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As was found for central government expenditures, the significance of other variables as determinants of per capita infrastructure expenditure is quite robust across the alternative bases. Because the regressions defining the stock of infrastructure as the total kilometers of road and rail per 1000 square kilometers land area, FCINFPA, perform marginally better overall, this definition also will be used as the base for the sensitivity analyses exploring the determinants of consolidated budget per capita infrastructure expenditures. The full results of the five regressions (MI through M5) using FCINFPA to define the stock of infrastructure are shown in Table 9.

The results shown in Table 9 do not include any correction for possible autocorrelation. Table 10 provides the basis for our test for autocorrelation. Table 10 shows the results of the same regression set, but includes dummy variables for year. As before, Y81 is set equal to 1 if the observation is from 1981, but zero otherwise, and so on; the omitted year is 1980. There is no evidence of autocorrelation. The F values for the joint significance tests of the dummy variables for year are 0.51, 0.42, 0.47, 0.35, and 0.62, for models Ml, M2, M3, M4, and M5, respectively. The critical FQ-K,,- value at the 20 percent significance level is greater than 1.40 in all cases. Further, the coefficient magnitudes are quite comparable between estimates, as are significance levels for the independent variables. As before, in the absence of any evidence of autocorrelation, the sensitivity analyses are undertaken excluding any control for the year of observation.

# _2. Population Density_

As can be seen from Tables 9 and 10 and Appendix C, population density is significant at the .01 level or better in all base runs and all sensitivity analyses with the exception of those including poverty level as opposed to poverty commitment. The relationship is strictly negative (whereas an inverted U relationship was found in the central budget case); higher population densities are associated with lower per capita consolidated budget infrastructure expenditures. The estimated sign reflects the fact that high population densities necessitate lower expenditure for a given level of infrastructure service. In the consolidated budget case, this factor offsets any tendency for economies of scale to dictate a high optimal level of provision in more densely populated countries.

At the sample mean the estimated elasticity calculated from Table 9 is -.33, opposite in sign and somewhat larger in magnitude than that found for central budget expenditures. At the sample mean, population density decreases infrastructure expenditures by about (1980) US $ 6.43. The size of the estimated impact is somewhat smaller (in absolute value) in the case of the alternative bases and the sensitivity analyses utilizing alternative indicators of the sectoral imbalance, but somewhat larger in the sensitivity analyses measuring development level in purchasing power parity dollars, measuring debt obligations as DODGDP1, and measuring the external balance as DOLLAR4, as well as in the runs using HDRPOOR and WBPOOR. Overall, the differences in the size of the estimated coefficients found are not large.