Policy Research Working Paper 10834 The Socioeconomic Outcomes of Native Groups in Argentina Pedro Dal Bó Carolina Lopez Development Economics Development Research Group June 2024 Policy Research Working Paper 10834 Abstract This study uses individual-level census data from Argen- non-Natives: for each level of education of the parents, the tina to examine the socioeconomic disparities between children of Natives have, on average, fewer years of educa- Native and non-Native people. Native people fare worse tion than the children of non-Natives. Finally, the study also across a variety of indicators, including housing, education, reveals large differences between Native groups: while some employment, and health. On average, the observed dispar- achieve average outcomes that surpass those of the non-Na- ities amount to 12 percent of the standard deviation and tive population, others significantly lag behind. Notably, persist even after controlling for factors such as geographic these differences are correlated with a characteristic of their location. Furthermore, there are differences in the intergen- pre-Columbian economy: the practice of agriculture. erational transmission of education between Natives and This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at carolina_lopez@worldbank.org and pedro_dal_bo@brown.edu. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team The Socioeconomic Outcomes of Native Groups in Argentina ´ and Carolina Lopez∗ Pedro Dal Bo JEL codes: I3, J15, O15. Keywords: Indigenous people, human development, persistence, Argentina. ∗ We thank Cynthia Marchioni and Ignacio Lopez Erazo for excellent research assistantship and Anna ˜ Aizer, Laura Fejerman, Hugo Nopo and audiences at Universidad de San Andr´ es, University of British Columbia, and Universidad Torcuato Di Tella for very useful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily repre- sent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own. Pedro Dal Bo: ´ Brown Uni- versity & NBER, pedro dal bo@brown.edu; Carolina Lopez: Development Research Group, World Bank, carolina lopez@worldbank.org. 1 Introduction While the population of Argentina descended mainly from the combination of Native, African and European ancestors (see Wang et al., 2008; Corach et al., 2010; Avena et al., 2012; Parolin et al., 2019), it is a common stereotype to see this population as mainly of European origin.1 This incorrect view of the Argentine population had a correlate on the scarcity of national statistics on the numbers and characteristics of the native population for most of the national history.2 We use the data from the 2010 National Census (Instituto Nacional de Estad´ ıstica y Censos, 2010), the first census to ask about native identity or ancestry at the individual level, to study the socioeconomic outcomes of the Native people of Argentina. We compare Native and non-Native Argentines in terms of outcomes related to hous- ing, education, labor market and health. Consistently with evidence of Native disadvan- tage in other Latin American countries (see Gandelman et al., 2011; Freire et al., 2015), we find that Natives, on average, fare significantly worse on all of these dimensions in Argentina. The magnitudes of the differences are moderate, corresponding on average to 12 percent of the standard deviation of the outcomes. These differences persist even af- ter controlling for location, showing that differences are not only related to geographical differences. We also find differences in the transmission of education from parent to children. For each level of education of the parents, the children of Natives have, on average, fewer years of education than the children of non-Natives. This is consistent with what is found for other Latin American countries (see Cruces et al., 2012; Berniell et al., 2021). Given the importance of education for economic outcomes and human development (see, for exam- ple, Angrist and Krueger, 1991; Lleras-Muney, 2005), the differences in the transmission of education between Natives and non-Natives may help perpetuate the observed disad- vantage of the Native population. We also study the differences in socioeconomic outcomes across Native groups. We find large differences across groups. While some groups obtain average outcomes above those of the non-Native population, other groups obtain outcomes well below. For exam- ple, the group with the highest average years of education, the Charrua, ´ has educational 1 The Mexican poet Octavio Paz wrote “Mexicans descend from the Aztecs, Peruvians from the Incas, and Argentines... from the ships.” Argentine president Alberto Fern´ andez echoed this stereotype by saying “Mexicans come from the Indians, Brazilians from the jungle, but us Argentines come from the ships, and these were ships coming from Europe...” 2 This was also the case in other countries in the region. Until recent censuses, the statistical offices of many Latin American countries did not systematically gather data on ethnicity or race (see Loveman, 2014; Freire et al., 2018). 2 levels above those of the non-Native population and twice the years of education than those of the group with the lowest educational attainment. Considering all outcomes, the group with the best outcomes has an average standardized outcome almost 9 percent of a standard deviation higher than the general population, while the group with the worst outcomes has an average outcome that is lower than the general population by 60 percent of a standard deviation. These large differences in outcomes across native groups stress the importance of not considering the native population of Argentina as homogeneous. While there may be many possible determinants of the differences across groups, we show that there is one historical characteristic of these groups that correlates with their outcomes: groups that practiced agriculture before the arrival of the Spanish tend to have better outcomes today than those that were hunter gatherers. This is consistent with what was found for Sub-Saharan Africa by Michalopoulos et al. (2018). 2 Size and Distribution of Native Groups The main data used in this study come from the 2010 National Census collected by the National Institute of Statistics (Instituto Nacional de Estad´ ıstica y Censos, 2010). In par- ticular, we use data collected with the “long-form” version of the census based on a prob- abilistic sample of households. In addition to the questions on the short form (regarding gender, age, level of education, dwellings’ characteristics, employment status, etc.), the long-form questionnaire includes questions on fertility, contributions to retirement plans, health insurance and whether the respondent is Afro-descendant or belongs to or is de- scendant from an indigenous or Native group (Instituto Nacional de Estad´ ıstica y Censos, 2010). The 2010 National Census is the first decennial census in Argentina to ask about racial or ethnic identification at the individual level which allowed for a description of the native population (Instituto Nacional de Estad´ ıstica y Censos, 2015a,b,c,d,e,f).3 4 Table 1 shows the population of each of the 32 native groups described in the 2010 census. The largest group is the Mapuche with more than 200,000 people, and the small- est one is the Tapiete with 407 people. Almost a million people self-identify as Native 3 In 1869, the first national census estimated the indigenous population based on reports from local chiefs and they were not considered Argentines. In the national censuses of 1895 and 1914, the population was also estimated. The 2001 census asked if one of the members of the household self-identified or belonged to a native group but did not identify this member individually. In total 281,959 households were identified with at least one indigenous person. A sample of individuals in these households was surveyed by the National Institute of Statistics in 2004 and 2005. We study the data from the 2010 census as it allows for a cleaner comparison as all individuals were surveyed on the same day. 4 In Appendix A, we have included a screenshot of the questionnaire form from the 2010 National Census, showing the section in which the questions to identify Native and Afro-Argentine groups were included (Figure A1). 3 or descendant of Natives, which corresponds to 2.4 percent of the total population of Argentina.5 Table 1: Native Groups (1) (2) (3) (4) Number of % of Total Economy before Approximate year natives Population conquest of conquest Atacama 13,936 0.035 Advanced agriculture 1,593 Ava Guaran´ ı 17,899 0.045 Incipient Agriculture 1,801 Aymara 20,822 0.052 Advanced agriculture 1,533 Chan´ e 3,034 0.008 Incipient Agriculture 1,879 Charrua ´ 14,649 0.037 Hunter-gatherers 1,752 Chorote 2,270 0.006 Hunter-gatherers 1,861 Chulup´ ı 1,100 0.003 Hunter-gatherers 1,881 Comechingon ´ 34,546 0.087 Advanced agriculture 1,632 Diaguita-Calchaqu´ ı 67,410 0.170 Advanced agriculture 1,630 Guaran´ ı 105,907 0.267 Incipient Agriculture 1,843 Huarpe 34,279 0.086 Incipient Agriculture 1,623 Kolla 65,066 0.164 Advanced agriculture 1,660 Lule 3,721 0.009 Hunter-gatherers 1,889 Maimar´ a 1,899 0.005 Advanced agriculture 1,596 Mapuche 205,009 0.517 ? 1,881 Mby´ a Guaran´ ı 7,379 0.019 Incipient Agriculture 1,710 Mocov´ ı 22,439 0.057 Hunter-gatherers 1,913 Omaguaca 6,873 0.017 Advanced agriculture 1,594 Ona 2,761 0.007 Hunter-gatherers 1,893 Pampa 22,020 0.056 Hunter-gatherers 1,787 Pilag´a 5,137 0.013 Hunter-gatherers 1,883 Quechua 55,493 0.140 Advanced agriculture 1,554 Querand´ ı 3,658 0.009 Hunter-gatherers 1,795 Rankulche 14,860 0.037 Hunter-gatherers 1,849 Sanaviron ´ 2,871 0.007 Incipient Agriculture 1,647 Tapiete 407 0.001 Hunter-gatherers 1,876 Tehuelche 27,813 0.070 Hunter-gatherers 1,890 Toba-Qom 126,967 0.320 Hunter-gatherers 1,909 Tonocot´ e 4,853 0.012 Incipient Agriculture 1,894 Tup´ı Guaran´ ı 3,715 0.009 Incipient Agriculture 1,831 Vilela 519 0.001 Hunter-gatherers 1,891 Wich´ ı 50,419 0.127 Hunter-gatherers 1,881 Other 5,301 0.013 - . Total Natives 955,032 2.407 - . Source: REDATAM INDEC Census Argentina 2010 for columns (1) and (2), see text for columns (3) and (4). There is geographical variation in the distribution of Natives across provinces as shown by Figure 1. The province with the greatest prevalence of Natives is Chubut, with 8.7 per- 5 It is important to note that 149,000 respondents self-identified as Afro-Argentine (“afrodescendientes”), which corresponds to 0.4 percent of the population. While the main focus of this paper is on the native population, we will also provide an analysis of their outcomes as a point of comparison. 4 cent of the population, and the province with the smallest is Corrientes (0.5 percent). Figure 1: Percentage of Natives by Province (7.6,8.7] (6.5,7.6] (5.4,6.5] 7.9 (4.3,5.4] (3.3,4.3] (2.2,3.3] 6.6 (1.1,2.2] 6.1 [0.5,1.1] No data 3.9 1.3 1.2 1.9 1.3 0.5 1.2 1.2 1.5 1.6 1.1 1.9 2.4 2.2 1.9 4.5 8.0 7.2 8.7 3.6 2.9 Source: REDATAM INDEC Census Argentina (Instituto Nacional de Estad´ ıstica y Censos, 2010). Disclaimer: Falkland Islands - Islas Malvinas: A dispute concerning sovereignty over the islands exists between Argentina who claims this sovereignty and the U.K. which administers the islands. How does our measure of Native self-identification relate to native ancestry? To an- swer this, we study the correlation of our self-identified measure of Native prevalence with a measure of prevalence based on genetic studies (Wang et al., 2008; Corach et al., 2010; Avena et al., 2012; Parolin et al., 2019) across regions of the country. As Figure 2 5 shows, the areas with higher self-reported Native identity tend to also have higher mea- sures of Native genetic ancestry. This shows that Native self-reported identity correlates with Native ancestry. Note, however, that Native genetic ancestry is several times larger than the self-identified measure we use in this paper. As such, the results discussed in this paper do not correspond directly to people with genetic Native ancestry but to those who self-identify as Natives or descendant of Natives. Figure 2: Genetic and Self-Reported Native Prevalence 80 Salta Average Native Genetic Ancestry Salta 60 Catamarca Chubut NEA 40 Río Negro Chubut Tucumán Río Negro Formosa 20 Buenos Aires Corrientes Buenos Aires Misiones 0 0 2 4 6 8 10 % Identifies as Native or Descendant (Census 2010) Parolin et al (2019) Avena et al (2012) Corach et al (2010) Wang et al (2008) Sources: REDATAM INDEC Census Argentina (Instituto Nacional de Estad´ ıstica y Censos, 2010), Parolin et al. (2019), Avena et al. (2012), Corach et al. (2010) and Wang et al. (2008). Notes: NEA (Northeast of Argentina) includes the provinces of Formosa, Chaco, Corrientes, and Misiones. 6 3 The Socioeconomic Outcomes of Natives In this section, we study the socioeconomic outcomes of the Native population of Ar- gentina and compare them with those of non-Native individuals. The outcomes we con- sider are whether the person lives in a precarious house, whether the family owns the house and land, years of education (for people 24 years and older), whether they use a computer (for people 14 years and older), whether they are legally married conditional of having a partner (for people 18 years and older), whether they are economically active (employed or looking for employment, for ages 18 to 65), whether they are employed in the formal sector conditional on employment (for ages 18 to 65), whether they are un- employed, whether they have health insurance, the number of disabilities (intellectual or physical), and teen pregnancy (measured as having a child below age 20). While the relevance of most of these outcomes is clear, the marriage outcome may require an expla- nation. This measure may capture the stability of family relationships and access to legal protections provided by the state. 3.1 Measures of Native disadvantage Columns (1) and (2) in Table 2 show the average of these outcomes for Natives and non- Natives while column (3) shows the difference between the two groups and the stan- dard errors. We find that the native population is, on average, disadvantaged in all the measures of economic and human development. The differences are statistically signifi- cant. Column (4) shows the estimated differences between Natives and non-Natives after adding age and gender fixed effects. The differences remain significant after adding these controls, showing that the native disadvantage is not related to the age and gender com- position of the groups. Table 3, column (1), shows that, on average across the variables, the difference between Natives and non-Natives corresponds to 12.3 percent of the standard deviation of the variables. Table 3, column (2), shows that, on average across the variables, the difference between Natives and non-Natives corresponds to 23 percent of the difference between the top and bottom decile by department (a government geographic unit similar to county in the US). In other words, the difference between Natives and non-Natives is small relative to the geographical variation we observe. The magnitude of the differences between Natives and non-Natives can also be com- pared with the differences between Afro and non-Afro-Argentines, which are shown in Table A1 in the Appendix. The differences between Natives and non-Natives in Argentina 7 Table 2: Differences between Natives and Non-Natives (1) (2) (3) (5) (4) + department Average Average Difference with age and gender and urban-rural Natives Non-Natives (SE) Fixed effects Fixed effects Precarious housing 0.152 0.067 0.086*** 0.083*** 0.049*** (0.015) (0.015) (0.008) Ownership land & house 0.652 0.692 -0.040*** -0.032*** -0.021*** (0.006) (0.006) (0.006) Years of education [24+) 9.111 9.856 -0.745*** -0.984*** -0.589*** (0.134) (0.140) (0.085) Use computer [14+) 0.489 0.517 -0.028** -0.063*** -0.031*** (0.012) (0.014) (0.007) Legally married [18+) 0.520 0.598 -0.078*** -0.064*** -0.038*** (0.010) (0.007) (0.004) Economically active [18-65] 0.735 0.750 -0.014* -0.022*** -0.009** (0.008) (0.008) (0.004) Formal employment [18-65] 0.704 0.744 -0.039*** -0.038*** -0.039*** (0.006) (0.005) (0.004) Unemployment [18-65] 0.073 0.061 0.012*** 0.012*** 0.012*** (0.002) (0.002) (0.002) Health insurance 0.526 0.642 -0.116*** -0.097*** -0.065*** (0.014) (0.013) (0.006) Number of disabilities 0.243 0.197 0.046*** 0.082*** 0.057*** (0.005) (0.005) (0.003) Teen pregnancy [14-19] 0.128 0.111 0.016** 0.021*** 0.008* (0.007) (0.007) (0.005) Source: REDATAM INDEC Census Argentina 2010. Notes: SE clustered at department level in parenthesis. Column 4 includes gender and age fixed effects and column 5 also includes department and urban fixed effects. * p < 0.10, ** p < 0.05, *** p < 0.01. are greater than the differences between Afro-Argentines and the rest of the population on average. While Afro-Argentines obtain, on average, statistically significant worse out- comes with respect to home ownership, being legally married and number of disabilities, they obtain statistically significant better outcomes regarding years of education, use of computer and being economically active. Another benchmark for comparison is the differences between Natives and non-Natives in the US, see Tables A2 and A3 in the Appendix. These tables are constructed us- ing the five-year sample (2010-2014) of the American Community Survey (see Ruggles et al., 2021). We chose outcomes that are somewhat comparable with the outcomes in Argentina, including home ownership, years of education, being economically active, unemployment, precarious housing, health insurance, number of disabilities and teen motherhood. Natives face a disadvantage in both countries for all outcomes except health insurance. For health insurance, Natives have a small advantage in the US thanks to the Indian Health Services, while Natives experience a disadvantage in Argentina. For the other seven outcomes in which Natives face a disadvantage in both countries, the disad- vantage is larger in Argentina than in the US for three outcomes, and smaller for the other 8 Table 3: Magnitude of the Difference between Natives and Non- Natives (percentages) (1) (2) Difference Difference / SD Total / [Decil 9 - Decil 1] Precarious housing 34.28 45.00 Ownership land & house 8.77 22.22 Years of education [24+) 16.89 19.89 Use computer [14+) 5.56 8.11 Legally married [18+) 15.95 36.36 Economically active [18-65] 3.36 4.76 Formal employment [18-65] 8.97 14.29 Unemployment [18-65] 4.99 20.00 Health insurance 24.15 33.33 Number of disabilities 7.53 29.41 Teen pregnancy [14-19] 5.29 25.00 Average 12.34 23.49 Source: REDATAM INDEC Censo Argentina 2010. Notes: Difference corresponds to the columns without FE in Table 2. In Column (1), the difference is divided by the standard deviation of each outcome. In Col- umn (2), the difference is divided by the difference between the top and bottom decile of each outcome (calculated at the department level). four outcomes. Table A3 in the Appendix allows us to compare the magnitudes of these disadvantages in terms of standard deviations and relative to the difference between counties in the first and ninth deciles. In terms of standard deviations, the disadvantage faced by Natives is greater in the US than in Argentina in all outcomes but precarious housing. And relative to the difference across counties, the magnitude of the disadvantage of Natives is greater in the US than in Argentina for all outcomes in which Natives face a disadvantage. While a comparison of the disadvantages faced by Natives across Latin American countries falls beyond the scope of this paper, we can compare the disadvantages we document in Argentina with the disadvantages regarding labor force participation, un- employment and use of computers documented for other countries by Freire et al. (2015). Freire et al. (2015) document small unemployment gaps between Natives and non-Natives based on census data from urban areas in seven Latin American countries. In some of these countries, Natives have a lower unemployment rate, while in others they have a higher unemployment rate (the differences are 2 percentage points or less). As shown in Table 3, Natives experience 1.2 percentage points higher unemployment in Argentina (without distinguishing between rural and urban areas). For urban areas this gap is 1.3 9 percentage points in Argentina. Regarding labor force participation, Freire et al. (2015) again find gaps that favor Natives in some countries and non-Natives in others for urban areas. These differences go from 8 percentage points in favor of natives in Ecuador to 9 percentage points in favor of non-natives in Colombia. The Native disadvantage of 1.4 percentage points that we find in Argentina regardless of location falls in the middle of this range. For urban areas in Argentina, Natives have an advantage of 0.4 percentage points in labor force participation. Freire et al. (2015) find a disadvantage for Natives in access to computers that goes from 3 percentage points for El Salvador to 27 percent- age points for Brazil and Panama. These gaps are greater than the 2.8 percentage points disadvantage that we document for Argentina for the use of computers.6 3.2 The role of location in explaining differences in outcomes To assess whether the differences in outcomes are due to differences in the geographical distribution of the Native and non-Native population, we control for location. Column (5) in Table 2 shows the estimated difference after adding location controls (department and rural/urban fixed effects). Adding location controls reduces the differences between Natives and non-Natives for most outcomes (on average by 35%), but it does not elimi- nate the differences. Thus, the differences in outcomes between Natives and non-Natives are not all due to Natives and non-Natives living in different parts of the country or in rural versus urban areas. Even within small geographical units, we find that, on average, Natives tend to have worse economic outcomes than non-Natives.7 Of course, the fact that the documented disadvantage of Natives survives adding loca- tion fixed effects does not mean that the disadvantage is constant across locations. Table 4 shows the differences in outcomes between Natives and non-Natives in rural and ur- ban areas. While for a majority of outcomes the disadvantage is significantly greater in rural areas, this is not the case for all outcomes. The difference in disadvantage between rural and urban areas is not statistically significant for formal employment and unem- ployment. In addition, Natives in rural areas are more likely to own the house and land in which they live than non-Natives, contrary to what is observed in urban areas. 6 This comparison is limited by the differences across censuses of different countries on the questions on the use of or access to computers. 7 Table A2 in the Appendix shows that this is also the case for the US. 10 Table 4: Differences between Natives and Non-Natives in Rural and Urban Areas (1) (2) (3) Difference Rural Difference Urban (2) − (1) (SE) (SE) (SE) Precarious housing 0.225*** 0.041*** -0.184*** (0.028) (0.009) (0.025) Ownership land & house 0.050*** -0.048*** -0.098*** (0.016) (0.006) (0.017) Years of education [24+) -1.224*** -0.375*** 0.849*** (0.145) (0.101) (0.140) Use computer [14+) -0.060*** 0.006 0.065*** (0.012) (0.009) (0.012) Legally married [18+) -0.166*** -0.058*** 0.108*** (0.019) (0.007) (0.017) Economically active [18-65] -0.062*** 0.004 0.066*** (0.016) (0.005) (0.015) Formal employment [18-65] -0.024* -0.035*** -0.011 (0.014) (0.006) (0.014) Unemployment [18-65] 0.014*** 0.013*** -0.001 (0.003) (0.002) (0.003) Health insurance -0.178*** -0.085*** 0.092*** (0.020) (0.010) (0.017) Number of disabilities 0.088*** 0.033*** -0.055*** (0.012) (0.004) (0.012) Teen pregnancy [14-19] 0.058*** 0.002 -0.056*** (0.015) (0.005) (0.014) Source: REDATAM INDEC Census Argentina 2010. Notes: SE clustered at department level in parenthesis. * p < 0.10, ** p < 0.05, *** p < 0.01. 3.3 The role of education in explaining differences in outcomes Given that the level of education is an important determinant of socioeconomic outcomes (see, for example, Angrist and Krueger, 1991; Lleras-Muney, 2005), we study whether the differences in these outcomes disappear once we control for the level of education (we consider the education of the parents in the case of minors). Table 5 shows that that is not the case for most outcomes. The only two differences that disappear, or turn around, are in using computers and being economically active. The differences for all the other variables are reduced by controlling for education but they remain statistically significant. On average, the difference in outcomes between Natives and non-Natives is reduced by 43% after we control for the years of education. This suggests that the differences in outcomes between Natives and non-Natives are not only due to differences in years of 11 education. Table 5: Differences between Natives and Non-natives after Controlling for Education Precarious Ownership land Use Legally Economically housing & house computer [14+) married [18+) active [18-65] (1) (2) (3) (4) (5) Natives 0.078∗∗∗ -0.040∗∗∗ 0.019∗∗∗ -0.073∗∗∗ -0.000 (0.013) (0.006) (0.005) (0.009) (0.006) Years of Educ. -0.010∗∗∗ 0.001∗∗ 0.064∗∗∗ 0.005∗∗∗ 0.016∗∗∗ (0.000) (0.001) (0.000) (0.001) (0.000) Observations 16,641,852 16,640,667 12,277,326 7,032,487 9,565,399 R-squared 0.029 0.000 0.294 0.003 0.023 Formal Unemploy- Health Number Teen employm. [18-65] ment [18-65] insurance of Disabilities Pregnancy (6) (7) (8) (9) (10) Natives -0.022∗∗∗ 0.011∗∗∗ -0.093∗∗∗ 0.025∗∗∗ 0.011∗ (0.005) (0.002) (0.010) (0.004) (0.006) Years of Educ. 0.027∗∗∗ -0.002∗∗∗ 0.030∗∗∗ -0.026∗∗∗ -0.007∗∗∗ (0.001) (0.000) (0.001) (0.000) (0.000) Observations 5,493,000 6,884,064 16,641,852 16,641,852 944,534 R-squared 0.062 0.001 0.069 0.034 0.008 Source: REDATAM INDEC Census Argentina 2010. Notes: Standard errors clustered at the department level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. There are several reasons why differences in outcomes may survive controlling for years of education. Firstly, there may be other personal characteristics or attributes that may affect outcomes which we do not observe and may differ between Natives and non- Natives. For example, there may be differences in wealth and social capital. Secondly, there may be differences on the quality of education available to the two groups, which may make “years of education” an imperfect measure of human capital. Unfortunately, we lack a method to assess the quality of the education available to Natives and non- Natives. And thirdly, labor market discrimination may result in different returns to edu- cation for the two groups, but further research is needed to confirm this potential channel in this setting.8 3.4 The transmission of education We study the transmission of education across generations and whether this differs be- tween Natives and non-Natives. To do so, we focus on the more than 700,000 households ˜ 8 On discrimination by race in Latin America, see Chong and Nopo ˜ (2008), Nopo et al. (2010), Arceo-Gomez and Campos-Vazquez (2014), and Gerard et al. (2021). 12 in the 2010 Census consisting of parents and children between 19 and 24 years old, where all members of the household are Native or non-Native. This excludes families whose children do not reside with them. This selection is smaller than in other countries, as chil- dren in Argentina tend to remain in the household of their parents for years after reaching adulthood: in 2010, 60 percent of all people between 19 and 24 years old still resided with their parents.9 We focus on households in which all members are Native or non-Native for simplicity.10 For each household, we calculate the average years of education of the parents, in case more than one is present, and the average years of education of the chil- dren. Figure 3 shows the distribution of years of education of the parents disaggregated by ancestry (Natives and non-Natives). While there is great overlap in the distribution, non- native parents have on average 1.20 more years of education than native parents. For the next generation, the difference in education between Natives and non-Natives is smaller (0.85 years) but still large. Figure 3: Education by Generation and Ancestry Parents Children 30 30 Average Education Average Education Natives: 8.05 Natives: 10.50 Non-natives: 9.24 Non-natives: 11.35 20 20 Percent Percent 10 10 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Years of Education Years of Education Natives Non-Natives Natives Non-Natives Notes: Households with children between 19 and 24 years old and all of same ancestry. 9 This number is higher for native families; with more educated native youth living with their parents than for non-natives. This “positive selection” in education for natives living at home relative to non-natives suggests that the differences in education transmission that we describe in this section may underestimate the actual differences. 10 This consists of 98.8% of the households. We have also studied households with a combination of natives and non-natives; they exhibit a transmission of education similar to non-natives. 13 Does the fact that the average years of education increased more across generations for Natives than non-Natives imply that the difference in years of education will disappear with time? The answer is no. The reason is that there are different patterns in the trans- mission of education between Natives and non-Natives. As shown in Figure 4, for each level of education of the parents, the children of Natives tend to have, on average, fewer years of education than the children of non-Natives. Table 6 shows that this difference is statistically significant and robust to adding rural/urban and department fixed effects. We find that the interaction of years of education of the parents with Native is not statis- tically significant. On average, native children have one-third of a year less of education even after controlling for the education of the parents. This difference is somewhat larger than the difference in education of the children due to an extra year of education of the parents. Figure 4: Education of the Parents and the Average Education of Children 14 Years of Education Children 8 106 12 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Years of Education Parents Natives Non-Natives Notes: Households with children between 19 and 24 years old and all of same ancestry. Assuming that the transmission of education is a Markov process that depends only on the education of the parents and whether they are native or not, we can calculate the limit distribution of years of education for both groups. These limit distributions are shown in Figure 5. While there is a large overlap in the distribution for the two groups, the distribution of education for Natives is to the left of that for non-Natives. In the limit, Natives have on average 0.57 fewer years of education than non-Natives. That implies 14 Table 6: Ancestry and the Transmission of Education Years of Education Children (1) (2) (3) (4) Years of Education Parents 0.338*** 0.328*** 0.309*** 0.310*** (0.005) (0.004) (0.005) (0.005) Edu. Parents x Native -0.008 (0.014) Native -0.441*** -0.390*** -0.346*** -0.276* (0.072) (0.067) (0.062) (0.165) Urban 0.717*** 0.593*** 0.593*** (0.051) (0.039) (0.039) Constant 8.221*** 7.646*** 8.162*** 8.160*** (0.055) (0.064) (0.061) (0.060) Observations 733,020 733,020 733,020 733,020 R-squared 0.221 0.225 0.237 0.237 Department FE N N Y Y Source: REDATAM INDEC Census Argentina 2010. Notes: Standard errors clustered at the department level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. that if the transmission of education by ancestry would continue as observed in the two generations we study, the distribution of education of Natives and non-Natives would not converge to be the same — a large difference would persist. 15 Figure 5: Stationary Distribution of Education by Ancestry 25 Average Education Natives: 11.90 Non-natives: 12.47 20 Percentage 10 5 0 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Years of Education Natives Non-Natives 4 Differences in Socioeconomic Outcomes across Groups While in previous the sections we focused on the differences between Natives and non- Natives, in this section we focus on differences across the different native groups. The panels in Figures 6, 7, and 8 show the average outcome by group for each of the outcomes we study.11 The last panel in Figure 8 shows the average standardized outcome by group (with higher numbers denoting better outcomes). These figures also show the average outcome for Natives and non-Natives, and the outcome at the first and ninth deciles by department as benchmarks for comparison. There is large variation across groups in all outcomes. In fact, one-third of the groups have average years of education above the national average. The magnitude of the differences in outcomes across groups can be appreciated in sev- eral ways. First, it is always the case that the difference in the average outcome between the first and last group is greater than the average difference between Natives and non- Natives. Consider for example the case of years of education. While non-Natives have 9.86 years of education on average, the Charrua ´ have 11 years of education on average and the Vilela have 5.4 years of education on average. Second, it is always the case that the difference between the first and last groups is greater than the difference between a 11 Table A4 in the Appendix provides these numbers. 16 department in the first and ninth deciles. Third, while some groups obtain average out- comes well below the average for Natives and non-Natives, some groups obtain outcomes above the average for non-Natives. That is, some native groups obtain higher outcomes than non-Natives. For example, in the case of years of education, one-third of the groups (consisting of 20 percent of the Native population) obtain averages above the average for non-Natives. Fourth, while the group with the best outcomes has an average standard- ized outcome 10 percent of a standard deviation higher than the general population, the group with the worst outcomes has an average outcome that is lower than the general population by 63 percent of a standard deviation. These large differences across groups cannot be fully attributed to differences in loca- tion. Large differences remain after controlling for the locations of the different groups as shown in Figures A4, A5, and A6. For example, after controlling for location the dif- ference between the groups with the best and worst average standardized outcomes is 42 percent of a standard deviation which is more than half the difference of 73 percent of a standard deviation without location controls. In conclusion, the differences across native groups are greater than the differences between Natives and non-Natives and differences across locations. 17 18 0 5 10 0 .2 .4 .6 Otros Tonocoté Charrúa Chorote Querandí Pilagá Comechingón Mbyá Guaraní Sanavirón Wichí Ona Vilela Pampa Chulupí Rankulche Lule Aymara Ava Guaraní Diaguita-Calchaquí Toba-Qom Huarpe Kolla Tehuelche Tapiete Maimará Tupí Guaraní Tupí Guaraní Chané Quechua Omaguaca Guaraní Mocoví Atacama Ona Mapuche Atacama Lule Quechua Kolla Diaguita-Calchaquí Omaguaca Guaraní Toba-Qom Otros Precarious housing Mocoví Aymara Years of education [24+) Chané Huarpe Ava Guaraní Sanavirón Tapiete Mapuche Chorote Maimará Tonocoté Tehuelche Chulupí Querandí Wichí Pampa Pilagá Charrúa Mbyá Guaraní Comechingón Vilela Rankulche 0 .2 .4 .6 .8 0 .2 .4 .6 .8 Sources: REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad´ Ona Lule Querandí Vilela Charrúa Chané Comechingón Rankulche Otros Diaguita-Calchaquí Rankulche Tehuelche Pampa Maimará Tehuelche Mapuche Figure 6: Average Outcomes by Group Sanavirón Kolla Mapuche Wichí Huarpe Sanavirón Tupí Guaraní Toba-Qom Aymara Atacama Quechua Querandí Diaguita-Calchaquí Comechingón Guaraní Omaguaca Atacama Charrúa Maimará Pilagá Toba-Qom Guaraní Kolla Mocoví Mocoví Huarpe Omaguaca Pampa Use computer [14+) Lule Tupí Guaraní Ownership land & house Chané Ava Guaraní Tapiete Tonocoté Ava Guaraní Otros Mbyá Guaraní Tapiete Chorote Quechua Wichí Mbyá Guaraní Chulupí Ona Tonocoté Chorote Pilagá Chulupí Vilela Aymara Natives, dashed horizontal lines show the average for non-Natives, and the dotted horizontal lines show the first and ninth decile by department. ıstica y Censos, 2010). Notes: Solid horizontal lines show average for 19 0 .2 .4 .6 .8 0 .2 .4 .6 Querandí Sanavirón Tehuelche Huarpe Charrúa Comechingón Sanavirón Pampa Comechingón Rankulche Pampa Charrúa Diaguita-Calchaquí Diaguita-Calchaquí Mapuche Otros Rankulche Tehuelche Ona Querandí Tupí Guaraní Maimará Huarpe Atacama Chané Chané Lule Quechua Tapiete Ona Kolla Mapuche Atacama Aymara Guaraní Tupí Guaraní Otros Guaraní Maimará Kolla Toba-Qom Omaguaca Tonocoté Lule Legally married [18+) Quechua Tonocoté Wichí Mocoví Formal employment [18-65] Chulupí Vilela Omaguaca Toba-Qom Mocoví Ava Guaraní Pilagá Chorote Mbyá Guaraní Chulupí Aymara Wichí Chorote Mbyá Guaraní Ava Guaraní Tapiete Vilela Pilagá 0 .05 .1 .15 0 .2 .4 .6 .8 Sources: REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad´ Chorote Ona Tupí Guaraní Querandí Chané Maimará Ona Rankulche Wichí Aymara Ava Guaraní Tehuelche Toba-Qom Comechingón Huarpe Pampa Figure 7: Average Outcomes by Group Pilagá Quechua Lule Charrúa Rankulche Mapuche Pampa Guaraní Mapuche Otros Guaraní Lule Tehuelche Diaguita-Calchaquí Diaguita-Calchaquí Huarpe Mocoví Sanavirón Quechua Tupí Guaraní Tonocoté Atacama Querandí Omaguaca Sanavirón Mocoví Kolla Toba-Qom Charrúa Kolla Unemployment [18-65] Comechingón Vilela Economically active [18-65] Tapiete Ava Guaraní Chulupí Chulupí Otros Tapiete Atacama Chané Aymara Mbyá Guaraní Maimará Wichí Omaguaca Chorote Mbyá Guaraní Pilagá Vilela Tonocoté Natives, dashed horizontal lines show the average for non-Natives, and the dotted horizontal lines show the first and ninth decile by department. ıstica y Censos, 2010). Notes: Solid horizontal lines show average for 20 0 .1 .2 .3 .4 .5 0 .2 .4 .6 .8 Mbyá Guaraní Querandí Wichí Rankulche Vilela Sanavirón Pilagá Comechingón Chulupí Charrúa Chorote Tehuelche Tonocoté Ona Toba-Qom Pampa Lule Maimará Tupí Guaraní Diaguita-Calchaquí Ona Mapuche Ava Guaraní Huarpe Mocoví Atacama Otros Guaraní Tapiete Otros Kolla Omaguaca Querandí Lule Mapuche Kolla Omaguaca Quechua Atacama Tupí Guaraní Rankulche Toba-Qom Health insurance Guaraní Mocoví Chané Chané Teen pregnancy [14-19] Quechua Ava Guaraní Sanavirón Aymara Aymara Tapiete Diaguita-Calchaquí Tonocoté Tehuelche Vilela Maimará Chorote Comechingón Wichí Huarpe Mbyá Guaraní Charrúa Pilagá Pampa Chulupí -.6 -.4 -.2 0 .2 0 .1 .2 .3 .4 Sources: REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad´ Querandí Omaguaca Comechingón Maimará Charrúa Chorote Rankulche Kolla Pampa Tapiete Sanavirón Wichí Tehuelche Pilagá Otros Diaguita-Calchaquí Figure 8: Average Outcomes by Group Ona Lule Huarpe Chulupí Maimará Tupí Guaraní Diaguita-Calchaquí Vilela Mapuche Ava Guaraní Guaraní Mapuche Atacama Tonocoté Quechua Tehuelche Tupí Guaraní Guaraní Aymara Toba-Qom Kolla Atacama Omaguaca Huarpe Lule Charrúa Chané Ona Mocoví Quechua Number of disabilities Toba-Qom Mocoví Tapiete Querandí Ava Guaraní Sanavirón Average of standardized outcomes Chulupí Comechingón Vilela Rankulche Tonocoté Pampa Wichí Aymara Chorote Chané Mbyá Guaraní Mbyá Guaraní Pilagá Otros Natives, dashed horizontal lines show the average for non-Natives, and the dotted horizontal lines show the first and ninth decile by department. ıstica y Censos, 2010). Notes: Solid horizontal lines show average for As a comparison point, Figures A7, A8 and A9, and Table A5 in the Appendix pro- vide the average outcome by group for the US. While there are also large differences in outcomes across groups in the US, these differences are smaller than in Argentina. For example, if we focus on the average standardized outcome, the difference between the maximum and minimum across groups in the US is 38 percent of a standard deviation while it is 72 percent in Argentina. The large differences in outcomes across native groups in Argentina stress the impor- tance of not considering the native population as homogeneous. There may be many reasons for the observed differences across groups. In the following section we explore a particular one. 5 Historical Determinants of Socioeconomic Outcomes Can the differences in outcomes across Native groups be explained by the type of econ- omy they had before the arrival of European colonizers? In particular, given the exist- ing work on the importance of pre-colonial agriculture in current economic development (see Michalopoulos et al., 2018), we focus on whether these groups relied primarily on hunting-gathering, or had an incipient or advanced agriculture. Table 1 column (3) shows our measure of the type of economy of each group before colonization. This measure is based on the previous literature on the Native groups of Argentina (Colombres, 2008; Ib´ ˜ anez, 2008; Lobos, 2011; Mandrini, 2008; Molocznik, 2011; Murdock, 1967; Nesis, 2005; Nordenskiold, ¨ 2002; Outes and Bruch, 1910; Sacco, 2011; Mart´ınez Sarasola, 2011, 2014; Serrano, 2012).12 Given that groups with agriculture tended to be colonized earlier than hunter-gatherer groups and there is evidence that years since colonization affects development (Feyrer and Sacerdote, 2009), we control for the years since colonization in some of the analysis. Our measure of the year of colonization is presented in the last column of Table 1.13 12 We were not able to find a clear assignment for the Mapuche, and as such they are dropped from the analysis in this section. While the Mapuche relied heavily on agriculture to the west of the Andes (see for example Murdock, 1967), in their expansion to the east they incorporated hunter-gatherer groups and relied less on agriculture. 13 We use several data sources to construct the variable shown in Table 1 measuring the year of colonization of each group. Firstly, we define the ancestral area for each group based on the literature (Colombres, 2008; Ib´ ˜ anez, ¨ 2002; Outes 2008; Lobos, 2011; Mandrini, 2008; Molocznik, 2011; Nesis, 2005; Nordenskiold, and Bruch, 1910; Sacco, 2011; Mart´ ınez Sarasola, 2011, 2014; Serrano, 2012). Secondly, we use the 2010 Census to calculate the population density of Natives of each group across counties. Thirdly, for each group, we take into account the three counties with the largest concentration of people from that group among counties in the ancestral land of the group. Fourthly, we find the year of colonization of those counties based on historical records of arrival of the Spanish or foundation of the county or main city in 21 In this section, for simplicity, we focus on the average of the standardized outcomes by group and the average years of education by group (for individuals aged 24 years old and older).14 In addition to studying the outcomes of native groups in the whole country, we will also study outcomes of groups in the north of the country.15 The reason is that a large part of Argentina, the pampas and Patagonia, only had original Native groups without agriculture. The north of the country, on the contrary, has greater variation in the type of pre-Columbian economy of the Native groups.16 Figure 9 shows the average standardized outcome by type of precolonial economy for all groups in Argentina and also for those in the north of the country. In both cases, the standardized outcome is increasing on agriculture with larger differ- ences in the north of the country. Figure 10 shows a similar pattern for years of education. Figure 9: Precolonial Agriculture and Current Development 0 Average of standardized outcomes -.6 -.4 -.8 -.2 Hunter-gatherers Incipient Agriculture Advanced agriculture Argentina North Table 7 provides the related regression analysis. We consider each group as the unit of observation and the statistical analysis is done weighting each group by its population. the county (we exclude the capital cities). Finally, we take the weighted average of the year of colonization of these counties as the year of colonization of the group. 14 The analysis for all outcomes and for the first component is presented in the Appendix. 15 The north of the country consists of the following provinces: Chaco, Formosa, Jujuy and Salta. For the analysis focusing on this part of the country, we only consider the 19 groups with ancestral land in these provinces. This sample consists of 21 percent of the native population of the country. 16 Of the 19 groups with ancestral lands in the north of the country, nine were hunter-gatherers, four had incipient agriculture, and six had superior agriculture. 22 Figure 10: Precolonial Agriculture and Current Education 10 8 Years of education 4 2 0 6 Hunter-gatherers Incipient Agriculture Advanced agriculture Argentina North Considering groups throughout the entire country, we find that groups with superior agriculture before colonization obtain significantly better outcomes compared to those without agriculture. However, when we control for the centuries since colonization, the coefficient on superior agriculture becomes negative (but not statistically significant). The positive estimated coefficient on centuries since colonization is consistent with the evi- dence provided by Feyrer and Sacerdote (2009) on the positive causal effect of years since colonization and economic development on islands around the world. We find stronger results on the relationship between pre-colonial economy and cur- rent outcomes for the north of the country. Groups which had incipient agriculture have significantly better outcomes than those that were hunter-gatherers at the 1 percent sig- nificance level, regardless of the specification. Groups which had superior agriculture have significantly better outcomes than those that were hunter-gatherers at the 5 percent significance level. Our evidence of a relation between the type of economy native groups had before col- onization and current development is consistent with what was found for Sub-Saharan Africa by Michalopoulos et al. (2018). They find that individuals from ethnicities which relied more on agriculture for subsistence before colonization are more educated and wealthier today than those relying on herding. There are many other possible determinants of native outcomes that we do not study 23 here due to data limitations or lack of variation. These possible determinants include pre-colonial institutions, and experiences during and after colonization. For evidence on these dimensions from elsewhere, see Michalopoulos and Papaioannou (2013), Nunn (2008), Dell (2010), Valencia Caicedo (2019), Dippel (2014), Akee et al. (2015), Akee et al. (2015), Feir (2016), and Feir et al. (2022). Table 7: Precolonial Agriculture and Current Outcomes Argentina North Average Outcomes Education Average Outcomes Education (1) (2) (3) (4) (5) (6) (7) (8) Incipient Agriculture 0.084 0.022 0.726 0.174 0.241*** 0.314*** 1.917*** 1.777*** (0.078) (0.090) (0.536) (0.602) (0.046) (0.066) (0.281) (0.429) Superior Agriculture 0.131* -0.053 1.173** -0.467 0.414*** 0.689*** 3.171*** 2.647** (0.070) (0.153) (0.479) (1.029) (0.037) (0.188) (0.223) (1.212) Centuries since Colonization 0.068 0.609* -0.108 0.207 (0.051) (0.341) (0.073) (0.469) Constant -0.217*** -0.305*** 8.424*** 7.635*** -0.671*** -0.547*** 4.890*** 4.653*** (0.048) (0.081) (0.329) (0.544) (0.024) (0.086) (0.143) (0.556) Observations 31 31 31 31 19 19 19 19 R-squared 0.116 0.171 0.180 0.266 0.890 0.904 0.928 0.929 Pvalue Incipient=Superior 0.560 0.536 0.422 0.434 0.00260 0.0191 0.000633 0.362 Source: REDATAM INDEC Census Argentina 2010. Notes: Weighted Least Squares. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01 6 Conclusion We contribute to the study of differences in socioeconomic outcomes across Native and non-Native people by using new Argentine census data to describe the economic out- comes of Native Argentines. We find that, on average, Native Argentines obtain worse outcomes than non-Natives. These differences cannot be explained only by differences in location or educational attainment. We also find differences in the transmission of ed- ucation that suggest that differences in educational attainment will not disappear with time. Interestingly, we find that differences among Native groups are much larger than the differences between Natives and non-Natives. We find that the economic outcomes of groups correlate with the practice of agriculture before the arrival of the Spanish. 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PLoS Genetics 4(3), e1000037. 29 A Appendix Table A1: Differences between Afro-Argentines and Non-Afro-Argentines (1) (2) (3) (4) (5) + department Average Average Difference with age and gender and urban-rural Afro-Argentines Non-Afro (SE) Fixed effects Fixed effects Precarious housing 0.072 0.069 0.003 0.003 0.003 (0.004) (0.004) (0.004) Ownership land & house 0.642 0.691 -0.050*** -0.047*** -0.038*** (0.010) (0.010) (0.009) Years of education [24+) 10.533 9.837 0.696*** 0.552*** 0.395*** (0.070) (0.069) (0.052) Use computer [14+) 0.600 0.516 0.084*** 0.066*** 0.049*** (0.008) (0.008) (0.005) Legally married [18+) 0.567 0.596 -0.029*** -0.030*** -0.025*** (0.008) (0.007) (0.007) Economically active [18-65] 0.787 0.749 0.037*** 0.027*** 0.022*** (0.005) (0.005) (0.004) Formal employment [18-65] 0.746 0.743 0.003 0.001 -0.009 (0.007) (0.007) (0.006) Unemployment [18-65] 0.061 0.061 0.000 0.001 0.001 (0.003) (0.003) (0.003) Health insurance 0.639 0.639 0.000 0.008 -0.002 (0.007) (0.007) (0.007) Number of disabilities 0.214 0.198 0.016*** 0.034*** 0.039*** (0.005) (0.005) (0.005) Teen pregnancy [14-19] 0.098 0.112 -0.014 -0.014* -0.012 (0.009) (0.008) (0.008) Source: REDATAM INDEC Census Argentina 2010. SE clustered at department level in parenthesis. Column 4 includes gender and age fixed effects and column 5 also includes department and urban fixed effects. * p < 0.10, ** p < 0.05, *** p < 0.01. 30 Table A2: Differences between Natives and Non-Natives in the US (1) (2) (3) (4) (5) + department Average Average Difference with age and gender and urban-rural Natives Non-Natives (SE) Fixed effects Fixed effects Precarious housing 0.022 0.008 0.014*** 0.014*** 0.012*** (0.005) (0.005) (0.004) Ownership 0.570 0.658 -0.088*** -0.070*** -0.070*** (0.010) (0.010) (0.008) Years of education 12.438 13.033 -0.596*** -0.669*** -0.607*** (0.058) (0.059) (0.048) Economically active 0.706 0.770 -0.064*** -0.065*** -0.060*** (0.006) (0.006) (0.005) Unemployment 0.099 0.068 0.031*** 0.028*** 0.031*** (0.003) (0.003) (0.002) Health insurance 0.872 0.859 0.014* 0.018** 0.017** (0.008) (0.008) (0.008) Number of disabilities 0.410 0.282 0.128*** 0.194*** 0.186*** (0.009) (0.008) (0.010) Teen motherhood 0.036 0.024 0.012*** 0.011*** 0.008*** (0.002) (0.002) (0.003) Household income 65524.406 83434.773 -17900.000*** -18400.000*** -15600.000*** (1778.393) (1780.529) (857.582) Source: IPUMS-USA (Ruggles et al., 2021). Notes: SE clustered at department level in parenthesis. Column 4 includes gender and age fixed effects and column 5 also includes department and urban fixed effects. Table A3: Magnitude of the Difference between Natives and Non-Natives in the US (percentages) (1) (2) Difference Difference / SD Total / [Decil 9 - Decil 1] Precarious housing 15.35 155.56 Ownership 18.51 39.29 Years of education 18.25 37.94 Economically active 15.28 61.54 Unemployment 12.44 68.89 Health insurance 3.89 12.17 Number of disabilities 14.09 72.73 Teen motherhood 7.83 30.77 Household income 21.61 39.60 Average 14.14 57.61 Source: IPUMS-USA (Ruggles et al., 2021). Notes: Difference cor- responds to the columns without FE in Table 2. Deciles calculated at the county level. 31 Table A4: Average Outcomes by Groups (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Precarious Ownership Years of Use Legally Economically Formal Uneploy- Teen [14-19] Health Number of Average Housing land & house education [24+) computer [14+) married [18+) Active [18-65] Employment [18-65] ment [18-65] Pregnancy Insurance disabilities Std. Outcomes All 0.069 0.691 9.836 0.516 0.596 0.749 0.743 0.061 0.111 0.639 0.198 -0.000 Non-Natives 0.067 0.692 9.856 0.517 0.598 0.750 0.744 0.061 0.111 0.642 0.197 0.003 Atacama 0.128 0.658 9.219 0.499 0.554 0.719 0.696 0.056 0.104 0.568 0.223 -0.081 Ava Guaran´ ı 0.268 0.619 7.448 0.266 0.431 0.647 0.533 0.084 0.151 0.359 0.254 -0.357 Aymara 0.105 0.392 10.007 0.530 0.530 0.791 0.545 0.054 0.095 0.357 0.176 -0.153 Chan´ e 0.172 0.696 7.897 0.330 0.544 0.604 0.714 0.100 0.097 0.367 0.167 -0.214 Charrua ´ 0.036 0.641 11.039 0.659 0.603 0.779 0.762 0.067 0.068 0.673 0.220 0.077 Chorote 0.538 0.530 6.847 0.237 0.429 0.525 0.538 0.115 0.181 0.289 0.365 -0.565 Chulup´ ı 0.459 0.525 6.772 0.223 0.409 0.638 0.624 0.058 0.209 0.190 0.258 -0.493 Comechingon ´ 0.031 0.652 10.867 0.637 0.623 0.789 0.755 0.060 0.070 0.675 0.200 0.084 Diaguita-Calchaqu´ ı 0.122 0.688 9.948 0.506 0.587 0.747 0.746 0.071 0.092 0.621 0.284 -0.035 Guaran´ ı 0.116 0.639 9.541 0.506 0.523 0.761 0.693 0.074 0.103 0.550 0.230 -0.081 Huarpe 0.096 0.630 9.838 0.531 0.646 0.746 0.715 0.082 0.069 0.600 0.222 -0.023 Kolla 0.208 0.674 8.611 0.404 0.516 0.685 0.701 0.068 0.116 0.479 0.323 -0.189 Lule 0.301 0.749 8.938 0.370 0.497 0.747 0.712 0.078 0.159 0.497 0.279 -0.201 Maimar´ a 0.072 0.681 9.727 0.488 0.562 0.807 0.671 0.048 0.080 0.650 0.365 -0.029 Mapuche 0.073 0.679 9.000 0.541 0.532 0.774 0.744 0.074 0.109 0.607 0.250 -0.041 Mby´ a Guaran´ ı 0.501 0.586 5.623 0.251 0.247 0.581 0.561 0.026 0.451 0.245 0.165 -0.604 Mocov´ ı 0.167 0.633 8.018 0.404 0.455 0.700 0.614 0.070 0.149 0.419 0.205 -0.225 Omaguaca 0.169 0.651 8.542 0.376 0.504 0.702 0.622 0.047 0.106 0.514 0.415 -0.197 Ona 0.129 0.558 10.674 0.694 0.532 0.815 0.726 0.089 0.156 0.668 0.219 -0.021 Pampa 0.040 0.623 10.259 0.618 0.622 0.788 0.751 0.076 0.062 0.667 0.183 0.055 Pilag´a 0.518 0.640 5.807 0.172 0.207 0.485 0.587 0.078 0.280 0.209 0.294 -0.628 32 Quechua 0.124 0.593 9.602 0.527 0.542 0.785 0.655 0.070 0.097 0.477 0.206 -0.093 Querand´ ı 0.047 0.655 10.999 0.682 0.567 0.811 0.834 0.068 0.111 0.753 0.202 0.100 Rankulche 0.028 0.691 10.145 0.620 0.615 0.799 0.732 0.078 0.104 0.689 0.198 0.057 Sanaviron ´ 0.092 0.671 10.752 0.580 0.650 0.745 0.761 0.068 0.096 0.676 0.202 0.039 Tapiete 0.204 0.609 7.071 0.313 0.238 0.625 0.709 0.059 0.133 0.337 0.300 -0.336 Tehuelche 0.057 0.687 9.786 0.605 0.573 0.791 0.773 0.072 0.083 0.669 0.235 0.034 Toba-Qom 0.220 0.664 8.142 0.415 0.446 0.687 0.665 0.082 0.169 0.423 0.223 -0.240 Tonocot´ e 0.605 0.614 6.817 0.217 0.468 0.473 0.661 0.069 0.179 0.308 0.246 -0.516 Tup´ı Guaran´ ı 0.187 0.620 9.682 0.531 0.529 0.737 0.724 0.104 0.159 0.471 0.255 -0.146 Vilela 0.484 0.744 5.379 0.152 0.455 0.652 0.523 0.006 0.300 0.291 0.254 -0.497 Wich´ ı 0.497 0.672 6.561 0.234 0.308 0.550 0.637 0.086 0.306 0.269 0.297 -0.544 Other 0.111 0.612 11.123 0.635 0.583 0.756 0.681 0.057 0.141 0.547 0.157 -0.014 Natives 0.152 0.652 9.026 0.484 0.516 0.732 0.698 0.073 0.126 0.526 0.243 -0.125 Source: REDATAM INDEC Census Argentina 2010. Table A5: Average Outcomes by Groups in the US (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Precarious Years of Economically Uneploy- Health Number of Teen [14-19] Household Average Housing Ownership Education [24+) Active [18-65] ment [18-65] Insurance disabilities Motherhood Income Std. Outcomes All 0.008 0.657 13.023 0.769 0.068 0.859 0.284 0.024 83116.811 -0.000 Non-Natives 0.008 0.658 13.033 0.770 0.068 0.859 0.282 0.024 83434.771 0.002 Alaskan Athabaskan 0.149 0.573 11.950 0.731 0.167 0.968 0.408 0.075 68150.310 -0.320 Aleut 0.014 0.595 12.346 0.667 0.137 0.920 0.491 0.038 73136.461 -0.131 Eskimo 0.234 0.661 11.728 0.675 0.152 0.958 0.290 0.043 72483.834 -0.380 Tlingit-Haida 0.035 0.536 12.437 0.712 0.136 0.947 0.356 0.006 74361.840 -0.106 Apache 0.041 0.491 11.959 0.664 0.154 0.877 0.437 0.086 50730.687 -0.282 Blackfoot 0.014 0.463 12.484 0.641 0.122 0.887 0.561 0.017 49369.658 -0.193 Cherokee 0.012 0.648 12.659 0.673 0.077 0.888 0.599 0.057 61798.998 -0.130 Cheyenne 0.017 0.481 12.469 0.667 0.139 0.942 0.325 0.032 48674.880 -0.160 Chickasaw 0.005 0.699 13.033 0.701 0.069 0.941 0.428 0.063 79820.571 -0.028 Chippewa 0.014 0.602 12.467 0.689 0.101 0.932 0.347 0.076 56590.728 -0.131 Choctaw 0.009 0.654 12.694 0.717 0.076 0.937 0.432 0.044 67228.438 -0.059 Colville 0.006 0.589 12.836 0.695 0.132 0.957 0.359 0.118 65957.876 -0.136 Comanche 0.022 0.564 12.798 0.696 0.095 0.910 0.404 0.010 62535.169 -0.093 Creek 0.007 0.640 12.779 0.698 0.071 0.933 0.401 0.089 60469.033 -0.099 Crow 0.013 0.485 12.532 0.764 0.255 0.921 0.222 0.056 52022.180 -0.184 Delaware 0.003 0.503 12.636 0.609 0.097 0.893 0.493 0.000 56041.001 -0.131 Iowa 0.000 0.587 10.664 0.595 0.031 0.726 0.479 0.000 60375.743 -0.195 33 Iroquois 0.015 0.608 12.714 0.691 0.096 0.927 0.438 0.050 59112.156 -0.111 Latin American 0.014 0.478 10.042 0.802 0.080 0.706 0.273 0.033 62221.380 -0.228 Lumbee 0.007 0.693 11.811 0.650 0.078 0.797 0.410 0.051 53978.981 -0.160 Menominee 0.010 0.535 12.737 0.781 0.216 0.917 0.226 0.000 58506.031 -0.092 Native Hawaiian 0.013 0.554 12.760 0.771 0.086 0.884 0.284 0.026 83039.593 -0.039 Navajo 0.121 0.606 11.513 0.631 0.115 0.904 0.346 0.034 49974.635 -0.304 Paiute 0.005 0.444 11.982 0.718 0.163 0.958 0.378 0.024 57885.247 -0.150 Pima 0.018 0.494 11.591 0.557 0.127 0.946 0.276 0.061 45724.995 -0.229 Potawatomie 0.002 0.643 12.942 0.705 0.048 0.939 0.350 0.011 68136.514 0.001 Pueblo 0.044 0.735 12.475 0.690 0.120 0.922 0.305 0.036 57957.350 -0.113 Puget Sound Salish 0.001 0.507 12.215 0.649 0.131 0.942 0.362 0.034 63678.350 -0.129 Seminole 0.004 0.519 12.198 0.615 0.106 0.935 0.335 0.027 68529.744 -0.116 Sioux 0.020 0.418 12.270 0.648 0.134 0.940 0.335 0.055 46597.263 -0.209 Tohono O’Odham 0.065 0.504 11.608 0.576 0.146 0.931 0.223 0.006 40461.453 -0.253 Yakima 0.007 0.578 12.438 0.535 0.100 0.815 0.270 0.000 49538.027 -0.153 Yaqui 0.009 0.515 11.813 0.677 0.125 0.902 0.420 0.036 52555.198 -0.177 Yuman 0.009 0.450 11.971 0.721 0.171 0.961 0.458 0.057 50257.656 -0.199 Other Am. Indian / Alaska Nat. 0.013 0.541 11.811 0.699 0.102 0.844 0.398 0.037 60163.104 -0.166 Multiracial 0.011 0.569 12.821 0.724 0.095 0.864 0.426 0.030 70890.648 -0.091 Natives 0.022 0.570 12.446 0.706 0.099 0.872 0.410 0.036 65524.408 -0.130 Source: IPUMS US (Ruggles et al., 2021). Table A6: Precolonial Agriculture and Current Outcomes (Argentina) Precarious Housing Ownership Use Computer Legally Married Economically Active Formal Employment Unemployment Health Insurance Number of Disabilities Teen Pregnancy First Component (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) Incipient Agriculture -0.065 -0.022 -0.027 0.004 0.030 -0.026 0.065 0.018 0.038 0.010 -0.004 -0.006 -0.004 -0.002 0.055 0.018 -0.006 0.017 -0.049 -0.031 1.488 0.335 (0.058) (0.067) (0.025) (0.027) (0.054) (0.060) (0.039) (0.043) (0.033) (0.038) (0.028) (0.033) (0.004) (0.005) (0.058) (0.067) (0.021) (0.023) (0.029) (0.034) (1.285) (1.463) Superior Agriculture -0.091* 0.036 -0.026 0.067 0.064 -0.104 0.094** -0.044 0.056* -0.027 0.014 0.008 -0.014*** -0.008 0.073 -0.034 0.025 0.096** -0.071** -0.017 2.330* -1.099 (0.052) (0.114) (0.022) (0.046) (0.048) (0.103) (0.035) (0.074) (0.029) (0.064) (0.025) (0.056) (0.004) (0.008) (0.052) (0.115) (0.019) (0.040) (0.026) (0.059) (1.148) (2.502) Centuries since Colonization -0.047 -0.035** 0.062* 0.051** 0.031 0.002 -0.002 0.040 -0.026* -0.020 1.273 (0.038) (0.015) (0.034) (0.025) (0.021) (0.019) (0.003) (0.038) (0.013) (0.019) (0.830) Constant 0.223*** 0.284*** 0.660*** 0.705*** 0.437*** 0.356*** 0.462*** 0.396*** 0.690*** 0.651*** 0.681*** 0.678*** 0.079*** 0.082*** 0.464*** 0.413*** 0.234*** 0.268*** 0.168*** 0.194*** -1.541* -3.190** (0.035) (0.060) (0.015) (0.024) (0.033) (0.054) (0.024) (0.039) (0.020) (0.034) (0.017) (0.030) (0.003) (0.004) (0.036) (0.061) (0.013) (0.021) (0.018) (0.031) (0.788) (1.322) Observations 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 R-squared 0.105 0.154 0.062 0.216 0.059 0.163 0.211 0.321 0.120 0.183 0.018 0.018 0.334 0.346 0.071 0.107 0.086 0.204 0.213 0.242 0.131 0.201 Pvalue Incipient=Superior 0.656 0.524 0.971 0.0892 0.546 0.344 0.471 0.292 0.590 0.474 0.526 0.752 0.0319 0.359 0.758 0.564 0.157 0.0183 0.476 0.766 0.527 0.471 Source: REDATAM INDEC Census Argentina 2010. Notes: Weighted Least Squares. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01 34 Table A7: Precolonial Agriculture and Current Outcomes (North) Precarious Housing Ownership Use Computer Legally Married Economically Active Formal Employment Unemployment Health Insurance Number of Disabilities Teen Pregnancy First Component (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) Incipient Agriculture -0.196*** -0.296*** -0.036 0.046 0.078*** 0.110*** 0.177*** 0.176*** 0.121*** 0.116*** 0.024 0.064* -0.001 0.020* 0.164*** 0.140** 0.007 -0.050 -0.103*** -0.155*** 2.953*** 2.937*** (0.057) (0.080) (0.027) (0.030) (0.019) (0.027) (0.026) (0.040) (0.022) (0.033) (0.025) (0.035) (0.008) (0.010) (0.033) (0.049) (0.030) (0.041) (0.032) (0.045) (0.439) (0.675) Superior Agriculture -0.264*** -0.639** -0.005 0.305*** 0.211*** 0.331*** 0.277*** 0.273** 0.178*** 0.160 0.178*** 0.330*** -0.024*** 0.055* 0.288*** 0.197 0.100*** -0.114 -0.145*** -0.342** 5.941*** 5.878*** (0.045) (0.225) (0.022) (0.086) (0.015) (0.077) (0.021) (0.114) (0.017) (0.093) (0.020) (0.099) (0.006) (0.027) (0.026) (0.139) (0.024) (0.116) (0.025) (0.127) (0.349) (1.908) Centuries since Colonization 0.148 -0.122*** -0.047 0.002 0.007 -0.060 -0.031*** 0.036 0.084* 0.077 0.025 (0.087) (0.033) (0.030) (0.044) (0.036) (0.038) (0.011) (0.054) (0.045) (0.049) (0.738) Constant 0.509*** 0.340*** 0.688*** 0.828*** 0.115*** 0.169*** 0.229*** 0.227*** 0.473*** 0.465*** 0.495*** 0.563*** 0.092*** 0.128*** 0.167*** 0.126* 0.271*** 0.175*** 0.278*** 0.190*** -2.534*** -2.562** (0.029) (0.103) (0.014) (0.040) (0.010) (0.035) (0.013) (0.052) (0.011) (0.043) (0.013) (0.046) (0.004) (0.012) (0.017) (0.064) (0.015) (0.053) (0.016) (0.058) (0.224) (0.875) Observations 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 R-squared 0.698 0.747 0.101 0.525 0.923 0.934 0.920 0.920 0.876 0.876 0.845 0.867 0.506 0.689 0.888 0.891 0.548 0.634 0.689 0.733 0.948 0.948 Pvalue Incipient=Superior 0.274 0.0640 0.292 0.00133 6.39e-06 0.00193 0.00232 0.282 0.0234 0.544 2.01e-05 0.00320 0.0140 0.111 0.00235 0.601 0.00908 0.481 0.223 0.0741 8.00e-06 0.0613 Source: REDATAM INDEC Census Argentina 2010. Notes: Weighted Least Squares. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01 Figure A1: Screenshot of questions included in the census to identify Natives and Afro- Argentines. Notes: Screenshot of the 2010 Census questionnaire (long version): Question 5 “Is any person in this household indigenous or a descendant of indigenous peoples (native or aboriginal)? Yes (indicate the ID of the individual, indicate which group)/No/Ignored” and Question 6 “Are you or any person in this household of African descent or have ancestors of African de- scent (father, mother, grandparents, great-grandparents)? Yes (indicate the ID of the individ- ual)/No/Ignored.” 35 Figure A2: Percentage of Natives by Departament 46.9 (50.3,57.5] 23.0 45.8 48.3 (43.2,50.3] 44.433.3 56.3 14.2 (36.0,43.2] 57.5 54.2 32.6 11.2 23.7 33.6 (28.8,36.0] 38.8 7.8 24.9 41.1 4.7 7.3 25.0 (21.6,28.8] 4.1 2.8 4.0 3.9 2.92.2 (14.4,21.6] 27.8 2.21.0 1.9 10.1 11.0 1.2 1.9 4.2 (7.2,14.4] 18.7 3.5 2.1 1.0 [0.0,7.2] 22.6 18.9 4.0 0.4 0.4 1.8 2.4 1.5 11.6 1.3 3.2 2.0 No data 9.9 6.5 0.6 3.4 12.0 0.8 38.7 0.7 1.4 6.2 11.6 1.1 1.3 0.31.25.9 1.3 2.5 1.1 1.6 0.5 0.6 0.4 1.1 13.1 0.4 5.5 4.4 2.2 3.2 1.0 0.6 2.5 0.7 1.41.8 0.7 0.5 1.2 0.8 0.4 0.4 0.5 6.0 1.6 0.5 3.8 2.8 3.4 0.4 0.6 1.2 1.2 0.4 1.4 0.9 2.3 0.4 0.2 0.8 0.1 3.0 4.0 1.8 0.5 0.2 0.2 0.7 0.6 0.5 0.9 0.7 0.5 0.5 0.3 1.5 0.9 0.5 0.7 1.1 0.40.5 0.7 0.211.8 0.6 1.9 0.6 0.6 1.7 0.3 0.7 1.0 1.2 0.8 0.3 1.6 0.7 0.8 0.3 6.1 0.6 0.4 1.9 1.0 0.5 6.2 1.5 0.2 1.9 0.7 1.4 0.6 0.6 0.60.6 0.4 1.8 0.4 0.4 2.4 2.1 1.5 0.7 0.4 1.6 3.4 1.2 8.5 0.5 0.7 1.8 0.5 0.5 0.8 0.5 0.8 0.6 1.3 1.0 3.2 0.4 0.6 0.5 1.9 0.3 0.8 0.3 1.1 2.1 3.9 5.2 0.5 0.7 0.0 0.9 0.6 0.6 0.6 0.8 3.2 0.6 3.6 0.3 2.5 2.0 0.8 1.0 1.3 0.9 0.4 1.0 0.8 0.6 0.9 0.8 1.6 0.6 1.0 1.8 1.7 0.81.00.9 1.1 0.6 1.6 1.5 1.5 1.3 0.4 1.0 5.0 2.5 1.4 2.2 0.8 0.9 1.0 0.8 2.9 1.2 1.1 0.7 1.8 2.4 10.9 1.5 1.3 0.1 1.5 0.9 0.8 2.4 2.6 1.3 0.9 0.7 2.7 2.0 2.12.2 1.0 1.1 0.8 0.8 1.8 1.8 0.9 1.6 2.2 1.4 1.4 1.3 1.7 2.1 1.4 1.5 1.2 1.0 0.9 1.7 1.1 0.9 1.2 2.2 1.7 1.3 1.51.0 2.0 1.3 1.41.6 1.1 1.3 1.7 1.31.31.7 2.3 1.3 3.0 1.0 0.9 1.1 2.01.8 1.3 1.7 1.3 2.1 1.8 2.1 2.0 1.9 1.7 1.8 1.2 1.5 1.5 1.8 1.6 2.51.2 2.0 2.6 1.42.7 2.4 2.3 1.2 2.7 3.5 2.3 1.2 0.81.1 1.72.0 1.9 2.0 3.7 1.6 1.5 2.0 1.4 2.3 2.02.2 2.1 1.82.02.2 1.6 13.6 0.80.8 0.8 1.3 2.3 1.8 2.2 1.3 2.1 1.91.7 1.5 2.3 3.6 2.1 1.2 1.5 1.0 4.6 1.1 1.4 0.9 1.0 1.7 2.0 2.5 3.1 2.0 1.7 0.8 1.6 1.8 0.9 1.0 1.6 4.7 2.7 2.3 1.3 2.9 1.1 1.4 1.1 1.6 1.3 7.4 21.0 10.4 1.0 1.20.5 5.7 1.7 1.2 1.5 6.3 1.4 1.5 1.1 1.02.2 6.4 2.9 1.6 2.2 1.6 1.3 2.3 1.1 1.43.0 6.9 5.3 1.8 1.9 3.5 4.0 1.9 1.4 2.9 19.4 1.4 1.0 1.7 2.5 2.0 1.3 22.3 6.9 1.7 1.9 0.8 1.5 2.1 2.5 5.2 4.5 2.9 1.1 2.2 5.1 1.3 1.0 9.5 2.8 3.3 1.4 9.2 6.3 1.8 2.9 29.0 3.2 29.9 6.7 4.2 3.8 24.1 13.2 20.4 4.8 11.1 7.0 10.6 17.5 8.5 20.1 9.5 6.4 15.4 10.7 43.5 22.4 16.9 7.5 29.4 15.5 9.4 28.3 10.3 6.6 24.1 15.9 9.4 17.8 11.8 4.3 4.9 4.2 7.2 2.4 4.1 2.5 3.0 2.9 2.9 Sources: REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad´ ıstica y Censos, 2010). Disclaimer: Falkland Islands - Islas Malvinas: A dispute concerning sovereignty over the islands exists between Argentina who claims this sovereignty and the U.K. which administers the islands. 36 Figure A3: Percentage of Natives by Departament. Buenos Aires Metropolitan Area 1.6 2.3 1.8 2.0 1.7 1.3 1.3 1.9 1.7 1.8 1.51.2 1.5 2.5 1.6 2.0 1.8 1.2 2.7 2.1 1.2 2.4 2.3 2.6 1.4 2.7 3.5 2.3 2.0 2.7 3.7 1.6 1.9 1.5 2.0 1.4 2.3 2.0 2.2 2.0 2.1 2.2 1.8 2.0 1.8 2.3 2.2 1.7 1.9 Sources: REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad´ ıstica y Censos, 2010). Notes: Buenos Aires metropolitan area includes the 15 communes of the city of Buenos Aires, and the following departments in the province of Buenos Aires: Almirante Brown, Avellaneda, Berisso, Berazategui, Canuelas, Ensenada, Escobar, Esteban Echeverr´ ıa, Ezeiza, Florencio Varela, General Rodr´ ıguez, General San Mart´ın, Hurlingham, Ituzaingo,´ Jos´ e C. Paz, La Matanza, La ´ Lomas de Zamora, Malvinas Argentinas, Marcos Paz, Merlo, Moreno, Moron, Plata, Lanus, ´ Pilar, ´ Quilmes, San Fernando, San Isidro, San Miguel, San Vicente, Tigre, Tres de Presidente Peron, ´ Febrero y Vicente Lopez. 37 38 -2 -1 0 1 0 .1 .2 .3 .4 Sanavirón Mbyá Guaraní Charrúa Tonocoté Querandí Pilagá Other Chorote Comechingón Chulupí Rankulche Wichí Huarpe Toba-Qom Diaguita-Calchaquí Mocoví Ona Tupí Guaraní Pampa Vilela Tupí Guaraní Ava Guaraní Omaguaca Lule Kolla Ona Maimará Quechua Guaraní Other Lule Aymara Atacama Tapiete Tehuelche Atacama Tonocoté Kolla Aymara Guaraní Quechua Diaguita-Calchaquí Mapuche Huarpe Precarious housing Chané Mapuche Years of education [24+) Chorote Sanavirón Mocoví Tehuelche Toba-Qom Omaguaca Wichí Rankulche Ava Guaraní Pampa Chulupí Querandí Vilela Comechingón Pilagá Maimará Mbyá Guaraní Chané Tapiete Charrúa -.1 -.05 0 .05 .1 .15 -.3 -.2 -.1 0 .1 Sources: REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad´ Querandí Vilela Charrúa Lule Ona Chané Sanavirón Omaguaca Comechingón Wichí Other Pilagá Rankulche Tehuelche Pampa Maimará Huarpe Huarpe Tupí Guaraní Mapuche Diaguita-Calchaquí Sanavirón Atacama Diaguita-Calchaquí Tehuelche Rankulche Kolla Kolla Guaraní Comechingón Omaguaca Toba-Qom relative to non-Natives in the same place (department and rural/urban). Maimará Mocoví Tonocoté Querandí Quechua Guaraní Figure A4: Average Outcomes by Group after Controlling for Location Mapuche Charrúa Lule Ava Guaraní Mocoví Atacama Use computer [14+) Toba-Qom Tapiete Ownership land & house Mbyá Guaraní Tonocoté Aymara Tupí Guaraní Wichí Other Chorote Pampa Chané Quechua Vilela Chorote Pilagá Mbyá Guaraní Chulupí Ona Ava Guaraní Chulupí Tapiete Aymara ıstica y Censos, 2010). Notes: Average outcome of Natives by group 39 -.2 -.1 0 .1 -.3 -.2 -.1 0 .1 Querandí Chané Sanavirón Sanavirón Tonocoté Maimará Chané Omaguaca Diaguita-Calchaquí Pampa Comechingón Charrúa Lule Comechingón Charrúa Rankulche Tapiete Tehuelche Huarpe Chorote Omaguaca Diaguita-Calchaquí Pilagá Huarpe Pampa Other Kolla Tupí Guaraní Rankulche Kolla Tupí Guaraní Atacama Tehuelche Chulupí Atacama Querandí Mapuche Mapuche Maimará Vilela Wichí Guaraní Guaraní Quechua Legally married [18+) Toba-Qom Tonocoté Vilela Ona Formal employment [18-65] Ona Lule Other Aymara Mbyá Guaraní Ava Guaraní Mocoví Wichí Chulupí Toba-Qom Quechua Pilagá Ava Guaraní Mocoví Chorote Mbyá Guaraní Aymara Tapiete -.04 -.02 0 .02 .04 -.15 -.1 -.05 0 .05 Sources: REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad´ Chorote Maimará Tupí Guaraní Lule Pilagá Vilela Ona Querandí Chané Ona Rankulche Rankulche Lule Comechingón Toba-Qom Charrúa Wichí Diaguita-Calchaquí Pampa Huarpe Ava Guaraní Guaraní Huarpe Quechua Mapuche Pampa Tehuelche Tehuelche Mocoví Sanavirón Guaraní Aymara relative to non-Natives in the same place (department and rural/urban). Sanavirón Omaguaca Charrúa Mapuche Diaguita-Calchaquí Kolla Figure A5: Average Outcomes by Group after Controlling for Location Tonocoté Atacama Quechua Tupí Guaraní Querandí Chulupí Kolla Mocoví Unemployment [18-65] Atacama Other Economically active [18-65] Omaguaca Toba-Qom Comechingón Ava Guaraní Chulupí Wichí Other Chané Aymara Tapiete Tapiete Mbyá Guaraní Maimará Tonocoté Mbyá Guaraní Chorote Vilela Pilagá ıstica y Censos, 2010). Notes: Average outcome of Natives by group 40 -.1 0 .1 .2 .3 -.3 -.2 -.1 0 .1 Mbyá Guaraní Querandí Pilagá Sanavirón Wichí Maimará Vilela Comechingón Ona Charrúa Toba-Qom Rankulche Tupí Guaraní Diaguita-Calchaquí Other Huarpe Ava Guaraní Omaguaca Mocoví Pampa Tonocoté Ona Chulupí Lule Lule Atacama Aymara Kolla Querandí Tehuelche Mapuche Guaraní Kolla Mapuche Rankulche Tonocoté Sanavirón Chorote Quechua Wichí Guaraní Vilela Health insurance Maimará Chané Chorote Toba-Qom Teen pregnancy [14-19] Atacama Other Diaguita-Calchaquí Tupí Guaraní Omaguaca Ava Guaraní Tehuelche Pilagá Comechingón Mocoví Tapiete Quechua Huarpe Tapiete Charrúa Chulupí Pampa Mbyá Guaraní Chané Aymara -.4 -.3 -.2 -.1 0 .1 -.1 -.05 0 .05 .1 .15 Sources: REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad´ Querandí Maimará Sanavirón Omaguaca Charrúa Tapiete Comechingón Chorote Rankulche Tupí Guaraní Huarpe Diaguita-Calchaquí Pampa Tehuelche Diaguita-Calchaquí Mapuche Maimará Vilela Omaguaca Lule Tehuelche Charrúa Chané Ona Kolla Wichí Atacama Pilagá Lule Guaraní Other Kolla relative to non-Natives in the same place (department and rural/urban). Guaraní Querandí Mapuche Ava Guaraní Ona Huarpe Figure A6: Average Outcomes by Group after Controlling for Location Vilela Rankulche Tupí Guaraní Chulupí Quechua Quechua Tonocoté Comechingón Number of disabilities Mocoví Toba-Qom Toba-Qom Sanavirón Wichí Pampa Average of standardized outcomes Ava Guaraní Mocoví Chorote Tonocoté Tapiete Atacama Aymara Aymara Chulupí Other Pilagá Chané Mbyá Guaraní Mbyá Guaraní ıstica y Censos, 2010). Notes: Average outcome of Natives by group 41 0 5 10 15 0 .05 .1 .15 .2 .25 Chickasaw Eskimo Potawatomie Alaskan Athabaskan Colville Navajo Multiracial Tohono O'Odham Comanche Pueblo Creek Apache Native Hawaiian Tlingit-Haida Menominee Comanche Iroquois Sioux Choctaw Pima Cherokee Cheyenne Delaware Iroquois Crow Aleut Blackfoot Blackfoot Pueblo Latin American Cheyenne Chippewa Chippewa Other Am. Indian / Alaska Nat. Yakima Native Hawaiian Tlingit-Haida Crow Aleut Cherokee Sioux Multiracial Puget Sound Salish Menominee Seminole Yaqui Paiute Choctaw Years of education Precarious housing Yuman Yuman Apache Yakima Alaskan Athabaskan Lumbee Yaqui Creek Lumbee Colville Other Am. Indian / Alaska Nat. Chickasaw Eskimo Paiute Tohono O'Odham Seminole Pima Delaware Navajo Potawatomie Iowa Puget Sound Salish Latin American Iowa 0 .2 .4 .6 .8 0 .2 .4 .6 .8 Latin American Pueblo Menominee Chickasaw Native Hawaiian Lumbee Crow Eskimo Alaskan Athabaskan Choctaw Multiracial Cherokee Yuman Potawatomie Paiute Creek Choctaw Iroquois Tlingit-Haida Navajo Potawatomie Chippewa Chickasaw Aleut Other Am. Indian / Alaska Nat. Colville Figure A7: Average Outcomes by Group in the US Creek Iowa Comanche Yakima Colville Alaskan Athabaskan Iroquois Multiracial Pueblo Comanche Chippewa Native Hawaiian Yaqui Other Am. Indian / Alaska Nat. Eskimo Tlingit-Haida Ownership Cherokee Menominee non-Natives, and the dotted horizontal lines show the first and ninth decile by county. Aleut Seminole Cheyenne Yaqui Economically active Apache Puget Sound Salish Lumbee Tohono O'Odham Puget Sound Salish Delaware Sioux Pima Blackfoot Apache Navajo Crow Seminole Cheyenne Delaware Latin American Iowa Blackfoot Tohono O'Odham Yuman Pima Paiute Yakima Sioux Source: IPUMS US (Ruggles et al., 2021). Notes: Solid horizontal lines show average for Natives, dashed horizontal lines show the average for 42 0 .2 .4 .6 0 .05 .1 .15 .2 .25 Cherokee Crow Blackfoot Menominee Delaware Yuman Aleut Alaskan Athabaskan Iowa Paiute Yuman Apache Iroquois Eskimo Apache Tohono O'Odham Choctaw Cheyenne Chickasaw Aleut Multiracial Tlingit-Haida Yaqui Sioux Lumbee Colville Alaskan Athabaskan Puget Sound Salish Comanche Pima Creek Yaqui Other Am. Indian / Alaska Nat. Blackfoot Paiute Pueblo Puget Sound Salish Navajo Colville Seminole Tlingit-Haida Other Am. Indian / Alaska Nat. Potawatomie Chippewa Chippewa Yakima Unemployment Navajo Delaware Sioux Iroquois Number of disabilities Seminole Comanche Cheyenne Multiracial Pueblo Native Hawaiian Eskimo Latin American Native Hawaiian Lumbee Pima Cherokee Latin American Choctaw Yakima Creek Menominee Chickasaw Tohono O'Odham Potawatomie Crow Iowa 0 .05 .1 .15 0 .2 .4 .6 .8 1 Colville Alaskan Athabaskan Creek Yuman Apache Eskimo Chippewa Paiute Alaskan Athabaskan Colville Chickasaw Tlingit-Haida Pima Pima Yuman Cheyenne Cherokee Puget Sound Salish Crow Chickasaw Sioux Sioux Lumbee Potawatomie Iroquois Choctaw Figure A8: Average Outcomes by Group in the US Choctaw Seminole Eskimo Creek Aleut Chippewa Other Am. Indian / Alaska Nat. Tohono O'Odham Yaqui Iroquois Pueblo Pueblo Puget Sound Salish Crow Navajo Aleut Latin American Menominee non-Natives, and the dotted horizontal lines show the first and ninth decile by county. Cheyenne Comanche Health insurance Teen motherhood Multiracial Navajo Seminole Yaqui Native Hawaiian Delaware Paiute Cherokee Blackfoot Blackfoot Potawatomie Native Hawaiian Comanche Apache Tlingit-Haida Multiracial Tohono O'Odham Other Am. Indian / Alaska Nat. Yakima Yakima Menominee Lumbee Iowa Iowa Delaware Latin American Source: IPUMS US (Ruggles et al., 2021). Notes: Solid horizontal lines show average for Natives, dashed horizontal lines show the average for 43 0 20k 40k 60k 80k 100k 120k -.4-.3-.2-.1 0 .1 .2 .3 Native Hawaiian Potawatomie Chickasaw Chickasaw Tlingit-Haida Native Hawaiian Aleut Choctaw Eskimo Multiracial Multiracial Menominee Seminole Comanche Alaskan Athabaskan Creek Potawatomie Tlingit-Haida Choctaw Iroquois Colville Pueblo Puget Sound Salish Seminole Comanche Puget Sound Salish Latin American Cherokee Cherokee Chippewa Creek Delaware Iowa Aleut Other Am. Indian / Alaska Nat. Colville Paiute Iroquois Yakima Menominee Household Income Cheyenne Pueblo Lumbee Paiute Other Am. Indian / Alaska Nat. Chippewa Average of standardized outcomes Yaqui Delaware Crow Lumbee Blackfoot Yaqui Figure A9: Average Outcomes by Group in the US Iowa Crow Yuman Apache Sioux Yuman Latin American Navajo non-Natives, and the dotted horizontal lines show the first and ninth decile by county. Pima Yakima Tohono O'Odham Blackfoot Apache Cheyenne Navajo Sioux Alaskan Athabaskan Pima Eskimo Tohono O'Odham Source: IPUMS US (Ruggles et al., 2021). Notes: Solid horizontal lines show average for Natives, dashed horizontal lines show the average for