Enforcement Process Tracing: Forbearance and Dilution in Urban Colombia and Turkey


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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  1. 1.

    For instance, see Beach and Pedersen (2013), Bennett and Checkel (2014), Collier and Brady (2010), and Hall (2008).

  2. 2.

    A few examples include Björkman (2015), Chubb (1982), Cross (1998), Fischer (2008), and Onoma (2010).

  3. 3.

    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.

  4. 4.

    For additional methods to identify forbearance, see Holland (2016).

  5. 5.

    On hoop tests, see Van Evera (1997: p. 31).

  6. 6.

    Author interview with judicial coordinator, Ciudad Bolívar, Bogotá, Colombia, October 14, 2011.

  7. 7.

    This contrasts with the organized land invasions and resulting popular movements in other time periods and Latin American cities, such as Lima (Dosh 2010).

  8. 8.

    Author interview with judicial advisor, District of Bosa, July 30, 2010.

  9. 9.

    Author interview with housing bureaucrat, District of Rafael Uribe Uribe, July 7, 2010.

  10. 10.

    Author interview with coordinator of inspectors, Subsecretary of Control, District Housing Secretary, Bogotá, Colombia, July 27, 2011.

  11. 11.

    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).

  12. 12.

    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).

  13. 13.

    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.

  14. 14.

    This difference is also statistically significant, as shown in the Appendix at Table 5.

  15. 15.

    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).


  1. Amengual M. Politicized enforcement: labor and environmental regulation in Argentina. New York: Cambridge University Press; 2015.

    Google Scholar 

  2. Amengual M, Dargent E. Enforcement: integrating politics with limited state capacity. Working Paper. 2018

  3. Beach D, Pedersen RB. Process-tracing methods: foundations and guidelines. Ann Arbor: University of Michigan Press; 2013.

    Google Scholar 

  4. Becker G. Crime and punishment: an economic approach. J Polit Econ. 1968;76(2):169–217.

    Article  Google Scholar 

  5. Becker G, Stigler G. Law enforcement, malfeasance, and the compensation of enforcers. J Leg Stud. 1974;3(1):1–19.

    Article  Google Scholar 

  6. Bennett A, Checkel JT. Process tracing: from metaphor to analytic tool. New York: Cambridge University Press; 2014.

    Google Scholar 

  7. Bergman M. Tax evasion and the rule of law in Latin America: the political culture of cheating and compliance in Argentina and Chile: Pennsylvania State University Press; 2009.

  8. Björkman L. Pipe politics, contested waters: embedded infrastructures of millennial Mumbai: Duke University Press; 2015.

  9. Borg MJ, Parker KF. Mobilizing law in urban areas: the social structure of homicide clearance rates. Law & Society Review. 2001;35(2):435–66.

    Article  Google Scholar 

  10. Brinks DM. The judicial response to police killings in Latin America: inequality and the rule of law. New York: Cambridge University Press; 2007.

    Google Scholar 

  11. Brollo F, Kaufmann K, La Ferrara E. The political economy of program enforcement: evidence from Brazil. CEPR Discussion Paper No. DP11964. 2017. 

  12. Buenker JD. The urban political machine and the seventeenth amendment. J Am Hist. 1969;56(2):305–22.

    Article  Google Scholar 

  13. Burgess R, Olken BA, Sieber S. The political economy of deforestation in the tropics. Q J Econ. 2012;127(4):1707–54.

    Article  Google Scholar 

  14. Caldeira T. City of walls: crime, segregation, and citizenship in. São Paulo: University of California Press; 2000.

    Google Scholar 

  15. Camargo A, Hurtado A. La Urbanización Informal En Bogotá: Panorama a Partir Del Observatorio. Colombia: Bogotá; 2011.

    Google Scholar 

  16. Casaburi L, Troiano U. Ghost-house busters: the electoral response to a large anti tax evasion program. Q J Econ. 2015;

  17. Chubb J. The social bases of an urban political machine: the case of Palermo. Polit Sci Q. 1981;96(1):107–25.

    Article  Google Scholar 

  18. Chubb J. Patronage, power and poverty in southern Italy: a tale of two cities: Cambridge University Press; 1982.

  19. Collier D. Understanding process tracing. PS: Polit Sci Polit. 2011;44(4):823–30.

    Google Scholar 

  20. Collier D, Brady HE. Rethinking social inquiry: diverse tools, shared standards. New York: Rowman & Littlefield Publishers; 2010.

    Google Scholar 

  21. Comptroller. Gestión adelantada por la administración distrital en el manejo de los cerros orientales. Bogotá, Colombia: Contraloría; 2004.

  22. Cross J. Informal politics: street vendors and the state in Mexico City. Stanford, CA: Stanford University Press; 1998.

    Google Scholar 

  23. Culpepper PD. Quiet politics and business power: corporate control in Europe and Japan. New York: Cambridge University Press; 2010.

    Google Scholar 

  24. Dargent E, Urteaga M. Respuesta Estatal Por Presiones Externas: Los Determinantes Del Fortalecimiento Estatal Frente Al Boom Del Oro En El Perú (2004–2015). Revista de ciencia política. 2016;36(3):655–77.

    Article  Google Scholar 

  25. Dimitrov MK. Piracy and the state: the politics of intellectual property rights in China: Cambridge University Press; 2009.

  26. DNP. Suelo Y Vivienda Para Hogares de Bajos Ingresos. In: Bogotá. Colombia: Departamento Nacional de Planeación; 2007.

    Google Scholar 

  27. Dosh P. Demanding the land: urban popular movements in Peru and Ecuador, 1990–2005: Penn State Press; 2010.

  28. Esen B, Gumuscu S. Rising competitive authoritarianism in Turkey. Third World Q. 2016;37(9):1581–606.

    Article  Google Scholar 

  29. Fischer B. A poverty of rights: citizenship and inequality in twentieth-century Rio de Janeiro: Stanford University Press; 2008.

  30. Gabaix X. Power laws in economics and finance. Annu Rev Econ. 2009;1:255–94.

    Article  Google Scholar 

  31. Gallagher J. The last mile problem: activists, advocates, and the struggle for justice in domestic courts. Comp Polit Stud. 2017;50(12):1595–631.

    Article  Google Scholar 

  32. Gingerich DW. Political institutions and party-directed corruption in South America: stealing for the team. New York: Cambridge University Press; 2013.

    Google Scholar 

  33. Goodfellow T. Taming the ‘rogue’ sector: studying state effectiveness in Africa through informal transport politics. Comp Polit. 2015;47(2):127–47.

    Article  Google Scholar 

  34. Hall PA. Systematic process analysis: when and how to use it. Eur Polit Sci. 2008;7:304–17.

    Article  Google Scholar 

  35. Holland AC. The distributive politics of enforcement. Am J Polit Sci. 2015;59(2):357–71.

    Article  Google Scholar 

  36. Holland AC. Forbearance. Am Polit Sci Rev. 2016;110(2):232–46.

    Article  Google Scholar 

  37. Holland AC. Forbearance as redistribution: the politics of informal welfare in Latin America. New York: Cambridge University Press; 2017.

    Google Scholar 

  38. Hummel C. Disobedient markets: street vendors, enforcement, and state intervention in collective action. Comp Polit Stud. 2017;50(11):1524–55.

    Article  Google Scholar 

  39. Levitsky S, Murillo MV. Variation in institutional strength. Annu Rev Polit Sci. 2009;12(1):115–33.

    Article  Google Scholar 

  40. Mahoney J, Thelen KA. Advances in comparative-historical analysis. New York: Cambridge University Press; 2015.

    Google Scholar 

  41. Markus S. Property, predation, and protection: piranha capitalism in Russia and Ukraine. New York: Cambridge University Press; 2015.

    Google Scholar 

  42. Min B, Golden M. Electoral cycles in electricity losses in India. Energy Policy. 2014;65:619–25.

    Article  Google Scholar 

  43. Onoma AK. The politics of property rights institutions in Africa. New York: Cambridge University Press; 2010.

    Google Scholar 

  44. Post AE. Foreign and domestic investment in Argentina: the politics of privatized infrastructure. New York: Cambridge University Press; 2014.

    Google Scholar 

  45. Resnick D. Urban poverty and party populism in African democracies: Cambridge University Press; 2013.

  46. Rosenfeld B, Imai K, Shapiro JN. An empirical validation study of popular survey methodologies for sensitive questions. Am J Polit Sci. 2016;60(3):783–802.

    Article  Google Scholar 

  47. Sinha A. Rethinking the developmental state model: divided leviathan and subnational comparisons in India. Comp Polit. 2003;35(4):459–76.

    Article  Google Scholar 

  48. Skouras S, Christodoulakis N. Electoral misgovernance cycles: evidence from wildfires and tax evasion in Greece. Public Choice. 2014;159(3):533–59.

    Article  Google Scholar 

  49. Slater, Dan, and Diana Kim. 2015. Standoffish states: nonliterate leviathans in Southeast Asia. TRaNS: Trans-Regional and -National Studies of Southeast Asia 3(1): 25–44.

  50. Sun X. Selective enforcement of land regulations: why large-scale violators succeed. The China J. 2015;74(1):66–90.

    Article  Google Scholar 

  51. Tendler J. Small firms, the informal sector, and the devil’s deal. Inst Dev Stud Bull. 2002;33(3):1–14.

    Article  Google Scholar 

  52. Treisman D. What have we learned about the causes of corruption from ten years of cross-national empirical research? Annu Rev Political Sci. 2007;10:211–44.

    Article  Google Scholar 

  53. Tsai LL. Constructive noncompliance. Comp Polit. 2015;47(3):253–79.

    Article  Google Scholar 

  54. Van Evera, Stephen.Guide to Methods for Students of Political Science. Ithaca, New York: Cornell University Press. 1997

  55. Weinstein L. Mumbai’s development mafias: globalization, organized crime and land development. Int J Urban Reg Res. 2008;32(1):22–39.

    Article  Google Scholar 

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Correspondence to Alisha C. Holland.



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.

Table 2 Regression results

Model Specification

$$ {Y}_{ist}=\alpha +\upgamma {\mathrm{TreatmentGroup}}_{\mathrm{s}}+\delta {\mathrm{TimeDummy}}_{\mathrm{t}}+\lambda {\mathrm{MunicipalityDummy}}_{\mathrm{i}}+\beta {D}_{\mathrm{i}}{D}_{\mathrm{s}\mathrm{t}}+{\in}_{\mathrm{i}\mathrm{st}} $$

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.

Table 3 Summary Statistics: the Monthly Number of Residential Construction Permits Issued in Non-Election Years, by Type of Municipality
Table 4 Main results including rural municipalities
Table 5 Results with Party and Core/Swing Interaction Variables
Table 6 Results of the Poisson Regression Model

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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

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  • Informality
  • Informal sector
  • Enforcement
  • Urban politics