nual for the period 1980-2019.<sup>38</sup> The sources are IFS (IMF) for GDP and government expenditure; World Bank for terms of trade; and Gruss and Kebhaj’s (2019) database for commodity export price indices.<sup>39</sup>

# 5.2 Effects of commodity prices on GDP

The first stage is to run a fixed-effects panel regression of output on commodity export prices. The results are summarized in Table 3. We use two control variables: terms of trade (as a way of controlling for trade effects) and the lagged dependent variable (to capture underlying growth unrelated to commodity prices).<sup>40</sup> We can see that the coeffi cient for EMDE (0.085) in Column (1) is about 3.5 times as much as the coeffi cient for AE (0.024) in Column (4).<sup>41</sup> In other words, an increase of 10 percent in commodity export prices increases GDP by 0.63-0.85 percent in EMDE and 0.18-0.26 percent in AE.<sup>42</sup> The estimates are quite robust across specifications.<sup>43</sup>

# 5.3 Effects of commodity prices on fiscal response

The second stage is to run a fixed-effect panel regression of government spending on commodity export prices. As in the case of Table 3, we control for terms of trade and the lagged dependent variable. The results are summarized in Table 4.<sup>44</sup> From

> 38For the computation of fiscal multipliers below, we use quarterly data from IFS (IMF).

> 39It is worth mentioning that the commodity price indices from Gruss and Kebhaj’s (2019) are countryspecific. Specifically, for each country, the change in the international price of up to 45 individual commodities is weighted using commodity-level data for the period 1962-2018.

> 40If we use the Arellano-Bond estimator for dynamic linear panel methods to deal with lagged-dependent variables, the same results hold for regressions (3) and (6). Also, using as controls world trade (as percentage of GDP) instead of terms of trade and public debt as percentage of GDP does not change the results.

> 41Notice that regressions (4)-(6) control for the EMDE response by introducing an interactive term.

> 42Since all variables are log-differences, the estimated coeffi cients are elasticities.

43 Comparing our quantitative estimates to those of other studies is not easy since empirical specifications vary considerably. With that caveat in mind, our estimates are very much comparable to those of, for example, Richaud et al. (2019). Based on a sample of 87 EMDE commodity exporters, Richaud et al. report a range of 0.34 to 1.25 percent for the cumulative impulse response of GDP to a one standard deviation shock in commodity prices. In our case, the sample is 11 EMDE with a coeffi cient range in the panel regressions in Table 3 of 0.063-0.085. Since the standard deviation of changes in commodity prices for EMDE is 14.4 percent, the range for the impact on GDP is 0.92 to 1.22 percent. Notice that the comparison is valid because changes in commodity prices tend to be very persistent (see Cashin et al., 2000).

> 44Again, if we use the Arellano-Bond estimator for dynamic linear panel methods to deal with laggeddependent variables, the same results hold for regressions (3) and (6). Further, if we use as control variables (i) volume of world trade as percentage of GDP (in lieu of terms of trade) and (ii) government debt as a percentage of GDP, the same results obtain.

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