rafmacalaba/datause-encoder-data huggingface dataset:
45

**Effects** of Energy Price Changes

_Diesel Price Increases 1982-85: 7he Industrial Sector_ What was the impact of these price increases on the industrial sector? The output effect of an increase in energy prices are determiined by energy costs in total cost of production and elasticity of demand for output for different subsectors. Table 3.2 shows the share of energy in total costs and the index of industrial production for a selected group of industries for the period 1981-88.

As table 3.2 shows, except for glass and glass products, energy cost shares are relatively low and lie between 0.26 percent for tobacco and 2.93 percent for cement. The pattem of changes in output is as expected, with highest output losses for industries with high shares of energy costs and lowest changes in output for those with low energy cost shares. During the first year of the reform, output for glass and glass products fell by nearly 20 percent For other industries the change in output varied between 0 and 11 percent. Food processing and the tobacco industiy did not experience any changes in output For the whole period of the reform, the largest losses were observed for industies such as yam and thread, weaving mills and plywood, a sub-group where energy costs constitute a relatively high share of total costs. Loss of output in the motor vehicle industry as fuel costs increased during ihe period of pnce reform. is as expected. However, the general index of production shows a gradual increase in aggregate output during the period of energy price increases.

The effect of a price increase on demand for individual fuels occurs as a result of a reduction in aggregate demand for energy, interfuel substitution generated by price increases and price elasficity of demand for industial output The effect on aggregate demand for energy depends both on the output effect of the energy price change and a substitution effect which occurs between energy and labor. Interfuel substitution on the other hand will be determined by the extent to which relative fuel prices are altered. Given that price changes during 1982-85 covered all petroleum products and electricity, interfuel substitution should be less important. To get m idea about likely effects on energy consumption we may draw upon the findings of an earlier World Bank study (1983), which analyzed energy price changes in Indonesia. Vanous energy price scenarios were simulated and table 3.3 presents the results of two scenarios.30 As expected, the change in energy consumption is much higher when wage rates are held constant as compared to wage rates that are allowed to increase by 26 percent Moreover, the change in The energy price change scenario underlying these simulations assume increases in product price as follows: Scenario A. electricity (+39%), gasoline (+60%). fuel oil (+66%). diesel (+66%), kerosene (+60%) and a 28 percet increase in wage rates. Scenario B is the same as scenario A except that wage rate is assumed constant. See World Bank (1983).
directly (and correctly) aimed at avoiding that demand falls too much and consumers’ hording may move demand for food up, future work could use general equilibrium models to quantify this channel. Finally, we abstracted in this paper from changes in import policy. Tariffs and other barriers to imports are high on food products and governments may lower them as food prices rise. While reduction in trade barriers is generally efficient, short-term policy changes such as tariff suspensions may contribute to price escalation by shifting out demand in the short term without bringing the longer-term benefits of more open food markets (Martin and Anderson, 2012).

# **References**

<mark>Aguiar, A., Chepeliev, M., Corong, E., McDougall, R., & van der Mensbrugghe, D. (2019). The GTAP Data Base: Version 10.</mark> _<mark>Journal of Global Economic Analysis, 4(1)</mark>_ <mark>, 1-27.</mark> Baldwin, R. (2020). “The supply side matters: Guns versus butter, COVID-style.” VoxEU. March 22, 2020.

Baldwin, R. and B. Weder di Mauro (eds) (2020a). Economics in the Time of COVID-19, a VoxEU.org eBook, CEPR Press.

Baldwin, R. and B. Weder di Mauro (eds) (2020b). Mitigating the COVID economic crisis: Act fast and do whatever it takes, a VoxEU.org eBook, CEPR Press.

Espitia, Alvaro, Nadia Rocha, Michele Ruta (2020a). “Trade and the COVID-19 crisis in developing <u>countries”. Washington, DC: World Bank; and VoxEU. April 9, 2020).</u> Espitia, Alvaro, Nadia Rocha, Michele Ruta (2020b). “Database on COVID-19 trade flows and policies”. Washington, DC: World Bank.

EUI-GTA-WB (2020). “Covid-19 Trade Policy Database: Food and Medical Products”.

FAO (2018). The future of food and agriculture. Alternative pathways to 2050. Food and Agriculture Organization. Rome, Italy.

FAO (2009). The State of Agricultural Commodity Markets. High Food Prices and the Food Crisis: Experience and Lessons Learnt. Food and Agriculture Organization. Rome, Italy.

Fontagné, L., H. Guimbard and G. Orefice (2019). “Product-Level Trade Elasticities.” CEPII working paper 2019-17.

Freund, C. and C. Ozden (2008). "Trade Policy and Loss Aversion". _American Economic Review_ , 98:4, 167591.

Giordani, P. N. Rocha, M. Ruta (2016). “Food prices and the multiplier effect of trade policy.” _Journal of International Economics_ , Volume 101, Pages 102-122.

Grossman, G.M. and E. Helpman (1994). "Protection for Sale." _American Economic Review_ , 84:4, 833-850.

Kee, H.L. (2020). “COVID-19 and Trade Disruptions Due to China.” Washington, DC: World Bank.

Kee, H.L. and A. Nicita (2020). “Trade Frauds, Trade Elasticities and Non-Tariff Measures.” Work in progress. Washington, DC: World Bank.

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The second issue concerns the interpretation of year-to-year fluctuations in household per capita income. These fluctuations in part reflect changes in household size, as well as changes in the number of income earners in the household, which may not be well-captured by our simple lognormal data generating process. While it is possible to directly observe household size, it is not possible to cleanly identify fluctuations in household income per capita due to changes in the number of income earners in the household.<sup>13</sup> Beyond these concerns, there are also perennial thorny questions concerning the effects of measurement error in family income on the estimates of ���� , which could lead to different biases depending on its correlation with income and over time.<sup>14</sup> One crude way of partially addressing both these concerns simultaneously is to filter our estimation sample for influential observations. We do this using a Cook’s distance criterion, and drop all household-year observations in our pooled baseline regression sample corresponding to the top one percent of observations on the Cook’s distance statistic.

The bottom panel of Figure 1 superimposes these two alternative micro estimates of ���� on the same grey shaded region corresponding to the bounds on mobility based on aggregate data. Both variants result in slightly higher micro estimates of ���� , with both averaging to 0.86, as opposed to 0.83 in the baseline. These alternative micro estimates of ���� now fall closer to the center of the range based on the macro data, and particularly so in the middle decade of our 20-year sample. Overall, the picture that emerges from Figure 1 is that the bounds on mobility we can estimate using only data on the evolution of aggregate moments of the income distribution are reasonably narrow, and for the most part include estimates of mobility estimated from the micro panel data.

# **5.  Cross-Country Estimates of Mobility Using Only Aggregate Data**

Encouraged by the results of the previous section, which show that our approach delivers reasonable bounds on mobility in the PSID where mobility can be directly observed in the record-level panel data, in this section we obtain bounds on the mobility parameter ���� in two multi-country datasets where we have data only on aggregate moments of the income distribution. We work with two such datasets. The first is the World Wealth and Income Database (WID), which assembles estimates of top

> 13 This is because the PSID collects information on family income and income of the household head, but does not collect data separately on incomes of other household members.

> 14 The limited available information based on linking survey incomes with administrative data in the US suggests that the overall effect of measurement error on mobility measures is small (see Jantti and Jenkins (2015), Section 10.4.1), although these authors caution that this evidence is best interpreted as a reflection of how little is known about this issue.

15
also be the case if countries’ liberalization strategies simply called for liberalizing all tariffs (preferential and multilateral) by a given percent. On the other hand, we find that while lagged preferential reduction positively and significantly predicts MFN liberalization (columns 2-3), lagged multilateral liberalization does not positively predict preferential reduction (columns 5-6). In addition, simple correlations show that MFN tariff changes are more correlated with lagged preferential tariff changes (correlation 0.36) than with lagged MFN tariff changes (correlation 0.25). This suggests that MFN tariff cuts may be more influenced by past preferential tariff reductions than by past MFN cuts. In contrast, the reverse is not true—preferential tariff reductions are more correlated with past preferential reductions (0.20) than with past MFN reductions (0.13).

While the figures and correlations are supportive of a sanguine view of preferential liberalization, where preferential tariffs and MFN tariffs are complements, it is possible that Latin America high preference sectors simply happen to be those where multilateral negotiations were most effective in bringing tariffs down during the 1990s, for example. In the next section, we control for this and other potential factors behind changes in MFN tariffs.

# **V. The Effect of Regional Agreements on MFN Tariffs in Latin America**

Table III reports the results from estimating equation (4). The dependent variable is the percentage point change in the MFN tariff, and the independent variables of interest are the lagged percentage point changes in the preferential tariff with and without interaction with the customs union dummy. (All variables are described in detail in Appendix A2.) We include a large set of fixed effects, for each country-year and for each country-industry pair. Standard errors are adjusted for clustering at the country-industry level.

The first column in Table III reports the results from estimating equation (4) using OLS.

25
WPS3784

**<u>Table 3. Source of Household Income</u>**

|Income Category|Rural|Urban|National|
|---|---|---|---|
|Labor Market Income|42.5%|83.6%|58.6%|
|Agriculture/Livestock/Sylvic/Fishing/Hunting|7.9%|0.8%|5.1%|
|Manufacturing Industries|3.3%|6.0%|4.4%|
|Buildings and Public Works|3.1%|5.6%|4.1%|
|Commerce|7.3%|20.3%|12.4%|
|Services a la Collectivite|16.0%|31.3%|22.0%|
|Other Sectors|5.0%|19.6%|10.7%|
|Agricultural Income|41.6%|0.7%|25.5%|
|Industrial Crops:|2.9%|0.0%|1.8%|
|Coffee|2.4%|0.0%|1.5%|
|Tea|0.4%|0.0%|0.3%|
|Food Crops:|33.2%|0.4%|20.3%|
|Beans|3.6%|0.0%|2.2%|
|Sorghum|2.2%|0.0%|1.3%|
|Cassava|3.7%|0.1%|2.3%|
|Sweet Potatoes|2.6%|0.0%|1.6%|
|Livestock Income:|5.5%|0.3%|3.4%|
|Cattle|3.5%|0.3%|2.2%|
|Sheep|1.2%|0.0%|0.7%|
|Transfers|8.9%|3.6%|6.8%|
|Other Income|7.0%|12.1%|9.0%|
|Total Income|100.0%|100.0%|100.0%|

Note: All farm households in urban provinces are considered rural

**<u>Table 4. Percentage of Farmers Growing Coffee, by Income Decile</u>**

|**Expenditure decile**<br>Poorest decile|**% of farmers growing coffee**<br>8.4|
|---|---|
|2nd decile|9.7|
|3rd decile|10.3|
|4th decile|13.5|
|5th decile|12.0|
|6th decile|11.8|
|7th decile|13.6|
|8th decile|10.4|
|9th decile|8.7|
|Richest decile|1.5|
|Total|10.0|

21
inclusion of these interaction terms with quantity sold raises the possibility of endogeneity of the interaction terms. The instruments used to identify the instrumenting equation for the interaction terms include interaction of quantity sold with market access and with wealth. The fi rst stage regressions are reported in appendix Table A.2. As before, the identifying instruments are highly statistically signi fi cant in the relevant regressions.

The results from the CMLE are reported in column 1 of Table 5. The inclusion of so many interaction terms, which are understandably collinear, now renders most of the estimated coe ffi cient statistically insigni fi cant. The coe ffi cient of interaction of market access and land ownership has a negative sign and is statistically signi fi cant at 5 percent level. None of the residuals are individually statistically signi fi cant. The joint test of signi fi cance of fi rst stage residuals shows that endogeneity is still not a problem. In order to deal with the collinearity problems, we dropped the interaction of quantity sold and market access index, which has the lowest t value in the regression. The precision of estimated coe ffi cients in column 2 improves, but the results again highlight the over-parameterization of the regression equation with heavily collinear terms. We again drop the interaction term with the lowest t-value, that is, the interaction of quantity sold and wealth. Dropping this interaction term signi fi cantly improves the statistical precision of the estimated coe ffi cients reported in column 3. The fi rst stage residual has a t-value of 0.49 with a P-value equal to 0.62. This implies that endogeneity of quantity sold is not at all a major concern in the regression. Accordingly, we drop the residual term in our fi nal speci fi cation and estimate it using the standard Probit technique.

The marginal e ff ects from the Probit estimation along with respective t-values are reported in the fi nal column in Table 5. Consistent with our results discussed so far, the market access variable is highly statistically signi fi cant (t-statistic=4.57). The positive sign of the marginal 20
# **Part II:  Fiscal revenues, output composition and financial cycles**

The literature analyzing the relationship between tax receipts and output composition is limited. Gabriela Dobrescu and Ferhan Salman (2011) discuss the relationship between a country’s fiscal stance and domestic absorption. Specifically, they show that just as ignoring output gaps can bias assessments of a country’s true fiscal stance, so can ignoring domestic absorption cycles -cycles during which net exports boom and fall- bias assessments. They find net exports to be anti-cyclical in many emerging and advanced economies. In tax systems that rely heavily on indirect taxes, periods of low net exports will be associated with high fiscal revenues, _ceteris paribus_ . Thus a period of high GDP growth and low net exports will be a period during which indirect tax receipts will be higher relative to GDP than a period of high GDP growth associated with high net exports. If net exports change in an anti-cyclical fashion, then periods of recession will see tax revenues to GDP fall more than would be expected with net exports remaining constant. Standard measures of cyclically adjusted fiscal balances would not capture this additional impact of GDP composition, which may vary with the GDP cycle, on revenues. These authors conduct a cross country analysis covering 59 advanced and emerging economies during 1990-2009 and examine fiscal outcomes during domestic absorption changes. They find that absorption booms (periods when net exports are low because domestic absorption is higher than normal) are associated with procyclical fiscal policy. They conclude that pro-cyclicality arises because policymakers mistake absorption booms for permanent (structural) increases in revenues.

Li and Lopez-Murphy (2010) examine tax revenue downturn episodes, that is, episodes where tax revenue to GDP ratios decline sharply, and explore the relationship between tax revenues and imports. Their analysis covers 63 countries during 1977-97 and 26 countries during 1995-2007. They find that tax revenue to GDP ratios in emerging and developing countries are almost twice as volatile as in advanced countries; import to GDP ratios are also much more volatile in emerging and developing countries. They also find that expenditure taxes account for the bulk of the downturn in emerging and developing countries, while income taxes account for the bulk in developed countries. Changes in the import to GDP ratio are a statistically significant determinant of changes in the tax revenue to GDP ratio even when controlling for changes in real output and the terms of trade. Rahman (2010) examines pro-cyclicality of revenues and expenditures with respect to output and domestic absorption in Eastern Europe during 20032007. She concludes that short term revenue booms during higher output gaps were driven more by increases in domestic absorption than output gaps. She finds that when domestic absorption was high, short term revenue booms were taken for longer term revenue growth; at the same time, there was procyclicality in expenditure growth.

12
runoff (Coe et al., 2009). Therefore, the net effect of land cover changes on river regimes depends on the heterogeneity, extent and spatial pattern of the deforested area (D’Almeida et al., 2007).

Various ecological impacts could result from either increasing or decreasing river discharge, depending on the scale of these changes. Considering the local effects of deforestation only on surface processes, the increased surface runoff together with soil exposure due to forest clearing would increase sediment loads and nutrient transported from terrestrial environment to river system ( _e.g._ Walling & Fang, 2003; Latrubesse et al., 2009). Consequently, changes in water quality and riverbed morphology could alter aquatic ecosystem and fish community structures (Poff & Allan, 1995). Some studies have also shown that deforestation could cause increase in flood frequency and severity that may entail large economic losses (Bradshaw et al., 2007).

In turn, the complex feedbacks between deforestation, climate, and hydrology may also diminish annual and seasonal precipitation patterns. Climate data observations in Rondonia pointed out an 11-days delay on the onset of rainy season after a period of 30 years of intense deforestation (Butt et al., 2011). The increase in the length of the water deficit period could interfere on navigability of rivers, water supply and hydroelectric power generation (Brown, 2006; Marengo et al., 2008).

Even in the (fortunate) case where these local and regional hydrological impacts of forest cover changes are well understood, the issue of their economic value remains. In large measure this is related to agricultural and electrical power generation impacts, mostly in the vicinity of forests (lost, or remaining), but also in some cases far from the forest. Water flows are of course closely connected to precipitation patterns, which are bound to change with major changes in forest cover, with resulting impacts on agricultural outputs.

# **2.6.2  Impacts on Hydroelectric Potential**

Regulation of river discharge is a fundamental ecosystem service that the Amazon forest provides, and therefore must economically be valued in order to draw attention to the need of reconciling conservation with sustainable development in the Amazon. Much of the valuation work associated with water flow benefits in the Amazon has focused on hydroelectric potential, since localized water availability has not historically been problematic (though the effect of the quality of such supplies on human health is an important issue, intertwined with child mortality).

Brazil and other South American countries facing future energy shortages are building hydroelectric dams in remote regions of the Amazon. However, hydropower production in the Amazon may depend upon its forests because of their role in maintaining the region’s rainfall. For example, Belo Monte complex, expected to be the world’s third largest hydroelectric complex, is a key component of Brazil’s energy strategy. In the next decade, energy demand in 19 | P a g e
on agriculture by implementing urban-biased policies. A World Bank multi-country study led by Anne Krueger, Maurice Schiff and Alberto Valdès provides empirical support for the view that price policy, trade policy and exchange rate policy in virtually all developing countries have discriminated against agriculture, either directly through food subsidies or taxes on agricultural exports, or indirectly through manufacturing protection and exchange rate overvaluation. Over 1960 – 1984, these policies extracted an average of 46 percent of agricultural GDP from this sector in 18 developing countries (Krueger, Schiff and Valdes 1998). This massive study confirmed the hypothesis of T.W. Schultz who, in his famous 1964 book _Transforming Traditional Agriculture,_ argued that peasants in poor countries were not backward and ―traditional,‖ but, on the contrary, rational decision-makers who maximized the return from their resources. Their apparent unwillingness to innovate, he argued, was rational because governments of developing countries often set artificially low prices on their crops and taxed them heavily. In other words, peasants respond to incentives.

The Krueger, Schiff and Valdès study was recently updated, under the direction of Kym Anderson. The new study reaches similar conclusions but shows that, since the mid-1980s, the inter-sector bias against agriculture and the antitrade bias have been substantially reduced (Anderson 2009). Many developing countries have undertaken a great deal of policy reform and opened to trade and benefited proportionately more (relative to GDP) than high-income economies from those trade-related policy reforms. Developing countries would gain nearly twice as much as richer economies by completing the reform process – with 72 percent of those prospective gains to developing countries coming from agricultural and food policy reform. In developing countries, net farm income (agricultural value added) is estimated to have been 5 percent higher in 2004 than it would have been without the reforms since the mid-1980s. And if policies remaining in 2004 were removed, net farm income would rise by another 6 percent (far more than the proportional gain to nonagricultural households). These reforms could further alleviate global inequality and poverty, since three-quarters of the world‘s extreme poor are in farm households in developing countries. One way to look at policy changes over the past 25 years would be to say that developing countries follow the example of higher-income countries in moving from anti to pro-farmer policies as they develop. The Anderson study shows that import-competing farmers in developing countries are being increasingly protected over time.

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