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Link between Political Competition and Local State Capacity: How Often Do Mayors Update Their Municipality’s Local Cadastre?

To operationalize the decision that best shows the intent of the mayors to have financial autonomy in spending and prioritizing the available resources, we focus on the decision to update the cadastre. The updates of the local cadastre allow us to calculate the underestimation of the values of local properties—both urban and rural. Thus, once the undervaluation of properties is estimated it will be used as the instrumental variable of local tax revenues in the econometric exercises aimed at explaining coverage and quality outcomes in education and water. In fact, tax revenues may be endogenous to the provision of education and services; local officials may decide to increase local taxes when they face higher demand for such public goods.

Cadastral information indicates that the municipalities take on the task of cadastral updating approximately nine years after the last update took place. Nevertheless, 20 percent of the municipalities undertake updates after five years or less following the last urban update, or seven years or less of the last rural one. In order to determine the variables explaining the cadastral updates we estimate a number of hazard models with the following specification that includes the contextual variables that may affect the decision to update, as well as the political competition variables to observe whether it has the effect we have so far described:

where UPDATE equals one in the years in which the local cadastre (either urban or rural) has been updated, and zero for the other years. Tt and DPj, stand for time dummies and departmental fixed effects, respectively. It is expected that the longer it takes to update the local cadastre, the greater the likelihood of updating (f1 > 0). We would also expect that the situation of coverage or quality of the local public goods should not be related to the cadastral updates (fa = 0) and that, as mentioned earlier, the political context, particularly political competition, would influence the cadastral updates.

After estimating the cadastral update hazard model, the undervaluation of local properties can be assessed. Such undervaluation depends on the number of years when there was no cadastral update and the number of updates within a given period. In other words, as the cadastre becomes older, the values and the number of properties tend to be lower than they should be. Thus, the lack of updates erodes the tax base, with tax collection remaining far below its potential. To determine the effect of the lack of updates on the value of properties, we first estimate a panel fixed effect model for the per capita value of municipal properties. Such value will be determined by the structural characteristics of the municipality—such as GDP per capita, poverty rates, concentration of land, participation of urban population, among others—and by the number of years to the last update, as well as the number of updates undertaken. Thus, the per capita value of properties is estimated with the following fixed effect panel equation (see appendix for details):13

Both the number of years to the last cadastral update and the number of cadastral updates are indicators of the local fiscal effort and should affect local tax revenue. The first is positively related and the second, negatively related to undervaluation.

Once Eq. (3.2) is estimated, we calculate the undervaluation of properties by using the following equation:

Calculations based on the information from the cadastral office indicate that the average undervaluation of properties is about 0.2 log points

(about 20 percent) with a standard deviation of 0.25 log points. The distribution goes from -0.06 to nearly 0.9 log points.

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