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Latin America’s infrastructure gap: a macroeconomic perspective Luis Servén The World Bank ECLAC January 2005 Plan 1. The changing policy framework 2. The infrastructure gap 3. The output cost 4. The lessons The changing policy framework • Until the 1970s, the public sector dominated infrastructure provision in both industrial and developing countries. • Since the 1980s (earlier in Chile and the UK) Latin America led the worldwide drive towards opening up of infrastructure to private initiative – in various forms and extents. • The drive was propitiated by a hardening of fiscal discipline in response to financial instability and macroeconomic crises • In most countries, the fiscal retrenchment led to a sharp contraction of public infrastructure investment (similarly to the post-Maastritch fiscal adjustment in the EU) The changing policy framework Latin America: Public Investment in Infrastructure (weighted average of 7 countries, percent of GDP) 4.0 3.5 3.0 2.5 % 2.0 1.5 1.0 0.5 Total Roads plus Rails Note: 7 Latin Am erica countries, ARG, BOL, BRA, CHL, COL, MEX, PER. Power Water Telecommunications 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 0.0 The changing policy framework Latin America’s fiscal adjustment: Contribution of consumption and investment Changes between 1980-84 and 19992001 (percent of GDP) Public infrastructure investment (1) Public consumption (2) Argentina -2.87 8.22 Bolivia Brazil Chile Colombia Mexico Peru -2.48 -2.57 -1.38 -0.59 -2.20 -1.43 2.38 9.97 -2.51 11.30 1.31 0.53 Source: Calderón and Servén (2004b). (a) Contributions to fiscal adjustment Primary surplus (3) Public infrastructure investment -(1)/(3) Public consumption -(2)/(3) 6.23 0.46 -1.32 0.40 0.62 1.89 0.17 0.42 2.10 -0.39 -2.42 3.45 -3.23 -0.25 -0.78 6.15 4.12 0.73 3.50 5.24 0.68 (b) The changing policy framework Latin America: Private Investment in Infrastructure (weighted average of 7 countries, percent of GDP) 1.6 1.4 1.2 1.0 % 0.8 0.6 0.4 0.2 Total Roads plus Rails Note: 7 Latin Am erica countries, ARG, BOL, BRA, CHL, COL, MEX, PER. Power Water Telecommunications 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 0.0 The changing policy framework Latin America: Total investment in Infrastructure (weighted average of 7 countries, percent of GDP) The changing policy framework Latin America: Total investment in Infrastructure (6 major countries, percent of GDP) The changing policy framework Latin America: Investment in Infrastructure (public + private) (weighted average of 7 countries, percent of GDP) 4.0 3.5 3.0 2.5 % 2.0 1.5 1.0 0.5 Total Roads plus Rails Note: 7 Latin America countries, ARG, BOL, BRA, CHL, COL, MEX, PER. Power Water Telecommunications 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 0.0 The changing policy framework The private sector response • Private initiative surged in the 1990s -- but with diversity across industries (and countries) • Strong response in telecommunications, much less in transport. • Evidence of public-private complementarity, not only substitution: countries maintaining higher public investment attracted more private investment (Chile, Bolivia, Colombia) • The rise in private investment was not enough for asset accumulation to keep up with other world regions • The investment fall contributed to widen Latin America’s infrastructure gap – in terms of quantity and quality -widened over the 1980s and 1990s The changing policy framework Brazil: the power sector Investment Capacity change The infrastructure gap Power Generation Capacity (megawatts per 1,000 workers, Medians by Region) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 1980 1985 LAC (19) 1990 EAP7 (7) 1995 MIDDLE (64) IND (21) 2001 The infrastructure gap Road plus Railway Length (km per area, Medians by Region ) 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 1980 1985 LAC (19) 1990 EAP5 (5) 1995 MIDDLE (53) IND (21) 2001 The infrastructure gap Total Telephone Lines (lines per 1,000 workers, Medians by Region) 3,000 2,500 2,000 1,500 1,000 500 0 1980 1985 LAC (19) 1990 EAP7 (7) 1995 MIDDLE (64) IND (21) 2001 The infrastructure gap Perceived infrastructure quality Figure 2.17. Overall Infrastructure Quality (Medians by region, 2000) Medians by region and income level, 2000 6 5 4 3 2 1 0 LAC (11) EAP7 (7) MIDDLE (27) Question: The quality of the infrastructure is among the best in the world (1=strongly disagree; 7 =strongly agree). Source: World Competitiveness Report. IND (24) The output cost Why do we care about infrastructure ? • The availability and quality of infrastructure services is key for productivity and profitability • Robust association between infrastructure availability and aggregate output / growth within and across countries • Partly driven by reverse causality (growth encourages demand for infrastructure services) • But there is broad agreement that infrastructure development has a strong causal effect of on economic development. • Evidence that infrastructure development helps reduce income inequality – makes it easier for the poor to access economic opportunities, jobs, health and education. Infrastructure Stocks and Economic Growth (1960-2000) 8% Growth Rate of GDP per capita 6% BWA THA CHN -4 SGP 7% y = 0.0056x + 0.0206 R2 = 0.2547 TWN KOR 5% HKG JPN CYP IRL PRT MUS ROM IDN ESP GRC TUN 3% AUTBEL PAK FINITANOR DOM ISR IND EGY MAR SYRBRA FRA USA PAN NLD CHL CAN TUR TTO LKA HUN DNK AUS SWE GBR IRN DEU 2% MEX COL ZWE PRY NPL DZA ECU CHE PHL JOR CRI UGA GNB URY NZL BGD KEN GHA GTM ZAF 1% ARG PER SLV JAM PNG BFA MRT ETH TZA HND BOL CIV 0% MLI RWA GINSLE SEN VEN -3 -2 -1 ZMB 0 1 POL 2 MDG NGA -1% NIC NER -2% 4% MYS Index of Infrastructure Stocks (1st. Principal Component) Source: Calderón and Servén (2004b) 3 Growth in GDP per worker Infrastructure accumulation and growth (1960-2000 country averages, percent) 7% 6% 5% 4% 3% 2% 1% 0% -1% -2% -3% -2% y = 0.505x + 0.0006 R2 = 0.3253 0% 2% 4% 6% 8% Growth in infrastructure stocks per worker Rest lac Source: Calderón, Easterly and Servén (2003) eap7 10% Infrastructure Stocks and Income Inequality (1960-2000) 0.7 0.6 ZWEBRA KEN HND BWA MEX COL PAN ZAF ECU CHL BOL GTM 0.5 MDG PER BFA ZMB PHL TUR DOM SLV THA MYS VEN CRI NGA TTO PNG IRN PRY URY JAM MAR TUN TZA ARG CIV LKA HKGSGP 0.4 ETH JOR MUS UGA EGY AUS IRL FRA PRT NZL USA GHA GRC ITA JPN CHN KOR NOR BGD IDN DNK YSR SWECHE IND PAK 0.3 ISR DEU TWN CAN RWA NLD FIN GBR AUT ROMPOLESP BEL CYP HUN BGR Gini Coefficient (0-1) SEN 0.2 y = -0.0303x + 0.403 R2 = 0.2157 0.1 0.0 -4 -3 -2 -1 0 1 Index of Infrastructure Stocks (1st. Principal Component) Source: Calderón and Servén (2004b) 2 3 The output cost • What is the contribution of infrastructure services to aggregate output and/or its growth rate ? Three main empirical approaches in the literature: 1. Empirical growth models 2. Augmented production (or cost) function 3. VARs Caveats: -- technical problems often severe (identification / reverse causality, spurious regressions…) -- all else equal: the costs of “getting there” are not explored – large tax rises or cuts in other expenditures that may have an output cost… The output cost The long-run growth approach: • Adding infrastructure into a standard growth regression • Infrastructure usually proxied by telecommunications indicators (e.g., Easterly 2001, Loayza et al 2003) Calderón and Servén 2004b: panel of 100+ countries, 40 years Consider both infrastructure quantity and quality Synthetic infrastructure indicator: first principal component of {power, roads, telecom} – accounts for 80% of their variance. Endogeneity: identification via GMM-IV with (a) internal instruments; (b) demographic variables Growth contribution of infrastructure quantity and quality is statistically and economically significant. The output cost Additional growth in LAC countries due to increased infrastructure development Source: Calderón and Servén 2004b The output cost The augmented production function approach: • Unlike VARs and growth regressions, it is a structural approach Y = F (K, H, Z); K = physical capital; H = human capital (often omitted) ; Z = infrastructure capital (power, phone lines, roads) • Productive services assumed proportional to asset stocks • In actual data, Z often is already included in K: The coefficient on Z captures the return differential on Z over K • In addition to usual reverse causality problem, spurious correlation problem when using time series: nonstationarity of Y, K, Z leads to huge infrastructure coefficient estimates (Aschauer 1990) The output cost The augmented production function approach Calderón and Servén 2005: panel time-series estimation for 90 countries, 40 years. • Spurious regression problem does not arise here (due to large N) • Only one long-run relation found – resolves identification problem • Pooled and country-specific estimates – permit assessing heterogeneity across countries / regions • Synthetic index and disaggregated infrastructure assets • Results broadly similar to Calderón, Easterly and Servén 2003 – in spite of very different approach (GMM-IV to deal with identification; first-differencing to deal with nonstationarity) The output cost Estimated (log) infrastructure coefficients (DOLS estimates, 1960-2001, synthetic index) Coefficient S.E. 0.091 0.013 All (89 countries) 0.130 0.019 Industrial (21) 0.080 0.027 Developing (68) 0.145 0.024 Pooled Country-specific: mean by group Source: Calderón and Servén 2005 The output cost Country-specific estimates [Synthetic Infrastructure Index, GLS--PIC (1,1)] 40 35 30 25 20 15 10 5 0 <0.20 -0.10 0.00 0.10 Source: Calderón and Servén 2005 0.20 0.30 The output cost • The estimated return on infrastructure assets is significantly higher than that on other physical capital in the vast majority of countries. • Infrastructure has significantly lower returns than other capital only in 3 out of 89 countries [none in LAC] • Across LAC countries, some heterogeneity too: The differential return on overall infrastructure is significantly higher than average in Peru, Mexico, Colombia… Differences also across assets – e.g., the differential return on power generation capacity is significantly lower than average in Paraguay, but higher in Brazil The output cost Estimated (log) infrastructure coefficients (DOLS estimates, 1960-2001) Electricity Generation Capacity Pooled DOLS Roads Main Telephone Lines 0.074 ** 0.018 0.060 ** 0.022 0.046 ** 0.015 All (89 countries) 0.115 ** 0.022 0.104 ** 0.042 0.052 ** 0.026 Industrial (21) 0.120 ** 0.030 0.135 * 0.070 -0.016 0.038 Developing (68) 0.113 ** 0.027 0.094 * 0.051 0.073 ** 0.032 Country-specific: means by Group Source: Calderón and Servén 2005 The output cost The cost of the widening infrastructure gap: EAP vs LAC Avg. 1991-00 vs. Avg. 1981-90 Avg. 1996-00 vs. Avg. 1981-85 1. Change in relative infrastructure endowments (%) Main Phone Lines Electricity Generating Capacity Roads 27.6 37.9 30.3 41.1 58.0 50.3 2. Change in Relative GDP per worker (%) 31.6 41.9 3. Contribution of the infrastructure gap 9.3 14.5 4. Relative contribution (as % of [2]) 29.2 34.7 Source: Based on Calderón and Servén 2005 The lessons (1) Fiscal adjustment, as commonly measured and enforced, tends to have an anti-investment bias • One (not the only) major factor is the use of inappropriate fiscal rules targeting liquidity, the cash deficit and gross public debt – rather than solvency and net worth, which are key to fiscal sustainability. • Infrastructure projects have a negative short-run liquidity effect -- it takes time to build the assets and get the returns. • The focus on fiscal liquidity discourages such projects – even if they are consistent with good public economics; i.e., they enhance solvency. The lessons (2) Infrastructure investment cuts represent an inefficient fiscal adjustment strategy • The direct effect of the spending cut is to raise liquidity and public sector net worth • But there is an opposing indirect effect: less infrastructure means less output and lower fiscal revenues tomorrow • The indirect effect offsets partly the direct effect – and can even make fiscal adjustment self-defeating. Summary • Latin America’s infrastructure gap widened in the 1980s and early 1990s, at a substantial cost in terms of output and productivity. • A major factor in the process was the investment slowdown – caused by a public investment decline not offset (except in telecom) by private sector participation. • The public investment compression reflected a biased and inefficient fiscal adjustment, encouraged by rules targeting liquidity and debt rather than solvency and net worth. • Ensuring adequate room for productive spending requires fiscal rules that reconcile solvency and growth. End The changing policy framework Fiscal discipline has led to a public investment fall not only in developing countries – also in the EU • The fiscal targets imposed in the Maastritch Treaty contributed to a decline in public investment across Europe: • Out of 9 countries exceeding the Maastritch deficit limit in 1992, 8 met it in 1997. Public investment had fallen in all 8 ! • Infrastructure investment fell along with the total The changing policy framework Fiscal adjustment and public investment (average of 9 EU countries, percent of GDP) 1.4 3 2 1.3 1 1.2 0 -1 1.0 -2 0.9 -3 Transport Investment, mean (left scale) Primary Deficit, mean (right scale) Sources: World Developm ent Indicators - World Bank; and provisional data from ECMT. Notes: (a) Total = Roads + Rails + Airports. (b) 9 EU countries: Austria, Finland, France, Netherlands, Norw ay, Portugal, Spain, Sw eden and United Kingdom . 2000 1999 1998 1997 1996 1995 1994 1993 1992 -6 1991 0.6 1990 -5 1989 0.7 1988 -4 1987 0.8 % GDP % GDP 1.1