WAGE INEQUALITY, TRADE, AND FOREIGN INVESTMENT 89 with trade may also have an effect on the way a government handles wage inequality

Thus, policy variables can hardly be used to identify the impact of trade.

Because a country’s geographical characteristics are not affected by income or policies, and geographical characteristics are supposed to have no effect on income, except for the impact on trade, they can be used to obtain instrumental variables.

Hence, I instrument a country’s trade activity with a constructed geographical com-ponent of bilateral trade, relying on a very similar approach to that proposed by Frankel and Romer (1999). Afterwards, I compare the results to those of an OLS estimation, controlling for the validity of the instrumental variable estimation.

The estimation procedure of the geographical component is quite standard in empirical literature, and is based on the following estimation equation ⁷:

ln(T_ij/GDP_i) = a₀+a₁lnD_ij +a₂lnN_i+a₃lnA_i+a₄lnN_j (4.4) +a₅lnA_j +a₆(L_i+L_j) +a₇lnB_ij +a₈B_ijlnD_ij

+a₉B_ijlnN_it+a₁₀B_ijlnA_i+a₁₁B_ijlnN_j +a₁₂B_ijlnA_j+a₁₃B_ij(L_i+L_j) +e_ij,

where ln(T_ij/GDP_i) is the log of bilateral trade (exports and imports) between countries i and j in relation to the GDP of country i. D_ij is the log distance between i and j, measured as log of the distance between countries’ capital cities.

lnN_i and lnN_j are the log populations of both countries, lnA_i and lnA_j are the log areas of both countries. L_i and L_j are dummy variables containing information whether a country is landlocked, B_ij is a dummy for a common border between countries iand j. All other variables are interaction terms with the border-dummy B_ij. e_ij is the error term.

First, as the values of GDP, trade, and population change from one year to another, I estimate equation (4.4) separately for each year of the sample. In a second step, I use the estimated results to calculate annual fitted values of trade between countries i and j relative to GDP of country i. Third, these fitted values are exponentiated and aggregated over all countries j for each country i and year.

7See e.g. Frankel and Romer (1999) for a detailed presentation.

geographic components of its bilateral trade with each other country in each year of the sample.

Frankel and Romer (1999) show that the generated geographical component is now usable to instrument a country’s trade activity. The trade share is treated as endogenous, and the constructed geographical component of trade is used as an instrument. I use a two-stage instrumental variable regression estimation (IV), also including country- and year-fixed effects, as well as several control variables (for example GDP per capita, unemployment rate, and exchange rate). Following Frankel and Romer (1999), I include the log of population as an explanatory variable to get an approximation of the within-country trade.

The constructed geographical instrument and the instrumented trade measure are highly positive correlated (around 0.809 in each of the subsamples). Moreover, the first stage regression has reasonable explanatory power, and the coefficient of the constructed geographical component is positive, as expected, and highly statistically significant. As the F−statistic is considerably larger than the rule of thumb value of 10, the geographical component does not seem to be a weak instrument.

I present the results for the OECD in Table 4.6 for each type of relative wage. The left hand side of the table shows the results using the unbalanced sample, the right hand side using the reduced sample. However, the differences in the results of the two samples are quite small. The IV estimates are compared to OLS estimates, based on a fixed effects estimation using country- and year fixed effects and the analogous explanatory variables. The estimated coefficients of the trade share are robust to the exclusion of the control variables, as the sign does not change and the estimated coefficients of trade stay almost unchanged. Moreover, the estimated coefficient of trade differs between the OLS and IV estimation, which is a strong evidence for the endogeneity of trade. The almost unchanged standard errors indicate there was no loss in efficiency due to the instrumental variable estimation. This result is also supported by the Hausman test.

4.4. WAGE INEQUALITY, TRADE, AND FOREIGN INVESTMENT 91 I find small significant negative effects of trade on relative wages in the OECD. The lower the relative wage ratio, the higher the wage inequality. Therefore, a negative estimated coefficient of trade share indicates increasing wage inequality. Using the reduced sample, I find evidence that an increasing trade share decreases the relative wage and thus increases wage inequality significantly by 0.5 percent. The strongest negative effect of trade on wage inequality is observed for relative wages in non-manufacturing sectors, where trade increases the wage spread by one percent. But still, the effect is quite small. Moreover, trade affects theMin/Max ratio significantly negative, as the ratio decreases by about 0.8 percent.

The results for all types of relative wages for the total number of countries, the EU, and High Income Countries (HIC) are given in Table (4.7). I run both approaches, the IV estimation and the OLS, and compare the results, respectively.

Again, a negative coefficient indicates that wage inequality is rising with increasing trade activity. Hence, using the IV approach, I find a slightly significant negative relationship between trade and relative wages in the EU. Compared to the OECD, the effect is quite small. However, I do not find any significant effect of trade on relative wages in manufacturing sectors, neither in the entire sample, nor in the EU or in HIC. Instead, using the IV approach, I find that trade affects relative wages in non-manufacturing sectors significantly negative in all of the three country samples.

However, I find no evidence that trade has a negative effect on the ratio of minimum and maximum wages. Thus, results are not as clear-cut as for the OECD.

In summary, I find evidence that the trade share has a small but significant negative effect on relative wages in the OECD. Thus, the gap between the average wages of low skilled and high skilled workers is rising with increasing trade flows.

Moreover, I can show that this negative effect in the OECD is driven by increas-ing wage inequality in non-manufacturincreas-ing sectors, which is a little puzzlincreas-ing. The results for the whole number of countries, the EU, and HIC are not clear-cut. The hypothesis that all workers can gain is not verified by the empirical analysis.

In a next step, I focus on the effect of FDI flows on relative wages and wage inequality, which are a good approximation for the theoretically relevant capital flows. Again, I proceed in two steps. First, following the idea of Frankel and Romer (1999), I construct a geographical component that can be used as an instrument for the ratio of total FDI to GDP, as there might be a problem of endogeneity in the analysis of the effect of FDI on wage inequality. Second, I estimate the effect of FDI share on wage inequality using a instrumental variable approach, and compare the results to an OLS estimation. Results are given for the OECD using the unbalanced and the reduced sample, as well as for the whole number of countries, the members of the EU, and High Income Countries.

First of all, I will briefly address to the suspected endogeneity problem. Wage inequality in a country may be high for reasons that have nothing to do with in-come, and foreign investment can be increasing because of increasing wage spreads.

Moreover, the way a country is dealing with foreign investment activities might also reflect the domestic policy and therefore affect income and wage inequality, which leads to an endogeneity problem, too. Therefore, I generate a geographical instrument to account for endogeneity, again following Frankel and Romer (1999).

The argumentation is analogous to the previous section: Because a country’s geographical characteristics are not affected by income or policies, and geographical characteristics are supposed to have no effect on income, except for a supposed impact on foreign investment, they can be used to obtain an instrumental variable.

The estimation strategy of generating the instrumental variable is given by the following equation:

ln(Capital_ij/GDP_i) = a₀+a₁lnD_ij +a₂lnN_i+a₃lnA_i+a₄lnN_j (4.5) +a₅lnA_j+a₆(L_i+L_j) +a₇lnB_ij +a₈B_ijlnD_ij +a₉B_ijlnN_it+a₁₀B_ijlnA_i+a₁₁B_ijlnN_j

+a₁₂B_ijlnA_j +a₁₃B_ij(L_i+L_j) +e_ij.

4.4. WAGE INEQUALITY, TRADE, AND FOREIGN INVESTMENT 93