Abstract
Argentina: Selected Issues
How Large is Argentina’s Capital Accumulation Gap and How Can it Be Reduced? Lessons From Past Investment Surges1
A. Introduction
1. Over the last three and a half decades, Argentina’s investment rate has been among the lowest among peer advanced and emerging market countries. Investment rates (defined in this paper as gross fixed capital formation in percent of GDP) fell in the 1980s from already relatively low levels and recovered strongly in the 1990s. After rebounding rapidly from the historical lows experienced during the 2001 economic crisis, the investment rate fell again over the last decade, reflecting the deterioration of the macroeconomic environment and increasing government interventionist policies (Figure 1). As of 2015, Argentina’s investment rate was well below the average of Latin American countries and that of a peer group of advanced and emerging market countries, with a larger gap in private investment (Figures 2 and 3).2 The low investment rates may have contributed to the relative decline in Argentina’s GDP per capita over the same period (Figure 4).
Argentina and Peers: Nominal Fixed Capital Formation: 1981–2015
(Percent of GDP)
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: IMF World Economic Outlook database.Argentina and Peers: Private Nominal Fixed Capital Formation: 1981–2015
(Percent of GDP)
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: IMF World Economic Outlook database.Argentina and Peers: Public Nominal Fixed Capital Formation: 981–2015
(Percent of GDP)
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: IMF World Economic Outlook database and IMF staff estimates.Argentina and Peers: GDP Per Capita: 1981–2015
Millions of 2011 U.S. dollars per person (from constant 2011 national prices, logs)
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: Penn World Tables (version 9.0), INDEC, and IMF staff calculations.2. Raising investment prospects would be essential to boost economic activity. The administration that took office in December 2015 has emphasized the importance of generating an investor friendly environment that allows Argentina to recover some of the growth opportunities lost over the last few decades. With this in mind, the administration promptly removed exchange and capital controls, resolved its outstanding external debt disputes to regain access to international capital markets, and started the process of subsidy reform. In addition to structural reforms that foster investment (IMF, forthcoming), stabilizing the macroeconomic environment would be an important trigger for an investment rebound.
3. In this paper we address the following questions:
How large is the “capital accumulation gap” from Argentina’s low investment rate of the last two decades? What would be the increase in investment needed to close that gap, and how sizeable would be the boost on output?
What lessons can Argentina learn from episodes of significant investment surges in the past?
While quantifying the capital accumulation gap is a clearly a difficult task, one way of doing so is to look at the difference between Argentina’s capital-labor ratio and that of the selected peer group of countries. We also compare Argentina’s investment rates and capital-output ratios with estimates of their steady state values derived from standard neoclassical growth models. Given the uncertainty over the calibration and over estimates of capital stock in Argentina, these results would need to be interpreted as illustrative, rather than a precise numerical exercise. We then quantify the potential impact on GDP growth from the rebound of investment needed to fill these gaps, by estimating the links between output per capita and both investment and capital-labor ratios. Finally, we conduct an event analysis aimed at identifying episodes of significant and persistent investment surges in the past, and look at the conditions and policies that tended to be associated with those episodes.
4. Our main conclusions are twofold:
Argentina’s investment rate would need to increase significantly to eliminate the capital accumulation gap built during the last two decades, and this could significantly accelerate GDP growth. For example, for Argentina’s capital-labor ratio to return to the median in the peer country group, the real investment rate would need to increase over the next 15 years by about 10 percentage points of GDP, with a potential positive impact on GDP growth by between about 1 and 3 percent per annum over the same period. The existence of a capital accumulation gap suggests that Argentina could well be bound to experience a significant investment surge.
The event analysis shows that investment surges generally build over many years, and tend to coincide with an increase in national saving rate supported by fiscal consolidation. This suggests that a prudent fiscal policy, able to reallocate spending to increase public investment while gradually reducing the fiscal deficit, would facilitate the needed investment rebound.
B. Assessing Argentina's "Capital Accumulation Gap"
5. Estimates of Argentina’s capital stock are subject to severe data limitations. There are a number of cross-country databases that can be used for international comparisons, but the data for Argentina reflect official statistics that have been recently revised (including new National Account data for the period 2004–15). Alternative sources for capital stock include Maia and Nicholson (2001), Maia and Kweitel (2003), the Arklems project (Coremberg 2008, 2011,2012,2014), the Penn World Tables ((Feenstra, Inklaar, and Timmer,2013), or the TED database (The Conference Board, 2015), some of which differ significantly from each other (Figure 5). To estimate Argentina’s capital stock, we use a standard perpetual inventory model which starts from the Penn World Tables capital stock in 2003 and updates it with investment data from the latest revised system of Argentina’s National Accounts.
Argentina: Factors of Production, Alternative Data Sources: 2981–2015
(Percent of GDP)
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: Coremberg (2008, 2011, and 20145, Maia (2001 and 2003), and Penn World Tables (version 9.0).6. According to these data, Argentina’s capital-to-output ratio has failed to keep up with the increase in our peer country group. In the early 1980s, Argentina was slightly above the median of the distribution of capital-to-output ratio among emerging market peers. Although the capital-to-output ratio has remained relatively stable around its mean, the ratio has increased more in other peer countries with low capital-to-output ratios. By 2014, Argentina was 15 percent below the capital-to-output ratio of median EM peer and 24 percent lower than the median peer in the full sample (Figure 6). Most of the relative decline is due to lower pace of capital accumulation in Argentina—its capital stock grew at an annual average of 2.5 percent between 1980s and 2014, compared with a median annual average growth over 35 years of 3.5 to 3.6 percent in the EM and full sample peer country groups respectively (average output growth in this period was 2.3 percent in Argentina and 2.6 for peer countries in full sample). Argentina’s relatively high growth given its capital stock is to a significant extent attributed to periods of high commodity prices during the mid-2000s to the mid-2010s.
Argentina and Peers: Capital-to-Output Ratio: 1980–2014
(Integers)
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: Penn World Tables (version 9.0) and INDEC.7. Argentina’s has lost ground also in terms of the capital-to-labor ratio. In the early 1980s, Argentina’s workers were slightly above the full sample median in terms of their availability of capital. The capital-labor ratio increased only modestly in Argentina since then through the year 2000. It declined with the end-of-convertibility crisis, and while it increased with the commodity boom, it never recovered the ground lost. Compared to the EM subsample, Argentina has lost its advantageous position of the early 1980s and now lags behind (Figure 7). Most of the gap in the capital-to-labor ratio corresponds to the slower growth in the capital stock, as employment growth in Argentina was close to the median level in the full-sample peer distribution over this period.
Argentina and Peers: Capital-to-Labor Ratio: 1980–2015
(Millions of 2011 U.S. dollars, from constant national prices, logs)
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: Penn World Tables (version 9.0), INDEC., and IMF World Economic Outlook database.8. Argentina’s “capital accumulation gap” could be defined as the increase in the capital-labor ratio that would allow Argentina to return to the median of the full-sample peer country distribution. In 2015, Argentina’s capital per worker was 24.2 percent below the 2014 median in our distribution. The increase of Argentina’s capital-labor ratio required to eliminate this gap depends on assumptions about i) the number of years this would take and ii) the increase of the capital-labor ratio in the peer countries group over this period of time. For example, eliminating this gap in 15 years would require Argentina capital-labor ratio to grow at an average annual 5.1 percent, assuming that the median capital-labor ratio in the peer group will continue to grow at the same annual rate of 3 percent experienced on average between 2004 and 2014, and that Argentina’s employment will increase at an average growth rate of 1 percent.3 Figure 8 shows the range of growth rates of Argentina’s capital-labor ratio required to return to the median of the distribution depending on different assumptions on these parameters.
Argentina: Growth in Capital-Labor Ratio Required to Catch Up with Peer Median
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: IMF staff calculations.9. What does this imply in terms of investment rates? The relationship between the required increase in the capital-labor ratio and investment rates can be derived from the laws of motion of capital formation
which could be transformed into the law of motion of the capital-output ratio,
or the law of motion of the capital-labor ratio
Where g is the growth rate of output, kl,t is the capital-labor ratio, nt is the growth rate in employment, dt is the depreciation rate, it is the (real) investment rate, gK is the growth rate of the capital stock, and kY,t is the capital-output ratio. Introducing assumptions on the depreciation rate, the employment growth, and the dynamics of the capital-output ratio, this expression allows deriving the investment rate consistent with the growth rate of the capital-labor ratio needed to eliminate the gap. For this calculation we assume a depreciation rate of 4.5 percent (the median in the Penn-World Tables data), an employment growth rate of 1 percent, and that the capital-output ratio averages 3 over the transition period.4,5 This implies that returning to the median capital-labor ratio in 15 years would require an average annual investment rate of 32 percent, in real terms. Figure 9 shows how the required real investment rate varies with time horizons and employment growth rates.
Argentina: Investment Required to Reach Median Capital-to-Labor Intensity
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Source: IMF staff calculations.10. Another way of assessing if investment in Argentina is too low is to calibrate long-run (steady state) levels of investment using standard neoclassical growth models. In these models, the steady-state level of investment rates is a function of the steady-state capital-output ratio, the depreciation rate, and the trend growth rate of output:
where i* is the (steady-state) ratio of investment to real output, kY* is the (steady state) capital-output ratio, g is potential output growth, and d is the depreciation rate. We calibrate the expression linking the steady state investment rate to the steady state capital-output ratio for all countries in the peer group, assuming a 4.5 percent depreciation rate (the rate used in the Penn-World tables to compute capital stocks), a potential GDP growth rate of 3.3 percent for Argentina (in line with the October 2016 World Economic Outlook), and a steady-state capital-output ratio (kY*) which is the median of the long-run capital-output ratios in peer countries.6 Figure 10 shows that Argentina’s investment rate and capital-output ratios in 2014 were below these estimated long-run levels. Moving to the long-run capital-output ratio would require Argentina’s investment rate to be above its steady-state level. Once the transition is completed, sustaining the steady-state level of the capital-output ratio would require an investment rate of about 24 percent of GDP.7
Argentina: Capital/Output and Investment/Output Relative to Those to Needed to Support Median Long-Term Capital/Output Ratio: 2014
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: Penn-World Tables (version 9), IMF’s World Economic Outlook and IMF staff calculations.11. Using alternative combinations of key parameters of a standard production function also suggest the current investment rate is well below its likely steady-state value. Starting from a Cobb Douglas production function, output growth can be expressed as follows:
Alternative Parameter Specification
Alternative Parameter Specification
| Depreciation rate (percent) | 4.0 | 4.5 | 6.0 |
| Growth rate (percent) | 2.6 | 3.3 | 4.0 |
| Employment growth (percent) | 0.8 | 1.0 | 1.2 |
| Capital to output ratio | 2.7 | 3.0 | 3.3 |
| Technological growth rate (percent) | 0.7 | 1.0 | 1.3 |
| Capital share of income | 0.5 | 0.6 | 0.7 |
Steady-State Investment Rate |
18.9 | 22.9 | 30.7 |
Alternative Parameter Specification
| Depreciation rate (percent) | 4.0 | 4.5 | 6.0 |
| Growth rate (percent) | 2.6 | 3.3 | 4.0 |
| Employment growth (percent) | 0.8 | 1.0 | 1.2 |
| Capital to output ratio | 2.7 | 3.0 | 3.3 |
| Technological growth rate (percent) | 0.7 | 1.0 | 1.3 |
| Capital share of income | 0.5 | 0.6 | 0.7 |
Steady-State Investment Rate |
18.9 | 22.9 | 30.7 |
where At+1/At is the rate of technological progress (or total factor productivity) and α is the capital share of income. Recognizing the uncertainty over the long-run value of the parameters of the production function, a baseline set of values is assumed for each of them (middle column of the table) together with a realistic range of values around the baseline ones. In addition, all possible combinations of the parameters in the table are considered so as to estimate a frequency distribution of steady-state investment rates. The results are shown in Figure 11, and give a sense of the uncertainty over the estimated steady-state investment rate for Argentina. The results of the calibration suggest that staff baseline estimate for Argentina’s steady-state real investment rate is around 23 percent, while the median of the frequency distribution is close to 24 percent.
Sensitivity of Investment Rates to Alternative Parameter Specification
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
12. Argentina’s capital accumulation gap with respect to its peers is evident when it comes to infrastructure. Electricity production and access to electricity is significantly lower. Argentina ranks in the low 12 percent in access to electricity and 30 percent in production of electricity among its peers. On both of these counts, Argentina has declined since the 1990s. It ranks better in the production of energy, an area where it has improved with the discoveries of energy resources in the period. The quality of trade and transport related infrastructure is also relatively low. On communications, the picture is mixed. Argentina ranks relatively high on cell phone penetration, but ranks below the median on internet access (Figure 12).
Argentina Infrastructure Indicators: Argentina’s Percentile in Distribution of Peer Advanced and Emerging Markets
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Source: World Bank’s World Development Indicators database.13. If Argentina’s investment rate were to increase, what would be the impact on output? While there is a vast literature on the link between investment and output, there is a great range of estimates on the impact of an exogenous investment shock on GDP growth, Among the others, Barro (1991), Barro and Lee (1993), Houthakker (1961), Modigliani (1970) and Carroll and Weil (1994) all find a positive association between investment and growth using growth-regression and cross-section studies. In this section we customize our empirical analysis to Argentina’s peer group using two separate methodologies:
A panel regression that directly relates the increase in investment rates to GDP growth, and thus provides an empirical estimate of the investment accelerator effects. Because the answer may vary with the time horizon, we estimate the panel relationship over five- and ten-year frequencies, (see Annex I). The exercise controls for growth in relative export prices, deviations of the real exchange rate from its long-term average, fiscal balances to GDP, and net foreign direct investment to GDP.
A production function approach, that measures the impact of an increase of the capital-labor ratios on output per worker. This allows us to determine the impact on potential (long-term) growth from filling the capital accumulation gap, controlling for country specific demographic factors, labor market developments and relative export prices (Annex II).
14. We find that a 1 percentage point permanent increase in the investment rate could increase annual output growth between 0.1 and 0.3 percentage points.
The panel regressions suggest that a one percentage point increase in the investment rate over a 5 or 10-year period would raise GDP growth by an annual average of about 0.1 percentage points over the same period. The panel regression also shows that increases in relative export prices, a depreciation of the real exchange rate, and stronger fiscal balances all have positive effects on growth.
The production function approach yields that for each percentage point increase in the capital-labor ratio, output per worker would increases by 0.6 percentage points. This estimated parameter can be interpreted as the capital share of income (α) in a Cobb Douglas production function shown above. Based on the calibration of the production function described by the mid-column of the previous Table, a (permanent) increase in the investment rate by 1 percentage point would raise potential growth by about 0.3 percent.
C. What Drives Investment Surges? An Event Analysis
15. In this section, we focus our attention on past episodes of persistent investment surges. The objective is to identify the stylized behavior of key macroeconomic and financial variables during those episodes, so as to infer under what conditions a surge in investment would likely take place in Argentina over the coming years and what could the Argentine authorities do to increase the likelihood of such a surge taking place. Investment surges episodes are identified as periods in which the three-year moving average of investment rates (share of nominal GDP) increases by at least 2 percent of GDP. The “duration” of each episode is the number of years between the troughs and peaks of the investment series, whereas the “size” of each episode is the trough-peak increase.
16. In general, investment surges took place over a number of years. We found 57 episodes in our sample (Table 1) with an average duration of 4.8 years and an average size of 5.9 percent of GDP (a median duration of 5 years for a median increase of the investment rate of 5.1 percentage points of GDP). The longest episode took place in Spain between 1996 and 2007. Argentina experienced two surges. The first took place at the beginning of the convertibility period between 1992 and 1995. The second one right started in 2004 and ended with the global financial crisis in 2008. Also, we found that investment surges generally built up gradually, with the median investment rate increasing on average by 2 percentage points over the first two years, about 40 percent of the overall increase in the episodes.
Argentina and Peers: Investment Surge Episodes: 1981–2015
Change in the three-year moving average of the rate of fixed capital formation to nominal GDP over the period.
Argentina and Peers: Investment Surge Episodes: 1981–2015
| Country | Start year | End year duration | Investment rate  1 |
Country | Start year | End year duration | Investment rate 1 |
||
|---|---|---|---|---|---|---|---|---|---|
| Argentina | 1992 | 1995 | 3 | 4.4 | Mexico | 1998 | 2001 | 3 | 4.0 |
| Argentina | 2004 | 2008 | 4 | 7.0 | Mexico | 2004 | 2009 | 5 | 2.8 |
| Australia | 1986 | 1990 | 4 | 2.5 | New Zealand | 1980 | 1985 | 5 | 3.7 |
| Australia | 2003 | 2009 | 6 | 3.8 | New Zealand | 1994 | 1997 | 3 | 4.0 |
| Brazil | 1986 | 1989 | 3 | 6.4 | New Zealand | 2001 | 2006 | 5 | 3.0 |
| Brazil | 2006 | 2013 | 7 | 3.9 | New Zealand | 2012 | 2016 | 4 | 2.7 |
| Canada | 2003 | 2008 | 5 | 3.3 | Peru | 1993 | 1997 | 4 | 6.7 |
| Chile | 1985 | 1991 | 6 | 10.0 | Peru | 2005 | 2010 | 5 | 7.6 |
| Chile | 1993 | 1995 | 2 | 2.9 | Philippines | 1988 | 1991 | 3 | 5.1 |
| China | 1984 | 1988 | 4 | 3.1 | Poland | 1995 | 2000 | 5 | 7.9 |
| China | 1992 | 1995 | 3 | 10.0 | Poland | 2005 | 2009 | 4 | 3.6 |
| China | 1998 | 2006 | 8 | 7.3 | Russia | 2006 | 2010 | 4 | 3.5 |
| China | 2009 | 2013 | 4 | 5.7 | Saudi Arabia | 1982 | 1984 | 2 | 4.0 |
| Colombia | 1993 | 1996 | 3 | 7.4 | Saudi Arabia | 2003 | 2010 | 7 | 6.5 |
| Colombia | 2002 | 2009 | 7 | 8.1 | South Africa | 2003 | 2009 | 6 | 6.5 |
| Colombia | 2011 | 2016 | 5 | 3.3 | Spain | 1987 | 1991 | 4 | 5.5 |
| Hungary | 1996 | 2001 | 5 | 3.0 | Spain | 1996 | 2007 | 1 | 9.4 |
| India | 1984 | 1991 | 7 | 6.2 | Thailand | 1985 | 1992 | 7 | 16.3 |
| India | 1994 | 1998 | 4 | 2.6 | Thailand | 2001 | 2007 | 6 | 5.3 |
| India | 2003 | 2009 | 6 | 8.7 | Turkey | 1985 | 1989 | 4 | 6.3 |
| Indonesia | 1980 | 1983 | 3 | 5.1 | Turkey | 1992 | 1998 | 6 | 2.7 |
| Indonesia | 1987 | 1991 | 4 | 3.5 | Turkey | 2004 | 2007 | 3 | 5.0 |
| Indonesia | 1993 | 1997 | 4 | 4.2 | Ukraine | 2003 | 2008 | 5 | 6.2 |
| Indonesia | 2004 | 2010 | 6 | 8.7 | United Kingdom | 1984 | 1990 | 6 | 5.3 |
| Israel | 1991 | 1993 | 2 | 8.2 | United States | 1994 | 2001 | 7 | 2.8 |
| Japan | 1987 | 1991 | 4 | 4.1 | Vietnam | 1992 | 1998 | 6 | 13.2 |
| Korea | 1987 | 1992 | 5 | 8.5 | Vietnam | 2000 | 2005 | 5 | 5.5 |
| Malaysia | 1980 | 1983 | 3 | 4.7 | |||||
| Malaysia | 1989 | 1997 | 8 | 20.1 | |||||
| Malaysia | 2009 | 2015 | 6 | 4.7 |
Change in the three-year moving average of the rate of fixed capital formation to nominal GDP over the period.
Argentina and Peers: Investment Surge Episodes: 1981–2015
| Country | Start year | End year duration | Investment rate 1 |
Country | Start year | End year duration | Investment rate 1 |
||
|---|---|---|---|---|---|---|---|---|---|
| Argentina | 1992 | 1995 | 3 | 4.4 | Mexico | 1998 | 2001 | 3 | 4.0 |
| Argentina | 2004 | 2008 | 4 | 7.0 | Mexico | 2004 | 2009 | 5 | 2.8 |
| Australia | 1986 | 1990 | 4 | 2.5 | New Zealand | 1980 | 1985 | 5 | 3.7 |
| Australia | 2003 | 2009 | 6 | 3.8 | New Zealand | 1994 | 1997 | 3 | 4.0 |
| Brazil | 1986 | 1989 | 3 | 6.4 | New Zealand | 2001 | 2006 | 5 | 3.0 |
| Brazil | 2006 | 2013 | 7 | 3.9 | New Zealand | 2012 | 2016 | 4 | 2.7 |
| Canada | 2003 | 2008 | 5 | 3.3 | Peru | 1993 | 1997 | 4 | 6.7 |
| Chile | 1985 | 1991 | 6 | 10.0 | Peru | 2005 | 2010 | 5 | 7.6 |
| Chile | 1993 | 1995 | 2 | 2.9 | Philippines | 1988 | 1991 | 3 | 5.1 |
| China | 1984 | 1988 | 4 | 3.1 | Poland | 1995 | 2000 | 5 | 7.9 |
| China | 1992 | 1995 | 3 | 10.0 | Poland | 2005 | 2009 | 4 | 3.6 |
| China | 1998 | 2006 | 8 | 7.3 | Russia | 2006 | 2010 | 4 | 3.5 |
| China | 2009 | 2013 | 4 | 5.7 | Saudi Arabia | 1982 | 1984 | 2 | 4.0 |
| Colombia | 1993 | 1996 | 3 | 7.4 | Saudi Arabia | 2003 | 2010 | 7 | 6.5 |
| Colombia | 2002 | 2009 | 7 | 8.1 | South Africa | 2003 | 2009 | 6 | 6.5 |
| Colombia | 2011 | 2016 | 5 | 3.3 | Spain | 1987 | 1991 | 4 | 5.5 |
| Hungary | 1996 | 2001 | 5 | 3.0 | Spain | 1996 | 2007 | 1 | 9.4 |
| India | 1984 | 1991 | 7 | 6.2 | Thailand | 1985 | 1992 | 7 | 16.3 |
| India | 1994 | 1998 | 4 | 2.6 | Thailand | 2001 | 2007 | 6 | 5.3 |
| India | 2003 | 2009 | 6 | 8.7 | Turkey | 1985 | 1989 | 4 | 6.3 |
| Indonesia | 1980 | 1983 | 3 | 5.1 | Turkey | 1992 | 1998 | 6 | 2.7 |
| Indonesia | 1987 | 1991 | 4 | 3.5 | Turkey | 2004 | 2007 | 3 | 5.0 |
| Indonesia | 1993 | 1997 | 4 | 4.2 | Ukraine | 2003 | 2008 | 5 | 6.2 |
| Indonesia | 2004 | 2010 | 6 | 8.7 | United Kingdom | 1984 | 1990 | 6 | 5.3 |
| Israel | 1991 | 1993 | 2 | 8.2 | United States | 1994 | 2001 | 7 | 2.8 |
| Japan | 1987 | 1991 | 4 | 4.1 | Vietnam | 1992 | 1998 | 6 | 13.2 |
| Korea | 1987 | 1992 | 5 | 8.5 | Vietnam | 2000 | 2005 | 5 | 5.5 |
| Malaysia | 1980 | 1983 | 3 | 4.7 | |||||
| Malaysia | 1989 | 1997 | 8 | 20.1 | |||||
| Malaysia | 2009 | 2015 | 6 | 4.7 |
Change in the three-year moving average of the rate of fixed capital formation to nominal GDP over the period.
17. We now look at what distinguishes these episodes from the rest of our sample. For a series of variables, we calculate the median during the investment rebound episodes and compare it with the median during all other years. A statistical test is performed to determine whether the difference between the two medians is significant at different confidence levels (Table 2).8 Figure 13 presents the stylized evolution of the key variables around investment surge episodes.
Event Analysis Charts
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Hodges-Lehman Median Differences on Investment Surge Episodes
Percent deviation from long-term average. Results also hold for levels (at 10 percent level).
Hodges-Lehman Median Differences on Investment Surge Episodes
| Investment-to-GDP ratio | 2 349 *** | Inflation user cost | −0.002 |
| Public investment to GDP ratio | Inflation | −0.168 | |
| Private investment to GDP ratio | 0.872 | ||
| Credit to GDP ratio | −9.296 | ||
| National saving rate | 1.689 * | Effective corporate tax rate | 0.007 |
| Fiscal balance to GDP | 1.921 ** | ||
| Current account balance to GDP | −0.327 | Capital account liberalization | 0.000 |
| Current account controls | −6.250 * | ||
| Export Prices relative to those in the U.S. | 0.573 *** | ||
| Global risk aversion (CBOE VIX index) | −1.246 ** | Net capital flows to GDP | 0.765 |
| Bank Lending Conditions US | −2.025 | Net foreign direct investment to GDP | 0.465 |
| Consumer confidence index in US | 3.950 | Net portfolio flows to GDP | 0.000 |
| Net other investment to GDP | 0.299 | ||
| Real effective exchange rate (+ appreciation)1 | −4.151 ** |
Percent deviation from long-term average. Results also hold for levels (at 10 percent level).
Hodges-Lehman Median Differences on Investment Surge Episodes
| Investment-to-GDP ratio | 2 349 *** | Inflation user cost | −0.002 |
| Public investment to GDP ratio | Inflation | −0.168 | |
| Private investment to GDP ratio | 0.872 | ||
| Credit to GDP ratio | −9.296 | ||
| National saving rate | 1.689 * | Effective corporate tax rate | 0.007 |
| Fiscal balance to GDP | 1.921 ** | ||
| Current account balance to GDP | −0.327 | Capital account liberalization | 0.000 |
| Current account controls | −6.250 * | ||
| Export Prices relative to those in the U.S. | 0.573 *** | ||
| Global risk aversion (CBOE VIX index) | −1.246 ** | Net capital flows to GDP | 0.765 |
| Bank Lending Conditions US | −2.025 | Net foreign direct investment to GDP | 0.465 |
| Consumer confidence index in US | 3.950 | Net portfolio flows to GDP | 0.000 |
| Net other investment to GDP | 0.299 | ||
| Real effective exchange rate (+ appreciation)1 | −4.151 ** |
Percent deviation from long-term average. Results also hold for levels (at 10 percent level).
18. Public investment was typically higher during these episodes. The median value of public investment to GDP was 4.4 over the whole sample. Controlling for cross-country differences, public investment was almost one percentage point higher during the investment surge episodes than elsewhere in the sample, at the five percent significance level. The median private investment was also higher during the episode, although the 0.8 percentage point median difference was not statistically significant. Notable increases in public investment took place in Saudi Arabia during 2000–03 (4.9 percentage points), Malaysia during 1980–83 (4.8 percentage points), Peru during 2005–10 (2.7 percentage points), Philippines during 1988–91 (2.3 percentage points), and Vietnam during 2000–05 (1.9 percentage points).
19. The rebound in investment rates tended to coincide with an increase in gross national saving rates. The median national saving rate rose during the investment surge episodes (a difference of 1.7 percentage point of GDP with the rest of the sample, which is significant at the 10 percent level). This accounts for a large fraction of the 2.3 percentage points of GDP increase in the investment rate around these episodes. Consistent with this, the investment surges have also tended to take place with stronger fiscal balances (whose median was 1.9 percent of GDP higher than when the episodes did not take place, a statistically significant difference). This suggests that policies that strengthen the fiscal balance position while at the same time reshuffling public spending to boost public infrastructure are the most favorable fiscal policies for encouraging investment. Current account balances tended to be weaker during the episodes, but the difference is not statistically significant, suggesting that the investment surge episodes captured in this sample were generally funded domestically.
20. Real exchange rates have tended to be more depreciated during the investment surge periods. For each country and each year in the sample, we calculate the difference between the real effective exchange rate and its long-run average (over the whole sample period) as a variable indicating currency misalignments. We found that domestic currencies were about 4 percent more depreciated (further below the long-term average) in real terms during the investment surge episodes than in the rest of our sample, a difference that was significant at the 5 percent.
21. The external environment played a crucial role. In our sample, it is possible to identify three global investment surges: the first one in mid-to-late 1980s, the second one in the mid-1990s and the final one in the mid-2000s (Figure 14). The investment episodes identified in Argentina occurred at the time of the second and third global investment surges. For emerging markets, it has been documented that export prices (typically of commodities) and capital inflows have played a crucial role in explaining investment dynamics (Magud and Sosa (2015), IMF (2015). The main external variables that seem to be associated to the episodes in our sample are:
Higher relative export prices, defined as export prices of any given country relative to those in the United States (at the 1 percent significance level). This seem to reflect the importance of commodity price booms for commodity exporters, consistent with the large number of episodes during 2003–07. In fact, most of the episodes were associated with an improvement in the countries relative export prices (Table 3).
Lower global risk aversion (at the 5 percent significance level), which signals greater availability of marginal funding from abroad. In addition, bank lending standards in the U.S. (a measure of availability of funding for foreign direct investment) tended to be less tight and U.S. consumer confidence stronger, although the differences were not statistically significant. In about two-thirds of the episodes for which data is available, global risk aversion was falling (Table 3).
Greater net foreign direct investment (at the 10 percent level). Portfolio flows, however, were not significantly higher. Also, the rebound of investment in the episodes identified here does not seem to be associated with an increase in the credit-to-GDP ratio (difference in median across the two groups was not significant). This seems to suggest that investment sentiment may have played a larger role than easier access to finance in triggering the investment surges identified in this paper. In about three-fourths of the episodes for which data is available received larger FDI flows (Table 3).
Argentina and Peers: Frequency of Investment Surge Episodes: 1981–2015
(Number of episodes)
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Sources: INDEC., IMF World Economic Outlook database, and IMF staff calculations.Direction of Variation of Key Variables During Episodes
(Number of episodes)
Direction of Variation of Key Variables During Episodes
(Number of episodes)
| Increase | Decrease | No change | Observations | |
|---|---|---|---|---|
| Relative export price change | 42 | 3 | 0 | 45 |
| Change in global risk aversion | 12 | 24 | 0 | 36 |
| Foreign direct investment change | 26 | 9 | 0 | 35 |
Capital account liberalization |
15 | 5 | 31 | 51 |
| Liberalization of current account controls | 17 | 2 | 31 | 50 |
| Tariff reduction (weighted average) | 0 | 0 | 11 | |
| Political stability | 29 | 15 | 3 | 47 |
Direction of Variation of Key Variables During Episodes
(Number of episodes)
| Increase | Decrease | No change | Observations | |
|---|---|---|---|---|
| Relative export price change | 42 | 3 | 0 | 45 |
| Change in global risk aversion | 12 | 24 | 0 | 36 |
| Foreign direct investment change | 26 | 9 | 0 | 35 |
Capital account liberalization |
15 | 5 | 31 | 51 |
| Liberalization of current account controls | 17 | 2 | 31 | 50 |
| Tariff reduction (weighted average) | 0 | 0 | 11 | |
| Political stability | 29 | 15 | 3 | 47 |
22. Structural reforms were also present in many of the episodes. Liberalization of current and capital account controls were present in about a third of the episodes, and in the nonparametric analysis presented in Table 2 the liberalization of current account controls comes out as a statistically significant characteristics of the investment surge episodes captured in this paper. Trade liberalization was also present in some of the episodes. Although information on import tariffs is scant, the 11 episodes for which data is available experienced a reduction in the weighted average of import tariffs. Finally, the political environment seems to have improved in more than half of the episodes (Table 3).
23. These macroeconomic and structural factors mattered to different degrees to each of the episodes considered in the sample. To give a flavor, we will discuss here a few stylized facts for a few of the episodes and present some panel charts in Annex III. For example,
In the 2004–08 episode in Argentina, most of the increase in the investment rate took place in the private sector, as public investment increased slightly and only in the later years of the episode, and accounted only a small fraction of the total increase. The external environment was favorable with rising export prices and falling global risk aversion. Part of the windfall gains were saved, as fiscal balance improved significantly giving room for the private sector to invest. A very depreciated real exchange rate helped with external competitiveness.
A similar story holds for Mexico during roughly the same period 2004–09, with a favorable external environment for export prices and external borrowing, was reflected in an improvement in the fiscal balances, and the backdrop of recent currency depreciation.
In Peru 2005–10, foreign direct investment played a more prominent role as it doubled to 5 percent of GDP during the episode. Public investment also rose by about 3 percentage points of GDP, in an episode characterized by rising commodity prices and a 4 percentage point improvement in fiscal balances. In Turkey 2004–07, FDI also played an important role, as did fiscal consolidation over the period.
In New Zealand 2001–05, a significant part of the increase in the investment rate came from the rise in public investment, despite a strong improvement of overall fiscal balances. The significant real currency appreciation, associated with strong capital inflows as global risk aversion fell sharply, may have contained investment opportunities in the private sector.
In Australia, 1986–90, wide-ranging structural reforms contributed to a favorable environment for investment. These reforms “freed up markets, promoted competition and generally sought to ensure that prices did their job of signaling costs and relative returns” (Banks, 2005). The package included trade liberalization, enhanced competition policy, and labor market reforms such as change in the wage settlement system. Important changes to the macroeconomic policy framework were also introduced with the adoption of inflation targeting and changes to the tax structure, including lowering the corporate tax rate. The investment rate rose by about 2 percentage points over the period, in a context of rising export prices and stronger fiscal balances. Most of the increase in the investment rate took place in the outer years, as the cost of structural reforms was paid up-front while the benefits take some time to materialize.
24. Bilateral panel logit regressions are consistent with these results. Because we are interested in the time (rather than the cross-country) dimension of the panel, we include country-fixed effects in these logit regressions The logit regressions suggest that the probability that a country would experience an investment surge increases in the presence of greater relative export prices, national saving rates, and foreign direct investment, while it falls with rising global risk aversion and currency appreciation. However, the increases in public investment did not increase the probability of an investment surge (Table 4).
Bilateral Panel Logit Regressions
Bilateral Panel Logit Regressions
| Coefficient | Odds-ratio | Signficance 1 | |
|---|---|---|---|
| External variables | |||
| Relative commodity prices | 0.611 | 1.843 | *** |
| Global risk aversion (VIX index) | −0.048 | 0.953 | *** |
|
Domestic variables |
|||
| National domestic saving | 0.054 | 1.055 | *** |
| Fiscal balance to GDP | O.380 | 1.462 | *** |
| Net foreign direct investment to GDP | 0.138 | 1.147 | *** |
| Real effective exchange rate | −0.005 | 0.995 | * |
| Public investment to GDP | −0.033 | 0.030 |
Bilateral Panel Logit Regressions
| Coefficient | Odds-ratio | Signficance 1 | |
|---|---|---|---|
| External variables | |||
| Relative commodity prices | 0.611 | 1.843 | *** |
| Global risk aversion (VIX index) | −0.048 | 0.953 | *** |
|
Domestic variables |
|||
| National domestic saving | 0.054 | 1.055 | *** |
| Fiscal balance to GDP | O.380 | 1.462 | *** |
| Net foreign direct investment to GDP | 0.138 | 1.147 | *** |
| Real effective exchange rate | −0.005 | 0.995 | * |
| Public investment to GDP | −0.033 | 0.030 |
25. Multivariate panel logit regressions confirm that investment surge episodes tended to be associated with an improvement in fiscal balances. The multivariate regression relates the investment surge dummy to the ratio of public investment to GDP, the national saving rate, relative export prices, global risk aversion (VIX index), the real effective exchange rate, net foreign direct investment rate, and the fiscal balance to GDP ratio. Controlling for fixed and time effects, the only significant variables are the fiscal balance to GDP ratio (at the one percent significance level) and the real effective exchange rate (although the latter only at the 10 percent significance level). The odds ratio suggests that every percentage point of GDP improvement of the fiscal balance increases the probability of an investment surge by 1.4 percentage point. The sign of the real exchange rate, however, suggests that an appreciation would increase the likelihood of an investment surge, which seems counterintuitive (Table 5). For robustness, multivariate regressions were also conducted controlling separately for fixed and time effects (not shown). In both versions, the fiscal balance remained significant at the 1 percent level, with similar figures for the odds ratio (exceeding 1). In contrast, the real exchange rate was not significant in either specification. In the fixed effects specification, global risk aversion was significant at the 5 percent level, suggesting that fluctuations in the perceptions of risk by global investors may explain the bunching of investment episodes across time. Controlling only for time effects, which emphasizes the cross-country dimension, the national saving rate, the public investment to GDP, and relative export prices were also significant at the 1 percent level.
Multilateral Panel Logit Regressions
Significance at the 1,5, and 10 percent levels, respectively (***,**,*).
Multilateral Panel Logit Regressions
| Coefficient | Odds-ratio | Signficance 1 | |
|---|---|---|---|
| Public investment to GDP | 0.105 | 1.111 | |
| National savingto GDP | −0.036 | 0.965 | |
| Relative export prices | −1.446 | 0.236 | |
| Global risk aversion (VIX index) | −0.030 | 0.971 | * |
| Real effective exchange rate (+ = appreciation) | 0.013 | 1.013 | |
| Net foreign direct investment to GDP | 0.094 | 1.098 | |
| Fiscal balance to GDP | 0.311 | 1.365 | *** |
Observations |
395 | ||
| LR chi 2(47) | 181.6 | ||
| Prob>chi2 | 0 | ||
| Pseudo R2 | 0.3649 | ||
| Log likelihood | −158 | ||
| Fixed effects | Yes | ||
| Time Effects | Yes |
Significance at the 1,5, and 10 percent levels, respectively (***,**,*).
Multilateral Panel Logit Regressions
| Coefficient | Odds-ratio | Signficance 1 | |
|---|---|---|---|
| Public investment to GDP | 0.105 | 1.111 | |
| National savingto GDP | −0.036 | 0.965 | |
| Relative export prices | −1.446 | 0.236 | |
| Global risk aversion (VIX index) | −0.030 | 0.971 | * |
| Real effective exchange rate (+ = appreciation) | 0.013 | 1.013 | |
| Net foreign direct investment to GDP | 0.094 | 1.098 | |
| Fiscal balance to GDP | 0.311 | 1.365 | *** |
Observations |
395 | ||
| LR chi 2(47) | 181.6 | ||
| Prob>chi2 | 0 | ||
| Pseudo R2 | 0.3649 | ||
| Log likelihood | −158 | ||
| Fixed effects | Yes | ||
| Time Effects | Yes |
Significance at the 1,5, and 10 percent levels, respectively (***,**,*).
References
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Annex I. Panel Investment and Growth
The international experience on the relationship between investment and growth can be summarized through a panel regression. The unbalanced panel is estimated for the period 1980–2014 controlling for growth in relative export prices as well as for fixed and time effects. Two versions of the panel are estimated to assess the relevance of how long an increase in the potential capital formation rate is sustained: 5 years, and 10 years. For example, the interpretation of the coefficient on the real investment (fixed capital formation) rate in the 10-year version of the panel would be that a 1 percentage point increase in the fixed capital formation rate sustained for 10 years would deliver an annual percent increase of 0.0954 percent. Over ten years, this would imply about a 1 percent increase in GDP.
The results are robust to different versions of the capital formation ratio and the inclusion of additional controls, including the real exchange rate deviations from long-run trends, the fiscal balance to GDP and foreign direct investment (FDI) in percent of GDP. The impact of commodity prices, however declines, suggesting that commodity prices may in turn affect the other controls, like the real exchange rate, fiscal balances, and even FDI. The difference in the growth impact of fixed capital formation when expressed as percent of nominal or real GDP virtually disappears at the 10 year frequencies. The estimates suggest that real exchange rate appreciation hurts economic growth (perhaps through competitiveness considerations) as do fiscal deficits sustained over 5 to 10 year horizons (perhaps through crowding out of the private sector). The results also suggest that private investment is more important than public investment in growth fluctuations, and that non-FDI funded fixed capital formation has a stronger impact than FDI on economic activity, perhaps through lower import leakages.
Growth and Investment Panel Regressions, Various Controls, Five Year Horizons
Growth and Investment Panel Regressions, Various Controls, Five Year Horizons
| A5 | B5 | C5 | D5 | ||
|---|---|---|---|---|---|
| Fixed capital formation | |||||
| Total, percent of real GDP | 0.08530 *** | ||||
| Total, percent of nominal GDP | 0.10600 *** | ||||
| Private, percent of nominal GDP | 0.12800 *** | ||||
| Private without FDI, percent of nominal GDP | 0.12800 *** | ||||
| Public, percent of nominal GDP | 0.06000 | 0.06000 | |||
eal export prices (change in logs) |
0.16300 *** | 0.15400 *** | 0.13600 *** | 0.13600 *** | |
| Real exchange rate (Percent deviation from | |||||
| long-term average) | −0.01180 *** | −0.01110 *** | −0.01300 *** | −0.01300 *** | |
| Fiscal balance (percent of nominal GDP) | 0.11300 *** | 0.11800 *** | 0.10500 *** | 0.10500 *** | |
| FDI (percent of dollar GDP) | −0.19300 *** | −0.16400 *** | −0.16700 *** | −0.03880 | |
Observations |
640 | 645 | 604 | 604 | |
| Countries | 26 | 26 | 25 | 25 | |
| R-squared | |||||
| Overall | 0.727 | 0.731 | 0.741 | 0.741 | |
| between | 1 | 1 | 1 | 1 | |
| within | 0.453 | 0.463 | 0.470 | 0.470 | |
| rmse | 0.0115 | 0.0114 | 0.0112 | 0.0112 | |
| Fixed effects | Yes | Yes | Yes | Yes | |
| Time effects | Yes | Yes | Yes | Yes | |
Growth and Investment Panel Regressions, Various Controls, Five Year Horizons
| A5 | B5 | C5 | D5 | ||
|---|---|---|---|---|---|
| Fixed capital formation | |||||
| Total, percent of real GDP | 0.08530 *** | ||||
| Total, percent of nominal GDP | 0.10600 *** | ||||
| Private, percent of nominal GDP | 0.12800 *** | ||||
| Private without FDI, percent of nominal GDP | 0.12800 *** | ||||
| Public, percent of nominal GDP | 0.06000 | 0.06000 | |||
eal export prices (change in logs) |
0.16300 *** | 0.15400 *** | 0.13600 *** | 0.13600 *** | |
| Real exchange rate (Percent deviation from | |||||
| long-term average) | −0.01180 *** | −0.01110 *** | −0.01300 *** | −0.01300 *** | |
| Fiscal balance (percent of nominal GDP) | 0.11300 *** | 0.11800 *** | 0.10500 *** | 0.10500 *** | |
| FDI (percent of dollar GDP) | −0.19300 *** | −0.16400 *** | −0.16700 *** | −0.03880 | |
Observations |
640 | 645 | 604 | 604 | |
| Countries | 26 | 26 | 25 | 25 | |
| R-squared | |||||
| Overall | 0.727 | 0.731 | 0.741 | 0.741 | |
| between | 1 | 1 | 1 | 1 | |
| within | 0.453 | 0.463 | 0.470 | 0.470 | |
| rmse | 0.0115 | 0.0114 | 0.0112 | 0.0112 | |
| Fixed effects | Yes | Yes | Yes | Yes | |
| Time effects | Yes | Yes | Yes | Yes | |
Growth and Investment Panel Regressions, Various Controls, Ten-Year Horizons
Growth and Investment Panel Regressions, Various Controls, Ten-Year Horizons
| A10 | B10 | C10 | D10 | ||
|---|---|---|---|---|---|
| Fixed capital formation | |||||
| Total, percent of real GDP | 0.11100 *** | ||||
| Total, percent of nominal GDP | 0.11000 *** | ||||
| Private, percent of nominal GDP | 0.13700 *** | ||||
| Private without FDI, percent of nominal GDP | 0.13700 *** | ||||
| Public, percent of nominal GDP | −0.01750 | −0.01750 | |||
Real export prices (change in logs) |
0.14400 *** | 0.13800 *** | 0.10600 *** | 0.10600 *** | |
| Real exchange rate (Percent deviation from | |||||
| long-term average) | −0.00587 *** | −0.00351 * | −0.00679 *** | −0.00679 *** | |
| Fiscal balance (percent of nominal GDP) | 0.05700 *** | 0.07920 *** | 0.08340 *** | 0.08340 *** | |
| FDI (percent of dollar GDP) | −0.14600 *** | −0.09520 ** | −0.10100 ** | 0.03630 | |
Observations |
635 | 640 | 599 | 599 | |
| Countries | 26 | 26 | 25 | 25 | |
| R-squared | |||||
| Overall | 0.796 | 0.795 | 0.811 | 0.811 | |
| between | 1 | 1 | 1 | 1 | |
| within | 0.404 | 0.405 | 0.437 | 0.437 | |
| rmse | 0.00820 | 0.00821 | 0.00793 | 0.00793 | |
| Fixed effects | Yes | Yes | Yes | Yes | |
| Time effects | Yes | Yes | Yes | Yes | |
Growth and Investment Panel Regressions, Various Controls, Ten-Year Horizons
| A10 | B10 | C10 | D10 | ||
|---|---|---|---|---|---|
| Fixed capital formation | |||||
| Total, percent of real GDP | 0.11100 *** | ||||
| Total, percent of nominal GDP | 0.11000 *** | ||||
| Private, percent of nominal GDP | 0.13700 *** | ||||
| Private without FDI, percent of nominal GDP | 0.13700 *** | ||||
| Public, percent of nominal GDP | −0.01750 | −0.01750 | |||
Real export prices (change in logs) |
0.14400 *** | 0.13800 *** | 0.10600 *** | 0.10600 *** | |
| Real exchange rate (Percent deviation from | |||||
| long-term average) | −0.00587 *** | −0.00351 * | −0.00679 *** | −0.00679 *** | |
| Fiscal balance (percent of nominal GDP) | 0.05700 *** | 0.07920 *** | 0.08340 *** | 0.08340 *** | |
| FDI (percent of dollar GDP) | −0.14600 *** | −0.09520 ** | −0.10100 ** | 0.03630 | |
Observations |
635 | 640 | 599 | 599 | |
| Countries | 26 | 26 | 25 | 25 | |
| R-squared | |||||
| Overall | 0.796 | 0.795 | 0.811 | 0.811 | |
| between | 1 | 1 | 1 | 1 | |
| within | 0.404 | 0.405 | 0.437 | 0.437 | |
| rmse | 0.00820 | 0.00821 | 0.00793 | 0.00793 | |
| Fixed effects | Yes | Yes | Yes | Yes | |
| Time effects | Yes | Yes | Yes | Yes | |
Annex II. Panel Production Function
Output per worker (or labor productivity) is a function of the capital stock and relative export prices. This relationship holds as a panel for Argentina’s peer countries under a variety of measurements for the capital stock and employment identified in the earlier sections. Estimates for the elasticity of labor productivity to relative export prices varies between 10 and 14 percent across the different measurements of the capital-to-labor ratio. In turn, the elasticity of the capital-to-labor ratio to labor productivity ranges from 50 to 61 percent (Annex II. Table 1). Fixed effects for Argentina came out negative under all specifications. The relationship between labor productivity, the capital-to-labor ratio, and relative export prices also holds as a vector error correction form in a dynamic panel specification. The long-term coefficient is 0.5 (Annex II Table 2).
GDP per capita depends, by definition, on the same variables than output per worker and on the share that the population that is employed. A panel regression under this specification delivers all variables statistically significant and with the right signs (Annex II. Table 3). This prompts the question of what determines the level of employment of the population. Although the share of the population in working age (15–64) is a likely candidate, it turns out to be not significant under a variety of specifications. Two important variables appear to play an important role. The first is the female participation in the labor force, which is a slow moving (structural) characteristic of an economy. The second is related to the business cycle fluctuations of an economy, and could be proxied by the investment rate (Annex II. Table 4).
Production Function Panel Regressions
Production Function Panel Regressions
| GDP per capita | Estimate p-val | Estimate p-val |
|---|---|---|
| Capital-to-labor ratio | 0.611 0.000 | 0.558 0.00 |
| Employment-to-population ratio | 0.739 0.00 | |
| Relative export prices | 0.106 0.030 | 0.135 0.02 |
Memo items: |
||
Fixed-effects |
Yes | Yes |
| Argentina FE | 0.215 | 0.110 |
| Time-effects | Yes | Yes |
| Period | 1992–2014 | 1992–2014 |
| Robust standard errors | Yes | Yes |
| Observations | 593 | 593 |
| Number of clusters (countries) | 26 | 26 |
| R-squared (overall) | 0.995 | 0.995 |
Production Function Panel Regressions
| GDP per capita | Estimate p-val | Estimate p-val |
|---|---|---|
| Capital-to-labor ratio | 0.611 0.000 | 0.558 0.00 |
| Employment-to-population ratio | 0.739 0.00 | |
| Relative export prices | 0.106 0.030 | 0.135 0.02 |
Memo items: |
||
Fixed-effects |
Yes | Yes |
| Argentina FE | 0.215 | 0.110 |
| Time-effects | Yes | Yes |
| Period | 1992–2014 | 1992–2014 |
| Robust standard errors | Yes | Yes |
| Observations | 593 | 593 |
| Number of clusters (countries) | 26 | 26 |
| R-squared (overall) | 0.995 | 0.995 |
Vector Error Correction Production Function Panel
Vector Error Correction Production Function Panel
| Output per employed person | Esti mate | P-val | Estimate | P-val |
|---|---|---|---|---|
| Short-run adjustment | ||||
| Capital-labor ratio | 0.527 | 0.000 | 0.340 | 0.000 |
| Relative export prices | 0.106 | 0.000 | 0.111 | 0.000 |
| VIX index | −0.001 | 0.006 | ||
| Long-run relationship | −0.136 | 0.001 | −0.094 | 0.000 |
| Lagged Long-run relationship | ||||
| Capital-labor ratio | 0.509 | 0.000 | 0.456 | 0.000 |
| Relative export prices | 0.148 | 0.000 | −0.047 | 0.000 |
| VIX index | 0.000 | 0.441 |
Vector Error Correction Production Function Panel
| Output per employed person | Esti mate | P-val | Estimate | P-val |
|---|---|---|---|---|
| Short-run adjustment | ||||
| Capital-labor ratio | 0.527 | 0.000 | 0.340 | 0.000 |
| Relative export prices | 0.106 | 0.000 | 0.111 | 0.000 |
| VIX index | −0.001 | 0.006 | ||
| Long-run relationship | −0.136 | 0.001 | −0.094 | 0.000 |
| Lagged Long-run relationship | ||||
| Capital-labor ratio | 0.509 | 0.000 | 0.456 | 0.000 |
| Relative export prices | 0.148 | 0.000 | −0.047 | 0.000 |
| VIX index | 0.000 | 0.441 |
Production Function Panel Regressions on GDP Per Capita
Production Function Panel Regressions on GDP Per Capita
| GDP per capita | Estimate p-val | |
|---|---|---|
| Public captial-to-labor ratio | 0.297 | 0.00 |
| Private capital-to-labor ratio | 0.157 | 0.00 |
| Relative export prices | 0.203 | 0.00 |
| Employment to population ratio | 0.007 | 0.00 |
Memo items: |
||
Fixed-effects |
Yes | |
| Argentina FE | −0.221 | |
| Time-effects | Yes | |
| Period | 1992–2011 | |
| Robust standard errors | Yes | |
| Observations | 592 | |
| Number of clusters (countries) | 26 | |
| R-squared (overall) | 0.995 | |
Production Function Panel Regressions on GDP Per Capita
| GDP per capita | Estimate p-val | |
|---|---|---|
| Public captial-to-labor ratio | 0.297 | 0.00 |
| Private capital-to-labor ratio | 0.157 | 0.00 |
| Relative export prices | 0.203 | 0.00 |
| Employment to population ratio | 0.007 | 0.00 |
Memo items: |
||
Fixed-effects |
Yes | |
| Argentina FE | −0.221 | |
| Time-effects | Yes | |
| Period | 1992–2011 | |
| Robust standard errors | Yes | |
| Observations | 592 | |
| Number of clusters (countries) | 26 | |
| R-squared (overall) | 0.995 | |
Panel Regressions on Employment to Population Ratio
Panel Regressions on Employment to Population Ratio
| Employment to Population ratio | Estimate p-val | |
|---|---|---|
| Working-age population (15–64) | −0.324 | 0.09 |
| Female share of labor force | 1.025 | 0.00 |
| Nominal fixed capital formation to GDP ratio | 0.353 | 0.01 |
Memo items: |
||
Fixed-effects |
Yes | |
| Argentina FE | −8.177 | |
| Time-effects | Yes | |
| Period | 1992–2014 | |
| Robust standard errors | Yes | |
| Observations | 756 | |
| Number of clusters (countries) | 33 | |
| R-squared (overall) | 0.945 | |
Panel Regressions on Employment to Population Ratio
| Employment to Population ratio | Estimate p-val | |
|---|---|---|
| Working-age population (15–64) | −0.324 | 0.09 |
| Female share of labor force | 1.025 | 0.00 |
| Nominal fixed capital formation to GDP ratio | 0.353 | 0.01 |
Memo items: |
||
Fixed-effects |
Yes | |
| Argentina FE | −8.177 | |
| Time-effects | Yes | |
| Period | 1992–2014 | |
| Robust standard errors | Yes | |
| Observations | 756 | |
| Number of clusters (countries) | 33 | |
| R-squared (overall) | 0.945 | |
Annex III. Panel Charts for Selected Investment Surge Episodes
Argentina 2004–08
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Australia 1986–90
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Mexico: 2004–09
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
New Zealand: 2001–05
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Peru: 2005–10
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Turkey: 2004–07
Citation: IMF Staff Country Reports 2016, 347; 10.5089/9781475552645.002.A001
Prepared by Jorge Iván Canales-Kriljenko.
To bring into the discussion the international experience, we compare developments in Argentina with those in a group of 32 other peer countries, selected to include all G-20 countries, 20 large emerging markets, all BRICS, five other large Latin American countries and peer historical development partners like Spain. All key trading partners are included. The peer group attempts to inform the evolution of developments in Argentina from countries that either face similar issues or that have higher income levels to which Argentina may aspire. The peer group includes, among advanced economies, Australia, Canada, Czech Republic, France, Germany, Israel, Italy, Japan, Korea, New Zealand, Spain, United Kingdom, and the United States, and among emerging markets, Argentina, Brazil, Chile, China, Colombia, Hungary, India, Indonesia, Malaysia, Mexico, Peru, Philippines, Poland, Russia, Saudi Arabia, South Africa, Thailand, Turkey, Ukraine, and Vietnam.
United Nations demographic projections imply an average growth rate of 1 percent in Argentina’s working age population between 2015 and 2025. This suggests that a 1 percent growth rate on employment, abstracting from cyclical fluctuations, is a valid reference point.
This depreciation rate may be conservative for an economy such as Argentina experiencing a large shift in relative prices that may alter the profitability of sectors across the economy, and therefore the economic viability of the existing capital stock. Higher depreciation rates would require more investment to sustain a higher level of output.
Based on the assumed parameters, the capital-to-output ratio would rise to 4 by 2030 (3.4 by 2025) from 2.5 in 2015, with an average level of 3 during the transition.
For each county, the long-run capital-to-output ratio was calculated as the maximum value of the capital-output ratio over 15-year averages between 1950 and 2014, following IMF (2005).
An increase of Argentina’s real investment rate to its peak during the period 1980–2015 (21 percent of GDP) would close 50 percent of the estimated “capital accumulation gap” in the long run.
In particular, the method used is the Hodges-Lehmann robust median difference confidence interval tests. To identify the episodes, the confidence intervals are used to identify the values equivalent to a 1, 5, and 10 percent significance level (Newson 2002). The confidence intervals are computed with robust variances that take account of the clustering of observations for individual countries.
