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This paper was prepared when Jun Il Kim was in the Research Department of the International Monetary Fund. We would like to thank Hugh Bredenkamp, Valerio Crispolti, George Tsibouris, Rex Ghosh, Joshua Aizenman, Oliver Jeanne, Eduardo Levy-Yeyati, and seminar participants at DFID and the SPR seminar series for their useful comments. Ke Wang provided excellent research assistance.
In this paper, “low-income countries” refers to all countries shown on the IMF’s list of countries eligible for the Poverty Reduction and Growth Trust (PRGT) at end-December 2010.
The categorization of fragile states relies on the definition of fragility adopted by the World Bank.
For example, countries may also accumulate reserves in order to pursue an export-led growth strategy by artificially maintaining an undervalued exchange rate (Dooley et al., 2004).
Valencia (2010) develops a precautionary savings model for optimal reserves for Bolivia. In the face of income uncertainty - represented by the occurrence of current account shocks—agents in the model self-insure by managing a stock of riskless assets to buffer consumption against adverse shocks. Our paper focuses on precautionary reserve holdings rather than precautionary savings, which arise from the inability to mitigate external shocks due to limited and uncertain market access or multilateral/bilateral aid flows.
Short-term debt by remaining maturity, another commonly used measure for reserve adequacy in countries that face capital account pressures, is not reported because of the poor quality of short-term external debt data in a large number of low-income countries. For countries with reliable short-term debt data, reserve holdings were found to be significantly above the rule of thumb, reflecting their limited market access and reliance on concessional longer-term financing from official sources.
It is also worth noting that the increase in reserves in 2009 is largely attributable to the SDR allocation in response to the global financial crisis. See http://www.imf.org/external/np/sec/pr/2009/pr09283.htm.
IMF (2003) and Aizenman and Marion (2002) develop models for EMs. The use of this model for assessing reserve adequacy relies on the assumption that averaged over countries and over the regression sample period there is no systematic bias over under- or over-insurance across countries.
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 foregone consumption or investment (see Hauner, 2005; Jeanne and Ranciere, 2006).
EMs 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.
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.
We also experimented with alternative definitions of crisis events. For example, we defined an absorption drop when real absorption per capita growth fell below the bottom tenth percentile of the country- specific distribution. This definition gave more frequent occurrence of crisis events, with real absorption growth remaining positive in some cases. 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 3 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).
The marginal product of capital is an important measure for LICs given their large investment needs. Caselli and Freyer (2007) calculate a range of 3 to 8 percent for the marginal product of capital in low-income countries.
Alternatively, shock values could be simulated by assuming a multivariate normal distribution for shocks, with the variance-covariance matrix estimated from the sample. Optimal reserves could then be calibrated for each set of simulated shock values, and then averaged to yield final results. While computationally demanding, this option allows for explicitly accounting for the correlation among shocks.
In view of the large uncertainty surrounding estimates of risk-aversion parameters, experimenting with more extreme shock values or larger adjustments while assuming risk-neutral utility, could be a practical approach to address differences in the risk attitude across countries.
Since a large shock event is defined as a union of six individual shock events (defined at or below the 10th percentile of the country-specific sample distribution), the unconditional probability q should be close to 0.6 if individual shocks are uncorrelated. The sample estimate of 0.5 thus suggests that individual shocks are positively (albeit weakly) correlated in the sample.