We broaden our presentation of the statistical results by summarizing the evidence obtained from the multi-variate econometric specifications, where a fuller set of controls in multiple specifications explaining service performance were included. As per description of the simple model and legend with the variables presented in the previous section, we performed econometric tests through OLS specifications with the various service performance outcomes as dependent variables, and with city characteristics, city and country governance and globalization indicators, as well as country characteristics, as independent variables.

Table 4 below presents the combined synthesis of the results of all econometric specifications. This synthesis presents the magnitude values (+,0,- on the left) and significance (with asterisks, on the right), which we obtain by averaging the magnitude and significance of the regression results of 21 different OLS specifications, for each city performance indicator.<sup>17</sup> From the implementation of the fuller econometric model, for which the results are summarized in Table 4 above, we obtain a number of salient results. There is an effect of City Globalization on performance using both the Taylor (Global City variable) and the UN (water, electricity, telephone, and sewerage access variables) databases. This effect is relatively modest in some cases yet present, though with varying levels of significance depending on the type of service, controls used, and model specifications. The variable that is most correlated to city globalization (global city (3)) is the quality of electricity.

Other city globalization proxies, such as city web portal with information on steps to start a firm (business dummy), and whether the budget is transparently available on the web (budget dummy), show positive and significant results, but these results are not robust across all service variables. In particular, access to cell phones (as opposed to telephones), and the overall quality of infrastructure are significant, while the other service variables, such as access to water, electricity and sewerage, and to telephone lines show less significance. See Section 6 below for a more detailed treatment of some of the city characteristics and related results. Bribery in utility seems to also be important, showing significant results for all service variables in Table 4.

Definition of city as a space is sensitive to the definitions of city boundaries, whether core city or broader agglomeration. We have corrected for thisby using city as an outcome for the analysis. Such a construct: (a) allows us to use “city as a place” as an input to the analysis and hence is less sensitive to the boundary definitions; and (b) the UN database 17 In particular, for each independent variable, for calculating the magnitude of the coefficient rating (left hand side), we computed the product of the average coefficient magnitude across all specifications times twice the standard deviation of the variable. Then we assigned magnitude values according to the following criteria: for positive coefficients, we applied a “++++” for any value above 1.2; “+++” for any value between 0.7 and 1.2; “++” for any value between 0.4 and 0.7; “+” for any value between 0.1 and 0.4; and finally a “0”’for any value below 0.1. Similarly, minus signs were applied to negative coefficients. For the rating on the significance of the coefficient in each cell (right hand side), we averaged the various significance levels across the 21 specifications, where “***” indicates significance at 1%, “**” indicates significance at 5%, “*” indicates significance at 10% and ‘ indicates significance at 15%.

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