Credit Stagnation in Latin America
Author: Mr. Adolfo Barajas and Mr. Roberto Steiner1
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address: abarajas@imf.org, rsteiner@uniandes.edu.co
Publication Date:
01 Mar 2002
eISBN:
9781451847390
Language:
English
Keywords:
WP; credit growth; central bank; credit slowdown; net credit; credit supply; credit demand; expansion period; deposit money bank; credit channel
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This study examines the recent marked slowdown in bank credit to the private sector in Latin America. Based on the study of eight countries (Argentina, Bolivia, Brazil, Chile, Colombia, Peru, Mexico, and Venezuela), the magnitude of the slowdown is documented, comparing it to historical behavior and to slowdown episodes in other regions of the world. Second, changes in bank balance sheets are examined to determine whether the credit slowdown is merely a reflection of a slowdown in bank deposits, or whether the asset side has changed. Third, following an econometric disequilibrium approach used in recent studies of credit slowdowns in East Asia and Finland, the paper investigates possible causes for the slowdown in three countries: Colombia, Mexico, and Peru. While both supply and demand factors appear to have played key roles, their relative importance has varied across countries.

Abstract

This study examines the recent marked slowdown in bank credit to the private sector in Latin America. Based on the study of eight countries (Argentina, Bolivia, Brazil, Chile, Colombia, Peru, Mexico, and Venezuela), the magnitude of the slowdown is documented, comparing it to historical behavior and to slowdown episodes in other regions of the world. Second, changes in bank balance sheets are examined to determine whether the credit slowdown is merely a reflection of a slowdown in bank deposits, or whether the asset side has changed. Third, following an econometric disequilibrium approach used in recent studies of credit slowdowns in East Asia and Finland, the paper investigates possible causes for the slowdown in three countries: Colombia, Mexico, and Peru. While both supply and demand factors appear to have played key roles, their relative importance has varied across countries.

I. Introduction

After experiencing moderate to high rates of growth during most of the 1990s, several Latin American countries witnessed a significant slowdown in growth over the past two years. As Table 1 below illustrates, Argentina, Bolivia, Brazil, Chile, Colombia, Peru, and Venezuela all have recently experienced declines in their growth rates in recent years, declines which have ranged from about 1 percentage point in Brazil to 7 percentage points in Argentina. One prominent exception to this behavior is Mexico, for which economic growth accelerated by about 1 percentage point on average after 1995.

Table 1.

Average Real Growth Rates of GDP and Credit to the Private Sector Selected Latin American Countries

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Source: IMF International Financial Statistics.

In many cases, the evolution of commercial bank credit to the private sector has followed a similar, and even more pronounced, cyclical pattern. When deflated by consumer prices, during the above periods credit growth decelerated by about 5 percentage points in Chile, by over 9 percentage points in Brazil, by 13 in Argentina and 18 percentage points in Bolivia, and by over 20 percentage points in Colombia and Peru. The Mexican and Venezuelan cases stand in contrast to the rest, however; in Mexico credit growth fell much more sharply than in the other countries, by over 34 percentage points, even as economic growth was accelerating; and in Venezuela economic growth declined while credit enjoyed a modest recovery.

In policy circles in several countries it is widely believed that the slowdown in financial sector credit is an important driving force behind the recent economic slump. According to some interpretations, growth will only be restored once the “credit channel” becomes operative once again. As the above figures suggest, this line of reasoning is challenged by the Mexican case, where two possible explanations have been offered for this apparent lack of a relationship between credit growth and economic activity. On the one hand, it has been pointed out that large Mexican corporations have ample access to foreign capital markets, thereby being able to invest and grow regardless of the fact that, following the 1994 “Tequila crisis,” the Mexican financial system has been in very bad shape. A second interpretation has to do with accounting issues—while we observe a decline in credit when we look at bank’s assets, corporations who have not repaid their credits or who have had access to several debt restructuring programs have not seen their effective financing curtailed.

Accounting issues aside, and given that in most Latin American countries only a very few large corporations can tap foreign capital markets, the recent slowdown in credit becomes a very relevant issue.2 In this paper we focus on the eight countries mentioned above, and try to identify, in a systematic manner, what are the factors that explain the evolution of bank credit to the private sector. It should be pointed out that the principal variable we examine is credit as reported in bank’s balance sheets. However, in certain countries, and under certain conditions, it may not fully capture what is actually happening to the effective financing being received by households and firms. For this reason, in some cases we also consider an alternative definition of credit.3

The paper is divided into five sections, including this introduction. In the second section we provide a very brief analytical discussion of issues related to the “credit channel” and review some of the recent empirical literature. In the third section we take a first look at the data, following a very simple accounting framework. This will allow us to look at the recent credit slowdown episode both from a historical as well as from a comparative perspective. In the fourth section we undertake the econometric analysis, and in the fifth section we present our conslusions.

II. The “Credit Channel:” A Brief Survey of the Literature

In the textbook IS/LM model, in which bank loans are not distinguished from other assets in the bond market, monetary policy has real effects because (i) it affects the interest rate at which the money market clears; (ii) the change in the interest rate, in turn, affects private expenditure. Following the work of Bernanke and Blinder (1988), Romer and Romer (1990) and others, increasing attention has been devoted to the role of banks in the provision of credit. The so-called “credit channel” has become instrumental in understanding the link between monetary policy and overall economic activity.

Banks hold assets in the form of reserves (mandatory and/or “excess” reserves), loans and bonds. While the overall size of assets is determined by the provision of bank reserves on the part of the central bank and by the public’s willingness to own bank deposits, the composition of assets is affected by several considerations, including the demand for loans from the corporate sector and the willingness of banks to satisfy that demand.

One would like to distinguish among three factors affecting the evolution of financial sector credit. On the one hand, there is the demand for credit from the corporate sector, which in turn can depend on several factors, including observed and expected economic activity. Regarding supply, it is critical to distinguish between a bank’s ability to lend—which might be constrained by the level of deposits—and its willingness to do so—in turn, closely associated to its perception of risk.

Evidently, relevant policy implications as to how to address a credit contraction critically depend on a correct interpretation of the factors that explain a particular episode. Interventions, if warranted, might range from programs to alleviate corporate debt burden, enhancing the provision of liquidity on the part of the central bank, or revising the regulatory framework regarding the level of provisions.

A vast empirical literature has recently emerged, trying to identify whether or not specific situations of credit contraction can be identified as being characterized by a “credit crunch,” loosely defined as a situation in which, for a given level of deposits, bank’s opt to purchase low-yielding securities (that is, government bonds) rather than to increase interest rates on their loans, in order to allow the credit market to clear, and thus, excess demand for credit remains unsatisfied. It is important to keep in mind that such a situation can emerge either because bank’s perceptions of corporate risk are too high, or because they simply do not have enough capital to accommodate riskier loans.

One of the first empirical approximations to the “credit crunch” phenomenon was provided by Bernanke and Lown (1991). Using state-by-state data, they found support for the “credit crunch” hypothesis in the case of the United States. Specifically, they identified that bank’s level of capital was actually restricting their ability to supply credit.

More recently, Pazarbasioglu (1997) estimated a disequilibrium model of credit demand and supply for Finland, following Laffont and Garcia (1977), using monthly data for the 1981-1995 period. In particular reference to the sharp decline in bank lending following the banking crisis of 1991-92, this paper provides evidence that such a decline was not the result of a credit crunch but, rather, the reflection of a cyclical decline in credit demand, in turn associated with borrowers’ high level of indebtedness.

A disequilibrium approach is also used by Ghosh and Ghosh (1999) to analyze the contraction of credit during the 1997-98 East Asian crisis in Indonesia, Korea, and Thailand. By including as an explanatory variable of credit supply bank’s actual lending capacity, they are able to differentiate between the ability to lend and the willingness to do so. Their results suggest that while real credit supply to the private sector diminished, estimated demand declined even more sharply. In that sense, they find no evidence of a credit crunch. Cautiously, the authors do not preclude the possibility that a few creditworthy firms were in fact supply-constrained, something they obviously cannot capture when using aggregate data.4

Several papers offer a different interpretation of events in the aftermath of the East Asian crisis. For example, Agenor et al (2000) develop and estimate a model according to which the contraction of bank lending in Thailand was basically the reflection of a supply phenomenon. Their model is based on a demand function for bank’s excess reserves. The estimation of a dynamic version of the model indicates that excess reserves were rather modest. If the slowdown in credit had been the result of a reduction in the demand for loans, then there would have been an important “involuntary” accumulation of reserves.

With specific reference to the case of Korea, Kim (1999) uses different methodological approaches—including the estimation of a disequilibrium model of the bank loan market—and also reports evidence that the severe credit contraction following the Asian crisis was mainly driven by a sharp decline in credit supply. The excess demand for bank loans would have in turn originated in a stringent regulation regarding capital requirements, at a time when nonperforming loans were mounting.

In the Latin American context, a growing empirical literature on credit contractions has recently emerged. For example, in reference to the conditions of the credit market following the 1995 Mexican crisis, Catao (1997) estimates an aggregate model of credit supply and demand for Argentina, using monthly data for the June 1991-1996 period. He reports that while the sharp credit contraction observed in the first half of 1995 was driven by a significant outflow of deposits from the banking system, bank’s did recuperate their lending capacity towards mid-year. The credit contraction that ensued was partially driven by bank’s having become more cautious in their lending practices—bank’s opted to lend to the government, rather than to less-known or more-risky borrowers—and especially by a decline in credit demand—in turn the result of high private sector indebtedness and adverse expectations regarding economic activity.

Also, in reference to the experience of Argentina, Braun and Levy-Yeyati (2001) use a panel data estimation for the 1996–99 period to show that while credit contraction in small banks was mainly caused by a decline in deposits, that was not the case of the larger banks. The latter, in fact, were the main recipients of the deposits leaving the smaller banks. In the case of the larger banks, the paper provides evidence that the decline in their loans was mainly determined by their decision to move away from risky assets in the corporate sector, into safer assets—including cash and public sector debt.

In a recent paper, Berrόspide and Dorich (2001) analyze the evolution of credit in Peru between 1997 and 2000. Using panel data estimation on monthly information for all 27 commercial banks, they estimate credit as a function of demand, supply and regulatory elements. Demand is proxied by GDP; supply is determined by loanable funds and proxies for sovereign as well as bank-specific risk; regulation is captured through the leverage coefficient. They report evidence in the sense that a period of credit slowdown associated with low GDP growth and a decline in loanable funds (late 1998 to late 1999) was followed by a credit slowdown that responded both to a tightening in regulation and to an increase in bank’s risk perceptions. Interestingly, when controlling by size they find evidence that for large banks all credit slowdowns are supply-determined.

Using a very different approach, Carrasquilla et al. (2000) claim that the severe credit contraction observed in Colombia after 1998 was mainly due to bank’s inability, rather than because of their unwillingness, to lend. They propose and estimate a model in which bank loans as a proportion of their liquid assets is positively associated with the level of deposits and negatively associated with bank’s perceptions of risk in the corporate sector. They provide econometric evidence that, by far, the dominant factor in explaining the sharp contraction in bank loans was the decline in deposits which, in turn, they associate with an inadequate provision of liquidity on the part of the central bank.

In a recent paper, Gourinchas et al (2001) take the discussion a step forward. They explicitly introduce credit booms as an explanatory argument for eventual financial crises. They report evidence that in Latin America lending booms do tend to make the economy more vulnerable to an eventual crises—that is, both in the financial sector and in the balance of payments. Interestingly, this type of regularity does not seem to hold for other regions in the world.

Finally, Braun and Hausmann (2001) focus on credit crunches5 throughout the world and find that in Latin America they tend to be closely associated with the deterioration in the region’s access to foreign financing. Interestingly, this deterioration appears to be associated not only with the generalized curtailment of capital inflows to emerging markets, but also a sharp with decline in the terms of trade which made it that much more difficult for Latin American economies to provide the much needed collateral required to access foreign financing.

III. Credit Stagnation in Latin America: A First Look

In this section we describe the recent performance of bank credit in eight Latin American countries, including the seven largest—Argentina, Brazil, Chile, Colombia, Mexico, Peru, Venezuela, and Bolivia—highlighting the recent slowdown in many of these countries. First, we show the evolution of bank credit in its historical context, pointing out how the recent behavior compares to previous credit cycles over a period of about 30 years, and making an initial assessment of the relative severity of recent slowdowns. Secondly, we compare the slowdown with several international cases where the credit crunch phenomenon was hypothesized and studied. Finally, for each Latin American country in the study we focus on the last 20 years, using a simple balance sheet decomposition to detect where the major changes occurred in the behavior of banks from one period to the next.

A. The Latin American Credit Slowdown in Historical and International Context

Several differences and similarities arise in comparing the evolution of bank credit over the past 30 years in the Latin American countries in our sample. Using IFS data, in Figure 1 we plot the ratio of private sector bank credit to GDP for the 1960-2000 period. We show private sector credit both from Deposit Money Banks (DMB), as well as from the entire banking system, DMB plus Other Banking Institutions (OBI),6 While six out of eight countries—Brazil and Chile being the exception—exhibit a slowdown with some amount of decline in the ratio in recent years,7 the patterns for the entire period are not all alike. For Peru and Bolivia, the slowdown appears to be a recent interruption in a long process of rapid credit growth after hyperinflation had virtually driven bank credit to zero. Brazil, Chile, Mexico and, especially, Argentina have experienced pronounced cycles since the 1960s. For Colombia there has been a more modest but steady upward trend in credit since 1960. Finally, Venezuela has experienced a sustained downward trend since the early 1980s.8

Figure 1.
Figure 1.

Credit-GDP Ratios in Latin America, 1960–2000

Citation: IMF Working Papers 2002, 053; 10.5089/9781451847390.001.A001

Given this decline in overall bank credit to the private sector, it is relevant to examine whether there has been a substitution towards financing by nonbank financial institutions. For most of the countries (Argentina, Bolivia, Brazil, Colombia, and Venezuela) this is clearly not the case, since the definition of OBIs is sufficiently broad to include such institutions as savings and loan or mortgage corporations, credit cooperatives, investment banks, and even financial funds9 (see Appendix Table A.l). However, in two of the countries, IFS provides an additional category, “Nonbank Financial Institutions” (NFI), which corresponds to leasing companies, stock brokerage houses, and distributor companies in Brazil, and to pension funds in Chile. As we show in Appendix Table A.2, substitution toward NFIs does not appear to have taken place in Brazil, where its relative importance actually fell during the credit slowdown of the late 1980s and has increased in recent years, precisely while bank credit is expanding. In Chile, on the other hand, the credit slowdown corresponded to a sharp rise of NFIs—pension funds—in the late 1980s. Finally, in the case of Mexico, we obtained Bank of Mexico information for Savings and Loan institutions (SLIs), investment societies, and stock brokerage houses, all of which we grouped into a nonbank category. Although SLIs constituted a very small share of credit—less ½ a percentage point—security holdings by the other nonbank institutions have been expanding rapidly since late 1996, thus suggesting that corporate financing through the stock market has substituted to some degree for the slowdown in bank credit.

In order to assess the severity of the recent credit slowdown in historical context, we undertook a second exercise using 1960-2000 data, based on the Gourinchas, et al. (2000) study of credit booms throughout the world. The procedure consists of calculating the trend in the credit-GDP ratio and then defining a boom as a period in which the (relative or absolute) deviation from trend is above a certain threshold. As Gourinchas, et al. use several alternative threshold levels, we chose an intermediate level of 5 percent in absolute terms. We also defined a credit “bust” symmetrically as an observation in which the credit-GDP ratio lies more than 5 percentage points below trend. We used a Hodrick-Prescott filter10 to capture the trend, then calculated the absolute deviations from this trend. These are plotted in Figure 2, and the results are summarized in Table 2.

Figure 2.
Figure 2.

Latin America—Absolute Deviations in the Credit-GDP Ratio with Respect to Trend

Citation: IMF Working Papers 2002, 053; 10.5089/9781451847390.001.A001

Table 2.

Latin American Credit Slowdowns in Historical Context

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Sources: International Financial Statistics, and authors’ calculations.

Defined as a period containing at least one year in which the credit-GDP ratio is at least 5 percentage points (absolute) or 25 percent (relative) above its trend value. See Gourinchas, et al. (2000) for details.

Trend calculated using a Hodrick-Prescott filter on the original series, with smoothing factor = 100.

Twelve-month growth rate based on the latest monthly observation available: August 2001 (Brazil, Venezuela, Mexico, and Peru) and September 2001 (Chile, Argentina, Bolivia, and Colombia).

Average taken over the longest period within 1960-2000 for which data is available.

Among the countries experiencing a credit slowdown in recent years, only Bolivia, which seemed to show only a modest decline in credit growth in 1999-2000 from Figure 1, meets this criterion for a credit bust in recent years, with the credit-GDP ratio falling more than 6 percentage points below its trend in 2000. On the other hand, Venezuela and Chile, for whom credit peaked in the early 1980s and thus experienced the slowdown much earlier than in the other six countries, had at least one episode of credit bust in the late 1980s or early 1990s. For example, in Chile the credit-GDP ratio fell almost 7 percentage points below trend in 1991, and in Venezuela it fell 7 percentage points below trend in 1990.11

Although the above analysis would suggest that the recent slowdowns have not been very severe, several issues suggest that we may be observing the initial stages of a more pronounced credit bust, or that by limiting the sample to banks we are also missing key information regarding the severity of the slowdown. First, although the Gourinchas, et al. methodology defines a boom episode as a period containing at least one observation outside the threshold, the entire episode is said to begin and end when a less stringent limit threshold is crossed. If the limit threshold is 2 percent, Peru may have entered a credit bust in 2000. The same could be said for Mexico and, to a lesser degree, for Argentina and Colombia. Second, in 2001 credit has continued to decline in real terms in most of the countries in the sample, falling by between 0.4 percent (Peru) and 11 percent (Mexico), as Table 2 shows. Second, the experience of the earlier slowdowns, in Chile and Venezuela, demonstrated that they did become quite severe over time, qualifying as credit busts. So far the recent slowdowns have been relatively short, lasting about three years as opposed to thirteen years in Venezuela and seven years in Chile. Third, as in the earlier case of Venezuela, in Colombia the recent decline in the broader aggregate—all banks—is much more pronounced than for DMBs and might meet the criteria for a credit bust. A more complete data set would allow us to determine whether this is true.

How does the recent credit slowdown in Latin America compare to other well-known cases of credit stagnation around the world? In Figures 3 and 4 we plot credit-GDP ratios and deviations from trend for several countries where serious credit slowdowns have been studied: Finland, Indonesia, Japan, Korea, Thailand, and the U.S. In Figure 3 we have indicated the periods studied previously: the 1997-99 for Indonesia, Korea and Thailand in Ghosh & Ghosh (1999), post-1992 for Finland in Pazarbasioglu (1997), post-1990 Japan in Woo (1999), and post-1989 for the U.S. in Bernanke & Lown (1991) and Peek & Rosengren (1995).

Figure 3.
Figure 3.

Credit-GDP Ratios 1960-2000—Selected Cases of Credit Slowdown

Citation: IMF Working Papers 2002, 053; 10.5089/9781451847390.001.A001

Figure 4.
Figure 4.

Absolute Deviations in the Credit-GDP Ratio with Respect to Trend Selected Cases of Credit Slowdown

Citation: IMF Working Papers 2002, 053; 10.5089/9781451847390.001.A001

We also find significant variability in these experiences. Finland, Indonesia,12 and Thailand all register dramatic declines in credit-GDP in a relatively short period of time, Korea experienced a short-lived and small drop, while Japan and the U.S. exhibit much more modest although extended downturns in credit.

We repeated the boom-bust analysis for this set of countries (Figure 4). Finland, Indonesia, Thailand, and the U.S. all clearly fit the criteria for a boom and bust cycle. Finland experienced a credit boom in the early 1990s which brought credit-GDP to 18 percentage points above trend, followed immediately by a bust in which credit plummeted to 8 percentage points below trend by 1997. Indonesia and Thailand both reached the peak of their credit booms in the late 1990s, with credit-GDP well above the threshold, then went into a bust after the crisis, and continue to lie below the threshold as of 2000. Finally, for the entire banking system,13 the U.S. registered a credit bust in the early 1990s which was preceded by a boom from 1986 to 1989. On the other hand, Japan experienced a credit boom in the early 1990s, but although credit has slowed appreciably, it has not met the criteria for an outright bust. According to our analysis, Korea experienced a boom in bank credit in 2000, after a one-year decline that approached but did not breach the bust threshold.

The differences between the recent Latin American experience and that of other credit slowdowns is highlighted in Table 3, where we show the absolute declines in credit-GDP with respect to a peak level for each of the slowdown episodes. The largest total decline in credit- GDP was 56 percentage points, experienced by Venezuela during 1983–95, followed closely by Finland’s 44 percentage point contraction during 1992-97. On an annual basis, the largest declines were those of post-crisis Thailand and Indonesia, where over 10 percentage points were lost per year. In addition to Venezuela, other Latin American slowdowns have been quite substantial; credit-GDP fell by almost 30 percentage points in the case of all banks in Chile (1985–91) and about 19 percentage points for DMBs in Mexico, or over 3 percentage points per year, and over 4 percentage points per year for all banks in Colombia, all of which are comparable to or even greater than the credit crunch of the U.S. in the early 1990s. Again, since the recent Latin American slowdown cases are still relatively short, as opposed to a six-year decline in Finland, for example, we maybe observing the initial stage of a longer and more pronounced credit slowdown.

Table 3.

Latin American Credit Slowdowns in Comparison with Selected International Cases

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Sources: International Financial Statistics, Bank of Korea, and authors’ calculations.

B. The Latin American Credit Slowdown—Some Stylized Facts

We now turn to a closer look at the evolution of credit and bank behavior in our eight countries over a shorter sample. In Tables 4 and 4a we show average annual real growth rates of credit and deposits for different sub periods, as well as the ratios of credit to deposits and credit to total bank assets. For the six countries in which the credit slowdown is relatively recent (Argentina, Bolivia, Brazil, Colombia, Mexico, and Peru; Table 4), we divide the period into three portions: (1) the 1980s, which are characterized generally by relatively repressed financial markets and thereby low credit growth, (2) the credit expansion period of the early 1990s, spurred in part by financial liberalization measures undertaken at the beginning of the decade, and (3) the recent slowdown. The expansion is defined as ending in the year when credit-GDP reached its peak of the 1990s, thus leading to the subsequent slowdown period. In Brazil and Mexico, credit reached its peak in 1995. In Argentina, Bolivia, Colombia, and Peru, credit peaked in 1998.

Table 4.

The Recent Credit Slowdown in Latin America: A Summary

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Source: International Financial Statistics, and authors’ calculations.
Table 4a.

Earlier Credit Slowdowns in Latin America: A Summary

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Sources: International Financial Statistics and authors’ calculations.

For the other two counties, Chile and Venezuela (Table 4a), the slowdown occurred earlier, and the more recent period is characterized by a recovery in credit growth. In Chile, financial markets were liberalized much sooner than in the rest of the region, while Venezuela observed a positive and significant terms of trade shock after 1973, and faced severe macroeconomic distress in the early 1990s. In both cases, there is significant credit expansion during the late 1970s and early 1980s, followed by sharp contractions—until 1991 in Chile and until 1995 in Venezuela.

There are several consistent patterns across the first group of countries, those experiencing the recent slowdown. While in all cases real credit growth accelerated during the early 1990s, and subsequently slowed in the late 1990s, this behavior also occurred in bank deposits. The latter may have been the result of deregulation and financial reform programs undertaken early in the decade, which reduced taxes on financial intermediation, liberalized interest rates, and thereby encouraged savings through the banking system. Similarly, in the more recent period there may have been a disintermediation process following a period of financial turmoil, in which capital outflows took place and domestic bank deposits fell. Thus, it may be that the slowdown in credit was driven by a slowdown in deposits, and banks merely reacted passively in response to a squeeze on their loanable funds. However, our third observation is that although deposit growth was indeed a key factor, it does not appear to be the entire story. Indeed, except in the case of Brazil, in the other five countries the slowdown in credit is more pronounced than that in deposits. In fact, in some countries deposits continued to grow in real terms while real credit fell. Consequently, loans fell in relation to total assets, thus reflecting a change in the composition of bank balance sheets.

This behavior is not always symmetrical across the expansion and slowdown. During the expansion, three countries, Brazil, Colombia, and especially Argentina, registered deposit growth rates greater than credit growth rates. For Argentina and Colombia, the expansion phase was more pronounced for deposits while the slowdown phase was more pronounced for credit. For the remaining three countries, the entire cycle observed in the 1990s was more pronounced on the credit side, thus the loan-deposit and loan-asset ratios increased during the expansion and fell during the slowdown.

In the cases of Chile and Venezuela, during the slowdown credit fell more rapidly than deposits, and during the recent recovery credit is growing faster than deposits. In fact, in the case of Venezuela, the credit recovery period is characterized by a slowdown in deposits. As a result, there is a sharp increase in loans in relation to deposits and total assets.

C. A Decomposition of Credit Growth

In this section we present a summarized balance sheet for commercial banks, and observe the major changes that took place in bank activities in the sub periods described above. We use the balance sheet to decompose credit growth to the private sector into banks’ sources of funds or in the alternative uses of funds. Based on IFS data, the major balance sheet items are:

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The distinction between “sources” and “uses” is not clear-cut. For example, accumulating foreign assets may be viewed as an alternative use, while contracting a long-term foreign debt should be considered a source of funds. However, the purpose was to keep a simple, sectoral classification that would yield a small number of groups that could be easily identified. We proceeded to decompose real credit growth to the private sector in the three periods by using the following balance sheet identity:

CREDITt=SFt−AUFt⇒ΔCREDITCREDITt=ΔSFCREDITt−ΔAUFCREDITt

We present this decomposition in Tables 5 and 5a, which show the contribution of each source or alternative use to bank credit growth in the three sub periods and for the two groups of countries mentioned in Table 4.15

Table 5.

Decomposition of Credit Growth - Recent Cases of Slowdown

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Source: International Financial Statistics and authors’ calculations.
Table 5a.

Decomposition of Credit Growth: Earlier Cases of Slowdown

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Source: International Financial Statistics and authors’ calculations.

Again we observe how changes in deposit growth contributed to both the expansion and slowdowns of credit in the 1990s—and to the slowdown and subsequent expansion in Chile and Venezuela. We also see that other balance sheet items changed as well, thus credit did not move one-for-one with deposits. In general, credit and deposits move in the same direction, with the most notorious exceptions being Argentina and Mexico during the recent credit slowdown, in which deposits and other liabilities with the private sector actually increase, and Venezuela’s recent credit recovery, in which deposits are falling.

In all countries but Bolivia and Chile, net credit to the public sector accelerated during the slowdown period, presumably competing with credit to the private sector and contributing to the slowdown. However, the opposite was not always true during the prior expansion. In that regard, maybe the most interesting case is the recent credit recovery in Venezuela, which has taken place in the context of a sharp contraction of net credit to the public sector—that is to say, by a rapid buildup of bank deposits by the decentralized agencies (that is, public enterprises).

In most cases, net foreign liabilities moved procyclically in both phases. For example, in Mexico and Peru they increased during the early expansion and then reversed sharply during the subsequent slowdown, thus reducing the amount of resources available for lending to the private sector. The notorious exception is Argentina,16given its very particular exchange rate regime. The net foreign position had a dampening effect on credit growth; by expanding in the early 1990s and declining in the late 1990s, it offset the expansion as well as the slowdown. Upon closer inspection it becomes apparent that, for most countries, foreign liabilities rather than assets have tended to register the largest movements from one period to the next, thus generating the largest impact on credit growth. However, two major exceptions are Chile and Mexico, which registered substantial increases in foreign assets throughout most of the 1990s.17

In order to focus on the factors underlying credit growth in the 1990s, we highlight the changes from period to period in Tables 6 and 6a. For the six recent slowdown cases, we show the changes in average growth rates, first from the 1980s to the expansion period, and then from the expansion to the slowdown period. For the two early slowdown cases, we focus on the differences between the slowdown and expansion period first, then on the differences between the recovery and the slowdown. Taking Argentina, for example, we have real credit growth accelerating from a negative rate of 6.7 percent during the 1980s to a positive rate of 7.7 percent early 1990s, thus, a turnaround of 14.4 percentage points. As the decomposition shows, this is matched to a large extent by a turnaround in deposit growth, the major contributing factor, amounting to 20.7 percentage points, and offset by several factors, including an acceleration in banks’ net position with respect to the central bank of 4.8 percentage. In turn, during the slowdown credit decelerated by 10 percentage points, with a 10.3 point decline in deposit and capital growth.

Table 6.

Components of Credit Growth: Differences with Respect to Previous Periods

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Source: International Financial Statistics and authors’ calculations.
Table 6a.

Components of Credit Growth: Differences with Respect to Previous Period

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Source: Table 4.

We then summarize the major changes in banks’ balance sheets in Tables 7 and 7a, where we also indicate the four balance sheet factors that had the greatest impact on credit growth in each case, and distinguish whether their behavior contributed to or offset the changes in credit from one period to another. A number of characteristics of the credit cycle in Latin America become apparent. First, the single most important factor contributing to the changes in credit growth was deposit growth. For five of the eight countries it had the largest impact during the expansion, and for (another set of) five it had the largest impact during the slowdown. There is not one case in which deposits were not the dominant factor in at least one of the two phases, and in four countries—Argentina, Bolivia, Colombia, and Peru—they were the dominant factor in both phases.

Table 7.

Major Factors Contributing to Changes in Credit Growth

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Source: Table 4.
Table 7a.

Major Factors Contributing to Changes in Credit Growth

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Source: Table 4.

Second, the co-movement of deposit and credit growth dropped somewhat during the slowdown; taking a simple average of the relative contribution of deposits to credit growth changes across the eight countries, we find that deposits accounted for 63 percent of the acceleration in credit growth during the expansion, and for 53 percent of the recent deceleration. Excluding Venezuela, where the recent credit upswing came together with a decline in deposits, the co-movement drops from 77 to 50 percent, when comparing expansions and contractions.

Third, the net position with the central bank generally played an important role, albeit for different reasons. In three countries net central bank credit—a proxy for monetary policy—has been highly procyclical. In the case of Brazil, it was the dominant accelerating factor during the expansion and the dominant decelerating factor during the contraction. In Chile it was the dominant accelerating element during the expansion period and a major decelerating factor during the credit contraction. In Mexico net central bank credit was the dominant factor decelerating credit during the recent slowdown. On the other hand, in Argentina and Peru, and to a lesser extent in Colombia and Bolivia, net credit to the central bank played an off-setting role, contracting during the expansionary period and expanding during the contraction. It is worth highlighting that only in the case of Peru it holds that the central bank was the major off-setting factor in both phases, as reserve requirements were kept high during the expansion and were relaxed during the decline.

Fourth, except in the cases of Colombia and Mexico, net foreign liabilities (NFL) of banks played an important role in credit growth. They were a major contributing factor during both phases in Bolivia, Peru,18 and Chile and played an accelerating role in Venezuela’s contraction. On the other hand, They were a major offsetting element in the recent credit contractions of Argentina and Brazil.

Fifth, a fiscal factor (labeled NFPS in Table 7) played a major role in accelerating Mexico’s credit in the early 1990s and in Venezuela’s recent credit recovery. In Bolivia and Chile, and to a lesser extent in Argentina and Colombia, credit to the public sector on occasions acted as a major offsetting factor—for example, accelerating during the expansion period. Regarding the slowdown, net credit to the NFPS was a significant contributing factor—albeit not always a major one—for all countries except Bolivia and Chile. This may reflect a fiscal expansion which squeezed out private sector credit, and/or a reduction in risk-taking by banks, who may have decided to increase their holdings of relatively safe government securities rather than increase loans, as argued by Catao (1997) in the Argentinean case.

Finally, the private sector credit slowdown appears to be more complex than the expansion, in the sense that it involves changes in a larger number of balance sheet items, as Tables 6 and 7 show. Except in the case of Chile, in all countries we find that the aggregate relative contribution of a small number of factors, two contributing, two offsetting, falls, and sometimes considerably, from the expansion to the slowdown. For example, in Argentina the major factors accounted for 115 percent of the credit expansion, but for only 77 percent of the slowdown.

IV. Econometric Analysis

A. Methodological Issues

Following recent studies of credit stagnation in selected East Asian and Latin American countries (Ghosh & Ghosh, 1999; Pazarbasioglu, 1997; Kim, 1999; and Barajas, López, and Oliveros, 2001) we estimated aggregate demand and supply functions for credit in three of the countries analyzed: Colombia, Mexico, and Peru. The econometric approach used is based on pioneering work by Laffont and Garcia (1977) and Sealey (1979), and consists of estimating the system of supply and demand functions under the assumption that, at a given point in time, the credit market may either exhibit equilibrium, or temporary excess demand or supply owing to imperfect flexibility in the interest rate in the short run. Thus, in addition to capturing the main determinants of credit growth, the approach also allows one to assess whether a situation of excess demand, or credit crunch, occurred during an episode of declining credit growth.

Actual credit observed at time t, Ct is defined as lying either on the supply curve (excess demand), on the demand curve (excess supply), or on both (equilibrium):

Ct=min(Cts,Ctd),(1)

where Cst and Cdare the supply and demand functions, respectively, defined as a function of the vectors of explanatory variables X1t and X2t, and error terms:

Cts=X1tβs+u1tCtd=X2tβd+u2t(2)

Without adequate information on the price adjustment process, and assuming that the errors u1 and u1 are normally distributed, a likelihood function may be determined for the above model. Defining λt as the probability that a given observation t lies on the supply function (that is, demand is greater than supply), and given that g(·) is the joint density function for supply and demand derived from the joint density function for u1 and u2, then the density function for Ct under the assumption of excess demand is:

h(Ct|Ct=Cts)=∫ct0g(Ctd,Ct)∂Ctd/λt(3)

Similarly, the density function under the assumption of excess supply is:

h(Ct|Ctd)=∫Ct0g(Ct,Cts)∂Cts/(1−λt)(4)

The unconditional density function is then equal to

h(Ct)=λth(Ct|Ct=Cts)+(1−λt)h(Ct|Ct=Ctd)=∫Ct0g(Ctd,Ct)∂Ctd+∫Ct0g(Ct,Cts)∂Cys(5)

The likelihood function is L=Πth(Qt), and the corresponding log likelihood to be maximized subject to the parameter values is:

∑t=0Tlogh(Qt)(6)

Maximization of equation (6) permits the estimation of both equations as well as the estimation of the probability of observed credit lying on either of the curves, λ.19 As in previous studies, we used OLS estimates of the supply and demand functions to provide initial values for the coefficients and standard errors for each equation. Although Maddala (1983) warns that the likelihood function is unbounded for certain parameter values, we found the OLS estimates to perform well as starting values, and convergence tended to occur relatively quickly.

In considering the specification of supply and demand functions, several issues emerge. First, identification of the model requires that one or more variables included in one function be excluded from the other. The past studies using this approach have used one key variable, lending capacity, (LC), to distinguish the supply from the demand function. Lending capacity, defined as the availability of loanable funds to banks, would affect banks’ ability to lend but would not have impact on firm or household demand for credit. We also followed this approach, including as LC a subset of loanable funds over which banks have little discretionary power to influence in the short run, therefore constituting an exogenous determinant of bank credit.

A second specification issue involves variables reflecting the macroeconomic and business environment, since one expects credit demand to be positively related to these present and future expected conditions, and credit supply to respond to these variables to the extent that they would affect the riskiness of loans. As in previous studies, we included manufacturing production indices (MANUF), quarterly (or, in some cases, monthly) GDP measures (y), the output gap (GAP),20 the expected inflation rate (INFE)21 as a measure of macroeconomic stability, and the stock market index (STKMKT). It should be noted that the latter variable, as discussed in Ghosh & Ghosh (1999), may also reflect the availability and attractiveness of alternatives to bank credit—equity finance in particular—from the demand side. Thus, the stock market index will have a positive coefficient if the macro conditions effect dominates, or a negative coefficient if the substitution effect dominates.

Third, also as in previous studies, we included interest rates on deposits (id) and on government securities (ig) as proxies for the opportunity cost of bank credit either from the demand or supply side.

Fourth, in contrast to previous studies, for Peru and Mexico we also included the country-specific JP Morgan EMBI price,22 which we expected to have dual effects similar to those of the stock market index. A positive impact on credit demand would arise if the dominant effect was an improvement in macroeconomic conditions, while a negative effect would arise when the EMBI signals an increase in foreign investors’ willingness to lend to domestic residents, thus drawing customers away from bank credit.

Finally, also in contrast to previous studies, we included two additional variables specific to the supply function, the ratio of nonperforming loans total loans (NPL), and the ratio of loan-loss provisions to nonperforming loans (PROV).23 The former reflects past credit risk and may signal financial difficulties in the banking system, while the latter reflects the severity of regulations on risk-taking in lending activities. If banks are reasonably well behaved, they would tend to lower their credit supply in response to mounting credit risk and/or increasing loan-loss provisions being imposed upon them.24

Given the econometric approach and the specification described above, in the next sections we describe the specific estimation results for each of the three countries, as shown in Tables 8-10. In all cases, the dependent variable was the natural log of real credit to the private sector, LRCRED, with subtle differences in definition from country to country, as we explain below. In each table we report the estimated parameters with their respective t-statistics, the value of the log-likelihood function, and the R2 for the initial OLS estimations of the supply and demand functions. Finally, since the dependent variable was found to exhibit a unit root in all three countries,25 we conducted tests to determine whether real credit and its predicted value in each case formed a cointegrating vector.26 In the bottom portion of Tables 8-10, for each regression we show the trace statistic,27 which rejects the null hypothesis of no cointegration in virtually all cases and at least at the 5 percent level. Thus, even though real credit has a unit root, it is appropriate to estimate the model in levels.

Table 8.

Colombia. Credit Demand and Supply Estimations

(Maximum likelihood disequilibrium estimation)

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Notes: t-statistics shown in parentheses. Significance levels of 1 percent (***), 5 percent (**) and 10 percent (*) indicated. The letter L at the beginning of a variable name denotes natural logarithm.

Adjusted credit is defined as the stock of credit plus loan write-offs. In addition, in regression (4), two small state-owned financial institutions are excluded.

Table 9.

Mexico: Credit Demand and Supply Estimations

(Maximum likelihood disequilibrium estimation)

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Notes: t-statistics shown in parentheses. Significance levels of 1 percent (***). 5 percent (**) and 10 percent (*) indicated. The letter L at the beginning ofa variable name denotes natural logarithm.

Defined as the stock of credit plus adjustments to incorporate private sector loan restructuring programs beginning in mid-1995.

This dummy variable takes a value of zero up until 1994:12, and then unity thereafter, thus testing for a structural change alter the credit expansion period.

Table 10.

Peru: Credit Demand and Supply Estimations

(Maximum likelihood disequilibrium estimation)

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Notes: t-statistics shown in parentheses. Significance levels of 1 percent (***), 5 percent (**) and 10 percent (*) indicated. The letter L at the beginning of a variable name denotes natural logarithm.