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Latin American earnings inequality in the long run

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Abstract

This paper traces between-group earnings inequality for six Latin American countries over two centuries based on wage and income series compiled from a large array of primary and secondary sources. We find that inequality varied substantially by country and by period, questioning the notion that colonial legacies largely dominated the evolution of inequality. There is a broader inequality trajectory over the long run in the form of an “m” pattern with peaks around 1880 and the 1990s and a trough around 1920/1930s. Export-led growth does not necessarily imply a rise in inequality, while the import-substitution industrialisation efforts did not translate into a more egalitarian distribution of income. More notably, Latin America’s experience does not exhibit the great inequality levelling as seen in the North Atlantic economies from the 1930s to the 1970s.

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Fig. 1

Sources see “Appendix”; GiniB4w for Chile (1862–1900): Rodríguez Weber (2014)

Fig. 2

Sources see “Appendix”; GiniB4w for Chile (1862–1900): Rodríguez Weber (2014)

Fig. 3

Sources see “Appendix”; Uruguay: Bértola (2005)

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Notes

  1. Although our measures largely rely on wage data to estimate inequality, we also make allowances for non-labour income, especially during the twentieth century.

  2. These countries have accounted for about three-quarters of the population over the last two centuries and thus are representative of the inequality in the region as a whole.

  3. Because the focus of this paper is inequality, we do not discuss real wage performance.

  4. The periods are as follows: post-independence and the first globalisation wave (1830s–1910s); the state-led industrialisation period (ISI) under protection (1920s–1970s); and the second wave of export-led growth that started with a move towards trade liberalisation and market-friendly reforms—in some countries starting in the 1970s (1970s–2010).

  5. This method is akin to the construction of dynamic social tables (combining benchmark years from census data with annual data on income from other sources) used for Uruguay (Bértola 2005), Chile (Rodríguez Weber 2014), and pre-industrial societies (Milanovic et al. 2010; Lindert and Williamson 1982).

  6. For instance, our estimations using data on rural wages in a sample of 15 states in Brazil (IBGE, 1924) show that whilst the regional dispersion (measured by the coefficient of variation) of wages for unskilled rural labourers dropped from 0.81 in 1911 to 0.40 in 1921, and that for carpenters from 0.57 to 0.37, the regional dispersion in the wage gap of the two occupations (reflecting between-group regional dispersion) only moved from 0.31 in 1911 to 0.37 in 1921.

  7. According to Brazilian census data, the proportion of the black population that completed primary school was below 5% in 1940, 1950 and 1960, rising to 10% in 1980 and above 30% by 2000. In addition, black workers were predominately in low skills occupations in agriculture (Bucciferro 2016).

  8. It is likely that most of the potential differences between our occupational Gini and a hypothetical Gini based on quartiles over the whole distribution will cancel out. To illustrate the point, suppose that Group 4 accounts for 50% of the EAP, Group 2 for 25%, Group 3 for 15%, and Group 1 for 10%. Compared with a quartiles Gini, our Gini is based on a less convex Lorenz curve for the first half of the distribution (resulting in lower inequality), a similar curve for the third 25%, and a more convex Lorenz curve for the upper section of the distribution (resulting in higher inequality).

  9. A non-monetary economy that relies on hunting, gathering, and agriculture to provide for basic needs.

  10. The long-term evidence in developed economies (Piketty 2014) shows that income from property tends to be concentrated in the top group, which means that our understatement of property income of the middle and bottom groups is small.

  11. Alvaredo (2010) calculates the top 1% income share for Argentina (1932–1972 and 2002) using tax data. Rodríguez Weber (2015) offers an estimation of the top 1% income for Chile (1913–1973) based on dynamic social tables. In both cases, secular patterns are roughly in line with those shown by our s 1 for both countries. Astorga (2015a) offers evidence showing that, in general, our estimates for the mean income of Group 1 in the first half of the 20th century are consistent with data available on top earners; and that our calculated s 1 is also broadly consistent with available official estimates of property income shares for the second half of that century.

  12. We are not accounting for fringe payments. According to the ILO’s October Inquiry, in Chile (1953–59) they represented, on average, about 16% of total earnings for construction workers and in Venezuela about 20% in 1981. The evidence available for the 1950s and 1960s indicates that fringe benefits only have a limited effect on skill differentials (Berg 1968).

  13. Suppose total income = 200; EAP = 100 with full employment; so that average income per person engaged = 2. The mean incomes are: Group 1 = 9; Group 2 = 3; Group 3 = 1.5; Group 4 = 0.4. The EAP shares are: e 1  = 0.1, e 2  = 0.2, e 3  = 0.2, e 4  = 0.5. Based on these data, r 1  = 4.5 (=9/2), r 2  = 1.5, r 3  = 0.75, r 4  = 0.2; and GiniB4 according to (1) = 0.565. Now suppose overall unemployment of 5% (affecting all groups equally); no data on Group 1’ mean income; and no change in the mean income of the remaining three groups. The new income per person engaged is 1.9 (=190/100). In this case, r 2  = 1.58 (=3/1.9), r 3  = 0.79, r 4  = 0.21. Then calculate r 1  = 4.21 as a residual using (2). The Gini now falls to 0.547, when it should not.

  14. We perform such an adjustment in all six countries from around 1970 onwards based on official unemployment rates, and during the 1930s using estimates available for Chile, Mexico, and Venezuela (in Argentina, Brazil, and Colombia, we apply the same adjustment as in Mexico). Otherwise we assume that unemployment is at its natural rate. For additional details, see Astorga (2015a).

  15. These structural differences are reflected in a relatively low average correlation between both measures (0.4) in the LA-6 over the twentieth century. The paired correlations are calculated using a five-year panel data, so as to minimise distortions caused by interpolation.

  16. Table 1 in the Appendix also offers average annual rates of growth of terms of trade as well as weighted-average real wages by country in selected periods.

  17. The main commodities for the countries in our sample were as follows: Argentina (wool, beef, and wheat), Chile (nitrates and copper), Colombia (coffee and gold), Mexico (silver and copper), and Venezuela (coffee and cacao). In many cases the two most important commodities represented at least 50% of total exports (Arroyo Abad 2013a; Bulmer-Thomas 2013).

  18. In theory, improved terms of trade should benefit the factor used intensively in the production of the exportable good (e.g., land), and, assuming concentrated ownership, it should increase inequality. But the impact on the wage structure is complex depending on the relative skill intensity of the tradable and non-tradable sectors (Galiani et al. 2010).

  19. On average, the net migration during the 1880s was around 2.2% of the total population per year. In comparison the prior and subsequent decades, the rates were 1.2% and 0.9%, respectively (Departamento General de Inmigración 1895, 1914). This rise in inequality driven by immigration (measured through the land rent-wage ratio) is also consistent with the experience of Uruguay as documented by Bértola and Williamson (2006) and Arroyo Abad (2013a). Arroyo Abad (2013a) estimates that population would have been 48.2% smaller in 1900 in the absence of migration.

  20. While around 40% of the foreigners’ occupations were as day workers and agricultural workers, the rest worked in a wide array of semi-skilled occupations such as masons, smiths and machinists (Comisión Directiva del Censo 1898). For an analysis on the integration of the Italians and Spaniards in the Buenos Aires labour market, see Arroyo Abad and Sánchez-Alonso (2015).

  21. Yet, while all these indicators point to an overall drop of inequality during the period, it appears that the landowning class enjoyed better income trends than the average wage earner (see rent/average wage ratio in Fig. 1).

  22. In addition, educational expansion also changed the relative scarcity of skilled to unskilled labour (Rodríguez Weber 2014).

  23. The urbanisation rates in the 1920s and the 1970s were, respectively, 38 and 81% in Argentina, 15 and 62% in Brazil, 38 and 79% in Chile, 15 and 61% in Colombia; 15 and 63% in Mexico, 24 and 76% in Venezuela (Astorga et al. 2005).

  24. Although our wage data cannot fully reflect the urban–rural divide, the conditional correlation between GiniB3w and the labour productivity gap between agriculture and manufacturing in the LA-6—after controlling for other key variables such as terms of trade and factor endowments—is significant and positive during the period 1935–2011. However, a similar econometric exercise using GiniB4 lacks significance (Astorga 2015b).

  25. Although we cannot rule out that some of these differences may reflect estimation shortcomings and data limitations.

  26. The 1917 Constitution set new minimum wage levels as well as profit sharing. Higher real wages and higher living standards emerged as priorities for the government after the revolution Bortz (2005).

  27. GiniB4 for Brazil shows a relatively low level until the early 1930s. At this time, according to the 1920 population census, about 80% of the labour force was rural, illiterate, and low skilled (Bértola et al. 2009). Under such circumstances, it is likely that the maximum feasible income Gini was relatively low reflecting the fact that poorer societies have a smaller surplus for the elite to extract (Milanovic et al. 2010).

  28. This could reflect increased profits of firms operating under protection. However, the relationship between ISI and property income is complex and requires further research. In general, the combination of industrialisation with a decline in agriculture generated winners (the new industrialists) as well as losers (the traditional landlords). Also the state became a key economic actor via state-owned enterprises. See Rodríguez Weber (2015) for the analysis of Chile.

  29. Favourable minimum wages policies and high rates of unionisation in Mexico contributed to a long spell of wage compression in this period (Márquez Padilla 1981).

  30. Intra-industry wage inequality also rose in Argentina (from early 1960s), Brazil, and Chile (Frankema 2012) suggesting skill-biased technological change.

  31. In a panel data study, Székely and Sámano (2012) found that greater trade openness was associated with widening income inequality in the region during the period 1980–2000. They also found that, once fully implemented, trade liberalisation did not lead to further inequality rises in the first decade of this century.

  32. The average skilled wage (Group 2) in Venezuela fell by an annual rate of −2.4% in the 1980s and 1990s, after rising by 2.1% in the 1970s. The same calculation for the average unskilled wage shows a fall of −2.1% after a 1.9% rise.

  33. According to our calculations based on official figures, the population in the LA-6 (simple averages) grew 74% between 1950 and 1970, and 56% between 1970 and 1990. The corresponding growth rates for the EAP are 64% and 85%. The same calculations for the 1930–1950 period shows a more even process with population and EAP growth both at 57%. As to female participation rates, there were few changes in 1940–1970, and an explosive growth in participation from 1970 to 2000 (Camou and Maubrigades 2016).

  34. A population-weighted average (where developments in Brazil and Mexico have a larger weight) produces a similar—though more pronounced—shape but with a peak around 1980.

  35. These three periods also match contrasting developments in real wages. The LA-6 real wage grew at average annual rates of 0.7% in 1870–1915, 2.2% in 1940–1980, and 0.7% in 1980–2011 (see Table 1).

  36. The lack of a clear link between inequality and the trade cycle does not necessarily invalidate the Heckscher–Ohlin model as its predictions differ according to the country’s relative abundance of factor endowments. But, changing inequality trends within a period dominated by either openness or closeness complicates a simple interpretation based on this model.

  37. Workers were granted one free day a week, typically Sundays, in addition to the numerous religious holidays celebrated throughout the year (González Pérez 2012). Note that Urrutia and Arrubla (1970) assume 360 working days per year.

  38. The dataset is part of an ongoing project on Mexican inequality in the long run by L. Arroyo Abad, A. Challú and A. Gómez Galvarriato.

  39. For the late colonial period, Lana-Berasain (2014) notes that the cost of subsistence for slaves was 1.25 reales per person per day.

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Authors and Affiliations

  1. Middlebury College, Middlebury, USA

    Leticia Arroyo Abad

  2. Institut Barcelona d’Estudis Internacionals, IBEI, Barcelona, Spain

    Pablo Astorga Junquera

Authors
  1. Leticia Arroyo Abad
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  2. Pablo Astorga Junquera
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Corresponding authors

Correspondence to Leticia Arroyo Abad or Pablo Astorga Junquera.

Additional information

We are grateful to Peter Lindert and Jeffrey Williamson for comments, and to Marcelo de Paiva Abreu, Florencia Aráoz, Ame Bergés, Eduardo M. Cuesta, Raymundo Campos Vázquez, Amílcar Challú, José Díaz, Ewout Frankema, Aurora Gómez Galvarriato, María Gómez León, Michael Huberman, María López Uribe, Gerardo Lucas, Oscar Nupia, Mario Matus, Brian McBeth, Marco Palacios, Eustáquio Reis, Javier Rodriguez Weber, Carmen A. Romero, Héctor Valecillos, Henry Willebald, and Alan Wittrup for kindly providing wage data and ideas.

Appendix

Appendix

Notes on estimation methodolgy and data sources. Selected data series can be downloaded from the Global Prices and Income History Group website (Tables 1, 2).

Table 1 Terms of trade and real wages in selected periods.
Full size table
Table 2 Representative occupations by group
Full size table

1.1 Nineteenth century

  • Employment shares

    In general terms, we used the occupation classification available in census when available. Each occupation was classified according to ECLAC 9-category list and then further summarised in 4 groups: Group 1 (employers, managers, and professionals), Group 2 (technicians and administrators), Group 3 (urban workers), and Group 4 (rural workers and personal services including low-skilled urban workers and street vendors) following the methodology by Astorga (2015). As census data are not available for many Latin American countries during this period, we used other estimations as detailed below.

    • Argentina: From 1869 to 1900, we used the 1869 and 1895 census with direct interpolation for the intervening years.

    • Chile: We used the censuses of 1865, 1875, 1895, and 1905 to estimate the shares.

    • Colombia: Unfortunately for Colombia, there are no data on occupations until the twentieth century. We used Venezuela’s shares.

    • Mexico: We assumed stable shares based on the 1895 Census.

    • Venezuela: The shares are estimated using the social tables by Brito Figueroa (2002).

  • Population and terms of trade

    • Argentina, Mexico, and Venezuela: Arroyo Abad (2013a).

    • Brazil: IBGE website.

    • Chile: EH ClioLab.

    • Colombia: Meisel and Ramirez (2007).

  • Wages

    • Argentina: Rural wages are for day workers with board from Cortés Conde (1979), Sábato (1990), Cuesta (2012), and Gelman and Santilli (2014) for the provinces of Buenos Aires, Entre Ríos, and Santa Fe. Unskilled urban wages correspond to servants from Barba (1999) and Arroyo Abad (2013a) for Buenos Aires. Semi-skilled data came from an array of sources: Carrasco (1886), Buchanan (1898), Patroni (1897), Cortés Conde (1979), and Barba (1999). Skilled wages were compiled from Dirección General de Estadística (1894), the National Archives (Archivo General de la Nación de Argentina 1820–1900), national and provincial budgets, railroad and other public utilities budgets (Congreso de la Nación 1860–1900; Ketzelman and Souza 1930; Barba 1999).

    • Brazil: Wages in Rio de Janeiro from Lobo (1978). For unskilled wages, we used series of labourers of a small plot to produce fruit and vegetables (hortelão) and porters (porteros). For semi-skilled wages are an average wage for masons and carpenters. For skilled workers, we used wages for clecks (escriturario)–only available post 1890.

    • Chile: Wages are from Rodríguez Weber (2014) and Matus (2012).

    • Colombia: Wages are from Urrutia and Arrubla (1970) and Meisel and Ramirez (2007). For rural agricultural workers, wages refer to dayworkers as reported in Meisel and Ramirez (2007) and from Acevedo Echeverri et al. (1989). The daily wages are annualized assuming 235 working days per year. Footnote 37

    • For blue-collar workers we used the category servant or construction worker (obrero de la construcción) from Urrutia and Arrubla (1970). For skilled and semi-skilled workers, we compiled data from national budgets (Leyes de los Estados Unidos de Colombia 1830–1900).

    • Mexico: Wages are from the Mexican National Archives (Archivo General de la Nación and Archivo Histórico del Instituto Nacional de Antropología e Historia, Hospitales), the Mexico City Archive (Archivo Histórico del Distrito Federal, Empleados and Beneficencia Pública del Distrito Federal), several national budgets, and Challú and Gomez-Galvarriato (2015). For rural wages, we used a variety of estimates from several sources including Bazant (1975) and Semo (1988), and all estimates include corn rations priced at market value from most of Mexican regions. For late nineteenth century, we used the minimum wage for the agricultural sector (de México 1965).Footnote 38 For high skilled and semi-skilled workers, the data are from the Mexico City National Archives and national budgets (Ministerio de Hacienda y Crédito Público 1881–1892; Arrillaga 1830–1900; Cámara de Diputados 1820–1900). These budgets provide wages by state in some occupational categories; the data collected were mainly from state capital cities, covering around 90% of the population.

    • Venezuela: Wages for skilled, semi-skilled and urban unskilled occupations are from the National Archives (Archivo General de la Nación de Venezuela). For rural wages, several sources show that the average monetary wage was 2 reales per day. Both urban servants and rural workers received in-kind rations (Lucas 1991; Yarrington 1997; Cartay 1988). No source indicates with precision the ration composition; however, a couple of sources provide rough estimations of around 1.5 reales per day in 1830s (Archivo Arzobispal 1830–1900).Footnote 39

    • Taking that figure into consideration, we assumed that the rural day workers and urban servants were provided corn and beans for a household subsistence based on the basket and prices published by Arroyo Abad (2013b).

  • Price deflators

    • Argentina: Consumer Price Index (CPI) published by Ferreres (2005).

    • Brazil: Wholesale price index from Catão (1992).

    • Chile: ClioLab (2015) provides a CPI for the reference period.

    • Colombia: The deflator used is based on Meisel and Ramirez (2007)

    • Mexico: We used the respectable basket by Challú and Gomez-Galvarriato (2015).

    • Venezuela: We combined the CPI for Caracas (Arroyo Abad 2013b) with the GDP deflator (De Corso 2013).

      • Land Rents: Arroyo Abad (2013a); Chile: Willebald (2015) estimated benchmarks for 1875, 1885, 1895, and 1907 with interpolation for the intervening years.

1.2 Twentieth century

  • Employment shares: see Astorga (2015a, Annex C).

  • Terms of trade: Brazil from IBGE (2015) website, Chile from Díaz et al. (2005). Otherwise MOXLAD (2015) database.

  • Wages

To assemble comparable and consistent wage series, we first set comparable wage levels in the core period of 1965–1980 and then proceed to complete the series back and forth by using rate of growth of a number of wage series from various sources. To set comparable levels across the LA-6, we do the following:

  • For the unskilled workers circa 1970, we relied on comparable series of agricultural wages for unskilled workers in agriculture and the urban minimum wage from PREALC (1982) and ECLAC website.

  • For semi-skilled workers, we used the average wage in most cases including seven occupations (bricklayers and masons, structural iron workers, concrete workers, carpenters and joiners, painters, plumbers, and electrical fitters) in the construction industry collected in ILO’s (International Labour Organization 1936–1964, 1964–1982) October Enquiry, Part I.

  • For relatively skilled workers, where possible, we used monthly wages for clerks (an average of bank tellers and accountants) available in ILO’s (International Labour Organization 1936–1964, 1964–1982) October Enquiry, Part II, or average wages in manufacturing from PREALC (1982) otherwise.

  • Finally, to have comparable wages in a single currency across countries we calculate PPP$ values using the PPP exchange rates available for 1970 (ECLA 1978).

Regarding rate of growth: for Group 2, we use series of manufacturing wages and, when not available, wages in the public sector for relatively skilled employees such as mechanics or mid-range officers (e.g., Colombia). For Group 3, we generally use wage series in the construction sector or in other relatively low-skilled sectors such as retailing. For Argentina, we use average wages of non-agricultural sectors (excluding government) pre-1965. Chile pre-1930 uses wages in low-productivity sectors (food and drinks and textiles). For Mexico, we used the changes in minimum wages for the early decades. For Group 4, pre-1965, when possible we compiled wages for unskilled rural and urban workers. In some cases, they are supplemented with wages for unskilled government employees (e.g., Colombia). And in the post-1980 period, we use a combination of rural and urban minimum wages, or relatively low-productivity sectors such as retailing and personal services. Figure A-5 in Astorga (2015a) shows the real wage series by the three occupational groups and the estimated income per person engaged series. We use the same deflator (usually the CPI) for both overall income per person engaged and wages, so that the inequality measures are equivalent to those calculated from nominal values. All series are updated up to September 2016. Full details on sources and methodology will be included in a forthcoming publication. Contact the author for more details.

  • Price deflators: see sources and notes in Astorga (2012). In Argentina to avoid the under-reporting of CPI inflation by INDEC (2015) in 2006-11, we use a CPI index reported by seven provinces compiled by CENDA (2005–2011).

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Arroyo Abad, L., Astorga Junquera, P. Latin American earnings inequality in the long run. Cliometrica 11, 349–374 (2017). https://doi.org/10.1007/s11698-016-0150-9

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