How dirty are quick and dirty methods of project appraisal

Public Disclosure Authorized Public Disclosure Authorized wPs1qID POLICY RESEARCH WORKING How Dirty Are "Quick and Dirty" M ethods Appraisal? ofProject Appraisal? Project of PAPER 1908 Routinequick-and-clrty methods of project appraisal can be so dirty in guiding projectselectionasto wipe uteescilanfol out the net socialgainsfromi Dominique van de Walle Dileni Gunewardena Public Disclosure Authorized Public Disclosure Authorized public investment The World Bank DevelopmentResearchGroup April1998 Poi,icy RESEARCIH WORKING PAPER 1908 Summary findings Routine "quick-and-dirty" methods of project appraisal can be sc dirty in guiding project selection as to wipe out the net social gains from public investment, contend van The quick-and-dirte method performs lcl iHn esinmatm- average becnefitsrnationallybut can be misleading for some regions a;nd, by ignni-ing de Walle and Gunewardena, illustrating their point with a case study of irrigation projects in Vietnam They test a common quick-and-dirty method for estimating benefits from irrigation investmnents, using data for Vietnam They compare the results with impacts assessed through econometric modeling of marginal returns, which allows for household and area heterogeneity using integrated household-level survey data heterogeneity, overestimates now muc¢h the p ;iro i At moderate to high project cost lev¢ls, quick-i K d-cirry makes enoc¢gh mistakes to e iinate the llct benetits fromti public investmrent WhIen- irrigaltng as littie as pircent of Vietnam s nonirrigated land:1, the sax igs -omn he more data-intensive method are enough to cover -he costs or the extra data required This paper -a product of the Development Research Group -is part of a larger effort in the group to assess the welfare impacts of public spending Copies of the paper are available free from the World Bank, i 18 H Street NW Washington, DC 20433 Please contact Cynthia Bernardo, room MC2-501, telephone 202-473-114', fax 2)2-22-1 iS4 Internet address cbernardoCc@worldbank.org April 1998 (38 pages) The Policy Research WYorking Paper Scries disseminates the ,indings of wvork in progress /0 ess ;ape thse exchange ofc u ii' de¢elozp;nentissues An objective of the series is to get the fizdings out quickly, evenn ithe ins are iessthan hruesetat uc o-,nlcpapers carr the names of the authors and should be cited accordingly The findings nuterOretaOnnt'Mc;OnCillSio(ns sio lO e -cfprssi 'a lUi paper are entirely those of the authors They not necessarily represenlt the lzen' of the 'rVnld Bsok, its lstxlccieuZiue > countries they represent Produced by the Policy Research Dissemirna-ion Ceinter a (J';< How Dirty are "Quick and Dirty" Methods of Project Appraisal? Dominiquevan de Walle and Dileni Gunewardena' Key, words: Poverty; Welfare; Project evaluation; Irrigation; Viet Nam JEL classi ication: H43; 022; 139 Dominique van de Walle is with the Development Research Group of the World Bank Dileni Gunewardena is with the Department of Economics at the University of Peradeniya, Sri Lanka Correspondence to van de Walle, MC2-507, 1818H St., NW, Washington, DC, 20433; (tel) 202-473-7935; (fax) 202-522-1154; (email) dvandewalle@worldbank.org Introduction An essential input to any project appraisal is an estimate of the quantity changes induced by the project Ideally, these estimates will allow for heterogeneity across projects and beneficiaries Ignoring such heterogeneity-by looking only at aggregates-can bias estimates of both average benefits and their distribution Typically, however, rapid assessments must be resorted to, and the analysis conducted for a representative project and/or beneficiary It is recognized that the methods used in practice for assessing quantity changes in project appraisal simplify reality in this respect; they often ignore household and area heterogeneity, behavioral responses and general equilibrium impacts It is implicitly assumed that economic losses due to these deficiencies are of second order importance Is that right? Would collecting better data, or using better methods, make an appreciable difference to the social welfare outcomes from public investments? Would it be worth investing the extra resources needed to make a more thorough assessment? Indeed, could the deficiencies of commonly used methods be so great as to eliminate the entire social gains from the investments? This paper addresses these questions for a commonly used "quick-and-dirty" (QD) method for assessing rural infrastructure investments The QD method we study is a stylized version of the method that is normally used in practice to assess both the average gains and the distributional impacts of an irrigation project The method is implemented and the implied average benefit from irrigation in Viet Nam is compared to the marginal benefit estimated by an alternative, more sophisticated econometric method for estimating impacts on farm profits at the farm-household level We call this the "slow-andclean" (SC) method This is closer to a well-defined theoretical ideal, and is about as sophisticated a method as one would expect to find in a small research project set up for the evaluation task While it is not perfect, we believe it represents a distinct improvement, and hence a reasonable test of the conventional QD approach The comparison of our stylized QD and SC methods allows uIsto estimate the potential gains from using more theoretically sound but costly behavioral methods, where those gains are assessed by the same criteria used to assess the projects themselves Here we will be concerned with both the impacts on average incomes and the distribution of income QD methods found in practice often appeal to both efficiency and equity criteria for project selection For example, appraisals of rural infrastructuLralprojects in developing countries often argue that since the project is to be located in a poor area, it will help reduce poverty However, this may be quite deceptive The benefits from physical infrastructure investments will be influenced by a number of factors which will typically be hidden by quick-and dirty methods Behavioral responses on the part of households may alter expected outcomes There mav be complementarities between physical and human infrastructure such that the returnisto individual households depend in part on the household's level of human capital (van de Walle 1997) f wealthier households have higher human capital, they may also have higher gains Retums to irriation on the family farm may further depend on household size and composition in settings with underdeveloped labor markets The size of landholdings may also matter, again with obvious potential skewness of benetits A project analysis which ignores these factors may seriously misinform policy conclusions about the impacts on poverty of public investments The next section outlines the theoretical ideal and the principles underlying the SC and QD methods It also briefly discusses the setting and the data which are used to implement the methods Section then conmparesand contrasts results obtained by the alternative approaches including implicationsfor distributionalassessments,for projectselectionand for the net social gains from public investments Section4 concludeswith a few comments Methods In this sectionwe start with a descriptionof the theoreticalideal and then describetwo approximations-the SC and QD methods 2.1 Tlte ideal An importantinputto the appraisalof irrigationprojectsis assumedto be an estimateof the gain in farm profit from irrigatinggiven amountsof previouslyunirrigatedland.2 The ideal method would start with a generalspecificationof the profitfunctionfor a farm household We measurefann householdprofit from crop productionby total revenueminus total productioncosts, whichwe term net crop income This is assumedto be a functionof output and input prices(p), non-irrigated(LN)and irrigated(LI)annualcrop land amounts,and other relevantvariables(z) The genericprofit function is: (1) ,T,= ;r(pj, Lv, L'.zj) which is the maximumprofitreceivedby thej'th household,G=1, ,n) The vector z will includeother fixed factorsand parametersof the productionfunctionused by the j'th household In the case wherea completeset of perfectmarketsexistsfor all farm outputsand inputs, variablesinfluencingconsumptiondecisions,such as the prices of consumergoods and the size and demographiccompositionof the household,wouldnot alter the maximumprofitfrom farming However,when marketsare incomplete-so that the conditionsrequiredfor separabilitybetween productionand consumptiondecisionsdo not hold-such variableswill spillover into production decisions(Strauss 1986) For example,in Viet Nam, rural labor marketsare thin, or non-existent, reflectingthe dual effectsof the past socialistorganizationof rural productionand relianceon selfsubsistencefarmingas well as possiblyhigh supervisioncosts and limitedmobilityin the early stagesof transition Variablessuch as familysize and compositionwill then influencethe amountof labor availablefor farmingand hence, maximumprofits Thenz may includefactors besidesthe parameters of the farm household'sproductionfunction The specificationin (I) can thus be made general enough to encompassmarketeffectsof credit or labormarketfailure Now considera projectwhichinvolvesirrigatingamountsA Lj1 of previouslyunirrigatedland for each of n households(possiblyzero for some) The benefitto thej'th farm-householdis givenby the incrementto its profits from farming,i.e., the benefit is (2) B, = ;r(P,,L,'-AL'.L +ALj,zj) - fr(P,.LA,L1.z,- One would then calculateaveragebenefit( JBj / n) or some distribution-weightedaveragebenefit In the special case in which one unit of land is irrigated it is useful to define the marginal benefit finction as (3) MB =1 - r(p, L,, L',,) If we knew the profit functionthen the task would be complete In the rest of this sectionwe describe two approximationsto this ideal,one of which-the SC method-is undoubtedlymore accuratethan the other but is still an approximation.But firstwe need to describesome key featuresof our data 2.2 Settinogand data We test irrigationprojectappraisalmethodsusing data fromthe Viet Nam LivingStandards Survey(VNLSS)of 1992-93 This is a nationallyrepresentative,high qualityhouseholdconsumption The data includedetailedcoverageof agricultural surveycoveringa sampleof 4800 households productionand incomeswhichallowsus to constructa comprehensivemeasureof annualcrop incomes net of all productioncosts The surveyalso collectsdetailedinformationon land assets, including qualityof plots, and other inputsto crop production,includingfamilylabor inputs We use total householdper capita expenditures(includingthe imputedvalue of consumptionfrom own production), appropriatelydeflatedto allow for spatialcostof livingdifferences,as our welfaremeasure Viet Nam is a largelyagriculturaleconomywhere in 1992/9384 percentof the rural labor force aged years or older claimedagricultureas theirprimaryoccupation A majorityof householdsare engagedin small-scaleself-subsistencefarmingrelyingalmostexclusivelyon householdlabor and traditionalinputs About half of the country'sarablecrop land is under irrigation It is generallyagreed that there is great potential foran expansionof the areaservedby new irrigationinfrastructureas well as by the rehabilitationof long non-functioningirrgation networks(Barker1994) Such investmentshave not been undertakendue to the combinationof historicalfactorssuch as war, highlyconstrainedpublic budgets.and lack of accessto credit The currentdistributionof accessto crop land and irrigationvaries acrossregions but much less so witlhinregionsdue to past land refonn In general,land endowmentsare relativelyequitably distributedin the North but less so in the Southwhereon averagethe poor have access to less than half the amountof land the non-poorhave (van de Walle 1996) The existingdistributionof irrigationis somewhat more equitable than that of land, though similar Given its current distribution, it cannot be argued that irrigation investments will necessarily benefit the poor more than the rich Although Viet Nam has been undergoing reform since 1986, markets were still relatively underdeveloped in 1992-93 Field work suggested that, in some parts of the country, labor and land markets did not exist at all Using the same data, van de Walle (1997) finds evidence that household demographics and human capital exert considerable influence on farm household crop incomes As already discussed, this would not be the case if markets performed well such that households could buy and sell labor time and skills 2.3 A slow amdclean approach The SC method works by assuming a functional form for the profit function which is then estimated by regression methods using suitable micro data-in this case the 1992/3 VNLSS The chosen specification allows a number of variables-including land itself, demographics, education variables and regional dummnyvariables for Viet Nam's regions-to have direct effects on the marginal returns from irrigated and non-irrigated crop land For the SC method used here, the profit function is assumledto have the following parameterization: (4) X, = a+±IvLv+>j/L+rzj+ 5d,+-, where (5) A, =b, + bPY dj + b2 z+ b3YL and (6) bo+bjdy + bdbzj+b'L1 and where d is a vector of regional dummy variables which are assumed to fully capture the variation in prices faced.4 The error term e is assumed to be independently and identically normally distributed The country's regions are made up of provinces, districts and, at the lowest level, communes Dummnyvariables for 119 out of the 200 sampled conimunes are included in the intercept of the profit function (d in equation 4) to capture variations in prices and any other spatial, cross-commune variations in omitted or fixed factors such as land and soil quality Thus, prices of outputs and variable inputs are assumed to vary between but not within communes The conmmunedummies will also pick up the influences of geographical and social and physical infrastructure variations at the commune level We also collapse the commune dummies into regional dummy variables and interact them with irrigated and non-irrigated land (d in equations and 6) and other land types, thus permitting regional effects on the marginal returns to land Of course this specification is only one of a number that might be proposed However, by allowing nonlinearitv in land and interaction effects with other variables, it is a reasonably flexible functional form for the present purposes.5 The vector: includes other land in agricultural production, land tenure variables, education, health and demographic variables, and location specific agro-ecological variables As discussed, a range of variables are included in z in order to capture characteristics specific to a transition economy in which markets are still underdeveloped OLS is used on a sample consisting of the 3049 farm households in the data set (including some urban farming households) Table describes the variables Regression results are given in Table 2.6 Table2: RegressionResults:CropIncomes cropinc Coefficient t-ratio urban sick hhsize propO6 hed hedIThedi oed I*oed I oed2 irrigated irr*irr nonirrigated nonirrig*nonirrig perennial perennialperennial forest forest*forest waterland*waterland otherland otherland*otherland propauct proppriv propall hed I * irrigated hed I *otherland hed2*irrigated hed2*perennial hed2*forest hed2*%aterlaid hed2 otherland oed I irrigated oed I nonirrigated oed I perennial ocd I * torest oed I otherland oed2 irrigated oed2*nonirrigated oed2*perennial oed2*otherland hhsize*irrigated bhsize*nonirrigated hhsize*perennial hhsize t forest hhsize*otherland pfadlt irrigated praditnonirrigated pfaditperennial pfadVt*otherland pmadVtirrigated 1093640 -318465.4 81451.7 -586514.9 -468646.3 67862.6 -1932.7 21023.8 352.40 -0.0030 238.40 -0.0036 -277.04 -0.0097 -372.88 -0.0026 -0.0401 -611.27 -0.0024 1116555 325505.5 470198.4 47.87 -113.39 -6.46 21.90 23.01 72.61 33.45 20.74 7.27 5.42 -21.37 49.20 4.179 1.741 -10.914 33.814 -35.991 4.639 52.933 37.473 79.081 -176.63 -137.02 610.10 1941.44 -162.40 1.08 2.59 2.12 1.18 2.69 2.66 2.48 1.24 4.27 4.42 3.81 9.60 1.73 6.43 1.35 1.38 3.80 1.22 1.32 2.54 1.49 2.10 6.06 2.58 1.53 2.53 1.62 1.58 1.51 8.03 3.38 1.21 1.72 4.66 2.18 1.04 2.65 4.0 6.94 1.13 4.62 1.68 2.62 2.16 2.07 3.33 4.18 1.71 26 cropinc pmadlt*perennial prop716*irrigated rr*irrigated rr*forest mk*irrigated mk*perennial nu*irrigated nu*perennial nu*otherland nc*perennial nc*otherland cc*irrigated cc*nonirrigated cc*perennial ch*irrigated ch*nonirrigated ch*perennial ch*waterland Coefficient 289.39 155.85 271.75 135.35 -67.71 -158.92 255.81 -199.58 434.86 -218.53 528.01 -203.38 -152.25 -228.68 -973.79 -134.37 310.57 5195.78 t-ratio 1.92 2.03 4.06 1.03 1.94 2.94 3.47 2.27 1.20 3.02 1.39 3.63 2.62 1.26 1.66 2.65 4.37 1.31 Numberof obs = 3049 F(233,2815)= 19.06 Prob> F = 0.0000 R-square = 0.6120 Adj R-square = 0.5799 Root MSE=1.5e+06 Note: For ease of presentation we have not reported results for the 119 commune dummies many of which are significant The table also omits regressors with t-stats less than The model also contained the following variables: demographic composition variables, pnum716, pfadlt, pmadlt and interactions with land variables; education variables: hed2, hed22, oedl, oed22 and interactions with land; land: waterland and interactions between types of land and regions; propit, propshare 27 Table 3: Marginal Benefit Function for Irrigation VARIABLE bil- bhN t-ratio 114.0 1.24 irrigated land -0.006 4.42 non-irrigated land 0.0072 9.60 h'hold head primary ed 7.87 5.56 hWholdhead other education -5.131 1.07 other adult primary ed 13.472 4.69 -5.92 2.57 h'liold size -40.63 7.04 proportion of female adults -39.61 0.44 -100.14 1.00 148.1 1.94 Red River 315.81 3.05 Mekong Delta -72.08 3.44 Northern Uplands 274.64 1.63 North Coast 98.67 1.14 Central Coast -51.13 0.75 -839.42 1.41 intercept other adult other ed proportion of male adults proportion of children to 16 Central Highlands 28 Table 4: Benefits by Region North Uplands Red River North Coast Central Coast South East Mekong Delta (I) Slow and Clean 449 488 272 102 175 100 (2) Quick and Dirty 541 392 342 48 184 129 92 -96 70 -54 29 Benefits (Dongs/year/m2 ) (2) - (1) 29 Table 5: IrrigationProjectsApprovedunderQD and SC Benefits Cost (Dong/ m2) 200 250 300 350 400 425 450 475 500 % Projects Accepted by Region Benefit Rule Total % projects accepted North Uplands Red River North Coast Central Coast South East Mekong Delta SC 99 100 87 45 13 68 QD 100 100 100 0 65 SC 98 100 67 17 61 QD 100 100 100 0 65 SC 95 99 32 52 QD 100 100 100 0 65 SC 89 97 46 QD 100 100 0 0 47 SC 79 92 1 41 QD 100 0 0 19 SC 69 88 0 38 QD 100 0 0 19 SC 53 81 0 33 QD 100 0 0 19 SC 38 65 0 0 26 QD 100 0 0 19 SC 26 45 0 0 17 QD 100 0 0 19 30 Table 6: Overall Ex-post Assessment of Quick-and-Dirty Method Cost (per m2 ) (1) (2) (3) (4) (5) (6) Projects on which the rules agree (as a-% of all projects) % projects accepted under QD rule Average net benefit realized: projects accepted under QD rule (per m2 ) % projects accepted under SC rule Average net benefit realized: projects accepted under SC rule (per m2 ) % loss from using QD instead of SC 200 91.80 64.97 216.08 68.15 212.32 2.97 250 91.17 64.97 166.08 61.09 183.88 3.94 300 85.71 64.97 116.08 52.36 160.34 10.16 350 95.35 46.77 122.30 45.94 129.48 3.82 400 69.52 18.70 49.43 41.15 91.52 75.46 425 69.12 18.70 24.43 38.00 73.05 83.55 450 68.38 18.70 -0.57 32.85 57.46 100.57 475 69.89 18.70 -25.57 25.66 44.95 141.47 500 73.47 18.70 -50.57 17.40 35.21 254.41 Note: For all projects accepted, benefits are evaluated using the SC estimates and expressed on a basis per mn Column (6) = I - [Col(2) x Col(3)]/[Col(4) x Col(5)] 31 Dongsperyearpermetersquareirrigatedland 800 NU O RR NC 600 MD NU = NorthernUplands RR = Red River NC = North Coast CC = CentralCoast / O SE MD= South East MekongDelta 0 SE 400SE 40 SC regional means CC 0 200 C) 45' 200 400 QD 600 Figure1.Comparing SCandQD 32 800 North (D 800- U Northern Uplands ,o o O Red River 600 - ,o °0 @ 600 400 t; 5541 , U) ~~400- I a 09 0 200 00 - 39 E ~~200 L CL 0S ~~~~~~~~ - c 00o 200 ~.5 36 Per cap expenditure, mil.Dongs _ _ ! _ 'O 392 a ~~~400 0) 0~~~~~~0 0a _ _ Per cap expenditure, _ _ 6i mil.Dongs >) QL 600 North Coast o Central 300- U) U) 4200 - 1.5 Per cap expenditure, 0 48 0 o 342 0) Coast U 200 - Per cap expenditure, mil.Dongs mil.Dongs Figure 2a Incidence of Benefits by Region 33 South South East 0) ~~~~~~~~~;0~ 400 :J L- E QL 3300 U) Mekong @t 0^ Delta 600 184 184 - ) 0) (100 Per cap expenditure, mil.Dongs 4000 0 0%~~~~~~~0 200 Per cap expenditure, 129 36 6.51 mil.Dongs 0) ac Figure 2b Incidence of Benefits by Region 34 a) 800 0 a).I0.0 0 600 ~ U) E ~ ~~0~0 0~ 400 ~ a)0 U)00Q 0) ~ L ~P00 M expnd t ongs 0& o &00 ooc~~~0 0 O6 D o 0) 0) 0 ~ Figure 00 ~ ~ 0 o I o oo RD &)~00 6P C O0 o 0) ~ ~ c 20 ~ Figur erca 00 xpndtue,mi.Dng 00 National inie ce of Benefit 0)~~~~~~~~~~~~~~~~3 QO C2) ON 0~~~3 Comparing SC and QD 1_ , _- I _ _I_ _1_ 350 (I) E S 300 U)~~~~~~~~~~~~Q 0~ L 4) (7> 250 - a) C a) / 200 Per cap expenditure, mil.Dongs Figure National Incidence of Benefits 36 II Comparing SC and QD (a) Policy I: Simulated total impacts (per capita) (b) Policy II: Simulated 20000 - total impacts (per capita) 40000 SC F5 I of i ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0~ a, a, C C Pe a expenditure, mil.Dongs (c) Policy 1:,Siuae mat s%oousehold Per cap expenditure, mil.Dongs expenditure (d) Policy IIt: Simulated impacts as % of household expenditure (D 10 4-I ~~~~~~~~~~~~~~~~~QD OD E4 37 c~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Cax, FL 0 Per cap expenditure mit tDongs Figure Per cap, expenditure, mil.Dongs Impacts of irrigation expansion 37 (a) - 300 a) (b) 300 0)~~~~~~~~~~~~~~~~~~~~~~~~~C 200 E o 200 E J0 4- 00 4- (I) (I) C/) Cl) o 100 0 200 _ _ _ _ _ 300 _ _ _ _0 400 - 20 500 % projects Costs per meter square 40 accepted 60 Figure Loss from using QD instead of SC 38 80 under SC PolicyResearchWorkingPaperSeries Title Author Date Contact for paper WPS1883 intersectoralResourceAllocationand FumihideTakeuchi its Impacton EconomicDevelopmentTakehikoHagino in the Philippines February1998 K Labrie 31001 WVPS1884 Fiscal Aspectsof Evolving 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Routine "quick- and -dirty" methods of project appraisal can be sc dirty in guiding project selection as to wipe out the net social gains from public investment, contend van The quick- and- dirte