Abstract
Export restrictions, such as export taxes, have increased over the last ten years. In addition, export taxes often occur when food prices are high and/or volatile. As such, export taxes have received a lot of attention in the literature, but these studies tend to examine only a single commodity or country. This study seeks to provide more detail into the linkages among export taxes, trade, food prices, and poverty by utilizing a global economic model with detail on export tax occurrence in agriculture. Results show that export taxes do not have a widespread impact on international agricultural prices, but rather that the impact is concentrated in few goods: wheat, coarse grains, and beef. Removing export taxes would benefit regions currently applying taxes through an increase in production and exports and a reduction in poverty. In other regions, which are major agricultural exporters, an increase in competition of exports in international markets could lead to a fall in domestic prices. Our analysis does not find a significant impact of export tax removal on poverty, except among some export tax imposing countries for which poverty falls as a consequence of the removal of export taxes. These results highlight the need to consider the general equilibrium effects of the removal of export taxes.
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Notes
Other examples include: export bans, export license requirements, and price references for exports.
Export taxes were, also, only used by 36 countries in this period. However, most of them were applied in key staples such as rice and grains, which could have impacted food security among food importing countries.
List of countries with no export taxes in place: Australia and New Zealand (AUSNZL), Japan (JPN), Korea and Taiwan (DVDASIA), Philippines (PHL), Thailand (THA), Bangladesh (BGD), India (IND), Canada (CAN), United States (USA), Mexico (MEX), Brazil (BRA), Colombia (COL), Peru (Per), Venezuela (VEN), EU-EFTA (EUEFTA), Rest of Europe (XER), South Africa (ZAF), Malawi (MWI), Mozambique (MOZ), Tanzania (TZA), Uganda (UGA).
See Hertel et al. (2001) for more information on the ams variable.
The baseline for the model is 2001, while the Estrades et al. (2017) database is from 2004 to 2014. To reconcile the two, we take the baseline taxes and apply any changes that Estrades et al. report in 2008.
We aggregate into these two parts by taking a weighted share of each result. We also mention the main region that drives each result, especially for the export tax group. Results by individual regions or countries are available upon request to authors.
This point is further made by noting that the change in exports of processed oilseeds (12.51%) is greater than the change in exports of oilseeds (6.66%).
This phenomenon is known as Differential Export Taxes (DET): some countries choose to apply lower export taxes along the production chain in order to promote production of higher value added products. In 2008, it is verified for all countries that apply export taxes on both products except Rest of South America (XSM), which is a region with different countries applying high export tax rates along the oilseed value chain, among them Argentina, one of the main countries applying DETs. For this reason, our aggregated results indicate the opposite than expected; when export taxes are removed, production and exports of produced oilseeds increase more than of raw oilseeds. When we exempt XSM from the removal of export taxes (see section 5.2), the impact on exports of raw oilseeds is larger and on exports of produced oilseeds is smaller, which shows the reversal of DETs.
Along with agricultural self-employment, the other strata (or sub-populations) are: non-agricultural self-employment, rural wage labor, urban wage labor, transfer payments, rural and urban diversified.
Information is from Energy Information Agency and changes are reported in U.S. prices.
This is just an illustration to compare available information from the model with historical data. Each series (FAO, OECD, and WB) do not necessarily capture the same information as each other and with the model, thus readers should be cautious in making direct comparisons.
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The findings and conclusions in this preliminary publication have not been formally disseminated by the U.S. Department of Agriculture and should not be construed to represent any agency determination or policy. This research was supported in part by the intramural research program of the U.S. Department of Agriculture, Economic Research Service.
Appendices
Appendix 1
Appendix 2: Updating/Validating the Model
From Jorgenson (1984) insight into the importance of utilizing econometric work in parameter estimation, to more recent calls for rigorous historical model testing (Kehoe 2003; Grassini 2013), it is clear that CGE models must be adequately tested against historical data to improve their performance and reliability. The article by Valenzuela et al. (2007) shows how patterns in the deviations between CGE model predictions and observed economic outcomes can be used to identify the weak points of a model and guide development of improved specifications for the modeling of specific commodity markets in a CGE framework. More recent work by Hertel and Beckman (2011) and Beckman et al. (2012) has focused on the validity of the GTAP-E model for analysis of global energy markets. Accordingly, we carried out a similar historical validation exercise. In particular, we examined the model’s ability to reproduce observed price changes in global commodity markets. Those authors showed that by shocking population, labor supply, capital, and investment (see Appendix Table 9), along with the relevant energy price shocks, the result is a reasonable approximation to key features of the more recent economy. The shocks shown in the table are generally positive, as economic and population expansion helped drive some of the agricultural commodity price increase over the time period. There are some negative values, primarily in Japan (see Appendix Table 8 for the regional mapping) for investment and labor; and to XER for population and labor. Note that most of these variables are exogenous in the model (population, labor, and capital); for those that are not, we have to do some work to make them exogenous. To do so, we swap them with a variable that was previously exogenous in the model (for example, productivity with GDP), then we shock the variable of interest. We then swap the variables, again, in the actual experiments to make investment and GDP endogenous. Apart from the exogenous shocks presented in Appendix Table 9, we also shocked energy prices by: Oil: 225%, Natural Gas: 75%, and Coal: 100%.Footnote 10 Note that we do not directly shock agricultural commodity prices, as these are the measure with which we use to validate the model.
The first of column in Appendix Table 10 indicates the ‘historical’ price change. This is based on food (or consumer) price indices from different sources listed under the regions name.Footnote 11 These data highlight the fact that prices have been highest in Venezuela and many of the African regions. By comparison, prices were very stable in Japan (2.4%). The final two columns report the model-generated percentage change in commodity. These results are built on the construction of a ‘food basket’ price for GTAP results. This household food basket is built on the share of agricultural consumption for each region, with the Other Food and Beverages (OthFdBev) sector accounting for more than 50% of the share in developed countries and about 25% in developing countries. The model results indicate that the exogenous shocks alone do not get us to the historical price change for food, except for a handful of countries: China (CHN), Developed Asian countries (DVDASIA), Thailand (THA), Rest of South and East Asia (XSE), India (IND), Canada (CAN), and European countries (EUEFTA and XER). In most cases, the predicted price change is smaller than the historical shock, which reflects the fact that the model cannot capture other drivers of large price changes. In very few cases, the predicted change in prices is higher than the observed, which highlights the fact that the model might not be capturing price isolating policies applied in those countries - Japan (JPN) and Indonesia (IDN). The final column indicates the changes in the food basket price when export taxes are overlaid on the exogenous shocks. In general, export taxes increased from 2001 to 2008 for any region using them. These results show that export taxes do not really impact price changes for the regions in the model. There are small decreases (between −0.28% and − 6.30%) in prices for those regions that use export taxes. This decrease indicates that export taxes did insulate countries from the global price changes; but the importance of the Other Food and Beverages sector in the consumption bundle is limiting the price changes that might arise from export taxes.
The next table shows global price changes across commodities. For grains and derivatives of grains, prices shown in Appendix Table 11 are greater than 100%. The second and third column show the CGE model generated price changes. Again, the differences between the CGE predicted price increase and the historical figures highlight the fact that the model is not capturing the main driving forces behind the surge in prices, mainly for grains. When export taxes are overlaid on the exogenous shocks, most agricultural commodities have a decrease in the change in price. On the other hand, fish and forest products are not affected by export taxes.
Appendix 3
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Beckman, J., Estrades, C. & Aguiar, A. Export taxes, food prices and poverty: a global CGE evaluation. Food Sec. 11, 233–247 (2019). https://doi.org/10.1007/s12571-018-0876-2
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DOI: https://doi.org/10.1007/s12571-018-0876-2