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GLOBALIZATION, WAGES, AND THE QUALITY OF JOBS: FIVE COUNTRY STUDIES
The second and third columns in table 5.9 present the results for 2004 and 2005. In the agricultural food sector the wage differential, although still negative, rose considerably compared with 2000. The main reason behind the large negative number in 2000 was the drop in coffee prices—El Salvador’s main agricultural export crop—during the period 1997–2001. Although 2004 and 2005 were recovery years for agriculture, the wage differential was still negative. Before 2005, the MFA was in effect, favoring countries in Central America with preferential access to the U.S. market through the Caribbean Basin Initiative. On January 1, 2005, the MFA expired. The MFA’s coming demise was known by 2004, and many firms in the sector began to move their operations to countries with comparative advantages after the elimination of quotas in the sector (mainly South Asian countries and China). Table 5.9 shows how the IIWD for apparel declined in those years, as did the industry’s share in employment (shown in table 5.6). The rising wage premium (2004 to 2005) coincides with a falling female share of total employment in apparel.
THE RELATIONSHIP BETWEEN FDI AND IIWDS Ideally, sufficient data would be available to allow IIWDs to be regressed on a full set of variables that characterize globalization. Such data are not available for El Salvador. This section presents some evidence about the link between globalization—as measured by FDI—and IIWDs. The available FDI data are categorized by different industries that are used for the estimated IIWD results. These two data sets can be compared by reclassifying the industries in table 5.9 into the following 10 industries: agriculture, fishing, mining, apparel, nonapparel manufacturing, utilities, construction, sales, services, and financial. Estimating IIWDs, ω, for these 10 industries for each year from 1997 to 2005 (inclusive) creates a data set of 10 industries with 8 years of data that can be matched with the FDI data described earlier. These data represent FDI stocks, rather than flows. Differencing the FDI data captures the net effects of FDI flows. The following regression equation allows for the possibility that FDI flows take time (one year) to affect wages: wit = a + bΔFDIit−1 + uit.
(5.1)
The random-effects estimate for b is 0.0005628, which is significant at the 5 percent level. The standard deviation of the change in FDI is 106.8591, which implies that a onestandard-deviation change in FDI would lead to an increase in the IIWD of 0.06. The estimated positive link between FDI and IIWDs is consistent with the literature that estimates the link between nonwage measures of working conditions and FDI (Daude, Mazza, and Morrison 2003; Mosley and Uno 2007). These and previous studies generally find nonnegative (either zero or positive) relationships between FDI and working conditions. (None of these studies, however, directly analyzes IIWDs.) There are still several reasons to interpret the results obtained here with caution. First, these results are estimated over very small samples (only seven time-series observations). Although these differentials are estimated controlling for demographic characteristics, they do not include other macroeconomic explanatory variables that might also contribute. Nevertheless, there is no evidence that increasing FDI lowers wage differentials.