Selected Issues

Abstract

Selected Issues

Real Exchange Rate and Sectoral Competitiveness in Uruguay1

Starting in 2003, Uruguay’s real effective exchange rate (REER) has appreciated, while the composition of exports shifted towards primary sectors at the expense of manufacturing products. We analyze the sectoral trends and the impact of the REER changes on sectoral exports using the detailed product data from the United Nations’ Commodity Trade Statistics Database (Comtrade). We conclude that Uruguay’s manufacturing exports are sensitive to the changes in REER, and, accordingly, that productivity-enhancing measures to promote competitiveness would be beneficial.

1. In the wake of the 2002 crisis, Uruguay underwent a remarkable economic recovery accompanied by the significant changes in the composition of its export basket. Between 2003 and 2017, the real GDP expanded at an annual average rate of 4.3 percent and the per capita income increased by almost 80 percent. Even as investment-driven imports have been volatile, exports stayed broadly constant as a share of GDP—in the context of a slowdown in global trade— and Uruguay has remained one of the more open countries in the region.2 The composition of exports shifted towards primary commodities (their share rose from 5 percent of total exports in 2000 to 30 percent in 2017) at the expense of manufacturing products3, where textile and vehicles share contracted sharply (Figure 1).

Figure 1.
Figure 1.

Uruguay: Export Developments

Citation: IMF Staff Country Reports 2019, 065; 10.5089/9781484399958.002.A001

Sources: Haver Analytics, and IMF staff calculations.

2. Uruguay’s REER has appreciated during that period (Figure 2). We use four distinct measures of the REER: (i) export-destination-weighted; (ii) competitor-weighted; (iii) a combination of export-weighted and competitor-weighted (IMF methodology); and (iv) the REER calculated by the Banco Central del Uruguay (BCU) (Box 1). According to any of the four measures, Uruguay’s REER has appreciated since 2003; of particular interest, the competitor-weighted REER has appreciated the most, suggesting that Uruguay’s competitiveness may be affected.

Figure 2.
Figure 2.

Uruguay: Real Effective Exchange Rate (2003m1=100)

Citation: IMF Staff Country Reports 2019, 065; 10.5089/9781484399958.002.A001

3. This paper analyzes the trends in Uruguay’s competitiveness. Competitiveness is defined as ability to offer products and services of desired quality at prices that compare favorably with the prices charged by others.4 To assess competitiveness, this paper focuses on Uruguay’s product- and sector-specific global export market shares. It also estimates the sensitivity of these market shares to real effective exchange rate by using the product data from the Comtrade database and building on the work presented in IMF (2017)

4. We begin by tracking the evolution of market shares for individual products exported from Uruguay between 2004 and 2015. Market share of product k in year t is the ratio of Uruguay’s exports of k to the world exports of k in year t.5 Products are defined according to the Standard International Trade Classification (SITC, Revision 2) at the four-digit aggregation level; there are 763 product lines reported for Uruguay. To avoid being swayed by the year-to-year volatility, we compare the average shares observed during the three years from 2013 to 2015 to the average shares for 2004–2006.

Calculation of REER

Following IMF (2017), the REER of country i is calculated as a weighted geometric average of bilateral real exchange rates:

REERi=Πj(PiEiPjEj)wij

where Ei is the nominal exchange rate of the currency of county i vis-a-vis the U.S. dollar, Pi is the consumer price index (or an appropriate price deflator) for country i, and wij is the weight of the trading partner j for country i.

In assessing external competitiveness, many relative prices are relevant, and can motivate alternative choices of weights wij. The first is the relative price of exports with respect to goods that are produced in the destination country, a concept that is approximated by weights equal to the shares of each partner j in country i’s total exports (the export-destination-weighted REER). Another is the relative price of exports with respect to those of competing exporters that sell the same products, with which country i may or may not trade directly (the competitor-weighted REER). The trade weights used to compute the combined REER incorporate information along both export and competitor dimensions. For a detailed discussion see Zanello and Desruelle (1997).

5. The evolution of Uruguay’s market shares through 2015 does not point to an obvious competitiveness problem (Figure 3). Across the product space, Uruguay both gained and lost shares, so that the distribution is not skewed to either side. In contrast, should Uruguay have lost overall competitiveness, we would have expected to see more share losses and fewer share gains. Aggregating the products by SITC sectoral groups does indeed show that the market share of agricultural raw materials increased the most, followed by food products, while textiles posted declines. An aggregation across the sectoral groups used by the Banco Central de Uruguay (BCU), however, indicates that manufacturing exports posted market share gains while commodity exports posted losses. The BCU classifies products in the manufacturing category as long as there is an element of manufacturing value added. In other words, the BCU’s definition of manufacturing is broader than the one used in SITC.6 Overall, looking at the individual products that posted the largest market share gains and losses (see Table 1 and Figure 4), Uruguay has increased its market share in soybeans and wood pulp and lost market share in some textile and leather products.

Figure 3.
Figure 3.

Uruguay: Export Shares by Product, Group, and Class

(Percentage points)

Citation: IMF Staff Country Reports 2019, 065; 10.5089/9781484399958.002.A001

Table 1.

Uruguay: Largest Increases and Declines in Market Share

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Source: UN – COMTRADE and Fund staff calculations.

1/ Market share is defined as a ratio of Uruguay’s export of a given product to the world’s total trade of that product.

According to the SITC classification. See Appendix I for details.

According to the BCU classification. See Appendix I for details.

In percentage points.

Figure 4.
Figure 4.

Uruguay: Increases and Declines in Market Share for Large Products

(Percentage points; BCU classification)

Citation: IMF Staff Country Reports 2019, 065; 10.5089/9781484399958.002.A001

6. IMF (2017) estimates the elasticities of product market shares with respect to the real exchange rates for country groups. The overall elasticity for a country group is a weighted average of country-product elasticities. The weight attached to an elasticity associated with product k exported from country i is the average share of country i in global exports of product k between the years 2009 and 2015. IMF (2017) also uses time dummies to isolate the impact of global trends. IMF (2017) finds that elasticities are negative and statistically significant for Latin America and for emerging Asia. The elasticities in Latin America are about one half of what they are in Asia, possibly reflecting the dominance of commodity exports in Latin America—most commodities are priced in dollars reducing the estimated elasticity.

7. This paper estimates the elasticities of product market shares with respect to real exchange rates for Uruguay only. Rather than using time dummies to isolate the potential impact of the time trend, we add the lagged value of the change in shares as an additional independent variable.7 We also do not use sectoral weights, since those were needed for a multi-country estimation to ensure that the relatively small countries did not unduly affect the result.

8. Formally, we estimate the following model:

xiktxikt1=α(xikt1xikt2)+β(REERit1REERit2)+const,

where xikt is a share of (i) a country i’s export of product k at time t to (ii) the total world exports of product k; REERit is the real exchange rate of country i at time t; and (iii) const is the constant.

9. The resulting elasticities have the correct (negative) sign and are significant for the manufacturing products (Figure 5 and Tables 2 and 3):

  • For the panel that is estimated across all products, the elasticities are negative and significant for two of the four measures of the REER, with values close to -0.8.

  • For the panel estimated across the SITC product groups, the elasticities are negative and significant for the manufacturing products all measures of the REER except competitor-weighted, with the values between --1.5 and -1.9.

  • For the panel estimated across the BCU product groups, the elasticity is negative and significant for the manufacturing products using any of the four REER measures, with the values between -0.7 and -1.3.

  • The elasticities are not significant for textiles—one product group where Uruguay experienced a significant loss of market share—suggesting that other factors apart from the real exchange rate might have been at play. In particular, the model does not control for market access and market entry of global players (such as China)—as data on market access by product/sector and time are not available—which could bias the results.

  • The impact of the competitor-weighted REER on export market shares is less pronounced than the impact of the export-weighted REER.

  • The robustness check—when we exclude the products with the smallest (below the 5th percentile) and the largest (above the 95th percentile) shares—confirms the above conclusions (Tables 4 and 5).

  • Finally, we conducted the same analysis incorporating the data for 2016 and 2017, which have recently become available. These new results (presented in Appendix II) confirm the above conclusions as well. Specifically, even though the magnitude of the new coefficients is somewhat lower, the elasticities for manufacturing remain negative and significant.

Figure 5.
Figure 5.

Uruguay: Global Market Share Elasticities: Point Estimates and 90-Percent Confidence Intervals 1/

Citation: IMF Staff Country Reports 2019, 065; 10.5089/9781484399958.002.A001

Sources: Comtrade, Banco Central del Uruguay, and IMF staff calculations.1/ Estimated for four different measures of REER: (i) export-destination-weighted (xREER); (ii) competitor-weighted (cREER), (iii) combined (REER-IMF); and (iv) calculated by the Banco Central del Uruguay (REER-BCU).
Table 2.

Uruguay: Results by Product Group According to UN S2AG4

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Notes: This table reports the coefficient for the change in the REER, xREER, cREER, REER from BCU and REER used by Staff between URY and each country lagged one period. BCU data downloaded from BCU website on Nov 16, 2018. Staff data estimated using scenarios of CPI for some commercial partners. Each model contains the on-lag version of the dependent variable. Product groups were organized according to UN definition found in UN webpage; data attached in annex. Clustered by sector standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 3.

Uruguay: Results by Product Class According to BCU Classification

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Notes: BCU data downloaded from BCU website on Nov 16, 2018. Standard errors clustered by sector in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 4.

Uruguay: Robustness Test by Product Group According to UN S2AG4

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Notes: BCU data downloaded from BCU website on Nov 16, 2018. Standard errors clustered by sector in parentheses. *** p<0.01, ** p<0.05, * p<0.1

10. Our Uruguay-specific results are broadly in line with the group-wide results from IMF (2017), although the magnitudes obtained for Uruguay are larger. IMF (2017) finds elasticities on the order of -0.10 for Asia, -0.05 for LA58, about -0.15 for the manufacturing products and close to -0.07 for textiles, while commodities are shown to respond little to the real exchange rate movements.

11. With Uruguay’s manufacturing exports sensitive to real exchange rate, measures are needed to maintain competitiveness. Despite the sustained appreciation since 2003, Uruguay has managed to increase its market share in certain sectors, mainly primary activities. At the same time some manufacturing sectors have experienced a decline in their market share. This paper shows that while Uruguay’s exports are sensitive to changes in real effective exchange rate, this is mainly driven by the sensitivity of the manufacturing sector. Commodities and primary activities are not found to respond to real effective exchange rate (in line with findings for the region). In this context, as the real effective exchange rate will be determined by the fundamentals and global trends, measures that would ensure competitiveness are more structural in nature. These could include (i) closing infrastructure gaps; (ii) keeping inflation low and ensuring that real wages do not grow faster than productivity; (iii) further improving business environment and access to credit; and finally (iv) further diversifying export markets and products, with an eye towards reducing exposure to commodity super cycles and weather-driven supply shocks.

Table 5.

Uruguay: Robustness Test by Product Class According to BCU Classification

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Notes: BCU data downloaded from BCU website on Nov 16, 2018. Standard errors clustered by sector in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Appendix I. Product Classifications

According to the Standard International Trade Classification (SITC, Revision 2), products are divided into seven broad groups (see Table A1).

Table A1.

UN SITC Product Classification (Group)

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The Banco Central del Uruguay uses its own classification, which consists of three broad categories: primary activities, manufacturing industries, and electricity, gas, and water. Products are classified as part of manufacturing as long as there is an element of post-primary value added (see Table A2).

Table A2.

BCU Product Classification (Class)

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Appendix II. Results with the Data for 2016 and 2017

Table A1.

Uruguay: Results by Product Group According to UN S2AG4

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Notes: This table reports the coefficient for the change in the cREER, xREER, cREER, REER from BCU and REER used by Staff between URY and each country lagged one period. BCU data downloaded from BCU website on Nov 16 2018. Staff data-estimated using scenarios of CPI for some commercial partners. Each model contains the on lag version or the dependant variable. Sectors were organized according to L’N definition found in UN webpape; data attached in annex, Clustered by sector standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table A2.

Uruguay: Results by Product Class According to BCU

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Notes: BCU data downloaded from I30U website on Nov 16 2018. Standard errors clustered by sector in parentheses. *** p<0.01, ** p<0.05, * p<0.1

References

  • BusinessDictionary. No date.Competitiveness.” Retrieved January 18, 2019, from http://www.businessdictionary.com/definition/competitiveness.html.

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  • English Oxford Living Dictionaries. No date.Competitiveness.” Retrieved January 18, 2019, from https://en.oxforddictionaries.com/definition/competitiveness.

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  • International Monetary Fund (IMF). 2016. “Global Trade: What’s Behind the Slowdown.” World Economic Outlook, pages 63–119. Washington, DC, October.

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  • International Monetary Fund (IMF). 2017. “External Adjustment to Terms-of-Trade Shifts.” Western Hemisphere Regional Economic Outlook, pages 55–80. Washington, DC, April.

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  • Organization for Economic Cooperation and Development (OECD). 1992. Technology and the Economy: The Key Relationships. Paris, France.

  • World Economic Forum. 2017, September 27. “What Exactly Is Economic Competitiveness?” Retrieved January 18, 2019, from https://www.weforum.org/agenda/2017/09/what-is-economic-competitiveness/.

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  • Zanello, Alejandro, and Dominique Desruelle. (1997). “A Primer on the IMF’s Information Notice System.” IMF Working Paper 97/71, International Monetary Fund, Washington, DC

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1

Prepared by Dmitry Gershenson, Carlos Goncalves, and Luis Omar Herrera Prada. We are grateful to Juan Yepez Albornoz and Yan Carriere-Swallow for making their datasets available for our analysis and to Pelin Berkmen, Jorge Restrepo, and seminar participants at the Banco Central del Uruguay and at the Universidad de la Republica for constructive comments. All errors are the authors’.

2

That slowdown followed from the overall weakness in economic activity post-global financial crisis, as well as from the slower growth in global value chains and the waning pace of trade liberalization. See IMF (2016) for further discussion.

3

It is worth noting that the shift from manufacturing to primary commodities does not necessarily imply a shift to “simpler” economic activities. As one example, Uruguay’s highly mechanized agriculture is a far cry from what is was half a century ago. We incorporate this observation into our analysis by using the product classification of the Banco Central del Uruguay (see paragraph 5 and Appendix I).

4

In the literature, there no single definition of the term “competitiveness.” We follow closely the dictionary definition of competitiveness as “ability of a firm or a nation to offer products and services that meet the quality standards of the local and world markets at prices that are competitive and provide adequate returns on the resources employed or consumed in producing them” (BusinessDictionary, no date). Other similar definitions are “the quality of being as good as or better than others of a comparable nature” (English Oxford Living Dictionaries, no date) and “the degree to which, under free and fair market conditions, a country can produce goods and services which meet the test of foreign competition while simultaneously maintaining and expanding the real income of its people” (OECD 1992). Yet another, and more productivity-tilted definition is “the set of institutions, policies and factors that determine the level of productivity of a country” (World Economic Forum 2017).

5

For instance, a 3-percent market share of soybeans (observed on average in 2013–15) means that during that period Uruguay accounted for 3 percent of global soybean exports.

6

These classifications are presented in Appendix I.

7

Time trend is correlated with the real effective exchange rate given trend appreciation.

8

Brazil, Chile, Colombia, Mexico, and Peru.

References

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  • Bucacos, E., A. Graña, Licandro, G. and Mello, M., forthcoming, “On Forex Intervention in Uruguay and its effects, 2005 to 2017,” in forthcoming Werner, A. Chamon, M., Hofman, D., Magud, N., forthcoming book, “Foreign Exchange Interventions in Inflation Targeters in Latin America.”

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  • International Monetary Fund. 2017. “Drivers of Capital Flows and the Role of the Investor Base in Latin America.” In Western Hemisphere Regional Economic Outlook. Washington: IMF, April, pp. 81108.

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Appendix I. Robustness Tests

Table A1a.

Uruguay: Determinants of FX Intervention – with Minimum Amount Threshold

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Notes: Numbers in parentheses are t-values. *** p<0.001, **p<0.01, *p<0.05
Table A1b.

Uruguay: Impacts of FX Intervention on Exchange Rate Level and Volatility – with Minimum Amount Threshold1

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Estimated using IV (2SLS) method. Predicted values of FX sale and purchase amounts from first stage regressions used as instruments.

Change in principal component of exchange rates in Argentina, Brazil, Chile, Colombia, Mexico and Peru. Numbers in parentheses are t-values. ***p<0.001, **p<0.01, *p<0.05

Table A2a.

Uruguay: Determinants of FX Intervention––with Sample Period 2010–2017

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Notes: Numbers in parentheses are t-values. *** p<0.001, **p<0.01, *p<0.05
Table A2b.

Uruguay: Impacts of FX Intervention on Exchange Rate Level and Volatility––with Sample Period 2010–20171

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Estimated using IV (2SLS) method. Predicted values of FX sale and purchase amounts from first stage regressions used as instruments.

Change in principal component of exchange rates in Argentina, Brazil, Chile, Colombia, Mexico and Peru. Numbers in parentheses are t-values. ***p<0.001, **p<0.01, *p<0.05

Table A3a.

Uruguay: Determinants of FX Intervention–– with Tolerable Range Estimated by 6-month MA

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Notes: Numbers in parentheses are t-values. *** p<0.001, **p<0.01, *p<0.05
Table A3b.

Uruguay: Impacts of FX Intervention on Exchange Rate Level and Volatility -with Tolerable Range Estimated by 6-month MA1

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Estimated using IV (2SLS) method. Predicted values of FX sale and purchase amounts from first stage regressions used as instruments.

Change in principal component of exchange rates in Argentina, Brazil, Chile, Colombia, Mexico and Peru. Numbers in parentheses are t-values. ***p<0.001, **p<0.01, *p<0.05.

1

Prepared by Yehenew Endegnanew.

2

FX interventions in this paper refer to intervention conducted exclusively by the BCU and exclude any possible intervention by other government institutions. The paper does not also make distinction between sterilized and non- sterilized as well as direct and indirect interventions.

3

For more on the different types and ways of FX intervention, please see Bucacos and others (forthcoming).

Uruguay: Selected Issues
Author: International Monetary Fund. Western Hemisphere Dept.
  • View in gallery

    Uruguay: Export Developments

  • View in gallery

    Uruguay: Real Effective Exchange Rate (2003m1=100)

  • View in gallery

    Uruguay: Export Shares by Product, Group, and Class

    (Percentage points)

  • View in gallery

    Uruguay: Increases and Declines in Market Share for Large Products

    (Percentage points; BCU classification)

  • View in gallery

    Uruguay: Global Market Share Elasticities: Point Estimates and 90-Percent Confidence Intervals 1/