Assessing reserve adequacy requires an understanding of the benefits of reserves in smoothing domestic consumption and absorption in response to external shocks, as well as the costs of holding reserves. In what follows, we present a stylized analytical framework that captures the costs and benefits of holding reserves. Empirically grounded estimates of both the crisis prevention and mitigation benefits of holding reserves are presented to evaluate the optimal level of reserves.
Analytical Framework
As discussed in previous chapters, LICs are routinely faced with substantially different shocks than EMEs, including sharp swings in foreign aid, remittances, and FDI, as well as natural disasters. Although both sets of countries may be affected by shocks to the terms of trade, the frequency and incidence of such shocks tend to be higher in LICs (IMF, 2011). Moreover, whereas crises in EMEs are generally characterized by pressures on the capital account, reflecting access to market financing, most LICs still have limited access to capital, so that external drains are primarily on the current account. As a result, current account-based measures, such as reserve coverage in months of imports, remain useful indicators of reserve adequacy.
From an insurance perspective, reserves can help reduce the likelihood and magnitude of abrupt drops in consumption and absorption, and consequently a loss in welfare, arising from large fluctuations in imports in the face of large external shocks. As documented in the previous chapter, reserves appear to have cushioned countries against sharp drops in consumption and absorption for a wide range of shocks, including during the recent global financial crisis.
The cost of holding reserves is typically defined as the difference between the return on short-term foreign currency assets and the return on more profitable alternative investment opportunities. The simplicity of this definition, however, masks thorny issues regarding the appropriate definition of alternative investment opportunities.1 For EMEs, the net financial cost of holding reserves—the difference between the external funding cost of reserves and the return obtained in relatively safer and more liquid foreign assets—is commonly used as a proxy for the opportunity cost. This reflects the fact that EMEs with significant market access (at least during normal times) can, in principle, accumulate reserves by issuing foreign liabilities without affecting net foreign assets or unduly compromising optimal investment or consumption decisions.
For most LICs with limited market access, the net financial cost is likely to be negative because the funding cost, if measured by the concessional interest rate on official borrowing, is typically lower than the return on reserves. Given large infrastructure needs in LICs, reserves could alternatively be channeled into productive investment. Hence, a more relevant measure of opportunity cost in these countries is the difference between the return in risky but high-yield assets, including domestic capital, and safe, low-yield foreign assets. However, consistent estimates of the marginal product of capital across countries are often not available, and the differential between the domestic and foreign real interest rate is widely used as a proxy.
Building on the above theoretical considerations, the objective function that LICs seek to maximize reflects the trade-off between the opportunity cost of holding foreign reserves and the marginal benefit from being able to smooth domestic absorption in the event of large external shocks. Specifically, countries are assumed to maximize the net benefit of holding reserves (NBR):
where P and C represent, respectively, the conditional probability of a crisis given a large shock event and the utility cost of a crisis, where a crisis is defined by a sharp drop in absorption or consumption. Both of these variables depend on reserves (R) and other control variables (Z). The parameters q and r refer to the unconditional probability of a large shock event and the unit cost of holding reserves, respectively. The first two terms on the right-hand side of the equation reflect the benefit of holding reserves (in terms of reducing the expected cost of a crisis), whereas the second term captures the opportunity cost of holding reserves. Given the dependence of the probability and cost of a crisis on Z, the maximization of NBR yields optimal reserves as a function of Z and r.
The specification of NBR reflects the precautionary motive for holding reserves, but it is subject to several limitations. First, the utility cost is simply measured by the loss in real absorption (in percent of GDP), assuming risk-neutral utility. It is well known that optimal reserves derived from a cost-benefit analysis are particularly sensitive to the assumed degree of risk aversion in the utility function. In light of this, our framework aims to simulate a lower bound of optimal reserves by assuming linear utility. Assuming risk-neutral utility may also appear at odds with the precautionary motive for holding reserves. But precautionary reserve holdings are not equivalent to precautionary savings, which would not arise under risk-neutral utility. In our analysis, the precautionary motive for holding reserves corresponds to the incentive to guard against the inability to finance large external shocks due to limited market access and uncertain aid flows.
Second, the objective function is static in nature. This is clearly a limitation, but alternative options are likely to be unduly complicated, if not intractable. In the context of LICs, assuming a static objective function would not be too unrealistic in light of the fact that other forms of insurance are typically available from multilateral institutions or bilateral donors in case of prolonged shocks.
A number of papers have examined the role of reserves as a self-insurance mechanism against external risks. Jeanne and Ranciere (2006, 2008), Aizenman and Lee (2008), and Durdu, Mendoza, and Terrones (2009), among others, develop models of optimal reserves for EMEs, in which countries aim to self-insure against sudden stops in capital inflows. A small number of stylized models have also been developed for low-income countries with limited access to foreign capital markets. These studies primarily focus on risks stemming from the current account. Barnichon (2009) models insurance against natural disasters or terms-of-trade shocks, whereas Drummond and Dhasmana (2008) extend the Jean—Ranciere framework to examine the implications of aid and terms-of-trade shocks (see also Valencia, 2010). The optimal level of reserves in these models is highly sensitive to assumptions about the size and probability of external shocks, the potential loss in output and consumption, the opportunity cost of holding reserves, and the degree of risk aversion. In contrast to these studies, we provide empirically grounded estimates of both the crisis prevention and mitigation benefits of holding reserves against a range of shocks routinely faced by low-income countries. Moreover, our framework has a more tractable structure, making it easier to calibrate using country-specific data.
External Shocks and Crisis Events
In the empirical analysis below, external shocks and crisis events are defined as follows. As in the event study analysis, large negative external shock events in LICs are identified if the annual percentage change of the relevant variable falls below the 10th percentile in the left tail of the country-specific distribution. In particular, shock episodes cover one or more of the following: (1) external demand, (2) terms of trade, (3) FDI, (4) aid, (5) remittances, and (6) climate (large natural disasters).2 Defining large negative shocks over country-specific distributions can better capture cross-country heterogeneity with respect to economic structure and vulnerability to external shocks. It implies that each country experiences the same frequency of shocks, so that the focus is on the reaction to the shock. Moreover, in the context of LICs, these shocks can be assumed to be exogenous to country-specific fundamentals or policy. The sample used for the analysis spans the period 1990–2008 for 71 LICs. For each shock, only the first year of the shock event is considered in the final set, giving us a total of 645 shock observations (out of 1349 observations).
Figure 5.1 shows the annual changes in external demand (proxied by trading partners’ growth), terms of trade, aid, and FDI for the sample of individual shock episodes and non-shock episodes.3 As can be seen from the figure, there is a marked difference in the size and severity of shocks between the two samples. For instance, the drop in terms of trade is more than 30 percent for the bottom 25th percentile of the shock sample, as compared to less than 5 percent for the non-shock sample. This suggests that our shock definition captures reasonably severe events.
Within the sample of identified shock events, a crisis is defined as a large drop in real absorption (or consumption) per capita. Specifically, we assume that a crisis occurs when the following two conditions hold: (1) the post-shock two-year average (t and t+1) level of real absorption per capita falls below the pre-shock three-year trend; and (2) growth of real absorption per capita is negative at time t. In principle, a crisis event can be defined in various ways. We use the above definition to highlight the extreme nature of the event, which is the main focus of our analysis.4
Table 5.1 summarizes the median growth rate of real absorption and consumption per capita for the identified shock sample, distinguishing between crisis and non-crisis events. The median growth of real absorption per capita is positive for the entire shock sample, implying that not all shocks incur a drop in real absorption. Indeed, the unconditional probability of a crisis within the shock sample is less than 30 percent. However, there is a substantial difference in real absorption growth per capita of more than 8 percentage points between crisis and non-crisis cases, which is statistically significant. A similar pattern holds for real consumption per capita.
Median Growth Rate of Absorption and Consumption
(Percent, otherwise indicated)
Median Growth Rate of Absorption and Consumption
(Percent, otherwise indicated)
| Growth Rate | Sample probability of crisis |
|||
|---|---|---|---|---|
| All shock episodes |
Crisis episodes |
No crisis episodes |
||
| Absorption | ||||
| 1.4 | –5.2 | 3.2 | 27.1 | |
| Observations | 446 | 121 | 325 | |
| Consumption | ||||
| 1.1 | –4.6 | 2.8 | 29.1 | |
| Observations | 446 | 130 | 316 | |
Median Growth Rate of Absorption and Consumption
(Percent, otherwise indicated)
| Growth Rate | Sample probability of crisis |
|||
|---|---|---|---|---|
| All shock episodes |
Crisis episodes |
No crisis episodes |
||
| Absorption | ||||
| 1.4 | –5.2 | 3.2 | 27.1 | |
| Observations | 446 | 121 | 325 | |
| Consumption | ||||
| 1.1 | –4.6 | 2.8 | 29.1 | |
| Observations | 446 | 130 | 316 | |
Benefits of Holding Reserves
Estimating the Probability of a Crisis
In this section, we estimate the effect of holding reserves on the likelihood of a crisis (a real drop in absorption per capita). A panel probit model is estimated for the conditional probability of a crisis for 49 LICs—that is, the probability of a crisis given a shock—using country characteristics and fundamentals as control variables. The dependent variable is a zero-one binary variable, which takes the value of one if a real absorption drop occurs, and zero otherwise. A general-to-specific approach was used to reach the preferred specification of the model, starting from a set of 21 potential regressors (see Appendix 3). The final explanatory variables used include reserves (in months of imports), the ratio of government balance to GDP, the World Bank’s Country Policy and Institutional Assessment (CPIA) index as a proxy for policy and institutional quality, a dummy for flexible exchange rate regime, and a dummy for IMF-supported programs.5 All explanatory variables are lagged by one year, except for the dummy for IMF-supported programs, and are thus predetermined with respect to the crisis event.6
Table 5.2 reports the estimation results for absorption drops. The baseline probit regression is reported in column (1). Results from a logit regression in column (2) and probit regressions for various country groups in columns (3)-(6) are also reported for comparison. We find that the probability of a crisis decreases with the quality of a country’s institutions and the ratio of government balance to GDP. Importantly, the coefficient on reserves is of the expected sign, statistically significant, and broadly similar across specifications and estimation methods. These results point to a statistically significant crisis prevention role for reserves and sound fundamentals (such as stronger fiscal position and better institutional quality). The exchange rate regime and IMF support are also important determinants of the likelihood of a crisis, given an external shock. These results are consistent with the existing view that greater exchange rate flexibility helps facilitate economic adjustment to real shocks (Broda, 2004), and broadly in line with the evidence of the crisis prevention role of IMF-supported programs in emerging markets (Becker and others, 2007).
Probability of Absorption Drops
(Panel probit regression, 1990–2007)
Probability of Absorption Drops
(Panel probit regression, 1990–2007)
| Absorption | ||||||
|---|---|---|---|---|---|---|
| Baseline probit (1) |
Logit (2) |
Excluding fragile states (3) |
Excluding commodity exporters (4) |
Excluding oil exporters (5) |
Excluding island economies (6) |
|
| Reserves, months of imports (t–1) | –0.0896*** | –0.1556*** | –0.1018** | –0.1333*** | –0.0949*** | –0.0734** |
| (0.0339) | (0.0595) | (0.0490) | (0.0446) | (0.0357) | (0.0354) | |
| Flexible exchange rate regime (t–1) | –0.3801*** | –0.6568*** | –0.1392 | –0.5043*** | –0.4106*** | –0.3884*** |
| (0.1366) | (0.2340) | (0.1779) | (0.1700) | (0.1400) | (0.1492) | |
| Government balance, percent of GDP (t–1) | –0.0323*** | –0.0537** | –0.0175 | –0.0312** | –0.0343*** | –0.0363*** |
| (0.0125) | (0.0220) | (0.0169) | (0.0149) | (0.0132) | (0.0138) | |
| CPIA index (t–1) | –0.3090*** | –0.5129*** | –0.3805* | –0.4028*** | –0.3245*** | –0.2560** |
| (0.1056) | (0.1766) | (0.2080) | (0.1209) | (0.1083) | (0.1256) | |
| IMF program (t) | –0.3021** | –0.5223** | 0.1042 | –0.1440 | –0.2820* | –0.3642** |
| (0.1409) | (0.2374) | (0.2016) | (0.1741) | (0.1453) | (0.1561) | |
| Constant | 0.8648** | 1.4790** | 0.7844 | 1.2357*** | 0.9175** | 0.6974* |
| (0.3614) | (0.6039) | (0.7989) | (0.4296) | (0.3803) | (0.4130) | |
| Number of observations | 445 | 445 | 282 | 311 | 427 | 385 |
| Pseudo R2 | 0.1099 | 0.1103 | 0.0457 | 0.1431 | 0.1105 | 0.1080 |
Probability of Absorption Drops
(Panel probit regression, 1990–2007)
| Absorption | ||||||
|---|---|---|---|---|---|---|
| Baseline probit (1) |
Logit (2) |
Excluding fragile states (3) |
Excluding commodity exporters (4) |
Excluding oil exporters (5) |
Excluding island economies (6) |
|
| Reserves, months of imports (t–1) | –0.0896*** | –0.1556*** | –0.1018** | –0.1333*** | –0.0949*** | –0.0734** |
| (0.0339) | (0.0595) | (0.0490) | (0.0446) | (0.0357) | (0.0354) | |
| Flexible exchange rate regime (t–1) | –0.3801*** | –0.6568*** | –0.1392 | –0.5043*** | –0.4106*** | –0.3884*** |
| (0.1366) | (0.2340) | (0.1779) | (0.1700) | (0.1400) | (0.1492) | |
| Government balance, percent of GDP (t–1) | –0.0323*** | –0.0537** | –0.0175 | –0.0312** | –0.0343*** | –0.0363*** |
| (0.0125) | (0.0220) | (0.0169) | (0.0149) | (0.0132) | (0.0138) | |
| CPIA index (t–1) | –0.3090*** | –0.5129*** | –0.3805* | –0.4028*** | –0.3245*** | –0.2560** |
| (0.1056) | (0.1766) | (0.2080) | (0.1209) | (0.1083) | (0.1256) | |
| IMF program (t) | –0.3021** | –0.5223** | 0.1042 | –0.1440 | –0.2820* | –0.3642** |
| (0.1409) | (0.2374) | (0.2016) | (0.1741) | (0.1453) | (0.1561) | |
| Constant | 0.8648** | 1.4790** | 0.7844 | 1.2357*** | 0.9175** | 0.6974* |
| (0.3614) | (0.6039) | (0.7989) | (0.4296) | (0.3803) | (0.4130) | |
| Number of observations | 445 | 445 | 282 | 311 | 427 | 385 |
| Pseudo R2 | 0.1099 | 0.1103 | 0.0457 | 0.1431 | 0.1105 | 0.1080 |
More informative are the marginal effects of these explanatory variables on the probability of an absorption drop (the crisis event). One interesting feature of the probit model is that it can be used to compute the impact of a change in fundamentals on the crisis probability. For example, a country with relatively strong economic fundamentals (e.g., higher government balance) is likely to face a smaller probability of a crisis for a given level of international reserves. This could reflect greater fiscal space to mount a countercyclical fiscal response to the external shock. Additionally, the probit model can also show how the crisis probability varies across different levels of reserves.
The top panel of Table 5.3 shows the marginal effects of changes in explanatory variables on the probability of a crisis. Assuming that the country has an IMF-supported program in the event of a shock, the third column reports estimated changes in crisis probability under a flexible exchange rate regime. Evaluating all other variables at their sample means, an increase in reserves from 3.2 months of imports (the sample mean) to 5 months of imports reduces the probability of an absorption drop by around 3.6 percentage points. As can be seen from the table, the estimated change in the crisis probability tends to be higher (more than 5 percentage points) under fixed exchange rate regimes. An improvement in the CPIA from 3.3 to 3.7 reduces the crisis probability by 3 percentage points under a flexible regime and by 4.2 percentage points under a fixed regime. Finally, an improvement in the government balance from 4.5 to 3 percent of GDP reduces the estimated annual probability of a crisis by more than 1 percentage point.
Fundamentals and Marginal Effects on Crisis Probability
Fundamentals and Marginal Effects on Crisis Probability
| Estimated Change in Crisis Probability with IMF Program | ||||
|---|---|---|---|---|
| Fundamentals | Sample mean | Parameter change | Flexible exchange rate | Fixed exchange rate |
| Reserves, months of imports | 3.2 | 3.2 → 5 | –3.6 | –5.1 |
| Government balance, percent of GDP | –4.5 | –4.5 → – 3 | –1.1 | –1.6 |
| CPIA index | 3.3 | 3.3 → 3.7 | –3.0 | –4.2 |
| IMF program dummy | no program → program | –8.8 | –10.9 | |
| Exchange rate regime dummy | fixed → flexible | ‒11.1 | ||
| Evaluated at mean value, percent | ||||
| Probability of crisis (no IMF program) | 25.2 | 38.5 | ||
| Probability of crisis (with IMF program) | 16.5 | 27.6 | ||
Fundamentals and Marginal Effects on Crisis Probability
| Estimated Change in Crisis Probability with IMF Program | ||||
|---|---|---|---|---|
| Fundamentals | Sample mean | Parameter change | Flexible exchange rate | Fixed exchange rate |
| Reserves, months of imports | 3.2 | 3.2 → 5 | –3.6 | –5.1 |
| Government balance, percent of GDP | –4.5 | –4.5 → – 3 | –1.1 | –1.6 |
| CPIA index | 3.3 | 3.3 → 3.7 | –3.0 | –4.2 |
| IMF program dummy | no program → program | –8.8 | –10.9 | |
| Exchange rate regime dummy | fixed → flexible | ‒11.1 | ||
| Evaluated at mean value, percent | ||||
| Probability of crisis (no IMF program) | 25.2 | 38.5 | ||
| Probability of crisis (with IMF program) | 16.5 | 27.6 | ||
Table 5.3 also suggests that shifting from a fixed to a flexible exchange rate regime has a significant impact on the crisis probability, reducing the likelihood of an absorption drop by 11 percentage points. This is confirmed from the bottom panel of Table 5.3, which reports the probability of a crisis under fixed and flexible regimes, evaluating all other variables at their sample means. Indeed, the probability of a crisis tends to be significantly lower under flexible exchange rate regimes. An IMF-supported program also has a significant effect in reducing the likelihood of absorption drops. In particular, having an IMF-supported program in the event of a shock reduces the crisis probability by approximately 8.8 percentage points under a flexible regime, and by 10.9 percentage points under a fixed exchange rate regime.
The relationships between the crisis prevention role of reserves and exchange rate regimes, and between reserves and IMF-supported programs are further illustrated in Figure 5.2. Panel a shows the probability of a crisis and the level of reserves under different exchange rate regimes, assuming there is no IMF program and evaluating all other variables at their sample means. Increasing reserve coverage from three to four months of imports reduces the probability of a crisis by about 3.5 percentage points under a fixed exchange rate regime. But the same increase in reserve coverage yields a smaller reduction in the crisis probability under a flexible regime, implying that the marginal effect of reserves is dependent on policy frameworks. Similarly, Panel b shows the probability of a crisis and the level of reserves with and without an IMF-supported program under a fixed exchange rate regime. These results reflect the inherently nonlinear relationship between the probability of a crisis and reserve levels.
Estimating the Cost of a Crisis
The next step involves estimating the severity of a crisis. Reserves not only help in crisis prevention but also play a role in mitigating the consequences of a crisis. To capture the crisis mitigation role of reserves, we estimate the real absorption loss (normalized in percent of GDP) in the event of external shocks as a function of reserves and other variables. The explanatory variables considered include the log of reserves (in months of import), the exchange rate regime, and the size of shocks.7 The regressions include country fixed effects to control for unobserved cross-country heterogeneity. The log of reserves (in months of imports) is used as the relevant dependent variable to capture the nonlinearity in the crisis mitigation role of reserves, that is, the marginal effect of reserves on real absorption loss diminishes by the level of reserves.8 The inclusion of the size of shocks is necessary to control for the income effect of shocks on real absorption. The regression analysis experimented with other explanatory variables, including a dummy for IMF-supported programs, but found them to be statistically insignificant. The regressions are estimated over the same sample period and countries used for the probit regressions.
The regression results, summarized in Table 5.4, support the crisis mitigation role of reserves. Higher reserve holdings are associated with lower absorption losses. The coefficients on the shock variables are of the expected sign (negative as the dependent variable is constructed to be positive to denote a real absorption loss) except for foreign aid shock (which is insignificant in most specifications). The results suggest that positive external demand and terms-of-trade shocks are associated with lower absorption losses shown in column (1). Most striking is the effect of the shown in exchange rate regime. Even after controlling for the size of shocks and including country fixed effects, the regressions suggest that a flexible regime helps reduce real absorption loss by about 9 percent of GDP, relative to the fixed regime. As in previous regressions, these results are largely robust to various sample restrictions, with the estimated coefficients on reserves varying only slightly across various specifications, as shown in columns (2)–(5) in Table 5.4.
Absorption Loss Regression
(Fixed effects regression with the probit sample)
Absorption Loss Regression
(Fixed effects regression with the probit sample)
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Baseline | Excluding fragile |
Excluding commodity exporters |
Excluding oil exporters |
Excluding island economies |
|
| Log of reserves, months of imports (t–1) | –2.2403*** | –2.0268* | –1.5548** | –2.0425*** | –2.5021*** |
| (0.6677) | (1.1416) | (0.6324) | (0.6634) | (0.7306) | |
| Flexible exchange rate regime (t–1) | –8.6983*** | –8.4203** | –5.6632** | –8.6269*** | –7.8198*** |
| (2.1689) | (3.3245) | (2.2809) | (2.2192) | (2.5429) | |
| External demand growth (t) | –0.9320** | –1.1587* | –0.8478** | –0.8066* | –0.5799 |
| (0.4356) | (0.6734) | (0.4294) | (0.4242) | (0.4415) | |
| Terms of trade growth (t) | –0.0841* | –0.0704 | 0.0072 | –0.0732 | –0.1193** |
| (0.0484) | (0.0431) | (0.0226) | (0.0478) | (0.0561) | |
| Change in FDI to GDP (t) | –0.0159 | 0.6605** | –0.7468 | 0.1236 | –0.1136 |
| (0.3391) | (0.2762) | (0.4908) | (0.4551) | (0.3237) | |
| Change in aid to GDP (t) | 0.0527 | 0.2125 | 0.0941 | 0.0615 | 0.0427 |
| (0.0839) | (0.2199) | (0.1081) | (0.0855) | (0.0904) | |
| Number of observations | 418 | 264 | 287 | 401 | 360 |
| Adjusted R2 | 0.34 | 0.37 | 0.47 | 0.34 | 0.33 |
| Country fixed effects | Yes | Yes | Yes | Yes | Yes |
Absorption Loss Regression
(Fixed effects regression with the probit sample)
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Baseline | Excluding fragile |
Excluding commodity exporters |
Excluding oil exporters |
Excluding island economies |
|
| Log of reserves, months of imports (t–1) | –2.2403*** | –2.0268* | –1.5548** | –2.0425*** | –2.5021*** |
| (0.6677) | (1.1416) | (0.6324) | (0.6634) | (0.7306) | |
| Flexible exchange rate regime (t–1) | –8.6983*** | –8.4203** | –5.6632** | –8.6269*** | –7.8198*** |
| (2.1689) | (3.3245) | (2.2809) | (2.2192) | (2.5429) | |
| External demand growth (t) | –0.9320** | –1.1587* | –0.8478** | –0.8066* | –0.5799 |
| (0.4356) | (0.6734) | (0.4294) | (0.4242) | (0.4415) | |
| Terms of trade growth (t) | –0.0841* | –0.0704 | 0.0072 | –0.0732 | –0.1193** |
| (0.0484) | (0.0431) | (0.0226) | (0.0478) | (0.0561) | |
| Change in FDI to GDP (t) | –0.0159 | 0.6605** | –0.7468 | 0.1236 | –0.1136 |
| (0.3391) | (0.2762) | (0.4908) | (0.4551) | (0.3237) | |
| Change in aid to GDP (t) | 0.0527 | 0.2125 | 0.0941 | 0.0615 | 0.0427 |
| (0.0839) | (0.2199) | (0.1081) | (0.0855) | (0.0904) | |
| Number of observations | 418 | 264 | 287 | 401 | 360 |
| Adjusted R2 | 0.34 | 0.37 | 0.47 | 0.34 | 0.33 |
| Country fixed effects | Yes | Yes | Yes | Yes | Yes |
Cost of Holding Reserves
As discussed earlier, consistent estimates of the marginal product of capital across countries are not easy to obtain. In the calibration exercise in the following chapter, we consider several reference values taken from the literature as the unit cost of holding reserves, based on various estimates of the marginal product of capital and interest rate differentials.
Indicators identified in the literature include sterilization costs, the differential between domestic and foreign real interest rates, the net financial cost of holding reserves (typically using measure of reserves of sovereign bond spreads), and the opportunity cost of forgone consumption or investment (see Hauner, 2006; Jeanne and Ranciere, 2006).
FDI, aid, and remittances are measured as ratios to GDP. Large natural disasters are identified if the number of people affected and the economic damage was considered to be among the top 25th percentile of the distribution. Data on natural disasters are drawn from the Emergency Events Database (EM-DAT), published by the Center for Research on the Epidemiology of Disasters (CRED).
The correlation between different shocks ranges from –0.05 to 0.05. While remittances are included in defining shock episodes, the number of observations for remittances is rather limited, making it difficult to analyze remittances shock episodes separately.
Research suggests that adverse external shocks tend to induce breaks in trend growth rather than fluctuations around a trend. Our definition of crisis events thus attempts to capture the combined effects of level drops and growth declines.
The CPIA is a broad indicator of the quality of a country’s present policy and institutional framework. It is based on 16 criteria, which are grouped into four clusters: economic management, structural policies, policy for social inclusion and equity, and public sector management and institutions.
Various specifications for the IMF program dummy were considered, including a one-year lag and a combined dummy for lagged and contemporaneous IMF programs. The lagged dummy variable was not statistically significant in any of the specifications. One would expect a positive coefficient for the contemporaneous IMF dummy if there is endogeneity. However, the regression results in Table 5.2 show a statistically significant negative coefficient for the IMF program dummy, indicating that our results are not due to endogeneity.
Remittance shocks are not included in the regressions as the number of observations drop substantially if the variable is included in the regression.
Alternative specifications were considered for the reserve variable, including R* = R/(1 + R). The resulting optimal reserve levels turned out to be very similar to those obtained by assuming a log specification (results available upon request).
