Skip to main content
SpringerLink
Log in

Regulatory compliance, information disclosure and peer effects: evidence from the Mexican gasoline market

  • Original Article
  • Published:
Journal of Regulatory Economics Aims and scope Submit manuscript

Abstract

Policy makers and regulators are increasingly interested in the use of information disclosure as a regulatory instrument to improve firms’ behavior. However, little has been done using micro-level data to investigate whether information provision may trigger peer influence among firms that affects their compliance behavior. Using station-level inspection verification data from the Mexican gasoline market, this paper examines whether gas stations react to peers’ performance to adjust their own compliance decisions. The information disclosure policy assigned each inspected gas station with green, yellow, or red colors to indicate the status of compliance, minor violation, and severe violation, respectively. We find strong evidence of peer influence triggered by information spillover. The probability of being in compliance increases as the number of “green” peers increases. We use both municipalities and postal codes as geographic boundaries to define potential peers, and find similar results. Our findings also suggest that the magnitude of peer effects varies across municipalities: the effects appear to be greater in wealthier, more educated communities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: http://webapps.profeco.gob.mx/verificacion/gasolina/home_11.asp, accessed March, 4, 2013. The information is collected from the State of Aguascalientes and Municipality of Pabellon de Arteaga. There are six gas stations in the municipality. The stations with id 5167 and 10438 received 2 inspections in the previous 6 months, stations #11032, #5117, #1121 received 1 inspection, and station #6778 was not inspected. None of the gas stations got a red rating in the previous 6 months

Fig. 2

Source: http://webapps.profeco.gob.mx/verificacion/gasolina/home_11.asp, accessed March, 4, 2013. The information is collected from the State of Chiapas and Municipality of Palenque. There are six gas stations in the municipality. The stations with id 10687 and 6875 were rated as red stations, and station #3917 was rated as yellow station. Station #2525 was not inspected

Similar content being viewed by others

Notes

  1. For example, the U.S. Environmental Protection Agency (EPA) has applied a few public disclosure policies as regulatory instruments for improving environmental performance. Examples include the Toxic Release Inventory (TRI), the Consumer Confidence Reports (CCR), and mandatory lead paint disclosure. Environmental economists have referred to information provision as the third-wave regulatory instrument in environmental regulation, after the first wave of command-and-control policy and the second wave of market-based instruments (Tietenberg 1998).

  2. That is, there is no brand effect in the Mexican gas markets.

  3. Depending on different settings, past literature also called “peer effects” as social interactions, social networks, social multipliers, peer influences, conformity and contagion effects.

  4. See Blume et al. (2011) for a survey of papers on social interactions.

  5. Although determined by the government, gasoline prices are not completely identical throughout Mexico. The government allows for lower gasoline prices in some northern border areas because many Mexican consumers chose to come over the border to purchase gasoline in the US for cheaper fuel price.

  6. A red color is assigned to a gas station if the following circumstances occur: refusal to be inspected by PROFECO staff, selling incomplete liters that dispense less than 0.985 L per liter sold, gasoline price forgery, or altered electrical and synchronizing components of the machine. Yellow colors are assigned given the following circumstances: selling incomplete liters that dispense greater than 0.985 but smaller than 0.995 L per liter sold, leaking pipes, repeatability error, and dispensers temporarily out of service due to mechanical or electronic failure.

  7. This online disclosure program was discontinued in January 2014 due to strong protest from gas station owners. Since that, PROFECO continuously publishes the verification outcomes through its website in the section of “Quién es Quién en los combustibles”.

  8. When reporting violators, newspapers provide the station’s PEMEX ID and address, so consumers can easily identify which stations have recently dispensed incomplete liters.

  9. For example, recent studies have found that Mexican newspaper reports on stations’ violation are very effective to help consumers identify local cheating gas stations (Guerrero 2012).

  10. The most important transaction cost here is transportation costs.

  11. A better choice is to use the number of surrounding competitors according to the proximity to the station. For example, count the number of stations within a one or two-mile radius of each station. However, this measure is not available in this study.

  12. In our sample, the median number of gas stations for a municipality is 4, and the mean is 9.

  13. We use “t − 1” to denote that the compliance information is based on the inspection records over the previous 6 months, not including current month.

  14. For example, in November 2010, the PROFECO website should disclose the compliance information during the period May 2010 to October 2010. For a gas station i, if it observes that 10 colors were assigned to its peers in the municipality, and 6 were green colors, then the peer effect is measured as 0.6 (6/10).

  15. For example, suppose a municipality with 10 gas stations. For a focal gas station i, if only 4 peer stations were inspected in the previous 6 months, then Eq. (1) is only determined by the performance of the 4 stations, and the other 5 stations are irrelevant in this period, though they are potential peers for the station i.

  16. Here, market size refers to the total number of gas station in the municipality.

  17. This paper does not consider the effect of inspector heterogeneity on compliance outcomes, since inspector personal characteristics are not available. Macher et al. (2011) find that compliance outcomes are likely to be determined by regulator heterogeneity, such as personal training and inspection experience.

  18. In many cases, PROFECO just sent warnings to yellow stations and made no monetary penalties on them.

  19. In related literature, it is a popular strategy to solve the problem of correlated effects by using within transformation in linear panel data models to eliminate unobserved variables (Bramoulle et al. 2009).

  20. Because the latest official norm required PROFECO to inspect each operating gas station at least once a year, we believe that the inspection verification records should cover most Mexican operating gas stations.

  21. The primary performance measures include: whether the station refused to be verified by inspectors, amount of incomplete liters, failing calibration, repeatability error, electrical anomalies and leaking pipes.

  22. The Federal Law of Transparency and Access to Public Government Information of Mexico allows anyone to request information from federal government departments and bodies. We sent our requests directly to PEMEX and obtained the list.

  23. For example, about 84% of red stations were assigned red colors because they either sold incomplete liters or refused to be verified by inspectors. Because refusal to inspection automatically results in receiving red color, gas stations that refused to be inspected were very likely to sell incomplete liters in a significant amount.

  24. Although the dependent variable is binary, we do not use fixed effect probit model to avoid the inconsistency caused by incidental parameter problem. The problem cannot be ignored in this study since we have a large number of fixed effects.

  25. Throughout the paper, we cluster errors at the municipality level to address potential autocorrelation. In separate regressions, we cluster errors at the gas station level and find similar results.

  26. Some gas stations were always in compliance (green) or always in violation (non-green) due to some unobservable factors. These gas stations are unlikely to be subject to peers’ behavior.

  27. The fixed-effect logit model cannot provide average partial effects estimation, since we have no information about the distribution of the fixed effect. See Wooldridge (2010).

  28. We ran additional regressions by using alternative variables to replace the dummy of red color spillovers to test the robustness of this result. We first use a similar pattern to define spillovers of both red and yellow colors: a dummy equals to 1 if any peer station within a municipality was assigned either yellow or red color over the previous six months. The regression results show that this spillover effect is insignificant. We then use percentage of red color received by peer stations in the same municipality over the previous six months. Again, the coefficient remains insignificant.

  29. Column 4 in Table 3 shows that the peer effect is even negative in the least educated areas. The effect turns to positive and the magnitude increases as educational level increases. For example, the 25th percentile, median, 75th percentile values of the distribution of years of schooling are 6.7, 7.6 and 8.7 years. The combined effect of the peer effect for the 3 points are 0.072, 0.103 and 0.142 respectively.

  30. Because we have collected repeated observations for each gas station and PROFECO was officially required to inspect every operating gas station at least once per year, this problem can be more accurately described as an unbalanced panel: some stations may be more frequently observed than others.

  31. This means the inspection decision is correlated with the idiosyncratic errors \( \upvarepsilon_{\text{ijt}} \) in Eq. (3).

  32. Gas stations characteristics include number of pumps, sales in year 2010, whether the station provides full service, whether the station is first-hand sale station, whether the station was awarded by PEMEX for compliance. Municipality characteristics include demographics in year 2010: population, average years of schooling, economic participation rate, mean household income, and percentage of gasoline employees over population.

  33. The marginal effects are for the conditional probability of compliance given the station was inspected, that is, E(Compliance | Inspection = 1).

References

  • Akerlof, G. A. (1970). The market for ‘lemons’: Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488–500.

    Article  Google Scholar 

  • Arora, S., & Cason, T. (1999). Do community characteristics influence environmental outcomes? Evidence from the toxics release inventory. Southern Economic Journal, 64, 691–716.

    Article  Google Scholar 

  • Arora, S., & Gangopadhyay, S. (1995). Toward a theoretical model of voluntary overcompliance. Journal of Economic Behavior & Organization, 28, 289–309.

    Article  Google Scholar 

  • Bandiera, O., Barankay, I., & Rasul, I. (2005). Social preferences and the response to incentives: Evidence from personnel data. Quarterly Journal of Economics, 120, 917–962.

    Google Scholar 

  • Becker, G. (1968). Crime and punishment: An economic approach. Journal of Political Economy, 76, 169–217.

    Article  Google Scholar 

  • Bennear, L., & Olmstead, S. (2008). The impacts of the ‘right to know’: Information disclosure and the violation of drinking water standards. Journal of Environmental Economics and Management, 56, 117–130.

    Article  Google Scholar 

  • Bertrand, M., Luttmer, E. F. P., & Mullainathan, S. (2000). Network effects and welfare cultures. Quarterly Journal of Economics, 115(3), 1019–1055.

    Article  Google Scholar 

  • Blume, L., Brock, W., Durlauf, S., & Ioannides, Y. (2011). Identification of social interactions. Handbook of Social Economics, 1B, 853–964.

    Article  Google Scholar 

  • Bramoulle, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150, 41–55.

    Article  Google Scholar 

  • Brock, W., & Durlauf, S. (2001). Discrete choice with social interactions. Review of Economics Studies, 68, 235–260.

    Article  Google Scholar 

  • Brooks, N., & Sethi, R. (1997). The distribution of pollution: Community characteristics and exposure to air toxics. Journal of Environmental Economics and Management, 32, 233–250.

    Article  Google Scholar 

  • Clark, A. E., & Lohéac, Y. (2007). “It wasn’t me, it was them!” Social influence in risky behavior by adolescents. Journal of Health Economics, 26(4), 763–784.

    Article  Google Scholar 

  • Cohen-Cole, E., & Fletcher, J. (2008). Is obesity contagious? Social networks versus environmental factors in the obesity epidemic. Journal of Health Economics, 27, 1382–1387.

    Article  Google Scholar 

  • Conley, T., & Topa, G. (2002). Socio-economic distance and spatial patterns in unemployment. Journal of Applied Econometrics, 17(4), 303–327.

    Article  Google Scholar 

  • Cooley, J. C. (2007). Desegregation and the achievement gap: Do diverse peers help? University of Wisconsin Working Paper.

  • Delmas, M., Montes-Sancho, M., & Shimshack, J. (2010). Mandatory information disclosure policy: Evidence from the electric utility industry. Economic Inquiry, 48(2), 483–498.

    Article  Google Scholar 

  • Earnhart, D. (2004). The effects of community characteristics on polluter compliance levels. Land Economics, 80(3), 408–432.

    Article  Google Scholar 

  • El Economista. (2012). Balconear Gasolineras Ayuda a Reducir Venta?de Chiquilitros: Banxico.

  • El Universal. (2006). Hackean Portal de Profeco, Tras “ventilar” a Gasolineras.

  • Falk, A., & Ichino, A. (2006). Clean evidence on peer effects. Journal of Labor Economics, 24, 39–58.

    Article  Google Scholar 

  • Ferraz, C., & Finan, F. (2008). Exposing corrupt politicians: the effects of Brazil’s publicly released audits on electoral outcomes. The Quarterly Journal of Economics, 123(2), 703–745.

    Article  Google Scholar 

  • Fourlon, J., Lanoie, P., & Laplante, B. (2002). Incentives for pollution control: Regulation or information? Journal of Environmental Economics and Management, 44, 169–187.

    Article  Google Scholar 

  • Glaeser, E. L., Sacerdote, B., & Scheinkman, J. A. (1996). Crime and social interactions. Quarterly Journal of Economics, 111(2), 507–548.

    Article  Google Scholar 

  • Gray, W. B., & Deily, M. E. (1996). Compliance and enforcement: Air pollution regulation in the US steel industry. Journal of Environmental Economics and Management, 31, 96–111.

    Article  Google Scholar 

  • Greenstone, M., Oyer, P., & Vissing-Jorgensen, A. (2006). Mandated disclosure, stock returns, and the 1964 securities acts amendments. Quarterly Journal of Economics, 121, 399–460.

    Article  Google Scholar 

  • Guerrero, S. (2012). Who is selling you chiquilitros of gasoline? Evidence from a public disclosure policy. Working Papers 2012-04, Banco de Mexico.

  • Hamilton, J. T. (1995). Pollution as news: Media and stock market reactions to the toxics release inventory data. Journal of Environmental Economics and Management, 28, 98–113.

    Article  Google Scholar 

  • Harford, J. D. (1991). Measurement error and state-dependent pollution control enforcement. Journal of Environmental Economics and Management, 21, 67–81.

    Article  Google Scholar 

  • Harrington, W. (1988). Enforcement leverage when penalties are restricted. Journal of Public Economics, 37, 29–53.

    Article  Google Scholar 

  • Helland, E. (1998). The enforcement of pollution control laws: Inspections, violations, and self-reporting. The Review of Economics and Statistics, 80, 141–153.

    Article  Google Scholar 

  • Jin, G. Z., & Leslie, P. (2003). The effect of information on product quality: Evidence from restaurant hygiene grade cards. The Quarterly Journal of Economics, 118(2), 141–153.

    Article  Google Scholar 

  • Knez, M., & Simester, D. (2001). Firm-wide incentives and mutual monitoring at continental airlines. Journal of Labor Economics, 19, 743–772.

    Article  Google Scholar 

  • Konar, S., & Cohen, M. A. (1997). Information as regulation: The effect of community right to know laws on toxic emissions. Journal of Environmental Economics and Management, 32, 109–124.

    Article  Google Scholar 

  • Macher, J. T., Mayo, J. W., & Nickerson, J. A. (2011). Regulator heterogeneity and endogenous efforts to close the information asymmetry gap. Journal of Law and Economics, 54, 25–54.

    Article  Google Scholar 

  • Manchana, P., Xie, Y., & Youn, N. (2008). The role of targeted communication and contagion in product adoption. Marking Science, 27(6), 961–976.

    Article  Google Scholar 

  • Manski, C. (1993). Identification of endogenous social effects: The reflection problem. Review of Economic Studies, 60, 531–542.

    Article  Google Scholar 

  • Mathios, A. D. (2000). The impact of mandatory disclosure regulations on health choices: An analysis of the salad dressing market. Journal of Law and Economics, 43, 651–678.

    Article  Google Scholar 

  • Moffitt, R. A. (2001). Policy Interventions, low-level equilibria, and social interactions. Social Dynamics, 4, 6–17.

    Google Scholar 

  • Pargal, S., & Wheeler, D. (1996). Informal regulation of industrial pollution in developing countries: Evidence from Indonesia. Journal of Political Economy, 104(6), 1315–1327.

    Article  Google Scholar 

  • Patacchini, E., & Zenou, Y. (2012). Juvenile delinquency and conformism. Journal of Law Economics and Organization, 28, 1–31.

    Article  Google Scholar 

  • Plante, M., & Jordan, A. (2013). Getting prices right: Addressing Mexico’s history of fuel subsidies (pp. 10–13). Dallas: Federal Reserve Bank of Dallas Southwest Economy, Third Quarter.

    Google Scholar 

  • PROFECO. (2005). Encuesta de percepcion en el consume de gasoline 2005: resultados nacionales. http://www.profeco.gov.mx/encuesta/histo_mirador.asp.

  • Reid, E. M., & Toffel, M. W. (2009). Responding to public and private politics: Corporate disclosure of climate change strategies. Strategic Management Journal, 30, 1157–1178.

    Article  Google Scholar 

  • Sacerdote, B. (2001). Peer effects with random assignment: Results for Dartmouth roommates. Quarterly Journal of Economics, 681–704.

  • Shimshack, J. P., & Ward, M. B. (2005). Regulator reputation, enforcement, and environmental compliance. Journal of Environmental Economics and Management, 50, 519–540.

    Article  Google Scholar 

  • Shimshack, J. P., & Ward, M. B. (2008). Enforcement and over-compliance. Journal of Environmental Economics and Management, 55, 90–105.

    Article  Google Scholar 

  • Shimshack, J. P., Ward, M. B., & Beatty, T. K. M. (2007). Mercury advisories: Information, education, and fish consumption. Journal of Environmental Economics and Management, 53, 158–179.

    Article  Google Scholar 

  • Stigler, G. J. (1970). The optimum enforcement of laws. Journal of Political Economy, 78, 526–536.

    Article  Google Scholar 

  • Tietenberg, T. (1998). Disclosure strategies for pollution control. Environmental & Resource Economics, 11, 587–602.

    Article  Google Scholar 

  • Topa, G. (2001). Social interactions, local spillover and unemployment. Review of Economic Studies, 68, 261–295.

    Article  Google Scholar 

  • Van de Ven, W. P. M. M., & Van Pragg, B. M. S. (1981). The demand for deductibles in private health insurance: A probit model with sample selection. Journal of Econometrics, 17, 229–252.

    Article  Google Scholar 

  • Weinberg, B. A., Reagan, P. B., & Yankow, J. J. (2004). Do neighborhood affect hours worked? Evidence from longitudinal data. Journal of Labor Economics, 22(4), 891–924.

    Article  Google Scholar 

  • Woittiez, I., & Kapteyn, A. (1998). Social interactions and habit formation in a model of female labor supply. Journal of Public Economics, 70, 185–205.

    Article  Google Scholar 

  • Wooldridge, J. (2010). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tianjin University of Finance and Economics, Tianjin, China

    Xian Liu

  2. University of Illinois at Urbana-Champaign, 1301 W Gregory Dr, Urbana, IL, 61801, USA

    Barrett Kirwan

  3. KU Leuven, Naamsestraat 69, 3000, Louvain, Belgium

    Andrea Martens

Authors
  1. Xian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barrett Kirwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Martens
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xian Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Kirwan, B. & Martens, A. Regulatory compliance, information disclosure and peer effects: evidence from the Mexican gasoline market. J Regul Econ 54, 53–80 (2018). https://doi.org/10.1007/s11149-018-9362-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11149-018-9362-1

Keywords

JEL Classification

Search

Navigation