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Argentina: Selected Issues

Author(s):
International Monetary Fund. Western Hemisphere Dept.
Published Date:
November 2016
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Argentina’s Exports and Competitiveness: a Sectoral View on Trends and Prospects1

A. Introduction

1. Argentina’s export performance has worsened markedly since 2012. Overall export values contracted at an average of over 9 percent per year between 2012 and 2015, nearly four times faster than world exports (Chart).

2. This underperformance coincided with tighter trade and foreign exchange controls and an overvalued currency. On the back of high inflation and a tightly managed exchange rate, Argentina’s real effective exchange rate (REER) accelerated its upward trend after 2011, with staff estimates suggesting an overvaluation that peaked at over 50 percent by November 2015 (Figure 1, left chart). Export taxes on commodities, some as high as 35 percent, added a further wedge to the relative prices for Argentine exporters. A complex array of foreign exchange controls (the so-called “cepo cambiario”), led to a parallel foreign exchange market and further hindered external trade. External competiveness was also hampered by fast increasing unit labor costs (Figure 1, right chart), with wages growing well above productivity, a high tax and administrative burden, deteriorating infrastructure, limited access to markets and financing for exporters, and restrictions on intermediate and capital goods imports that disrupted production in exporting firms.

Goods Exports: Argentina vs. World 1/

(Percent change, y/y)

Source: Direction of Trade Statistics.

1/ Under/overperfomance indicates whether Argentina’s export growth is lower/ greater than the world’s export growth.

Figure 1.Argentina: Price Competitiveness

3. With the removal of the “cepo”, the free float of the peso, and the elimination of most export taxes, Argentina’s export performance has the potential to improve. The peso has depreciated over 50 percent in nominal terms since the foreign exchange controls were relaxed in December 2015, although, in real effective terms, it remained some 20 percent above its historical average by September 2016. In early 2016, export taxes were eliminated for all products except soybeans and soy derivatives (oil and meal), for which taxes were reduced by 5 percentage points. Also, quantitative restrictions (through a system of permits) on agriculture exports were removed, and there are signs that the producers started reacting to these changes: for example, the meat sector has begun to rebuild the breeding stock, while the cereal area is projected to increase sharply in the 2016/2017 agricultural season, potentially leading to higher production and exports in coming years.

4. The main objective of this paper is to assess to what extent a more competitive exchange rate can boost Argentina’s exports. To do so, we estimate export demand equations at the product level as the structure of Argentina’s trade means that estimating aggregate export equations may be subject to a specification bias. In particular, the product-level equations allow us to determine which price competiveness indicator is the most appropriate for a set of major Argentina’s export products. We then aggregate up the product-level estimates of export price and demand elasticities, which can be compared with estimates based on aggregate exports to gauge the extent of the bias. We conclude with a discussion of a number of key non-price competitiveness factors that would need to be improved to sustain and broaden Argentina’s export growth beyond the near-term horizon.

5. The main conclusion of the paper is that while greater price competitiveness would boost the exports of a few products, its effect on overall export is likely to be modest. Our estimates show that export elasticity to relative prices is high in some sectors (especially, automotive, machinery and equipment) but low in others (cereals, soybean). Overall, aggregating up the product-level elasticities suggests that a 10 percent real depreciation of the Argentine peso would boost overall export growth by less than one percentage point. For demand elasticity, we find that a one percent increase in trading partners’ growth would increase overall export growth by about 2 percentage points. Improving non-price competiveness factors could help obtain more broad-based and permanent results.

B. Argentina’s Exports Over the Last Decade: A Few Trends

6. Argentina’s export share, both in the world market and in its own economy, has declined since 2004 (Chart). By 2016, Argentina’s exports were less than 0.4 percent of world’s exports, falling more than 0.1 percentage points from the peak in 2009, close to its longer-term historical minimum. The share of exports in GDP also fell by nearly 13 percentage points since 2004 to about 11 percent in 2015, the lowest level among its peers in Latin America.

Argentina: Export Share in GDP and Export Market Share

(Percent, 4-quarter m.a.)

Sources: Direction of Trade Statistics and IMF World Economic Outlook database.

7. The decline in export market share reflected a severe loss of external competitiveness.

  • During 2004–14, the composition of exports shifted toward agriculture and automotive products (Figure 2, left chart), while the destination shifted more toward Brazil, Venezuela, and the Asian EMs (except China) away from China, the United States, Chile, and Europe.

  • A “shift-share” decomposition of Argentina’s exports shows that these compositional changes had a positive impact on Argentina’s exports (Figure 2, right chart), reflecting Argentina’s greater exposure to more dynamic destination markets (‘geography effect’) and specialization in high growth sectors (‘product mix effect’).

  • The decline in Argentina’s export market share can therefore be attributed to a loss of competitiveness, that is, the residual in our decomposition (for details, see Gaulier and others, 2013).

Figure 2.Argentina’s Exports: Changes in Composition and Market Share

8. Looking at a product level, however, suggests a more nuanced story. The relative performance of Argentina’s exports has varied by product, reflecting in part sector-specific circumstances. In 2004–14, nearly half of Argentina’s top 22 export products (comprising about 90 percent of its total exports in 2014), grew faster than the world’s exports of these products (Chart). For example, Argentina’s exports of motor vehicles (about 12 percent of Argentina’s total exports) increased on average 15 percent per year over this period, three times faster than the world’s exports of motor vehicles. With 80 percent of this category destined for Brazil, this impressive growth mainly reflects the impact of a special trade agreement between the two countries.2 Argentina’s largest export item— soybean meal (“residues and waste from food industries” in the COMTRADE classification, comprising almost 20 percent of total exports)—grew largely in line with the world’s exports of soybean meal. In contrast, its main primary products (cereals, soybeans) and energy products underperformed the world exports, being affected, among other factors, by high export taxes, export restrictions, and domestic policies of regulated prices and subsidies that discouraged investment and activity in these sectors.

Argentina vs. World: Export Growth by Product 1/

(Percent change, annual average between 2004 and 2014)

Source: COMTRADE.

1/ Numbers in the bubbles refer to product classification according to HS 2002: e.g., 23=residues and waste from food industries; 87=vehicles; 10=cereals; 15=animal/vegetable fats and oils; 12=oil seeds and oleaginous fruits; 27=mineral fuel; 38=chemical products; 71=precious/semi-precious stones; 26=ores, slag, and ash; 41=raw hides and skins.

C. Price Competitiveness: A Product-Level Analysis

9. When looking at the sensitivity of exports to prices it is important to take into consideration the structure of Argentina’s exports. Estimating the elasticity of aggregate export volume to REER casts doubts on whether price competitiveness is a factor for Argentina’s exports— changes in REER enter the export growth equations with the wrong sign and/or are not statistically significant (Appendix Table A1). Estimating the elasticity through panel data models allows to control for product-specific prices and foreign demand, and shows the expected negative and statistically significant relation between exports and prices (Appendix Table A2), although with little overall explanatory power and important sensitivity of the results to outliers. Failing to take into account that different products could respond to different price indicators may lead to model misspecification.3

10. In this paper, we estimate export growth equations at product level, using different indicators of relative prices for each product.4 For example, soybean meal represents about 20 percent of Argentina’s overall exports but very little of this product is exported to the top 10 trade partners of Argentina. This suggests that REER, which uses total export shares as the weights, may not be the best price competitiveness indicators for this product. Instead, the demand for this product is likely to depend more on its export price (in absolute terms, or relative to the prices in the export destination countries) and the cost structure relative to main competitors. Since both Argentina and Brazil have relatively large market shares in total world exports of soybean meal (36 and 22 percent, respectively), the demand for Argentina’s export of this product could be particularly affected by its bilateral real exchange rate with Brazil (facing the same level of international prices, the country with a lower relative cost of production should be able to produce and export more). For transport equipment, Argentina’s second largest export item, the bilateral real exchange rate with Brazil would be the most appropriate price indicator given that 80 percent of the exports of this product goes to Brazil, and large car manufacturers operating in both countries can potentially react to changes in their relative cost structure.5

11. In particular, for each productp, we estimate the following equation:

where Xpt is Argentina’s export volume; Y¯p,t is the demand for that product from Argentina trading partners (using export share as weights); Pp, are indicators of price competitiveness as described below; φp,t is product-specific factors, such as harvest-related factors; σt is country-wide factors affecting exports, such as exchange rate uncertainty; τp,t are time dummies (where significant); and ϵpt is the error term.6 For the sources and description of the data, see Appendix 1. The variables denoted by Δ are growth rates calculated as quarterly year-on-year log differences. Eq. (1) is estimated with OLS with robust standard errors, or Prais-Winston GLS estimator to correct for the first-order error autocorrelation when such is detected in residual diagnostic tests. The sample period is 2004Q1-2016Q2, reflecting the availability of product-level export data. The choice of a proxy for price competitiveness or relative prices is not straightforward, and most studies simply use the indicator with the best fit.7 Ideally, the relative price would capture not only the relation between export prices of the home country and domestic prices of the trading partners, but also the export price of the home country relative to export price of potential competitors.8 We use the following price indicators:

  • Real effective exchange rate (REER), CPI (or GDP deflator) based, defined as etPArg,tP¯p,t$, where et is the nominal exchange rate (U.S. dollars per peso), PArg,t is Argentina’s prices (CPI or GDP deflator), and P¯p,t$ is product-specific export share-weighted foreign U.S. dollar prices (CPI or GDP deflator);

  • Bilateral real exchange rate with respect to the Brazilian real, similar to REER but the denominator represents only the U.S. dollar prices in Brazil;

  • Export prices, product-specific unit value prices of Argentina’s exports in U.S. dollar, Pp,tX;

  • Relative export prices defined as Pp,tXP¯p,t$; and

  • Commodity exporter exchange rates, a proxy of the relative prices faced by Argentina’s exporters. calculated as Pp,t*(1τp,t)etPArg,t, namely the ratio of the international price of a given product, Pp,t*, adjusted for Argentina’s (time-varying) export tax rate, rp¡t, to Argentina’s domestic prices in U.S. dollars. An increase in this ratio as a result of higher international price, lower tax rate, lower domestic inflation, or nominal depreciation of the peso would imply more favorable relative prices for the Argentine commodity exporters.

12. The results confirm that different products tend to react to different indicators of price competitiveness (Appendix Table A3). The final specification for each product has been selected based on its fit (measured through R2 and RMSE), which in most cases also matches the characteristics of trade. The fit of the estimated equations for six largest export products (which comprise about two-thirds of Argentina’s total exports) is shown in Appendix Figure A2.9 Among the main results we find that:

  • For exports of automotive sector, chemicals, and machinery/equipment, the relative price that matters is the RER with the Brazilian real with an estimated elasticity of about -0.3.

  • Exports of soybean meal depend on international export prices and the bilateral real exchange rate with the Brazilian real, in line with our expectations. For soybean oil (fats and oils in Appendix Table A3), export depend on international prices only, reflecting the fact that Argentina has the largest market share in the world (40 percent of total world exports) and is therefore a price setter. Exports of cereals (mainly corn) depend on the commodity exporter exchange rate (though less robustly), consistent with the hypothesis that export taxes particularly affected the export of this product which (unlike soybean meal) is also consumed domestically.

  • Export sensitivity to trading partner growth appears to be particularly high for automotive, machinery/equipment, and soybean. For soybean meal, partner demand does not appear to be very robust, which in part can reflect the fact that some countries (like the Netherlands) are among the main importers of this product but part of the imports is being re-exported to the markets of final consumptions.

  • Rainfall is a key determinant of export supply for all agriculture products, while a negative downward trend is detected for export growth in basic metals and meat. Export regressions, not surprisingly, perform relatively poorly for the energy sector, reflecting distortions introduced by the domestic energy policies.

  • Exchange rate uncertainty, proxied by the unconditional three-quarter moving standard deviation of REER (but also nominal or parallel exchange rates) does not appear to be robust in export equations (unlike Catão and Falcetti, 1999, who however cover a much earlier period).

13. The export elasticity to REER derived from aggregating up these product-specific elasticities is relatively low. To gauge the aggregate export elasticity to REER, we perform a simple simulation exercise. First, we calculate export growth rates for each product using the elasticity estimates from the regressions and the forecast of the determinants (relative prices and trading partner growth) from the latest (October 2016) World Economic Outlook projections. Then, we derive the projected growth rate for the overall exports, as the weighted sum of product-level projections, using export shares as weights.10 Third, we re-calculate export growth rates for those products for which the price indicator depends on the exchange rate using a modified path of the nominal exchange rate, which implies a 10 percent depreciation in real effective terms in 2017 relative to the baseline, and aggregate again to get a projection of overall export growth. The difference between the two projected overall export growth rates yields an approximate elasticity of total exports growth to the change in REER. We use a similar approach to estimate the demand elasticity of the aggregate exports. Our findings suggest that that 10 percent real depreciation of the Argentine peso would boost total exports growth by less than one percentage point, and a one percent increase in trading partner growth is associated with about 2 percentage points higher total export growth. While price elasticity of exports appears on the lower side of estimates found in the literature, our estimate of demand elasticity is close to the average for a number of other emerging market economies (see, for example, IMF, 2015).

D. Non-Price Competitiveness: A Few Considerations

14. Interventionist policies over the past years contributed to the weak export performance, suggesting that their removal could help improve external competitiveness. Quantifying the potential gains to exports from the removal of the restrictions associated with the cepo cambiario is not straightforward, mainly as the cepo came on top of other restrictions that had already been in place for much of the last decade (for example, exports of meat, cereals and other agricultural products, have been subject to quotas since 2006). Still, our product-level regressions over-estimate actual export growth after 2012 for a series of products with relatively higher import content, such as chemicals, machinery and equipment, and plastic products—suggesting that import restrictions affecting industrial inputs disrupted production and exports of these manufactured goods (Appendix Figure A3). Focusing on these products, our simulations suggest that the annual average growth in total exports could have been ½ percentage points higher in 2012–15 without the restrictions imposed by the cepo.11

15. While potential gains from price competitiveness appear to be modest for Argentina’s exports, non-price factors could play a significant role in enhancing its external competitiveness. External competiveness and the evolution of global market shares can be explained only partially by looking at changes in price competiveness (Benkovskis and Worz, 2013). A series of non-price factors could have a substantial positive impact on export performance, including improving the quality of infrastructure, reducing tax pressure and administrative burden, easing access to financing, and pursuing trade policies that facilitate market access.

  • Tax regime. At 34 percent of GDP (in 2015), the tax burden in Argentina is one of the highest in the region and among emerging market economies. In the food industry, the key export sector, taxes and social security contributions comprised an estimated 58 percent of the value added (COPAL, 2015). Immediately after taking office, the current government started to address this issue by eliminating or reducing export duties,12 thus improving the effective exchange rate the exporters perceive. However, there is still room for improvement, as corporate income taxation is not designed to stimulate investment, while the large tax wedge makes labor expensive.

  • Infrastructure and logistics. Although infrastructure access in Argentina is above the regional average, infrastructure quality has declined steadily over the past years. The World Economic Forum (WEF) ranking of infrastructure quality shows that between 2006 and 2015 Argentina lost 62 positions, falling from 61 to 123. Over the past 15 years, public investment grew much less than other types of spending, such as subsidies to energy and transportation, while private financing of infrastructure was very limited (World Bank, 2015). Access to appropriate infrastructure in terms of roads, railways, ports and energy is especially important for exporting firms outside the central region and metropolitan areas, which tend to be the most vulnerable to REER appreciation. The logistic cost of exporting a 40-foot container by land is US$1,842 in Argentina compared to US$1,000 in Brazil, with data as of 2014 (World Bank, 2015). According to the World Bank Logistics Performance Index (LPI), Argentina’s gap with the best performing OECD country is about 1 point and 0.3 points with the average of its peers,13 and improving the LPI score by 1 could increase labor productivity by close to 35 percent on average (OECD, CAF, and ECLAC, 2013).

Change in Export Market Share and Transport Infrastructure

Sources: Direction of Trade Statistics and Global Competitiveness Report (2014).

  • Administrative procedures. Excessive bureaucracy, lack of coordination between government agencies, and inefficient customs systems undermine export competitiveness by increasing transaction costs. Argentine exporters face comparatively high costs to export, according to the World Development Indicators (WDI) index that measures the fees associated with completing the export procedures (Chart).14

  • Trade policy. A dynamic and ambitious trade policy can substantially improve export performance by expanding market access for domestic exporting firms and facilitating their integration into global value chains. As part of the MERCOSUR trade bloc, in recent years Argentina has lagged behind other regional peers, such as the members of the Pacific Alliance (Chile, Mexico, Colombia and Peru), which have been more aggressive in securing new export markets.15 More recently, seeking closer relationships with the Pacific Alliance and advancing the trade negotiations between MERCOSUR and the EU might mark a shift in trade policy.

  • Access to financing. Recent studies confirm that access to bank credit is important for the decision to enter export markets. And access to foreign financing helps firms to reach more developed and more distant markets, increasing also the number of destinations and of products exported (Castagnino, D’Amato, and Sangiácomo, 2013). The underdevelopment of the Argentina’s financial system, which is small and mostly transactional, combined with limited access to international capital markets, has affected export competitiveness of local firms in recent years. The recent resolution of the holdouts conflict, following the settlement with the Paris Club in 2014 marked a crucial step in the financial normalization process that will likely pave the way to increase external trade financing to Argentine exporters.

Change in Export Market Share and Administrative Costs

Sources: Direction of Trade Statistics and World Development Indicators (2014).

References
Appendix I. Data Sources and Description

Argentina’s exports: Argentina’s export volume data are based on the monthly export volume indices available from INDEC for 39 groups of products.

Argentina’s prices: The CPI is constructed using the official INDEC GBA index until 2006M12, appended by the private estimates of inflation until 2012M8, and the City of Buenos Aires CPI thereafter.

Product-specific trading partner weights: The weights are derived from the COMTRADE exports data for 2014. To obtain the weights consistent with the exports according to INDEC product classification, the COMTRADE HS2002 level-2 product classification, which includes 97 product categories, is mapped to the INDEC classification. Export data for Argentina vis-à-vis 182 partner countries/areas are presented as 32 individual partner countries, which comprised about 83 percent of Argentina’s total exports in 2014, and a group of other countries/areas.

Trading partner demand: External demand is proxied by the real GDP of the trading partners, using the quarterly data (in constant 2010 prices and exchange rate) available from the WEO dataset. For each product, total foreign demand is calculated as the product-specific export share-weighted average of real GDP of the 32 partner countries.

Foreign U.S. dollar prices: Foreign price indices are the quarterly CPI data (2010 base year) available from the WEO database. For each product, foreign U.S. dollar prices are calculated as the product-specific export share-weighted average of the CPI of the 32 partner countries.

Rainfall: The indicator is constructed using data on monthly rainfall in a group of five weather stations representing the main grain production area of the country. The indicator for each period reflects the relevant rainfall for the corresponding harvest, taking into account the crop annual cycle for oilseeds and cereals.

Figure A1.Argentina: Product-Country Export Matrix 1/

1/ For a given product, the bars indicate the share Argentina’s exports to a column-country in its total exports of the product in 2014.

Sources: COMTRADE and Fund staff calculations.

Appendix II. Empirical Results and Robustness Analysis
Table A1.Aggregate Export Growth Equations: Time Series Models(All variables are in year-on-year log differences)
Dependent variable
PrimaryAgricultureIndustrialFuels and
Total exportsproductsmanufact.manufact.energy
(1)(2)(3)(4)(5)(6)(7)(8)(9)
External demand 1/3.31***3.30***3.44***3.36***3.42***9.25***1.285.22***−7.10***
(3.08)(2.89)(3.34)(3.03)(3.69)(3.65)(0.92)(4.78)(−3.64)
REER, CPI-based 1/0.06
(0.33)
REER, GDP deflator-based 1/0.03
(0.15)
Relative export price 2/−0.25
(−1.17)
REER WEO, CPI-based 3/0.080.110.75−0.14−0.120.10
(0.49)(0.91)(1.67)(−0.62)(−0.65)(0.35)
Constant−10.13**−9.93** -10.41***−10.49**−9.68**−30.96**−1.11−14.84***9.41
(−2.22)(−2.11)(−2.73)(−2.19)(−2.45)(−2.66)(−0.19)(−3.32)(1.23)
Number of observations464646465046464646
R20.290.280.310.290.360.310.050.420.33
Notes: OLS estimator; robust t-statistics in parentheses; *** (**, *) = significant at the 1 (5, 10) percent level.Columns (1)–(4) and (6)–(9): exports from monthly INDEC trade data; column (5): real exports from national accounts.

Trade-weighted with the weights derived from the COMTRADE exports data for 2014; see Appendix 1 for further details.

Ratio of the total export unit price in U.S. dollars to trade-weighted foreign GDP deflator.

Trade-weighted with the weights according to the IMF INS database.

Notes: OLS estimator; robust t-statistics in parentheses; *** (**, *) = significant at the 1 (5, 10) percent level.Columns (1)–(4) and (6)–(9): exports from monthly INDEC trade data; column (5): real exports from national accounts.

Trade-weighted with the weights derived from the COMTRADE exports data for 2014; see Appendix 1 for further details.

Ratio of the total export unit price in U.S. dollars to trade-weighted foreign GDP deflator.

Trade-weighted with the weights according to the IMF INS database.

Table A2.Export Growth Equations: Panel Data Models(All variables are in year-on-year log differences)
Fixed-effects modelRandom-effects model 1/Mean group estimator 2/
(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)
External demand 3/0.751.48**1.15**1.06*1.39***1.32***1.16**0.681.052.04**1.95***
(1.27)(2.35)(2.03)(1.90)(2.69)(2.60)(2.02)(0.78)(1.29)(2.27)(3.12)
REER, CPI-based 3/−0.26**−0.25**−0.25**−0.14−0.25**
(−2.19)(−2.13)(−2.26)(−1.16)(−2.54)
REER, GDP deflator-based 3/−0.36***
(−2.80)
Relative export price, with foreign CPI 4/−0.32***
(−2.85)
Relative export price, with foreign GDP deflator 4/−0.33***−0.31*0.45***
(−2.92)(−1.81)(−5.89)
REER WEO, CPI-based 5/−0.15
(−1.31)
Constant−1.00−3.74−2.76−2.41−4.44**−4.38**−3.27−2.37−2.35−7.81***−5.77**
(−0.45)(−1.57)(−1.36)(−1.23)(−2.29)(−2.26)(−1.57)(−1.04)(−0.98)(−2.68)(−2.49)
Time dummy for 2009Q2noyesyesyesyesyesyesyesyesyesyes
Outlier robust meansnoyesnoyes
Number of observations1,4721,4721,4721,4721,4721,4721,4721,4721,4721,4721,472
Number of product groups3232323232323232323232
Overall R20.010.010.010.010.020.020.01
Notes: t-statistics in parentheses; robust errors in columns (3)–(7); *** (**, *) = significant at the 1 (5, 10) percent level.

Hausman test suggests that random-effects estimator is consistent.

Mean group estimator allows for heterogeneous slope coefficients across product groups; columns (8) and (10): coefficient averages computed as unweighted means; columns (9) and (11): coefficient averages computed as outlier-robust means.

Product-specific export share-weighted with the weights derived from the COMTRADE exports data for 2014; see Appendix 1 for further details.

Ratio of product-specific export unit price in U.S. dollars to product-specific export share-weighted foreign CPI or GDP deflator.

Trade-weighted with the weights according to the IMF INS database.

Notes: t-statistics in parentheses; robust errors in columns (3)–(7); *** (**, *) = significant at the 1 (5, 10) percent level.

Hausman test suggests that random-effects estimator is consistent.

Mean group estimator allows for heterogeneous slope coefficients across product groups; columns (8) and (10): coefficient averages computed as unweighted means; columns (9) and (11): coefficient averages computed as outlier-robust means.

Product-specific export share-weighted with the weights derived from the COMTRADE exports data for 2014; see Appendix 1 for further details.

Ratio of product-specific export unit price in U.S. dollars to product-specific export share-weighted foreign CPI or GDP deflator.

Trade-weighted with the weights according to the IMF INS database.

Table A3.Export Growth Equations by Product(Variables are in year-on-year log differences unless otherwise indicated)
RER_BrazilRER_Brazil_l ag1REER, CPI basedREER, CPI based_lag1REER, CPI based_lag2Exporter ER with tax_lag2 1/Export price_lag1Relative export price (defl)Foreign demandForeign demand_lag1Foreign demand_lag2Foreign demand_lag3Foreign demand_lag4Rainfall_soyRainfall_soy_lag1Rainfall Rainfall _lag2TrendR-sq.
(1) Soybean meal−0.24**−0.37***2.310.06***0.54
(2) Automotive−0.43**5.70***0.80
(3) Cereals0.37*5.98−4.122.07*** 1.99***0.35
(4) Chemicals−0.27***−0.220.28
(5) Fats and oils−0.36***6.35***0.11***0.51
(6) Soybean−1.03*17.90***0.30***0.56
(7) Fuels−0.173.35*0.09
(8) Basic metals1.43***−2.49***1.41**−7.40***6.60***−0.52***0.44
(9) Precious metals−1.70**−2.780.17
(10) Meat−0.45−6.91***−0.71**0.27
(11) Machinery and equipment−0.30***6.77***0.63
(12) Crude oil−0.38**−13.69***0.40
(13) Dairy products1.00***−1.850.59
(14) Unprocessed vegetables and legumes0.69−1.78**0.95−7.900.62
(15) Fresh fruits−0.36−0.142.98−0.010.25
(16) Tobacco−0.62−0.59−2.112.970.05
(17) Cotton fiber1.810.0025.72−50.25*0.10
(18) Ores, slag and ash−1.67**2.03*−4.52−0.310.25
(19) Other primary products0.15−0.9513.73*−5.490.17
(20) Fish and processed seafood−0.67**−0.065.72−7.13*0.45
(21) Coffee, tea, mate and spices0.21−0.040.710.950.15
(22) Grain mill products−0.14−0.040.340.260.31
(23) Sugar and sugar confectionery−0.220.36−2.88−3.440.07
(24) Preparations of vegetables−0.69*0.84*4.84*−0.360.19
(25) Beverages, spirits and vinegars0.17−0.060.342.53*0.27
(26) Extracts tanners dyes−0.021.85−5.578.110.09
(27) Hides and leathers−0.06−0.3512.77**−10.15**0.45
(28) Fabricated wool−0.36−0.409.34***−5.00*0.48
(29) Plastic materials and products−0.45**−2.94**−1.322.88***0.44
(30) Rubber and rubber products−1.19***0.22−1.863.610.44
(31) Paper, cardboard, printed publications−0.08−0.53**0.270.920.41
(32) Textiles and clothing−0.57***−0.212.18**0.620.75
Notes: OLS or GLS estimates; robust t-statistics (not reported); *** (**, *) = significant at the 1 (5, 10) percent level. Number of observations=46; time dummies and constant term are not reported. Rainfall variables are in levels.Total share of products (1)–(13) is about 82 percent of total Argentina’s exports; products (14)–(32) each comprise 2 or less percent of total exports.

Exporter exchange rate with tax is defined here as the ratio of international price of corn adjusted for Argentina’s export tax to Argentina’s CPI in U.S. dollars. See the main text for the definition of the price variables.

Notes: OLS or GLS estimates; robust t-statistics (not reported); *** (**, *) = significant at the 1 (5, 10) percent level. Number of observations=46; time dummies and constant term are not reported. Rainfall variables are in levels.Total share of products (1)–(13) is about 82 percent of total Argentina’s exports; products (14)–(32) each comprise 2 or less percent of total exports.

Exporter exchange rate with tax is defined here as the ratio of international price of corn adjusted for Argentina’s export tax to Argentina’s CPI in U.S. dollars. See the main text for the definition of the price variables.

Figure A2.Exports by Product: 2005Q1–2017Q4

(Percent change, y/y, log differences)

Sources: INDEC and Fund staff estimates.

Figure A3.Residuals from Product-level Regressions: 2005Q1-2016Q2

(Percent change, y/y, log differences)

Source: Fund staff estimates.

Prepared by Lusine Lusinyan and Mariano Ortiz Villafañe.

Since the early nineties, Argentina and Brazil have a special trade agreement for the auto sector which allows for duty-free bilateral trade in vehicles and auto parts within certain limits, defined by the so-called ‘flex’ coefficient. With the current ‘flex’ of 1.5, for each dollar of automotive products exported to Brazil, Argentine car makers can import US$1.5 from Brazil duty free. In June 2016, the pact was extended until June 2020, with a provision allowing for an increase of the ‘flex’ to 1.7 in June 2019 if certain conditions are met.

For a discussion of other econometric issues that may complicate the estimation of trade elasticities, see IMF (2015).

The differences among Argentina’s main export products were emphasized in Catão and Falcetti (1999). In particular, they argued that, given the specific features of manufacturing trade within the South American common market (MERCOSUR), Argentina’s exports comprise two different groups of products in terms of their economic determinants: while the price of exports of primary and lightly manufactured goods is largely determined at the world market, Argentina’s industrial exports (to MERCOSUR) are mostly influenced by such factors as intra-bloc trade policies, income growth in the region, and geographical proximity. Consequently, they use two different specifications for the export functions—an export supply function for total exports excluding manufacturing exports to Brazil, and a two-equation, supply-and-demand system for manufacturing exports to Brazil.

If both currencies appreciate and the bilateral real exchange rate remains unchanged, Brazilian importers would still find difficult to switch to non-Argentine products owing to the very high (up to 35 percent) tariffs in the auto sector.

Note also that this specification differs from more standard export demand equations which use the level of real exports as a dependent variable. The aggregate product-level data from INDEC, which we would like to explore in this chapter, are available as indices (see Appendix 1).

Furthermore, as a measure of domestic price of importers, it would be more preferable to use producer price indices (PPIs), which however are not readily available for all the countries in our sample, and we use CPI or GDP deflator indices instead. However, IMF (2015) finds that using CPI instead of PPI does not significantly impact the estimated trade elasticities.

For products for which Argentina enjoys a large export market share, such as soybean oil, a system of export supply and demand equations is also estimated (not reported here) which yields largely similar results.

The assumption of unchanged export shares following exchange rate devaluation appears appropriate for deriving aggregate export growth projections over a short-term horizon given that sectoral restructuring will likely to take time, the changes to export shares observed in the first half of 2016 (mainly favoring cereals and soybean/oil), and the evidence from Argentine exports’ reaction following the 2002 devaluation.

In this simulation, we assume that the difference in the actual and fitted values of growth in exports of chemicals, machinery and equipment, and plastic products in 2012–15 is fully attributed to the effects of restrictions, and that the exports of these products (which together comprise about 13 percent to total exports) grew as projected by the model after 2012. The choice of these sectors has also been confirmed through a t-test on the equality of means of regression residuals before and after 2012Q1, showing that the average residuals after 2012Q1 (included) were significantly different and smaller than the average residuals before 2012.

In mid-December 2015, the new administration eliminated export duties on most products except for soybeans and soy derivatives, which were reduced by 5 percentage points. The system of export permits for agricultural exports was also removed.

The LPI has a scale of 1 to 5, where 5 represents the best logistics performance.

The fees covered include costs for documents, administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges and inland transport, but does not include tariffs or trade taxes.

Argentina currently has trade agreements with 43 countries, while Chile has agreements with 91 countries.

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