What Drives Cross-Regional Differences in Returns to Higher Education?

1. What Drives Cross-Regional Differences in Returns to Higher Education? Aleksey Oshchepkov (Higher School of Economics, Moscow) ERSA Congress, Barcelona, 30 August 2011

2. Motivation - I • Estimation of the rate of returns to education is a topic of hundreds of studies around the world. Most of them produce a single country-level estimate of the rate completely ignoring spatial issues (Psacharopulos&Patrinos, 2004). • There is, however, a relatively small but growing body of literature which shows that returns to education vary significantly across regions within countries (e.g.,USA: Hanushek (1981), Beeson (1991), Black et al. (2009); Great Britain:O'Leary-Sloane (2008); Spain:Casado-Lillo (2005); Portugal: Vieira et al. (2006); Sweden: Backman-Bjerke (2009); Cheque Republic: Jurajda (2004); Brazil: Behrman-Birdsall (1984)).These findings raise many questions.

3. Motivation –II • The standard country-level is a simplification • Regions with a relatively high rate of return may well exist in “low-return” countries, and vice versa. Estimating the rate of return to education at the regional level seems to be an important extension of the standard approach, since high returns in the country as a whole does not guarantee that investing in education is beneficial in all its regions. • Implicit assumptions of the HC theory are not valid? • The theory implicitly assumes that the national labour market is unified and homogenous, and all individuals without any restrictions may offer their labour on this market. • Should government\firms\people increase investments in education in “high-return” regions? • Relatively high returns to education are generally regarded as a sign of underinvestment in education. They have served as the basis for recommendations to increase investments in education and enrollment which are common in the case of developing countries; it is expected that these measures will raise incomes and reduce inequality. Is it correct to transfer these recommendations to certain regions exhibiting high rates of returns to education? • What drives cross regional differences in returns to education? • Could potentially help to shed some light on cross-country discrepancies

4. Motivation –III The number of studies estimating rates of returns to education at the regional level is small, and the number of studies addressing these issues is even more limited. In this paper, we • present new empirical evidence on significant cross-regional differences in returns to education within one country, Russia. We are first who provides region-specific estimates of the return to higher education for Russian regions. • document very large differences. The returns (using basic Mincerian specification) to higher education are in the range from 32% to 140% (compared to the secondary education) against 65% for the country on average. • try to reveal factors associated with the rates of return to education in the regional level. For this, we regress the estimated rates of returns on a set of regional characteristics

5. Data The main reason for the limited number of studies is a lack of appropriate regionally representative micro-data. In Russia: • LFS does not contain info on wages or incomes • RLMS data, widely used to estimate returns to education, are not regionally representative We use a unique set of matched employer-employee data from the Occupational Wages Survey (OWS) of 2005 and 2007. • The OWS covers enterprises, which have more than 15 employees and are obliged to submit statistical forms to the Russian Statistical Agency (Rosstat). • Almost all branches of the economy are covered (except state administration, agriculture with fishery, and financial sector) • The sample represents about 80% of employment in covered industries • The size of the sample is about 700 000 each year • The average number of observations in regional subsamples is about 9500 with the minimum of about 1500. • Specific regional based design: regional subsamples formed separately for each region

6. Stage 1: estimation of the returns to education For each region: 1) Basic equation: Ln(W)= α + β*Education + γ1*exp + +γ2*exp2 + γ3*gender + γ4*ln (hours) + ε1 Education is the highest educational level achieved. We distinguish 6 levels: 1-higher and postgraduate education, 2-undergraduate, 3-vocational, 4-basic vocational, 5-complete general secondary and 6- basic general and below. 2) Extended equation: Ln(W)= α + β*Education + γ1*exp + γ2*exp2 + γ3*gender + +γ4*ln (hours) + γ5*industry + γ6*ownership+ε2 Industry at the 1-st level of NACE; ownership – public/private. Returns to higher education (with respect to complete secondary): (Halvorsen, Palmquist, 1980) +Correction: (Kennedy, 1981)

7. Stage 1: Interpreting regional returns to education • The β-coefficient can be regarded as an estimate of the return to investments under several assumptions: • costs of receiving education are equal to the potential income which could get an individual if instead of training he went to work (Chiswick, 1997) • assumptions on system of the taxation of labor income, uncertainty at the time of making investment decision about the size of future incomes, etc.are needed (Heckman et al., 2003). • All those conditions needed to interpret β as the rate of returns should hold true in each region • There is yet a more principal difficulty due to interregional migration • The β-coefficient is an estimate for the rate of returns to education in a region if and only if all individuals are employed in the regions where they received education. We do not interpret the estimates of β, in terms of the return to investments in education. We treat them as conditional relative wage of workers with higher education (compared to the wage of workers with secondary education). But in order not to abandon the generally accepted terminology, we continue to "return to education" minding, however, that such use is conditional.

8. Fig. 1. Point estimates of s with 95% confidence intervals (basic specification, OWS 2007)

9. Fig.2. Rates of returns adjusted for absence of some industries (% of average earnings of workers with secondary education). Only in three regions (including Moscow) the absolute value of the bias is more than 5% of the initial rate.

10. Overview of estimation results The estimates increase markedly after controlling industries and the type of ownership Many workers with higher education work in public sector (health, education), where wages are low Transition from the basic to the extended wage equation reduces inter-regional variation in the rate of returns to higher education, but it is still very high A substantial part of this variation is caused either by differences between regions in the wage structure or by differences in the distribution of workers in different jobs, or by both these factors A strong correlation between regional estimates of returns obtained from the basic and extended wage equations (more than 0.9). In other words, the ranking of regions by the rate of return to higher education is virtually independent of the specification we use

11. Fig.4. Regional rates of returns to higher education in 2005 and 2007

12. Fig.3. Mapping the returns to higher education in Russia

13. Stage 2: What drives cross-regional differences in returns to education? We regress estimated returns on a set of regional characteristics: βj = β0 + φb*RCj+ ξj There are only a limited number of studies attempting to explain cross-regional differences in returns to education. We have managed to find only two published articles where these differences are modeled explicitly (Beeson (1991); Black et al., (2009), both are for USA). Both of them view the differences as a result of asymmetric influence of compensating mechanismto workers with different level of education. We use this approach as a starting point of our analysis.

14. Asymmetric compensating mechanism • “Standard” compensating mechanism in the regional labour markets: workers receive wage compensations for living in regions or cities with relatively less favorable characteristics(e.g., Roback (1982,1988), Dumond et al., (1994)) • However different (groups of) workers may receive different compensations for living in the same conditions, suggesting the existence of differences in relative wages. Due to: • different preferences • different willingness to pay for favorable regional characteristics (income effect) • different propensities to move

15. What regional characteristics (RC) matter? Previous studies [Bignebat (2004), Berger et al., (2008), Oshchepkov (2009)] suggest: • Price level • Flat price (for 1 sq.m) • Crime rate • Air pollutions • Medical staff (per 10 000 citizens) • Average temperature in January • Life expectancy • Unemployment rate • Net migration • …

16. Stage 2: alternative explanations? • How is the return connected with the stock? Connection with the proportion of workers with higher education Negative relation: diminishing marginal return (Middendorf (2008) for EU countries). Positive relation: Black et al (2009) for MSA across USA. Russia: HC externalities in the city level(Muravyev (2008)) • Level of economic development Cross-country comparisons of rates of returns (Psacharopulos & Patrinos (2004) : more developed have lower returns, but relation may be non-linear. • Employment in public sector Positive relation expected, which is suggested by our descriptive analysis and by the fact that in Russia returns to education in public sector are higher than in private sector.

17. Correlations between regional returns and regional characteristics Note: OLS with Huber-White standard errors.

18. Robustness check Note: pool 2005+2007 years

19. Correlations with other regional characteristics Note: OLS with Huber-White standard errors.

20. Summary-I • This paper belongs to a relatively small number of studies showing that rates of returns to education may vary greatly across regions within a country. • We first provide region-specific estimates of the return to higher education for regions-subjects of the Russian Federation. • The results indicate that the returns to higher education extremely vary across Russian regions.

21. Summary-II • The standard country-level approach to estimate the returns to education is an oversimplification. • The Russian case clearly shows that it may hide behind a huge regional variation. In some Russian regions the rates of return to higher education are comparable with the rates of return existing developing countries, while in other regions the rates correspond to those existing in developed countries. • Assessing the rate of returns to education at the regional level seems to be an important extension of the standard country-level procedure. • It is difficult to expect that the single country-level estimate of the rate of return to education will be linked (as required by theory) with the decision to invest in obtaining or continuing education, as this decision is made taking into account conditions at the regional or local level.

22. Summary-III Our robust findings: the return to higher education is higher in regions • which are less attractive for living • with higher unemployment rate • with higher proportion of employment in the public sector. Relatively high returns to higher education in some regions should not be interpreted as a signal for investment, these are rather a signal of “bad” regional performance(surprisingly?)