Estimating infrastructure financing needs in the Asia-Pacific least developed countries, landlocked developing countries, and small island developing states

Branchoux, Candice; Fang, Lin; Tateno, Yusuke Article Estimating infrastructure financing needs in the Asia-Pacific least developed countries, landlocked developing countries, and small island developing states Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Branchoux, Candice; Fang, Lin; Tateno, Yusuke (2018) : Estimating infrastructure financing needs in the Asia-Pacific least developed countries, landlocked developing countries, and small island developing states, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 6, Iss. 3, pp. 1-21, https://doi.org/10.3390/economies6030043 This Version is available at: https://hdl.handle.net/10419/197098 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ economies Article Estimating Infrastructure Financing Needs in the Asia-Pacific Least Developed Countries, Landlocked Developing Countries, and Small Island Developing States Candice Branchoux 1, Lin Fang 2,* and Yusuke Tateno 3 1Global Environment Finance, United Nations Development Programme, Bangkok 10200, Thailand; [email protected] 2Independent Researcher, Yanji 133000, China 3Macroeconomic Policy and Financing for Development Division, United Nations Economic and Social Commission for Asia and the Pacific, Bangkok 10200, Thailand; [email protected] *Correspondence: [email protected]; Tel.: +86-133-0443-7826 Received: 23 April 2018; Accepted: 11 July 2018; Published: 1 August 2018   Abstract: To assist with the achievement of the Sustainable Development Goals by 2030, this paper develops a framework to estimate infrastructure financing needs of the Asia-Pacific least developed countries (LDCs), landlocked developing countries (LLDCs), and small island developing States (SIDS) by 2030. The framework takes into account the financing required to close existing infrastructure gaps, keep up with growing demands for new infrastructure, maintain existing infrastructure, and mitigate the vulnerability of infrastructure to climate-related risks. Based on a panel of 71 developing economies from 1990 to 2015 and the application of unit costs to the level of physical infrastructure stock projected to 2030, the required resources are estimated to amount to 8.1% of GDP per annum on weighted average, which exceeds current levels of infrastructure funding of 5–7% of GDP. The paper finds that a large proportion of financing needs in LDCs and SIDS arises from the current infrastructure shortages, particularly in the transport and energy sector, implying that provision of universal access to basic infrastructure services would require large outlays of resources. The results also suggest that LLDCs and some SIDS require over one-third of their spending to be allocated to maintenance and replacement of existing assets, while those in low-lying coastal areas face substantial long-run costs in improving infrastructure to mitigate climate change and protect them against loss and damages caused by extreme weather events. Meeting future infrastructure financing needs will require greater engagement of the private sector and other global and regional initiatives to ensure that sufficient resources can be raised for investment in infrastructure. Keywords: infrastructure financing; sustainable development; Asia-Pacific; developing countries with special needs JEL Classification: O18; Q01; H54 1. Introduction The Asia-Pacific least developed countries (LDCs), landlocked developing countries (LLDCs), and small island developing States (SIDS) continue to face significant challenges and constraints in achieving inclusive growth and sustainable development. Such challenges and constraints are associated with remoteness; geographic features; availability of resources; demography; weather; or, most commonly, a combination of these factors. The result has been limited progress in structural Economies 2018,6, 43; doi:10.3390/economies6030043 www.mdpi.com/journal/economies Economies 2018,6, 43 2 of 21 transformation, slower development of productive capacities and heightened vulnerability to external shocks, such as those arising from volatile commodity prices, climate change, and natural disasters. While each of these economies faces its own unique circumstances, they share one characteristic that applies to most developing countries—a significant deficit in physical infrastructure such as transport, energy, information and communications technology (ICT), and water supply and sanitation (WSS). In many of these economies, particularly in the least developed ones, access to basic infrastructure services is still far from universal; in Afghanistan, Cambodia, and Solomon Islands, more than 70 percent of the rural population does not have access to improved water sources and more than half of the population lacks access to electricity. A lack of physical infrastructure is the principle obstacle to sustainable development as it not only limits opportunities to expand productive capacities and improve connectivity across and among countries, thereby restricting economic growth, but also constrains social development and harms environmental sustainability (United Nations Economic and Social Commission for Asia and the Pacific (ESCAP 2017)). Paying full regard to the critical development need, the 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs) aim at providing universal access to water, sanitation, electricity, and so on, by 2030. In addition to the current infrastructure deficit, the Asia-Pacific LDCs, LLDCs, and SIDS will face new demands for physical infrastructure stemming from their rising wealth and rapid urbanization. Although population growth is expected to slow down over the medium-term, urban growth pressures will remain high in coming decades, particularly in LDCs and LLDCs. While in these economies, only one in three persons lived in urban areas as of 2014, projections suggest that about half of the population will live in urban areas by 2050, aggravating infrastructure shortages in cities ( ESCAP 2015a ). In addition, a rapidly rising middle-income class in LLDCs and an expansion of the transitional income category—defined as people in the income bracket right below the middle-income class—in LDCs will create further demand for public infrastructure services that go beyond basic needs, including reliable energy and ICT infrastructure. Inadequate maintenance could also add to the expansion of future infrastructure deficits. Countries tend to prioritize development of new infrastructure over maintenance of existing facilities and end up reducing the useful life of these assets (Rioja 2003;Kalaitzidakis and Kalyvitis 2004). World Bank (2005 ) estimates that preference towards building new road infrastructure, for instance, has led investments in maintenance to be only between 20% and 50% of what they should be to effectively maintain the road network. Similarly, the Pacific Region Infrastructure Facility (2013) admits that “there is common agreement that maintenance is being avoided within the ‘build–neglect–rebuild’ paradigm.” To make matters worse, in countries that have chronically weak public revenues, such as LDCs, shortfalls in government review targets are often accompanied by cuts in maintenance spending. The degradation of existing infrastructure not only diminishes the benefits of network development, but also results in costly reconstruction projects or repair jobs in the future. Moreover, climate change will necessitate the development of more sustainable and climate-resilient infrastructure. For instance, in the 2030 Agenda for Sustainable Development, Goal 7 is set to ensure universal access to affordable, reliable, sustainable, and modern energy. Goal 7 also aims to substantially increase the share of renewable energy in the global energy mix and to support the expansion of modern and sustainable energy services, particularly in LDCs, LLDCs, and SIDS, which will involve additional costs. The United Nations estimated that developing Asia will need an additional investment of $232 billion annually to double its renewable energy consumption by 2030, plus $211 billion for energy efficiency improvements in a scenario that is consistent with the two-degree target of the Paris Agreement (SE4All, 2015). SIDS and other countries with low-lying coastal areas also face substantial long-run infrastructure costs to mitigate losses and damages caused by climate change or extreme weather events. For instance, the International Monetary Fund (IMF) estimates that Kiribati’s operating expenditures related to climate change contingencies and to new infrastructure maintenance costs amount to 2–3% of GDP (IMF 2016). Economies 2018,6, 43 3 of 21 While it is clear that the Asia-Pacific LDCs, LLDCs, and SIDS will have to invest significant financial resources to address these issues, quantifying how much is needed for these economies is not an easy undertaking. This is partly because information on the magnitude of their past infrastructure investment is often not available. Thus, although some studies have included these economies as the “rest” of the world or of the region, those estimates are typically extrapolated from data for other countries (see, for instance, McKinsey Global Institute 2013,2016). The main challenge in estimating financing needs for LDCs, LLDCs, and SIDS arises from differences in the nature of infrastructure needs in these economies and other developing countries. In the latter, most needs are a result of either increasing demand for new infrastructure or maintenance and rehabilitation of existing infrastructure. Thus, estimating future levels of infrastructure can be based upon historical trends of infrastructure provision and projections of demand arising from population growth, increasing urbanization and per capita income growth assumptions. However, infrastructure needs in LDCs, LLDCs, and SIDS may be more related to supply constraints and resulting infrastructure shortages. Therefore, estimates for these economies cannot be based solely on historical trends and need to include a component of financing needs that would be required to fill the existing infrastructure gaps. This paper, therefore, aims to develop a framework to estimate the infrastructure financing needs of the Asia-Pacific LDCs, LLDCs, and SIDS, taking into account four components: (1) financing that is needed to meet the growing demand for new infrastructure as populations increase and become more urbanized; (2) financing that is needed to effectively maintain existing infrastructure; (3) financing that is needed to fill existing infrastructure shortages; and (4) financing that will be needed for improving infrastructure to mitigate losses and damages caused by climate change or extreme weather events. The methodology developed in this paper partly builds upon the “top-down” approach developed by Fay (2000) and Fay and Yepes (2003) and later extended by Bhattacharyay (2012), Ruiz-Nunez and Wei (2015 ), and Asian Development Bank (ADB 2017). It first estimates financing needs to meet the growing demand for infrastructure and to effectively maintain existing infrastructure (i.e., the first and second components). Second, for countries or sectors in which universal access to physical infrastructure will not be achieved by 2030, the estimated capital costs of universal access are added (i.e., the third component). Finally, these estimates are adjusted by factoring in the costs of climate mitigation and adaptation (i.e., the fourth component). The paper contributes to the literature of estimating infrastructure financing needs in the following ways. First, it focuses on a number of ‘small’ Asia-Pacific countries that have been often omitted from existing analyses or included only as part of the ‘rest’ of the world because of limited data availability. Second, in addition to the conventional factors of infrastructure financing needs that arise from growing future demand for infrastructure, the paper considers financing that is needed to fill existing infrastructure shortages. This component is typically assumed to constitute only a small proportion of total financing needs, and is thus excluded from analyses. This is a valid assumption to be made as long as countries/sectors assessed are sufficiently developed and have adequate provision of basic services. As the focus of the paper is on the Asia-Pacific LDCs, LLDCs, and SIDS, this assumption has to be relaxed. Third, this paper partly addresses the drawback of the “top-down” approach by actively incorporating country-specific unit cost data to universal unit cost estimates. Finally, this paper considers climate adaptation and mitigation as one of the key drivers of financing needs in these economies, especially in the Pacific. With a notable exception of the latest study by ADB (2017 ), this component has not been taken into account in the context of estimating infrastructure financing needs. The rest of the paper is organized as follows: Section 2reviews the current state of infrastructure in the Asia-Pacific LDCs, LLDCs, and SIDS to demonstrate how universal access to basic services is still limited and narrow in some economies; Section 3provides the overview of previous studies and methodologies adopted to estimate infrastructure financing needs; Section 4presents the methodologies used for estimating the four components of infrastructure financing needs; Section 5 Economies 2018,6, 43 4 of 21 provides the results from the estimation of infrastructure financing needs of 29 countries by sector; Section 6discusses the policy implication of the findings; and Section 7draws conclusions. 2. The State of Infrastructure in the Asia-Pacific LDCs, LLDCs, and SIDS The infrastructure sectors covered in this paper are (1) transport, (2) energy/electricity, (3) information and communications technology (ICT), and (4) water supply and sanitation (WSS). The review of the state of infrastructure in the Asia-Pacific LDCs, LLDCs, and SIDS is based on the following eleven indicators representing the four categories of physical infrastructure: •Paved roads (total route km per 1000 people); •Unpaved roads (total route km per 1000 people); •Rail lines (total route km per 1,000,000 people); •Electric power consumption (kWh per capita); •Access to electricity (% of population); •Fixed telephone subscriptions per 100 people; •Mobile telephone subscriptions per 100 people; •Access to improved water sources, rural (% of rural population); •Access to improved water sources, urban (% of urban population); •Access to improved sanitation facilities, rural (% of rural population); and •Access to improved sanitation facilities, urban (% of urban population). Appendix A(Table A1) provides a list of countries and country groupings used in the paper. Detailed definitions and sources of the infrastructure indicators can be found in Appendix B(Table A2). Table 1offers a review of access to infrastructure services by presenting simple averages by indicator for each of the three country groups, LDCs, LLDCs, and SIDS, as well as for other Asian developing countries. It reveals that overall access to physical infrastructure is significantly less developed in LDCs than in LLDCs and SIDS, while that in LLDCs and SIDS is still much lower than the average of other Asian developing counties in many aspects. Across the four sectors of infrastructure, LLDCs perform relatively well in transport and energy sectors, while they still have room for improvement in access to water sources and sanitation facilities, particularly in the rural areas. In SIDS, provision of energy infrastructure services should be ameliorated as more than 30 percent of the population still lacks access to electricity. LDCs severely lack access to infrastructure services across all sectors. All three groups share a similar pattern for the WSS sector: the rural population has a significantly lower accessibility to both water sources and sanitation facilities than the urban population. What is also evident from observing these infrastructure indicators is the significant variations in access to basic services across countries, even within each of the three country groups. To illustrate the high degree of variations, index scores are calculated for each country and for each of the four sectors of physical infrastructure; each of the eleven infrastructure indicators is first standardized to have a mean value of 1 and a standard deviation of 0.15 and then averaged by country and by sector. As an example, Figure 1presents a scatterplot of the two index scores—one for the energy sector and the other for the WSS sector—of twelve LDCs, eight LLDCs, nine SIDS, and thirteen other Asian developing countries. These countries present a great variety of combinations with regard to accessibility to energy and WSS infrastructure. Across the three country groups, LDCs are all located in the bottom-left part of the graph, which reconfirms the presence of severe infrastructure shortages in both sectors in LDCs. LLDCs are all lined up horizontally on the top part of the graph. This implies that all LLDCs provide decent accessibility to energy infrastructure, relative to the Asia-Pacific developing countries average, while access to WSS infrastructure services varies widely by country. In contrast, most SIDS are scattered vertically on the right part of the graph. This indicates that they hold among the best scores in the WSS sector, but the pace of development in the energy sector is more disparate. Economies 2018,6, 43 5 of 21 The exceptions to this tendency are the Federal States of Micronesia and Papua New Guinea. These two countries have many dispersed islands and archipelagos and may encounter additional difficulty providing access to energy and WSS infrastructure. Other Asian developing countries are all located at the top-right corner of the graph, with little variation of achievements across countries. Table 1. Access to infrastructure by country groups. LDCs—least developed countries; LLDCs—landlocked developing countries; SIDS—small island developing States. Infrastructure Indicator LDCs LLDCs SIDS Other Asian Developing Countries Total route km of paved roads per 1000 people 1.1 4.7 2.1 2.2 Total route km of unpaved roads per 1000 people 3.0 3.1 5.3 1.0 Total route km of rail lines per 1,000,000 people 10.7 376.6 74.0 52.2 Electric power consumption (kWh per capita) 410.7 2321.5 1851.4 3091.0 Access to electricity (% of population) 49.5 98.6 67.9 96.4 Number of fixed telephone subscriptions per 100 people 3.8 13.0 15.2 17.6 Number of mobile phone subscriptions per 100 people 84.1 115.5 89.4 109.8 Access to improved water sources (% of rural population) 50.2 80.3 84.9 75.4 Access to improved water sources (% of urban population) 77.2 88.8 92.5 87.6 Access to improved sanitation facilities (% of rural population) 69.9 73.3 84.5 92.5 Access to improved sanitation facilities (% of urban population) 85.7 92.2 95.9 97.6 Sources: Authors’ calculations based on data from various sources. See Annex 2 for details; Note: These figures are simple averages and for 2015 data or the latest available year. Economies 2018, 6, x FOR PEER REVIEW 5 of 22 What is also evident from observing these infrastructure indicators is the significant variations in access to basic services across countries, even within each of the three country groups. To illustrate the high degree of variations, index scores are calculated for each country and for each of the four sectors of physical infrastructure; each of the eleven infrastructure indicators is first standardized to have a mean value of 1 and a standard deviation of 0.15 and then averaged by country and by sector. As an example, Figure 1 presents a scatterplot of the two index scores—one for the energy sector and the other for the WSS sector—of twelve LDCs, eight LLDCs, nine SIDS, and thirteen other Asian developing countries. These countries present a great variety of combinations with regard to accessibility to energy and WSS infrastructure. Across the three country groups, LDCs are all located in the bottom-left part of the graph, which reconfirms the presence of severe infrastructure shortages in both sectors in LDCs. LLDCs are all lined up horizontally on the top part of the graph. This implies that all LLDCs provide decent accessibility to energy infrastructure, relative to the Asia-Pacific developing countries average, while access to WSS infrastructure services varies widely by country. In contrast, most SIDS are scattered vertically on the right part of the graph. This indicates that they hold among the best scores in the WSS sector, but the pace of development in the energy sector is more disparate. The exceptions to this tendency are the Federal States of Micronesia and Papua New Guinea. These two countries have many dispersed islands and archipelagos and may encounter additional difficulty providing access to energy and WSS infrastructure. Other Asian developing countries are all located at the top-right corner of the graph, with little variation of achievements across countries. Figure 1. Index scores in the energy and WSS sectors in Asia and the Pacific. Source: Authors’ calculation. See Appendix B for details. Notes: Appendix A provides a complete list of countries and country groupings. “Asia-Pacific non-CSN developing economies” refers to the thirteen other Asian developing countries. The figures are based on data from 2015 for the WSS sector and 2012 for the energy sector. The energy index score is calculated, for each country, as the simple average of standardized values of the two energy infrastructure indicators. The WSS index score is the simple average of standardized values of the four WSS infrastructure indicators. Each standardized value is computed to have a mean of 1 and a standard deviation of 0.15 so that the units of these values are consistent. LDCs—least developed countries; LLDCs—landlocked developing countries; SIDS— small island developing States; WSS—water supply and sanitation. Malaysia China Afghanistan Armenia Azerbaijan Bangladesh Bhutan Cambodia Fiji French Polynesia Kazakhstan Kiribati Kyrgyzstan Lao PDR Maldives Micronesia (Federated States of) Mongolia Myanmar Nepal New Caledonia Palau Papua New Guinea Samoa Solomon Islands Tajikistan Timor-Leste Tonga Turkmenistan Tuvalu Uzbekistan Vanuatu India 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 Energy index score WSS index score LDCs LLDCs SIDS Asia-Pacific non-CSN developing economies Figure 1. Index scores in the energy and WSS sectors in Asia and the Pacific. Source: Authors’ calculation. See Appendix Bfor details. Notes: Appendix Aprovides a complete list of countries and country groupings. “Asia-Pacific non-CSN developing economies” refers to the thirteen other Asian developing countries. The figures are based on data from 2015 for the WSS sector and 2012 for the energy sector. The energy index score is calculated, for each country, as the simple average of standardized values of the two energy infrastructure indicators. The WSS index score is the simple average of standardized values of the four WSS infrastructure indicators. Each standardized value is computed to have a mean of 1 and a standard deviation of 0.15 so that the units of these values are consistent. LDCs—least developed countries; LLDCs—landlocked developing countries; SIDS—small island developing States; WSS—water supply and sanitation. Economies 2018,6, 43 6 of 21 3. Literature A wide range of estimates on infrastructure financing needs has been produced in recent years. For instance, ESCAP (2015b) estimated that the Asia-Pacific developing region would need to mobilize $800–900 billion annually for the provision of transport infrastructure services, ICT, water supply and sanitation, and electricity access. Bhattacharyay (2012) reported that Asia-Pacific will need to spend approximately $8 trillion in infrastructure investment for the period 2010–2020 or equivalent to $800 billion per year in order to maintain current levels of economic growth. Similarly, Fay and Toman (2010 ) estimated that up to an additional $1.5 trillion will be necessary annually through 2020 to help lowand medium-income countries establish adequate levels of infrastructure. McKinsey Global Institute (2016) assessed that global infrastructure financing requirements for the period 2016–2030 would be around $3.3 trillion annually, 60% above the 2000–2015 trends. Most recently, ADB (2017) estimated that, over the period 2016–2030, developing Asia’s infrastructure investment needs would reach $26 trillion or $1.7 trillion per annum. Although these studies agree that bridging infrastructure gaps will require massive investment, their estimates vary significantly as they rely upon various assumptions and definitions. The use of different assumptions, for instance, on future infrastructure needs, estimated rates of economic and population growth, assumed increases in rates of urbanization, and policy shocks, necessarily translates itself into wide discrepancies between the estimates. Moreover, as there is no universal database on infrastructure investment, different databases follow their own definitions and cover different aspects of infrastructure investment. In terms of the methodologies, existing studies can be broadly classified into two categories based on the approaches adopted to estimate infrastructure financing needs: the “top-down” and the “bottom-up” approaches. The “bottom-up” approach assesses the total infrastructure services demand by reviewing infrastructure investments demand at the project level. The methodology consists of reviewing the implementation costs of individual infrastructure projects and compiling the estimates to obtain the total demand by country and by sector. However, lack of relevant data obscures what is needed at a project level. For instance, data on projects or plans are often not available or confidential so the cost of these projects must be estimated, with varying assumptions based upon costs of past infrastructure projects, which are assumed to be in line with best practice scenarios. The “top-down” approach quantitatively estimates infrastructure needs at the national level using econometric analysis techniques. This approach follows the works of Fay (2000) and Fay and Yepes (2003 ) that developed a model to predict future demand for infrastructure, which was later applied in a number of studies, including Bhattacharyay (2012), Ruiz-Nunez and Wei (2015), and ADB (2017 ). In this approach, the relationships between demand for infrastructure services and economic/demographic variable are established for each sector and extrapolated into the future using predicted growth rates. Once the projections of the infrastructure stock are obtained, standardized unit costs based on international best practice norms are applied to estimate the financing requirements for new infrastructure. However, by construction, such projections rely on unit cost estimates and ignore many national and regional specificities, as it is assumed that what happened in some countries in the past is a good predictor of what might happen in some other countries in the future (Fay and Toman 2010). Despite these caveats, the ‘top-down’ approach still forms the basis for many of the current estimates of multi-country infrastructure financing needs as the data requirements are relatively modest. 4. Methodology for Estimation A conventional “top-down” approach to forecast infrastructure financing needs is to apply unit capital costs and unit maintenance costs to projected changes of physical infrastructure stock and to existing stock, respectively. However, earlier sections of this paper have pointed out that many LDCs, LLDCs, and SIDS currently lack basic infrastructure, and also that some of them will Economies 2018,6, 43 7 of 21 incur climate-related costs. Thus, the methodology developed in this paper takes into account these additional costs of filling those shortages and adapting to climate change. The financing needs estimates are calculated using the following steps: a. Needs of physical infrastructure stocks for each type of infrastructure are projected to 2030 using a dynamic data panel model to meet rising demographic, urbanization, and economic growth rates; b. Current infrastructure stock shortages are estimated based on the current level of access to each type of infrastructure; c. Unit costs are applied to the estimated lacking infrastructure stocks, thus calculating the financing requirements induced by the construction of the additional infrastructure facilities to be built by 2030; d. Maintenance costs of the existing infrastructure stock are added to the previous financial estimate; and e. Additional costs related to infrastructure climate-proofing and climate change mitigation are added to obtain the final financing needs estimates. Following a similar methodology to Fay (2000), Fay and Yepes (2003), Bhattacharyay (2012), Ruiz-Nunez and Wei (2015), and ADB (2017), it is assumed that physical infrastructure stock is correlated with several variables including lagged values of the infrastructure stock, gross domestic product (GDP) per capita, shares of agriculture and industrial value-added in GPD, urbanization rate, and population density. As a result, the annual financing needs by 2030, excluding climate-related considerations, are decomposed and expressed as follows: Fi,t=∑ j Fj i,tand Fj i,t=maxIj i,T−Ij i,t T−t, 0×cj i+Ij i,t×mj i+maxUj−Ij i,T T−t, 0×cj i where Fi,t represents the total annual financing needs for country iat time t; Fj i,t indicates financing needs for infrastructure type j; Ij i,t is the infrastructure stock of type jin country iat time t; Uj denotes the infrastructure stock of type jrequired to provide universal access; cj i and mj i are the annual unit capital costs and unit maintenance costs of infrastructure of type jin country I, respectively; and Tis a targeted time period by which universal access should be provided. The three terms of Fj i,t represent the first three components of annual financing needs, respectively; the first term (1) indicates the costs induced by the construction of infrastructure stock to meet the rising demand driven by demographic evolution, economic growth, and urbanization by 2030; the second term (2) represents the maintenance cost of the existing stock of infrastructure; and the third term (3) signifies the additional financial cost required to palliate the existing infrastructure shortages by 2030. The fourth component of annual financing needs, which is associated with additional costs required for climate change mitigation and adaptation, will be factored in into each of the three terms of Fj i,t through the annual unit capital cost cj iand unit maintenance cost mj i. The same set of infrastructure indicators reviewed in Section 2is used for estimating infrastructure financing needs. These indicators range from 1990 to 2015, except for that covering mobile phone subscriptions, which only starts in 2004. Because of limited availability of data, three-year-averages have been used instead of yearly data. This transformation also captures the fact that infrastructure development is a slow process. Linear intra/extrapolations have been performed to fill in the missing values and thus obtain a balanced data panel. The methodology developed in this paper first estimates the component of financing needs that correspond to the growing demand for new infrastructure based on the “top-down” approach Economies 2018,6, 43 8 of 21 described above. This is done by projecting the demand for infrastructure to 2030 under the assumption that infrastructure services are demanded both as consumption goods by individuals and as inputs into the production process by firms, in accordance with the work of Fay (2000), Fay and Yepes (2003), Bhattacharyay (2012), and Ruiz-Nunez and Wei (2015). Once the new demand is projected to 2030, financing needs can be calculated by applying it to a set of unit cost estimates. The projection of each indicator to 2030 is performed using an Ordinary Least Squares (OLS) regression with fixed effects on a sample of 71 developing economies, of which 29 are Asia-Pacific LDCs, LLDCs, and SIDS. In theory, the use of instrumental variables (IV)/generalized method of moments (GMM) estimator would be more appropriate than OLS given the presence of the lagged variable in the model. However, ADB (2017) found that its explanatory power was actually lower than OLS and that the performance in out-of-sample forecasting was uneven and unsatisfactory. The future infrastructure demand can thus be described by the following process: Ij i,t=αj 0+αj 1Ij i,t−1+αj 2yi,t+αj 3Ai,t+αj 4Mi,t+αj 5Ui,t+αj 6Pi,t+αj 7Dj i+αj 8t where Ij i,t is the infrastructure stock of type jneeded in country iat time t; yi,t , Ai,t , and Mi,t represent the GDP per capita and shares of agriculture and manufacture value added in GDP, respectively; Ui,t and Pi,t stand for the urbanization rate and the population density, respectively; Dj i is the country fixed effect; and t is a time trend, used to capture time effect. All the variables in the equation are expressed in natural logarithm to linearize the model. The data sources of the independent variables and their projections are displayed in Appendix C (Table A3) and Appendix D(Table A4), respectively, and the regression results can be found in Appendix E(Table A5). Because of the absence of future estimations for gross domestic product (GDP) composition, the shares of agriculture and manufacture value added in GDP have been projected using basic linear extrapolations. Table 2presents the unit costs employed in the paper. For transport, the estimated unit costs for paved roads, unpaved roads, and railways per kilometer are obtained from various studies, such as Collier et al. (2015), ADB (2012), Fay (2000), Ruiz-Nunez and Wei (2015 ), and Eliste and Ivailo (2015). Table 2. Unit capital cost of physical infrastructure. Sector Unit Cost in 2010 US Dollars Paved roads, per kilometer 200,000 for a 6 m wide road (two lanes) Unpaved roads, per kilometer 50,000 Rail lines, per kilometer 1,200,000 Electricity generation, per kilowatt of generating capacity 1400 for fossil fuel-based electricity generation, 2200 for hydro power-based, and 1800 for mixed sources, depending on the composition of current generating capacity mix Access to electricity, per person Unit cost of electricity generation per kilowatt multiplied by the average power consumption of people who have access to electricity Fixed telephone, per subscription 250 Mobile telephone, per subscription 100 in urban area and 160 in rural area Access to water supply, per person 75.5 in rural area and 151 in urban area Access to sanitation, per person 117 in rural area and 190.5 in urban area Sources: Authors’ estimation based on various sources. Economies 2018,6, 43 15 of 21 but it does not consider “better” quality infrastructure or improved efficiency. While we consider the unit cost of building 6 m wide road, basic railway, 3G connection, and so on, in our estimation, the required financing would be much higher if 7 m wide road, advanced railway, or 4G connection is to be developed. Considering the high financing requirements, governments would need more strategy as they finance infrastructure projects. Table 5. Comparison with other existing studies. (Percent of GDP) Financing Needs Without Climate—Related Risks Financing Needs With Climate—Related risks Limitations that This Paper Addresses This paper LDCs, LLDCs, and SIDS 6.0–7.4% 7.6–8.9% LDCs 10.0–12.1% 12.2–14.4% LLDCs 2.8–3.6% 4.0–4.8% SIDS 6.4–8.0% 8.5–10.1% Asian Development Bank (ADB 2017) Asia-Pacific 5.1% 5.9% - When calculating projected infrastructure demands, the industrial share of GDP is kept constant; - Absence of financing needs estimates at the country level; - Absence of consideration for the LDC/LLDC/SIDS status of countries; - Current shortages in economic infrastructure are not accounted for in the financing needs estimates. By sub-region Central Asia 6.8% 7.8% East Asia 4.5% 5.2% South Asia 5.0% 5.7% The Pacific 8.2% 9.1% By income level Low income 9.9% 10.5% Lower middle income 7.1% 8.2% Upper middle income 4.9% 5.7% High income 1.9% 2.3% Ruiz-Nunez and Wei (2015) World 2.2% - Absence of country/region-specific unit costs; - Absence of climate change-related financing components; - When calculating projected infrastructure demand, industrial and agricultural shares of GDP are kept constant; - Absence of financing needs estimates at the country level; - Current shortages in economic infrastructure are not accounted for in the financing needs estimates. By income level Low income 14.1% Lower middle income 3.4% Upper middle income 2.6% High income 0.8% Bhattacharyay (2012) Total Asia-Pacific (32) 6.5% - Absence of climate change-related financing components; - When calculating projected infrastructure demand, industrial and agricultural shares of GDP are kept constant; - Current shortages in economic infrastructure are not accounted for in the financing needs estimates. Re-calculated using our country grouping LDCs, LLDCs and SIDS (22) 9.1% LDCs (12) 10.2% LLDCs (7) 7.9% SIDS (4) 3.6% Source: Asian Development Bank (ADB) (2017), Ruiz-Nunez and Wei (2015) and ADBI/ADB (2012). Author Contributions: This paper is conceptualized by Y.T., and its methodology and first draft is prepared by C.B., L.F. and Y.T. C.B. performed data curation, and Y.T. edited the final draft. Funding: This research received no external funding. Acknowledgments: The authors would like to thank Alberto Isgut, Oliver Paddison, Vatcharin Sirimaneetham and Mathieu Verougstraete for useful comments and suggestions on a previous version of this article. This article was initially prepared as a background document for the Asia-Pacific Countries with Special Needs Development Report 2017 (ISBN: 978-92-1-120746-9). The results reported in this article are not entirely identical to those Economies 2018,6, 43 16 of 21 previously reported in the above publication due to differences in sample countries and time periods covered by the study as well as differences in the methodology for calculating regional aggregates. Part of the article was written while the first and second authors were with the United Nations Economic and Social Commission for Asia and the Pacific. The views expressed in this article are those of the authors and should not necessarily be considered as reflecting the views or carrying the endorsement of the United Nations. Conflicts of Interest: The authors declare no conflicts of interest. Appendix A Table A1. List of countries and country groups. LDCs—least developed countries; LLDCs—landlocked developing countries; SIDS—small island developing States. LDCs LLDCs SIDS Other Asian Developing Countries Developing Countries Afghanistan * Armenia Fiji China Algeria Bangladesh Azerbaijan French Polynesia India Argentina Bhutan * Kazakhstan Maldives Indonesia Benin Cambodia Kyrgyzstan Micronesia Iran Botswana Kiribati ** Mongolia New Caledonia Malaysia Brazil Lao PDR Tajikistan Palau Pakistan Cameroon Myanmar Turkmenistan Papua New Guinea Philippines Chile Nepal * Uzbekistan Samoa Republic of Korea Colombia Solomon Islands ** Tonga Singapore Cote d’Ivoire Timor-Leste ** Sri Lanka Egypt Tuvalu ** Thailand Gabon Vanuatu ** Turkey Ghana Viet Nam Jordan Kenya Mexico Morocco Mozambique Namibia Nigeria Paraguay Peru Saudi Arabia Senegal South Africa Tunisia Uruguay Yemen Zambia Zimbabwe Notes: (*) For simplicity, LDCs that are also LLDCs (Afghanistan, Bhutan, and Nepal) belong to the LDC group only. (**) Similarly, LDCs that are also SIDS (Kiribati, Solomon Islands, Timor-Leste, Tuvalu, and Vanuatu) belong to the LDC group only. In this way, these three groups are mutually exclusive (non-overlapping). Economies 2018,6, 43 17 of 21 Appendix B Table A2. Definition and sources of the infrastructure indicators. ADB—Asian Development Bank; CIA—Central Intelligence Agency; IEA—International Energy Agency. Type of Physical Infrastructure Name of Indicator Definition/Sources Transport Paved roads (total route km per 1000 people) Paved roads are those surfaced with crushed stone (macadam) and hydrocarbon binder or bituminized agents with concrete or with cobblestones. World Bank Development Indicators, ADB, CIA World Factbook Unpaved roads (total route km per 1000 people) Total road network excluding the paved road network. World Bank Development Indicators, ADB, CIA World Factbook Rail lines (total route km per 1,000,000 people) Rail line is the length of railway route available for train service, irrespective of the number of parallel tracks. World Bank, Transportation, Water, and Information and Communications Technologies (ICT) Department, Transport Division. Energy Power consumption (kWh per capita) Electric power consumption measures the production of power plants and combined heat and power plants less transmission and other losses and own use by heat and power plants. IEA Statistics, Organisation for Economic Co-operation and Development (OECD)/IEA Access to electricity (% of population) Access to electricity is the percentage of population with access to electricity. World Bank, Sustainable Energy for All (SE4ALL) database from World Bank, Global Electrification database. ICT Fixed telephone subscriptions per 100 people Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, Integrated Services Digital Network (ISDN) voice-channel equivalents, and fixed public payphones. International Telecommunication Union, World Telecommunication/ICT Development Report and database. Mobile telephone subscriptions per 100 people Mobile telephone subscriptions refers to the subscriptions to a public mobile telephone service and provides access to Public Switched Telephone Network (PSTN) using cellular technology. This should include all mobile cellular subscriptions that offer voice communications. Water supply and sanitation (WSS) Access to improved water sources, rural (% of rural population) The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps, standpipes, tube wells, etc.). World Bank Development Indicators Access to improved water sources, urban (% of urban population) Access to improved sanitation facilities, rural (% of rural population) Improved sanitation facilities include flush/pour flush, ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet. Access to improved sanitation facilities, urban (% of urban population) Economies 2018,6, 43 18 of 21 Appendix C Table A3. Definition and sources of the independent variables. GDP—gross domestic product. Name of indicator Sources GDP per capita (constant 2010 US$) GDP in constant 2010 U.S. dollars, comes from the World Bank Development Indicators Agriculture, value added (% of GDP) Agriculture, value added (% of GDP) comes from the World Bank Development Indicators Manufacturing, value added (% of GDP) Manufacturing, value added (% of GDP) comes from the World Bank Development Indicators Urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Population density (people per sq. km of land area) Population density, midyear population divided by land area in square kilometers, comes from the World Bank Development Indicators. Notes: As a result of the absence of data, agriculture value added (% of gross value added (GVA)) and manufacture value added (% of GVA) have been used instead of GDP composition for French Polynesia and Samoa. Likewise, for French Polynesia and New Caledonia, GDP per capita (current US$) has been used instead of GDP per capita (2010 US$). Appendix D Table A4. Sources of the projections of the independent variables. Name of Indicator Sources Urban population (% total population) United Nations, World Urbanization Prospects Population density Population GDP per capita (2010 USD) Economic Research Service of the United States Department of Agriculture Notes: The projections of GDP per capita for French Polynesia, Kiribati, New Caledonia, Palau, Timor-Leste, and Tuvalu have been obtained by using the average growth rate of Asia Pacific SIDS (Fiji, Maldives, Federated States of Micronesia, Papua New Guinea, Samoa, Solomon Islands, Tonga, and Vanuatu). Economies 2018,6, 43 19 of 21 Appendix E Table A5. Regression results. Independent Variables\Infrastructure Indicators Paved Roads Unpaved Roads Rail Lines Power Consumption Access to Electricity Mobile Phones Fixed Telephones Water Sources Rural Water Sources Urban Sanitation Facilities Rural Sanitation Facilities Urban Lagged variable 0.7930 *** 0.6787 *** 0.8215 *** 0.8137 *** 0.9062 *** 0.5430 *** 0.7954 ** 0.7271 *** 0.9119 *** 0.8107 *** 0.9403 *** (0.0293) (0.0332) (0.0191) (0.0170) (0.0145) (0.0391) (0.0297) (0.0115) (0.0079) (0.0094) (0.00732) GDP per capita 0.0307 0.1006 −0.0062 0.1584 *** 0.0128 −0.3840 0.1350 * 0.0356 ** 0.0063 0.0525 *** 0.0080 *** (0.0580) (0.0766) (0.0182) (0.0292) (0.0081) (0.2888) (0.1750) (0.0170) (0.0040) (0.0095) (0.0031) Urbanization 0.1945 0.0990 0.0597 −0.0189 0.0111 0.3521 0.4764 0.1463 *** 0.0296 *** 0.0037 −0.0030 (0.1239) (0.1662) (0.0388) (0.0574) (0.0169) (0.6823) (0.1750) (0.0353) (0.0082) (0.01976) (0.0065) Population density 0.0162 −0.1387 −0.1738 *** −0.0152 0.0526 ** 0.0619 0.6649 0.0593 0.0218 ** 0.1115 *** 0.0357 *** (0.1496) (0.2042) (0.0502) (0.0703) (0.0222) (0.7886) (0.2070) (0.0430) (0.0103) (0.0242) (0.0079) Manufacture 0.0473 −0.0262 −0.0118 0.0496 *** 0.0078 −0.3149 ** −0.1308 ** −0.0315 *** −0.0039 −0.1625 *** 0.0022 (0.0384) (0.0503) (0.0121) (0.0192) (0.0053) (0.1420) (0.0526) (0.0111) (0.0026) (0.0063) (0.0020) Agriculture −0.0907 ** 0.0130 0.0011 0.0014 0.0059 −0.4406 ** 0.0230 −0.0019 −0.0007 0.0141 * −0.0003 (0.4488) (0.0626) (0.0141) (0.0216) (0.0065) (0.1905) (0.0607) (0.0128) (0.0030) (0.0073) (0.0024) Period −0.0069 0.0056 0.0019 0.0085 * −0.0009 0.0271 −0.4145 *** 0.0003 −0.0014 ** −0.0030 * −0.0020 *** (0.0094) (0.0127) (0.0030) (0.0045) (0.0013) (0.0499) (0.0137) (0.0027) (0.0006) (0.0016) (0.0005) Constant −0.8474 −0.4127 1.1584 *** 0.0090 0.0381 2.4860 −1.3903 0.1025 0.1651 *** −0.1759 0.0905 * (0.7952) (1.0342) (0.2831) (0.3762) (0.1114) (4.2535) (1.0824) (0.2324) (0.0535) (0.1331) (0.0510) Rho 0.6456 0.7142 0.9767 0.6997 0.8812 0.8961 0.7237 0.9022 0.9248 0.9607 0.9644 Notes: Standard errors are in parentheses. The levels of significance are as follows: *** p< 0.01, ** p< 0.05, and * p< 0.1. Rho represents the estimated variance of the overall error accounted for by the individual effect. Economies 2018,6, 43 20 of 21 Appendix F Table A6. Composition of annual financing needs, by country and sector, % of GDP, 2018–2030. Country Transport Energy ICT WSS Total Afghanistan 9.9% 12.3% 5.9% 0.7% 28.8% Armenia 0.7% 0.9% 0.6% 0.4% 2.6% Azerbaijan 0.8% 0.9% 0.6% 0.3% 2.5% Bangladesh 3.9% 4.9% 2.1% 0.4% 11.4% Bhutan 3.7% 3.3% 0.7% 0.1% 7.8% Cambodia 4.3% 5.1% 2.2% 0.6% 12.2% Fiji 1.4% 1.2% 0.5% 0.4% 3.5% French Polynesia 0.5% 0.4% 0.1% 0.1% 1.1% Kazakhstan 0.8% 0.9% 0.3% 0.1% 2.1% Kiribati 4.3% 4.1% 1.3% 0.7% 10.5% Kyrgyz Republic 10.2% 4.5% 3.4% 1.2% 19.3% Lao PDR 3.5% 3.6% 2.6% 0.4% 10.2% Maldives 0.7% 0.7% 0.5% 0.1% 1.9% Micronesia (F.S. of) 2.4% 2.3% 0.7% 0.4% 5.7% Mongolia 2.3% 1.5% 0.6% 0.3% 4.7% Myanmar 2.9% 3.9% 2.5% 0.4% 9.8% Nepal 6.6% 8.1% 4.3% 0.4% 19.4% New Caledonia 0.7% 1.5% 0.1% 0.1% 2.4% Palau 0.5% 1.2% 0.3% 0.2% 2.2% Papua New Guinea 4.2% 4.0% 2.0% 0.6% 10.8% Samoa 1.8% 1.7% 0.6% 0.3% 4.4% Solomon Islands 5.9% 5.6% 1.8% 1.0% 14.2% Tajikistan 3.4% 6.2% 4.4% 1.2% 15.2% Timor-Leste 5.9% 6.3% 5.0% 0.8% 17.9% Tonga 1.8% 1.8% 0.7% 0.6% 4.8% Turkmenistan 3.4% 1.5% 0.3% 0.2% 5.4% Tuvalu 1.5% 1.6% 0.6% 0.4% 4.1% Uzbekistan 2.1% 2.9% 1.9% 0.6% 7.4% Vanuatu 2.8% 2.9% 1.0% 0.5% 7.3% Weighted average 2.7% 3.1% 1.6% 0.8% 8.1% References Asian Development Bank (ADB). 2012. 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