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Foreign Competition and Banking Industry Dynamics: An Application to Mexico

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Abstract

Our paper develops a simple general equilibrium framework to study the effects of global competition on banking industry dynamics and welfare. It applies the framework to the Mexican banking industry, which underwent a major structural change in the 1990s as a consequence of both government policy and external shocks. Given high concentration in the Mexican banking industry, domestic and foreign banks act strategically in the framework. After calibrating the model to Mexican data, the paper examines the welfare consequences of government policies which promote global competition. It finds relatively high economy-wide welfare gains from allowing foreign bank entry.

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Notes

  1. Claessens and Van Horen (2014) document the importance of foreign banks across countries during the last three decades.

  2. Our Bankscope data only begins in 1998.

  3. See Figure 2 of Banco de Mexico (2013).

  4. This literature is also related to Jayaratne and Strahan (1996) and Berger, Demsetz, and Strahan (1999) who analyze deregulation of interstate banking in the United States during the 1990s. In Corbae and D’Erasmo (2011), we provide a spatial model with imperfect competition in the banking sector and study the effects of the removal of branching restrictions.

  5. Since limited liability for foreign banks applies for its operations in Mexico, we are implicitly assuming that foreign banks enter as subsidiaries and not as branches. This is consistent with the way most foreign banks operate in Mexico.

  6. A different implementation requires verifying that the entry value is lower than ϒθ when the number of incumbent banks is (that is, the upper bound is not binding).

  7. This is the simplest market structure sufficient to capture the main features of the highly concentrated Mexican banking sector. Without introducing further heterogeneity or considering a different type of equilibrium, additional foreign and domestic banks will behave identically to the representative bank of type θ we consider, so analyzing the case with a larger number of banks will not change our qualitative results. We discuss the possible impact of adding more banks on our quantitative results in Section VI.

  8. It can be shown (see Corbae and D’Erasmo, 2011) that a sufficiently risk-averse household would never find it optimal to try to match with an entrepreneur to make a nonintermediated loan that exposes it to the firm’s idiosyncratic uncertainty.

  9. Suppose not and The net cost of doing so is rδ≥0, while the net gain on is zero, so it is weakly optimal not to do so.

  10. See the appendix for a detailed description of sources and variables as well as the computational algorithm.

  11. To estimate the domestic productivity process we maximize the size of the data to capture periods of domestic crisis (such as those arising from fiscal or exchange rate crisis). Output fluctuations in the model (driven not only by exogenous factors but also by industry dynamics) capture all sources of GDP fluctuations in the data.

  12. Top 10 banks hold, on average, well above 80 percent of total assets and total loans in Mexico during the period analyzed.

  13. Bankscope provides information on the nationality of the controlling shareholder and the history of ownership. When data were missing or incomplete we complemented Bankscope with information from the official websites of each bank, banking publications and country experts.

  14. For every set of parameters, we simulate 18 panels of banks for 7,000 periods. To compute the moments, we discard the initial 2,000 periods and average over all the panels created.

  15. The average equity return and its volatility are taken from Diebold and Yilmaz (2009), who study the evolution of the equity markets across countries from 1992 to 2007.

  16. As we describe later, exit by foreign banks is more likely when a global crisis is followed by a domestic crisis, and domestic banks are more likely to exit when we enter into a domestic crisis. Moreover, bank risk-taking is directly connected with the likelihood of moving into a crisis or staying in bad times. For these reasons, asset returns for domestic and foreign banks provide good information into the likelihood of moving into a crisis or staying in bad times.

  17. The parameter determines the slope of the loan demand and in place the elasticity of the loan interest rate to changes in the loan supply. Since a key component of the dividend to asset ratio is the loan return, provides useful information on this moment.

  18. Equity issuance by domestic and foreign banks provides information on η b and η g since bank risk-taking and the likelihood of a bank issuing equity (as opposed to exiting) depend on the strength of its competitor and whether the competitor decides to stay and issue equity or to exit.

  19. We do not report decision rules for zero probability events (that is, recall that for G(z, z′, η′) in Equations (26) and (27), the probability of transiting from z g to z c is zero).

  20. We note that the standard deviation of real HP filtered GDP in Mexico is 2.98 percent while the figure for the model counterpart is 5.09 percent. One reason for this discrepancy between the model and the data is the parsimonious representation of the production sector in the model that assumes that all output is intermediated via the banking sector.

  21. Beck, Demirgüç-Kunt, and Levine (2003) also include other controls like “economic freedom,” which are outside of our model.

  22. In the counterfactual economy, domestic banks are ex-ante identical (they have the same cost structure) and fully owned by the domestic consumers. Since profitability of both banks is exactly the same, multiple equilibria can arise. We assume that if at the exit stage there is more than one equilibrium (for example where one bank stays if the other exits and vice versa), we select the equilibrium where one of the active banks stays.

  23. We assume that households can only hold shares in domestic banks.

  24. As stated (pp. 52–53) by Beck and Peria (2010), “Mostly, the increase in foreign bank participation in Mexico resulted from foreign acquisitions of domestic banks, as opposed to de novo foreign bank entry.”

  25. An important point to note here is how Bankscope treats consolidation of subsidiaries. We set our search settings to primarily pull consolidated statements, and if those are unavailable for a subset, pull unconsolidated statements. This avoids duplication issues if a bank is a subsidiary of another bank.

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Authors
  1. Dean Corbae
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  2. Pablo D’Erasmo
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Additional information

*Dean Corbae is US Bank Professor of Finance and Professor of Economics at the University of Wisconsin-Madison. Pablo D’Erasmo is Senior Economist at the Federal Reserve Bank of Philadelphia. The authors wish to thank the editors Pierre-Olivier Gourinchas, Galina Hale, Katheryn Russ, and two anonymous referees for helpful comments on an earlier version of the paper. They also thank Linda Goldberg, Vincenzo Quadrini, and Robert Townsend for their comments and the Consortium for Financial Systems and Poverty at the University of Chicago for early financial support of this project, as well as participants at the IMF/De Nederlansche Bank Conference, Bank of Chile, and SED 2015. Finally, they thank Shu Lin Wee, Anton Babkin, and Neeraj Goyal for excellent research assistance. This paper is available free of charge at www.philadelphiafed.org/research-and-data/publications/working-papers/. The views expressed in this paper are those of the authors and do note necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System.

An erratum to this article is available at http://dx.doi.org/10.1057/s41308-017-0034-4.

Electronic supplementary material

Appendix I

Appendix I

Data Appendix

Banking sector variables are from Bureau Van Dijks Bankscope data set. To create our sample, we first screen for location, focusing on Mexico, and then we screen for the banks specialization, that is, commercial banks. We consider “active” and “inactive” banks at the moment of downloading the data to incorporate banks which may have existed in the sample but are currently inactive. This leaves us with a sample of 61 banks.Footnote 25 Our sample starts in 1998 and goes to 2012, the earliest date available through Bankscope to the most recent date with consistent data. For balance sheet variables as well as income statement variables, we censor the top and bottom 1 percent of the sample.

We use real Mexican GDP data from World Bank via Haver Analytics. We convert the data to United States Dollars (Thousands) with each periods closing date exchange rate.

Identifying Ownership

We follow Micco, Panizza, and Yanez (2007) when coding ownership. This is time-consuming because there is no ownership identifier in the data. There is a field with a brief history of each institution that allows to track ownership changes. We also gathered information from individual bank websites as well as performed several internet searches, business week company profiles, and consulted country experts. For this reason, we focus on the the 10 largest banks in Mexico. The top 10 banks represent, on average, more than 80 percent of total assets in the industry.

Bankscopes Ownership Database shows an active banks organization hierarchy, the percentage of the bank owned by the parent, and the country where the parent is headquartered, all through time. For example, if Bank A is owned domestically by Bank B, which is ultimately owned by a foreign bank, Bank C, Bankscope will provide either the exact percentage of Bank A owned by Bank B and of Bank B owned by Bank C; or it will show if Banks A and B were wholly owned or majority owned. Looking through time, this allows us to check whether a bank is foreign or domestically held. Its worth noting that these data largely go back only to 2002. We then complement the data with the “bank history” variable, which expounds on the banks story and explains if it was merged, liquidated, or altered in some way. As we explained above, we complement this with other sources.

In cases where Bankscope does not have complete information, we determine foreign/domestic ownership status by looking at total foreign ownership as a percentage of all information available. For example, in the case of Banco Del Bajio is owned by Temasek Holdings (13.3 percent), Banco De Sabadell SA (20 percent), International Finance Corporation (10 percent), and a number of foreign entities whose combined reported ownership is 85.3 percent. Foreign institutions hold 50.7 percent of the reported total, allowing us to designate Banco Del Bajio as a foreign institution. Banco Azteca SA was the final case where ultimate ownership was unclear. Here, Bankscope reports three individuals as owning 72 percent of Banco Azteca SA, and two of those three individuals are Mexican. As such, we designate the bank as being domestic.

Computational Strategy

In this section, we describe the computational algorithm that allows us to compute bank strategies and the equilibrium of the model. We follow an extension of the algorithm proposed by Pakes and McGuire (1994).

After giving a functional form to the utility function, the failure probability, the equity issuance cost and the distribution of net costs for domestic and foreign banks as well as defining a grid and a transition probability for the aggregate shocks, the steps to compute the algorithm are the following:

  1. 1

    Solve the entrepreneur’s problem. This amounts to defining a grid over rL and for each 2-tuple {rL, z} obtaining the type of project the entrepreneur operates Rj(rL, z) and the decision on whether to operate the technology or not ι(ω, rL, z). Using ι(ω, rL, z) we can derive the loan demand: Ld(rL, z).

  2. 2

    Define a grid over the number of possible active domestic and foreign banks {{0, 0},{0, 1}, {1, 0},{1, 1}} where the first element in each set corresponds to an indicator of whether the representative domestic bank is active or not and the second element corresponds to an indicator for the foreign bank.

  3. 3

    For each state {μ, z, η}, solve the problem of foreign and domestic banks. We use value function iteration in this step:

    1. a)

      Guess a loan decision rule and value function for each bank type: and V0(θ, μ, z, η) for θ=d, f.

    2. b)

      For each bank, solve the optimal loan and exit decision rule taking as given the strategy of other banks. Note that the equilibrium in the loan market is derived from the following equation:

      The solution to this problem will provide a loan decision rule , an exit decision rule, and a new value function V1(θ, μ, z, η).

    3. c)

      If ∣ V1(θ, μ, z, η)−V0(θ, μ, z, η) ∣ <ɛv and ∣ for ɛv and small you have obtained the solution to the bank problem and can continue. If not, set V0(θ, μ, z, η)=V1(θ, μ, z, η), and return to previous step.

    4. d)

      The equilibrium loan decision rules determine the equilibrium loan interest rate rL(μ, z, η).

  4. 4

    For each state {μ, z, η, z′, η′} define the distribution of survivors (that is, the distribution of banks that arises after exit decisions but prior to entry):

  5. 5

    Solve the entry problem of each bank type, that is select e(θ, μx, z, η)∈{0,1}. Each bank will enter (that is, e(z, η)=1) if

    where is the distribution that would arise if the bank of type θ enters and takes into account the entry decision rule by other banks starting from μx.

  6. 6

    Using the exit and entry decision rules of dominant banks we can define the evolution of the distribution μ′=H(μ, z, η, z′, η′):

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Corbae, D., D’Erasmo, P. Foreign Competition and Banking Industry Dynamics: An Application to Mexico. IMF Econ Rev 63, 830–867 (2015). https://doi.org/10.1057/imfer.2015.40

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