The role and impact of infrastructure in middle-income countries: Anything special?

Abiad, Abdul; Debuque-Gonzales, Margarita; Sy, Andrea Loren Working Paper The role and impact of infrastructure in middle-income countries: Anything special? ADB Economics Working Paper Series, No. 518 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Abiad, Abdul; Debuque-Gonzales, Margarita; Sy, Andrea Loren (2017) : The role and impact of infrastructure in middle-income countries: Anything special?, ADB Economics Working Paper Series, No. 518, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS178974-2 This Version is available at: https://hdl.handle.net/10419/169349 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK AsiAn Development BAnk 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org The Role and Impact of Infrastructure in Middle-Income Countries: Anything Special? This paper finds that the provision of infrastructure varies across different levels of development and growth performance. Basic infrastructure, such as transport, water, and sanitation, are emphasized more during early stages of development, while “advanced” infrastructure, such as power and especially information and communication technology, become more important during later stages. In addition, better-performing middle-income countries tend to have more information and communication technology infrastructure than their peers, and tend to invest more in infrastructure. Finally, public investment is shown to have a more significant and sustained impact on output in middle-income countries than in low-income countries. About the Asian Development Bank ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries reduce poverty and improve the quality of life of their people. Despite the region’s many successes, it remains home to a large share of the world’s poor. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration. Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. adb economics working paper series NO. 518 august 2017 The ROle AND IMpACT Of INfRASTRuCTuRe IN MIDDle‑INCOMe COuNTRIeS: ANyThINg SpeCIAl? Abdul Abiad, Margarita Debuque-Gonzales, and Andrea Loren Sy ADB Economics Working Paper Series The Role and Impact of Infrastructure in Middle-Income Countries: Anything Special? Abdul Abiad, Margarita Debuque-Gonzales, and Andrea Loren Sy No. 518 | August 2017 Abdul Abiad ([email protected]) is economic advisor at the Economic Research and Regional Cooperation Department, Asian Development Bank, Mandaluyong City, Philippines. Margarita Debuque-Gonzales ([email protected].ph) is an assistant professor, School of Economics, University of the Philippines, Quezon City, Philippines. Andrea Loren Sy ([email protected]) is a consultant at the Economic Research and Regional Cooperation Department, Asian Development Bank, Mandaluyong City, Philippines. This paper was prepared as background material for the A sian Development Outloo k 2017 theme chapter, “Transcending the Middle-Income Challenge” (ADB 2017b). We thank seminar participants at the Asian Development Outlook Workshop on “Transcending the Middle-Income Challenge” for helpful comments and suggestions. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2017 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 632 4444; Fax +63 2 636 2444 www.adb.org Some rights reserved. Published in 2017. ISSN 2313-6537 (Print), 2313-6545 (e-ISSN) Publication Stock No. WPS178974-2 DOI: http://dx.doi.org/10.22617WPS178974-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area, or by using the term “country” inthis document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be bound bytheterms of this license. For attribution, translations, adaptations, and permissions, please read the provisions andterms of use at https://www.adb.org/terms-use#openaccess This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributed toanother source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Notes: 1. In this publication, “$” refers to US dollars. 2. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda CONTENTS TABLES AND FIGURES iv ABSTRACT v I. INTRODUCTION 1 II. PRIMER: INFRASTRUCTURE AND THE ECONOMY 2 III. TAKING STOCK: THE STYLIZED FACTS 4 IV. INFRASTRUCTURE PROVISION AND INVESTMENT: GOING DEEPER 8 V. THE MACROECONOMIC EFFECTS OF PUBLIC INVESTMENT 16 VI. CONCLUSION 19 APPENDIXES 21 REFERENCES 25 TABLES AND FIGURES TABLES 1.a Sectoral Infrastructure Regressions, Without Fixed Effects 10 1.b Sectoral Infrastructure Regressions, With Fixed Effects 11 2 Sectoral Infrastructure Regressions Using Lagged GDP per Capita, Level, and Quadratic Terms 12 3.a Sectoral Infrastructure Regressions for the Developing Country Subsample 13 3.b Sectoral Infrastructure Regressions for the Middle-Income Country Subsample 13 4 Using the Composite Infrastructure Index of Donaubauer, Meyer, and Nunnenkamp 14 5 Public Investment Regressions 15 A1 Data Sources 21 A2 List of Countries 22 A3 Summary Statistics 23 A4 Sectoral Infrastructure Regressions for the Middle-Income Country Subsample Using Lagged GDP per Capita 24 FIGURES 1 Infrastructure, Income, and Growth Performance 6 2 Shares of Different Types of Infrastructure in Total Infrastructure Stock 7 3 Total Infrastructure Investment 8 4 Effects of Public Investment on Output 18  ABSTRACT We examine the evolution of infrastructure, and the impact of infrastructure investment, in middleincome countries (MICs). We document how different types of infrastructure stocks, as well as infrastructure investment, vary with the level of development and growth performance. We then use the two-stage approach of Corsetti, Meier, and Müller (2012) to identify exogenous public investment shocks and investigate the macroeconomic impact of these shocks. We find that the provision of infrastructure varies across development stages; there is a focus on basic infrastructure, such as transport, water, and sanitation, during early stages, and an emphasis on “advanced” infrastructure, such as power and especially information and communication technology, in later stages. Better-performing MICs tend to invest more in infrastructure. They also have more information and communication technology infrastructure. Finally, we find a more significant and sustained impact of exogenous public investment shocks on output in MICs than in low-income countries. Keywords: economic development, infrastructure, middle income JEL codes: H54, O18, O40, Q40 I. INTRODUCTION The provision of infrastructure has long been considered a crucial element of economic development— a widespread view not only in the academic setting but also in policy circles (Calderón and Servén 2008). Although related empirical studies dating back to the late 1980s (beginning with Aschauer in 1989) have not always been in complete agreement, a majority finds a strong positive link between infrastructure and development outcomes (see Straub 2008 for a survey). Middle-income countries (MICs) often face challenges in sustaining growth, and hence could potentially benefit from increased infrastructure investment. As they develop, countries often experience substantial slowdowns in growth when they reach middle-income status, preventing many of them from making a quick transition to high-income status.1 Eichengreen, Park, and Shin (2014) find that growth decelerations usually occur at per capita income levels of about $10,000–$11,000 and again at $15,000–$16,000 a year in 2005 international purchasing power parity (PPP) dollars. Historically, it takes about 55 years for a country to graduate from lower-middle-income to upper-middle-income status, and another 15 years to graduate to high-income status, with only a few economies, mostly in Asia, able to do so significantly faster (Felipe, Kumar, and Galope 2017). By raising productive capacity, infrastructure investment could play a critical role in sustaining growth in MICs. In a theoretical treatment of the phenomenon, Agénor and Canuto (2015) hypothesize that investing in advanced infrastructure, such as information and communication technology (ICT), can raise potential growth in MICs. But the type of investment undertaken is important, as an economy’s infrastructure needs may change as it develops. While the transition from low income to middle income corresponds to a basic shift from sectors with low to higher productivity, the transition to high-income status is more complex, requiring countries to diversify into a wider set of products, innovate rather than just imitate, and upgrade to more complex products with higher value added. The empirical literature to date is sparse on whether infrastructure plays a special role in overcoming the development challenges faced by MICs. The few studies that have examined the issue simply include a few indicators of infrastructure as one of many right-hand-side controls, in the search for correlates of growth. The results are unsurprisingly diverse. Using probit analysis, Aiyar et al. (2013) find that better infrastructure (measured by roads and telephone lines) lowers the risk of a growth slowdown in MICs. Examining differences in group means, Vandenberg, Poot, and Miyamoto (2015) find that middle-income economies tend to be weak in infrastructure (measured by roads, electricity, and phone lines) relative to high-income countries (HICs). And Han and Wei (2017), applying newly developed nonparametric classification techniques (conditional tree and random forest analysis), do not find any special role for infrastructure (measured by electricity, roads, and rail) in MICs in terms of separating fast-growing and slow-growing economies, although they do find that good transport infrastructure matters for low-income countries (LICs). The goal of the paper is to better understand the evolution and nature of infrastructure in MICs. We begin by investigating whether the level and sector compositions of infrastructure, as well as patterns of infrastructure investment, change as countries attain middle-income status and, further, if these vary with growth performance within an income group. 1 While this has often been referred to in the literature as a “middle-income trap” (a term first used by Gill and Kharas in 2007), other authors argue that this label is not accurate, as the data do not support the notion that MICs are more likely than other income groups to be stuck, or that they have a high probability of being caught in such a trap (see for example, Han and Wei 2017; and Felipe, Kumar, and Galope 2017). 2 | ADB Economics Working Paper Series No. 518 We then examine whether the macroeconomic impact of infrastructure investment differs for MICs. The direction of expectations here is less clear. We expect middle-income economies to have smaller infrastructure shortfalls than LICs, which would lower the marginal productivity of infrastructure investment. However, investment efficiency and absorptive capacity could also be higher in MICs because of stronger institutions, resulting in better selection and execution of infrastructure projects. To address these questions, this paper presents stylized facts on the provision of infrastructure across the different country income groups and the different levels of performance within these groups, where the latter is measured in terms of growth in gross domestic product (GDP) per capita. Physical measures of different types of infrastructure capital are used, supplemented by an overall measure of infrastructure investment that combines public investment and private infrastructure investment data. Using the data on infrastructure stocks, we probe the robustness of the stylized facts by estimating panel regressions that formally test the relationship between a country’s income level and growth performance on the one hand, and infrastructure stocks and infrastructure investment on the other. Finally, an empirical method based on Corsetti, Meier, and Müller (2012) is adopted to identify exogenous public investment shocks and examine whether the effects of infrastructure investment on economic output are different for MICs. The study reveals several interesting results regarding MICs. First, there is a clear pattern in the sectoral provision of infrastructure across development stages, with basic infrastructure such as transport and water and sanitation emphasized more during the early stages, and more advanced infrastructure such as power and ICT becoming more important during later stages. Second, fastergrowing MICs invest more in infrastructure than slower-growing countries. They also tend to have a greater share of infrastructure in ICT than other MICs. Finally, there is a more significant and sustained impact of public infrastructure investment on output in MICs relative to LICs. The rest of the paper is organized as follows. Section II provides a primer on infrastructure that outlines our conceptual framework. Section III discusses the stylized facts regarding infrastructure provision across development stages and levels of growth performance, with section IV providing more solid econometric backing to the findings. Section V presents the analysis on the macroeconomic effects of infrastructure investment, while section VI concludes. II. PRIMER: INFRASTRUCTURE AND THE ECONOMY Infrastructure typically refers to the basic structures that facilitate and support economic activity. In this paper, we use the term to denote network infrastructure—transport by roads and rails, electricity, water and sanitation, and telecommunication by landlines, mobile phones, and internet systems. By providing essential services and connecting markets, infrastructure is essential for the smooth functioning of the economy. It is highly complementary to labor and other types of capital. Therefore, its contribution to output gains can be potentially large. Infrastructure differs from other types of capital in a few important ways. Infrastructure projects are often big and capital intensive, making them natural monopolies. They have large upfront costs, but benefits accumulate over very long periods. They also tend to generate positive externalities, as social returns can exceed the private gains that can be generated. Because of these peculiarities, which make private financing and provision of infrastructure difficult, infrastructure is still commonly provided or regulated by the public sector. For example, ADB (2017a) finds that over 90% of infrastructure The Role and Impact of Infrastructure in Middle-Income Countries: Anything Special? | 9 the infrastructure stocks and find no unit root, except in the case of water access, which could not be tested due to the insufficient number of observations per cross section. The key explanatory variables enter the regressions as dummy variables, representing income groups (with low-income economies serving as the omitted group) and growth performance (with the slowest-growing countries serving as the omitted group).9 Using a standard set of controls, we estimate the following equation: ,   ,  ,  ,  ,  ,  ,  ,  ,    , where , is the log of the level of infrastructure stock in country  at time  (except for water and sanitation, where access is measured in percent of population); , , ,, and , are the income group dummies; , and , are the growth performance dummies; , is the percent share of agriculture in GDP; , is the log of population density; , is the degree of urbanization, defined as urban population as a percent of total population; and  and  are the country and time fixed effects, respectively. The auxiliary controls used are similar to those used in Fay and Yepes (2003) and Ruiz-Nuñez and Wei (2015), among others. Measures of economic structure (percent share of agriculture in GDP), population density, and degree of urbanization are included as more industrialized and more densely populated and urbanized countries can be expected to have more infrastructure. We present the results both without and with country and year fixed effects, with the latter being the baseline specification as it controls for systematic unobserved heterogeneities across countries and over time. The regressions confirm that LMICs tend to have greater infrastructure stocks than LICs, and that UMICs tend to have higher infrastructure stocks than both groups (Tables 1.a and 1.b, first two rows; formal tests of differences in coefficients are presented at the bottom of each table). This is true for most types of infrastructure. The link between countries’ level of development and infrastructure provision is strong; regressions with only the income group dummies as explanatory variables and without fixed effects can already explain 15%–45% of the variation in advanced ICT (mobile and internet) and transport infrastructure, and 70% or more of the variation in telephone, energy, water, and sanitation infrastructure. A few results stand out that add nuance to our previous observations on infrastructure provision across development stages. One is the continued accumulation of mobile, internet, and energy infrastructure throughout and beyond the middle-income stage, as evidenced by the increasing size of the country income dummy coefficients (Table 1.b, first three rows). The other, also based on these coefficients, is the tendency for road and telephone line provision, as well as water and sanitation, to level off following a run-up during the upper-middle-income phase. A formal test of differences in coefficients between the HIC dummy and UMIC dummy confirms these results, as reported at the bottom of Table 1.b. For roads, a possible explanation is that, during early stages of development, the focus is on expanding the road network—building new roads where none existed. But at later stages of development, the focus shifts to increasing the quality and capacity of existing roads (e.g., upgrading from two lanes to four, or from provincial roads to national roads and highways), and this is not captured in the indicator we use, which measures the length of the road network. Water and sanitation access also tends to rise during the early middle-income stages, but provision of these services naturally tapers as 9 For completeness, we also introduce dummies for HICs with GDP per capita (year 2011 PPP) ≥ $17,600. Results are similar when regressions are estimated on a sample that excludes HICs (see Table 3.a.). (1) 10 | ADB Economics Working Paper Series No. 518 access becomes practically universal by the time a country reaches high-income or even upper-middleincome status. The regressions also confirm the positive association between growth performance and some types of infrastructure stocks. Most notably, better performers (the top 25%) tend to have more ICT infrastructure (Table 1.b, columns 1–3). Countries in the top quartile of growth performance tend to have about 25%–50% more telephone mainlines, mobile subscriptions, and internet usage than those in the bottom quartile of growth performance. Good growth performers also seem to have slightly greater (2%–4% more) access to water and sanitation. Exceptions to the positive relationship are rail provision, which seems unrelated to growth performance, and energy and roads, where good growth performers seem to have slightly less (8%–14%) provision. Coefficients on the auxiliary control variables generally have the correct signs, with higher population density and urbanization associated with more infrastructure, and a larger agriculture sector associated with less infrastructure. Table 1.a: Sectoral Infrastructure Regressions, Without Fixed Effects (1) (2) (3) (4) (5) (6) (7) (8) Variables Telephone Mainlines Mobile Subscriptions Internet Users Electricity Total Roads Rails Water Access Sanitation Access (per 100 people) (per km2 ) (% of population) Lower-middle income 0.875*** –0.049 0.338 0.928*** 0.271*** 0.160*** 10.527*** 24.877*** (0.050) (0.287) (0.250) (0.046) (0.039) (0.047) (1.942) (1.466) Upper-middle income 1.764*** 0.668** 0.826*** 1.498*** 0.741*** 0.936*** 16.076*** 36.785*** (0.052) (0.306) (0.263) (0.053) (0.049) (0.058) (2.298) (1.484) High income 2.479*** 1.056*** 1.650*** 2.404*** 1.625*** 1.598*** 17.423*** 46.718*** (0.063) (0.366) (0.309) (0.064) (0.056) (0.068) (2.663) (1.737) Mid50 0.089*** –0.216 0.179 –0.042 –0.072*** –0.008 1.676* 1.261** (0.027) (0.135) (0.116) (0.027) (0.023) (0.027) (0.917) (0.582) Top25 0.182*** 0.601*** 0.599*** 0.263*** –0.524*** –0.302*** 1.718 –0.942 (0.044) (0.211) (0.188) (0.042) (0.054) (0.055) (1.404) (1.076) Agriculture, share of GDP –0.037*** –0.053*** –0.057*** –0.028*** –0.011*** –0.012*** –0.338*** –0.298*** (0.002) (0.010) (0.008) (0.002) (0.002) (0.002) (0.075) (0.044) Population density 0.089*** –0.012 0.057* –0.024** 0.672*** 0.503*** 2.617*** 3.571*** (0.009) (0.041) (0.035) (0.009) (0.009) (0.010) (0.350) (0.246) Urbanization 0.020*** 0.010** 0.017*** 0.021*** 0.001 0.003** 0.183*** 0.324*** (0.001) (0.004) (0.004) (0.001) (0.001) (0.001) (0.044) (0.026) Constant –0.540*** 1.373*** –0.413 1.386*** –4.576*** –7.169*** 56.321*** 11.239*** (0.086) (0.471) (0.390) (0.091) (0.090) (0.103) (4.097) (2.530) Observations 3,249 2,016 1,779 3,713 3,291 3,225 525 2,045 R-squared 0.897 0.185 0.374 0.870 0.810 0.703 0.766 0.859 Formal test of differences in coefficients UMIC > LMIC Yes Yes Yes Yes Yes Yes Yes Yes HIC > UMIC Yes Yes Yes Yes Yes Yes Yes Yes Top25 > Mid50 Yes Yes Yes Yes No Yes No No GDP = gross domestic product, HIC = high-income country, km2 = square kilometer, LMIC = lower-middle-income country, UMIC = upper-middleincome country. Notes: Robust standard errors are in parentheses below the coefficients. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimates. The Role and Impact of Infrastructure in Middle-Income Countries: Anything Special? | 11 Table 1.b: Sectoral Infrastructure Regressions, With Fixed Effects (baseline) (1) (2) (3) (4) (5) (6) (7) (8) Variables Telephone Mainlines Mobile Subscriptions Internet Users Electricity Total Roads Rails Water Access Sanitation Access (per 100 people) (per km2 ) (% of population) Lower-middle income 0.518*** 0.892*** 0.760*** 0.327*** 0.300*** –0.013 3.540*** 5.007*** (0.049) (0.125) (0.162) (0.031) (0.024) (0.016) (1.363) (0.798) Upper-middle income 0.900*** 1.390*** 1.602*** 0.485*** 0.339*** 0.067** 4.917*** 7.665*** (0.064) (0.164) (0.212) (0.046) (0.042) (0.028) (1.753) (0.885) High income 0.817*** 1.845*** 1.880*** 0.600*** 0.277*** 0.069** 4.027* 6.390*** (0.072) (0.245) (0.268) (0.054) (0.046) (0.031) (2.163) (1.011) Mid50 0.030 0.017 0.336*** –0.079*** –0.037* 0.033* 1.994 1.498** (0.039) (0.129) (0.124) (0.026) (0.020) (0.017) (1.470) (0.684) Top25 0.231*** 0.500*** 0.268* –0.080** –0.138*** –0.011 3.861** 1.953*** (0.053) (0.184) (0.161) (0.040) (0.033) (0.020) (1.578) (0.632) Agriculture, share of GDP –0.033*** –0.081*** –0.071*** –0.012*** –0.011*** 0.000 –0.095 –0.148*** (0.002) (0.009) (0.009) (0.002) (0.001) (0.001) (0.081) (0.027) Population density 0.164** 6.941*** 5.482*** –0.302*** 0.274*** 0.045 21.519*** 6.401*** (0.076) (0.515) (0.447) (0.049) (0.042) (0.038) (3.094) (1.017) Urbanization 0.027*** –0.003 0.044*** 0.020*** 0.005*** 0.002* 0.308*** 0.440*** (0.003) (0.012) (0.014) (0.002) (0.002) (0.001) (0.102) (0.043) Constant –1.161*** –35.147*** –32.377*** 3.009*** –2.219*** –4.120*** –24.508* 31.082*** (0.320) (2.480) (2.166) (0.270) (0.197) (0.127) (13.818) (4.790) Observations 3,249 2,016 1,779 3,713 3,291 3,225 525 2,045 R-squared 0.968 0.915 0.931 0.978 0.978 0.981 0.965 0.993 Formal test of differences in coefficients UMIC > LMIC Yes Yes Yes Yes Yes Yes No Yes HIC > UMIC No Yes Yes Yes No No No No Top25 > Mid50 Yes Yes No No No No Yes No GDP = gross domestic product, HIC = high-income country, km2 = square kilometer, LMIC = lower-middle-income country, UMIC = upper-middleincome country. Notes: Robust standard errors are in parentheses below the coefficients. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimates. These results are robust to various changes in specification. First, we estimate an alternative regression that replaces the country income group dummies with a continuous variable, the lagged value of the log of GDP per capita; we include linear and quadratic terms to allow for possible nonlinearities (Table 2). In line with our original regressions, infrastructure stocks tend to rise with per capita income, which represents the level of economic development. The negative and significant coefficient on the quadratic term suggests that infrastructure increases at a declining rate as countries develop. The coefficients on the growth performance dummies and auxiliary controls remain similar as in the baseline regression. Second, we restrict the estimation sample to the developing country subsample, and further to just the MIC subsample (Tables 3.a and 3.b). These shrink the estimation sample size substantially— by between one-half and two-thirds of observations in the case of the MIC subsample—and so it is not surprising that the results are generally weaker than in the baseline. They are nonetheless similar to the results in the baseline regression, and confirm that the documented relationships hold in developing countries in general, and in the MICs in particular.10 10 In ADB (2017b), the specification uses the lagged value of the log of GDP per capita (instead of a UMIC dummy) in the MIC subsample regression. The results are robust to this change, and that regression is presented in Appendix Table A4. 12 | ADB Economics Working Paper Series No. 518 Third, we run the same set of regressions using a composite infrastructure index constructed by Donaubauer, Meyer, and Nunnenkamp (2016), which combines a large number of indicators of both the quantity and quality of different types of infrastructure using an unobserved components model. One disadvantage is that this composite measure is substantially shorter than our original time series (starting only in 1990 or later), and the data has substantially less time variation and is, thus, much more cross sectional in nature; country dummies alone can explain 96% of the overall variation in the composite index. For this reason, we do not include fixed effects in the specification. The results (Table 4) are similar to those in the baseline regression; infrastructure as measured by this overall composite index tends to be positively associated with both level of development and with growth performance. Table 2: Sectoral Infrastructure Regressions Using Lagged GDP per Capita, Level, and Quadratic Terms (1) (2) (3) (4) (5) (6) (7) (8) Variables Telephone Mainlines Mobile Subscriptions Internet Users Electricity Total Roads Rails Water Access Sanitation Access (per 100 people) (per km2) (% of population) Lagged GDP per capita 5.314*** 15.861*** 13.307*** 0.809*** 1.464*** –0.061 48.796*** 37.284*** (0.281) (0.973) (1.250) (0.155) (0.148) (0.113) (5.957) (3.289) (Lagged GDP per capita)^2 –0.286*** –0.950*** –0.782*** –0.020** –0.080*** 0.006 –2.903*** –2.103*** (0.015) (0.055) (0.067) (0.009) (0.009) (0.007) (0.341) (0.179) Mid50 0.028 –0.070 0.427*** –0.046* –0.000 0.036** 1.934 1.445** (0.035) (0.144) (0.128) (0.025) (0.021) (0.016) (1.484) (0.600) Top25 0.283*** 0.809*** 0.685*** –0.006 –0.109*** –0.004 4.136*** 2.574*** (0.052) (0.176) (0.150) (0.038) (0.033) (0.019) (1.489) (0.595) Agriculture, share of GDP –0.017*** –0.056*** –0.052*** –0.006*** –0.010*** 0.000 0.009 –0.089*** (0.002) (0.009) (0.009) (0.002) (0.001) (0.001) (0.072) (0.030) Population density 0.068 4.582*** 3.534*** 0.111* 0.246*** 0.090* 14.913*** 3.698*** (0.080) (0.468) (0.464) (0.062) (0.051) (0.049) (2.963) (1.206) Urbanization 0.021*** –0.020* 0.044*** 0.014*** 0.004*** 0.002** 0.262*** 0.411*** (0.003) (0.011) (0.014) (0.002) (0.002) (0.001) (0.099) (0.045) Constant –25.089*** –91.243*** –80.291*** –3.621*** –8.506*** –4.221*** –198.038*** –117.140*** (1.303) (4.174) (5.308) (0.707) (0.662) (0.379) (29.957) (15.631) Observations 3,241 2,015 1,779 3,711 3,288 3,220 520 2,041 R-squared 0.973 0.929 0.939 0.979 0.978 0.981 0.969 0.993 GDP = gross domestic product, km2 = square kilometer. Notes: Robust standard errors are in parentheses below the coefficients. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimates. The Role and Impact of Infrastructure in Middle-Income Countries: Anything Special? | 13 Table 3.a: Sectoral Infrastructure Regressions for the Developing Country Subsample (1) (2) (3) (4) (5) (6) (7) (8) Variables Telephone Mainlines Mobile Subscriptions Internet Users Electricity Total Roads Rails Water Access Sanitation Access (per 100 people) ( per km2 ) (% of population) Lower-middle income 0.431*** 0.524*** 0.675*** 0.317*** 0.284*** –0.013 3.007** 4.412*** (0.045) (0.114) (0.156) (0.031) (0.024) (0.016) (1.365) (0.772) Upper-middle income 0.624*** 0.100 1.145*** 0.424*** 0.277*** 0.074** 2.771 5.692*** (0.062) (0.163) (0.212) (0.047) (0.041) (0.030) (1.869) (0.850) Mid50 –0.023 –0.042 0.192 –0.090** –0.035 0.063*** 3.125 1.843** (0.050) (0.125) (0.149) (0.036) (0.026) (0.024) (1.998) (0.929) Top25 0.182*** 0.790*** –0.066 –0.117** –0.186*** 0.026 5.352** 1.008 (0.070) (0.215) (0.221) (0.058) (0.055) (0.030) (2.286) (0.885) Agriculture, share of GDP –0.029*** –0.033*** –0.037*** –0.011*** –0.010*** 0.000 –0.036 –0.091*** (0.002) (0.007) (0.008) (0.002) (0.001) (0.001) (0.086) (0.027) Population density –0.931*** 3.645*** 2.534*** –0.587*** 0.014 0.042 15.539*** –4.575*** (0.108) (0.524) (0.462) (0.080) (0.065) (0.058) (3.994) (1.460) Urbanization 0.029*** –0.044*** 0.021 0.018*** 0.003 0.001 0.259** 0.426*** (0.003) (0.014) (0.016) (0.003) (0.002) (0.001) (0.129) (0.052) Constant 3.063*** –21.451*** –20.535*** 4.135*** –1.225*** –4.121*** 1.853 80.479*** (0.439) (2.365) (2.194) (0.373) (0.264) (0.187) (18.392) (7.170) Observations 2,472 1,349 1,208 2,929 2,545 2,486 375 1,496 R-squared 0.956 0.945 0.939 0.962 0.968 0.972 0.955 0.991 GDP = gross domestic product, km2 = square kilometer. Notes: Robust standard errors are in parentheses below the coefficients. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimates. Table 3.b: Sectoral Infrastructure Regressions for the Middle-Income Country Subsample (1) (2) (3) (4) (5) (6) (7) (8) Variables Telephone Mainlines Mobile Subscriptions Internet Users Electricity Total Roads Rails Water Access Sanitation Access (per 100 people) (per km2 ) (% of population) Upper-middle income 0.280*** 0.124 0.713*** 0.035 –0.049* 0.067** 2.251 0.264 (0.035) (0.116) (0.139) (0.026) (0.028) (0.031) (1.367) (0.465) Mid50 0.188*** 0.307* 0.500*** 0.093** 0.082** 0.046* –0.345 2.350*** (0.053) (0.167) (0.187) (0.037) (0.032) (0.024) (2.228) (0.657) Top25 0.316*** 0.722*** 0.259 –0.037 –0.388*** –0.125*** 1.704 3.657*** (0.090) (0.253) (0.210) (0.074) (0.133) (0.035) (2.331) (0.797) Agriculture, share of GDP –0.037*** –0.014 –0.025* –0.016*** –0.019*** 0.005*** 0.051 –0.070** (0.003) (0.014) (0.014) (0.003) (0.002) (0.001) (0.116) (0.035) Population density –0.574*** –2.588*** –0.828 0.163 0.088 0.111** 9.994 14.948*** (0.141) (0.668) (0.858) (0.112) (0.096) (0.050) (6.612) (2.805) Urbanization 0.014*** –0.027* 0.042** 0.008** 0.020*** 0.001 –0.018 0.285*** (0.004) (0.014) (0.018) (0.003) (0.003) (0.001) (0.159) (0.051) Constant 2.728*** 5.566* –5.730 1.970*** –1.166*** –4.515*** 40.977 –1.439 (0.574) (2.849) (3.742) (0.451) (0.375) (0.183) (27.575) (12.359) Observations 1,355 861 755 1,497 1,316 1,330 221 865 R-squared 0.940 0.954 0.940 0.946 0.966 0.969 0.928 0.987 GDP = gross domestic product, km2 = square kilometer. Notes: Robust standard errors are in parentheses below the coefficients. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimates. 14 | ADB Economics Working Paper Series No. 518 Table 4: Using the Composite Infrastructure Index of Donaubauer, Meyer, and Nunnenkamp (2016) Variables Total Infrastructure Index Lower-middle income 0.135*** (0.033) Upper-middle income 0.165*** (0.040) High income 1.826*** (0.060) Mid50 0.195*** (0.023) Top25 0.196*** (0.051) Agriculture, share of GDP –0.011*** (0.001) Population density 0.050*** (0.008) Urbanization 0.007*** (0.001) Constant –1.029*** (0.073) Observations 1,733 R-squared 0.825 GDP = gross domestic product. Notes: Robust standard errors are in parentheses below the coefficients. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimates. We now turn to an examination of the correlates of infrastructure investment, focusing on public investment which, as noted above, comprises more than 90% of overall infrastructure investment. Our regression specification draws on the literature on fiscal policy reaction functions, beginning with Bohn (1998). In this literature, public spending is a function of the state of public finances (typically proxied by the lagged level of public debt) and cyclical macroeconomic conditions (often measured by the output gap), and various other controls. Here, we adopt a parsimonious specification which includes the income group and growth performance dummies used in Section II, the lagged level of public debt as a percent of GDP, and controls for macroeconomic conditions. Given the difficulty of measuring the output gap in developing countries—see, for example, Aguiar and Gopinath (2007)—we proxy cyclical macroeconomic conditions using lagged GDP growth, as well as expectations for contemporaneous growth. We also include the lag of investment to account for persistence, and country and time fixed effects to account for systematic unobserved heterogeneities across countries and over time. The specification is: ,  ,  ,  , _ ,  ,  ,  , ,  ,    , where , refers to public investment as a share of GDP;  is the debt-to-GDP ratio;  denotes output growth;_, is the expectation about current economic activity, proxied by GDP growth forecasts for the current year made in October of the prior year, from the IMF’s World Economic Outlook; and  and  are the country and time fixed effects, respectively. (2) The Role and Impact of Infrastructure in Middle-Income Countries: Anything Special? | 15 The investment regression results are in Table 5. We begin with column 1, which presents the regressions without lagged investment and the fixed effects; the subsequent columns add these in sequence. The coefficient on the lagged level of public debt is negative; higher public debt in the previous period is associated with lower levels of public investment, other things being equal. This is consistent with the findings in most of the fiscal reaction function literature, which finds that, as public debt rises, government spending declines and the primary balance improves. Public investment is also positively associated with lagged GDP growth and with expectations of contemporaneous growth. Turning to our variables of interest, we find no association between income group levels and public investment, consistent with the finding in Figure 3 that investment levels do not vary much across income groups. Finally, we find that the top25 growth performance dummy is positively associated with public investment, which is also consistent with what was observed in Figure 3. The inclusion of lagged public investment as a control (column 2) does not qualitatively affect these results. When country and year fixed effects are included (columns 3 and 4), lagged GDP growth continues to be significantly associated with public investment; lagged public debt and the top25 growth performance dummy retain the same sign, but are no longer statistically significant. This suggests that the higher infrastructure investment we observed in fast-growing countries relative to other countries, which was documented in Figure 3, is mainly driven by differences across countries, rather than by within-country variations in growth performance. Table 5: Public Investment Regressions No Fixed Effects With Fixed Effects (1) (2) (3) (4) Variables Public Investment Public Investment Lagged public investment (% of GDP) 0.909*** 0.773*** (0.020) (0.045) Lagged public debt (% of GDP) –0.009** –0.001*** –0.006 –0.002 (0.004) (0.000) (0.004) (0.001) Lagged GDP growth (%) 0.046** 0.014* 0.032*** 0.016* (0.022) (0.008) (0.012) (0.008) Forecast of current GDP growth made in previous period 0.137* 0.022 0.111** 0.032 (0.074) (0.018) (0.051) (0.022) Lower-middle income –0.656 –0.035 0.337 0.236 (0.619) (0.092) (0.552) (0.204) Upper-middle income –0.319 –0.052 0.115 0.014 (0.601) (0.070) (0.661) (0.272) High income –0.762 –0.082 0.476 –0.088 (0.586) (0.069) (0.851) (0.310) Mid50 –0.365 –0.023 –0.361 0.050 (0.359) (0.045) (0.392) (0.137) Top25 2.728** 0.199* 0.111 0.272 (1.187) (0.101) (0.452) (0.170) Constant 4.690*** 0.406*** 4.386*** 1.162*** (0.681) (0.123) (0.874) (0.279) Observations 2,263 2,263 2,263 2,263 R-squared 0.176 0.881 0.719 0.894 GDP = gross domestic product. Notes: Robust standard errors are in parentheses below the coefficients. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimates. 16 | ADB Economics Working Paper Series No. 518 To sum up our findings to this point, the stylized facts and regression results on infrastructure provision suggest a hierarchy of needs where countries are more likely to invest in basic infrastructure such as water and sanitation, roads, power, and telephone mainlines during early stages of development. Countries then seem to turn their attention to advanced infrastructure, such as mobile cellular and internet connections, when they reach the upper middle-income stage, with power also continuing to be a priority. Moreover, fast-growing economies within each income group generally invest more and tend to have higher infrastructure stocks than their weaker counterparts for certain types of infrastructure, notably ICT. V. THE MACROECONOMIC EFFECTS OF PUBLIC INVESTMENT The previous sections have established an association between infrastructure provision and investment, on the one hand, and level of development and growth performance on the other. But the bivariate charts and multivariate regressions cannot shed light on the direction of causality between infrastructure and output. The positive relationship can arise because output responds to infrastructure investment, with the latter boosting short-run demand and increasing the productivity of existing factors of production. It can also arise from infrastructure responding to output—either because higher output growth in the past makes it easier to pay for infrastructure, or because expectations of higher growth induce greater investment in infrastructure. This is a problem that has long plagued the literature on the macroeconomic effects of infrastructure investment. The challenge is to identify changes in infrastructure investment that are not driven by contemporaneous or lagged output, nor by expectations of future output growth. To tackle this issue, we follow the empirical strategy of Corsetti, Meier, and Müller (2012), which in turn builds on Perotti (1999). The idea is to employ a two-stage strategy, relying on the fact that significant parts of government spending are likely to be determined by past information and cannot easily respond to current economic conditions. This is especially true of public investment, which operates with substantial lags and is more difficult to adjust quickly than current spending. Based on this assumption, one can first estimate a fiscal policy rule where public investment is a function of past information on macroeconomic conditions (lagged growth and past expectations of contemporaneous growth), and from this obtain a series of exogenous shocks to public investment.11 The estimated policy shocks can then be used to trace the dynamic effects of public investment on output. In principle, the assumption that public investment cannot easily respond to current economic conditions can be violated for two reasons. First, public spending can automatically respond to cyclical conditions. This should not pose a problem for public investment because these expenditures are discretionary; automatic stabilizers operate mostly via revenues and social spending. Second, discretionary public investment spending could be in response to output conditions. As discussed in Corsetti, Meier, and Müller (2012), the relevance of this concern relates to the precise definition of contemporaneous feedback effects. Although it is typically assumed in the literature that government spending does not react to changes in economic activity within a given quarter (Blanchard and Perotti 2002), whether it may respond in a period longer than a quarter is an open question. Recent evidence suggests that the restriction that government spending does not respond to economic conditions within a year cannot be rejected (Beetsma, Giuliodori, and Klaassen 2009; Born and Müller 2012). 11 This identification strategy is very similar to the structure embedded in fiscal policy vector autoregressions. The fiscal policy rule links the change in government spending to its lags, lagged growth, current and lagged public indebtedness, and expectations of next year’s growth. The Role and Impact of Infrastructure in Middle-Income Countries: Anything Special? | 17 To implement this two-step approach, in the first step an annual time series of public investment shocks is derived by estimating a fiscal policy reaction function, where public investment as a share of GDP is a function of its own lag, lagged public debt, previous period output growth, and expectations about current economic activity. But this is precisely the specification adopted in Table 5, column 4, of Section IV. Thus, the residuals from that regression provide estimates of the public investment shocks, which by construction do not contain the response of public investment to macroeconomic conditions. In the second step, following the “local projections” approach proposed by Jordà (2005) in estimating impulse response functions, the impact of public investment innovations on output is estimated through the equation ,  ,      ,    where  is the log of output;  and  are the country and time fixed effects, respectively; and , represents the exogenous public investment shocks derived from the fiscal policy reaction function. This approach has been advocated by Stock and Watson (2007) and Auerbach and Gorodnichenko (2013), among others, as a flexible alternative that does not impose the dynamic restrictions embedded in vector autoregression (autoregressive-distributed lag) specifications. The equation is estimated for each 󰇛 , , , , , , , 󰇜 representing the time horizon in years after a shock. Impulse response functions are computed using the estimated coefficients , and the confidence bands associated with the impulse response functions are generated using the estimated standard errors of the coefficients , based on clustered robust standard errors. We apply this approach specifically to explore the macroeconomic effects of infrastructure investment in developing economies and, more specifically, to examine whether macroeconomic effects are different in middle-income economies relative to low-income economies.12 To compare the country income groups, we generate the impulse response functions for each income group separately. The results suggest that public investment has a positive and persistent impact on output in developing countries (Figure 4, Panel A). The contemporaneous effect of a 1 percentage point of GDP increase in public investment is a 0.3% increase in output. Using the sample average of government investment as a percentage of output, this implies short-term investment spending multipliers of about 0.3, consistent with other estimates of the overall government spending multiplier reported in the literature (see Coenen et al. 2012 and literature cited therein). This gradually increases to just above 1.2% 7 years after the shock, with the impact being significantly different from zero in both the short and long run, suggesting an expansion of the productive capacity of the economy as public investment augments the physical infrastructure stock. 12 Abiad, Furceri, and Topalova (2016) examine the macroeconomic effects of public investment using a more precise measure of shocks to public investment, namely forecast errors in public investment derived from OECD Economic Reports. This approach is not feasible for our investigation of developing economies due to lack of similar forecast data. (3) 18 | ADB Economics Working Paper Series No. 518 Figure 4: Effects of Public Investment on Output Notes: t = 0 is the year of the shock; dashed lines denote 90% confidence bands. Shock represents an exogenous 1 percentage point of gross domestic product increase in public investment spending. Source: Authors’ estimates. When splitting the developing economy sample into LICs and MICs, one sees a slight difference in effects (Figure 4, Panel B versus Panel C). For LICs, public investment shocks do not raise output on impact. There is an increase in long-term output of 1.1% after 7 years, but with the substantially larger standard errors in the LIC subsample, the estimated impact is not significantly different from zero. In MICs, public investment shocks raise output by 0.6% on impact, and the effect is significantly different from zero. Over time, the impact increases to 1.1% by the 7th year after the shock, and this long run effect is also significantly different from zero. The greater variance (and consequent statistical insignificance) of the impact of public investment in LICs is consistent with evidence from existing studies. Warner (2014), for instance, finds only a weak and fleeting relationship between public investment spending and growth in LICs, noting in his paper how past investment drives were typically weighed down by incentive and agency problems –1.0 –0.5 0 0.5 1.0 1.5 2.0 2.5 –1 0 1 2 3 4 5 6 7 (%) Year Panel A. Developing (Low and middle income) –1.0 –0.5 0 0.5 1.0 1.5 2.0 2.5 –1 0 1 2 3 4 5 6 7 (%) Year Panel B. Low income –1.0 –0.5 0 0.5 1.0 1.5 2.0 2.5 –1 0 1 2 3 4 5 6 7 (%) Year Panel C. Middle income REFERENCES Abiad, Abdul, Davide Furceri, and Petia Topalova. 2016. “The Macroeconomic Effects of Public Investment: Evidence from Advanced Economies.” Journal of Macroeconomics 50: 224–40. Agénor, Pierre-Richard, and Otaviano Canuto. 2015. “Middle-Income Growth Traps.” Research in Economics 69 (4): 641–60. Aguiar, Mark, and Gita Gopinath. 2007. “Emerging Market Business Cycles: The Cycle is the Trend.” Journal of Political Economy 115 (1): 69–102. Aiyar, Shekhar, Romain Duval, Damien Puy, Yiqun Wu, and Longmei Zhang. 2013. “Growth Slowdowns and Middle-Income Trap.” IMF Working Paper 13/71. Washington, DC. Aschauer, David. 1989. “Is Public Expenditure Productive?” Journal of Monetary Economics 23 (2): 177–200. Asian Development Bank (ADB). 2017a. Meeting Asia’s Infrastructure Needs. Manila. https://www.adb.org/publications/asia-infrastructure-needs ————. 2017 b. Asian Development Outlook 2017. Transcending the Middle-Income Challenge. Manila. https://www.adb.org/publications/asian-development-outlook-2017-middle-income-challenge Auerbach, Alan J., and Yuriy Gorodnichenko. 2013. “Fiscal Multipliers in Recession and Expansion.” In Fiscal Policy After the Financial Crisis, edited by Alberto Alesina and Francisco Giavazzi. NBER Books. Cambridge, MA: National Bureau of Economic Research, Inc. Bae, Kyoung Yul. 2011. “ICT and Broadband in Korea.” Presentation. http://unpan1.un.org/intradoc/ groups/public/documents/ungc/unpan047290.pdf Beetsma, Roel, Massimo Giuliodori, and Franc Klaassen. 2009. “Temporal Aggregation and SVAR Identification, with an Application to Fiscal Policy.” Economics Letters 105 (3): 253–55. Blanchard, Olivier J., and Roberto Perotti. 2002. “An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output.” Quarterly Journal of Economics 107 (4): 1329–68. Bloomberg. 2015. The Bloomberg Innovation Index. http://www.bloomberg.com/graphics/2015 -innovative-countries/ Bohn, Henning. 1998. “The Behavior of US Public Debt and Deficits.” The Quarterly Journal of Economics 113 (3): 949–63. Born, Benjamin, and Gernot Müller. 2012. “Government Spending Shocks in Quarterly and Annual Time Series.” Journal of Money, Credit and Banking 44 (2–3): 507–17. Calderón, César, Enrique Moral-Benito, and Luis Servén. 2015. “Is Infrastructure Capital Productive? A Dynamic Heterogeneous Approach.” Journal of Applied Econometrics 30 (2): 177–98.  ADB recognizes “Korea” and “South Korea” as the Republic of Korea. 26 | References Calderón, César, and Luis Servén, 2008. Infrastructure and Economic Development in Sub-Saharan Africa. World Bank Policy Research Working Paper No. 4712. Washington, DC (September). Canning, David. 2007. “A Database of World Stocks of Infrastructure: Update 1950–2005. Version 1. http://hdl.handle .net/1902.1/11953. Updated version of A Database of World Stocks of Infrastructure: 1950–1995.” World Bank Economic Review 12 (3): 529–48. Coenen, Günter, Christopher J. Erceg, Charles Freedman, Davide Furceri, Michael Kumhof, René Lalonde, Douglas Laxton, Jesper Lindé, Annabelle Mourougane, Dirk Muir, Susanna Mursula, Carlos de Resende, John Roberts, Werner Roeger, Stephen Snudden, Mathias Trabandt, and Jan in't Veld. 2012. “Effects of Fiscal Stimulus in Structural Models.” American Economic Journal: Macroeconomics 4 (1): 22–68. Corsetti, Giancarlo, Andre Meier, and Gernot J. Müller. 2012. “What Determines Government Spending Multipliers?” Economic Policy 27 (72): 521–65. Donaubauer, J., Birgit E. Meyer, and Peter Nunnenkamp. 2016. “A New Global Index of Infrastructure: Construction, Rankings and Applications.” The World Economy 39 (2): 236–59. Eichengreen, Barry, Donghyun Park, and Kwanho Shin. 2014. “Growth Slowdowns Redux.” Japan and the World Economy 32: 65–84. Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What is Needed from 2000 to 2010?” World Bank Policy Research Working Paper No. 3102. Washington, DC (July). Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer. 2015. “The Next Generation of the Penn World Table.” American Economic Review 105 (10): 3150–82. Felipe, Jesus, Utsav Kumar, and Reynold Galope, 2017. “Middle-Income Transitions: Trap or Myth?” Journal of the Asia Pacific Economy 22 (3): 429–53. Gill, Indermit, and Homi Kharas. 2007. An East Asian Renaissance. Washington, DC: The World Bank. Gleditsch, Nils Petter, Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg, and Håvard Strand. 2002. “Armed Conflict 1946–2001: A New Dataset.” Journal of Peace Research 39 (5): 615–37. Han, Xuehui, and Shang-Jin Wei. 2017. “Re-examining the Middle-Income Trap Hypothesis (MITH): What to Reject and What to Revive?” Journal of International Money and Finance 73 (PA): 41–61. International Monetary Fund (IMF). 2014. “Is it Time for an Infrastructure Push? The Macroeconomic Effects of Public Investment.” World Economic Outlook, Chapter 3. Washington, DC (October). ————. 2017. Investment and Capital Stock Dataset. http://www.imf.org/external/np/fad/ publicinvestment/ Jordà, Òscar. 2005. “Estimation and Inference of Impulse Responses by Local Projections.” American Economic Review 95 (1): 161–82. References | 27 Kraay, Aart. 2012. “How Large is the Government Spending Multiplier? Evidence from World Bank Lending.” The Quarterly Journal of Economics 127 (2): 829–87. Lee, Sang M. 2003. “South Korea: From the Land of Morning Calm to ICT Hotbed.” The Academy of Management Executive 17 (2): 7–18. Lee, Yong-Hwan. 2015. ICT as a Key Engine for Development: Good Practices and Lessons Learned from Korea. World Bank Note. http://siteresources.worldbank.org/INTEGOVERNMENT/ Resources/NoteKoreaICT.doc Maddala, G. S., and Shaowen Wu. 1999. “A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test.” Oxford Bulletin of Economics and Statistics 61 (S1): 631–52. Oh, Myung, and James F. Larson. 2011. Digital Development in Korea: Building an Information Society. Routledge. Perotti, Roberto. 1999. “Fiscal Policy in Good Times and Bad.” The Quarterly Journal of Economics 114 (4): 1399–436. Pettersson, Therése, and Peter Wallensteen. 2015. “Armed Conflicts, 1946–2014.” Journal of Peace Research 52 (4): 536–50. Presbitero, Andrea F. 2016. “Too Much and Too Fast? Public Investment Scaling-up and Absorptive Capacity.” Journal of Development Economics 120: 17–31. Röller, Lars-Hendrik, and Leonard Waverman. 2001. “Telecommunications Infrastructure and Economic Development: A Simultaneous Approach.” American Economic Review 91 (4): 909–23. Ruiz-Nuñez, Fernanda, and Zichao Wei. 2015. “Infrastructure Investment Demands in Emerging Markets and Developing Economies.” World Bank Policy Research Working Paper No. 7414. Washington, DC (September). Stock, James H., and Mark W. Watson. 2007. “Why has US Inflation Become Harder to Forecast?” Journal of Money, Credit and Banking 39 (s1): 3–33. Straub, Stéphane, 2008. “Infrastructure and Growth in Developing Countries: Recent Advances and Research Challenges.” World Bank Policy Research Working Paper No. 4460. Washington, DC (January). Vandenberg, Paul, Lilibeth Poot, and Jeffrey Miyamoto, 2015. “The Middle-Income Transition around the Globe: Characteristics of Graduation and Slowdown.” ADBI Working Paper No. 519. Tokyo (March). Warner, Andrew M. 2014. “Public Investment as an Engine of Growth.” IMF Working Paper 14/148. Washington, DC: IMF. Yeo, Young-Hyun, Sung-Ki Kim, Ji-Hye Bae, and Byung-Gyu Kim. 2014. “The Assessment of Information and Communication Technology (ICT) Policy in South Korea.” In Advances in Computer Science and Its Applications, 1241–49. Berlin, Heidelberg: Springer. ASIAN DEVELOPMENT BANK AsiAn Development BAnk 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org The Role and Impact of Infrastructure in Middle-Income Countries: Anything Special? This paper finds that the provision of infrastructure varies across different levels of development and growth performance. Basic infrastructure, such as transport, water, and sanitation, are emphasized more during early stages of development, while “advanced” infrastructure, such as power and especially information and communication technology, become more important during later stages. In addition, better-performing middle-income countries tend to have more information and communication technology infrastructure than their peers, and tend to invest more in infrastructure. Finally, public investment is shown to have a more significant and sustained impact on output in middle-income countries than in low-income countries. About the Asian Development Bank ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries reduce poverty and improve the quality of life of their people. Despite the region’s many successes, it remains home to a large share of the world’s poor. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration. Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. adb economics working paper series NO. 518 august 2017 The ROle AND IMpACT Of INfRASTRuCTuRe IN MIDDle‑INCOMe COuNTRIeS: ANyThINg SpeCIAl? Abdul Abiad, Margarita Debuque-Gonzales, and Andrea Loren Sy