By A EKAPOL C HONGVILAIVAN Institute of Southeast Asian Studies (ISEAS), Singapore AND

1. Structural Change and Relative Demand for Skilled Workers: New Evidence from the U.S. Manufacturing By AEKAPOL CHONGVILAIVAN Institute of Southeast Asian Studies (ISEAS), Singapore AND JUNG HUR Department of Economics, Sogang University, Korea

2. What is “structural change”? • Structural change is “change in labor productivity fuelled by labor reallocation across industries”. • It can be either “positive” or “negative”. • Structural change is typically “positive” in developing Asia, while Latin American countries (as well as most developed countries) discern “negative” structural change (McMillan and Rodrik, 2011)

3. U.S. Manufacturing Sectors: Some Stylized Facts • “The largest productivity gains have been in the sectors that have seen large employment reductions” (McKinsey Global Institute, 2011). • Rapid productivity growth over that past decades spurs widespread concerns whether boosting productivity is a “job-killing exercise”. • That is, proliferation of the American economy had been coupled with “negative” structural change whereby labor is displaced in high-productivity industries and reallocated to low-productivity industries.

4. Some possible Explanations • Modest inter-sectoral productivity differentials imply limited room for efficiency gains from labor reallocation. • Advanced economies typically thrive on “industry rationalization” whereby drastic competition forces the least productive firms to exit the markets, thereby enhancing industry productivity. • As this process continues, labor is displaced in high-productivity and eventually ends up in low-productivity industries.

5. Does negative structural change lead to wage inequality between skilled and unskilled workers? • In principle, negative structural change is naturally pertinent to a shift of resources away from labor- or low skill-intensive activities toward capital- or high skill-intensive activities. • Hence, it can be hypothesized this pattern of inter-sectoral labor movement is skill-biased and results in a shift in relative demand for skilled workers. • If the evidence supports this, structural change will be another catalyst of widening wage inequality between skilled and unskilled workers.

6. Yes, we found the evidence 

8. Key Contributions to the Literature • The past studies on structural change put emphasis on the extent to which labor reallocation within sectors contributes to overall productivity growth. • This is the first attempt in investigating the skill-biased effects of structural change! • More importantly, it ushers in a new dimension of wage inequality drivers – negative structural change!

9. Conceptualization of Structural Change • As in McMillan and Rodrik (2011), the overall labor productivity in an economic sector can be decomposed into two components – “within” and “between” components. • The “between” component captures change in sectoral labor productivity contributed by labor reallocation across industries – so called “structural change”.

10. Empirical Model • The most straightforward empirical strategy is to model the relative demand for skilled workers based on the translog cost function (Brown and Christensen, 1981). • As is well known, Sheppard’s Lemma, together with linear homogeneity and symmetry restrictions, yields the following expression:

11. Econometrics Specifications and Estimations • Base-line Specifications: Wage Share Equation • Robustness Check: Employment Share Equation • Three econometric issues to be addressed – heteroskedasticity; unobserved sector-specific effects; and endogeneity biases.

12. Data Sources and Measurements • The 6-digit NAICS U.S. manufacturing industry dataset for the period of 1958-2005 is retrieved from the NBER-CES Manufacturing Industry Database. • To measure structural change, the 6-digit NAICS industries are aggregated into 4-digit NAICS sectors. • Therefore, the notion of structural change pertains to productivity change at the 4-digit level associated with labor reallocation across the 6-digit industries.

13. Cleaning Dataset • All variables reported in a nominal term are reverted into a real term using the relevant industry-specific price indices. • Seven 4-digit NAICS sectors contain only one six-digit industries; as such, we decide to drop those industries. • We meticulously examine potential errors from change in industry specifications from SIC to NAICS in 1997. Our matching exercise reveals that the errors are unlikely. • Finally, our data construction yields 79 4-digit NAICS U.S. manufacturing sectors spanning over the period of 1973-2005 (33 years).

14. Summary of Statistics

15. Correlation Matrix of Control Variables

16. Empirical Results: Wage Share Equation • We find rather strong evidence that the negative structural change in the U.S. manufacturing sectors, given all other things unchanged, contribute to the wage inequality. • The intuition rests with labor displacement associated with structural change. • This evidence cautions that growth – reducing structural change may undermine unskilled jobs and serve as another driving force of rising wage inequality.

17. Supplementary Findings • Capital stock appears to be complementary to relative demand for skilled workers. • The negative scale effect on relative demand for skilled workers is observed. • Although sensitive to IV estimation, high-tech capital intensity is found to be a substitute for skilled workers. • Outsourcing puts upward pressure on relative demand for skilled workers.

18. Supplementary Findings (continued) • We also try to factor out the indirect effects of high-tech capital accumulation and outsourcing on the relative demand for skilled workers vis-à-vis structural change. • High-tech capital intensity seems to amplify the skill-biased effects of (negative) structural change. • Outsourcing activities mitigate the effects of growth-reducing structural change on wage inequality.

19. Robustness Check: Employment Share Equation • The main findings are satisfactorily robust. • Some slight sensitivity, however, is observed. • The interaction term between structural change and high-tech capital intensity, albeit still negative, becomes statistically insignificant.

20. Alternative Measure of Structural Change • So far, the measure of structural change implicitly imposes an unappealing assumption that labor productivity remains unchanged between the time interval between t and t - k. • To get around this problem, we resort to an alternative measure of structural change as suggested by Griliches and Regev (1995).

21. Alternative Measure of Structural Change (continued) • The estimates are virtually identical to those with the standard measure of structural change, in terms of both signs and statistical significance. • The only sensitivity is found in the employment share equation where the coefficient of the interaction term between structural change and outsourcing becomes insignificant.

22. Conclusions and Limitations • The evidence substantiates the hypothesis that structural change is negatively correlated with relative demand for skilled workers. • Therefore, the negative structural change observed in the U.S. manufacturing sectors may be another driving factor of widening wage inequality. • Some limitations: aggregation biases due to the absence of firm-level data; endogeneity problems in other control variables?

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