Gender differences in the impact of banking services: evidence from Mexico

For example, a recent study by the European Commission, Directorate-General Enterprise and Industry (2008) shows that difficulties in access to finance are among the main obstacles for female entrepreneurs-to-be. Aidis et al. (2007) show that access to funds represents a more significant barrier to the progress of female business owners in Lithuania and Ukraine than to males. Muravyev et al. (2008) illustrate that female-managed firms are less likely to obtain a bank loan than their male-managed counterparts, and the FAO (2011) reports that, in seven out of nine countries in the RIGA dataset, rural female-headed households are less likely to use credit than those headed by men.

See, for example, Pitt and Shahidur (1998), Coleman (1999), Kaboski and Townsend (2005), McKernan (2002), and Pitt et al. (2003).

In fact, Banco Azteca’s motto is, “We changed banking, now it’s your turn to change.” (“Cambiamos la banca, cambia tú también”).

Azteca charged interest rates of about 50% per annum, while commercial banks at the time charged rates of 20–40%. However, commercial banks rejected all but the most creditworthy customers (The Dallas Morning News, 31 October 2002). Compartamos, the largest microfinance institution in Mexico, was charging close to 100% per annum, while pawn shops charged an average annual percentage rate (APR) of 220% (Ruiz and Claudia 2011).

We use “not employed” to denote individuals who are either not participating in the labor force or who are unemployed.

Microfinance loan size is the average for all microfinance institutions operating in Mexico at the time, available on MIXmarket.​org.

Authors’ calculations, based on figures from MIXmarket.​org, covering the following four Mexican microfinance institutions: ADMIC, Compartamos, FINCOMUN, and Pro Mujer.

Kochar (2005) and Panagariya (2006) cast doubt on their findings.

An alternative strategy would be to interact the post-Azteca opening dummy with the number of Azteca branches (which is equivalent to Grupo Elektra stores) in each municipality in the fourth quarter of 2002. However, we do not have accurate data on the number of Azteca branches or Grupo Elektra stores for the fourth quarter of 2002.

Using the same ENE survey data and a similar empirical approach, Bruhn (2011) finds that using probit regressions produces similar results to OLS.

There are two reasons why we do not use data from the new survey. First, the question that distinguishes informal from formal business owners is different in the new survey, implying that one of our main outcome variables is inconsistent across the two surveys. Second, Banco Azteca started opening new branches after initially establishing branches in all pre-existing Elektra stores. This means that our identification strategy is less valid for later years.

It is important to point out that the way the ENE sample is constructed implies that a municipality–year average is not necessarily representative of the municipality in that year. The sample selection procedure randomly selects households in a small geographic unit, the AGEB (Basic Geo-Statistical Area). All AGEBs within a state are first stratified by socio-economic characteristics. Within each stratum, a certain number of AGEBs is chosen at random. Households are then chosen at random within the AGEB. This procedure implies that it could happen that only some socio-economic groups are selected in a given municipality in a given year. However, since the strata are randomly chosen, this remains in expectation a random sample of the households in a municipality, so that the estimate should remain unbiased.

A detailed description of how these variables were constructed is available in Bruhn (2008). The sample includes dual job holders, but the ENE does not collect detailed information on a person’s second job. All occupation and labor income information used in this paper refers to the person’s main job.

We convert the income of the 95.8% of individuals who reported it as amounts to multiples of the minimum wage using information on the minimum wage from the National Commission for Minimum Wages (Comision Nacional de Salarios Minimos (CONASAMI; http://​www.​conasami.​gob.​mx).

Note that Fig. 2 reports raw percentages, while in our regressions we control for municipality fixed effects (which capture the level differences) and municipality-specific time-trends (which capture any differences in time trends across municipalities). The figure suggests that controlling for trends is important, which is why our preferred specifications are those which use municipality-specific time-trends.

Note that our data allow us to observe whether an individual is an informal business owner in each period, but we cannot be certain that the informal business is the same business in each period. That is, it is in principle possible that somebody closes one informal business and opens another.

We use the quadratic root as an alternative to log(x + 1) since it also allows us to keep zero incomes and mimics the logarithmic function well for positive numbers (see Thomas et al. 2003, who choose the quadratic root of income instead of log to include zero and negative yields).

The New York Times, 31 December 2002.

The New York Times, 31 December 2002.

By Brendan M. Case, The Dallas Morning News, 31 October 2002.

Reuters 2002.

Business Week, 13 January 2003.

Business Week, 13 January 2003.

Reuters News, 21 September 2003.

Reuters News, 21 September 2003.

According to the CNBV, commercial bank interest rates in Mexico at the end of 2002 stood at 10.6% per year on average, taking into account all different types of loans. The highest interest rates charged by commercial banks prevailed among consumption loans (27.4% per year).