metadata: authors: 0: author: Estache, Antonio
1: author: Kouassi, Eugene
count: Africa
docna: 0: docna: Sector organization, governance, and the
            inefficiency of African water utilities
docty: Policy Research Working Paper
subtopic: Labor Markets,State Owned Enterprise Reform,Public Sector Administrative & Civil
            Service Reform,Water and Human Health,Hydrology,Privatization,Water Supply and Sanitation Economics,Energy Privatization,Small Private Water Supply Providers,Energy Demand,Energy and Environment,Energy and Mining,Town Water Supply and Sanitation,Democratic Government,Economics and Finance of Public
            Institution Development,Public Sector Administrative and Civil
            Service Reform,De Facto Governments
teratopic: Water Resources
abstracts: cdata!: The authors analyze the determinants of
            the efficiency levels reached by twenty one African water
            utilities. They assess efficiency through the estimation of
            a production frontier for the sector in Africa. The
            efficiency estimates confirm much of the common perceptions
            from partial productivity indicators. They point to a great
            heterogeneity in the African water utilities'
            performances, the predominance of constant returns to scale,
            and the great rate of technological progress. And the
            authors show that the institutional capacity of the country,
            as well as its governance quality, are significant driving
            factors in the performance of each firm.
repnme: repnme: Sector organization, governance, and the
            inefficiency of African water utilities
display_title: Sector organization, governance, and
            the inefficiency of African water utilities
listing_relative_url: /research/2002/09/2026461/sector-organization-governance-inefficiency-african-water-utilities
topicv3: Water m1326522 137,Energy and Extractives m1326595 507,Water Supply m1326961 138,Privatization m1327236 1283,Production Frontier m1503947 2688
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Water Resources
origu: WBI
no_of_pages: 26
model_extractions: PoLcY RiESEARCI-I WORKING PAI'ERI 2890

# Abstract

Estacihe an1d Kouassi analyze thc deteriilnanits of the efficiency levels reached bv 21 African vwater utilities. They assess efficiency throLighi the estimationl of a production frontier for the sector in Africa. T1-he efficiency estimates confirm mucIh of the Commllonl perceptions from partial productivity indicators. They point to a great heterogeneity in the African water utilities' performanices, the preclomi nancc of constant returins to scale, and the great rate of technological progress. And the authors show that the institutionial capacity of the countrv, as well as its governanice quality, are significant driving factors in the performance of each firm.

Tshis paper-a product of the Governanice, Regulation, and Finance Division,<sup>WorldBanikInstitute-is part of alargereffort</sup> in the institute to promote the uliderstanidinig of regulatory issues. Copies of<sup>thepaperareavailahlefreefromiithe World</sup> B3ank, 1818 H Street NW, Washingtoni, DC 20433. Please contact<sup>GahrielaCheniet-Smilithl,room13-147,telephiolic202-</sup> 473-6370, fax 202-676-9874, email address gcheneit(PNorldbanik.org.<sup>P'olicyResearchWorkinlgPapers arealso postedon</sup> the Web at http://econ.worldhank.org. Antonio Estache **may** he contacted<sup>at aestache(0worldhank.org.September2002.</sup> (2 1 pages)

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# SECTOR ORGANIZATION, GOVERNANCE, AND THE INEFFICIENCY OF AFRICAN WATER UTILITIES<sup>1</sup>

Antonio Estache* _The World Bank Institute,_ and ECARES, Universite Libre de Bruxelles Eugene Kouassi

> _Universite de Codody,_ and _Resource Economics -_ West Virginia University

Keywords: Water utilities, unbalanced panel data, efficiency analysis, stochastic frontier models.

JEL Classification: C22, E23, E27 'We are grateful to I. Alikhani, A. Locussol, S. Perelman, L. Trujillo and all the participants to various seminars on regulation in West Africa for helpful discussions and suggestions.

> * Direct correspondence to: Antonio Estache, the World Bank Institute, WBI, 1818 H St NW, Washington, DC 20433., USA. Tel. (202) 458-1442 Fax. (202) 676-9874 Email: Aestache@worldbank.org.
# 1. INTRODUCTION

Africa faces increasingly critical resource constraints in its effort to<sup>extendwater</sup> services of acceptable quality to the vast majority of its people.<sup>(e.g.,seePouliquen,2000;</sup> Sandelin, 1994; Snell, 1998; World Bank, 1999). The inefficiency of<sup>waterutilitiesisoften</sup> identified as one of the major factors. in explaining the slow progress<sup>and themany setbacks</sup> in improving access to water and water distribution (e.g., see Schuebeler,<sup>1995;WorldBank,</sup> 1999). Yet, there is a surprising dearth of literature attempting to<sup>measuretheefficiencyof</sup> operators in a way that would allow economic regulators to introduce<sup>explicitly performance</sup> incentives in the regulation of the operators in African countries.<sup>Perhapsbecausethepartial</sup> productivity indicators, such as water losses or number of employees<sup>perconnection, have</sup> generally been so poor that radical operational reforms were easy<sup>topropose,muchof the</sup> attention of policymakers, donors and researchers seems to have<sup>focusedontheinstitutional</sup> and financing aspects of water sector reforms. The need to mobilize<sup>additionalresourcesfor</sup> water through fees and other modalities of financing and the potential<sup>for an increasedpublic-</sup> private partnership in the sector were particularly emphasized<sup>(e.g.,World Bank1999;</sup> UNDP-World Bank Water and Sanitation Program -West and<sup>CentralAfricaOffice,1998</sup> and Snell, 1998) The main purpose of this paper is to show that it is worth assessing<sup>morecarefullythe</sup> potential efficiency improvements that should result from the much<sup>emphasizedreforms.This</sup> would allow quantitative assessments of the potential improvements<sup>intheoverall useof</sup> inputs and a more analytical discussion of the optimal scale of<sup>operation.Bothpotential</sup> sources of efficiency gains could be set as targets for the restructured<sup>sectorsandgoa long</sup> way in cutting the financing requirements. This, in<sup>turn,impliesthatthe needfortariff</sup> increases may not be as high as sometimes argued for some of<sup>themarkets. Moreover,it</sup> implies that there is scope for regulatory supervision which ensures<sup>thattheefficiencygains</sup> are not simply turned into pure rent for the new operators but<sup>areeventuallysharedwith the</sup> consumers.

We assess the potential for efficiency improvements and the importance of<sup>the scaleof</sup> operation by estimating a production frontier for the<sup>region.Forlackofbetterdata,we</sup> estimate this production function from an unbalanced panel of<sup>data fora sample of 21African</sup> water utilities covering the 1995-1997 period. To contribute to<sup>thedesignofreforms,the</sup> 2
paper also quantifies the joint effect of various institutional sources of inefficiencies and in particular assesses the costs of the interactions between inefficiency and major institutional problems, in particular governance problems hurting many African countries.

The paper is organized as follows. Section 2 presents a brief overview on the African water utility sector. Section 3 discusses the analytical and conceptual framework and introduces the panel data method for unbalanced data analysis. Sector 4 explains the data and empirical model selection procedure. Section 5 presents the empirical results; Section 6 discusses the results. Section 7 concludes with some policy implications.

# **2. SOME STYLIZED FACTS** ON **AFRICA'S WATER SECTOR**

Currently, only 64% of Africa's urban population has access to safe water supply and 55% have access to sanitation. Of these, 14% have house connections and fewer have access to sanitation. The correlation of water coverage levels with income levels is clear as seen in Figure 1 but also more complex than many would expect since income levels explain only 40% of the differences in water coverage. This in turn suggests that there is more to improving access than waiting for growth to accelerate.

**Figure 1: Access to Water and Income Level**

<!-- Start of picture text --> 120 -<br>100-<br>0<br>80<br>o 60<br>40<br>o° 40 X K _--<br>0 20 -<br>0 500 1000 1500 2000 2500 3000 3500 4000<br>GNP/capita (1999)<br><!-- End of picture text --> 3
> One of the most obvious additional explanation for the differences in water coverage is the institutional arrangements adopted for the sector. Table 1 gives a snapshot of the

> organization of the sector in Africa during the period covered by this analysis. The data is collected from a survey of twenty one Africa water utilities. From a statistical viewpoint, it

> yields an unbalanced panel of twenty one utilities observed over the period 1995-1997 is

> representative of the total of one hundred and fifty water utilities in the region. The salient

> feature are that: (i) most utilities are in the public domain (85,71%); (ii) most of them are also

> water suppliers (77%); (iii) private sector participation in these utilities was very limited

> during the period analyzed (9.52%).

**Table 1: African Water Utility Firms 1995-1997**

|Country<br>|Water<br>**Utility**<br>|Typeof<br>Utility<br>|Areaof<br>Jurisdiction<br>|ScoreofActivity<br>|PrivateSector<br>Participation<br>|
|---|---|---|---|---|---|
|Benin<br>|SBEE<br>|Public<br>|Country<br>|WaterSupply<br>|No<br>|
|Burkina<br>|ONEA|Public|Country|WaterSupply and Sewerage|No|
|Faso<br>||||||
|CBte<br>'|SODECI|Private|Country|WaterDistribution|Leasing|
|dIvoire<br>||||||
|Ethiopia<br>|AA WSA<br>|Public<br>|Municipal<br>|Water Supply<br>|No<br>|
|Ghana|GWSC<br>|Public<br>|Country<br>|WaterSupply|No|
||CWA|Public|Country|Water Supply|No|
|Mauritius<br>||||||
|Morocco<br>|ONEP<br>|Public<br>|Country<br>|WaterSupply<br>|No<br>|
|Morocco<br>|RED<br>|Public<br>|Country<br>|WaterSupply<br>|No<br>|
|Namibia<br>|WM<br>|Public<br>|Province<br>|Water SupplyandSewerage<br>|No<br>|
|Niger<br>|SNE<br>|Public<br>|Country<br>|Water Supply<br>|No<br>|
|Nigeria<br>|KdSWB<br>|Public<br>|Region<br>|Water Supply<br>|No<br>|
|Nigeria<br>|KtSWB<br>|Public<br>|Region<br>|Water Supply<br>|No<br>|
|Nigeria<br>|BoSWB<br>|Public<br>|Region<br>|Water Supply<br>|No<br>|
|Nigeria<br>|EdSWB<br>|Public<br>|Region<br>|Water Supply<br>|No<br>|
|Senegal<br>|SDE<br>|Private<br>|Country<br>|Water Supply<br>|Affermage<br>|
|South<br>|UMGENI|BothPublic<br>|Region|WaterSupplyandSewerage|No|
|Africa<br>||andPrivate<br>||||
|South<br>Africa<br>|RAND-W<br>|Public<br>|Region<br>|WaterProduction<br>|No<br>|
|Togo<br>|RNET<br>|Public<br>|Country<br>|Water Supply<br>|No|
|Tunisia<br>|SONEDE<br>|EPIC2<br>|Country<br>|Water Supply<br>|No<br>|
|Uganda<br>|NWSC<br>|Public<br>|Country<br>|Water Supply<br>|No<br>|
|Zambia<br>|LMSC<br>|Public<br>|Province<br>|Water Supply<br>|No|
|Notes: aPu|blicenterpri|sebutwithap|rivatizedmanag|ement.||

What the table does not tell (partially because there is little specific data on this topic), is that the organization of the sector is shifting towards a community-driven development 4
approach, ignoring the basic traditional assumption<sup>inthesectorthateconomiesofscale</sup> justify large utilities in order to reduce costs<sup>by making themost of economiesof scale.This</sup> preference for smaller scale operations is<sup>clearin rural andperi-urbanareasbutitisalso</sup> increasingly important in urban areas<sup>whichcontinuetobe underthecontrolofmore</sup> traditional water utilities. Indeed, even for these<sup>utilities,the need for constantinterfaceswith</sup> the users to ensure that supply meets the<sup>demandandwillingnesstopayleadstoactual</sup> payments is now recognized as a necessary,<sup>although not a sufficient, conditionfor success.</sup> The fact that utilities follow the demand orientation<sup>pathadoptedforrural and peri-</sup> urban approaches also reflect a desire to increase<sup>accountabilitywiththehopethatitwill</sup> indeed improve and accelerate access<sup>towaterservices.Thiswould implythat lower</sup> corruption or governance problems levels are<sup>expectedto beassociatedwith bettercoverage</sup> levels. Figure 2 provides a naive confirmation<sup>of this intuition (with thenotableexceptionof</sup> Ethiopia where geological conditions offset the<sup>benefitof a good governancescore).</sup> Figure 2 Correlation between Water Coverage<sup>and GovernanceProblemsn</sup> <!-- Start of picture text --> 120<br>¢ 40<br>60<br>20 40 O' Ml ERoN'i<br>0 5 10 15 20<br>Perceived corruption (3 = very corrupt; 16= clean)<br><!-- End of picture text --> This quick, and somewhat naive, overview of<sup>the sectorshowsthatthefrustrated</sup> demand is likely to be great as revealed by<sup>thelow coverageratesbutalsothatthe</sup> _5_
organizational structure of the sector and governance quality are likely to be important factors to account for. A more subtle third message is that it may be useful to have a more quantitative view of the supply side of this market to ensure that cost are minimized. Finally, it is also important to check the extent to which the institutional arrangements contribute to the reduction of these costs and hence to the better use of the resources available to expand and accelerate the investments needed to meet the frustrated demand.

# 3. ANALYTICAL AND CONCEPTUAL FRAMEWORK

In spite of the diversity of experiences with water sector provision in Africa, the overview presented in section 2 reveals the need to tackle four main policy issues. The authorities responsible for the water sector need to be able to: (i) compare as rigorously as possible the firms they are responsible for with similar firms in the region as, implying that an efficiency ranking of firms would be useful, (ii) assess fully potential efficiency gains for individual firms from better joint uses of all inputs, (iii) assess the efficiency costs of ignoring potential scale economies by focusing on smaller scale operations and (iv) assess the efficiency effects of governance problems.

While partial productivity indicators generally prove to be useful instruments to get a quick overview of the performance of any enterprise, they can often be misleading when comparing various firms. Not all partial indicators necessarily yield the same ranking and this is why we need to have a joint assessment of the effects of the key inputs if robust ranking are expected. Second, when setting targets for operational improvements and in particular cost reductions, once more, we need to account for the joint effect of all inputs rather than for the effects specific inputs and to come up with a single sector or firm specific figure which will serve as a target. Third, in view of the institutional changes which seem to spreading throughout the region and resulted in the decision to go for smaller scale operations, we also need to have an instrument that allows a fair assessment of the opportunity cost in terms in terms of economies of scale to be tapped. Finally, the limits of what can be done when corruption interferes with the process of reform must be recognized and assessed as well if targets are to reachable. The focus on technical efficiency as defined by economists allows us to address all of these issues.

6
## **3.1 Technical Efficiency: Concept and Measurement**

The measurement of efficiency in the water sector is complicated by the nature of the production process (e.g, see Sengupta and Monsour, 1986; Eglal et al., 1996; Hunt and Lynk, 1995; Bishop and Thompsom, 1992; and Ashton, 2000). Complications arise from the fact that water production is a function of many variables, many of which are exogenous to the water sector - for example household income, chemical products prices, and intrahousehold decisions etc.

Farell (1957), drawing upon the work of Debreu (1951) and Koopmans (1951), introduced a measure of productive efficiency that avoids the problems associated with traditional average productivity measures (ratios). He proposed that efficiency relative to a best-performance frontier determined by a representative peer group. In the Farell framework, a firm's efficiency is measured relative to the efficiency of all other firms in the industry, subject to the restriction that all firms are on or below the frontier. A firm is regarded as technically efficient if it is operating on the best-practice production frontier in the industry. The degree of technical efficiency is given by the ratio of the minimal input required to the actual input use, given the input mix used by the firm. It tells the utility manager the amount by which all inputs could be reduced without a reduction in output. Technical efficiency takes the values between zero and one (0 < TE, < 1). Technically inefficient production units have a TE1 value less than one, while the efficient ones have a TEi value of 1.

As seen in the foregoing discussion, empirical estimates of efficiency measures involve two steps: (i) estimation of the frontier and (ii) calculation of the individual water utility deviations from the frontier. Currently, there are two approaches used in estimating frontiers (see for instance, Coelli et alt 1998 or Coelli et alt. 2001). These are the _parametric_ approach, which relies on econometric methods, and the _non-parametric_ approach, which involves _linear programming_ techniques. The _stochastic_ and _parametric_ frontiers are considered in the present study, and computed through panel estimation techniques.

# **3.2 Stochastic Frontier Models and Unbalanced Panel Data**

Our empirical work focuses on the estimation of a stochastic frontiers for a panel of data. This implies, among other problems, having to make assumptions about the distribution 7
of the technical efficiency, also accepting that the distribution of the technical efficiency of a firm and the regressors (inputs) are independent. This assumption is very restrictive because it is reasonable to think that if the firm knows its inefficiency level, the selected quantities of the inputs can be affected. The statistical analysis of econometric models with panel data allows applications to the estimations of frontier models to be developed and, with them, one can partially solve the estimation problems<sup>2</sup> .

Schmidt and Sickles (1984) established the basic idea and Comwell extended it in 1990 (Comwell et al., 1990). The model suggested was as follows: if we have a data panel composed by T temporal observations for N productive units, we can represent the technology with the following production function (we assume a linear technology for simplicity): _K yj, =aoc+ I xki,C,k+ ej,, i = N; ,.......... t =1 k .. =1 ,T; (1).. ,K_ **_k=1_** where

**_-'i =_ )** **_- u,_**

where y denotes the output, **Xk** represents the kth inputs and Pk stands for the output elasticity with respect to the kth input. Finally, s is a composed error terms; **jit** is a disturbance term with the usual characteristics [iid, N(0,&2,)] that captures the random factors that can explain the divergence between the observed and the potential output enumerated above and ui captures the time-invaring latent individual effects. Then, the ui's are positive and iid with mean **t** and variance o2u and they are independent of uit. That is, [ui % D(gL, c&2u)].

> Therefore the parameter **t** represents the latent average inefficiency level of technology. We can also estimate (or not) a particular distribution for ui, and we can assume (or not) that inefficiency is correlated with the inputs. The technical efficiency measurement of an ith firm will be obtained from

_ET = e-u_ This model is a simple generalization of the stochastic frontier models corresponding to the usual literature of panel data models with individual effects. The only difference with the standard panel data models is that in Equation 1 the individual effects (u;) are one-sided.

> **_2_** Simar's article (1992) constitutes a good survey of the frontier methodology with panel data, with an

> application of the different methods and estimators proposed.

8
Following Schmidt and<sup>Sickles (1984) the model can be managedin the followingway.Since</sup> we know that E(uj) = 11 **>**<sup>**0,**wecan define:</sup> a _=a- p_ (4) u, = u,-P and consequently u*j is<sup>independentand identicallydistributed withE(u*i)= 0.Therefore,the</sup> model (1) can be expressed<sup>as follows:</sup> _K yi,=a +Zx,J,Jk+vi_-ui_ **_k=1_** Now, the two errors have<sup>mean zeroand therefore we can directlyapply all the results</sup> of panel data models. As<sup>aresult, wecanusethedifferent estimatorsproposedin the</sup> econometric literature of<sup>paneldata,thefixedeffectsmodelortherandomeffectsmodels.</sup> The choice between these<sup>twomodels,asiswell known, willdependon the possible</sup> correlation between the<sup>individualeffectsandtheobservable explanatory variables,inthis</sup> case, the inputs (xk).

If this correlation exists,<sup>the parametersof the model Equation5 can be estimatedwith</sup> the within groups (WG)<sup>estimators. Theindividualeffects canbe definedas</sup> as = a - ui = a - ui and<sup>their estimation will beobtainedfromthe withinestimatorsof the</sup> ^ _WG_ parameters of the model<sup>**_(Bk_**</sup> From this estimation of<sup>theNindependent terms</sup> _al,a2,...aN_ an estimation of the independent term and<sup>thelevelof(in)efficiency(ui)canbeobtained froma simple</sup> procedure: a = max(ai) _ui_ = a-a, (8) _ET7_ = _e-U'_ 9
This transformation is necessary in order to obtain positive values for all the ui. It is a translation of the frontier suggested by Greene (1980). With this operation the technical efficiency index of the most efficient firm will be equal to one3. **3**

> The second way to estimate Equation 5 proposed in the panel data literature is the random effects models Generalized Least Squares (GLS) estimator. These models must be used when unobservable individual effects are not correlated with the regressors because they are more efficient than the within estimators. Thus, the problem of this estimator lies in the necessity to assume that the individual effects (efficiency level of the firms) and the explanatory variables (inputs) are not correlated. That is, in this case, one does not admit the possibility that if the firm know its inefficiency level it will be conditioned to choose the quantities of inputs in its productive process.

^ **_GIs_** From this GLS estimation of the parameters _1k_ , one can recover the individual effects from the residuals, and with them one makes the same operation as in the fixed effects models to recover the technical efficiency index. Note that this procedure also gives us an estimation of **o**<sup>**2**</sup> **U .**

# **4.** EMPIRICAL SPECIFICATION

> We specify a Cobb-Douglas production frontier. Output is measured by the yearly water production, and labor, capital and materiel quantities are the main inputs considered.

> Other variables of interest are: the energy cost and the number of connections. Taking logarithms from this Cobb-Douglas production function we have:

**_y,=_** _a +&ku_ **_+_** _flmm+_ **_flLhu_** _+ PECeCgi + I3PSPsj + acucu, +_ **_-t_** _+ R_ (10)

g,, **=** **_v,,a-u,,_** (1 1) where y, k, m and h represent, respectively, the logarithms of the real output, the real capital stock, the materials in constant prices, the hours of work, the energy costs in constant prices and the number of connections; the coefficients **py,** PM, **PL** , PEC and pNC are the output

**_3_** We can find a problem of inconsistency as theory tells us that **_cti_** estimations will be consistent if T **om.**

10
elasticities of inputs, and the sum of them gives us the elasticity of scale, which indicates<sup>the</sup> returns to scale. There is also the variable cu which is a measurement of capacity<sup>utilization;</sup> the role of this measure is to introduce links with input flows, and t is a variable<sup>added here to</sup> measure the Hicks-neutral technical change, that is common among firms in the same<sup>sector.</sup> The composed error term combines sit, which is assumed to be normally distributed<sup>and</sup> uncorrelated with the ui and with the explanatory variables, with ui, which captures<sup>the level</sup> of inefficiency of the water firm and so it will be greater or equal to zero.

The main justifications for the selection of this specification of a water<sup>production</sup> function for Africa are the following. First, in most African countries, the production<sup>cost</sup> structure is not know or the degree of uncertainty surrounding cost structures is<sup>relatively</sup> high, therefore it is better to estimate a production function rather than a cost<sup>function.</sup> Second, in most classical papers, capital and length of network are two key variables;<sup>whilein</sup> the present case, those two variables are highly correlated (multi-colinearity issue).<sup>That</sup> means that one of these two variables should be used but not both of them. Third,<sup>inthe</sup> specific context of African countries, the number of connections is a very important<sup>variable</sup> since the average size family is between 7 and 9 for some African countries and even<sup>more for</sup> others (free rider issue). Finally, the variable t should capture technological impact within<sup>the</sup> water industry in Africa.

As for the mains issues relating to the estimation procedure, they can be<sup>summarized</sup> as follows. Since we have an unbalanced panel of data and water firms are of diverse<sup>size</sup> (small, medium and large), it is unlikely that the model would pass a test of homoskedastic variances. Even logarithmic specifications postulating percentage variation<sup>across cross-</sup> sectional units are likely to be heteroskedastic, because observations for lower output<sup>firms</sup> are likely to evoke larger variances [e.g., see Kumbhakar and Bhattacharyya,<sup>1996;Baltagi</sup> and Griffin, 1988]. Moreover, the estimation of a seemingly unrelated regression<sup>modelwith</sup> an unbalanced panel of data set gives rise to some estimation problems [e.g., see<sup>Baltagi,1995</sup> and Schmidt, 1977]. For different time periods, there are different number of units<sup>(i.e.,firms</sup> drop out without replacement) which change the ordering of observations. Since<sup>it dictatesthe</sup> structure of the variance-covariance matrix [e.g., see Baltagi, 1985], the ordering is important.

Furthermore, as N -+ Xo we can consistently separate the intercept a from the<sup>one-sidedindividualeffects.</sup>
Therefore, the process of estimation proposed is the following:<sup>First,weestimatethe</sup> model Equation (10) with the Within Group (WG) estimator. This<sup>estimator isconsistent</sup> when the individual effects (inefficiency) are correlated with the other<sup>variablesin the model</sup> (productive factors) or when this correlation does not exist. Second,<sup>oneobtainsGeneralized</sup> Least Squares (GLS) estimators. This estimator is more efficient than<sup>theWG, whennoneof</sup> the variables are correlated with the individual<sup>effects.Ifthis correlationexists,WG</sup> estimation is required. To determine the most suitable<sup>model,aHausmantestto decide</sup> whether to use one estimator or the other is provided.

Another problem presented in the estimation of a production<sup>functionisthe possible</sup> endogeneity of the explanatory variables. In general, one<sup>canexpectthatlaborinput may be</sup> simultaneously determined with output. In order to<sup>takethisproblem intoaccount,the</sup> instrumental variable (IV) estimation of model Equation<sup>10 is also presented.The instruments</sup> used in these estimations are based on energy costs (contemporaneous<sup>or one-periodlagged).</sup> To check the endogeneity of labor a Hausman simultaneity test<sup>is also provided.</sup> Additional problems exist with this last estimation procedure:<sup>forexample,thereisa</sup> question as to whether one should use differences or levels<sup>asinstruments.Arellano(1989)</sup> gives evidence that the latter is preferable. More recently,<sup>Ahn and Schmidt(1993)observed</sup> that the IV estimator neglects quite a lot of information<sup>and is therefore inefficient.They thus</sup> proposed a more general estimator based on GMM.

# 5. **RESULTS**

The results summarized here provide an answer to each<sup>oneof thepolicyconcerns</sup> identified earlier. In addition, they allow to point to more<sup>structuralissues in the sectorthanks</sup> to an analysis of the shifts of the frontier during the period of<sup>analysis.</sup>

# **5.1 A synthetic indicator of Africa's water utilities**

Table 2 displays the estimated coefficients and statistical<sup>significancetests.In</sup> addition, we also present the Hausman test result<sup>todiscriminateamongmethodsof</sup> estimation, based on econometric contrasts. We start by<sup>estimating Equation (10)based on the</sup> WG estimator. Then we apply the statistical tests on<sup>theresiduals:testsforautocorrelation,</sup> tests for heteroskedasticity, tests for multicollinearity, tests<sup>for normalityetc.Thediagnostics</sup> 12
clearly indicate, as expected, a serious problem of heteroskedasticity. Second, as suggested by Greene (1980), we apply a transformation to the original data and re-estimate the model in order to get the GLS estimates. The results are very satisfactory. Out of six explanatory variables, four are significant. Capital input is not significant across all the estimations and has an unexpected sign. The explanation may be that the selected functional form is not adequate; and/or that there is a great heterogeneity among firms within the African water industry. It may also reflect to trend to focus more on small scale operations discussed earlier.

Results confirm the non-endogeneity of labor as seen from the Hausman simultaneity test. The estimation with the GMM has given similar results. Note also that the GLS is the favorite due to the absence of correlation between the individual effects and the explanatory variables confirmed by the Hausman test.

Table 2: Estimation Results and Diagnostic Tests

|Dependent|variable:Totalwaterp|roduction||
|---|---|---|---|
|Regressor<br>Specificati<br>|on(standard deviation<br>|*<br>||
|WG|GLS|GMM|IV|
|Constant<br>1.7128<br>(0.133)|0.001a<br>(0.125)|1.7358<br>(0.145)|1.775a<br>(0.135)|
|Capital<br>0.0017<br>(0.00037)|0.00016<br>(0.00034)|0.00015<br>(0.00033)|0.00015<br>(0.00034)|
|Materials<br>0.00092a<br>(0.00031)|0.00013a<br>(0.00030)|0.00014a<br>(0.0004)|0.00015a<br>(0.0005)|
|Labor<br>0.0016a<br>(0.00046)|0.0017a<br>(0.00044)|0.0016a<br>(0.00045)|0.0018a<br>(0.00047)|
|EnergyCosts<br>-0.00006<br>(0.00018)|-0.00007<br>(0.00017)|-0.00006<br>(0.00019)|-0.0008<br>(0.0002)|
|Technology<br>-0.00037<br>(0.00039)|-0.00037<br>(0.0004)|-0.00037<br>(0.0005)|-0.00039<br>(0.0006)|
|<br> <br>|<br>Diagnostics:<br>|<br>||
|DegreeofFreedom<br>31|31|31|31|
|_~**2**<br>0.38|0.39|0.27|0.28|
|S.e.e<br>2.30%|2.11%|2.40%|2.43%|
|Error<br>auto-1.81<br>correlation***|1.86|1.83|1.79|
|Durbin'h'||||
|LM(1)<br>F6,31 =0.63|0.54|0.72|0.81|
|ARCH(1)**<br>F(1,68=0.05|0.02|0.04|0.05|
|Normality<br>Z(2)=0.76|0.75|0.77|0.78|
|Reset(1)<br>F(153)**=**0.22|0.18|0.25|0.27|
||HausmanTest|||
|x2(5)<br>6.86|1.21|2.23|NO|
|<br> <br>Notes:*'t'statistics are derivedusingheterosce<br>**the errors auto-correlation tests andtheARCH|<br>dastic-consistent estim<br> tests**are**alladjusted for|<br>atesofstandard-errors.<br>gaps.||

13
For the sample analyzed and the period<sup>covered,theaverageperformance is only54%which</sup> is not great and the standard deviation<sup>is.19.Thetop performersscorehigh at85and 83%which is</sup> close to three times the score of the bottom<sup>performers who score between30 and35%.</sup>

# 5.2 Is Small Costly?

Table 3 reports the elasticity of scale<sup>and therateoftechnicalprogress withinthe</sup> water sector. These two variables<sup>areimportantto theextent thatthey providesomeinsights</sup> on the trade-offs between a CDD<sup>approach towatermanagementandautilitiesbased</sup> approach. The scale indicator suggests<sup>thatconstantreturntoscaleprevailsin the WSSUsin</sup> African water industry. This implies<sup>thatthereisnomajor reasontoworry forthecosts</sup> consequences of the leakage of clients<sup>from utilities towardssmaller scale operations.</sup> The second indicator is the rate<sup>of technical progressreveal.Itturnsoutthatthe</sup> impact of technology is very limited<sup>in thecontextof WSSUsinAfricanwater industry</sup> during the period under analysis.<sup>Suggesting oncemorethatthecost of thediversificationof</sup> water supply providers is not<sup>costlyatleast in termsof notbenefiting from technological</sup> improvement often expected from<sup>largeutilities. Thislatterresultshould be takenwith</sup> caution due to the sample size.

**Table 3: RELEVANT PARAMETERS**

|**Country**|**Water**<br>**Utility**|_Elast_|_icityo _|_fScale_||**Rat**|**eTechnic**|**alProgress**|**(%)**|
|---|---|---|---|---|---|---|---|---|---|
|||_WG_|**GLS**|**GMN**|**IV**|_WG_|**GLS**|**GMN**|**IV**|
|Benin|SBEE|0.55|0.51|_0.55_|0.48|0.3b|O.lb|0.4b|0.3b|
|Burkina Faso|ONEA|0.43|0.44|0.47|0.40|_0 _<sup>_.5b_</sup>|0.4b|0.3b|0 <sup>.</sup>8 <sup>b</sup>|
|Coted'Ivoire|SODECI|0.44|0.41|0.40|0.39|0.2b|0.3b|0.3b|0.9b|
|Ethiopia|AAWSA|0.33|0.30|0.30|0.31|0.4b|o.gb|o.8b|0.7b|
|Ghana|GWSC|0.68|0.70|0.70|0.71|1.7|1.8|1.8|1.9|
|Mauritius|CWA|0.53|0.51|0.51|0.52|0.3b|0.4b|0.4b|0.3b|
|Morocco|ONEP|0.93a|0.98a|0.99a|0.98a|2.1|2.0|1.8|1.8|
|Morocco|RED|0Q94a|0.91a|0.92a|0.96a|1.8|2.0|1.9|1.9|
|Namibia|WM|0.55|0.48|0.46|0.44|0.4b|0.3b|**0.2b **|0.3b|
|Niger|SNE|0.34|0.33|0.31|0.35|0 <sup>.</sup>5<sup>b</sup>|0.7'|0.7b|0.7b|

14
|Nigeria|KdSWB|0.61|0.60|0.64|0.71|0<sup>.6b</sup>|0.7b|0.9b|o.gb|
|---|---|---|---|---|---|---|---|---|---|
|Nigeria|KtSVTB|0.47|0.44|0.45|0.48|0.4b|0.4b|0.4b|**0 **<sup>**.**</sup>**5**<sup>**b**</sup>|
|Nigeria|BOSWB|0.48|0.47|0.48|0.41|**0.5b**|0<sup>.</sup>5<sup>b</sup>|0.8b|0.8b|
|Nigeria|EdSWB|0.39|0.39|0.33|0.35|0.8b|0.9b||0.gb<br>o.9b|
|Senegal|SDE|0.41|0.40|0.41|0.41|0.70b|06b|_o.5b_|0.4b|
|South Africa|TMGENI|0.97a|0.98g|**1.04a**|1.088|3.1|3.0|3.1|3.3|
|SouthAfrica|RAND-W|0.96a|**097T**|1.038|1.09a|2.4|2.3|2.3|2.5|
|Togo|RNET|0.31|0.30|0.30|0.33|1.1|1.1|_1.5_|_1.5_|
|Tunisia|SONEDE|0.88a|0.868|0.86a|1.Ola|1.7|1.8|1.8|1.9|
|Uganda|NWSC|0.76|0.77|0.78|0.79|1.2|1.2|1.0|1.3|
|Zambia|LMSC|**1.01a**|_1.05_a|_1.058_|1.068|1.5|1.4|1.3|1.2|

# **5.3 Do Institutional factors such a ownership and governance matter?**

Recent studies have shown that institutional factors at the discretion of the management as well as environmental factors beyond the control of managers or regulators affect water efficiency (e.g, see Ferrier and Valdmanis 1996, Valdamnis 1992, Ozcan and Luke 1993, Rosko et al. 1995). Some of the factors that influence the efficiency of water utilities cited in the literature are: corruption (various indices), governance (various indices) etc. This can be tested from the results obtained here.

> The efficiency scores of water utilities are examined using a _censored tobit model_ to

> identify factors influencing inefficiency. The environmental variables are excluded as their numbers are not sufficiently large to undertake a multivariate analysis.

> In the tobit model, for computational convenience it is preferred to assume a censoring point at zero (e.g, see Greene, 1993).

Formally, the tobit model is defined as follows: Y= ii+ (i Y = _Y; f Y'_ > 0 (12) Y= =Oif _y;_ <O where ij _-_ N(O, a2), and yj is the observed inefficiency score; _15_
I is a k x 1 vector of unknown parameters; xi is a k x 1 vector of explanatory variables.

The empirical model, therefore, takes the following form: _INEFF = 0 +/3,CORRflDEX +/ 2 GOVERIVDEX +/3 3DUM + 5 (13)_ where: INEFF is the inefficiency score; CORRINDEX is the corruption index; GOVERNINDEX is the govemance index; DUM is a dummy variable: = 1 if the water utility is private; = 0 otherwise. Statistical analyses are performed using STATA 5 statistical software (Statacorp 1997).

Results are displayed in Table 7. An important feature of the results is that institutional variables are statistically significant at the five percent level; their signs are also as expected. An interpretation of these results corroborate the fact that corruption is negatively linked to efficiency while govemance is positively linked. As a consequence, water utilities should also focus on institutional variables when trying to improve their efficiency scores.

**Table 5: Inefficiency and Institutional Variables**

|Independent variable|Coefficient|Standarddeviation|
|---|---|---|
|Constant|1.989**|0.508|
|Corruption|0.0344***|0.048|
|Governance|0.041**|0.0247|
|Dummy(Privatelyoperated=1)|-0.028**|0.015|
|LR tes<br>|35.28***||
|McFadden's**R**<sup>**2**</sup>|0.348||

Notes: Numbers in parentheses are standard errors. Single, double, and triple asteriks **(*)** denote significance<sup>atthe</sup> 10%, **_5%_** and 1%, respectively.

A final interesting result is the effect of ownership on the efficiency of the firm.. In the present context, the dummy variable which captures the effect of privatization is statistically significant at the five percent level. In terms of policy implications, this suggests that privatization has had an impact on water utilities in the African context. This is in contrast to the results found by Estache and Rossi (2001) for Asia where no significant difference was found.

16
## **6. Conclusions**

The results of this study provide preliminary empirical evidence on the performance of water utilities in many African countries. The findings suggest that many of the water utilities operate technical efficiency levels well below a best-practice frontier that is determined by the relatively efficient ones from the group. Only about 12.9 per cent of the water utilities operate efficiently as compared to their peers.<sup>4</sup> This finding supports the commonly held view that Africa's water sector operate at unacceptable levels of technical inefficiency but may surprise some by the extent of the problem.

The policy implications can be summarized as follows. The improvement in the efficiency of the sector should go a long way in financing the need to improve access and/or quality of water production and distribution. Continuing the public or private financing of the sector without significant efficiency improvement is a major waste of scarce resources in the region. Efficiency savings exceeds revenue from user fees which implies that average tariff levels continue to be too high as compared to what they would be if firms were operated efficiently. The poverty alleviations implications are obvious. Water should become more accessible and more affordable with major improvements in the operation of the sector and private operators have so far been able to work in that direction, even if they did not operate the best performing companies during the period under study.

The main challenges are however not in the water sector. Governance issues and the weakness of institutions have been and continue to contribute to explain a large share in the excess of costs. These problems are just as important as the ownership debate and need to be addressed as well.

# REFERENCES

# [1] Ahn S, and Schmidt PJ (1995) Efficient Estimation of Models for Dynamic

Panel Data. Journal of Econometrics, 68: 5-28.

[2] Aigner DJ, Lovell CAK, and Schmidt PJ (1977) Formulation and Estimation of

> Stochastic Frontier Production Function. Journal of Econometrics, 6: 21-37.

> Bear in mind that stochastic and parametric methods do not give a measure of absolute efficiency and that the efficiency scores only reflect the performance and production technology of the group. It is possible that the addition of more water utilities in the sample or increasing the panel is likely to increase the number of inefficient water utilities.

17
- [3] Arellano, M (1989) A Note on the<sup>Anderson-HsiaoEstimatorfor Panel Data.</sup> Economics Letters, 31: 337-341.

- [4] Ashton, JK (2000) Cost Efficiency in<sup>the UK's Water and SewerageIndustry',</sup> Applied Economics Letters, 7: 455-458.

- [5] Baltagi BH (1985) Pooling Cross-Sections<sup>With UnequalTime-SeriesLengths.</sup> Economics Letters 18, 133-146.

- [6] Baltagi BH (1995) Econometric Analysis<sup>of Panel Data.New-York: JohnWileyand</sup> Sons, 1995.

- [7] Baltagi BH, and Griffith JM (1988)<sup>A GeneralizedError Component Model with</sup> Heteroskedasticic Disturbances. International<sup>Economic Review29,745-753.</sup>

- [8] Battese, GE, and Coelli TJ (1988). Prediction of<sup>farm-leveltechnicalefficiencieswith</sup> a generalized frontier production and panel<sup>data.Journalof econometrics38, 387-</sup> 399.

- [9] Battese, GE, and Coelli, TJ (1992). Frontier production<sup>functions,technical</sup> efficiency and panel data: with application<sup>to paddyfarmersin India.Journal</sup>

- of productivity analysis 3, 153-169.

- [10] Bauer PW, Berger AN, Ferrier GO, and<sup>HumphreyDB (1998)Consistency</sup> Conditions for Regulatory Analysis of<sup>FinancialInstitutions:A Comparisonof</sup> Frontier Efficiency Methods. Journal of<sup>Economicand Business,50:85-114.</sup>

- [11] Bhattacharyya A, Harris TR, Narayanan R, and Rafflee<sup>K (1995)Technical</sup> Efficiency of Rural Water Utilities. Journal of Agricultural<sup>and Resource</sup> Economics 20, 373-391.

- [12] Bishop M and Thompson D (1992) Regulatory Reform<sup>and ProductivityGrowthin</sup> the UK's Public Utilities', Applied Economics, 24:<sup>1181-1190.</sup>

- [13] Bravo-Ureta BE, and Rieger L (1991)<sup>Dairy FarmEfficiency Measurementusing</sup> Stochastic Frontiers and Neo-Classical Duality.<sup>AmericanJournalof Agricultural</sup> Economics 10: 421-428.

- [14] Chamberlain, G (1980) Analysis of<sup>covariancewith qualitativedata.Review of</sup> Economic Studies 47, 225-238.

- [15] Coelli, TJ and S. Perelman 1998. A comparison<sup>of parametricand non parametric</sup> distance functions: with application to<sup>European railways.EuropeanJournal of</sup>

18
Operations Research 117: 326-339.

> [16] Comwell C, Schmidt P, and Sickles RC (1990) Production Frontiers with Cross

- -Sectional and Time Series Variation in Efficiency Levels. Journal of Econometrics, 46: 185-200.

- [17] Djerrari, FM, and Janssens, JG (1998) Overview paper on Institutional Options For Urban Water and Sanitation Services in Africa', WUP, Abidjan.

- [19] Eglal R, Rathgeber E and Brooks DB (1996) Water Management in Africa and the Middle East. Edited by Eglal R, Rathgeber E and Brooks DB

> [17] Farell MJ (1957) The Measurement of Production Efficiency. Journal of Royal Statistical Society, Series A, 120: 253-281.

> [18] Featherston AM, Langemeier MR and Ismet M (1997) A Non-Parametric Analysis of Efficiency for a Sample of Kansas Beef Cow Farms. Journal of Agricultural and Applied Economics, 29: 175-184.

> [19] Ferrier GP, and Lovell CAK (1990) Measuring Cost Efficiency in Banking-

- Econometric and Linear Programming Evidence. Journal of Econometrics, 46: 229-245.

> [20] Forsund FR, Lovell CAK, and Schmidt P (1980) A survey of Frontier Functions and of their Relationship to Efficiency Measurement. Journal of Econometrics, 13: 5- 25.

- [21] Greene WH, (1980) Maximum Likelihood Estimation of Econometric Frontier Functions. Journal of Econometrics, 13: 27-56.

- [22] Greene WH, (1997) Frontier Production Functions. In M. Pesaran and P. Schmidt, Handbook of Applied Econometrics: Volume II: Microeconomics. London: Blackwell Publishers.

> [23] Hausman, JA and Taylor WE (1981) Panel Data and Unobservable Individual

> Effects. Econometrica 30, 34-53.

> [24] Hunt LC, and Lynk EL (1995) Privatisation and Efficiency in the UK Water Industry: An Empirical Analysis. Oxford Bulletin of Economics and Statistics 57, 371-388.

> [25] Jondrow J, Lovell CAK, Materov IS and Schmidt P (1982) On the Estimation of

> Technical Inefficiency in the Stochastic Frontier Production Function Model.

19
> Journal of Econometrics, 19: 233-238.

- [26] Kumbhakar, SC, and Bhattacharyya A (1996) Productivity Growth in Passenger-Bus Transportation: A Heteroskedastic Error Component Model with Unbalanced Panel Data. Empirical Economics, 21: 557-573.

> [27] Meeusen W, and van den Broeck J (1977) Efficiency Estimation from Cobb

- -Douglas Production Functions with Composed Error. International Economic Review, 18: 435-444.

- [28] Mundlak Y (1978) On the Pooling of Time Series and Cross Section Data. Econometrica 46, 69-85.

- [29] Perelman S, and Pestieau P (1988) Technical Performance in Public Enterprises European Economic Review, 32: 432-441.

- [30] Sanchez PC, and Villarroya JM (2000) Efficiency, Technical Change and Productivity in The European Rail Sector: A Stochastic Frontier Approach. International Journal of Transport Economics, 27: 55-76.

- [31] Schmidt P, and Lovell CAK (1979) Estimating Technical and Allocative Inefficiency Relative to Stochastic Production and Cost Frontiers. Journal of Econometrics, 9: 343-366.

- [32] Schmidt P, and Sickless R. (1984) Production Frontier and Panel Data. Journal of Business and Economics Statistics, 2: 367-374.

- [33] Sengupta JK, and Monsour K (1986) Efficiency in Water Allocation under stochastic Demand. Applied Economics, 18: 37-48.

- [34] Seale, J.L., Jr. (1990) Estimating Stochastic Frontier Systems With Unbalanced Panel Data: The Case of Floor Tile Manufactories in Egypt. Journal of Applied Econometrics, 8: 59-74.

[35] Shepard RW (1970) Theory of Cost and Production Functions. Princeton: Princeton University Press.

- [36] Simar L, (1992) Estimating efficiencies from frontier models with panel data: a comparison of parametric, non-parametric and semi-parametric methods with Bootstrapping, Journal of productivity analysis 3, 171-203.

- [37] Taylor, WE (1980) Small sample considerations in estimation from panel data. Journal of Econometrics 13, 203-223.

20
[38] Vickers J and Yarrow G (1988) Privatization: An Economic Analysis MIT, Press; Cambridge, MA.

- [39] Water Utilities Partnership (2000) Performance Indicators of Some African Water Supply and Sanitation Utilities. Abidjan, Cote-d'Ivoire, February 2000.

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