Mr. Mario Catalan, Alexander W. Hoffmaister, and Cicilia Anggadewi Harun
This paper studies the transmission of bank capital shocks to loan supply in Indonesia. A series of theoretically founded dynamic panel data models are estimated and find nonlinear effects of capital on loan growth: the response of weaker banks to changes in their capital positions is larger than that of stronger banks. This non-linearity implies that not only the level of capital but also its distribution across banks in the financial system affects the transmission of shocks to aggregate lending. Likewise, the effects of bank recapitalization on loan growth depend on banks’ starting capital positions and the size of capital injections.
This paper seeks to uncover the main drivers of credit growth in emerging Asia using a multi-country structural vector autoregressive (SVAR) model. Taking a novel approach, we developed a two-block SVAR whereby shocks within blocks are identified using sign restrictions, whereas shocks across the blocks are identified using a recursive (block-) Cholesky structure. We find that domestic factors are more dominant than external factors in driving rapid credit growth in emerging Asia. This is particularly true for domestic monetary policy, which can play a pivotal role in terms of managing rapid credit growth in emerging Asia.
Current estimates of global poverty vary substantially across studies. In this paper we undertake a novel sensitivity analysis to highlight the importance of methodological choices in estimating global poverty. We measure global poverty using different data sources, parametric and nonparametric estimation methods, and multiple poverty lines. Our results indicate that estimates of global poverty vary significantly when they are based alternately on data from household surveys versus national accounts but are relatively consistent across different estimation methods. The decline in poverty over the past decade is found to be robust across methodological choices.
Using two recently constructed measures of trade liberalization dates, this research studies the impact of trade liberalization on imports, exports, and overall trade balance for a large sample of developing countries. We find strong and consistent evidence that trade liberalization leads to higher imports and exports. However, in contrast Santos-Paulino and Thirwall (2004) who find a robustly negative impact of trade liberalization on the overall trade balance, we only find mixed evidence of such a negative impact. In particular, we find little evidence of a statistically significant negative impact using our first measure of liberalization dates which extends Li (2004). Using a second measure of liberalization dates compiled by Wacziarg and Welch (2003), we find some evidence that liberalization worsens the trade balance, but the evidence is not robust across different estimation specifications, and the estimated impact is smaller than that reported by Santos-Paulino and Thirwall (2004).
Previous early-warning systems (EWSs) for currency crises have relied on models that require a priori dating of crises. This paper proposes an alternative EWS, based on a Markov-switching model, which identifies and characterizes crisis periods endogenously; this also allows the model to utilize information contained in exchange rate dynamics. The model is estimated using data for the period 1972-99 for the Asian crisis countries, taking a country-by-country approach. The model outperforms standard EWSs, both in signaling crises and reducing false alarms. Two lessons emerge. First, accounting for the dynamics of exchange rates is important. Second, different indicators matter for different countries, suggesting that the assumption of parameter constancy underlying panel estimates of EWSs may contribute to poor performance.
This paper takes the Asian crisis as an example to show that the Autoregressive Conditional Hazard (ACH) model is a powerful tool for studying the time series features of speculative attacks. The ACH model proposes a duration variable to capture the changes in the frequency of attacks, which might be an important factor influencing investors' expectations. The empirical results show that the ACH model explains the crisis far better than the Probit model. The duration variable is highly significant while most fundamentals are not. The contagion effect is tested and accepted under the ACH specification.
This paper investigates whether Indonesia’s recent currency crisis was due to domestic fundamentals, common external shocks (“monsoons”), or contagion from neighboring countries. Markov-switching models attribute speculative pressure on Indonesia’s currency to domestic political and financial factors and contagion from speculative pressures in Thailand and Korea. In particular, the results from a time-varying transition probability Markov-switching model (which overcomes some drawbacks of previous methods) show that inclusion of exchange rate pressures from Thailand and Korea in the transition probabilities improves the conditional probabilities of crisis in Indonesia. There is also evidence of contagion in the stock market.