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The structure of relative wages in the U.S. economy has undergone a major transformation in the last 30 years. Wage differentials between more- and less-educated workers have risen sharply (Katz and Autor 1999; Lemieux 2008). Within narrow groups of workers defined by education, gender, and birth cohort, the distribution of wages has become much more dispersed (Juhn, Murphy, and Pierce 1993). This increase in within-group dispersion reflects wider fixed individual wage differentials and more pronounced volatility in both persistent and transitory shocks (Gottschalk and Moffitt 1994, 2009). Overall, the U.S. wage structure has become much more unequal.
This surge in U.S. economic inequality has generated great interest among labor economists and macroeconomists. A vast theoretical and empirical literature investigates the sources of the phenomenon. The leading explanation is that the widespread adoption of new information and communication technologies has raised the relative productivity of more-skilled labor – complementary to the new technologies in production – and lowered the demand for less-skilled workers employed in tasks easily replaceable by the new machines (Krusell, Ohanian, Ríos-Rull, and Violante 2000; Acemoglu 2002; Autor, Katz, and Kearney 2006; Acemoglu and Autor 2010). A less-prominent role is attributed to falling demand for unskilled-intensive goods produced in the United States because of greater openness to trade and offshoring of unskilled stages of production (Feenstra and Hanson 1996; Burstein and Vogel 2010). The rise in idiosyncratic volatility is viewed as the result of a more turbulent work environment and faster skill obsolescence (Violante 2002); changes in wage-compressing labor-market institutions such as unions (DiNardo, Fortin, and Lemieux 1996); and a contractual shift toward performance-based and piece-rate pay (Lemieux, MacLeod, and Parent 2009).
The purpose of this chapter is to explore in detail a number of econometric issues associated with the specification and estimation of a non-linear, latent variable aggregate production function developed in Krusell, Ohanian, Rios-Rull, and Violante (1995) (hereafter KORV). In particular, we discuss how different simulation-based methods can be used to address a number of difficult problems associated with our particular model, and evaluate the relative performance of these methods. Since some of these issues have not been analyzed in much detail using simulation-based methods, the findings reported here may be of use to other researchers working in similar environments.
In our earlier paper, we developed an aggregate production function that differs substantially from the standard production function used in macro-economic analysis. The development of our alternative model was motivated by a key fact of the US economy. In the last 30 years, a substantial difference has emerged in the growth rates of wages for workers with different educational levels. John, Murphy, and Pierce (1993) report that the median wage earner among college graduates experienced a 15 percent increase in inflation-adjusted wages between 1964 and 1988, while the median wage earner among high school graduates experienced a 5 percent decline in real wages over the same period. The widening gap in the relative wage of skilled or unskilled labor is commonly referred to as the “wage premium” or “skill premium.”
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