The Evolution and Impact of Infrastructure in Middle-Income Countries: Anything Special?

ABSTRACT

We examine the evolution of infrastructure, and the impact of infrastructure investment, in middle-income 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 on 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.

KEY WORDS:

• economic development
• infrastructure
• middle income

JEL CLASSIFICATION:

• H54
• O18
• O40
• Q4

Notes

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 e.g., Han and Wei 2017; and Felipe, Kumar, and Galope 2017).

2. Previous research on the macroeconomic effects of public investment (e.g., Abiad, Furceri, and Topalova 2016) confirms the role that efficiency plays in shaping the macroeconomic impact of such investment. However, that study focused only on public investment in advanced (Organisation for Economic Co-operation and Development [OECD]) economies. One difficulty is that measures of the efficiency of public investment are available only for a limited number of countries, and these measures are only cross-sectional.

3. Because of congestion, pollution, and other problems associated with urbanization that takes place in the middle-income phase, urban infrastructure is also likely to become more important for MICs. However, lack of comprehensive data on such urban infrastructure (such as mass transit) prevents us from analyzing this further, and we leave this for future research.

4. The list of data sources used in this section and the rest of the article is provided in Appendix Table A.1. The sample comprises the 99 countries listed in Appendix Table A.2, and is an unbalanced panel with some data starting from 1960, subject to data availability.

5. Specifically, the thresholds were derived from the World Bank country income thresholds for fiscal year 2017 (1st July 2016–30th June 2017), which use gross national income per capita to classify countries. To identify corresponding thresholds in Penn World Tables 9.0, which uses GDP per capita in constant 2011 PPP dollars, we calculate the 2015 ratios of average gross national income per capita to GDP per capita in constant PPP per income group (i.e., LIC, LMIC, UMIC, and HIC), and use these ratios to derive the equivalent thresholds in GDP per capita in constant 2011 PPP dollars. United States GDP per capita in 1960 is used to identify the threshold for HICs. The resulting thresholds are: GDP per capita (year 2011 PPP) ≤ $2,585 for LIC; $2,585 < GDP per capita (year 2011 PPP) ≤ $5,351 for LMIC; and 5,351 < GDP per capita (year 2011 PPP) ≤ $17,600 for UMIC.

6. Small states are defined as countries with populations of less than one million. Conflict countries are those with conflict intensity equal to 2 or “war” based on the UCDP/PRIO Armed Conflict Dataset (see Gleditsch et al. 2002; and Pettersson and Wallensteen 2015), and that also experienced a decline in GDP per capita of more than 10%. Two other countries excluded from the analysis were Bosnia and Herzegovina (a conflict country) and Zimbabwe (hyperinflation and macroeconomic instability).

7. Physical quantities of infrastructure (e.g., road and rail kilometers, kilowatts of power generation capacity) are multiplied by unit costs and aggregated; the shares of various types of infrastructure in the total can then be calculated. Fay and Yepes (2003, p. 10) provide such unit costs for telephone landlines, mobile lines, roads, rail, electricity, water supply, and sanitation. Costs of internet provision are omitted in the calculations for Figure 2 because unit costs are not available for internet infrastructure provision; inclusion would raise the share of ICT in overall infrastructure. Using unit costs from ADB (2017a) produces a similar picture.

8. Public investment can include investment in non-infrastructure items such as machinery and equipment, inventories, valuables, and land.

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.).

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 A.4.

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.

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.