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Educational and income inequality in Europe, ca. 1870–2000

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Abstract

In this paper, we revisit the relationship between educational and income inequalities in a historical perspective, using a newly developed annual dataset of average years of education in Europe. Theoretically one would expect a reduction in educational inequality should, given the positive correlation between education level and income, initially increase and then, at a later stage, reduce income inequality. Testing for such a Kuznets-type relationship between educational and income inequalities yields an unexpected result: we find the expected inverse U-curve before the 1950s, but the relationship changes into a normal U-curve afterward. We explain this observation by a change in the trend of skill premium during the second half of the twentieth century due to an increased relative demand for skills, which contradicts the usual assumption of decreasing returns to education. Due to lack of appropriate wage data, we cannot directly capture this effect. Yet, once we use an instrumental variable estimation method to filter out the effect of the omitted skill premium, the expected inverse U-curve also appears for the latter decades of the twentieth century.

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Notes

  1. While several theoretical models were developed (see Banerjee and Newman 1993; Perotti 1993; Galor and Tsiddon 1996) that produce the inverted U-curve, the empirical results are inconclusive. Bruno et al. (1996), Deininger and Squire (1998), Matyas et al. (1998) and Fields (2001) for example cannot find empirical support for it.

  2. The enrollment data for the UK by Mitchell (2003), however, do not include enrollment to non-state inspected schools and can hence lead to and underestimate of educational attainment for the second half of the nineteenth century. For this reason, we rather used Lindert’s (2004) enrollment data, but also run all regressions excluding the UK. Since the difference was not significant, we do not report those results in the paper.

  3. The most recent estimates by Barro and Lee (2010) employ additional statistical data to correct for the problem of the relationship between mortality and educational attainment. Such an approach would not be feasible on historical data; for this reason we believe that the method by Foldvari and van Leeuwen (2009) may be advantageous for historical research.

  4. For clarity, let us take an example: if we know the average years of education from census data for the years 1960 and 1970, we can estimate the average years of education in 1965 as an average of the 1965 value estimated backward from 1970 and forward from 1960. Similarly, we can use this corrected 1965 estimate together with the census data from 1960 to estimate a corrected value for 1963 and so on for all unknown years. The main assumption is that the bias resulting from neglecting the interrelation between mortality and education depends on the distance from the point of departure, but not from the year. In this case the two biases should have equal magnitude and opposite sign and they should cancel out when taking an average.

  5. An obvious advantage of annual data is that one can rely on time-series techniques (autoregressive models, especially VAR and VEC) that would not be applicable if only benchmarks were available.

  6. The dataset by Morrisson and Murtin does not contain Eastern Europe (with the sole exception of Bulgaria and Hungary) and omits several of the, admittedly smaller, Western European countries as well. In addition, it reports the data by decade rather than annually. Methodologically, the dataset of Morrisson and Murtin is very similar to ours with the main distinction that their data are based on a strong assumption regarding enrollment numbers in the nineteenth century. They assume very low primary school enrollment rates for less developed countries for the start of the nineteenth century, and then they link these assumptions to the first available observation by assuming a constant growth rate. This basically means that the longer the missing period is, the larger the measurement error for a country becomes at the start of their series. Other assumptions used such as for drop-out rates and duration of schooling do not significantly bias the estimates in either series (Morrisson and Murtin 2009).

  7. They use the following identity from Robinson (1976): \(\sigma^{2} = p_{s} \sigma_{s}^{2} + (1 - p_{s})\sigma_{u}^{2} + p_{s} (1 - p_{s})(\bar{y}_{s} - \bar{y}_{u})^{2}\), where σ, σ s , and σ u denote the total income inequality, the income inequality of schooled and unschooled worker respectively, ps is the share of schooled workers in total population, and finally \(\bar{y}_{s}, \bar{y}_{u}\) are the mean incomes of the two groups. When they difference this expression with respect to p s they assume that \(\frac{{\partial \bar{y}_{s}}}{{\partial p_{s}}} < 0,\frac{{\partial \bar{y}_{u}}}{{\partial p_{s}}} > 0\), that is they assume that the wage of educated individuals is monotonic function of their share in the population i.e. the expansion in education necessarily compresses wages.

  8. He shows that \(G \approx \frac{1}{\sqrt 3}\frac{\sigma}{{\bar{y}}}\rho (y,r_{y})\), where G denotes the Gini coefficient, and ρ(y, r y ) is the rank correlation coefficient between income and rank. The latter we can take as constant for a given population.

  9. If we take the total derivative of the CV, with skilled wages and the share of skilled workers allowed to change, we obtain the following:

    \(d{\text{CV}}(y) = \left[ {\frac{{p_{s} (1 - p_{s} )(\bar{y}_{s} - \bar{y}_{u} )}}{{\sigma \bar{y}}} - \frac{{p_{s} \sigma }}{{\bar{y}^{2} }}} \right]d\bar{y}_{s} + \left[ {\frac{{0.5\left( {\sigma_{s}^{2} - \sigma_{u}^{2} + (1 - 2p_{s} )(\bar{y}_{s} - \bar{y}_{u} )^{2} } \right)}}{{\sigma \bar{y}}} - \frac{{\sigma (\bar{y}_{s} - \bar{y}_{u} )}}{{\bar{y}^{2} }}} \right]dp_{s}\)

    As long as only the share of schooled workers (p s ) is allowed to increase, the effect can be either negative or, if the first term in the respective bracket is high enough and p s  < 0.5, then initially positive and at higher values of ps negative. This can give rise to an inverse U-curve. When there is a wage change as well, then the coefficient of \(d\bar{y}_{s}\)will affect the behavior of the CV, which can be both positive and negative, depending on the parameters and ps, resulting in an inverse or a normal U-curve.

  10. In every recent article Morrisson and Murtin (2013) take increasing returns to education into account, but they still find an inversed U-curve in their global dataset, even though less pronounced. A possible explanation is that the increase the rate of returns was strong enough in Europe in the second half of the twentieth century to cause such a reversal of the relationship, while outside Europe and North-America this effect was simply not strong enough to dominate the composition effect.

  11. Hall and Jones (1999) assume 13.4 % for the first 4 years of education, 10.1 % for the second 4 years and 6.8 % for any additional years, while Morrisson and Murtin (2013) estimate the rate of returns as a linear function of average years of schooling: \(\hat{r}_{i} = 0.125 - 0.004S_{i}\).

  12. The importance of the state is initiating mass education is well described in the literature (e.g., Ramirez and Ventresca 1992; Cummings 2003). In Europe, several countries saw earlier proclamations from King and Church to parents to educate their children. It has been argued that these played an important role in the spread of literacy in Europe (Graff 1987; Mitch 1992; Vincent 2000). This led gradually to the spread of compulsory education. Compulsory education laws were first enacted in Prussia (1763) and Denmark (1814), followed by several South European and Scandinavian countries (Soysal and Strange 1989, 278). In the second half of the nineteenth century compulsory education laws were made in several Northwest European countries, followed by Eastern Europe (Benavot and Resnik 2006, p. 11). Yet, in all cases, the State played an important role by enacting compulsory mass education be it either because “the establishment of compulsory education addressed narrowly defined educational problems; in others, it was employed as a strategy to “solve” or defer solving long-standing economic, cultural, or social problems” (Benavot and Resnik 2006, pp. 13–14). Indeed, in a quantitative analysis Soysal and Strange (1989, p. 285) find that only the effect of the state on the enactment of compulsory education and increasing enrollments seems to matter.

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Acknowledgments

The authors wish to express their gratitude to the two anonymous referees for their valuable and constructive comments and suggestions.

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  1. Utrecht University, Utrecht, The Netherlands

    Péter Földvári & Bas van Leeuwen

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  1. Péter Földvári
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Appendix

Appendix

See Tables 8, 9 and 10.

Table 8 Average years of education in Europe
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Table 9 Average years of education in Europe
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Table 10 Attainment by level of education in Europe, 1870–2007
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Földvári, P., van Leeuwen, B. Educational and income inequality in Europe, ca. 1870–2000. Cliometrica 8, 271–300 (2014). https://doi.org/10.1007/s11698-013-0105-3

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