Robustness

It remains to make sure that the estimates of the impact of shifts in industrial composition on city-industry wages are robust to introduction of alternative explanations for differences in wage growth across cities. Specifically, these alternative explanations include diversity of employment in a city (Glaeser, Kallal, Scheinkman, and Shleifer, 1992) and education levels (Moretti, 2004;

Acemoglu and Angrist, 1999). Additional variables representing these alternatives are added to

equation (3.1) to ensure of the robustness of previous estimates. The results are shown in table (5.6), and in table (5.8) for the decomposed changes in the measure of industrial composition. The coefficient of the measure of industrial composition is fairly stable and remains highly significant after the introduction of the new variables one at a time or altogether at once. Among the new controls only the diversity of employment in a city is highly significant, which positively affects city-industry wages.

Glaeser et al. (1992) examine predictions of various theories of growth externalities

(knowledge spillovers) within and between industries at city level in the U.S. during 1956 and 1987. They try to verify whether it is the geographic specialization or competition of geographically proximate industries that promote innovation spillovers and growth in those industries and cities. One measure of city growth they use is growth in wages. By testing empirically in which cities industries grow faster, as a function of geographic specialization and competition, they find that specialization has no effect on wage growth and diversity in a city helps the wage growth of the industries. Here, following Beaudry et al. (2009), a measure of “diversification” of employment in each city at the start of the decade measured by one minus the Herfindahl index, or one minus sum of squared-industry-shares in the city, is introduced. The results are reported under columns OLS (1) and IV (1) in table (5.6), and table (5.8) for the decomposed changes in measure of industrial composition. The change in the measure of industrial composition in table (5.6) and the share-based change in the measure of industrial

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The P-value for endogeneity test (that the specified endogenous regressors can actually be treated as exogenous) of ∆𝑅_𝑐1 is 0.68, of ∆𝑅_𝑐2 is 0.04, and of ∆𝐸𝑅_𝑐 is 0.00.

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The P-value for the null hypothesis of having equal IV estimates for the coefficients of ∆𝑅_𝑐1 and ∆𝑅_𝑐2 is 0.87, and for them to be jointly zero is 0.006.

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composition in table (5.8) are both robust to introducing this alternative explanation for growth in wages at the city level.

Notice that with inclusion of the measure of diversification in employment, both the OLS and IV estimates of the coefficient on ∆𝑅_𝑐 in table (5.6) and the coefficient on ∆𝑅_𝑐1 in table (5.8) become larger. The measure of industry diversification is significant and positive both in OLS and after using instruments, which confirms the finding of Glaeser et al. (1992) for the case of Brazilian cities. It is indicating that cities with a more diversified composition of industrial employment in 1991 – that is a composition of employment in which industries have relatively similar shares employment – experienced higher growth rates in industry wages during 1991- 2000 due to more competition between different industries. Notice that an increase in the measure of industrial composition (∆𝑅_𝑐 or ∆𝑅_𝑐1) is the result of a move away from a diversified composition of employment toward a polar composition in favour of high-paying industries. In other words, ∆𝑅_𝑐 or ∆𝑅_𝑐1 are negatively correlated with the measure of diversification in 1991 (see Graphs (1.5), (1.6), and (1.7)). As a result, given the positive impact of both measures on growth in industry wages and given the negative correlation between the two measures, not controlling for the measure of diversification dampens the coefficient on change in the measure of industrial composition. In other words, the estimates of the G.E. wage impacts of shifts in industrial composition presented in tables (5.6) and (5.8) are more precise than the estimates presented in previous tables.

Moretti (2004) examines wages in U.S. cities in the 1980s and finds that cities with greater

increase in the proportion of workers with a BA or higher education have higher wage gains.

Acemoglue and Angrist (1999) find weaker results for the impact of education using average

years of education in a state. Because here it is already controlled for the level of education in estimating the industry wage premia and therefore, the measure of industrial composition does not reflect cities with higher wages due to having higher levels education. However, it will be controlled for both measures of education discussed in the two studies mentioned above; one measure is the change in the proportion of workers with a BA or higher education and the other is using average years of schooling as an alternative measure of the education level of a city. The results are shown under columns OLS (2) and IV (2) in tables (5.6) and (5.8), where initial levels of workers with a Bachelor or higher and initial average year of schooling among workers are used as instruments in addition to the instruments used so far. The change in the measure of industrial composition in table (5.6) and the share-based change in the measure of industrial

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composition in table (5.8) are both robust to introduction of these variables. Neither of the new variables is significant after instrumenting, which is similar to the results in Acemoglue and

Angrist (1999).

Finally, the last two columns in tables (5.6) and (5.8) under columns OLS (4) and IV (4) report the results of the robustness tests, introducing all the alternative explanations discussed above at once. The wage impact of change in local industrial compositions remains intact.