# PALGRAVE STUDIES IN ECONOMIC HISTORY

# SPANISH ECONOMIC GROWTH, 1850–2015

*Leandro Prados de la Escosura*

# Palgrave Studies in Economic History

Series editor Kent Deng London School of Economics London, UK

Palgrave Studies in Economic History is designed to illuminate and enrich our understanding of economies and economic phenomena of the past. The series covers a vast range of topics including financial history, labour history, development economics, commercialisation, urbanisation, industrialisation, modernisation, globalisation, and changes in world economic orders.

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# Leandro Prados de la Escosura Spanish Economic Growth, 1850–2015

Leandro Prados de la Escosura Department of Social Sciences Universidad Carlos III Madrid, Spain

Palgrave Studies in Economic History ISBN 978-3-319-58041-8 ISBN 978-3-319-58042-5 (eBook) DOI 10.1007/978-3-319-58042-5

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# Acknowledgements

I am most grateful to Albert Carreras, César Molinas, Patrick O'Brien, Joan Rosés, Blanca Sánchez-Alonso, James Simpson, David Taguas† and, especially, Angus Maddison† for their advice and inspiration over the years. Nelson Álvarez, Juan Carmona, Albert Carreras, Sebastián Coll, Francisco Comín, Antonio Díaz Ballesteros, Rosario Gandoy, Antonio Gómez Mendoza, Alfonso Herranz-Loncán, Stefan Houpt, Pablo Martín-Aceña, Elena Martínez Ruíz, Vicente Pérez Moreda, David Reher, Blanca Sánchez-Alonso, María Teresa Sanchis, James Simpson, Antonio Tena and Gabriel Tortella kindly allowed me to draw on their unpublished data. Pilar Martínez Marín and Begoña Varela Merino, at the Spanish Statistical Institute, kindly help me with some technicalities of the latest national accounts. I thank Julio Alcaide†, Bart van Ark, Carlos Barciela, Francisco Comín, Antonio Díaz Ballesteros, Rafael Dobado, Toni Espasa, Ángel de la Fuente, Ángel García Sanz†, Pedro Fraile Balbín, Pablo Martín-Aceña, César Molinas, Jordi Palafox, Vicente Pérez Moreda, Carlos Rodríguez Braun, Nicolás Sánchez-Albornoz, Blanca Sánchez-Alonso and Piero Tedde de Lorca for their valuable comments. Of course, this project would have not been completed without the stimulating academic environment of the Department of Social Sciences at Carlos III University. Lastly, I would like to express my gratitude to Kent Deng, series editor and two anonymous referees, for their useful suggestions, and especially to Laura Pacey, Economics Commissioning Editor, for her encouragement and patience. A research grant from Fundación Rafael del Pino (Cátedra Rafael del Pino) is gratefully acknowledged.

# Contents




# List of Figures




# List of Tables


xv




# Introduction

What does GDP really mean? Is it a measure of material welfare or simply a measure of output? In its report to President Sarkozy of France, the Commission on the Measurement of Economic Performance and Social Progress claimed 'GDP is an inadequate metric to gauge well-being over time particularly in its economic, environmental, and social dimensions' (Stiglitz et al. 2009: 8). A wave of critical publications (Coyle 2014; Masood 2016; Philipsen 2015, among others) followed rejecting any pretence for GDP to capture anything other than market economic activity.

Calls have been made to broaden the narrow focus of GDP with a more comprehensive measure of quality of life that includes health, education, non-market activities, the environment, political voice and personal security (Stiglitz et al. 2009; OECD 2011). This approach does stress capabilities, that is, the ability of individuals to choose among different functionings. It is actually with this perspective that the United Nations Development Programme (UNDP) introduced the concept of human development, defined as 'a process of enlarging people's choices'(UNDP 1990: 10) and has published an index, the HDI, that fails, though, to incorporate agency—that is, the ability to pursue and realize goals a person has reasons to value—and freedom (Ivanov and Peleah 2010).

The novelty of these claims is arguable since the depiction of GDP as a crude measure of economic progress and an even poorer measure of welfare—because it does not take into consideration informal and non-market activities, leisure and human capital investment, while ignores environmental costs and income distribution—has been shared among economists from the inception of national accounts (Beckerman 1976; Engerman 1997; Nordhaus 2000; Syrquin 2016). More than four decades have passed since William Nordhaus and James Tobin (1972: 4) wrote in a classical paper, 'GNP is not a measure of economic welfare … An obvious shortcoming of GNP is that it is an index of production, not consumption. The goal of economic activity, after all, is consumption'.

Moreover, it has long been acknowledged that, in defining GDP, the concerns of public economists during World War II and its aftermath played a decisive role, a fact that its critics now emphasise (Coyle 2014; Syrquin 2016). It is worth noting, for example, the inclusion of government services as part of GDP, a criterion of the US Department of Commerce which Simon Kuznets rejected, as he saw them as intermediate, not final goods (Higgs 2015). The problem of measuring non-market services, such as health or education, often provided by the government, is largely its legacy.

Interestingly, those who claim that GDP is a flawed measure of economic welfare tend to accept that GDP per head is highly correlated with non-monetary dimensions of well-being (Oulton 2012). In a recent contribution, Jones and Klenow (2016), after claiming that GDP is a flawed measure of economic welfare and putting forward an alternative comprehensive measure of welfare which combines data on consumption, life expectancy at birth, leisure and income inequality, come to the conclusion that per capita GDP 'is an informative indicator of welfare' as it presents a 0.98 correlation with their consumption-equivalent welfare index for a sample of 13 countries.

Such conclusion lends support to Kuznets' depiction of GDP as a measure of economic welfare from a long run perspective (Syrquin 2016). It is also provides grounds for mainstream economists to argue that GDP provides a measure of material prosperity (Broadberry et al. 2014; Mankiw 2016).

Can we, then, rely on historical estimates of GDP to assess output and material welfare in the long run? In the early days of modern economic quantification, Kuznets (1952: 16–17) noticed the 'tendency to shrink from long-term estimates' due to 'the increasing inadequacy of the data as one goes back in time and to the increasing discontinuity in social and economic conditions'. Cautious historians recommend to restrict the use of GDP to societies that had efficient recording mechanisms, relatively centralized economic activities, and a small subsistence sector (Hudson 2016; Deng and O'Brien 2016). But should not the adequacy of data be 'judged in terms of the uses of the results' (Kuznets 1952: 17)?

It is in this context that a new set of historical national accounts for Spain, with GDP estimates from the demand and supply sides, is presented and used to draw the main trends in Spanish modern economic growth.

The new set of historical national accounts revises and expands the estimates in Prados de la Escosura (2003). Firstly, historical output and expenditure series are reconstructed for the century prior to the introduction of modern national accounts. Then, available national accounts are spliced through interpolation, as an alternative to conventional retropolation, to derive new continuous series for 1958–2015. Later, the series for the 'pre-statistical era' are linked to the spliced national accounts providing yearly series for GDP and its components over 1850–2015.

All reservations about national accounts in currently developing countries do apply to pre-1958 Spain.<sup>1</sup> In fact, Kuznets' (1952: 9) sceptical words are most relevant, 'Consistent and fully articulated sets of estimates of income, … and its components, for periods long enough to reveal the level and structure of the nation's economic growth, are not available … The estimates … are an amalgam of basic data, plausible inferences, and fortified guesses'. Thus, despite the collective efforts underlying the historical output and expenditure series offered here, the numbers for the 'pre-statistical era' have inevitably large margins of error. <sup>2</sup> This warning to the user is worth because as Charles Feinstein (1988: 264) wrote, 'once long runs of estimates are systematically arrayed in neat tables they convey a wholly spurious air of precision'.

Nonetheless, the new series represent an improvement upon earlier estimates, as they are constructed from highly disaggregated data grounded on the detailed, painstaking research on Spain carried out by economic historians. A systematic attempt has been made to reconcile the existing knowledge on the performance of individual industries, including services (largely neglected in earlier estimates), with an aggregate view of the economy.

The book is organized in two parts. The first one offers an overview of Spain's long-run aggregate performance, on the basis of the new GDP, population and employment series. Thus, GDP per head is derived, decomposed into labour productivity and the amount of work per person and placed into international perspective. Later, the extent to which GDP captures welfare is discussed. Part II addresses measurement and provides a detailed discussion about how GDP estimates are constructed. Thus, it includes two sections on the 'pre-statistical era' (1850–1958) describing the procedures and sources used to derive annual series of nominal and real GDP for both the supply (section I) and the demand (section II). Then, in section III, the new results are compared to earlier estimates for pre-national accounts years. Lastly, in section IV, the different sets of national accounts available for 1958–2015 are spliced through interpolation, and the resulting series compared to those obtained through alternative splicing procedures and, then, linked to the pre-1958 historical estimates in order to obtain yearly GDP series for 1850–2015. Additionally, details are provided on the estimates of population and employment.

## Notes


## References


# Part I

# Main Trends

1

# GDP and Its Composition

Aggregate economic activity multiplied fifty times between 1850 and 2015, at an average cumulative growth rate of 2.4% per year (Fig. 1.1). Four main phases may be established: 1850–1950 (with a shift to a lower level during the Civil War, 1936–1939), 1950–1974, 1974–2007 and 2007–2015, in which the growth trend varied significantly (Table 2.1).1 Thus, in the phase of fastest growth, the Golden Age (1950–1974), GDP grew at 6.3% annually, four and a half times faster than during the previous hundred years and twice faster than over 1974–2007, while the Great Recession represented a fall in real GDP between 2007 and 2013 (8%), and the 2007 level had not been recovered by 2015. Gross Domestic Income (GDI), that is, income accruing to those living in Spain, as opposed to output produced in Spain, shadows closely GDP evolution.

A look at the evolution of output and expenditure components of GDP provides valuable information about its determinants. Changes in the composition of demand are highly revealing of the deep transformation experienced by Spain's economy over the last two centuries.

#### 4 L. Prados de la Escosura

Fig. 1.1 Real GDP at market prices, 1850–2015 (2010 = 100) (logs)

The share of total consumption in GDP remained stable at a high level up to the late 1880s, followed by a decline that reached beyond World War I (Fig. 1.2). Then, it recovered in the early 1920s, helped by the rise in government consumption (Fig. 1.2, right scale), stabilizing up to mid-1930s. The Civil War (1936–1939) and World War II (even if Spain was a non-belligerent country) accounted for the contraction in private consumption and the sudden and dramatic increase in government consumption shares in GDP. The share of total consumption only fell below 85% of GDP after 1953, when a long-run decline was initiated reaching a trough (at three-fourths of GDP) by the mid-2000s. Such a decline in the GDP share of total consumption conceals an intense decline in private consumption (which contracted from 75% of GDP in 1965 to a historical trough, 56%, in 2009) paralleled by a sustained rise in government consumption (which jumped from a 7.5% trough in the mid-1960s to a 20% peak in 2009–2010) that resulted from the expansion of the welfare state and the transformation of a highly centralized state into a de facto federal state (Comín 1992, 1994).

Investment oscillated around 5% of GDP in the second half of the nineteenth century except during the late 1850s and early 1860s railways construction boom, when it doubled (Fig. 1.3). From the turn of the

Fig. 1.2 Private, government and total consumption as shares of GDP, 1850–2015 (% GDP) (current prices)

Fig. 1.3 Capital formation as a share of GDP, 1850–2015 (%) (current prices)

#### 6 L. Prados de la Escosura

Fig. 1.4 Fixed capital formation and its composition, 1850–2015 (% GDP) (current prices)

century, a long-term increase took place with the relative level of capital formation increasing from around 5 to above 30% of GDP in 2006. Phases of investment acceleration appear to be associated with those of faster growth in aggregate economic activity, namely the late-1850s and early 1860s, the 1920s, from the mid-1950s to the early 1970s, and between Spain's accession to the European Union (EU) (1985) and 2007. Nonetheless, the long-run increase was punctuated by reversals during the World Wars and the Spanish Civil War, the transition to democracy (1975–1985), which coincided with the oil shocks, and the Great Recession (2008–2013).

The breakdown of gross domestic fixed capital formation shows the prevalence of residential and non-residential construction as its main components over time, with a gradual rise of the share of more productive assets (machinery and transport equipment) during the twentieth century up to 1974 that stabilized thereafter (Fig. 1.4). The urbanization and industrialization push in the 1920s and between 1950 and the early 1970s reflects clearly across different types of assets. It is worth noting the increase in the share of infrastructure after Spain's accession to the EU

Fig. 1.5 Openness: exports and imports shares in GDP (%) (current prices)

and the residential construction bubble between the late 1990s and 2007.

The exposition of Spain to the international economy also increased but following a non-monotonic pattern, with three main phases: a gradual rise in openness (that is, exports plus imports as a share of GDP) during the second half of the nineteenth century that at the beginning of the twentieth century stabilized at a high plateau up to 1914; this was followed by a sharp decline from the early 1920s to mid-century that reach a trough during World War II (Fig. 1.5). A cautious but steady process of integration in the international economy took place since the 1950s, facilitated by the reforms associated with the 1959 Stabilization and Liberalization Plan.

How gradual was the post-1950 recovery is shown by the fact that only in 1955 the level of openness of 1929 was reached and that the historical maximum of the pre-World War I years was overcome in 1970. It took longer for exports than for imports to recover pre-World War I relative size (only in 1980 that of the 1910s was overcome). Spain's increasing openness during the last four decades suffered, nonetheless,

#### 8 L. Prados de la Escosura

Fig. 1.6 Gross fixed capital formation and imports, 1850–2015 (8% GDP) (current prices)

reversals in the second half of the 1980s and, again, in the 2000s as a result of a contraction in exports.

It is worth mentioning the concordance observed between investment and imports, which suggests a connection between economic growth and exposure to international competition (Fig. 1.6). Furthermore, phases of more intense imports and investment are also those of deficit in the balance of goods and services, which suggests an inflow of capital and a link between the external sector and capital formation.

The composition of GDP by sectors of economic activity between 1850 and 2015 highlights the transformations associated with modern economic growth (Fig. 1.7).

Agriculture's share underwent a sustained contraction over time, but for the autarkic reversal of the 1940s, which intensified during the late 1880s and early 1890s, the 1920s and over 1950–1980. Industry, including manufacturing, extractive industries and utilities, followed an inverse U, expanding its relative size up to the late 1920s and, after the 1930s and 1940s backlash, resumed its relative increase to stabilize at a high plateau (around 30% of GDP). Since the mid-1980s, the share of

Fig. 1.7 GDP composition from the output side (%) (current prices)

industry dropped sharply, as sheltered and uncompetitive industries collapsed due to liberalization and opening up after EU accession. By 2010, the relative size of industry had shrunk to practically one-half of its peak in the early 1960s. Construction industry remained stable below 5% of GDP until mid-twentieth century (but for expansionary phases in the late 1850s and early 1860s, 1920s and 1950s), exhibiting a sustained increase since the early 1960s that peaked during the mid-2000s, more than doubling its relative size. The end of the construction bubble during the Great Recession implied a return to the mid-1960s relative size.

Services made a high and stable contribution to GDP, fluctuating around 40%, between mid-nineteenth and mid-twentieth century, but for the 1930–1940s parenthesis of depression, civil war and autarky, and expanded from less than one-half to three-fourths of GDP between the early 1960s and 2015.

The evolution of services as a share of GDP in Spain, with a high share of GDP in early stages of development (around 40%), conflicts with the literature on structural change, which suggests a growing contribution of services to GDP as per capita income increases (Chenery and Syrquin 1975; Prados de la Escosura 2007a). A path dependency explanation

Fig. 1.8 Employment: hours worked distribution by economic sectors, 1850–2015 (%)

could be hypothesized as the arrival of American silver remittances in the early modern era (sixteenth and eighteenth centuries), altered the relative prices of tradable and non-tradable goods, in an early experience of 'Dutch disease', shifting domestic resources towards non-tradable production (Forsyth and Nicholas 1983; Drelichman 2005).<sup>2</sup>

Comparing the sectoral composition of GDP to that of labour can be illuminating. Figure 1.8 presents the composition of employment in terms of hours worked across industries.

Agriculture's share exhibits a long-run decline from above three-fifths to less than 5% since 2006. It fell more gradually up to 1950—but for the sharp contraction of the 1920s and early 1930s—reverted during the Civil War (1936–1939) and its autarkic aftermath, and accelerated over 1950–1990, when it shrank from half the labour force to one-tenth. Even though its numbers might be over-exaggerated prior to mid-twentieth century due to peasants' economic activities outside agriculture, agriculture provides the largest contribution to employment up to 1964, when it still represented one-third of total hours worked. The evolution of the relative size of services, whose figures may be underestimated before 1950, for the same reasons of agriculture's over-exaggeration, presents a mirror image of agriculture, taking over as the largest industry from 1965 onwards and reaching three-fourths of total hours worked by 2015. Industry's steady expansion, but for the Civil War reversal, overcame agriculture's share by 1973 and peaked by the late 1970s reaching one-fourth of employment to initiate a gradual contraction that has cut its relative size by almost half by 2015. Construction, in turn, more than trebled its initial share by 2007, sharply contracting as the sector's bubble ended during the Great Recession.

As already observed in GDP composition, an initial phase of structural change, in which the agricultural sector contracted and that of industry expanded—only broken by the post-war falling behind—was followed by a second phase since 1980, in which the relative decline involved, in addition to agriculture, the industrial sector, while employment in services accelerated its escalation.

Comparing the sectoral distribution of GDP and employment allows us to establish labour productivity (measured as Gross Value Added [GVA] per hour worked) by industry relative to the economy's average (Fig. 1.9). Several features stand out. Relative industrial productivity

Fig. 1.9 Relative labour productivity (GVA per hour worked), 1850–2015 (average labour productivity = 1)

increased to reach a plateau over the late 1880s and World War I in which it doubled it. Episodes of intensified industrialization and urbanization in the 1920s and, to a larger extent, between the mid-1950s and mid-1970s, were accompanied by the expansion of industrial employment, which underlies the decline in the relative productivity of industry and services.

Agricultural labour productivity fluctuated between one-half and two-third of the economy's average (exceptional peaks and troughs aside) and tended to be rather stable. Such stability between 1890 and 1960, hardly affected by the gradual contraction of agricultural share in employment, shows the moderate and gradual structural transformation of the Spanish economy. In the 1960s accelerated industrialization, upheld by capital intensification and the incorporation of new technologies, and industrial restructuring in the late 1970s explain the sharp drop in the relative productivity of the agricultural sector. In turn, the recovery of agriculture's relative productivity in the late 1980s and early 1990s is attributable to the destruction of agricultural employment that cut its share by half.

The gradual reduction in productivity differences across sectors during the last half a century suggests moderate convergence in factor proportions and could be interpreted as a result of improved resource allocation.<sup>3</sup>

## Notes


would reveal a lesser proportion of employment in agriculture and a greater one in services, with consequent repercussions on the relative productivity of labour in each sector.

### References


#### 14 L. Prados de la Escosura

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/ 4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# 2

# GDP and GDP Per Head

Modern economic growth is defined by the sustained improvement in GDP per head. From 1850 to 2015 while population trebled, real GDP per head in Spain experienced nearly a 16-fold increase, growing at an annual rate of 1.7% (Fig. 2.1 and Table 2.1). GDP growth was intensive, that is, driven by the advance in GDP per person, but for exceptional periods of Civil War, Depression, and Recession (Fig. 2.2). Such an improvement took place at an uneven pace. Per capita GDP grew at 0.7% over 1850–1950, doubling its initial level. During the next quarter of a century, the Golden Age, its pace accelerated more than sevenfold so, by 1974, per capita income was 3.6 times higher than in 1950. Although the economy decelerated from 1974 onwards, and its rate of growth per head shrank to one-half that of the Golden Age, per capita GDP more than doubled between 1974 and 2007. The Great Recession (2008–2013) shrank per capita income by 11%, but, by 2015, its level was still 83% higher than at the time of Spain's EU accession (1985).

Different long swings can be distinguished in which growth rates deviate from the long-run trend as a result of economic policies, access to international markets, and technological change. Growth rates, measured as average annual logarithmic rates of variation, are provided in Table 2.1

L. Prados de la Escosura, Spanish Economic Growth, 1850–2015, Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5\_2

Fig. 2.1 Real absolute and per capita GDP, 1850–2015 (2010 = 100) (logs)


Table 2.1 Growth of GDP and its components, 1850–2015 (%) (average yearly logarithmic rates)


Table 2.1 (continued)

Fig. 2.2 Real GDP growth and its breakdown over long swings, 1850–2015 (logarithmic growth rates) (%). Note Real GDP growth results from adding up the growth rates of GDP per person and population

for main phases of economic performance (Panel A) and long swings (Panel B). A further breakdown into short cycles is presented for –1950 (Panel C).

During the first long swing, 1850–1883, the rate of growth of product per person was well above the 1850–1950 average. Institutional reforms that brought higher economic freedom seem to lie beneath the significant growth experienced during these three decades (Prados de la Escosura 2016). Opening up to international trade and foreign capital made it possible to break the close connection between investment and savings and contributed to the economic growth (Prados de la Escosura 2010). It is worth stressing that, contrary to common economic wisdom, robust economic performance took place in a context of persistent political instability which included the 1854 liberal uprising and the 1868 Glorious Revolution. This suggests that an improved definition and enforcement of property rights and openness to goods and ideas contributed to offset political turmoil and social unrest.

Growth slowed down between the early 1880s and 1920s. Restrictions on both domestic and external competition help explain sluggish growth during the Restauración (1875–1923), despite the fact that institutional stability should have provided a favourable environment for investment and growth (Fraile Balbín 1991, 1998). Increasing tariff protection (Tena Junguito 1999), together with exclusion from the prevailing international monetary system, the gold standard, may have represented a major obstacle to Spain's integration in the international economy (Martín-Aceña 1993; Bordo and Rockoff 1996). The Cuban War of Independence, despite the already weakened economic links between the metropolis and its colony, caused significant macroeconomic instability that brought forward the fall of the peseta and increased Spain's economic isolation (Prados de la Escosura 2010). Macroeconomic instability, together with a 'sudden stop', reduced capital inflows leading to the depreciation of the peseta (Martín-Aceña 1993; Prados de la Escosura 2010) that, in turn, increased migration costs and reduced the outward flow of labour (Sánchez-Alonso 2000). Cuban independence had little direct economic impact on Spain's economy but a deep indirect one, as the intensification of protectionist and isolationist tendencies in the early twentieth century seem to be its political outcome (Fraile Balbín and Escribano 1998). World War I hardly brought any economic progress and GDP per head shrank, a result in stark contradiction with the conventional stress on the war stimulating effects on growth.1

The 1920s represented the period of most intense growth prior to 1950. The hypothesis that Government intervention, through trade protectionism, regulation, and investment in infrastructure, was a driver of growth has been widely accepted (Velarde 1969). The emphasis on tariff protectionism tends to neglect, however, that Spain opened up to international capital during the 1920s, which allowed the purchase of capital goods and raw materials and, hence, contributed to growth acceleration.

A fourth long swing took place between 1929 and 1950, which includes the Great Depression, the Civil War, and post-war autarkic policies, is defined by economic stagnation and shrinking GDP per head. The impact of the Depression, measured by the contraction in real GDP per head, extended in Spain, as in the USA, until 1933, with a 12% fall (against 31% in the USA), lasting longer than in the UK (where it ended in 1931 and real per capita GDP per head shrank by 7%) and Germany (1932 and 17% decline, respectively), but less than in Italy (1934 and 9% contraction) and France (1935 and 13% fall). Thus, the Depression, with GDP per head falling at −3.1% annually (−1.5% for absolute GDP), was milder than in the USA but similar in intensity to Western Europe's average (Maddison Project 2013), a finding that challenges the view of a weaker impact due to Spain's relative international isolation and backwardness. The Civil War (1936–1939) prevented Spain from joining the post-Depression recovery and resulted in a severe contraction of economic activity (31% drop in real per capita income between levels in 1935 and the 1938 trough) that, nonetheless, did not reach the magnitude of World War II impact on main belligerent countries of continental Western Europe (in Austria, the Netherlands, France, and Italy per capita income shrank by half and in Germany by two-thirds) (Maddison Project 2013).<sup>2</sup>

The weak recovery of the post-World War years stands out in the international context. Spain's economy did not reach its pre-war GDP per head peak level (1929) until 1954 (1950 in absolute terms) and that of private consumption per head until 1956. In contrast, it only took an average of 6 years to return to the pre-war levels in Western Europe (1951).<sup>3</sup> It is true that warring countries surrounded post-Civil War Spain (Velarde 1993), but the fact that its economy only grew at a rate of 0.2% yearly between 1944 and 1950 suggests a sluggish recovery after a comparatively mild contraction.

In the search for explanations, the destruction of physical capital does not appear to be a convincing one as it was about the Western European average during World War II (around 8% of the existing stock of capital in 1935), although its concentration on productive capital (especially transport equipment) meant that levels of destruction caused by the conflict in Spain were far from negligible (Prados de la Escosura and Rosés 2010). However, exile after the Civil War and, possibly to a larger extent, internal exile resulting from political repression of Franco's dictatorship, meant the loss of a considerable amount of Spain's limited human capital (Núñez 2003; Ortega and Silvestre 2006).4 Thus, it can be put forward the hypothesis that the larger loss of human capital vis-à-vis physical capital contributed to the delayed reconstruction (Prados de la Escosura 2007).

The change in trend that began after 1950 ushered in an exceptional phase of rapid growth lasting until 1974. During the 1950s, though, industrialization in Spain was largely dependent on internal demand. Import volatility rendered investment risky and tended to penalize capital accumulation, while inflows of foreign capital and new technology were restricted. However, increasing confidence in the viability of Franco's dictatorship after the US—Spain military and technological cooperation agreements (1953), together with the regime's moderate economic reforms, favoured investment and innovation contributing to accelerated economic growth (Calvo-González 2007; Prados de la Escosura et al. 2012).

An institutional reform initiated with the 1959 Stabilization and Liberalization Plan, a response to the exhaustion of the inward-looking development strategy, set policies that favoured the allocation of resources along comparative advantage and allowed sustained and faster growth during the 1960s and early 1970s.<sup>5</sup> Without the Stabilization and Liberalization Plan, per capita GDP would have been significantly lower at the time of Franco's death, in 1975. However, without the moderate reforms of the 1950s and the subsequent economic growth, it seems unlikely the Stabilization Plan would have succeeded (Prados de la Escosura et al. 2012). This view challenges the widespread perception of the first two decades of Franco's dictatorship as a homogeneous autarchic era and the 1959 Stabilization and Liberalization Plan as a major discontinuity between autarky and the market economy.

The oil shocks of the 1970s happened at the time of Spain's transition from dictatorship to democracy that brought with it further opening up and economic liberalization. During the transition decade (1974–1984), GDP growth rate fell to one-third of that achieved over 1958–1974, and to one-fourth when measured in per capita terms. Was the slowdown exogenous, a result of the international crisis? Did it derive from the Francoism legacy of an economy still sheltered from international competition? Or was the outcome of the new democratic authorities' policies? Answering these questions represents a challenge to researchers. Accession to the European Union heralded more than three decades of absolute and per capita growth that came to a halt with the Great Recession. Again, the deeper contraction and weaker recovery calls for investigation on the underlying foundations of the 1985–2007 expansion.

## Notes


## References


#### 24 L. Prados de la Escosura

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/ 4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# 3

# GDP per Head and Labour Productivity

A breakdown of GDP per head into labour productivity and the amount of labour used per person can be made. Thus, GDP per person (GDP/N) will be expressed as GDP per hour worked (GDP/H), a measure of labour productivity, times the number of hours worked per person (H/N), a measure of effort.

$$\text{GDP/N} = \text{GDP/H} \ast \text{H/N} \tag{1}$$

And using lower case to denote rates of variation,

$$(\text{gdp/n}) = (\text{gdp/h}) + (\text{h/n})\tag{2}$$

GDP per head and per hour worked evolved alongside over 1850– 2015, even though labour productivity grew at a faster pace—labour productivity increased 23-fold against GDP per head 16-fold—as the amounts of hours worked per person shrank—from about 1000 h per person-year to less than 700—(Table 3.1 and Fig. 3.1). Thus, it can be claimed that gains in output per head are fully attributable to productivity gains, with phases of accelerating GDP per head, such as the 1920s

L. Prados de la Escosura, Spanish Economic Growth, 1850–2015, Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5\_3


Table 3.1 GDP per head growth and its components, 1850–2015 (%) (average yearly logarithmic rates)

or the Golden Age (1950–1974), matching those of faster labour productivity growth.

A closer look at the last four decades reveals, however, significant discrepancies over long swings. In fact, a pattern can be observed

Fig. 3.1 Real per capita GDP and its components, 1850–2015 (logs)

according to which phases of acceleration in labour productivity correspond to those of GDP per person slowdown, and vice versa (Fig. 3.2). Thus, periods of sluggish (1974–1984) or negative (2007–2013) per capita GDP growth paralleled episodes of vigorous or recovering productivity growth, although only in the first case, during the 'transition to democracy' decade, labour productivity offset the sharp contraction in hours worked—resulting from unemployment—and prevented a decline in GDP per head. Conversely, the years between Spain's accession to the European Union (1985) and the eve of the Great Recession (2007), particularly since 1992, exhibited substantial per capita GDP gains, while labour productivity slowed down. Thus, during the three decades after Spain joined the EU, in which GDP per head doubled, growing at 3.0% per year, more than half was contributed by the increase in hours worked per person.

Thus, it can be concluded that since the mid-1970s, the Spanish economy has been unable to combine employment and productivity growth, with the implication that sectors that expanded and created new jobs (mostly in construction and services) were less successful in attracting investment and technological innovation. Actually, labour

Fig. 3.2 Real per capita GDP growth and its breakdown over long swings, 1850–2015 (logarithmic growth rates) (%). Note per capita GDP growth results from adding up the growth rates of GDP/hour and hours per person

productivity in construction and services grew at a yearly rate of −0.2 and 0.3%, respectively, compared to 1.1% for the overall economy over 1985–2007 (Table 3.2).

Gains in aggregate labour productivity can be broken down into the contribution made by the increase in output per hour worked in each economic sector (internal productivity) and by the shift of labour from less productive to more productive sectors (structural change).1 The level of aggregate labour productivity (A), which is obtained by dividing gross value added (GVA) by the number of hours worked (H) for the economy as a whole in the year t, can be expressed as the result of adding up labour productivity (GVAi /Hi ) for each economic sector i (i = 1, 2, …, n), weighted by each sector's contribution to total hours worked (Hi /H).<sup>2</sup>

$$\mathbf{A}\_{\mathbf{t}} = (\mathbf{GVA/H})\_{\mathbf{t}} = \Sigma(\mathbf{GVA\_{i}/H\_{i}})\_{\mathbf{t}}(\mathbf{H\_{i}/H})\_{\mathbf{t}} = \Sigma(\mathbf{A\_{it}U\_{it}}) \tag{3}$$

where Ai is gross value added per hour worked in sector i, and Ui is the contribution of sector i to total hours worked.


Table 3.2 Labour productivity growth by sectors, 1850–2015 (%) (GVA per hour worked) (average yearly logarithmic rates)

Using lower case letters to represent rates of change,

$$\mathbf{a}\_{\text{t}} = \boldsymbol{\Sigma}\,\mathbf{a}\_{\text{it}}\,\mathbf{U}\_{\text{it}} + \,\boldsymbol{\Sigma}\,\mathbf{A}\_{\text{it}}\,\mathbf{u}\_{\text{it}} \tag{4}$$

The method usually employed in this calculation, shift-share analysis, involves estimating, in the first place, internal productivity growth (the first term on the right-hand side of expression (4), that is, the result obtained by adding up the labour productivity growth of GVA per hour worked in each economic sector weighted by the initial composition of employment (expressed in hours worked). The difference between aggregate productivity and internal productivity will then provide the contribution of structural change. Structural change would have made a positive contribution to productivity growth over 1850–1974 by shifting labour from agriculture into industry (Table 3.3, column 3 and Fig. 3.3). Conversely, since 1985, structural change, represented by the shift of labour from both agriculture and industry into services, would have slowed down aggregate productivity growth. Carrying out the shift-share analysis at a high level of aggregation, that is, between main economic sectors, precludes a more nuanced picture, as within industry and services there were shifts from sectors of lower productivity levels or growth rates to others of higher productivity levels or more intense growth.

Nonetheless, the shift-share analysis is based on the assumption that, in the absence of labour shift between sectors, each sector's productivity would have been identical to the actual ones. This is an unrealistic assumption when labour is rapidly absorbed by industry and services and productivity tends to stagnate or even decline in these sectors. This seems to be the case in Spain.<sup>3</sup> It would appear more plausible to assume that agricultural productivity partly improved, say, between 1950 and 1975, due to the reduction in the number of hours worked in the sector. Furthermore, during the 'transition to democracy' (1975–1985) GVA per hour worked in industry would have grown more slowly had employment not fallen in the sector, a result of industrial restructuring that shrank or eliminated less competitive branches. Thus, the result for the contribution of structural change to productivity growth obtained


Table 3.3 Labour productivity growth and structural change, 1850–2015 (%) (average yearly logarithmic rates)

Fig. 3.3 Labour productivity growth and structural change over long swings: shift-share, 1850–2015 (logarithmic growth rates) (%)

using the conventional shift-share analysis (Table 3.3) would arguably represent a lower bound.

Alternatively, an upper bound can be derived using Broadberry's modified version of the shift-share analysis. <sup>4</sup> The contribution of structural change is derived by subtracting from aggregate productivity the figure that would result by weighting output per hour worked growth in each sector according to its contribution to total employment in the initial year, but with an exception for those sectors whose contribution to employment falls (e.g. agriculture over the entire time span considered and industry since 1975). In such a case, the differential between the rate of variation in hours worked for the economy as a whole and for the relevant sector would be subtracted from the latter's productivity growth.<sup>5</sup> As Table 3.3 shows, the difference between upper and lower bounds can be significant for some periods.

Structural change, derived with the modified shift-share approach, would account for 38% of the aggregate productivity growth achieved over the last 166 years. This figure is not far below from Broadberry's own findings for Germany and the USA.<sup>6</sup> Over 1850–1950, its

Fig. 3.4 Labour productivity growth and structural change over long swings: modified shift-share, 1850–2015 (logarithmic growth rates) (%)

contribution would reach two-fifths of labour productivity growth, against the one-fourth suggested by the conventional shift-share approach. A closer look indicates that structural transformation made a larger contribution to productivity growth between the 1870s and 1929, with decade 1874–1883, the long decade before World War I, and the 1920s as the most intense episodes (Fig. 3.4).

According to the modified shift-share analysis, it is in the Golden Age (1950–1974) when structural change would have made the larger and more sustained contribution to productivity growth.

Since 1975 and up to the eve of the Great Recession (2007), structural change accounted for more than one-third of the increase in aggregate labour productivity and avoided an even deeper productivity deceleration after 1984. This result is at odds with the negative contribution of structural change to productivity advance suggested by the conventional shift-share analysis. In this phase, the transfer of labour away from agriculture (which still absorbed one-fifth of the total number of hours worked in 1975 and, since then, hours worked in agriculture declined at yearly rate of −4% to 2007) was accompanied by a sustained destruction of employment in less competitive manufacturing industries, which intensified during the 'transition to democracy' decade (−3.8% yearly decline of hours worked in industry during 1974–1984). Since 2007, structural change prevented labour productivity from stalling contributing a moderate increase in output per hour worked during the Great Recession.

A clearer picture of the evolution of the number of hours worked per person, (H/N), is obtained by breaking it down into its components (Table 3.4). Thus, (H/N) equals hours worked per full-time equivalent worker, L, (H/L), times the participation rate—that is, the ratio of L, to the working age population, WAN-, (L/WAN), times the share of WAN in total population, N, (WAN/N),

$$(\mathbf{H}/\mathbf{N}) = (\mathbf{H}/\mathbf{L}) \, \ast \, (\mathbf{L}/\mathbf{W}\mathbf{A}\mathbf{N}) \, \ast \, (\mathbf{W}\mathbf{A}\mathbf{N}/\mathbf{N}) \tag{5}$$


Table 3.4 Hours worked per head growth and its composition, 1850–2015 (%) (average yearly logarithmic rates)


Table 3.4 (continued)

That in rates of change (lower case letters) can be expressed as:

$$(\mathbf{h}/\mathbf{l}) = (\mathbf{h}/\mathbf{l}) + (\mathbf{l}/\mathbf{w}\mathbf{an}) + (\mathbf{w}\mathbf{an}/\mathbf{n})\tag{6}$$

The change in hours per full-time equivalent worker-year (H/L), which fell from 2800 by mid-nineteenth century to less than 1900 at the beginning of the twenty-first century, represents the main driver of the amount of work per person, especially in periods of industrialization and urbanization such as the 1920s (to which the gradual adoption of the 8 h per day standard also contributed) and the Golden Age (1950–1974) (Fig. 3.5).

Changes in the participation rate (L/WAN) also made a contribution (Fig. 3.6). For example, in the Golden Age (1950–1974), it mitigated the decline in hours worked per person. However, it is since 1975 when the participation rate becomes the main determinant of changes in the amount of hours worked per person. Thus, (L/WAN) accounts for two-thirds of its contraction during the 'transition to democracy' decade (1975–1984). Such a decline was due to a dramatic surge in unemployment, largely resulting from the impact of the oil shocks and the exposure to international competition on traditionally sheltered industrial sectors, plus the return of migrants from Europe. The higher bargaining power of trade unions and industrial restructuring made the rest. Another surge in

Fig. 3.5 Hours per full-time equivalent worker, 1850–2015

Fig. 3.6 Hours worked per head growth and its breakdown over long swings, 1850–2015 logarithmic growth rates (%)

unemployment made the participation rate accountable for most of the reduction in hours worked per person during the Great Recession (2008– 2013).

Conversely, between Spain's EU accession and the Great Recession (1985–2007), the increase in (L/WAN) was the main contributor to the increase in the number of hours worked per person, helped by increasing female participation and the post-1990 inflow of migrants. Again, the rise in the participation rate, as unemployment has gradually declined, is a main actor in the post-2013 recovery in hours worked per person. Lastly, a demographic gift, as the dependency rate fell increasing the share of potentially active over total population, prevented a further decline of hours worked per person during the 1930s, contributed to its recovery in the 1940s and helped the surge in employment over 1985–2007.

## Notes


#### 38 L. Prados de la Escosura


## References


Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/ 4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# 4

# Spain's Performance in Comparative Perspective

A long-run view of Spain's economic performance cannot be completed without placing it in comparative perspective. In Fig. 4.1, Spain's real GDP per head is presented along estimates for other large Western European countries: Italy, France, the UK, and Germany, plus the USA, the economic leader that represents the technological frontier, all expressed in purchasing power parity-adjusted 2011 dollars to allow for countries' differences in price levels (Fig. 4.1).<sup>1</sup> A caveat is needed about this kind of exercise. Per capita income levels obtained through backward projection of PPP-adjusted GDP levels for a given benchmark year (2011, in this case, or 1990 in Maddison's estimates) with volume indices derived at domestic relative prices from historical national accounts provide a convenient way of comparing of countries' income levels over time, as it is easy to compute and does not alter national growth rates. However, it also presents a huge index number problem that gets bigger as the time span considered widens, rendering comparisons less significant. This is so because this computation procedure implicitly assumes that the basket of goods and services and the structure of relative prices for the benchmark year remain unaltered over time, something definitively misleading as long-run growth is about change in

L. Prados de la Escosura, Spanish Economic Growth, 1850–2015, Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5\_4

Fig. 4.1 Spain's comparative real per capita GDP (2011 EKS \$) (logs)

relative prices (Prados de la Escosura 2000). As a matter of fact, this type of series only provides an effective comparison between the level of the benchmark year (2011 here) and that of any other year at the former's relative prices.

Several findings emerge from Fig. 4.1. Firstly, Spain's long-term growth appears to be similar to that of Western nations.<sup>2</sup> Secondly, Spain's level of GDP per head is systematically lower than those of other large Western European countries. Lastly, the improvement in Spain's GDP per head did not follow a monotonic pattern, a feature that shares with Italy and Germany, but differs from the steady progress experienced by the UK and the USA and, to less extent, with France.

The first two results would lend support to the view that the roots of most of today's difference in GDP per person between Spain and advanced countries should be searched for in the pre-1850 era.<sup>3</sup> However, the fact that Spain's initial level was lower would suggest within a neoclassical framework—a potential for growth that would have not been exploited.

A closer look reveals that long-run growth before 1950 was clearly lower in Spain (as in Italy) than in the advanced countries (Table 4.1).


Table 4.1 Comparative per capita GDP growth, 1850–2015 (%) (average annual logarithmic rates)

Source Spain, see the text; rest of countries, Maddison Project

Sluggish growth over 1883–1913 and not taking advantage of its World War I neutrality to catch up, partly account for it. Furthermore, the progress achieved in the 1920s was outweighed by Spain's short-lived recovery from the depression, brought to a halt by the Civil War (1936– 1939), and by a long-lasting and weak post-war reconstruction. In fact, although less destructive than World War II, and despite being Spain non-belligerent in World War II, post-Civil War's recovery in Spain was longer and less intense than in the warring Western European countries after 1945.

Thus, Spain fell behind between 1850 and 1950 (Fig. 4.2). The second half of the nineteenth century and the early twentieth century witnessed sustained per capita GDP growth, while paradoxically the gap with the industrialized countries widened over 1883–1913. Moreover, the gap deepened during the first half of the twentieth century.

The situation reverted from 1950 to 2007. The Golden Age (1950– 1973), especially, the period since 1960 (a common feature of countries in the European periphery: Greece, Portugal, Ireland) stands out as years

Fig. 4.2 Spain's relative real per capita GDP (2011 EKS \$) (%)

of outstanding performance and catching up to the advanced nations. Steady, although slower, growth after the transition to democracy years (1974–1984) allowed Spain to keep catching up until 2007. The Great Recession reversed the trend, although it is too soon to determine whether it has opened a new phase of falling behind.

To sum up, the liberal regime of the Restauración (1875–1923), which provided political stability, but largely failed to offer incentives for accelerated growth; the 1930s and 1940s, with the Civil War and its slow and autarkic recovery; the 'transition to democracy' decade after General Franco's death (1975); and the Great Recession (2008–2013) stand out as those phases responsible for Spain's falling behind Western Europe. Conversely, over 1950–2007, especially during the Golden Age, Spain outperformed the advanced nations improving her relative position.

On the whole, Spain's relative position to Western countries has evolved along a wide U-shape, deteriorating to 1950 (except for the 1870s and 1920s) and recovering thereafter (but for the episodes of the transition to democracy and the Great Recession). Thus, at the beginning of the twentieth-first century, Spanish real GDP per head represented a similar proportion of USA and Germany's income to the one back in mid-nineteenth century, although had significantly improved with respect to the UK and kept a similar position to that of the 1870s with regard to France. Lastly, compared to Italy, Spain has reached parity, as had been the case in the late nineteenth century and, again, in the 1920s.

A final reminder: the choice of splicing procedure for the modern national accounts can result in far from negligible differences in the relative position of a country over the long run. Moreover, the difference between the resulting series of interpolation and retropolation procedures appears much more dramatic when placed in a long-run perspective, that is, when the spliced national accounts are projected backwards into the nineteenth century with volume indices taken from historical national accounts. This is due to the fact that most countries, including Spain, grew at a slower pace before 1950, so the level of per capita GDP level by mid-twentieth century largely determines its relative position in country rankings in earlier periods.

In order to illustrate this point, I have constructed long-run estimates of real GDP per head for Spain using the retropolated series for 1958– 2015 and compared them to the series obtained through interpolation (Fig. 4.3).4 The retropolation approach is the one conventionally used

Fig. 4.3 Spain's comparative real per capita GDP with alternative splicing (2011 EKS \$) (logs)

(as discussed in Part II, Chap. 9) and has been employed, for example, in the Penn Table 9.0 (RGDPNA series).<sup>5</sup> It can be observed that when adopting the retropolated series, Spain overcomes Italy in terms of GDP per head over 1850–1950 (but for the Civil War years), matching France and Germany in the early 1880s.

Moreover, I have computed Spain's position relative to France and the UK (Fig. 4.4). The choice of yardstick countries obeys to the purpose of comparing a country of fast growth and deep structural change in the second half of the twentieth century, such as Spain, with others more mature and in which economic growth proceeded at steadier pace. The reason is that it is fast growth and deep structural transformation what produces the large disparities between new and old benchmark national accounts series for the overlapping year. In most countries, national accounts have been spliced through retropolation. However, in the yardstick countries, the method of splicing national accounts is not a relevant issue because, as their structural transformation was largely completed before the modern national accounts era (post-World War), differences between new and old national accounts estimates are small at the overlapping year.

Fig. 4.4 Spain's real per capita GDP relative to France and the UK with alternative splicing (2011 EKS \$)

According to the figures derived from using the retropolation splicing procedure, during the second half of the nineteenth century, real per capita GDP in Spain would have matched that of France in the mid-1850s and, again, between the mid-1870s and mid-1880s. Furthermore, when its retropolated series are considered, Spain would have practically matched British per capita income during the last quarter of the twentieth century with a sorpasso in 1974 and, again, at the beginning of the 1990s. These results are in stark contrast with those derived by splicing national accounts through interpolation. Thus, Spanish GDP per head would have represented above four-fifths of the French over 1973–1984 and would have represented less than 90% of the British with a brief takeover during 1990–1993. It can be, then, concluded that whatever the measurement error embodied in the interpolation procedure may be, its results appear far more plausible than those resulting from the conventional retropolation approach.

### Notes


## References


Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/ 4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# 5

# GDP, Income Distribution, and Welfare

But how did GDP per head gains affect economic well-being? Within the existing national accounts framework, Sitglitz et al. (2009: 23–25) recommend to look at net rather than gross measures, in order to take into account the depreciation of capital goods. Net National Disposable Income (NNDI) measures income accruing to Spanish nationals, rather than production in Spain, and also accounts for capital consumption. NNDI provides, therefore, a more accurate measure of the impact of economic growth on average incomes than GDP.

In Fig. 5.1, a long-term decline in the NNDI share of GDP is observed. The reason is that as the stock of capital gets larger and its composition shifts from assets with long lives but low returns (i.e. residential construction) to shorter life assets but with higher returns (i.e. machinery), capital consumption increases. The integration of Spain into the global economy since the last quarter of the twentieth century accentuated this process.

Nonetheless, it can be noticed that per capita NNDI grows in parallel with GDP per head, although at a slower pace from 1960 onwards, that resulted in its 13-fold increase over 1850–2015, against 16-fold for per capita GDP (Fig. 5.2 and Table 5.1).

L. Prados de la Escosura, Spanish Economic Growth, 1850–2015, Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5\_5

Fig. 5.1 Net national disposable income ratio to GDP 1850–2015 (current prices) (%)

Fig. 5.2 Real per capita GDP and net national disposable income, 1850–2015 (2010 = 100) (logs)


Table 5.1 Real per capita GDP, NNDI, private consumption, and Sen-welfare growth, 1850–2015 (%) (average yearly logarithmic rates)

In their Report, Sitglitz et al. (2009) also advise focusing on household consumption, rather than on total consumption, to capture the effect of growth on material welfare. This way, government consumption that

Fig. 5.3 Real per capita GDP and private consumption, 1850–2015 (2010 = 100) (logs)

could be deemed, in Nordhaus and Tobin (1972) words, 'defensive' expenditures—namely services that represent inputs for activities that may yield utility—would be excluded.

A look at the behaviour of real private consumption per person shows a narrow parallelism with that of GDP per head, but with a lower rate of growth (Fig. 5.3), as reflected by its declining contribution to GDP (Fig. 1.1), and that implied, nonetheless, multiplying 10 times its initial level over 1850–2015. Solely during the long decade preceding World War I, the Civil War (1936–1939), and the Great Recession (2008– 2013) did private consumption growth fall ostensibly behind that of GDP (Table 5.1). In short, it can be suggested that the fruits of growth were passed on to the population, so present consumption was not sacrificed to greater future consumption and, hence, no parallelism can be drawn with the post-1950 experience of former socialist countries in Europe or East Asian countries (Krugman 1994; Young 1995).

Another major objection to GDP per head is that it takes no account of income distribution. In fact, the conviction that averages fail to give 'indication of how the available resources are distributed across persons or

Fig. 5.4 Income inequality, 1850–2015: Gini coefficient. Source Prados de la Escosura (2008), 1850–1994; Eurostat 1995–2015

households' (Stiglitz et al. 2009: 32) recommends that average income should be accompanied by measures of its distribution.

How have the fruits of growth been distributed in Spain? Trends in income distribution measured by the Gini coefficient are presented in Fig. 5.4. <sup>1</sup> Its evolution has not followed a monotonic pattern and different phases can be observed. A long-term rise inequality is noticeable between mid-nineteenth century and World War I reaching a peak in 1918. Then, a sustained inequality reduction took place during the 1920s and early 1930s, stabilizing in the years of the Civil War (1936– 1939) and World War II. A sharp reversal was experienced during the late 1940s and early 1950s, with an inequality peak by 1953, similar in size to that of 1918. Then, a dramatic fall in inequality occurred in the late 1950s and early 1960s. Henceforth, income distribution stabilized fluctuating within a narrow 0.30–0.35 Gini range.

A comparison of the evolution of real per capita NNDI and income distribution (Figs. 5.2 and 5.3) shows no trade-off between inequality and growth, by which higher living standards resulting economic growth compensate for higher inequality and vice versa, seems to exist. Moreover, there is no clear association between them over time. Thus, in the most dynamic phases of economic performance, inequality declined (the 1920s, the Golden Age, 1950–1974), but it also increased (1850– 1883); while in years of sluggish performance, inequality deepened (1880–1920, the post-Civil War autarchy), although it shrank too (during the II Republic, 1931–1936, and the transition to democracy, 1975–1984).

But how severe has been inequality in terms of well-being? Branko Milanovic et al. (2011) proposed the concept of Inequality Extraction Ratio, defined as the ratio between the actual Gini [G] and the maximum feasible Gini (G\*), which is obtained as

$$\mathbf{G}^\* = (\mathbf{z} - \mathbf{1})/\mathfrak{a} \tag{5.1}$$

Where a = average incomes, expressed in terms of subsistence (1.9 2011 EKS dollars a day).

Thus, the Inequality Extraction Ratio (IER) measures the actual level of inequality as a proportion of its potential maximum. The closer a country is to the maximum potential inequality, the stronger the negative impact of inequality on welfare.

The negative effect of inequality on welfare, as measured by the IER, increased during the early twentieth century peaking at two-thirds of its potential maximum by the end of World War I and, then, declined until the mid-1980s, but for a dramatic reversal at the end of the autarchic period, fluctuating thereafter around one-third of its potential maximum (Fig. 5.5).

It is worth noticing that, in Spain, similar levels of inequality are significantly different in terms of its impact on well-being. For example, although exhibiting similar levels of inequality (around 0.35 Gini), during 1850–1883, actual inequality oscillates around one-half of its potential maximum, while over 1960–2015 it fluctuates around one-third.

But can the effect of changes in income distribution on welfare be quantified? Amartya Sen's (1973) proposed to adjust the level of net national disposable income for the evolution of income distribution. Thus, I have computed the so-called Sen Welfare,

Fig. 5.5 Inequality extraction ratio 1850–2015 Note Actual Gini as a proportion of the maximum potential Gini

Sen Welfare ¼ Real Per capita NNDI x 1ð Þð Gini 5:2Þ

Figure 5.6 compares GDP per head with the Sen-Welfare measure. It can be observed that except for the early twentieth century—especially in the 1910s and 1920s and in the late 1940s and early mid-1950s—when Sen-Welfare level fell behind per capita GDP, both measures exhibit similar long-run performance.

During the 1920s and, especially, the 1950s, Sen Welfare improved faster than real GDP per person, while this situation reversed from the end of the nineteenth century to the end of World War I. Moreover, in phases of income contractions such as the Civil War and its autarchic aftermath, and the Great Recession (2008–2013), welfare worsened more intensively than GDP per head. On the whole, Sen Welfare increased 13-fold over 1850–2015.

To sum up, net disposable income and private consumption exhibit similar trends to GDP but with less steep acceleration since mid-twentieth century, while the negative impact of inequality on

#### 54 L. Prados de la Escosura

Fig. 5.6 Real per capita GDP and Sen welfare, 1850–2015 (2010 = 100) (logs)

economic welfare was softened from 1960 onwards and inequality decline made significant contributions to well-being in the 1920s and the 1950s. Thus, it can be concluded that in modern Spain long-run economic growth was accompanied by a substantial improvement in material welfare.

A substantive objection to GDP per head is that it fails to incorporate non-income dimensions of well-being. Human welfare is widely viewed as a multidimensional phenomenon, in which per capita income (and its distribution) is only one facet. Critics of GDP as a measure of welfare have signalled the Human Development Index as a better alternative (Coyle 2014). Human development has been defined as 'a process of enlarging people's choices'(UNDP 1990: 10), namely enjoying a healthy life, acquiring knowledge and achieving a decent standard of living, that allow them to leading 'lives they have reasons to value' (Sen 1997).

The Human Development Index (HDI), published by the United Nations Development Programme (UNDP), has three dimensions: a healthy life, access to knowledge and other aspects of well-being. It uses reduced forms of these dimensions, namely life expectancy at birth as a proxy for a healthy life, education measures (literacy, schooling) as a short cut for access to knowledge, and discounted per capita income (its log) as a surrogate for other aspects of well-being (Anand and Sen 2000; UNDP 2001). These are combined into a synthetic measure using a geometric average (UNDP 2010). Since all dimensions are considered indispensable they are assigned equal weights.

It matters how progress in the dimensions of human development is measured. Often social variables (life expectancy, height or literacy) are used, either raw (Acemoglu and Johnson 2007; Becker et al. 2005; Soares; Lindert 2004) or linearly transformed (UNDP 2010). This causes measurement problems when a social variable has asymptotic limits. An example would be life expectancy. Consider two improvements, one from 30 to 40 years and another from 70 to 80 years. These increases are identical in absolute terms, but the second is smaller in proportion to the initial starting level. When original (or linearly transformed, as happens in the case of the UNDP's HDI) values are employed, identical changes in absolute terms result in a smaller measured improvement for the country with the higher starting point, favouring the country with the lower initial level (Sen 1981; Kakwani 1993).

The limitations of linear measures become more evident when quality is taken into account. Life expectancy at birth and literacy and schooling rates are just crude proxies for the actual goals of human development: a long and healthy life and access to knowledge. Research over the last two decades concludes that healthy life expectancy increases in line with total life expectancy, and as life expectancy rises, disability for the same age-cohort falls (Salomon et al. 2012). Similarly, the quality of education, measured in terms of cognitive skills, grows as the quantity of education increases (Hanushek and Kimko 2000; Altinok et al. 2014). The bottom line is that more years of life and education imply higher quality of health and education during childhood and adolescence in both the time series and the cross section.

My alternative to the UNDP's conventional HDI is a historical index of human development (HIHD) in which non-income variables are transformed nonlinearly, rather than linearly as in the HDI, in order to allow for two main facts: (1) increases of the same absolute size represent greater achievements the higher the level at which they take place, and

Fig. 5.7 Real per capita GDP (2010 = 100) (logs) and historical index of human development [HIHD\*] (excluding income dimension), 1850–2007. Source Real per capita GDP, see the text; human development, Prados de la Escosura (2015) and http://espacioinvestiga.org/home-hihd/countries-hihd/hihd-esp-eng/?lang=en#

(2) quality improvements are associated with increases in quantity (see Prados de la Escosura 2015).

When per capita GDP and Human Development (in which the income dimension has been excluded) are compared, they exhibit similar long-term trends (Fig. 5.7), although improvements in the Historical Index of Human Development are more intense between 1880 and 1950 and slower thereafter (Table 5.2). A major discrepancy is observed for the 1930s and 1940s, when human development thrived, driven by improving life expectancy at birth—a result of the epidemiological transition related to the diffusion of the germ theory of disease—and broadening primary education, while GDP per head contracted as consequence of the Depression and the Civil War and its autarchic aftermath.


Table 5.2 Real per capita GDP and human development growth, 1850–2007 (%). (average yearly logarithmic rates)

Source Real per capita GDP, see the text; Human Development (excluding income dimension), Prados de la Escosura (2015) and http://espacioinvestiga.org/homehihd/countries-hihd/hihd-esp-eng/?lang=en#

All in all, it can be concluded that GDP per head captures long-run trends in welfare in Spain, but fails to do it in the short and medium term.

## Note

1. The Gini coefficient measures the extent to which the distribution of income (or consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Gini of 0 represents perfect equality, while an index of 1 (100) implies perfect inequality. This paragraph draws on Prados de la Escosura (2008).

## References


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# Part II

Measurement

# 6

# Measuring GDP, 1850–1958: Supply Side

In historical national accounts, as for most developing countries, the most reliable and easiest to estimate GDP figures are those obtained through the production approach.<sup>1</sup> As for most developing countries, real product has been computed from physical indicators rather than as a residual obtained from independently deflated output and inputs. The components' method has prevailed over the indicators' method as much as the data permitted it, and both direct and indirect estimating procedures have been employed.<sup>2</sup>

Estimating constant gross value added series involved several steps. In the first place, Laspeyres quantity indices were built up for each major component of output using 1913, 1929 and 1958 value added as alternative weights. Value added for 1913 and 1929 benchmarks was computed through either direct estimate or, more often, gross value added levels for 1958, taken from the input–output table (TIOE58) and the national accounts (CNE58) were projected backwards to 1913 and 1929 (with quantity and price indices expressed as 1958 = 1). Then, in an attempt to allow for changes in relative prices, these volume indices were spliced into a single series. The estimates with 1913 weights have been accepted for 1850–1913, while variable weighted geometric averages of

63

L. Prados de la Escosura, Spanish Economic Growth, 1850–2015, Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5\_6

the indices obtained with 1913 and 1929 (1929 and 1958) weights have been adopted for 1913–1929 (1929–1958), a procedure that allocates a higher weighting to the closer benchmark. Lastly, a volume index of real gross value added (GVA) for 1850–1958 was constructed by weighting output chain volume Laspeyres indices for each major branch of economic activity with their shares in total gross value added for 1958.

An effort to construct price indices was carried out from a wide range of price series of uneven quality and coverage.<sup>3</sup> Chain Paasche price indices for agriculture, industry and services were built up.<sup>4</sup> In fact, since volume indices are of Laspeyres type, that is,

$$\mathbf{Q}^{\rm L} = \sum \mathbf{q}\_{\rm i} \mathbf{p}\_{\rm o} / \sum \mathbf{q}\_{\rm o} \mathbf{p}\_{\rm o},\tag{6.1}$$

Paasche price indices,

$$\mathbf{P}^{\mathbf{P}} = \sum \mathbf{q}\_{\mathbf{i}} \mathbf{p}\_{\mathbf{i}} / \sum \mathbf{q}\_{\mathbf{i}} \mathbf{p}\_{\mathbf{o}},\tag{6.2}$$

are, then, required to derive current values,

$$\mathbf{V} \mathbf{V} = \mathbf{Q}^{\mathrm{L}} \ast \mathbf{P}^{\mathrm{P}} = \sum \mathbf{q}\_{\mathrm{i}} \mathbf{p}\_{\mathrm{i}} / \sum \mathbf{q}\_{\mathrm{o}} \mathbf{p}\_{\mathrm{o}} \tag{6.3}$$

where q and p are quantities and prices at the base year o or any other year i.

Yearly series of gross value added at current prices were derived for each branch of economic activity by projecting backwards its level at the 1958 benchmark, provided by official national accounts (CNE58), with its Laspeyres quantity and Paasche price indices, expressed with reference to 1958 = 1.<sup>5</sup> Total gross value added at current prices was derived by aggregation of sectoral value added. An implicit Paasche GVA deflator was calculated by dividing current and constant price series. Adding indirect taxes (net of subsidies) to total current GVA provided nominal GDP at market prices. Real GDP at market prices was obtained by deflating nominal GDP with the GVA deflator.

Four major branches of economic activity are taken into account: (a) agriculture, forestry and fishing; (b) manufacturing, extractive industries and utilities; (c) construction; and (d) services.

### 6.1 Agriculture, Forestry and Fishing

#### 6.1.1 Agriculture

Two steps were followed in computing agricultural value added.<sup>6</sup> Firstly, final output, that is, total production less seed and animal feed, was constructed. Then, gross value added was derived by subtracting purchases of industrial and services inputs, from final output.

Unfortunately, annual data on crops and livestock output are incomplete and their coverage uneven over time. Nonetheless, available data allowed me:

(a) To compute agricultural final output at different benchmarks: circa 1890, 1898/1902, 1909/1913, 1929/1933, 1950 and 1960/1964 by valuing physical output for each product at farm-gate prices.<sup>7</sup>

(b) And, then, to derive, Laspeyres real output (Q<sup>L</sup> ) for each benchmark (bk) by deflating current values (V) with a Paasche chain price index built on a large sample of agricultural goods (q and p are quantities and prices at the base year o or any other year i).8 That is,

$$\mathbf{Q}\_{\rm bk}^{\rm L} = \mathbf{V}\_{\rm bk} / \mathbf{P}\_{\rm bk}^{\rm P} \\ \rm bk = 1890, 1898/1902, 1909/13, 1929/33, 1950, 1960 \,\tag{6.4}$$

being P<sup>P</sup> bk a chain Paasche,P<sup>P</sup> bk ¼ Ppipqip= Ppip1qip

The lack of quantitative evidence on low acreage, high-value crops such as fruits and vegetables that increase its importance at higher income levels and urbanization, makes the deflation of current value estimates a preferable alternative to the construction of volume indices on reduced quantitative information.<sup>9</sup> Actually, prices tend to move together within closer bounds than quantities.<sup>10</sup>

(c) Next, real final agricultural output series was derived splicing each pair of adjacent benchmarks with a yearly index of final output built on reduced information.<sup>11</sup> The procedure was to project each benchmark with a quantity index constructed at its relative prices and to compute, then, a weighted geometric average of the series resulting from each pair of adjacent benchmarks, in which the closer benchmark to each particular year was allocated a higher weighting,

$$\begin{split} \mathbf{Q}\_{\mathbf{t}}^{\rm L} &= \left( \mathbf{Q}\_{\mathbf{b}\mathbf{k}o}^{\rm L} \ast \mathbf{O}\_{\mathbf{t}}^{\rm L} \right)^{(\rm n-t)/(\rm n-o)} \ast \left( \mathbf{Q}\_{\mathbf{b}\mathbf{k}n}^{\rm L} \ast \mathbf{O}\_{\mathbf{t}}^{\rm L} \right)^{(\rm t-o)/(\rm n-o)} \\ &= \left. \mathbf{O}\_{\mathbf{t}}^{\rm L} \ast \left( \left. \mathbf{Q}\_{\mathbf{b}\mathbf{k}o}^{\rm L} \right)^{(\rm n-t)/(\rm n-o)} \left( \mathbf{Q}\_{\mathbf{b}\mathbf{k}n}^{\rm L} \right)^{(\rm t-o)/(\rm n-o)} \right. \end{split} \tag{6.5}$$

where Q is Laspeyres real final output index, O is a Laspeyres quantity index (built on reduced information) for year t, bk represents each benchmark estimate, and o and n are the initial and final years within each period.<sup>12</sup>

(d) Lastly, agricultural final output at current prices was obtained by extrapolating the 1958 level of final output (CEN58) backwards with the real final output index and a Paasche price index.<sup>13</sup> The Paasche price index was constructed by interpolating each pair of adjacent chain price benchmarks (Table 6.1, column 2) with a yearly Paasche price index derived on reduced information.<sup>14</sup> The linkage procedure for each pair of adjacent benchmarks was projecting each benchmark price level with the


Table 6.1 Agricultural final output: benchmark estimates, 1890–1960/64

Notes <sup>a</sup> value at 1960 prices. <sup>b</sup> 1960 price level. <sup>c</sup> 1960 prices

Incomplete coverage led to assumptions about the production of several crops in 1890 and 1900. Total output for major groups (vegetables, raw materials, fruits and nuts, meat, and poultry and eggs) was inferred on the basis of observed sample-to-total output ratios for 1909/1913

Source Quantities, prices and values derive from GEHR (1991), Simpson (1994) (unpublished data set), and the original sources quoted there, and Ministerio de Agricultura (1979a)

Ratios of final output to total production for each crop are shown in the Appendix, Table A.1. Coefficients to transform livestock output into quantities of meat, wool and milk are presented in the Appendix, Table A.2

variations of the annual price index and, then, computing a variable geometric mean in which the closer benchmark to a particular year received the higher weighting.15

## The Construction of Annual Quantity and Price Indices on Reduced Information

The annual quantity and price indices constructed on a sample of agricultural produce, and employed to interpolate adjacent benchmark estimates of real final output, deserve some comments. A two-stage procedure was followed to build the quantity index in order to prevent undesired over-representation of particular crops in aggregate output. Ten groups of products were firstly defined, for which independent indices were constructed. This procedure did not prevent adding guesses to the data since it was assumed that, within each group, those products not included in the sample moved exactly like those that were part of it. However, the more homogeneous the group of goods is, the less strong the implicit assumptions of this method are. In any case, when output is directly estimated from a sample of products, the implicit assumptions are stronger than in my proposed two-stage calculation procedure.<sup>16</sup> Thus, index numbers were built for major groups of products: cereals, legumes, vegetables, raw materials, fruits and nuts, must, unrefined olive oil, meat, poultry and eggs, and milk and honey.<sup>17</sup>

Incomplete production data constitute a major obstacle to the construction of an agricultural output index for nineteenth-century Spain. Assumptions and conjectures are required, then, to establish trends in agricultural output and to fill in the missing data. Estimating output trends under information constraints can be approached through (a) the volume produced, in which most is made of the scattered evidence available; (b) the commercialization of crops deflated by the (expanding) length of the transportation network (road and rail) in order to prevent an upward bias in the rate of growth of agricultural production, as mercantilization evolved faster than production in the early stages of development; and (c) the demand approach, in which output is deducted from an estimate of consumption derived from a demand equation calibrated with levels of disposable income (real wages) and relative prices for food, together with their relevant elasticities.<sup>18</sup> The volume and commercialization approaches are used here to derive output levels.

Data coverage of crop output is much lower prior to 1891 than thereafter, and it is practically non-existent for the period 1850–1881.<sup>19</sup> Output for major agricultural groups had to be derived from scattered information on the production of wheat, barley, must, raw olive oil and sugar cane and beet, plus fruit export data for the period 1882–1890, whose data coverage represents 64% of final production (excluding livestock) in 1890.<sup>20</sup> Up to 1882, non-livestock agricultural output was proxied by trading series for major crops using evidence from maritime and rail transportation (the latter previously deflated by the network's length).<sup>21</sup> The commercialization series included cereals, legumes, wine, olive oil, fruits and nuts, and raw materials (raw silk, sugar cane).<sup>22</sup> Accepting traded crops as proxies for crops output implies the arguable assumption of a highly commercialized agriculture in which both distribution and production show a similar profile.<sup>23</sup> If trade in agricultural products rose faster than output, the resulting index would incorporate an upward bias.<sup>24</sup>

Estimates are even weaker for the years 1850–1865, when only maritime transportation data were available (coastal transport since 1857), and in the cases of wheat and legumes, output had to be derived from consumption estimates (by arbitrarily assuming a constant consumption per head times population) adjusted for net imports.<sup>25</sup>

Once quantity series were established for the main commodity groups, the calculation procedure used for the post-1865 estimates was applied to compute output.<sup>26</sup>

Evidence on livestock prior to 1905 is only available for 1865 and 1891.<sup>27</sup> Meat and milk outputs were obtained by applying conversion coefficients to livestock numbers for 1865, 1891 and 1905/1909 and valued at 1891 prices.<sup>28</sup> Annual figures for livestock output were derived through log-linear interpolation, both for 1865–1891 and for 1891– 1905. The case for accepting such a crude procedure is to reach a wider coverage for agricultural production by including livestock output, which apparently had an opposite trend to that of crops output over the late nineteenth century.<sup>29</sup> However, it is worth noticing that a decline in livestock numbers does not necessarily mean that livestock output fell as an increased turnover of animals took place stimulated by the rise in the demand for meat and dairy products associated with urbanisation.<sup>30</sup> For the earlier years 1850–1864, output was obtained under the assumption that per caput consumption remained constant and equivalent to that of 1865.<sup>31</sup>

Then, a second step was estimating the aggregate index as a weighted average of output indices for major agricultural groups with their shares in the benchmark's agricultural final output as weights (Table 6.2). Volume indices were computed for different time spans valuing quantities of each product at the farm-gate prices for each benchmark (Table 6.3).

To construct a yearly price index, single series for a sample of goods within each agricultural subsector were gathered from a wide range of sources.<sup>32</sup> Individual price series were assembled for cereals (wheat, barley, rice), legumes (chick peas), vegetables (potatoes), fruits and nuts


Table 6.2 Agricultural final output at current prices, 1890–1964 (%)

Note <sup>a</sup> 1960/1964 final output computed at 1960 prices

Source Quantities are derived mostly from GEHR (1989, 1991), completed with Comín (1985a), Simpson (1986, 1994) (unpublished data set) and Carreras (1983) for the pre-Civil War years; and Barciela (1989) and Ministerio de Agricultura (1974, 1979a) for the 1940–1964 period. Prices are taken from GEHR (1989), Simpson (1994) (unpublished data set) and Ministerio de Agricultura (1974, 1979a)


Table 6.3 Construction of agricultural volume indices, 1850–1958

Sources Appendix, Table A.3

(oranges and almonds), must, unrefined olive oil, raw materials (sugar beet, wool), meat (beef, veal, pig and lamb), eggs and milk. Laspeyres price indices were constructed, then, for each group of goods with benchmarks' weights. An aggregate price index was, in turn, obtained as the average of subsectoral Laspeyres price indices weighted by their annual quantity indices.<sup>33</sup>

### Gross Value Added

Nominal gross value added was obtained by deducting purchases outside the agricultural sector from final output at current prices. Real gross value added was derived, in turn, by subtracting industrial and services inputs at constant prices from real final output. An implicit deflator was derived from nominal and real gross value added series. Purchases outside the agricultural sector were proxied by the consumption of mineral fertilizers, and the level of non-agricultural inputs for 1958 was backcasted with the annual rate of variation of mineral fertilizers consumed in agriculture.<sup>34</sup>

#### 6.1.2 Forestry

Evidence for forestry is only available since 1901 and quantities of wood, firewood, resin, cork and esparto grass were valued at 1912/1913, 1929/1933 and 1960 prices and added up into single values from which a chain quantity index was derived.<sup>35</sup> Output at current prices is available since 1901.<sup>36</sup> Gross value added at current prices was computed through backward projection of the 1958 level in national accounts (CNE-58) with the value index.<sup>37</sup> An implicit deflator was derived from the current value and volume indices.

#### 6.1.3 Fishing

For fishing, quantity and current value series are available from 1904 onwards, but only scattered information exists for 1878, 1883 and 1888–1892 (and no data at all for 1935–1939).<sup>38</sup> The quantity of fresh fish captured is available but, since no allowances can be made for composition changes, the alternative of deflating the current value of fish captures was preferred on the grounds that, within a given industry, price variance is lower than quantity variance. Gross value added at current prices was obtained through backward extrapolation of the 1958 level (CNE58) with the rate of variation of the total value of captures.<sup>39</sup> When current values of total production were missing (1850–1903), gross value added was extrapolated backwards on the basis of output (computed under the assumption of constant per capita consumption times the population and adjusted for net exports) and a price index for cod.40 An implicit deflator was derived from the current value and volume indices.

#### 6.1.4 Value Added for Agriculture, Forestry and Fishing

Value added at current prices for agriculture, forestry and fishing was reached by adding up each subsector's estimates. Aggregate volume indices for agriculture, forestry and fishing output were derived as an average of the subsector indices with their share in its aggregate gross value added for 1913, 1929 and 1958 as weights, respectively.<sup>41</sup> Then, a single quantity index was computed as a variable weighted geometric average of the three indices.<sup>42</sup> The composition of the aggregate index is as follows: for 1850– 1913, 1913 weights were accepted; for 1913–1929, a weighted geometric average of 1913 and 1929 weighted indices; for 1929–1958, a weighted geometric average of 1929 and 1958 weighted indices. An implicit deflator was obtained from current and constant price value added.

## 6.2 Industry

New series of industrial output and its main components, in nominal and real terms, are constructed in this section. The pathbreaking research carried out by Albert Carreras supplied the basis from which new series for extractive industries, utilities and manufacturing output were built up.<sup>43</sup>

The difficulties faced by historical attempts to produce hard empirical evidence on industrial performance can be illustrated by assessing Carreras' seminal contribution.44 His index of industrial production used a fixed weighting system with alternative base years (1913, 1929, 1958, and 1975) that were, in turn, spliced into a single series using end years. For the period under study here, the 1958 input–output table (TIOE58) supplied the unit value added used as weights that were, then, extrapolated backwards to 1929 and 1913 with industrial prices, under the assumption that they approximated the trends in unit value added.<sup>45</sup> Unfortunately, the author was unable to establish earlier base years for the nineteenth century, and as no regard was paid to changes in relative prices, the further back in time we move from 1913, the less representative of industrial performance his index becomes. In addition to the use of fixed weights, limited coverage is usually a major liability for any industrial index. Carreras' index reaches an acceptable coverage, 65% in 1958 and approximately 50 and 70% for 1929 and 1913.<sup>46</sup>

The main objection to Carreras' index is its weighting scheme. At each benchmark (1913, 1929, 1958 and 1975), annual physical output for every product was weighted by its unit value added to compose an aggregate series that was, then, spliced into a single chain index using end years.<sup>47</sup> The final series approximates well overall industrial performance insofar the sample of goods from which the industrial output index is derived remains 'representative' for the whole industry. Unfortunately, the coverage of different sectors is asymmetrical in Carreras' index, and as one moves backwards in time, it declines and becomes more uneven, increasing the risk of undesired over-representation of particular products since a mere fraction of a subsector may eventually dominate the overall index.<sup>48</sup>


Table 6.4 Composition of manufacturing value added in 1958

Note <sup>a</sup> [100 \* ln ((1)/(2))]

Source Carreras (1983) and Spanish National Accounts Base 1958 (CNE58)

An illustration of this argument is provided by the coverage of Carreras' index at the 1958 benchmark. A glance at Table 6.4 shows the extent to which its coverage is asymmetrical. Metal industries (basic and transformation), for instance, are clearly over-represented conditioning the aggregate industrial index when it is computed directly, as in Carreras' case. Industrial growth might suffer, then, from an upward bias as a result of over-weighting capital goods, whose growth rate is usually higher than the industry's average.49 In the construction of quantity indices for manufacturing industry, an attempt will be made to prevent some of the shortcomings in Carreras' industrial production index.

#### 6.2.1 Manufacturing

Lack of information prevented the computation of total production and inputs, at current and constant prices, separately, from which nominal and real value added would be derived. In turn, changes in real value added are represented by variations in quantity indices constructed from production evidence for each manufacturing sector, as it is usually done in historical national accounts and occasionally in developing countries.<sup>50</sup>

In order to construct an index for manufacturing output, Laspeyres indices for each branch (Qi ,t) were, firstly, computed, and then, the

#### 74 L. Prados de la Escosura

aggregate index (Qt \* ) was obtained as their average, using each branch's share in total manufacturing value added at the benchmark year as weights (Pi ,o).51 That is,

$$\mathbf{Q}\_{\rm i,t} = \sum \mathbf{q}\_{\rm jt}^{\rm i} \mathbf{p}\_{\rm jo}^{\rm i} / \sum \mathbf{q}\_{\rm jo}^{\rm i} \mathbf{p}\_{\rm jo}^{\rm i} \tag{6.6}$$

and then,

$$\mathbf{Q}\_{\mathbf{i}}^{\*} = \sum \mathbf{Q}\_{\mathbf{i},\mathbf{t}} \mathbf{P}\_{\mathbf{i},\mathbf{o}} / \sum \mathbf{Q}\_{\mathbf{i},\mathbf{o}} \mathbf{P}\_{\mathbf{i},\mathbf{o}} \tag{6.7}$$

where

$$\mathbf{P}\_{\rm i,o} = \sum \mathbf{q}\_{\rm jo}^{i} \mathbf{p}\_{\rm jo}^{i} / \sum \mathbf{q}\_{\rm jo} \mathbf{p}\_{\rm jo} \tag{6.8}$$

Here q and p represent quantities and prices; subscripts o and t are the benchmark year and any other year, respectively; j = 1,… n are goods, and i = 1, … s are sectors; superscript i denotes quantities and prices of goods included in sector i. Goods in sector i are not included in any other sector.

Using this approach, the problem of lack of representativeness will be less acute than in the case of Carreras index, since the assumptions that (a) total output evolves as its main components, and (b) its coverage remains unchanged over a given period, are more easily acceptable at branch level than for the industry as a whole.

For manufacturing, eleven branches have been distinguished (Table 6.5). Basic series of physical quantities were taken from Carreras (1983, 1989), supplemented with production data on wine, alcohol, brandy, beer, meat slaughtering and timber.52 Thus, most data employed in the construction of the manufacturing output index correspond to intermediate and primary inputs that would lead, in turn, to underestimating industrial growth, as efficiency gains in the use of inputs are not allowed for. In order to offset this shortcoming, I arbitrarily assumed a yearly 0.5% efficiency increase in the use of inputs for engineering industry and incorporated quality adjustments in the transport equipment industry.<sup>53</sup>


Table 6.5 Breakdown of manufacturing value added, 1913–1958 (%)

Source CNE58 for 1958; for 1913 and 1929, see text

In the construction of a Laspeyres quantity index for manufacturing production, a two-stage procedure was followed.

(a) Quantity indices for each manufacturing branch. Unit value added for each product in 1958 was backward extrapolated to 1929, 1913, 1890 and 1870 with its own price indices under the arbitrary assumption that the value added/total production ratio remained stable over time.<sup>54</sup> Whenever possible, direct estimates of unit value added were applied.<sup>55</sup> Also, adjustments by Morellá on Carreras' unit value added estimates for 1958 were accepted.<sup>56</sup> Then, for each branch of manufacturing, Laspeyres quantity indices were constructed with each benchmark's unit value added estimates as weights.<sup>57</sup>

(b) Quantity index for aggregate manufacturing. A Laspeyres quantity index for total manufacturing was obtained by adding up all branch indices with their benchmark shares in 1913, 1929 and 1958 current value added as weights (Table 6.5) that were obtained by extrapolation of 1958 levels (CNE58) with each branch's Laspeyres quantity and Paasche price indices. The resulting three indices were, then, spliced using a variable weighted geometric mean, in which the closer to a given year t, the larger the weight allocated to a particular benchmark.58

Paasche price indices for each branch of manufacturing industry were constructed by dividing, for a given sample of goods, its current value (expressed in index form) by a Laspeyres quantity index.<sup>59</sup> Current values for the sample of goods were obtained by multiplying quantities by prices that were, then, added up. An important caveat is that manufacturing price indices were constructed on very scant price data, strongly skewed towards raw materials and intermediate goods that, in turn, would tend to bias upward current manufacturing value added.<sup>60</sup> Later, an implicit Paasche deflator was obtained for aggregate manufacturing by dividing total current value added (in index form) by the Laspeyres quantity index.

## 6.2.2 Extractive Industries

As regards extractive industries, mining and quarrying were considered, with the latter usually representing less than 10% of sectoral value added. The construction procedure of quantity and price indices and of nominal and real value added levels was identical to the case of manufacturing.<sup>61</sup>

## 6.2.3 Utilities

Only gas and electricity output series were available on yearly basis, and an aggregate chain index was obtained by weighting gas and electricity output with their contributions to sectoral value added for 1913, 1929 and 1958, in which gas was allocated a larger share to include water supply.<sup>62</sup> Nominal gross value added was reached through the backward extrapolation of 1958 levels with Laspeyres quantity and Paasche price indices. Quantity indices were spliced into a single index following the same procedure used for manufacturing and extractive industries. In turn, the same construction method of price indices applied to manufacturing and extractive industries was adopted.

## 6.2.4 Value Added for Manufacturing, Extractive Industries and Utilities

Finally, an aggregate quantity index for industry (excluding construction) was derived as an average of manufacturing, extractive industries and utilities indices using their 1913, 1929 and 1958 sectoral shares in industrial gross value added as weights. Then, to obtain a single Laspeyres chain index of industrial gross value added, the three indices were spliced through a variable weighted geometric mean in which weighting varied according to the distance from the considered year (as in (12)). Current price estimates were obtained by adding up each industry's value added. An implicit deflator was derived from current and constant price estimates.

### 6.3 Construction

Five subsectors were distinguished in the construction industry, residential and commercial, railway, road building, hydraulic infrastructure and other public works.

#### 6.3.1 Residential and Commercial Construction

I started from the available information on the stock of urban and rural dwellings and derived the number built in each inter-censal period by adding a rough estimate of the number of houses demolished in the period to the net increase in the stock.63 Also, size and quality changes in housing were taken into consideration and overall improvements were arbitrarily assumed to take place at 0.5% annually.<sup>64</sup> Demolition rates were obtained through alternative methods that cast very close results. One procedure, adopted from the British case, was to derive decadal rates for demolition by assuming that 85% of the new homes built a century earlier would be demolished while the surviving 15% would disappear steadily over the next century (Feinstein 1988: 388). An alternative was the demolition rates computed for Spain by Bonhome and Bustinza that I accepted up to 1940.<sup>65</sup> For the years 1940–1958, I derived them from existing sources (Nomenclators and Censuses of dwellings).<sup>66</sup> The resulting demolition annual rates were 1861–1910, 0.21; 1911–1940, 0.28; 1940s, 0.36; and 1950s, 0.26.

To sum up, the change in the quality-adjusted stock of dwellings includes the net increase in stock plus the replacement of demolished dwellings, that is, the increase in gross stock, to which a yearly 0.5% quality improvement was applied. In order to distribute the inter-censal increase in the gross stock annually, available figures for the consumption of cement and timber were used for 1850–1944, while the annual number of new dwellings (mostly subsidized construction) was taken for 1944–1958.<sup>67</sup> To obtain yearly output figures, repairments and maintenance expenses were added to the quality-adjusted increase in gross stock. Repairments and maintenance were assumed to represent 1% of the current stock (which was obtained through log-linear interpolation between pairs of adjacent censal benchmarks). Finally, urban and rural construction indices were combined into a single index using their respective shares in the total value of dwellings.68 A specific deflator was, in turn, built up that combined construction materials costs and mason wages with 1958 input–output weights (TIOE58).<sup>69</sup> Annual current value added for the residential and commercial construction industry was obtained by projecting the level of gross value added for 1958 backwards with the quantity and price indices.<sup>70</sup>

## 6.3.2 Non-Residential Construction

## Railways

Expenditure on investment and maintenance in railways at 1990 prices computed by Cucarella (1999) is the basis of my estimates. He relied on decadal averages of nominal expenditure on investment and maintenance in railways estimated by Gómez Mendoza (1991) that were distributed annually over 1850–1920 using the number of kilometres under construction, for investment, and those under exploitation, for maintenance, and that he completed for the late 1920s and early 1930s with his own estimates (Cucarella 1999: 84–85). In addition, government's and Spanish national railways company's (RENFE) investment and maintenance expenditures in railways estimates by Muñoz Rubio (1995) were employed from 1940 onwards. Cucarella (1999: 78–80) deflated his current value estimates with a wholesale price index. I converted Cucarella's constant price estimates into nominal values using his own deflator and deflated the series again with a specific railway construction price index that combines the costs of railway materials and mason wages with 1958 input–output weights (TIOE58).<sup>71</sup>

### Roads

Investment, repairs and maintenance expenditures on roads at current prices are available since 1897 (Uriol Salcedo 1992). Nominal road expenditure was backcasted to 1850 with the rate of variation of public expenditure on roads (Comín 1985b). The resulting yearly figures for 1850–1935 were adjusted to match the decennial estimates by Gómez Mendoza (1991). Finally, current expenditure estimates were deflated with a specific price index computed by combining materials costs and mason wages with 1958 input–output weights (TIOE58).<sup>72</sup>

#### Hydraulic Infrastructure and Other Public Works

Investment, maintenance and repairs expenditures on hydraulic infrastructure and maritime and harbour expenditure by the central government were deflated with a specific price index including construction materials and wages.<sup>73</sup>

Indices of non-residential construction were built up combining railway and road construction, hydraulic infrastructure and other public works with their 1913, 1929 and 1958 shares in the sector's value added.<sup>74</sup> A compromise, single quantity index for the whole period 1850–1958 was built up as a variable weighted geometric average of each pair of adjacent benchmark's indices (as in the case of manufacturing).

It is worth mentioning that Alfonso Herranz-Loncán (2004) estimated output in infrastructure for 1860–1935 at a more disaggregated level than the one presented here. His results are coincidental with mine but show higher volatility, due to the fact that only investment is considered while maintenance is neglected (Fig. 6.1). For this reason, I have not incorporated Herranz-Loncán estimates here.

Fig. 6.1 Non-residential construction volume indices, 1850–1935: alternative estimates (1913 = 100). Source Prados de la Escosura, see the text; Herranz-Loncán (2004)

Current value series for each branch of non-residential construction was obtained by linking the level of gross value added for 1958 to its Laspeyres quantity and price indices and, then, added up to represent total value added in non-residential construction. An implicit deflator was computed.

#### 6.3.3 Value Added in Residential and Non-Residential Construction

Residential and non-residential construction output was, then, combined into a single index for the construction industry with their 1913, 1929 and 1958 shares in the sector's value added, from which a spliced volume index was derived using a variable weighted geometric average.

Nominal gross value added for the entire construction industry was obtained by adding up residential and non-residential construction value added at current prices. An implicit (semi-Paasche) deflator was derived from current value (in index form) and the aggregate volume index.<sup>75</sup>

## 6.4 Services

Estimating value added in services represents the main obstacle in the construction of historical national accounts, especially in the case of those services for which no market prices exist, and also an unsurmountable problem in international comparisons.<sup>76</sup> In the present estimate, the use of employment data has been avoided and output indicators used instead.<sup>77</sup> When the output of services is derived using labour input data, productivity cannot be estimated since by construction it is implicitly assumed that output per worker remains stagnant. Major subsectors considered here are transport and communications, trade (wholesale and retail), banking and insurance, ownership of dwellings, public administration, education and health, and other services including restaurants, hotels and leisure, household services and liberal professions. Several steps were taken to produce annual quantity and price indices for the different branches of the service sector.

#### 6.4.1 Transport and Communications

Transportation and communication services include water (coastal and international), road, urban, air and rail transport plus postal, telegraph and telephone services.

For transportation by rail, merchandise and passenger output series are available for the period 1868–1958 and were backcasted to 1859 with the volume of merchandise and passengers transported.78 A spliced index of total rail transport output was obtained with rates per passengerkilometre and ton-kilometre for 1913, 1929 and 1958 as weights over 1859–1964 that was extrapolated back to 1850 with the rate of variation of railway tracks. Thus, 1913 weights were applied for the period 1868– 1913, while variable weighted geometric averages of 1913 and 1929 (1929 and 1958) weighted indices were accepted for 1913–1929 (1929– 1958). Prices, that is, average output per passenger-kilometre and ton-kilometre (in pesetas), were taken from Gómez Mendoza (1989) and Muñoz Rubio (1995). Value added at current prices in rail transport was obtained by linking the 1958 level (CNE58) to quantity and price indices (average prices per passenger-kilometre and ton-kilometre).

For maritime transport, coastal and international transport services were distinguished. For coastal transport, merchandise output (expressed in tons-kilometre), available since 1950, was projected backwards to 1857 with tons of merchandise transported, while only the number of passengers transported was available from 1928 onwards. An unweighted average of the quantity indices of passenger and merchandise coastal transport was computed for 1928–1958 that was, then, spliced with the merchandise index in order to cover the period 1857–1958.<sup>79</sup> International transport services for 1942–1958 were measured by the total value of passenger and merchandise freights received by Spanish ships and, then, deflated by their respective freight indices.<sup>80</sup> For 1850– 1942, merchandise transport was computed by applying a freight factor to the total value of exports and imports carried under Spanish flag that was, then, deflated by a freight index.<sup>81</sup> An index for international sea transport was computed using 1958 passenger and merchandise freight rates as weights for 1942–1958 and, then, projected backwards with the merchandise index to 1850. Finally, value added for maritime transport at current and constant prices was derived projecting value added for 1958 (CNE58) backwards with freight and quantity indices for coastal and international transport.<sup>82</sup>

For road transport, merchandise and passenger outputs are available since 1950 and were backward projected to 1940 with the number of tons and passenger transported.83 A road transport output index was computed as an average of merchandise and passenger output for 1940– 1958 and backward projected to 1850 with the road length that, to allow for its use, was weighted by the stock of motor vehicles over 1900– 1940.<sup>84</sup> Value added at current prices in road transport was obtained by linking the 1958 level (CNE58) to the output index and a price index for gasoline.<sup>85</sup>

Urban transport was approximated by the number of passengers transported by tramways, trolley buses, buses and metro from 1901 onwards (Gómez Mendoza 1989). Value added at current prices was reached through backward projection of the 1958 level (CNE58) with the rates of variation of the sector's revenues. 86

For air transport, passenger output is available since 1929 and merchandise output from 1950 onwards that was projected backwards to 1930 with the rate of variation of total merchandise transported; both series were combined into a single quantity index using with equal weights.<sup>87</sup> Value added was computed annually by backcasting the level for 1958 with the output index and a price index for gasoline.<sup>88</sup>

Finally, road, urban, water, air and rail indices weighted by their contributions to transport gross value added in 1913, 1929 and 1958 (CNE58) provided an aggregate index for transport services.<sup>89</sup> A spliced quantity index was constructed for 1850–1958 as a variable weighted geometric average of each pair of adjacent benchmark's indices.

Annual value added in transport services (at current prices) was reached by adding up rail, water, road, air and urban transport value added derived through linking 1958 value added levels (CNE58) to their quantity and price indices. An implicit deflator resulted of dividing current value added (in index form) by the aggregate volume index.

For communication services, postal (number of letters and parcels sent), telegraph (number of telegrams) and telephone (calls from 1924 onwards, backcasted with lines in service to 1897) indices were merged into an aggregate index using their 1913, 1929 and 1958 revenues as weights that were, then, spliced into a single index using variable weighted geometric average.<sup>90</sup> The current value of communications services was derived by linking the 1958 value added level (CNE58) to each subsector's yearly revenues.<sup>91</sup> An implicit deflator resulted from current value added (in index form) and the quantity index.

#### 6.4.2 Wholesale and Retail Trade

Due to dearth of data on distribution, it was assumed that trade output was a linear function of physical output, and a quantity index was derived by combining, with 1958 weights, agricultural (including fishing), mining and manufacturing output plus imports of goods, from which a 2-year moving average was computed to allow for inventories.<sup>92</sup> Value added at current prices was obtained by linking the 1958 level to the quantity index and a price index (computed on the basis of the same trade components and 1958 shares).

### 6.4.3 Banking and Insurance

Value added at current prices was computed by splicing 1958 value added for banking and insurance services (CNE58) with the joint index of banking deposits and insurance premia. Deposits in commercial and savings banks and the value of insurance premia, expressed in index form (with 1958 = 1), were weighted according to their shares in the 1958 input–output table's sectoral value added (TIOE58) to derive an aggregate nominal index. Value added at current prices was deflated with a wholesale price index.<sup>93</sup>

### 6.4.4 Ownership of Dwellings

It was assumed to evolve as the quality-adjusted stock of dwellings.<sup>94</sup> Value added at current prices was derived splicing the 1958 level (CNE58) to the quantity index and a rent of dwellings deflator.<sup>95</sup>

## 6.4.5 Public Administration

Services output for public administration was measured by wages and salaries paid by the central government, which were deflated by a cost of living index.<sup>96</sup> Value added at current prices was obtained by backcasting the 1958 benchmark level with the rate of variation of wages and salaries paid by the central government.

#### 6.4.6 Education and Health

For education services, an index of schooling weighted by deflated government expenditure on education, to allow for quality changes, was used.<sup>97</sup> For health, the number of hospital patients was combined with deflated public expenditure on health in order to incorporate quality improvements.<sup>98</sup> Value added in education and health was obtained by projecting value added in 1958 with their quantity indices and a wholesale price index.

#### 6.4.7 Other Services

In the cases of household services and liberal professions, the usual assumption that output evolved as the labour force employed in each sector was accepted, namely that no productivity growth occurred, and yearly figures were obtained from log-linearly interpolating census data.<sup>99</sup> Value added was reached by linking the 1958 level to the quantity index and a wage index (household services) or the wholesale price index (liberal professions). Finally, for hotel, restaurant and leisure services were crudely approximated combining indices of room occupancy and leisure. <sup>100</sup> Value added was derived by splicing 1958 level with the quantity index and the cost of living.

#### 6.4.8 Value Added in Services

Next, index numbers for the different branches of services were merged into an aggregate index, with 1913, 1929 and 1958 weights, which correspond to their contributions to total gross value added in services (Table 6.5). A compromise, single index was computed through a variable weighted geometric average, as in the cases of agriculture and industry.

Aggregate gross value added at current prices was computed by adding up all services' value added. An implicit deflator was obtained from current value (in index form) and the aggregate quantity index.

## 6.5 Total Gross Value Added and GDP at Market Prices

A real gross value added index was constructed for 1850–1958 by weighting output volume indices for each major branch of economic activity (agriculture, industry, construction and services) with their shares in total gross value added for 1958.<sup>101</sup> Nominal gross value added was obtained by adding up GVA at current prices for each major branch of economic activity. GDP at market prices resulted from adding indirect taxes less subsidies to total GVA. An implicit gross value added deflator was derived from nominal and real values expressed in index form (1958 = 1). Real GDP at market prices was derived with the GVA deflator.

# Notes


unpublished agricultural quantity and price data set for 1890–1930 that underlies his own work (Simpson 1994).


such crude proxies within reasonable limits. The source for the 1958 benchmark was Ministerio de Agricultura (1979b: 155). The N+P2O5 + K2O content of mineral fertilizers in Gallego (1986) and Barciela (1989) provides a homogeneous annual indicator for the years 1892–1958 that was backcasted with fertilizer imports to 1850. Missing values for the content of mineral fertilizers in 1935–1939 and 1945– 1950 were log-linearly interpolated from available data for 1935, 1945 and 1950. For 1940–1944, it was assumed the same value as for 1945. For mineral fertilizers, prices were taken from Pujol (1998), Carreras (1989) and Anuario(s) Estadístico(s). Quantities and prices for fertilizer imports were derived from Estadística(s) del Comercio Exterior.


manufacturing, price indices for different subsectors (food, textile, shoemaking, metal, chemical, cement, timber, paper) were constructed from a wide variety of sources. Thus, for food industry, its price index was based on price series for wine, brandy, beer, olive oil, flour, rice, sugar, coffee, cocoa and tobacco. Prices for yarn and semi-manufactures of cotton, silk, wool, hemp and jute were, in turn, the basic ingredients of the textile price index. Again, for metal industries, both basic and transforming, iron ingots, steel and cast iron, tin, lead, copper, blister, zinc, tin, silver and mercury, that is, inputs prices, were the almost exclusive ingredients of their price indices. Prices for shoes, corks, common and Portland cement and paper were the available information for shoemaking, cork, cement, paper and printing industries. For the chemical industries, a wider coverage was achieved. In any case, price coverage was uneven and the sources quite heterogeneous. The main sources for industrial prices used, including mining, utilities and construction, were Arenales (1976), Barciela (1989), Carreras (1989), Coll (1985, 1986), Martín Rodríguez (1982), Ministerio de Trabajo (1942), Paris Eguilaz (1943) and Prados de la Escosura (1981).


Barcelona non-residential dwellings did not reach 5% of total dwellings, with the ground floor of residential buildings being commonly allocated to industrial and services' activities. The sources are Nomenclators and Censos de viviendas. Residential construction indices are available for several cities, including Madrid and Barcelona for the late nineteenth and early twentieth century, i.e. Tafunell (1989b); Gómez Mendoza (1986). Data on the stock of urban dwellings are available in Tafunell (1989a).




Carreras (1989). For the late nineteenth century, it was assumed that road transport prices fluctuate along rail transport prices.


## References


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# 7

# Measuring GDP, 1850–1958: Demand Side

Measuring aggregate economic activity through the expenditure side represents adding up all final products or sales to final demand. Ideally, each expenditure component should be computed with actual data from households, firms and public administration. Unfortunately, lack of direct evidence renders such a task impossible and the so-called commodity flows approach provides a second-best alternative.1 This method uses output figures for agriculture and industry that are adjusted to include imports and to exclude exports in order to derive estimates of consumption and investment. An implication is that the GDP output and expenditure estimates are not independent from each other.

I will succinctly describe the procedures and sources used to derive estimates for private and public consumption of goods and services, domestic investment and net exports of goods and services. In all cases, except for net exports of goods and services, the same method employed in the output approach to obtain GDP levels will be followed. That is, in order to compute annual nominal GDP, the level for each expenditure component in 1958 was backcasted with the yearly variations of Laspeyres quantity and Paasche price indices and the resulting series added up. For investment, private consumption and gross domestic

L. Prados de la Escosura, Spanish Economic Growth, 1850–2015, Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5\_7

expenditure quantity indices at 1913, 1929 and 1958 relative prices were constructed and, then, a single index for each demand component was obtained by splicing the three volume indices using a variable weighted geometric average. A volume index of real GDP results from adding up its component indices with weights from 1958 national accounts.

A word of warning is necessary. GDP estimates from the expenditure and output sides are not coincidental. Since it is widely accepted that measurement errors tend to be smaller when the production approach is used, I have chosen GDP computed from output side as the 'control final', and private consumption, the largest expenditure component, was adjusted so GDP from the demand side conforms to GDP derived from the supply side.

# 7.1 Consumption of Goods and Services

Consumption represents the part of final output used up for its own sake. Current expenditure on goods and services by consumers (households and non-profit organizations) and by public administration (central and local government) can be distinguished. While tastes, incomes and relative prices will determine household consumption, political motives are behind public consumption (Beckerman 1976).

### 7.1.1 Private Consumption

To derive yearly estimates of private consumption, quantity and price indices were constructed for its major components: foodstuffs, beverages and tobacco; clothing; current housing expenses, including the rent of dwellings, heating and lighting, plus current expenses on household maintenance; household consumption of durable goods; hygiene and personal care; transport and communications; leisure; and other services including education and financial services. Most of the available evidence for private consumption's components comes from output estimates to which net imports were added. I will discuss briefly the construction of indices for each consumption component. Paasche price indices were computed for each private consumption component using, unless otherwise stated, the same method and evidence described for agriculture and industry in the previous section.<sup>2</sup>

#### Foodstuffs, Beverages and Tobacco

This was still the main component of private consumption by 1958 and includes bread and cereals, meat, fish, milk, cheese and eggs, oil and fat, potatoes, legumes, vegetables and fruit, coffee and cocoa, and sugar, plus beverages (beer, wine, brandy) and tobacco. Evidence on quantities and prices gathered to compute output in agriculture and in food industry in the previous section together with net imports has been used to produce constant and current price series of foodstuffs consumption.<sup>3</sup> Major consumption groups in national accounts (CNE58) were disaggregated into its individual components using the input–output table for 1958 (TIOE58). Consumption, in most cases, was estimated from final output figures, that is, total output less seed and animal feed, to which net imports were added.<sup>4</sup> Wheat and rice milling output were accepted as indicators for bread and cereals. Evidence on meat consumption in capital cities was used to cross check estimates of total consumption on the basis of meat output plus net imports.<sup>5</sup> Fish captures plus net imports were used for fish consumption. For milk, cheese and eggs, output figures were used. For oil and fat, evidence on the proportion of human consumption of olive oil and its derivatives was employed.<sup>6</sup> Data on final output less net exports were used for potatoes, legumes, vegetables and fruits. The consumption of sugar (both cane and beet) was obtained by adding up output and net imports.<sup>7</sup> Imports were accepted for the consumption of tobacco, chocolate (cocoa) and coffee.<sup>8</sup> Quantity indices were computed with 1870, 1890, 1913, 1929 and 1958 benchmarks and, then, spliced into a single index using variable weighted geometric averages in which the larger weight corresponds to the closer benchmark (see expression 12). Individual price series were taken from the section on output. A Paasche price index was derived from current values (in index form) and the chain Laspeyres quantity index.<sup>9</sup>

### Clothing and Other Personal Articles

The output and price series for clothing and shoemaking were accepted and aggregated with weights from 1958 national accounts (CNE58). For clothing, a spliced index for the whole period under consideration was constructed using 1913, 1929 and 1958 weights.

## Housing Current Expenses

Under this label, dwelling rents, heating and lighting, and maintenance expenses are included. For rents paid for dwellings and for those imputed when occupied by their owners, quantities and prices from the output series were accepted. For heating and lighting, figures on domestic consumption of electricity and gas are provided by Anuario(s) Estadístico(s) since 1901 and 1930, respectively. I have computed figures for the earlier years by extrapolating consumption levels with the rate of variation for electricity and gas total output. Domestic consumption of coal was also added, but lack of direct evidence led me to assume that household consumption of coal evolved as total coal consumption. Prices were taken from the output estimates. Household maintenance expenses were computed by adding up domestic services and the consumption of non-durable goods with 1958 input–output weights.<sup>10</sup> Output and price estimates for domestic services were employed. Non-durable goods consumption was estimated through backward projection of 1958 levels, taken from the input–output table (TIOE58), with the rates of variation of its output, under the arbitrary assumption that household consumption represented a stable proportion of its production.<sup>11</sup>

### Household Consumption of Durable Goods

Household consumption of durables was approximated with furniture consumption. 1958 consumption levels were backcasted with rates of variation for timber and furniture output under the arbitrary assumption that the proportion allocated to private consumption was constant over time. Price indices for output were accepted.

## Hygiene and Personal Care

The output and price series for health services were used to approximate the expenses on personal care.

#### Transport and Communications

Expenses on transport services included purchases of automobiles and transport and communications expenses. 1958 levels were projected backwards with the number of registered automobiles and the rate of variation in the number of registered cars and in transport and communications output, respectively.<sup>12</sup>

#### Leisure

The corresponding series for the output of restaurants, hotels and leisure services were accepted, while the paper industry's output was used to approximate books and periodicals consumption.13 Weights were taken from the 1958 input–output weights (TIOE58).

#### Education, Financial and Other Services

The output of education services has been adopted for education and research consumption. The consumption of financial services was also approximated through its output. Liberal professions employment represented the consumption of other services. The price index for 'other household consumption services' was used back to 1939 and spliced with the cost of living index back to 1850 (de Ojeda 1988).

Nominal private expenditure on goods and services was derived by projecting the current value of each of its components in 1958 (CNE58) backwards with their quantity and price indices (expressed a 1858 = 100) and, then, adding them up.

An aggregate volume index of real private consumption was, then, computed. Quantity indices were, firstly, built up on the basis of volume

Fig. 7.1 Private consumption paasche deflator and laspeyres consumer price index, 1850–1958 (1913 = 100) (logs). Sources Private Consumption Deflator, see the text: CPI, Maluquer de Motes (2006)

indices for private consumption components at 1913, 1929 and 1958 relative prices and, later, spliced into a single index for 1850–1958 resulted from splicing all three segments using a variable weighted geometric average of quantity indices at 1913 and 1929 prices for 1913– 1929, and at 1929 and 1958 prices for 1929–1958. An implicit deflator was calculated with current and constant price estimates. The resulting Paasche deflator of private consumption and Maluquer de Motes (2006) Laspeyres consumer price index are highly coincidental, somehow an unexpected result due to their different weighting (Fig. 7.1).

#### 7.1.2 Public Consumption

Wages and salaries and purchases of goods and services by the central Government are both provided for the entire period 1850–1958 by Francisco Comín (1985), while no data on rents imputed to public buildings were available. Annual figures for local government consumption are only available from 1927 onwards, but scattered evidence exists for 1857–1858, 1861–1863, 1882 and 1924.<sup>14</sup> I have rescaled central government figures with their ratios to local and central government consumption for these years.15 Yearly public consumption at current prices was derived through backward projection of the level for 1958 (CNE58) with the annual rate of variation of central and local government consumption estimates. Nominal public consumption was deflated with the cost of living, a wholesale price index and the rent of dwellings deflator weighted with the shares of salaries, goods purchased and rents imputed to public buildings in 1958.<sup>16</sup>

## 7.2 Gross Domestic Capital Formation

The current output of goods and services devoted to increase the nation's stock of capital and, hence, to raise the future potential income flow is called domestic investment or capital formation. Fixed capital formation and changes in inventories are the components of domestic investment.

#### 7.2.1 Gross Domestic Fixed Capital Formation

Gross fixed capital formation can be defined as capital expenditure on domestic reproducible fixed assets (including both new investment and replacement). More frequently, it is described as the value of purchases and construction of fixed assets by residents firms and government, and all durable production goods lasting more than a year, are included. In addition, major alterations of existing assets are considered capital formation and this includes all of those affecting buildings and construction. Inventories, in turn, refer to raw materials, work in progress and stored finished goods.

Gross domestic fixed capital formation was classified in the OECD national accounts system according to three criteria: products, branches of activity and institutions (CNE58). More detailed breakdown is presented in the contemporary input–output table for 1958 (TIOE58). Given data constraints, the products criteria will be followed to compute historical capital formation in pre-1958 Spain. As for consumption, the way of constructing current and constant price series for gross domestic capital formation was to start from the 1958 benchmark level and to extrapolate each of its individual components back to 1850 with quantity and price indices.<sup>17</sup>

Two alternative ways are used in capital formation estimates: the expenditure and the commodity flows approaches. The expenditure approach establishes the actual investment by firms or by the government, and it is the most rigorous and data demanding one. Its large data requirements, however, make it also the less frequent procedure in historical accounts and in present-day developing countries national accounts. In the present historical estimates, this expenditure approach was exceptionally used for private investment (only for telephone communications). The alternative commodity flows method reaches investment figures by adding net imports to domestic output of capital goods. In other words, the commodity flows approach is not independent from the output method, but it is the only feasible way to compute investment in historical cases, aside from the most recent period or from those countries with exceptionally good records (i.e. the UK and the USA).

An additional difficulty comes from the lack of evidence on prices for capital goods. With the exception of unit value data from commercial statistics from trading partners (UK, France, Germany, the USA) and occasional evidence for bulky and expensive capital goods (locomotives, ships), deflators had to be constructed on the basis of input prices, wages and raw materials, combined with input–output weights (TIOE58). This means that usually no allowances are made for productivity change in capital goods' industries.<sup>18</sup>

In the classification by products, fixed capital formation is distributed into dwellings, other buildings, other constructions and works, transportation material and other materials (machinery and equipment). In the following paragraphs, a brief description of the sources and procedures used to construct quantity and price indices for the main categories of fixed capital formation and for variations in stocks are provided.

#### Dwellings and Other Buildings

Data restrictions prevent to consider dwellings and other buildings separately.<sup>19</sup> Capital formation in dwellings and other buildings is represented by the output index of residential and commercial construction, excluding repairs and maintenance expenses. The output deflator was used.

#### Other Constructions and Works

Roads, streets, sanitation, railways, docks, tunnels, bridges, dams, harbours and airports, drainage, irrigation and land improvement, electric installations, telegraph and telephone lines are all included in this category.

For capital formation in railway and road construction, hydraulic infrastructure and other works (maritime and harbours), output (quantity and price) indices have been accepted.<sup>20</sup>

Land improvement was approximated, in addition to central government investment on irrigation and drainage (already included under hydraulic infrastructure), through fertilizer consumption and afforestation (after 1900).<sup>21</sup> Price indices were built up on the basis of input costs.<sup>22</sup>

Capital formation in gas and mining was computed under the arbitrary assumption that the capital–output ratio was stable over time.<sup>23</sup> First differences (excluding negative values) in the output series provide, hence, new capital formation to which scrapping is added to obtain gross investment figures. <sup>24</sup> Scrapping is computed assuming an average asset life of 50 years.<sup>25</sup> When evidence on scrapping, that is, new capital formation 50 years back in time, was not available, I assumed it was proportional to fixed capital formation. A price index was computed with input prices.<sup>26</sup>

Capital formation on electricity structures was assumed to represent 15% of total capital expenditure on electricity supply, and the level for 1958 was projected backwards with the rate of variation in installed capacity (kilowatts) to 1890, to represent new investment, while scrapping was estimated assuming 60 years average life.<sup>27</sup> The deflator was constructed with input prices for construction costs (0.8) and costs of plant and machinery (0.2).<sup>28</sup>

For communications works, private investment in telephone buildings and works was assumed to represent 15% of total investment outlays over 1925–1958.<sup>29</sup> A deflator computed with construction materials and wages, combined with 1958 input–output weights, was used to derive constant price estimates.<sup>30</sup> For the years 1903–1924, real investment was extrapolated backwards with an index of investment. On the basis of the number of telephone offices, available since 1902, and assuming an average life above 60 years, real investment was computed as first differences from which a 3 year moving average was accepted as the investment index.<sup>31</sup>

Once quantity and price indices were built up for each major component of capital formation on 'other constructions and works', current price series were obtained by projecting 1958 levels (derived from CNE58 and TIOE58) backwards to 1850 with quantity and price indices that were, then, added up into a single series.<sup>32</sup> Quantity indices for total investment on 'other constructions and works' were, then, constructed on the basis of its components' indices with 1913, 1929 and 1958 weights, and a single index was derived through variable weighted geometric mean. The comparison between my estimates and those obtained by Herranz-Loncán shows a substantial degree of coincidence, although Herranz-Loncán series exhibits higher volatility (Fig. 7.2). An implicit deflator was derived from current and constant price indices.

## Transportation Material

Under this concept, all expenses on ships, vans, commercial vehicles, vehicles for public transport, airplanes and rolling stock for railways and tramways are included. Purchases of transport vehicles for private use (i.e. automobiles) are not considered as investment but as private consumption. Given the dearth of reliable data, only capital formation in railway rolling stock, ships and road vehicles will be considered here.

Fig. 7.2 Gross investment in non-residential construction volume indices, 1850– 1935: Alternative Estimates (1913 = 100). Sources Prados de la Escosura, see the text: Herranz-Loncán (2004)

As for capital formation in railway rolling stock, new investment was derived as first differences from the stock of locomotives, cars and wagons to which scrapping obtained by assuming an average life for each type of asset was added.<sup>33</sup> Quality adjustments were introduced to allow for the locomotives' increasing power.<sup>34</sup> Quantity indices of investment in locomotives, cars and wagons were computed at 1913, 1929 and 1958 prices and, then, a single index was derived as a variable weighted geometric average. Current price estimates up to 1940 were obtained with quantities (unadjusted for quality) and available prices for locomotives, cars and wagons.<sup>35</sup> After 1940, data on current capital expenditure, available for Spanish state company, RENFE, were deflated with a price index constructed with input costs.<sup>36</sup> An implicit deflator was obtained from current values and the quality-adjusted quantity index.

The estimates of capital formation in merchant shipping include all sailing and steam ships.<sup>37</sup> No evidence on capital expenditure on shipping exists but yearly additions to tonnage can be computed through domestic production and net imports available from 1850 onwards.<sup>38</sup> A quantity index for investment has been obtained by adding net imports to domestic output.<sup>39</sup> A quality adjustment constructed for Britain, adapted to the case of Spain, was introduced in the investment series.<sup>40</sup> Feinstein's price index (adjusted for exchange rate fluctuations between the sterling and the peseta) was used for 1850–1920 and a deflator was built using weighted input prices for 1920–1958.<sup>41</sup>

For capital formation in road vehicles (excluding automobiles owned for private use which are classified as consumer goods), domestic output (since 1946) plus imports (since 1906) were added up and backcasted to 1900 with yearly registered vehicles.<sup>42</sup> A deflator was built up with input prices for labour and construction materials.<sup>43</sup>

Current price series of fixed capital formation on transportation material were obtained through backwards projection of the 1958 levels for each of its components (derived from CNE58 and TIOE58) with their quantity and price indices that were, in turn, aggregated into a single series.<sup>44</sup> Quantity investment indices were constructed with 1913, 1929 and 1958 weights, and a single index was obtained as a variable weighted geometric mean. An implicit deflator was computed from current and constant price indices.

### Other Materials

Machinery and equipment are the main components under this category, including electrical implements, tractors, office equipment and furniture, research equipment, construction and mining materials, and school and hospital materials. Dearth of data precludes estimating capital formation except for electric and non-electric machinery and equipment.

Mains and other plant and machinery were assumed to represent 85% of total investment outlays in electricity supply.<sup>45</sup> As capital stock was highly correlated with installed power, first differences in kilowatts of installed capacity were, hence, accepted as a proxy for new capital formation to which scrapping was added in order to obtain total capital formation.<sup>46</sup> Scrapping was derived assuming an average assets life of 30 years.<sup>47</sup> The deflator was constructed with input prices (copper, 0.5; engineering wages, 0.5) (Feinstein 1988).

Investment on telephone equipment and plant was obtained by assuming it represented 85% of total capital outlays by Spanish telephone company for the years 1924–1958.<sup>48</sup> A constant price series was computed with a deflator constructed with input prices and weights from the 1958 input–output table (TIOE1958).<sup>49</sup> Real investment was backcasted to 1903 with an investment index built from first differences in the number of telephone lines plus scrapping under the assumption of 30 years average (Feinstein 1988: 354).

As for non-electric machinery, while quantities and values are available for imports, no historical series exists for the production of machinery.<sup>50</sup> I have backcasted the level for 1958 with the rate of variation of an index of input consumption in the engineering industry computed through the commodity flows method. Iron and steel output plus net imports, from which iron and steel consumption in the construction of dwellings, shipping and railway rolling stock was deducted, are the basic series available to compute the output of machinery and equipment.<sup>51</sup> A 3 year moving average for the iron and steel available for machinery industry's consumption was computed to allow for stocks and, then, a quality adjustment of 0.5% per year was applied.<sup>52</sup> A machinery output deflator was constructed by combining engineering wages and steel prices with 1958 input–output weights.<sup>53</sup>

As for other components of fixed capital formation, investment on 'other material' (machinery and equipment) at current prices was obtained by extrapolating 1958 levels backwards with quantity and price indices for its components that, later, were added up into a single series.<sup>54</sup> Real indices for investment in machinery and equipment were constructed with its components' volume indices using 1913, 1929 and 1958 weights, and a compromise index was reached through variable weighted geometric mean. An implicit deflator was derived from current and constant price series.

Gross domestic fixed capital formation at current prices was obtained by adding up its components' nominal value. Quantity indices for fixed capital formation were constructed combining its main components at 1913, 1929 and 1958 prices that were, in turn, spliced into a single index using a variable weighted geometric average. An implicit deflator was derived from current and constant price series.

In order and to keep consistency with post-1958 national accounts, fixed capital formation was distributed into four main categories: residential structures (dwellings), non-residential structures (other buildings and other constructions and works), transportation material and machinery and equipment.<sup>55</sup>

## 7.2.2 Variations in Stocks

Purchases of raw materials for further elaboration, work in progress, or partially transformed products that are not on sale unless a final transformation takes place, plus stored finished goods for future sale, are all included in this category. Variations in livestock, agriculture, trade and manufacturing also are taken into account.

Lack of historical data on inventories has frequently forced researchers to look for short-cut estimates. In their pioneer contribution on the British case, Jefferys and Walters (1955: 7) assumed that the annual variation in the stocks value was 'equal to 40% of the first difference between national income estimates in successive years'. Feinstein (1972, 1988) assumed, in turn, that the ratio of stocks to output was stable over time and, hence, the change of final expenditure corresponded to stock building. For Spain, a similar approach was followed, and I accepted the rate of variation of final demand at current prices (GDP at market prices, derived from the output approach, plus imports of goods and services) to approximate stock building and spliced it to the level of variations in stocks in 1958 (CNE58). A wholesale price index was used to deflate the series.

Lastly, variations in stocks were added to gross domestic fixed capital formation to obtain total domestic investment.

# 7.3 Net Exports of Goods and Services

To compute GDP from the expenditure side, the net value of goods and services supplied to the rest of the world (excluding net returns to factors of production) should be added to consumption and capital formation. Two main categories are included under this label: net exports of goods and services and non-residents expenses in Spain (net of resident expenses abroad). Free on board (fob) value of goods exported and imported, commodity transport services provided by residents to foreigners, and by foreigners to residents, and other incomes (insurance, communications, patents' royalties) derived from non-residents, and those paid by residents, are considered under traded goods and services. Under the second label are included consumption expenses in Spain by non-residents less expenditures of residents abroad, payments by non-residents to nationals for passenger transport services net of those payments by residents to foreign passenger carriers and any other net expenses by non-residents within Spanish boundaries.

Current values of exports and imports of goods and services for 1940–1958 are from Elena Martínez Ruíz (2003).56 For the period 1850–1939, the sources and procedures used to construct current values for the main components of exports and imports of goods and services are briefly described below.

#### 7.3.1 Net Exports of Goods

Free on board (fob) value of goods exported and imported needs to be computed. Data from Spanish official trade statistics have been corrected for quantity underestimation and price biases through a comparison of Spanish trade with its main trading partners on the basis of foreign and Spanish trade statistics by Prados de la Escosura (1986) for 1850–1913 (who included an estimate of smuggling through Gibraltar and Portugal), Antonio Tena Junguito (1992) for 1914–1935 and Martínez Ruíz (2003, 2006) for 1936–1939. Cost, insurance and freight (cif) imports were converted into fob imports to comply with balance of payments conventions.<sup>57</sup> In addition, exports and imports were grossed-up to include the Canaries, while trade between these islands and the Peninsula was excluded.<sup>58</sup>

## 7.3.2 Gold and Silver

Quantities of gold and silver as recorded in trade statistics (coins, bars and paste) are considered as monetary gold and silver and, therefore, non-monetary gold and silver trade was not included in the estimates of net exports of goods and services.<sup>59</sup>

## 7.3.3 Freight and Insurance

Freight income received for exports carried in Spanish ships less freight expenses paid for imports transported in foreign vessels constitute the first item to be computed under this label. Following North and Heston, the freight-value method, or freight factor, was preferred to the earnings per ton method.<sup>60</sup> Total freight revenues on exports and imports were first computed by applying freight factors to the fob value of exports and imports and, then, to ascertain freight income on exports (a credit for Spain) the share of tonnage exported carried under Spanish flag was used, while the share of imported tonnage in foreign ships was employed to compute freight expenses on imports.<sup>61</sup> In addition, freight income from carrying trade between foreign ports was assumed, following North (1960) and Simon (1960), to represent a percentage of freight earnings and a 10% of freight income on exports was accepted.<sup>62</sup> Port outlays by Spanish ships in foreign ports and by foreign ships in Spain's harbours as payments for port dues, loading and unloading expenses and coal are assumed to represent a fixed share of shipping earnings and expenses.<sup>63</sup> Foreign ships transported more tonnage than in Spanish vessels as they exhibited, according to Valdaliso (1991: 71), a more efficient transport capacity ratio. I assumed that more fully loaded vessels made smaller outlays per ship and, hence, port outlays by Spanish ships abroad (a debit) were established at 30% of the freight income on exports, while port outlays by foreign ships in Spain (a credit) were fixed at 20% of freight expenses on imports.<sup>64</sup> Finally, marine insurance income and expenses were computed under the widely shared assumption that underwriting follows the flag and exports in Spanish ships were, hence, usually insured by Spanish companies, while imports in foreign vessels were insured by foreign companies.<sup>65</sup> I arbitrarily assumed that insurance rates were identical by Spanish and foreign companies and accepted those used by Prados de la Escosura (1986) for 1850–1913 and by Tena for 1914–1939, to which I added an extra 2% to include shipping commissions and brokerage.<sup>66</sup>

#### 7.3.4 Tourism, Emigrants' Funds, Passenger Services and Other Services

Yearly income from tourist services was derived on the basis of expenses per visitor (net of Spanish tourist expenses abroad) calculated by Jáinaga for 1931, times the annual number of tourists and, then, reflated with a cost of living index to obtain current price estimates.67 Unfortunately, the total number of tourists is only known since 1929 and was backward projected to 1882 with the rate of variation of passengers arriving by sea, while no tourism was assumed to exist over 1850–1881.<sup>68</sup>

Spain was a net emigration country over the late nineteenth and early twentieth centuries (Sánchez Alonso 1995, 2000). Emigrants carried small sums with them to cover their arrival expenses. It can be reckoned that, in 1931, emigrant funds to America represented, on average, 200 gold pesetas, that is, 400 current pesetas, including the fare and small amounts to cover arrival expenses.<sup>69</sup> If the fare represented around 340 current pesetas, 60 pesetas corresponded to emigrant's funds.<sup>70</sup> However, its author only added 'a small amount for unavoidable expenses', to the cost of the passage, and this sum is most likely an underestimate.<sup>71</sup> I, therefore, accepted a higher estimate of 100 pesetas for those emigrating to America and one-tenth, 10 pesetas, for those to Algeria (and to France) in the eve of World War I.<sup>72</sup> These average sums times the number of emigrants to America, Algeria and France cast a yearly series of emigrants' funds that were reflated with a wage index.<sup>73</sup>

In addition, revenues and expenses from passenger transport have to be taken into account. Fares paid by tourists carried by Spanish ships and by immigrants returning in Spanish vessels are included on the credit side, while fares paid by emigrants to foreign shipping companies represented a debit. The number of migrants provided by Sánchez Alonso (1995) for 1882–1930 was completed up to 1939 with Spain's official migration statistics and those from the main destination countries, plus an estimate of migration for the years 1850–1881 on the basis of scattered foreign evidence.<sup>74</sup> The share of arrivals and departures in Spanish and foreign ships is provided by official migration statistics from 1911 onwards, and shows a stable pattern, roughly one-third of emigrants returned home under Spanish flag and three-fourths left in foreign ships, except during World War I when the distribution pattern was reversed.<sup>75</sup> These shares were accepted for the nineteenth and early twentieth centuries. The fares for trips to Argentina, Cuba and Algeria are obtained from Vázquez, Llordén and official emigration statistics.76

Lastly, Government transactions (credits and debits) were taken from official accounts were added up (Instituto de Estudios Fiscales 1976).

Total exports and imports of goods and services at current prices were reached by adding up its components. Constant price values were obtained with price indices for commodity exports and imports.77

## 7.4 Gross Domestic Product at Market Prices

A yearly series of nominal gross domestic product at market prices was obtained by adding up individual indices for private and public consumption, capital formation and net exports of goods and services. A GDP volume index was constructed by weighting each expenditure series with their shares in nominal GDP in 1958. An implicit deflator was derived from current and constant price GDP series.

However, the resulting GDP estimates from the demand side do show discrepancies with those obtained through the supply side. As discussed before, it is widely accepted that both in present time developing countries and in historical accounts measurement errors are smaller when GDP is computed from production rather than from expenditure.<sup>78</sup> Hence, I have chosen GDP derived from the output approach as the control final and adjusted private consumption (at both current and constant prices), the largest expenditure component, so GDP from the expenditure side equals to GDP derived through production.<sup>79</sup> The consumption structure remained, however, unchanged.

## 7.5 Gross National Income

Net payments to foreign factors must be added to gross domestic product in order to compute gross national income. Martínez Ruiz (2003) provides the data for 1940–1958. Jáinaga's contemporary estimates of net factor incomes, converted from gold to paper pesetas, were accepted for 1931–1934.<sup>80</sup> Due to the dearth of data, only very crude estimates of foreign capital incomes (dividends and interest payments to private foreign capital and external debt service), on the debit side, and of Spanish labour returns abroad (wages and salaries), on the credit side, could be carried out. These are the main components of net factor payments abroad, as neither Spanish investments abroad nor foreign labour in Spain was significant over the long period considered.

Assessing returns to Spanish labour employed abroad is a complex task because labour incomes (wages and salaries), the relevant concept for GNI estimation, have to be distinguished from emigrants' remittances, a variable not included in the calculation.<sup>81</sup> Actually, such a distinction can only be made since 1917. For the period 1850–1913, I accepted that only 5% of those migrating to America and 60% of those migrating to Algeria returned within the year.<sup>82</sup> The next step was to assess the amount that, on average, was brought home by returning Spanish workers after 1 year, or less, away from home. I computed an average sum that was taken home by the temporary emigrant or sent annually by the long-term emigrant to their relatives and friends.<sup>83</sup> García López (1992) presents the most comprehensive estimates for the years prior to World War I, 250–300 million pesetas as an annual average over 1906– 1910, that amounts to 340–400 pesetas per emigrant (either returning home or sending remittances). I accepted 400 pesetas per emigrant as a benchmark that was, then, projected backwards and forward with a nominal wage index constructed for the destination countries and adjusted for exchange rate between the peseta and each destination country's currency. <sup>84</sup> Finally, returns to Spanish labour abroad were obtained by multiplying the annual sum per head times the number of emigrants returning home within their first year abroad.

On the debit side, three main items can be distinguished: the external debt service, dividends and interests paid to railway shares and debentures owned by foreigners, and returns to foreign factors in mining, to which crude estimates of incomes paid to foreign capital invested in insurance, tramways and utilities, were added for the twentieth century.<sup>85</sup>

Service payments on the external debt have been computed by applying specific interest rates to each class of Government bonds.<sup>86</sup> After the debt conversion of 1882 in which existing foreign debt was given in exchange for new bonds (at 43.75% of its nominal value), and simultaneously with the abandonment of gold convertibility of Spanish currency, debt repatriation started as Spaniards found more secure to invest in bonds serviced in gold pesetas as a shelter against currency depreciation.<sup>87</sup> Since 1891, when the peseta's depreciation took actually place, Spanish citizens purchased external debt bonds while foreign bondholders were trying to get rid of them. A government measure intended to cut short such a trend was the introduction of the so-called affidavit in 1898, which implied that only non-resident bondholders would continue receiving their interests in gold pesetas (or francs), while the rest would be paid in current pesetas (and offered to convert their external debt bonds into internal debt). As a result, the external debt fell, in 1903, to 52.7% of its volume in 1898; in other words, it proves that Spanish residents had purchased almost half Spain's external debt between 1891 and 1898. Hence, only half of the interest paid (52.7%) on external debt should be computed as payment to foreign capital invested in external debt over 1891–1898. Moreover, in so far debt service was in gold pesetas, the amount of interests paid (obtained by applying the interest rate to foreign debt in non-residents' hands) had to be increased by the depreciation rate of the current peseta with respect to the gold peseta over 1891–1914.<sup>88</sup> After World War I, unlike the experience of the 1890s, Spanish foreign debt in foreign hands tended to disappear. I have computed the share of interest payments that accrued to foreign citizens on the basis of Banco Urquijo data.<sup>89</sup> Fortunately, for the purpose of this study, railway companies were highly concentrated, and the detailed studies by Pedro Tedde de Lorca provide enough evidence to estimate dividends on share capital and interests on debentures paid to non-residents.<sup>90</sup> Dividends paid to shareholders and interest payments on debentures issued by the three major railway companies are available from the mid-nineteenth century up to the Civil War.<sup>91</sup> Both the percentage represented by the three main companies in total capital invested in railways and the proportion of railways capital in foreign hands have to be ascertained in order to compute the returns to foreign capital invested in Spanish railways. Tedde de Lorca (1978, 1980) provides total capital shares and bonds held by the three major companies and its proportion in total investment, and, based on Broder's research, also the participation of French capital in total capital invested in 1867, at the time of network construction, and over the nineteenth century. Broder's (1976) estimates of foreign investment in railways allowed, in turn, to gross-up French railways capital to cover all foreign capital. For the interwar years, I have had access to estimates of the proportion of shares and debentures in non-resident hands.<sup>92</sup>

Foreign capital in mining was mainly British. On the basis of effective capital invested by British companies and cumulated total foreign investment in mining, it can be suggested that, over 1870–1913, more than half of all foreign capital in Spanish mining came from the UK, while the British share raised to three-fourths in the interwar years.<sup>93</sup> Decadal averages of dividend and interest payments to British companies are provided by Harvey and Taylor that were grossed-up to include all payments to foreign capital in Spanish mining for 1851–1913, assuming similar rates of return in non-British foreign investment, and using the estimated British participation in total foreign capital.<sup>94</sup> Estimates of foreign capital returns in mining derived through this procedure were, then, distributed annually with an index of non-retained value in Spanish mineral exports.<sup>95</sup> Dividend and interest payments from 1914 onwards were estimated by projecting the average level for 1911–1913 with an index of non-retained export proceeds.

Finally, crude estimates of incomes paid to foreign capital invested in tramways, electricity, gas and water supply, and insurance were carried out through backwards extrapolation of an estimate for 1931–1934 (Jáinaga) with the rates of variation of their output.<sup>96</sup> For foreign insurance companies, the volume of declared premia times the yield of British consols provided their yearly returns.<sup>97</sup>

The difference between credit and debit estimates provided the value of net payments to foreign factors abroad. To derive constant price series, the import price index was used as a way of assessing its purchasing power.<sup>98</sup> Gross National Income was, in turn, computed adding net factor payments abroad to gross domestic product at market prices.

# 7.6 Net National Income

Net National Income was obtained by subtracting capital consumption provided in Prados de la Escosura and Rosés (2010)—from Gross National Income.

# 7.7 Net National Disposable Income

Net National Disposable Income was derived by adding an estimate of net transfers to the rest of the world to Net National income. Emigrants' remittances constituted its main historical component in Spain. Not all emigrants sent money home while being abroad. In historical estimates, it is usually accepted that most of those who established themselves abroad stopped sending money after 5 or 6 years either because they have already payed for their debts or because they planned to invest in the receiving country. I arbitrarily assumed that emigrants only sent money home within their first 5 years and computed emigrants' remittances by multiplying the estimated average sum per emigrant times the cumulative figure of emigrants arrived in the last 5 years, after deducting those migrants who returned home within 1 year.<sup>99</sup>

# Notes

1. The commodity flows approach is common in present time developing countries (Heston 1994) and in historical national accounts. Cf. the pioneering work by Jefferys and Walters (1955) on the UK, extended by Deane (1968) and Feinstein (1972), and more recently, the research by Carreras (1985) on Spain, Vitali (1992) and Baffigi (2013) on Italy, and Smits, Horlings and van Zanden (2000) on the Netherlands.


the case of Britain, according to (Feinstein 1988). Given the longer life of assets in developing countries, I assumed a 50 year average for both buildings and plants and machinery. As a consequence of this decision, capital formation in other construction and works is overexaggerated, as it also includes plant and machinery in gas and mining. However, such an upward bias is small given the size of capital formation in mining and gas.


construction of freight factors. Thus, the 1913 export freight factor (ratio of freight costs to the value of commodities traded) from Prados de la Escosura (1986) has been extrapolated with iron ore freights (from (1998), expressed in index form, as the numerator, and the export price index, as the denominator. As regards imports, Tena Junguito (1992) freight factor for 1926 has been projected over time with a freight index computed as a trade weighted average of coal and wheat freights (tons imported are the weights) and the import price index.


Emigration to Algeria was derived from Spanish arrivals in Alger and Oran for the years 1872–1881, while the figures for 1850–1871 were estimated under the arbitrary assumption that the share of emigrants remaining in Algeria after 1 year was similar to the one over the period 1872–1881 (25%). Estimates for returned migration were computed by assuming that the average returns from America for 1869–1873 were acceptable for 1850–1868 while 92% of emigrants to Algeria returned home within the first year. A consistency check of the yearly migration data was performed using the migration balances from population censuses along the lines described in Sánchez Alonso (1995). Data for returned migration from America, 1869–1881, were taken from Yáñez (1994: 120). Data on presents the data on migration to Algeria, 1850– 1881, come from Vilar (1989).


the available quantity indices for 1914–1958, as the latter are built up on the basis the official trade statistics in which quantities and prices are mismeasured (Cf. Tena Junguito 1992).


onwards. French nominal wages from Williamson (1995) are used for emigrants to France and Algeria. The trading exchange rates of the peseta against the peso, the French franc and the US dollar are computed on the basis of Cortés Conde (1979), Della Paolera (1988), and Martín Aceña and Pons (2005). I assumed that no labour returns were sent home during the Civil War years (1936–1939).


(and only 22–41% in the earlier period 1851–1870). When, alternatively, Broder's estimates of non-railway investment from other countries are cumulated, British capital represented from 52 to 61% over 1870–1900 (22–31% in 1851–70). Evidence in Muñoz, Roldán and Serrano (1976) indicates that British capital was above 50% in the years 1900–1913 (53% on average for 1900 and 1912), while its contribution rose up to three-fourths in the interwar years (76.6% on average for 1923 and 1931).


## References


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# 8

# New GDP Series and Earlier Estimates for the Pre-national Accounts Era

How do the new GDP series compare to earlier estimates?<sup>1</sup> Let us examine them first. Unlike contemporaries who were interested in assessing national income levels, early Spanish research has been concerned with trends and fluctuations in real output and expenditure.<sup>2</sup> All available GDP estimates are output indices constructed with a fixed, single benchmark level whose economic significance tends to decline as one moves away from the base year.<sup>3</sup> Moreover, trends in real gross value added are proxied by production indices, which imply the unlikely assumption that total output and input consumption evolve in the same direction and with the same intensity.<sup>4</sup> Three types of yearly GDP estimates can be distinguished: official estimates by the Consejo de Economía Nacional, its revisions and extensions, and independent estimates.

## 8.1 Consejo de Economía Nacional Estimates

In 1944, the Consejo de Economía Nacional or National Economic Council (CEN, thereafter) was asked to estimate a set of national accounts for Spain (CEN 1945, 1965). Three were the main targets: to provide income figures for the years prior to the Civil War (1936–1939), to evaluate 1940 GDP on the available, fragile statistical basis, and to design a direct method to estimate national income for the years to come (Schwartz 1977: 460).

Dearth of data forced CEN to split output indices into two segments with 1929 as the link year. In each case, independent production indices for agriculture and industry were obtained, from which an aggregate index was derived to approximate national income. No regard was paid to services and was implicitly assumed that output in services evolved as a weighted average of agricultural and industrial production.

For the earlier period, 1906–1929, an agricultural output index was built up on the basis of eleven products, mostly dry farming crops (while no livestock output was included), representing half the value of total output. The index of industrial production included eighteen products, rendering a good coverage for mining, but insufficient for manufacturing and construction. Output indices were obtained for agriculture and industry by weighting each single product with its average price over 1913–1928, and the aggregate results were expressed by taking the average for 1906–1930 as 100.

The composition of agricultural and industrial indices changed from 1929 onwards. Thirteen new crops were added to the agricultural index, distributed into eight main groups of products, that reached up to 80% of total production, while the industrial index's coverage rose to 38 products distributed into ten different groups.<sup>5</sup> To derive output indices for agriculture and industry, quantities were weighted by 1929 farm-gate prices and unit value added, respectively.<sup>6</sup> Improvements in data coverage took place in the 1950s, but the method remained practically unaltered until 1956.

An index of total production was obtained by combining agricultural and industrial indices with fixed weights (0.6 and 0.4, respectively, over 1906–1929, and 0.5 each, thereafter). In addition, to allow for short-term fluctuations over the period 1906–1935, a de-trended nuptiality index was combined with the total production index. Nuptiality was excluded after the Civil War (1936–1939) as unsuitable for post-war cycles.

In a second stage, the total production index was linked to an estimate of national income for 1923 in order to derive national income at constant prices.<sup>7</sup> A further step was to obtain national income figures at current prices by reflating real income with a wholesale price index. Finally, for the years 1957–1964, CEN computed national income directly.

## 8.2 Revisions and Extensions of CEN Estimates

Modern national accounts constructed according to OECD rules are available in Spain since 1954. Attempts to extend them backwards led to revisions of CEN figures that, occasionally, were expanded to cover the expenditure side. Three estimates are worth mentioning.

#### 8.2.1 Comisaría del Plan de Desarrollo

A first attempt to revise CEN's estimates was carried out by Comisaría del Plan de Desarrollo, the Development Planning Authority (CPD, thereafter), and covered the period 1942–1954 (CPD 1972).8 CPD economists were concerned with the high volatility shown by CEN figures that they attributed to its high dependence on agricultural output and to the exclusion of services. The alternative proposed by CPD was to construct a new index of aggregate performance in which services were added to CEN's indices of agricultural and industrial output. Services output was obtained by combining series on transport and communications and banking.<sup>9</sup> A real product index was calculated by weighting each sectoral index with the shares of agriculture, industry and services in 1954 GDP at factor cost, as established in official national accounts (CNE58).10 GDP at constant prices for 1942–1953 was, then, derived through backward extrapolation of the 1954 GDP level with the real product index. GDP at current prices was computed, in turn, by reflating real output with a composite index of wholesale prices (0.3) and the cost-of-living index (0.7).11

GDP was completed with a breakdown of its expenditure components that included direct estimates of investment, public consumption and net exports of goods and services. To approximate private non-residential fixed capital formation, a physical index of private investment was built up by combining, with 1954 weights (CNE58), steel and cement output, machinery imports, electric power and registered transport vehicles. An index of residential investment was proxied by the number of completed dwellings. Public investment, in turn, resulted from adding up investment in agriculture and public works and provincial and local public investment, deflated by a wholesale price index. Levels of each type of investment for 1954 were taken from the national accounts and projected backwards with each investment index to derive real capital formation series and, then, reflated with price indices for production goods and construction materials. Total expenditure of public administration (central, provincial and local governments) re-scaled to match national accounts was used for public consumption and, then, deflated with a wholesale price index. Net exports of goods (at current and constant prices) were used as a proxy for net exports of goods and services, except in the case of tourism, in which the number of tourists (and the cost of living index as deflator) was accepted. Private consumption was obtained as a residual from GDP at market prices (derived by adding indirect taxes net of subsidies to GDP at factor cost, obtained through the production approach) and the directly estimated components of expenditure.

## 8.2.2 Alcaide

A revision of CEN series was also attempted by Julio Alcaide, a pioneer of Spanish national accounts, who, concerned for its volatility and cyclical behaviour, attempted to smoothing CEN's real output (Alcaide 1976).12 For the period 1901–1935, Alcaide derived an index of domestic production by combining, with 1906 fixed weights, CEN indices for agricultural and industrial output, and total employment in services, as a proxy for its output.<sup>13</sup> GDP at current prices was obtained by reflating real output with a wholesale price index.<sup>14</sup>

#### 8.2.3 Naredo

An apparent inconsistency in the CEN series that would have led to underestimating national income for the post-Civil War years motivated José Manuel Naredo's revision of CEN's national accounts (Naredo 1991). The rationale for the under-registration of economic activity in official national accounts lies in the response of economic agents to systematic regulation and intervention of markets under Francoist autarchy.<sup>15</sup> He also noticed that CEN's implicit income-elasticity of demand for imports in the 1940s was too low. Naredo proposed, then, an alternative real GDP series for 1920–1950 based upon the revision of official national account estimates by hypothesizing higher incomeelasticity of the demand for imports in the 1940s and by assuming a 10% fall in GDP resulting from the Spanish Civil War (1936–1939).

## 8.3 Independent Estimates

#### 8.3.1 Información Comercial Española

The contribution by the research unit of the Ministry of Commerce and published in its journal, Información Comercial Española (ICE, thereafter), represented a major improvement over earlier indices of Spanish aggregate performance (ICE 1962).<sup>16</sup> The 'general index of total production', as its authors named it, covered 1951–1960 and represented a Laspeyres volume index in which three major sectors, agriculture and fishing, mining, manufacturing and construction, and trade and services, were combined with 1958 gross value added as weights. For each sector, a Laspeyres volume index with 1958 weights was constructed, in which four branches were included for agriculture, sixteen for industry, and six for services, the latter appearing for the first time in pre-national accounts GDP estimates.<sup>17</sup>

Real product series was complemented with a quantity index for investment based on construction and public works, afforestation and the consumption (production plus imports) of machinery and equipment.

## 8.3.2 Schwartz

A major attempt at overcoming CEN's estimates for the period 1940– 1960 was carried out by Pedro Schwartz, at the Bank of Spain's research unit, where he assembled new empirical evidence and used transparent methods in which indirect methods and regression analysis were combined (Schwartz 1976). In the new series, gross value added for every major sector in the economy was obtained by regressing their value-added levels (derived from official national accounts) on a set of indicators over 1954–1960, and the resulting structural relationship was applied to the set of variables or indicators to compute sectoral value added for the earlier pre-national accounts period 1940–1953. Gross domestic product (nominal and real) was derived by aggregation.<sup>18</sup>

## 8.3.3 Carreras

The most ambitious attempt to derive historical series of real GDP was produced by Albert Carreras (1985) who built up an index from the demand side, covering a longer time span, 1849–1958.<sup>19</sup> Weights for the main aggregates (private and public consumption, investment, net exports) were derived from the 1958 benchmark from the national accounts, while the 1958 input–output table allowed the breakdown of each series into its main components.<sup>20</sup>

However, a few shortcomings can be observed in an otherwise major piece of research. For example, the consumption series only cover food, beverages and tobacco, and clothing while services are neglected.<sup>21</sup> Actually, it could be argued that consumption growth may be possibly biased downwards since the goods included in the series (food and clothing) are those of lower income-elasticity of demand.<sup>22</sup> In addition, the use of end-year (1958) fixed weights could underestimate GDP growth since relative prices for capital goods, the fastest growing component of expenditure, declined over time rendering, hence, a lower weight for investment than would have been the case if relative prices of any previous year were used.<sup>23</sup>

## 8.4 Comparing the New and Earlier GDP Estimates

How does the new GDP series compare to the earlier estimates? There is a significant agreement about performance over the long run between Carreras estimates and my new series, although significant discrepancies emerge in the short term. During the first half of the twentieth century, the new GDP series present slower growth than those by Alcaide and CEN (Fig. 8.1).

When the focus is placed on specific periods, the variance across different estimates emerges. World War I years seem to have been of fast

Fig. 8.1 Alternative real GDP estimates, 1850–1958 (1958 = 100) (logs)

growth (CEN, Alcaide and Carreras), in which the economy would have taken advantage of Spain's neutrality to cater for the needs of belligerent nations while domestic industry expanded on the basis of import substitution. This conventional depiction is challenged by the new GDP series. Then, the post-war years and especially the 1920s exhibit accelerated growth in CEN and Alcaide's. estimates while Carreras' suggest deceleration. The new GDP series provide an even more optimistic picture than Alcaide's.

The impact of Great Depression in Spain (1929–1933) varies dramatically according to different authors. Spain's economy decelerated but continued growing in Alcaide's view, stagnated in Naredo's, mildly contracted in Carreras' computations and definitely shrank in CEN's estimates. The new series side along CEN's but with a less intense decline.

Earlier estimates are discontinued between 1936 and 1939, so comparing output levels in 1935 and 1940 is the only way to assessing the impact of the Civil War (Fig. 8.2). A consensus exists about a substantial contraction in economic activity during the war years, around 6% per annum, but for Naredo's mild −2.1%. In my new estimates, the Civil War represented a milder but still deeper shrinkage than Naredo's. 24

Fig. 8.2 Alternative real GDP estimates, 1900–1958 (1958 = 100) (logs)


Table 8.1 Real GDP growth in the pre-national accounts era: alternative estimates, 1850–1958 (%)

Note 'New Series' are GDP estimates at market prices. Sources New Series, see the text. CEN (1945, 1965), ICE (1962), CPD (1972), Alcaide (1976), Naredo (1991), Schwartz (1976), and Carrerras (1985)

The post-war recovery was mild (but for Carreras and Naredo estimates) and short-lived (CEN, Carreras and Schwartz), and only resumed at a fast pace in the 1950s (except for Alcaide) (Table 8.1). The new GDP estimates concur with the view of a post-Civil War mild and long recovery, which makes Spanish post-war experience different from western Europe's fast return to pre-war output levels (Maddison 2010).

## Notes

1. Attempts to provide historical GDP at benchmark years have been carried out by economic historians. Bairoch (1976) and Crafts (1983, 1984) included Spain in their estimates for the nineteenth century computed along Beckerman and Bacon (1966) indirect approach. Following Deane (1957), Prados de la Escosura (1982) reconstructed Mulhall (1880, 1884, 1885, 1896) figures in a consistent way and derived a set of benchmark estimates for Spanish national income for 1832–1894. In addition, GDP estimates for seven benchmarks over the period 1800–1930 from the industry of origin approach are provided in Prados de la Escosura (1988).


activities, was constructed as a Laspeyres volume index with 1958 weighting. In ICE estimates, the coverage of output was far superior to CEN's, with 227 and 45 basic series for industry and services. For agricultural output (excluding livestock, forestry and fishing, for which 21 basic new series were used), CEN revised index was adopted. Weights applied to agriculture, industry and services to derive the "general index of total production" were 0.2693, 0.3200 and 0.4107, respectively.


official values for exports and imports that exaggerate commodity trade deficit for most of the period up to 1913 (see Sect. 7.3).

24. Actually, my yearly estimates indicate a sharper decline between 1935 and 1938, at −11% per year, followed by a recovery up to 1944.

## References


1977. El Producto Nacional de España en el siglo XX, 209–215. Madrid: Instituto de Estudios Fiscales.


Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/ 4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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# 9

# Splicing National Accounts, 1958–2015

National accounts rely on complete information on quantities and prices to compute GDP for a single benchmark year, which is, then, extrapolated forward on the basis of limited information for a sample of goods and services. To allow for changes in relative prices and, thus, to avoid that forward projections of the current benchmark become unrepresentative, national accountants periodically replace the current benchmark with a new and closer GDP benchmark. The new benchmark is constructed, in part, with different sources and computation methods.<sup>1</sup>

## 9.1 National Accounts in Spain

In Spain's national accounts, benchmarks for 1958 (CNE58) and 1964 (CNE64) were derived using OECD criteria, while the United Nations System of National Accounts (SNA) was used for all the rest (CNE70, CNE80, CNE86, CNE95, CNE00, CNE08, CNE10) (Table 9.1).2 Detailed sets of quantities and prices (derived from the closest input-output table) were employed to compute GDP at the benchmark year (1958, 1964, 1970, 1980, 1986, 1995, 2000, 2008, 2010).<sup>3</sup>

L. Prados de la Escosura, Spanish Economic Growth, 1850–2015, Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5\_9


Table 9.1 Spain's national accounts, 1954–2015

Note Direct estimates only refer to years after the benchmark. Sources IEF (1969), INE (various years)

Differences in a new benchmark year between 'new' and 'old' national accounts stem from statistical (sources and estimation procedures) and conceptual (definitions and classifications) bases. Once a new benchmark has been introduced, newly available statistical evidence would not be taken on board to avoid a discontinuity in the existing series (Uriel 1986: 69) so the coverage of new economic activities may explain the discrepancy between the new and old series. Furthermore, discrepancies between 'new' and 'old' benchmarks for the year in which they overlap also stem from statistical (sources and estimation procedures) and conceptual (definitions and classifications) differences. As a result, the consistency between the new and old national account series breaks.

The obvious solution to this inconsistency problem would be recompilation, that is, computing GDP for the years covered by the old benchmark with the same sources and procedures employed in the construction of the new benchmark. However, national accountants do not follow such a painstaking option.

A simple solution, widely used by national accountants (and implicitly accepted in international comparisons), is the retropolation approach, in which the new series (Y<sup>R</sup> ) results from accepting the reference level provided by the most recent benchmark estimate (YT) and, then, re-scaling the earlier benchmark series (Xt) with the ratio between the new and the old series for the year (T) at which the two series overlap (YT/XT).

$$Y\_t^R = (Y\_T/X\_T) \ast X\_t \quad \text{for} \quad 0 \le t \le T \tag{9.1}$$

For example, in order to obtain CNE70 estimates for 1964–1969, Spanish national accountants projected backwards (retropolated) the new 1970 GDP level (CNE70) with the rates of variation derived from the old benchmark series (CNE64). The retropolation approach was also adopted to derive series levels for the years 1964–1979 in both the 1980 and the 1986 benchmarks (CNE80 and CNE86).4

The choice of the retropolation procedure was made on the arguable assumption that growth rates originally calculated could not be improved (Corrales and Taguas 1991). Underlying this approach is the implicit assumption of an error level in the old benchmark's series whose relative size is constant over time. In other words, no error is assumed to exist in the old series' rates of variation that are, hence, retained in the spliced series Y <sup>t</sup> <sup>R</sup> (de la Fuente 2014). Official national accountants have favoured this procedure of linking national accounts series on the grounds that it preserves the earlier benchmark's rates of variation.<sup>5</sup> The retropolation approach pays no regard to the unpredictable but significant effects of using a set of relative prices from the old benchmark to project the level of the new benchmark backwards.

The main methodological discontinuity in Spanish national accounts occurred when the SNA substituted for the OECD method in the late 1970s. Table 9.2 provides the values of each benchmark series at base years and the ratio between each pair of adjacent 'new' and 'old' benchmark values. Substantial discrepancies are noticeable between CNE64 (constructed with OECD criteria) and CNE70 (derived with SNA criteria), benchmarks within a period of fast growth and deep structural change (Prados de la Escosura 2007).

It is worth noting that the most recent benchmark usually provides a higher GDP level for the overlapping year, as its coverage of economic activities is wider. Thus, the backward projection of the new benchmark GDP level with the available growth rates—computed at the previous benchmark's relative prices—implies a systematic upward revision of GDP levels for earlier years.<sup>6</sup> The evidence in Table 9.2 highlights the



impact of successive one-side upward revisions, which widens the gap over time. In fact, the GDP figure obtained by the cumulative re-scaling different national accounts subseries from 2010 backwards (that is, using the retropolation approach) is 28.4% higher for 1970 than the one computed by CNE64 (and 24.6% higher than the one directly calculated for 1964).<sup>7</sup>

Would it be reasonable to expect such an underestimate from a direct GDP calculation on the basis of 'complete' information about quantities and prices of the goods and services in the old benchmark? Can the direct measurement of GDP level at an early benchmark year be really improved through the backward projection of the latest benchmark year with earlier benchmarks' annual rates of variation?

The challenge is to establish the extent to which conceptual and technical innovations in the new benchmark series hint at a measurement error in the old benchmark series. In particular, whether the discrepancy in the overlapping year between the new benchmark (in which GDP is estimated with 'complete' information) and the old benchmark series (in which reduced information on quantities and prices is used to project forward the 'complete' information estimate from its initial year) results from a measurement error in the old benchmark's initial year estimate, or it is the cumulative result of the emergence of new goods and services not considered in the old benchmark series.

An alternative to the retropolation method is provided by the interpolation procedure that accepts the levels computed directly for each benchmark year as the best possible estimates—on the grounds that they have been obtained with 'complete' information on quantities and prices and distributes the gap or difference between the 'new' and 'old' benchmark series in the overlapping year T at a constant rate over the time span in between the old and new benchmark years.<sup>8</sup>

$$Y\_t^I = Y\_t \ \* \left[ \left( Y\_T / X\_T \right)^{1/n} \right]^t \quad \text{for } 0 \le t \le T \tag{9.2}$$

Being Y <sup>I</sup> the linearly interpolated new series, Y e X the values pertaining to GDP according to the new and old benchmarks, respectively; t, the year considered; T, the overlapping year between the old and new benchmarks' series; and n, the number of years in between the old (0) and the new benchmark (T ) dates.<sup>9</sup>

Contrary to the retropolation approach, the interpolation procedure assumes that the error is generated between the years 0 and T. Consequently, it modifies the annual rate of variation between benchmarks (usually upwards) while keeps unaltered the initial level that of the old benchmark. As a result, the initial level will be probably lower than the one derived from the retropolation approach.

In Spanish national accounts, a break in the linkage of GDP series through retropolation was introduced in CNE86, when national accounts were spliced using the interpolation approach and the GDP differential between CEN86 and CEN80 in 1985 was distributed at a constant rate over the years 1981–1984 (expression 16) (INE 1992). However, a new national accounts benchmark in 1995 (CNE95) did not bring along a splicing of CNE95 and CNE86 series.<sup>10</sup> In later benchmarks (CNE00, CNE08 and CNE10), the interpolation method was resumed, but only after adjusting upwards the old benchmark for methodological changes. <sup>11</sup> Thus, the gap between, say, CNE10 and CNE00-08, in the year 2010, was decomposed into methodological and statistical plus other differences.<sup>12</sup> Firstly, CNE00-08 series for 1995–2009 were adjusted upwards for methodological discrepancies with CNE10. Then, the residual gap, due to statistical and other differences, was distributed at a constant rate (using expression 16) over the in-between benchmark years, 2001–2009.<sup>13</sup> As a result, no officially spliced GDP series are available at the present for the entire national accounts era.

# 9.2 Splicing National Accounts Through Interpolation

A straightforward procedure would be, then, splicing the all benchmark series available by accepting the levels directly computed for each benchmark year and distributing the gap between each pair of adjacent benchmark series at their overlapping year at either a constant rate over the time span between them. This solution has the advantage of being transparent and linking different benchmarks equally.

Nonetheless, before computing and comparing alternative splicing results, pre-1980 national accounts need to be examined because, as mentioned earlier, it is during the transition between OECD and SNA methodologies when larger disparities between adjacent benchmarks series emerged in overlapping years. By examining the way OECD (CNE64) and SNA (CNE70) benchmarks were constructed, an attempt to reconcile their differences can be made.

In pre-1980 official national accounts, annual nominal series of, say, industrial value added were usually obtained through back and forth extrapolation of the benchmark year's gross value added with an index of industrial production that was, then, reflated with a price index for industrial goods. Projecting industrial real value added with an index of industrial production amounts to a single deflation of value added, in which the same price index is used for both output and inputs.<sup>14</sup> However, only if prices for output and intermediate inputs evolve in the same direction and with the same intensity, real value added is accurately represented by an industrial production index. In periods of rapid technological change (or external input price shocks), significant savings of intermediate inputs do take place while relative prices change dramatically, and, hence, the assumption of a parallel evolution of output and input prices does not hold.<sup>15</sup> This description applies well to Spain in the 1960s and 1970s, when the country opened up to foreign technology and competition and suffered the oil shocks.<sup>16</sup> Fortunately, alternative estimates of gross value added at constant prices derived through the Laspeyres double deflation method<sup>17</sup> are available for industry and construction over the years 1964–1980 (Gandoy 1988).<sup>18</sup> Gandoy's value added series exhibit higher real growth rates than CEN70 series since her implicit value added deflator grows less than the national accounts' deflator (biased towards raw materials and semimanufactures).19 This is what should be expected in a context of total factor productivity growth, such as was the case of Spain in the 1960s and early 1970s, with output prices growing less than inputs prices, as inputs savings resulted from efficiency gains (Prados de la Escosura 2009).20

Thus, CEN70 series for GDP have been revised for 1964–1980. Firstly, Gandoy Juste (1988) alternative value added estimates for industry and construction (GVAi <sup>G</sup> and GVAc <sup>G</sup> ) were substituted for those in official national accounts (GVAi cen70 and GVAc cen70).<sup>21</sup> CNE70 value added figures for agriculture (GVAa cen70 ) and services (GVAs cen70) were kept.<sup>22</sup> Total gross value added was reached by adding up sectors' gross value added.

$$\text{GVA}^T = \text{GVA}\_a^{\text{cin70}} + \text{GVA}\_i^G + \text{GVA}\_c^G + \text{GVA}\_s^{\text{cin70}} \tag{9.3}$$

GDP at market prices was derived, in turn, by adding taxes on products net of subsidies to total gross value added.

CEN70 GDP estimates on the expenditure side were also adjusted. While Gandoy (1988) provides alternative value added series at factor cost for industry (VAfci <sup>G</sup>) and construction (VAfcc <sup>G</sup> ), Gómez Villegas (1988) presents new series for fixed domestic capital formation in industry (GCFi <sup>G</sup>) and construction (GCFc <sup>G</sup>). Thus, in order to adjust the aggregate figure for investment in CNE70 (GCFcen70 ), I firstly computed the share of value added at market prices (VAmp) allocated to investment in industry and construction, according to Gandoy (1988) and Gómez Villegas (1988), (GCF <sup>i</sup> <sup>G</sup>/VAmpi <sup>G</sup> and GCF <sup>c</sup> <sup>G</sup>/VAmpc <sup>G</sup> ), which implied adjusting value added to include taxes on production and imports net of subsidies.<sup>23</sup> Then, I applied this share to the difference between the value added estimates at factor cost in Gandoy's (VAfci <sup>G</sup> and VAfcc <sup>G</sup> ) and in CEN70 (VAfci cen70 and VAfcc cen70).

$$GCF\_i^{\text{add}} = \left(GCF\_i^G / \text{VA}\_{mpi}^G\right) \,\* \, \left(VA\_{fc}^G - VA\_{fci}^{con70}\right) \tag{9.4}$$

$$\left(\text{GCF}\_c^{\text{add}} = \left(\text{GCF}\_c^G / \text{VA}\_{mpc}^G\right) \ast \left(\text{VA}\_{fcc}^G - \text{VA}\_{fcc}^{cen70}\right)\right) \tag{9.5}$$

So the additional investment—that is, the portion of gross capital formation not included in CNE70—was obtained. Thus,

$$GCF^{add} = GCF\_i^{add} + GCF\_c^{add} \tag{9.6}$$

And the revised figure for gross capital formation was derived as,

$$GCF^{1970R} = GCF^{\text{cent}70} + GCF^{add} \tag{9.7}$$

Then, I adjusted private consumption figures in CEN70 for the changes introduced in gross capital formation. That is, I assumed that the additional value added in industry and construction (derived by deducting CNE70 value added from Gandoy's estimates) less the additional investment (GCF add ) accrued to private consumption, since the values for net exports of goods and services (NX cen70 ) and public consumption (GOVTcen70 ) provided by CEN70 were obtained from a sound statistical basis.<sup>24</sup> That is,

$$\text{CONS}^{\text{add}} = \left( \left( \text{VA}\_{fci}^{G} + \text{VA}\_{fcc}^{G} \right) - \left( \text{VA}\_{fci}^{cen70} + \text{VA}\_{fcc}^{cen70} \right) \right) - \text{GCF}^{\text{add}} \tag{9.8}$$

And the revised figure for total private consumption was reached as,

$$CONS^{1970R} = CONS^{cen70} + CONS^{add} \tag{9.9}$$

Lastly, the new estimates of GDP at market prices were obtained as,

$$GDP\_{mp}^{1970R} = CONS^{1970R} + GCF^{1970R} + GOV^{\text{cen70}} + NX^{\text{cen70}} \tag{10.1}$$

How are interpolated, then, earlier, pre-1980, national account benchmark series? CNE70<sup>R</sup> series have been accepted for the years 1964–1969, rather than distributing the difference in 1970 between CNE70<sup>R</sup> and CNE64 over these years. The reason of this choice is that CNE70<sup>R</sup> series have been mainly derived through double deflation, as opposed to CNE64 single deflation series. CNE70<sup>R</sup> and CNE58 series were, in turn, interpolated by distributing their gap in 1964 over 1959– 1963.<sup>25</sup> Lastly, in order to I derived a single series for GDP and its components for the pre- and post-1980 series, I distributed their gap in the overlapping year, 1980, over 1971–1979. Aggregated GDP figures result from adding up its previously spliced components.<sup>26</sup>

This strict interpolation procedure has, nonetheless, the shortcoming of deviating from official national accounts series for the years 1995–2009. The reason is that, as observed above, in post-2000 Spanish national accounts its splicing is performed in two stages: firstly, the old benchmark series are adjusted upwards for methodological changes in the new benchmark; and, then, the remaining statistical gap is distributed at a constant rate over the years between the new and the old benchmarks.

Thus, an alternative to deriving GDP series through strict interpolation appears, namely accepting the official interpolation linkage for 1995– 2010 and interpolating the different benchmark (CNE58 to CNE95) series for the previous years, 1958–1995.<sup>27</sup>

It is worth noting, however, that, in CNE10 series, the GDP level for 1995 is higher (4.9%) than the one originally computed with complete information in CNE95 (Table 9.3). What share of this gap is attributable to methodological differences? The CNE10 linkage procedure consisted in adjusting the CNE00 series for methodological differences back to 1995 and, then, distributing the remaining, mostly statistical, gap over 2001–2009, under the assumption that no statistical error exists in 2000. Thus, the entire discrepancy in 1995 between CNE10 and CNE95 could be attributable to methodological differences.<sup>28</sup> Should pre-1995 series, resulting from splicing all previous benchmarks (CNE58–CNE95), be raised, then, by a fixed ratio (1.0492)? This option does not seem reasonable, as it can be conjectured that the impact of methodological changes would be larger the closer the year's estimate to CNE10 benchmark year, 2010. A compromise solution would be to distribute the entire gap over the 1954–1994 series. Therefore, I have


Table 9.3 Real GDP Growth: Alternative Splicing, 1958–2010 (annual average rates %)

Fig. 9.1 Ratio between hybrid linearly interpolated and retropolated nominal GDP series, 1958–2000. Sources See the text

spliced the pre- and post-1995 series through a 'hybrid' interpolation, with an adjustment for methodological differences as described above.

Figure 9.1 presents the ratio between the figures for nominal GDP obtained by splicing national accounts through 'hybrid' linear interpolation and those derived through extrapolation. It can be observed how the over-exaggeration of GDP levels derived through retropolation cumulates as one goes back in time, reaching around one-fifth by the late 1950s.

Once GDP series at current prices were obtained, the next task was to deflate them in order to obtain GDP volume indices. Deflators for each CNE benchmark GDP series were also spliced through 'hybrid' linear interpolation as well as through retropolation. Interestingly, deflators derived through alternative splicing methods do not exhibit the far from negligible differences observed for current values.

Figure 9.2 presents the evolution of GDP at constant prices, expressed in log form, using alternatively the interpolated and retropolated series over 1958–2000. It can be observed that their differential widens

Fig. 9.2 Real GDP, 1958–2000 (2010 Euro) (logs): alternative estimates with hybrid linear interpolation and retropolation splicing (logs). Sources see the text

significantly over time suggesting lower levels and faster growth for GDP estimates derived through interpolation.29

Table 9.3 compares the resulting GDP growth rates between National Accounts benchmark years derived by splicing national accounts alternatively with 'hybrid' linear interpolation and retropolation approaches. GDP estimates derived through the interpolation procedure cast higher growth rates over the entire time span considered than those estimates resulting from the conventional retropolation method. The annual cumulative rate per person over 1958–2000 is 4.5% compared to a 4.0% for the retropolated series, respectively. The main discrepancies correspond to period 1970–1995, and particularly during the 1970s, in which the interpolated series exhibit a more than one-third faster growth rate. The implication is that, in the period of rapid expansion 1958–1974, Spain's delayed Golden Age, and, again, between Spain's accession to the European Union (1985) and the eve of the Great Recession (2007), the interpolated series grew faster that the retropolated ones. However, it is during the so-called transition to democracy period (1974–1984), when the positive growth differential between the interpolated and the retropolated series reached its peak (2.3 and 1.3%, respectively). As a result, the deceleration following the exceptional growth of Spain's delayed Golden Age was less dramatic than suggested by conventional narrative. It is worth comparing the results to another alternative to the retropolation procedure provided by the 'mixed splicing', in which Ángel de la Fuente (2014, 2016) proposes an intermediate position in which an initial error in the old series, stemming from the insufficient coverage of emerging economic sectors, grows at an increasing rate. Unfortunately, the correction to the growth rate of the original series implies an arbitrary assumption about its size (see the discussion in Prados de la Escosura 2016).

Since de la Fuente (2016) favours Gross Value Added (GVA, equivalent to GDP at basic prices), the comparison is carried out in terms of real GVA (Fig. 9.3). It can be observed that the results from 'mixed splicing' are not far apart from those I obtained through hybrid linear interpolation. Discrepancies only appear in the pre-1980 period for which de la Fuente (2016) linked his series to Uriel et al. (2000) GDP series spliced through retropolation.

Fig. 9.3 Real gross value added, 1958–2015 (2010 Euro) (logs): alternative estimates with hybrid linear interpolation and mixed splicing, 1958–2015. Sources Hybrid linear interpolation, see the text; Mixed splicing, de la Fuente (2016)

# Notes


deflation in CNE58, splicing through interpolation provides a correction of its series that somehow amounts to an allowance for efficiency gains.


## References


C. Molinas, M. Sebastián, and A. Zabalza. Una perspectiva macroeconómica, Barcelona/MadridAntoni Bosch/Instituto de Estudios Fiscales' 583–646.


Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/ 4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# 10

# Population, 1850–2015

Spain's Statistical Office (Instituto Nacional de Estadística, INE) provides yearly series of 'resident' population from 1971. INE also presents annual series of 'de facto' population for 1900–1991, in which figures for census benchmark years are linearly interpolated. Roser Nicolau (2005) collected and completed the series back to 1858. More recently, Jordi Maluquer de Motes (2008) has constructed yearly estimates of 'de facto' population for 1850–1991 and spliced them with 'resident' population for 2001. In order to do so, Maluquer de Motes started from census figures at the beginning of each census year adding up annually the natural increase in population (that is, births less deaths) plus net migration (namely immigrants less emigrants). I have followed Maluquer de Motes's approach with some modifications. Thus, I have accepted census benchmark years' figures and Gustav Sündbarg (1908) estimate for 1850 and obtained the natural increase in population with Nicolau (2005) figures for births and deaths from 1858 onwards, completed for 1850–1857 with Sündbarg (1908) net estimates at decadal averages equally distributed.1 My main departure from Maluquer de Motes approach has been with regards to net migration for which I have accepted Blanca Sánchez-Alonso (1995) estimates for 1882–1930, completed back to 1850 and forth to 1935 with statistical evidence from Spanish and main destination countries' sources (see Sect. 7.3.4). For the years of the Civil War (1936–1939) and its aftermath (1940–1944), I have accepted José Antonio Ortega and Javier Silvestre (2006) gross emigration estimates for 1936–1939, assuming no immigration during the war years, and distributing evenly an upward revision of their return migration estimates for 1940–1944, while assuming no gross emigration during World War II.2 In order to obtain a consistent series for 1850–1970, I have spliced population estimates linearly by distributing the difference between the estimated population obtained by forward projection of the initial census benchmark figure for the year of the next census benchmark and the observed figure at the new census using expression (16). Lastly, I have linked the linearly interpolated series for 'de facto' population for 1850–1970 with the 'resident' population series from 1971 onwards to get a single series.<sup>3</sup> Fortunately, the difference between the 'de facto' and 'resident' series over 1971–1991 is negligible.<sup>4</sup>

# Notes


# References


Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/ 4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# 11

# Employment, 1850–2015

The latest round of national accounts (CNE10) provides data on the number of full-time equivalent (FTE) workers and hours worked and its distribution by industry from 1995 to 2015. Unfortunately, no similar data are provided in earlier rounds of national accounts that present only figures for the number of occupied back to 1980 (CNE80 and CNE86). However, the 1995-based quarterly national accounts (CNTR95) provide data on FTE workers for 1980–1995. I have, then, spliced the two sets of FTE workers through linear interpolation to get consistent estimates over 1980–2015.<sup>1</sup>

For the pre-1980 years, García Perea and Gómez (1994) provide estimates of employment back to 1964 that can be pushed further back to 1954 with the rate of variation of employment provided in earlier national accounts (CNE64) (Instituto de Estudios Fiscales 1969: 33–34). I have assumed that the number of FTE workers evolved alongside employment and, thus, projected its 1980 level backwards to 1954 with the employment rate of variation to derive FTE employment series for the period 1954–2015 for the economy as a whole and its main economic sectors.

The next challenge was to link the post-1954 series with the historical evidence back to 1850. Thus, on the basis of population censuses, I constructed yearly employment estimates for 1850–1954 for the four main sectors: agriculture, forestry, and fishing; industry, mining, and utilities; construction; and services. Major shortcomings appear in Spanish census data: working population is only available at benchmark years and refers to the economically active population [EAN, thereafter], with no regard of involuntary unemployment.<sup>2</sup> Moreover, censuses tend to only record one activity per person, the one that individuals consider to be their principal activity, and this is usually 'farmer'. However, in a developing society the division of labour is low and a single person might undertake various work tasks over the course of a year.<sup>3</sup> Henceforth, activities corresponding to the industrial and, particularly, service sectors end up being underestimated in population censuses.<sup>4</sup> In addition, figures for female EAN in agriculture seem to be inconsistent over time.<sup>5</sup> Therefore, I have been forced to make some choices. For example, in order to derive consistent figures over time for EAN in agriculture, I excluded the census figures for female population, while assumed that female labour represented a stable proportion of male labour force in agriculture and, hence, increased the number of days assigned to each male worker (see below).<sup>6</sup> Moreover, as the share of EAN in agriculture is suspiciously stable over 1797–1910, in spite of industrialization and urbanization, I corrected it by assuming that the agricultural share of EAN moved along, and could not exceed, the proportion of rural population (living in towns with less than 5,000 inhabitants) in total population.<sup>7</sup> Thus, I adjusted downwards the percentage of EAN employed in agriculture between 1887 and 1920 by redistributing 'excess' agricultural workers proportionally between industry, construction, and services.<sup>8</sup> The next step was to obtain yearly EAN figures through log-linear interpolation of benchmark observations. Since the resulting estimates do not capture yearly fluctuations in economically active population, a partial solution has been, firstly, to compute EAN share in working age population (WAN) and WAN share in total population (N), being WAN and N computed through linear interpolation (i ) between population censuses.<sup>9</sup> Then, these ratios have been multiplied by the new yearly population estimates (N) to derive annual figures of economically active population (EAP). Thus,

$$\mathbf{EAP} = (\mathbf{EAP}^i / \mathbf{WAN}^i)(\mathbf{WAN}^i / \mathbf{N}^i)\mathbf{N} \tag{11.1}$$

Later, in order to adjust for differences in labour intensity across main economic sectors and obtain a crude measure of full-time equivalent worker by industry, the data on EAP were converted into days worked per year. I assumed that each full-time worker was employed 270 days per annum in industry, construction, and services. Such figure results from deducting Sundays and religious holidays plus an allowance for illness. This assumption is in line with contemporary testimonies and supported by the available evidence.<sup>10</sup> In agriculture, however, contemporary and historians' estimates point to a lower figure for the working days per occupied, as full employment among peasants only occurred during the summer and, consequently, workers were idle for up to four months every year. It can be assumed that the working load per year for the average male worker in agriculture would range, at most, between 210 and 240 days.<sup>11</sup> However, in order to make for the exclusion of female employment in agriculture (due to the absence of consistent data), I increased the number of days assigned to male workers employed in agriculture to match the figure used for the rest of economic sectors (270).<sup>12</sup>

Lastly, figures for full-time equivalent employment by economic sector for 1850–1953 were derived by assuming that their yearly changes mirrored those in economically active population and, thus, FTE employment estimates for 1954 were backwards projected with those for economically active population (EAN). Total FTE employment for 1850–1954 resulted from adding up figures for sectoral estimates. It is worth noting that, in 1954, the ratio between FTE employment and EAN for each economic sector is 1.003 (agriculture), 0.872 (industry), 1.095 (construction), and 1.069 (services), and 1.000 for the aggregate. The implication, in the case of agriculture, is that the upper bound figure for male employment (resulting from an attempt to make for missing female labour figures) matches that of full-time equivalent total employment (including female work).

The final step has been to derive hours worked in which I draw on Prados de la Escosura and Rosés (2010: 526). For mid-nineteenth-century agriculture, Caballero (1864) estimated 10 h per day and a similar average figure, 9.7 h, was found for the mid-1950s.<sup>13</sup> Thus, I accepted 10 h per day for 1850–1911, interpolated these two figures over 1912–1935, and retained 9.7 h for the period 1936–1954. For industry and services, I interpolated Huberman's (2005) figures for 1870–1899 to derive annual hours worked, and the number of hours worked in 1870 was accepted for 1850–1869. I adopted Domenech's (2007) estimates for different industries and services in 1910 for 1900–1910, and Silvestre's (2003) annual computations for industry for 1911–1919. As regards the interwar years, Soto Carmona (1989: 596–613) provides some construction and services figures. Data on hours worked for the early 1950s are often close to those of 1919. I accepted the number of working hours per occupied in 1954 for the years 1936–1953, and interpolated the figures for 1919 and 1936. For the post-1954 period, hours worked for each branch of economic activity derive from Sanchis (private communication) for the 1950s, Maluquer de Motes and Llonch (2005) for 1958–1963, Ministerio de Trabajo (1964–1978) for 1964–1978, and OECD (2006) for 1979–1994. From 1995 onwards, the latest round of national accounts (CNE10) provides annual figures of hours worked. The resulting estimates show that the amount of total hours worked increased moderately, multiplying by 2.1 over the 166 years considered, but falling short of the increase in population that multiplied by 3.1.

## Notes


was a lot of seasonal as well as hidden unemployment in the agricultural sector (labour hoarding) (Pérez Moreda 1999: 57).


living in rural population centres. Moreover, as income levels increase, both the rural population and the overall population of agricultural workers will decrease, although the latter does so at a faster rate, as there always exists some part of the population that opts to live in the countryside despite not being employed primarily in either agriculture or the raising of livestock (Prados de la Escosura 2007).


## References


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# Appendices

#### Appendix. Final Output and Gross Value Added in Agriculture, 1850–1958

See Tables A.1, A.2 and A.3.


Table A.1 Ratios of final output to total production for main crops

a 0.37 in the 1950s

Sources Simpson (1994); Federico (1992); Ministerio de Agricultura (1979b)

© The Editor(s) (if applicable) and The Author(s) 2017

L. Prados de la Escosura, Spanish Economic Growth, 1850–2015,

Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5


Table A.2 Conversion coefficients applied to livestock numbers to derive meat, wool and milk output, 1891–1924

a Kilograms per unit of total livestock (not just slaughtered livestock), 1865. The share of livestock slaughtered comes from García Sanz (1994), but for cattle the share has been raised from 6.36%, the figure provided by García Sanz, to 11.36% in order to include slaughtered young animals. Such proportion is obtained as follows: in the 1933 cattle census, adult animals slaughtered represented 15.68% of its total. However, according to Simpson (1994), when young animals are considered, the percentage increases to 28%. A similar correction for 1865 would result in 11.36% of livestock slaughtered [28 \* 6.36/15.68 = 11.36]. Lack of information led me to accept dressed carcass weights for 1920 from Flores de Lemus (1926), 38.472 kg per livestock unit and 3.753 kg per sheep and goat unit, 1891/1924. For sheep and pigs, coefficients provided by Simpson (1994) and Comín (1985) were applied. Simpson (1994) assumes, following the 1929 Census, that 37.5% of sheep and 59.6% of pigs were slaughtered annually. Comín (1985) provides dressed carcass weight per unit, 9.8 kg per sheep and 86.5 kg per pig. For cattle and goats, total dressed carcass weight/livestock number ratios for 1925– 1935 were accepted, while for horsemeat it was the 1950 ratio, all from Ministerio de Agricultura (1979a). If, alternatively, Simpson (1994) approach, which assumes that 28% and 38.3% of cattle and goats were sacrificed each year, was used, and average dressed carcass weight of 137.4 kg and 9.8 kg, respectively, from Comín (1985) was applied, the resulting conversion coefficients would be slightly higher than those adopted here

b Simpson (1994), Comín (1985), Carreras (1983) and Prados de la Escosura (1983) accept this figure. Alternatively, Parejo (1989) suggests 2 kg <sup>c</sup>

Litres per unit of total livestock (not per females), 1865. He aplicado los rendimientos que proporciona Simpson (1994) yields 700 l per milking cow-year, being milking 45% of all cows that, in turn, represented 59% of total cattle. I have adjusted this figure (186 l per cattle unit) downwards with the ratio between milk production derived by me and by Simpson for 1891/1924 (363/387). In the cases of sheep and goat, female represented 69.5% and 73.4% of the total, respectively, and I have accepted the milking female/total female ratio for 1929/33, 1891–1924.

1925–1935 average milk/livestock unit ratios were accepted from Ministerio de Agricultura (1979a). Simpson (1994) estimates for 1929/33 are very close. For cows, Simpson assumed that females represented 75% of cattle, from which 45% were milked, yielding 1,146 l per head per year. For sheep, the corresponding figures were 62.7%, 23.4% and 25.8 l and for goats, 65.2%, 60% and 175 l Sources Carreras (1983); Comín (1985); Simpson (1994); Ministerio de Agricultura (1979a)


Table A.3 Coverage of the sample of products included in the annual index for each agricultural group at benchmarks (%) (current prices)

a Wheat, barley, rye, oats, maize, rice

b Chickpeas, broad beans, beans

c Potatoes, onions

d Sugar beet, sugar cane, wool, silk cocoons, cotton (since 1950), tobacco (since 1950)

e Almonds, oranges, carobs, apples, chestnuts, lemons, bananas (only almonds and oranges before 1910)

f Olive oil, no olives and sub products included

g Beef and veal, lamb and mouton, goat, pork, horsemeat (since 1950) h Milk only

Sources See the text

#### References


#### Statistical Appendix: Spain's Historical National Accounts: Expenditure and Output, 1850–2015

See Tables S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, S16, S17, S18, S19, S20, S21, S22, S23, S24, S25, S26, S27 and S28.



206 Appendices

Table S1


Table S1 (continued)



Table S1 (continued)

(continued)


210 Appendices

Table S1


Table S1 (continued)


Table S1

(continued)

SourcesPleasecitethedatabaseas:LeandroPrados de la Escosura(2017),SpanishEconomicGrowth,1850–2015

#### 212 Appendices


Grossdomesticproduct,grossandnetnationalincome,1850–2015(millionEuro)

(continued)


214 Appendices

Table S2


Table S2

(continued)

Appendices 215


Table S2


Table S2 (continued)


Table S2


Table S2 (continued)


220 Appendices

Table S2


Table S3

Absolute

 and per capita gross domestic

 product,

 gross and net domestic

 income,

1850–2015

 (million Euro and

Appendices 221


222 Appendices

Table S3


Table S3 (continued)


224 Appendices

Table S3


Table S3 (continued)


226 Appendices

Table S3


Table S3 (continued)


Table S3 (continued)


Appendices 229

(continued)


230 Appendices

Table S4


Table S4 (continued)


232 Appendices

Table S4


Table S4

(continued)

Appendices 233


234 Appendices

Table S4


Table S4 (continued)


236 Appendices

Table S4



238 Appendices

Table S5


Table S5 (continued)


#### 240 Appendices

Table S5


Table S5 (continued)


242 Appendices

Table S5


Table S5 (continued)


244 Appendices

Table S5



246 Appendices

Table S6


Table S6

(continued)

Appendices 247


248 Appendices

Table S6


Table

S6

(continued)


250 Appendices

Table S6


Table S6 (continued)


252 Appendices

Table S6


Table S6

(continued)

Appendices 253


Table S7

Deflators

 of gross domestic

 product and its

expenditure

components,

1850–2015

 (2010 = 100)


Table S7

(continued)

Appendices 255


256 Appendices

Table S7




258 Appendices

Table S7


Table S7 (continued)


260 Appendices

Table S7


Table S8 Gross domestic fixed capital formation, 1850–2014 (million Euro)


Table S8 (continued)


Table S8 (continued)



Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015

\*includes other concepts such as "agriculture" and "other". The latter including intellectual property since 1995


Table S9 Composition of gross domestic fixed capital formation, 1850–2015 (percentages)


Table S9 (continued)


Table S9 (continued)


Table S9 (continued)


Table S9 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015

\*includes other concepts such as "agriculture" and "other". The latter including intellectual property since 1995


Table S10 Volume indices of gross domestic fixed capital formation, 1850–2015 (2010 = 100)


Table S10 (continued)


Table S10 (continued)


Table S10 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015

\*includes other concepts such as "agriculture" and "other". The latter including intellectual property since 1995


Table S11 Deflators of gross domestic fixed capital formation, 1850–2015 (2000 = 100)


Table S11 (continued)


Table S11 (continued)


Table S11 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015

\*includes other concepts such as "agriculture" and "other". The latter including intellectual property since 1995


TableS12Grossdomesticproductanditsoutputcomponents,1850–2015(millionEuro)


Table S12

(continued)

Appendices 279


280 Appendices

Table S12


Table S12

(continued)

Appendices 281


282 Appendices

Table S12


Table S12 (continued)


Table S12 (continued)


Table S12

(continued)


Table S13 Absolute and per capita gross value added and gross domestic product at market prices, 1850–2015 (million Euro and Euro)


Table S13 (continued)


Table S13 (continued)


Table S13 (continued)


Table S13 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S14 Volume indices of absolute and per capita gross domestic product at market prices and gross value added, 1850–2015 (2010 = 100)


Table S14 (continued)


Table S14 (continued)


Table S14 (continued)


Table S14 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S15 Shares of output components in gross value added, 1850–2015 (percentage)


Table S15 (continued)


Table S15 (continued)


Table S15 (continued)


Table S15 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S16 Volume indices of gross value added and its output components, 1850– 2015 (2010 = 100)




Table S16 (continued)


1992 79.6 72.1 89.7 56.2 62.5

Table S16 (continued)


Table S16 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S17 Deflators of gross value added and its output components, 1850–2015 (2010 = 100)


Table S17 (continued)




Table S17 (continued)



Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S18 Employment (full-time equivalent), 1850–2015 (million)




Table S18 (continued)




Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S19 Sector shares in employment (full-time equivalent), 1850–2015 (percentage)


Table S19 (continued)


Table S19 (continued)


Table S19 (continued)


Table S19 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S20 Relative sector labour productivity (full-time equivalent employment), 1850–2015 (Average productivity = 1)


Table S20 (continued)


Table S20 (continued)


Table S20 (continued)



Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S21 Labour productivity indices (gross value added per full-time equivalent occupied), 1850–2015 (2010 = 100)


Table S21 (continued)




Table S21 (continued)


Table S21 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S22 Hours worked, 1850–2015 (million)



Table S22 (continued)




Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S23 Sector shares in worked hours, 1850–2015 (percentage)


Table S23 (continued)


Table S23 (continued)


Table S23 (continued)


Table S23 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S24 Relative sector labour productivity (hours), 1850–2015


Table S24 (continued)


Table S24 (continued)


Table S24 (continued)


Table S24 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S25 Labour productivity levels (per worked hour), 1850–2015 (2010 = 100)


Table S25 (continued)




Table S25 (continued)


Table S25 (continued)

Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


Table S26 Hours worked per full-time equivalent occupied/year, 1850–2015




Table S26 (continued)




Sources Please cite the database as: Leandro Prados de la Escosura (2017), Spanish Economic Growth, 1850–2015


S27Realpercapitagrossdomesticproduct,1850–2015(EKS



358 Appendices

 Germany

 5530

 5749

 5893

 5932

 6024

 6201

 6219

 6258

 6398

 6514

 6734

 6972

 5847

 5540

 5608

 5642

 5701

 4943

 5343

 5882

 6366

 5255

 6113

 6749

 6889

 7532 (continued)

1916

1917

1918

1919

1920

1921

1922

1923

1924

1925

1926

1927

 3740

 3664

 3622

 3677

 3943

 4038

 4166

 4173

 4277

 4528

 4437

 4813

 4116

 4129

 4025

 3864

 3956

 3821

 4100

 4435

 4514

 4782

 4778

 4650

 5780

 4972

 3998

 4692

 5386

 5131

 6026

 6264

 6975

 6953

 7092

 6933

 7862

 7915

 7971

 7111

 6640

 6482

 6770

 6950

 7185

 7511

 7207

 7760

 8836

 8494

 9160

 9195

 8987

 8616

 8967

 9978

 10088

 10169

 10687

 10645






Sources (2017), Spanish Growth, Spain, see the text; Italy, Baffigy (2011) completed with Conference Board for 2011–2015; France, UK, USA, and Germany,Maddison Project completed with Conference Board for 2011–2015


Appendices 363




366 Appendices





 

Maddison

 Project

completed

 with

Conference

Board

for

2011–2015

#### Appendices 369

# Author Index

A Acemoglu, D., 55 Alcaide Inchausti, J., 156, 159–161, 163 Almarcha, A., 88, 93, 99, 100 Alonso Alvarez, L., 133 Altinok, N., 55 Álvarez-Nogal, C., 45 Anand, S., 55 Andrews, D., Annual Statement Of Trade and Navigation, 136 Antolín, F., 94 Anuario(s) Estadístico(s) de España, 90 Arenales, M.C., 90, 91, 94 Ark, B. van, 184 Arrow, K.J., 184 Artola, M.,

B

Bacon, R., 161 Baffigi, A., 133, 142 Baiges, J., Bairoch, P., 161, 198 Bakker, G. den, 182 Balke, N.S., 86, 164 Ballesteros, E., 86, 90, 99, 140 Banco Central, 135, 137 Banco Urquijo, 130, 143, 162 Barciela López, C., 69, 88, 90, 91, 94, 134 Bardini, C., 164 Batista D., 93, 98, 142 Becker, G.S., 55 Beckerman, W., 112, 161 Bolt, J., 21 Bonhome L., 77, 95 Bordo, M.D., 18

© The Editor(s) (if applicable) and The Author(s) 2017 L. Prados de la Escosura, Spanish Economic Growth, 1850–2015, Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5 Boskin, M.J., 182 Broadberry, S.N., 32, 37, 38, 164 Broder, A., 131, 143, 144 Bustinza, P., 77, 95

C Caballero, F., 196 Cairncross, A.K., 95, 138 Calvo, A., 98 Calvo González, O., 20 Carreras, A., 69, 72–75, 88, 90–95, 98, 133, 135–137, 158–161, 164 Cassing, S., 162, 183 Castañeda, J., 163 Catalan, J., 23, 108 Chamorro, S., 142 Chenery, H.B., 9 Coll, S., 93, 94, 97 Comin, F., vii, 4, 79, 88, 90, 93, 96, 99, 116, 134 Comisaria del Plan de Desarrollo Economico y Social, 155 Consejo de Economia Nacional (CEN), 153, 154 Consejo Superior de la Emigración Española, 141 Cordero, R., 93, 136 Corrales, A., 86, 90, 171, 182, 185 Cortes-Conde, R., 98 Coyle, D., 54 Crafts, N.F.R., 161 Cucarella, V., 78, 182

D

David, P.A., 92, 183, 184 Deane, P., 132, 161 de la Fuente, A., 171, 181 Della Paolera, G., 143 Demeulemeester, J.L., 55 Deng, K., xxi Diebolt, C.R., 55 Doménech Felíu, J., 198 Domínguez Martín, R., 89 Drelichman, M., 10

#### E

Edwards, S., 21 Engerman, S.L., xx Eng, P. van der, 98, 142 Erdozáin Azpilicueta, P., 197 Escribano, A., 18 Escudero, A., 93, 94, 144 Estadística(s) de Comercio Exterior, 90, 137 Estadística(s) de fletes y seguros, 97 Eurostat, 51

#### F

Federico, G., 89 Feenstra, R.C., Feinstein, C.H., 77, 93, 122, 124, 132, 134–138, 145 Fenoaltea, S., 88, 92, 94, 183 Fernández Acha, 143 Fernández Fúster, L., 140

Fischer, S., 21 Fitoussi, L.P., xix Flaxman, A.D., 55 Flores de Lemus, A., 89, 90 Forsyth, P.J., 10 Fraile Balbín, P., 18 Frax, E., 97, 99, 144 Freeman, M.K., 55 Fremdling, R., 92 Fuentes Quintana, E., 162 Fundación BBV, 96, 163

#### G

Gallego, D., 89, 91, 133, 134 Gandoy Juste, R., 92, 175–177, 184 Garcia Barbancho, A., 133, 164 García Delgado, J.L., 21 García López, J.R., 129, 142 García Sanz, A., 89, 90, 198 Garrués Irurzun, J., 94 General de la Emigración, Giraldez, J., 91 Gomez Mendoza, A., 78, 79, 81, 82, 90, 91, 93, 95, 97, 98, 133, 135, 136, 138, 144, 197, 198 Gómez Villegas, J., 176, 184 Gordon, R.J., 86, 164 Grupo de Estudios de Historia Rural (GEHR), 66, 69, 87–91, 134 Guerreiro, A., 162

#### H

Hansen, B., 184 Hanushek, E.A., 55 Harley, C.K., 92

Hemberg, P., 91, 162 Heston, A., 86, 87, 92, 99, 126, 132, 139, 142 Higgs, R., xx Hoffmann, W.G., 92 Holtfrerich, C.L., 92 Horlings, E., 133 Huberman, M., 196

#### I

Informacion Comercial Española, 157 Inklaar, R., Instituto de Estudios de Transportes y Comunicaciones, 97 Instituto de Estudios Fiscales, 86, 128, 193 Instituto Nacional de Estadística (INE), 189 Isserlis, L., 97 Ivanov, A., xx

#### J

Jáinaga, F., 127, 129, 131, 140, 142 Jefferys, J.B., 124, 132 Jerven, M., xxii Johnson, S., 55 Jones, C.I., xx

#### K

Kakwani, N., 55 Kimko, D.D., 55 Kindleberger, C.P., 37 Klenow, P.J., xx

Krantz, O., 96, 98, 99, 184 Krugman, P., 50 Kuznets, S., xx, xxi

#### L

League Of Nations, Lewis, W.A., 92, 93, 95, 98, 100, 138 Lindert, P.H., 55 Llordén, M., 128, 140, 141 Lluch, C., 164 Lopez, A.D., 55 López Carrillo, J.M., 97, 137 López García, S.M., 21

#### M

Maddison, A., 87, 96, 161, 183 Maddison project, 19, 21, 41, 45 Maluquer de Motes Bernet, J., 86, 93, 116, 133, 185, 189, 190, 196 Marolla, M., 140 Martin Aceña, P., 18, 99, 143 Martínez-Galarraga, J., Martínez Ruiz, E., 97, 125, 129, 138, 139 Martín Rodríguez, M., 90, 94, 133 Martins, C., 93, 98, 142 Masood, E., xix Matilla, M.J., 99, 144 Matthews, R.C.O., 37 Melvin, J.R., 96 Menéndez, F., 93, 136

Miguel, A. de, 92 Mikelarena Peña, F., 197 Milanovic, B., 52 Ministerio de Agricultura, 66, 69, 86–88, 90, 91 Ministerio de Trabajo, 90, 94, 140–142, 196 Mitchell, B.R., 89, 98, 99, 144 Mohr, M.F., 184 Molto, M.L., 142, 182 Morales, R., 142 Morella, E., 75, 93, 94 Muñoz, J., 143, 144 Muñoz Rubio, M., 78, 81, 97, 136 Mulhall, M.G., 161, 162 Murray, C.J.L., 55

#### N

Nadal, J., 144 Naredo, J.M., 157, 160, 161, 163, 164 Nicholas, S.J., 10 Nicolau, R., 140, 189, 197, 198 Nordhaus, W.D., 50 North, D.C., 97, 126, 139 Núñez, C.E., 20, 99

#### O

O'Brien, P.K., vii Odling-Smee, J.C., 37 OECD, 117, 155, 169, 171, 175, 196 Ojeda, A. de, 99, 115, 140

Organización Sindical Española, Ortega, J.A., 20, 190 Oulton, N., xx

#### P

Parejo Barranco, A., 88 Paris Eguilaz, H., 90, 94 Peleah, M., xx Pérez Moreda, V., 190, 197 Philipsen, D., xix Philipson, T.J., 55 Pinheiro, M., 93, 98, 142 Pinilla, V., 88, 133 Piqueras, J., 90 Pla Brugat, D., 21 Pons, M.A., 143 Prados de la Escosura, L., 9, 12, 18, 20, 39, 45, 51, 56, 57, 80, 88, 92, 94, 97, 121, 125, 127, 132, 133, 138, 139, 141, 144, 161, 171, 175, 181, 183, 185, 196, 198 Pujol, J., 91

#### R Reher, D.S., 86, 90, 99, 140, 198 Reis, J., 93, 98, 142 Rey, G.M., 162 Roccas, M., 140 Rockoff, H., 18 Roldán, S., 21, 144 Rooijen, R. van, 182

Rosés, J.R., 20, 92, 132, 175, 196

#### S

Salomon, J.A., 55 Sánchez Alonso, B., 18, 127, 141, 190 Sanchez-Albornoz, N., 90 Sanz-Villarroya, I., 20 Sardá, J., 86, 99, 141, 143 Schwartz, P., 98, 138, 142, 154, 158, 161, 162 Sen, A.K., 52–55 Serrano, A., 144 Shleifer, A., 23 Silvestre, J., 20, 190, 196 Simon, M., 126, 139, 140, 145 Simpson, J., 66, 69, 86–90, 198 Sims, C.A., 184 Smits, J.P., 98, 133, 138, 139 Soares, R.R., 55 Srinivasan, T.N., xxii Stiglitz, J., 51 Sündbarg, G., 189, 190 Syrquin, M., 9

# T

Tafunell, X., 94, 95 Taguas, D., 86, 90, 171, 182, 185 Tedde de Lorca, P., 130, 131, 143 Tena Junguito, A., 18, 125, 138–140, 142 Tobin, J., 50

Tortella, G., 99, 139, 163

U

United Nations Development Program (UNDP), 54, 55 Uriel, E., 142, 170, 181, 182, 185 Uriol Salcedo, J.I., 79

V

Valdaliso, J.M., 97, 126, 136, 139 Vandellos, J.A., 198 Vazquez, A., 128, 140–142 Velarde Fuertes, J., 19, 140, 142 Vilar, J.B., 141 Vitali, O., 133 Vos, T., 55

W Walters, D., 124, 132 Wang, H., 55 Williamson, J.G., 142 World bank, 45

Y

Yáñez Gallardo, C., 141, 142 Young, A., 50

Z

Zanden, J.L. van, 21, 133

# Subject Index

A Agricultural census, 77 Agricultural final output, 65, 66, 69, 87, 90, 92 Agricultural volume indices, 70 Agriculture, 8, 10, 11, 13, 30, 32, 33, 64, 68, 70, 71, 85–87, 89–91, 93, 98, 111, 113, 124, 154–157, 163, 164, 176, 184, 194–198 Air transport, 83, 98

#### B

Backwardness, 19 Balance of payments, 125, 142, 145 Banco de España, 00 Bank deposits, 99 Banking, 84, 155 Banking and insurance, 81, 84

Base year, 64, 65, 72, 153, 162, 171, 182, 183 Basic prices, 181 Buildings, 95, 116–120, 124, 134, 135, 137, 138

#### C

Capital, 6, 8, 12, 18, 20, 21, 47, 94, 113, 117–124, 129–131, 134–137, 143, 144, 177 Capital formation, 6, 8, 117–120, 122–124, 128, 135, 138, 156, 176, 177 Capital goods, 19, 47, 73, 118, 159 Cereals, 67–69, 113 Civil war, 3, 4, 6, 9, 11, 12, 15, 19–21, 41, 42, 44, 50, 52, 53, 56, 69, 88, 91, 96, 99, 131, 134, 139, 142, 143, 154, 155, 157, 160, 190

© The Editor(s) (if applicable) and The Author(s) 2017 L. Prados de la Escosura, Spanish Economic Growth, 1850–2015, Palgrave Studies in Economic History, DOI 10.1007/978-3-319-58042-5 Clothing, 112, 114, 158, 164, 197 Coal, 94, 97, 114, 126, 139 Commercialization, 67, 68, 88 Communications, 83, 115, 118, 120, 125, 155 Constant prices, 70, 73, 82, 128, 155, 156, 175, 179, 184 Construction, 7, 9, 27, 28, 47, 64, 65, 67, 73–81, 85, 88, 92, 94–96, 112, 117, 118, 119, 120, 122, 124, 131, 133–135, 139, 156–158, 162, 164, 170, 175–177, 184, 194–196 Consumer price index (CPI), 116, 164, 185 Consumption goods, 164 Consumption of goods and services, 111, 112 Cost of living, 84, 85, 99, 115, 117, 127, 134, 140, 156, 163 Crops, 65, 67, 68, 86, 90, 154 Cuba, 18, 128, 140, 142 Current prices, 64, 66, 69–71, 79, 80, 83–85, 117, 123, 124, 135, 136, 156, 157

#### D

Deflation, 65, 141, 177, 183, 184 Deflator, 64, 70, 71, 76–80, 83–87, 94–96, 99, 118–123, 128, 133, 134, 136, 145, 156, 164, 175, 179, 185 Demand, 3, 20, 37, 67, 69, 89, 111, 112, 118, 124, 128, 157, 159, 163, 164 Dependency rate, 37 Depreciation, 18, 47, 99, 130

Distribution, 11, 12, 51, 54, 57, 68, 83, 98, 128 Dividends, 129, 130, 143 Domestic trade, 111, 128 Double deflation, 162, 177, 184 Drink, 00 Dwellings, 77, 78, 84, 94, 95, 112, 114, 118, 123, 124, 134, 138, 156

#### E

Education, 55, 56, 81, 84, 99, 100, 112, 115 EKS \$2011, 356 Electricity, 76, 94, 114, 119, 131, 135, 137, 143, 162 Emigrants, 127–129, 132, 141, 142, 145, 189 Emigration, 127, 128, 140, 142, 190 Employment, 10, 30, 32, 34, 81, 99, 115, 157, 163, 193–195, 198 Equipment, 74, 118, 122, 123, 138, 158 Exchange rate, 122, 129, 138 Exports, 7, 8, 71, 82, 89, 111, 125, 126, 128, 133, 139, 144, 156, 165 Exports of goods and services, 111, 125, 128 Extractive industries, 8, 64, 72, 76,

#### 94

F Fertilizers, 70, 91, 134 Finance, 99, 112, 115 Fiscal policy, 21

Fisher, 92 Fishing, 64, 71, 83, 91, 157, 164, 194 Foodstuffs, 112, 113, 133 Foreign trade, 18, 125, 126, 129 Forestry, 64, 70, 71, 91, 164, 194 Freight, 82, 97, 125, 126, 138, 139

#### G

Gas, 76, 94, 114, 119, 131 GDP at market prices, 64, 85, 86, 124, 156, 172, 176, 177, 184 GDP per head, 15, 18, 19, 21, 25, 27, 39, 40, 42–45, 47, 50, 53, 56, 57 Geary Khamis \$1990, 363 General price index, 65 Gini coefficient, 51, 57 Gold standard, 18, 86, 139 Government, 18, 21, 78, 79, 84, 96, 99, 112, 116, 117, 119, 134, 156, 163, 164 Government consumption, 4, 49, 116, 117, 164 Great Depression, 19, 160 Great Recession, 3, 6, 9, 11, 15, 21, 27, 37, 42, 53, 180 Gross domestic capital formation, 118 Gross domestic fixed capital formation, 6, 117, 123, 124 Gross domestic product at market prices, 128, 132 Gross National Income, 3, 129, 132 Gross Value Added (GVA) per Hour Worked, 28

Gross Value Added, 11, 28, 63–65, 70, 71, 76–78, 80, 85, 86 Growth, 3, 8, 12, 18, 41, 44, 171, 175, 178, 180, 181, 183 Guerreiro, A., 162

#### H

Health, 54, 55, 84, 100, 115 Heating, 112, 114 Historical index of human development (HIHD), 55, 56 Hours worked per full-time equivalent occupied, 351 Hours worked, 10, 11, 25, 27, 28, 33, 34, 37, 193, 196 Hours worked per head, 34 Human Development, 54 Human development, 55 Hybrid linear interpolation, 179–181

#### I

II Republic, 52 Imports, 7, 8, 68, 82, 83, 89, 91, 97, 111, 113, 118, 122, 123, 125, 126, 128, 133, 138, 156, 165 Imports of goods and services, 124, 125, 128 Income distribution, 50–52 Income Inequality, xx, 51 Independence, 18 Industrial, 11, 73, 77, 92, 95, 154, 175, 184, 197 Industrial census, 11 Industrialization, 6, 12, 20, 35, 194 Industrial vehicles, 137

Industry, 8, 9, 11, 12, 30, 37, 64, 72, 73, 75, 77, 78, 80, 85, 94, 113, 123, 134, 154, 158, 160, 161, 163, 164, 175–177, 193, 195, 196 Inequality, 51, 52, 57 Inequality Extraction Ratio, 52 Input–output table, 63, 72, 84, 94, 96, 99, 114, 123, 137, 138, 158, 164, 182 Interest rate, 99, 130, 143 Interpolation, 43, 45, 78, 173, 174, 178–180, 193, 196 Interpolation Splicing, 178 Investmen in capital goods, 120 Investment in machinery, 123 Investment in trasport material, 123

#### L

Labour productivity, 11, 12, 25, 27, 99, 100, 163 Land transportation, 133 Laspeyres index, 133 Leisure, 81, 85, 100, 115 Life expectancy, 54, 55 Literacy, 55 Livestock, 65, 68, 69, 89, 90, 124, 154, 164, 198

#### M

Machinery, 6, 47, 118, 120, 122–124, 135, 156, 158 Manufacturing, 8, 34, 64, 72, 73, 75, 76, 79, 83, 92, 94, 124, 154, 184 Market prices, 64, 81, 144, 176

Merchant shipping, 121 Modified shift-share, 32 Monetary policy, 18, 126

#### N

National Accounts, 39, 43, 64, 71, 86, 94, 112, 114, 132, 142, 154, 157, 162, 171, 175, 180, 182, 185 Net exports of goods and services, 111, 126, 156, 177 Net National Disposable Income, 47, 52, 132, 142 Net National Income, 132 Non-residential construction, 6, 79, 80, 95, 96, 134 Non-residential investment, 124, 138, 156

#### O

Occupied, 114, 193, 196, 198 OECD, xix, 117, 155, 169, 171, 175, 196 Openness, 7, 18 Output, 25, 28, 32, 34, 63–65, 67–72, 76, 78, 79, 81, 82, 88, 90, 113, 120, 124, 133, 135, 164, 175, 184 Ownerhip of dwellings, 81, 84

#### P

Paasche, 65, 75 Per Capita GDP (GDP per head), 16, 19, 20, 27, 47, 49, 57 Per capita GDP, 43

Per hour worked, 34 Peseta, 18 Population, 15, 17, 50, 89, 189, 191, 194, 198 Population censuses, 100, 141, 162, 194, 197 Post, 7, 11 post, 7, 9, 11, 12, 18–20, 37, 40, 45, 50, 51, 56, 57, 68, 80, 88, 92, 94, 97, 98, 121, 124, 125, 127, 132, 133, 138, 139, 141, 144, 155, 160–164, 175, 178, 181, 183, 185, 196 Private consumption, 4, 19, 50, 111–114, 128 Productivity, 12, 27, 29, 30, 32 Public administration, 81, 84, 112, 156 Public consumption (government consumption), 112, 117, 134 Public investment, 156 Public works, 77, 79, 96, 158 Purchasing power parity, 39, 45

#### R

Railways, 78, 88, 119, 131, 143 Railways construction, 4 Raw materials, 19, 66, 67, 69, 76, 117, 175 Real per capita GDP, 28, 40, 42, 43, 45 Real per capita GDP and private consumption, 50 Real per capita NNDI, 51, 53 Relative labour productivity, 11 Rents, 114, 117 Residential and commercial construction, 78, 119 Residential investment, 156

Retropolation, 43, 44, 170, 173, 174, 185 Retropolation splicing, 45, 180 Roads, 79, 119 Rural dwellings, 95

S

Savings, 18, 84, 175 Schooling, 55, 84 Sea transport, 82, 98 Sen Welfare, 49, 52, 53, 54 Services, 8–10, 12, 27, 30, 50, 64, 70, 81, 83, 85, 95, 98, 99, 112, 114, 115, 125, 133, 154, 155, 158, 163, 164, 173, 184, 194, 196 Shift-share, 30, 32, 33 Shift-share analysis, 30, 33 Single deflation, 92, 175, 183, 184 Splicing, 43 Splicing national accounts, 44, 45, 179, 180 Standard of living, 54 State, 4, 121 Structural change, 9, 11, 28, 30, 33, 37, 171 Subsidies, 64, 86, 156, 176 Supply, 37, 94, 122, 135, 198

#### T

Tariff, 18, 19 Taxes, 64, 86, 156, 163, 176 Telegraph, 81, 83, 98 Telephone, 81, 83, 98, 119, 123, 137 Tobacco, 73, 75, 94, 112, 113, 133, 158 Tourism, 127, 140, 156 Transfers, 132, 142

Transport and communications, 81, 112, 115 Transport equipment, 6 Transport material, 20

U

Unemployment, 27, 37, 194, 196, 197 Urban dwellings, 95, 99 Urbanization, 6, 12, 35, 65, 90, 194 Utilities, 64, 72, 94, 130, 144, 162, 194

V

Value added, 63, 64, 70, 72, 73, 75–77, 80–84, 86, 91, 93, 96, 153, 158, 175, 176, 184 Variation in the stocks, 124

Vehicles, 82, 97, 120, 122, 156 Volume indices, 39, 43, 63, 65, 69, 71, 80, 85, 112, 123, 179

#### W

Wages, 68, 78, 79, 84, 95, 96, 99, 118, 129, 135, 138, 164 War, 18, 160, 190 Water, 83 Water supply, 76, 131 Welfare, 52–54 Wholesale and retail trade, 83 Wholesale trade, 100 Working age population, 34, 194 World War I, 4, 7, 12, 18, 33, 50, 52, 53, 127–130, 140, 159 World War II, 4, 7, 19–21, 41, 134, 190

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