A city of trades: Spanish and Italian immigrants in late-nineteenth-century Buenos Aires, Argentina


The city of Buenos Aires in the 1890s is an extreme case in immigration history since the native workers accounted for less than one-third of the labour force. In this paper, we look at the labour market performance of Argentineans vis-à-vis the largest two immigrant groups, Italians and the Spaniards. We find that, on average, Argentineans enjoyed higher wages, but workers specialised in particular occupations by nationality. Immigrants clustered in occupations with lower salaries. Despite higher literacy levels and the language advantage, Spaniards did not outperform Italians in earnings. Ethnic networks facilitated the integration of immigrants into the host society and played a role in the occupation selection of immigrants. Our results suggest that Italian prosperity in Buenos Aires was not based on superior earnings or skills but on older and powerful networks.

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Fig. 1

Sources: Calculated with data from Table 3

Fig. 2

Sources: Argentina: Somoza and Lattes (1967), Foreign (=Italians + Spaniards): see text. Note Smooth series with winsored outliers

Fig. 3

Source: Italy and Spain calculated from census sample, 1895, Argentina: Somoza and Lattes (1967)

Fig. 4

Source: Resumen estadístico del movimiento migratorio en la República argentina, 1857–1924. Buenos Aires. Dirección General de Inmigración (1925)

Fig. 5

Sources: calculated from the census sample 1895 according to the age of first child born in Argentina. See text


  1. 1.

    Other classic studies include Bourdé (1977) and Sargent (1976). For an analysis on the city’s industrial development, see Rocchi (1999, 2006). See also Gutiérrez (1981) and Suriano (1994) on labour movements and living conditions.

  2. 2.

    In his pioneer work on the Spaniards in the city of Córdoba, Szuchman (1980) concludes that none of the semi-skilled [Spanish] workers improved their position. Moya (1998, p. 276) draws a more optimistic conclusion for the city of Buenos Aires: “few became Anchorenas (…) but many saved some pesos, sent millions in remittances back home, raised families, and became fathers and mothers of teachers and bookkeepers (…) For most of the immigrants, that was what ‘making America’ was all about”. [In Argentina, the name Anchorena, from the Anchorena dynasty, evokes wealth and power.].

  3. 3.

    The national censuses corresponded to the years 1869, 1895 and 1914. For the city of Buenos Aires, three municipal censuses (1887, 1904 and 1909) are also available.

  4. 4.

    It provides original information by individual such as name and surname, sex, age, marital status, birthplace, literacy, occupation, ownership, attendance to school for children and for women years of marriage and number of children.

  5. 5.

    See Kim et al. (2014) on the use of common surnames to build representative samples. There is no reason to believe that M and G surnames tend to be drawn from specific regions of Spain or Italy.

  6. 6.

    Linking individuals through time would definitely be desirable; Moya (1998, p. 263) tried unsuccessfully to track individuals between censuses.

  7. 7.

    Less than 5% of immigrants in our sample reported place of birth. Among Italians, 43% were from Liguria and Lombardy in the North while 58% of Spaniards were from the northern regions Galicia, Asturias and the Basque country. However, 15% of the very few reporting place of birth came from the South both in Italy and Spain.

  8. 8.

    Under the direction of Gino Germani, Somoza and Lattes carried out the 1869 and 1895 census data sampling and published a long working paper (Somoza and Lattes 1967) explaining their sampling technique and possible errors. They also presented basic tables with the main results but they never used the database for further analysis. Many years later the S&L database have been digitized and are available at www.censos1869-1895.sociales.uba.ar; see McCaa et al. (2001) and Quartulli (2014).

  9. 9.

    The occupations in S&L sample are grouped in very broad categories and codified according to Argentine census occupational classification. Therefore, the imputation of wages to occupation of natives and of other European immigrants is according to more general occupational groups.

  10. 10.

    Data on women’s occupations and wages are scarcer. In historical terms, female labour force participation and the range of occupations were limited.

  11. 11.

    Social historians starting with the pioneer research by Thernstrom (1973) have frequently used occupational coding variations. Moya (1998), for example, follows a similar classification for Argentina.

  12. 12.

    Cortés Conde (1979) pioneer research also used Buchanan data for real wages. Recently, Vence Conti and Cuesta (2016) presented a new series of prices and wages for Buenos Aires in the late nineteenth century. However, both studies deal with the evolution of average real wages over time and our focus here is wage differences by occupations in the census year.

  13. 13.

    This publication also provides information on nationality. We find that there was no difference in the wages paid to foreigners relative to Argentineans for the same occupation.

  14. 14.

    ‘Other Europeans’ include French (70.58%), English (16.28%) and German (13.14%) immigrants.

  15. 15.

    Even though 50% of the population was Argentine-born, the native share of labour force was barely over 30%.

  16. 16.

    Our estimations assume that both groups would have faced the same average wage per occupational group while changing the occupational distribution to match the Argentine one. We are ignoring general equilibrium changes that would have resulted from a change in the relative supply of labour by skill.

  17. 17.

    The available data on migration outflows do not distinguish the place of residence within Argentina.

  18. 18.

    For the US case, selective emigration of below-average earnings immigrants is qualitatively important. As a result, analysts using census data have systematically overestimated the assimilation of immigrants to the US labour market (Lubotsky 2007).

  19. 19.

    More than 75% of Italians applying for a passport after 1901 declared their intention to return regardless of their destination (Cinel 1982, pp. 47–49).

  20. 20.

    Subsidies may have affected negatively migrants’ selection in Spain as provinces with little or no tradition of emigration to Argentina—such as Málaga in the South—benefitted greatly (Mateo Avilés 1993).

  21. 21.

    Our findings for Buenos Aires are in line with Moya’s (1998, Table 32) analysis of Spanish immigrants’ occupations in the city for this period. He compiled a random non-stratified sample of every employed adult from only three heavily Spanish city districts. This methodology overrepresented Spaniards while in reality Italians outnumbered Spaniards in the city. Moya’s sample is biased towards working-class neighbourhoods. The sample we used here is not selected by district or other individual characteristics. Re-classifying our data using Moya’s categories results in a higher share of Italians and Spaniards in the professional occupational group and a higher concentration of Spaniards as sales and clerical workers. Despite these differences, the broad characterisation remains very similar.

  22. 22.

    Except for the Spaniards from the Canary Islanders migrating to Cuba, as they were the least educated of the population at home (Juif 2015).

  23. 23.

    The Italian census of 1881 shows 76% of male illiteracy rate in Calabria versus 54.5% in Piedmont. Sori (1979, p. 209) presents data on primary school enrolment per region in 1881–1882: 58% in Liguria compared to 38% in Campania.

  24. 24.

    Differences in female literacy were smaller. Literacy rates for different nationality groups were calculated from 1895 Argentine census for population older than 7 years of age.

  25. 25.

    Germani (1966) was the first in pointing out the rapid social and occupational upgrading by immigrants in Argentina.

  26. 26.

    Experience in the American labour market was more important than skills, measured by literacy, in explaining Jewish occupational distribution in the USA at the turn of the century (Chiswick 1991).

  27. 27.

    We also experimented with another dependent variable, the index of socio-economic prestige (SEI). Literacy turns out to be, once again, a highly significant explanatory variable.

  28. 28.

    To potentially tease out the impact of qualifications such as literacy, we carried out an Oaxaca decomposition based on separate regressions for Italians and Spanish with poor results.

  29. 29.

    The use of prestige rankings in historical research (such as the SEI) based on surveys and income from the 1950 American census can be problematic. However, Hauser (1982) reports a quite satisfactory fit between status rankings assigned by historians in five nineteenth century American cities and the Duncan’s SEI. See also Treiman (1976) and Sobek (1996) stressing the likelihood that occupational categories and prestige seem to have been stable over time.

  30. 30.

    The regression analysis confirms these results. Using the log of SEI as the dependent variable, we find that literacy allowed Spaniards to find opportunities in more prestigious occupations. The coefficient is highly significant and much higher than for Italians.

  31. 31.

    Spaniards also concentrated in unskilled occupations.

  32. 32.

    Beaman (2012) and Pattel and Vella (2013) find that individuals choosing the most common occupation of their compatriots enjoyed large and positive earnings effects. Given the construction of our wage data, we cannot test the impact of networks on wages. The wage matching does not take into account experience or other individual-based characteristics.

  33. 33.

    Our analysis differs from the residential analysis for the city in 1855 carried out by Moya (1998, chap. 5). He analysed Spanish immigrant networks from the sociological point of view including cultural capital and invisible skills related to the place of origin.

  34. 34.

    The correlation coefficient between our main variable of interest, C on, in 1869 and 1895 is 0.6.

  35. 35.

    By using the main occupational group, we are considering around 20% of the immigrant population. Using the two main occupational groups is problematic as it would entail nearly 50% of the immigrant population.

  36. 36.

    To pin down these estimates, we use the 1869 census. In the first counterfactual, we allocated all Italian and Spanish immigrants in equal number across neighbourhoods and then calculated their average wage using the prevailing distribution of occupations for all people in each neighbourhood. For the second counterfactual, we placed both immigrant groups according to the relative share of Argentines per neighbourhood and occupation to obtain average earnings. These estimations aim at presenting crude counterfactual scenarios. In particular, they do not take into account changes in labour demand resulting from changes in the relative supply of skills and their corresponding effects on relative wages.

  37. 37.

    We exclude single immigrants from the sample.

  38. 38.

    Based on association data available by date of foundation from the 1904 Buenos Aires city census. We used the current membership reported in that year given the lack of earlier information. According to the 1914 national census (vol. 10, pp. 240–243) Italians founded 43 mutual aid societies in Buenos Aires before 1895 compare to 7 founded by Spaniards.

  39. 39.

    Gandolfo (1992) argues that there was also considerable tension within the Italian community in Buenos Aires and even among members of the same mutual aid societies.

  40. 40.

    We are not considering here the possibility that the larger the size of the community and the more powerful the networks, the more negatively selected the immigrants.


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We thank Timothy J. Hatton, Javier Silvestre, anonymous reviewers and the editor of this Journal for very useful comments and suggestions. This article also benefited greatly from discussions in the Economic History Seminar of the Universitat de Barcelona and the XI Conference of the European Historical Economics Society (Pisa). The usual disclaimer applies.

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Correspondence to Blanca Sánchez-Alonso.


Appendix 1: Occupation and Wages 1890s

Our original information refers to occupations declared by individuals recorded in our sample to census-takers in 1895. We have codified original occupations by IPUMS 1950 codes. Different occupations were then grouped by the ten occupational categories in IPUMS. Occupational structure of migrants and natives are presented in percentages over the total of the sample (Table 4).

We imputed a wage w o to each specific occupation o based on the sources described above. Then, we calculated the average wage W gc for the occupational group g (from 0 to 9, see Table 9) for each immigrant country c by weighting the wage w o assigned to each specific occupation o by the number of individuals L oc employed in each occupational group o for country c.

$$W_{gc} = \sum w_{o} L_{oc} /\sum L_{oc}$$

In those cases with no information in the sources we have assigned the wage of a similar occupation or the average wage of the occupational category. For example ‘hornero’ (ovenbird) like ‘fraguador’ (forgemen), or ‘hotelero’ (hotelier) the average wage for occupational category 7. ‘Comerciantes’ (shopkeeper, trader) have been assigned a wage 50% higher than ‘dependiente de comercio’ (shop assistant, salesperson) given in Patroni (1897).

Table 9 Occupation and Wages 1890s—wage in pesos moneda nacional

We followed the same procedure with the sample by Somoza and Lattes for ‘Argentines’ and ‘Other Europeans’, although in this case the original data on occupations were more aggregated that in our sample directly taken from census records.

All wages are annual and in m$n (pesos moneda nacional).

Sources for wages in occupational categories 4, 5, 6, 7 and 9: we have used two sources for wages: Adrian Patroni (1897) Los trabajadores en la Argentina, Buenos Aires, Imprenta Litografia y Encuadernación, Chacabuco 664 y 670 and William Buchanan (1898) “La moneda y la vida en la República Argentina”, Revista de Derecho, Historia y Letras, año I, tomo II, Buenos Aires.

When the same occupation appears in both sources a simple average was used. Buchanan gives daily wage data for 1894 and 1896, so we used a simple average of the two years.

Patroni (1897) provides daily wages and the number of working days by occupation for most occupations. When Buchanan wages are used, the number of working days is the average of the same category according to Patroni (1897). When there is no information for the number of working days per year for a particular occupation, a simple average for the rest of occupations according to Patroni (1897) is used (261 working days).

If wages were given in ranges (for example $3.00–$5.00 for ‘relojeros’ watchmakers) we converted these wages using simple averages (for the example given, the figure used was $4.00).

In most cases sources distinguish between male and female wages. The average female–male earnings ratio for those occupations with information is 0.67 (CV = 0.18). In the few cases of women with recorded occupations in the census sample and no wage information in our sources, the ratio was applied to convert male to female wages. The majority of women wages (groups 5–6–7) come from original sources.

Source for wages in occupational categories 0, 2, 3: Dirección General de Estadística (1894) Censo de empleados administrativos, funcionarios judiciales, personal docente, jubilados y pensionistas civiles de la República Argentina correspondiente al 31 de diciembre de 1893, Buenos Aires: Compañía Sudamericana de Billetes de Banco.

Wages are monthly wages multiplied by 12 for annual wages. We used the average wage for each category when there is no information.

Appendix 2: Comparison of samples

See Table 10.

Table 10 Comparison of our sample with Somoza and Lattes (1967)

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Arroyo Abad, L., Sánchez-Alonso, B. A city of trades: Spanish and Italian immigrants in late-nineteenth-century Buenos Aires, Argentina. Cliometrica 12, 343–376 (2018). https://doi.org/10.1007/s11698-017-0164-y

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  • Migration
  • Wages
  • Networks
  • Labour force
  • Buenos Aires

JEL Classification

  • N36
  • F22