1. See OECD (2010), Causa et al. (2009), and Blanden et al. (2005, 2006). Of course, looking beyond income, education is in itself also associated with social status.
  2. Psacharopoulos and Patrinos (2004).
  3. Fajardo and Lora (2010).
  4. A clear example is a publicly funded university system to which mainly the affluent have access.
  5. Atal et al. (2009).
  6. OECD (2009a).
  7. See Björklund et al. (2007).
  8. OECD (2008).
  9. This is true provided “nature” factors do not vary greatly across countries, which seems a reasonable working assumption.
  10. While the literature on mobility in principle is concerned with income mobility across generations, parental income is subject to considerably larger measurement errors than education. Even when income data are available many researchers focus on the transmission of educational outcomes. The sociological literature often focuses on occupational categories in addition to education as an indicator of social status.
  11. The middle sectors are defined as individuals in households with household-adjusted income between 50% and 150% of the median; with the disadvantaged below this range, and the affluent above.
  12. This could be almost tautological, especially for older cohorts: education determines a significant part of income and people are classified by income group.
  13. Thomas et al. (2001).
  14. The primary source of data for this analysis is the 2008 Latinobarómetro survey conducted in 18 countries of the region, covering around 1 000 persons in each. This captures several socio-economic characteristics of its subjects as well as their opinions and perceptions regarding public policies and politics.
  15. Parental educational attainment is taken as the higher of the mother’s or the father’s, whether the measure is years of education completed or highest level of education achieved.
  16. Daude (2010) does find a downward trend, such that for younger generations a difference in one year of parental education matters less than it did for the older generations if an alternative measure of inter-generational transmission is considered (the elasticity coefficient underlying the regressions used to compute the correlations). However, this effect is mainly driven by the reduction in the dispersion of parental education documented in Table 3.1.
  17. Hertz et al. (2007).
  18. Psacharopoulos and Patrinos (2004).
  19. Of course, many of the differences between the point estimates are not statistically significant at standard levels of confidence.
  20. It is interesting to note that these estimates based on those household surveys that have information on parental education are confirmed (in magnitude) by those based on the Latinobarómetro database, although the resulting country ranking is slightly different.
  21. The figures are 81.6% for women and 78.2% for men.
  22. Of course, there are differences across countries that are ignored in Figure 3.5. In a very similar exercise, Torche (2007) shows that in Chile the greatest hurdle is access to tertiary education, while in Mexico it falls much earlier in the educational system, in the steps between primary and secondary education.
  23. See Anderson (2001), Behrman et al. (2001) and Conconi et al. (2007). The region is a good target as the required data are available for a large number of countries.
  24. Larrañaga and Teilas (2009).
  25. This is consistent with the evidence presented in Figure 3.2. Of the six countries covered by PISA, Colombia exhibits the lowest inter-generational correlation for educational attainment.
  26. The correlation coefficient is 0.74, significant at standard levels of confidence.
  27. Of course, it is hard to establish causality. If the objective were to analyse the impact of income inequality on inter-generational mobility, the Gini index lagged by at least one or two decades should be considered.
  28. Again, the correlation coefficient (-0.52) is significant at standard levels of confidence.
  29. See OECD (2010).
  30. Becker and Tomes (1979 and 1986); and Solon (2004).
  31. Of course, such financial policy instruments should also be available for disadvantaged households. In practice, though, for poorer households public interventions in early childhood would probably be more relevant in most countries, given their stage of development. Even if financing were available to all households, it would probably be used most intensively by the middle sectors.
  32. A country-by-country analysis shows that the exceptions to this are among the poor countries, in particular El Salvador, Guatemala, Honduras and Nicaragua.
  33. There are important differences across countries. The best in terms of relatively high rates of enrolment at the secondary level and minor differences across quintiles are Chile, Colombia, Mexico and Venezuela. Differences are severe in the poor countries of Central America where a child from the highest income quintile is four to five times more likely to be enrolled at the secondary level than a child from the first quintile. Brazil, Uruguay and Panama are middle-income countries that also exhibit large disparities across income quintiles in secondary enrolment. The good performers at the secondary level, in addition to Argentina, also exhibit fewer differences across income groups at the tertiary level. On the other hand, Central America, Bolivia, and to some extent also Brazil, Uruguay and Panama, present higher levels of inequality in tertiary enrolment.
  34. The index is based on a variance decomposition between and within schools of an index of economic, social and cultural status (ESCS). Values close to 0 imply that most of the variation in the ESCS is due to differences across schools, such as that individuals who go to the same school tend to have similar backgrounds, while a value close to 1 implies that students with very different socio-economic backgrounds go to the same school.
  35. Calónico and Ñopo (2007). Not all private schools are the same; within the private system there is a considerable amount of heterogeneity in terms of the quality of education.
  36. Of course, this finding does not necessarily imply any causality.
  37. The correlation coefficient is 0.82, significant at conventional levels.
  38. Studies based on PISA data for OECD member countries show that a difference of 38 points in science scores corresponds on average to a difference of one year of study.
  39. Estimations were performed separately for women and men to adjust the female wage equation for self-selection (given that the decision to participate in the labour market is not random). Therefore, we estimate a standard Heckman-correction estimation for women, and simple ordinary least-squares estimates for men (the number of children under 5 and elderly over 65 years in the household is used as exogenous shift variable to identify the participation equation).
  40. See Vegas and Santibáñez (2010).
  41. de Janvry et al. (2006).
  42. Causa and Chapuis (2009).
  43. Of course, a careful analysis of the incentives and cost-recuperation aspects for non-poor households should be an important part of any public programme in this area.
  44. The main exceptions are the extremely poor in the region’s middle-income countries and some of the poorer countries in Central America.
  45. Oreopoulos et al. (2006).
  46. Of course, compulsory education could also be extended to pre-school levels, in combination with ECD programmes.
  47. MacLeod and Urquiola (2009).
  48. See Field et al. (2007) for more details, especially chapters 3 and 5.
  49. See Akerlof and Kranton (2002).


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