metadata: authors: 0: author: Ruiz Nunez,Fernanda
1: author: Wei,Zichao
count: World
docna: 0: docna: Infrastructure investment demands in
            emerging markets and developing economies
docty: Policy Research Working Paper
subtopic: Sanitation and Sewerage,Engineering,Energy Policies & Economics,Water and Human Health,Hydrology,Water Supply and Sanitation Economics,Sanitary Environmental Engineering,Small Private Water Supply Providers,Environmental Engineering,Health and Sanitation,Energy Demand,Energy and Environment,Energy and Mining,Transport Services,Town Water Supply and Sanitation
teratopic: Law and Development,Informatics,Infrastructure Economics and Finance,Public Sector Development
abstracts: cdata!: The authors have assembled 1960–2012
            infrastructure stock data from 145 countries to estimate the
            demand for infrastructure services in emerging markets and
            developing economies. This paper identifies that the
            required resource flows to satisfy new demand while
            maintaining service for existing infrastructure amounts to
            $836 billion or 6.1 percent of current gross domestic
            product per year over the period 2014–20. The annual
            infrastructure investment gap for emerging markets and
            developing economies is $452 billion per year, which implies
            that emerging markets and developing economies should almost
            double their current spending. The paper also estimates that
            half of the spending should be allocated to maintenance of
            existing assets. Acknowledging the challenges to compare
            infrastructure investment estimates across different
            methodologies, the authors recognize this result as a lower
            bound estimate and compare the results with others available
            in the literature.
repnme: repnme: Infrastructure investment demands in
            emerging markets and developing economies
display_title: Infrastructure investment demands in
            emerging markets and developing economies
listing_relative_url: /research/2015/09/25057696/infrastructure-investment-demands-emerging-markets-developing-economies
topicv3: Sanitation m1326453 2000,Water m1326522 137,Energy and Extractives m1326595 507,Transport m1326661 135,Power and Electricity Sector m1327094 515
publishtoextweb_dt: 2015-09-17T00:00:00Z
historic_topic: Law and Development,Informatics,Infrastructure Economics and Finance,Public Sector Development
no_of_pages: 30
author_keys: 0: author_key: 880384
1: author_key: 1317515
model_extractions: WPS7414 Policy Research Working Paper 7414 Infrastructure Investment Demands in Emerging Markets and Developing Economies _Fernanda Ruiz-Nuñez Zichao Wei_ Public-Private Partnerships (PPPs) Group September 2015
Policy Research Working Paper 7414

# **Abstract**

The authors have assembled 1960–2012 infrastructure stock data from 145 countries to estimate the demand for infrastructure services in emerging markets and developing economies. This paper identifies that the required resource flows to satisfy new demand while maintaining service for existing infrastructure amounts to $836 billion or 6.1 percent of current gross domestic product per year over the period 2014–20. The annual infrastructure investment gap for emerging markets and developing economies is $452 billion per year, which implies that emerging markets and developing economies should almost double their current spending. The paper also estimates that half of the spending should be allocated to maintenance of existing assets. Acknowledging the challenges to compare infrastructure investment estimates across different methodologies, the authors recognize this result as a lower bound estimate and compare the results with others available in the literature.

This paper is a product of the Public-Private Partnerships (PPPs) Group. 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://econ.worldbank.org. The authors may be contacted at fruiznunez@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
# **Infrastructure Investment Demands in Emerging Markets and Developing Economies**

## **Fernanda Ruiz-Nuñez and Zichao Wei**<sup>**1**</sup>

_JEL Classification_ : H54, O13, O18, R42, E22 _Keywords_ : Infrastructure, Infrastructure Investment Demand, Investment Requirement, Government Spending, Infrastructure Gap.

> 1 Fernanda Ruiz Nuñez is a Senior Economist at the Public-Private Partnerships (PPPs) Group at the World Bank; and Zichao Wei is a Financial Analyst (STC) at World Bank’s PPPs Group. The authors are grateful to Francesca Guiso for assisting with data analysis, and Tito Yepes who has provided database used in Yepes (2008)’s paper.  The author would like to thank Marianne Fay, Tito Yepes and Atsushi Iimi for their valuable comments and discussions. The findings, interpretations and conclusions expressed herein do not necessarily reflect the views of the Board of the Executive Directors of the World Bank or the governments they represent. The authors’ email addresses are: <u>fruiznunez@worldbank.org,</u> and zwei@worldbank.org.
# **I. Introduction**

The links between infrastructure and development are well established, including the impact of infrastructure on poverty alleviation, equality, growth and specific development outcomes such as job creation, market access, health and education.<sup>2</sup> These relationships are complex and dynamic; even with respect to growth and job creation, infrastructure’s impacts are felt through multiple channels.<sup>3</sup> The demand for infrastructure is rising with the accelerating pace of globalization and urbanization. Every month in the developing world more than five million people migrate to urban areas. This demand trend is compounded by the growing need for low CO2 and climateresilient investments to combat the challenges of climate change.<sup>4</sup> The developing world suffers from an undersupply of infrastructure, constraining economic growth rates, leaving the world’s most vulnerable communities without access to basic services, and hampering attempts to achieve broad-based poverty reduction. Currently, it is estimated that some $700 billion to $800 billion is invested worldwide ($550 billion in emerging markets and developing economies (EMDE)) in infrastructure every year, although data are scarce.<sup>5</sup> In a nutshell, more than 70% of the current infrastructure funds come from the public sector. The second largest source of financing comes from the private sector, roughly 20% of the total funds. The remaining is covered by ODA.<sup>6</sup> A range of estimates on infrastructure financing requirements applying different assumptions and methodologies for achieving various global goals have been estimated over the years. Despite the challenges comparing results across different methodologies, all the latest estimates of the global infrastructure investment requirements indicate that there is a significant gap.

This paper reviews the recent global estimates and summarizes the main findings. Using a top-down approach, this paper estimates the global estimates on infrastructure investment requirements to satisfy consumer and producer’s demand for infrastructure services, assuming specific economic and demographic growth rates. This paper uses Yepes’ (2008) model addressing some of the limitations of the original model developed by Fay (2000), and gathers the most recent data on infrastructure stock and macroeconomic variables up to 2012.<sup>7</sup> It is important to address that the purpose of longterm projections is not to forecast the future. Rather, it is to explore how the future infrastructure investment might evolve given a set of economic, social, technical and

> 2 Straub (2008); and Serven and Calderon (2004b, 2008 and 2010)

> 3 Agénor and Moreno-Dodson (2006) for an overview. Estache, et al (2013) and Schwartz, et al (2009) for a treatment of infrastructure impacts on jobs and growth.

> 4 Fay and Toman (2010) and Bhattacharya, et al (2013)

> 5 These figures are author’s own estimates based on a number of sources. See table 5.

> 6 Adopted as a concept by the Development Assistance Committee (DAC) of the Organization for Economic Cooperation and Development in 1969, official development assistance (ODA) is a classification of resource flows from 22 DAC donor countries.

> 7 It also explicitly list the countries used for the estimation for which data is available and extrapolates results for all Emerging Markets and Developing Economies (EMDE). Moreover, one of the main contributions of this study is to simplify future updates of investment needs by providing a model and database simple to update when most recent data is available.

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political assumptions. The estimates from this paper should be considered as complement to the existing estimates in the literature by providing lower bound figures and filling the gap for certain sectors or regions.

# **II. Definitions and Previous Literature**

A range of estimates on infrastructure financing requirements applying different assumptions and methodologies for achieving various goals have been estimated over the years. Terms such as “needs”, “demand”, and “gap” have been used in the literature interchangeably generating some confusion when comparing results. In order to quantify “needs”, normative targets should be established usually assuming an optimal level of infrastructure. Different targets will lead to different estimated needs. “Demand,” as used in this paper, refers to the infrastructure investment requirements to satisfy the consumer and producer demand, based on predicted GDP growth and other growth trends. The terms “gap” is used as the difference between the estimated requirements and the actual level of investments.

There are various methodologies used in the literature to estimate infrastructure investment requirements; they can be broadly classified in two categories: “top-down” and “bottom-up” (See Annex I). As the data of macroeconomic variables are relatively more comprehensive compared with the data of individual infrastructure projects, majority of the studies in fact adopt the “top-down” approach to conduct the empirical analysis.

The most recent estimates of annual infrastructure investment requirements for Emerging Markets and Developing Economies (EMDE) are Fay et al (2011) which estimate the annual needs of US$0.9-1.1 trillion with an annual gap of US$100-300 billion by extrapolating estimates from Yepes (2008) using price inflation rate. A recent G24 policy paper, Bhattacharya et al. (2013), calculates investment annual needs of US$2 trillion and investment gap of US$1 trillion by extrapolating Fay et al (2011) using economic growth rate and price inflation rate. Both studies are extrapolations of previous studies and they do not account for actual changes in infrastructure stock since 2006.<sup>8</sup> McKinsey (2013)’s uses three approaches to estimate infrastructure investment requirements. First, they use the historical spending approach, which assumes that future needs will follow past spending as percentage of GDP. Using this assumption, they project spending to 2030. The main limitation of this approach is that the data are mainly limited to OECD countries and extrapolated for the rest of the world. Secondly, they use the stock of infrastructure approach that estimates its value by using a perpetual inventory model. Unfortunately, they only have data for 12 countries<sup>9</sup> and

> 8 Moreover, they fail to mention the list of countries for which data was not available.

> 9 The model was populated with data from the following countries: Brazil, Canada, China, Germany, India, Italy, Japan, Poland, South Africa, Spain, United Kingdom and United States

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they use this information to estimate the average value of infrastructure stock (calculated at 70% of GDP). This average value is used to extrapolate results for the rest of the countries until 2030. Thirdly, they use the independent estimates of future needs approach that aggregate the estimates from three different sources: (i) Global Water Intelligence for the water sector; IEA (2011) for the power sector and OECD (2012) for the remaining infrastructure sectors. Based on these three approaches, they estimate that global infrastructure investment requirements for 2013-2030 range from US$57 trillion to US$67 trillion, or about US$3.2 trillion to US$3.7 trillion annually on average.

The OECD (2006) report concluded that global infrastructure investment requirements across the transport (road, rail), telecom, electricity and water sectors would amount to around US$53 trillion over 2010-30, or about US$ 2.7 trillion annually average. Annual investment requirements for these sectors amount to some 2.5% of world GDP, which would rise to 3.5% of GDP; if electricity generation and other energy-related infrastructure investments in oil, gas and coal are included. The methodology used built on existing estimates, and extrapolates those to 2030. For the transport sector, they apply the estimated regional level elasticity (the ratio of paved road capital stock in USD billion to GDP) from Fay and Yepes (2003) assuming a fixed convergence rate. Asset replacement is calculated by assuming the economic life provision of 30 years without controlling by the current quality level. For the water sector, they extrapolate the proportion of GDP allocated to water services, varying from 0.3% to 2% based on different levels of income. For telecoms, they identify the underlying trends and drivers, and extrapolate the percentage of GDP invested in telecoms from Fay and Yepes (2003. Lastly, for the energy sector, they adopt estimates from IEA’s World Energy Outlook, which uses a scenario approach to analyze the possible evolution of energy markets to 2030.

There are other estimations in the literature, however, they are based on Fay and Yepes (2003), McKinsey (2013), and OECD (2006). For example, Kennedy and CorfeeMorlot (2013), based on OECD work, are in the high part of the range, with annual investment requirements estimated at US$ 6.6-7.2 trillion when buildings and vehicles included at low carbon (2°C) scenario, and US$ 2.7-3.2 trillion when those are excluded. Using McKinsey’s (2013) estimates of infrastructure investment requirements, KPMG (2014) and Standard & Poor's Ratings Services present the annual investment requirements of $3.2 trillion by 2030. The World Economic Forum’s (2013) estimation of annual infrastructure spending need for 2020 is calculated by taking the Fay et al (2010) estimate of US$ 1.25-1.5 trillion annually in 2013 and assuming a 4% annual growth rate from 2013-20, and an additional US$ 200-300 billion annual requirement to make the infrastructure sustainable (by providing for climate change mitigation and adaptation).

There have been previous efforts to calculate regional estimates, but some of them are outdated and do not account for changes in infrastructure stock or have significant 4
drawbacks. Gill and Kharas (2007) estimate infrastructure investment requirements for East Asia and the Pacific using data up to 2005. Data for Latin America and the Caribbean were collected from primary sources for 1990–2005 and then extrapolated in posterior work as presented in Yepes (2008). The most recent figures are from South Asia, where Andres et al. (2013) have extrapolated previous estimations at the country level for South Asia.<sup>10</sup> The estimations from Sub-Saharan Africa come from Foster and Briceño-Garmendia (2010) that made a significant effort to collect data on infrastructure stock and present a bottom-up approach.

Some of the most recent sectoral estimates come from the International Energy Agency (IEA). IEA indicates that the annual investments in energy supply of $1.6 trillion need to rise steadily over the coming decades towards $2 trillion. Under IEA’s expectation, future electricity usage underpins changes in the installed capacity of power generation technologies and in transmission and distribution networks, which assume global electricity demand growth rate of 2.2% per year, projected to future demand. Annual spending on energy efficiency, measured against a 2012 baseline, needs to rise from $130 billion today to more than $550 billion by 2035. OECD presents estimates at sectoral level for transport, telecoms, and water and sanitation.

Comparing the different estimates presented some significant challenges (see Annex II). Recognizing the limitation of comparing results from different methodologies, the table below presents the most recent estimates on infrastructure investment requirements/demand in the literature.

> 10 Estimates by Chatterton and Puerto (2006) follow Fay (2000) methodology to estimate investment needs in South Asia but estimates are up to 2010.

5
**Table 1. Estimations on annual infrastructure investment requirements (US$ billion)**

||**Global Estim**|<br>**ates(USD billion)**|
|---|---|---|
||$3,200-$3,700|McKinsey,2013|
|Global|$2,680-$3,200|Kennedy&Corfee-Morlot,2013|
||$2,650|OECD,2006|
||$836|Ruiz-Nuñez&Wei,2015|
||$2,000|Bhattacharya,2013|
|Develoin|$1,250-$1,500|WEF,2013|
|pg|$900-$1,100|Fay,2011|
||$1,113|Yepes,2008|
||$465|Fay&Yepes,2003|
||**Regional Esti**|<br>**mates(USD billion)**|
||$498|Bhattacharyay,2010|
|EAP|$165|Gill and Kharas,2007|
||$34|Bhattacharyay,2010|
||$260-$434|Kohli&Basil,2011|
|LAC|$186|Perrotti& Sanchez,2011|
||$57|Fay,2000|
|MENA|$78.50|World Bank. 2009|
||$137-$206|Andrés et al,2013|
|SAR|$234|Bhattacharyay,2010|
||$62|Chatterton&Puerto,2006|
|SSA|$93|Foster&Briceño-Garmendia,2010|
||**Sectoral Esti**|**mates(USD billion)**|
|Electricity|$2,000|IEA,2012|
|Transport|$805|OECD,2011|
|Telecoms|$600|OECD,2007|
|W&W+WW|$1,320|OECD,2007|

Source: Authors’ summary

# **III. Methodology**

This paper uses Fay’s (2000) model to estimate infrastructure investment demand and tries to address some of the limitations in the methodology following Yepes (2008). See Annex III with details of the methodology used. The model estimates the infrastructure investment requirements needed to satisfy consumer and producer demand given an expected growth rate. The model follows a top-down approach and accounts for global, regional and sectoral estimates.

As described in the previous section, most of the methodologies in the literature have significant drawbacks, and Yepes (2008) is not innocuous to those. This methodology does not account for regional or sector specificities and supply constraints.<sup>11</sup> Importantly, the model does not estimate the level of infrastructure that would maximize growth or socio-economic targets, that is to say the model assumes no optimality. The relationship between income level and infrastructure service demand is

> 11 Data limitations does not allow to address those constraints.

6
established on the basis of past observed behavior in a sample of countries and extrapolated for the future using predicted income growth.

Using Yepes (2008) as starting point, we constructed an annual panel of infrastructure stocks, macro variables, and demographics for 145 countries for 1960–2012. Due to missing data the numbers of periods observed for each country differ and therefore, the database is an unbalanced panel. (See Annex IV for data sources.) The infrastructure variables gathered are:

- Telephones (mobile and landlines) subscribers per 1,000 persons;

- Kms of paved roads per Km2 of land

- Kms of unpaved roads per Km2 of land

- Kms of rail per 1,000 persons

- Total TEU handled

- KW of installed electricity generation capacity per capita;

- Percentage of households with access to electricity

- Percentage of households with access to water and sanitation

- Percentage of households with access to sanitation

- Percentage of households with access to wastewater treatment

After obtaining the annual database, we followed Yepes (2008) by taking five-year averages of all variables for each country, and obtaining a panel that contains the fiveyear average of annual flows (for flow variables) or the average stock held over the five-year period (for stock variables).

The main motivation for this transformation comes from the lack of annual data on infrastructure stock and the fact that infrastructure accumulation is a slow process that happens over long periods of time. Therefore, we are not able to capture the short-term dynamics of the investment process. Instead, Yepes’s (2008) model aims to reveal the relationship between income flows and infrastructure stocks at low frequencies. Therefore the model is estimated on a panel with five-year periodicity.

The benchmark GDP growth scenarios are created by extrapolating the 5-year-long regional growth rate to predict future GDP. This approach provides a region-specific scenario that is tailored to forecast. Then, the model applies the average growth rate of 5 years (2010–2015) GDP series, including the forecasted value of year 2014 and 2015, to predict the GDP trend for 2014–2020, which results in an implied annual growth rate for regions GDP projection.

**Table 2. Regional GDP growth rate used to forecast GDP 2014-2020**

|Region|EAP|ECA|MENA|LAC|SAR|SSA|
|---|---|---|---|---|---|---|
|GDP Growth rate|7.79%|3.95%|1.64%|3.32%|6.32%|4.63%|

Source: Authors’ estimation based on Global Economic Prospects, WDI 7
To obtain the demand function for infrastructure, the model includes some additional regressors: (i) agricultural share of value added, (ii) manufacturing share of value added, (iii) urbanization rate, and (iv) population density. For each country, we created scenarios for these variables by obtaining their growth rate for the 1990–2012 period, and then applying that growth rate to obtain a forecast for the 2014–20 period. Projections of GDP composition are not available (share in agriculture and in manufacturing) so we keep them at 2010 values. For urbanization and population, we use the 2014–20 projections provided by the United Nations.

Since infrastructure stocks tend to change reasonably slowly over time and have a long life span, we included a lagged value of the dependent variable in the regression in order to increase the explanatory power. We take Fay and Yepes (2003) estimate equation below to estimate the infrastructure demand: where all variables are in natural logs to linearize the model, I<sup>j</sup> i,t is demand for infrastructure stock of type j in country i at time t; Iji,t-1 is the lagged value of the infrastructure stock; y is income per capita; A is share of agriculture value added in GDP; M is the share of manufacturing value added in GDP; Di is a country fixed effect; Dt is a time dummy; and  is the error term.<sup>12</sup> In order to calculate maintenance cost, the report provides estimates based on a topdown approach of using data on infrastructure and parameters for maintenance costs. The maintenance cost is calculated by applying a depreciation rate to the predicted total value of the capital stock.

The unit costs used to value infrastructure were obtained by applying GDP deflator inflation rate to previously estimated unit cost values presenting prices at 2011 level. Unit costs are assumed constant over time. Since more recent data were not available, these numbers were validated by World Bank experts.

For transport, energy and water sectors, a GDP deflator inflation rate is applied to the lasted available price in the literature and they were later validated by World Bank experts. In terms of telecoms sector, the costs have come down significantly in the last decade, mainly due to competition, technological change, market entry by Chinese companies and market volume. However, according to experts in the field, the projected price level is expected to remain constant in the coming decade.<sup>13</sup>

> 12 Manufacturing rather than industry was used here because industry includes mining, which has very different implications on the demand for infrastructure.

> 13 This is due to: (i) decline in shipments, due to market saturation, (ii) technological change is now pushing in favor of higher performance networks (eg DSL, fibre, 4G) which will tend to swap out performance for price, (iii) the shake up to the market

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**Table 3. Unit Costs (USD), by Sector**

||<br>**2011 Price**|**Level**|
|---|---|---|
|**_Sector_**|_Costper unit_|_Unit_|
|Electricity generation|2,700|kW|
|Paved roads|500,000|kilometer|
|Unpaved roads|51,000|kilometer|
|Rails|1,200,000|kilometer|
|Rural water and sanitation|150|person|
|Urban sanitation|150|person|
|Urban water|80|person|
|Main telephone lines*|200-300|line|
|Mobile lines*|90-130|line|
|Access to electricity|250|person|
|Ports|360|TEU|
|Wastewater treatment|150|person|

Source: Authors’ estimation validated with World Bank experts.

*Main telephone lines unit price ranges from US$200-US$300 per line per connections in areas greater than 3km of an existing exchange.  The lower bound is used for India (US$200) and the upper bound for the rest of the countries.

*Mobile lines unit price is related to the urbanization rate. US$100 for new 3G connection in urban areas and US$ 160 for new 3G connections in rural areas. We take the average of the rural and the urban areas, as the projected urbanization rate is nearly 50% in developing countries. Therefore, US$130 unit price is used all countries except India where the numbers would be closer to US$60 and US$120, respectively with an average of US$90 per connection.

# **IV. Main Results**

The results of the estimated regression are included in Annex V. This study indicates that the developing world will require annual investments about $819 billion (6 percent of GDP in developing countries) through 2020 to satisfy consumer and producer demand for infrastructure services. Of this amount, about 49 percent is required for capital expansion of infrastructure and 51 percent for maintenance of both current and future infrastructure. In addition, high income countries will require about $ 285 billion (0.8 percent of GDP in developed countries) of annual infrastructure investments during the 2014- 2020 period.

The table below describes the results for the expected annual investment requirements, including capital expenditure and maintenance cost, by income groups (low income, upper caused by the entry of Chinese manufacturers like Huawei and ZTE won’t happen again for a while.

9
middle-income and lower middle income), regions, and infrastructure sector (i.e transport, telecom, electricity, and water and sanitation).

As expected, the investment requirements as a share of GDP are the highest in low-income countries (14.1%). This is followed by lower-middle income (3.4%), and upper-middle income (2.6%) countries. The sectors accounting for the greatest share of infrastructure investment requirements is electricity (39%). Transport is the second largest (31%), followed by telecommunications (23%). The Water and sanitation sector only account for 7% of the share.

**Table 4: Expected Annual Investment Requirements, 2014–20** (at 2011 prices)

||Ca<br>|pital<br>|Maint<br>|enance<br>|Tot<br>|al<br>|
|---|---|---|---|---|---|---|
||US$Mn|%GDP|US$Mn|%GDP|US$Mn|%GDP|
|_By income group_|||||||
|Low Income|176,217|7.1%|172,089|6.9%|348,306|14.1%|
|Lower Middle Income|35,821|1.1%|72,063|2.3%|107,884|3.4%|
|Upper Middle Income|160,251|2.0%|203,047|2.6%|363,298|2.6%|
|_Developing countries by region_|||||||
|SAR|159,074|7.8%|144,427|7.1%|303,501|14.9%|
|SSA|28,946|3.2%|27,108|3.0%|56,054|6.2%|
|EAP|115,897|2.0%|96,244|1.7%|212,140|3.7%|
|MENA|14,826|1.1%|32,244|2.5%|47,070|3.7%|
|LAC|65,828|1.7%|74,450|1.9%|140,277|3.6%|
|ECA|24,178|0.8%|36,267|1.1%|60,445|1.9%|
|_Developing countries by sector_|||||||
|Electricity|152,577|1.1%|167,754|1.2%|320,331|2.4%|
|Transport|76,201|0.6%|178,818|1.3%|255,019|1.9%|
|Telecoms|123,351|0.9%|63,755|0.5%|187,105|1.4%|
|W&S+WW|20,160|0.1%|36,873|0.3%|57,033|0.4%|
||||**_All developi_**|**_ng countries_**|819,488|6.0%|
||||**_High Incom_**|**_e countries_**|285,045|0.8%|
||||**_World_**||1,104,532|2.2%|

Source: Authors’ estimation.

Note: GDP percentage is calculated based on the projected GDP2015 by income level and by region. For sectors, we used average projected GDP2015 for developing countries.

Income level, regions and sectors are defined according to the World Bank’s World Development Indicators Report.

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<!-- Start of picture text --> = 7 -e<br><!-- End of picture text --> <!-- Start of picture text --> § 350 ss<br>wi<br>2 \ |<br>250<br>150<br><!-- End of picture text -->

Capital TM Maintenance
# **V. Infrastructure Investment Gap**

The infrastructure investment gap is estimated as the difference between the estimated investment requirements and actual spending on infrastructure. The lack of data on infrastructure spending has been long recognized as a serious impediment to deriving reliable gaps estimates (Fay et al., 2011). There have been many efforts to calculate figures on infrastructure spending but they have had significant drawbacks (See Annex VI with the description of those attempts). The table below describes total spending in infrastructure by applying GDP deflator for inflation to the latest and most reliable regional data.

**Table 5: Total Spending in Infrastructure by region in 2011 USD billion.**

|**Region**|**% of**<br>**GDP**|**Spending**<br>**in**<br>**2011**<br>**billion**|**USD**|**Source of figure as reported in Fay and Toman (2010)**|
|---|---|---|---|---|
|EAP|6.8||356|<sup>Yepes (2008a) with Original source from Gill and</sup><br>Kharas(2007)|
|SAR|4.2||67|<br>Estimates of India’s public infrastructure spending from<br>the country’s public expenditure plans and extrapolated<br>to the rest of the region.|
|LAC|1.2||40.3|<sup>Estimates from original data collection efforts. Data was</sup><br>collected for 1990–2005 aspresented in Yepes(2008b).|
|SSA|3.2||27.4|<br>Foster and Briceño-Garmendia, 2010.|
|ECA|1.2||34.6|<sup>No data. Investments share is assumed to be similar to</sup><br>that of Latin America.|
|MENA|4.2||46|No data. Assumed to be the weighted sample average.<br>Based on Agénor et al. (2005), Public capital<br>expenditure in infrastructure (as % of GDP) is estimated<br>as 2percent.|
|**TOTAL**|**4.3**||**571.3**||

Source: Authors’ estimation Note: The developing countries that are included in our dataset are listed by regions: EAP ( Indonesia, Lao PDR, Fiji, Malaysia, Mongolia, China, Papua New Guinea, Cambodia, Vietnam, Philippines, and Thailand); SAR ( Pakistan, Sri Lanka, India, Nepal, and Bangladesh); LAC (Argentina, Uruguay, Costa Rica, Guatemala, Guyana, Trinidad and Tobago, Jamaica, El Salvador, Venezuela, RB, Colombia, Peru, Bolivia, Nicaragua, Honduras, Brazil, Suriname, Ecuador, Haiti, Panama, Paraguay, Mexico, Dominican Republic, Belize, and Chile); SSA (Chad, Niger, Angola, Mauritania, Kenya, Swaziland, Gambia, The, Tanzania, Comoros, Burundi, Ghana, Botswana, Cape Verde, Equatorial Guinea, Liberia, Nigeria, Central African Republic, South Africa, Mali, Zambia, Mozambique, Cameroon, Lesotho, Ethiopia, Congo, Rep., Zimbabwe, Malawi, Uganda, Senegal, Cote d'Ivoire, Sudan, Benin, Madagascar, Namibia, Djibouti, Guinea-Bissau, Guinea, Rwanda, Gabon, Togo, Mauritius, Sierra Leone and Burkina Faso); ECA (Lithuania, Belarus, Russian Federation, Armenia, Estonia, Turkey, Albania, Croatia, Kyrgyz Republic, Tajikistan, Bulgaria, Poland, Azerbaijan, Georgia, Kazakhstan, Slovak Republic, Czech Republic, Romania, Uzbekistan, Latvia, Turkmenistan, Hungary, and Ukraine); and MENA (Tunisia, Iran, Islamic Rep., Oman, Saudi Arabia, Egypt, Arab Rep., Jordan, Morocco, Syria, Yemen, and Algeria) 12
The figure below shows the annual infrastructure investment requirements against actual investments disaggregated over 2014-2020 for the countries included in the sample.

**Figure 3. Infrastructure investment requirements Vs. Actual for the subset of EMDE countries that were included in the estimation**

<!-- Start of picture text --> 900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>EAP SAR LAC SSA ECA MENA Total<br>Actual 356 67 40 27 35 46 571<br>Needs 212 304 140 56 60 47 819<br>Amount, in 2011 US$(billion)<br><!-- End of picture text --> Source: Authors’ calculation for the countries included in the estimation (See Note in Table 2).

In order to present figures for all EMDE countries,<sup>14</sup> we extrapolated the results of investment requirements and spending as percentage of GDP for the countries not included in our estimation due to data limitations.<sup>15</sup> We apply the estimated regional investment requirements (as percentage of GDP) to the countries not included in the estimation. Figure 4 illustrates infrastructure investment requirements for EMDE countries and disaggregates the results by region. The infrastructure investment requirements for EMDE countries is $836 billion per year over 2014-2020. The estimated actual spending is $580 billion per year. This estimated gap only includes the following sectors: roads (paved and unpaved), ports, railways, water, sanitation, waste water management, electricity (generation and distribution), telephone and mobile lines.

As presented in the figure below, South Asia has the highest investment gap. The opposite occurs in East Asia and the Pacific as China drives up the average. In that region, investments actually exceed requirements to keep up with economic and

> 14 The definition of EMDE country list comes from two parts: Emerging market countries come from IMF World Economic Outlook Update: http://www.imf.org/external/pubs/ft/weo/2012/update/02/index.htm, and Developing economies list come from WBG: <u>http://data.worldbank.org/about/country-and-lending-groups.</u>

> 15 List of countries not included in the regression due to limited data: EAP : American Samoa, Kiribati, Korea, Dem. Rep., Marshall Islands, Micronesia, Fed. Sts, Myanmar, Palau, Samoa, Solomon Islands, Timor-Leste, Timor-Leste, Tuvalu, Tonga, Vanuatu; SAR: Afghanistan, Bhutan, Maldives; LAC: Belize, Bolivia, Grenada, St. Lucia, St. Vincent and the Grenadines SSA: Botswana, Cabo Verde, Côte d'Ivoire, Eritrea, São Tomé and Principe, Seychelles, Somalia ECA: Kosovo, Macedonia, FYR, Moldova, Montenegro, Serbia MENA: Djibouti, Lebanon, Libya, Tunisia, West Bank and Gaza

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demographic growth. However, if we exclude China from the EAP estimation, the rest of EAP countries do have a positive investment gap.

**Figure 4. EMED Infrastructure investment requirements Vs. Actual**

<!-- Start of picture text --> 900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>EAP EAP* SAR LAC SSA ECA MENA Total<br>Actual 356 35 68 41 28 35 52 580<br>Needs 212 87 309 141 58 62 53 836<br>Amount, in 2011 US$(billion)<br><!-- End of picture text --> Source: Authors’ estimation Note: EAP*: Exclude China’s data from EAP region.

# **VI. Main Conclusions**

This study presents estimations of investment requirements over 2014-2020 accounting for the most recent changes in stock of infrastructure. The annual infrastructure investment requirements for EMDE countries is $836 billion or 6.1% of current GDP per year over 2014-2020.

The table below shows the investment gap for EMDE excluding China.<sup>16</sup> The annual infrastructure investment gap for EMDE countries is $452 billion over 2014-2020, which implies that EMDE countries should almost double their current spending.

> 16 China is overinvesting in infrastructure, and including it in the calculation of the infrastructure investment gap will underestimate the gap for the rest of EMDE countries since investment is done at country level.

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**Table 6: EMDE* Infrastructure investment requirements, Actual Spending and Investment Gap in annual US$ billion over 2014-2020**

||EAP*|SAR|LAC|SSA|ECA|MENA|**Total**|
|---|---|---|---|---|---|---|---|
|Requirements|87|309|141|58|62|53|711|
|Actual|35|68|41|28|35|52|259|
|**Gap**|**52**|**241**|**100**|**30**|**27**|**1**|**452**|

Source: Authors’ estimation Note: (*) EAP* includes EMDE in EAP excluding China.

The breakdown between capital expenditure and maintenance cost shows that roughly half of the estimated amount should be spent on maintenance.

When results are disaggregated by region, SAR has the biggest gap followed by LAC and EAP (without China). It is important to note that the results are less robust when data are further disaggregated as the quality of data varies significantly across regions.

Our estimates are lower than the most recent extrapolation presented in the literature. While our methodology has some drawbacks, other available estimates have used extrapolation techniques not accounting for infrastructure stock changes in developing countries and are based on a very small sample of countries mainly in the developed world.

The estimation of this study should be used to complement the existing ones and it should be considered as a lower bound of the actual global requirements since, due to data limitations, it does not include the cost of upgrading and rehabilitating existing infrastructure in bad condition (except for roads where the regional percentage of road in bad conditions is available). Moreover, this estimation does not include sectors such as airports, urban transport, energy transmission, and solid waste management that could account for a significant cost.

Expenditures related to GHG mitigation and adaptation to climate change could add some $170-$220 billion per year to the cost of developing country infrastructure.<sup>17</sup> The table below compares the estimations of this paper with the latest estimates in the literature. While the methodologies are not strictly comparable, it offers a sensitivity analysis around estimates that are highly constrained by data availability.

> 17 From Fay and Toman (2010): This includes some $30-40 billion for adaptation in infrastructure (World Bank 2009a) and some $140-175 billion associated with mitigation in the energy sector (World Bank 2009b).

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**Table 7: Global Estimates Comparison**

||**Global Estima**|<br>**tes(USD billion)**|
|---|---|---|
||$836|Ruiz-Nuñez&Wei,2015|
||$2,000|Bhattacharya,2013|
|Develoin|$1,250-$1,500|WEF,2013|
|pg|$900-$1,100|Fay,2011|
||$1,113|Yepes,2008|
||$465|Fay&Yepes,2003|
||$3,200-$3,700|McKinsey,2013|
|Global|$2,680-$3,200|Kennedy&Corfee-Morlot,2013|
||$2,650|OECD,2006|

Source: Authors’ summary 16