There is a growing body of literature that seeks to identify, or even predict, circumstances under which countries may suffer balance of payments crises. Much of this literature, inspired by the theoretical models of Krugman (1978) and Flood and Garber (1984), emphasizes the role of macroeconomic imbalances—large fiscal deficits or excessive rates of credit expansion—as the underlying cause of currency crises (while the proximate triggers may be contagion effects or imprudently low levels of foreign exchange reserves).
Yet the Asian crisis countries, in particular, do not readily fit this mold. Exchange rates in these countries were not especially overvalued, fiscal deficits were small, and macroeconomic performance had generally been exemplary. Rather, structural weaknesses in the corporate and financial sectors appear to have been at play. This paper seeks to complement much of the existing literature on currency crises by examining the role of structural factors and vulnerabilities.2
At least in East Asia, weak corporate and public sector governance appears to have encouraged an environment of excessive risk-taking by the corporate and financial sectors, resulting in highly vulnerable corporate financing structures, with too much reliance on debt rather than equity issuance, and a large fraction of short-term rather than long-term borrowing.3 But such vulnerable corporate financing structures, while a feature of the East Asian experience, are by no means unique to it, and may have been at play in other currency crises as well.4
Identifying such structural determinants of currency crises is important for at least two reasons. First, inasmuch as these weaknesses, like macroeconomic imbalances, contribute to the probability of a currency crisis, eliminating them is clearly a priority. Second, if structural factors are at play, then faced by such a crisis, announcing and implementing structural reforms may be crucial in restoring confidence to the markets.
We examine the role of corporate sector vulnerabilities in currency crises using a panel dataset covering some 40 industrialized and emerging market countries, over the period 1987-1999. Our list of crises is taken from Glick and Hutchison (1999), except that, for the bulk of our analysis, we focus on “deep” currency crises—that is, those in which there was an appreciable decline in real GDP growth.5 In addition to the usual macroeconomic suspects, we consider four broad categories of structural indicators. The first pertains to what might be termed the country’s overall “rule of law,” including ratings on public sector corruption, risk of government expropriation or contract repudiation, as well as efficiency of the juducial system and legal and accounting standards. The second and third categories concern corporate governance directly, and pertain to the rights and responsibilities of shareholders and creditors, respectively6. Weak corporate governance, resulting, in part, from inadequate shareholder and creditor rights, may be manifested in a risky financing structure of corporations (e.g. with an over-reliance on short-term debt). As a final category, therefore, we also include corporate debt- equity ratios and structure of debt (medians for a sample of firms in each country).
Much of the literature on currency crises to date has used probit analysis to relate the probability of a crisis to vector explanatory variables. Such probit models underlie most of the “early warning systems” for currency crises being implemented in both the private and public sectors.7 Although the precise explanatory variables differ across models, they normally include indicators of macroeconomic imbalances—current account deficits, real exchange rate overvaluation, rapid rates of credit growth, and budget deficits—and, in some recent studies, various indicators of structural vulnerability as well.
These probits give the marginal effect on the probability of a crisis of each of the explanatory variables, holding the others constant at their mean values. While this “other things equal” (ceteris paribus) assumption is common in economics, it is not the most natural assumption to make when assessing the risk of an event because it does not readily allow for interactions between the various explanatory variables; indeed, in many other contexts, the ceteris paribus assumption would be considered quite odd.
Take, for instance, a doctor diagnosing a patient’s risk of a heart attack, and suppose that both a history of heart problems in the immediate family (hereditary factors) and high (LDL) cholesterol levels are known to contributory factors. The equivalent of a “probit” approach would be one in which the doctor considers the marginal effect of the patient’s cholesterol level, holding constant his family history at the (population) mean. But no doctor would do this. Rather, it would be much more natural to first ascertain whether there was any history of heart attacks among the patient’s relatives. If the answer were yes, then the “danger” level of cholesterol may be 130, and the patient’s cholesterol assessed in relation to this level. On the other hand, if the answer to a family history of heart attacks were no, a higher level of cholesterol, say 150, may be tolerable.8
In much the same vein, a country may be vulnerable to a crisis because of structural deficiencies, but only suffer a currency crisis when macroeconomic imbalances become sufficiently severe. As a methodological innovation of this paper, therefore, we go beyond standard probit analysis and use a decision-theoretic classification technique known as a binary recursive tree (BRT). This technique is particularly well-suited to situations in which there may be threshold effects and non-linear interactions between the explanatory variables. Such interactions could be especially important here because structural factors typically do not change very rapidly, so their ability to predict crises in a panel (or time series) context may be limited. Rather, we would expect the interaction of relatively long-standing structural vulnerabilities and high(er)-frequency movements in macroeconomic variables to account for currency crises. For instance, the East Asian crisis countries had structural vulnerabilities for a number of years prior to the onset of the crisis; there must have been a confluence of events—structural vulnerabilities, macroeconomic imbalances, and perhaps contagion—to have actually triggered the crisis.
Our main results may be summarized briefly. First, we confirm that macroeconomic imblances, most notably a large current account deficit, are often the proximate trigger of a crisis. Second, we find that a weak “rule of law” may make countries particularly vulnerable to the effects of macroeconomic imbalances. Third, a risky corporate finance structure—high debt-equity ratios and short maturity of corporate debt—is an important determinant of currency crises. When these debt-equity ratios and maturity composition of corporate debt are included, the indicators of shareholder and creditor rights figure less prominently, suggesting that the effect of the latter on the probability of a crisis is manifested mostly through the financing structure of corporations. Finally, we find that the interaction between structural vulnerabilities and macroeconomic imbalances in determining crises is often highly complex, highlighting the difficulties of undertaking effective surveillance and monitoring of countries’s potential vulnerability to crises.
The remainder of this paper is organized as follows. Section 2 briefly outlines why structural vulnerabilities may be important in currency crises. Section 3 describes the methodology of binary recursive trees. Section 4 describes the data. Section 5 presents the empirical results. Section 6 concludes.
II. Corporate Governance and Structural Vulnerabilities
Although structural factors may have been at play in previous crises, it was the Asian currency crises at end-1997 and 1998 that brought them to the fore.
A key hypothesis that has been put forward in the context of the East Asian crisis, is that the corporate incentive structure encouraged a rapid pace of investment that was of increasingly uncertain quality.9 The rapid pace of investment and in some cases, progressively lower returns on these investments, made it necessary for firms to seek financing outside of retained earnings. Given the corporate governance environment, and a traditional reluctance to dilute family shareholdings, this demand for outside financing took the form of borrowing rather than equity issuance.
Corporate governance refers to the rules, standards and organizations that govern the behavior of corporate owners, directors and managers and that define their duties and accountabilities to outside investors (Prowse 1998). It is thus a key element in exercising discipline on firms and defining the overall incentive framework for firms, and is therefore essential for efficient, productivity-driven investments and safeguards against excessive risk-taking.10
Mechanisms that facilitate good corporate governance may be grouped into those that govern the rights of (especially minority) shareholders, those that govern the rights of creditors, and those that facilitate enforcement of these rights as well as monitoring and disciplining.11
Within the first category, there are measures that strengthen shareholders’ rights in general, those that strengthen minority shareholders’ abilities to exercise governance, and those that strengthen the rights of “strategic” investors. If the minimum percentage of ownership of share capital required to call an emergency shareholders’ meeting is relatively low, for instance, this makes it easier for minority shareholders to organize a meeting to challenge or oust the management. (The percentage varies around the world from 1% in US to 33% of share capital in Mexico). Or, if proxy by mail is allowed, (any) shareholders’ ability to exercise their voting rights is considerably facilitated. For strategic investors, the right to hostile takeovers may be an important disciplining mechanism.
Creditor rights are conceptually more complex, as creditors exercise their power in several ways. Perhaps the most basic creditor right is the right to repossess and then liquidate—or keep—the collateral (La Porta et al. 1998). Creditor rights are strengthened if, for example, the bankruptcy or reorganization laws stipulate restrictions on re-organization, such as the need for creditors’ consent to file for reorganization; or if secured creditors are ranked first in the distribution of the proceeds that result from the disposition of assets of a bankrupt firm. Also important however, is the incentive structure of creditors or financial institutions themselves, which is shaped not only by their own corporate governance, prudential norms, and regulatory and supervisory framework, but also by the perception of implicit government guarantees. Strong creditor rights without the corresponding good governance of financial institutions will still result in weak disciplining of corporations.
Finally, there are a set of rules and regulations that facilitate monitoring and disciplining, including the legal framework (enforcement and insolvency/bankruptcy or exit mechanisms), accounting standards, transparency and disclosure etc.
Weak corporate governance is often reflected in a divergence between “control rights” and “cash-flow rights”12; in turn, encouraging excessive risk-taking in investments. Through control enhancing mechanisms such as pyramiding13, cross-holdings, or having a chief executive officer, board chairman or vice chairman related to the controlling family, cash-flow rights can deviate substantially from control rights. Such deviations, if significant, can provide incentives for greater risk-taking (both in the nature of investments, and in their volume and pace), as corporate owners have less to lose if the project goes wrong (since their cash flow stake is relatively small), but can benefit if the project is successful—because, through their effective control, they can more easily expropriate the gains. Particularly in East Asia, these perverse incentives were exacerbated because management was generally not separated from ownership control14. The combination of concentrated family control rights that exceeded cash flow rights, and close control of management by family owners, provided corporate owners both greater incentives for risk-taking and the means for effecting this.
Excessive risk-taking, in turn, can result in a fast pace of investment (often with progressively declining rates of return), necessitating financing outside of firms’ retained earnings. This financing often takes the form of debt, because of the incentive structure of debt, where default allows the borrower to limit the downside risk (particularly in countries where creditors’ recourse to bankruptcy proceedings is limited), while capturing the gains if the project is successful. Beyond this, however, particularly in the East Asian context, there is often a general reluctance by family owners to dilute their share ownership as well.
Greater integration of the world capital markets allows for easier access to foreign borrowing by domestic corporations—be it directly, or indirectly through the intermediation of domestic financial institutions—so that a sizable proportion of this borrowing maybe external. Moreover, the macroeconomic policy mix used to deal with capital inflows and attendant macroeconomic overheating, can itself further encourage unhedged short-term borrowing,15 exacerbating the accumulation of large short-term external liabilities. At the micro level, this can result in highly leveraged corporations and sizable currency and maturity mismatches in the balance sheets of both corporations and financial institutions.
The standard approach in the currency crisis literature is to estimate a probit of the occurrence of a crisis on a set of explanatory variables.16 Such an approach has the benefit of being familiar, with well known statistical properties, and of being able to isolate the marginal effect of each individual explanatory variable, holding the others constant at their mean values. This “other things equal” (or “partial derivative”) assumption is such a standard part of economists’ toolkit that it is seldom questioned. As noted in the introduction, however, it is not the only approach, and not necessarily the best approach for analyzing crises.
In particular, standard economic analysis implicitly assumes some continuity in the relationships between economic variables. That is, if increasing variable x elicits a certain response in variable y, then doubling the increase in x should induce a correspondingly large response in y. Currency crises differ in that they are fundamentally discontinuous; that is, they represent a confluence of factors that trigger a discrete event (the crisis), but only once certain thresholds have been crossed. For instance, in Indonesia and Korea, there were longstanding weaknesses in the financial (and, in Korea, the corporate) sector. It required a particular confluence of events—terms of trade shocks, contagion, political uncertainty—interacting with these weaknesses to trigger the crisis.
Accordingly, analyzing crises requires a technique that allows both for thresholds in the effects of an independent variable on the probability of a crisis and, moreover, for the thresholds themselves to depend upon interactions between the variables.17
In principle, it would be possible to capture such interactions within a probit framework by including sufficiently many interactive dummy variables—for instance, estimating the probit with an interaction term between cholesterol level with gender. When there are several explanatory variables, and if they are continuous, however, such an approach soon becomes impractical.
Fortunately, more systematic methods are available. One such technique is known as a binary recursive tree (BRT).18 Formally, it is a sequence of rules for predicting a binary variable, y, on the basis of a vector of explanatory variables, xj j = 1,…, J. At each branch of the tree, the sample is split according to some threshold value,
To illustrate, let y be the event of a crisis (equal to 1 if there is a crisis, and 0 otherwise). The sample is randomly separated into a core sample and a smaller test sample, which is used for “out-of-sample” robustness checks. For the core sample, the algorithm searches for sequential splits, each consisting of the explanatory variable, and its threshold value, which best discriminates between the groups. Suppose, for example, that a large current account deficit is associated with currency crises, and is thus a potentially useful discriminator variable. There will, however, be countries that have small current account deficits, yet suffer a currency crisis (a type I error), and others that have large current account deficits but (nonetheless) do not have a crisis (a type II error). The algorithm searches over all observed values of the current account deficit in the sample until it finds that threshold value,
The minimum sum of errors provides a natural gauge of the ability of the current account deficit variable to predict crises. The same procedure is applied to each of the explanatory variables; then, sorting these variables by their minimum error scores provides a ranking of their ability to discriminate between crisis and non-crisis countries. (To check robustness, the threshold value for each variable is also applied to the test sample, yielding a second error score). The variable (together with its associated threshold value,
For each sub-branch, the algorithm is repeated. In principle, this process could continue until every observation has been placed into its own branch. This would be akin to including as many explanatory variables as observations in a standard regression and thus getting a “perfect,” if meaningless, fit. Some termination rule is required. The rule used is roughly the same as an adjusted R2 rule. After each split, the improvement in the overall fit (which, just like the change in the raw R2 upon adding another variable in a regression, is always non-negative) is combined with a penalty on the number of branches, which promotes parsimony. If the penalty exceeds the improvement, the branch is terminated at the prior node; otherwise, the branching continues.
Several aspects of the algorithm are noteworthy. First, the algorithm automatically establishes orderings among explanatory variables both globally (toward the top of the tree) and locally (along each of the various sub-branches). Although an explanatory variable that appears toward the top of the tree is more “important” in discriminating between crisis and non-crisis countries, an explanatory variable may appear several times along various sub-branches. To return to the heart attack example, if the critical levels of cholesterol differ across men and women, one branching of the tree might split the sample according to gender, then, along each sub-branch the level of cholesterol might be the next discriminator (albeit at different threshold levels). Second, by its very nature, the algorithm captures interactions between explanatory variables. Third, the algorithm is good at capturing threshold effects, which may be particularly important in looking at the effects of structural variables. By the same token, however, if the effect is truly continuous, the algorithm simply finds the value that best discriminates between crisis and non-crisis countries. For example, if the probability of a crisis increases linearly in the current account deficit, the algorithm would still try to find the best “threshold” value for discriminating between crisis and non-crisis countries. (This is relatively easy to detect, however, because, when the effects are continuous, they tend to show up by repeated branchings by the same explanatory variable along the same branch.20) Fifth, the procedure is very robust to outliers since it splits on an interior threshold (rather like using medians instead of means). Finally, the decision tree is invariant to any monotone transformation of the variables. Again, this is a very important property when looking at structural variables, several of which are rank indexes.
But the methodology is also not without its own limitations. Most importantly, the statistical properties are not yet well-known, and formal statistical tests are not available. As such, the only way to assess the model is in terms of its ability to predict crises (more exactly, the likelihood that the model makes either a type I or type II error). Second, as noted above, the procedure is less well-suited when the effects are genuinely continuous. Third, at each branch, the procedure picks out the explanatory variable that best discriminates between crisis and non-crisis countries; this is not to suggest, however, that others may not be important (i.e., beaten by only a small margin)21. Fourth, toward the lower branches of the tree, the number of crisis cases may become very small, sometimes leading to counterintuitive results,22 though this can be avoided by more stringent stopping or pruning rules to limit the number of sub-branches.
In our view, these limitations do not preclude the usefulness of this technique, at least as a complement to the more standard probit/regression analysis. As discussed below, the resulting decision trees require careful interpretation but, if nothing else, they make clear that currency crises occur as a result of a complex confluence of factors—an insight that is perhaps lost in the simplicity of the standard probit output.
IV. Macroeconomic and Structural Data
Our dataset covers 42 industrialized and mainly emerging market countries over the period 1987-99; with missing data, there are 624 observations.23 There are 52 currency crises, of which 19 involve a fall in real GDP growth of at least 3 percentage points, and 14 involve a fall in real GDP growth of at least 5 percentage points.24
The traditional currency crisis literature has suggested a smogarsbord of both macroeconomic policy and performance indicators (in addition to “vulnerability indicators” such as the external debt ratio or the level of foreign exchange reserves). Following this literature, but with a view to parsimony, we select five “macroeconomic” indicators: (i) the percentage real exchange rate appreciation over the previous three years (i.e. t-3 to t-1); (ii) the current account balance as a ratio to GDP, averaged over the previous three years, (iii) the central government balance as a ratio to GDP, averaged over the previous three years; (iv) the growth of the ratio of banking system credit to GDP, averaged over the previous three years; and (v), the ratio of total external debt to reserves.25 While not exhaustive, this set captures most of the variables that have been identified in the literature as relatively robust predictors of currency crises: external vulnerability, fiscal laxitude, and excessive rates of credit growth.26
As noted above, for our structural variables, we include four broad categories, each with a number of separate indicators.27
The first category consists of six indicators pertaining to the country’s rule of law. These concern both broad governance issues—corruption and property rights (such as risk of expropriation or contraction repudiation by the government, efficiency of judicial system)—as well as those more narrowly related to the corporate sector such as accounting standards.
The second category consists of eight indicators related to shareholders’ rights. These include investor protection (whether ordinary shares carry one vote per share, the percentage of mandatory dividend), as well as indicators of the ease with which investors can exercise their rights (whether proxy by mail is allowed; whether firms can block shares prior a general stockholders meeting; whether minority shareholders have a judicial venue to challenge the decisions of management; whether minority shareholders can name a proportional number of directors to the board). Of the two remaining indicators, one is a composite index of shareholders’ rights vis a vis company directors, while the other is percentage of mandatory dividend.
The third category consists of five indicators of creditors’ rights. These include the legal requirement for a firm to seek its creditors’ consent prior to filing for reorganization; the requirement that management not stay during the period of reorganization (with management in the hands of an official appointed by the court instead); the requirement that secured creditors be paid first in any bancruptcy proceedings; as well as legal reserve requirements (which can force automatic liquidation before all the capital is wasted or stolen).
Finally, we include the ratio of short-term to total corporate debt, and the ratio of debt to (common) equity for a sample of non-financial firms in each country, taken from the Worldscope database.
In general, within each category, the indicators tend to be correlated across countries. The correlations are greatest for the “rule of law” indicators, ranging from 0.6 to 0.9, with a single principal component capturing almost 80 percent of the total variation. The creditor rights variables are somewhat less correlated, but a single principal component captures more than 50 percent of the total variation, while the shareholder variables are the least correlated, with a single principal component capturing only 35 percent of the variation.
The correlation among the various indicators seems intuitive, since countries that have stronger creditor or shareholder rights along one measure are likely to have strong rights along other measures. But it also means that these indicators are subject to nralticollinearity, and econometrically it may be difficult to isolate which among them matters (especially since the indicators are qualitative scores along arbitrary scales). Accordingly, in interpreting the results, if it is found that one or more of the indicators (or the first principal component) of a given category is significant, it is perhaps more useful to take this to mean that “shareholder rights” or “creditor rights” broadly construed may be important, and not just the individual indicator that happens to be significant.
There is also the correlation across categories. The first principal component of the shareholder rights category has a correlation of 0.29 (t-stat: 3.29**) with the (first principal component of) the creditor rights category; it is rather less correlated with the “rule of law” category (correlation = 0.05). Stronger shareholder rights are also (negatively) correlated with higher debt-equity ratios or a larger fraction of short-term debt (with t-statistics of 3.67** and 6.13**). Again, this makes intuitive sense. Countries with strong shareholder rights are also likely to have strong creditor rights28 and, as a result of the better corporate governance and matching of cashflow and control rights, lower debt-equity ratios and a better maturity of debt. By the same token, however, to the extent that better shareholder and creditor rights affect the probability of a crisis through their effect on corporate debt-equity ratios and financing structure, they are unlikely to be significant in a probit or binary recursive tree once the short-term debt and debt-equity ratios are included directly.
V. Empirical Results
A. Probit Results
Since probit analysis is generally familiar, we begin by estimating standard probits for three dependent variables (i) the occurrence of a BOP crisis; (ii) a BOP crisis with at least a 3 percentage point growth swing; (iii) a BOP crisis with at least a 5 percentage point growth swing.
We begin, in panel  of Table 1, with only the macroeconomic indicators. These are mostly consistent with intuition: a greater real exchange rate appreciation is associated with a higher probability of a crisis, while a larger current account balance (i.e. smaller deficit) is associated with a lower probability of a crisis. A rapid expansion of banking system credit (relative to GDP) and a higher ratio of external debt-to-reserves are positively correlated with crises, but the coefficients are not statistically significant. Counter-intuitively, a larger fiscal balance is also positively related to a crisis, though the coefficient is generally not significant.
|BOPCrisis||Crisis with GDP growth|
swing of at least
3 percentage points
|Crisis with GDP growth|
swing of at least
5 percentage points
|ΔREER 1/||0.027||2.30**||0.035||2.06 **||0.036||1.86*|
|CAB/GDP 1/||-0.086||-3.18***||-0.119||-2.79 ***||-0.122||-2.46**|
|GovB/GDP 1/||0.012||0.51||0.039||1.08||0.066||1.52 *|
|Ext. Debt/Reserves 2/||0.000||0.97||0.000||0.32||0.000||0.89|
|Constant||-2.522||-5.16 ***||-4.510||-4.31 ***||-5.322||-3.90 ***|
|CAB/GDP 1/||-0.121||-2.94 ***||-0.311||-3.01 ***||-0.278||-2.49 **|
|Δ(DC/GDP) 1/||0.032||3.71 ***||0.032||2.33 **||0.036||2.29 **|
|Ext. Debt/Reserves 2/||0.000||1.81 *||0.000||1.61 *||0.000||1.97 **|
|Shareholder Rights Prin. Comp. 1||0.038||0.31||-0.135||-0.66||-0.276||-1.12|
|Shareholder Rights Prin. Comp. 2||0.121||1.11||-0.045||-0.23||0.077||0.33|
|Shareholder Rights Prin. Comp. 3||0.154||1.02||0.517||1.65 *||0.539||1.44|
|Creditor Rights Prin. Comp. 1||0.091||0.70||0.108||0.49||-0.101||-0.30|
|Creditor Rights Prin. Comp. 2||0.171||1.50 *||0.265||1.34||0.402||1.57 *|
|Rule of law Prin. Comp. 1||-0.299||-1.89 *||-0.758||-2.29 **||-0.553||-1.35|
|Corp. Short-term/total debt||0.019||0.03||0.794||0.67||2.288||1.19|
|Corp. Debt/equity||0.345||2.28 **||0.510||2.16 **||0.451||1.64*|
Average for years t-3, t-2, t-1
Average for years t-3, t-2, t-1
Next, we turn to the structural factors, augmenting the probit with our structural indicators (Panel  of Table 1). Unfortunately, there are too many individual structural variables to be included simultaneously in the probit (individually they tend to be insignificant, and in some cases the probit estimation does not converge). Instead, therefore, we use the principal components of the three categories of structural indicators: corporate governance, creditor rights, and rule of law29; the debt-equity and short-term debt ratios are included directly.30
Of the macroeconomic variables, the current account balance continues to be highly significant, while the real exchange rate appreciation loses its statistical significance. The rate of credit growth in the economy, however, now becomes highly significant. Turning to the structural variables, the results are decidedly mixed. The main principal components of better shareholder rights are (slightly) negatively correlated with the occurrence of a crisis, with the exception of the third component, which is positively correlated. Counter-intuitively, the estimates suggest that better creditor rights appear to be positively correlated with the occurrence of crises; however, stronger rule of law is negatively correlated with crises (with a statistically significant coefficient). Finally, a higher proportion of corporate short-term debt or a higher debt-equity ratio are unambiguously correlated with higher probabilities of a crisis (the latter being statistically significant).
The statistical significance of the latter two variables suggests that they may be masking the effects of stronger shareholder and creditor rights (i.e. the effect of better shareholder and creditor rights on the probability of a crisis happens mostly through the corporate financing structure). Dropping the short-term debt and debt-equity variables confirms this in that the shareholder rights variable now has a negative, and statistically significant coefficient (although the coefficients on the creditor rights variables remain positive, albeit insignificant).31
One interpretation of the results is that structural factors are not very important determinants of currency crises. In a sense, this should be none too surprising, particularly in the context of a panel dataset. Most structural variables change very slowly (some are constant for the country) so they have difficulty in explaining why a crisis occurs when it does, though they may do better at explaining where crises occur (i.e. in a purely, or largely, cross-country dataset). Put differently, Korean corporations have had high debt-equity ratios for a number of years—why did the crisis occur in 1997 and not in 1995? This leads to a second interpretation, however, namely that there may be important interactions between structural vulnerabilities and macroeconomic performance (which can change rapidly) that can explain currency crises. In principle, this can be done within the probit framework by interaction terms between the structural and macroecomic variables. In practice, deciding which interactions to include in the probit estimation is difficult because there are potentially many. Therefore, we turn next to an approach that allows for such interactions more systematically.
B. Binary Recursive Trees
As discussed above, a binary recursive tree is simply a technique for classifying observations on a binary dependent variable (in our case, the occurrence of a currency crisis) on the basis of a set of explanatory variables (in our case, the macroeconomic and structural variables). Since the resulting trees can be quite involved, requiring some interpretation, and in order to avoid a tedious taxonomy, we focus on the case of crises with at least a 3 percentage point swing in the real GDP growth rate.32
Again, we begin with only the macroeconomic variables. Figure 1 illustrates the resulting binary recursive tree, where the dependent variable is a currency crisis with a 3 percentage point real GDP growth swing, and the explanatory variables are (i) the current account balance; (ii) the real exchange rate appreciation; (iii) the government balance; (iv) the growth in the credit-to-GDP ratio; and (v), the ratio of external debt to reserves.
Figure 1.Macroeconomic Determinants of Currency Crises 1/
1/ Probability of a currency crisis involving a decline in real GDP growth of at least 3 percentage points. Figures in italics refer to within-node (i.e. conditional) probabilities of a crisis.
The tree turns out to be particularly simple as there is a single node, with the current account balance being the explanatory variable at threshold level of about 2½ percent of GDP. The tree branches to the left node when the current account balance is less than -2.6 percent of GDP (i.e. the deficit is greater than 2½ percent of GDP), and to the right node, otherwise. Along the left hand branch, the “within-node” probability of a crisis if 0.72; this can be interpreted, roughly, as the probability of a crisis conditional on being at that node (in fact, it is the ratio of the probability that a crisis country has a current account balance below -2.6 percent of GDP, to the probability that a country without a crisis has a current account balance below -2.6 percent.33). Along the right hand branch (countries with current account balances above -2.6 percent of GDP). By contrast, for these countries, the within-node probability of a crisis is only 0.27.
Among the macroeconomic variables, the algorithm thus picks out the current account balance as the most important variable distinguishing crisis from non-crisis countries. Note that nothing prevents the algorithm from further splitting the tree (using cither the current account balance or any of the other potential explanatory variables); however, the improvement in the fit is not sufficient to justify the additional complexity of the tree, given the stopping rule.
Next, we add the various structural variables (individually, not in terms of their principal components). The resulting tree, again with the conditional probabilities of a crisis at each node, is illustrated in Figure 2.
Figure 2.Macroeconomic and Structural Determinants of Currency Crises 1/
1/ Probability of a currency crisis involving a decline in real GDP growth of at least 3 percentage points. Figures in italics refer to within-node (i.e. conditional) probabilities of a crisis.
2/ Country scores in the top half of the sample countries on the International Country Risk assessment of public sector governance (low levels of public sector corruption).
The first branching of the tree is now the index of public sector governance, with lower scores indicating greater corruption or that “high government officials likely to demand special payments” and “illegal payments are generally expected throughout lower levels of government in the form of bribes connected with import and export licenses, exchange controls, tax assessment, policy protection or loans.” The conditional probability of a crisis in countries that score in the lower half of this governance index (across our sample of countries) i.e. that have worse public sector governance, is 0.62, versus 0.19 for countries that score well.
Continuing along the left hand branch of the tree, the second node (2) depends on the current account balance; again with a threshold value of a deficit of about 2½ percent of GDP. The conditional probability of a crisis in countries with larger deficits is 0.76 compared to 0.42 in countries with smaller current account deficits. Continuing along the left hand branch (node (3)), the next variable is the corporate debt-equity ratio (with a threshold at about 100 percent), and a conditional probability of crisis of 0.85 for countries that exceed this threshold. Finally, at node (4), countries that have a high level of total external debt to reserves have a much higher conditional probability of a crisis.
Returning to the right hand branch of node (2) (countries that score badly on the governance index but have a current account balance greater than 2.6 percent of GDP), it is the real exchange rate that matters (node (5)), with a conditional probability of a crisis of 0.7 for countries whose average real exchange rate appreciation exceeds 2½ percent.
Finally, returning to the right hand branch of node (1) (countries that score well on the governance index), it is the corporate debt equity ratio that matters; those with very high debt-equity ratios have a much higher conditional probability of a crisis.
How should the tree be interpreted?
The algorithm seems to identify two broad groups of crisis countries. For one group, which consists mainly of the advanced industrialized countries, and that scores well on the public sector corruption index—probably a proxy for stronger “rule of law” or governance generally—the distinguishing characteristic of countries that suffer currency crises34 are their banking and corporate sector vulnerabilities (rather than macroeconomic imbalances). Put differently, these countries can better support macroeconomic imbalances, such as current account deficits or real exchange rate appreciations, with relatively less risk of crisis.
The other group, mainly emerging market and developing countries, which tend to score worse on the public sector corruption index, are more vulnerable to macroeconomic imbalances. For this latter group of countries, the most crucial variable is the current account deficit. Even if the current account deficit is modest (less than 2½ percent of GDP), however, they may still be vulnerable to the effects of real exchange rate appreciations.
Again for this group of countries, when the current account deficit is large, the corporate debt-equity ratio matters, with a cut-off at about 95 percent. Notice that these countries can support a much lower corporate debt equity ratio (95 percent) compared to the advanced industrialized countries, with good governance, who can support much higher debt-equity ratios (the threshold at node 6 is 380 percent).
How well does the tree perform?
A simple metric of the tree’s performance is the number of misclassified observations (either crisis countries predicted to be non-crisis, or vice versa). Using only the macroeconomic variables, 179 out of 624 observations are incorrectly classified (in-sample), out-of-sample35, 180 out of 624 observations are incorrectly classified. Once the structural variables are added, the number of incorrectly classified observations drops to 130 out of 624 observations, in-sample. In fact, all of the 19 crisis observations are correctly classified (so that all of the 130 incorrect classifications correspond to “false positives”). In the out-of-sample predictions, 156 out of 624 observations are incorrectly classified (of which 10 correspond to crisis cases, and 146 are “false positive” non-crisis cases). Taking the least favorable results, therefore, about 75 percent of all observations are correctly classified, and about one-half of the currency crises that occurred would have been predicted by the tree.36 The score on predicting crises could, presumably, be improved by weighting Type I errors more heavily in the algorithm’s objective function, albeit at the cost of calling more false positives.
Two additional points are worth noting. First, if the short-term debt and debt-equity ratios are dropped from the list of explanatory variables, the resulting tree (not shown) again branches on the index of public sector corruption and the current account deficit, but also on accounting standards, and the composite index of shareholder rights vis a vis company directors (MCORP7).37 As above, this suggests that the impact of corporate governance on the probability of a crisis occurs mostly through the corporate financing structure. Second, if the Asian crisis countries (Indonesia, Korea, Thailand) are dropped from the panel (to see whether the results are being driven by them), structural variables continue to be included in the tree. In particular, node 1 again splits on public sector governance, and node 6 splits on the debt-equity ratio, and a sub-branch of node 5 splits on the corporate short-term debt ratio; however, node 3 no longer exists.
Naturally, different crisis definitions yield somewhat different trees. For instance, if currency crises with the larger swing in real GDP growth is used as the dependent variable instead, the first node no longer splits on the public sector corruption index, simply because there are no observations with such deep crises among the group of advanced industrialized countries that score well on the public sector governance index (i.e. corresponding to node (6) in Figure 2). The tree therefore starts with the current account balance (again picking the threshold of about 2½ percent of GDP). Among countries with large current account deficits, it is then the structure of corporate short-term debt and the debt equity ratio that matter, especially in an environment in which credit has been growing rapidly.38
The precise structure of the trees, therefore, is perhaps of less importance than the general conclusions that emerge. Of these, three bear emphasizing. First, currency crises come in a variety of flavors: occurring both in advanced industrialized countries, with generally sound governance and stronger regulatory frameworks, and in emerging market and developing countries, with much weaker records of governance. Second, there are complex interactions between governance, macroeconomic, and corporate indicators that may contribute to the likelihood of a crisis, and that are not easily captured with the very linear structure of a standard probit. Third, given differences in the overall “rule of law” or governance, countries’ resilience to either macroeconomic imbalances or corporate sector vulnerabilities may differ markedly.
In this paper, we have examined the role of structural factors in currency crises. Given that structural variables typically do not change much, their ability to predict crises—especially in a panel or time-series context—is necessarily limited. Nonetheless, the findings suggest that weak governance may make countries particularly vulnerable to the effects of macroeconomic imbalances and corporate sector weaknesses.
These interactions mean that standard regressions or probits may not be able to identify vulnerabilities arising from a confluence of legal, macroeconomic, and corporate factors. To this end, we have proposed the use of an alternative technique, known as a binary recursive tree, that is better suited to identifying such interactions. While we consider our results to be mostly illustrative and, at best, preliminary, we believe that this approach shows some promise.
The interactions also have implications for monitoring and country surveillance work. In particular, they suggest that assessing countries according to a given list of vulnerability indicators is unlikely to suffice. Rather, the “danger” thresholds depend very much on the particular combination of institutional, macroeconomic, and corporate governance/financial structure indicators, and each country must be assessed in light of these.
In the text, reference is made to a number of structural indicators. This appendix provides a detailed description of them.
We consider four broad categories of structural indicators: rule of law; corporate governance (shareholder rights); corporate governance (creditor rights); and corporate finance.
1. Rule of Law (MRULE)
MRULE1 AND MRULE2 are measures that pertain to law enforcement. A strong system of legal enforcement could even substitute for weak rules, to some extent, since active and well-functioning courts can step in and rescue investors abused by the management.
MRULE1 This measures the efficiency of judicial system. Assessment of efficiency and integrity of the legal environment as it affects business, particularly foreign firms produced by country risk rating agency Business International Corp. The index is the average between 1980 and 1983, and the scale ranges from 10 (most efficient) to zero (least efficient). A higher score indicates a better rule of law.
MRULE2 This variable is an assessment of the law and order tradition or rule of law produced by the international rating agency International Country Risk (ICR). The index is an average of the months of April and October of the monthly index between 1982 and 1995. La Porta et al. change the scale of the index from its original range which went from 6 to zero) into one that ranges from 10 (greatest tradition for law and order) to 1 (least tradition for law and order). A higher score indicates a better rule of law.
MRULE3 and MRULE4 are variables that reflect how government affects businesses.
MRULE3 This variable is an assessment by ICR of the corruption in government. The scale ranges from 10 to zero (again La Porta et al. changed the original range which went from 6 to zero), with lower scores indicating greater corruption or that “high government officials likely to demand special payments” and “illegal payments are generally expected throughout lower levels of government in the form of bribes connected with import and export licenses, exchange controls, tax assessment, policy protection or loans.” A higher score indicates a better rule of law.
MRULE4 This is ICR’s assessment of the risk of outright confiscation or forced nationalization, i.e. risk of expropriation The score rages from 10 (low risk) to zero (high risk of expropriation). A higher score indicates a better rule of law.
MRULE5 This variable is ICR’s assessment of the “risk of a modification in a contract taking the form of a repudiation, postponement or scaling down” due to “budget cutbacks, indigenization pressure, change in government, or a change in government economic and social priorities” or repudiation of contracts by government. The scale ranges from 10 (lowest risk) to 0 (highest risk). A higher score indicates a better rule of law.
MRULE6 This an index of accounting standards created by examining and rating companies’ 1990 annual reports on their inclusion or omission of 90 items. These items fall into seven categories (general information, income statements, balance sheets, funds flow statement, accounting standards, stock data and special items). A minimum of three companies in each country were studies, These companies represent a cross section of various industry groups: industrial companies represented 70 percent and financial companies represented the remaining 30 percent. The index ranges from 100 (highest) to 0 (lowest). A higher score indicates better rule of law.
2. Corporate Governance: Shareholders’ rights
MC0RP1 reflects investor protection. If the law stipulates that ordinary shares carry one vote per share, La Porta et al. assign it a value of one. In general, investors are better protected when dividend rights are tightly linked to voting rights, i.e. one share-one vote: when votes are tied to dividends, insiders cannot appropriate cash flows to themselves by controlling only a small share of the company’s cash flows but still maintaining voting control. Equivalently, this variable equals 1 if the law prohibits both the existence of multiple voting and nonvoting shares, and does not allow firms to set a maximum number of votes per shareholder irrespective of the number of shares owned (all of which are ways in which the one share one vote principle can be circumvented). It is set to zero otherwise. A higher score indicates stronger shareholder rights.
The measures MCORP2 through to MCORP5 measure the ease with which shareholders can exercise their voting rights. Because these rights measure how strongly the legal system favors shareholders vis a vis managers in the voting process, La Porta et al. refer to them as anti-director measures.
MCORP2 This is assigned a value of one if proxy by mail is allowed and zero otherwise. Clearly proxy by mail facilitates shareholders’ ability to exercise their voting rights. In fact, when proxy by mail is not allowed, it can render it considerably more difficult and onerous for shareholders to exercise their votes (unless they go through the legal procedure of designating proxies at meetings), especially if companies hold their annual meetings around the same time (as tends to be the case in Japan where about 80 percent of the companies tend to hold their annual meetings on the same week). A higher score indicates stronger shareholder rights.
MCORP3 This is assigned a value of one if the company law or commercial code does not allow firms to block shares prior to a general shareholders’ meeting and zero otherwise. In some countries the law requires that shareholders deposit their shares with a company or financial intermediary prior to a shareholder meeting. The shares are kept in custody until a few days after the meeting which prevents shareholders from selling their shares for several days around the time of the meeting. A higher score indicates stronger shareholder rights.
MC0RP4 This is assigned a value of one if the company law or commercial code allows shareholders to cast all their votes for one candidate standing for election to the board of directors or allows for a mechanism of proportional representation in the board by which minority interests may name a proportional number of directors to the board i.e. if it allows cumulative voting or proportional representation and is assigned a value of zero otherwise. A higher score indicates stronger shareholder rights.
MC0RP5 This is assigned a value of one if the company law or commercial code grants minority shareholders either a judicial venue to challenge the decisions of management (including, the right to sue directors as in American derivative suits) or of the assembly, or the right to step out of the company by requiring the company to purchase their shares when they object to certain fundamental changes such as mergers, asset dispositions, and changes in the articles of incorporation. Thus this variable reflects minority shareholders’ legal mechanisms against perceived oppression by directors. The variable is set to zero otherwise. (Minority shareholders are defined as those shareholders who own 10 percent of share capital or less). A higher score indicates stronger shareholder rights.
MCORP6 This is the minimum percentage of ownership of share capital required to call an emergency shareholders’ meeting. Clearly, the higher the percentage this is the harder it is for minority shareholders to organize a meeting to challenge or oust the management. (The percentage varies around the world from 1% in US to 33% of share capital in Mexico). A higher score indicatesweakershareholder rights.
MCORP7 La Porta et al. construct an aggregate measure of shareholders’ rights vis a vis company directors, formed by adding 1 when i) the country allows shareholders to mail their proxy vote; ii) shareholders are not required to deposit their shares prior to a general shareholders meeting; iii) cumulative voting or proportional representation is allowed; iv) oppressed minorities mechanism are in place; v) the minimum percentage share capital that entitles a shareholder to call for an extraordinary meeting is less than or equal to 10% (the sample median in La Porta et al.), or vi) shareholders have preemptive rights that can be waived only by a shareholders’ vote. The index ranges from zero (low protection) to six (high protection). A higher score indicates stronger shareholder rights.
MCORP8 This variable equals the percentage of firms’ declared earnings that the company law or commercial code requires them to distribute as dividends among ordinary shareholders i.e. the percentage of mandatory dividend. Although earnings can, of course, be misrepresented within the limits allowed by the accounting system, it at least prevents declarations of high earnings by firms (which might be needed to raise additional funds) without requiring dividend pay-outs. The mandatory dividend right (which is slightly different to the other shareholder rights listed above) may be needed when other rights of shareholders are too weak to induce them to invest. The variable is assigned a value of zero when no such requirement exists in the law or commercial code. A higher score indicates stronger shareholder rights.
3. Corporate Governance: Creditors’ Rights (CCRED)
MCRED1 If the bankruptcy or reorganization laws stipulate restrictions on reorganization such as the need for creditors’ consent to file for reorganization, the variable is assigned a value of one. It equals zero if no such restrictions exist. A higher score indicates stronger creditor rights.
MCRED2 This variable is assigned a value of one if there is no automatic stay on assets. In some countries the reorganization procedure imposes an automatic stay on the assets upon filing the reorganization petition. An automatic stay prevents secured creditors from gaining possession of their security. It is assigned a value of zero if the law stipulates an automatic stay on assets. A higher score indicates stronger creditor rights.
MCRED3 This variable is assigned a value of one if secured creditors are ranked first in the distribution of the proceeds that result from the disposition of assets of a bankrupt firm, i.e. if secured creditors are paid first. In some countries secured creditors are not assured the right to collateral in reorganization (although this is rare). In Mexico, for example, various social constituencies need to be repaid before the secured creditors, often leaving the latter with no assets to back up their claims. The variable is set to zero if non secured creditors, such as the government and workers are given absolute priority. A higher score indicates stronger creditor rights.
MCRED4 If an official appointed by the court or by the creditors is responsible for the operation of the business during reorganization this variable is assigned a value of one. Equivalently, the variable is assigned a value of one if the debtor does not keep the administration of its property pending the resolution of the reorganization process, i.e. if management does not stay in reorganization. In some countries management stays pending resolution of the reorganization procedure, whereas in other countries, such as Malaysia, management is replaced by a party appointed by the court or creditors. The threat of dismissal may enhance creditors’ power. The variable is given a value of zero if no such threat exists. A higher score indicates stronger creditor rights.
MCRED5 This variable is the percentage of total share capital needed to avoid the dissolution of an existing firm as mandated by the corporate law i.e. the legal reserve requirement. This requirement forces firms to maintain a certain level of capital to avoid automatic liquidation. As with the mandatory dividend ion the case of shareholders, the legal reserve requirement protects creditors when they have few other powers in that it forces an automatic liquidation before all the capital is stolen or wasted. The variable takes value of zero for countries without such a legal reserve requirement. A higher score indicates stronger creditor rights.
4. Corporate Performance
Finally, we construct a set of corporate vulnerability and performance indicators.39
CorpStDebt The ratio of short-term corporate debt to total corporate debt, and as such measures firms’ vulnerability to liquidity squeeze. It is the median of all (non-financial) firm in the WORLDSCOPE database.
CorpDtEq The debt to (common) equity ratio, which provides an indication a firms’ vulnerability to interest rate spikes. It is the median of all (non-financial) firms in the WORLDSCOPE database.
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