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.
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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
hugediversities within the 2 nations
The Race between Education and Technology (Goldinand Katz, 2008)
Universal high school and mass higher education (Wang, 2009)
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.
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.
Goldin and Katz (2008)
Data. Yearly CPS and Census (when available) data, 1915-2005
Method. Regress the college-high school wage premium (log ratio) on relative supply, with institutional factors and time trends controlled
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
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 sixadministrative divisions
Data. Same for national replication, but 1976-2010 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)
The Model Works for 52% of the US Population
Relatively optimistic actual premiums evolutions for WNC and SA, and the predicted ones are even more optimistic
THE DIFFERENCE: Quite obvious…
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
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
Larger gap of return to higher education
Lower marginal return of higher education, BUT still can tell it is big
Similar shapes are found for East and South Central. About 57% of the Chinese population live in these two regions.
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.
Over college-oriented high school education?
No strong marginal return to higher education.
And it seems for Northwest the priority should be lower secondary education
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