_Brazilian spatial labor dynamics Page 8_ Tables 4 and 5 show also that about two-thirds of employment is in the metropolitan agglomerations. Over the nine year interval, employment grew a total of 15% in the nonmetropolitan areas vs 20% in the metropolitan areas. Dynamic areas (W+E+) account for a much larger share of employment in metropolitan areas than in nonmetropolitan areas. Stagnant (W-E+) areas are much more prevalent in the nonmetropolitan areas.

Figure 4 shows an important correlate of wage growth that is associated with the north/south differential. It plots, in ln-ln form, the mean educational level of 1991 employees against wage growth over 1991-2000, by MCA. MCAs from the North and Northeast are shown in red; others are in blue<sup>5</sup> . The figure shows that wage growth is strongly and significantly associated with initial educational level. It shows also that a significant disjunction in these variables between north and south. Northern and northeastern MCAs have markedly lower educational levels, and markedly lower wage growth.

Is this a causal relationship? Places with higher human capital may also have higher levels of social capital and may therefore be better able to attract further investment in human and physical capital, and to boost local productivity. On the other hand, causality may go in the opposite direction. Places with inherent social capital or other local advantages may experience both more rapid growth and greater investments in education. The labor supply and demand equations estimated below attempt to hold constant other characteristics that might be associated with educational levels.

# **Econometric model**

We estimate independent, ln-linear approximations to the wage (labor demand) equation of (3) substituted into (1), and the labor supply equation (2). Endogenous variables are instrumented with time-lagged or space-lagged exogenous determinants. In the wage equation, ∆lnL is instrumented with lagged demographic determinants of labor force growth; the exclusion of these variables from the wage equation identifies it. The labor supply equation is identified, more tenuously, via the exclusion of the market access variables and the government transfer variables<sup>6</sup> , and by the use of initial period wage rather than wage change. Table 6 lists the instruments for each endogenous variable.

Finally, a key issue in estimation is the likelihood of spatial autocorrelation of unobserved variables. Failure to account for this autocorrelation could lead to inaccurate estimates of standard errors. To address this issue, we use the GMM technique of Conley (1999) (as did Araujo, de Janvry and Sadoulet 2004 and Baicker 2005). In contrast to the more standard spatial econometric approach using a weight matrix, Conley’s nonparametric technique requires only that users locate data points in a real or virtual space. Users specify a cutoff distance beyond which spatial autocorrelation is thought to be unimportant. For our analysis we use the latitude and longitude of the MCA centroid to locate the data points. This technique is attractive because it is computationally feasible for large numbers of observations; allows for the use of

> 5 Readers are again referred to the color pdf version of this paper.  The figure shows that the northeastern and northern observations are clustered in the lower left of the diagram.

> 6 We assume that the transfers go mostly to old-age pensioners and thus do not affect labor supply