Infrastructure

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Infrastructure

The most recent and complete infrastructure model documentation is available on Pardee's website. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.

The current version of the infrastructure model within IFs was developed in concert with the production of Building Global Infrastructure, the fourth volume in the Patterns of Potential Human Progress series (Rothman et al 2013). Further details on the model and analyses can be found in that volume.

Overview

The purpose of the infrastructure model is to forecast the following:

  1. the amount of particular forms of infrastructure;
  2. the level of access to these particular forms of infrastructure;
  3. the level of spending on infrastructure; and
  4. the effect of infrastructure development on other socio-economic and environmental systems

The infrastructure model includes parameters that allow users to explore a range of alternative scenarios around infrastructure. These can be used to ask questions such as:

  1. What would be the costs and benefits if countries were to accelerate infrastructure development above that seen in the Base Case?
  2. What if the unit costs of infrastructure development or infrastructure lifetimes were to differ from the assumptions used in the Base Case?
  3. What if the impacts of infrastructure development on economic productivity and health were to differ from the assumptions used in the Base Case? 

Unlike many previous studies, which estimate only the demand for infrastructure, IFs forecasts a path jointly determined by both the demand for infrastructure and the funding available to meet that demand. Therefore, the amount of infrastructure forecasted in IFs in each year explicitly accounts for expected fiscal constraints. Furthermore, the socio-economic and environmental effects of infrastructure feed forward to the drivers of infrastructure demand and supply in future years. 

The figure below provides an overview of the infrastructure model within IFs. In brief, the infrastructure modeling in IFs involves moving through the following sequence for each forecast year:

  1. Estimating the expected levels of infrastructure
  2. Translating the expected levels of infrastructure into financial requirements
  3. Balancing the financial requirements with available resources
  4. Forecasting the actual levels of attained infrastructure
  5. Estimating the social, economic, and environmental impacts of the attained infrastructure 
Each of these steps are described in more detail below.
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For more on the infrastructure module, see the links below

Structure and Agent System: Infrastructure

System/Subsystem
Infrastructure
Organizing Structure
 
Stocks
Physical infrastructure, Access rates
Flows
Spending (public and private on ‘core’ infrastructure; public on ‘other’ infrastructure)
Key Aggregate  Relationships 
(illustrative, not comprehensive)
Demand for physical infrastructure and access changes with population, income, and other societal changes
 
More infrastructure helps economic growth and reduces health effects from specific diseases
 
Public spending available for infrastructure rises with income level
 
Public spending leverages private spending
 
Lack (surplus) of public spending on ‘core’ infrastructure hurts (helps) infrastructure development 
Key Agent-Class Behavior  Relationships
(illustrative, not comprehensive)

Government revenue and expenditure on infrastructure

 

Infrastructure Types

IFs distinguishes between ‘core’ and ‘other’ infrastructure. Core infrastructure refers to those types of infrastructure that we represent explicitly in IFs—roads, electricity generation, improved water and sanitation, and ICT. Other infrastructure refers to those types that we do not represent explicitly—e.g., railroads, ports, airports, and types of infrastructure yet to be envisioned. The choice of what to include as core infrastructure reflects the availability of historical data and understanding of what can be modelled.

Infrastructure Access and Stocks

The table below summarizes the primary variables in IFs related to infrastructure stocks and access. From these and other variables forecasted by IFs, we are able to calculate numerous other indicators—for example, the number of persons with access to electricity.

  Variable Name in IFs (dimensions) Description Units
Access INFRAROADRAI* Access to rural roads percentage of rural population living within 2 kilometers of an all-season road
INFRAELECACC* (rural, urban, total) Access to electricity percentage of population with access 
ENSOLFUEL Solid fuel use percentage of population using solid fuels as their main household energy source
WATSAFE* (none, other improved, piped) Access to improved water percentage of population with access by type
SANITATION* (other unimproved, shared, improved) Access to improved sanitation percentage of population with access by type
WATWASTE Access to wastewater collection connection percentage of population with wastewater collection
WATWASTETREAT* Access to wastewater treatment percentage of population with wastewater treatment
INFRATELE* Fixed telephone lines lines per 100 persons
ICTBROAD* Fixed broadband subscriptions subscriptions per 100 persons
ICTMOBIL* Mobile telephone subscriptions subscriptions per 100 persons
ICTBROADMOBIL* Mobile broadband subscriptions subscriptions per 100 persons
Physical Stocks INFRAROAD* Total road density  kilometers per 1000 hectares
INFRAROADPAVEDPCNT* Percentage of roads paved percentage
INFRAELECGENCAP* Electricity generation capacity per capita kilowatts per person
LANDIRAREAEQUIP Area equipped with irrigation 1000 hectares
*Note: Each of these variables has a companion variable with the extension DEM; for example, the variable INFRAROADRAI has a companion variable named INFRAROADRAIDEM. These companion variables indicate the amount of the infrastructure stock or access that would be expected to exist in the absence of financial constraints.

 

Infrastructure Spending

The following table summarizes the primary variables in IFs related to infrastructure spending. As with the access and stock variables, from these and other variables forecasted in IFs, we are able to calculate numerous other indicators—for example, the ratio of total public to private spending on infrastructure. Please note that although we do not represent these other forms of infrastructure explicitly, we do estimate spending on them in order to avoid almost certainly underrepresenting the total demand for infrastructure. This is given by the variable GDS(InfraOther).

Variable Name in IFs Description Units
GDS (infrastructure, infraother) Government consumption, by category[1] billion dollars
INFRAINVESTMAINT Total (public plus private) investment for infrastructure maintenance, by type of infrastructure[2] billion dollars
INFRAINVESTMAINTPUB[3]
Public investment for infrastructure maintenance, by type of infrastructure[2] billion dollars
INFRAINVESTNEW Total (public plus private) investment for construction of new infrastructure, by type of infrastructure[2] billion dollars
INFRAINVESTNEWPUB[3] Public investment for construction of new infrastructure, by type of infrastructure[2] billion dollars

[1] The categories are military, health, education, R&D, Infrastructure, InfraOther, Other, and Total.

[2] The types of infrastructure included are RoadPaved, RoadUnPaved, ElectricityGen, ElectricityAccRural, ElectricityAccUrban, Irrigation, SafeWaterHH, SafeWaterImproved, SanitationHH, SanitationImproved, WasteWater, Telephone, Mobile, Broadband, BroadbandMobile, and Total. Currently, no cost is assumed for access to Unimproved water, Other unimproved sanitation, solid fuel use, or a wastewater collection connection. [3] Each of these variables has a companion variable, which indicates the amount of public investment that is desired based upon the expected levels of infrastructure.  For INFRAINVESTMAINTPUB, the companion variable is named INFRABUDDEMMNT and for INFRAINVESTNEWPUB, the companion variable is named INFRABUDDEMNEW. The differences between the desired and actual amounts of public investment result from the budgeting process described below.

 

Forward Links from Infrastructure

Although there are a wide range of potential social, economic, and environmental impacts of infrastructure, we limit our modeling of the direct effects of infrastructure to its effects on economic productivity and a small set of health impacts. Currently, the empirical research on these effects are more advanced—and the effects themselves more amenable to modeling—than the direct effects of infrastructure on factors such as income inequality, educational attainment, or governance. To the extent direct effects and other aspects, such as spending on infrastructure that reduces spending on other categories, affect other systems included in IFs, infrastructure will have a number of indirect effects.

Sources of Infrastructure Data

Infrastructure Stocks and Access

In terms of historical data on infrastructure stocks and access, we can turn to various international organizations with specific emphases. These include the International Road Federation (IRF) for transportation, the International Energy Agency (IEA) for energy, and the International Telecommunication Union (ITU) for telecommunications. No one organization focuses on water and sanitation systems, but a number of different organizations, such as the Joint Monitoring Programme (JMP) of WHO and the United Nations Children’s Fund (UNICEF), the United Nations Statistics Division, and the United Nations Food and Agriculture Organization (FAO), maintain global data related to certain aspects of water infrastructure. The table below summarizes a number of the datasets these groups maintain.

Infrastructure Type Organization Spatial Coverage Temporal Coverage Infrastructure Coverage
Transportation   International Road Federation Global  Annual data: 1968–2009 Total road network length, percent of road network paved, and road density 
World Bank Global  Data for most recent year only  Percentage of rural population with access to an all-season road 
Electricity and Energy   United States Energy Information Administration  Global  Annual data: 1980–2010  Total installed electricity generation capacity and generation capacity by energy type 
International Energy Agency  Global  Annual data: 1960–2009  Electricity production by source type; total electricity production; percent of total, urban, and rural population with access to electricity 
Water and Sanitation    WHO and UNICEF Joint Monitoring Programme for Water Supply and Sanitation  Global  Annual data: 1990−2010  Percent of population with access to improved, piped, other improved, and unimproved water, and to sanitation facilities 
Food and Agriculture Organization AQUASTAT database  Global  Annual data: 1960–2010  Percent of arable land equipped for irrigation and water use/withdrawals by sector 
United Nations Statistics Division  Global  Data for most recent year available only  Percent of population with wastewater connection and percent with connection to wastewater treatment 
Information and Communication Technologies  International Telecommunication Union  Global  Annual data: 1960–2011  Number of telephone mainlines, cell phone subscriptions, broadband subscriptions, mobile broadband subscriptions, and number of computer/internet users 

In addition to these primary data sources, the World Bank’s World Development Indicators (WDI) and the World Resources Institute’s Earth Trends databases act as clearinghouses for much of the same data. We can turn also to Canning (1998), Canning and Farahani (2007), and Estache and Goicoechea (2005),who have drawn on these and other sources in attempts to create global databases of infrastructure stocks and access, increase the number of years covered for certain time-series while maintaining consistent definitions, and correct errors. Further, as part of the Africa Infrastructure Country Diagnostic (AICD), the World Bank and the African Development Bank developed an extensive database on infrastructure in Africa. Finally, G. Hughes, Chinowsky, and Strzepek (2009) and Calderón and Servén (2010a; 2010b), among others, have used and modified a number of these databases in their own studies.

Infrastructure Spending

There exist relative little organized historical data on infrastructure spending. In considering public investment in infrastructure (PII), some researchers have used other measures in the Systems of National Accounts, usually fixed capital formation or government outlays by economic sector, as proxies (Agénor, Nabli, and Yousef 2007; Cavallo and Daude 2008; Organisation for Economic Co-operation and Development 2009a; Ter-Minassian and Allen 2004). Lora (2007: 7), however, strongly argued against this practice

because capital expenditures by the central or the consolidated government as measured by the International Monetary Fund’s Government Financial Statistics . . . are a very poor measure of actual PII, which in many countries is mostly undertaken by state-owned enterprises or local governments whose operations are not well captured by this source.

Estache (2010: 67) adds:

Neither the national accounts nor the IMF [International Monetary Fund] Government Finance Statistics (GFS) report a disaggregation of total and public investment data detailed enough to allow identifying every infrastructure sub-sector. In national accounts, energy data cover both electricity and gas but also all primary-energy related products such as petroleum. Similarly, the data do not really distinguish between transport and communication. Water expenditures can be hidden in public works or even in health expenditures.

The World Bank does collect data on private investment in infrastructure in its Private Participation in Infrastructure Project Database. Unfortunately, limitations to this database make us hesitant to rely on it as a primary source of data on infrastructure investment. First, it provides data only on projects in low and middle-income countries in which there is private participation. Second, the amounts in the database primarily reflect commitments, not actual investments. Third, it relies exclusively on information that is made publicly available. Finally, the Bank itself states that it “should not be seen as a fully comprehensive resource.”

This leaves us needing to rely on national, regional, and global studies and reports that provide estimates of infrastructure spending. Given their varied purposes, these studies and reports tend to differ in a number of significant dimensions: temporal coverage; types of infrastructure included; sources of funding (e.g., public versus private); and purpose of expenditure (e.g., new construction versus maintenance). Therefore, we need to be careful in comparing data across studies and in drawing conclusions from them. Even so, they provide a starting point for our exploration. The following table lists a number of these studies and summarizes some of the major elements in their approaches.

Study Spatial Coverage Temporal Coverage Infrastructure Coverage  Source of Funds Purpose of Expenditure
Trends in Transport Infrastructure Investment 1995–2009 (International Transport Forum and Organisation for Economic Co-operation and Development 2011) Albania, Australia, Austria, Azerbaijan, Belgium, Bosnia, Bulgaria, Canada, Croatia, Czech Republic, Denmark, Estonia, Finland, , France, Georgia, Germany, Greece, Hungary, Iceland, India, Ireland, Italy, Japan, Korea, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia, Malta, Mexico, Moldova, Montenegro, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russia, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States Annual data: 1992–2009 Separate data for rail, road, inland waterways, maritime ports, and airports Combined public and private sources for investment; only spending by public authorities for maintenance Separate data for investment and maintenance
Africa Infrastructure Country Diagnostic (http://www.-infrastructureafrica.-org/aicd/tools/data)  Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Chad, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Uganda, Zambia Annual average for one period: 2001–2006 Separate data for electricity, ICT, irrigation, transportation, and water supply and sanitation Public and private Separate data for new construction and for operation and maintenance
Infrastructure in Latin America (Calderón and Servén 2010b) Argentina, Brazil, Chile, Colombia, Mexico, Peru Annual data: 1980–2006 Separate data for telecommunications, power generation, land transportation (roads and railways), and water and sanitation Separate data for public and private Total spending (construction, operations, and maintenance)
Public Spending on Transportation and Water Infrastructure (Congressional Budget Office 2010) United States Annual data: 1956–2007 Separate data for highways, mass transit, rail, aviation, water transportation, water resources, and water supply and wastewater treatment Public only, broken down by (1) federal, and (2) state and local Separate data for capital expenditures and for operation and maintenance
Infrastructure Development in India and China—A Comparative Analysis (Kim and Nangia 2010) China, India Annual data: 1985–2006 Combined data for electricity, water, gas, transport, and communications Combined public and private Not stated
Going for Growth: Economic Policy Reforms (Organisation for Economic Co-operation and Development 2009a) Australia, Austria, Belgium, Canada, Finland, France, Iceland, Ireland, Italy, Netherlands, New Zealand, Norway, South Korea, Spain, Sweden, United Kingdom, United States Annual averages for four periods: 1970–1979, 1980–1989, 1990–1999, 2000–2006 Aggregate data provided separately for (1) electricity, gas, and water, and (2) transport and communications Combined public and private Aggregate investment (from national accounts)
Connecting East Asia: A New Framework for Infrastructure (Asian Development Bank, Japan Bank for International Cooperation, and World Bank 2005)
Cambodia, China, Indonesia, Laos, Mongolia, Philippines, Thailand, Vietnam
Annual data for select years: 1998, 2003 Separate data for transportation, telecommunications, water and sanitation, other urban infrastructure, and power Separate data for national government, local government, state owned enterprises, and private Not stated

Dominant Relations: Infrastructure

The dominant relations in the Infrastructure model are those that determine the expected levels of infrastructure stocks and access, spending on infrastructure, and the impacts of infrastructure on health and productivity. The expected levels of infrastructure stocks and access are influenced by socio-economic factors related to population, economic activity, governance, and educational attainment. In almost every case there are also path dependencies that supplement the basic relationships, reflecting the considerable inertia in infrastructure development.

Spending on infrastructure is divided into private and public spending, with the latter further divided into ‘core’ and ‘other’ infrastructure. ‘Core’ infrastructure refers to those types of infrastructure that are explicitly represented in the model; ‘other’ infrastructure refers to those types of infrastructure that are not explicitly represented in the model (see Infrastructure Types). Public spending on core infrastructure, GDS(Infra), is driven by the required spending to meet the expected levels of infrastructure (INFRABUDDEMMNT and INFRABUDDEMNEW), total government consumption (GOVCON), and the demands on government consumption from other categories. Public spending on other infrastructure, GDS(InfraOther), is driven by average GDP per capita (GDPPCP), total government consumption (GOVCON), and the demands on government consumption from other categories. Deficits and surpluses of government funds will affect the actual levels of funds allocated for both core and other infrastructure. The public spending on core infrastructure leverages a certain amount of private spending on core infrastructure, with the amount leveraged depending upon historical relationships found in the literature, which nominally reflect the variation in public and private returns between particular types of infrastructure. Finally, in recognition of the incremental approaches that public budgeting decisions usually follow, our model avoids unusually sharp increases in public spending on infrastructure by smoothing it out over time.

Infrastructure development directly affects multifactor productivity, with this effect being treated separately for non-ICT and ICT related infrastructure. The use of solid fuels in the home and access to improved water and sanitation directly affect human health through their effects on the mortality and morbidity rates of specific diseases—diarrheal diseases, acute respiratory infections, and respiratory diseases.

For detailed discussion of the model's causal dynamics, see the discussions of flow charts (block diagrams) and equations.

Initializing the Infrastructure Data

 The IFs preprocessor uses historical data to prepare data for the base year of the model, currently 2010. We describe the general workings of the IFs preprocessor here. However, there are some peculiarities in the infrastructure model, specifically related to the initialization of the variables related to spending on infrastructure.

Because of the paucity and inconsistency of the historical data on infrastructure spending discussed above, IFs does not use actual historical data on spending, but rather estimates spending in the first year of the model based upon data on the stocks of and access to infrastructure after the pre-processor has filled any gaps in the historical data. The procedure is as follows:

  • We assume that: 1) the amount of infrastructure requiring maintenance in the base year is given by the level of infrastructure in the previous year (2009) times a factor based on the lifetime of the infrastructure (see table 5 below), and 2) the amount of newly constructed infrastructure is the difference between the amount of infrastructure in the base year (2010) and the previous year (2009).
  • Total spending on maintenance, INFRAINVESTMAINT, is estimated as the amount of infrastructure requiring maintenance times the unit cost for each type of infrastructure (see Table 6 below).
  • Total spending on new construction, INFRAINVESTNEW, is estimated as the amount of new construction times the unit cost for each type of infrastructure. If the amount of newly constructed infrastructure is less than or equal to zero, spending on that type of infrastructure is set to zero.
  • For each type of infrastructure, public spending on maintenance, INFRAINVESTMAINTPUB, and new construction, INFRAINVESTNEWPUB, are estimated by multiplying the total spending by infrastructure specific parameters, infrainvmaintpubshrm and infrainvnewpubshrm , indicating the share of total spending that is assumed to be public.
  • The sum of estimated public spending on maintenance and new construction, across all types of core infrastructure, provides an initial estimate of government consumption for core infrastructure, GDS(Infrastructure).
  • If, in the first year budgeting process, total estimated government consumption on core infrastructure is reduced, an infrastructure cost adjustment factor, INFRACOSTADJFAC, is calculated as the ratio of the final to the initial value of GDS(Infrastructure). The value of INFRACOSTADJFAC is also used to adjust infrastructure spending in future years. It gradually converges to 1 over the time period given by the parameter infracostadjfacconvtime .
  • The initial estimates of INFRAINVESTMAINT, INFRAINVESTNEW, INFRAINVESTMAINTPUB, and INFRAINVESTNEWPUB are each multiplied by INFRACOSTADJFAC to calculate their final values.
  • The initial value of public spending on other infrastructure, GDS(InfraOther ), is calculated as a function of average income, GDPPCP, multiplied by INFRACOSTADJFAC. This function is:

http://www.du.edu/ifs/help/media/images/infraeqa1.PNG

GDS(InfraOther) = government spending on other infrastructure in billion constant 2005 dollars

GDP = gross domestic product at market exchange rates in billion constant 2005 dollars

GDPPCP = gross domestic product per capita at purchasing power parity in thousand constant 2005 dollars <header><hgroup>

Infrastructure Flow Charts

</hgroup></header>

Overview 

The introduction provided an overview of the infrastructure model within IFs, noting that this involves moving through the following sequence for each forecast year. This section describes each of these five steps:  <header><hgroup>

Estimating the Expected Levels of Infrastructure

</hgroup></header> At the core of our forecasts of the expected levels of infrastructure is a set of estimated equations embedded within a set of accounting relationships. The equations are presented here.

Additional elements beyond the estimated equations are involved in specifying the expected values of infrastructure, and we handle some of these elements algorithmically. For instance, the base year calculated estimations will most often not match exactly the historical data for countries in the base year.[1] Each country has peculiarities that differentiate it from the “typical pattern”; among the factors not captured by our equations for estimating the base year country values are many aspects of geography, culture, and unique historical development paths. And sometimes, of course, data errors account for such differences.

To deal with this issue of differences between our estimated values and reported data in the base year, the model calculates an additive or a multiplicative country and variable specific shift factor representing that difference; we allow those shift factors to gradually diminish over time, thereby causing countries to approach the expected value function. Among the reasons for allowing convergence is that we quite consistently see that the patterns of higher-income countries are more similar and more like those of our general equations than are those of lower-income countries. On the assumption that countries will seldom abandon infrastructure they have already developed, however, our downward convergence is extremely slow relative to our upward convergence.

A second instance in which we make adjustments to our core estimated equations is when the dynamic trajectory of demand/supply growth in a country in recent years is inconsistent with the forecasts produced by the equations. For instance, a policy-based surge of infrastructure development like that seen recently in China may result in a historical growth rate well above the one that our functions produce in the first years of our forecasting. Making a simplifying assumption that these growth rates will change only gradually, we estimate the growth rate of physical infrastructure stock using the historical data over three to five recent years and incorporate that growth rate in the demand estimation through a moving average-based extrapolative formulation.

We make a final adjustment in those cases where we wish to modify the estimates of expected infrastructure for scenario analysis. This can be accomplished in several ways. First, most of the estimates can be adjusted with the use of a simple multiplier. Second, we can stipulate specific levels for specific types of infrastructure in a specific future year; in this case, the model will automatically forecast a linear approach to the targeted level from the base year. Third, we can modify both the rates at which the country shift factors converge and the levels, in relation to the expected values, to which the shift factors converge. For example, we can drive the shift factors to those of the best performing countries, i.e., those that perform better than expected, by a certain date. This will, in turn, affect the levels to which the physical infrastructures themselves converge (see Standard Error Targeting).

Transportation

The primary indicators of transportation infrastructure included in IFs are: 1) the total road density in kilometers per 1000 hectares, INFRAROAD, 2) the percentage of roads that are paved, INFRAROADPAVEDPCNT, and 3) the Rural Access Index, INFRAROADRAI, the percentage of the rural population living within two kilometers of an all-season road. From these, we can calculate additional indicators, such as the expected lengths of paved and unpaved roads.

The general sequence of calculations for estimating the expected values of these variables is shown in the figure below. We begin by estimating road density (INFRAROAD) as a function of income density, population density, and land area. The percentage of roads that are paved (INFRAROADPAVEDPCNT) is then calculated as a function of the estimated road density, GDP per capita (GDPPCP), population (POP), and land area (LANDAREA). In parallel, the Rural Access Index(INFRAROADRAI) is calculated as a function of the estimated road density (kilometers per person) and income density (dollars per hectare).

Infrastructure2.png

Electricity

Our focus in the energy sector is on the generation and use of electricity. In terms of physical infrastructure, the key indicator we forecast is the level of electricity generation capacity, INFRAELECGENCAP. From the user perspective, we forecast the percentage of the rural and urban populations that have access to electricity, INFRAELECACC(rural) and INFRAELECACC(urban). These access rates, in combination with the forecasts for population and average household size, are used to calculate the number of household connections, which drive the cost calculations described below. Finally, given its connection to electricity access, we also forecast the percentage of the population that uses solid fuels as the main source of energy, ENSOLFUEL. At the moment, no physical infrastructure is associated with solid fuels, so this value does not enter into the cost calculations.

The following figure presents an overview of the submodel that forecasts access to electricity and electricity generation capacity in IFs. It is fully integrated with the larger IFs system, which provides forecasts of critical variables such as energy demand, energy production by primary type, poverty, and governance character. The electricity submodel contains three components—estimating consumption, estimating production, and sending a signal for additional generation capacity in the case of a gap between production and consumption.

Beginning with consumption, we first estimate the percentage of the population with access to electricity (INFRAELECACC). This is forecast as a function of poverty levels (INCOMELT1CS/POP) and a measure of government effectiveness (GOVEFFECT). The levels of access, along with average income (GDPPCP) determine the share of the population using Solid Fuel for heating and cooking (ENSOLFUEL). Next, the levels of access and average income (GDPPCP), along with the historic ratios of fossil fuel and non-fossil fuel production to total primary energy use (FossilFuelShare and NonFossilFuelShare), are used to forecast the expected ratio of electricity use to total primary energy use (INFRAELECSHRENDEM). With this ratio and the level of total primary energy use (ENDEMSH)[2], forecast elsewhere in IFs, we then calculate the desired electricity use (INFRAELEC * POP).

The amount of domestically produced electricity (INFRAELECPROD) is determined by the existing generation capacity (INFRAELECGENCAP), adjusted by a capacity utilization factor (INFRAELECTADJFACT). We estimate the initial capacity utilization factor for each country based on historical data related to generating capacity and electricity production. Over the forecast horizon, the capacity utilization factor is assumed to converge, over a 50 year period, to a global average value, 0.55, which we derived from current data on generation capacity and production in high-income countries. We also account for transmission and distribution loss (INFRAELECTRANLOSS), which we forecast as a function of average income (GDPPCP) and a measure of governance regulatory quality (GOVREGQUAL). This allows us to calculate post-loss production of electricity.

The desired electricity use can be met by either the domestic post-loss production or imports. Similarly, the post-loss production can be used for either domestic use or exports. At the moment, we assume that the imports are available, when necessary, and that any excess post-loss production can be exported; i.e., we do not attempt to balance the trade in electricity. In parallel, we use the ratio of desired electricity use to post-loss production (INFRAELECCONSPRODRATIO) as a driver of future levels of generating capacity. Each year the computed ratio is compared to a historical value calculated in the pre-processor. We make the simplifying assumption that countries wish to keep this ratio constant over time. A growing ratio implies that domestic consumption is increasing at a faster rate than domestic production, which sends a signal indicating a desire to build additional capacity. A declining ratio implies that domestic consumption is increasing at a slower rate than domestic production. While this could send a signal to remove existing capacity, the model does not do so; rather it calls for no new construction and less than full replacement of depreciated capacity. Over time, this should bring the production and use back into historical balance.

Infra3.png

 

Water and Sanitation

Access to Water, Access to Sanitation, and Wastewater Treatment

The key access indicators we include for water and sanitation infrastructure are the percentages of the population with access to different levels of improved drinking water and sanitation and whose wastewater is collected and subsequently treated. The physical quantities include the number of connections providing these services and the amount of land that is equipped for irrigation.

We originally introduced forecasts of access to improved sources of drinking water and sanitation into IFs in support of the third volume in the PPHP series, Improving Global Health (Hughes, Kuhn, et al. 2011), because of the health risks associated with a lack of clean water and/or improved sanitation. We have extended this portion of the model to include forecasts of the share of wastewater that is collected and then treated prior to being returned to the environment. In addition, we have added a component to forecast the area equipped for irrigation.

The WHO and UNICEF (2013) use the concept of “ladders” for drinking water sources and sanitation systems. They currently include four steps for both drinking water (surface water, unimproved, other improved, and piped on premises) and sanitation (open defecation, unimproved, shared, and improved). As countries develop, more of their citizens ascend these ladders. We have combined these into three categories each; for drinking water these are unimproved, other improved, and piped; for sanitation, these are other unimproved, shared, and improved. Notably, using international standards, estimates of the total population with access to improved sanitation does not include the shared category.

We forecast the shares of the population in each of the water and sanitation ladder categories using average income, poverty levels (measured as the percentage of the population living on less than $1.25 per day), educational attainment (measured as the average number of years of formal education for adults over 25), and public health expenditures as explanatory variables (see the next figure). These results then feed into the forecasts of the percentage of population with wastewater collection and wastewater treatment.

Finally, these access rates, in combination with the forecasts for population and average household size, are used to calculate the number of safe water, sanitation, and wastewater treatment connections, which drive the cost calculations described below.

Infra4.png

Area Equipped for Irrigation

There have been few forecasts of the area equipped for irrigation, and those that do exist tend to be based on very detailed analyses of specific situations. In a recent report from the United Nations Food and Agriculture Organization (FAO) looking out to the year 2050, Bruinsma (2011: 251) stated that the “projections of irrigation presented in this section are based on scattered information about existing irrigation expansion plans in different countries, potentials for expansion (including water availability) and the need to increase crop production.” Another report looking at global agriculture over the next half century (Nelson et al. 2010), this one from the International Food Policy Research Institute, relies on exogenous assumptions of the growth in irrigated area. The authors do not specify the source of these assumptions, but some of the same authors (You et al. 2011) have reported on the irrigation potential for Africa, basing their conclusions on agronomic, hydrological, and economic factors.

Rather than attempt to replicate the level of detailed analysis of most previous studies, we forecast the area equipped for irrigation based on data from the FAO’s FAOSTAT and AQUASTAT databases on historical irrigation patterns and the area that could potentially be equipped for irrigation. These data are incomplete; for area equipped for irrigation, data are provided for 168 of the 186 countries included in IFs, and for the potentially irrigable area, data are provided for 117 of 186 countries. In our examination of these historical data, we found that a number of countries had already reached an apparent plateau in the amount of area equipped for irrigation that was often well below the potential indicated. For example, Argentina’s equipped area has stayed at a bit over 1.5 million hectares since the late 1970s, even though its potential is given as more than 6 million hectares. Why a country saturates below its ultimate potential is often unclear, but one obvious reason for some countries is that they receive enough rainfall to not warrant further irrigation.

In any case, once we have determined an appropriate saturation level for each country and a recent historical growth rate, we assume that the expected area equipped for irrigation gradually approaches the saturation level. The rate of growth starts at the historical growth rate, with the growth rate slowing as the saturation level is approached. The user can modify this path using the parameter ladirareaequipm , which acts as a multiplier. Still, the amount of area equipped for irrigation cannot exceed the specified saturation level for the country.

 

ICT

We forecast four basic indicators of ICT infrastructure: fixed telephone lines, fixed broadband subscriptions, mobile telephone subscriptions, and mobile broadband subscriptions, all per 100 persons. Our forecasts for the expected levels of these different forms of ICT infrastructure are driven in part by cross-sectional relationships with average income and government regulatory quality. As the next figure illustrates, however, there are also interactions among the different forms of ICT.

For each technology, we found strong relationships indicating that usage levels (our proxies in this case for access) increase with rises in average income and governance regulatory quality; in the case of fixed broadband, we also found urbanization to be important, as one might expect for a technology whose installation is supported by population density.

As for the interactions between the different forms of ICT, we start with fixed telephone lines. Given the potential for substitution by mobile telephone lines, we assume that the demand for fixed telephone lines will decline as mobile usage increases. Already we see this happening in the data, especially, but not exclusively, in high-income countries. Our analysis of the historical data indicates a level of approximately 30 mobile telephone subscriptions per 100 persons as the point at which fixed-line telephone decline begins, so we build this into our forecasts algorithmically. We do not expect that fixed telephone line usage will completely disappear. Rather, we assume arbitrarily that it will settle at a low level; this is set by default to 2.5 lines per 100 persons. Furthermore, we also assume that: (1) mobile broadband subscriptions will never exceed mobile telephone subscriptions; and (2) any decline in fixed telephone lines will boost the growth in fixed broadband because countries that have existing investments in fixed-line infrastructure are able to leverage these networks to provide broadband access with rather modest investments.

The cross-sectional relationships with income do not remain static across time for mobile phones, fixed broadband, and mobile broadband. The last figure shows this for mobile telephone subscriptions. The individual points reflect historical data for country access rates for the years 2000, 2005, and 2010. The lines are logarithmic curves fit through these data. The upward shift over time reflects advances in information and communication technologies that are making ICT cheaper and more accessible around the world. These advances are, in turn, driven by various systemic factors ranging from product and process innovation to network effects.

In order to capture the effect of this rapid change in our forecasts of future access, we combine the use of the cross-sectional function with an algorithmic approach that simulates the upward shift of the curves for mobile phones, fixed broadband, and mobile broadband. The algorithmic element assumes a standard technology diffusion process in which the growth in penetration rate associated with the technological shift rises from a low annual percentage point increase at low levels of penetration to a maximum at the middle of the range (the inflection point) and falls again as saturation is approached. For each of the three technologies, we have looked at historical patterns to estimate the minimum and maximum growth rates, expressed as annual percentage points of absolute change.

The choice of saturation levels is obviously quite important. Data from the International Telecommunications Union show penetration rates for mobile phones that exceed 100 subscriptions per 100 persons (e.g., approaching 200 in Hong Kong). At the same time, some countries (e.g., Denmark) seem to be reaching a saturation level for fixed broadband well below 100 subscriptions per 100 persons. Uncertainty remains over the proper level of saturation to assume for these subscriptions, and therefore, different researchers use different values. Specifically, we define saturation as 50 subscriptions per 100 persons for fixed broadband and 150 subscriptions per 100 persons for both mobile technologies. In addition, we assume that mobile broadband penetration cannot exceed mobile phone penetration.

Similarly to the other access rates, the numbers of lines and subscriptions per 100 persons, in combination with the forecasts for population, are used to calculate the absolute number of lines and subscriptions, which drive the cost calculations described below.