Relative wages, labor supplies and trade in Mexican manufacturing: Evidence from two samples

Abstract

How do relative wages (between skilled and unskilled workers) respond to technical progress and to relative supply shifts? An empirical model of the wage premium for Mexican manufacturing is employed on two monthly data samples: one, from 1987 to 1995, displays the well-documented rising trend in wages right after Mexico joined GATT; the other, from 1994 to 2007, suggests slightly decreasing wages. The model provides support for skill-biased technical change (SBTC) and yields plausible elasticity of substitution for the first sample (σ = 1.03) and higher elasticity for the second (σ = 1.71). Allowing export intensity and the real exchange rate to modify the factor augmenting technology ratio, negative relationships are found for the earlier sample: the higher the export intensity or real exchange rate the lower relative wages. The error correction methodology and the bounds approach confirm these results. Combining trade and SBTC, this study supports the view that trade considerations have an impact on wage premiums at the very beginning of trade liberalization. In contrast, the benchmark model seems a more adequate representation when NAFTA and a market-oriented peso help consolidate Mexico in its path towards sustainable growth.

Keywords:

• export intensity
• Mexico
• real exchange rate
• relative wages
• SBTC
• wage premium

Notes

1. Export intensity and import penetration ratios, controlled for demand, do not help explain the rise in relative wages of white to blue-collar workers during 1987–1995 in Mexico, according to Mollick (2002). For Argentina, in industries where import penetration increased mostly, Galiani and Sanguinetti (2003) find that wage inequality widened more in favor of skilled workers, although trade does not explain a large portion of the rise in the skilled premium. In addition, a burgeoning literature follows the two-stage framework of Gaston and Trefler (1994) at the individual worker level, such as: Pizer (2000) for US industries; the impact of Colombian trade reforms on wages by Attanasio et al. (2004); Pavcnick et al. (2004) for Brazil; and Milner and Tandrayen (2004) for Sub-Saharan countries.

2. Topel (1997) discusses certain changes in labor supply (immigration and increased female labor force participation) exacerbating inequality, while there is also the likelihood that human capital investment mitigates wage inequality. Will increased supply of college graduates reduce the relative wages? The answer depends on how well different skill groups substitute for one another in production, through the elasticity of substitution: σ. When the demand for college-education labor is fairly inelastic (high school and college are poor substitutes), increased supply of college graduates will reduce their relative wage.

3. This definition of xint is very close to the alternative (xint2) in which exports are divided by the sum of imports, domestic sales, minus exports, as employed by Pizer (2000). See Figures 2a and 2b for the visual trend of both measures. With the 1994 devaluation, exports surge and imports fall, contracting the denominator and leading the figure slightly over 1 for the earlier sample. A different definition, which bounds export intensity to lay within 0 and 1 divides exports by the sum of domestic sales plus exports. Qualitatively, the results are the same under this new measure but we would not have a measure that is as commonly used as xint or xint2 have been. Revenga (1992) has another import share definition as well.

4. See also Revenga (1992) for the US and Li and Xu (2003) for export intensity's role in explaining the skilled labor share in China. After estimating a Mincerian equation for wages in the first-stage, a second stage estimation regresses the industry wage premiums on a vector of trade-related industry characteristics, of which export intensity is a regressor. For example, Attanasio et al. (2004) and Pavcnick et al. (2004) use lagged exports; Pizer (2000) uses lagged export intensity; and Milner and Tandrayen (2004) employ a dummy variable for exporting firms.

5. Kurozumi (2002) discusses further KPSS tests with breaks and Carrion-i-Silvestre and Sansó (2006) handle the calculation of the long-run variance in the KPSS statistic. Herein, we chose the truncation lag (l) in such a way that l = [k(T/100)^0.25], where k ∊ {4, 12}. The long-run variance and therefore the KPSS-statistic were estimated exactly as in KPSS (1992).

6. See Slaughter (2001) for individual firms facing perfectly elastic labor supplies when employing four-digit data. In his case, industry labor supply is closer to perfectly elastic than to perfect inelastic. An alternative identification strategy to the one above is to use instrumental variables, which are, however, difficult to obtain in practice. See Revenga (1992) and Mollick (2002) for examples in this context.

7. This theoretical possibility is derived fully in Acemoglu (2002, 38–39). It is possible to show that rwhr = (p_HN_H /p_LN_L ) = (H/L)^{(2ρ − 1)/(1-ρ}) = (H/L)^σ − 2, where N is the number of machines used by each type of worker. If σ > 2 (or ρ > 0.5), the skill premium is an increasing function of the relative supply of skills.

8. We also lag export intensity in order to allow for the possible endogeneity of trade flows. The lagged one period procedure was adopted by Attanasio et al. (2004) and Pavcnick et al. (2004) but did not change the qualitative nature of our findings. Rather, the magnitude of the β₃ coefficient decreased marginally. For the statistically significant coefficients, β₃ was found to be (standard error in parenthesis): −0.083 (0.024).

9. The weak exogeneity methodology adopted is as follows. We first regress relative wages on labor supplies and save these first residuals. We then regress labor supplies on its own lagged terms as well as lagged wages and save these second residuals. Finally, we regress the first residuals on a constant, labor supplies and the second residuals. We repeat the procedure for the other two models, in which export intensity and real exchange rates are present in turn in the right-hand side. Under the null of weak exogeneity, nR ² from this regression is asymptotically distributed as a χ² distribution. The null is rejected if nR ² is greater than a critical value. It was never possible to reject the weak exogeneity null. The results for the nR ² statistic were as follows: for the 1987–1995 sample, 0.0047 for the benchmark model, 2.4503 for the model with exports, and 0.8346 for the model with the real exchange rate; and for the 1994–2007 sample, 0.0003 for the benchmark model, 0.6552 for the model with exports, and 0.0005 for the model with the real exchange rate.