Abstract
This paper empirically examines how job destruction affects the impact of imports on U.S. wages from 1983 to 1999. Based on Helpman et al. (Econometrica 78:1239–1283, 2010), I raise the concern that if the probability of displacing workers by imports depends on their wage level, job destruction is likely to reduce the negative effects of import competition on average industry wage. To connect Helpman, Itskhoki, and Redding to my empirical analysis, I focus on variations in workers’ residual wages obtained from estimating the Mincerian wage equation. This is because Helpman, Itskhoki, and Redding focus on the wage distribution of workers with the same observed characteristics. I find that the lower the job destruction is than average, the more significant and sizeable the negative effect of import competition on the average residual wage, while the effect of import competition on the average residual wage is positive but insignificant at the 10% level at maximum job destruction. The robustness checks support this evidence. The findings imply that unemployment by increased imports leads to underestimating workers’ anxiety about the negative wage effects of imports.
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Notes
The Mincerian wage equation is used to estimate the premium of observed characteristics such as education, gender, and experience. In this equation, the residual wage is empirically defined by the residual term.
This strategy offers the advantage of avoiding the Moulton problem. If we constructed the estimation equation with the individual-level dependent variable and industry-level independent variables, the Moulton problem would lead to underestimation of the standard errors. According to Angrist and Pischke (2009), using group averages instead of microdata is a good way to avoid the problem.
The reader can find a more detailed discussion about the solutions in HIR.
For simplification, this paper assumes that M and H are constant.
Equation (8) is not differentiable at θ x because it is jump-discontinuous at θ x .
Menezes-Filho and Muendler (2011)showed the interesting evidence that tariff cuts and additional imports trigger worker displacement but that neither comparative-advantage sectors nor exports absorb the trade-displaced workers.
According to Boyrie and Kreinin (2012), the U.S. Grubel-Lloyd index of intra-industry trade increased from 0.3 in 1960 to above 0.6 in 2000. The United States experienced a particularly drastic increase in intra-industry trade from 1985 to 1999.
Technical changes in the opening of trade could potentially affect workers’ employment status. Alexandre et al. (2011) show that the technology level affects the negative impact of exchange rates on employment destruction.
White (2008) showed that increased exports created jobs in the U.S. manufacturing sector, whereas increased import penetration reduced both production and non-production employment in the sample period, 1972–2001.
For this paper, I use the list of low-wage countries in Bernard et al. (2006): Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bhutan, Burkina Faso, Burundi, Cambodia, Central African Republic, Chad, China, Comoros, Congo, Equatorial Guinea, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guinea, Guinea-Bissau, Guyana, Haiti, India, Kenya, Lao PDR, Lesotho, Madagascar, Malawi, Maldives, Mali, Mauritania, Moldova, Mozambique, Nepal, Niger, Pakistan, Rwanda, Samoa, Sao Tome, Sierra Leone, Somalia, Sri Lanka, St. Vincent, Sudan, Togo, Uganda, Vietnam, and Yemen.
If hourly wages were absent and only weekly wages were recorded, the rate would be defined as weekly wages divided by the usual weekly hours for salaried workers.
Top-coded weekly and hourly wage are controlled in multiple ways. DiNardo, Fortin, and Lemieux (1996) used the upper tail of the 1986 wage distribution to impute a wage distribution to the observations censored at the top code in other years. Moreover, the CPS questionnaire recommends their removal.
Here, I calculated the college premium by subtracting the coefficient of ed6 from that of ed8. Given that ed7 requires 13–15 years of schooling, it is not relevant for college premium. Therefore, I assumed that a bachelor’s degree requires 16 years of schooling, and the college premium is the wage difference between high school and college graduates.
To match the import, export, and job flow indices, I used the output and employment data in Becker et al. (2013) as weights; I used the CIF (cost, insurance, and freight) values of imports in cases of matching tariffs.
CPS has its own industry classification based on the SIC codes. Some sectors cannot be divided by the three-digit SIC87 codes and I merged them as follows: I merged primary aluminum with other primary metal industries, and I merged the scientific and controlling instruments industries with medical, dental, and optical instruments and supplies. I separately excluded leather tanning and finishing and watches, clocks, and clockwork devices because of inadequate observations, and I also excluded seven industries that had no information on imports and exports.
The fixed effects estimates of the lagged dependent variable can be severely biased downwards for small T, as shown by Nickell (1981).
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Acknowledgments
I would like to thank Robert McNown, Wolfgang Keller, Brian Cadena, Carol Shiue, Will Olney, the editor and two anonymous referees for their helpful comments.
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Kang, Y. Job Destruction and the Impact of Imports on Wages in U.S. Manufacturing. Open Econ Rev 28, 711–730 (2017). https://doi.org/10.1007/s11079-017-9432-5
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DOI: https://doi.org/10.1007/s11079-017-9432-5