Cities are complex regulatory environments. Attempts to regulate urban behavior create opportunities for politicians to manipulate enforcement to win votes and reward supporters. While some politicians choose not to enforce regulations, or forbearance, others undercut their intent, or dilution. Empirical research on enforcement has lagged behind due to the identification challenges in distinguishing weak state capacity from political manipulations. We develop a structured approach to process tracing that follows enforcement decisions sequentially across bureaucracies and specifies statistical distributions as counterfactuals to identify the causes of limited enforcement. We illustrate these strategies through original data on enforcement against squatters in urban Colombia and the provision of building permits in urban Turkey. Enforcement process tracing helps to document a form of distributive politics that is common to cities in the developing world.
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For instance, the 2014 Latin American Public Opinion Project (LAPOP) attempted to have citizens estimate enforcement probabilities. The survey asks “If someone in your neighborhood occupied an uninhabited piece of land, how probable is it that s/he would be sanctioned by the authorities?” The results bear little relationship at the country or individual level with factors known to affect the objective enforcement probability.
For additional methods to identify forbearance, see Holland (2016).
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Author interview with judicial coordinator, Ciudad Bolívar, Bogotá, Colombia, October 14, 2011.
This contrasts with the organized land invasions and resulting popular movements in other time periods and Latin American cities, such as Lima (Dosh 2010).
Author interview with judicial advisor, District of Bosa, July 30, 2010.
Author interview with housing bureaucrat, District of Rafael Uribe Uribe, July 7, 2010.
Author interview with coordinator of inspectors, Subsecretary of Control, District Housing Secretary, Bogotá, Colombia, July 27, 2011.
Prior to a recent administrative reform that decreased the total number of municipalities to 1397, there were around 2000 township (belde) municipalities, the smallest political units in rural areas. The results are unchanged when small and rural municipalities are included (see Appendix Table 4).
In a DiD framework, municipalities in election years (in months) correspond to the treatment group, and the same municipalities in non-election years (for the same months) correspond to the control group. The “treatment” corresponds to the month when elections occur (March in election years).
The increase in August 2014 may be due to the presidential election, held in 2015 for the first time after a constitutional change. The other unexpected increase in the number of construction permits occurs in December and particularly in December 2003. It is possible that this reflects greater citizen demand to improve homes in the new year, given that smaller upticks can be seen across all years. In addition, a major earthquake struck Turkey in 2003, so it is possible that people were rebuilding from the destruction at the end of the year. Even if we drop this election cycle from our analysis, the findings hold.
This difference is also statistically significant, as shown in the Appendix at Table 5.
Process-tracing research designs do not require the same pieces of evidence across cases or even different parts of the mechanism, giving them greater flexibility compared to dataset observations that require unit homogeneity (Beach and Pedersen 2013: pp. 79–81).
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The Difference in Difference Model
Conventionally, a difference-in-difference (DiD) strategy compares the pre- and post-treatment differences in the outcome of a treatment and a control group. In our model, municipalities in election years (in months) correspond to the treatment group, and the same municipalities in non-election years (for the same months) correspond to the control group. In estimating the effect of elections on construction permits, we define month right before the election as the “treatment” period and the month preceding the treatment period as the “pre-treatment” period. Local elections occur on the last Sunday in March so we define March as an election period and February as a non-election period. We then analyze how permitting behavior changes in this two-month period preceding the election, compared to the same period in non-election years.
Table 2 presents the regression results. Our dependent variable is the monthly number of construction permits issued in a given municipality. The first column shows the results for all urban municipalities. An additional four construction permits are issued in the period prior to an election (March) compared to the same period in non-election years. Given that the average number of permits issued in the same month in non-election years is four, this result suggests that permitting activity doubles in the month leading up to an election.
Where DiDst = TreatmentGroups × Timet is a dummy variable which equals one for treatment units in the treatment time (March) and is zero otherwise. TimeDummyt denotes the time dummy that we use for each February and March since we pool multiple time periods in our model.
TreatmentGroupt equals one for treatment units (those in election years) and is zero otherwise (those from the previous year). Finally, MunicipalityDummyi, the municipal-level fixed effects, controls for the time invariant municipality specific characteristics.
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Bozçağa, T., Holland, A.C. Enforcement Process Tracing: Forbearance and Dilution in Urban Colombia and Turkey. St Comp Int Dev 53, 300–323 (2018). https://doi.org/10.1007/s12116-018-9274-1
- Informal sector
- Urban politics