||||||| The MIT Press The Impact of Outsourcing and High-Technology Capital on Wages: Estimates for the United States, 1979-1990 Author(s): Robert C. Feenstra and Gordon H. Hanson Source: The Quarterly Journal of Economics, Vol. 114, No. 3 (Aug., 1999), pp. 907-940 Published by: The MIT Press Stable URL: http://www.jstor.org/stable/2586887 Accessed: 27/07/2010 22:33 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher?publisherCode=mitpress. 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Trade is measured by the foreign outsourcing of intermediate inputs, while technological change is measured by expenditures on high-technology capital such as computers. The estimation procedure we develop, which modifies the conventional "price regression," is able to distinguish whether product price changes are due to factor-biased versus sector-biased technology shifts. In our base specification we find that computers explain about 35 percent of the increase in the relative wage of nonproduction workers, while outsourcing explains 15 percent; both of these effects are higher in other specifications. I. Introduction The recent economic performance of less-skilled workers in industrial countries is an important policy topic and the subject of intense academic attention. During the 1980s and 1990s the wages of low-skilled workers have fallen relative to those of high-skilled workers. In the United States the earnings of the low-skilled have also declined in real terms. The two most widely cited explanations for the rise in wage inequality are skill-biased technical change and trade with low-wage countries. Of these two, technical change due to the use of computers is often believed to be the dominant explanation. The goal of this paper is to develop a new methodology and estimate the impact of trade and technology on wages, for the United States over the period 1979-1990. We will measure trade by the foreign outsourcing of intermediate inputs,1 while we will * The authors thank James Anderson, Robert Baldwin, Alan Deardorff, James Harrigan, Lawrence Katz, Edward Learner, David Richardson, and Matthew Slaughter for very useful comments. Financial support from the National Science Foundation is gratefully acknowledged. 1. Foreign outsourcing was first considered by Lawrence and Slaughter [1993] and more recently by Feenstra and Hanson [1996a, 1996b]. Lawrence and Slaughter [1993] and Berman, Bound, and Griliches [1994] argue that the amount of outsourcing from the United States is too small to explain the change in wages, but this was due to the narrow measure of outsourcing that they used (see Feenstra and Hanson [1996a], pp. 106-107). We will be using a measure of outsourcing constructed as in Feenstra and Hanson [1996b], which is estimated imports of intermediate inputs into each industry. This measure may also miss aspects of outsourcing, such as the use of computer programmers in India for products otherwise manufactured in the United States. Learner [1998] introduces © 1999 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. The Quarterly Journal of Economics, August 1999 907 908 QUARTERLY JOURNAL OF ECONOMICS measure potential technical change by the shift toward high-technology capital such as computers. The starting point for our analysis is a popular method to predict wage changes under zero-profits: a regression of the change in industry prices on the level of factor cost-shares in that industry, where the estimated coefficients are interpreted as the predicted change in factor-prices that are consistent with the movement in product prices. This "price regression" was first used by Baldwin and Hilton [1984], and more recently by Learner [1994, 1998], Baldwin and Cain [1997], and Krueger [1997]. In contrast to existing literature, we argue that when fully specified, this regression becomes an identity and cannot offer any prediction of the implied changes in factor prices, other than that which actually occurred. To move beyond this stalemate, we shall modify the conventional price regression using a two-stage estimation procedure. First, we examine how changes in structural variables, such as foreign outsourcing and high-technology capital, affect industry prices and productivity. By treating industry prices (and productivity) as endogenous, we allow for a large-open-economy setting. From these first-stage results, we decompose price and productivity changes into portions that are attributable to each structural variable. Second, using a modified version of the price regression, we use the decomposed price and productivity changes from the first stage to estimate the change in primary factor prices that is attributable to each structural variable separately. The results indicate how much of the observed rise in wage inequality is attributable to foreign outsourcing or high-technology capital. While we focus on these two explanations, the methodology we develop is quite general and could be used to examine the relationship between factor prices and many types of changes in production techniques. Our approach may help resolve an apparent conflict in the literature over whether it is the factor bias or the sector bias of technological changes that matters for wages.2 Krugman [2000] and Learner [1998, 2000] have debated this point, with Krugman arguing that factor bias is important in a closed or large open economy, and Learner arguing that sector bias is all that matters the broader term "derealization" to indicate the many ways that pieces of the research/production/marketing processes can be moved offshore. 2. See Haskel and Slaughter [1998] and Kahn and Lim [1998] for evidence on the sector-bias of technical change and Berman, Bound, and Machin [1997] for international evidence on skill-biased technical change. OUTSOURCING AND HIGH-TECHNOLOGY CAPITAL 909 in a small open economy (or even with log-linear pass-through from productivity to prices). To resolve this, we need to have an indication of which setting is empirically relevant. This will turn out to be a by-product of our analysis, since our first-stage regression can distinguish between sector-biased and factor-biased technological changes: both of these changes affect industry prices, but (with Cobb-Douglas preferences) only the factor-biased changes will have an impact on wages and prices over and above their impact on productivity. Thus, in a regression of industry prices on total factor productivity, a test for the presence of additional structural variables can be interpreted as a test for nonneutral technological change (conditional on finding complete pass-through from productivity to prices). The specification of our model is derived in Sections II and III; while the data are discussed in Section IV, and empirical results are presented in Section V. In our empirical results, we begin by examining the impact of foreign outsourcing and alternative measures of high-technology capital on the relative demand for skilled labor. This allows comparison with existing literature and our later results. We then consider two specifications to explain industry changes in prices and productivity. In the first, we assume that the structural variables enter linearly as independent variables. In that case we find that computers explain about 35 percent of the increase in the relative wage of nonproduction workers, while outsourcing explains 15 percent. In the second specification we allow for interactions between the structural variables and quantities of primary factors, which is a more direct method to control for the contribution of the structural variables to nonneutral technological change. We then find that foreign outsourcing explains about 40 percent of the increase in the relative nonproduction wage, whereas computer expenditures can explain 75 percent of this increase. Our conclusions are discussed further in Section VI. II. Price Regression The first step in our empirical specification is derive the "price regression" that has been used by Baldwin and Hilton [1984], Learner [1994, 1998], Baldwin and Cain [1997], and Krueger [1997]. While the exact specification that is estimated varies across different studies (see Slaughter [1998] for a survey), the typical regression has the change in industry product prices as a 910 QUARTERLY JOURNAL OF ECONOMICS dependent variable and industry factor cost shares as independent variables, with total factor productivity sometimes included as a regressor. Following the terminology in Learner [1998], the coefficients on the factor cost shares are interpreted as the change in factor prices that are mandated by the change in product prices and, possibly, productivity. The standard method to derive the price regression is to totally differentiate the zero-profit condition for each industry. That is, we treat the product prices as changing due to (unspecified) market forces, leading to an implied change in equilibrium factor prices. Expressing this in first-differences, the relationship is (1) A \npff = -TFPa + y2(^-! + suYA In wit, where pff denotes the value-added price in industry i — 1, . . . , N, TFPit denotes total factor productivity, wit denotes the vector of primary factor prices in industry i, and sit-i and sit are the primary factor cost-shares that are averaged over the two periods.3 The ability of (1) to hold in the data will depend on the measure of total factor productivity that is used. In particular, the dual Tornqvist index of TFP [Caves, Christensen, and Diewert 1982a, 1982b] is defined as the difference between the log change in industry prices, and the cost-shared weighted change in factor prices. Using this particular measure of productivity, (1) clearly holds as an identity, as we assume. It is perhaps more common to work with the primal Tornqvist index of TFP, which equals the log change of output minus the share-weighted growth of inputs. While the primal and dual measures are not equal in general, their difference is extremely small in our sample.4 In order to move from equation (1) to the price regression, as it is conventionally applied, we treat A In wit as a random variable over industries i and denote its mean value by wf. Then using this 3. The value-added price is constructed as A In pj^ = [A Inpit - 1/2(r'it-i + r^)'A In pJ/Ejli V&rijt-i + fßt)], where is the cost-share of intermediate input j used in the production of industry i = 1, . . . , N. We impose the assumption of perfect competition, so that revenue equals costs, and the cost-shares are measured by the revenue shares. Hall [1988] and Domowitz, Hubbard, and Petersen [1988] suggest that imperfect competition may bias standard measures of total factor productivity and that one should account for this bias by introducing controls for price-marginal cost markups. In our empirical analysis we find that introducing such controls (output-capital ratios) has little effect on parameter estimates. 4. The primal measure of TFP is denned as the growth of value-added minus the weighted average growth of primary factors. It has a correlation of 0.999 with the dual measure of TFP denned by (2), for 1979-1990. OUTSOURCING AND HIGH-TECHNOLOGY CAPITAL 911 notation in (1), we readily obtain (2) A lnpjf = -TFPlt + ^(sft-i.+ sft)'o>, + elt, where the final term appearing on the right is (3) eit = 1/2(sit-1 + sit)'(A In wlt - coa). This term equals the average deviation of industry-specific factor-price changes from their mean levels. We refer to the magnitude in (3) as the "change in wage differentials," since it reflects the change in the industry-specific wages for labor (and rental price of capital) in relation to their manufacturing-wide levels. This term is usually excluded from estimation of the price regression and hence implicitly treated as an error term. The change in wage differentials can be measured with available data, however, and we shall explicitly account for its presence in our work. There are two general sources of variation in factor prices across industries, leading to interindustry wage differentials: unobserved variation in factor quality and industry-specific rents. There is extensive empirical literature on interindustry differences in wages, much of which is devoted to ascertaining their source (e.g., Krueger and Summers [1988], Murphy and Topel [1990], and Gibbons and Katz [1992]). Since we examine long-run changes in factor prices in an environment where factors are assumed to be perfectly mobile across industries, we prefer to interpret interindustry factor-price variation as resulting from variation in factor quality across industries, which is consistent with the neoclassical trade model that is the foundation for our analysis. Under this assumption, the effective wages paid by industries—after accounting for quality differences—are properly measured by the manufacturing-wide wages, or cof. It follows that the effective total factor productivity is measured by (4) ETFPit = TFPit - eit. Combining (4) and (2), we obtain an alternative version of the price regression that incorporates the interindustry wage differentials: (2') A \npf = -ETFPit + V6(sft_i + sa)'