prices and incomes change . This chapter compares the macroeconomic adjustment in Chile, Colombia, and Peru in response to fluctuations in commodity prices. This comparison is relevant as these are commodity exporters’ emerging economies (mainly copper for Chile and Peru, and oil for Colombia) that are comparable in size and have sound macroeconomic frameworks in place, including fiscal rules and inflation-targeting regimes. 3. First, macroeconomic responses to a commodity ToT shock are estimated . Using a vector auto-regression methodology (VAR), the implications of
Macroeconomic Performance Pre/Post Commodity Shock 8. Baseline: Impulse Responses to a 10 Percent Drop in Commodity Terms of Trade 9. Impulse Responses of Real GDP per capita Growth to a 10-percent Negative Commodity ToT Shock under Alternative Model Specifications 10. Gradual Exits to Floating through More Flexible Pegged Regimes 11. Exit Episodes Toward Greater Flexibility 12. Evolution of Key Economic Indicators Before and After the Exit to More Flexible ER Regime Boxes 1. Ingredients of a Successful Move to Exchange Rate Flexibility 2. Transitions to
crawl-like arrangements as “pegged exchange rate regimes” and the rest as “floating regimes.” The distribution of exchange rate regimes between 2013–17 suggests that most commodity-exporting countries maintain pegged exchange rate regimes (Section II). 31. The PVAR impulse-response functions (IRFs) of the key macroeconomic variables support the event-study findings that adjustment following a commodity ToT shock is smoother under flexible regimes . The IRFs of real GDP per capita, real exchange rate, real government consumption, and consumer price index to a 10
macroeconomic responses to a commodity ToT shock. Using a vector auto-regression methodology (VAR), the implications of movements in the ToT (using a country-specific commodity price index) on government revenues and expenditures, GDP growth, the real effective exchange rate (REER), and the current account are analyzed. Once the relevant shocks are identified, impulse response and forecast error variance decomposition analyses are conducted. Second, we conduct an event study of the actual adjustment to the recent drop in commodity prices. In the three economies, current
least 0.3 percent of GDP (excluding countries with a positive overall commodity ToT shock). The impact of the ToT shock on LICs differs significantly from that on MICs . The overall price shock for 2022 is higher for LICs than for MICs both in nominal terms (US$3.3 billion compared to US$1.4 billion) and as a percent of GDP (0.3 and 0.1 percent respectively). The underlying drivers of the shock are also different: MICs mostly suffer from higher fertilizer prices, whereas cereals and fertilizer price hikes contribute almost equally to the expected increase in the
.892) Consensus floating ERR (dummy) 3 −2.316 * −2.311 1.121 2.652 *** −1.324 (1.297) (1.920) (0.924) (1.001) (0.938) Commodity TOT shock −0.366 *** −0.339 *** −0.481 *** −0.424 *** 0.483 ** 0.705 ** −0.348 *** −0.346 *** −0.347 *** −0.345 *** −0.196 *** −0.386 *** (0.054) (0.027) (0.075) (0.071) (0.192) (0.312) (0.026) (0.023) (0.023) (0.028) (0.072) (0.061) CTOT shock * intermediate ERR dummy 0.244 *** 0.188 0.077 0.107 0.116 0.345 0.159 0