West indian countries

Skill-based production makes highly skilled workers more efficient in rich countries

Skill-based production makes highly skilled workers more efficient in rich countries

The availability of highly skilled labor varies considerably between poor and rich countries. Take, for example, India and the United States. In 2010, the share of India‘s working-age population with tertiary education was 9%, while the corresponding share in the United States was 56% (Barro and Lee 2013). At the same time, wage premium differentials for highly skilled labor appear small. According to estimates collected by Caselli et al. (2014), in India as in the United States, an additional year of schooling is associated with a salary increase of around 10%; more generally, returns to schooling are only weakly correlated with development.

The combination of large differences in the quantity of skilled labor and small differences in the skill premium has led previous work to conclude that the relative efficiency of highly skilled workers increases with development (Caselli and Coleman 2006) . The logic of this argument is simple: under standard assumptions, the abundance of skilled workers should, all other things being equal, lower their relative earnings in rich countries because the services they produce are more widely available. The absence of this pattern in the data suggests that the downward pressure on the skill premium is offset by the fact that skilled workers in rich countries are relatively more productive, pushing up their wages.

The literature has suggested two possible reasons for this. On the one hand, the productive environment in rich countries could be more “skill-driven”, either because firms adopt technologies that are more suited to skilled workers, as Caselli and Coleman (2006) propose, or, more generally, because the institutional framework or the sectoral composition of these countries is favorable to highly qualified labour. On the other hand, as in Jones (2014), highly skilled workers could embody more human capital in rich countries, due to differences in the quality of education, training or intrinsic characteristics of workers. The distinction between these two explanations is very important for development accounting: if the “human capital view” is correct, all of the cross-country variation in GDP per worker can be explained by highly skilled labor having relatively more human capital in rich countries; according to the other view, the contribution of relative human capital is much lower (Caselli and Ciccone 2018).

Measuring relative skill effectiveness with microdata

In recent work, I revisit both the measurement and interpretation of relative skills efficiency gaps across countries (Rossi, 2022). Using micro-level data in 12 countries at different levels of development, I construct comparable measures of the relative supply and wage premium of workers with higher education. Unlike previous work, these measures incorporate cross-country variation in employment rates and hours worked, and are based on comparable wage data as opposed to collections of estimated returns to schooling from different sources. Based on these measures and assuming a standard production function, I calculate for each country the implied relative efficiency of highly skilled labour. As shown in Figure 1, there is a close correlation with GDP per worker. The productivity gap between high-skilled and low-skilled labor is more than 20 times larger in the United States than in India, a difference of a magnitude similar to that of GDP per worker between these two countries. To a large extent, the cross-country dispersion of relative skill efficiency is not determined by differences in industry composition, incidence of self-employment, or gender- and experience-related returns.

Figure 1

Remarks: The figure plots the logarithm of the relative efficiency of skills and the logarithm of GDP per worker for the 12 countries for which microdata are available. The relative efficiency of skills is normalized to take the value 1 (0 in log) for the United States. The solid line represents the best linear fit.

Interpreting the relative efficiency of skills: Evidence from international migrants

Why are highly skilled workers more productive in rich countries? I exploit the variation in the skill premia of international migrants to shed light on this question. Intuitively, foreign-educated migrants employed in the same labor market are subject to an equally skill-based production environment, but have different levels of human capital depending on the quality and characteristics of the educational environment in their native country. Comparing skill premiums between nationalities within the same host country then makes it possible to separate the differences between countries in the relative human capital of the highly skilled labor force and in the skill bias of the labor force. production.

Figure 2 shows the skill premiums for US immigrants relative to GDP per worker in the source country. Highly skilled migrants from rich countries are relatively better paid in the United States, as their relative human capital is higher. However, these differences between nationalities are small compared to the differences between countries in the relative efficiency of skills shown in Figure 1. Indeed, these data (combined with similar patterns in other receiving countries) lead me to conclude that less than 10% of the variation in the overall relative efficiency of skills can be explained by human capital endowments of a highly skilled workforce. To a large extent, it is the productive environment that makes highly skilled labor relatively more efficient in rich countries.

Figure 2

Remarks: The figure plots the logarithm of the skill premium in the source countries of US immigrants, relative to the logarithm of GDP per worker in the source country. The solid line shows the best linear fit.

Implications for country differences in human capital

How can we reconcile these results with recent literature that finds an important contribution of human capital in accounting for income differences between countries (Hendricks and Schoellman 2018)? My results in Rossi (2022) suggest that the cross-country variation in the human capital gap between high-skilled and low-skilled workers is somewhat limited. This means that a strong contribution of human capital to income differences requires uniform skill gaps – that is, all workers, regardless of skill level, having more human capital in rich countries. This echoes recent work by Hendricks and Schoellman (2022), which shows that the wage gains of high- and low-skilled migrants in the United States are consistent with large differences in human capital for both groups.

This conclusion imposes important restrictions on theories of human capital accumulation and economic development. The large cross-country differences in human capital for all levels of education call attention to factors affecting the human capital formation of all workers, which become natural candidates to explain the cross-country variation in human capital. human. This includes the quality of early education (Fazzio et al. 2020), family contributions (De Philippis and Rossi 2020), cultural traits (Hanushek et al. 2020) and opportunities to acquire skills throughout life. life cycle (Lagakos et al. 2017).

What makes the productive environment more skill-based in rich countries?

The accounting results in my article leave open the question of why and through which channels production is more skill-biased in rich countries. A popular view is that differences in skill biases are endogenous responses to the availability of highly skilled labor: as highly skilled workers are abundant in rich countries, firms in those countries have greater incentives to adopt technologies that complement these workers. The idea that technology adoption responds to factor availability has received empirical support in a variety of contexts (eg, Beaudry et al. 2010).

Moreover, country differences in institutional quality, organization of production, and prevalence of large modern firms could all disproportionately benefit highly skilled workers in rich countries, perhaps also helping to explain why more individuals choose to accumulate skills. in these countries. Documentation and quantification of these channels are important tasks for future work.

References

Barro, RJ and JW Lee (2013), “A new dataset on educational attainment around the world, 1950-2010”, Journal of Development Economics 104(C): 184–198.

Beaudry, P, M Doms and E Lewis (2010), “Should the personal computer be considered a technological revolution? Evidence from United States Metropolitan Areas,” Journal of Political Economy 118(5): 988-1036.

Caselli, F and A Ciccone (2018), “Imperfect Substitution and Development Accounting”, VoxEU.org, 9 June.

Caselli, F and WJ Coleman (2006), “The Global Technological Frontier”, American Economic Review 96(3): 499–522.

Caselli, F, J Ponticelli and F Rossi (2016), A new dataset on technology differences in mincer yields across space and timePrinceton University Press.

De Philippis, M and F Rossi (2020), “Parental influence and human capital gaps between countries”, VoxEU.org, 3 November.

Fazzio, I, A Eble, R Lumsdaine, P Boone, B Bouy, Pt J Hsieh, C Jayanty, S Johnson and F Silva (2020), ” VoxEU.org, 16 December.

Hanushek, EA, L Kinne, P Lergetporer and L Woessmann (2020), “Patience, risk-taking and international differences in student achievement”, VoxEU.org, 2 August.

Hendricks, L and T Schoellman (2018), “Human Capital and Development Accounting: New Evidence of Wage Gains During Migration”, The Quarterly Journal of Economics 133(2): 665–700.

Hendricks, L and T Schoellman (2020), “Skilled Labor Productivity and Cross-country Income Differences”, working paper, May.

Jones, BF (2014), “The Stock of Human Capital: A Generalized Approach”, American Economic Review 104(11): 3752–3777.

Lagakos, D, B Moll, T Porzio, N Qian and T Schoellman (2017), “Life-Cycle Wage Growth across Countries”, Journal of Political Economy.

Rossi, F (2022), “The Relative Efficiency of Skilled Labor Across Countries: Measurement and Interpretation”, American Economic Review 112(1): 235–266.


Source link