Globalization, the skill premium, and income distribution: the role of selection into entrepreneurship


This paper proposes a novel channel by which trade affects the skill premium and the household income distribution: the selection-into-entrepreneurship mechanism. Trade liberalization intensifies competition for profit, discouraging less able educated workers from sorting into entrepreneurship and increasing the skill supply. As a result, the return to college declines, leading college enrollment to decrease. We illustrate this mechanism with a simple trade model and show that while highly talented households optimally respond to export opportunities by engaging in entrepreneurial investment and moving up the income distribution, less able educated households self-select downward along the income distribution, which gradually results in household income polarization. Using Chinese household survey data, we employ a Bartik-type instrument for export expansion to investigate how globalization affects the return to college, the selection into entrepreneurship, and the income distribution. The analysis shows that regions facing more export exposure are associated with a larger drop in the skill premium, a greater selection effect on household business ownership, and a stronger polarization pattern in the household income distribution. The entrepreneurship angle highlighted by this paper offers another lens through which to study the broader impact of trade shocks on workers’ occupation sorting and the distribution of income.

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  1. 1.

    For instance, International Labour Organization reports that the middle class in Europe shrank by 2.3% between 2004 and 2011, and the drop has continued since then ( In the US, Valletta (2015) also finds polarization in the earning distribution, which can be partly explained by the flattened wage premium for individuals with high education. For policymakers, maintaining a prosperous middle-class population not only matters for sustainable economic growth (Jones 2014; Lucas 2015; Hanushek et al. 2017; Hendricks and Schoellman 2017; Blanchard and Olney 2017), but is also related to keeping a healthy inequality level (Blanchard and Willmann 2016).

  2. 2.

    The business income share has increased from 0.7% in 1995 to 5.8% in 2007 in CHIP, and a similar trend can also be observed in the China Health Nutrition Survey (CHNS).

  3. 3.

    This is also consistent with the findings of Gabaix and Landier (2008), who argue persuasively that market capitalization of large firms can fully explain changes in CEO pay.

  4. 4.

    In each regression, we include not only the labor share of export-intensive industries but also an interaction term between the labor share and our variable of interest depending on the specification. Other robustness checks include controlling for the city (or city-year) fixed effect, as suggested in Li (2018), and constructing the Bartik IV using the employment weights calculated from the 1990 census. Detailed discussions are provided in Sect. 4.

  5. 5.

    Li (2018) also estimates the impact of a Bartik-style export shock on educational outcomes such as the skill premium. While she separates the trade shock into a low- and a high-skill component and evaluates their heterogeneous effects separately, she doesn’t report an overall effect of the export shock. We complement her empirical findings, showing that the overall effect of a trade shock on the skill premium tends to be negative in the context of China.

  6. 6.

    Substantial research has been done on how trade or trade-induced technology change affects the skill premium. Examples include Matsuyama (2007), Verhoogen (2008), Costinot and Vogel (2010), Harrigan and Reshef (2011), Parro (2013), Burstein et al. (2013), Raveh and Reshef (2016), and Burstein and Vogel (2017). However, all these studies assume away the endogeneity of the skill supply.

  7. 7.

    Li (2018) separately constructs export shocks to skilled and unskilled labor and directly evaluates their heterogeneous impacts on individual educational attainment. The main mechanism is in line with the finding of Atkin (2016) and Blanchard and Olney (2017) that the expansion in skill-demand embodied exports changes the demand for skilled labor and the skill premium, which in turn affects educational choice. Li (2018) also investigates the impact of import shocks and finds the opposite pattern. Taking an entrepreneurship angle, we empirically study the overall effect of export shocks on the skill premium, household business activity, and the income distribution in China. Because of data limitations, we cannot directly test the effects of export shocks on schooling decisions.

  8. 8.

    Davidson and Sly (2014) study how informational asymmetries affect the interaction between globalization and skill acquisition. Research investigating educational effects resulting from a certain shock or event (other than trade) that change local production patterns includes Foster and Rosenzweig (1996), Shastry (2012), and Cascio and Narayan (2015) for studying technology; Black et al. (2005), Emery et al. (2012), and Morissette et al. (2015) for natural resources; Munshi and Rosenzweig (2006) for institutions; Aggarwal (2018) for road infrastructure; and others (Jaworski 2014; Muralidharan and Prakash 2017).

  9. 9.

    Occupational choice in Dinopoulos and Unel (2015) and Dinopoulos and Unel (2017) consists of only one type of labor and entrepreneurship, which is not a suitable way to study any policy effect on the skill premium. In the extended framework, we model education as a signaling device similar to Davidson and Sly (2014).

  10. 10.

    Early work by Chesnokova (2007) finds that trade liberalization can potentially lead entrepreneurs to under-invest in industry under credit constraints, possibly decreasing welfare.

  11. 11.

    See the references listed in footnote 6 and Burstein and Vogel (2017) for detailed information on each aspect.

  12. 12.

    Detailed information on CHIP and CHNS is provided in “Appendix C1 (Electronic supplementary material)”. Note that we also use the China Household Financial Survey (CHFS) for the year 2012.

  13. 13.

    The pattern remains similar when we use the CHNS sample, which is shown in Figure A.5 of “Appendix D (Electronic supplementary material)”.

  14. 14.

    See Alichi et al. (2016) for details.

  15. 15.

    The pattern of a shrinking middle class is robust to using different cutoffs to group households and using different datasets. The robustness is provided in “Appendix D (Electronic supplementary material)”.

  16. 16.

    Taking business income into consideration is also motivated by the findings of Tan et al. (2017), who investigate the role of income sources in shaping overall inequality. They find that income sources between the rich and the poor are systematically different, which explains a sizable margin of overall inequality. Business income accounts for the largest share (59.09%) in the total income of the top 1% households, whereas labor income accounts for only a small share (21.35%). In contrast, only 7.43% of income is from business income for the bottom 5% earners, and the main sources for this group are transfer income (63.15%) and labor income (22.68%).

  17. 17.

    The rising business income share among the high-income group is also found in the CHIP dataset, as shown in Figure A.6 of “Appendix D (Electronic supplementary material)”.

  18. 18.

    The absentee agents use the collected costs to consume the final product; that is, education costs can be considered as payment transfers from citizens to the schooling sector so that there is no income loss.

  19. 19.

    We model education as a signaling device that allows workers to distinguish themselves from unskilled workers. Schooling alone does not improve employees’ ability in our case. The assumption is quite standard in the human capital literature and is also used in a similar way in Davidson and Sly (2014). The schooling cost in Davidson and Sly (2014) is in the form of dis-utility.

  20. 20.

    Effort z captures factors that can affect a firm’s production efficiency, such as managerial decisions. As also used in Dinopoulos and Unel (2015), the way of modeling the firm’s productivity \(\phi =z^{1/(\sigma -1)}\) is for algebraic simplicity. Results do not change qualitatively if the exponent of z varies. This parameter mirrors the spirit of the human capital theory of Becker (2009), which implies that entrepreneurs with higher managerial ability incur a lower marginal cost of improving firm efficiency through better management of the firm’s operation. This specification is also used in Dinopoulos and Unel (2015, 2017), Unel (2015, 2018).

  21. 21.

    The full solution to the model is presented in the “Appendix A1 (Electronic supplementary material)”.

  22. 22.

    The full expression to determine \(a_{e}\) is \([\kappa _{\pi }A^{\sigma }\tilde{w}^{1-\sigma }]^{2}a_{e}/(2\lambda )=w\), where the left-hand side of equality denotes the profit of a domestic firm with manager’s ability at \(a_{e}\). Profit function is provided in (31) of “Appendix (Electronic supplementary material)”.

  23. 23.

    In algebra, we let \(T=(\eta ^{2}+2\eta )^{k}/f_{x}^{k-1}\) where \(\eta =\tau ^{1-\sigma }\); that is, the reduction in \(f_{x}\) or \(\tau\) suggests a greater value of T. For a detailed derivation refer to “Appendix A1 (Electronic supplementary material)”.

  24. 24.

    In this case, the relative skill supply is barely changed across ability when \(|c'(a)|\) is close to zero; that is, \(d\ln \left( H^{S}/L^{S}\right) =-c'(a_{s})\times da_{s}/w\approx 0\).

  25. 25.

    The summary for selection effects in different models is provided in “Appendix A8 (Electronic supplementary material)”.

  26. 26.

    If labor is highly mobile across regions, trade may affect workers without its consequences being identifiable at the regional level.

  27. 27.

    We construct the Bartik IV using both the lagged (\(L_{rk,t-2}\)) and the initial (the average \(L_{rk,0}\) between 1997 and 1999) city-sector employment in computing the Bartik weights. The specialization pattern (i.e., the sectoral employment shares by region) remain highly persistent over time, which barely contributes to the temporal variation of Bartik IV even in the case where we use lagged employment as Bartik weights. Results remain similar regardless of using initial or lagged city-sector employment. Details are provided in Sect. 4.6.

  28. 28.

    The results remain similar if we construct \(ExportTariff_{k,t}\) without export weights.

  29. 29.

    The relationship between exports and tariffs is displayed in Figure A.3 of “Appendix B (Electronic supplementary material)”. The negative slope indicates that a 1% rise in foreign tariffs imposed on Chinese exports decreases China’s exports by 0.19% on average. This effect is highly significant and economically sizable. The strong correlation remains robust after we exclude outliers. In Table A.2 of “Appendix B (Electronic supplementary material)”, we report the performance for Bartik IV. In both specifications, the F-statistics are all greater than 10, indicating a strong correlation between IV and the instrumented variable. We also show that the strong correlation is not from the employment weights, as the employment share of export-intensive industries is not correlated with the instrumented variable, as also reported in columns (3) and (4) in Table A.2. In addition, we report the first-stage regression results for all specifications in Table A.3, and all F-statistics are well above 10.

  30. 30.

    Detailed information on data sources is provided in “Appendix C1 (Electronic supplementary material)”.

  31. 31.

    We refer to a region as a city in CHIP and as a province in CHNS due to restrictions on information disclosure in CHNS. We also include the region-year fixed effects as additional specifications. As discussed earlier, the region (region-year) fixed effects also take care of any city (city-year) specific predetermined trends in outcome variables that can be correlated with the initial (or lagged) industry specialization.

  32. 32.

    In the baseline regression, we consider a business activity as a real business if the generated income is large enough; that is, the business income accounts for at least 50% of total household income. In robustness checks, we relax this restriction: any business activity that generates a positive income will be considered as a real business.

  33. 33.

    We combine the middle- and low-income groups because of the lack of enough observations of households who own a business in the low-income group. Similarly, we merge middle- and low-income groups into one in regression (18).

  34. 34.

    In 2007, \(ExportShock_{ct}\) has increased by about $16,640 per worker on average relative to 1999.

  35. 35.

    In the probit model, the two numbers are 2.14% and 2.26% for the high- and middle/low-income households, respectively.

  36. 36.

    These empirical findings are consistent with the prediction of Proposition 3. So far, we have completed testing all implications of Proposition 3.

  37. 37.

    The description of the method is provided in “Appendix E (Electronic supplementary material)”.

  38. 38.

    The results hardly change if we use employment in a single year only; that is we also use \(ExportShock_{r,t}^{IV}=\sum _{k}\frac{L_{rk,0}}{L_{r,0}}\frac{\Delta \hat{E}_{k,t}}{L_{k,t}}\), and the results remain similar.

  39. 39.

    The high-income group refers to households with income more than 175% of the median level; the middle-income group refers to households with income between 25% to 175% of the median; and the low-income group refers to households with income less than 25% of the median income.

  40. 40.

    A similar issue results from the migration of workers, which has been investigated by Li (2018). In the same context as China, she finds that selective migration on the worker side doesn’t have much of an effect on the estimation of how trade affects the educational choice.


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Correspondence to Mingzhi Xu.

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We are deeply grateful to the two anonymous referees for their extremely constructive comments and instructions. Thanks also go to Emily Blanchard, Eugene Beaulieu, Loren Brandt, Belton M. Fleisher, Jason Garred, Shengyu Li, Ruijuan Liu, Hong Ma, Priya Ranjan, Alan Taylor, Miaojie Yu, and seminar participants at UC Davis, CES Conference in Sacramento, RMET Conference, NSD Trade Conference, and SJTU Biennial Conference for providing insightful feedback. We are extremely grateful to Robert C. Feenstra, Giovanni Peri, Deborah L. Swenson and Ina Simonovska for their guidance and support. All errors are mine. We especially thank Robert C. Feenstra for generously providing the China Custom Dataset. Financial support from the China Scholarship Council is gratefully acknowledged.

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Xu, M. Globalization, the skill premium, and income distribution: the role of selection into entrepreneurship. Rev World Econ 156, 633–668 (2020).

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  • Trade
  • Occupational choice
  • Entrepreneurship
  • Income polarization
  • China

JEL Classification

  • F11
  • F16
  • L2
  • I24
  • J24