Industry effects of offshore outsourcing on prices.

摘要

The observed and likely effects of offshore outsourcing on the United States labor market continue to draw increasing attention of those in the media and academia. Discussions of outsourcing extend to popular culture, including jokes by late night television talk show hosts, editorial cartoons, and television programs such as the Lou Dobbs Tonight show on CNN1 . The debate centers on whether outsourcing is contributing to the increasing relative unemployment rate and reduced reward of the less skilled workers in the United States. An issue that is often overlooked is how firms respond to foreign competition and how the labor market is affected by those responses both in the short and long run. Specifically, how employment and wages of low skilled workers (relative to their high skilled counterparts) are affected by those responses. Most existing studies have centered around explaining the long run effects of international trade on the declining relative employment and wages of low skilled workers, thereby assuming the short run effects (Feenstra and Hanson (1999), Leamer (1996)). In this light; the objective of our research is to analyze the effect of offshore outsourcing on the United State's manufacturing industries. Relative to the aforementioned studies, our focus is on the industry rather than the workers. Our analysis is firmly rooted in the structural modeling of the Heckscher-Ohlin-Samuelson (H-O-S) framework. We employ the bound's testing approach to error correction and cointegration to the price equation as specified in Feenstra and Hanson (1999). The effective price is explained by outsourcing, high technology usage and skill intensity. In particular, we study the short run responses of industry's growth to changes in outsourcing, high technology usage and skill intensity, as well as consequent adjustment process to long run equilibrium are analyzed. Based on time series analysis of data on 324 United States manufacturing industries for the period of 1958--2001, we observe that 52% of the industries have their short run adjustments lasting into the long run. Some possible explanations for the 48% that were phased out in the short run are the effects of (a) the cost of maintaining the outsourcing-vendor relationship, (b) transition costs from in-house production to offshore, (c) complex import regulations, (d) relative scale economies enjoyed by the large scale outsourcing and (d) other factors including quality concerns, culture, language and time zone differences and shipping costs. All these factors can translate to huge fixed costs overshadowing the anticipated variable costs savings. Our study also reveals that 22% of those industries that converge to their long run equilibrium are jointly affected by outsourcing, education and technology. This finding justify the need to include internal factors namely education and technology measure in addition to international trade. Also, a sizeable proportion of those industries converging to their long run equilibrium are positively affected by outsourcing and negatively by both education and high technology usage. Regarding each industry's rate of adjustment to its long run equilibrium as portrayed by the estimated value of the lagged error correction term, we find that the rate differs across industries. The industries adjustment costs are inherent in the act of changing their production process and thus, the response in shocks will not be uniform or instantaneous. Simply stated, industries have different short run periods. Some have their short run period lasting three months, other last more than one year. As such, one plausible explanation for the variation in the rate of adjustment is due to the fact that adjustment costs are not uniform across industries2. Unlike the kitchen-sink approaches and/or ad-hoc regressions our analysis stems from a structural modeling of the H-O-S framework. Our contribution complements the existing literature in the following way; we capture the effects of adjustment costs to the industry by analyzing both the short run and long run responses, using the bound's testing approach to cointegration. Furthermore, by using time series data, we focus on the individual industry characteristics that are not captured in panel data.;1See Lou Dobbs (2004). 2See Chakrabarti (1999, 2004).