National Replication vs. Regional Replication ---- How Reliable is the OLS-Based Evidence of College Wage Premium? Haogen Yao, Steve Simpson Teachers College, Columbia University Sui Yang, Shi Li Beijing Normal University 38% of the world’s tertiary graduates 33% of the world’s GDP in 2011 huge diversities within the 2 nations

The Studies Replicated The Race between Education and Technology (Goldin and Katz, 2008) Summary: Apply the basic regression and aggregated indicators (yearly-national level) to find that the relative lag of college graduate supply is the main reason of expanding wage premium. Universal high school and mass higher education (Wang, 2009) Summary: Use extended Mincer earning function and the Chinese Census data to find a very high marginal return to higher education for both urban and rural populations. Problem statement: We know OLS is problematic. Before applying advance methods like IV and RD, maybe we should firstly ask HOW reliable (unreliable) the OLS-based evidences are? Here is a straightforward answer relying on large-scale datasets: regional replication.

The Implementation Wang (2009) Data. 1% sample of the 2005 Chinese Census Method. Includes variables indicating the lengths of 4 levels of education, with individual characteristics and provincial dummies controlled Our Replication Data. 20% resampling of the 1% sample Method. The same regression with the same set of variables/ But not sure if they are constructed in identical way/ Replication for the nation and the six administrative divisions Goldin and Katz (2008) Data. Yearly CPS and Census (when available) data, Method. Regress the college-high school wage premium (log ratio) on relative supply, with institutional factors and time trends controlled Our Replication Data. Same for national replication, but CPS for regional replication b/c previous data are inappropriate Method. while the original one weighed data by gender, race and experience, we use personal weight but control these 3 factors in regression/ Use the national equation to predict regional premium

The United State

Result from Goldin and Katz (2008) Our Result

The Model Works for 52% of the US Population

THE DIFFERENCE Relatively optimistic actual premiums evolutions for WNC and SA, and the predicted ones are even more optimistic WHY Variable Construction? Omitted Variable?

THE DIFFERENCE: Quite obvious… WHY The quality of “supply” variable? Industrial structure? Path dependency? SES? Yes fixed-effect can close the gap between lines, but it gives an elasticity of substitution between skilled and unskilled as high as 9, much higher than the suggested one of 1.4

China

lower estimates Larger gap of return to higher education Lower marginal return of higher education, BUT still can tell it is big Our data does not allow for a strict classification of rural/urban population. Our urban group contains rural residents that may drag the estimates down Pretty high marginal return of higher education

Similar shapes are found for East and South Central. About 57% of the Chinese population live in these two regions.

THE DIFFERENCE Low overall returns Upper secondary education looks too “risky” to the rural Northeast: Those entered college gain big, while losers swallow the pain of 3-years cost with no human capital accumulation. WHY Industrial Structure? Market openness? Over college-oriented high school education?

THE DIFFERENCE No strong marginal return to higher education. And it seems for Northwest the priority should be lower secondary education Hint These are the real RURAL China

Closer look at the marginal returns The low return to upper secondary education is as eye-popping as the high return to higher education

To Sum Up  The study is simple and straightforward-- Firstly have a national replication to make sure we get results similar to the original study’s, then compare them to regional results. By looking at the nation-region disparities, we are able to assess the OLS-based evidence of college wage premium.  GK(2008) and Wang(2009) advocate mess higher education, but our replications caution on this suggestion. Even assuming the OLS results are enough for causal identification, mess higher education may only benefit half of the population for both countries. Since we are unable to perfectly replicate the two studies, the best way to clear up the worry is regional replication from the authors.