During Portugal's Estado Novo, 1945-1974

ECONOMIC GROWTH AND WAGE INEQUALITY DURING PORTUGAL’S ESTADO NOVO, 1945-1974

Pedro Lains1, Ester Gomes da Silva2 and Jordi Guilera3

November 2007

Abstract

This paper relates the changes in the structure of the Portuguese economy during its golden age of growth, from 1945 to 1974, to changes in wage inequality at the national level. The paper also aims at contributing to a better definition of the political nature of the Portuguese dictatorial regime. We present a new data set based on surveys conducted by the National Statistics Office (INE), which are put together for the first time. The data covers the whole economy and includes 17 sectors plus data on skilled and unskilled labour, as well as data on male and female labour force in agriculture. We estimate a Theil index for wage inequality that reveals an inverted-U Kuznets’ curve with a peak in 1959. The fact that wage inequality declined after 1959 leads to the conclusion that the fall of the dictatorship in 1974 was not preceded by an increase in social injustice. Finally, the paper estimates econometrically the relationship between wage inequality and per capita income growth, controlling for the influence of additional variables that capture the effects of industrialization, investment in human and physical capital, emigration, foreign trade and the size of the government. Keywords: Kuznets curve; Wage Inequality; Structural change; Portugal.

JEL-Codes: D31, O15

1 [email protected]; Instituto de Ciências Sociais, University of Lisbon. 2 [email protected]; Faculdade de Letras, University of Oporto. 3 [email protected]; University of Barcelona. 2

1. Introduction Between 1945 and 1974, the Portuguese economy experienced deep transformations, which included rapid industrialization, opening up to international trade, the increase in State intervention, as well as urbanization. It is our purpose in this paper to relate changes in the structure of the economy with changes in wage inequality at the national level. We want to explore the impact of the size of the industrial and export sectors on the dispersion of wages within the country. We also analyse how the increase on the size of the government and large- scale emigration impacted on inequality. The analysis of wage inequality is also relevant for a better understanding of the political nature of the Portuguese regime. During the period under analysis, Portugal was ruled by a dictatorial regime, the Estado Novo, which repressed political rights and is frequently pictured as being an anti-labour regime which favoured large scale industrial and agricultural capitalists4. The Estado Novo had its origins in a military coup in 1926, and was institutionalized after Salazar became Prime Minister, in 1932, and a Constitution was approved by plebiscite in the following year. During the years from 1926 to the mid-1930s, the dictatorial government was particularly tough and many expected that the regime would open up after 1945, under the influence of Western democratic governments. Yet, the advent of the Cold War, in the early 1950s, turned Salazar’s regime bearable to Western governments and the pressure to change died out. In the following decades, an ageing dictator secured a tight grip on the country and the regime became more and more closed and isolated from democratic influences. Salazar left power due to his health condition in 1968 (he died in 1970), and his successor was unable to introduce significant changes because of the colonial war in Africa and also because of the semi-presidential character of the regime5. The dictatorship came to an end finally in 1974 with another military coup. The analysis of wage inequality provides a better understanding of the evolution of social pressures to change the regime that occurred with the 1974 coup d’état. The dictatorship is frequently characterized as socially unfair. And the fact is that Portugal still was one of the most unequal countries in Western Europe in 1974 and remains so in the present times6. But what happened to wage inequality during Salazar’s long reign?

4 See for example Rosas (1994). 5 The President of the Republic, who was a minor figure whilst Salazar was in power, had a crucial role in the survival of the regime to 1974. A survey of Portugal’s political history is provided by Pinto (Ed.) (2003). 6 According to Silva (1982), in 1973, about one third of Portuguese households were poor (Silva, 1982: 1079). 3

Despite the interest on the matter, the analysis of income inequality during this period has remained a relatively unexplored issue, with only a few authors addressing the topic.7 Based on qualitative insight and the partial evidence for the 1970s, Manuela Silva (1982) has posited that industrialization and urbanization was responsible for the increase in income inequality during the period from 1950-1974.8 Castanheira and Ribeiro (1977) show an increase in the Gini coefficient between 1967-68 and 1973-74, from 0.35 to 0.39, but an increase in the income share of the bottom 25 percent share of the families (Castanheira and Ribeiro, 1977:1081-1082). On the other hand, Pereirinha (1988) goes in the opposite direction. Based on industrial wages for the period from 1953, he finds an inverted-U curve with the peak of maximum wage inequality in 1959. Regarding household income inequality, he shows a slight reduction of inequality between 1967-68 and 1973-74.9 More recently, Guilera (2007) shows an increase of top income shares after WWII that lasted until the early fifties and a reversal of the trend in the following three decades. The data set we use provides the most complete coverage in terms of economic activity and time span. It is also highly representative of the income of the labour force, as about 75 percent of it was formed by wage earners. The paper is structured as follows. Next section sets down the theoretical framework. Section 3 presents the macroeconomic background in terms of trends in growth rates, structural change and degree of openness of the Portuguese economy. Section 4 presents an overview of the data. Section 5 discusses the evolution of wage inequality in Portugal during the period under study. Section 6 attempts to provide an explanation for the observed inequality trends. Section 7 concludes.

2. Inequality and growth: theory and evidence The relationship between inequality and growth is far from being established. In theoretical terms, higher levels of inequality can foster growth if savings rates rise with income levels (e.g., Lewis, 1954; Kaldor, 1957). Yet, inequality can also hamper growth if the rich invest unproductively, if it induces distributional conflicts and political instability (e.g., Persson and Tabellini, 1994; Aghion et al., 1999), or if it is associated with lower levels of human capital held by the poor. In this respect, Galor and Zeira (1993) showed that higher inequality may have a negative impact on economic growth if capital market imperfections limit human capital

7 See, for example Pereirinha (1988) Carvalho and Moura (1964), Carvalho (1967, 1969), Castanheira and Ribeiro (1977), and Silva (1982). 8 This was inferred from the fact that at the end of the period, in 1973, poverty rates in the urban areas were lower then in the rural areas (Silva, 1982: 1081-1082). 9 The Lorenz curves for both years are, however, crossed, which hampers a precise statement of the evolution of inequality during this period. 4 accumulation. At this level, Barro (2000) stated that capital market imperfections were more damaging in poor countries, and that was the reason why the negative relationship between inequality and growth only existed in such countries,. Inequality may also affect growth through its influence on the definition of government policies. More precisely, it may lead to an increase in social spending, particularly in democratic countries, where the masses have a larger share of political power. This in turn may lead to different outcomes. On the one hand, it may divert taxed income from investment towards higher consumption levels by the poor, diminishing the country’s growth potential. But, on the other hand, it may stimulate growth, if it increases the lower classes’ human capital.10 Yet, Benabou (1996) criticized both models because two of the unrealistic character of their premises. More precisely, he found that neither higher inequality was associated with higher redistribution, nor higher redistribution was associated with lower growth. More recently, Rodrigues (2004) provides a theoretical model that relates higher inequality with lower redistribution and more rent-seeking activities that divert public resources towards unproductive investments that harm economic growth. In empirical terms, the evidence regarding the impact of inequality on economic growth has led to contradicting results. A significant number of studies focusing on world income inequality conclude that inequality is harmful for growth, but their results have been put into question because of the poor quality of data. At the same time, studies using fixed effects estimates to take into account omitted country specific effects have reached different results. As Banerjee and Duflo (2003: 268) summarize, ‘while OLS regressions typically found a negative relationship between inequality and subsequent growth, the fixed effect approach yields a positive relationship’. From a different point of view, growth can also have an influence on inequality, though the precise assessment of its impact may be difficult to disentangle from the influence of other factors. During the early phases of development, countries may experience an increase in income inequality, as population shifts from informal and low paid agricultural jobs to manufacturing and service sectors, which have both higher productivity and wage levels (Kuznets, 1955). Moreover, labour supply in developing countries tends to be elastic (Lewis, 1955), whereas capital supply is less elastic. This means that in the early development phases, the rise in profits will tend to be accompanied by stagnant or even falling wages, giving rise to further inequality. Yet the Kuznets curve linking the early stages of industrialization with rising inequality has not been found everywhere, as a considerable amount of evidence documents.11 The contradicting results suggest

10 See in this respect Alesina and Rodrik (1994), Persson and Tabellini (1994) and Li and Zou (1998). 11 In particular, Williamson (1985), Scholiers (1991), Van Zanden (1995), Morrisson and Snyder (2000), Sodeberg (1991) and Morrisson (2000) have found evidence in agreement with the Kuznets’ 5 that there may be other forces at work, namely changes in human and physical capital investment, changes in demographic variables and in foreign trade, which may counterbalance the negative impact of industrialization on wage differentials. According to the Hecksher-Ohlin trade model, for example, inequality should decline in developing and transition economies that opened up to trade, since those countries would specialize in export goods that use more intensively unskilled labour, leading to a reduction of the gap between , unskilled workers’ and skilled workers’ incomes. Empirical evidence on the impact of trade on wage inequality has also led to mixed results. Anderson (2001) finds two different scenarios between 1870 and 1914 and 1914 and 1970. In the first period, wage inequality rose in the US, Canada and Australia, whereas it fell in the UK, France, Germany, Sweden and Denmark. According to the expressed views, trends in wage inequality in these two regions were a consequence of emigration, mainly of unskilled workers, from the Old to the New World. The author also shows that the impact of globalization on wage dispersion was less important than the impact on the wage-rental ratio proposed by O´Rourke et al. (1996) and Williamson (1997). For the period after 1914, Anderson (2001) does not find a significant relationship between globalization trends and variations in wage inequality. He concludes that changes in wage inequality became more closely linked to changes in domestic factors. In a related study, Wood (1997) shows that greater openness to trade led to a reduction in wage inequality in the 1960s and 1970s in a number of East Asian developing economies. By contrast, in the case of Latin American countries, there was an overall increase in wage inequality as the economies opened up in the 1980s. According to the same author, the contrasting evidence for both groups of countries is due to the fact that world trade conditions were different in the two periods considered. In particular, the 1980s were characterized by increasing competition from exports of low skill manufactures from countries such as China and India, which influenced negatively wages of less skilled workers in Latin American economies. Therefore, the impact on wage inequality of opening up to international trade may depend on the specific circumnstances regarding the structure of world trade in the period in which it occurs. In the industrialized countries for which long-term data on inequality is available, inequality shows a decline in most of the 20th century (Akinson and Piketty, 2007), but this trend changes from the 1980s onwards, period in which overall inequality shows an increase. The rise in liberalization and competition levels of international trade, along with rapid technological change and the decline in state intervention may have affected adversely income inequality during

hypothesis for Great Britain, Belgium, France, Sweden, Finland and Germany, whereas Thomas (1991), Rossi et al. (1999), Feinstein (1989) and Bértola (2005) have rejected it for Australia, Italy, Great Britain and Uruguay. 6 this period, since they tended to favor economic segments with higher income and human capital levels (e.g., Gouveia and Tavares, 1995; Aghion et al., 1999; Forbes, 2000). During the period under study, the Portuguese economy became increasingly open, with a rise in foreign trade, capital inflows and emigration. The gradual removal of trade barriers was, however, mostly limited to Portugal’s more developed European partners. As such, we might expect increasing openness to have caused a reduction in wage inequality levels, as the country’s comparative advantage was concentrated in unskilled manufactures, whereas it imported manufactures with high capital and technological contents. The expansion of exports was, nevertheless, accompanied by the increase in the size of the industrial sector and by significant shifts of labour from agriculture to manufacturing, which might have implied the opposite effect. The overall impact of trade and industrialization on wage inequality is thus dependent on the relative forces of both factors, along with the impact of other variables, such as demography (population growth, emigration and changes in activity rates) and investment in human and physical capital.

3. The economic background The golden age of growth was paramount for the change in the structure of the Portuguese economy and that can be better assessed by the analysis of its long-run performance During the first half of the twentieth century, the Portuguese economy entered a period of growth at unprecedented speed, which commenced in the 1930s, even before the golden age of growth after 1950. During 1950-1973, Portuguese GDP per capita increased at 5.5 percent per year and the country overcame a substantial part of the gap in income levels with regard to the European core. Table 1 depicts a comparison of growth rates of Portugal with other peripheral countries and the European core. Economic growth before 1960 was accompanied by relatively slow change in the shares of the three main sectors of economic activity (primary, secondary and tertiary), whereas after 1960 industrialization and the growth of the service sector gained momentum and the agricultural sector stalled (Lains 2003). There were however relevant changes in the composition of each sector which are not fully reflected at this broad-level of analysis. Within the agricultural sector, for example, there was an increase in the production of goods with higher income elasticities, namely meat, fruits and vegetables. Within manufacturing, there was also an increase in the relative importance of capital intensive industries, such as chemicals and cement (Lains forthcoming). [Table 1] The evolution of the output of agriculture, manufacturing and services is shown in Figure 1. From its inspection, it can be seen that World War I affected negatively the three broad sectors 7 of the economy and from the mid 1920s down to the mid 1950s they had similar trend growth rates. This pattern of growth is consistent with the relative constancy of the Portuguese economic structure during this period. From the mid-1950s onwards the manufacturing sector experienced rapid growth, and from the beginning of the 1960s onwards agriculture entered a period of virtually flat trend growth rate. [FIGURE 1] Tables 2 and 3 depict changes in the structure of labour force and output at the three sectoral level. In 1930, 60.9 percent of total male labour force was in agriculture and contributed with 31.5 percent to total output. In 1950, those shares were still 53.8 and 32.1 percent, respectively. It was only afterwards that the share of agriculture in total employment declined systematically to 27.6 percent, in 1970, and 13.1 percent, in 1990. The share of agriculture in total GDP declined less than on total population after 1970, and it was only then that there was some convergence in terms of sectoral labour productivity levels. During the last decades of the twentieth century, the productivity gap declined for the first time, and in 1990 labour productivity in the agricultural sector was 80 percent of labour productivity in the rest of the economy. [Tables 2 and 3] Table 4 provides the long-term view for decennial growth rates for output, labour and labour productivity for the three sectors and the whole economy during the four epochs of growth defined for the agricultural sector. Industry expanded faster than the rest of the economy in all periods except 1960-1990, when it was surpassed by the service sector, albeit by a thin margin. Agricultural labour force increased until 1930 and remained stagnant in 1930-1960 to decline steeply in 1960-1990. Labour productivity in agriculture increased at rates which compare rather well with those of the other sectors, except in 1900-1930. We may also see in that figure that the periods in which labour productivity in agriculture increased fastest, namely 1930-1960 and 1960- 1990, are also those in which labour employed in the sector was either not expanding or declining. This is a common feature of labour productivity growth in agriculture in the industrialized world and implies that changes in labour productivity in the primary sector depend on the capacity of the other sectors (or emigration) to attract agricultural workers. Figures 2 and 3 also depict the evolution of labour and output shares and Figure 4 shows the evolution of relative productivity levels. Convergence between services and industry is patent from 1920 to 1970, whereas agriculture converged only after 1970. These trends of relative productivity levels would imply only a partial reduction in wage inequality indicators. [Table 4] [Figures 2 to 4] The expansion of the foreign sector was also an important feature of the period under study. As may be seen in Figures 5 and 6, which show the ratios between exports and imports and 8

GDP, the export share declined from 1910 to 1930, recovering thereafter towards pre-World War I levels. Exports had a big spurt during World War II, as Portugal benefited from its status of non- belligerent country. Between 1955 and 1973 the export share increased further to 20 percent in 1973. In the same period, the share of imports in GDP expanded faster, from about 20 percent of GDP in 1950 to about 30 percent in 1973. Such an increase in the external sector of the economy had obviously a very important impact on the composition of economic activity and employment, contributing to a large extent to the growth of the manufacturing sector and to the substantial decline of agriculture. In this process, the share of labour intensive export industries, such as textiles and processed food, increased substantially, as did the share of imported foodstuffs in domestic consumption12. [FIGURES 5 AND 6]

4. The wage data set In this study we use wage and employment data from different surveys conducted by the Portuguese National Statistics Office (INE) since 1944.13 We build a wage data set that covers 17 economic sectors, including agriculture, mining and quarrying, nine manufacturing industries, electricity, gas and water supply, construction and four service sectors, as well as data on male and female wages for agriculture, and skilled and unskilled labour force for manufacturing. The manufacturing survey provides information on the total amount of workers or number of days worked per year, as well as on the total amount of wages paid for industrial units with 10 or more workers. From 1944 to 1955, the survey separates workers in three major groups: employees (empregados), industrial workers (assalariados industriais) and other workers (outros assalariados). This information is given for either December 31st or for the period of maximum activity. Employees are those with a longer term contract, whereas workers earn daily wages.14 This distinction allows for the estimation of skill premiums. For

12 … 13 The sources for manufacturing are Anuário Estatístico (1944-1974): “Produção e Consumo”, “Indústrias extractivas”, “Indústrias transformadoras”, “Rendimentos, salários e preços” and “Mão- de-obra”; for agriculture are Anuários Estatísticos de Portugal and for services are Estatísticas das Sociedades. Data for employment is from Valério (2001) and Pinheiro (1997). 14 More precisely, employees are owners with directive responsibilities and a regular remuneration, administrators, managers, services chiefs, highly skilled staff (e.g., economists, engineers, technical directors), secretaries, stenographers, typists, accountants, staff in charge of ordinary tasks in laboratories, personnel recruitment and staff of the social services of the company (clinics, schools, sports and other leisure activities). Workers comprise all the personnel that participate directly in the production system, including masters and foremen. 9 the period from 1956 to 1970, the same source provides information on just two occupational groups: employees (empregados, administrativos, técnicos e de escritório), as well as wage- earners. The rest of the information is similar to the previous period with one exception: after 1956, the number of workers is also given as a monthly average, which is the information considered in the construction of our dataset. Finally, for the years after 1970, there is another change in the structure of data. The information on wages is again given for three groups: dirigentes, outro pessoal (both employees) and workers. The database gives employees’ monthly wages and the workers’ hourly salaries. We use in this paper daily wages. For the period until 1970, daily wages of employees are estimated by dividing the total amount paid in each year by the number of employees and then dividing the outcome by 304 working days.15 For industrial and other workers the daily wage is calculated by dividing the total amount of wages paid to each group by the total number of days worked per year by this group (given in the official statistics). From 1971 onwards, the employees’ daily wage is calculated by dividing the monthly wage per 25.33 (i.e., 304 working days divided by 12 months), and the workers’ daily salary is calculated by multiplying the hourly salary per 8 hours worked per day. A final adjustment regards the aggregation level of industrial sectors. The number of industrial sectors considered in the survey is extremely volatile: during these thirty years it varied from 21 to 187 sectors. In order to homogenize the high variability of the data, the data structure used by Pinheiro (1997) has been taken as reference, and the information regarding the other years has been aggregated to fit this sectoral decomposition (CAErev1).16 Furthermore, in order to aggregate the different sub-sectors into the reference sectors, the wages have been weighted according to the number of workers of each sub-sector.

15 This assumption is based on 6 working days per week and the deduction of official and religious holidays. The 6 days working week was reaffirmed in 1934, Decreto n. 24402, See Patriarca (1995), pp. 372. The 5-days working week was established after 1974 (see in this respect, Leite. and Almeida (2001:169) and Barreto (1990: 57-117). During the Estado Novo there were nine days of official and religious holidays per year (Araújo. et all, 1969: 207), this situation did not change until 1976, Decreto 874/76 (see Leite and Almeida, 2001: 200-201). 16 Mining and quarrying; Food, drink and tobacco; Textiles, clothing and leather and footwear; Wood and products of wood and cork and furniture; Pulp, paper, paper products, printing and publishing; Chemicals; Non-metallic mineral products; Basic metals; Metallic products and transport equipment; Other manufacturing 10

The sources for agriculture are the Portuguese Statistical Yearbooks. In order to estimate national aggregates for male and female agricultural wages, we estimated average wages for each of the 18 districts and then for the national level.17 The source for services - Estatísticas das Sociedades - provides the number of workers and the total amount of wages paid, from which we estimate average annual wages. In order to estimate daily wages, annual wages were divided by 304 days, as explained above. The source starts in 1950 and provides information for 15 sectors for 1950-1952. From 1953 onwards the source gives data for 21 sectors, which has been aggregated as follows: Electricity, gas and water supply; Construction; Trade; Transports, storage and communications; Banks, insurances and real estate; and Services. The process of aggregation took into account the relative importance of employment in each subsector. Table 5 shows the coverage of our data base for manufacturing and services.18 The coverage of our data set starts at 9.3 percent in 1944-49, and increases steeply to 28.4 percent in 1950-54, and then more slowly to 44.2 percent in 1970-74. Manufacturing is better covered throughout the period under analysis but at the end the differences between manufacturing and construction and services are lower. [Table 5] The wage database has some discontinuities regarding the number of sectors for which there is available information. From 1944 to 1949 there are 8 sectors: agriculture, mining and quarrying and industry (6 sectors). In 1950 there is information for five additional sectors: construction, trade, transports, storage and communications, banks, insurances and estates and other services, that increase a 48 percent the sample size and a 61 percent the total wage earners coverage. In 1953 there are 4 new sectors: industry (3 sectors) and electricity, gas and water supply that imply and increase of a 6 percent of the sample size and a 3 percent the wage-earners coverage. Data on employment for the 17 sectors that we use is available from 1953 onwards in Pinheiro (1997). For the previous years, from 1944 to 1953, we extrapolate backwards the structure given by the same source, with the growth rate of labour force by sectors given by Valério (2001, Tables 4.6 and 4.7). The fact that the ratio of total wage-earning population and total labour force remained relatively constant between 1944 and 1953, ranging between 70 and 75 percent, as shown in Figure 7, provides the support for that option.

17 The source does not provide data for 1955, which was linearly interpolated using data for 1954 and 1956. 18 The source does not provide similar information for the agricultural sector. 11

[Figure 7] 5. Trends in wage inequality In the manufacturing sector, the evolution of the share of skilled workers within the total number of workers in the survey is as shown in Figure 8. The proportion of skilled workers remained relatively stable, around 8 percent, down to 1960, and then went up in two stages, between 1960 and 1965, and after 1970, reaching 14 percent in 1974. This significant change in the composition of the labour force is likely to have had a relevant impact on wage inequality. Figure 9 shows the distribution of labour force among sectors of different skill levels. The degree of qualification of each industrial sector has been defined according to the average participation of skilled labour in total employment in each sector during the last four years of the period studied.19 The figure shows that the share of the industrial sectors that used more intensively unskilled labour declined from around 75 percent in 1950 to 53 percent in 1974. The figure also shows that the share of semi-skilled labour-intensive sectors increased faster than the share of skilled labour-intensive ones. [Figures 8 and 9] Figure 10 shows the evolution of the skill premium in the industrial sector defined as the ratio between the daily wage of skilled and unskilled labour. The figure indicates that the skill premium remained relatively stable, around 3, between 1950 and 1960, and that it increased between 1961 and 1969, to decline again in the later part of the period. The figure clearly shows an inverted-U curve for the years after 1960. The interpretation of that curve can be related to the fact that the 1960s were the most dynamic period (with the most intense structural change) of the Portuguese industrial sector history. Moreover, the 1960s were also a period of intense emigration which also had an impact in the relative endowments of skilled and unskilled labour force. After 1970, the skill premium declined. That reversal of the trend may be partially related to changes in labour policy. [Figures 10] Figure 11 shows the evolution of the coefficient of variation in the industrial sector, which measures the evolution of wage dispersion. An increase in the index implies an increase in inequality. The evidence collected reveals an upward inequality trend from 1946 to 1967 and the decline of wage inequality after that year. This is just a confirmation of the inverted-U curve already detected in the evolution of the skill premium. To some extent, the

19 Skill intensive sectors are those in which skilled labour participation is above 25%; semi-skill are those sectors in which skilled labour participation is between 15% and 25%; and those in which skilled labour participation is below 15% are considered as non-skill intensive. 12 left-hand part of the curve might be related to the increase in the participation of skilled labour within total employment in a context of increasing skill premium. By contrast, the right-hand side of the inverted-U might only be explained on the basis of the evolution of the skill premium. [Figure 11] We now revert to the estimation of the Theil index, which is a descriptive measure of inequality that allows for the decomposition in two main components: inequality within and between sectors. This feature is quite interesting because it gives additional information that might be hidden in other aggregated and synthetic measures such as the Gini index (Cowell, 1998). The Theil index is defined as:

1  µ  I = ⋅ ln  ∑i   n  yi  where n is the number of individuals of the sample, µ the average wage, and yi the individual wage. The Theil index can be decomposed as:

n n  µ  I = k ⋅ I k + k ⋅ ln  ∑k ∑k   n n  µk  where nk is the number of individuals in sector k and µk the average wage of sector k. The first term of the equation is the within group inequality and the second is the between group inequality. The evolution of the Theil index in the manufacturing sector is partially consistent with the coefficient of variation, since it shows also an increasing trend until the mid 1960s. However, the existence of a downward trend during the last years of the period is much less clear. Figure 12 reports our estimates for the Theil index. The within coefficient measures inequality due to the wage dispersion inside each industrial sector, that is to say, between skilled and unskilled workers. The evolution of the “within” inequality increased until 1969 and then suddenly decreased; this is obviously consistent with the evolution of the average skill premium. On the other hand, the between coefficient measures the part of inequality that is due to differences in the average wage of each sector. Figure 12 shows that the “between” component was relatively stable until 1961, and it began to increase afterwards. This upward trend indicates that the dispersion between the average wages of the different industrial sectors increased since the early 1960s. In order to better understand the evolution of the “between” component of the Theil Index, Figure 13 presents the coefficients of variation of the wages of skilled workers, unskilled workers and all the industrial labour force. The 13 coefficient of variation of the skilled workers wage decreased smoothly along this period. By contrast, the coefficient of variation of the unskilled workers wage remained more or less stable until 1961, period after which it rose sharply. Therefore, it may be concluded that rising inequality between sectors mainly affected the unskilled workers, increasing their wages in some sectors and diminishing them in others. A plausible explanation of that evolution could be related with labour market regulation under the Estado Novo or other institutional factors that may have protected skilled workers wages from the market forces. [Figures 12 and 13] Finally, figure 14 shows the evolution of the Theil index for the whole economy. As it can be seen, global wage inequality increased till the late fifties and it declined afterwards. The analysis of the two subcomponents of the Theil index gives additional clues to characterize the evolution of wage inequality during this period. On the one hand, the “within” coefficient remained relatively stable along this period, although increasing smoothly till 1970 and declining afterwards. On the other hand, the “between” coefficient evolved parallel to the Theil index drawing an inverted U curve with a maximum inequality in 1959. It could be concluded that the evolution of wage differences across sectors of activity (agriculture, industry and services) was the main contributor behind the evolution of global wage inequality because wage differences “within” each sector played a minor role. The changes in the degree of coverage of each economic sector can affect the robustness of our estimations of the wage inequality index presented. The data characteristics could provoke three different kinds of problems. First, the low coverage of a sector may lead to biased wage estimates if the omitted wages are significantly different from the computed average wages. Second, the increasing coverage of a sector could generate distortions if the new-covered wage earners had different wages than the previous ones. And last, the incorporation of new sectors, which were previously omitted, could generate an artificial increase (or decreases) of wage inequality. The effect of the second problem is studied in Figure 15 that shows the correlation between wage increases and increases in coverage. As it may be seen, it does not exist any kind of relation between both variables. Furthermore, the first and the second problems are highly related: if the increasing coverage of the wage earners does not have any potential distortion it is reasonable to think that the non covered wage earners may have similar characteristics than the covered ones. In this case, it seems fair to conclude that the wages calculated from the sample are representative of the wage-earners population. [Figure 15] 14

Figure 16 below addresses the last issue. i.e., the incorporation of new sectors into the sample. Three alternative inequality indexes have been calculated in order to discern the potential distortions in 1949-50 and 1952-53 due to the incorporation of new sectors previously omitted. Theil (8 sectors) and Theil (13 sectors) measure wage inequality among the sectors considered between 1944-49 and 1950-52 for the four following years after 1949 and 1952, whereas Theil (full sample) measures wage inequality among all the sectors with available information, which increases over time. As it can be seen from Figure 16, the differences between the Theil (full sample) index and the two alternative indices are quite insignificant and always evolve in the same direction. These checking tests seem to suggest that the results are robust and are unaffected for the unbalanced coverage of the wage sample used to derive the Theil index. [Figure 16]

6. Wage inequality and economic growth The evidence presented so far shows that the evolution of wage inequality during the Estado Novo followed the inverted U shape characteristic of the Kuznets curve. In order to achieve a greater understanding of the phenomenon and provide a tentative explanation for the observed pattern, in this section we assess econometrically the relationship between wage inequality and per capita income growth, controlling for the influence of additional variables that might influence inequality, such as industrialization, investment in human and physical capital, migrations and foreign trade. We thus estimate the following specification of the Kuznets curve: THEIL = β + β (ln INC ) + β (ln INC )2 + β MANUF + β GFCF + β HK + β OPEN t 0 1 t 2 t 3 t 4 t 5 t 6 t + β7 EMIGt + β8GOV + β9SPEND + µt The dependent variable, wage inequality, is represented by THEIL, which indicates the Theil coefficient computed in the previous section. The variable INC stands for real income per capita, MANUF and GFCF represent manufacturing and GFCF shares in GDP, respectively, HK is a measure of human capital, represented in our regressions either by an index of human capital (HK) or by school enrolment ratios at the primary and secondary levels (SCHOOL1 and SCHOOL2, respectively), OPEN is a measure of openness and consists in the ratio of exports plus imports to GDP, EMIG is the ratio of emigration to total 15 population, and finally, GOV and SPEND represent government spending and social expenditure shares in GDP20.

The verification of an inverted-U Kuznets curve requires β1 and β2 to have a positive and negative sign, respectively. The sign of β3 is expected to be positive because the process of industrialization was characterized by the appearance and increasing importance of the skilled labour intensive sectors (see the discussion in the previous section). GFCF captures a similar relationship, and therefore its coefficient is expected to assume a positive value. Investment in human capital may be expected to attenuate inequality levels, if it is directed to the lower income classes. However, if that investment is concentrated in privileged segments of the population, it will amplify the inequality levels, and thus we expect the sign of β5 to be positive. Regarding β6,, according to the Hecksher-Ohlin theory, its sign is expected to be negative, given the country’s relative abundance of unskilled labour. The role of emigration may have contributed to raise average wages, since most of the emigrants are probably from 21 the lower income classes, so that β7 is expected to be negative. Finally, β8 and β9 are expected to be negative because the increasing state intervention in the economic and social assistance spheres has probably been most beneficial to the lower income segments of the population. Table 6 reports the results of the 5 models estimated, considering different variations of Equation 3. [Table 6] The fit of the proposed relationship is rather good, as the adjusted R-squared values for all models estimated range from 0.75 to 0.82. Thus, the level of economic development thus seems to explain most of the variations in wage inequality in Portugal during the period under study. The results suggest furthermore that the quadratic specification gives evidence of an inverted U-shaped curve, demonstrating support for the Kuznets curve. That is, the parametric regression conclusion is that Portugal has undergone a Kuznets process of inequality transformation between 1944 and 1974, with an increase followed by a later decrease in inequality. The results also show that human capital, as expected, was in this period, positively and significantly related to inequality. The coefficients of GOV, EMIG and SPEND variables also have the expected signs, although only GOV is statistically significant (models 1 and 4).

20 The sources are … 21 However, the relationship can take the opposite direction if we consider that most of the emigrants came from the littoral regions (Baganha, 2000) and that the coastal population was most skilled than that of the interior regions. 16

Finally, the two measures of industrialization, MANUF and GFCF do not show any explanatory power for the evolution of Theil coefficient. This may be related to the inclusion of the real income per capita variable that might already capture the effects associated with these variables.

7. Concluding remarks and research agenda In this study an attempt was made to provide a quantitative analysis of wage inequality trends in Portugal during the 1944-1974 period, an issue that despite its relative importance has been practically ignored in the literature. To this purpose an extensive effort was made in order to compile and homogenize the time series data on wages from the National Statistics Office (INE) during the period under study. Along with sectoral wage inequality analysis, made possible through the comparison of average sectoral wages, we also take into account intra-sectoral wage inequality, considering wage differentials between skilled and unskilled work and female and male work, for the sectors in which this information is available. The calculus and graphic representation of the Theil index (the inequality measure that has been chosen) shows a configuration in line with Kuznets’ predictions: as Portugal switched from agricultural based production to manufacturing, income inequality first increased and then decreased. This finding is also confirmed by regression analysis, which shows evidence of an inverted U-shaped Kuznets curve. Along with shedding light in a relatively unexplored issue, using data that had not yet been explored, our study distinguishes from the voluminous amount of evidence regarding the empirical confirmation of the Kuznets curve, by considering inequality changes over time, using annual time-series data for a single country, rather than relying in cross-sectional or pooled data. The parametric analysis conducted does not however remove the criticism according to which the confirmation or rejection of the Kuznets curve is dependent upon the particular functional form chosen (in this case, a quadratic specification on real income per capita).22 Therefore, the natural following step in our study will be to remove this kind of criticism by investigating the presence of a Kuznets curve by using nonparametric analysis. Such an approach will enable us to estimate the path of inequality in Portugal as the level of GDP per capita changes without any parametric assumptions.

22 See, for example, Frazer (2006). 17

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Table 1 Growth of real income per capita in the European periphery and core, 1913-1986 (Maddison’s phases of development; annual growth rates between 3-years averages; per cent)

Portugal Spain Greece Ireland Core(1) 1913-1929 1.35 1.65 2.45 0.33 1.39 1929-1938 1.28 -3.53 1.50 0.87 1.16 1938-1950 1.56 1.48 -2.72 0.94 1.00 1950-1973 5.47 5.63 5.99 2.98 3.55 1973-1986 1.52 1.31 1.75 2.47 2.01 (1) 9 European forerunners Source: Lains (2003, ExEH)

Table 2 – Male labour force (1911-1950) and total employment (1960-1990) Total male Agriculture Industry Services labour force Percent 000 1911 61.0 21.7 17.3 1,629 1920 [60.9] [21.2] [17.9] 1,691 1930 60.9 20.7 18.4 1,967 1940 57.8 21.0 21.1 2,241 1950 53.8 24.6 21.6 2,562 1960 43.1 28.2 28.7 2,713 1970 27.6 33.9 38.6 2,263 1980 19.2 37.7 43.1 2,544 1990 13.1 37.3 49.6 2,476 Sources: Lains (2006) for 1911-1950, and Valério (ed.) (2001), p. 164 for 1960- 1990. 22

Table 3 – Portugal: Composition of GDP, 1910-1990 (percent) Agriculture Industry Services 1910 37.1 27.1 35.8 1920 30.4 25.8 43.9 1930 31.5 28.0 40.5 1940 30.6 28.7 40.6 1950 32.1 30.3 37.6 1960 27.2 37.0 35.7 1970 15.3 48.8 35.9 1980 10.5 48.8 40.7 1990 10.4 44.6 45.0 1958 prices Sources: Lains (2003c) and (2006). 23

Table 4 – Growth of output, labour force and labour productivity, 1860-1950 (annual growth rates, percent) Agriculture Industry Services Total Output (1) 1910-1920 -1.64 0.15 2.14 0.31 1920-1930 4.51 4.35 2.97 3.83 1934-1940 1.81 2.02 1.73 1.84 1940-1950 2.82 4.16 2.66 3.15 1950-1960 2.63 5.89 3.83 4.23 1960-1970 -0.67 8.93 5.44 5.81 1970-1980 1.23 4.66 6.19 4.86 1980-1990 3.43 2.78 4.90 3.75 Labour force (2) 1910-1920 0.40 0.16 0.80 0.42 1920-1930 1.52 1.28 1.80 1.52 1934-1940 0.78 1.46 2.71 1.31 1940-1950 0.62 2.96 1.59 1.35 1950-1960 -1.03 3.16 1.43 0.74 1960-1970 -3.46 2.82 3.98 0.95 1970-1980 -2.76 1.90 1.94 0.81 1980-1990 -2.99 0.72 2.25 0.82 Labour productivity 1910-1920 -2.04 -0.01 1.34 -0.11 1920-1930 2.99 3.07 1.17 2.31 1934-1940 1.03 0.56 -0.98 0.53 1940-1950 2.20 1.20 1.07 1.80 1950-1960 3.66 2.73 2.40 3.50 1960-1970 2.79 6.11 1.46 4.86 1970-1980 3.99 2.76 4.25 4.05 1980-1990 6.42 2.06 2.65 2.93 Sources: Lains (2006) for 1910-1950; and Pinheiro (Ed.) (1997) for 1950-1990.

Table 5 - Number of workers in the wage survey / total employment (%) 1944-49 1950-54 1955-59 1960-64 1965-69 1970-74

Manufacturing 26,2 41,5 46,7 43,0 41,9 58,6

Construction 0,0 11,2 13,3 17,1 24,2 33,9

Services 0,0 22,2 25,0 28,9 33,1 36,4

Total 9,3 28,4 31,8 32,8 35,5 44,2

Source: Own elaboration from Pinheiro (1997), Valério (2001), Estatística das Sociedades and Portuguese Statistical Yearbooks.

Table 6 – Regression results Dependent variable: Theil index Model 1 Model 2 Model 3 Model 4 Model 5 Constant -625,6* -404,5** -384,0* -734,7* -446,4* (-4,070) (-2,594) (-3,019) (-6,852) (-3,218) LGDPpc 145,9* 90,1** 85,7* 169,0* 100,4* (4,382) (2,549) (2,915) (7,197) (3,157) LGDPpc2 -8,437* -4,960** -4,723** -9,659* -5,637* (-4,576) (-2,431) (-2,763) (-7,373) (-3,083) MANUF/GDP -0,096 -0,047 -0,060 (-0,637) (-0,352) (-0,553) GFCF/GDP -0,076 -0,097 -0,112 0,095 (-0,637) (-0,934) (-1,069) (-1,037) HK 2,499** 2,253* (3,347) (3,764) SCHOOL1 0,105* 0,090* 0,107* (4,031) (4,918) (4,877) SCHOOL2 -0,063 0,066 -0,010 (-0,411) (-0,595) (-0,082) OPEN/GDP 0,054 0,052 0,072*** 0,056 0,076*** (0,984) (1,080) (1,698) (1,194) (1,807) EMIG 0,00001 0,000008 (1,066) (0,658) GOV/GDP -0,810** -0,517 -0,512 -0,832** -0,455 (-2,115) (-1,478) (-1,502) (-2,442) (-1,390) SPEND/GDP 0,0003 -0,001 (0,199) (-1,052) N 31 31 31 31 31 Adjusted R2 0,753 0,812 0,817 0,771 0,823 DW statistic 1,121 1,455 1,336 1,082 1,264 F statistic 11,14 13,96 21,13 17,83 20,90 Notes: Models estimated by OLS. t-statistics are in brackets. *, **, *** denote significant at the 1, 5 and 10% level, respectively, in a two-tailed test.

Figure 1 - Growth of GDP and its components, 1910-1990 (semi-log scale; 1953=100) 1000

Agriculture Industry Services GDP

100

10 1910 19151920 19251930 1935 1940 19451950195519601965197019751980 1985 1990

Figure 2 - Labour shares, 1911-1990

0,70 Lagr Lind 0,60 Lser

0,50

0,40

0,30

0,20

0,10 1911 1920 1930 1940 1950 1960 1970 1980 1990

Figure 3 - Output shares, 1841-1993

0,70

0,60 Yagr Yind

Yser 0,50

0,40

0,30

0,20

0,10 1911 1920 1930 1940 1950 1960 1970 1980 1990

Figure 4 - Relative productivity levels, 1910-1990

3,00 Y/Lagr Y/Lind 2,50 Y/Lser

2,00

1,50

1,00

0,50

0,00 1910 1920 1930 1940 1950 1960 1970 1980 1990

Figure 5 - Export and Import shares, 1910-1990 (current market prices)

0,50

0,45

0,40 X/GDP M/GDP

0,35

0,30

0,25

0,20

0,15

0,10

0,05

0,00 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990

Figure 6 - Trade shares [(X+M)/GDP], 1910-1990 (current market prices)

0,70

0,65

0,60

0,55

0,50

0,45

0,40

0,35

0,30

0,25

0,20 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990

Figure 7 - Wage-earners (TCO)/ Active population

0,9

0,85

0,8

0,75

0,7

0,65

0,6 1950 1955 1960 1965 1970 1975 1980

TCO/Active population

Source: Pinheiro (1997)

Figure 8 - Participation of skilled workers in total employment (%) in the industrial sector

16,00

14,00

12,00

10,00

8,00

6,00

4,00

2,00

0,00 1940 1945 1950 1955 1960 1965 1970 1975 1980

Source: Portuguese Statistical Yearbooks 30

Figure 9 - Sectors participation in total labour force in the industrial sector

0,90

0,80

0,70

0,60

0,50

0,40

0,30

0,20

0,10

0,00 1940 1945 1950 1955 1960 1965 1970 1975 1980 Non-skill intensive sectors (%) Semi-skill intensive sectors (%) Skill intensive sectors (%)

Source: Portuguese Statistical Yearbooks

Figure 10 - Skill premium in the industrial sector (1944-74)

4,5

3,5

2,5

1,5

1 1940 1945 1950 1955 1960 1965 1970 1975 1980

Source: Portuguese Statistical Yearbooks 31

Figure 11 - Coefficient of variation in the industrial sector

3 8 2 946 955 960 965 974 1944 1945 1 1947 1948 1949 1950 1951 1952 195 1954 1 1956 1957 195 1959 1 1961 1962 1963 1964 1 1966 1967 1968 1969 1970 1971 197 1973 1

Sources: Portuguese Statistical Yearbooks and Valerio et al (2001) Tab 4.7 pp. 181-185

Figure 12 - Wage Inequality in the industrial sector (1944-1974)

0,05

0,04

0,03 Within Between Theil

0,02

0,01

0 1940 1945 1950 1955 1960 1965 1970 1975 1980

Source: Portuguese Statistical Yearbooks

Figure 13 - Coefficient of variation of skilled and unskilled wages

\ 30

0 1940 1945 1950 1955 1960 1965 1970 1975 1980 Average wage Skilled workers Unskilled workers

Source: Portuguese Statistical Yearbooks

Figure 14 - Global wage inequality (Theil index)

0,14

0,12

0,1

0,08

0,06

0,04

0,02

0 1940 1945 1950 1955 1960 1965 1970 1975 1980

Theil Within Between

Source: Portuguese Statistical Yearbooks 33

Figure 15 - Correlation wage growth-increasing coverage

60%

50%

40%

30%

20%

10%

Wage growth (%) growth Wage 0% -60% -40% -20% 0% 20% 40% 60%

-10%

-20% y = -0,0004x + 0,0984 R2 = 2E-05 -30%

-40%

Figure 16 - Robustness of the results

0,14

0,12

0,1

0,08

0,06

0,04

0,02

0 1940 1945 1950 1955 1960 1965 1970 1975 1980

Theil (full sample) Theil (8 sectors) Theil (13 sectors)