Studying the Effects of Household and Firm Credit on
the Trade Balance: The Allocation of Funds Matters
Berrak Buyukkarabacak and Stefan Krause∗
April 2005
Abstract
One of the most widely used indicators of financial development in the empirical
literature is the Private Credit to GDP ratio. A key shortcoming of this measure is
that it does not distinguish between the share of credit extended to households visà-vis firms. It is our contention that this distinction is crucial to analyze the effects
of financial development on the trade balance: Changes in the composition of private
credit should have an impact on the foreign trade deficit.
Our empirical findings show that: 1) private credit to households is negatively and
significantly correlated with net exports; 2) private credit to firms is not significantly
correlated with net exports; and 3) the allocation of credit matters; a higher proportion
of firm credit is positively and significantly correlated with net exports.
A key implication of these results is that, whenever there is a sizeable trade deficit
or a high risk of a currency crisis, policy makers should limit the growth of household
credit while, at the same time, encourage further allocation of funds to firms.
JEL classification: F32, F41, G21
Keywords: Firm and household credit, financial development, trade balance
∗
Department of Economics, Emory University. We thank Neven T. Valev for useful discussion. For
comments, please contact us at bbuyukk@emory.edu or skrause@emory.edu.
1
Introduction
A well functioning financial system plays a key role in the economic performance of a
country, so factors that perversely affect the intermediation process will negatively impact
the economy. Bernanke and Gertler (1990, 1995) argue that a reduction in the supply
of bank credit is likely to increase the external finance premium and therefore reduce real
economic activity. Turning to the empirical evidence, Roubini and Sala-i Martín (1992) show
that, after controlling for other determinants of economic development, various measures of
financial repression affect growth negatively. Rajan and Zingales (1998) find that financial
development decreases the costs of external finance thus facilitating economic growth. More
recently, Beck, Levine, and Loayza (2000), and Levine, Loayza, and Beck (Levine et al, 2000)
find strong statistical evidence that a larger participation of financial intermediaries causes
an increase in economic growth, at least in the long-run.1
However, other studies find inconclusive evidence, or even suggest detrimental effects that
stem from financial development. For countries with very low levels of financial development,
Rioja and Valev (2004a,b) observe that the effect of additional improvements in financial
development on output growth is uncertain. Aghion, Bacchetta, and Banerjee (2004) show
that economies at an intermediate level of financial development are more unstable than
either very developed or very underdeveloped economies and argue that countries which are
going through a phase of financial development may become more unstable in the short run.
Demirgüç-Kunt and Detragiache (1999, 2002) argue that the relationship between financial liberalization, financial development and growth should be analyzed cautiously whenever prudential regulation and supervision are not fully developed. De Gregorio and Guidotti
(1995) find evidence suggesting negative effects of financial development on growth for twelve
Latin American countries, and argue that the negative effect is the result of financial liberalization in a poor regulatory environment. Finally, Demirgüç-Kunt and Detragiache (1998)
find that a high share of credit to the private sector and elevated past economic growth may
1
See King and Levine (1993a,b) and Levine (1997) for an exhaustive literature review.
1
be associated with a higher probability of a crisis.
A widely used indicator of financial development in this rich literature is the relative
importance of loans issued by commercial banks and other financial intermediaries to the
entire private sector; i.e., the Private Credit to Gross Domestic Product (GDP) ratio, following the definition by King and Levine (1993a,b). However, this measure has one major
shortcoming: It does not distinguish between the fraction of credit extended to households
vis-à-vis firms. It is our contention that this distinction is crucial to analyze the effects of
financial development on several key macroeconomic performance variables, such as growth
and inflation among others.
Out of the several possible macroeconomic variables of interest, in this paper we concentrate our efforts on a particular one: The trade balance. Our rationale is that for small,
open economies the external sector plays a pivotal role in determining overall macroeconomic performance. We show empirically how the allocation of loans to the private sector
between consumers and producers affects a country’s foreign trade position. While an increase in household credit, all other things equal, results in an overall rise in consumption
and, therefore, in the purchase of imported goods, an increase in direct lending to firms will
affect investment and eventually contribute towards the production and export capacity of
a country. Therefore, changes in the composition of private credit should have an impact on
the trade deficit.
The main objective of our study is to analyze the effect of the allocation of credit to
the private sector on the trade balance. As argued by Wood (1997), "[...] the [International
Monetary] Fund continues to place most emphasis within its programmes on the stabilisation
objective and therefore the core of IMF programmes continues to be based upon the restriction of domestic credit creation [...]." The practice of limiting credit growth has been adopted
by several developing countries; in particular Mexico during the 1994-1995 crisis (Gruben
and McComb, 1997) and Brazil in 1999 (International Monetary Fund, 2003). However, even
though the distinction is made between credit to the public versus the private sector, there
2
is no mention as to different outcomes resulting from restricting household or firm credit.
More recently, Chan-Lau and Chen (2002) find that in the developing stages of a financial
system, the country is more exposed to exchange rate and financial crises. In their extensive
and detailed review of the causes and consequences of the East Asian Crisis, Ito and Krueger
(2001), found one common underlying explanation: the expansion of private domestic credit,
combined with weak regulation, is considered the main catalyst of the currency crises and
trade imbalances in East Asian countries during the latter 1990s.2 Again, these studies focus
on total lending to the private sector and do not empirically distinguish between the separate
effects household and firm credit have on currency crises and trade imbalances.3
The remainder of the paper is organized as follows: We first describe our sample selection
and data in Section 2. Clearly, we are restricted to analyzing only countries and time periods
for which data on household and firm credit was, not only available, but reliable as well.
These restrictions lead us to examine an unbalanced panel of nine countries.4 As we elaborate
below , we focus exclusively on developing and transition economies for two main reasons:
1) these countries have undergone a financial liberalization process more recently than most
industrialized economies; and 2) for all of them, the main source of funds for consumers
and producers is bank lending and financial intermediation, with stock markets playing a
relatively minor role.
In Section 3 we detail the instrumental variable estimation of the household and firm
credit ratios, which we use in order to overcome potential endogeneity issues. We then
utilize the first-stage estimates of these two main components of private credit in Section
4 to perform several dynamic panel data analyses. Here we model the trade surplus as a
function of private lending to consumers and producers, and the relative importance of each
2
See Corsetti, Pesenti, and Roubini (1999) for the derivation of a model of financial crisis that focuses on
moral hazard as the common source of overinvestment, excessive borrowing, and current account deficits in
an economy with a poorly supervised and regulated financial sector.
3
One exception in the theoretical literature is the work by McKinnon and Pill (1997). They develop a
model that shows how, whenever domestic consumer credit is insufficiently restrained, the effect of moral
hazard in banks becomes more damaging, generating financial instability and exchange rate crises.
4
These countries are: Argentina, Brazil, Bulgaria, Costa Rica, Hungary, Mexico, Poland, Turkey, and
Uruguay.
3
component, alongside some control variables. Our results show that: 1) private credit to
households is negatively and significantly correlated with net exports; 2) private credit to
firms is not significantly correlated with net exports; and 3) the composition of credit to the
private sector matters; a higher share of firm credit is positively and significantly correlated
with net exports. These findings are robust to alternative specifications and estimation
methods.
We perform several comparative statics exercises in Section 5 and provide some policy
prescriptions based on our main observations. A key implication of our findings is that in
the presence of a sizeable trade deficit or a high risk of a currency crisis, policy makers
should limit the growth of household credit only, rather than restricting the expansion of
total domestic credit to the private sector. Section 6 concludes and elaborates on possible
extensions.
2
Sample Selection and Data Description
As we point out above, we look only at developing and transition economies for which data
on lending to consumers and producers is both available and reliable. Nine countries fit these
criteria: Argentina, Brazil, Bulgaria, Costa Rica, Hungary, Mexico, Poland, Turkey, and
Uruguay. We list the maximum number of sequential quarters, between 1990:I-2004:IV, for
which information on household and firm credit was obtainable and display this information
in Table 1.
The data periods coincide with the ones provided by the national central banks in their
respective websites with three exceptions. First, for the case of Argentina there exists information beyond 1999:IV; nevertheless, we found a change in the reporting methods that
renders the data on the decomposition of credit to the private sector starting 2000:I impossible to compare with the information reported for the 1990s.
4
Table 1: Data Description
Country
Period
Variable used for
Household Credit
Variable used for
Firm Credit
Argentina
1993:I-1999:IV
Family & Individual
Loans
Primary, Industry &
Services Sector Loans
Brazil
1995:I-2004:II
Housing & Individuals'
Loans
Industry, Rural &
Commerce Sector
Loans
Bulgaria
1998:I-2004:II
Household Credits
Firm Credits
Costa Rica
1996:I-2004:I
Consumption Loans
Agricultural, Industry &
Services Sector Loans
Hungary
1995:I-2004:II
Households' Balance
Sheet
Firms' Consolidated
Balance Sheet
Mexico
1995:I-2004:III
Credit to Housing &
Consumption
Credit to Primary,
Industry & Services
Poland
1995:I-2001:IV
Claims to Household
Sector
Claims to Corporate
Sector
Turkey
1990:I-2003:IV
Private Credit to Firms
Private Credit to
Consumers
Uruguay
1990:I-2001:IV
Credit to Consumption
Credit to Agriculture,
Industry, Commerce &
Service
Second, Brazil does have information on lending to consumers and producers between
1991:I and 1994:IV, while Bulgaria has data on these two categories between 1996:I and
1997:IV. However, Brazil experienced an average annual inflation of nearly 1,500% between
1991 and 1994; meanwhile, Bulgaria had an inflation rate close to 700% on average in 1996
and 1997. The outcome of these extremely inflationary periods is that, since these two
central banks report nominal values, and we are unable to determine during what time of
5
the month or quarter the information is collected (and whether or not an adjustment has
been made), we cannot rely on this data for our analysis.5
Another important criterion for our data selection was to consider countries that have
undertaken measures to eliminate financial repression either slightly before or during the
1990s. The nine countries in our sample fit this description. Also, some of these countries
have been either directly or indirectly affected by recent balance-of-payments and foreign
exchange crises. The Mexican currency crisis of 1994/1995 had consequences Argentina,
Brazil, Costa Rica and, to a lesser extent, Uruguay (through the "Tequila Effect"); the
East Asian and Russian Crisis of 1997/1998 impacted the economies of former communist
countries, Turkey and several South American countries; while the Brazilian and Argentinean
fiscal troubles and banking panics, at the end of the 1990s and in 2002, respectively, caused
severe problems for those two countries and some of their trading partners. One final criterion
is that all of these countries had relatively small stock markets during most of the period
of study, as shown in Table 2. With the exception of Mexico, Brazil and Argentina, who
have experienced a steep development of external financing since the latter part of the 1990s,
stock market capitalization does not even exceed ten percent of GDP during the past decade.
This implies that the private sector in these economies heavily relies on banks and financial
intermediaries as a source of funds for consumption and investment.
5
Fortunately, no other country or period in our sample has such high inflationary outcomes. The closest
is Turkey in 1994, where average inflation was about 100%; this number is more manageable.
6
Table 2: Stock Market Size (1990-1999)
Country
Avg. stock market
capitalization
to GDP (in %) /a
Avg. stock market
total value traded
to GDP (in %) /b
Argentina
12.71
4.03
Brazil
18.92
12.98
Bulgaria
2.76
0.19
Costa Rica
6.60
0.29
Hungary
9.11
9.66
Mexico
26.93
11.81
Poland
3.97
3.60
Turkey
9.86
20.87
Uruguay
1.06
0.03
Average
10.21
7.05
/a:
Source: Own computation based on data from the Standard and Poor's Emerging Market
Database (and Emerging Stock Markets Factbook) and the World Development Indicators (2004)
/b:
3
Source: Own computation based on data from the World Development Indicators (2004)
First Stage: Instrumental Variable Estimation
We now turn our attention to the measurement of the household and firm credit de-
composition. We argue that the distinction between these two components of total private
credit is of great value for many empirical applications since changes in the allocation of
funds between consumers and producers will have an important effect on macroeconomic
variables, including output growth. We choose to focus our efforts on the effects of loans to
7
households and firms on the trade balance, given the crucial link that exists between macroeconomic outcomes and the external sector for small, open economies. This way, we hope to
provide some insight into preventing future currency and foreign trade crises in developing
and transition economies.6
Before we proceed to estimating the relationship between private credit allocation and
the trade balance, we need to deal with potential endogeneity issues. Clearly, the level of
aggregate spending is directly related to bank lending and conversely. Also, by national
accounting, both exports and imports depend on the level of aggregate demand and viceversa, which implies that net exports and domestic credit are determined simultaneously.
Therefore, we require a first-stage estimation to obtain instrumented estimates of the
credit to households and credit to firms variables. To do this we run the following two
autoregressive models:
ct + εht
ht = α0h + α1h ht−1 + α2h rt−1 + α3hb
ft = α0f + α1h ft−1 + α2f rt−1 + α3fbit + εf t ,
(1)
(2)
where h and f represent, respectively, household and firm credit (as defined in Table 1)
as ratios of GDP; r is the (effective) real interest rate; b
c is the instrumental variable (IV)
predicted value of aggregate consumption over GDP; and bi represents the IV-estimate of the
aggregate investment-GDP ratio. b
c is the outcome of modeling the consumption-GDP ratio
as a linear function of its one-period lagged value, one-period lag of credit-to-household-GDP
ratio, imports and time; while the regressors used to obtain bi are its autoregressive term,
the lagged value of credit-to-firm-GDP ratio, exports and time. We report the R-squared
and the estimates of the α1 ’s, α2 ’s, and α3 ’s for equations (1) and (2) in Tables 3a and 3b,
respectively.
Prior to describing the results of this first stage process, it is imperative that we justify
6
For example, Mendoza and Uribe (2000) show how devaluation risk leads to distortions in real prices
and economic activity through the increase in real interest rates resulting from a higher risk premium.
8
the choice of our instruments. Employing the lagged real interest rate in equations (1) and
(2) is relatively straightforward; credit to the private sector, whether it is used to finance
spending or production, depends on the cost of funds, albeit not immediately but with at
least a few months of delay. Also, as argued by Bernanke and Gertler (1990, 1995) and
others, the (real) interest rate first affects credit and then spending (through the lending
channel), which justifies our exclusion of this variable in the consumption and investment
IV equations.
We also find it reasonable to instrument consumption through lagged imports. The reasoning for this is that a higher level of imports makes more goods available in the economy
and, as a consequence, all sectors (in particular households) will purchase more goods. This
effect need not be immediate, but eventually (a three-month lag is our assumption) consumers’ spending will increase as a result of a rise in imports. As an outcome of the higher
level of consumption, credit to households will also rise through the increased spending effect.
Finally, we can apply a similar logical argument to the export-investment relationship:
A boost in exports could indicate a growing foreign market with more opportunities for
domestic firms. These firms will have to invest in order to expand production capabilities,
resulting eventually in the need for more bank loans.
Table 3.a describes the regression results of the household credit ratio to GDP. In general,
the results are as expected: consumer credit is positively and significantly correlated with
its own past value and either negatively or uncorrelated with the lagged real interest rate.
Finally, for all countries we find that the IV estimate of consumption is either positively and
significantly correlated or uncorrelated with household credit. In general, the goodness of
fit criterion also shows that these regressors provide an important explanatory power (over
63% for all countries) in accounting for the variance of consumer credit.
9
Table 3.a.: First Stage: IV regression
(Dependent Variable: Household Credit /GDP)
Country
Lagged
household
credit
Lagged
real rate
Consump.
(IV-Estim.)
R2
Argentina
1.0307
-0.0026
-0.0275
(0.000)
(0.941)
(0.952)
0.9036
Brazil
0.4956
-0.0111
0.4901
(0.030)
(0.045)
(0.158)
Bulgaria
1.1780
-0.0613
0.3248
(0.000)
(0.003)
(0.023)
Costa Rica
0.8692
-0.1766
-0.2298
(0.000)
(0.448)
(0.253)
Hungary
1.7164
-0.2533
-0.4759
(0.000)
(0.746)
(0.346)
Mexico
0.5597
-0.0167
-0.3443
(0.002)
(0.346)
(0.154)
Poland
0.9570
-0.0482
0.3802
(0.000)
(0.662)
(0.088)
Turkey
1.0047
-0.0321
-0.0970
(0.000)
(0.013)
(0.652)
Uruguay
0.9851
0.0171
-0.0457
(0.000)
(0.112)
(0.349)
0.6341
0.9722
0.9422
0.7613
0.6827
0.9594
0.7749
0.9152
Turning to the equation of firm-credit-to-GDP ratio in Table 3.b, the lagged dependent
variable once again has the expected sign and is always significant. Not too surprisingly, we
encounter no correlation between the real interest rate and loans to producers. Consistent
with our expectations, we do find that the effect of the IV estimate for investment is positive
and significant for six countries, and the coefficient is only not distinguishable from zero for
Bulgaria, Costa Rica and Mexico. The goodness of fit criterion shows that the explanatory
variables account for well over 80% of the variance with the exception of three countries:
Argentina, Brazil and Turkey. Nevertheless, we note that for these countries the coefficients
10
on lagged firm credit and the IV estimate for investment are significant and have the expected
positive sign, which brings further support to our specification.
Table 3.b.: First Stage: IV regression
(Dependent Variable: Firm Credit /GDP)
Country
Lagged
firm
credit
Argentina
0.3760
0.0426
0.2735
(0.021)
(0.111)
(0.016)
Brazil
0.5191
0.0004
1.7026
(0.001)
(0.971)
(0.045)
Bulgaria
1.0351
0.1004
-0.2341
(0.000)
(0.190)
(0.549)
Costa Rica
0.9566
0.1461
1.1793
(0.000)
(0.431)
(0.113)
Hungary
0.9376
0.7171
1.5813
(0.000)
(0.349)
(0.005)
Mexico
1.0251
-0.0146
-0.1317
(0.000)
(0.289)
(0.129)
Poland
0.8069
-0.1147
1.0035
(0.000)
(0.684)
(0.000)
Turkey
0.6307
-0.0405
0.6105
(0.000)
(0.436)
(0.074)
Uruguay
0.9919
0.0081
0.5897
(0.000)
(0.766)
(0.068)
Lagged Investment
real rate (IV-Estim.)
R2
0.3132
0.5263
0.8514
0.9555
0.8192
0.9609
0.8089
0.4933
0.8203
Summarizing the results from this section, we believe that the first-stage adjusted values
for both household and firm credit are warranted and can therefore be employed to analyze
the implications that the composition of private credit has on the trade balance.
11
4
Second Stage: Panel Data Analysis
Our main objective is to determine whether or not the particular allocation of private
credit between consumers and producers has an effect on the trade balance. We believe that
this composition is important: household credit will have a different effect than firm credit
on net exports. Formally, our main hypotheses can be summarized as follows:
• Hypothesis 1: A rise in household credit causes a decrease in the foreign trade surplus
through its effect on consumption spending.
• Hypothesis 2: Firm credit has no immediate effect on net exports.7
• Hypothesis 3 : Controlling for total credit to the private sector, a higher share of credit
extended to producers (vis-à-vis credit extended to households) increases the foreign
trade surplus .
We now proceed to study how the trade balance, as a fraction of GDP, is associated with
household and firm credit. We perform an unbalanced dynamic panel analysis, controlling
for the current value and the one-quarter lag of the real devaluation rate. The models we
estimate are represented by the following equations:
f
+ γ 11 di,t + γ 12 di,t−1 + ν i +
ni,t = β 11 hfi,t + β 12 fi,t
1i,t
,
(3)
ni,t = β 21 sfi,t + β 22 pfi,t + γ 21 di,t + γ 22 di,t−1 + η i +
2i,t
,
(4)
where: t represents the quarter; n represents exports minus imports divided by GDP; αi
is the country-specific effects; hf and f f are the fitted values of household-credit-to-GDP
ratio and household-credit-to-GDP ratio, obtained from equations (1) and (2), respectively;
sf (≡
ff
)
hf +f f
represents the share of total private credit that is directed to firms; pf is the
amount of total private credit relative to GDP; and d is the real exchange devaluation rate.
7
Eventually, an expansion in firm credit should lead a rise in net exports by boosting capital accumulation
and production. However, since it is quite difficult to empirically determine how long will it take for this full
effect to take place, we will refrain from testing this implication in our present analysis.
12
Hypothesis 1 and Hypothesis 2 imply that β 11 < 0 and β 12 = 0 in equation (3). We test
Hypothesis 3 by estimating β 21 in equation (4). Regarding our control variables, we expect
a positive impact of the real devaluation rate (γ 11 > 0 and γ 21 > 0) on net exports.
We use two econometric techniques to estimate the relationship between trade balance
and credit levels: (1) an uncorrected random effects model; and (2) a random effects model
with AR(1) disturbances. We note that in 11 out of the 12 specifications we analyze, the
Hausman (1978) test does not reject the hypothesis that individual country effects are uncorrelated with other regressors at the 5% significance level, which leads us to conclude that
the random effects model is indeed the best choice.
Table 4.a: Panel Data Analysis
Household and Firm Credit Ratios
Explanatory variable
Specification 1
Specification 2
Specification 3
-0.0670
-0.0674
-0.0668
(0.003)
(0.002)
(0.003)
-0.0237
-0.0223
-0.0220
p-value
(0.113)
(0.126)
(0.129)
Real Devaluation
0.0020
0.0100
p-value
(0.899)
(0.342)
Lag. Real Devaluation
0.0085
0.0097
(0.482)
(0.239)
Household Credit /a
p-value
Firm Credit /a
p-value
Chi2-statistic
22.56
21.61
22.02
p-value
(0.000)
(0.000)
(0.000)
/a:
First stage estimates of household credit to GDP and firm credit to GDP.
13
Finally, the reason we do not employ a dynamic panel generalized method of moments
(GMM) approach is that our cross-section sample size is restricted with nine countries. Since
we deal with endogeneity problems by instrumenting our credit variables, we believe that
the two techniques above will provide us robust estimators.
To verify the robustness of our results, we employ an alternative estimation method;
namely, a random effects model with autoregressive (AR(1)) disturbances (Baltagi and Wu,
1999).
Table 4.b: AR (1) Random Effects
Household and Firm Credit Ratios
Explanatory variable
Household Credit /a
p-value
Firm Credit /a
Specification 1
Specification 2
Specification 3
-0.0557
-0.0560
-0.0560
(0.015)
(0.014)
(0.014)
-0.0056
-0.0055
-0.0056
p-value
(0.678)
(0.679)
(0.674)
Real Devaluation
0.0129
0.0158
(0.415)
(0.201)
p-value
Lag. Real Devaluation
p-value
0.0036
0.0097
(0.766)
(0.303)
Chi2-statistic
10.37
10.30
9.82
p-value
(0.035)
(0.036)
(0.044)
/a:
First stage estimates of household credit to GDP and firm credit to GDP.
Our results from Tables 4.a. and 4.b. support both Hypothesis 1 and Hypothesis 2 : the
ratio of household credit to GDP has a negative and (at the 1% and 2% level, respectively)
significant impact on the trade balance, whereas firm credit has no significant effect on net
14
exports. These results are robust under the three different specifications and across the two
alternative estimation models. Finally, we note that the coefficients on current and lagged
real devaluation are positive but insignificant under both estimation procedures.
Table 5.a: Panel Data Analysis
Share of Total Private Credit Directed to Firms
Explanatory variable
Specification 1
Specification 2
Specification 3
Share of Credit to Firms/a
6.1828
6.1475
6.1106
p-value
(0.040)
(0.037)
(0.038)
Total Private Credit/a
-3.2748
-3.1696
-3.1541
p-value
(0.001)
(0.001)
(0.001)
Real Devaluation
0.0035
0.0118
p-value
(0.822)
(0.259)
Lag. Real Devaluation
0.0087
0.0108
(0.469)
(0.186)
p-value
Chi2-Statistic
25.04
23.65
24.19
p-value
(0.000)
(0.000)
(0.000)
/a:
Share of Credit to Firms and Total Private Credit are computed using first stage estimates of
household credit to GDP and firm credit to GDP.
We present the estimation results of (4) in Tables 5a and 5b. The data strongly favors
Hypothesis 3 : controlling for total credit to the private sector, the share of firm credit to
total private credit has a positive impact on net exports, and this effect is significant in
all three specifications and in both alternative estimation procedures (with the exception of
the specification with only lagged real devaluation with the AR-correction model, where it
becomes not-significant at the 10% level).
15
It is worth noting that the coefficient on total private credit over GDP is negative and
significant at the 5% level, and this observation consistent with the policy prescription recommended by the IMF; namely, that restricting total domestic lending will indeed result in
a more favorable trade balance. Nevertheless, our initial claim that the composition of credit
matters is validated by our results. Finally, we again fail to encounter any effect of current
and lagged real devaluation on the trade balance.
Table 5.b: AR (1) Random Effects
Share of Total Private Credit Directed to Firms
Explanatory variable
Specification 1
Specification 2
Specification 3
Share of Credit to Firms/a
5.1609
5.1880
5.0596
p-value
(0.100)
(0.098)
(0.106)
Total Private Credit/a
-1.8587
-1.8662
-1.8707
p-value
(0.041)
(0.040)
(0.040)
Real Devaluation
0.0139
0.0171
(0.382)
(0.167)
p-value
Lag. Real Devaluation
p-value
0.0039
0.0106
(0.747)
(0.263)
Chi2-Statistic
10.33
10.24
9.68
p-value
(0.036)
(0.037)
(0.046)
/a:
Share of Credit to Firms and Total Private Credit are computed using first stage estimates of
household credit to GDP and firm credit to GDP.
Overall, the empirical evidence provides strong and robust support for our claim that
consumer credit and producer credit have sharply different impacts on the trade balance.
We should also note that the above results are robust to employing the actual data on the
16
household and firm credit ratios.8 In the next section we look more closely at the implications
of these empirical outcomes and provide a few comparative statics exercises.
5
Comparative Statics and Implications
Exactly how big are the effects of changes in the allocation of funds to consumption
and the composition of private credit on the trade balance? Table 6.a runs a comparative
statics exercise for the two countries for which we have the longest time series: Turkey and
Uruguay. From 1990:I until 2003:IV, Turkey experienced an increase of household credit
from 0.53 percent of GDP to 16.52 percent of GDP. This dramatic change, all other things
equal, translates into an average of nearly one percent rise in the foreign trade deficit.
The numbers for Uruguay are smaller, but nonetheless still important: as a ratio of GDP,
consumer credit rose from 2.46 percent in 1990:I to 8.33 percent in 2001:IV, resulting in an
average contribution of 0.37 percent increase to the external deficit. For all other countries
except Brazil, private lending to households also increased as a percentage of GDP. We
should point out that the Brazilian economy has been experiencing trade surpluses that
average over 3 percent of GDP since 2002; larger than any of the other eight countries in
our sample.
Table 6.a.: Comparative Statics:
Effect of Change in Household Credit Ratio on Net Exports
Period of Interest
Actual Increase in
Household Credit
Predicted Range of Change
on Surplus
Turkey
From 1990:I to 2003:IV
15.99% of GDP
-1.08% to -0.89% of GDP
Uruguay
From 1990:I to 2001:IV
5.87% of GDP
-0.40% to -0.33% of GDP
Country
8
The results of these alternative tests are available upon request from the authors.
17
The measured effects of the composition of credit to the private sector on the foreign
trade account are even more astonishing, as can be seen in Table 6.b. Looking once again
at the case of Turkey, the participation of firm credit in total credit to the private sector fell
from 0.987 during the first quarter of 1990 to 0.562 for the last quarter of 2003, resulting in an
average increase in the trade deficit of 5.37 percent of GDP. This change in the composition
of credit alone accounts for two-thirds of the reported foreign trade deficit of 2003!
Table 6.b.: Comparative Statics:
Effect of Change in Composition of Private Credit on Net Exports
Period of Interest
Change in Share of
Firm Credit
Predicted Change in Trade
Surplus
Turkey
From 1990:I to 2003:IV
-0.425
-2.63% to -2.15% of GDP
Uruguay
From 1990:I to 2001:IV
-0.133
-0.82% to -0.67% of GDP
Country
For Uruguay, the comparative statics exercise indicates that the decline in the portion of
private credit extended to firms from 0.884 in 1990:I to 0.751 in 2001:IV is responsible for an
average of 1.68 percent of the trade deficit: Close to two-fifths of the reported value of 2.04
percent for 2001. As stated before, all countries but Brazil experienced a decrease in the
relative importance of credit to producers vis-à-vis consumers over the period of analysis.
We therefore should expect that their foreign trade deficits would have been smaller if it
were not for the increase in both the relative size of consumer credit and its share in the
total funds destined to the private sector.
There are several key implications that stem from our results in Section 4 and the comparative statics analyses:
• private credit to firms has no significant effect on the trade balance;
• a decrease in net exports is associated with both an increase in household credit and
18
a larger share of funds allocated for consumption expenses;
• the effects are sizeable: a one percent rise in the foreign trade deficit may result from
either an increase in consumer credit as a fraction of GDP in the order of only less
than 14 percent, or a larger participation of loans to households in total lending in the
order of merely 0.15;
• as a direct consequence, policy makers should pay more attention to the composition
of domestic credit rather than the absolute size of it.
6
Conclusions
We have argued and presented empirical evidence showing that analyzing the effects
of the particular distribution of funds between households and firms is more important for
explaining foreign trade imbalances than the size of domestic credit per se. The measured
effects are quite large; therefore, policy makers, faced with either an unmanageable trade
deficit or the risk of a currency crisis, should focus their efforts on restricting consumer credit,
instead of total private credit.
We do not encounter any evidence suggesting that a larger amount of funds allocated to
firms has any significant effect on foreign trade deficit. Our findings support the hypothesis
that a higher relative share of credit to producers is both significantly and in a sizeable
manner associated with a boost to net exports. The discussion on the effects of firms’ credit
on foreign trade deficit can be extended by analyzing the imports of goods and raw materials.
Contingent on data availability, we would be interested in studying the likely outcomes of
the allocation of funds to the private sector by way of considering the value added from
exports relative to the purchase of (imported) raw materials and capital. Nevertheless, we
emphasize that in the present study we fail to find any direct positive effect of firm credit
on the trade balance.
19
Limitations of our study are mainly related to data availability, both in terms of time
series and the number of countries. We look forward to the possibility of obtaining additional
reliable data to broaden the scope of our analysis.
On a final note, making the distinction between private credit to households versus
firms can have enormous implications in potential empirical studies that involve analyzing
the effects of financial development on other key macroeconomic variables such as growth,
inflation, interest rates, and exchange rates. We hope to extend our line of research into this
direction in the very near future.
7
Appendix: Data Sources
The countries included in the sample are: Argentina, Brazil, Bulgaria, Costa Rica,
Hungary, Mexico, Poland, Turkey, and Uruguay. Firm Credit and Household Credit data
was obtained from the individual central banks’ websites for each individual country, while
information for the remaining variables was obtained from the International Monetary Fund’s
International Financial Statistics database. Gross Domestic Product is line 99b; Private
Consumption is line 96f. Gross Fixed Capital Formation, our measure for Investment is on
line 93e. Inflation is given by the percentage change in the Consumer Price Index with
respect to the corresponding quarter of the previous year, and it is taken from line 64.
Devaluation is the percentage change in the exchange rate of national currency per U.S.
dollar at the end of the quarter, with respect to the corresponding period of the preceding
year. The information for Exports and Imports of goods and services was obtained from
lines 90c and 98c, respectively. The Interest Rates used differed from country to country
depending on data availability: the money market rate for Argentina, Brazil and Poland; the
discount rate for Hungary and Costa Rica; the interbank rate is for Bulgaria and Turkey;
the time deposit rate for Uruguay; and the bankers’ acceptances rate for Mexico.
20
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