For official use only. Subject to further revision. Economic Growth in Peru1 Background paper for the report “Peru Building on Success – Boosting Productivity for Faster Growth” Peru’s strong growth performance over the last two decades was supported by macroeconomic stability, structural reforms, and favorable external conditions. Like in other commodity- exporting countries, growth acceleration in the late 2000s relative to the late 1990s was supported by favorable external conditions. A wave of structural reforms initiated in the 1990s has also created the conditions for stronger growth however. Structural and external factors help explain nearly a quarter and a fifth of the average annual growth over this period. In the last decade, one-third of growth is explained by productivity growth, but the productivity gap with high-income countries is still large. Before the 1990s, growth was driven by factor accumulation, with a negative contribution from TFP. Macroeconomic stabilization and structural reforms during the last two decades have improved efficiency in allocating resources and have led to more productivity-driven growth In the 2000s, TFP contributed around a third of Peru’s growth. This kind of productivity driven growth represents a pattern similar to that seen in other fast-growing developing economies. 1 This background note has been prepared by Cristina Savescu (Senior Economist, World Bank), Ekaterina Vostroknutova (Lead Economist, World Bank), with inputs from Daniel Francisco Barco Rondan (Economist, World Bank). For official use only. Subject to further revision. I. Historical growth perspective 1. Peru’s long-term growth performance has been undermined by recurrent short- term crises that took a heavy toll on output and income levels, heightening their volatility. Peru’s economy grew at 3.6 percent annually over the 1960-2014 period. After robust economic performance during the 1960s, averaging more than 5 percent annually, growth decelerated in the 1970s, followed by dismal economic performance during the 1980s, with output contracting 14.4 percent between 1981 and 1990, as a result of economic crisis in mid and late 1980s. Growth has accelerated following the structural reforms implemented during the 1990s, with growth averaging 5 percent per year during the 1991- 2014 period. Figure 1: GDP growth by decades (average Figure 2: Standards of living annual percent change) 6 5.5 5.3 5.4 120 300 5 100 250 3.7 3.9 4 80 200 3 60 150 2 40 100 1 0 20 50 -1 0 0 -1.0 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 -2 Peru/LAC peers Peru/Core high-income Peru/Emerging Asia (left axis) Source: Central Bank and own estimations Source: Maddison Project, World Bank. Note: Note: Simple average calculated using 1990 Gheary- Khamis international dollars 2. Peru’s long-term economic performance has lagged that of its peers. When benchmarked against both structural peers and countries that graduated to high-income levels over the course of this period Peru's relative standards of living have deteriorated, especially when compared to Emerging Asia, and to a lesser degree when compared to Latina American peers. Peru’s income per capita at PPP was twice as large as that of its regional peers in mid- 1960s, and twice and a half that of Emerging Asia peers. Even though Peru’s per capita income has grown at a good pace in the last two decades, by 2010, it was only two thirds of that of its Latin American peers, and 40 percent of that of Emerging Asia comparator countries. 3. Dramatic changes in policy orientation and lack of clear growth strategies have afflicted growth. Since late 1960s until mid-1970s, the left-leaning military government implemented policies that followed an import substitution strategy, and also carried out nationalization of firms that exploited natural resources, and a land reform. These policies were accompanied by an acceleration of public spending and strong capital accumulation with an important fiscal component, financed in part through external borrowing. However For official use only. Subject to further revision. this model of growth fueled growing domestic and external imbalances that resulted in an economic crisis that ended in an institutional succession. Economic policies under the renewed military rule remained populist, increasing government’s intervention in the economy. Economic mismanagement resulted in heightened output volatility, rising levels of inflation and large oscillation in international reserves. 4. Democracy was restored in 1980, but economic policies kept in disarray and rising terrorism added to the plight. The decision not to service international debt led to a loss of access to international financial markets. Concomitantly the country turned increasingly protectionist, adopting tariff and non-tariff trade barriers. Attempts of economic stabilization failed and only deepened the crises. During the late 1980s, hyperinflation made its appearance and reached almost 3,400 percent at the end of that decade. Also, there was mismanagement in public accounts and deficit was over 10 percent between 1987 and 1989. 5. The following decade marked a drastic change in Peru’s policies. Drastic stabilization measures were adopted in order to control hyperinflation, while also eliminating price controls and fiscal subsidies. High-scale privatization of public enterprises had the main goal of increasing efficiency in product markets, but also was key to restore credibility in public finances. The government also implemented other important structural reforms starting in early 1990s. They sought to increase trade openness, foster the development of financial and capital markets, increase the flexibility of the labor markets, and improve the efficiency of monetary and fiscal policies. 6. Economic growth remained volatile however through early 2000s, before entering a sustained phase thereafter. Despite a more stable macroeconomic environment output growth was still quite volatile, undermined by external shocks and a loss of reform momentum during the last years of the Fujimori government. Growth resumed in mid-2001 and was thereafter sustained by a favorable external environment, characterized by strong external demand, rising commodity prices, and increased liquidity in the international financial system. This resulted in significant capital flows to developing economies, including Peru. Notably Peru’s inflation throughout this period was low, fiscal accounts continued to improve, and international reserves rose to historically high levels. 7. Macroeconomic stability has played an important role in reducing output volatility. As a result of more prudent macroeconomic policies inflation decelerated markedly and enabled a deepening of financial markets. Increased financial intermediation helped to smooth private consumption, although most of the increase in financial intermediation was in foreign currency, thus preventing the de-dollarization of the financial system. With the aim of strengthening the credibility of domestic currency, in 2002 the Central Bank adopted an explicit inflation target regime, using the short-term interest rate as the operational instrument. Since then, the degree of dollarization gradually declined, in particular with respect to deposits. 8. The economy underwent important structural changes over time as a result of the structural reforms of the early 1990s which have resulted in an increase exposure For official use only. Subject to further revision. to the external environment. Trade and export have more than doubled their share of GDP, while FDI’s share of GDP has increase four fold, as the high barriers to trade that were in place during the 1980s have been gradually removed, and as free trade agreements with Peru’s main trading partners, including US, the European Union and China, have improved access to these markets. Primary fiscal balance improved while growth in monetary aggregates stabilized. At the same time, financial intermediation increased significantly, with the credit to private sector surpassing 30 percent of GDP, while the liquidity of the financial system also improved. Figure 3: Fiscal outturns 1981-2014 Figure 4: Investment ratio 4 2 0 -2 -4 -6 -8 -10 -12 -14 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Source: Central Bank of Peru Source: Central Bank of Peru, World Bank. 9. The structural changes have also create growth opportunities for Peru. Increased openness of the economy allowed Peru to take advantage of the rapid growth in world trade, while enabling it to attract international capitals to finance its investments; in particular, those undertaken by the private sector. Macroeconomic stability as a result of more prudent monetary and fiscal policies have also been an important determinant of increased access to foreign financing for investments. 10. Privatization programs in 1990s and access to international capital markets have resulted in an important acceleration of private investment. It is notable the marked acceleration in private investments in the post-reform period, with FDI contribution playing a key role. In this sense, privatizations encouraged the presence of private actors in almost every sector of the economy, reducing the share of government spending in GDP and giving room for an increasing share of private investment in total investment. For official use only. Subject to further revision. II. Growth Accounting: Sources of Growth in Peru 11. A growth accounting exercise using a Cobb-Douglas production function with constant returns to scale reveals that factor accumulation (physical and labor) explains most of Peru’s long-run growth (Equation 1). During the 1960-2013 period, capital and labor force grew at average annual rates of 5.1 percent and 3.2 percent, contributing 2.2 and 1.8 percentage points to growth, respectively (Table 4). Most of the growth over this period is thus explained by factors accumulation, while the contribution to growth from improvements in human capital is less than 20 percent. This growth pattern is similar to that of many other Latin American countries over this period. Equation 1: 1− = where: A represents Total Factor Productivity (TFP), or efficiency capturing the non- observable or unmeasurable factors K is the stock of physical capital calculated using the Perpetual Inventory Method 2 α is the income share of capital H is the human capital adjusted by quality of labor 3 = ℎ ℎ = ( ) Φ is returns to education S is average years of schooling 12. By differencing equation 1 we obtain the growth accounting decomposition. 13. Peru’s weak long-run output growth is explained by a lack of TFP growth and relatively weak improvements in human capital. Notably, the Solow residual, or Total Factor Productivity (TFP) has actually subtracted from growth over this period on average close to 1 percentage point per year, underperforming many of the countries in the region in terms of TFP growth and performing significantly worse than Emerging Asia. 14. Long-term average masks however stark differences in economic performance across time. The 1960s were characterized by strong economic growth, which averaged 5.3 percent annually, largely explained by strong capital accumulation, as it has been the case for many countries in the region. Capital accumulation explains more than 60 percent of growth, as the capital stock growth at average annual rate of 8 percent. Labor force growth explains an additional 45 percent, as the economically active population grew at an average annual rate of 2 The Capital stock is estimated using the PIM, using investment data from 1960 from the Central Bank of Peru and a depreciation rate of 4%. 3 We use the Economically Active Population (PEA). For official use only. Subject to further revision. 4.1 percent. Meanwhile both human capital explain less than 15 percent of growth each during this period, while TFP subtracted from growth 1.2 percentage points per year. 15. Economic performance deteriorated markedly during the 1970s and 1980s notwithstanding continued factor accumulation. Economic growth decelerated to an annual average of 1.5 percent, and was quite volatile. Capital accumulation continued during the crisis years, with the capital stock expanding at 4.5 percent, while the labor force growth decelerated marginally to an average of 3.6 percent annually. While human capital continued to increase at a similar path to that observed during the previous decade, the TFP deteriorated markedly over this period, declining at a 3.3 percent annual pace, explaining the growth collapse during this period, as economic mismanagement and large macroeconomic imbalances, together with increasing protectionism led to inefficient use of productive resources. 16. Peru entered a new growth phase in the 1990s with the start of the implementation of structural reforms and stabilization policies that was sustained through mid-2010s. Growth has accelerated to 3.9 percent for the period 1990-2000, accelerating further to 5.7 percent for the period 2000-2013 as TFP stabilized in the first period and accelerated to 1.7 percent in the second one. This stand in stark contrast with the previous growth periods with the TFP being the second largest contributor to growth (30 percent), growing at an annual pace of 1.7 percent. This marked improvement in the efficiency of combining the factors of production is linked to the reforms introduced in the 1990s. Growth in human capital across this period continued to decelerate marginally, as growth in average years of schooling leveled off. Capital accumulation accelerated from 3.5 percent in over the 1990-2000 period to 5.7 percent during the 2000-2013, with investment nearing 30 percent of GDP and a capital output ratio of 2.64 by 2013. Figure 5: The contributions from TFP to Figure 6: Peru compares well to other fast-growing growth turned positive after the reforms economies on TFP’s contribution to growth 8 80 Real GDP growth and contributions, Shar eof contributions to growth, percent 6 60 4 percentage points 40 2 0 20 -2 0 Peru Chile Colombia* Mexico Thailand Malaysia -4 1990-2013 1990-2012 2000-2012 1990-2012 1990-2012 1990-2012 -20 1960-1970 1970-1990 1990-2000 2000-2013 Capital Stock Labor Human Capital per Labor Total Factor Productivity -40 Real GDP Capital Stock Labor Human Capital per Labor Total Factor Productivity Source: Authors’ calculations. Source: Authors’ calculations. 17. The TFP contribution to growth is slightly lower if one controls for capital quality and utilization ratio. Céspedes and Ramírez-Rondán (2014) find that the capital contribution to growth for the period 2003-2012 increases 0.3 percentage points to 3 percent when adjusting For official use only. Subject to further revision. for quality. MEF 4 (2013) estimates that do not account for the quality and utilization ratio find the contribution to growth of capital, labor, and productivity to be 2 percent, 1.7 percent, and 2.5 percent respectively. 18. If one adjusts for the utilization ratio the cyclicality of the TFP declines as utilization of both capital and labor decline during the deceleration periods of the business cycle. Data on capacity utilization of capital is not readily available however. Loayza (2005) used the unemployment rate as a proxy for physical capital utilization, while Fuentes et al. (2006) construct and index of deviation of effective consumption of electricity from its long- run trend. Similarly one can adjust for the number of hours worked to capture the changes in intensity of the use of the labor force. Céspedes (2011) finds that hours worked have been declining for the period covered in his study. While one should adjust for utilization ratios when measuring the year-to-year changes in TFP (through the business cycle) when looking at longer-term growth adjusting for utilization ratios might lead to underestimating TFP. III. Estimating Peru’s Potential Growth 19. Potential GDP estimates play an important role in guiding macroeconomic policy and in assisting with macroeconomic forecasting. We estimate Peru’s potential GDP by employing a Cobb-Douglas production function with constant returns to scale and augmented for quality augmented for quality of labor. We assume that TFP (A) grows at its trend growth rate, that the capital is at steady state level and is fully employed, and that labor force adjusted by the quality of labor is fully employed 5: ∗1− Equation 2 ∗ = ∗ 20. To estimate trend TFP we use the Hodrick-Prescott filtering technique, making adjustments to address end-of-sample problem of the HP (Mise et al., 2005). We calculate TFP out to 2100, assuming TFP will growth at the annualized rate observed over the 1995- 2013 period, which we than smooth using HP filter to obtain the TFP trend level. Human capital per labor is assumed to grow at the rate observed since 2011 6, with constant returns to schooling. 21. Under these assumptions Peru’s potential GDP growth is estimated to decline to 4.7 percent by 2017, from 5.6 percent in 2013. Given a less favorable external environment, including markedly lower commodity prices relative to the boom-years, softer external demand as China goes through structural slowdown, investment could slow down more sharply than in our assumptions which will lower potential output growth. Furthermore monetary policy normalization in high-income countries and the US in particular could result in more volatile capital flows, and higher long-term interest rates that could affect investment decisions. 4 The MEF (2013) assume a value of 0.42 for the capital share of output and a depreciation rate for the capital of 4 percent. 5 Unemployment is at its natural rate of unemployment. 6 In 2011 there is a significant jump in the years of schooling, a performance we do not expect to be replicated. For official use only. Subject to further revision. Table 4: Growth accounting 7 Depreciation = 4% Alfa =42% ϕ=0.1116 Growth Accounting Compound Annual Growth Rates (percentage points) Contributions to Growth (α = 42%) † 1960-2013 1960-1970 1970-1990 1990-2000 2000-2013 1990-2013 1960-2013 1960-1970 1970-1990 1990-2000 2000-2013 1990-2013 Capital Stock gK 5.1 8.0 4.5 3.5 5.2 4.4 2.2 3.4 1.9 1.5 2.2 1.9 Labor gL 3.2 4.1 3.6 2.7 2.2 2.4 1.8 2.4 2.1 1.6 1.3 1.4 Human Capital per Labor gh 1.2 1.2 1.5 1.1 1.0 1.1 0.7 0.7 0.9 0.7 0.6 0.6 Total Factor Productivity gA -1.0 -1.2 -3.3 0.2 1.7 1.0 -1.0 -1.2 -3.3 0.2 1.7 1.0 Real GDP gY 3.7 5.3 1.5 3.9 5.7 4.9 3.7 5.3 1.5 3.9 5.7 4.9 Growth Accounting Contributions to Growth (α = 42%) † (%) ‡ 1960-2013 1960-1970 1970-1990 1990-2000 2000-2013 1990-2013 Capital Stock gK 59 63 127 37 38 38 Labor gL 50 45 139 40 22 28 Human Capital per Labor gh 20 14 57 17 10 12 Total Factor Productivity gA -28 -22 -224 6 30 21 Real GDP gY 100 100 100 100 100 100 Source: World Bank staff calculations 7 The growth accounting uses an augmented Cobb-Douglas production function, with a capital share of output of 0.42 and adjusting for human capital assuming a return to education of 11.16 percent. The Capital stock has been derived using the perpetual inventory method, assuming a constant depreciation rate of 4.5 percent. For official use only. Subject to further revision. Figure 7: Growth accounting: contribution to growth 15 10 5 0 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 -5 -10 Labour Capital -15 TFP Human capital per labor Potential growth Source: World Bank staff calculations. IV. Key Determinants of the Recent Growth Acceleration 22. Understanding the key determinants of growth is essential for informing policy choices. To explain the past economic performance of Peru we use a cross-country system GMM regression model developed by Brueckner (2013) which follows Loayza et al. (2005). Loayza, Fajnzylber, and Calderon (2005) using cross-country data estimate the main determinants of growth looking at five key potential determinants: the initial level of income; the position in the economic cycle; structural policies in the areas of education, financial markets development, trade openness, and provision of public infrastructure; macroeconomic stabilization policies including price stability, inflation targeting, cyclical volatility, and frequency of systemic banking crisis; and external conditions as captured by terms of trade and global growth. Following Araujo et al. (2014), we group the explanatory variables into three general categories: structural factors (secondary enrollment rate that proxy human capital, credit to the private sector as a share of GDP as proxy for financial development, trade openness, main telephone lines as proxy for infrastructure, government consumption, and institutions captured by polity2), stabilization policies (as captured by inflation, real exchange rate, and occurrence of banking crisis), and external conditions (terms of trade changes and export prices. 23. Econometric analysis indicates that structural and external factors have played a key role in the growth acceleration of the 2000s. Figure 9 illustrates the main results of the GMM model for four periods (The 2000s, Early 2000s, Late 2000s, 2010-2013). The model does of a relatively good job in predicting the growth for the 2000s (or between 1996-2000 and the 2006-2010 (“late 2000s”)). The growth (real GDP per capita at PPP) predicted by the model over this period is of 3.5 percent, in line with observed growth (Figure 10). Structural and external factors have contributed an estimated 0.8 and 0.7 percentage points to growth For official use only. Subject to further revision. annually, explaining 25 and 21 percent of the 3.5 percent annual growth recorded over this period. Meanwhile persistence effects help explain slightly more than 50 percent of growth. 24. Improvements in structural factors help explain about a quarter of the growth observed over this period, in particular infrastructure spending. Infrastructure 8 has had by far the largest contribution to growth over this period, explaining close to 80 percent of the growth attributable to structural factors. More generally, gross fixed investment has increased rapidly over this period, increasing at a 9.5 percent annualized rate, and averaging close to 18 percent of GDP in real terms over the 2000s. Private investment growth has almost doubled to an annual 10 percent during the 2000s, accounting for more than 80 percent of the gross fixed investment, while public investment continued to grow at a robust pace of nearly 8 percent. As a consequence the investment rate surged to 25.7 by 2010, rising further to a record 28.5 by 2013. Meanwhile, a slight reduction over this period in government size 9 is estimated to have supported growth. Meanwhile the share of current government expenditure in nominal terms averaged 14.9 percent during the 2000s, relatively unchanged from the previous decade, while public investment during this period averaged 3.8 percent of GDP. The government financing needs were mostly financed externally during the first half of the 2000s, while in the second half the government balances were mostly in surplus. This in conjunction with the relative size of public to private investments suggests that there has been limited crowding out of private investments over this period. 25. Increased trade openness has also had a positive, albeit more mute contribution to growth. When measured in constant prices, trade openness increased more than 10 percentage points of GDP in the 2001-2010 period relative to the previous decade, as exports grew at an annual 6.5 percent over this period, while imports increased at a 9.1 annualized pace, above the 5.6 percent economic growth pace over this period. Meanwhile our estimates show that slow financial development has hindered growth during this period. The degree of financial development has been empirically shown to have an important impact on trade flows and economic growth (Manova, 2013). Peru’s credit to the private sector is much lower than in other countries with similar income levels (Figure 10). In particular, small and medium enterprises (SME) face more constraints to accessing credit than larger firms, on account of lack of collateral or guarantees and asymmetry of information, among other constraints. Furthermore a relatively high degree of financial dollarization, and in particular a relatively large share of foreign currency-denominated loans, have generated balance sheet effects during periods of depreciations, and likely have affected the real economy and the financial sector. 26. Notable is the muted estimated contribution to growth from the human capital variable over this period. Although Peru’s secondary enrollment rates increased from an average of 78.7 percent in the 1996-2000 period to 89.7 percent in the 2006-2010 period, reaching 94 percent by 2013, human capital as measured by secondary enrolment rate has a relatively small predicted impact on growth. However the true impact is likely 8 Infrastructure is proxied by the number of fixed telephone lines per capita. 9 Government sized is proxied by the ratio of real government consumption expenditures over GDP (at PPP) For official use only. Subject to further revision. underestimated 10. When using average years of schooling as proxy for human capital per labor the unconditional impact on the dependent variable is a much larger and statistically significant (0.2 percentage points per year). Furthermore, empirical analysis by Hanusheck and Woessman (2012) using cross-sectional data shows that despite having relatively high levels of school attainment Latin American countries have had relatively low growth rates in per capita income over the past half century because of low school achievement. The measure of Peru’s human capital 11 that captures the years of schooling and the return to schooling shows that after an acceleration in the rate of accumulation of human capital during the 1990s there is a notable slowdown in the rate of accumulation of the human capital in the years 2000s, turning negative in the late 2000s. Figure 10: Financial development and income level 250 Credit to the Private Sector as share of GDP 200 150 100 50 0 0 20 40 60 80 100 120 -50 Per capita GDP thousands USD Source: WDI. 27. Similarly, the governance, as proxied by Polity2, has a marginal estimated impact on growth. Nevertheless, in line with Loayza 2005 we interpret this to mean that the effects of governance on growth work indirectly through the impact of the structural and stabilization policies implemented by the government. Governance as proxied by Polity2 IV Score 12 has improved markedly in the early 2000s in Peru, with the score rising to 9 by 2001, close to the maximum score of 10, and remaining stable during the 2000s. Peru has also improved its ranking in most of the Worldwide Governance indicators (WGI) (Kaufman et al. 2010). Government effectiveness has improved considerably, rising to percentile 48.3 by 2011, from a low of 31.7 in 2006. On the Voice and Accountability its percentile rank increased to 53.1 by 2011 from 38 in 2000, while on Political Stability and Absence of Terrorism its percentile 10 Araujo et al 2014 suggest that the statistically insignificant impact of education and political institutions could be explained by the fact that the five-year timeframe used to estimate the parameters might be too short to properly capture the impact of educational attainment, that the school attainment data is prone to mismeasurement and might not properly capture actual skills, and that they act mostly through other channels. 11 The human capital per unit of labor input ht = exp(φSt ) , where ϕ is the return to education, and St is the average years of schooling in year t. 12 The Polity2 IV Score is a combined democracy index which ranges from -10 to 10, with higher scores indicating more democratic institutions. For official use only. Subject to further revision. ranking improved to 23.11 by 2011 from 15.4 in 2000. Peru continued to score well on Regulatory Quality, ranking in the top third percentile, and has improved its ranking in Rule of Law. In terms of Control of Corruption it ranked in the percentile 52 by 2011, an important improvement over the 2000 ranking, when it ranked in 39th percentile. 28. On the external front, marked improvements in Peru’s terms of trade have also played an important role in supporting growth, explaining close to a fifth of the growth in the per capita GDP for this period. Notwithstanding significant increases in oil prices, Peru’s terms of trade over this period have improved markedly and have contributed to growth in a significant manner, as the international export prices for the main commodities exported by Peru have increased rapidly over this period. 29. The persistence effect 13 is also significant, explaining 51 percent of the predicted growth. Meanwhile the impact from stabilization has been muted over this period, and working mostly through the real exchange rate appreciation. The impact of stabilization policies implemented in earlier periods are captured by the persistence term. 30. In early 2000s, growth in per capita GDP at PPP has decelerated, and the model over predicts growth for this period. During this period the persistence effect overwhelmingly explains growth (close to 75 percent of the predicted growth), with the structural and stabilization policies of the 1990s giving an important impulse to growth in early 2000s. Meanwhile the contemporaneous external factors and stabilization policies explain less the 10 percent each. Among the structural factors lack of financial deepening is one of the key deterrents to growth during this period with a negative contribution estimated at 0.4 percentage points. 31. The model under predicts the growth acceleration of the late 2000s. Real GDP grew at an annualized 6.9 percent over the 2006-2010 period, up from 4.3 percent annualized growth during the previous five years. In per capita PPP terms using the five-year periods growth accelerated to 5.5 percent in the late 2000s from 1.5 during the early 2000s. For the late 2000s structural factors explain 40 percent of the predicted growth (of 3.8 percent) while external and the persistence effect each explain a third of the predicted growth rate. Among the structural factors the contribution from investment is by far the most important accounting for close to 30 percent of growth. Among the external factors the improvement in the terms of trade helps explain about 24 percent of the predicted growth. 13 The persistence effect captures the impulse effect from improvements in structural, stabilization, and external factors in previous periods that have an “echoing” effect this period. For official use only. Subject to further revision. Figure 11: Key growth determinants (real GDP per capita) Figure 12: Model performance The 2000s Early 2000s Late 2000s Late 2000s-2013 The 2000s Early 2000s Late 2000s Late 2000s-2013 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 Annualized GDP p.c. growth (in %) 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 1 1 1 1 0 1 1 1 1 0 0 0 -1 -1 0 0 0 0 -1 -1 persistence structural stabilization external actual predicted Source: Authors’ calculations. Source: Authors calculations For official use only. Subject to further revision. Figure 13: Counterfactual* GDP per capita Figure 14: Counterfactual GDP per capita 7800 PERU 14 18000 FINANCIAL DEVELOPMENT 20 16000 7600 12 GDP per capita (USD, PPP) 15 14000 GDP per capita (USD, PPP) 7400 12000 10 In percent 10 7200 10000 8 8000 5 7000 6000 6 6800 4000 0 4 2000 6600 0 -5 6400 2 6200 0 Financial Infrastructure Trade Real Inflation Schooling Polity 2 Government Banking Development (telecom) Openness Exchange Size Crisis Rate Counter-Factual Actual % Change - rhs Actual Counter-Factual % Change - rhs Note: * using unconditional coefficients Source: Authors calculations, adapted from Araujo et al. 2014 Source: Authors calculations, adapted from Araujo et al. 2014 For official use only. Subject to further revision. Figure 15: Counterfactual GDP per capita Figure 16: Counterfactual GDP per capita 18000 INFRASTRUCTURE (TELECOM) 20 18000 TRADE OPENNESS 14 16000 16000 12 GDP per capita (USD, PPP) GDP per capita (USD, PPP) 15 14000 14000 10 12000 12000 8 In percent In percent 10 10000 10000 6 8000 8000 4 5 6000 6000 2 4000 0 4000 0 2000 2000 -2 0 -5 0 -4 Actual Counter-Factual % Change - rhs Actual Counter-Factual % Change - rhs Source: Authors calculations, adapted from Araujo et al. 2014 Source: Authors calculations, adapted from Araujo et al. 2014 For official use only. Subject to further revision. Table 1: Growth effects of structural, stabilization and external factors in Peru Log changes Predicted effect, Annualized parameter The 2000s Early 2000s Late 2000s Early 2010s The 2000s Early 2000s Late 2000s Early 2010s Persistence 0.781 0.23 0.15 0.08 0.27 1.79 2.38 1.21 4.26 Structural 0.87 0.20 1.54 0.76 Schooling (Secondary enrollment rates) 0.018 0.13 0.08 0.05 0.02 0.02 0.03 0.02 0.01 Credit to the Private sector (% of GDP) 0.074 -0.15 -0.25 0.10 0.23 -0.11 -0.37 0.15 0.34 Government consumption (% of GDP) -0.262 -0.05 -0.03 -0.02 -0.02 0.12 0.13 0.11 0.11 Infrastructure proxy (Main lines per 100 people) 0.141 0.51 0.11 0.40 0.06 0.71 0.31 1.12 0.16 Institutions (polity2) -0.003 0.45 0.45 0.00 0.00 -0.01 -0.02 0.00 0.00 Trade openness 0.082 0.16 0.07 0.09 0.09 0.13 0.12 0.15 0.15 Stabilization 0.08 0.29 -0.13 -0.13 lnflation -0.011 -0.04 -0.05 0.01 0.00 0.00 0.01 0.00 0.00 Real Exchange Rate -0.064 0.00 -0.10 0.10 0.10 0.00 0.12 -0.13 -0.13 Banking crisis -0.040 -0.20 -0.20 0.00 0.00 0.08 0.16 0.00 0.00 External factors 0.74 0.27 1.21 -1.09 Commodity export price 10.482 0.00 0.00 0.00 0.00 0.33 0.35 0.30 -0.60 Terms of trade 0.118 0.35 -0.03 0.39 -0.21 0.41 -0.08 0.91 -0.49 Predicted per capita GDP at PPP 3.49 3.14 3.83 3.80 Source: Staff calculations, adapted from Araujo et. al 2014 For official use only. Subject to further revision. V. Benchmarking: Policy outcomes that would benefit growth the most in Peru 32. To estimate the potential effect of various structural and stabilization policies we simulate the explanatory variables in the model interpreting them as policy outcome. For this exercise we benchmark Peru against regional peers, using the 90th percentile of best performing Latin American and Caribbean country for each variable for the late 2000s. 33. Despite Peru’s an impressive economic performance over the past decade and a half, and important achievements on the structural and stabilization front, there are areas where Peru lags with respect to the benchmarks. These means that there are potential growth benefits to be had if Peru continues to close to gap with the benchmark countries in these areas. We follow the approach proposed by Araujo et al. (2014) to identify the most important potential determinants of growth for Peru. Catching up in areas with the largest gaps could potentially be easier to achieve but the decision on where the efforts are concentrated should be balanced against the potential gains in income engendered by these efforts. 34. For example, catching up with the 90th percentile most financially developed country in LAC, as measured by the domestic credit to the private sector as a share of GDP, would have a large income growth effect for Peru (Figure 13 and Figure 14). This catch up is estimated to lead to an increase of 12.8 percent in per capita income. This does not mean however necessarily that Peru has the largest gap with respect to the benchmark country in financial development, as the impact is determined also by the economic returns to the change in these variables (estimated coefficients). Furthermore, while Peru has had an impressive investment effort, both private and public, the infrastructure as proxied by the main telephone lines still lags the benchmark and closing the gap with the 90th percentile best performing LAC country would result in an estimated 8.3 percent increase in its real per capita GDP at PPP, one of the largest in the region (Figure 15). 35. Further increasing trade openness also has the potential to bring about important per capita income gains. In this area closing the gap with the benchmark is estimated to result in per capita income gains of 6.9 percent (Figure 16). 36. There are some caveats in relation to the estimates of the benchmarking exercise. As discussed in the previous section it is likely that the impact of catching up in education are underestimated, as efforts in this area take a longer term to come to fruition. Furthermore governance changes are also happening over longer period of time and are difficult to capture in fixed effects models, and therefore the estimated coefficient could also perhaps be considered as a lower bound. In addition there could be an omitted variables bias, i.e. other structural and stabilization factors that have an effect on growth and that are not captured by the model use. For official use only. Subject to further revision. VI. Sectoral Performance During the Growth Acceleration 37. Growth has been broad-based in Peru, driven by productivity growth that has underpinned the structural transformation 14 of the economy. Services 15 were the largest contributor to growth over the past 4 decades, contributing on average about 3.3 percentage points to annual growth, while the industry sector contributed on average 1.4 percentage points and the primary sector contributed less than 1 percentage points. During the 1970s and1980s services contract or had a muted contribution to growth, but they reemerged as the main contributor to growth since the early 1990s. 38. Strong growth and rising incomes during the most recent growth acceleration period bolstered demand for services in particular for retail, transportation, as well as other services such as education and health. Telecommunication and other information services also grew more rapidly, but its contribution to growth was less than a third of a percentage point, similar to the contributions from business services. Notable is the relatively modest contribution to growth of the financial services, which contributed on average less than 0.3 percentage points to annual growth, with their contribution 39. Manufacturing has also made an important contribution to growth over the 2001- 2013 period surpassing the contribution from the extractive sector. The manufacturing sector contributed 0.9 percentage point on average to growth over this period as production in the metal, machinery and equipment and rubber and non-metallic subsectors grew rapidly (Figure 13). 40. The extractive industry’s contribution to growth has been more subdued despite high international commodity prices and significant investment in the sector. The sector’s contribution to growth has been slight less than that of the manufacturing sector, supported by higher external demand for mineral exports (copper and gold in particular). 41. Low productivity growth in the agriculture sector has undermined growth in this sector, even as commodity prices have surged. Agriculture and fisheries have contributed less than a quarter of a percentage point annually to growth, despite employing a large share of the labor force. Beside the traditional coffee exports there have been a few success stories, in particular the fruit and vegetables exports (table grapes, asparagus, processed artichoke, mangoes, avocadoes) which have increased markedly over time, benefiting from free or preferential trade agreements (e.g. with the US, EU, China), relatively cheap labor and good climate. In particular exports of coffee and grapes have increased significantly. However large distances to export markets, which make transportation cost an important cost factor, export infrastructure, water scarcity in coastal areas and unresolved land issues have hindered more rapid growth. 14 The structural change is defined as the reallocation of resources across sectors with different productivities and is consistent with that used by Duarte and Restuccia (2010), McMillian and Rodrik (2011) and others. 15 Services include commerce, transport, telecommunication, health, education, public sector administration, financial services, business services, and other services. For official use only. Subject to further revision. Figure 17: Contributions to growth (supply side) Figure 18: Contributions to growth (demand sside) 8 12 10 6 0.92 8 0.85 4.28 1.33 3.63 4 2.45 3.89 3.80 6 2.35 3.24 1.84 2.29 4 2.86 2 0.13 1.74 1.39 0.95 1.70 1.61 0.18 0.24 4.29 1.83 2.17 0.37 2 3.81 3.63 1.84 3.55 0.01 1.50 1.05 1.04 0.83 0.91 1.02 1.94 0.00 0.26 0.55 0.53 0.21 0 -0.05 -0.06 0.44 0.24 0.67 -0.51 0 -0.25 -0.45 -0.32 -0.33 -1.26 -0.05 -1.12 -1.94 -1.61 -2.30 -0.71 -2.54 -2.73 -2 -2 -0.29 -0.28 -4 1971-1975 1976-1980 1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010 2011-2013 -4 1971-1975 1976-1980 1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010 2011-2013 Private consumption Public consumption Gross investment Primary sector Industry Services sector Exports Imports GDP Source: Authors’ calculations. Source: Authors calculations For official use only. Subject to further revision. Table A1. Description of Variables Variable Description Source Growth Rate of GDP per capita The change in the natural logarithm of real PPP GDP per capita PWT 7.1 between period t and t-1. Schooling The natural logarithm of the secondary school enrolment rate. WDI (2014) Private Credit/GDP The natural logarithm of the ratio of domestic credit to the private WDI (2014) sector divided by GDP. Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. Trade Openness The natural logarithm of the ratio of exports plus imports over PPP PWT 7.1 GDP adjusted for countries' population size. Telephone Lines The natural logarithm of main telephone lines per capita. Telephone WDI (2014) lines are fixed telephone lines that connect a subscriber's terminal equipment to the public switched telephone network and that have a port on a telephone exchange. Integrated services digital network channels and fixed wireless subscribers are included. Government Size The logarithm of the ratio of government consumption expenditures PWT 7.1 over GDP. Polity2 The polity2 score measures the degree of political constraints, Polity IV political competition, and executive recruitment. It ranges between - (2012, 2014) 10 to 10 with higher values denoting more democratic institutions. CPI Inflation The natural logarithm of 100+consumer price inflation rate. CPI WDI (2014) inflation reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services. Real Exchange Rate The natural logarithm of the GDP price level divided by the nominal PWT 7.1 exchange rate. Banking Crisis Indicator Variable that is unity in period t if the country experienced Reinhart and a banking crisis. Rogoff (2011) Terms of Trade Growth The change in the natural logarithm of the net barter terms of trade WDI (2014) index. The net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. ComPI Growth The change in an international commodity export price index. The Arezki and index is constructed as Brueckner (2012) = � ∈ where ComPriceit is the international price of commodity i in year t, and θic is the average (time-invariant) value of exports of commodity i in the GDP of country c. Data on international commodity prices are from UNCTAD Commodity Statistics and data on the value of commodity exports are from the NBER-United Nations Trade Database (Feenstra et al., 2004). 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