metadata: authors: 0: author: Foster,Vivien
1: author: Rana,Anshul
2: author: Gorgulu,Nisan
count: World
docna: 0: docna: Understanding Public Spending Trends for
            Infrastructure in Developing Countries
docty: Policy Research Working Paper
subtopic: Transport Economics Policy & Planning,Roads & Highways,Financial Sector Policy,Energy Policies & Economics,Transport Services
teratopic: Energy,Transport
abstracts: cdata!: Evidence of public expenditure on
            infrastructure is extremely sparse. Little is known about
            the trends and patterns of infrastructure expenditure, and
            there is no real basis for assessing the adequacy and
            efficiency of infrastructure spending. Drawing on the World
            Bank&#x2019;s novel BOOST database, this paper provides
            a first relatively disaggregated picture of infrastructure
            spending trends and patterns for a large sample of more than
            70 developing countries covering 2010&#x2013;18,
            drilling down into expenditure by sector for roads as well
            as electricity, and distinguishing operating from capital
            expenditure. Complementary sources of data are tapped to
            allow comparison between expenditure patterns on and off
            budget. The study finds that on-budget expenditure on
            infrastructure has been low both in absolute terms (1
            percent of gross domestic product) and relative terms (5
            percent of total public spending), as well as declining over
            time. Overall, infrastructure spending declined by about
            one-third over 2010&#x2013;18 (with the road sector
            bearing the brunt of the decrease), and now lies well below
            estimates of the required levels, except in a handful of
            cases. There is evidence that low-income countries, despite
            lower spending envelopes, attach greater priority to public
            investment and infrastructure spending than their
            middle-income counterparts. Econometric analysis suggests
            that infrastructure spending in low- and middle-income
            countries has been historically procyclical, although to a
            lesser degree than total expenditure. In the transport
            sector, road funds are shown to play a substantial role in
            funding road maintenance, appearing to improve the adequacy
            of funding, while attenuating pronounced capital biases in
            road sector spending, but there is little evidence of
            efficiency improvements over time.
repnme: repnme: Understanding Public Spending Trends for
            Infrastructure in Developing Countries
display_title: Understanding Public Spending Trends
            for Infrastructure in Developing Countries
listing_relative_url: /research/2022/01/33704044/understanding-public-spending-trends-infrastructure-developing-countries
topicv3: Transport m1326661 135,Road Funds m1326663 1816,Power and Electricity Sector m1327094 515,Financial Sector Development m1327681 1920,Road Maintenance m1505354 4031
publishtoextweb_dt: 2022-01-14T00:00:00Z
dois: 10.1596/1813-9450-9903
historic_topic: Energy,Transport
origu: Chief Economist Infrastructure (INFCE)
no_of_pages: 44
author_keys: 0: author_key: 452278
1: author_key: 1272539
2: author_key: 1761041
model_extractions: Policy Research Working Paper 9903 Understanding Public Spending Trends for Infrastructure in Developing Countries _Vivien Foster Anshul Rana Nisan Gorgulu_ Infrastructure Chief Economist Office January 2022
Policy Research Working Paper 9903

# **Abstract**

Evidence of public expenditure on infrastructure is extremely sparse. Little is known about the trends and patterns of infrastructure expenditure, and there is no real basis for assessing the adequacy and efficiency of infrastructure spending. Drawing on the World Bank’s novel BOOST database, this paper provides a first relatively disaggregated picture of infrastructure spending trends and patterns for a large sample of more than 70 developing countries covering 2010–18, drilling down into expenditure by sector for roads as well as electricity, and distinguishing operating from capital expenditure. Complementary sources of data are tapped to allow comparison between expenditure patterns on and off budget. The study finds that on-budget expenditure on infrastructure has been low both in absolute terms (1 percent of gross domestic product) and relative terms (5 percent of total public spending), as well as declining over time. Overall, infrastructure spending declined by about one-third over 2010–18 (with the road sector bearing the brunt of the decrease), and now lies well below estimates of the required levels, except in a handful of cases. There is evidence that low-income countries, despite lower spending envelopes, attach greater priority to public investment and infrastructure spending than their middle-income counterparts. Econometric analysis suggests that infrastructure spending in low- and middle-income countries has been historically procyclical, although to a lesser degree than total expenditure. In the transport sector, road funds are shown to play a substantial role in funding road maintenance, appearing to improve the adequacy of funding, while attenuating pronounced capital biases in road sector spending, but there is little evidence of efficiency improvements over time.

This paper is a product of the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at vfoster@worldbank.org, arana1@worldbank.org and ngorgulu@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Understanding Public Spending Trends for Infrastructure in Developing Countries**<sup>**1**</sup> **Vivien Foster, Anshul Rana, Nisan Gorgulu**

# **JEL:** E32, E62, H50, O10, R40, Q40

**Keywords:** public spending, infrastructure, fiscal policy, road spending, electricity, low- and middle-income countries

> 1 This paper has been peer reviewed by Robert Johann Utz, Lead Economist EMFTX, World Bank and Tomas Serebrisky, Principal Infrastructure Economist, Inter-American Development Bank. It was prepared as a background paper for the Infrastructure Vice-Presidency Flagship on “Fiscal Costs and Risks from Infrastructure in an Era of High Debt Vulnerabilities” and presented at the corresponding Authors’ Workshop held on July 12, 2021, benefiting from feedback provided by the workshop participants. The authors would also particularly like to thank Richard Damania, Matias Herrera Dappe, Jevgenijs Steinbuks, Teresa Ter-Minassian, and Burak Turkgulu for their helpful comments and suggestions.
# **1. Introduction**

Infrastructure is key to delivering several essential services and plays a central role in achieving the United Nations’ Sustainable Development Goals.<sup>2</sup> Goal 9 explicitly articulates the importance of building resilient infrastructure by promoting innovative sustainable technologies. However, it has been shown that “infrastructure either directly or indirectly influences the attainment of _all_ of the Sustainable Development Goals (SDGs), including 72% of the targets” (Thacker et al., 2019).

Developing countries face significant infrastructure needs if they are to deliver on the 2030 policy agenda (Doumbia et al., 2019; World Bank, 2019). There have been few rigorous attempts to quantify these spending needs, and until recently most of them were based on high-level cross-country panel econometric studies (such as Fay and Yepes, 2003), or limited in scope to individual regions or sectors (for instance Foster and Briceno Garmendia, 2009). A more recent study takes a micro-economic approach to modeling individual components of infrastructure needs, which are then built-up into an aggregate picture (Rozenberg and Fay, 2019). This research finds that, when combined with good policy choices, investments of 4.5 percent of GDP, combined with operations and maintenance expenditure of 2.7 percent of GDP, will together enable low and middle-income countries to achieve infrastructure-related Sustainable Development Goals and stay on track to full decarbonization by the second half of the century. These headline estimates can be further disaggregated at the sector level (see table 1).

**Table 1: Overview of estimates of infrastructure spending needs for energy and transport**

|% of GDP of LMICs|Investment needs|Operations and maintenance needs|Total expenditure needs|
|---|---|---|---|
|Energy|2.2|0.6|2.8|
|Transport|1.3|1.3|2.6|
|Water|1.0|0.8|1.8|
|Total|4.5|2.7|5.4|

Source: Derived from Rozenberg and Fay, 2019 However, evidence on how much countries are _actually_ spending on infrastructure has been sparse. This is due to limitations in the ability of traditional public finance sources – such as the IMF Government Finance Statistics – to provide sufficiently disaggregated measures to isolate infrastructure expenditure. A preliminary attempt to estimate aggregate financing flows to infrastructure in the developing world, leveraging a variety of different data sources, provided a central estimate of 4.0 percent of GDP for public investment in infrastructure by developing countries in 2011 (Fay et al., 2019). While this central estimate of spending is not far short of the projected investment needs above, the aggregate finding belies significant variation in the size of financing gaps across sectors and regions, with significant spending shortfalls estimated for Africa, Middle East, Europe and Central Asia, even as financing exceeds the minimum requirement for East Asia and Pacific.

Subsequent research has shed further light on the sectoral composition of capital spending on infrastructure projects and the extent to which it is delivered through public budgets, state-owned enterprises, or publicprivate partnerships (World Bank, 2020). This evidence, currently available for 2017 alone, suggests that 80 percent of infrastructure investment is publicly financed and two-thirds of that is channeled through

> 2 https://datatopics.worldbank.org/sdgs/sdg-goals-targets.html

2
state-owned enterprises, with the remainder on-budget. The same study also finds that almost the entirety of infrastructure investment goes to just two sectors: energy (50 percent) and transport (45 percent).

Beyond these high-level estimations of expenditure and spending needs, little is known about infrastructure spending trends and patterns at the global level. This paper fills this gap by analyzing a new source of disaggregated infrastructure spending data covering some 75 countries over a period of 10 years, drawing from the World Bank’s novel BOOST database, complemented with data from relevant extra-budgetary channels. On this basis, the study aims to answer the following questions. What have been long term infrastructure spending trends across different types of countries? What are the patterns of infrastructure spending across sectors, functional categories, and institutional channels? Is there evidence of inefficiency, incoherence or cyclicality in infrastructure spending?

The rest of the paper proceeds as follows. The next section describes the data and methodology used to identify the infrastructure public spending trends, while the following one presents the results with a focus on roads and electricity spending. A fourth concluding section discusses the central findings and their policy implications.

# **2. Data and Methodology**

Public expenditure on infrastructure can be classified into three separate categories. The first category is on-budget expenditure, undertaken primarily by central government entities, such as ministries, which is reported in the budget itself and financed from domestic tax revenue mobilization. The second category is development expenditure provided by traditional and new foreign donors to governments and typically channeled outside of the public budget. The third category is off-budget expenditure, undertaken by corporatized or otherwise decentralized state entities, such as power utilities or Road Funds, which is not reported on the budget but can be found in the respective financial accounts.

The World Bank’s Public Expenditure Database BOOST compiles information on the level and composition of the first category of on-budget government expenditures for a growing number of emerging and low-income countries at quite a high level of granularity, currently around 75.<sup>3</sup> The sample is heterogeneous ranging from large countries (e.g. Argentina, Brazil, Indonesia, Mexico, and South Africa) to several small islands. It spans all major country income groups, including 22 low-income (LIC), 26 lower-middle income (LMIC), 21 upper-middle income (UMIC), and 8 high-income (HIC) countries, classified according to the level of gross national income per-capita. Due to the relatively small number of high-income countries, no disaggregated analysis is performed for this group. While the sample is not representative by design, with disproportionate representation of Sub-Saharan Africa and Latin America, it does account for about 40 percent of the population and 30 percent of the GDP of low- and middle-income countries globally (Appendix Table A2.2). Moreover, by excluding China and India, which are known to be relatively large spenders on infrastructure and often disproportionately influence global developing country averages, the sample provides a more realistic picture of the infrastructure spending situation across the bulk of emerging nations.

> 3 While there are 77 countries covered in BOOST, two had to be dropped. The first is Pakistan, where coverage is limited to the province of Punjab. The second is São Tomé Príncipe, where there were some concerns about data quality and coverage.

3
The BOOST program was launched in 2010 as a platform for improving, standardizing and curating data collection associated with the World Bank’s Public Expenditure Reviews in countries. It tracks wellclassified and highly disaggregated budget data derived from engagements with individual countries in a country-specific manner. The BOOST database covers the period 2006 - 2018. The sample is quite unbalanced, with many earlier year observations missing. Furthermore, the available sample shrinks at increasingly fine levels of disaggregation. While aggregate public expenditure is available for over 70 countries, the number shrinks to 61 countries when looking at disaggregated expenditure for roads, and 46 countries for disaggregated expenditure on electricity. Coverage is also uneven depending on the subcategory of data considered, with greater availability of data on investments than on recurrent expenditure, and even less on subsidies. Combining BOOST data with other sources to build a wider picture of on- and off-budget expenditure further reduces the sample due to limited overlap in countries for which multiple data sources are available.

An important issue is differences between countries in the institutional coverage of the budget data provided. While 36 of the countries provide General Government data, which includes expenditure by subnational authorities, the remaining 41 countries provide only Central Government data. Examination of the General Government data shows that for most countries subnational expenditure represents no more than 6 percent of the General Government total, except in federal states. This suggests that pooling General Government and Central Government data is a defensible approximation, particularly for relatively small and unitary states as long as large federations supplying only Central Government data are excluded.<sup>4</sup> To build up a comprehensive picture of on-budget and off-budget expenditure, it is essential to combine BOOST data with other sources of information that capture development aid as well as domestic spending through off-budget vehicles. When aggregating these different sources of data some possibility of doublecounting is introduced, across some though not all of the categories. Wherever possible this issue is examined and any double-counting directly addressed through subtraction. However, due to data limitations this is not always possible. Table A2.5 qualitatively identifies the nature of the risks across each pair of categories. Since double-counting cannot be entirely eliminated, the resulting aggregate estimates must be considered as an upper bound. Given how low the resulting spending envelopes are relative to estimates of spending needs, this does not, however, affect the main conclusions of the paper.

The BOOST database does not capture foreign capital spending such as contributions made by multilateral, bilateral or other donor institutions, because these are typically not channeled through the budget. This source of funding plays an important role in infrastructure financing in the developing world and needs to be incorporated to get a complete picture. The OECD DAC database covers donor and multilateral commitments to developing countries across sectors. The database covers disbursements to 155 countries (from 29 donors) over the period 2005 – 2019. However, the OECD DAC database does not cover financing contributions by Non-Paris Club Members, notably China, which has become a significant source of infrastructure finance for some developing countries (Malik et al., 2021). Therefore, this study also draws upon the College of William and Mary’s AidData database that captures over 13,000 officially-financed Chinese projects (by Chinese government institutions and state-owned entities) in five regions (Africa, the

> 4 These considerations led to the exclusion of Argentina and Brazil from the BOOST analysis, due to the fact that they are large federal states known to have substantial infrastructure expenditure at the sub-national level, yet only reported central government expenditure to BOOST.

4
Middle East, Asia and Pacific, Latin America, and the Caribbean) and across 165 countries for the period 2000 – 2017.

In addition, the BOOST database does not capture domestic spending through extrabudgetary vehicles, including State-Owned Enterprises (SOEs) and Road Funds. The research drew upon financial statements of these entities from the World Bank’s Power and Transport State-Owned Enterprises (SOE) Database, comprising a panel of systematic standardized data drawn from company financial statements, which is consistent at the observation level and comparable across SOEs and years. The database covers 135 SOEs in the power and transport sectors (including road, rail, and air) across 19 countries. In the case of the road sector, it is known that an important subset of countries has implemented off-budget Road Funds, which capture earmarked revenues from transportation fuel levies and channel them towards road maintenance activities. Expenditure from Road Funds must be considered alongside recurrent budgetary expenditure in order to allow for cross-country comparisons on the adequacy of road maintenance allocations. It was established that 25 countries from the BOOST database have implemented Road Funds, and financial statements were collected for 20 of these, while the remainder of countries with Road Funds were dropped from the more detailed road sector analysis.

Cross-country comparisons of road maintenance expenditure also entail some normalization against the extent of the road network. For this purpose, the International Road Federation database was used, which provides information on road network characteristics across 205 countries. In particular, the total length of the primary and secondary network is used for normalization purposes since tertiary roads are typically a municipal responsibility.<sup>5</sup> The reported share of paved and unpaved roads was also used to adjust maintenance expenditure benchmarks for comparison purposes. The World Bank’s ROCKS database provides such regionally differentiated unit cost data for road maintenance activity.

Finally, to get a complete picture of infrastructure expenditure sources, the research also draws upon the World Bank’s Private Participation of Infrastructure (PPI) database that covers over 6,400 infrastructure projects in 137 middle- and low-income countries with private sector participation.

One big distinction between both the PPI database and the AidData’s Global Chinese Development Finance Dataset versus the others used in the analysis is that the latter provide actual executed expenditure, while the former only record financial commitments at the year of project closing, without recording how this is spent over time. Based on the assumption that infrastructure projects typically take five years from financial closing to complete, commitment data are converted to simulated disbursement data for greater compatibility with other sources. Similarly, AidData is also smoothened by taking five-year moving averages.

Exploratory data analysis is complemented with selective use of econometric techniques. To explore the relationship between infrastructure spending and GDP over time, with a view to assessing the extent of cyclicality, a fixed effects panel data model is estimated and further complemented with a panel vector autoregression (PVAR) analysis. In addition, the efficiency of infrastructure spending is assessed using Data Envelopment Analysis. The details of these techniques are discussed in the relevant sections.

> 5 Care was taken to establish whether the institutional responsibility for maintaining primary and secondary road networks in any given country was consistent with the corresponding level of the budget. Road network lengths were averaged over time to address some problematic fluctuations in the data over time.

5
<!-- Start of picture text --> (a) Total public expenditure (b) Public investments<br>35% 5%<br>30% — oooes oe —=<br>a 25%° a,4% a “Wc, reed<br>8 20% B 3%<br>8 15% aS \ 2%<br>10%<br>5% %<br>0% 0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>es|||) me UM eee Total eefe || UM! ee eeee Total<br><!-- End of picture text -->
<!-- Start of picture text --> (a) Public expenditure on infrastructure (b) Public investments on infrastructure<br>2.5% 2.0%<br>B 2.0%1.5%ey (1) 7 ay 1.5%<br>x 05%1.0% : afte. x 0.5%‘0, Oe eecccceccseeeree oo<br>0.0% 0.0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> <!-- Start of picture text --> (a) Infrastructure expenditure as share of total (b) Infrastructure investment as share of total<br>expenditure investment<br>40% 40%<br>30% 30%<br>20% 20%<br>eee cesesessseoces feonee® Bee,<br>10% wpa nnnnnnnnnnnnnne 10%<br>0% 0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text -->
<!-- Start of picture text --> (a) Roads expenditure (b) Electricity expenditure<br>2.0% 2.0%<br>1.5% 1.5%<br>3° 3<br>2x 1.0% ES fee 2x 1.0% +<br>0.5% 0.5% = ao;<br>ecescccesee®”<br>0.0% 0.0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>coe|| ce| [i] ee UM] ceceeee [otal ce|| ce| [| ee UV] ceeeee [otal<br>(a) Expenditure on Power (b) Expenditure on Transport<br>1.0% 1.0%<br>0.8% 0.8%<br>5 0.6% 5 0.6%<br>2 0.6% Q 0:<br>-20.4% =xe 0-4%<br>0.2%ox mH [1 fl i Zs 0.2%0.0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>investments m Maintenance Subsidies minvestments Maintenance mTM Subsidies<br><!-- End of picture text -->
<!-- Start of picture text --> Typical annual expenditure on subsidies in power<br>1.0%<br>0.8%<br>a<br>o 0.6%<br>-<br>2 0.4%<br>0.0%<br>Ethiopia Kosovo Guinea Dominican Argentina<br>Republic<br><!-- End of picture text --> <!-- Start of picture text --> (a) Execution ratio, 2010-18 (b) Sector specific execution ratios, 2010-18<br>14 ry 14<br>e bd e<br>e<br>12 °<br>12 - .e °<br>z1£) ° . ° B- 1 e o, e ©.°e e e<br>B08 Aad e e e oE 06 e e<br>ef ie § vo ts °<br>=<br>0.6 ee e ° ’ z 04 |@ e ® e<br>0.2 e<br>0.4 )<br>0) 2000 4000 6000 8000 10000 12000 14000 0 02 04 06 os 1 12 14<br>@ll @LMI @UMI GDP per capita @L! @LM! @UMI Investments, Power<br><!-- End of picture text -->
# **3.1.2 Patterns of Cyclicality**

According to Keynesian principles, counter-cyclical fiscal policy provides a means of smoothing out trends in economic fluctuations over time. Most studies in the literature support the economic cyclicality of fiscal policies, but the patterns differ according to a country’s stage of development. Government spending has typically been countercyclical in industrial countries and procyclical in developing economies (e.g. Alesina et al, 2008; Gavin and Perotti, 1997; Kaminsky et al., 2004). One strand of the literature relates this phenomenon to the lack of access to international credit markets (Calderón and Schmidt-Hebbel, 2008) by developing countries (especially during recessions) that might constrain their ability to spend during economic downturns. While another strand of the literature attributes this to restrictions on access to domestic markets (Caballero and Khrisnamurthy, 2004) and institutional and political structures (Alesina et al., 2008; Lane, 2003; Talvi & Végh, 2005). In addition, empirical studies indicate the differences in fiscal cyclicality between regions (Gavin and Perotti, 1997), between countries in different income groups (Ilzetzki & Végh, 2008; Kaminsky et al, 2004; Talvi & Végh, 2005), and across time (Fatás & Mihov, 2009; Thornton, 2008).

Evidence on the cyclicality of infrastructure spending specifically has been thin due to data limitations. The purpose of this section is to fill this gap with recourse to the BOOST database, which provides data on public investment on infrastructure in the form of capital formation by central and general government. One important limitation of the database for this type of analysis is that it only covers a 13-year time period, which is relatively short for the study of economic cycles. The cyclicality of infrastructure spending patterns is first explored visually, with more sophisticated statistical analysis subsequently applied to examine the evidence on cyclicality. In particular, this shifts the primary focus from the relationship between the business cycle and associated fiscal responses to a dynamic relationship between output growth and government expenditures on infrastructure, which helps to address concerns about endogeneity.

To begin with, the bivariate correlation coefficient is reported as a simple way of capturing how government expenditure on infrastructure per capita responds to changes in GDP per capita, without delving into underlying drivers. Figure 8 visually illustrates the cyclical movements between total spending on transport and energy per capita, and GDP in per capita terms for both developing and high-income countries. The visual evidence clearly suggests a countercyclical pattern for high-income countries, with infrastructure expenditure (blue) increasing relative to the expected trend when economic output (red) declines. On the other hand, GDP and infrastructure expenditure cycles seem to follow a procyclical pattern in developing countries. Procyclicality is particularly accentuated following the Global Financial Crisis of 2008–10, perhaps reflecting the depth of the crisis.

The nature of this relationship between GDP and infrastructure spending also varies across developing regions, but emerges as procyclical in most cases (Figure 9). Dependence of natural resources in many countries in these regions limits economic diversification and hence the ability to run countercyclical policies. Oil price fluctuations and volatility seem to lead to particularly large changes in infrastructure spending in oil-dependent MENA countries. However, this impact is not immediate (exhibiting a one to two year lag). For instance, world crude oil prices declined from USD 97.2 per barrel in 2008 to USD 61.5 10
<!-- Start of picture text --> High Income Low and Middle Income<br>N<br>N,<br>2005 2010 2015 20202005 2010 2015 2020<br>year<br>— (mean) log Infrastructure _——— (mean) log GDP<br><!-- End of picture text --> <!-- Start of picture text --> East Asia & Pacific Europe & Central Asia Latin America & Caribbean<br>wo<br>i)<br>Middle East & North Africa South Asia Sub-Saharan Africa<br>lo<br>° AAV POV fF<br>iQ<br>s,sa Ss ie<br>2005 2010 2015 20202005 2010 2015 20202005 2010 2015 2020<br>year<br>— (mean) log Infrastructure |.———— (mean) log GDP<br><!-- End of picture text -->
<!-- Start of picture text --> _<br>c<br>2005 2010 2015 2020<br>year<br>——— (mean) log Pow er Spending ——— (mean) log GDP<br><!-- End of picture text --> <!-- Start of picture text --> =<br>2cs<br>o<br>»ry<br>2005 2010 2015 2020<br>year<br>——— (mean) log Road Spending ——— (mean) log GDP<br><!-- End of picture text -->
|DepVar: Expenditure|Total|Infrastructure|Transport|Road|Energy|Power|
|---|---|---|---|---|---|---|
|GDP growth per capita|0.440***|0.448**|0.493*|0.356|0.265|0.617|
||(0.101)|(0.242)|(0.321)|(0.331)|(0.425)|(0.439)|
|laggedFiscalBalance|0.003**|0.0007|0.005**|0.005*|-0.003|-0.014*|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.003)|<br>(0.007)|
|Terms ofTrade growth|0.007|0.029|0.455|0.075|-0.135|0.186|
||(0.133)|(0.298)|(0.300)|(0.427)|(0.498)|(0.345)|
|N.Obs.|720|672|672|696|684|528|
|R-squared|0.13|0.05|0.04|0.03|0.02|0.04|
|Year Fixed Effect|Yes|Yes|Yes|Yes|Yes|Yes|
|CountryFixedEffect|Yes|Yes|Yes|Yes|Yes|Yes|

Robust Standard errors in parentheses. "op </0:1,."" p< 0.08) """ p< 0.03
<!-- Start of picture text --> .03<br>02<br>01<br>0<br>-.01<br>ty) 5 10<br>step<br>(«95% Cl ~—— Orthogonalized IRF<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>02<br>0<br>-.02<br>0 5 10<br>step<br>[1 95% Cl ~=——— Orthogonalized IRF<br><!-- End of picture text --> <!-- Start of picture text --> 06<br>04<br>02<br>0<br>-.02<br>0 5 10<br>step<br>(9) 95% Cl ~=——— Orthogonalized IRF<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>A<br>05<br>0<br>0 5 10<br>step<br>(9) 95% Cl ~——— Orthogonalized IRF<br><!-- End of picture text -->
**Table 4: Comparison of Output Multipliers from PVAR**

||Impact multiplier|Peak multiplier|Cumulative multiplier(after 10years)|
|---|---|---|---|
|Total Spending|0.008|0.008|0.023|
|Infrastructure Spending|0.010|0.010|0.020|
|Road Spending|0.021|0.021|0.043|
|Power Spending|0.058|0.058|0.130|

Note: All spending is in per capita terms

# **3.2 Roads**

# **3.2.1 Road Spending Adequacy**

Attention now turns to a more disaggregated analysis of spending trends at the sector level. In the case of roads, on-budget road expenditure does not tell the full story. While many countries do channel roads expenditure directly through the budget, a significant minority – particularly in Sub-Saharan Africa – have also established off-budget road funds that are typically resourced through earmarked fuel levies and used primarily to support road maintenance activities. This makes it particularly important to integrate road fund expenditure in any cross-country comparison of road sector spending or any assessment of its adequacy. In addition, several off-budget channels of expenditure potentially make a significant contribution to road sector funding. To begin with, OECD donor agencies (FCDO, JICA, MCC etc.) and multilaterals (WB, AfDB, ADB etc.) often fund or finance major investment projects in the sector, as increasingly do NonParis Club donors (notably China). In addition, a subset of countries has pursued toll road projects through public private partnerships, thereby tapping an additional source of finance for road investments.

For a better understanding of the sources of expenditure in the sector, budget expenditure needs to be combined with road fund statements, donor contributions and information on PPPs to build up a more comprehensive picture of financial flows. Due to constraints in data availability from different sources, the sample size shrinks to 49 countries where data is available and can be brought together from multiple sources to provide the full picture of road sector spending.<sup>11</sup> The relative importance and time trends for the different sources of road expenditures differ remarkably (Figure 12a). Budget expenditure was historically by far the largest source, but has fallen dramatically over the period 2010-18, and is now similar in overall magnitude to the resources from Road Funds, which have been more stable over time at just over 0.5 percent of GDP. Contributions from donors and private sector financiers have been steady but very small in magnitude, amounting to no more than 0.1 percent of GDP combined. Overall, public investment accounts for around 70 percent of road sector spending, with Road Funds making-up another 26 percent, donors a further 4 percent, and the private sector providing only a negligible amount (Figure 12c).

As anticipated, addition of off-budget expenditures significantly increases the estimate of resources allocated to the road sector. Whereas total on-budget spending on roads for the reported countries averages around 1 percent of GDP during the time period 2010-18, total spending on roads rises to 1.2 percent of

> 11 There is some potential risk of double counting to the extent that Road Agency expenditure may be dedicated to paying shadow tolls or availability payments to private road concessionaires. However, given the limited scope of toll road concessions, and the fact that these are more typically funded directly from toll revenue collected, the issue of double counting looks to be relatively insignificant in the roads sector.

15
<!-- Start of picture text --> (a) Overall expenditure on roads, by source, 2010-18 (b) Aggregate expenditure, all sources, 2010-18<br>1.5% 2.5%<br>1.3% 2.0%<br>a C)<br>a 1.0% =a 1.5% re<br>° x<br>SX 9.5% 0.5%<br>0.3% 0.0%<br>0.0% rr ———t 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 —— low income Lower middle income<br>——BOOST =——=RF =—— Private -——=Donors == Upper middle income= = = Grand Total<br>(c) Expenditure breakdown for RF countries, 2010-18 (d) Breakdown for largest spenders, 2010-18<br>Private, 6<br>0.01% 6.0%<br>) S 4.0%<br>C)<br>ia)<br>2 2.0%<br>RF, 0.57% 0.0% |<br>BOOST,1.50% s=2SPegggseeegs3ge8= WL £832F<c os© GOs2S$o4€2?35S22325sQreezgstsono8 a<br>“a3° <x-BD 2 ooNs(=)<br>TMDonors MBOOST @Private MRF<br><!-- End of picture text -->
<!-- Start of picture text --> (a) Annual expenditure by source, 2010-2017 (b) Aggregate expenditure, all sources, 2010-17<br>2.0% 4%<br>a<br>rc) 5 9,<br>a . . —~<br>x 1.0% - 2% Fr \<br>0.5% x 1% vor ro-e -<br>0.0% 0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017<br>——China —— Public =——Private ——— Low income == Lower middle income<br>== Donors == Road Funds === Upper middle income = = = Total<br>(c) Expenditure breakdown for RF Countries, 2010-17 (d) Breakdown for largest spenders, 2010-17<br>| Private , 0.02% 7 8%6%<br>i 3%© ao oqadl<br>=~ PET<br>0% : :<br>3ses= SmeeZ2zLFR_eeo SB RQE<br>=s ©588s = a e&§38~*5 LEE“oS<br>a°<br>=<br>China @ Public Private mDonors MRF<br><!-- End of picture text -->
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1.80 Kosovo Bulgaria R2-0.122<br>‘€ 1.60 e e<br>=<br>Q 1.40<br>yn<br>2 1.20<br>zh<br>x<br>5 1.00 Bangladesh Romania<br>8629 0.80 e e<br>3 060 Jordan Peru he<br>=9& Ethiopia Tunisia ‘i e.Labandsant coeneeeeeeneeet. Mauritius<br>e 0.40 020 Burubdi|ktTarzaniaKehva--8Ne ukraiee GeorgiaMoldovaMace@bniaEcuador1a| 4 "Mexicoe eCosta Rica.<br>0.00 '@ Myanmar M@ngolia® Jamaica 2<br>Guinea 2000 4000 6000 8000 10000 12000 14000<br>Gambia ~ Tajikistan GDP Per capita (USD), 2018<br>Top countries with largest volume of private investment in roads<br>2.50%<br>0.20%<br>2.00% 0.05%<br>0 Oo,<br>a tm 0.07% 0.18%<br>G)<br>-° 0.07%<br>S 1.00%<br>0.50% 0.09%i 0.02%<br>0.05%<br>0.31% fil 0.40% NN 0.99% [NNN 1.28% [NNN 1.30% [NMI 1.51% INN 1.789% [INN 2.00%<br>0.00%<br>Costa Rica Mexico Bangladesh Paraguay Ecuador Senegal Peru Kenya Albania<br>Public mTM Private<br><!-- End of picture text -->
<!-- Start of picture text --> (a) Breakdown of maintenance expenditure for countries with RF (b) Comparison of maintenance expenditure<br>3.0% 40000<br>a 25%<br>O 2.0% E<br>2 1.5% ~ 30000<br>0.5'<br>2 1.0% | g<br>05% ee ee = 20000<br>Ss2ZEES= RSS SSSEREE= 3 SS& 2a? 2= 10000<br>Saotgaetwsartrove regekoels s<br>~3 SE CER ZSPEREEE<br>rc]£go 2265a5 & ree"2” 2§ 0 2012| 2013a 2014a 2015| 2016| 2017= 2018a<br>@Road Fund m Budget @Non RF countries RF countries<br>0.10<br>0.09 R? = 0.0362<br>a Peru<br>E 0.08 e<br>a<br>S007 |-©<br>E 0.06<br>% 0.05 vaskes<br>S . Kosovo cee<br>E 0.03 aan<br>‘gqg~= 0.0 lyanmarooAcmenta”a Macedonia. R‘omaniaF<br>Ss Moldova e e<br>0.01 Tunisia ;_. Mauritius<br>Tajikista Bie porsaachador Lebanon Mexico 5<br>0.00 ambia Li<br>2000 Mee"? 6000 ~~ g000-~S«10000-Ss«12000-~—«14000<br>GDP Per capita (USD), 2018<br><!-- End of picture text -->
prioritize construction of new or upgraded roads over preservation of existing roads, spending around six times as much on investment as maintenance. Some countries appear to neglect road maintenance almost completely (such as Gabon, The Gambia, Jordan, Mexico and Paraguay). Even countries with Road Funds, while they may spend more on maintenance than their peers, still seem to dedicate more resources to investment than maintenance overall (such as Cameroon, Kenya, Senegal, Mozambique and Namibia). Mozambique and Ethiopia are among the few countries reporting both high and balanced levels of expenditure between road investment and road maintenance.<sup>13</sup> Only a minority of countries are dedicating adequate resources to either investment in or maintenance of roads. An important question is whether spending levels are adequate to meet development targets. High level estimates of spending needs for roads suggest that countries may need to spend similar amounts, of the order of 1 percent of GDP, on both capital and operating expenditure for roads (Rozenberg and Fay, 2019). These spending adequacy thresholds are represented by the green box in Figure 18, meaning that countries appearing in the green box are spending adequate amounts on both investment and maintenance. Strikingly, Ethiopia and Mozambique are the only two countries that fall into this category. Additionally, the red box represents the area where countries are neither spending enough on investment nor maintenance, and the majority of countries appear in this area. Namibia is the only country that spends adequately on maintenance, but not on investment. A larger group of countries, including some with Road Funds, spend adequately on investment but not on maintenance.

Countries with PPP programs for toll roads (represented as black triangles in Figure 18 tend to exhibit a particularly high level of capital bias in road spending allocation decisions. Countries with Road Funds (represented by blue circles in Figure 18) are more likely to allocate adequate resources to the road sector, either on maintenance and/or investment, than those without. Nevertheless, the presence of a Road Fund is not of itself a guarantee that maintenance expenditure will be _enough_ . Countries like Lesotho and Sierra Leone, for instance, do not appear to capture adequate resources through their Road Funds to support full maintenance requirements.

> 13 The above estimates are based on the BOOST, Road Fund and OECD-DAC data and do not include any maintenance expenditure undertaken by toll roads as this data is not available. However, based on the earlier analysis of Road Funds, this is likely to be relatively small overall.

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<!-- Start of picture text --> Capex vs Opex, Total over 2006-2018<br>5% -<br>= ;<br>=_<br>5 3% Mozambique >—<br>»oO e - _<br>& _-— Ethiopia<br>= Namibia ae<br>== Myanmar ®@ o_<br>3 1% Galvado? periegat ‘Sierra Leone esotno<br>~ alvador anzania<br>&<br>0% a C3 : @a %eA Peru ‘&Ibania ° Bhutan °| Kosovo@<br>0% Mexico 434 Kenya 2% 3% 4% 5%<br>Costa Rica Parag ay . .<br>cuador Full capital expenditure<br>@ Road Funds @NoRoadFunds A Countries with PPPs<br><!-- End of picture text -->
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panel data for 18 countries on input and outputs covered the period between 2006 and 2018. Data vary considerably across countries. While the median CAPEX per kilometer is USD 550 in Costa Rica, it is USD 71,872 in Guatemala. Similarly, Tanzania has the median OPEX per kilometer value of USD 0.41 while the OPEX by km is 7,065 in Bulgaria.

The particular form of DEA adopted is the input-oriented Envelopment Model, which minimizes input while keeping outputs at their current level. The technique estimates a productivity Malmquist index (Malmquist, 1953), which decomposes the change of productivity into technological change (TECCH) and efficiency change (EC), with the latter further decomposed into pure efficiency change (PECH) and scale efficiency change (SECH). The latter term captures economies of scale recognizing that a process may become more efficient simply by increasing the scale of production. A Malmquist Index (or any of its components) greater than one indicates improvement in productivity whereas values less than or equal to one denote stagnation. (Refer to Appendix 2 for further methodological details.) The overall results for the sample, reported in Table 5, indicate that on average there has been a very slight improvement in productivity, with a Malmquist index of 1.03, and this improvement is almost entirely attributable to technological change rather than any efficiency gain. Curiously, a 6 percent improvement in pure efficiency (1.06) has been almost entirely offset over the sample by an equivalent deterioration in scale efficiency (0.95). However, the average results conceal a wide variation in country results.

DEA shows substantial dispersion in efficiency performance by country (Figure 20). Some nine countries show an improvement of overall road sector productivity in excess of 3 percent, and as high as 70 percent in the case of Mauritius. In most of these cases (such as Ethiopia, Mexico, Paraguay, and Peru), the observed productivity improvements are driven by technological progress. Only a handful of countries (Macedonia and Mauritius) boosted productivity primarily through efficiency gains, and in most cases, these were attributable to a change in the scale of production.

It is striking that one third of the countries saw road sector productivity decline by 10 percent or more. In each case, this was attributable to a loss of efficiency large enough to overwhelm mainly positive technological trends. In the case of Afghanistan and Niger, both of which saw a massive decline in efficiency of 70-98 percent, scale effects seem to have played the largest role.

Figure 22 shows the summary of annual means of total productivity change (Malmquist Index) and its components, technical change, and efficiency change (decomposed into pure efficiency and scale efficiency) for the whole sample over the period 2006 - 2018. The pattern in Figure 22 shows that efficiency change (the product of pure efficiency and scale efficiency) and technology change move in opposite directions in general, diluting the overall growth in total productivity. The chart also indicates that movements in the indices can be quite volatile, with a large spike for technological progress in 2012, and smaller spikes for other indices at different points in time.

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**Table 5: Malmquist Index summary by country means**

|||||**Of w**<br>|**hich:**<br>|
|---|---|---|---|---|---|
|**Country**|**Total Factor**<br>**Productivity**<br>**Change**<br>**(TFPCH)**|**Technological**<br>**Change**<br>**(TECCH)**|**Technical**<br>**Efficiency**<br>**Change (EC)**|**Scale**<br>**Efficiency**<br>**Change (SECH)**|**Pure Efficiency**<br>**Change (PECH)**|
|**Afghanistan**|0.02|1.22|0.01|0.02|0.90|
|**Bulgaria**|0.76|0.94|0.81|0.71|1.13|
|**Burkina Faso**|0.20|0.41|0.48|0.49|0.97|
|**Costa Rica**|0.84|1.18|0.71|0.74|0.96|
|**Ethiopia**|1.44|1.51|0.96|0.90|1.06|
|**Guatemala**|0.81|1.20|0.68|0.63|1.07|
|**Kenya**|1.24|0.97|1.27|1.01|1.26|
|**Kosovo**|1.03|1.08|0.95|0.70|1.36|
|**Macedonia**|1.50|0.55|2.75|2.08|1.32|
|**Mauritius**|1.70|0.78|2.18|2.01|1.09|
|**Mexico**|1.17|1.17|1.00|1.00|1.00|
|**Namibia**|0.30|1.13|0.26|0.26|1.00|
|**Niger**|0.93|0.74|1.26|1.26|1.00|
|**Paraguay**|1.37|1.41|0.97|0.95|1.02|
|**Peru**|1.03|1.13|0.91|1.24|0.74|
|**Senegal**|1.37|0.94|1.46|1.20|1.23|
|**Tanzania**|0.92|0.97|0.95|0.88|1.07|
|**Tunisia**|0.97|0.98|0.99|1.34|0.74|
|**Mean**|1.03|1.04|1.00|0.95|1.06|

Source: Authors’ elaboration based on BOOST data. This table presents the results for the input–oriented Malmquist index and its components for the period 2006 – 2018.

**Figure 20: Mean Deviations from the Malmquist Index**

<!-- Start of picture text -->
1.00<br>0.50<br>0.00<br>-0.50<br>-1.00<br><!-- End of picture text -->

Source: Authors’ elaboration based on BOOST database. Mean deviation is calculated by subtracting 1 from the Malmquist Index presented in Table 5.

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**Figure 21: Total Factor Productivity Change over Time for Selected Countries**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>2010 2011 2012 2013 2014 2015 2016 2017<br>Bulgaria Mexico Peru<br><!-- End of picture text -->

**Figure 22: Malmquist Indices and Sub-Components Over Time**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Total Factor Productivity Pure Efficiency<br>Technological Change Scale Efficiency<br><!-- End of picture text --> Source: Authors’ elaboration.

# **3.3 Electricity Spending Trends**

As with roads, budgetary expenditure on electricity fails to fully capture resource flows to the sector due to widespread reliance on off-budget vehicles. Due to the prevalence of electric utilities, many of which are state-owned enterprises across the developing world, budget spending on electricity only tells a small part of the expenditure story for the sector. During the last 25 years, there has also been a substantial scale-up in PPPs, particularly for power generation projects. Finally, donors – from the OECD and beyond – have also contributed to spending in the sector, especially in the lower income country group. This means that budget expenditure needs to be combined with data from utility financial statements, as well as public private partnerships and donor engagements, to build-up a comprehensive picture of financial flows to the electricity sector.

By integrating off-budget data sources, a more complete picture can be provided. Over the period 20102018, public expenditure for the reporting 48 countries averages 0.35 percent of the GDP with a clear rising trend (Figure 23a). The private sector contributed 0.21 percent of GDP towards expenditure, while OECD donors contributed just 0.05 percent of GDP in the power sector during the same period (Figure 23b, c).

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<!-- Start of picture text --> (a) On-budget, 48 Countries (b) Donors, 48 countries<br>0.70% 0.70%<br>0.60% 0.60%<br>a 0.50% a 0.50%<br>6 0.40% & 0.40%<br>5 0.30% 5 0.30%<br>* 0.20% * 0.20%<br>0.10% 0.10%<br>0.00% 0.00%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>(c) Private sector, 48 countries (d) China, 35 countries, 2010-17<br>0.70% 0.70%<br>0.60% 0.60%<br>o 0.50% o 0.50%<br>6 0.40% 8 0.40%<br>8 0.30% S 0.30%<br>0.20%0.10% _— 0.20%0.10% —aa—=<br>0.00% 0.00%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->
<!-- Start of picture text --> (a) Aggregate expenditure, by source (with (b) Aggregate expenditure, by source (w/o<br>Brazil)- 2010-17 Brazil) 2010-17<br>(c) Aggregate expenditure breakdown, 2010-2017<br>6%<br>a 5%<br>2 4%<br>be 3%2%<br>0%1%% —- =o eo |<br>& & ee & ro se & s ss &<br>ws s&s aS oe & oe Rts) e Ss<br><& r o><br>se 9<br><<br>mChina mPublic mPrivate mDonors mSOEs<br><!-- End of picture text -->
<!-- Start of picture text --> (a) On budget expenditure, % of GDP, 2010-2018 (b) Utilities expenditure, % of GDP, 2010-2018<br>2.00% S> 2.00% ;7<br>9 1.50% a 9 1.50% S=<br>s a s mA<br>©cc1.00% a © 1.00% ;<br>:2 E a<br>0.50% ao = 050% sa<br>aa = ° e<br>0.00% @i=©e—eeas—_@-e e 0.00% “@pe—®@a ee oe . e<br>0.00% 0.50% 1.00% 1.50% 2.00% 0.00% 0.50% 1.00% 1.50% 2.00%<br>Investments Investments<br>1.0%<br>=<br>2<br>a=]<br>c<br>7& 0.6% SEE<br>3<br>wo e<br>c=° Solomon Islands south Africa Bhutan<br>& 0.4% e e<br>wv<br>£2<br>oO<br>= 0.2%<br>Kosovo<br>a e<br>Ethiopia Brazil @ Kenya<br>0.0% gentind Pee<br>0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 40% 45% 5.0%<br>Capital expenditure<br><!-- End of picture text -->
# **4 Discussion and Policy Implications**

Drawing on a variety of new and existing data sources, this paper has aimed to shed light on the patterns and trends of public spending on infrastructure in low- and middle-income countries.

Overall, public expenditure on infrastructure in the developing world was found to be low and declining, reaching its lowest levels since 2010 at just below 1 percent of GDP in 2018. Infrastructure accounts for a low and stable share of no more than 5 percent of total government expenditure. However, investment in infrastructure accounts for a somewhat higher share of 15 percent of overall public investment; albeit reaching higher shares of around 30 percent for some countries during limited time windows.

There is evidence that low-income countries, despite their lower overall levels of public expenditure, were inclined to devote significantly higher shares of public expenditure and public investment to infrastructure at least up until 2015, but with a marked decline subsequently. Over the period 2010-18, there has been a marked sectoral shift in public expenditure for infrastructure, with spending on roads halving over this period even as spending on electricity has doubled.

It is striking that these numbers are much lower than previous high-level aggregated estimates of developing country infrastructure spending presented in Fay et al. (2019). However, there are important differences in the methodology that can adequately explain the differences. First, this analysis is limited to actual spending data and does not rely on any estimates involving assumptions or extrapolations. Second, the current data set does not include any of the known high infrastructure spending countries such as India and China, which due to their large size tend to inflate global estimates of infrastructure spending. In that sense, the current research provides a higher resolution more accurate understanding of spending patterns in the developing world outside the largest middle-income countries.

Low levels of public expenditure on infrastructure are further exacerbated by low budget execution ratios. Comparing budget allocations to executed amounts shows that countries manage to execute 94 percent of the budget allocation for capital projects in roads but only 75 percent in power. There are marked differences according to income group, with middle-income countries (executing 99 percent of budget for roads and 82 percent for power), doing substantially better than their low-income counterparts (executing just 64 percent in roads and 39 percent in power).

When it comes to roads expenditure, the main sources of funding are government budgets, and for some countries Road Fund resources and official financing from China, with the private sector and donors contributing relatively modest amounts. Relatively few countries have been able to tap PPP resources for the road sector, and even where they have, these have typically accounted for no more than 5-10 percent of total road investment.

The vast majority of countries were found to exhibit a strong capital bias, allocating about six times as much expenditure to construction of new roads as to preservation of existing roads; a 50:50 balance would be closer to recommended norms. Countries adopting Road Funds were found to allocate substantially more to road maintenance on average than those without – nine times as much on a per kilometer basis – and this is particularly remarkable when their relatively low level of income is taken into account. Countries with Road Funds were also less likely to exhibit capital bias, or at least exhibit less pronounced capital bias, than those without. However, even then, the presence of a Road Fund did not necessarily seem to guarantee that 29
an _adequate_ resource envelope would be allocated to maintenance. Countries with road sector PPPs seemed to exhibit even stronger capital bias than those without.

Data Envelopment Analysis suggests that the productivity of road sector spending has barely improved over time, despite modest technological progress. This is due to a countervailing deterioration in technical efficiency that largely wipes out these gains. Countries that did see some improvement in productivity often did so as a result of scale effects rather than pure efficiency gains.

Turning to the electricity sector, budget expenditures were found to account for just 25 percent of the resource allocation to the sector. Investment in electricity is dominated by SOE or PPP financing, while operating expenditure comes almost exclusively from SOE revenues. Chinese financial flows have made a material contribution in some cases. Again, there is significant evidence of capital bias in observed spending patterns.

Regarding the adequacy of infrastructure spending relative to high-level estimates of what would be needed to deliver on 2030 development goals, actual spending looks well below the benchmarks in the majority of cases. Only two countries, Ethiopia and Mozambique, were found to have dedicated adequate resources to both the construction and preservation of roads. Similarly, only a handful of countries – including Bhutan, Kenya and Kosovo – are investing in the power sector at recommended levels. Overall budgetary expenditure on infrastructure looks to be no more than 0.7 percent of GDP; although this rises to around 2.5 percent of GDP when all off-budgetary sources are taken into account, still just a fraction of the estimated 4.5 percent of GDP minimum requirement. At the more disaggregated sector level, both capital and operating expenditures for both roads and electricity were found to lie below spending norms for the vast majority of countries studied.

Finally, while data for the turbulent post-2018 period is not yet available, infrastructure spending was already low and declining in many low- and middle-income countries prior to the global pandemic of 202021. The paper presents significant evidence regarding the procyclicality of infrastructure spending in lowand middle-income countries, which suggests that the explosion of competing demands for health and social safety net expenditures during this economic crisis is likely to have placed infrastructure spending under even further strain.

30