Decentralizing the Chilean miracle: regional intergenerational mobility in a developing country

ABSTRACT

We estimate spatially disaggregated measures of intergenerational mobility in Chile through an administrative dataset linking children’s and their parents’ earnings from the formal private labour sector. We report remarkable heterogeneity as we find higher and lower upward mobility in mining and agricultural regions, respectively, corroborating previous findings by Connolly et al. in 2019 with the distinction that Chile is a unitary state, implying that factors other than institutional differences shape mobility.

KEYWORDS:

• intergenerational mobility
• administrative data-based intergenerational mobility
• regional intergenerational mobility
• inequality and intergenerational mobility

JEL:

• J31
• J61
• J62

ACKNOWLEDGEMENTS

We thank Claudio Ferraz, Nicole Fortin, David Green, Kevin Milligan, Thomas Lemieux, Lars Osberg and the participants of the UBC Applied Economics seminar. We also thank the Budget Office for providing access to the data used in this work. This paper is based on the first chapters of the following PhD theses: Gutiérrez Cubillos (2020) and Villarroel (2021).

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. Endowment represents the combined effect of many children’s attributes influenced by nature or nurture. For example, sociocultural skills, cognitive abilities or access to networking.

2. Another finding from Becker et al. (2018) is that the relationship between inequality and mobility is ambiguous. It depends on the human capital transmission function and complementarities in the human capital production function of children.

3. This implies that changes in policies and institutions can affect long-run mobility trends. In addition, changes in mobility today can be caused by events of the far-reaching past. Thus, differences in mobility between geographical regions reflect the consequences of current and past policies, institutions and conditions.

4. For instance, when child earnings are observed at younger ages (before 25), or parental earnings are observed at older ages (in their 50s and 60s).

5. Recent studies have suggested additional reasons for concern if outcomes are not observed at a comparable time (e.g., see Engzell & Mood, 2021, for more details).

6. Despite this, several papers use this method to estimate IGM in developing countries (Dunn, 2007; Narayan et al., 2018; Núñez & Risco, 2004; Núñez & Miranda, 2010; Sapelli, 2013; Ferreira & Veloso, 2006; Jiménez, 2011; Torche, 2005).

7. CZs are geographical aggregations of counties that cover urban and rural areas in the United States.

8. Chetty et al. (2016) and Chetty and Hendren (2018) also estimate the impact of communities to determine IGM in the United States. The latter find that most of the variation in IGM between CZs and counties is driven by causal effects of place rather than differences in the types of people living in those places.

9. Corak (2020b) shows that inequality in the bottom half of the distribution may be vital to understanding social mobility, negatively correlating with a series of mobility indicators.

10. A possible explanation of this result would be that Australia is a more centralized federation than the United States, with less geographical variation in policies that might influence mobility.

11. They conclude that around half of the province effect is due to a selection of the resident population, and the other half is the result of local socio-economic conditions.

12. There is also a literature that studies IGM using education as a proxy for privileges. For instance, Alesina et al. (2021) find for Africa that geographical and location-specific features, including distance to the coast and capital and favourable ecology to malaria, correlate negatively with upward IGM. Muñoz (2021) documents wide cross- and within-country heterogeneity in Latin America and the Caribbean, whereas Asher et al. (2021) conclude that upward educational mobility is on average five points higher in urban areas from India.

13. However, a more desegregated geographical scale can better estimate neighborhood effects. These effects explain spillovers within distinct social environments that can generate persistent poverty, resulting in low IGM, giving a better estimate for IGM (Durlauf, 2006; Durlauf & Shaorshadze, 2015; Durlauf & Seshadri, 2017).

14. Andrews et al. (2004) and Hortas-Rico and Rios (2019) find similar results for Australia and Spain, respectively.

15. In the period 2007–18, the Región de Los Ríos (RIOS), de Arica y Parinacota (AyP) and de Ñuble (NUBLE) were created after dividing into two areas the Región de Los Lagos (LAGOS), de Tarapacá (TPCA) and del Biobío (BBIO), respectively. Ñuble was created in 2018 and there are no official GDP estimates for the region (BCCh, 2020).

16. There are no official Gini coefficients for each region in 2020.

17. Our linkage procedure is not exempt from further criticism. For example, in the case of legal parents absent from their child’s life, we are not taking into account how the economic resources of non-legal parents impact their dependent children while they are growing. However, a similar problem is also present when the linkage relies on cohabitation between parents and children, because we would be omitting all the economic resources coming from legal parents who are present in their children’s life but do not live with them. Unfortunately, we do not have information on earnings for non-legal parents. Nevertheless, according to the assortative mating literature, there exists a positive correlation between spouses’ educational attainment and income (Schwartz & Mare, 2005; Western et al., 2008; Eika et al., 2019; Alonzo, 2022). Thus, even in the case where the child lives with the non-legal parent, our linkage procedure would be partially considering the economic resources of the non-legal parent due to the fact that the correlation between earnings (economic resources) of the legal parent and the non-legal parent is probably relatively high (Gelissen, 2004).

18. Since we observe daughters around the time they are likely to become mothers, gender differences in part/full-time work could have implications for our analysis. Unfortunately, the data do not contain information on hours worked. Thus, we cannot differentiate between full- and part-time workers. Although this is a limitation of our data, our proxy of lifetime earnings (five-year average) and the robustness of our results suggest that this is not a major source of concern.

19. Our sample can potentially contain missing at non-random because we are working with the formal private sector. To reduce this phenomenon, we do not use data before 2003 to avoid potential bias stemming from the self-selection of workers into the programme.

20. This is similar to Chetty et al. (2014b) and Corak (2020b) with the difference that they impute missing earnings as zero. In Section A1 in the supplemental data online we perform a robustness check in which we impute these missing earnings as zero.

21. Before 2010, the official consumer price index only reflected price changes from the Región Metropolitana. From 2010 onwards, the index incorporates price changes in other regions.

22. Additionally, unofficial price indexes rely on the Chilean Socioeconomic Characterization Survey (CASEN), which is released every two or three years. Thus, the production of a complete regional price index would require the imputation of all monthly data points between survey waves, which could introduce further biases.

23. Children who did not repeat any grade and were born before 30 June should be 17 years old in 12th grade. Children born after 30 June should be 18 years old in 12th grade.

24. Additionally, neighbourhood effects seem to be of a larger magnitude during adolescence than in the earliest years of childhood (Chetty et al., 2014b). This is relevant to the Chilean context as 12th grade is the year in which students are tested in the national university selection test.

25. Section A1 in the supplemental data online explores a different ranking scheme in which parents and children are ranked in relation to their respective birth cohort.

26. Engzell and Mood (2021) suggest that while the rank–rank correlation might also be sensitive to the age of observation or the definition of income used, it is still more stable than log-based measures.

27. Overlapping error bars do not ensure that differences are necessarily insignificant.

28. Table A2 in Appendix A in the supplemental data online outlines regions with a prominent mining industry.