metadata: authors: 0: author: Fay, Marianne
1: author: Yepes, Tito
docna: 0: docna: Investing in infrastructure : what is needed
            from 2000 to 2010?
docty: Policy Research Working Paper
subtopic: Sanitation and Sewerage,Engineering,Telecommunications Infrastructure,Energy Policies & Economics,Water and Human Health,Hydrology,Food Security,Water Supply and Sanitation Economics,Sanitary Environmental Engineering,Small Private Water Supply Providers,Environmental Engineering,Health and Sanitation,Town Water Supply and Sanitation
teratopic: Urban Development
abstracts: cdata!: The authors estimate demand for
            infrastructure services over the first decade of the new
            millennium based on a model that relates demand for
            infrastructure with the structural change and growth in
            income the world is expected to undergo between now and
            2010. It should be noted that predictions are based on
            estimated demand rather than on any absolute measure of
            "need" such as those developed in the Millenium
            Development Goals. The authors also provide estimates of
            associated investment and maintenance expenditures and
            predict total required resource flows to satisfy new demand
            while maintaining service for existing infrastructure.
repnme: repnme: Investing in infrastructure : what is
            needed from 2000 to 2010?
display_title: Investing in infrastructure : what
            is needed from 2000 to 2010
listing_relative_url: /research/2003/07/2487584/investing-infrastructure-needed-2000-2010
topicv3: Agriculture and Food Security m1326205 435,Sanitation m1326453 2000,Water m1326522 137,Power and Electricity Sector m1327094 515,Telecommunications and Broadband Access
            m1327340 286
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Urban Development
origu: Fin, Private Sec & Infrastructur (LCSFP)
no_of_pages: 26
model_extractions: | POLICY RESEARCH WORKING PAPER 3102

# Abstract

Fay and Yepes estimate demand for infrastructure services over the first decade of the new millennium based on a model that relates demand for infrastructure with the structural change and growth in income the world is expected to undergo between now and 2010. It should be noted that predictions are based on estimated demand rather than on any absolute measure of "need" such as those developed in the Millenium Development Goals. The authors also provide estimates of associated investment and maintenance expenditures and predict total required resource flows to satisfy new demand while maintaining service for existing infrastructure.

This paper-a product of the Infrastructure Vice Presidency-is part of a larger effort in the vice presidency to improve knowledge of infrastructure needs. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Marianne Fay, room 15-007, telephone 202-458-7200, fax 202-676-9594, email address mfay@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mfay@worldbank.org or tyepes@worldbank.org. July 2003. (19 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by Partnerships, Capacity Building, and Outreach
# **INVESTING IN INFRASTRUCTURE: WHAT IS NEEDED FROM 2000**<sup>**to 2010?**</sup>

**Marianne Fay** LCSFP, The World Bank

**Tito Yepes** Consultant, The World Bank

This paper was commissioned by the<sup>INFVP Officeof theWorld Bank. We aregrateful to Judy Baker, John</sup> Besant-Jones, Cecilia Briceno, Antonio<sup>Estache, Pierre Guislain, JonathanHalpem,Charles Kenny, Bill</sup> Kingdom, Anat Lewin, Christina<sup>Malmberg,Kyron O'Sullivan,Peter Roberts, TomasSerebrisky, Richard</sup> Scurfield and Christine Zhen-Wei<sup>Qiangfor usefulcomments and/or contributions. We areparticularly</sup> indebted to Antonio Estache for<sup>long debatesin the preparation of this paper.</sup>
# **Introduction**

The world is expected to grow<sup>at2.7%perannumin thefirstdecadeofthe new</sup> millennium. Accompanying this<sup>growthwill beanincreasein demandfor infrastructure</sup> services, for both consumption<sup>and productionpurposes.Afailuretorespondtothis</sup> demand will cause bottlenecks<sup>to growth and hamper poverty alleviationefforts.</sup> This paper sets out to estimate<sup>the changein demandfor infrastructureservices thatwill</sup> spring from the expected structural<sup>changeandgrowth in income theworld isexpected to</sup> undergo in the next 7 years. We<sup>usethe same macromodelthat linksgrowthand demand</sup> for infrastructure services that<sup>wasdevelopedinFay(2000.)Toourknowledge, this</sup> paper is the only one that systematically<sup>triestoestimate infrastructure needacrossa</sup> cross section of countries and<sup>acrossavarietyofsectors.'</sup> We then discuss the implication for investment needs<sup>across regionand incomegroups.</sup><sup>_Theword"need"is_</sup> _used here only to refer to the_<sup>_investmentnecessary to satisfyconsumer and producer_</sup> _demand based on predicted GDP_<sup>_growth.Itdoesnotrefertoanysociallyoptimal_</sup> _measure of need for infrastructure_<sup>_service or infrastructure investment._</sup> The infrastructure sectors covered<sup>inthis paperare roads, railroads,telecommunications,</sup> electricity, water and sanitation. For lack of comparable data<sup>acrosscountries,we</sup> excluded ports, airports, and<sup>canals-</sup> which represent a small share<sup>ofoverall</sup> infrastructure endowments - and<sup>oilandgas.Table1 offersaquick reviewof accessto</sup> infrastructure services across<sup>low,middleand highincomegroups, showinghow</sup> infrastructure stocks or access<sup>increasesalongwithincome.</sup> This however, varies somewhat across different types<sup>of infrastructure.For waterandsanitation, where access,</sup> by definition, is bounded at 100,<sup>accesstowaterin highincomecountriesisonly1.3</sup> times what it is in poor countries,<sup>and 2.2 times higherfor sanitation.Incontrast,the ratio</sup> for mobile phones, a relatively<sup>newtechnologyis91:1infavorof richcountries.Note</sup> finally, that for most types of<sup>infrastructure,thedifferenceinaccessbetweenpoorand</sup> rich countries is much less than<sup>the differencein income(estimated hereat about 63: 1.)2</sup>

**Table 1. Access to infrastructure**<sup>**by incomegroup -**2000</sup>

||GDPper<br>capita|Electricity<br>Generation<br>(kwpercapita)<br>|Telecomm<br>(per100<br>Fixed<br>(lines)<br>|unications<br>0person)<br>Mobile<br> <br>(subscribers)<br>|Road<br>(km/1000 <br>person)<br>|Rail<br>(km/1000<br>person)<br>|Water<br>(%ho<br>conn<br>|Sanitation<br>usehold<br>ected)<br>|
|---|---|---|---|---|---|---|---|---|
|LIC|475|116<br>|28<br>|5.8<br>|1.06<br>|0.07<br>|76.26<br>|45.58<br>|
|MIC|1,919|406|127<br>|83.7<br>|1.10<br>|0.13<br>|81.82<br>|61.87<br>|
|HIC|29,808|2,031|582|526.0<br>|10.54<br>|0.44<br>|99.59<br>|98.07<br>|
|Ratio HICtoLIC<br>Source:seeAnnex|63<br>1.|18|21|91|10|6|1.3|2.2|

' Many countries, in the course of<sup>their investmentplans, makethis kindof estimates.These may be much</sup> more accurate inasmuch as they<sup>are basedon individual countryandsector data,althoughin manycases</sup> they are more "wishlists." Also,<sup>thereare sector studiesthat typically tackle onetypeof infrastructuresuch</sup> as one on energy by Moore and<sup>Smith(1990.)</sup> 2 Note that we are not taking<sup>into account differencesin quality.Thus accessto water in high income</sup> countries usually means reliable,<sup>continuousservice,whilein manydeveloping countriesit may only entail</sup> sporadic access to water of<sup>unreliable quality.</sup>
**2**

# **_The world's infrastructure endowments today_**

Using best practice average prices as discussed later on in this paper, the world's infrastructure stock today can be valued at about US$ 15 trillion (table 2.) Of this total, about 60% is in high income countries, 28% in middle income countries and 13% in low income countries. In contrast, the population shares are 16%, 45%, and 39% respectively.

> The composition of infrastructure also changes across income groups. In low income

> countries, roads tend to dominate, accounting for about 50% of infrastructure stocks,

> whereas in middle income countries, this share falls to 28% while electricity accounts for close to 50%. In high income countries, electricity and roads amount to about 40% to

> 45% each of overall infrastructure stocks. Everywhere, roads and electricity represent

> the bulk of investment accounting for 75 to 85 of total infrastructure value. Water and sanitation drop in relative importance as income increases, while the reverse is true for telecom.

**Table 2: The composition of infrastructure stocks, 2000**

||LowIncome|Middle income|Highincome|World|
|---|---|---|---|---|
|Electricity|25.6%|48.1%|<br>40.1%|40.4%|
|Roads|50.9%|28.1%|44.9%|41.0%|
|Water&sanitation|14.5%|9.9%|4.7%|7.5%|
|Rail|7.2%|7.0%|4.1%|5.3%|
|Telecom(fixed)|1.3%|3.2%|2.4%|2.5%|
|Telecom(mobile)|0.5%|3.7%|3.8%|3.3%|
|Total(%)|100.0%|100.0%|100.0%|100.0%|
|Total($billions)|1,968|4,194|8,804|14,966|

> *The composition of infrastructure has also changed quite dramatically over time.

> Whereas in the 1960s, rail accounted for almost a third of the value of infrastructure

> stocks, today this share has dropped to a mere 6%. In contrast, electricity's importance

> has doubled from about 22% to 44% and telecom has tripled, albeit from a very low 2%.

> **Table 3 How the composition of infrastructure stocks has changed over time, all countries:**

||1960|1970|1980|1990|2000|2010|
|---|---|---|---|---|---|---|
|Electricity|22%|32%|40%|43%|44%|42%|
|Roads|47%|46%|45%|44%|44%|43%|
|Rail|29%|19%|13%|9%|6%|5%|
|Telecom|2%|3%|3%|4%|6%|10%|
|Total|100%|100%|100%|100%|100%|100%|

> Note: water and sanitation are excluded for lack of historical data.
3

# **Projecting demand for new infrastructure**

The model developed below is from Fay<sup>(2000)andseeksto askthequestionofwhat</sup> infrastructure levels will be required in the<sup>future,eitherasconsumptiongoods,oras</sup> input into production function.

## **_A model of Infrastructure demand_**

We develop a model to estimate future demand<sup>forinfrastructure,whereinfrastructure</sup> services are demanded both as consumption<sup>goodsbyindividualsandasinputsintothe</sup> production process by firms. On the consumption<sup>side, the amount of service demandedis</sup> a function of income and prices: Demand for a particular type of infrastructure<sup>serviceI by individual j is a functionof j's</sup> income, Yj, and the price of infrastructure<sup>serviceI, ql.Aggregatingover the population,</sup> national per capita demand of infrastructure<sup>serviceforconsumption,lC,willthenbe</sup> given as: where Y/P is income per capita.

On the production side, each individual firm's<sup>demand for infrastructure serviceI willbe</sup> based on a profit maximization decision<sup>which yields the usualfirst order condition:</sup> where Y, is output of good i by the firm, and<sup>wiis the priceof that good.</sup> To go any further, we must adopt a specific<sup>functionalform for theproduction function.</sup> Assuming a Cobb-Douglas, we can rewrite the first<sup>order conditionas:</sup> where K is physical capital (excluding infrastructure),<sup>L is laboror human capital,andI is</sup> the flow of infrastructure services consumed<sup>bytheindividualfirminthe productionof</sup> good i. Solving for Ii yields the derived demand<sup>for infrastructureservicesof firm i:</sup>
_4_ Aggregating over all firms yields the following: The derived demand for any given infrastructure service IP is the sum of weighted individual firms' demands.

Equation 2 is however of limited usefulness since we do not have firm level data. A reasonable proxy for firms' aggregate demand for infrastructure is given by aggregate output. However, it is unlikely that the elasticity of demand for a particular infrastructure service, 4, is the same across sectors of the economy. Thus the weight attributable to a given firm's demand depends on the sectoral composition of the economy. Also, as technology changes, 4 may change. Finally, the weighted average of the relative price wi/qi can be proxied by the real price of the infrastructure good -- quw where w is the price level. The reduced form of equation 2, is then given as: where Y is aggregate output, **YAG** and YIND are the share of GDP derived from agriculture and industry, and A is a term representing technology level. Combining equations 1 and 3, and expressing infrastructure demand in per capita terms yields the following for overall production and consumption demand for infrastructure services: Note that to the extent that the model assumes a competitive market for infrastructure (prices are assumed to be given for any individual firm) and that it assumes a perfectly elastic supply of infrastructure.

# **_Estimating infrastructure demand empirically_**

The purpose of this paper is to estimate investment needs in infrastructure. For this the variable of interest is the stock of infrastructure, rather than the flow of services that will be produced from it. To the extent that services are proportional to the physical stock (though intensity of use may vary), equation 4 can easily be understood as demand for physical stocks of infrastructure.

# **_Proxies_**

Lacking measures of technological change or actual real prices of infrastructure services, we use time dummies and country fixed effects as proxy. The country fixed effect allows
_5_ each country to have a different intercept, which combined with the time<sup>dummyallows</sup> us to capture (albeit roughly) the price variable.

Note that our interest is not to establish a causal relationship between infrastructure<sup>stocks</sup> and various economic variables. Instead, since we want to use this regression for projection, our interest is to obtain the best fit possible and the highest explanatory<sup>power.</sup> Thus, since infrastructure stocks tend to change reasonably slowly over<sup>time and havea</sup> long life span, we include lagged value of the dependant variable in the<sup>regressionin</sup> order to increase explanatory power.

We therefore estimate equation 4 as follows: _5._ it, _=a_ + 1 _I aAi,t i,t 4 a5Di 5+ + a 6 Dt + 6i,t_ where all variables are in natural logs to linearize the model, Ii.t<sup>is</sup> demand for infrastructure stock of type j in country i at time t; Pit-i is the lagged<sup>valueofthe</sup> infrastructure stock, y is income per capita; A is share of agriculture value<sup>added inGDP;</sup> M is the share of manufacturing value added in GDP, Di is a country fixed<sup>effect,Dt isa</sup> time dummy; and £ is the error term.<sup>3</sup> Given then that there is no modeling<sup>of the supply</sup> side, equation (5) can be interpreted as a law of motion for infrastructure<sup>stock.</sup> Most infrastructure goods are provided through networks so that the price<sup>of the serviceis</sup> often reduced with higher population density. Urbanization, in particular,<sup>allowseasier</sup> and cheaper access to electricity and telephone. Average costs of<sup>waterandsanitation</sup> tend to be actually higher in urban areas, but this is because the standard<sup>serviceoffered</sup> there is typically much higher. Access is however always much higher<sup>incities,partly</sup> because of the higher income of the population, and partly because<sup>ofpublic health</sup> consideration that make piped water and reasonably sophisticated<sup>sanitation services</sup> necessary. In the case of roads, roads per capita tend to decrease with higher<sup>population</sup> density. We therefore also estimated a version of equation (5) that included<sup>urbanization</sup> and population density to capture the density effect and its impact on<sup>demand(bothdirect</sup> and through price.)

# **_Data_**

The infrastructure variables we use are telephone mainlines<sup>(lines per1000person),</sup> mobile phones (subscribers per 1000 persons), KW of installed electricity<sup>generating</sup> capacity per capita, km of rail per 1000 person, km of paved<sup>road perkm2of landand</sup> percentage of households with access to water and sanitation. The only<sup>reasonforusing</sup> land rather than population as the deflator for roads is that it<sup>yieldedaslightly betterfit.</sup> Annex 1 discusses the variables and their source.

For all but the mobile phone data, our data base is organized as an unbalanced<sup>panelwith</sup> observations every 5 years from 1960 to 2000 and includes all<sup>independentlow,middle</sup> and high income countries with population of more than 500,000 in 2000<sup>for whichdata</sup> was available (113 countries). In the case of mobile phones, this is a more recent

> 3Manufacturing rather than industry was used here because industry includes<sup>mining,whichhasvery</sup> different implications on the demand for infrastructure.
_6_ technology, that appeared in different countries in different years, starting in the eighties. Thus for mobile, we use an annual data base, of different "length" depending on the country (from a minimum of 1 in Sierra Leone to a maximum of 21 in Finland) For these regressions we added a variable called market maturity equal to the number of years the market has existed in a given country.

# _Results_

Using OLS with fixed effects, we ran both the basic model described by equation 5 and an extended model that included density and urbanization on all 5 infrastructure variables. In all cases, we ran regressions both on a full sample of up to 113 countries and then separately for low and middle income as one group and high income countries as another.<sup>4</sup>

> As mentioned, country fixed effects proxy for differences in technology and price across nations. Their use also allow us to obtain consistent parameter estimates. Canning (1998), shows that per capita infrastructure levels are nonstationary, which implies that running the regressions in levels may produce misleading results unless the variable

> variables used in the regressions are cointegrated. Unfortunately, cointegration would not yield an easy system with which to make predictions, leaving us with two possible solutions. One is to run the regressions on first differences, which Canning shows to be stationary. This would reduce our sample size considerably since we only have up to eight time series observations, and the series are often incomplete. The second possibility - which we use- is to include fixed effects. Kao (1997) shows that in this case parameters estimates are consistent even if the estimated relationship is not a cointegrating one.

A Chow test of structural change allows us to determine whether the estimated relationship is the same for developing countries and the high income sample.<sup>5</sup> With the exception of water, sanitation, and mobile we reject the hypothesis that coefficients are equal across samples and therefore present the results separately for developing and high income countries. For water, sanitation and mobile phones however, we cannot reject the hypothesis that they are equal, and therefore run the regression on the world sample.

For mobile phones and rail, we modified the basic regression structure since including a lagged variable or a time dummy resulted in projections that either went to zero (rail) or exploded into infinity (mobile phones.) In the case of mobile, we also found that the sectoral share of GDP did not add any explanatory power so we dropped it.

Table 4 presents the regressions that were subsequently used for the projections. Country fixed effects are not reported. For all but water, we obtain very high R2 (0.95 and above) which is our goal given that we want to predict infrastructure values as best as possible.

> 4When explanatory variables, the variable is set to zero and a dummy variable equal to 1 is included in that regression.

> _5_ Note that the presence of fixed effects somewhat complicated the estimation of Chow test. The hypothesis tested (Ho) was not in fact whether all coefficients were the same across samples, but only whether the coefficients on the explanatory variables other than the country fixed effects and the time dummies.
7 In the case of water we manage to explain 60% of cross country and over time variation in coverage.
> **Table 4. Estimated models for infrastructure predictions**

||Electricity<br>|Generation<br>|||<br>|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Capa<br>|city<br>|Telephone<br>|Mainlines<br>|<br>R<br>|ails<br>|Paved<br>|Roads<br>|Water<br>|Sanitation<br>|Mobile<br>|
||L&MIC|HIC|L&MIC|HIC|L&MIC|HIC|L&MIC|HIC|ALL|ALL|ALL|
|LaggedDependant Variable|0.52|0.68|0.22|0.47|||0.02|0.28|0.34|||
|(<br>|18.84)***|(16.43)***|(8.67)***|(11.13)***|||-1.01|(6.38)***|(3.78)***|||
|GDPpercapita|0.18<br>(2.79)***|0.11<br>(2.26)**|0.39<br>(7.95)***|0.36<br>(4.88)***|_-0.05_<br>-0.32|-0.28<br>(4.23)***|0.14<br>(1.87)*|0.23<br>(2.57)**|0.11<br>(2.79)***|0.19<br> (1.78)*|0.64<br>(10.20)***|
|Agriculture,shareofGDP|-0.04|-0.05|-0.02|0|0.5|-0.04|-0.22|0.03|0.02|0.11||
||(-0.74)|(2.04)**|-0.42|-0.07|(3.35)***|<br>-0.8|(2.89)***|<br>-0.53|-0.43|(1.91)*||
|Manufacture, shareofGDP|0.08|0.05|0.1|0.19|0.29|0.24|0.28|-0.14|0.02|0.11||
||-1.41|-1.18|(2.16)**|(2.71)***|(1.91)*|(2.51)**|(3.88)***|<br>-1.18|-0.4|-1.35||
|Year|0<br>-0.18|-0.01<br>(3.26)***|0.04<br>(9.58)***|0.01<br>(1.90)*||||||||
|PeopleperKmSquare|0.37<br>(2.07)**|0.33<br>(3.28)***|-0.04<br>-0.22|0.41<br>(2.96)***|-1.2<br>(4.47)***|-1.19<br>(11.42)***(|0.46<br>3.34)***|0.37<br>(1.96)*|0.04<br>(3.12)***|||
|%ofpeoplein UrbanAreas|0.06<br>-0.6|0.42<br>(4.50)***|0.51<br>(6.21)***|0.72<br>(5.29)***|0.39<br>(1.77)*|0.32<br>(1.68)*|1.11<br>(8.92)***|2.48<br>(7.30)***|0.12<br> (1.92)*|0.84<br>(5.48)***||
|Market maturity|||||||||||2.29<br>(56.26)***|
|DummyforIncome Group|-1.43||-0.2||2.82||0.38||-0.01|||
||(1.70)*||-0.27||(1.96)*||-0.72||-0.25|||
|Constant|4.05|11.56|-74.81|-7.88|-12.29|-4.5|-0.72|-2.05|2.05|3.23|-7.02|
||-0.38|(3.59)***|(8.10)***|-1.53|(4.76)***|(4.91)***|-0.82|(2.18)**|(4.23)***|(3.33)***|(14.71)***|
|N<br>|669|200|642|199|542|186|601|163|242|209|980|
|R-squared|0.96|0.99|0.97|0.97|0.93|0.99|0.95|0.98|0.58|0.93|0.69|
||Fixed|Fixed|Fixed|Fixed|Fixed|Fixed|Fixed|Fixed|Random|Fixed|Random|
|ModelType|Effects|Effects|Effects|Effects|Effects|Effects|Effects|Effects|Effects|Effects|Effects|
|Sample<br>|||||Quinque<br>|nnial<br>|||||Annual<br>|
|Absolutevalueoftstatistics<br>not reportedhereforlackof|inparenthe<br>space.Du|ses.*sign<br>mniiesform|ificantat1<br>issingobs|0%;**sig<br>ervationsi|nificantat<br>nexplanato|5%;***s<br>ryvariable|ignificant <br>swere inc|at1%.A<br>ludedbut|llregressio<br> not shown|nsinclude<br> here.|countryfixedeffects, whichare|
9

# **Projections**

The World Bank calculates an official set of GDP projections for in its annual Global Economic Prospects. No such projections are available for GDP composition (share<sup>in</sup> agriculture and in manufacturing) so we took take the simplistic and admittedly not<sup>so</sup> satisfactory approach of keeping them at 2000 values. For urbanization and population we have UN projections. Thus we have projections for up to 113 countries representing about 90% of world GDP.

Data on access to infrastructure are available for the year 2000 for another 34 other countries. We therefore expand our set of projections by using projected infrastructure growth rates for the region/income group to which an individual country belongs, and applying them to the 2000 actual infrastructure stock. As a result, we have projections for up to 147 countries (water, sanitation and rail have poorer coverage.) Looking at the results of our projections, it appears that the largest increases in coverage will occur in telecommunications, particularly mobile phones (table 5.) Fixed density<sup>is</sup> expected to more than double in low income countries and nearly triple in middle income countries. As to mobile density, it is projected to quadruple or quintuple in developing countries. Even high income countries should see a steady growth of 3 or 4% per annum in mobile and fixed lines.

Electricity generating capacity and road density should increase by similar orders<sup>of</sup> magnitude (2.3 to 2.5% per annum) in MICs. In low income countries, instead, electricity is expected to increase by about 3.2% p.a. while road density should only rise by about 1.4% p.a.. Water and sanitation should increase by about 2 to 2.5% p.a. in LICs and about 1.5% p.a. in MICs. For high income countries, increases are expected to be much smaller, except for roads density.

**Table 5: Infrastructure stocks, 2000-2010**

|||Electricity|Teleco|mmunications||||||
|---|---|---|---|---|---|---|---|---|---|
|||Generation|Fixed|Mobile|Roads|Rail|Water|Sanitation|Total|
|LIC|2000|504|26|9|1,001|142|139|147|1,968|
||2010|665|67|47|1,143|146|168|182|2,417|
|_Annua_|_l Increase_|_3.2%_|_15.9%_|_40.1%_|_1.4%_|_0.3%_|_2.1%_|_2.4%_|_2.3%_|
|MIC|2000|2,019|134|154|1,177|295|178|239|4,194|
||2010|2,528|350|562|1,450|298|204|280|5,673|
|_Annua_|_lIncrease_|_2.5%_|_16.2%_|_26.6%_|_2.3%_|_0.1%_|_1.5%_|_1.7%_|_3.5%_|
|HIC|2000|3,527|213|337|3,951|364|152|261|8,804|
||2010|3,920|290|437|4,587|343|157|271|10,005|
|_Annua_|_lIncrease_|_1.1%_|_3.6%_|_3.0%_|_1.6%_|_-0.6%_|_0.4%_|_0.4%_|_1.4%_|

Units: Electricity Generation: kilowatts per hab; Telephone Mainlines: lines per 1000 hab. Paved<sup>Road</sup> Length lineal km by square km of surface Rail Road Length: km per 1000 hab. Mobile: subscribers per 1000 hab Water; % Households with access Sanitation % Households with access
10 In the case of rail, we show a quasi stagnation in km of tracks per capita in developing countries. This is not particularly surprising as rail construction has largely stopped in the last 20 years and given the fact that in most countries, privatization of the railroads has brought with it the abandonment of unprofitable lines. In high income countries we also see a small decline - note that this is in per capita terms and need not therefore imply an absolute decline; Overall, the implication for the rail sector is not that no new investment will be taking place, but rather that it is more likely to take the shape of upgrading and rehabilitation rather than of an actual expansion of the network. This is indeed what has been happening in a number of countries (Mexico, Brazil) already.

# **_Implications for investment*_**

From our projections for infrastructure stocks in 2010, we can derive the associated flow of required new investment. To do so we simply look at the predicted increase in stock, and price it using best practice prices taking into account associated network costs. This is important notably in the case of power, where generating capacity is only a share of total infrastructure cost.<sup>6</sup>

**Table 6. Unit costs for infrastructure investment**

|Sector|$|Unit|
|---|---|---|
|Electricity|$1,900|per kilowattofgeneratingcapacity, includingassociated<br>networkcost.|
|Roads|$410,000|per kilometeroftwo lanepavedroad|
|Railway|$900,000|perkilometersorrail,including associatedrollingstock|
|Sanitation|$700|perconnected household|
|Water|$400|perconnected household|
|Mainlines|$400(from2000onward)|perline|
|Mobile<br>$70|0in2000and$580from 2005 on|per subscriber|

Source: Mobile, Pyramid Research, World Bank specialists; Mainlines: Ruzzier, Kennet, Benitez, and Estache (2000). Water, Sanitation, Roads, Electricity : World Bank specialists.

It would be misleading however, to only look at investment needs, in the sense that this seriously underestimates the flow of resources needed to maintain or improve access to services. Thus we also look at maintenance needs. These are calculated, in rough estimates, to be 2% of the replacement cost of the capital stock for electricity generation, rail and road; 3% for water and sanitation, and 8% for mobile and mainline. These numbers are not meant to represent an optimum for maintenance expenditures but are broadly seen as being the minimum annual average expenditure on maintenance, below which the network's functionality will be threatened.

An argument can be made that since many developing countries have substantially underinvested in maintenance, we should also include an estimate for rehabilitation. Unfortunately, the data is simply not available to make such an estimate. Nevertheless, it

> **6** Depending on the choice of technology and the population density, the proportion will vary, but a decent rule of thumb could be that 60% of the investment cost is for generation, 30% for distribution, and 10% for transmission.
> should be noted that our estimates of overall investment needs are probably lower bound estimates since they do not include rehabilitation needs. On the other hand, they may be appropriate estimates of what will actually be spent, except that the resources that in an ideal world would go to maintenance will more likely continue to be used for rehabilitation.

**Table 7. Expected annual investment needs 2005-2010**

||Ne|w|Mainte|nance|To|tal|
|---|---|---|---|---|---|---|
||US$Mn|%GDP|US$Mn|%GDP|US$Mn|%GDP|
|_Byincomegroup_|||||||
|LowIncome|49,988|3.18%|58,619|3.73%|108,607|6.92%|
|MiddleIncome|183,151|2.64%|173,035|2.50%|356,187|5.14%|
|Highincome|135,956|0.42%|247,970|0.76%|383,926|1.18%|
|_Developing countriesbyre_|_gion_||||||
|EastAsia&Pacific|99,906|3.67%|78,986|2.90%|178,892|6.57%|
|SouthAsia|28,069|3.06%|35,033|3.82%|63,101|6.87%|
|Europe&CentralAsia|39,069|2.76%|58,849|4.16%|97,918|6.92%|
|MiddleEast&N.Africa|14,884|2.37%|13,264|2.11%|28,148|4.48%|
|Sub-SaharanAfrica|13,268|2.84%|12,644|2.71%|25,912|5.55%|
|LatinAmerica&Caribb.|37,944|1.62%|32,878|1.40%|70,822|3.02%|
|_Alldevelopingcountries_|233,139|2.74%|231,654|2.73%|464,793|5.47%|
|_World_|369,095|0.90%|479,624|1.17%|848,719|2.07%|

GDP deflator used is an average of the 2005-10 projections.

New investment needs are estimated to be approximately US$370 Billion per annum for the period 2005-10, amounting to nearly 1% of worldwide GDP. Another $480 billion (1.2% of global GDP) are needed for maintenance. The total resources needed are therefore approximately 2.1% of GDP, excluding any expenditure on rehabilitation or upgrading. Results for each country are presented in Annex n.<sup>7</sup> The burden for developing countries is much heavier, however, both because of their greater need for new investments and because of their much smaller resource base. Estimated needed new investment decreases with income - from a high of 3.2% of GDP for low income countries to a low of 0.4% of GDP, with a middle point of 2.6% for middle income countries. Maintenance follows a similar pattern, so that total resources needed are 6.9% in low income countries and 5.1% in middle income countries, for a developing country average of 5.5% of GDP.

Our investment estimates are similar to the results obtained elsewhere. The 1994 World Development Report estimated that developing countries spent on average 4% of GDP on

> 7Note however that we are much more confident about regional or income group averages than we are about individual country results. This type of approach is indeed much better suited to producing aggregate results, which usually are fairly accurate, than it is to producing individual country predictions. Thus, whereas we are reasonably confident on the overall estimates, _we do not recommend relying on individual country level estimates except in a very indicative manner._
12 investments in infrastructure. Traditionally, most of this was publicly funded: in the eighties for example, public investment in infrastructure was estimated at 4.3% of GDP in middle income countries (Easterly and Rebelo, 1993). This, most certainly included rehabilitation, upgrading, and probably even some maintenance, given that few government budgets in developing countries make a clear distinction between these three categories.<sup>8</sup> Within developing countries, there is also substantial regional variation from a low of 3% of GDP in Latin America to a high of 6.9% in South Asia and Eastern Europe.

In terms of sectoral allocation, three sectors (electricity, mobile phones and roads) will absorb four fifth of developing country and worldwide new investment. Electricity generation is likely to absorb about 30% of new and total investments. This number is somewhat higher than Easterly and Rebelo's (1993) finding that developing countries in the 1980s were spending about a quarter of their infrastructure investments on the power sector.<sup>9</sup>

**Table 8. Sectoral allocation of investments, new and total**

||Developing|countries|Wo|rld|
|---|---|---|---|---|
||New|Total|New|Total|
|ElectricityGeneration|32%|30%|30%|30%|
|Roads|17%|19%|31%|31%|
|Mobile|32%|27%|23%|20%|
|TelephoneMainlines|13%|14%|11%|11%|
|Waterandsanitation|6%|8%|4%|6%|
|Rail|1 %|2%|0%|2%|
|Total(%)|100%|100%|100%|100%|
|Total(US$ Mn)|233,139|464,793|369,095|848,719|

Mobile is expected to be the next most important expenditure item, absorbing another third of new and total investments in developing countries. This implies developing countries would spend about 0.9% of their GDP in new investments in mobile but up to 1.5% if maintenance is included.

Finally, roads are projected to require about 17% of new investment (19% of total investment) in developing countries amounting to 0.5% to 1% of GDP depending on whether maintenance is included. This compares well with the estimates of Ingram and Fay (1994) who calculated that on average developing countries spend about 0.8% of GDP on roads (which certainly does not include full funding for maintenance.) As to

> 8 Typically the distinction is made on the basis of the amount of resources needed - if the amount is small, it is included in the current budget; if the amount is large (as for periodic maintenance expenditure) it would be included in the capital budget.

> 9 At the time, most of the electricity sector in developing countries was in the public sector, so public investment would have represented the quasi totality of investment in electricity.
13 mobile phone, a newcomer in the infrastructure world, is expected to absorb about 32% of developing country new investments.

When including high income countries we see that new investment will be just as concentrated, with power, roads, and mobile absorbing 84% of resources. Mobile, however, will absorb relatively less resources.

Telephone mainline will absorb about 13% of developing countries investment, while water and sanitation together should require about 6%. Including maintenance we expect that water and sanitation should add up to about 2% of GDP. Note that this, as mentioned earlier, is not calculated in relation to some normative goal of water and sanitation coverage. It is however substantially higher than Easterly and Rebelo's (1993) findings that in the 1980s, public investment in water and sanitation in developing countries absorbed about 0.4% of GDP in middle income countries. As to rail, it is expected to absorb very little in new investments but about 2% of GDP in maintenance.

Overall it seems new investment composition is in fact quite different across income groups. We verify that with Figure 1 below which shows expected new investment composition across income groups. The power sector is most important in low income countries where we expect it to require about 36% of all new investments. Mobile dominate in middle income countries accounting for a similar share of new investment. Finally, in high income countries, roads are expected to account for nearly 60% of all new investments.

**Figure 1: New investment composition varies across income group**

<!-- Start of picture text --> 100%<br>90% 14-O.37 i ,<br>913137 7Y'. 1~~~~~~~~~~~~~~~~~~~~~~8%~~5 .P<br>00% ~~~~27 2%<br>40%<br>20%<br>Low lnoco,e l I, ir Kgh Sddl 1b<br>El3 Electricity Generation 0 Paved Road Length<br>a0 Mobile Lo Telephone Mainl ines<br>10.RaiLERoad Lenath E ae ~dSntto<br><!-- End of picture text -->
14 How will this affect overall composition of infrastructure stocks? It will not change dramatically, but continue along the trend established since the 60s of a gradual shift towards telecommunications and power, and away from transport, with road dominating transport more and more.

Figure 2. Changing infrastructure stocks per capita, developing countries 1960-2010 <!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>-- Elecric Generation Capacity .UTelepthone Mainlines -&- Paved Roads -Rail Routh Length -MobiIe<br><!-- End of picture text -->

**Table 9: The composition of infrastructure stocks in 2010**

||Developingcountries<br>2000|Developing countries<br>2010|
|---|---|---|
|Power|41%|39%|
|Roads|35%|32%|
|Telephone(fixed)|3%|5%|
|Telephone(mobile)|3%|8%|
|Waterand Sanitation|11%|10%|
|Rail|7%|_5%_|
||100%|100%|
|Totalvalue(US$Billion)|6,162|8,089|
15

# Conclusion

We developed a model to predict future demand for infrastructure, which performs very well in all sectors, even in water and sanitation where poor data usually makes estimation difficult. It should be noted that ours are estimates of _demand,_ rather than some absolute measure of _"need."_ We also estimate needed resources for maintenance based on what is considered the minimum expenditure necessary to maintain the integrity of a system, and predict total required resource flows to satisfy new demand and maintain service for existing stocks.

Our overall estimates do not include resources that might be needed for rehabilitation ( to make up for deferred past maintenance) or for upgrading. As such they are likely to be lower bound estimates. Nevertheless they compare well with other studies estimates, notably with data on public expenditure on infrastructure from the 1980s.

The investments needed should amount to about $ 465 billion per annum or 5.5% of developing countries' GDP over 2005-2010. Most of it will go to the telecommunications sector ($187 billion), followed by the power sector ($138 billion), and roads ($90 billion), including maintenance. Estimates for ports, airports and canals are not available, but since these types of infrastructure represent but a fraction of the total, it is unlikely that including them would change our total estimates.

This study is an interesting, albeit limited, first foray into trying to systematically estimate investment needs. Like many study of its kind, it is surely broadly accurate in the order of magnitude that it projects - notably concerning the inability of private investment to satisfy demand in the near future. This work would however greatly benefit from complementary studies, notably at individual country level.