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

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  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 (https://www.ilo.org/global/about-the-ilo/newsroom/news/WCMS_535607/lang--en/index.htm). 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.

References

  1. Addison, D. (2003). Productivity growth and product variety: Gains from imitation and education. Washington, D.C.: The World Bank.

    Google Scholar 

  2. Aggarwal, S. (2018). Do rural roads create pathways out of poverty? Evidence from India. Journal of Development Economics, 133, 375–395.

    Google Scholar 

  3. Akerman, A., Helpman, E., Itskhoki, O., Muendler, M.-A., & Redding, S. (2013). Sources of wage inequality. American Economic Review, 103(3), 214–19.

    Google Scholar 

  4. Alichi, A., Kantenga, M. K., & Sole, M. J. (2016). Income polarization in the United States. Washington, D.C.: International Monetary Fund.

    Google Scholar 

  5. Atkin, D. (2016). Endogenous skill acquisition and export manufacturing in Mexico. American Economic Review, 106(8), 2046–85.

    Google Scholar 

  6. Becker, G. S. (2009). Human capital: A theoretical and empirical analysis, with special reference to education. Chicago: University of Chicago Press.

    Google Scholar 

  7. Black, D. A., McKinnish, T. G., & Sanders, S. G. (2005). Tight labor markets and the demand for education: Evidence from the coal boom and bust. ILR Review, 59(1), 3–16.

    Google Scholar 

  8. Blanchard, E., & Willmann, G. (2016). Trade, education, and the shrinking middle class. Journal of International Economics, 99, 263–278.

    Google Scholar 

  9. Blanchard, E. J., & Olney, W. W. (2017). Globalization and human capital investment: Export composition drives educational attainment. Journal of International Economics, 106, 165–183.

    Google Scholar 

  10. Bloom, N., Manova, K., Van Reenen, J., Sun, T. S., & Yu, Z. (2018). Managing trade: Evidence from China and the US. Technical Report, National Bureau of Economic Research.

  11. Bombardini, M., & Li, B. (2016). Trade, Pollution and Mortality in China. (Working Paper 22804). National Bureau of Economic Research November 2016.

  12. Brandt, L., Van Biesebroeck, J., & Zhang, Y. (2014). Challenges of working with the Chinese NBS firm-level data. China Economic Review, 30, 339–352.

    Google Scholar 

  13. Burstein, A., & Vogel, J. (2017). International trade, technology, and the skill premium. Journal of Political Economy, 125(5), 1356–1412.

    Google Scholar 

  14. Burstein, A., Cravino, J., & Vogel, J. (2013). Importing skill-biased technology. American Economic Journal: Macroeconomics, 5(2), 32–71.

    Google Scholar 

  15. Cascio, E. U., & Narayan, A. (2015). Who needs a fracking education? the educational response to low-skill biased technological change. Technical Report, National Bureau of Economic Research.

  16. Chakraborty, P., & Raveh, O. (2018). Input-trade liberalization and the demand for managers: Evidence from India. Journal of International Economics, 111, 159–176.

    Google Scholar 

  17. Chen, C., & Steinwender, C. (2019). Import Competition, Heterogeneous Preferences of Managers, and Productivity. Technical Report, National Bureau of Economic Research.

  18. Chesnokova, T. (2007). Immiserizing deindustrialization: A dynamic trade model with credit constraints. Journal of International Economics, 73(2), 407–420.

    Google Scholar 

  19. Costinot, A., & Vogel, J. (2010). Matching and inequality in the world economy. Journal of Political Economy, 118(4), 747–786.

    Google Scholar 

  20. Danziger, E. (2017). Skill acquisition and the dynamics of trade-induced inequality. Journal of International Economics, 107, 60–74.

    Google Scholar 

  21. David, H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review, 103(5), 1553–1597.

    Google Scholar 

  22. David, H., Dorn, D., & Hanson, G. H. (2013). The China syndrome: Local labor market effects of import competition in the United States. American Economic Review, 103(6), 2121–2168.

    Google Scholar 

  23. Davidson, C., & Sly, N. (2014). A simple model of globalization, schooling and skill acquisition. European Economic Review, 71, 209–227.

    Google Scholar 

  24. Dinopoulos, E., & Unel, B. (2015). Entrepreneurs, jobs, and trade. European Economic Review, 79, 93–112.

    Google Scholar 

  25. Dinopoulos, E., & Unel, B. (2017). Managerial capital, occupational choice and inequality in a global economy. Canadian Journal of Economics, 50(2), 365–397.

    Google Scholar 

  26. Dix-Carneiro, R., & Kovak, B. K. (2015). Trade liberalization and the skill premium: A local labor markets approach. American Economic Review, 105(5), 551–57.

    Google Scholar 

  27. Edmonds, E. V., Pavcnik, N., & Topalova, P. (2010). Trade adjustment and human capital investments: Evidence from Indian tariff reform. American Economic Journal: Applied Economics, 2(4), 42–75.

    Google Scholar 

  28. Edmonds, E. V., Topalova, P., & Pavcnik, N. (2009). Child labor and schooling in a globalizing world: Some evidence from urban India. Journal of the European Economic Association, 7(2–3), 498–507.

    Google Scholar 

  29. Emery, J. C. H., Ferrer, A., & Green, D. (2012). Long-term consequences of natural resource booms for human capital accumulation. ILR Review, 65(3), 708–734.

    Google Scholar 

  30. Erten, B., & Leight, J. (2017). Exporting out of Agriculture: The Impact of WTO Accession on Structural Transformation in China. Technical Report, (Working Paper).

  31. Falvey, R., Greenaway, D., & Silva, J. (2010). Trade liberalisation and human capital adjustment. Journal of International Economics, 81(2), 230–239.

    Google Scholar 

  32. Feenstra, R. C., Ma, H., & Xu, Y. (2017). US Exports and Employment. Technical Report, National Bureau of Economic Research.

  33. Foster, A. D., & Rosenzweig, M. R. (1996). Technical change and human-capital returns and investments: Evidence from the green revolution. The American Economic Review, 86, 931–953.

    Google Scholar 

  34. Frensch, R., & Wittich, V. G. (2009). Product variety and technical change. Journal of Development Economics, 88(2), 242–257.

    Google Scholar 

  35. Gabaix, X., & Landier, A. (2008). Why has CEO pay increased so much? The Quarterly Journal of Economics, 123(1), 49–100.

    Google Scholar 

  36. Goos, M., Manning, A., & Salomons, A. (2009). Job polarization in Europe. The American Economic Review, 99(2), 58–63.

    Google Scholar 

  37. Goos, M., Manning, A., & Salomons, A. (2014). Explaining job polarization: Routine-biased technological change and offshoring. American Economic Review, 104(8), 2509–26.

    Google Scholar 

  38. Goh, C. C., Xubei, L. U., & Nong, Z. H. (2009). Income growth, inequality and poverty reduction: A case study of eight provinces in China. China Economic Review, 20(3), 485–496.

    Google Scholar 

  39. Greenland, A., & Lopresti, J. (2016). Import exposure and human capital adjustment: Evidence from the US. Journal of International Economics, 100, 50–60.

    Google Scholar 

  40. Griffin, K., Zhao, R. W., et al. (1993). The distribution of income in China. New York: Macmillan Press Ltd.

    Google Scholar 

  41. Gustafsson, B. A., Shi, L., & Sicular, T. (2008). Inequality and public policy in China. Cambridge: Cambridge University Press.

    Google Scholar 

  42. Hanushek, E. A., Ruhose, J., & Woessmann, L. (2017). Knowledge capital and aggregate income differences: Development accounting for US states. American Economic Journal: Macroeconomics, 9(4), 184–224.

    Google Scholar 

  43. Harrigan, J., & Reshef, A. (2011). Skill biased heterogeneous firms, trade liberalization, and the skill premium. Technical Report, National Bureau of Economic Research.

  44. Harris, R. G., & Robertson, P. E. (2013). Trade, wages and skill accumulation in the emerging giants. Journal of International Economics, 89(2), 407–421.

    Google Scholar 

  45. Helpman, E., Itskhoki, O., & Redding, S. (2010). Inequality and unemployment in a global economy. Econometrica, 78(4), 1239–1283.

    Google Scholar 

  46. Helpman, E., Itskhoki, O., Muendler, M.-A., & Redding, S. J. (2017). Trade and inequality: From theory to estimation. The Review of Economic Studies, 84(1), 357–405.

    Google Scholar 

  47. Hendricks, L., & Schoellman, T. (2017). Human capital and development accounting: New evidence from wage gains at migration. The Quarterly Journal of Economics, 133(2), 665–700.

    Google Scholar 

  48. Jaworski, T. (2014). You’re in the army now: The impact of world war II on women’s education, work, and family. The Journal of Economic History, 74(1), 169–195.

    Google Scholar 

  49. Jones, B. F. (2014). The human capital stock: A generalized approach. American Economic Review, 104(11), 3752–77.

    Google Scholar 

  50. Kaplan, S. N., & Rauh, J. (2010). Wall street and main street: What contributes to the rise in the highest incomes? Review of Financial Studies, 23(3), 1004–1050.

    Google Scholar 

  51. Keller, W., & Olney, W. W. (2017). Globalization and executive compensation. Technical Report, National Bureau of Economic Research.

  52. Kovak, B. K. (2013). Regional effects of trade reform: What is the correct measure of liberalization? The American Economic Review, 103(5), 1960–1976.

    Google Scholar 

  53. Li, B. (2018). Export expansion, skill acquisition and industry specialization: Evidence from China. Journal of International Economics, 114, 346–361.

    Google Scholar 

  54. Lin, J. Y., Wang, G., & Zhao, Y. (2004). Regional inequality and labor transfers in China. Economic Development and Cultural Change, 52(3), 587–603.

    Google Scholar 

  55. Liu, H. (2008). The China health and nutrition survey: An important database for poverty and inequality research. The Journal of Economic Inequality, 6(4), 373–376.

    Google Scholar 

  56. Lucas, R. E. (2015). Human capital and growth. American Economic Review, 105(5), 85–88.

    Google Scholar 

  57. Matsuyama, K. (2007). Beyond icebergs: Towards a theory of biased globalization. The Review of Economic Studies, 74(1), 237–253.

    Google Scholar 

  58. Mau, K., & Xu, M. (2019). Economic growth and complexity across Chinese regions: The role of cost-saving production diffusion. Working Paper.

  59. McCaig, B. (2011). Exporting out of poverty: Provincial poverty in Vietnam and US market access. Journal of International Economics, 85(1), 102–113.

    Google Scholar 

  60. Melitz, M. J. (2003). The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica, 71(6), 1695–1725.

    Google Scholar 

  61. Meng, X. (2012). Labor market outcomes and reforms in China. Journal of Economic Perspectives, 26(4), 75–102.

    Google Scholar 

  62. Morissette, R., Chan, P. C. W., & Lu, Y. (2015). Wages, youth employment, and school enrollment recent evidence from increases in world oil prices. Journal of Human Resources, 50(1), 222–253.

    Google Scholar 

  63. Munshi, K., & Rosenzweig, M. (2006). Traditional institutions meet the modern world: Caste, gender, and schooling choice in a globalizing economy. American Economic Review, 96(4), 1225–1252.

    Google Scholar 

  64. Muralidharan, K., & Prakash, N. (2017). Cycling to school: Increasing secondary school enrollment for girls in India. American Economic Journal: Applied Economics, 9(3), 321–50.

    Google Scholar 

  65. Parro, F. (2013). Capital-skill complementarity and the skill premium in a quantitative model of trade. American Economic Journal: Macroeconomics, 5(2), 72–117.

    Google Scholar 

  66. Raveh, O., & Reshef, A. (2016). Capital imports composition, complementarities, and the skill premium in developing countries. Journal of Development Economics, 118, 183–206.

    Google Scholar 

  67. Shastry, G. K. (2012). Human capital response to globalization education and information technology in india. Journal of Human Resources, 47(2), 287–330.

    Google Scholar 

  68. Tan, J., Zeng, T., & Zhu, S. (2017). Earnings, income, and wealth distributions in China: Facts from the 2011 China Household Finance Survey. Wokring Paper.

  69. Topalova, P. (2010). Factor immobility and regional impacts of trade liberalization: Evidence on poverty from India. American Economic Journal: Applied Economics, 2(4), 1–41.

    Google Scholar 

  70. Unel, B. (2015). Human capital formation and international trade. The BE Journal of Economic Analysis & Policy, 15(3), 1067–1092.

    Google Scholar 

  71. Unel, B. (2018). Offshoring and unemployment in a credit-constrained economy. Journal of International Economics, 111, 21–33.

    Google Scholar 

  72. Valletta, R. (2015). Recent flattening in the higher education wage premium: Polarization, deskilling, or both?,” in “education, skills, and technical change: Implications for future US GDP growth. Chicago: University of Chicago Press.

    Google Scholar 

  73. Verhoogen, E. A. (2008). Trade, quality upgrading, and wage inequality in the Mexican manufacturing sector. The Quarterly Journal of Economics, 123(2), 489–530.

    Google Scholar 

  74. Zhang, Y., & Wan, G. (2006). The impact of growth and inequality on rural poverty in China. Journal of Comparative Economics, 34(4), 694–712.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Mingzhi Xu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Electronic supplementary material

Below is the link to the electronic supplementary material.

10290_2020_374_MOESM1_ESM.pdf

Supplementary material 1 (pdf 1073 KB)

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xu, M. Globalization, the skill premium, and income distribution: the role of selection into entrepreneurship. Rev World Econ 156, 633–668 (2020). https://doi.org/10.1007/s10290-020-00374-2

Download citation

Keywords

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

JEL Classification

  • F11
  • F16
  • L2
  • I24
  • J24