Abstract
Uruguay’s growth history has been characterized by the influence of external shocks—including commodity price cycles, regional crises and, recently, the pandemic. Underlying this volatile history, long-term trends have also shaped Uruguay’s growth. This SIP explores the role of these different factors to shed light on what Uruguay’s potential growth could be after the pandemic.
Uruguay’s Potential Growth: What to Expect After the Pandemic?
Uruguay’s growth history has been characterized by the influence of external shocks—including commodity price cycles, regional crises and, recently, the pandemic. Underlying this volatile history, long-term trends have also shaped Uruguay’s growth. This SIP explores the role of these different factors to shed light on what Uruguay’s potential growth could be after the pandemic.
A. A Long-term View on Growth
Over the last 6 decades, Uruguay’s growth (2.3 percent per year on average) has been considerably lower than in peer countries (3.6 precent) and subject to significant volatility. Long-term population dynamics can account for about ½ of this lower growth, while volatility has been driven by regional crises and commodity price cycles.
1. Uruguay’s economy has experienced pronounced volatility. While, on average, the economy growth 2.3 percent a year since 1963, the country experienced some pronounced setbacks—most notably due to the Latin American debt crisis of the early 1980s, the Brazil’ s crisis in the late 1990s, the Argentinian crisis of 2001 and the associated Uruguayan crisis of 2002—but also some periods of strong growth. By historical standards, GDP growth was exceptionally high during 2004–14, buoyed by the rebound from the 2002 crisis and a favorable commodity cycle. More recently, however, as the commodity boom came to an end, growth became stagnant (averaging 0.9 percent in 2015–19) despite the absence of any mayor negative external shock.1 This weak performance poses questions about medium-term growth prospects. The section dissect Uruguay’s economic performance over time and in comparison to peer countries to shed light on key factors that could drive growth going forward.
Long-Term View on Growth
(percentage change)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Long-Term View on Growth
(percentage change)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Long-Term View on Growth
(percentage change)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
2. A growth accounting exercise allows to examine the proximate drivers of economic growth and the recent slowdown. The exercise decomposes growth into contributions from labor, capital, and total factor productivity (TFP), relying on data from the Penn World Tables 10.0. The sample is restricted to 1978–2019 and 91 countries due to data availability issues. The labor contribution is further decomposed into average hours worked per worker, human capital (proxied by a Mincer equation of the average years of schooling) and the number of people employed (which is governed by demographic dynamics and the employment-to-population rate). 2 The physical stock of capital (henceforth capital) is estimated from data on investment in buildings and different types of machineries using the perpetual inventory method. TFP is estimated as a residual.3
3. Notwithstanding volatile contributions over different periods, capital accumulation was the primary driver of Uruguay’s growth (Table 1). Over the last 4 decades, nearly 60 percent of GDP growth reflected investment in physical capital while the contributions of TFP (about 15 percent), employment (20 percent) and human capital accumulation (15 percent) were more muted. Interesting, hours worked contributed negatively to growth, detracting about 0.2 percent per year. The latter has been a constant phenomenon throughout, as hours worked declined in all the subperiods of analysis in which data is available. GDP growth and its components have been sensitive to external forces, displaying strong positive contributions in the 1990’s and the commodity boom years (2006–09 and 2010–14), and significantly lower contributions in the early 2000’s (Argentina and Uruguay’s crises), and the pre-pandemic years that followed the commodity boom (2015–19).
Growth Accounting in Uruguay
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Growth Accounting in Uruguay
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Growth Accounting in Uruguay
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Uruguay: Growth Accounting
Uruguay: Growth Accounting
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP Growth | Employment | Human Capital | Hours | Capital | TFP | |
| 1978–2019 | 2.5% | 0.5% | 0.4% | -0.2% | 1.4% | 0.4% |
| 1990–2019 | 2.9% | 0.4% | 0.3% | -0.3% | 1.5% | 0.9% |
| 1991–99 | 3.6% | 0.6% | 0.4% | -0.2% | 1.6% | 1.2% |
| 2000–05 | -0.2% | -0.1% | 0.1% | -0.5% | 0.6% | -0.2% |
| 2006–09 | 5.4% | 1.5% | 0.2% | -0.2% | 2.0% | 1.9% |
| 2010–14 | 4.7% | 0.4% | 0.3% | -0.2% | 2.6% | 1.6% |
| 2015–19 | 1.3% | -0.1% | 0.4% | -0.2% | 1.0% | 0.2% |
Uruguay: Growth Accounting
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP Growth | Employment | Human Capital | Hours | Capital | TFP | |
| 1978–2019 | 2.5% | 0.5% | 0.4% | -0.2% | 1.4% | 0.4% |
| 1990–2019 | 2.9% | 0.4% | 0.3% | -0.3% | 1.5% | 0.9% |
| 1991–99 | 3.6% | 0.6% | 0.4% | -0.2% | 1.6% | 1.2% |
| 2000–05 | -0.2% | -0.1% | 0.1% | -0.5% | 0.6% | -0.2% |
| 2006–09 | 5.4% | 1.5% | 0.2% | -0.2% | 2.0% | 1.9% |
| 2010–14 | 4.7% | 0.4% | 0.3% | -0.2% | 2.6% | 1.6% |
| 2015–19 | 1.3% | -0.1% | 0.4% | -0.2% | 1.0% | 0.2% |
4. Uruguay’s long-term growth has been considerably lower than in peer economies—reflecting mainly slower capital accumulation and employment growth (Table 2). The pace of growth of the Uruguayan economy was 0.8 (1.1) percentage points lower than in other EMs (non-oil commodity exporting EMEs)—a difference that compounds to 39 (57) percent higher cumulative growth for the latter group. Within the region, Uruguay’s growth has been in line with that of Brazil but considerably below other Latin American countries (except Argentina), mainly driven by slower employment growth and capital accumulation.
Growth Accounting, 1978–2019
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: staff calculations, Penn World Tables 10.0Growth Accounting, 1978–2019
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: staff calculations, Penn World Tables 10.0Growth Accounting, 1978–2019
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: staff calculations, Penn World Tables 10.0Uruguay: Growth Accounting. Cross-Country Comparison, 1978–2019
Uruguay: Growth Accounting. Cross-Country Comparison, 1978–2019
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP growth | Employment | Human capital | Hours | Capital | TFP | |
| ARG | 1.7% | 0.8% | 0.4% | -0.2% | 1.2% | -0.4% |
| BRA | 2.4% | 1.0% | 1.0% | -0.2% | 1.4% | -0.7% |
| CHL | 4.3% | 1.1% | 0.3% | -0.2% | 2.8% | 0.3% |
| COL | 3.5% | 1.2% | 0.4% | -0.1% | 2.0% | -0.1% |
| PER | 3.0% | 1.3% | 0.4% | 0.0% | 2.2% | -0.9% |
| AE | 2.9% | 0.7% | 0.4% | -0.2% | 1.4% | 0.6% |
| EM commodity exp. | 3.0% | 1.1% | 0.5% | -0.1% | 2.6% | -1.1% |
| EM LAM | 2.8% | 1.1% | 0.5% | -0.1% | 1.7% | -0.4% |
| EM Asia | 5.3% | 1.0% | 0.6% | 0.0% | 3.1% | 0.6% |
| EM EUR | 2.6% | 0.1% | 0.4% | -0.1% | 2.0% | 0.2% |
| EM MENA | 3.5% | 1.2% | 0.5% | 0.0% | 3.5% | -1.8% |
| EM AFR | 2.2% | 1.1% | 0.6% | -0.1% | 1.2% | -0.5% |
Uruguay: Growth Accounting. Cross-Country Comparison, 1978–2019
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP growth | Employment | Human capital | Hours | Capital | TFP | |
| ARG | 1.7% | 0.8% | 0.4% | -0.2% | 1.2% | -0.4% |
| BRA | 2.4% | 1.0% | 1.0% | -0.2% | 1.4% | -0.7% |
| CHL | 4.3% | 1.1% | 0.3% | -0.2% | 2.8% | 0.3% |
| COL | 3.5% | 1.2% | 0.4% | -0.1% | 2.0% | -0.1% |
| PER | 3.0% | 1.3% | 0.4% | 0.0% | 2.2% | -0.9% |
| AE | 2.9% | 0.7% | 0.4% | -0.2% | 1.4% | 0.6% |
| EM commodity exp. | 3.0% | 1.1% | 0.5% | -0.1% | 2.6% | -1.1% |
| EM LAM | 2.8% | 1.1% | 0.5% | -0.1% | 1.7% | -0.4% |
| EM Asia | 5.3% | 1.0% | 0.6% | 0.0% | 3.1% | 0.6% |
| EM EUR | 2.6% | 0.1% | 0.4% | -0.1% | 2.0% | 0.2% |
| EM MENA | 3.5% | 1.2% | 0.5% | 0.0% | 3.5% | -1.8% |
| EM AFR | 2.2% | 1.1% | 0.6% | -0.1% | 1.2% | -0.5% |
After accounting for population growth, Uruguay’s performance has been in line with peers. GDP growth can be decomposed into population growth and per capita GDP growth. The demographic factors account for the observed difference in GDP growth between Uruguay and peers. As a matter of fact, Uruguay’s population has grown at a significantly slower pace than in other EMEs (0.5 percent per year in Uruguay compared to 1.1 percent in non-oil commodity exporting EMEs). Surprisingly, Uruguay’s population growth has been lower than in AEs too. On the other hand, GDP per capita grew on average at the same pace as in non-oil commodity exporting EMEs. For years, GDP per capita in Uruguay and peers grew more slowly than AEs, but growth sped up in the last years thanks to the commodity price boom. Overall, the patterns of GDP per capita in non-oil commodity exporting countries in the last twenty years and the strong correlation with commodity prices highlight the large role that commodity cycle has played for the economic development of these countries.
Population
(index 1978=1)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations. Note: country averages are unweighted.Population
(index 1978=1)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations. Note: country averages are unweighted.Population
(index 1978=1)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations. Note: country averages are unweighted.
GDP per capita
(index 1978=1)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0. staff calculations. Note: country averages are unweighted.GDP per capita
(index 1978=1)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0. staff calculations. Note: country averages are unweighted.GDP per capita
(index 1978=1)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0. staff calculations. Note: country averages are unweighted.5. The end of the commodity boom led to a marked growth slowdown across EMEs—although more pronounced in Uruguay—in the years preceding the pandemic. Uruguay’s low growth in the last five years mirrors the slowdown of the region and other non-oil commodity exporting EMEs, in the context of the end of the commodity boom. Both the direct effects from reduced commodity prices and spillovers from neighboring countries account for Uruguay’s weaker economic performance. However, compared to other non-oil commodity exporting EMEs, Uruguay’s slowdown was significantly more pronounced (1.7 percentage points larger), mostly explained by a starker slowdown in capital accumulation and productivity.
Growth Accounting. Difference between 2015–2019 and 2010–2014
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: staff calculations, Penn World Tables 10.0. Note: country averages are unweighted.Growth Accounting. Difference between 2015–2019 and 2010–2014
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: staff calculations, Penn World Tables 10.0. Note: country averages are unweighted.Growth Accounting. Difference between 2015–2019 and 2010–2014
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: staff calculations, Penn World Tables 10.0. Note: country averages are unweighted.Uruguay: Growth Accounting. Difference Between 2014–19 and 2010–14
Uruguay: Growth Accounting. Difference Between 2014–19 and 2010–14
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP growth | Employment | Human capital | Hours | Capital | TFP | |
| ARG | -3.1% | -0.3% | -0.3% | 0.1% | -1.0% | -1.7% |
| BRA | -3.9% | -1.1% | 0.0% | 0.0% | -1.4% | -1.4% |
| CHL | -2.5% | -0.6% | 0.0% | 0.0% | -1.6% | -0.3% |
| COL | -2.5% | -0.6% | 0.0% | 0.0% | -0.7% | -1.1% |
| PER | -2.5% | -0.3% | 0.0% | 0.1% | -2.2% | -0.1% |
| AE | 0.4% | 0.5% | 0.0% | 0.0% | 0.1% | -0.2% |
| EM commodity | -3.5% | -0.4% | 0.0% | 0.0% | -1.1% | -2.1% |
| EM LAM | -2.9% | -0.2% | 0.0% | 0.0% | -0.8% | -1.9% |
| EM Asia | -1.7% | -0.3% | -0.1% | 0.0% | -0.8% | -0.5% |
| EM EUR | 0.7% | 0.5% | 0.0% | 0.0% | 0.1% | 0.0% |
| EM MENA | -1.7% | -0.2% | 0.0% | 0.0% | -0.9% | -0.5% |
| EM AFR | -1.8% | 0.4% | 0.1% | 0.0% | -0.5% | -1.8% |
Uruguay: Growth Accounting. Difference Between 2014–19 and 2010–14
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP growth | Employment | Human capital | Hours | Capital | TFP | |
| ARG | -3.1% | -0.3% | -0.3% | 0.1% | -1.0% | -1.7% |
| BRA | -3.9% | -1.1% | 0.0% | 0.0% | -1.4% | -1.4% |
| CHL | -2.5% | -0.6% | 0.0% | 0.0% | -1.6% | -0.3% |
| COL | -2.5% | -0.6% | 0.0% | 0.0% | -0.7% | -1.1% |
| PER | -2.5% | -0.3% | 0.0% | 0.1% | -2.2% | -0.1% |
| AE | 0.4% | 0.5% | 0.0% | 0.0% | 0.1% | -0.2% |
| EM commodity | -3.5% | -0.4% | 0.0% | 0.0% | -1.1% | -2.1% |
| EM LAM | -2.9% | -0.2% | 0.0% | 0.0% | -0.8% | -1.9% |
| EM Asia | -1.7% | -0.3% | -0.1% | 0.0% | -0.8% | -0.5% |
| EM EUR | 0.7% | 0.5% | 0.0% | 0.0% | 0.1% | 0.0% |
| EM MENA | -1.7% | -0.2% | 0.0% | 0.0% | -0.9% | -0.5% |
| EM AFR | -1.8% | 0.4% | 0.1% | 0.0% | -0.5% | -1.8% |
6. The historical view presented above indicates that long-term trends in labor markets and commodity price cycles have been key drivers of Uruguay’s growth. The next sections shed further light on these specific aspects to help distill some lesson on how they could shape economic growth going forward.
B. Labor and Human Capital
The low growth contribution from labor reflects population dynamics and the erosion of human capital following the 2002 crisis.
7. Population growth has contributed less than in peer countries to raising GDP. A breakdown of the labor contribution into its components (employment, hours, and human capital) indicates that the difference in labor contribution vis-à-vis peers arises mainly from employment (which, in turn, reflects mainly differences in population growth). That is, Uruguay’s population has been growing considerably slower than in peer economies. The contributions from hours worked and the employment-to-population rate have also been lower, commanding an analysis of the frictions in the labor market. High school dropout rates may explain the lower human capital accumulation. Interestingly, while Uruguay’s labor share decreased in line with the global trend, it has done so by less than in peer economies thanks in part to long-standing policies of worker protection.
Decomposing the contributions from labor (1978–2019)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations.Decomposing the contributions from labor (1978–2019)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations.Decomposing the contributions from labor (1978–2019)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations.
Differences in the GDP contribution relative to non-oil commodity exp. EMEs
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations.Differences in the GDP contribution relative to non-oil commodity exp. EMEs
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations.Differences in the GDP contribution relative to non-oil commodity exp. EMEs
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations.8. Following a steady decrease over the years, average hours worked in Uruguay are now lower than in peer countries. On average, Uruguayan employees worked 38.4 hours per week in 2018, compared to an average of 43 hours in EMEs. Uruguay’s numbers are comparable to the average AE, despite having a considerably lower level of income. This is the result of a long-term downward trend that started in the 1990s.4,5 While in the same period average hours worked were decreasing also elsewhere in the world, Uruguay has a steeper downward trend than both AEs and other EMEs.
Mean Weekly Hours Worked
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: ILOSTAT. Note: last available year; unweighted country averagesMean Weekly Hours Worked
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: ILOSTAT. Note: last available year; unweighted country averagesMean Weekly Hours Worked
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: ILOSTAT. Note: last available year; unweighted country averages9. The 2002 crisis resulted in a (relative) erosion of human capital still visible two decades later and an acceleration in the reduction of hours worked, despite a full recovery in GDP. The 2002 crisis caused a significant slowdown in years of schooling—especially in tertiary and, to a lesser extent, secondary education—which was never recovered. This erased the advantage in human capital relative to other EMEs that the country had built in previous decades.6, 7 Similarly, hours worked remained 3% below the pre-crisis trend, adding to the long-term reduction and further increasing the distance between Uruguay and peers in this respect. The labor market issues arisen during the 2002 crisis for many years have been hidden by an acceleration in capital accumulation, partly driven by the commodity boom, which favored the full recovery of the GDP levels (Figure 1). When the commodity boom ended, these issues came back at the forefront.
Uruguay: Hours Worked and Human Capital in Uruguay
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Barro-Lee. Note: missing years are interpolated. Country averages are unweighted.Uruguay: Hours Worked and Human Capital in Uruguay
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Barro-Lee. Note: missing years are interpolated. Country averages are unweighted.Uruguay: Hours Worked and Human Capital in Uruguay
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Barro-Lee. Note: missing years are interpolated. Country averages are unweighted.C. The Role of the Commodity Price Cycle
Commodity prices have been an important driver of growth in Uruguay and other commodity exporters, accounting for a large fraction of the exceptional growth in 2010–14 and the subsequent slowdown. The effect of commodity prices is particularly visible in employment and capital accumulation.
10. Fluctuations of Uruguay’s commodity export prices have been sizable over the last 2 decades. Prices started rising gradually in early 2000 and, after a short pause during the 2008–09 global financial crisis, they resumed their upward trend. The boom ended in 2014 when prices started falling rapidly, up until the pandemic. As discussed before, the period of booming commodity prices was associated with higher growth in many emerging economies, including Uruguay. Symmetrically, the subsequent decrease saw a slowdown in growth.8 Disentangling the effect of commodity prices on growth is of the essence to shed light on potential growth going forward, whether a repeat of the last commodity price boom can be expected or not.
Export-Weighted Commodity Prices in Uruguay
(Index 100=2012)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Gruss and Kebhaj (2021)Export-Weighted Commodity Prices in Uruguay
(Index 100=2012)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Gruss and Kebhaj (2021)Export-Weighted Commodity Prices in Uruguay
(Index 100=2012)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Gruss and Kebhaj (2021)11. The impact of the commodity cycle on growth is estimated in a panel setting. The estimation follows the local projection method pioneered by Jorda (2005). Specifically, the following specification is estimated:
where si,t is the commodity export price constructed by Gruss and Kebhaj (2021). The index uses data on commodity prices weighted by lagged export shares for each country; K is number of lags of the dependent variable; J is the number of lags of the commodity terms of trade; H is the time horizon of the impulse response function. Regressions include time fixed effects and country fixed effects to control respectively for economic trends common to all countries, and country-specific characteristics that are constant over time. The model is estimated using an OLS.9 The sample encompasses commodity exporting EMEs during 1960–2019 and uses annual data from a number of sources, including Penn World Tables, IMF World Economic Outlook, and Gruss and Kebhaj (2021). Impulse responses are estimated with K=4 lags of the dependent variable, J=4 lags for the commodity terms of trade, H=5 years. Results are robust to alternative specifications of the impulse response function and to different time periods. In particular, results are not sensitive to the inclusion of the years of the global financial crisis.10
12. Swings in export prices have sizable and long-lasting effect on output and its components (Figure 2). A one percent increase in commodity export prices is estimated to increase GDP by 0.02 percent on impact and 0.1 percent after five years. The impact on output mainly reflects the effect through capital accumulation and employment. A one percent temporary increase in commodity export prices gradually increases investment, to raising it by about 0.3 percent at a five-year horizon and leading to an increase in the stock of capital of 0.1 percent over a five-year horizon. Meanwhile, employment also increases gradually to about 0.07 percent over a five-year horizon. The effect on average hours worked and TFP is not significant, and the magnitudes are small.11 These results indicate that temporary commodity price shocks can have sizable and long-lasting effects on the level of output (although not on growth) primarily through capital accumulation and employment.
Uruguay: Recovery from the 2002 Crisis
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations. Note: the linear trend is computed on 1990–2000 data.Uruguay: Recovery from the 2002 Crisis
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations. Note: the linear trend is computed on 1990–2000 data.Uruguay: Recovery from the 2002 Crisis
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: Penn World Tables 10.0, staff calculations. Note: the linear trend is computed on 1990–2000 data.13. The last commodity boom partially accounts for Uruguay’s growth during the 2004–14 period. Based on the estimates above and the observed commodity prices for Uruguay—which increased on average by 3 percent annually during 2004–14—the commodity boom accounts for 1.2 percentage points GDP growth per year above the historical average. Zooming in, estimates indicate that the commodity boom contributed to raising output growth primarily through faster capital accumulation (1 percent higher) and employment (0.7 percent higher). Similarly, the drop in commodity prices after 2014 accounts for about 0.2 lower growth annually during 2015–19. They also account for the negative employment growth and decreasing employment rates observed in the period.
Uruguay: Impulse Responses to a Commodity Export Price Increase
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Sources: Penn World Tables, IMF WEO, Gruss and Kebhaj (2021) and own estimations. P-values for an F-test on the coefficients being significant at any time horizon are reported.Uruguay: Impulse Responses to a Commodity Export Price Increase
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Sources: Penn World Tables, IMF WEO, Gruss and Kebhaj (2021) and own estimations. P-values for an F-test on the coefficients being significant at any time horizon are reported.Uruguay: Impulse Responses to a Commodity Export Price Increase
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Sources: Penn World Tables, IMF WEO, Gruss and Kebhaj (2021) and own estimations. P-values for an F-test on the coefficients being significant at any time horizon are reported.D. The Effects of the Pandemic on Growth
The pandemic is expected to lower potential growth relative to its pre-pandemic trend. The drop in potential output is accounted for by a strong reduction in employment and labor force participation, and a slowdown in investment and productivity. School closures also reduced human capital accumulation.
Employment. Selected Latin American Economies, 2020–21
(index Jan 2020=100, 3-month MA)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Employment. Selected Latin American Economies, 2020–21
(index Jan 2020=100, 3-month MA)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Employment. Selected Latin American Economies, 2020–21
(index Jan 2020=100, 3-month MA)
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
14. At the peak of the pandemic, employment in Uruguay fell by 8 percent, but overall, the labor market has remained resilient. While 152 thousand workers lost their job and those who retained their jobs worked significantly fewer hours, the impact on the labor market has been mild compared to other Latin American countries, thanks to early successes in the containment of the virus. The participation rate in Uruguay dropped by merely 1.7 percentage points, compared to an average among EM in Latin America of 4.1 percentage points. However, employment has yet to reach pre-pandemic levels. Those who left the labor force may never re-enter after health conditions normalize. Firms might be reluctant to hire, in a context of low productivity and uncertain global demand. Similarly, hours worked significantly dropped during the pandemic, with people in April 2020 working on average twelve fewer hours than they did at the beginning of the year. The situation has improved since the peak of the pandemic, but hours worked have not gone back to pre-pandemic levels, and, if the pandemic resembles the rebound following the 2002 crisis, they never will.
Change in Participation Rate 2019–2020
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: ILOSTATChange in Participation Rate 2019–2020
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: ILOSTATChange in Participation Rate 2019–2020
Citation: IMF Staff Country Reports 2022, 017; 10.5089/9798400200335.002.A001
Source: ILOSTAT15. While Uruguay entered the pandemic well positioned to face the challenges posed by the education system, the switch to distance learning resulted in more than ½ year of school lost. Uruguay has high levels of digitalization, internet access, and computer ownership in the population, including among the lowest quantile of the income distribution. In Uruguay, 77 percent of the population has Internet access, compared to an average in Latin America of 56 percent and the gap in Internet access between the poorest and the richest quintile is one of the smallest in Latin America (World Bank 2020). Access to a mobile phone with Internet in Uruguay is almost close to 100 percent (UNICEF, 2020). Nevertheless, relying on estimates from a World Bank study on the effectiveness of distance learning in Chile (World Bank, 2020b), Uruguayan students in 2020 enrolled in primary and secondary education may have lost between 0.79 and 0.63 years of school due to school closures.12 Because of school closures, the impact on human capital translates into 2.5 percent lower GDP per capita than it would have been otherwise.13 The pandemic was also felt on school enrolments and drop-out rates, reversing the progress made in this area.14 The experience of 2002 indicates that this effect—together with possible impact on college enrollments—may be long lasting.
16. The pandemic reduced investment, although the impact on the stock of capital is minimal. Investment contracted by 0.5 percent in 2020, implying a negative capital contribution of -1.3% in the year, although the impact on the overall capital stock was small. The recovery is also likely to spur new investment opportunities, in a similar fashion as the 2002 crisis, thanks to renewed interest in green energy and digitalization, unless the recovery from the pandemic stalls.
17. The extraordinary adjustments in production factors, in particular labor inputs, was offset by an increase in productivity during the pandemic. While the drop in production in 2020 was large, the reduction in production inputs (employment, hours worked, and capital) was even larger. As a result, productivity in 2020 increased, in stark contrast with past crises, when productivity was the largest margin of adjustment and production factors remained relatively stable. As a matter of fact, the magnitude of the reduction in hours worked and employment was extraordinary by historical standards.
Uruguay: Growth Accounting During the Pandemic
Uruguay: Growth Accounting During the Pandemic
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP growth | Employment | Human capital | Hours | Capital | TFP | |
| 2020 | -5.9% | -1.8% | 0.0% | -5.7% | -1.3% | 2.9% |
Uruguay: Growth Accounting During the Pandemic
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP growth | Employment | Human capital | Hours | Capital | TFP | |
| 2020 | -5.9% | -1.8% | 0.0% | -5.7% | -1.3% | 2.9% |
18. For the future, the COVID-19 crisis might erode past improvements in GDP per capita, but long-lasting negative effects on GDP growth are less likely. If human capital and hours worked remain permanently low, as they did during the 2002 crisis, the level of GDP per worker might never fully recover. As a matter of fact, primary and secondary school students may not be able to make up for the time lost during the pandemic. This would not have implications on GDP growth, but it would result in a significant erosion in GDP per capita and a setback in the fight for poverty reduction.
19. On the other hand, the structural changes spurred by the pandemic might translate into a boast in productivity and investment. The pandemic has also highlighted how advanced Internet access and digitization are important strengths Uruguay should continue to leverage and develop, and they might entail important productivity gains in the medium-term.
E. Post-Pandemic Growth
In absence of reforms, Uruguay is likely to return to pre-pandemic low growth although, if sustained, high commodity prices could provide support to growth again.
20. The next years are likely to see a strong rebound from the pandemic. In the past, Uruguay has displayed strong GDP rebounds after deep recessions. If history repeats itself, we might witness a full recovery in employment and capital, and at least a partial recovery in hours worked. The rebound from the pandemic will drive growth in the near future, mirrored by improvements in the labor market.
21. Faster potential growth might spur from a new commodity cycle, with benefits on GDP, employment, and capital accumulation. Fueled by the global demand and the recovery from COVID-19, food prices are expected to remain high in the foreseeable future (IMF October 2021 WEO), resulting in a positive impact on GDP and its components. The commodity cycle could add 1.3 percentage points to the annual GDP growth, favor employment growth and capital accumulation. This estimate is the average effect of commodity export prices on GDP growth in a sample of 32 countries in the last sixty years. While this model is a reasonable benchmark, the effect in Uruguay might differ from the average. Moreover, other forces may have been at play, including the rebound from the 2002 crisis and indirect effects from fast-growing neighboring countries. In general, these estimates are subject to significant uncertainty, for instance if commodity prices turned out lower than currently forecasted.
Uruguay: Growth Outlook for the Next Five Years
Uruguay: Growth Outlook for the Next Five Years
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP growth | Employment | Human capital | Hours | Capital | TFP | |
| Historical trend (1978–2019) | 2.5% | 0.5% | 0.4% | -0.2% | 1.4% | 0.4% |
| Pre-pandemic trends (2014–19) | 0.9% | -0.1% | 0.4% | -0.2% | 1.0% | -0.2% |
| - with high commodity prices | 1.3% | 0.5% | … | … | 0.8% | … |
| - with rebound from COVID-19 | 0.5% | 0.3% | 0% | 0.2% | 0% | 0% |
| Total growth average (2022–26) | 2.7% | 0.7% | 0.4% | 0.0% | 1.8% | -0.2% |
Uruguay: Growth Outlook for the Next Five Years
| Contributions | ||||||
|---|---|---|---|---|---|---|
| GDP growth | Employment | Human capital | Hours | Capital | TFP | |
| Historical trend (1978–2019) | 2.5% | 0.5% | 0.4% | -0.2% | 1.4% | 0.4% |
| Pre-pandemic trends (2014–19) | 0.9% | -0.1% | 0.4% | -0.2% | 1.0% | -0.2% |
| - with high commodity prices | 1.3% | 0.5% | … | … | 0.8% | … |
| - with rebound from COVID-19 | 0.5% | 0.3% | 0% | 0.2% | 0% | 0% |
| Total growth average (2022–26) | 2.7% | 0.7% | 0.4% | 0.0% | 1.8% | -0.2% |
22. Beyond the next five years, structural reforms in key sectors will be needed to sustain growth. Over the medium-term, the boost to growth from elevated commodity prices and the recovery from the pandemic is likely to fade away. Tackling structural impediments (e.g., enhancing labor market flexibility, improving efficiency and competitiveness through SOE reform, improving education outcomes, investing in digitalization and green energy, etc.) will be key to sustain strong growth.
References
Azevedo, Joao Pedro; Hasan, Amer; Goldemberg, Diana; Iqbal, Syedah Aroob; Geven, Koen. 2020. Simulating the Potential Impacts of COVID-19 School Closures on Schooling and Learning Outcomes: A Set of Global Estimates. Policy Research Working Paper; No. 9284. World Bank, Washington, DC.
Céspedes, Luis Felipe, and Andrés Velasco. 2012. “Macroeconomic Performance during Commodity Price Booms and Busts.” IMF Economic Review 60 (4): 570–99.
De Brun, Julio, 2004, Growth in Uruguay: factor accumulation or productivity gains? Inter-American Development Bank, Regional Operations Department 1. Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105 (10), 3150–3182.
Frache, Serafín, and Rodríguez, Helena (2021), Un modelo estocástico de equilibrio general para la economía uruguaya con producción de commodities, Documento de trabajo 006 – 2021, Banco Central del Uruguay.
Gruss, Bertrand, and Suhaib Kebhaj. 2021. Commodity terms of trade: A new database. International Monetary Fund.
INEEd (2021). Aristas 2020. Primer informe de resultados de tercero y sexto de educación primaria.
Jordà, Òscar, 2005, “Estimation and inference of impulse responses by local projections.” American economic Review 95, no. 1 (2005): 161–182.
World Bank, 2020, Impacto del COVID-19 en los Resultados de Aprendizaje y Escolaridad en Chile: Analisis con Base en Herramienta de Simulacion Proporcionada por el Banco Mundial (Spanish). Washington, D.C.: World Bank Group.
World Bank, 2020b, Latin American Economic Outlook 2020: Digital Transformation for Building Back Better. Chapter 3. The digital transformation for all.
UNICEF 2020, COVID-19 and primary and secondary education: the impact of the crisis and public policy implications for Latin America and the Caribbean. UNDP LAC C19 PDS No. 20.
The recent GDP rebasing lowered the initial growth estimates, as the contribution of some of the most dynamic sectors (e.g., transport and communications) was revised down.
Data on average hours worked are available only starting 1990. For the period 1978–89, these are assumed to be constant and equal to the 1990 value. Data for years of schooling comes from Barro-Lee and missing years are interpolated.
For further information on the Penn World Tables, please refer to Feenstra et al. (2015). Short-term changes in TFP also include changes in factors utilization.
ILO provides alternative data for average weekly hours worked, which display the same pattern.
The global decrease in average hours worked could be accounted for by the increase in female labor force participation.
Due to data limitations, we cannot exclude other factors contemporaneous to the 2002 crisis accounting for the observed drop in human capital and years of schooling.
On the role of human capital as driver of growth in Uruguay, see de Brun (2004).
See for instance Céspedes and Velasco (2012) on the relationship between terms of trade movements and output.
List of countries included in the analysis: Algeria, Angola, Argentina, Azerbaijan, Bahrain, Brazil, Brunei Darussalam, Chile, Colombia, Costa Rica, Ecuador, Gabon, Guatemala, Guyana, Indonesia, Iran, Kazakhstan, Kuwait, Libya, Malaysia, Oman, Paraguay, Peru, Qatar, Russia, Saudi Arabia, Syria, Trinidad and Tobago, Turkmenistan, United Arab Emirates, Uruguay, Venezuela.
For a general-equilibrium approach on the effect of commodity prices, see Frache, Rodríguez (2021).
During the period of high commodity prices, oil prices increased significantly as well. Being an oil importer, Uruguay’s terms of trade only improved around 2014. However, it turns out that the effect of import and export prices is asymmetric and import prices do not have a significant effect on economic conditions.
This assumes a loss in quality-adjusted years of education due to school closures between 12% and 3% and that schools in Uruguay have been closed or partially closed for at least 90 percent of the academic year. The resulting range of years of school lost is between 0.79 (= 0.9*(1.0–0.12)) and 0.63 years of school (= 0.9*(1.0–0.30)).
This figure is computed using a Mincer equation and years of schooling as a proxy for human capital accumulation.
