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This paper was developed as input to the IMF Policy Paper “Managing Volatility-A Vulnerability Exercise for Low-Income Countries” (IMF, 2011). The authors would like to thank Hugh Bredenkamp, Saul Lizondo, Sharmini Coorey, Andy Berg, Catherine Pattillo, Christian Mumssen, Raphael Espinoza, Marco Arena, and Enriko Berkes for helpful comments and suggestions.
Studies have found that the negative impact of exogenous shocks on growth and consumption volatility is especially pronounced in low-income countries (Becker and Mauro, 2007; Perry, 2009), and that such impact results mostly from crises or severe recessions rather than normal cyclical fluctuations (Hnatkovska and Loayza, 2005).
The importance of these factors in reducing resilience to shocks is well established in the empirical literature (see Collier et al., 2006; Loayza and Raddatz, 2007; Acemoglu et al., 2003; and Rodrik, 1999).
Studies find that shocks impact long-run growth through reductions in investment (Aghion et al., 2005), a worsening of economic policy (World Bank, 2006) and, in extreme cases, by increasing the risk of conflict (Bruckner and Ciccone, 2010).
If economic agents were liquidity-constrained or short-sighted, they may be more concerned about an immediately apparent sharp decline in growth than a slowdown in the long-run economic growth that has a similar impact in net present value terms (Becker and Mauro, 2007).
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. See Dabla-Norris et al. (2011) for details.
The severe state failure events are taken from Political Instability Task Force (PITF) dataset. Four types of political crises are included in this dataset: revolutionary wars, ethnic wars, adverse regime changes, and genocides. From this dataset the variable SFTPMMAX, which presents the maximum magnitude of all events in a year, exceeding 3.9 is taken as a severe state failure event.
Pooled probit models assume independence of observations over both t and i. A random effects (RE) probit model treats the individual specific effect, ci, as an unobserved random variable with ci|xit∼IN(μc, σ2c) if an overall intercept is excluded, and imposes independence of ci and xi. A fixed effects (FE) probit model treats ci as parameters to be estimated along with β, and does not make any assumptions about the distribution of ci given x¿. This can be problematic in short panels as both β and ci are inconsistently estimated owing to an incidental parameters problem. Finally, a correlated model relaxes independence between ci and xi using the Chamberlain (1982)-Mundlak (1978) device under conditional normality. In this specification, the time average is often used to save on degrees of freedom.
Results are available from authors upon request.
Drawing from indicators of speculative pressure in the crisis literature (Eichengreen et al., 1995, Kaminsky and Reinhart, 1999, and Herrera and Garcia, 1999), this study uses a composite indicator given by:
where EMPIit is the exchange market pressure index for country i at time t, xr is the exchange rate of national currency to U.S. dollar (an increase indicates a nominal depreciation), res is the stock of international reserves, mgs is the imports of goods and services, blackpr is the black market premium, and σ is the standard deviation of each variable. Weights are inverses of the standard deviation of each component for all countries over the full sample after removing the outliers. Higher levels of EMPI indicate increased pressures on the exchange rate. This version of the index was first used in Bal Gündüz (2009).
The World Bank’s CPIA (the Country Policy and Institutional Assessment) 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.
Small islands are defined as islands with a population less than 1 million.
Owing to the non-linearity of the model, estimated coefficients have no direct interpretation. Marginal effects of variables calculated at preset values of other explanatory variables (at their means or medians) are reported to present the relative impact of each variable.
Marginal effects of a specific covariate are averaged across the sample distribution of other covariates.
The ranges for individual covariates in Figure 3 are determined after removing outliers on both tails of their distributions in the estimation sample.
The less informative the model, the less dispersed the predicted probabilities, the limiting case being the flat sample probability predicted for both types of events.
Both approaches were implemented, but as in previous studies, the maximization of the signal-to-noise ratio led to corner solutions associated with very high type I or type II errors. Therefore, we reported thresholds based on the minimization of misclassification errors.
The weights of individual indicators are determined on the basis of their goodness of fit.
We rebalanced unconstrained weights predicted from the signaling power of indicators to account for potential correlation among variables both within a cluster and across clusters. Unconstrained weights obtained were 51 percent for overall economy and institutions; 28 percent for external index; and 21 percent for fiscal index. Adjustments to the weights were guided by model performance.
In the univariate probit regressions, the overall index and its sub-components are entered one-by-one as the only covariate in addition to the constant. In the multivariate probit regressions, all subcomponents of the index are included simultaneously.