Abstract
Selected Issues
Productivity, Foreign Demand and Factor Allocation in Uruguay1
Long-run economic growth in Uruguay has relied in equal parts on productivity and factor accumulation. As an open economy with imported intermediate inputs, external demand is found to explain some of this productivity growth through the terms-of-trade. This work provides a newly-compiled dataset on GDP, employment and labor productivity by sector of production. These new data suggest that labor may not be efficiently allocated across sectors of production relative to other Latin American countries. More worryingly, Uruguayan workers tend to move from high- to low-productivity sectors.
A. Introduction
1. After four decades of decline, relative living standards of the average Uruguayan are only now catching up to where they were in the 1960s. In this sense, Uruguayan living standards have not improved relative to those of the United States over the last five decades. Living standards are measured here as Gross Domestic Product (GDP) per person (Figure 1). These relative living standards in fact declined, from 38 percent of U.S. levels to 20 percent the end of the 2001–2 recession, and then recovered rapidly. Over this period, living standards in Uruguay remained above those of countries in Latin America and the Caribbean and below those of countries in the Organization for Economic Cooperation and Development. In the most recent years, growth rates in Uruguayan living standards have declined somewhat (Figure 2).
Per Capita GDP Relative to the U.S.
(percent)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Source: Penn World Tables version 9.0 and IMF Staff Calculations.Notes: Chart shows ratios of purchasing power parity gross domestic products per person that are comparable at each point in time.Per Capita GDP Relative to the U.S.
(percent)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Source: Penn World Tables version 9.0 and IMF Staff Calculations.Notes: Chart shows ratios of purchasing power parity gross domestic products per person that are comparable at each point in time.Per Capita GDP Relative to the U.S.
(percent)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Source: Penn World Tables version 9.0 and IMF Staff Calculations.Notes: Chart shows ratios of purchasing power parity gross domestic products per person that are comparable at each point in time.
Growth in Uruguay GDP Per Capita
(percent per year)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Source: World Economic Outlook Database (April 2017).Note: Growth rates shown are 10-year rolling averages of underlying annual data. The underlying value for year 2017 is an IMF staff estimate. Underlying GDP is measured in constant price national currency units.Growth in Uruguay GDP Per Capita
(percent per year)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Source: World Economic Outlook Database (April 2017).Note: Growth rates shown are 10-year rolling averages of underlying annual data. The underlying value for year 2017 is an IMF staff estimate. Underlying GDP is measured in constant price national currency units.Growth in Uruguay GDP Per Capita
(percent per year)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Source: World Economic Outlook Database (April 2017).Note: Growth rates shown are 10-year rolling averages of underlying annual data. The underlying value for year 2017 is an IMF staff estimate. Underlying GDP is measured in constant price national currency units.2. Low investment rates and weakness in education are likely holding back growth in Uruguay. Investment averaged only 17 percent of GDP between 1981 and 2016, which is second-lowest among large Latin American economies.2 Comparative statistics are 23 percent for Chile and 22 percent for Colombia. Uruguay achieves a competitive rate of completion of primary school education, but underperforms other Latin American countries in completion of secondary and tertiary education. The average Uruguayan over 25 years of age has only 2.3 years of secondary schooling and 0.3 years of tertiary schooling, which are second-lowest and lowest respectively among large Latin American economies.3 (The comparative statistics are 3.7 and 0.5 years for Chile, and 3.1 and 0.8 years for Colombia.) Uruguay’s relatively low completion rates for secondary education are associated with relatively high dropout rates, rather than low enrollment rates (OECD, 2014).
3. Nevertheless, this paper shows that productivity is the main driver of fluctuations in Uruguayan living standards. Uruguay’s swings in living standards are more than twice what can be explained by movements in the available stocks of factors of production alone. For example, the labor force and the physical capital stock together grew at about twice the rate of the Uruguayan economy during the 1980s. Then, in the decade since the 2001–2 economic crisis, these magnitudes reversed, so that the quantity of goods and services produced in Uruguay grew at twice the rate of growth of these combined factors of production. This excess volatility in Uruguayan production suggests that there are important trends in productivity in the economy.4
4. This work explores two potential explanations for these productivity developments: global demand for Uruguayan products and the efficiency of the allocation of workers across sectors of production. One potential hypothesis for the post-crisis experience is that increasing global demand for Uruguayan products has benefited the terms at which Uruguayan exports can be traded for imported intermediate inputs. The analysis supports this view somewhat, but finds that models of this feature can only explain about ½ of a percentage point of increased yearly growth since the crisis. A second hypothesis, which relates more to Uruguay’s lack of long-run convergence in living standards, is that labor resources may not be efficiently allocated to sectors of production. This paper shows that labor productivities are almost twice as spread out across Uruguayan sectors as they are across sectors in the United States, suggesting such labor market inefficiencies. Similarly, the evidence suggests that workers in Uruguay may not re-allocate from low-productivity sectors to high-productivity sectors, as they do in the United States. Instead, labor resources mostly flow in the wrong direction, hurting overall productivity growth.
B. Sources of Growth
5. The value of all goods and services than an economy can produce reflects the stocks of available factors of production, especially capital and labor. Total factor productivity (TFP) is the efficiency with which factor inputs are converted into produced goods and services, and is typically measured as a residual that reflects the difference between the observed value of produced goods and services and the accumulation in factor inputs. This method is the neoclassical approach to measuring productivity (Solow, 1956; Swan, 1956). The TFP residual reflects many forces, including the terms at which exported goods can be traded for intermediate inputs and the efficiency of labor markets. In addition to TFP, the concept of average labor productivity is defined as the value of produced goods and services per unit of labor input.
6. Productivity has contributed slightly more than factor accumulation to Uruguay’s long-run growth over the last four decades. Within the neoclassical paradigm, researchers disagree over the design of the shape of the production function and over the measurement of the stock of labor: the substitutability between capital and labor in production may be near-constant or may vary with the available stocks of these two factors; the share of output accruing to labor and its variation over time is uncertain;5 and the stock of labor may be measured by the number of people employed or may additionally reflect the average number of years of schooling in the population and the returns to education. However, a variety of methods decompose Uruguay’s growth similarly (Figure 3). Factor accumulation and decumulation seem to explain less than half of the variation in the growth of produced goods and services over this period, supporting the idea that productivity has been the primary driving force. The growth acceleration in recent decades also appears to come primarily from productivity improvements, although physical capital accumulation has picked up slightly. Periods of strong positive productivity growth in Uruguay contrast starkly with the experience in the rest of Latin America and the Caribbean, where productivity growth has been negative or almost zero in every decade since 1990 (IMF, 2017). Growth in the decades around Uruguay’s crisis years appeared to suffer from higher unemployment and weak productivity, rather than any underinvestment in physical capital.
Contributions to Growth in a Neoclassical Model
(in percentage points of 10-year average annual growth)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: Penn World Tables (PWT) version 9.0 and IMF staff calculations.1/ PWT method refers to the method followed in the PWT version 9.0. TFP growth is as published in the PWT, and the split of the factor accumulation contribution between capital and labor follows equation (C4) in Feenstra et al. (2015).2/ The parameter α in the Solow—Swan model refers to the elasticity of output with respect to the capital stock, which under ideal conditions implies a share of 1 – α of production accruing to the labor force.Contributions to Growth in a Neoclassical Model
(in percentage points of 10-year average annual growth)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: Penn World Tables (PWT) version 9.0 and IMF staff calculations.1/ PWT method refers to the method followed in the PWT version 9.0. TFP growth is as published in the PWT, and the split of the factor accumulation contribution between capital and labor follows equation (C4) in Feenstra et al. (2015).2/ The parameter α in the Solow—Swan model refers to the elasticity of output with respect to the capital stock, which under ideal conditions implies a share of 1 – α of production accruing to the labor force.Contributions to Growth in a Neoclassical Model
(in percentage points of 10-year average annual growth)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: Penn World Tables (PWT) version 9.0 and IMF staff calculations.1/ PWT method refers to the method followed in the PWT version 9.0. TFP growth is as published in the PWT, and the split of the factor accumulation contribution between capital and labor follows equation (C4) in Feenstra et al. (2015).2/ The parameter α in the Solow—Swan model refers to the elasticity of output with respect to the capital stock, which under ideal conditions implies a share of 1 – α of production accruing to the labor force.7. However, it may be important for an open economy like Uruguay to separate out the effects of foreign demand from this measured productivity. The above neoclassical approach ignores the possibility of imported intermediate inputs being used for production, which limits its applicability to a small, open economy like Uruguay. Uruguay imports crude oil and machinery as intermediate inputs into domestic production, and similarly exports intermediates in the form of agricultural products. Therefore, improvements in the terms at which Uruguay can exchange domestic goods for imported intermediate inputs (the “terms of trade”) allow it to produce more domestically by importing more inputs from abroad. In turn, increases or decreases in foreign demand for Uruguayan products would improve or deteriorate respectively these terms of trade. It is possible to extend the neoclassical model by adding imported intermediate inputs as an additional factor of production, in the context of two countries6 with one final good per country, where each country’s final good also serves as an intermediate input in the other country (Acemoglu and Ventura, 2002). The extended production function and the optimal domestic demand for imported intermediate inputs give the equations
respectively, where Y is domestic production in units of the domestic country final good, A is a domestic residual productivity parameter, K is the domestic physical capital stock, L is the domestic labor stock, X is the quantity of imported intermediate inputs used in domestic production (measured in units of the foreign country final good), p is the price of the foreign country final good in units of the domestic country final good (i.e. the reciprocal of the domestic country’s terms-of-trade), parameters α, β, γ are elasticities of domestic production with respect to each of the factor inputs. These two equations can be combined to obtain
which shows how the terms of trade 1/p enter the production function. Improvements in the terms of trade, arising for example from a surge in foreign demand for domestic goods,7 allow a country to produce more with the same quantities of labor and physical capital. Such an improvement would be attributed to the productivity residual in the preceding neoclassical model. The parameter γ determines the importance of these effects associated with foreign demand and terms of trade, and can be expected to be larger for a country that is more open to international trade and more specialized in its production.
8. Foreign demand appears to account for about ½ of one percentage point Uruguay’s long-run growth, which is small relative to the above measures of productivity. While estimates are available for the parameters α, β, they are difficult to obtain for the parameter γ. Under ideal conditions,8 the parameter γ should equal the share of production accruing to the owners of imported intermediate inputs. Johnson and Noguera (2012) find that Uruguay’s value-added in exports is 71 percent of its gross exports, suggesting that imported intermediates constitute the remaining 29 percent of gross exports. If Uruguay’s domestic production of all goods and services is comparable to its production of exports, then 0.29 would be an indicative value of γ under the above ideal conditions.9 However, it remains to reconcile such an estimate with those in the literature on the shares of production accruing to the owners of labor and physical capital. Figure 4 shows decompositions of long-run growth under a range of assumptions on the parameter γ. Historically, changes in Uruguay’s terms-of-trade correlate positively with changes in the naïve TFP residual from the neoclassical model above, so it is no surprise that terms-of-trade movements do absorb some of the contribution of movements in residual TFP. Values of γ of at least 0.3 seem necessary for terms-of-trade mechanisms to make a material contribution to explaining growth, but values larger than 0.4 seem to reverse the sign of the TFP residual in the 1990s and after 2007.10,11
Contributions to Growth in an Open-economy Extension of the Neoclassical Model
(in percentage points of 10-year average annual growth)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: Penn World Tables version (PWT) 9.0 and IMF staff calculations.The parameters α, β and γ control the shape of the production function, specifically the elasticities of physical output with respect to domestic capital, domestic labor and intermediate inputs, respectively. The parameters α, β are set at levels 0.4 and 0.5 respectively.Contributions to Growth in an Open-economy Extension of the Neoclassical Model
(in percentage points of 10-year average annual growth)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: Penn World Tables version (PWT) 9.0 and IMF staff calculations.The parameters α, β and γ control the shape of the production function, specifically the elasticities of physical output with respect to domestic capital, domestic labor and intermediate inputs, respectively. The parameters α, β are set at levels 0.4 and 0.5 respectively.Contributions to Growth in an Open-economy Extension of the Neoclassical Model
(in percentage points of 10-year average annual growth)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: Penn World Tables version (PWT) 9.0 and IMF staff calculations.The parameters α, β and γ control the shape of the production function, specifically the elasticities of physical output with respect to domestic capital, domestic labor and intermediate inputs, respectively. The parameters α, β are set at levels 0.4 and 0.5 respectively.C. Allocation of Labor Resources
9. The efficiency of the allocation of scarce resources across sectors of production affects the productivity of an economy. Ideally, sectors of an economy should compete for workers through the wage rate. Sectors with higher marginal productivities of labor should expand production by offering higher wages and attracting more workers, while other sectors should shrink, until the sizes of all sector settle at levels that equate the marginal labor productivities across sectors. Distortions to the labor market could disrupt this ideal mechanism. For example, segmentation of the labor market (perhaps through skills specificity) or immobility of workers between regions could limit the optimal allocation of labor resources. Similarly, government subsidies to production in a particular sector could lead that sector to grow beyond its efficient size and prevent workers from leaving that sector. As another example, barriers to entry of new firms in a particular sector could lead the incumbent firms in that sector to experience higher labor productivity than firms in other sectors that pay the same wage, and the absence of any wage premium in that sector would prevent workers from reallocating into that sector, where their marginal productivity is higher.
10. Existing evidence suggests that Uruguay’s economic performance may be held back by inefficient functioning of labor markets. A recent worldwide survey of executives ranked Uruguay 121st out of 137 countries in on labor market efficiency (Schwab, 2017). Within this category, respondents identified cooperation in labor-employer relations (rank 131), flexibility of wage determination (rank 135), hiring and firing practices (rank 126) and the effect of taxation on incentives to work (rank 132) as the most problematic issues. Kaldewei and Weller (2013) find that only 0.7 percent out of Uruguay’s 4.4 percent yearly growth in output per worker between 2006 and 2011 could be attributed to the reallocation of labor from low to high productivity sectors, with the large remainder coming from productivity improvements within sectors. Cassoni, Allen and Labadie (2004) find that from 1975 to 1997 the wage bargaining processes in Uruguay inhibited the wage flexibility that would encourage workers to move between sectors. Wage bargaining in Uruguay occurs in each sector between firms and trade unions, with participation of the government. The sector-by-sector bargaining process artificially segments the labor market, which produces wage differentials between sectors for similar workers and thus prevents a dynamic reallocation of labor across sectors. A historical lack of representation in the negotiations of some firms and the unemployed means that agreed wages for any sector may not reflect the preferences of all such parties, resulting in an excess supply of workers at agreed wages.
11. To analyze the allocation of labor resources across sectors of production, this study compiles new data on GDP, numbers of workers and production prices across sectors in Uruguay. The data are compiled by combining information on sectoral GDP in current and constant prices, employment rates by region, sectoral distributions of employees by region, and regional population sizes. The source for these data is the Instituto Nacional de Estadística (INE). Assumptions are required to match the three different vintages of sectoral classifications. A full description of the data compilation appears in Annex I.
12. Over the last two decades, these data show that the composite sector of transport, storage and communication has experienced the highest average labor productivity (20 thousand dollars per worker, in 2005 prices) and the fastest average growth in labor productivity (9.3 percent per year). Table 1 presents summary statistics of these data, according to the six-sector classification explained in Annex I. Employment is concentrated in the public sector, which makes up four-fifths of the workers shown in the “Financial and Community” sector. The construction sector has experienced the highest average labor productivity when measured in 2015 prices, because there has been large output price inflation in this sector.
Growth, Employment and Labor Productivity by Sector in Uruguay
Constant 2005 national prices.
Constant 2005 national prices and 2005 market exchange rates.
End-of-year market exchange rates.
Growth, Employment and Labor Productivity by Sector in Uruguay
| Agriculture, Mining | Manufactures, Utilities | Construction | Trade, Hospitality | Transport, Storage, Communication | Financial, Community | ||||
|---|---|---|---|---|---|---|---|---|---|
| Geometric mean annual growth in number of workers, 1991–2016 | |||||||||
| −0.3% | −1.6% | 0.8% | 1.2% | 2.7% | 0.7% | ||||
| Geometric mean annual growth in real GDP per worker, 1991–2015, in constant 2015 prices | |||||||||
| 2.9% | 2.9% | 1.1% | 3.6% | 9.3% | 2.1% | ||||
| Mean number of workers, 1991–2016 | |||||||||
| 92,000 | 291,091 | 147,847 | 399,425 | 113,050 | 825,100 | ||||
| Mean real GDP per worker, 1991–2015, thousands | |||||||||
| Pesos1 | 413 | 446 | 127 | 152 | 485 | 201 | |||
| USD2 | 17 | 19 | 5 | 6 | 20 | 8 | |||
| Mean nominal GDP per worker, 1991–2015, thousands | |||||||||
| Pesos | 431 | 300 | 219 | 153 | 292 | 227 | |||
| USD3 | 23 | 16 | 11 | 9 | 16 | 12 | |||
Constant 2005 national prices.
Constant 2005 national prices and 2005 market exchange rates.
End-of-year market exchange rates.
Growth, Employment and Labor Productivity by Sector in Uruguay
| Agriculture, Mining | Manufactures, Utilities | Construction | Trade, Hospitality | Transport, Storage, Communication | Financial, Community | ||||
|---|---|---|---|---|---|---|---|---|---|
| Geometric mean annual growth in number of workers, 1991–2016 | |||||||||
| −0.3% | −1.6% | 0.8% | 1.2% | 2.7% | 0.7% | ||||
| Geometric mean annual growth in real GDP per worker, 1991–2015, in constant 2015 prices | |||||||||
| 2.9% | 2.9% | 1.1% | 3.6% | 9.3% | 2.1% | ||||
| Mean number of workers, 1991–2016 | |||||||||
| 92,000 | 291,091 | 147,847 | 399,425 | 113,050 | 825,100 | ||||
| Mean real GDP per worker, 1991–2015, thousands | |||||||||
| Pesos1 | 413 | 446 | 127 | 152 | 485 | 201 | |||
| USD2 | 17 | 19 | 5 | 6 | 20 | 8 | |||
| Mean nominal GDP per worker, 1991–2015, thousands | |||||||||
| Pesos | 431 | 300 | 219 | 153 | 292 | 227 | |||
| USD3 | 23 | 16 | 11 | 9 | 16 | 12 | |||
Constant 2005 national prices.
Constant 2005 national prices and 2005 market exchange rates.
End-of-year market exchange rates.
13. The United States achieves a spread in labor productivities across sectors that is 43 percent lower than that of Uruguay, suggesting that substantial productivity gains are possible by eliminating distortions to the efficient allocation of labor across sectors of production. As explained above, labor should reallocate across sectors to equate these sectors’ marginal productivities of labor, in the absence of distortions. Larger differences of marginal labor productivities between sectors are therefore an indication of greater labor market distortions that could be a potential source of productivity improvements.12 The newly-compiled data above provide an opportunity to produce new measures of the spread of average labor productivities across sectors. Sectoral labor productivity data are available for other countries from the Groningen Growth and Development Center (GGDC) with a comparable sectoral classification to that available for Uruguay. A measure of the spread across sectors in average labor productivity13 places Uruguay between its two large neighbors, Brazil and Argentina, with which it shares historic and economic linkages (Figure 5). Other Latin American countries like Chile, Mexico, Peru and Colombia achieve a lower spread of average labor productivities across sectors.
Spread in Labor Productivities Across Sectors
(average coefficient of variation)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: Groningen Growth and Development Center (GGDC) 10-Sector Database, INE and IMF Staff calculations.Note: the chart shows the average over time of the coefficient of variation across sectors of the level of labor productivity. In turn, labor productivity is measured as GDP per worker in 2005 U.S. dollars, using constant national prices and market exchange rates.Spread in Labor Productivities Across Sectors
(average coefficient of variation)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: Groningen Growth and Development Center (GGDC) 10-Sector Database, INE and IMF Staff calculations.Note: the chart shows the average over time of the coefficient of variation across sectors of the level of labor productivity. In turn, labor productivity is measured as GDP per worker in 2005 U.S. dollars, using constant national prices and market exchange rates.Spread in Labor Productivities Across Sectors
(average coefficient of variation)
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: Groningen Growth and Development Center (GGDC) 10-Sector Database, INE and IMF Staff calculations.Note: the chart shows the average over time of the coefficient of variation across sectors of the level of labor productivity. In turn, labor productivity is measured as GDP per worker in 2005 U.S. dollars, using constant national prices and market exchange rates.14. In Uruguay, workers tend to move out of sectors with high labor productivity and into sectors with low labor productivity. Uruguay’s labor reallocation process produces a negative relationship between sectoral labor productivity and sectoral employment growth in the subsequent ten years (Figure 6).14 By contrast, in the United States, sectors with high labor productivity tend to attract more workers over the subsequent decade, while sectors with low labor productivity tend to lose workers. This phenomenon in Uruguay is particularly driven by the trade, restaurant and hotel sector, which attracts many workers despite low levels of labor productivity, and the composite sector of manufacturing and utilities, which experience declines in employment (or only modest increases in employment) despite relatively high levels of labor productivity.
Labor Reallocation Between Sectors According to Productivity
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: GGDC 10-Sector Database, INE and IMF Staff calculations.Note: GDP per worker is measured in 2005 U.S. dollars, using constant national prices and market exchange rates. Each point on the chart shows a sector—decade pair. The dashed line shows a least-squares fit and its associated equation is displayed.Labor Reallocation Between Sectors According to Productivity
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: GGDC 10-Sector Database, INE and IMF Staff calculations.Note: GDP per worker is measured in 2005 U.S. dollars, using constant national prices and market exchange rates. Each point on the chart shows a sector—decade pair. The dashed line shows a least-squares fit and its associated equation is displayed.Labor Reallocation Between Sectors According to Productivity
Citation: IMF Staff Country Reports 2018, 024; 10.5089/9781484339824.002.A004
Sources: GGDC 10-Sector Database, INE and IMF Staff calculations.Note: GDP per worker is measured in 2005 U.S. dollars, using constant national prices and market exchange rates. Each point on the chart shows a sector—decade pair. The dashed line shows a least-squares fit and its associated equation is displayed.D. Conclusion and Policy Discussion
15. Lifting policy-related obstacles to investment in physical and human capital could bring factor accumulation closer to other countries in the region. Productivity is important to Uruguay’s growth relative to factor accumulation, and compared with other countries in Latin America and the Caribbean. This suggests either that the Uruguayan economy is relatively good at innovating, or that there are limitations to the accumulation of factors of production in Uruguay. Persistent weakness in public investment, and a lack of availability of credit for firms with revenues denominated in pesos, are two potential policy-related limitations to investment. Similarly, weakness in the attainment rates for post-primary education and in the quality of education (as measured by domestic performance on internationally standardized tests) suggests that policies could alleviate constraints on the accumulation of human capital. Female labor force participation rates remain below rates for males, and policies to extend public childcare facilities or to extend paternity leave could help expand the labor force, especially with an aging population.
16. Enhancing the flexibility of the exchange rate regime could improve the ability of the exchange rate to absorb terms-of-trade shocks. Some of Uruguay’s productivity performance over the last four decades is due to changes in terms-of-trade that could reflect global factors, like the increase in demand from China for Uruguayan exports. Uruguay maintains a flexible exchange rate, which acts as an absorber of exogenous terms-of-trade shocks. However, official interventions in the foreign exchange market are common, influenced by a desire to smooth short-term volatility in the exchange rate that could have costly effects on wealth and consumption in this dollarized economy. An assessment of the optimal degree of smoothing seem warranted, to ensure that the benefits of the exchange rate as a shock absorber are maintained as much as possible.
17. Policies to eliminate distortions and foster labor mobility can improve the allocation of labor resources across sectors. A lack of wage flexibility prevents the price mechanism from re-allocating workers from low-productivity sectors to high-productivity sectors. Ensuring the availability of training programs and insurance mechanisms could assist in the mobility of labor between sectors of production. Passing through more of input price changes into administered prices across various sectors could remove time-varying distortions to their optimal size, and could thus increase labor mobility between these sectors.
18. Trade integration and labor productivity interact in a complicated manner in Latin America. McMillan and Rodrik (2011) explain that over the last half century, Latin America ambitiously reduced barriers to trade, exposing unproductive tradable sectors of production to competition from imports, while employing somewhat tight monetary policy to reduce inflation. Productivity improvements in manufacturing sectors came from their rationalization, but given capacity constraints to highly productive commodity-exporting firms, displaced workers moved into unproductive industries. In Uruguay, labor moved from manufacturing into hospitality.15 Along with these structural changes, trade integration brought significant productivity benefits to Uruguay’s export sectors, which are small and specialized relative to those of trading partners.
References
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Adler, G., R. Duval, D. Furceri, S. Çelik, K. Koloskova, and M. Poplawski, 2017, “Gone with the Headwinds: Global Productivity,” IMF Staff Discussion Note 17, International Monetary Fund, Washington, DC.
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Busso, M., and Madrigal, L. 2013. “Productivity and resource misallocation in Latin America,” The BE Journal of Macroeconomics 13(1), 903–932.
Cassoni, A., Allen, S.G. and G.J. Labadie, 2004, “Unions and Unemployment in Uruguay”, in Heckman, J. and C. Pages, Eds., “Law and Employment: Lessons from Latin America and the Caribbean,” University of Chicago Press.
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Kaldewei, C. and J. Weller, 2013, “Empleo, crecimiento sostenible e igualdad.” United Nations Economic Commission on Latin America and the Caribbean, Santiago.
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Annex I. Construction of Sectoral Data
This annex describes the sources, procedure and assumptions used to compile data on sectoral value-added in real and nominal terms, and sectoral employment.
Instituto Nacional de Estadistica (INE) provides data on:
The value added of each sector, in current and constant national price pesos. These data are provided yearly between 1983 and 2015. Between 1983 and 2006, the constant-price value-added data are provided in constant 1983 prices, and since 2007 they are provided in constant 2005 prices.
The aggregate rates of employment for each of the interior and Montevideo. These data are available monthly between 1991 and May 2017.
The distribution across sectors of the employed populations of the ‘urban’ interior1 and Montevideo. For the urban interior, these data are provided in non-overlapping quarters until 2000, overlapping quarters between 2000 and 2011, and monthly thereafter.2
The key complication with combining these data is that the classification of sectors of production differs between the value-added and employment data, and between the current and constant-price value-added data. The available sectoral classifications also vary over time. Until 2006, the sectoral classification of the value-added data follows the second revision of the International Standard Industrial Classification of All Economic Activities (ISIC) of the United Nations, and the third revision thereafter.3 The sectoral distribution of employed workers follows the ISIC revision two, three and four, in periods 1991—2000, 2000—2011, and 2011—2017 respectively. Some of the sectors are combined and presented as aggregates relative to the more detailed classification of the ISIC. This aggregation procedure differs between the constant-price value-added data, the current-price value-added data and the sectoral employment data, and between the different time periods.
At most six categories can be used to match the data on value added and employment. These categories are presented in Table 1. By aggregating data at more detailed sectoral levels up to this six-sector level, estimates are obtained at each year-end for value added in current prices by sector and for the distribution of employed workers across sectors.
Six-Sector Classification of Sectors of Production
Six-Sector Classification of Sectors of Production
| Short label | Description | ISIC codes |
|---|---|---|
| Agriculture, Mining | Agriculture, forestry, hunting, fishing, quarrying and mining | 1–2 (revision 2) A-C (revision 3) A-B (revision 4) |
| Manufactures, Utilities | Manufacturing industries, electricity, gas and water | 3–4 (revision 2) D-E (revision 3) C-E (revision 4) |
| Construction | Construction | 5 (revision 2) F (revision 3) F (revision 4) |
| Trade, Hospitality | Trade, restaurants and hotels | 6 (revision 2) G-H (revision 3) G, I (revision 4) |
| Transport, Storage, Communication | Transport, storage and communication | 7 (revision 2) I (revision 3) H, J (revision 4) |
| Financial, Community | Services including financing, insurance, real estate, business, community, social and personal | 8–9 (revision 2) J-Q (revision 3) K-U (revision 4) |
Six-Sector Classification of Sectors of Production
| Short label | Description | ISIC codes |
|---|---|---|
| Agriculture, Mining | Agriculture, forestry, hunting, fishing, quarrying and mining | 1–2 (revision 2) A-C (revision 3) A-B (revision 4) |
| Manufactures, Utilities | Manufacturing industries, electricity, gas and water | 3–4 (revision 2) D-E (revision 3) C-E (revision 4) |
| Construction | Construction | 5 (revision 2) F (revision 3) F (revision 4) |
| Trade, Hospitality | Trade, restaurants and hotels | 6 (revision 2) G-H (revision 3) G, I (revision 4) |
| Transport, Storage, Communication | Transport, storage and communication | 7 (revision 2) I (revision 3) H, J (revision 4) |
| Financial, Community | Services including financing, insurance, real estate, business, community, social and personal | 8–9 (revision 2) J-Q (revision 3) K-U (revision 4) |
In a given year, the number of workers in each of the six sectors is estimated by combining population estimates, employment rates and the proportion of employed workers in that sector. Populations of 1.4 and 2 million people are assumed for Montevideo and the interior respectively.4
Prepared by Galen Sher (WHD). The sector-level data produced in this study are available from the author on request.
According to the April 2017 vintage of the World Economic Outlook database published by the IMF.
In the year 2010, according to data published in Barro and Lee (2013).
This conclusion assumes that there are no omitted domestic factors of production and that there are no important domestic demand effects. However, it does allow for an additional factor of production, in the form of imported intermediates, and it does allow for demand effects from abroad, both of which are investigated in this work.
The share of output accruing to labor fell from 52 percent in 1997 to 47 percent in 2005, according to the Penn World Tables version 9.0.
The two countries can be thought of as Uruguay and the rest of the world.
A similar production function can be written down for the rest of the world economy, and terms of trade can be endogenized by defining it as the relative price that clears the market for international trade. These extensions would not change the dependence of domestic production on domestic terms of trade.
Specifically, perfect competition in the market for imported intermediate inputs and a production function exhibiting constant returns to scale, α + β + γ = 1.
Uruguay ranks as more sensitive to foreign factors than Chile (with a share of 0.2 accruing to intermediate inputs), Colombia (0.14) and the United States (0.23), but less sensitive than Mexico (0.33) (Johnson and Noguera, 2012).
As the parameter γ increases, the size of the contribution of growth in the terms-of-trade factor increases. At large values of γ, the combined contribution of accumulation of factors of production and growth in the terms-of-trade can exceed the growth in real GDP, leading to a negative productivity residual.
As a caveat, it should be mentioned that the success of terms-of-trade in explaining TFP in Uruguay does not extend to decades prior to the one ending in 1985.
Hsieh and Klenow (2009) apply this idea to measure the allocation efficiency of labor and physical capital across manufacturing firms in China and India.
Average labor productivities (i.e. GDP per worker) can be measured for each sector without additional assumptions on the shape of the production function. However, if an isoelastic (e.g. Cobb—Douglas) production function is assumed for every sector, with the same shape for each sector, then marginal and average productivities differ only by a constant of proportionality. In this setting, the labor allocation that would equate marginal labor productivities across sectors would be the same as the labor allocation that would equate average labor productivities across sectors.
A similar analysis is conducted for Argentina, Brazil, and groups of countries like Latin America, in McMillan and Rodrik (2011).
It is not obvious whether rationalization of employment in manufacturing sectors came primarily from technology gains and the falling price of capital or import competition.
The ‘urban’ interior consists of localities with more than five thousand inhabitants.
For the period 2000—2011, in which distributions of employed workers are provided in overlapping quarters, data for the last quarter of each year are used here as the relevant data for the end of that year.
Revisions two, three and four of the ISIC classify sectors of production into ten, seventeen and twenty-one categories respectively.
In future work, these estimates should be extended to allow for population growth over time.
