_Brazilian spatial labor dynamics Page 11_ elasticity of about 0.6. In contrast to the nationwide sample, the coefficient on government transfers here stays robust across the GMM specifications, at about 0.16. The coefficients on transport cost are here more significant, though still negligible in magnitude. These results (including the effect of rainfall) are essentially unchanged when the North region is excluded.

The supply-side results for the nonmetropolitan samples also are very similar to the nationwide results. The main difference is that the effect of the initial proportion in farming is reduced in magnitude, but it is still quite important quantitatively and statistically. In addition, the initial participation rate is no longer significant in the nonmetropolitan analyses.

A final caution on our results relates to the J-test. For most of the GMM regressions, the test statistic is significant at the 5% level, which suggests potential problems with the instruments. It is not however significant at that level for the Brazilwide demand regression (cutoff of 2.0) and the nonmetropolitan supply regressions excluding the North (cutoffs of 1.0 and 2.0).

# **Discussion**

We have found a number of relationships that appear to be robust to different choices of sample and different controls for spatial autocorrelation.

_Initial levels of workforce education are strongly related to subsequent wage growth_ , even after controlling for correlated variables such as remoteness and climate. Note that this is different from the well-known association between education and wage levels; it suggests an accelerator effect, where more-educated regions experience more rapid wage growth. And, the effect is large. In 1991, the mean educational level of workers was 3.9 years for the country as a whole and just 3.6 years for nonmetropolitan regions. The standard deviation for nonmetropolitan regions was 1.32 years. A change of this magnitude was associated with a 8% increase in wages over 1991-2000.

If confirmed, this a sobering finding from the viewpoint of reducing inter-regional inequalities. It suggests that wages will continue to diverge between the moreeducated south and the less-educated north. And because it takes 40 years to turn over the labor force, it will take a long time for lagging regions to reduce the educational gap with leading ones, even if the former were to make more rapid progress – itself a questionable assumption.

_Farming regions are losing employment, or growing more slowly, than other regions._ In the nationwide sample, each 10 percentage point increase in the initial proportion of farmers among workers was associated with a 10 to 12 percent decrease in the nineyear rate of employment growth. Even when the sample is restricted to nonmetropolitan areas, this relationship holds, though it is less steep. Further investigation is needed to understand the degree to which this represents ‘pull’ factors – attraction to growing areas, vs. ‘push’ factors – e.g. displacement of smallholders by large farming enterprises.

_Low rainfall areas lagged other areas in wage and employment growth._ These patterns were evident even controlling for remoteness and education, which might be correlated with rainfall.

_Wages respond relatively elastically to changes in labor supply._ This suggests that in-migration could substantially reduce wages in an MCA with vigorous demand growth; that