Abstract
There is no consensus on the impact of education on entrepreneurial choice in both theory and empirics. China’s Higher Education Expansion (HEE) policy initiated in 1999 provides us a unique opportunity to identity the causal relationship between college education and entrepreneurship by exploiting the Fuzzy Regression Discontinuity Design (FRDD) approach. In this paper, we use the China Household Income Project (CHIP) 2013 database, finding that China’s HEE policy significantly increases the probability of obtaining college education by 12%. There is suggestive evidence that college education decreases overall and self-employed-type of entrepreneurial choices, but increases boss-type activities; none of the coefficients are precisely estimated, though. Our results are robust to different inference approaches.
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Notes
This definition is consistent with Li and Wu (2014)’s research on China’s entrepreneurship.
Prior to 2003, the examination was held in July, but has since been moved to the month of June in consideration of the adverse effects of hot weather on students living in southern China and possible flooding during the rainy season in July.
Only few provinces (mainly the minority Autonomous Regions) set the entrance age of primary school to 7 years old.
In literature, the observed variable affecting treatment status is also called as score variable.
The program operates as follows: first, recentering the usual t-statistic with an estimate of the leading bias, which can correct the bias of RD estimator to account for the effect of a “large” bandwidth choice; second, rescaling the bias-corrected t-statistic with a novel standard error formula that accounts for the additional variability introduced by the estimated bias.
We will define these variables in data section.
Compared with the well-known McCrary’s manipulation, which requires pre-binning of the data and hence introduces additional tuning parameters, this new test requires choosing only one tuning parameter, avoids pre-binning the data and permits the use of simple well-known weighting schemes, and thus it removes the need of choosing the length and positions of bins, or employing complicated boundary kernels directly. In our test, we use the corresponding Stata package with default options.
In traditional analyses, RD plots are typically presented employing ad hoc choices of tuning parameters, which makes these procedures less automatic and more subjective. This new data-driven plot doesn’t require additional choices of tuning parameters, which is objective and automatic.
In the first version, we use the default spacings estimator. In fact, spacings is more suitable for continuous variable. We appreciate the referee points out our mistake, and we now use polynomial estimator.
In unreported results, we control all pre-determined variables in all robustness checks, and find that all results are still robust.
In our main analyses, we restrict our sample to a 10 years window. Other windows, such as 6 years, 7 years, and so on, have also been used but not reported. Results are still robust.
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Acknowledgements
We appreciate support from the Income Distribution and Development Policy Research Project at the Research Institute of Economics and Management of SWUFE. We are very grateful for the editor and reviewer’s insightful feedback. All errors are mine.
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Chu, T., Wen, Q. Does College Education Promote Entrepreneurship in China?. J Labor Res 40, 463–486 (2019). https://doi.org/10.1007/s12122-019-09293-0
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DOI: https://doi.org/10.1007/s12122-019-09293-0
Keywords
- College education
- Entrepreneurship
- Higher education expansion policy
- Fuzzy regression discontinuity design