Public Disclosure Authorized I rrn%r LSM135 LI I LU May 2002 Living Standards Measurement Study Working Paper No. 135 CGildelines for Conctriiwtinc Cnnznimntinn Aggregates c oC1 c, for Welfare -. -. - --. --- - - Analvsis Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized S!OSXIRUV a.IBjpaA* .IOJ n vi~i~ sdnnnn n---Jr °n The Living Standards Measurement Study The Living Standards Measurement Study (LSMS) was established by the World Bank in 1980 to explore ways of improving the type and quality of household data collected by statistical offices in developing countries. Its goal is to foster increased use of household data as a basis for policy decisionmaling. Specifically, the LSMS is working to develop new methods to monitor progress in raisn Le V els VI lUn V It LV i& . t1LLi Ue cLnOVuc.j-JLces vrJL hLousehold F.L Vf pasLU dJLIJprvI.eU rV I UL policies, and to improve communications between survey statisticians, analysts, and policy makers. The LSMS Working Paper series was started to disseminate intermediate products from the LSMS. Publications in the series include critical surveys covering different aspects of the LSMS Itn tnllpitinin nrornom nint rPnnrtc nn imnrnvprA mn¶thnAnnlgiPe fnr iding T d T;iG , ntinArti c Qllrup'i data. More recent publications recommend specific survey, questionnaire, and data processing designs and demonstrate the breadth of policy analysis that can be carried out using LSS data. LSMS Working Paper ~T - -- - - 't1 INumDer i35 Guidelines for Constructing Consumption Aggregates for Welfare Analysis Anm Usneaton and Sac&1aim 7.oiAi The World Bank Wwzhinortnn TD.C i 2002 The International Bank for Reconstruction and Development / The World Bank 1818 H Street, NW Washington, DC 20433 All rights reserved. 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All other queries on rights and licenses, including subsidiary rights, should be addressed to the Office of the Publisher, World Bank. 1818 H Street NW. Washington, DC 20433. USA, fax 202-522-2422, e-mail pubrights@worldbank.org. ISBN: 0-8213-4990-2 ISSN: 0253-4517 Angus Deaton is the Dwight D. Eisenhower Professor of International Affairs and Professor of Economics and International Affairs at the Woodrow Wilson School of Public and International Affairs at Princeton University. Salman Zaidi is an economist in the Poverty Reduction and Economic Management Sector Unit of the South Asia Regional Vice Presidency of the World Bank. Table of Contents FOREA WARD. R vii ABSTRACT ............................................................ ix ACKNOWLEDGEMENTS ............................................................ xi 1. INTRODUCTION ............................................................ 1 2. THEORY OF THE MEASUREMENT OF WELFARE ............................................................ 4 2.1 INTRODUCTION: ................................................................... 4 2.2 MONEY METRIC UTuIIY: ................................................................... 4 2.3 AN ALTERNATrVE APPROACH: WELFARE RATIOS: ................................................................... 8 2.4 INCOME VERSUS CONSUMPTION: ................................................................... I1 2.5 DURABLE GOODS: ................................................................... 3 2.6 THE EVALUATIO OF TIM N AND E LEISURE: ................................................................... 15 2.7 PUBLIC GOODS AND PUBUCLY SUPPLIED GOODS: ................................................................... 17 2.8 FARM HOUSEHOLDS: ................................................................... 18 2.9 DIFFERENCES IN TASTES ACROSS PEOPLE AND HOUSEHOLDS: ................................................................... 19 Box 1. SUMMARY OF THEORETICAL ISSUES AND RECOMMENDATIONS ................................................................. 21 3. CONSTRUCTING THE HOUSEHOLD CONSUMPTION AGGREGATE ....................................... 23 3.1 iNTRODUCTION: ............................................................ 23 3.2 FOOD CONSUMPTION: ................................................................... 25 3.3: CONSUMFTION OF NON-FOOD ITEMS: .............................................................................. 29 3.4 CONSUMER DURABLES: ................................................................... 33 Box 2. RECOMMENDATIONS FOR CONSTRUCTNG THE CONSUMPTION AGGREGATE .............................................. 38 4. ADUSTING FOR COST OF LIVING DIFFERENCES ............................................................ 39 4.1 !N i iRODUCUIION: ............................................................... ....................................... 39 4.2 PAASCHE PRICE INDEX: ................................................................... 41 4.3 %Ci-A--uL.-al iG ALINSPE-YKES uN-DEx:.. ........................................................................................... 4.3 5. ADUSTING FOR HOUSEHOL-D COMrOSMTONr .................................................................... 46 ......................................................................................................... 46 5.1INT RODUC TON : 5.2 EQUI-VAIENCE SCALES:...................................................................................................... ............................... 5.3 BEHAvioRAL APPROACH: .................................................................. 48 ). a . ~ tSUbJL - IIV E,APRO L-n A -H:.................... 1.40T. ............................................................................... 149 5.5 ................................ ARBITIARY APPROACH: .................................................................. 50 Box 3. ADrjus tXiTS i,-~a-viFOROST-OF-rLI-VLL,4%G D'iFFr-ERCACESAN KIVU S %CONrruS HL ,2 lkN ................................................. 6. iviEUiHODS urOF4aii x Avil, I' Ai'ALY SNi .......................................................................... 53 6.1 NTRODUCToN: ................................................................. 53 6.2 STUvCHSIsC W .uANCE.: .4................... 53 6.3 USING SUBSETS OF CONSUMDn1N AND THE EFFECTS OF MEASUREMEENT ERROR: ...................................... 55 6.4-Er Vi-IY ANALY IS-SVVI SFSi-I 5,8 rnEQ UVALENCE SCAl ES ........................................................................... REFEREKNClES ................................................................................................... 64 A PPEND IX .............................................................. 66 AN INTRODUCTION TO LIVING STANDARDS MEASUREMENT STUDY (LSMS) SURVEYS: 66 ........................................ AN INTRODUCTION TO THE PROGRAMS: ............................................................. 67 Al. 1995 NEPAL LIING STANDARD SURVEY (NLSS) STATA CODE ............................................................. 69 A2. PAASCHE PRICE INDEX: STATA CODE FOR NEPAL ............................................................. 89 A3. DURABLES CONSUMPTION SUBCOMPONENT: STATA CODE FOR VIETNAM ....................................................... 92 A4. DURABLES CONSUMPFTON SUBCOMPONENT: SPSS CODE FOR PANAMA ......................................................... 95 A5. DURABLES CONSUMPTION SUBCOMPONENT: STATA CODE FOR KYRGYZ REPUBLIC ........................................ 98 A6. HOUSING CONSUMPTION SUBCOMPONENT: STATA CODE FOR SOUTH AFRICA ................................................ 99 A7. HOUSING CONSUMPTION SUBCOMPONENT: STATA CODE FOR ViETNAM ....................................................... 102 Foreword In recent years, there has been a proliferation in use of household survey data to cast light on a range of policy issues related to welfare analysis. Data from LSMS as well as many non-LSMS household budget and consumption surveys are increasingly being used as a powerful tool for poverty and distriuUUonai analysis. However, despite the widespread use oi survey data for weiiare analvsis; there is relatively little research on the general princinles that should he annlied when constructing consumption aggregates from survey data. This paper seeks to fill this important gap by providing analysts some practical guidelines and techniques to help facilitate this task. Paui Collier, Director Development Research Group vii Abstract An nnalyst using houehnold s.rvey datn to consnuct a welfnar metric is oftpn confr%ontPd with a number of theoretical and practical problems. What components should be included in the overall welfare measure? Should differences in tastes be taking into account when making comparisons across people and households? How best should differences in cost-of-living and household composition be taken into consideration? Starting with a brief review of the theoretical framework unrderinning tmpical wvelfare analysis undertaken based on household s--ey data, this paper provides some practical guidelines and advice on how best to tackle such problems. It outlines a three-part procedure for constructing a consumption-based measure of individual welfare: (i) aggregation of different components of household consumption to construct a nominal consumption aggregate, (ii) construction of price indices to adjust for differences in prices faced by households, anu kill) aUJustLmVent of the real consuumpLtion aag,61iaW fiv dil.M M l in hI composition. iuousehIu Examples based on survey data from eight countries - Ghana, Vietnam, Nepal, the Kyrg Republic, Ecuador, South Africa, Panama, and Brazil - are used to illustrate the various steps involved in constructing the welfare measure, and the STATA programs used for this purpose are provided in the appendix. The paper also includes examples of some analytic techniques that can be used to examine thC roustness ul te estimatdUU. Welfare mltSUTi unueriying LOU assumpuuns. ix Acknowledgments We would like to acknowledge the invaluable assistance provided by Ludovico Carraro in analyzing the data sets from the country case studies reviewed in this paper, and in documenting the programs included in the appendix. We are grateful to Martin Ravallion for discussions on the relationship between money metric utility and welfare ratios. For their helpful comments on previous drafts we would like to thank Martha Ainsworth, Javier Ruiz-Castiilo, Lionel Demery, Paul Giewwe, Margaret Grosh, Jesko Hentschei, Maanny Jimenez, Jean 01'nn T.Ininlw Ravlvnn Oliver C(invanna Prennuqhi Martin Ravallion. Kinnon Scott. and Naoko Watanabe. xi I XThTVI2 ^1M TI"'VV^.TN A. AU JM AVV PL U-k L.P Pnvertvh is comnlex nhennmnenni invnlvrino niiiltinle .din iniw of(denrintwiV.nof whiAh the lack of goods and services is only one. Even so, there is a good deal of consensus on the value of using a consumption aggregate as a summary measure of living standards, itself an important component of human welfare. In recent years, in much of the World Bank's operational work as well as in applied research, consumption aggregates constructed from survey data have been used to measure poverty, to analyze changes in living standards ovAer tme, aned to assess tli A i.utirua. i ^r.:tof p m.s ar.a policies. Despite this widespread use of consumption aggregates, there is little in the way of guidelines on how to construct consumption aggregates from survey data. Researchers and analysts interested in using consumption as a welfare measure must often work from whatever documentation exists from earlier exercises, GULL - 1AA041 - I-- - UL DU1LiL W65VIO, _{_i A.1 iuIi uv10%11FL1IV1 _A _ A;_ II A aL'I suLLOui3J AAs9-Z_L us.11 L Luii A_ A U *1 UlUii9 . .__ 1A zao ui IA- -- a guu A 3- Oval vi. ULUMCadAMy replication with each analyst working afresh through the underlying theoretical and practical issues. This paper seeks to fill the gap by providing a brief theoretical introduction followed by practical advice on how to construct a consumption aggregate from household survey data. We re oguiizue hait tLhere are several distiict audiencus 0or nese dgueiines, wno wiu use dinerent parts of what follows, with different kinds of surveys; and for different purposes; so that it is useful to start with something of a road map: Audience. We hope that these guidelines will be useful, not only to those whose immediate task is to use a survey (or surveys) to construct consumption aggregates, but also to statisticians, economists, or advisors who are interested in why consumpntinn agrevateQ mioht be iiefiul and the venne l fe ires of their construction. This latter group includes those in Statistical Offices who might be considering instituting a new consumption survey, or in modifying an old one. The arguments for and against consumption, usually in comparison with income aggregates, come up often enough that is useful to have guidelines on the main arguments, and on what is involved in constructing a consumption aggregate. T he first part ofthese guidehnes, which outlines the mndevrhAing theo,r, as well as the 4,rnrnnr 1,Boxes,^ 1ll bU of,r.os* ineresat totgu. Issues of survey and questionnaire design are not dealt with in these guidelines but are dealt with in the companion piece by Deaton and Grosh (1998). At the same time, we have tried to discuss most of the detailed decisions that would have to be made by our first audience, those actually doing the calculations. There is illustrative code in the Appendix covering much of what has to be done, and there is discussion of most of the 1 practical issues that have arisen over the years. But it is important that the calculations not be done mechanically. Each survey is different from every other survey, if only in detail, and each country has its own institutions that need to be taken into account. Constructing consumption aggregates without knowledge ofthe country and it insLitutions will not give -Ueflul results. Inconsequen,ce, analysts n,eed to be famlliliar wiuh Ule theory in order to be able to make sensible decisions when a new Droblem presents itself. as is alwavs the case in practice. Surveys: LSMS versus others? These guidelines have been prepared by and for the LSMS group in the Bank, and the examples in tne Appendix are drawn from LSMS surveys around the worid. wnenever we require asptneifi examnle, we take it frnm some LSMS survey, and we generally assu-mme that some version .f LSMS protocols have been used. However, we believe that these choices should not compromise the usefulness of the guidelines for those who are constructing consumption aggregates from other surveys. The theory is general, and almost all of the details of the construction would have to be followed through in one form or another using any consumption survey. It shouid aiso be noted that as the number of LSMS surveys has grown there has bee.n a great deal of variation in sulrvPy desigm, so that thpre are very few consumption surveys around the world whose design would not be represented in one or more LSMS surveys. A more serious issue is that many non-LSMS surveys will lack at least some of the information used in constructing a comprehensive measure. r .. I osean A C_,4 T_ -,U-+ _fAO %A VLJL%AI. Ii4i WIVLaL LVJiLJ%WAvva WI,1.7 -;.., wYJiVaJL 11- .A4-- Ap_ fl+U-*AA G.aOLL1%" ULIM + A LaL uLw ,U11LOUIUU11 A_II a8&Vr,4IZ A Az__11 WIll Ur, used in poverty analysis, identifying the poor, and computing standard measures of poverty and ineaualitv. Such aggregates are also used for incidence analysis, to identify the position in the income distribution ofthose who are likely to benefit or lose from some policy, such as subsidies or taxes, or the provision of a service. We discuss the procedures that would normally be followed in constructing a consumption aggregate for such purposes. However, -we shall encounter a number of examples where procedures wii nave to be modified depending on the context and purpose. For example. some of the theoretically ideal concepts are hard to implement, and because the best is sometimes the enemy of the good, we will often recommend not trying to implement the theoretically ideal solution. But there will always be cases where the purpose of the exercise is compromised by such a decision, and attempts must be made. For example, it is very difficult to measure the welfare effects ofpublic good provision, and we recommend against the routine inclusion ofsuch valuations in the consnmption agregat.es. But if the aggregates arete to be uedto exami.ne the effects of public good provision on (for example) the regional distribution of poverty, then some attempt must be made. Again, the theoretical framework is the ultimate guide as to what to do. 2 The rest of the paper is laid out as follows: The theoretical framework underlying the use of the consumption aggregate as a welfare measure is briefly reviewed in Section 2, along with a discussion of some ILUNUVO I. Uu1115 r, V WILW OUCdI a 1inesiur W1VULUV UlvaUU%. LJjJ9%ILL'L.. rUIV&aL4O .ll LL VY &WS % consumption based measure of welfare are then presented in Sections 3-5. The paper outlines a three-part procedure for the construction of a consumption-based measure of individual welfare: the various steps involved in aggregating different components of household consumption to construct a nominal consumption aggregate are laid out in Section 3. The construction of the price index in order to adjust for differences in prices faced by nouseholds is then reviewed in Secuon 4. Tne aUJustntIL of ihe rew conswumpuior aggregate for differences in composition between households is then presented in Section 5. Finally, Section 6 provides examples of some of the analytic techniques that can be used to examine the robustness of the measure to assumptions and choices made at the construction stage. The consumption aggregates constructed in recent years from tne Living Standards Measurement Sthudyf zA SM) glruPv-tJt frnm i.irht CiCntrip.S GTh:nma Vietnam7 Npena the CvRermnhIir- FcIladnr Smniith Africa, Panama, and Brazil were reviewed for this paper (for a brief introduction to the LSMS project as well as a description of the main survey instruments typically used in these surveys, please consult the appendix). In none of the countries covered did we find the procedures followed to be fully in conformance with the recommendations provided in this paper; nonetheless, these case studies provided the basis for much of the prc.1 nvA..4- n-A rtolara. nspreser,ted *n ffaper. Th. nro m. s used to nc . * nnnrn.m nn aggregates in these countries are included in the appendix as they provide useful illustrations of the general steps involved in constructing the aggregates. 3 2. TiEOWY OF TkIL H ASUmKLENi OF WAWTLLFAL 2.1 Introduction: In this section, we discuss briefly the theoretical basis for the consumption-based measure of welfare whose detailed construction is explained elsewhere in the report. Our concern here is a fairly narrow one, focusing on an economic definution of living standards. 'w'edo not consider otiher important components of welfare, such as freedom, health status; life-exnectancyv or levels of education. all of which are related to income and consumption, but which cannot be adequately captured by any simple monetary measure. Consumption measures are limited in their scope, but are nevertheless a central component of any assessment of living standards. One important concept here is money metric utility, Samuelson (1974), which measures levels of living by the money required to sustain them. We start with this in Section 2.2 below. An alternative approach, based on Blackorby and Donaldson's (1987) concept of welfare ratios,whereby welfare is measured as multiples of a poverty line, is presented in Section 2.3. Eacn ofI te money-metric and weliare-rauo approacnes nas iLts strPngths and weaknesses; bnth start frnm a nominal consumption aggregate, but adiust it differently= These first subsections cover the basic ideas, and are followed by subsections on a range of theoretical issues that repeatedly come up in practice. A fuller, and only slightly outdated, treatment is given in Deaton (1980) in one of the earliest LSMS Working Papers (no. 7). Our treatment here skips theoretical developments that are of limited relevance in practice given the data that are typically available, or that can be calculated. For exampie, 4 ,,e r be~ now y,iing the choice to use inconme or consuempirtioin torneasClre liAng standards. In the theory outlined in the previous subsection, the choice between income and consumption did not arise because, in a single period model, there is no distinction; all income is consumed, and income and total -44p.^;on -e i-an4nal lt1, n,nrc ti-an- one period, i,o di,.ereroni.ce vte..m .An .d .nnn-co c - .fnt- -a;n is saving, or dissaving, so that in terms of the theory, the choice between income and consumption is tied to the choice of the period over which we want to measure welfare. Over a long enough period of time, such as a lifetime, and provided that we work in present value terms, the average level of consumption (including any bequests) must equal the average level of income (including any inheritances), so that, if the concern is to nLaeasure 1VU11i..LUi WeUlfar, Uie choIUeV UdVo In0t Ir,aI Iila ViI. is :,i,Uaeed a case LUe -114aUre fVI WUInLgII WIUI a lifetime measure. Many would argue that ineaualitv is overstated by including the comDonent that comes from the variation in living standards with age. According to this view, there is no inequality if, over life, everyone gets their turn to be relatively rich or relatively poor. But the argument for abolishing the concept of age-related 11 povertv is weaker; and policymakers (and their constituents) frequently show concern about child and old-age poverty. Even so, few would argue for very short reference periods for living standards; that someone is "poor" for a day or two is of little concern, since most people have ways of tiding themselves over such short periods. There is more concen about seasonal poverty, especially in agricultural societies with limited or very expensive credit availability. But most standard househoid surveys are not designed to capture seasonal fluictuiatinns in inenme or exnentitire- antd mnqt anti-nnvertv nolicies are directed at ln-nger term levels of living. On balance, and for most purposes, there is widespread agreement that a year is a sensible practical compromise for the measurement of welfare. In consequence, we must decide whether it is consumption, income, or wealth, or some combination of all three, that permits the best measure of living standards over a year. The empirical literature on the relationship between income and consumption has established, for both rich and poor countries, that consumption is not closely tied to short-term fluctuations in income, and that consumption is smoother and less-variable than income. Extreme versions of the smoothing story involve people evening out their resources over a lifetime, something for which there is little convincing evidence. But UJVt .s *U-.r iL goode.lc UJ Wu %VI%VLI1 , tz Ulat os.-,.cr AI UL. U."LiL sr.ohot %IUIL UIU .. P o.,Ui JCJLLI ;.tLeshr Ic-a.;rs LLU'..I.U4LLJ1I0 Iii U L% e..t . 1IWILL t1wI HI, e"t'yoe %,VLLUII3 VVVL seasons, and in most cases, over a few years. As a result, in circumstances where income fluctuates a great deal from year to year-as in rural agriculture-the ranldng of households by income will usually be much less stable than the ranking by consumption, though exceptions can occur as discussed in Chaudhuri and Ravallion (1994). Even limited smoothing gives consumption a practical advantage over income in the measurement of living standards because ooservmig consumption over a reiauveiy snort penou, even a weeK or two, will tell us a great deal more about annual-or even longer period-livingy standards than will a similar observation nn income. Although consumption has seasonal components-for example, those associated with holidays and festivals-they are of smaller amplitude than seasonal fluctuations in income in agricultural societies. In such communities, it is usually not possible to get a useful measure of living standards based on income without multiple seasonal visits to the household, something that has rarely been attempted within LSMS protocois. In 4 seasons wh2e.n people h.ave l.itl.e or no ircv.e their -ncmim..tr.n is ffr.an.ced frtomr assets, or from _nvits so fiiet an alternative way to measuring living standards without consumption data would be to gather data on income and assets. But assets are typically difficult to measure accurately, so that this is not usually a practical alternative. 1FIq- i UIL cuui D4.IILC IVui jAaAMA.IV IM4OL110 WJ113 IL 10 LJIVIUI- LU raUJIJI V.ULIOLUMPUL)II UljLIl iIL,LICLIXICL4 HIU most countries where an LSMS is beine run. Where self-emloyment, including small business and agriculture, 12 is common, it is notoriously difficult to gather accurate income data, or indeed to separate business transactions from consumption transactions. Income from self-employment is hard to measure in industrialized countries too, but self-employment is rarer relative to wage income, so that, for most households, a fairly accurate picture o0 no-usehiu micomre cain ub obuined fixum only a Iew qUesLiUI1b cUvenng UlfiLe-rr LypVs o1 UiWcIIlU. iiI uiL U.S.. it costs five times as much Der household to collect consumution (and other) information in the Consumer Expenditure Survey (CEX) as it does to collect income (and other) data in the Current Population Survey (CPS). As a result, the CPS can be much larger than the CEX, and it is the former that is used for poverty statistics because of the greater regional and racial disaggregation that the larger sample can support. In developing countries, the calculation of income often requires tne measurement of aii own-account tr~iisacinn m innimen uwth muijtinle -Asitc as weu!p as a host nf sassimntintin aonuit sci,h 12hteTs as tl depreciation of tools or animals. Consumption data are expensive to collect in poor countries as in rich, but the concepts are clearer, the protocols are well-understood, and less imputation is required. Perhaps in consequence, there is a long tradition of successful and well-validated consumption surveys in developing countries. One argument that can be made for income is that it is often possible to assign particular sources of income to particular members of the household; for example, earnings from the market can be attributed to the individual who did the work, and pensions are typically "owned" by an identifiable member of the household. By contrast, consumption is only occasionally measured for individual household members. While many aLUU1esIi UW U 1 LU4LLa laV, IL14UU r0'U UOV 01 OUWl1 U1UU11L a wLUUy U4aL4 L1ULdaU0UI WIUZI U1 LtUUWllUlU, "LiU to examine the effects of who "owns" the income on purchases, it should be clear that there is no very clear link between individual welfare and individual income. Earnes or pensioners share their incomes with non- earnes and non-pensioners, so that the attribution of individual welfare from individual income requires some sort of imputation scheme, just as it does for consumption. Although we shall discuss issues of how to adjust welfare for housenold size and composition in Section 5 below, we provide no guidance on how to use survey data on either consumntion or income to study the allocation of resources within the household. Such allocations are often best studied through other measures, for example anthropometric or educational status, though there is an extensive (though only occasionally successful) literature on using household consumption data to make inferences about intrahousehold allocation, see Deaton (1997, Chapter 3) for a review and discussion. 2.5 Durable goods: Because durable goods last for several years, and because it is clearly not thepurchaseofdurables that 13 is the relevant comnonent of welfare. they reauire special treatment when calculating total expenditure. It is the use of a durable good that contributes to welfare, but since use is rarely observed directly, it is typically assumed to be proportional to the stock of the good held by the household. In consequence, when we add up total household expenditures during the year, we add to expenditures on non-durables the annual cost of hoiding tne stock of each durable. This cost is estimated from a conceptual experiment in wnich we imagine the holusehold huviTi the tiurahle good at the beginningy nf each vear- and then selling it again at vear's end- The costs of doing this depend on the price at the beginning of the year, pt, say, its price at the end of the year, p,+,, on the nominal interest rate, rt, which is the cost ofhaving money tied up in the good for the year, and on the extent to which the durable good deteriorates during the year. Deterioration is modeled by means of the simple assumption that the quantity of the good is subject to "radioactive decay"' so that, if the household StutSa nff tl,p year w it the a.n,n,int St it w;ill li arn aarn..irit (11I S C' toep I back at th,e er'.d af t). year- Seen from the beginning of the year, the sales at the end of the year must be deflated to put them on discounted present value terms so that, in today's money, the discounted present cost (negative profit) ofthe transaction is St Pt - Pt+j_ (2.11) so that the cost of maintaining the st0cr-_,;r,h ;a iS of wi- r.ee to ad up total pei approximately (provided the interest rate and depreciation rate are small) .V^ n {w r+ ;){1< where t is the rate of inflation of the durable good price, ( pt,+ - Pt) / Pt. If it is assumed that the rate of inflation of the durable good is the same as that of other goods, the first two terms in the bracket give the real r.tafAAintoro en that thA - ,fia r t2Al1.A necaf 4 A,n.nlo A farS a^ ne afr ;e ;te. nneron nrina n...hln:AS k, * sum of the real interest rate and its rate of deterioration. This is typically referred to as "user cost" or, since it would be the rental charge for the durable in a competitive market, as the "rental equivalent." In Section 3.4 below, we discuss how the elements of (2.12) are computed from the LSMS data. Note that tue ap-proach based on -user cost makes nO allowance for the (oi;tn considerabie) ransactions costs involved in buying and selling durable goods, particularly used durable goods. Such cnots mean that households cannot easily take advantage of temporarily high real interest rates by reallocating their portfolios away from durables and holding money or other assets. Given this, it is important not to make user cost too sensitive to market fluctuations in real interest rates, and this can be accomplished by using, not today's real 14 interest rate, but some average computed over a number of years. One of the most important durable goods for many households is housing itself. Many people rent their accommodation, in which case the "rental equivalent" is actual rent, which is gathered in the surveys and dUUUU IL thLe cUUrUIlpL1U1 LUL.ol. rul UlUos WnhU LJWU U'uli IoIus11rg, uic ieutu1uu for ouier durables can sometimes be used, if people have some idea of what their house is worth, or the rental rate can be imputed by observing the rental costs of similar units. In Section 3.5 below, we discuss how this is calculated from the data gathered in LSMS surveys. 2.6 The evaluation of tlme and leisure: It is often pointed out that people's levels ofliving depend, not only on how much they spend, but also on the amount of leisure they have, so that using a pure consumption measure could be misleading. For example, if two people have the same income and expenditure, but one has a two hour daily commute to get to work, and the other none, they are not equally well off. Similarly, singie-parent households with children are li.kely to be shorrt of innn-ar..t-qt tsir..e crnm.pared xAth, twn-nnntC-rt h16miiAhicd .;it' i- te nm.. iine'n7.e ain. expenditure. Adding in an allowance for the value of leisure or of non-market work could eliminate these anomalies. The theory in Section 2.2 can readily be extended to tell us what to do. In the single period model, --. _.I_A..P_U1_ A. . -tl +-^A_n^+ -;- -+- Up .. . 4; U-A _ _+ CA_ __A -A 1_ _ - Ut___ -AA wuvav wuvin i avaiauivv aL a %vIJOaroLlt vvarLr LaL-,v w, uiv UUUr,%,L %VIIWUCU11L- i'I uVUL& aOu AUs UVI%UvIuII%O p q=w(= - e) + y (2.13) where T is the total time endowment, e is time spent in leisure, and y is income that is not associated with time in the market. Rewriting this gives p-q + we=wT + y (2.14) so that leisure takes itS place with the other gnond5- with price w- and the budget constraint says that expenditures on all goods, including leisure, must be no more than "full income," defined as non-market income plus the value of the time endowment. Leisure can then be incorporated into the welfare measure by working not with expenditure on goods, x, but with expenditure on goods and leisure together. 4 T1iics is cor.rea far as it goes, buIt if ,Ipfrar .eastepr.rt stops ha.rp c, rvrnl,, r o., a .i?rn pt.A-,,,- with full expenditure, a serious error will have been made. In the theory at the beginning ofthis section, money 15 metrin anid welfare ratio utility were rneasured- not hy expenditure.s x. but by x divided by a price index. In those situations where the prices of goods do not differ much across households, which apart perhaps from housing is the normal situation in industrialized countries, a welfare ranking ofhouseholds according to x will be very similar to a welfare ranking according to x deflated by the price index. But once leisure is introduced, the situation is quite different, because the price of leisure, the wage rate, differs across peopie. Rankings by fi1ll expenditare are thefew- uy Adif.frp-t frnm ranw-lOiin by rlef6at.eA fiill ey.p tiijbip. uvhe.re thF. AlPflatnr includes the wage as one of the prices. By the failure to deflate, the welfare of high wage people is overstated, and the welfare of low wage people understated. A high wage rate not only makes the time endowment more valuable-which is taken into account in full income or full expenditur-but it also makes leisure more expensive-which is not. It is incorrectto assess individualor household welfare levels usingfiull income or J"gt c,rr"sz UW"t&"sU Vs"I Suppose that the error is avoided, and a price index including the wage is constructed which is then used to deflate full expenditures. In some circumstances, the resulting welfare measure will be better than one based on expenditures ignoring leisure. But there are also a number of problems that cause us not to recoimmwenu uUs p[rocedure Hi g9VUVF4l. ILi,= zi 1s Uldt 'ul LrebuLts are bsels1vI* LU U-V Value assumieu for -ilu time-endowment. T.should this be 24 hours for each day, or should it be something less, to allow for sleep and "minimal personal maintenance?" More serious still is the real possibility that the simple model oflabor supply that underlies the calculations may be at odds with the facts. For example, suppose that we find an adult in the survey who does not work. According to the model, this person is voluntarily allocating resources to leisure, and although we don't oDserve tnat person's wage-because he or she is not worKing-we can impute some hv Atlw- _iiimlar peoplie w^ value based onr the pewrson'ns ednucalinn and e.xperience, nr using the wavges -reivj. h,o are working. But this person might be unemployed, and unable to find work, or maybe able to find work only at wages that are much lower than those who are working, and whose wages we are using to value "leisure." It adds insult to injury to class unemployed people as well-off by imputing to them a value of leisure based on wages in a formal sector to which they have no access. Because ofthese dangers, we believe that the attempt to value leisure introduces more problems than it is likely to solve, and may compromise the integrity and general credibility of the welfare measures produced from the survey data. Of course, we are not disputing that leisure is valuable, nor that there will be specific cases where assigning some value to it will generate useful supplementary evidence on levels of living. Indeed, time-use data, when available, are a val-uable comnplement to cons-umpLion aggregates for studying welfare. They allow us to identifv those-such as people who must travel long distances to work- or women who mu-st 16 combine childcare with market work-whose welfare is incorrectly assessed by their consumption alone, and permit at least rough-and-ready corrections in circumstances where such cases are a focus of interest. 2.7 Public goods and publicly supplied goods: Another important contribution to living standards that is ignored by private consumption is that made by publicly provided goods, the most important of which are education and health, but which also include such things as police, water, sanitation, justice, public parks, and national defense. The major problem with icuAdUUUig U1*Les is iiiuui a se; of prJces (or shadow pr1ices) U14t reflect what~vUI to'ea4h are WULUlU huUe11U1U. One approach to estimating prices is to look for effects of the provision of public goods on the demand for private goods. For example, we might be able to assess the value of a new public clinic by seeing how much less people spend on private doctors or clinics. But it is clear that this line of investigation, although useful in some cases, cannot work in general. If the publicly provided good is separable in preferences from private if~~~~~~~ _ consunption, or .~~~~~~ part _r* _ ofI ___ I is separable, - __ * s --_ cnanges - mi ~1 the-- ___@s _ .--- _ _rt_ provision l1_.s oI ~_ -- __ tIe former *. __ -_ -_- --_~_ *-s tor m its separablc part) will have no effect on the latter. In conseauence. there is no hone of comnuting the full shadow nrice based on observable behavior. The other approach, which has recently become popular in the project evaluation literature, is to ask people how much they would be prepared to pay for an additional unit ofthe good. Whether such "contingent valuation" procedures yield useful numbers remains controversial among both economists and psychologists, see Hanemann (I 994) for the arguments in favor, and Diamond and Hausman (i994) for the (,,ntiih mo,nre rn,nv u,n,r.in,g 1wmu.-m^^npt- _g:i~nct As . ith the i. nurn1t1atnn of leisulre, w e believe tha>t i em. tUtiniw*,finr public goods are likely to compromise the credibility and usefulness of welfare measures in general. None of which gainsays the fact that the documentation of who gets access to publicly provided goods and services, and whether these people are poor or rich, remains an important element in any overall assessment of living standards and poverty. It should be noted that there are some cases where the necessity to make some allowance for public goods cannot be avoided. The most obvious case is when maldng international comparisons where in one country, some good-health and housing are the obvious examples-is publicly provided or subsidized, while in the other it is obtained through the market. Even within a country, urban residents may have access to sUUbsUild hUospitals, cl iULc, rU "fai pFL-;ce shsIUF LU4L are IIUL availab:I iII U1e uULLUY51UV. %JIVVII LUhe difficulties of measurement, and the variety of possible cases, it is impossible to make useful general recommendations about how imputations might be done. It will sometimes be enough to be aware of the problem and its implication for certain types of welfare comparisons; in other cases, it will be necessary to try to revalue consumption at international or unsubsidized prices, even if such imputations carry a large margin of 17 error. 2.8 Farm households: 1Ws iV1aL _ . t AN - A1 UUULVUSIJIUJLUO UL AAA1 V9VqIVFJLL5r, %AJUlUlID CL'. _w 1_4A _A1 _--_ 11J Only ULJOWAl1ClIS0 -- _>s- A_1 rVVUQA. CJUKJ -- AA -_A . QUL V WC4 U-AA.1-af1l , U%l a4 producers. Many people have small, own-account business, and many more are farm-households who produce goods, sometimes for the market, and sometimes for their own consumption. The standard approach to these mixed entities is to split them into a consumption unit and a production unit. This can be done under the conditions of the "separation" property, see Singh, Strauss, and Squire (1976). Ifmarkets are perfect, so that all factors are perfectly oimiugencous and can be bought and sold at fixed prics m uimii ted quanuties, tnen a farm-ho:usehold behaves exactly as if it were the sum of a farm. which maxi-mizes nrofits at given market prices, and a household, which chooses its consumption bundle so as to maximize its welfare at fixed prices and subject to its income, including the profits from its farm. The assumptions of the separation theorem are more obviously appropriate to the owners of an agribusiness who live in New York city than to most subsistence farm househoids in developing countries, or eisewhere. Family labor is not the same as hired labor, wnrlr may not always be Avnilnhle 2t "the" w,2ge, and the costs of translport tno and frnm uwnrk may redluce the effective price of work on the home farm. All of these issues can be dealt with by suitable modifications of the theory, but only at the cost of introducing shadow prices that are even more difficult to observe and to calculate than the actual prices, the collection of which itself imposes considerable difficulty. "T,. jJSW.-c , it i8 Wiflc.uAAt to be.f.er - - h.. t-o- treat. fats -fl% b .4..es a0 distinct units, and to value the sales from one to the other at some suitable prices. These prices are of course not observed for the households for which they are required, but must be imputed from purchases of such goods by other households, or from prices collected in the community questionnaire. This tends to be a very approximate business, so that it is perhaps unreasonable to insist too strictly on abstract considerations. 1NUVVUIIAVIC65, IL lb WVLUI UIULULIg UIUL IIWLa1r.eL piIVre ofUIL r,IAIUUde aU eVIVIRVIL UL WUlUpnUL aLIU UdlfibUUUUHi costs that should not be included when evaluating consumption from home production: "farm-gate" not "market" prices are appropriate for imputation. It is also necessary to be careful about quality comparability, home produce may (or may not) be of lower quality, and water from the local pond is certainly different fromL 'Eau Perrier. Ac we shall qee helnw imnuitatinns are tvnicallv roiuh and readv and subjeihict tn a goon deal nf inaccuracy. In countries where a large fraction of food consumption comes from home production-see Table 3.1 for examples-imputations, and the role of the separation theorem, can generate considerable discomfort 18 with the resulting calculations. The methods of this paper make most sense where markets are active, and where the standard neoclassical model is a good approximation to reality. For many non-monetized subsistence economies, this is hardly the case. In such economies, the ratio of measurement to imputation is often quite low, and there is a real question about whether we are "'measuring" or "assuniing;. And even if imputations are accurate on av .agewhich would be assIMing a geat deAIt,ey tenrd to be less ,.4-able tkan would be the true data, so that their use tends to understate inequality and (in most cases) poverty. Money metric and welfare-ratio measures of welfare were developed to measure living standards for households who obtain their goods and services through the market and make the best choices that their incomes will permit given the prices that they face. In peasant economies, this neoclassical model is often a poor approximation to reality, 11Vd~U~ii1i1L GUU V11~~ d~U III ar.du we:fare-r,a1e.e. based. on. a %AJ IVUIjJII drL,-,L consun-.wYon - ggrga. lb a UHLKViy LV Ur, V1ULVI 4%'ALUILV, VI UbrIUI. .IL isurlk'ytbei.r cua o-sfl..e again. we have no useful counsel except to be aware of the issue, and sometimes to be prepared to concede defeat. 2.9 Differences in tastes across people and households: The theoretical framework of Section 2.2 works with a single set of nreferences so that when we rank different households according to their money metric utility, we are locating their different expenditures levels on the same set of indifference curves. Since different people have different tastes, it is not clear why this is the correct thing to do. O)ne' 2rclimtnt iq that there iS little intetpsqt i,n 2vltiintina xnv intdiv iVA12'S e!fi2r. aG nrAing tn hs or her own lights, but that we need to know about the welfare of a reference person given the circumstances ofthe individual. Hence, we need a reference set of preferences, as well as a reference set ofprices. The answer to the question "How well-off would John Doe be with household h's income?" is of more general- interest than allowing the idiosyncrasies of each person's tastes to affect the evaluation ofhis or her resources. For example, n... Aessr.t- a gntrn. r- ;nnnm,nrtles, but wve v,nou1d hardly cont.+ sor.eone as pr jusot bemaOus thai income did not match their greed. More seriously, altruists are not deemed to be rich because their neighbors are rich nor, in the same circumstances, are the envious deemed to be poor. Nevertheless, there are some taste factors that affect the translation of money into welfare for everyone, ard U14L are U4U11 LyUUre LgUU. Ill aODVbsLl* WeL1far. EIaVl'Ul bLaLUb Lb UIr, btll s IhU l d-IboUlI WULI lU ub'VU pllU a great deal of money for life-saving surgery or simply to stay alive would not be deemed to be rich because of such expenditure. But in practice, the most important taste-like factor that must be allowed for is household size and composition. There is a useful analogy here with prices; prices, like needs, moderate the way in which 19 expenditures on each good generate welfare. If the price of rice is three times as high, 50 rupees can onlybuy a third as much rice. Similarly, 50 rupees worth of rice buys only a third as much per person in a household of three persons as in a household of one. According to this analogy, expenditure must not only be deflated by a price index that reflects variations in the costs of goods and services, but it must also be deflated by some n-^easrve of household sL: U.L VA-Ud tL aaV b :r.Ud ViLUUa wefiUrn. SVecUUio 5 Ls coIr.CIMLlmedi-ui hiuw LU LIruCt Con the appropriate measures. There is another issue about taste variation. This is the question of "regrettable necessities," goods and services that yield no welfare in their own right, but that have to be purchased, for example, in order to earn Ub-viuus exaples, aid ule argument is tuh sucn items snouid be icorme. Work clotuhes or raIIsport to wUrk are deducted from income rather than included in consumntion. If this is not done- individualq with differen.t expenditures on regrettable necessities will not be correctly ranked if we rely only on their total consumption inclusive of such expenditures. Again, the theoretical validity of such points should not blind us to the practical difficulties. Transport to work is a regrettable necessity for someone who has little choice ofwhere to work or where to live, but is consumption for someone wno chooses to live in a pieasant suburb. Out-of-pocket medical eypen es are anecesritv for nomen hilt a rihnir- fnr nther, n in, Gritivp wvr. l eametic. m I ;ediu.i,e TIto;odt+o see how guidelines could be constructed that would allow one and not the other. The issue here is essentially the same as that facing a tax authority when deciding what expenses should be allowed as deductions against income in the computation of income tax. While recognizing the occasional injustice, such authorities tend to take a hard line on such deductions in order to avoid large scale abuse. Exactly the same arguments apply here. 20 Box 1. Summarv of Theoretical Issues and Reeommendations Issue Recommendation Money Metric Utility (MMU) vs. Welfare Rgftio(WR) MMU is the amount required to sustain a level of living and requires that consumnption Attempt should be made to UV 4UJuMLVU Uy a Paasche price iiidex ihai rfiecuts ta pnces uic hu-usehoid faces ana use Money Metric utility and whose weights are different for each household. to calculate the Paasche price indices with individuai WR is an indication of how much better or worse off a household is than a reference household weights. howehol (s y at the p .. n ay uu equis and cnu :onU tUV ausate blUy a Laspeyres price index that reflects the prices faced by the reference household but whose weights are tihe sam-e for oil hou6seholds. 1'lp NMUrT can cause o,~pf difficuti-es in a..... ing thhe ,w.pt of ri- b policy l l but, on the other hand, WR does not necessarily represent welfare correctly. The latter is the mnre serinus dra.whack- in nractic Ineome vs Cannnnintian IConsunmntion is a theoretically more satisfactorv measure of well-being Tinmost de-velopin contries where LSMS and /or Income is wed in industrial countries where self-employment is relatively rare so that household exnenditure most household income comes from a few sources, where annual income variation is surveys are available, low, and consumption data are relatively costly to gather. consumption is the appropriate measure to use. Consumption is less variable over the period of a year, much more stable than income in agricultural economies and makes it more reasonable to extrapolate from two weeks to a year for a survey household. When self-employment is common, income data is at | least as expensive and as difficult to collect as are onsumption data. Durable Goods and Housing A measure of use-value, not purchase, of durable goods is the right measure to include Exclude expenditures - in the consumption aggregate from a welfare point of view instead, calculate a rental equivalent / user cost for housing & durable goods owned by the household. Time and Leisure Households with more leisure time have a higher level of welfare than households with Omit time and leisure in the no leisure. However, valuing leisure for each individual is problematic. Furthermore, calculation of consumption. it is diffcult to distinguish between leisure, non-market work for the household, and l involuntary unemploymentl l l l~~~~~~~~~~L Issue IRecommendation Publc Goods Clearly presence of public goods such as hospitals and schools improves the welfare of J Do not include any nearby households more than that of households without good access to these services. valuation of public goods in However, estimating the value of those services is problematic. Households may the calculation of the choose private services even if public services are available. Contingent valuation of household consumption services that don't exist are sometims used but of questionable accuracy. aggregate. Frm Households It is possible to consider households as consumers separately from household Treat the farm household as businesses or farms in economies with active markets. In subsistence economies, this j a business selling to the assumption is sometimes hard to justify; however trying to separate the producer from household. Attempt to value the consumer using estimates of farm-gate prices is the best strategy in practice. In produce at "farmgate" rather countries where a large fraction of consumption comes from home production, and than "market" prices. markets are less active, the evaluation of welfare becomes sensitive to difficult decisions about imputations, and should be regarded with caution. l_l Differences in Tastes P,xpenditure on regrettable necessities should, in theory, be exciuded but in practice it Include expenditure on is impossible reliably to distinguish between necessities and choices. Household size, items that may or may not nowever, is importan gnu d auecis uae nousenoid weirare associatea witn a given ievei be regrettable necessities. of expenditure. Adjust household expenditure to refiect household size. 22 3. CONSTRUCTING THE HOUSEHOLD CONSUMPTION AGGREGATE 3.1 IntroIuction: Following the diiscussion of the basic theoretical firmewnrk inplicit in using c-nnlrnution as a measure of welfare, this section provides specific guidelines that the analyst can follow to construct a nominal consumption aggregate from a typical LSMS household survey. For the purposes of this paper, the procedures followed in constructing the consumption aggregate from recent household surveys in the following countries were reviewed in detail: Vietnam, Nepal, Ghana, the Kyrgyz Republic, Ecuador, South Africa, Panama, and One important preliminary issue should be emphasized, though it is one where it is hard to give any very precise guidelines. This is the issue of data cleaning. In most cases, analysts who are constructing consumption aggregates will be using a "clean" set of data that has already been subjected to the usual IoraLiI.ey cheLv-s and elllliUVi Vo gross VULIierlsUandUcin1g o1L1V U.-or. IUVV1 Ule:es, eAxp,ier.11V h1s soWrI that every new exercise reveals new problems with the data, and the construction of a consumption aaaregate is no exception. As we shall see, the construction of a consumption aggregate involves adding together a large number of items, many but by no means all from the consumption section of the questionnaire. It is of the greatest importance that the analyst check each of these items for the presence of "gross" outliers, typically by graphing the data, for exarmpic using iiie oneway andi oox options in STA I A. r or inherently posiuve quantities, it is often useful to do this in logs as well as in levels. Aggaregates and sub-aa repates should similarly be checked. Such checks often reveal, not only isolated outliers, but groups of outliers, for example if the units have been misinterpreted for all observations in a cluster. Sometimes, outliers can clearly be attributed to coding errors, as when the units have been misinterpreted, or where zeros have been added, and in such cases it is routine to impute an average (or better median) value for other househoids in the same ciuster or region. In other cases, it is unclear uwhether thi "Antlier" is genr.uini nr inot, and the analyst m.ust ma,e a judgment that balances the desirability of keeping any reasonable number against the risk of contaminating the aggregate. In Table 3.1, the components of consumption are aggregated into four main classes: (i)food items, (ii) nnfoodf ,tt*lI.-, (iii)j orfl f -- -lf -, ar.d (v ho uJin. A - lA WL LUlt WA t.r e WA UAtbOY. oAcasses U;n the overall consumption aggregate depends on many factors, including the average level of income in the country, prevalent tastes and norms, as well as the types of data collected in the survey. In this regard, it should be noted that there was considerable variation in the design of questionnaires across the various countries, so that the aggregates do not always include the same items. Nonetheless, the table is indicative of the order of magnitude and relative importance of the sub-aggregates. Table 3. 1: Main components of the consumption aggregate Share of consumption aggregate (per cent) Sub-aggregate Vietnam Nepal Ghana Kyrgyz Ecuador S. Africa Panama Brzil i]9S7J L996 1900Y7 ;796 19977A ;993 ;99I i99_Y Food 50.9 64.2 65.2 44.5 49.6 30.4 45.9 27.7 Purchases ' 34.1 29.0 44.4 33.4 44.3 28.2 39.8 21.0 Home oroduction b 16.8 35.2 20.8 11.1 5.3 2.2 6.1 6.7 Non-food Items: 28.7 194 28.0 22.5 29.1 45.1 45.8 32.0 Education 2.5 3.4 N/a 2.4 8.2 3.2 7.8 6.4 Health 5.7 3.2 N/a 1.0 . 1.7 0.9 4.5 Other non-foods 20.5 12.8 N/a 19.1 20.9 40.2 37.1 21.1 Consumer Durables 12.7 1.4 2.2 3.5 5.2 . 5.4 Housing 7.7 .1i 2.5 29.6 16.0 24.5 2.8 40.2 Rent 5.9 12.6 1.7 17.6 12.1 15.6 2.1 31.4 Utilities 1.8 2.5 0.8 11.9 3.9 8.9 0.7 8.8 OVERALL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 GNP per capita (S)' 170 210 390 550 1,280 2,980 3,080 4,400 a Includes meals taken away from the home. b Includes also food received from other household members, friends, and in the formn of in-lind payments. c GINP per capita is taken from intemational statistics for the same year of tne survey, except for Panama wnere the latest available estimate is for 1996. in generl, as we would e-xpect irom nEgel's law, huisnare of food items in the Lotal tends to be relatively more irnmortant the lower the level of income in tie countrv. The share of home-production in the food consumption aggregate tends to be higher in countries where relatively fewer transactions take place through the market place (Nepal, Vietnam) compared to those countries where agricultural markets are relatively well-developed (Ecuador, Panama, South Africa). 4 T,he c.kiva of onn.irn.wtin, atffhiiitahL. to oAipaftie%" ard health alse An'.vAa mn t+p iirn.-.e of leave of the country, as well as the extent to which these services are purchased through the market, or else are provided instead by the state at subsidized rates. A more detailed discussion of each of the main classes in the overall consumption aggregate is taken up in the sections that follow: 24 3.2 Food consumption: In principle, constructing a food consumption sub-aggregate is a straightforward aggregation exercise; all that is needed are data on the totai value of the various food consumed in the reference period, or else on the total quantities of Aiffp.rpn+t food iters conisurmed as a rpfprpt,on. mrel spt of nriw-es at u.hich tn v alue thenm. Tn practice, however, households consume food obtained from a variety of different sources, and so in computing a measure of total food consumption to include as part of the aggregate welfare measure, it is important to include food consumed by the household from all possible sources. In particular, this measure should include not just (i) food purchased in the market place, including meals purchased away trom home for consumption at Vl away 4 l.VUVIl', t VUU QIoL y.1J LULJ t saul o *1tJfl1flVy1tflW .. .'.A 1 *LJL UJ o. *.__. 1 _v L ° .W v* S* fl1fl to from other households, as well as (iv) food received from employers as payment in-kind for services rendered. In some cases where food can be and is stored over long periods of time, and where the questionnaire permits it, "food consumed" can be distinguished from "food purchased". In principle, it is the value of the former that should go into the consumption aggregate. A household that stocks up on cereals once every few months, and wh O-useU iS IUdUgL by Ulus ei svy, s,iUUIU :IUL Ur, UberLUy countUe Well-oIA., I.or should mone WIo did not stock up in the survey period be counted as poor. The food consumption module of most LSMS questionnaires typically contains separate sets of questions on (a) purchased and (b) non-purchased food items. As can be seen from Table 3.1, the relative importance ofthese two components in the food consumption sub-aggregate varies considerably by countiy in Nepal, home-produced food items constitute more than half of food consumption, while in Souith Aica they compri"se less than 10 per rent of food connqumption It is even more obvious that the extent of non-purchased food varies within countries, particularly between rural and urban sectors, but also within rural areas according to the level of living. As a result, failure to capture the value of consumption from home-production is likelyto overstate both poverty and inequality. 0 foodA "phiuc,nae Th. r,ei,lp- in T QM.if niipct. ninn.r-iva thnimnllv rnntnin niipQtinne nn inrrlknec of 2 fairly comprehensive list of food items (a) during a relatively short reference period, such as the last two weeks, and / or (b) during a typical month in which such purchases were made. Data are often collected on the total amount spent on purchasing each food item, and sometimes also on the quantities purchased, during the specified reference period. Calculating the food purchases sub-aggregate involves converting all reported exper:.^e on~ food it1,, nc to a ,;fo.... refere.cen nner,oo., nor. yer-arn.....dn tur ag egar;nog t*hes 1%0 VJIALW FFj.11lU1LLU J JV L 11.1.41 LU UL UAfLA.W11l W- ' .. WV "~ 1. JI'I3O - 01 4111 . 1U 6 1'1% expenditures across all food items purchased by the household. In surveys where information on food purchases has been collected for more than one recall period, the question arises as to which of the two sources of information should be used. Note once again that, in these guidelines, we are not concerned with how the data should be collected and what reference periods shouldbe used, but rather with the decisions that must be made by an analyst wno is confronted with multuple measures an already 1 conlljtPie Q1ru. y. Conlsumptionn survevy-includirng LSMS sqirvevs-have used several diffe-mrent designs in collecting consumption data, from a single question about purchases over the last two weeks, to multiple visits each with much shorter recall periods, to repeated visits over the year designed to capture seasonal variations in consumption patterns. There is large (but far from decisive) literature on the benefits and costs of these different designs, much of which is reviewed in the context of LSMS surveys in Deaton and #-At (1 [AflQ] Tf-s rsA olrza 1-..,, Al AtPr 4-tin iirw th-n~nnip ov vn th-ft thPrP ic _ rhif1P -n_lvct .JIU;:OII 1L770.. Li" 0113 ,..Vfl Ev* 0w fldJ * L- . ., , f. tf 0_._ should choose the altemative that is likely to provide the most accurate estimate of annual consumption for each household, not for households on average. In perhaps the ideal (but most expensive) case, where in each "season" the household has been visited on several occasions, estimates should be made of consumption in each of the seasons, and the seasonal totals added to get annual consumption. In most surveys, this will not be an op.UIo, d U1 Ll14 U±ML 4I.4uai LSM s veys, he-eI eiUN.4 Cs 11r.I CIlAice or ClL0IV%e LO UIiUL'..A oi z Q. ^o41I' 1U.t' LV weeks" (or shorter period) measure, and a "usual month" measure. The literature reviewed in Deaton and Grosh leads to a recommendation in favor of the latter over the former, at least for the present purpose. The former tends to be biased by progressive forgetting, as well as the occasional intrusion of (especially well- remembered) purchases from outside the period. The latter has the advantage of being closer to the concept that we want-usual consumption is a better weifare meas-ure than what actually happened in the las two -weeks, whi.-.h eniild have been unusual for any number of reasons--and reduces nroblems with seasonalitv. but will suffer from measurement error if respondents find it difficult to calculate a reasonable answer. In any case, and whenever possible, data from very short reference periods should be avoided. Over a period of a day or two, purchases are quite unrepresentative of consumption. Averaged over a large number of households, mean purchases will still be accurate for mean consumption, but dispersion will be exaggerated, with consequent exge-aiv o~f ,pii.qa1jh --nd (,i. no,rr.i1. p~cases flnurty. Co.nnsiimnrhnn mP--asirte based onf very 'shnr rpt,ldl are not suitable for the construction of consumption aggregates for welfare purposes. The total value of meals consumed outside the household (restaurants, prepared foods purchased from the market place) should also be included in the food consumption aggregate, as should the value of meals L4ZII by I3UalOULldU,-.L..bJL atl MI. I WeJLN LII.U1 -Arg.JLi, U V^GLJGVAL1, LIL. l LSJMIVI.U O11 V aIlJ VLys ask%L lAPL.ILtL1y about the total value of meals taken outside the home by all household members; this amount should also be included in the food consumption aggregate. In some cases, however, it is impossible to disentangle expenditure on some meals taken outside the home from other related (and more aggregate) non-food 26 expenditures such as miscellaneous schooling expenses, total expenditure on vacations, etc. reported elsewhere in the questionnaire. This need not be cause for concern as long as these expenditures are included in the o ve-r-al IlJ aggrer-ate hoLus,oVldVL ,,laurue irL oef 'ore cor,s U,,JL,UoIn u,e Ulu ie. Almost all LSMS questionnaires contain a separate set of questions or module on consumption of home-produced food items. Here it is more common to find questions only on the amount of home-produced food items consumed in a typical month (rather than in the past 2 weeks), as well as the number ofmonths each food item is typically consumed in a year. Data are often coliected on both the totai value and quantity of consumntion of each home-produced food item The home-production food sub-aggregate can th.us be calculated by adding the reported value of consumption of each of the home-produced food items in a manner analogous to that followed in the case of food purchases. in principle, it is possibie to calculate the food home-production sub-aggregate using data on reported quniinin f-e ninmeid in CnjiO.cioGinn uAth pr_es from the fnfood prkchases section. However, a pointed - section 2.8 above, and to the extent possible, "farm-gate" prices should be used when imputing values to home-produced food items. Moreover, home-produced food items consumed by the household may not be comparable in quality to items traded in the market place. Households' own valuation of the amount they would expect to receive (pay) if they had sold (bought) the home-produced food items that they consume are therefore likely to be a m.uch better approximation to their tUue "fz .. g;aLe" vaue, ratLher Uthn estLUm4,ates derived using prevailing market prices from the food purchases section. In most LSMS questionnaires, food received as payment in-kind, as well as in the form of gifts, remittances, etc., are usually lumped together into one set of questions (usually on total value of consumption .. _. - - - . -_ - - _ I - - -J !- - ~ - - - on nume prucuCtion. Consumption of Iood aerved .- .j ' _ - - _ !-- - - 1 _ - - _j_ - ___ . e- . ._ - I fUrM tiiib ,UFCui), U1 ES buUbmUeIdU unudr uth quesuunis from these sources should be added to the overall food aggregate, if it is not already imnlicitly included in the home-produced food sub-aggregate described above. In some cases, however, it may be that questions on consumption ofhome-produced food items are not included in the quesuonnaires explicitly, so that data are avaiiabie for consumption of purchased food items only. Tn such caseq- it may still he pnosihle to uSe data from the agrictultire section to dtpnve an es,t.i Em, te ofthe total value of home-produced food items. The section on crop production of most LSMS surveys typically includes a question of the type: "How much of ..[crop].. did your household keep for consumption at home?" as well as questions on dairy and other livestock products that the household consumed from its own 2I -7 production, so this information, in conjunction with data on prices, can be used to calculate the total value of home-produced food consumption. Pnr ivncta nnr i,n the apep nfthe 1QO . Tvrav7 T 1Rpn,hlinr LSMS rrnn,elinwintin nfhnmP.nrnrdm-l-. r,ngn and animal products was calculated from the "Agro-Pastoral Activities" section of the questionnaire, because the section on "Food Expenditure and Consumption" collected data on food purchases only. Exclusion ofthese items from the food consumption aggregate would have resulted in underestimating average food consumption by 30 per cent. Furthermore, because the share of home-produced food in rural areas was much higher than in -bLar aiLLr fl0 LLWA.r, .. LILJ ' UL ag51 gregate 5 COn.LarJL-IpIoJLL L..-.eQLL would have L%1..iLVU iLJLiI :.ns.LUously LurLUI- estimating the welfare of rural compared to urban households. Because all LSMS surveys collect information on total value of the food item consumed (for both pur- chased and non-purchased foods), the question of assigning monetary values does not arise. However, in cected on both va'ue as weii as quanuty of food ite surveeys wvhe-re datw iae consuimied, it may oe utax due to interviewer error-or a varietv of other reasons-we find households consuming non-zero quantities of a particular item, but where data on the total value of consumption may be missing. In such instances, the question arises as to what prices to use to value food consunption of these items - (i) average or median prices calculated from the survey data for other households, (ii) prices from the price (conmmunity) questionnaire, or else (iii) prices from some otner externai source? Faced with a choice ofprices, the best choice is usually the one that offers the closest approximation to the amount actually paid. Except where there is a large choice of quality, the values reported by the household are likely to be better guide than market prices, if only because they record actual, not hypothetical transactions. When such data are not available, the analyst can construct prices from the data for other t 4 h1, ns-ol-dn onAr-ae t k.n r.,n (n prefere..c to the"r.ear. chire ser,s;.. v1 to out'.ier;,) price -Ai.. *L.Jao.A.JalO,Wfl LW% ts% L - ALL ---1ALL F.L -- - -h. ILI -V lLULXV .h~ O A..L OfL,UV .tIOL I A, WUUALLIV 1 IA J L%A FtjaLU U) other households in the same cluster. When these data are not available, there is no choice but to use prices reported by other households in the same sub-region, district, division, or province, depending on whichever is the next higher level of aggregation for which price information is available. When making such substitutions, great care must be exercised, particularly through checking that the prices being imputed are reasonable. Mrech1anical imputatiVon uhat are inifact very different, witi can. resuUlt inI tUhe mLatcLhin1g of prices fu[ gouds catastrophic conseauences for consumDtion agzregates. In one famous example, a survey imputed a value for water collected by households from local wells by using the geographically nearest price for purchased water, which in this case turned out to be imported bottled water from a French spa. By this remarkable imputation, 28 rural households were given living standards well in excess of their urban counterparts. 3.3: Consumntion of non-food items: £LSMVLY3 4UL&VLL1aIUO L.yJiFaiy coal]ec UIIIRa.III .fo on1 co1rsU11,-JL,ti VI oL WLL ie4I Ira.o.I LII-.nVoU iLt.-.^ For example, data are collected on consumption of daily-use items such as soap and cleaning supplies, kerosene and petrol, newspapers, tobacco, stationary and supplies, recreational expenses and miscellaneous personal care items, as well as other less frequently purchased items such as clothing, footwear, kitchen equipment, household textiles such as sheets, curtains, bedcovers, etc., and other household use items. Data are also collected on education and health expenditures for all nousehold members. Expenditures on nousenold utilities are tvnicallv collected in the housing module- and for households that have small hbuines enterpn:ses, that module can provide information on non-food items that were produced for home consumption. Finally, these questionnaires typically also solicit information on other infrequent expenses such as legal fees and expenses, home repair and improvements, taxes and levies, as well as expenditure on social ceremonies, marriages, births, and funerais, etc. The actual computation of an annual non-food consumption aggregate is straightforward. The difficulties lie in the choice of which items to include. The choice depends not only on which data are available, but also on the analytic objectives of the study being undertaken. However, there are a few general issues that apply to most LSMS survey data and for the standard welfare analyses; these are taken up later in Unlike many homogeneous food items, most non-food goods are too heterogeneous to permit the collection of information on quantities consumed-exceptions are some fuels, like kerosene or electricity, and some transportation items-so that LSMS surveys collect data only on the value of non-foods purchased over Ulr.e .-efe.-e. J-.Lcpeiod. D.aa Von purchases1;; VI non, food itenrs are oferL collected forWLIVL dif-f,-- ,-eallp,iods, L for example over the past 30 days. the past 3 months, or the past 12 months. depending on how freauentlv the items concerned are typically purchased. Constructing the non-food aggregate thus entails converting all these reported amounts to a uniform reference period-say one year-, and then aggregating across the various items. As far as singling out which non-food "exnenditures" should be excluded from the consumntinn aggregates, some choices are straightforward. Expenditures on taxes and levies are not part of consumption, but a deduction from income, and should not be included in the consumption total. An apparent exception can 29 sometimes be argued for some local taxes, such as property taxes, that are used to provide local services, such as schools, policing, or garbage collection. In some locations, these taxes bear no relation to services provided and so should not be included in the consumption aggregate. But where such taxes are closely related to services provided, households that are paying more tax are receiving more services, are better off as a result, and A 4-, :-,-u of t -;1 d A- ". 'e to " e -k -- l A;ff o i, piublic goroA pi.vrridni between different households. Commodity taxes are included in the prices of goods, and so (correctly) find their way into the consumption aggregate through the prices-though it is also possible to imagine using reference prices for money metric utility that exclude commodity taxes. In any case, no special treatment is required for commodity taxes. As we have already argued, expenditure on "regrettable necessities", such as travel to wvork or wor'-rivelated clouuing, are b i.,cuded, u'uU1 bus sx ed - oneration of own-account business must be excluded. These distinctions are much more easily enunciated than implemented; the welfare analyst faces much the same difficulties as does a tax inspector! Some surveys list as "expenditures" items that are clearly capital account transactions, such as expenditures for a "saving club". All purchases of financial assets, as well as repayments of debt, and interest payments should be excluded from the consumption aggregate. More complex is the case of "lumpy" and relatively infrequent expenditures such as marriages and dowries, births, and funerals. While almost all households incur relatively large expenditures on these at some stage, only a relatively small proportion of households are likely to make such expenditures during the reference period typically covered by the survey. For instance, in the case of the LSMS survey conducted in p l tf (1001 PDT)I, lves thkai, 2 pe,r -er.t nflhk,mc.hntlA repourtpe hag iadep r a do, pa.urunupt Al,riun thep past 12 months; however, such expenses constituted 20 per cent oftheir total annual consumption, Howes and Zaidi (1994). Ideally, we would want to "smooth" these lumpy expenditures, spreading them over several years, but lacking the information to do so-which might come, for example, by incorporating multi-year reference periods for such items-we recommend leaving them out of the consumption aggregate. Note the 4RUiUr,y WlUl JIlUs4buremlent III LO-. t%IUIVU611 UaDIWLLI VAPVLULUtULVO WlG ilW4l VllvUUI, UIeilbUIIIMUUII aFsSa>4ur that include them can be thought of as "noisy" measures of the longer-run averaged totals that we would really like to measure. In this sense, measurement error and lumpiness can be thought of together, and the techniques we discuss in Section 6.4 below can be applied to both. Expenditure on healtn is an often lumpy expenditure where a decision almost aiways has to be made. Orn.e argum..e-nt for exclusion is that such ernendiit ure reflects a regrettable inecersity that dnes nothing to increase welfare. By including health expenditures for someone who has fallen sick, we register an increase in 30 welfare when, in fact, the opposite has occurred. The fundamental problem here is our inability to measure the loss of welfare associated with being sick, and which is (presumably) ameliorated to some extent by health expenditures. Including the latter without allowing for the former is clearly incorrect, though excluding health expendiLLtLUres d1LtgUeur r Learns uhatL We r,iUs UIhe UIIVIVII96.or )VW.I ILW peoUplJ, UVULV11 WHLUIH &-e sic, b-UL UILy one of which pavs for treatment. It is also true that some health expenditures-for example cosmetic expenditures-are discretionary and welfare enhancing, and that it is difficult to separate "necessary" from "unnecessary" expenditures, even if we could agree on which is which. It is also difficult without special health questionnaires to get at the whole picture of health financing. Some people have insurance, so that expenditures are only "out of poCket expendiu-res whmcn may be only a small fracton oI the total, while others have none, and may bear the whole cost Simply adding up expenditures will not give the right answer. Yet another approach is a pragmatic one that recognizes that measured health expenditures are a noisy approximation to what we would ideally like to have. As we shall see in Section 6.3 below, the decision about whether to include them in the total depends, not only on the extent of the measurement error, but also on elasticity of health expenditures with respect to totai expenditure. The higher the elasticity, the stronger the Pc2c fnr inet,hicinn Table 3. 2: Elasticity of Health and Education Expenditures Health Exnenditures Education Expenditures Country Year Estifm t- R Estim. t- R elascl a+:s4c~4 s22. s:c-;Sas4c qare ------- Vietnam 92-93 0.86 33.2 0.19 1.35 46.8 0.43 Nepal 1996 0.75 20.9 0.15 1.65 43.5 0.48 Kyrgyz Republic 1996 0.74 14.3 0.14 0.68 13.1 0.13 Ecuador 94-95 -- -- -- 1.38 46.6 0.37 South Africa 1993 1.14 58.7 0.40 1.32 67.2 0.45 Panama 1997 0.80 29.2 0.25 1.24 54.9 0.49 Brazil 96-97 0.85 31.0 0.26 1.25 47.9 0.45 The elasticity of expenditure on health was estimated from the LSMS data from the seven countries reviewed for this paper. With the exception of South Africa, the elasticities of health expenditures are estimated to be relatively low (see Table 3.2), a result that should be contrasted with the estimated elasticities for educationai expendiiures, which arc also shown in the table. Given these numbers, and given the measurement probhemns, we think that there is a relatively good case for exliidinog health expenditures in the consumption aggregate. Table 3.2 also shows elasticities for educational expenditures, for which similar issues arise as for health. Although educational expenses are not as irregular as health expenditures, they are located 31 at a particular point in the life-cycle, so that, even if all households paid the same for education and had the same number of children, some would appear better-off than others simply by virtue of their age. In this sense, educational expenditures, like health expenditures, would ideally be smoothed over life. There is also the argument that education is an imvestment, not consumpuon, ana snoula be mcluaed m saving, not m me conumpntion aagregate. But we follow standard natinnal income accauntina nraceice and recnmmend that it he included in the consumption aggregate. Another important group of items to consider are items such as consumer durables and housing whose useful life typically spans a time-period greater than the interval for wnich the consumption aggregate is being constructedA. As disc,sed 1n Sectionn 2.4 nnup, the revn-Iunt nnm"s-ie.,t nftAP tntal. is a not tinh f a "" on such items but a measure of the flow of services that they yield. How to calculate this measure of "user-cost" for consumer durables and for housing is taken up in more detail in Sections 3.4 and 3.5 respectively. Another group of expenditures are gifts, charitable contributions, and remittances to other households. A case cW* I- -. A. for I;A.clu ng i it, tonl, oU-A. +tI-,fat. ula, Lit.3 -flo mi,u ^-t.e1A as .- 11..u weL^h e'.oU transmitting household as do other consumption expenditures that could have been made with the funds. However, their inclusion in the consumption aggregate would involve double-counting if, as one would expect, the transfers show up in the consumption of other households. Average living standards could be increased without limit if each household were simply encouraged to donate its income to another household, and so on; noUthig would have changed except oUrU measure of welfare. Wue uie-refore r;mmend excludiig gifts and transfers, counting them as they are spent by their recipients. Finally, there are various miscellaneous non-food items that are worth mentioning. Expenditures at weddings and funerals are another lumpy and occasional item. In some countries, these expenditures are really transfers-to tne bride and groom, or to tneir parents-and shouid probably be treated as such and excluded frnm theaggoregate Their tranitoriness wouldlead to the saMe econcluscin-n So-mei hnusehoJds ownx. sm.1l enterprises which produce goods for own-consumption; such items should be treated analogously to home- produced food, priced as well as is possible in the circumstances, and added to the total. There are also a number of non-foods received as payment in kind; housing subsidies, transport to work, and education are probably the most important examples. In principle, all such items should be valued and included though, as -l1,,rnn th^.. 1st chn.ill1. hi,r tA tm. orlnn#l,nn ohnA ~.A t.nA ... A_ 1A AAOA2nrstrnnrla.t7roe _l_ . 64 VT ,4O IAVJ 6 1ab OflfFlf t_ *IS*. v .. t ... * ^t^V_V%d1li 14I4'.4 *11J11f.4lOVl1COtf UIt vIJUI llllV, uUIU1 fl.lUL41VLsl error on the other, again see Section 6 below. Expenditures on utilities, water, gas, electricity, or telephone can also be problematic if some households are subsidized and some are not. For example, some households may 32 receive high quality piped water at little or no cost, while others have to buy expensive, inconvenient, and lower quality water from local vendors. In some cases, making accurate regional (and certainly international) welfare comparisons will make it necessary to make corrections to (by repricing) the reported expenditures. 3.4 Consumer durables: From the point of view of household welfare, rather than using expenditure on purchase of durable goods during the recall period, the appropriate measure of consumption of durable goods is the value of se(VYCe ULL U".a L1UU1LU1U AVVl eives all 'U1 duu-able goosu IiI 1U in :'s u UVVe oUU U,e UIosessi .Ut1 rle-v-ar1L fIeIodU. As discussed earlier in Section 2.5, the "user cost" or "rental eQuivalent" for durable goods is apDroximately: where S,p, is the current value of the durable good, r, - r, the real rate of interest, and a the rate of depreciation for the durable good. Although in theory, r, is the general nominal rate at time t, and ;rt is the s-pecific rate of ifIUaLion for each durable good at tLime t, il practice it is best o collapse heivWo iinto a siigle real rate of interest. taken as an average over several years. and to use that real rate for all durable goods. Almost all LSMS surveys collect data on the stock of durable goods currently owned by the household. However, the amount of detailed information collected about each durable good varies quite considerably across surveys. 1nerefore, depending on uie type of data avaiiabie, tne analyst must cnoose between a number of different strategies when using (3 1) to estimate the durahle oods connumntion -mbh-am-ewate In the case ofthe Vietnam and Nepal LSMS surveys, the "Inventory of Durable Goods" module of the questionnaire collected information on (i) the current value of each durable good (S, p,), (ii) the age of the item Tinyears, as well as (iii) tne value of the item wnen purcnased (S, Pt-rTJ Using (3.1), consumption of Adlbleh goodsc WAC th-^. c- mlcuAti-edA fo!!owse: First the depreciation rate 6 for each type of durable good was calculated using: ( .. 'Yr _-r=1_(I V, (3.2) (. Pt-Tij For instance, estimates of 6 - ir calculated from the survey data in Nepal ranged from 13 per cent for 4 television sets, 1'7 per &,1VV .O1WA o0n.o, A~ ~fl --ert eSF.. b for u -A-o=csette a.n , Pl.,or, .. WJf ar t. onb. 1W far,s, -n IL o2 c^'f- 11 p e F -* W %A3t UIUY%..!LO. I ttA%,D 33 estimates were then used, in conjunction with data on the real rate of interest r, - ;r, and the current value of durable goods owned by each household S, p,, to calculate the durable goods consumption sub-aggregate. In order to minimize the influence of any outliers in the data, the median value of depreciation rates were used for each of the 16 items for which data were collected (i.e. rather than using household-specific values of 6 s In the case of the Ecuador and Panama data sets, information was available only on (i) current value of durable goods owned by the household St p, as well as (ii) the age of the item T in years. As the value of the item when new was not available in the data sets (i.e. SI P-rT), (3.2) could not be used to calculate the 5 s; iLLZtVadG aLL aLIiL-mat VI cvionuiLLIpLVIVIUo durabUI goUod was.O cLaCL.eu1C&.rU as IfIIUws. First, the average age for each durable good, T, is calculated from the data on the purchase dates of the goods recorded in the survey. We then estimate the average lifetime of each durable good as 2T under the assumption that purchases are uniformly distributed through time. (In some cases, for example where a good - - ouny rwcuntiy beun _____1- - __ ! __ - -- - _ _ - -_ _ __ _ _ t_-- A _- _-- - I - - -I - I - _- . - nas mitruuuocu, some other guess wouiu nave to be maue.) I ne remaminig nie oI eacn gooa j \s. is then calculated as 2T - T in this case- and somewhat arbitrarilvy this estimate is "rn'rndeduip" to 2 yenrc when the estimate was less. A rough estimate of the flow of services is then derived by dividing the current replacement value S, p, by its expected remaining life. For the countries, the interest component in the flow of services was ignored. zr-1 log zr.d .- ezzrgi. +'.e +erm.s som.ew"at (32 cnb rem;.e-l s GhLiir L'JrO GLIA i~GL a ,U CLIS~L.A.r II LII, U1Ia.vvLa. k .Z..a1 %CUL P% LIUWLIULV1i a~.. ln(p,) = 1n(P-T) - Tln(l -6 + it) (3.3) thus, in cases where data are available on the current value and age of the durable good only, using (3.3) 6- it can be estimated by regressing the current value of the durable good on a constant and T (i.e. by assuming that the current value of the durable good when new is a constant). LTi the L_QX' T[x.^ eovgsny fi. th 1T>..21; A.+t,. -U,- o w+leklo-o ; +_+1 1s9 s_ fla...~J,LV.S O - L.aJ. -lI flfl 8 Sn - -aI W lSM YO,,UuS. - MAIJ~1Jt UZI . tVW JLLV U LU1%.iiL VOLUq- Vk UIt stock of durable goods owned by each household. In this case, (3.1) was estimated directly assuming a value of 10 per cent for ( r. - ;t, + 6 ), a number that seemed reasonable given the prevailing real rate of interest and plausible values of 6. Finally, in the case of the Brazil and South Africa data sets, consumption of durable goods was not included in the overall consumption aggregate because of unavailability ofdata. Whenever good 34 data are available on the total stock of durable goods owned by the household, we would reconmnend incorporating in the overall consumption aggregate a measure ofthe flow of services accruing to the household from these goods. 3.5: Housing: Of all components of the household consumption aggregate, the housing sub-aggregate is often one of the most problematic. The underlying principle is the same as for other consumer durabies; what is required is a measure rn moneay terms of the flow of services that the household receives from occupying its dwelling. Because house purchase is such a large and relatively rare expenditure, under no circumstances should expenditures for purchase be included in the consumption aggregate. In the hypothetical case where rental markets function perfectly and all households rent their dwellings, the rent paid is the obvious choice to include in the consumption aggregate. Whenever such rental data are available, and provided the rents are a reasonable s 11 us-- itiL1lIr al a -- X I. -U11 A- U ;*% A SIkI U-- KFW -9UUX6oU t .-A +1IA - consumption total. In many cases, however, households own the dwelling in which they reside and do not pay rent as such. Others are provided with housing free of charge (or at subsidized rates) by their employer, a friend, a relative, government, or other sucn entities. Iimmany LSMS surveys, non-renter households are as'&ed how Muc-h it w ^uld cost them if they had to rent the dwelling in which they reside. and this "implicit rental value" can be used in place of actual rent. Such measures must be treated with caution and carefully inspected prior to use. Implicit rent is a hypothetical concept, perhaps to the interviewer as well as to the respondent, and the numbers reported may not always be credible or usable. Even when people are apparently confident about their estimates, they may do a very poor job of reporting market rents. Rents inown to them may be subsidized, out of at.e, or ainrPnresenentntive in. sorm.e .w. of the g.nernl run of nronertv in their area The hardest cases arise when there are data on neither actual nor imputed rent. In the case ofthe South African LSMS, in addition to information on rents, data were collected on the total property value (i.e. current sale value) of the dwelling. For households who reported property values but neither actual nor imputed rents, +Ult locAb UnAa.r;Jn ft L of .lWS LI. = j to I propeivIe tOi used to curatl … r*tt*l. tn.ir.puted * Tin WVc Wlher the property value of the dwelling was also missing, a median property value per room was used in each locality to assign a property value to the dwelling based on the total number of rooms, and the estimated property value used to estimate its rental value. 35 In the Nepal and Kyrgyz Republic LSMS data sets, hedonic housing regressions were used to impute a value of housing consumption wherever information on rents was missing. The idea behind this approach is to estimate an econometric model in which rents reported by a subset of the population (either actual or reported, as the case may be) are regressed on a set of housing characteristics including, for instance, the number of rooms and m.easures of quiality of fthe dweling such as tpe of roof, flnnri, innntriinicn maternia1 of walls, t,Vpe of sanitation, etc. as well as regional dummies. The parameter estimates obtained from this model are then used to calculate rents for that segment of the population for which data on rents are missing. In cases where data on imputed rental value for non-renting households are not available, or where Well estnimtsaLre dee-..e to,L1L% Le ur1reliaible or difficuh. to wee esuzmw bcue r=n.al L.. Mtkets zreU +U-.m (zs iAs tLe case, for instance, in rural areas in some countries), the hedonic regression approach can also be used to impute rents for such households. The regression model is first estimated using rent paid by renter-households as the dependent variable; the results of the model are then used to impute rents for the rest of the population. Because there may be systematic differences in characteristics between renters-and non-renter households, the nfCuKiiinn (17Y 6) Lw)U-5L48g bLUlhla4UUiH Xl-UIUU Lb oUI LUIV1V=5 UsVU WIIVHLrbLU1.4LU1r, bU%sIc islsU I,iUUedoic r1,,uoe, see for example Lee and Trost (1978) and Malpezzi and Mayo (1985). Finally, in cases where data on rental value are not available for both renters as well as non-renters, or where the percentage of the population renting their dwelling unit is so small as to make estimation of a hedonic housing model unfeasible, data on property values can be used to estimate the value of housing consumnpion. Followingarnanaprarlhsimilar tnthatii ed for conRil mer diirahle. oitlined earlier in Re.tinn 34 the value of the flow of services received by the household from housing can be calculated by using an appropriate guesstimate of the user cost per unit to derive a measure of housing consumption from the total property or "stock value" of the dwelling. This was the approach used in the case of the Vietnam LSMS data set. Once again, it is necessary to warn against the mechanical application of these (and other related) procedures. In some countries, housing and rental markets are not well enough developed to permit any serious estimate ofrental value, and attempts to repair the deficiency using data from a small number of households are unlikely to be effective, however sophisticated the econometric technique. Even if there is information on rents Hi someu par s VIof. wuleoltyIt iS obUviou-ly I*L'LUdUo LU appy It toU UUiI MUM, 4LU UUMlMUIVL1lU IjLes sometimes do no more than disguise the Droblem. In extreme cases. the best available solution may simplv be to exclude the housing component for all households. 36 Note finally that data related to expenditures on water, electricity, garbage collection, and other such utilities and amenities are usually collected in the housing module ofLSMS questionnaires. They should also be included in the housing sub-aggregate, and in the measure of totai expenditure. 37 Box 2. Recommendations for Constructing the Consumption Aggregate Food Consumption l Food purchased from market: amount spent in the typical month x 12 (or number of months typically consumed) rooa mat is nomne-producca: quanuity in typical montuhx Lari-gawe pnre x numoer of mouLn typicauy uonsumeu Food received as gift or in-kind payment: total value for a year Meacis coiisumed o-uLlbid heI hoIme: Amount spent in restaurants A *F,ULUL on FJLVja1VU _ount spen Z%¶JJIUUL redood 4rp L%;VJUD Amount spent on meals at work [here or in work-related expenditures] AmountIfl spentfl on mesls at scoo!Lf. LUr in o: U.GLf edu^ado ALSnIUtores Amount spent on meals on vacation [here or in vacation expenditures] Issues: Missing prices or unit values, first choice is price (unit value) reported by the household; if not available, use as a pro.xy the median - not mean - price paid by 'similar' housepholds in the neighborhood, ciihipt tn chefks thnt scih prices are I plausible. Check data for outliers; miscoding or misunderstanding of units for quantities causes errors in unit values. Non-Fnood Conn umntinn T)nDilv nze ittemnq annualize the value Clothing and housewares, annualize the value Health exnenses should only be included if they have high income elasticity in relation to their transitory variance or measurement error. Education expenses: Typically measured quite accurately in most surveys - our recommendation is to include them. Work-related expenses: To the extent possible, purely work-related expenditures should be excluded. This recommendation does not include transport to work or work clothing. Exclude taxes paid, purchase of assets, repayment of loans, expenditure on durable goods and housing, as well as other lumpy expenditures such as marriages and dowries. To the extent that local property taxes bear a relation to services rendered, we recommend their inclusion. Durable Goods Calculate an annual rental equivalent using an appropriate real rate of interest and median depreciation-values for each item calculated across all households owning that item. Housing If a household pays rent, annualize the amount of rent paid. Even if the dwelling is owned by the household or received free | of charge, an estirate of the annual rental equivalent must be included in the consumption aggregate. In countries where few households pay rent, rental equivalents are potentially inaccurate, and the benefits of completeness need to be weighted against the costs of error. l Weights or Raising Factors | If households interviewed in the survey had differing probability of being selected in the survey sample, household "weights" I (also known as expansion a raising factors) should be included in the data. Remember to use these when deriving l representative statistics for the entry under consideration. 38 A A ThTQT!ThTA! d'ID £'IQT fTV T TIrTNXTf "ITMI7EDU.'17f'1k C 1. v.~~ £ ^ As 5 xJ A ^A RA V AJ^. E.PAAT i^ Z~L3s^ 4.1 Introduction: In this' Section, we lay out some of the practical issues involved in calculating the price indexes that are used to deflate the nominal consumption aggregate. As we saw in the theory section, the caiculation of money mn,etrn na2oreO2te he diefl.ted by a Pans-he pnric.e index i.n u,hieh the wPeights utility reqnires that the nnrimil vary from household to household. If the analyst prefers to work with the welfare ratio approach to measurement, the deflator is a Laspeyres index whose weights are the same for all households. We present the price indexes in that order, which follows our recommendation in favor ofthe money metric approach. We note that these price indexes are of independent interest beyond their roles in deflating expenditures, simply for r.ea...g prices. Price indexes are used to aggregate a large number of individual prices into a single number, so that individual prices are the raw material for the indexes. In LSMS and other surveys, there are several possible sources for the prices, see Deaton and Grosh (1998) for further discussion of how prices can be collected and flo ar1 ar4aly-is UI ufll UI Ulu U1ie11r9LI%oVb Ur.LWreI Ul.111. III ULief, UwavL arv Uu rv pIJosblUiVsourc. I.Le fUL source is the survey itself, and the reports of purchases by the households surveyed. In many (but not all) surveys, households report both quantities and expenditures for most of the foods they purchase (three kilos of rice for 5 rupees) as well as for a few non-food items where quantities are well-defined, fuels being the obvious example. Dividing expenditures by quantities gives "unit values". These are affected by quality choices; someone wno buys beiter cuts of meat wili pay more per unit, but experience snows that the spatial variation of unit values is closely related to price variation. As a result; unit values provide good price informationn especially when averaged over households in a cluster. The second source of price information is a dedicated price questionnaire, often admninistered in each cluster as part of a community questionnaire. Tne price questionnaire seeks to measure prices in the markets aurt1al.lu ntrn1ni7eA hy _uurveu hAluup.Qhnlrl and iin nrininiple pnrnvides a direcmt mneie nf iwhat we need. Tn practice, there may be some compromise of data quality from the fact that the investigators do not actually make purchases. There are also sometimes problems of locating a wide enough range of homogeneous goods in all the relevant markets, so that it may be hard to match prices from the questionnaire with the expenditure patterns of the households in the survey. But this is the preferred source of price information when quantities are- not coillece frorac hP- -' Lvted LonI 'L ousehold, a.-A the orly sourcfor AL~ AAJA%&, 6411%&%4~ LA4_ 0WLL.U +lk-s goods, -uch r.ost- LUlLAtMOt. Lh'30, OLLO)A00 AUPJOL U1UA9UJUU AV no-fo itrsA^, ILdIJO, "IL%& 5 39 food eaten away from home, where quantity observation is not possible in principle. The third source of price data is ancillary data, for example from government price surveys. This is typically a source of last resort. Such data are often thin on the ground, and there will often be many householdQswhoe nenrest obsezrvedp nrie auwa8yv § ic SO far t,obe irrelevant.Neve.rtheless, such dnta sre sometimes the only information available, and it is usually better to use them than to make no correction at all. Note finally that the situation is somewhat different depending on whether we need to compute price indexes over space or over time. In the latter case, for example when we are comparing two surveys for the s.coin ttll%ltOWb sor.e ,rsaar,4 the.re tnl t,o.iamll't, ba o.,nllabl sor.e r.ativ.a! nnnrn,n,ar priamc irdx , ht tolla uso by how much the general price level has changed between the two surveys. In the absence of spatial data on prices, the temporal index should be used to deflate all nominal expenditures to ensure that welfare comparisons between the two periods are not being driven by inflation. BJeflUr LLLLILUr, ln Uto 2LLVAilD, It is LfuIl LU Ubgin Uy IVvall'ur,U UI'e LUILIUma fIo m lulvy'Iiivulit andU welfare-ratio utilities, whereby each is expressed as total expenditure deflated by a price index. For money metric utility, we have from (2.6) that n .a x U a ;Z ^ =- - (4.1) p P wlI.el- 'UL £.e.,IIV P JaIaIsc ULIiLxL L UUUI.e dro-ujIIaoi is g.IVeI Uy h h PP _ O _ h (4.2) P *q Here, the weights for the price index are the quantities consumed by the household itself and therefore differ from one household to another. By contrast, welfare-ratio utility uses a Laspeyres index so that, from (2.10) h h... X U, = n*(4.3) where, if we are using the poverty line as the base, the Laspeyres is given by (2.9) 40 PL. z nh(4.4) p p~ 4WiI q r P ~~PFo ( Most of past practice has been based on using Laspeyres indexes for adjustment, though not always with weights tailored to the poverty line as in (4.4), and relatively little attention has been given to the calculation of the Paasche index. In this section, we focus on the calculation of (4.2) and (4.4) using the data from a typical LSMS survey. 4.2 PaascIe price index: It is useful to express (4.2) in a manner that makes it easier to see how the Paasche index could be calculated from the type of data typically collected in an LSMS survey. Equation (4.2) can also be rewritten in inme orm: *(p k Wk /p k (4.5) where w. is the share of household h's budget devoted to good k. This formula can be calculated from expenditure data and price relatives alone. The following approximation may also be used: In ph Wk iFkJ (4.6) Note that these indexes involve, not only the prices faced by household h in relation to the reference prices, UUL also I.householdU Is e xApdUitUII1 eJaLLe11, sOr.IIVUUIP UI42L Ls n0t U-I 4 -UUC I.d4ZFVyIV5 1IIUr,e. il,. distinction is an important one; to convert total expenditure into money metric utility, the price index must be tailored to the household's own demand pattern, a demand pattern that varies with the household's income, demographic composition, location, and other characteristics. -0 l ne reerencV price veir p iemvitably seiected as a mauer of convenience, bui snould noi be very different from prices actually observed. A good choice is to take the median of the nrices observed from individual households (for foods and fuels, if unit values are collected) or from the community questionnaire (otherwise). Especially when using the unit values from individual records, there will be some outliers, not only for the usual reasons, but also because there are often misunderstandings about units--such as eggs being reported in dozens instead of in units. Use of medians rather than means reduces sensitivity to such accidents. The' iie nf a nntininl average nprie vectnr t-nmires that the m ney mptri, s m¶1Pnirp con,nfnrm no 1nlse.r n 41 Dossible to national income accounting practice, as well as eliminating results that might depend on a price relative that occurs only rarely or in some particular area. In general, even if quantities and unit values are available at the household level, this will only be the case for a limited set of goods, typicaily foods and perhaps some fuels. For nonfoods, and pernaps some foods, price relatives will come from communitv auestionnaires or even other regional sources. and will not be available at the household level. In such cases, we must use the price relative that seems most appropriate for each household, in which case (4.6), for example, becomes In Pp= 'wIn(P /kp.) + 5 wln(p,/p,) (4.7) k.F keNF where F denotes the set of goods (foods) for which we have individual household price relatives, and NFisthe set where we do not (nonfoods), and the superscript c denotes a cluster or regional price. One further refinement is likely to be useful. Because the household level unit values are likely to be noisy, and to contain focionaOl rltli-r1 it is nAaa ta replace 4,,. i"A;-.;A,-i, ph bky thnir rnA.i-oa.oe househomld in- s.n -- DVT T or locality. Analysts often want to use LSMS data for purposes other than deflating nominal consumption for each household, and calculate some indicator of regional price levels, or of regional price levels at different times LUilougll uLe sul vey year. LLis ca. be U doe USig SUC eViUlVl UlV I Pabsce ,nLUdAes of UlID sUUbsecUUIo, VI U1,e L-Y- indexes discussed below. The most straightforward procedure is simply to take means (or better. medians) within the relevant region or season of the individual Paasche indexes as calculated above. Such indexes could be made more relevant to the poor by averaging the individual household price indexes only over those at or below the poverty line, see the next subsection for discussion of procedures. Note that when all households he within a region R face isame prices, so th-at 1ph =EWi( Pt/ PW) (4.8) the average of the (log) prices is Riven by In ppR= Wk in (Pk / Pk (4.9) SO UI4L UIV 4ajJLVpIuaL%, 'WCIriLL LVI UIV, 4VV~Iar IIUVA ai:LV UIIU 14II19LI VL LIqI. UUUFr.L bUazes uVV, ~in kI,, poor) households. Note that is not the same as using the weights defined as the share of aggregate purchases in 42 aggregate total expenditure, weights that are typically used in computing consumer price indexes by statistical offices. These aggregate weights effectively weight each household, not on a "democratic" basis, with one household or individual getting equal weight, but on a "plutocratic" basis in which each household is weighted according to its total expenditure. Because better-off households have, by definition, larger total expenditures, ute YV.4iAtLL oJf UI LLuLV%146&c indxe zesLV,Ui;JiLve .nore ol rich'l UIofL Up ooUr VA,)11U1LLUr piaLrIIi1, a1 Uid hUaL causes problems when relative prices change in a way that affects the Door and the rich differently. For example, if the relative price of a staple food rises, a plutocratic price index will rise by less than a democratic price index if the staple is a necessity, and the poverty-increasing effects of the price change will be understated. 4.3 Calculating laspeyres index: For researchers who wish to follow the welfare-ratio rather than money-metric approach to measuring living standards, the relevant price index is not the Paasche index (4.2), but the Laspeyres index (4.4). Because this index uses the same weights for all households, it is typically more straightforward to caiculate than is the Pascehe, though in hnth ec the hardlest t2cL- is fine1lin the twrire re1ntivrec na raolltn t1Fsvnlt again, it is often useful to write the Laspeyres in terms of budget shares and price relatives so that, corresponding to (4.5), we now have pi f z Wk , (4.10) p .q Pk) which corresponds to (4.4) or, aitematively, corresponding to (4.6), } L EWk i n A (4.11) The discussion of measuring price relatives for foods and non-foods, and of aggregation over households goes through as before, though when we average the Laspeyres indexes, only the price relatives are being averaged, not the weights, though the principle of averaging price indexes over households remains uncnanged. The welfare ratio approach requires comparison of actual indifference curves with a baseline indifference curve, here taken to be the poverty-line indifference curve, and the theory requires that the weights for the Laspeyres index used for deflation be calculated at that indifference curve. In practice, it may not be 43 obvious how to do this. There are usually many households near the poverty line, though rarely many (or even any) exactly at it, so we lack the data for the quantity or budget share weights in (4.10) and (4.11). A useful solution to this problem is to calculate weights by averaging over the expenditure patterns ofhouseholds near the poverty line, with those closer to it given more weight than those further away. Weights with this property are conveniently provided by a "kernel" function, here denoted Kh(.) and the weigits in (4.4), (4.10) or (A 11 qre enl3iflated1 frnm H jjk = Kr(xh z)wk (4.12) h-I This sum is a weighted average over al households in the sample of the budget shares w using the k We.1iUts. The.re are a n1um11AJ l-...l of suimble chLoices CON fne kerr.el fi nictlionlwhI M.ust be posibel.-, r.ust sum to one over all households, and which must be smaller the larger is the absolute difference between xh and the poverty line z. One convenient choice is the "bi-square" function I ( _ _,) -- - K ((X-Z)=-tnLL± for-; •1 (4.13) and Kr(x - z) =0, otherwise. (4.14) The nuimbe-r T iS a "bandwidth" that controls how many households are included in the samnple. The larger is T, the more households are used, which makes the average more precise, but can cause bias by including households a long way from the poverty line. In practice, setting T so as to include a few hundred households around the poverty line will usually be satisfactory. These equations are also likely to work better if xtL and z in (4.12) to (4.14) are replaced by their logarithms, so that distances from the poverty line are m.easued proporti,onat.el.y, not absolut.e1 ". Note finally, that although different price indexes will sometimes be similar, it is dangerous to assume that this will always be true. Because of poorly developed infastructure, relative prices sometimes vary a good deal from one place to another, and when this is the case, price indexes are sensitive to the weights used to construct ulVIII. ,oeU uauil U18L U1U WVigLp Ail the Ulu Pauash IUrdeb xes aIushuUllIU spec-fILc WvlgUw , sO uIal because household level demand patterns are quite variable, the (apDroDriate) deflation of total expenditure by the household level Paasche index will generally give different money metric utility ranilings than will (the 44 inappropriate) deflation by local (e.g. Laspeyres) indexes that do not vary from household to household. Even when price data are sparse, and only available for a few regions, it is still desirable to calculate the household- specific indexes, not because prices vary from one household to another within the same region, but because the weights do. Our recommendation here follows from our original recommendation for the use of money metric utility. Money metric utility is calculated by deflating nominal consumption expenditures by the Paasche index (4.5) and (4.6), and that is what we recommend using. Calculation ofthe Laspeyres index might be marginally more convenient-though given the other household specific calculations, constructing household specific -r;- i-A-xs shou-I pose no Ad;.4-1n 1--pnAl.4 45 C ADUSTQlNGrWi2 1. Z'Z U%ULA AL Kl q.N VWD A' A UfFTTQPIUf%J AA%.P J.JU LJA~A~ JU %.ULJAJ ~._J'LVAA fL C4n%VDnQOTTIflnV A A.11 5.1 introduction: Sections 3 and 4 have presented guidelines on how to use LSMS data to construct a nominal measure of totai household consumption and of how to adjust it to take into account cost-of-living differences. How- ever, we are ulntimatelv interected in individualwelfare, nnt the welfare nfa lhoueholAd snme.thing that ic hard. to define in any very useful way. If it were possible to gather data on consumption by individual family members, we could move directly from the data to individual welfare, but except for a few goods, such data are not available, even conceptually-think of public goods that are shared by all household members. As it is, the best that can be done is to adjust total household expenditure by some measure of the number ofpeople in the ho,WWWWWld, ar.d to assibgn. +-..he result-.g mvae r.eas eacth l.ousehioldd r.e..e tow s l ik;vsul Equivalence scales are the deflators that are used to convert household real expenditures into money metric utility measures of individual welfare. If a household consists entirely of adults, and if they share nothing, each consuming individually, then the obvious equivalence scale would be household size, which is uhI u,,ALbUer of people ove;V WILIVIc howUUZVIdUU eApnILtULLrUe aiV JJIVadU. JEveVV- whUer hiiu-ulduu conssis of adu'its and children, welfare is often assessed by dividing expenditures by household size, as a rough-and-ready concession to differences in family size. However, such a correction does not allow for the fact that children typically consume less than adults, so that deflating by household size will understate the welfare of people who live in households with a high fraction of children. Moreover- simply deflating household expenditures hy total household size askn meane implircitly ignoring any economies of scale in consumption within the household. Some goods and services consumed by the household have a "public goods" aspect to them, whereby consumption by any one member of the household does not necessarily reduce the amount available for consumption by another person within the same household. Housing is an important household public goods, at least up to some limit, as are durable ite UVO. bCrwV l;ka e 6;neJ.byles tor ca, -n bea.st.A .U1 V.ll Iby -tt 1 ho-u1Aold r.A...Ub s+ at di.=-l- times. Because people can share some goods and services, the cost of being equally well-off does not rise in proportion to the number of the people in the household. Per capita measures of expenditure thus understate the welfare of big households relative to the living standards of small households. In. tUis SetIon wie discuss equi-valen.ce scales in geInera and o-utline some of thie miiairn approaches io 46 their calculation. But before doing so, it is worth emphasizing that we do not recommend abandoning the use of per capita expenditure. Twenty years ago, per capita expenditure was itself something ofan innovation, and many studies worked with total household expenditure or income without correction for household size. In the '- oa- A A 414- +A cap -_ba s h>as +1U 0 o+--A-A ---- A..o -A 1tA1 AOI;R O ; s UVLJaLJ1 LJl%1, a a per WjJI a UaOIO A1L.0 UVIIvJ1f U101 v.L ,uUJUBU11 "&I%& 1'u 0.L'. widely understood, none ofthe alternatives discussed have been able to command universal assent. As a result, no calculation of welfare or poverty profile should ever be done without the calculation of per capita expenditure as at least one of the alternatives. In part, this recommendation reflects the burden of the past; results are almost always compared with previous analyses for the same country, or with similar analyses for otner couiLiris w'nici use per caLpita expVndi Ure. But It tait v years of vxperiee -w-ith per cupiw is also tneui expenditure has given analvsts a good working understanding of its strengths and weaknesses. when it is sound (in most cases), and when it is likely to be misleading (for example, in comparisons of the average living standards of children and the elderly.) 5.2 Equivalence scales: To make welfare comparisons across households with different size and demographic composition, we need some way of adjusting aggregate consumption measures to make them comparable across households. In this regard, just as a price index is used in order to make comparable consumption levels of households with different cost-of-living, equivaience scales are a way to make comparable consumption aggregates of 1^h1.aA%^1Ae w.tl Adfifirxt eAavnnm.o,i1vhi.. eirm1tiiit.';o-,W, UJhul .my hwup AiffArp,t mptlinA, h . p'up wnv. cnA in. thin literature to calculate the exact conversion factors used in each particular set of equivalence scales, the underlying principle is often the same: the basic idea is that various members of a household have "differing needs" based on their age, sex, and other such demographic characteristics, and that these differing needs should be taken into account when making welfare comparisons across households. The costs of children relative to adults and the extent of economies of scale are of the first-order of importance for poverty and welfare calculations. Indeed, the direction of policy can sometimes depend on exactly how equivalence scales are defined. Larger households typically have lower per capita expenditure levels than small households but until we know the extent of economies of scale, we do not know which group 1i bULLtr ofl, of WheLUVI e, ilt-pOvc4VeLy JrJILdI4I b hol eliUIU Ur, Lu4gLvu to onL oI Ulu Lulur. Rurdl hliuseolius a re often larger than urban households, and we are sometimes unable to compare rural with urban poverty without an accurate estimate of the extent of economies of scale. Another frequent comparison is between children and the elderly, and both groups have claims for public attention on grounds of poverty. Children tend to live in larger households than do the elderly, and (obviously) live in households with a higher fraction of children. As 47 a result, comparisons of welfare levels between the two groups are often sensitive to what is assumed about both child costs and about economies of scale, see the calculations in Section 6 below. Issues involving comparison between children and the elderly have acquired a new salience in work on the transition economies of Eastern Europe wnich, compared with developing countries of Africa or Asia, have relatively large elderly ponpnatin"e uulirh _rjPpivP este linnnrt thrniiorh npneinn antid health uihsidie. As a remult the two aorun are in competition for welfare support, and an accurate assessment of their relative poverty has become an important issue. Unfortunately, there are no generally accepted methods for calcuiating equivalence scales, either for 1 4m.e .elah'vr Mr cots of nhulArnn or for pt-nonuic of scale.T.he.re are t vu,or. annp.roac. to Am--nrn-- equivalence scales: (i) one relying on behavioral analysis to estimate equivalence scales, (ii) one using direct questions to obtain subjective estimates, and (iii) one that simply sets scales in some reasonable, but essentially arbitrary, way. Each of these is discussed in turn in the sections that follow. Our recommendation, apart from the continuing use of per capita expenditure, is the arbitrary method, and we offer some suggestions for its .r-pcil tlnpICL;LMUUM 5.3 Behavioral approach: The behavioral approach has generated a large literature, much of which is reviewed in Deaton (1997). 'Wnhile there are metiods for calc-ulating t,e costs of children utat arc relatively soundly based - tiough not all woulld agree even with this -- there are so far no satisfactorv methods for estirnatinL economies of scale. Manv of the standard methods, such as Engel's procedures for calculating both child costs and economies of scale, are readily dismissed, see again Deaton (1997) and Deaton and Paxson (1998). One idea that seems correct, and that can sometimes give a useful if informal notion of the extent of economies of scale, is that shared goods within the household, or household public goods, are the root cause of economies of scale. In the simplest case, .- sor e twoof gi to Fh - - 1 l*- old, prgi .-- e -.. byor.e o r o.d person only and where consumption by one person precludes consumption by another, and public goods, where there is an unlimited amount of sharing, and where consumption by one member of the household places no limitation on consumption by others. In this case, Dreze and Srinivasan (1997) have shown that, in a household with only adults, the elasticity of the cost-of-living with respect to household size is the share of private goods 11Uo inILU4 ho-eh IUi ld conumpionU. If OLl gooU O.i Ie p;V8te, t'UsW 11Dei :,,^, p-oLUo UUUI toV 'd^,e ,,U,e U~ ofU1 peop: P.- "^ household, while if all goods are public, costs are unaffected by the number of people. This sort of argument supports the intuitive notion that, in very poor economies with a high share of the budget devoted to food- which is almost entirely private-the scope for economies of scale is likely to be smali. In other settings, where 48 housing-which has a large public component-is important, economies of scale are likely to be larger. Unfortunately, attempts to extend this sensible approach to a more formal estimation of the extent of economies of scale have not been successful, Deaton and Paxson (1998). 5.4 Subjective approach: The subjective approach to setting equivalence scales has attracted increased attention in recent years. One widely used technique is the "Leyden" method pioneered by van Praag and his associates, see van Praag and Wamaar (1997) for a recent review. In the household survey, each household is asked to provide estimates o~f th.e am .t of i,nmnp it 1x,ni1 nMeedA as that th.eir he A^otvib1dA as "e.ry 16aiatances badA '" d, "bA "insufficient," "sufficient," "good," and "very good." Suppose that the answer to the "good" question by household h is ch. From the cross-section of results, Ch is regressed on household income and family size (or numbers of adults and children) in the logarithmic form ln ch = a + f In nh + Inyh (5.1) _ - - .- .~~~~- ._ . l ms equaiuon ISuseu wL e ILu 1Sal;u1La ---- I or 1UV ci iume .- ,t < !_ y ._wun --- -I ,1 , ,,,,,,,,*_, us nousenolu woua nave lo nave m oruer to name its actual income as "good." Evidently, this is given by In yh = + p In nh (5.2) 1-;v 1-Y ii^ y is interpreted as a measure of needs in that it wouid be regarded by a househoid receiving it as goano "thpen the nii2nrtitv A / - vinedtohe inttprnret a the e1sqtiitu nfneptil'tn hn1,cPehnId Sj7'e nn (a negative) measure of economies of scale. van Praag and Warnaar report an estimate of,B / (1 - y) for the Netherlands of 0. 17, 0.50 for Poland, Greece, and Portugal, 0.33 for the US. Taken literally, these numbers indicate very large, not to say incredible, economies of scale. Eve if wxe accept the ger.eral trsAiodo ym, itiard ti tale se .Ir niat,s. .sseiously. T- -4l,- if the costs of children, or more generally the costs of living together, vary from household to household, the estimation of(5.1) will lead to downward biased estimates of fi. To see this, rewrite (5.1) including the error term as ln ch=a + l 1nnh + y n yh + uh (5. l a) r ne term u vanes irom one nousenotu to tne next, ania represenis uie iciosyncrauc costs oi iving ior 49 that household, the amount that household needs above the average for a household with its income and size. The trouble with this regression is that households choose their size nh, partly through fertility, but more importantly by adults (and some children) moving in and out. People who like living with lots of other people will live in large households (high nh) and will report that they need relatively little money to live in a large h;.ousehol.d (ow ,h) A a result, he W... b e,..t,rpl. l. .-A ,npthih-ouhold s h ar.d estimates of .0 will be biased downward, consistently with what van Praag and Warnaar report. 5.5 Arbitrary approach: ,al Wil U1i c.U.LI.e.^ ureliability of eiul.eU1 UIC beLIaV1i1rI or ULC OUjectI-ve ap-IJJIIGchI, LL'*.e. is Irl.uh to b said for maldng relatively ad hoc corrections that are likely to do better than deflating by household size. One useful approach, detailed in National Research Council (1995), is to define the number of adult equivalents by the formula AE=(A+aK)K (5.3) wnere A is ne numnber of aduits in e nousenoid, and k is me number o c'iidren. i ne parameter a is me cost of a rhild relative to that of an ad1ult, and lieS s-mewhere between 0 and 1.The nther parameter, 0, whic*h also lies between 0 and 1, controls the extent of economies of scale; since the elasticity of adult equivalents with respect to "effective" size, A + a K is 0, (1 - 9) is a measure of economies of scale. When both a and Oare unity-the most extreme case with no discount for children or for size-the number of adult equivalents is simply household size, and deflation by household size is equivalent to deflating to a per capita basis. An lora+h¶.r^at v,oemv.-in of (5 .3) use is f,eu.nn.nnlh.ap innn ntp,lmr.nle. *l,p th.ec fi adut col.rtsn nasor., . subseque.^o.t adults are discounted, so that the A in (5.3) is replaced by) + fi (A - 1) for some 0 less than unity. This is really an alternative treatment of economies of scale so that, if this scheme is used, the parameter 0 would normally be set to unity. Acae Ve carL *mUV 10fo ULU -poJs,UbioUn U14L tulAllltb UU l4%,r%Ac lb LVIUse (J.3) fox UIV iulL)Vln 01 1UUIL eauivalents. simplv setting a and 0 at sensible values. Most of the literature - as well as common sense - suggests that children are relatively more expensive in industrialized countries (school fees, entertainment, clothes, etc.) and relatively cheap in poorer agricultural economies. Following this, a could be set near to unity for the US and westem Europe, and perhaps as low as 0.3 for the poorest economies, numbers that are consistent with estimates based on Rothbarth's procedure for measuring child costs, Deaton and Muelrbauer (18ORI) nid Dle2ton (1997). Tf we think nf enononmies f scale s comincr frnm the PYiqtPnr-e nf haredhpu mih goods in the household, then Owill be high when most goods are private and low when a substantial fraction of 50 household expenditure is on shared goods, see Section 5.3 above. Since households in the poorest economies spend as much as three-quarters of their budget on food, and since food is an essentially private good, economies of scale must be very limited, and 9 should be set at or close to 1. In richer economies, 9 would be lower, er.iaps.nI'u. of 0.,5. _eio In Section 6 below, we argue that it is important to assess the robustness of poverty comparisons using stochastic domninance techniques, and we sketch out a simnple methodology for doing so. When the results are not robust, for example when the comparison of poverty rates between children and the elderly is sensitive to the cnoice of a and 6 within thle sensibie range for that couniry, there is probaDby not much aiternauve to farinog failuire snqarelv. Certainly the behavioral nnmroacrh is unlikely to nrovide estimates that would he sufficiently precise and sufficiently credible to support such fine distinctions. In such situations, it might be better to turn to other indications of well-being, such as mortality or morbidity. When the analyst is not concerned with situations in which everything depends on the choice of a and 9-for example in comparing the poverty of children and the eiderly-our recommendations are straightforward. At the first round, calculate ,ePr nariit~ pviermntliirp~ finr p!)fh hn,,cphn1,l hv, Al-y nf altPri-nn,tiveP souraes r for nrril im,l.ng,(in ) Imnit valueIII P w itTi wku.n-srvy rri.ces l from the survey itself, (ii) prices collected in the price (community) supplemented by prices from the %JUL&V0UiWUULQLV aW.A "I IJ) CmO.IIIUAJ. LELI(, IJLA"I .AGIIq, JLLIJIi rUV L. %-J 1 OL YVU_I VLLyV,J J9i. UVUV1U14UJLq, 1 avUaiV VU : ah informative to examine the sensitivity ofkey results to alternatives. In recent years, much use has been made of stochastic dominance analysis to examine the sensitivity of poverty measures to different poverty lines, and this work has led to a much closer integration between poverty measurement and welfare analysis more generally. Stochastic dominance techniques can also be useful in examining the sensitivity of poverty analyses LU U1, way Hi W.LL.c r.oneI)y 111r.LI,e UtiLty 1i coUnstUcted, InclUUdiLr,g L U1A, LcUL UcLoLU L VI eqULVa',1eIr, sal. III this Section, we explore some of these issues. Suppose that we ha-ve a money metric utility Measure which, for the mUmeL tIIU treUUdc LU nUoULaLIUW clutter- we denote by x. Su.npose too that we are interested in the headcount ratio (HCR). the .oronortion of people whose money metric utility is below the poverty line z. If F(.) is the cumulative density function ofx in the population, F(z) is the fraction below z, and thus is the HCR. The sensitivity of the HCR to changes in z, can be assessed simply by plotting the HCR as a function of z, i.e. by plotting the cdf F(z) as a function of z. Suppose tnen tnat we nave two measures oi money metric utility, xo and x1, corresponding to two different Apt-idiins ahnit c,n.rtriuc.tnn. Suinnnse~ th2t these decisnns a e su.h that it rnskes S-nc to USe th rie pvi'ertv Sam line for both - this will be the case if both are unbiased for the true money metric utility, and neither is more precise than the other. We discuss what happens when this is not the case in the subsections below, though it is sometimes obvious how to adapt the poverty line in moving from one situation to the other. Then if the two cdfs are F, ( and F2(, the two HCRs are F, (z) and F2 (Z). Plotting both of These functions against z on a s.i..gle. g... ,.4.1, how.. v.,1iich n.,tes th.e .h. e.ghe H-CR, arnd lx,v h A dflu,. .c in. upx,1it.e choice ofp FT.CR1s ,tirns~ rB.f poverty line z. Figure 2 illustrates the lower part of the cumulative distribution 4F(x) cdf of measure I / cdf of measure 2 I -s 1 i L. !h ~~~~~~~Z Za ! ~~Zb X Figure 2: Cumulative distribution functions of two measures of welfare functions for two (imaginary) measures of welfare. If the horizontal axis is thought of as the poverty line, each line tells us the fraction of people in poverty corresponding to that poverty line. Putting the two graphs on the be o ui coice -oI measure ad ifiierent poverty anes. same figure tells us how ronust the head count rano willb Fnr arnu low enouAh poverty line helow Lhe headcount ratio will be higher for measure 2. Between choice t ofpoverty line between Za, and Zb , measure 1 gives the higher poverty count, reversing again above Zb. Given some idea of the relevant poverty line, such figures tell us how the choice of measure affects the headcount. This rather mechanical exercise becomes more interesting when we come to construct poverty proniles, fovr example for Aifferent such ea voups, chilAre n.ad the elderly, or holcehkolds in differ.nt regins. Suppose 54 that we have two groups G and H, and that the conditional cdfs of the two measures are now F,( . G ) and F2 ( .j G) for G with similar expressions for H. What we are typically concerned about is whether the relative pov.rty rates of hPftp G arnd H are cp"a;ve ton Lh.e *hie%bt- teftk, t o m.easiiesr arA to .what extent the conclusion depends on the choice of the poverty line. For poverty line z, and measure i, for i equal to 1 or 2, the difference in poverty rates between the two groups is A i (z)=Fi(zlG) - Fi(zlH). (6.1) Plotting Ai ( z ) against z for a given i, and seeing whether it ever cuts the horizontal axis, tells us WIVUIVI LUIV fUVfIL- r iAiuung '- VI UW LWU UUJfO lb bTiseI-UV LU ULU ;UPVe Uhic VI pVVILj HIMie. PIULLUIg ULU LWU Li functions on the same graDh tells us whether, at any given poverty line, the ranking is sensitive to the construction of the utility measure, and whether that sensitivity (or lack of it) depends on the choice of poverty line. A worked example of this kind of analysis is given in Section 6.3 below. Sensitivity caicuiaiuons ior mie nea-c;ount ratio invoive ml'e comparison of ine cais o0iwo uismriouuons. Similar calculation-, are possible for other povertv measures- for example, the sensitivitv of the novertv gan measure to the poverty line can be examined by plotting the areas under the cdfs, see Deaton (1997) for a review of the literature and for examples. These higher order stochastic dominance comparisons can be used in the same way as above to examine the effects of construction on higher-order poverty measures. 6.3 Using subsets of consumption and the effects of measurement error: It is often clear from the data collection exercise or from the subsequent analysis of the data that some components of consumers' expenditure are much better measured than others. Food is sometimes thought to be easier to measure than non-food, if only because in households that eat from a common pot, there is a single 4 we1ll- .fo..,.edA al vuo can act s.-.- T-,..4 ,to - -a nt., q-te , wtn .- of r -1-,', +l,-o f-r imputed rent for owner occupiers in an economy where house tenancy is very rare. As a result, most analysts who have had to work through an LSMS survey, writing code to make the imputations, tend to be rather unwilling to make much use of the subsequent numbers. Whether it is better to use a subset of well-measured expenditures to assess poverty is an important question that has been raised by Lanjouw and Lanjouw (1996). tAs We have alrady ser,II, UbVIRIL4Iy Ulu sa,liv issuve aislv III UdUeIding - WilvUIvi Uo SLot to ulPiuUV air eAper,di.ure item where there are large. occasional expenditures. Transitory expenditure around a longer run mean is effectively the same as measurement error. In the rest of this subsection, we sketch out some results that are useful in thinking about measurement error and transitory expenditure. While we follow the lead of Lanjouw and Lanjouw, there are some differences in the analysis, both in methods and in results. 55 Before going on, it is worth noting that instrumental variable techniques for measurement error that are standard for making imputations, or for correcting regression analysis, are of more limited use when we are concermned with meaLuring povertv or inequality. The es-nettial probhl-m is that povertyv and inteqnulitv depend on dispersion, not means, or even conditional means. If we are trying to estimate the mean expenditure ofthe population on some item, and some households have missing or implausible values, it is standard practice to impute an estimate, often from the mean of similar households, or more generally, from a regression using instruments, variables that are thoughit to be correlated with the missing information. But because such regreacsiins -e,^-w^-.jonly cape a fraltin of a U.- v of the iariationinthe -e -_ "ie xr.le mid_l-,Uv -v ;fitedA value the swa -.. tb V lless -w swill *-&.0 varilkle IV than the actuals, and imputation will tend to reduce inequality and poverty (if the poverty line is low enough.) Of course, for transitory expenditures and for measurement error, variance reduction is exactly what we want. But imputations are likely to eliminate not only the measurement error, but also the genuine variation across households, something that we need to preserve. Start by assuming that there is a subset of total expenditure, such as food. expenditure on which is denoted by e, and that, conditional on total expenditure, x, we have E(elx)=m(x); V(elx)= 2 r (61, The regression function m( x ) can be thought of as an Engel curve, or as the true value of x whenx is measured witU error, or tue long-run vaiue ofx when x has a large transitory component. h ne poverty line in terms of xis. as before, z, and the cdfofx is FQ. so that the head count ratio is F! z ) Supnose that in qte-ti of defining the poor in terms of low x, we define them in terms of low e; to do so, we must select an appropriate poverty line for e, and one obvious choice is to take the level of e on the Engel curve where total expenditure is equal to the poverty line, i.e. m ( z ). The headcount ratio using e is then given by rs-. = _4 / _ / - It I . -re Fe I lS( h/ / ko9.3) where Fe.() is the cdf of e. If we assume that m ( x ) is monotone, and therefore invertible, it can be shown that pe is related to the "true" headcount ratio pX by the approximation Aa 2 f ( (f (z) m(z)) 56 where f (x) is the pdf ofx. (This result is closely related to those derived in a somewhat different context by Ravallion, 1988.) Note first that when the Engel curve fits perfectly (or there is no measurement error, or no transitory expenditure), so that a = 0, the two poverty lines coincide, a result that is exact. Otherwise, the two poverty counts will diverge in a way that depends on the slope of the density of x at the poverty line, and on the convexity or concavity of the Engel curve. When the Engel curve is linear or when we are dealing with transitory expendiures or measurement error, uie second term in brackets is zero, so that "food" poverty wiii overstate "true"poverty if f'( z) > 0 7which will occur if the density ofx is unimodal and the novertv line is below the mode. If this condition holds, the overstatement will be exacerbated if the Engel curve is concave, and moderated if it is convex. These results are a useful starting point, but are not directly practical. if we knew both x and its Cnm.nnnqint e there uiniil hbe nn nieead tn use the lattert Nevertheless, there tare twn imimedniate nrn11nri.es thAat are more useful. The first is the case where m ( x) = x, so that e is just an error ridden measure of x, so that (6.4) becomes Pe tsPr + ay 2 f'(z) (6.5) which gives us a guide about how measurement error inflates (or deflates) the poverty measure. This formula is ^.1-14c lal se, ,,1- .. 4.1 ., 1k - - _ WA-. o +uA wiane_ arth eo..-.c) 1 ^.^f -a-l,; ^ jim L'.I £3Q m Ly A.kI Wk.V'.A WV iGAY V D%JLLW. IUV"IJ1 UA VGaL1GALV- %JL IJ'u J..uIA 41'.A 4 L JIwL LAGLAIjJiL, Au C'VLA'4, could be estimated from two error-ridden but independent measures of x. Note also that (6.5) is the basis for the (often somewhat mysterious) result that for unimodal distributions, where f'(x) is first positive and then negative, adding measurement error increases the head count ratio if the poverty line is below the mode, so that f rz) > 0, and decreases it when the poverty line is above the mode, where f rz) < 0. Except in the very pooiesarea, we wouiU expect LIeV LU UV, UVIVW UVle UIUUV. 'Uep pover.; The approximation formula is also useful when considering whether or not to include a poorly measured component in the total. To simplify, suppose that e is the noncontroversial component of the total x, so that adding in the controversial component would, in principle, take us to the total x. Suppose that the Engel curve for e is linear, so t'ihat tihe denivaiive mm'(x) is corLstant, cqual to p say. To avoid confusion, rewrite its variance around the regression line as ar _where the subscrint e identifies the noneontroversial comnonent From (6.4), the poverty count using the comprehensive, but noisy measure is C57 .JI P.-P. + ac, f'(z) (6.6) where a. is the measurement error in the comprehensive (but noisy) total; c is for comprehensive. From (6.4), the poverty count using the non-controversial component alone is "r2 f'(z ) P,_Px + 2* ' (6.7) 3i11LA It is nUUnflfly uie UcasU UitLL LUle PUVV[L liU is below uie m euu, wu Ucn assume ulat X (z) is posiuv;, m which case the povertv count based on the comprehensive but noisy measure will be closer to the truth if p8 < ae (6.8) ac Note that , is the share of the marginal rupee devoted to the non-controversial good, and that 1 -/p is the ahare orning tn the rontirnvemrial cinnt sn that the p.q e fnr ineliminn nf the cnntmnrnversia! iteni ic strning if at the margin, a large share of total expenditure is devoted to it, while the case is weaker the larger is the ratio of variance in the comprehensive measure to the noncontroversial measure. This result is perhaps not surprising. A strong link to total expenditure is a case for inclusion, while making the total noisier is a case against conclusion. Note finally that (6.8) can be written in terms of the total-expenditure elasticity of the non- ('ffli(VI7PV,R,P; .1 Pof.f oYIlflh Tt . .2,IA^ th.G r.a1,,i-A,n i.rn.1^ o,¶p 1i-t VVWSO- OQ E_ < <6.9) /e 01/X /x S;UrV .ce %5.e LuuI s.o uhL coI. WAInuv vial .U noU.cL1L%-UUo-VVIid 'LdbLlLicLlre is UILY, (u.9) lb a prescription of including controversial items if their total expenditure elasticities are large, provided they do not add too much measurement error. Ofcourse, neither ae nor ac can actually be observed in practice, but the formulas (6.8) and (6.9) tell us what to look for and what to think about when making the decision to trade off comprehensiveness versus precision. 6.4 Sensitvity analysis with equivalence scales: Suppose that we are working with the formula (5.3) that links adult equivalents to the number of adults A and the number of children K according to 58 EA=(A + aK)° (6.9) and that we do not know a or O, though we may be prepared to commit to a range of values for each. Given values for the two parameters, we can compute money metric utility values for everyone so that, armed with a poverty line, we can calculate poverty rates for any groups. In this context, groups that we are particularly likely to be interested in are children, adults, and the elderly, as well as other groups where househoids have dL A i .t.d cor.J&.fl..ons, such* tas* .t a. ,.fo mon b. sSflt&c. S e. tJ *Jtv v of a, 9, and z, proceeds in very much the same way as discussed in Section 6.1 above. However, as in Section 6.2 but in contrast to Section 6.1, we cannot simply change the parameters and leave the poverty rate unchanged. For example, suppose that a is set at 1,and 0 is reduced from I to 0.5. As a lrsulL, el WUUU U 1%UU9QU V.1 _1OL40VAULUUD alv l VJLWVFL U1vOU W UI vuL/t_ 3 jrJ'. 'Jvll$ O *1 UIGL, ii fR), V'..W'mY line were held constant, poverty would be decreased. But this is not what we want changes in the parameters of the equivalence scale to do. Instead, we want to alter the relative standings of large households relative to small households, or households with large numbers of children relative to those with none. A straightforward way to do this is to select a particular household type as "pivot," and .to choose the equivalence scale in such a way tiat the money metric utility of people m such houshnoids are unafecteud by changes in the paraneters. Denote the number of adults and children in the reference or nivot household by (Ao . Ko );in practice this should be chosen as the modal type, for example, a two adult and three child household. We then define money metric utility, not as x divided by AE, but as x (+aK 0 ) ° (6.10) " (A+aK)° Ao+Ko 4 A^t an.y gSan".v.,alues of a an.d 9, X..¾ ~isjut a scaled ve.rsion of X / A F; but .fnr thep .referer.re hn,,csehd,i X is always equal to per capita expenditure, and is unaffected by,changes in a and 9. An alternative procedure, not pursued here but equally useful in practice, is to alter the poverty line for use with equivalent expenditure so as to hold constant the measure of interest, for example the head count I auu. 1LIa 1aE W * 1UOLDLIIMY UvIJ w u UUJ nAUL. i -uLwu"sWl JJI. L& V GW.4L. WS V11 UAJ *V ull 1wu VUJ %U31VLhi dividing total expenditure by equivalent adults calculated using the chosen values of a and 9. For a trial poverty line, calculate the head count ratio, and continue adjusting until the head count ratio returns to its value using per capita expenditure. Equivalently, the ratio of the new to the old poverty lines can be used to deflate expenditure per equivalent, at which point the original poverty line can be used. J7 Figures 3-5, reproduced from Deaton and Paxson (1997), show what happens to the relative poverty of children, non-eiderly adults, and the elderly in South Africa using the 1993 South African LSMS. 1flese calculations are done on an individual basis wihPrPey whker. mnen metric ltilityis assigned to ahusehioed, it is assigned to each person in that household. When we are doing population calculations, such as a mean or a measure of dispersion, the money metric utility of the household is weighted by the product of the number of people in the household and the household's sampling weight or inflation factor. Figure 3 shows the cdfs for the three groups, for a range of possible poverty lines, and for nine combinations of values for a and 6 . 11AQASpCAV ' JLCUAVM4UV ,, - - viL uw M4 *arA iA.J+-J 1- I-U., - lAA.13 _A- .. 1+a1- a waj U- Ivw%,J UJ. as9;Muskvs ^9 v uw avvwssXas. =LssiecB9,ls, poveri,y &LI1AI a LD CUsW CLUL&sW ala. Ll1v va headcount ratio than do children or the elderly. The poverty profile of the elderly versus that of children depends on the values of the parameters. In the top right of the figure, where children are cheap, and economies of scale are large, children do better than the elderly, who benefit relatively little from either economies of scale or inexpensive children. At the bottom left of the picture, where there are no discounts for WuIWWlulI U! I iaigv DLr Z%V UI4L IiiUiiry IILUULV UUIJLY lb CAPUI1UILWIC PU %RtplW.,Uir.UUIUM,WVII WV IIIU[U UKLY LU be poor than the elderly at all poverty lines. Figures 4 and 5 show plots of the difference between the cdf for the elderly and the cdf for children for the same range of the poverty line, but with plots for different values of a and a on the same graph. By discarding the automatic increase in tne cdf with fne level of the poverty line, and looking only at differences, these rapnhs permit reater focis on the differences of intrc-t here the elderlyverss c.hildre'n Figure 4 shouw the movement on Figure 3 from top right to bottom left, and shows how children become relatively poorer, and that, in the middle configuration, with a = 0 = 0. 75, the relative poverty rates depend on the value of the poverty line. Figure 5 shows the progress through Figure 3 from top left to bottom right, and shows a more muddied picture. All three graphs show that the relative poverty rates of the two groups depend on the poverty *fl1;. _l. f ^1.%; .l1 tan. be laes poor 'innM V (lWk.dC at 60 .8 ci~~~~~~~~~~~~~~~~~~~~~~~ederly elderlyr H 1 fiPi-[f- v 1 45a-w7Ss 5 < t s tht = . Il~~~~~~~~~~~~~~~~~lpha = 5,*c .6 O .S8 alpha 1, theta 5 alpha = .75, theta = .75 alpha = .5, theta = .75e .4 chhildree ell"dey c 7_ .2- {:8alpha 1, dtEa = 75, tbt ap -.-1a thleta =175 =.75, C5 .6 .4 eldery eledry childerl: E,preLivdrentepnlur diei .2 - 0 10 5 0 alpha0- 3: 1010 thelta fia200 povrt 250 0 ahe 50 ladun 100 atios ahta 150 vliu 200 250 oe 0 50 alnesamfo:r 100 theaniu I ISO i 200 250 ot n so cl Poverty linie in. PEX, per eq[uivatlent expenditure Filgure 3: Souith Africa, poverty headcount rastios at various poverty lines aind for varilous child costs and economnies of scale 61 .05 Ialpha=theta=05 - - ------- alpha=theta=0.75 I | \ alpha=theta= I -.05 . t Sc~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ I I 0 50 10O 150 200 250 Poverty line in PEX, per equivalent expenditure F;.mlre A4 Sutfh AfAro nnvpovrtu ra*hte nf thea iedrly and ,hdilArern alpha= I, theta=0.5 *~.02 alpha=0.5, theta=l -NA 04 alpha=0.75, theta=0.75 0 50 100 150 200 250 Poverty line in PEX. per equivalent exnenditure Figure 5: South Africa: poverty rates of the elderly and children 62 What should we conclude from sensitivity analyses like these? Much of the time, the desired result from a sensitivity analysis is to find ihat the results are robust, so that clear conclusions can be drawn. This will sometimes be the case, but rarely for the analysis of equivalence scales, where we know from a large body of WU.k+ U14L bVIIIV, I1irp LGULL 1aO.su are .- ot LoUUsL. LA16LVL, LJeDatLVL.9LLU aA.d CL&OWL DW s:.riGL DVIe.UVAUc U%,LW'..rLL the relative poverty rates of children and the elderly, not only for South Africa, but also for Ghana, Pakistan, Taiwan, and Thailand, but not Ukraine. In the absence of a breakthrough in behavioral and or subjective methods of measuring equivalence scales, it may simply be necessaxy for policy to be conducted in ignorance of the relative poverty of some groups. This section is somewhat more speculative (as well as more technical) than the other sections in these guidelines. Nevertheless, there are a number of general points and recommendations that should be drawn from the analysis. First, to the extent that the welfare measures are to be used for poverty analysis, and in particuiar the n 6.2 (illustratedfnr CAl-IlntiAnn Of ht-eadcoumt rtinos, the firto rderpsto.1hastic dnminanne ttec.ihnii nqf Sef.'iR equivalence scales in this Section) are easy to use and often provide useful insights. That said, these techniques should not be used to check out the results of every controversial decision in constructing the consumption aggregates. There are so many points where judgment calls have to be made, and they combine with one another to produce an impossibly large number of alternatives. Decisions have to be made for better or worse. But there are often. critica decisions, of whirh that ahout equvl.en.ce scales it o..e, an.d the ncluion of a noisy item of expenditure is often another, where we know in advance that the decision is going to matter for the poverty analysis, and where it is important to have more information on exactly how it matters. For this, stochastic dominance analysis is ideally suited. oewnU weQVL nLo6AV-LIL..,ILU.er.&f aLLv onLabLL howV* LV rIVLL%0.e . MVGOUL%ALLIL .VLV at LVp:. UL. LO LL.UL.r a auestion of survey design. The crucial point is always to be aware of it existence, and to ask, every time a decision is made, whether or not that decision would be different depending on the extent of measurement error. We hope that the formulas in Section 6.3, although no panacea, will be helpful in that enterprise. 63 Blackorby, Charles and David Donaldson, 1987, "Welfare ratios and distributionally sensitive cost-benefit analysis," Journalof PublicEconomics, 34, 265-90. Blackorby, Charles and David Donaldson, 1988, "Money metric utility a harmless normalization?" Journalof Economic Theory, 46, 120-29. Chaudhurii Shubhan and Martin Ravallion- 1994; "How well do static indicators identifv the chronically poor?" JournalofPublic Economics, 53, 367-94. Deaton, Angus S., 1980, "The measurement of welfare: theory and practical guidelines," LSMS Working 'n---XT- 7 l lTIv l IV" OU. I,v atIIIt6UI, r%f: iJ'.. i11 T BU I IU LdII. Deaton, Angus S., 1997, Tne analysis of nousenold surveys: microeconometric analysisfor development policy. Baltimore, Md. Johns Hopkins University Press for The World Bank. Deaton, Angus and Margaret Grosh, 1999, Chapter 17: Consumption, in Margaret Grosh and Paul Glewwe, eds., DesigningHouseholdSurvey Questionnairesfor Developing Countries:Lessonsfrom Ten Years of LSMS Experience, World Bank (forthcoming). Deaton, Angus and John Muellbauer, 1980, Economics and consumer behavior, New York, Cambridge University Press. Deaton, Angus S., and John Muellbauer, 1986, "On measuring child costs: with applications to poor countries-" Journalof PoliticalEconomy; 94; 720-44. fTlotnn Anmgu S. and Chrictinn 14 Paxsnn 1 O9R "1rnncnmieP nf seal, husehphnlA si7pz anti the diemai.nd for food," Journalof PoliticalEconomy, 106, 897-930. Deaton, Angus S., and Christina H. Paxson, 1998, "Poverty among children and the elderly in developing co-ur7tries," RXresea-ch K1r g in DrevelOp,l-ent SLuUies, PrincLUII U111vF.1Ly, p[cUUsseU. Diamond, Peter A., and Jerry A. Hausman, 1994, "Contingent valuation: is some number better than no number," JournalofEconomic Perspectives, 8, 45-64. Dreze, Jean and P. V. Srinivasan, 1997, "Widowhood and poverty in rural India: some inferences from household survey data," Journalof Development Economics, 54, 217-34. Grosh, Margaret, and Paul Glewwe, 1998, "The World Bank's Living Standards Measurement Study Household Surveys," Journalof Economic Perspectives. 12. Number 1 187-196. Hamnemann W Michael, 1994, "V2 th5 enivimnmentthmliirh cnntingent v2Al1tinnf final nf ,onom.ic Perspectives, 8, 19-43. Heckinan, J., 1976, "The Common Structure of Statistical Models of Truncation, Sample Selection and or Limited Dependent Variables and a Simple EstiMato fSuch Models," Annals of Economic and Social Measurement 5:475-92. Howes, Stephan and Salman Zaidi, 1994, "Notes on some household surveys from Pakistan in the eighties and 64 nineties," STICERD, London School of Economics, mimeo. Lanjouw, Jean Olson, and Peter Lanjouw, 1997, "Poverty comparisons with noncompatible data: theory and iUlUUsLAor,s, A Pol AP%ese&h1 TV UZLUPn Aper, Was:V GUAritor., "%P,.e DJC. TV VIAS CuLJ.f __T__ LeU, L. anu J I ~__ -= roUst, T% TN . In 70 19 I 8, XS_ __ __T: :._- 1 TS__ __ bEtimauun oL SomIe LimiteU Depunuunt V ariaule J- r_-1 L_ - t :s A_s_sw ivsoulb wiUI Appulcaton to :_ _. Housing Demand," JournalofEconometrics, 8, 357-382 Malpezzi, S. and Mayo, S., 1985 "Housing Demand in Developing Countries," WorldBank StaffPaperNo: 733, The World Bank Washington D.C. National Research Council, 1995, Measuringpoverty: a new approach,Washington, DC. National Academy Press. Ravallion. Martin. 1988. "ExDected povertv under risk-induced welfare variabilitv." EconomicJournal.98. 1171-82. Ravallion, Martin, 1998, "Poverty lines in theory and practice," LSMS Working Paper 133, Washington, D.C. Th.e World Bank 0aniauelbso, Paul AL., 197,4, C01'' CTCNml.wiUw..-j-An Cssay oni 'use. -v aruniveasaLy of 'uir ru'A-tlleu revolution in demand theory," Journalof Economic Literature, 15, 24-55. Singh, Inderjit, Lyn Squire, and John Strauss, 1986, Agricultural household models: extensions and applications,Baltimore, Md. Johns Hopkins University Press for The World Bank. van Praag, Bernard M. S. and Marcel F. Wamaar, 1997, "The cost of children and the use of demographic variables in consumer demand," Chapter 6 in Mark Rosenzweig and Oded Stark, eds., Handbook of Populationand Family Economics, IA, Amsterdam, North-Holland, 241-273. 65 An introduction to Living Standards Measurement Study (LSMS) Surveys: The Living Standards Measurement Study (LSMS) was established by the World Bank in 1980 to imDrove uie availability of higi quality household survey data collected uy statistical offlees in developing countries. One of the main nurmoses of surveys is to nrovide data on a number of different dimensions of household welfare, to better understand household behavior, and to evaluate the impact of various govemment policies and programs on living conditions. To-date, LSMS surveys have been conducted in over 40 countries throughout the world, and in a number of countries these surveys are now carried out at regular intervals by the statisticai offices as part of their routine data collection activities. For a more comprehensive introduction to the World Bank's LSMS su1rvPys, see Trosh and Glewwe (1998). LSMS surveys typically use a number of different survey instruments to collect data: (i) a household questionmaire, (ii) a community questionnaire, (iii) a price questionmaire, as well as (iv) a school or health facilities questionnaire. The household questionnaire is usually administered to a relatively small sample of nnn about 2,000-5,000 lsehold, ar.d tunica11.u _o!!e_tsa A+a on ak:dep rainge of+n" r.A. n nUjAs, ng ,00,f demographics, economic activities, consumption of goods and services, housing conditions, access to services and amenities, as well as data on the health and educational status of all household members. In each of the localities throughout the country in which households are interviewed, a community questionnaire is also administered. This questionnaire collects information on the quality of infrastructure as well as on access to e,-v vallous -- A -^u -4.-d ie1i-1. #U l.4-. A prcequs:--ei -1-pial als _Adr_s,,s.- n_ each community! and this instrument collects data on prevailing prices ofa wide range of goods and services on sale in the locality. Finally, a school and health facilities questionnaire is sometimes also administered in all school and health facilities that fall within the locality-, this questionmaire typically collects infornation on staffing, the quality of infrastructure and range of services provided at the facility. 66 An Introduction to the Programs: Tn fhe pages t1at follo, t-.e ,r.ogs u.A *, + -. nc..rnn;nn ocre.af.es frr. &Iota collected in LSMS surveys in Nepal as well as a few other countries is presented. For each of the major set of calculations discussed in the paper, the relevant section of the stata code used to construct this particular sub- aggregate is listed, along with copies of the relevant pages of the questionnaire as well as notes to guide the analyst through the syntax. These programs are included in the paper to provide "templates" for the user, rather Ulan a set of programs tLat can be iUmmediately executed as such to construct tuuCOumption aggregate in a given country. Each survev is at least a little different from everv other. so that the code that follows would- at a minimum-have to be modified for each country to take into account differences in structure of the questionnaire as well as to give due consideration to each country's unique circumstances and institutions, types of data collected in the survey, etc. A I incliidie' the 6.Stata progrms used to cnntrunct the ensnurmption aggregate from the Nepal Tiving Standards Survey (NLSS) data, the LSMS conducted in Nepal in 1995. A2 provides an example of the Stata code used to construct the Paasche price index based on the NLSS data set (the programs provided in Al construct a Laspeyres price index). A3-A5 present examples of the code used to construct the durable goods consumption sub-aggregate in Vietnam, Panama, and the Kyrgyz Republic respectively-in each of these co mrin,es. the ft,e of d.t e^11aeptoeA x,ar,spA ir. term. nfAdeta1. I;nalylx A AA snd A7 thke Qtato c-Aoe useA to construct the housing consumption sub-aggregate in South Africa and Vietnam respectively. 67 SECTION ! =PFOOD EXPENSES AND HOME PRODUCTION FOOD PURCHASES HOME PRODUCTION IN-KIND 1. 2. 3. _ . 5. 6. 7. 8. Have you co:nsumed .. [FOOD] .. during How many In a typical How much How marty In a typical How much What is the the past 12 montlhs? months in month dluring would you months in month during wou:Ld your total value the past which you normally the past whichL you ate household of the 12 months purchasied have to 12 months .. [FOOD] .. , have to *. (FOOD].. PUT A CHECK (1) IN THE APP:ROPRIATE did you .. (FOOD) . How spend in diid you how much did spend in consumed BOX FOR EACH FOO]D ITEM. IF THE purchase much did you total to consume your household the market thaLt you ANSWER TO Q. 1 IS YES, ASK Q. 2-8. . [FOOD]. 1s purchasie? buy tlhis . [FOOD]. consume of to buy received quantity? that you .. [FOOD] . .? thiis in-kind grew or- quanatity over the produced of past 12 yourself? . (FOOD] . Monrths IF NONE (i.le. the (waLges for WRITE ZER0 IF NONE amount work, ANI) 45 WRITE con:sumed etc. ) ? ZERO AND in a 4a 8typical IF NONIE month)? WRITE ZERO _ ____ | NO YES CODS MONTHS QUANTIIZ]| UIT RUPEES MONTIHS QUANIITY TUNIT RUPEES_ RUPEES WEi9S rI P ulO Ar __ -g *g __ 1 1 E 1 m - Fine rice 011 Coarse rice 012 ____. Beaten/flattened rice 013 _ = == Maize _014 _ _ . Maize flour 015 is . _ _ - - - Wheat flour _ 016 . _____ _ Millet 017 Other grainBTcere7als _-_ Black Pulse 021 __02______ __ __- Masoor 022 .__ _ _ -_ Rahar 023_ 0_ _ _ . - Gram _ . __ .i024 Other 'pullses. = = 5__ .68 Al. i995 Nepai Livig Standard Survey .NLSS)Stata Code PROGRAM 1: * This prugram co,uputes the annual househoid food consumption expenditure in * three different components: purchased, received and home produced. * wwwhh is a 5-diait code that uniquelv identifipR earh hoiusehold * * * Food consumption expenditure * use data\sectO5, clear * See Section 5 from the questionnaire on the facing page gen purchase = v0502 * v0504 * v0502 and v0;04 are variables with data from question 2 and 4 respectively * of section 5 drop v0502 vO503a v0503b v0504 gen hproduct = vO5O5 * v0507 drop v0505 vO506a v0506b v0507 rename v0508 inkind * Taking out tobacco eaen tobacco=rsum(nurchase horoduct inkind! if fooditm >=121 D fnnditm e=124 replace purchase=. if fooditm>=121 & fooditm<=124 replace hproduct=. if fooditm>=121 & fooditm<=124 replace inkind=. if fooditm>=121 & fooditm<=124 collapse 'sumi purclhlase hproduct inkind tobacco, by('wwwhh) egen food= rsum(purchase hproduct inkind) label var wwwhh "Household code" label var purchase "Food purchases" label var hproduct "Food home production" label var inkind "Food in-kind receipts" label var Lfood Food corisuIpIt'o.iLn" label var tobacco "Tobacco consumption" sort wwwhh save consumption\food, replace 69 SECTION 7. EDUCATION PART C CURRENTLENROLLIMENT (ALL PERSONS 5 YEARS AND OLDER) (CONT.)] 9. 10. 11. I How muc:h has your household spent during the past 12 montlhs for your schooling? Elid you How much dlid iD receive a you receive E IF NOTHING WAS SPENT, WRITE ZlERO. scholarship to over the past pg help pay for 12 months? 'T IP THE RESPONDEN1' CAN ONLY GIVE A TOTAL AMOUNT OF EXPENSES AND NOT lHE BREAKDOWN PER TYPE3, WRITE DK y pour I (DON' T KNOW) IN C'OLuMrS A 7O G, AND THE TOrAL ANOUNT IN COLUMN H. educational F expenses? I IC Th'S..... 1 A N'O..... 2 Tr | (+NEXrr PERSON) I N _______ A. B. C. D. E. F. G. H. O Admiss:ion, Exarmina- Transpor- Textbooks, Private BoaLrding Other ID Registrati tionI feeEs taLtion writing tutoring fees a fees and TOTAL E on and fees ar,d supp. expenses Tuition ccists stationery . _ _ RB = R n- = R- e - RUPEE:S j1 _ _ _ _ ._ r _ _ _ RE __E ~~i i 3! 3E_tall- - I _ _ _ _ R£_ @5 ~_ s iiiil _ -- $t ___ 07 i1I W I- - ___ I _ _ --- U-19 - _ _ E in - i _ I - _ __ FE-~fm mm ! mi m_ E i m_ -- 70 PROGRAM 2: * This program computes annual expenditure for education, health and other * non food consumption. * wwwhh is a 5-digit code that uniquely identifies each household. * * * 1TNon Food experditure * * * ----------- EDUCATION EXPENSES --------------- use data\sectO7, clear * See Sectior. 7, Part C off trhne nu.estr-nrnaire on the facing page * The total expenditure on education is taken to be either the sum of the * reported education expenditure sub-categories (a - g) or the total reported * in column h, whichever is greater. egen toteduc= rsum(vO7cO9a vO7cO9b vO7cO9c vO7cO9d vO7cO9e vO7cO9f vO7cO9g) replace toteduc= v07c09h if (toteduc < vO7cO9h) & toteduc-=. & vO7cO9h-=. * Addding- in v,alue of scholarship egen educatn= rsum(toteduc v07cll) collapse (sum) educatn, by(wwwhh) label var educatn "Education expenditure" sort wwwhh save consumption\educatn.dta, replace 71 SECTION 6. NON-FOOD EXPENDITURES AND INVENTORY OF D1JRABL}3 GOODS PART A FREQUENT NON-FOOD EXPENDITURESR Were any of the following items purchased or Whhat is the money Wrere any of the following :Ltems purchased or What is the money received in--kind over the past 12 months? va:Lue of the amourt received in-kind over the past 12 months? vaLue of the amount purchased or purchased or received in-kind by received in-kind by PUT A CHECK (1) IN THER APPROPRIATE BOX FOR ALL youar household PUT A CHECK (1) IN THE APPROPRIATE BOX FOR ALL your household ITEMS. IF TrHE ANSWER IS YES, ASK Q 2-3. during the past: ITEMS. IF THE ANSWER IS YES, ASK Q 2-3. during the paLst: AMOUNT IN RlUPEES; PMOUNT IN RUPEES 2. 3. 2. 3. NO YES CD 30 DAY'S 12 MONTHS NO YES CD 30 DAYS 12 MONTHS _____ ___ ~. 3 _ _ Q.E....E..P.NSI.S......_ .. -- Wood (bundlewood. logwood etc.) 211 Public transportation (buses, 231 _ . __ . _. _ ___ taxis, train tickets ietc.) Kerosene oil 212 petrol, diesel, motor oil fEor - _ 232 - . -- - _____ ~~~-rsonal vehicle only _ CoDal, charcoal . 213 _ _ _ _Entertainment (cinema, rad:io 233 - - -- -~~~ ~tax, _____ cassette rentals, etc.) Cylinder gas 214 ewspapers, books, statione.ry 234 Matches, candles, flint, 215 pocket money to children 235 lighters, lanterns, etc. I____ j 2m0 . . E:ducational and profe ssional 236 Ready-made clothing and apparel 221 Modern medicines&hlth. services 237 -…-- - (~~~~~~~~~~~~~~~~~~Ifees, hospital chargies etc.) Cloth, wool,, yarn, and thread rd222 1raitional medicines and 238 for making clothes anei sweaters hlealth. services Tailoring expenses 223 rages paid to servants, maLie, 239 chowkidars, etc. Footwear (shoes, slippers, 224 Light bulbs, shades, 241 chappals, etc.) _ batteries, etc. Toilet soap ousehold cleaning articles H225 - _ 242 __ _ _ . . _ _ __ _Z(soap, washingpowder, etc.) . _ __ . Toothpaste, tooth powder, 226 toothbrush, etc.) . OTL (2 Ci. - Olther ipersonial catre items 227- __L1___ (shampoo, cosmetics, etc.) = = =_ Dry cleaninq and washing 228 expenses - ._ PSK RESPONDENT TO ESTIMATE AVE. MONTE[LY & Personal services (haircuts, 229 ANNUAL 260 EXPENDITU&RE ON FREQlENTLY PURCASED s]having ,_shoeshine, et.c.) . _ _ON-FOOD N ITEMS lS , 72 ***----------- HEALTH EXPENDITURE --------------- use data\sectO6ab, clear keep if nfooditm==237 I nfooditm==238 gen hmonth=12*vO602 recode hmonth .=0 gen hannual=vO603 replace hannual=hmonth if hannual==. collapse (sum,) heallth= ha--1.al byw_ h sort wwwhh save consumption\health, replace --------- OTHER NON-FOOD EXPENSES ------------ use data\sectO6ab, clear * Drop subtotals drop if int(nfooditm/10) == (nfooditm/lo) * Drop expenditure on firewood drop if nfooditm==211 * Drop education drop if nfooditm==236 * Drop health drop if nfooditm==237 | nfooditm==238 * Drop taxes, etc. #delimit ; drop if nfooditm==312 | nfooditm==313 | nfooditm=,317 | nfooditm==318 nfooditm==319; #delimit cr exenes * Drop ,p,isc. drop if nfooditm>=321 & nfooditm<=328 * Drop durable qoods except 411 (crockery, cutlery and kitchen utensils) * and 413 (pillows, mattress, blankets,..) drop if nfooditm> 400 & (nfooditm-=411 & nfooditm-=413) * Drop fuels drop if nfooditm>=211 & nfooditm<=215 gen nfood_m = 12*v0602 recode nfood_m .=0 gen nfoodl= v0603 replace nfoodl= nfood_m if nfoodl== 0 | nfoodl==. collapse (sum) nfoodl, by(wwwhh) label var nfoodl "Non-food expenditures" e-ep nfood -wwwhh. sort wwwhh save consumption\nfoodl, replace 73 SECTION 2. HOUSIING PART' A I TYPE OF DWELLING J 1. Is tthis dweilling unit occupiedlby your household only? WOOD/BRANCHES ............ 3 CONCRETE ................. 4 YBS . 1 Cg UNBAKED BRICKS ........... 5 YEzS .......... 1 OTHCER PERM4ANENfT MAT'ERIAL, . 6 NO . 2 NO OUTSIDE WALLS . 7 5. MAIN FEiQORIN(: MATE'RIAI,: 2. How inany :room does your housebhold 'occupy? WOOD .... ... 2 = ....... ............. CBMElNTtTIBB I KITCHEN CEXENT/TILE.........4[1 OTHER........... 5 TOILET/BATHROOM _ 6. MAIN MATERIAL ROOEP IS MADE OF: BEDROOMS S TAC.. TRAW, . 1 LIVING/DINING ROOMS EARTH/MUD ................. WOOD, PLANOKS............... GALVANIZED IRON ........... 2 3 4 E I] BUSINESS CONCRETE, CEMENT. .CBMBli 5 ~ M1:XBD~ ~ ~~ ~ ~ ~ ~ ~ ~_ ~~~ ~~TIMI/LAE.SSJ,6 ~~~~~TLE/LX T......... B 6........ MIXED USE OTHER ................. 7 7 OTHER 7. THE WINDOWS ARE FITTED (CHECK THE FIRST THAr APPLIES) NO WINDOWS/ NO COVEtING ... 1 SHUTTERS. 2 INTERVIEWER: PiMASK 1PROVIDE THER FOLLOWING INFORMATION O)N SCREENS/GLASS. 3 _ TE, RESPONDN'T HOUSEHOLD'S' DWELLING UNIT (Q. OTHER. 4 3. IS THERE A KITCHEN G ARDENi? 8. HOW BIG IS THE' HOtUSING PLOT? SQ. P-F [ T7 YES .. mm NO .2 . .J9. 1 HOW BIG IS THE INSIDE OF THE DWELLING? .................. _ .................. <;~~~11Q. F :]r 4. MAIN CONSTRUCTION M[ATERIAL OP OUTSIDE WALLS: CEiMENT BONDED BRICKS/STONES1 MUD BONDED BRICKS/STONES . 2 EI] 74 1995 Nepal Livin2 Standards Survey (NLSS) Stata Code PROGRAM 3: * This program computes housing annual consumption in two different * co.poent: rent and util4t-i4-s * wwwhh iB a 5-digit code that uniquely identifies each household. * * * Housing consumption * * * ------------ RENT EXPENDITURE --------------- use data\sectO2, clear * Rename and prepare variables used to impute rents drop vO2d* gen housrent = v02b0.1 replace housrent = v02bO7 if vO2bO6==2 vO2bO6==3 v02bO6==4 replace housrent = v02bO9 if v02bO6==l gen rstatus=l if vO2bO6 == 1 replace rstatus=2 if v02bO1 == 1 replace rstatus=2 if v02b06 > 1 gen rooms vO2aO2a - vO2aO2b gen k-41.-ch-en = Av,2a02b >= 1.'1 gen dwelsize = vO2aO9 qen . walls = (v02a04==1 I vO2aO4==4) gen floor = (vO2aO5==3 vO2aO5==4) gen roof = (vO2aO6==4 vO2aO6==5) gen window = (vO2aO7==2 vO2aO7==3) gen water = (v02c02==1) gen sanitatn = (v02c02==1) gen garbage = (v02c05==l | v02c05==2) gen toilet = (v02c07==l) gen light = (v02c08==l) gen telephon = (v02c11==1) #delimit ; keep wwwhh www rstatus housrent rooms kitchen dwelsize walls floor roof window water garbage sani tatn. tolet4 lih tIelUephon #delimit cr sort wwwhh merge wwwhh using data\group drop _merge gen kathmand = (group==1) gen othurban = (group==2) gen rwhilla8 = (group==3)1 gen rehills = (group==4) qen rwterai = (qroup==5) gen lnrent = ln(housrent) gen lnrooms = ln(rooms) gen lndwsize = ln(dwelsize) 75 SECTION 2. HCIUSING PART B U IOUSING EXPEN9ES | 1. Is this dwelling yPours? RUPEES Y1ES .1. .............. NO. I .2 (46) 1 2. If you wanted to buy a dwelling just like t]his today, how much money would you have to pay? INCLUDE VALUE OF HOUSING PLOT RUPEES S LRU [ 11 J 8. From whom are you renting? PRIVATE INDIVIDUAL... 1 3. If someone wanted to rent this dwelling today, how much RELATIVE......2....... 2 money would they hLave to pay each month? E MPLYER. 3 .r _ OTHER .4 RUPEES . _ 4. Did you rent out part of this dwelling unit? 9. What is the rent: per month? (cash plus value of in-kind payments) Y'ES ....... 1 - - NO .2 (4 PART C) L]. RUPEES = : 5. 6. How much do you receive as r ent per month? Whlat is your present occupancy RENTER ............. 1 (48) status? L±] SELECTRICITY RUPEES 10. Does the rent NO . 2 include: ~~~~~ TELEPHONE E] ~~~~~~~~~~~~~~~~WATIER E ] PROVIDED FREE OF CHARGE BY RELATIVES, LANDLORD OR EMPLOYER ...... 2 [ SQUATTING ........... 3 OTHER ............. 4 7. If someone wanted to rent t]his dwellinlg tocday, how much money would they ]have to pay each month? 76 1995 Nepal Living Standards Survey (NLSS) Stata Code sort wwwhh save consumption\housing, replace * Add information on access facilities and durable assets use data\sectO6c collapse (sum) durasset=vO6cO6, by(wwwhh) sort w-W-w-1"' save templ, replace use data\sectO3, clear keep if fcode == 104 | fcode == 105 fcode == 106 gen proad = (v0302 == 6 & fcode 104) gen othroadl = (v0302 == 6 & fcode == 105) gen othroad2 = (v0302 == 6 & fcode == 106) collapse .~AJ.j. (s-ur..)1 p proa-d othoad %JOJ '. LUL --- Alww; JA 4.J. t od2 WILI±UJC OL%I4 AY%.W WW.ALJ.L1 7 sort wwwhh save temp2, replace use consumption\housing, clear merge wwwhh using temp2 drop _merge sort wwwhh r..erge w--w'"'I using te.-.pl gen lnasset = ln(durasset) dron _merge durasset save consumption\housing, replace * Predicting rents for households #delimit ; kathma.and othurban rwhills rehills rwterai l- reg lnrent lndws lnasset kitchen proad walls floor roof window water garbage toilet light telephon if lnrent> 0; #delimit cr replace lnrooms=ln(3) if lnrooms==. replace lndwsize=ln(500) if lndwsize==. recodle 1nasset ., recode kitchen .=0 recode Droad .=0 recode walls .=0 recode floor .=0 recode roof .=0 recode window .=0 recode wat"er .=10 recode garbage .=0 recode toilet .=0 recode light .=0 recode telephon .=0 predict renthat gen hrent - exft lnrent)*12 if lnrent > O replace hhrent = exp(renthat)*12 if hhrent 77 SECTION 2. HOUSING PART C = UTILITIES AND AM_ENI'IE_S PRIVATE CiOLLECrOR ............... 2 1. Where does your drinking water come from? DUMPED ............... .... 3 (4 7) PIPED) WATER SUPPLY ... 1 BURNED/BURIED .4 (4O7) COVERED WELL/HAND PUMP OPEN WELL ............ OTHER WATER SOURCE ... 2 3 4 1'43) (43) 1[43) E DUMPED AND USED FOR FERTILIZER OTHER. .O T H E 5 (47) 6 6. How much do you pay for garbage disposal over the last 2. Do you have water piped into your house? 12 months? YES .. 1 IF NOTHING, WRITE ZERO NO . 2 I_J Rl RUPEES_ 7. What type of toilet is used by your househo:Ld? 3. How much did you pay for water ovetr the last (EXCLUDE WATER USED FOR IRRIGATION) IF NOTHING, WRITE ZERO 12 months? I~1 HOUSEHOLD FLUSH (CONNECTED TOMNCPLSWR .... TO MjUNICI PAL SEWER) .1......... HOUSEHOLD FLUSH (CONNECTED TO SEPTIC TANK) .2 ................. l] RUJPEES HOUSEHOLD NON-FLUSH .:3 COMMUNAL LATRINE .4 NO TOILEI. 5 4. Are you connected to a sanitary system for liquid wastes? YES, UNDEIRGROUND DRAINS 1.... YES, OPEN DRAINS ..... ...... 2 YES, SOAK PIT ...... ........ 3 NO ........ ................... 4 5. How does your hbusehold dispose of its garbage? COLLECTED BY GARBAG3 TRUCK .... 1 F 78 1995 Nepal Living Standards Survey (NLSS) Stata Code keep wwwhh hhrent sort wwwhh save consumption\hhrent, replace erase templ.dta erase temp2.dta ***-------- CONSUMPTION OF UTILITIES --------- use data\sectO6ab, clear keep if nfooditm>=211 & nfooditm<=215. gen fuel_m= 12*vO602 recode fuel_m .=O replace fuel=fuel m if fuel==O I fuel==. collapse (sum) fuel, by(wwwhh) label var fuel "Fuel expenditures" keep wwwhh fuel sort wwwhh save consumption\fuel, replace use data\sectO2, clear keep wwwhh vO2cO6 v02clO v02c12 rename v02c06 garbage rename v02clO electric rename v02c12 telephon sort wwwhh 4 m.erge ...,hh ng nsu 4 tion\ l drop if _merge==2 drop merge egen utility= rsum(fuel garbage electric telephon) keep wwwhh utility sort wwwhh save consumption\utility, replace 79 SECTION 6. NON-FOOD EXPENDITURES AND INVENTORY OF DURABLE GOODS PART C INVENTORY OF DU3RABLEi GOODFS = 1. 2. 3. 4. 5. 6. Does your household oim any of the How many How many yeaLrs ago Did you purchase it, How much was if you wanted to following items? .. [ITEM].. d:id you acqukire receive it as a gift or it: worth when sell lthis ... [ITEM]. . does your .. (ITE1...? payment: for services, you acq[uired today, how much PUT A CHECK (') IN TH1E APPROPRIATE BOX household or receive it as dowry it? imoney wouldl you FOR ALr, ITEMtS. IF THE ANSiER IS YES, own? or inheritance? receive for it? ASK Q. 2-6 IF MORE THiN ONB ITEM OWNED, ASK PURCHASE .1 IF IYORE THAN ONE ABODUT MOST GIFT/PAYMENT . 2 ITEIM OWNED, ASK RECENTLY ACQUIRED DOWRY/INHERITANCE ... .3 ABOUT TOTAL VAL13E OF ITEM ALL ITEMS ITEM NO YES ICOD No: YEARS RUPEES RUP1EES E Radio/ cassette player _ 501 Camera/camcorder 502 Bicycl_e 503 Motorcycle / scooter j504 Motor car et-c. =5 RefrigeratoEr or freezer S06 Washin, machine 507 Fans 5_08 =_ . _== = Heaters 509 T,elevision /7 VCR 510 il___ __ _ _ . Pressure lais _ _ 511 petromax Telephone siets / 512 cordless Sewing machiLne 5;13 Parniture and rucg =s __ 514 5_ _ *__ Kitchen utensils 515 _ ___-- Jewelr_ (incl. watches . 16 * 80 1995 Nepal Living Standards Survey (NLSS) Stata Code PROGRAM 4s * This program computes a consumption value for durables * * * Durables consumption * * * use dt\et6 gen number=vO6cO2 gen age=vO6cO3 gen oldval=vO6cO5 gen curvai=vO6cO6 * iiupate nIAl value gen presval=oldval*number if age==0 replace presval=oldval*1.08*number if age== 1 replace presval=oldval*1.17*number if age== 2 replace presval=oldval*1.27*number if age== 3 replace presval=oldval*l.39*number if age== 4 replace presval=oldval*1.68*number if age== 5 replace nrPalu'=ondvaI*1 A4*numhAr if are== 6 replace presval=oldval*2.05*number if age== 7 replace presval=oldval*2.18*number if age== 8 replace presval=oldval*2.42*number if age== 9 replace presval=oldval*2.75*number if age==.0 replace presval=oldval*3.3i*number if age>=11 geyn derae=- (erva1InrARsval) A (1 Iage! sum deprate, d sort durbcode egen meddepr=median(deprate), by(durbcode) tab durbcode, summ(meddepr) gen durables= (meddepr+O.01)*curval/'(1-meddeprI sort wwwhh durbcode nllaapse (Rniml durables. bv(wwwhh) keep wwwhh durables label var durables "Durables consumption" sort wwwhh save consumption\durables, replace 81 1995 Nepal Living Standards Survey (NLSS) Stata Code PROGRAM 5: * This file aggregates all the consumption expenses: food, non-food, housing * durables and calculates total nominal consumption per household and per * capita *** FOOD use data\hhlist, clear keep wwwhh hhsize weight group urbrural sort wwwhh merge wwwhh using consumption\food drop _merge recode f ooo d .=0A sort wwwhh save consumption\agacons, replace *** NON FOOD merge wwwhh using consumption\educatn dirop _..erae recode educatn .=0 sort wwwhh merge wwwhh using consumption\health drop _merge recode health .=0 sort wwwnn merge hhwwh ina no consuimtion\nfoodil drop _merge recode nfoodl .=0 sort wwwhh save, replace *** HOUSING merge wwwhh using consumption\hhrent drop _merge recode hhrent .=O sort wwwhh merge wwwhh using consumption\utility Arop m,rge recode utility .=0 sort wwwhh save, replace *** DURABLES rmterge www'h usinrg cunsumption\durabiea drop _merge recnne dulrAhlen . =O sort wwwhh save, replace *** PUT ALL THE EXPENSES TOGETHER gen totcons= food+ nfoodl+ tobacco+ educatn+ durables+ hhrent+ utility On OZ. label var totcons "Total household consumption" gen pcapcons = totcons/hhsize label var pcapcons "Per-capita annual consumption" sort wwwhh save, replace * anperatinc main sharpR gen foodp=purchase recode foodp .=0 egen foodh=rsum(hproduct inkind) recode foodh .50 gen nfood=tobacco+educatn+health+nfoodI gen housecon=hhrent+utility gen foodpash=foodp--/toona gen foodhsh=foodh/totcons gen foodsh=food/totcons gen educatsh-educatn/totcons gen othnfosh=(nfoodl+tobacco)/totcons gen nfoodsh=nfood/totcons gen housesh=housecon/totcons ger. renta.h= I,.ren,t/"otcons gen utilsh= utility/totcons qen durabsh=durables/totcons gen weightl=totcons*weight #delimit ; collapse (mean) foodpsh foodhsh foodsh educatsh othnfosh nfoodsh housesh rentsh utilsh durabsh [weight=weightl];. -=i,,i cr save consumption\totshare, replace 1995 Nepal Living Standards Survey (NLSS) Stata Code PROGRAM 6: * This program generates a laspeyres regional price index using information on * food prices and housing prices * i * aLASPEYRES PRICE INDEX * * * ------------ FOOD PRICE INDEX --------------- * preparing weights use data\h'.hli-st, clear keep wwwhh weight gen sumcode=1 collapse (sum) sweight=weight, by(sumcode) sort sumcode save consumption\sweight, replace *generatir.g pr4Cea per standlard units use data\sectO5, clear sort wwwhh merge wwwhh using data\group drop _merge * Eliminating items for -which we do not ha-ve information on quantities drop if fooditm==018. Ifooditm==025. fooditm==026. | fooditm==036. dron if fooditm==044. I foodit-m==nsl fnhitm==nA I foodit-m--067 drop if fooditm==068. | fooditm==075. fooditm==082. fooditm==083. drop if fooditm==084. fooditm==085. fooditm==086. fooditm==094. drop if fooditm==103. fooditm==104. fooditm==lll. fooditm==112. drop if fooditm==113. fooditm==114. fooditm==124. fooditm==131. drop if fooditm==132. fooditm==102. fooditm==03j. drop if fooditm==121. | fooditm==122. | fooditm==123. * Converting all purchased quantities into grams gen gramyrp = vO503a* v0502*1000 if v0503b==l replace gramyrp = v0503a* v0502 if v0503b==2 replace gramyrp = vO503a* v0502*37500 if v0503b==3 replace gramyrp = vO503a* v0502*1000 if v0503b==4 replace gramyrp = vO503a* v0502*72000 if v0503b==5 rlace gram,y.p = vO50a* v0502*3600 i. V0503==6 replace gramyrp = vO053a* v0502*1000/2.2 if v0503b==7 replace gramyrp = vO503a* v0502*3600 if v0503b==8 * Converting eggs into grams (purchased) replace gramyrp = vO053a* v0502*60 if v0503b== 9. & fooditm -=31 replace gramyrp = vO503a* v0502*60*12 if v0503b==l0. & fooditm ==31 * Converting bananas into gram.s replace gramyrp = vO503a* v0502*127 if v0503b== 9. & fooditm ==61 renlace aramvrn = vOS03a* v0502*127*12 if v0503h==lo. &.fnnaitm- -- 91 * Converting pineapples into grams replace gramyrp = vO503a* v0502*500 if v0503b== 9. & fooditm ==65 replace gramyrp = v0503a* v0502*500*12 if v0503b==lO. & fooditm ==65 * Converting papayas into grams 84 replace gramyrp = vO503a* v0502*500 if v0503b== 9. & fooditm ==66 rpla--ep cyrRm,rn = v0503a* vOS02*500*12 if v0503b==10. & fooditm ==66 drop if gramyrp==O I gramyrp==. * Converting home-produced food quantities into grams gen gramyrh = v0506a* v0505*1000 if v0506b==i replace gramyrh = vO506a* v0505 if v0506b==2 re-lace -ramIrh = vO506a* v050S*37SOO if v0506h==3 replace gramyrh vO506a* 8 v0505*1000 if v0506b==4 replace gramyrh = vO506a* v0505*72000 if v0506b==5 replace gramyrh = vO5O6a* v0505*3600 if v0506b==6 replace gramyrh = v0506a* v0505*1000/2.2 if vO506b==7 replace gramyrh = vOS06a* vOS05*3600 if V050Sb==8 * Con.verting eggs into "gram.s (home-produced) replace gramyrh = vO506a* v0505*60 if v0506b== 9 & fooditm ==31 replace gramyrh = vO506a* v0505*60*12 if v0506b==10 & fooditm ==31 * Converting bananas into grams replace gramyrh = v0506a* v0505*127 if v0506b== 9 & fooditm ==61 replace gramyrh = v0506a* v0505*127*12 if v0506b==10 & fooditm ==61 * Converting pineapples into grams replace grayrh v O = ifv0506b== 5n6a* fooAdit v0505*500 r = = 9 M. replace gramyrh = vO506a* v0505*500*12 if vO506b==10 & fooditm ==65 * Converting papayas into grams replace gramyrh = vO506a* v0505*500 if v0506b== 9 & fooditm ==66 replace gramyrh = vO506a* v0505*500*12 if v0506b==10 & fooditm ==66 egen gramy=rsum(gramyrp gramyrh) drop i. gramLLy=-' I gra-,,,y-=. * Calculatina an averace Drice Der aram gen value = v0502*v0504 gen price = value/gramyrp * Setting extreme values in price to missing egen avgprice = mean(price), by(fooditm group) replace price=. if (price > 1O*avgprice I O.<*avgpieVe) label var price "price per standard unit" keep wwwhh fooditm gramy nrice groiin sort wwwhh merge wwwhh using data\hhlist keep if _merge==3 drop merge gen pricew=price*weight sort wwwhh fooditm sasve conBUinvt-ion\fAnprijesA -rreplace * generating the average quantities to use as weights for the price index gen qO=gramy*weight/hhsize collapse (sum) qO, by(fooditm) gen sumcode=1 sort sum.cd merge sumcode using consumption\sweight drop _merge replace qo=qO/sweight label var qO "average quantities" sort fooditm save consumption\qO, replace use consumption\fdprices, clear 85 drop if pricew==. I pricew==O sort wwwhh fooditm collapse (sum) regprice=pricew sweight=weight, by(fooditm group) replace regprice= regprice/sweight * there may be some items in a particular region for which we have not * prices. we need to exclude them gen one=l egen chk=sum(one), by(fooditm) drop if chk<-5 drop one save consumption\fdprices, replace sort fooditm merge fooditm using consumption\q0 keep if merge==3 drop_m e gen regexp=regprice*qO label var reqexp "reqional expenditure for the same food basket" save consumption\fdprices, replace * Create food item shares egen totfood=sum(regexp), by (group) gen sUare=regexp/totfoo collapse (mean) share, by(fooditm) save consumption\fshares, replace use consumption\fdprices collapse (sum) regexp, by(group) egen avg=sum(regexp)/6 gen fLn%dAex=regexp/a vg gen one=l gen reaion=sum(one) drop one label define KathmOthurRwhilRehilRwterReter 1 Kathm 2 Othur 3 Rwhil 4 Rehil 5 Rwter 6 Reter label values region KathmOthurRwhilRehilRwterReter keep region f index sort region 1iat- finder save consumption\findex, replace ------------ HOUSING PRICE INDEX --------------- * Regional housing price index using the hedonic regression as the basis use consumption\housing, clear sort wwwhh merge wwwhh using data\hhlist drop merge * generating output that would help calculate the housing price index #delimit ; reg lnrer.t .wil kath.,.and othrba Llnrooms rehills -w-terai lnd-wsze lnasset kitchen proad walls floor roof window water garbage toilet light telephon if lnrent> 0: #delimit cr replace lnrooms=ln(3) if lnrooms==. replace lndwsize=ln(500) if lndwsize==. recode inasset .=O recode kitchen .=0 86 recode proad .=0 recoue walls .=0 recode floor .=0 recode roof .=Q recode window .=0 recode water .=0 recode garbage .=0 recode toilet .=0 recode light .=0 recode telephon .=0 #delimit ; collapse (mean) lnrent kathmand othurban rwhills rehills rwterai (median) lnrooms lndwsize lnasset kitchen proad walls floor roof window water garbage toilet light telephon [weight=weight]; sum; gen av rent= b[_cons]+kathmand*_b[kathmand]+othurban*_b[othurban]+ r.wh1 118a*_h rrWhi 118 +arehi 1 8*_ [rehi 18] +rwte ,rai*_ h rrw1-prai + lnrooms*_b[lnrooms]+lndwsize* b[lndwsize]+lnasset*_b[lnassetl+ kitchen*_b[kitchen]+proad* b[proad]+walls*_b[walls]+floor*_b[floor]+ roof* b[roof]+window* b[window]+water* b[water]+garbage* b[garbage]+ toilet*_b[toilet]+light* b[light]+telephon* b[telephon]; gen reter_r=av_rent-katnmanda*b [kathmandj -othurban* b[otnurbanj- rwhills* b[rwhills]-rehills*_b[rehills]-rwterai*_b[rwterai]; gen kathm r=reter r+ b[kathmand] gen othur_r=reter_r+_b[othurban] gen rwhil_r=reter r+_b[rwhills] gen rehil_r=reter r+ b[rehills] gen rwter_r=reter_r+_D[rwterai] replace av_rnepa_eW replace reter r=exp(reter_r) replace kathm r=exv(kathm_r) replace othur_r=exp(othur_r) replace rwhil_r=exp(rwhil_r) replace rehil_r=exp(rehil_r) replace rwter_r=exp(rwter_r) keep av_rent reter_r kathm_r othur_r rwhil_r rehil_r rwter_r expand 6 gen one=1 gen region=sum(one) drop one label define KathmOthurRwhilRehilRwterReter 1 Kathm 2 Othur 3 Rwhil 4 Rehil 5 Rwter 6 Reter label values region KathmOthurRwhilRehilRwterReter gen hindex=kathm r/ay rent in 1 replace hindex=othur _r/avrent in 2 replace hindex=rwhilr/avy_rent in 3 replace hindex=rehil_r/av_rent in 4 replace hindex=rwter_r/av_rent in 5 replace hindex=reternr/av_rent in 6 keep region hindex enrt regi n, save consumption\hindex, replace ***------------ TOTAL PRICE INDEX --------------- use consumption\totshare expand 6 87 gen one=1 gen region-asu-m (o.e) drop one label define KathmOthurRwhilRehilRwterReter 1 Kathm 2 Othur 3 Rwhil 4 Rehil 5 Rwter 6 Reter label values region KathmOthurRwhilRehilRwterReter sort region merge region using consumption\hindex drop fh.rge sort region merge region usinq consumption\findex drop _merge * we have information on prices on some components only of the total * expenditure. the food price index is therefore used as a proxy for all but * rent prices gen pirdex_renth *hndex+(l-rentsh)*'index tj~~~~~" XL %I A*u,,..at L - . .~*L. ILI .8. LILLC.A. list findex hindex pindex keep region pindex sort region save consumption\pindex, replace *** ---- PRICE-ADJUSTED CONSUMPTION ------------- use consumption\aggcons gen reaion=aroup label define KathmOthurRwhilRehilRwterReter 1 Kathm 2 Othur 3 Rwhil 4 Rehil 5 Rwter 6 Reter label values region KathmOthurRwhilRehilRwterReter sort region merg region using cunsumption\pindex drop merge cen rtoton-totronrs /n nidepx label var rtotcons "real household consumption" gen rpccons=pcapcons/pindex label var rpccons "real per capita consumption" sort wwwhh save consumption\raggcons, replace 88 A2. Paasche Pce Index: Stata Code for Nepal * This program generates a paasche price index using data on food prices * * * PAASCHE PRICE INDEX * * * *1. Calculating the Abudg:ett ir.flCeO1I shares- forL e--ach it=em... use data\SectO5.dta. clear * Total consumption by household of each item drop if fooditm>=120 & fooditm<=130 drop if fooditm>=130 gen purch = v0502* vOs04 gen hncons = vUJU5* v0507 egen tcons - rsum( purch hcons vOS08) dron npirrh hcons label var tcons "Total consumption of item" egen totcons = sum(tcons), by(wwwhh) label var totcons "Total household consumption" gen wi = tcons / totcons label var wi "Budget share of item-- keep wwwhh www fooditm wi sort ,yw,whh fnnditm save fileOl, replace * 2. Calculating cluster-level median prices in fileO2 use data\SectO5.dta, clear Identifying whic-h c * 4o 4a rTporte mIoat fre,-er.tly fo- each food iter keep if v0502 > 0 & v0502-=. & vO503a>0 & vO503a-=. & v0503b>0 & v0503b<=lo & v0504>0 & v0504-=. drop if fooditm== 10 fooditm== 18 fooditm== 20 fooditm== 25 drop if fooditm== 26 fooditm== 30 fooditm-= 36 fooditm== 40 drop if fooditm== 44 fooditm== 50 fooditm== 55 tooditm.= 56 drop if fooditm== 60 fooditm== 67 fooditm== 68 fooditm== 70 drop 4f f-14 i.=-,-a 75 fo 4 -.-- f F I8 4 f-m.-W) _ f^-A4*_<=.0% drop if fooditm== 94 | fooditm==100 | fooditm==103 | fooditm==104 drop if (fooditm>=110 & fooditm<=120) I fooditm>=124 collapse (count) ncases=wwwhh, by( fooditm v0503b) egen maxfreq = max( ncases), by(fooditm) keep if ncases== maxfreq keep fooditm vOS03b sort foodlitm. ren v0503b code save templ, replace use data\SectOS.dta", clear sort fooditm merge fooditm using templ keep if _merge==3 drop _mJerge erase templ.dta keen if vn503b== code drop if fooditm== 10 I fooditm== 18 | fooditm== 20 | fooditm== 25 89 drop if fooditm== 26 fooditm== 30 | fooditm== 36 fooditm== 40 drop if fooditm.== 44 fooditm== gn I fooditm== 55 fnnAitm== 56 drop if fooditm== 60 fooditm== 67 fooditm== 68 | fooditm== 70 drop if fooditm== 75 fooditm== 80 (fooditm>=82 & fooditm<=90) drop if fooditm== 94 | fooditm==100 I fooditm==103 I fooditm==104 drop if (fooditm>=110 & fooditm<=120) I fooditm>=124 sort www merge www using group drop mer-e gen ph = v0504/ vO503a egen pc = median(ph), by(www fooditm) egen pg = median(ph), by(group fooditm) egen pO = median(ph), by(fooditm) keep wwwhh www fooditm ph pc pg pO collapse (mean) pc pg pO, by(www fooditm) sort wnn., fooditn m label var pc "Cluster Price" label var pg "Group Price" label var pO "Overall Price" replace pc = pg if pc==. replace pc = pO if pc==. drop if pc==. I pc==O save file.2, replace * 3. Food item price missing: Replace with next level of aggregation * (Food Group) in fileo3 * Item within food group reported most frequently use Cdata1\Sec1t05.d1ta, clear keep if v0502 > 0 & v0502-=. & v0503a>0 & vO503a-=. & v0503b>0 & v0503b<=10 & v0504>0 & v0504-=. gen foodgrp = int(fooditm/10) collapse (count) ncases=wwwhh, by(foodgrp fooditm) egen maxfreq = max( ncases), by(foodgrp) keep if ncases== maxfreq keep foodgrp fooditmn sort foodgrp ren fooditmi I-nrode save templ, replace use data\SectO5.dta", clear keep wwwhh www fooditm gen foodgrp = int(fooditm/l0) sort www fooditm me-re .. f leO-2 , foodi-tm 1u7-s-ineg drop -merge label var foodgrp "Food Group" sort foodgrp merge foodgrp using templ drop merge erase templ.dta s ort www merge www using group droD _merge gen pcgrp = pc if fooditm==code gen pggrp = pg if fooditm==code gen pogrp = pO if fooditm==code egen pc2 = mean(pcgrp), by(www foodgrp) 90 egen pg2 = mean(pggrp), by(group foodgrp) egen p02c= c y(foodgrp) iean(pgrp), reDlace pc = pc2 if pc==. replace pc = pg2 if pc==. replace pg = pg2 if pg==. replace p0 = p02 if p0==-. w-w-w'L keeWp -. 'w-w-w food-i... foodgrp p p p0 group sort wwwhh fooditm save fileO3. replace * 4. Calculating the index itself use fileOl merge wwwnh fUUUoodi L ± tUe uIsiEly Lg drop _merge Rnrt wwwhh fnoditm gen pratio = pc/po label var pratio "Cluster Price / Overall Price" gen lnprice = log(pratio) label var lnprice "Log pratio" gen lnpindex = w1i*lnprice collapse (sum) lnpindex, by(wwwhh) gen pindex = exp(lnpindex) drop lnpindex label var pindex "Household Paasche Index" save pindex, replace 91 A 1 fl,uahoa b nncnmpinntinn S,hi-bmnnnnnt- tcfao Vnden fnr V,it-nom * * * OBJECTIVE: This program imputes a consumption * * value from data on consumer durables (section 12c) * * * ** **** **** *** ****** ******** ***** *** ******* ******** ****** **** * version 4.0 clear set maxobs 130000 use data\sectl2c * CORRECTIONS *-----consumer durable corrections replace goodaCLc y=8- u2 hi-=2520C %.'. oodd=20 replace goodcv=. if hid==27902 & goodcd==202 & line==2 replace goodacy=78 if hid==20015 & goodcd==203 replace goodcv=1450 if-hid==19616 & goodcd==203 replace goodcv=ll00 if hid==20809 & goodcd==205 replace goodcv-800 if hid==24712 & goodcd==2L8 & line==10 replace goodbuy=ll0 if hid==20813 & goodcd==207 replace goodbuy=1000 if hid==14817 & goodcd==224 *…------------------------------------------------------------- save results\nfdcdurb, replace clear *---Depreciation rates calculations * Age of each item calculated, taking into account the survey date * Workl otiif- the date of the Q1tVr7O set maxobs 5000 use data\sectOOa keep hid datel gen svyyear=mod(datel,100) gen nnS'41 nnJ.eJJl) tsAIJ**i jih=... tab svymonth svyyear,m drop datel sort hid save results\svydate, replace c'lear set maxobs 32000 use results\nfdcdurb sort hid merge hid using results\svydate tab merag drop if _merge<3 *---these cds are producer durables drop if hid==8716 & goodcd==219 drop if hid==Q71A L goodcd==21Q drop if hid==13011 & goodcd==216 drop if hid==25501 & goodcd==216 9l2 7'- *----calculations based on accruisition s{ince 1985-- thpv nnlv non5ider * durables acquired after 1986 because earlier inflation indices to update * the purchase price do not exist. keep if goodacy>85 & goodacy<94 drop if goodbuy==0 I goodbuy==. *----generating an inflator variable to make all past values real 1993=100 gen inflator=52423.1/321.1 if goodacy==86 replace inflator=52423.1/1514.4 if goodacy==87 replace inflator=52423.1/7181.7 if goodacy==88 replace inflator=52423.1/14059.7 if goodacy2s89 replace inflator=52423.1/19177.9 if goodacy==90 replace inflator=52423.1/35038.2 if goodacy==91 r…P1ace inflat--r=97A71 1/48240.7 if replace inflator=52423.1/52423.1 if goodacy==93 gen realpurp=goodbuy*inflator *---determining duration for which household has had cd * ihadformni is the age of the durable expressed in months replace goodacm=svymon if goodacm==. gen hadform-A=s... ta,n,ar-goodacyr.*.k*1 + s r..y.on=goodac. sum hadformn,d 1 hid qoodacy qoodacm svvyear svvmon if hadformn<0 replace hadformn=0 if hadformn 0) f4 = v.du.a.m execute. ** still have 50 cases with missing values - for these, use the average values by geog-rahic region. if (f4 = - 1 & v.dura.m > 0) f4 = v.dura.m 95 execute. freq f4. recode f4 (0 thru 50=1) (50.00000001 thru 100=2) (100.000001 thru 500=3) (5--.00001 thvra Y.ghest=4) i-nto guov execute. variable label grupo.va 'Grouped value of durable good'. sort cases by equipo (A) grupo.va (A) . ** generate a file with average values. aggregate outfile 'c:\m.ecovi\sl.-.n\ag Isv break equipo grupo.va/ edad.g 'Age by group' = MEAN(f3). execute. match files/file */ table 'c:\mecovi\salman\aggr.savy/ ijy ecruipo gUrpo. va. execute. recode f3 (miss=-l) execute. if (f3 = - 1 & edad.g > 0) f3 = edad.g execute. ** Average aaa for carn (S-R' and boats (4-2!. ** do not appear to be representative of values we'd expect for Panama ** instead, we used Car=20, boats=15. if (equipo = 21) edad.m = 10 execute. if (equipo = 22) edad.m = 7.5 execute. ** Calculate total remaining useful life of each durable good. compute edad.que = (edad.m * 2) - f3. execute. variable labels edad.que !Total remaining life of durable good! ** Assj. a minimum useful life of 2 years. recode edad.que (lowest thru 2=2) execute. ** Assign a minimum useful life of 4 years for all goods with a value > $5,000. do if (f4 >= 5000) recode ed"A - IT owest th-ru A=A) end if. execute. ** In 4 cases, change minimum with 4 years. compute V.USO = f4 / edad.que execute. recode f2 (9=1) (sysmis=1) execute. compute v.equipo = f2 * v.uso . execute. variable label v.equipo 'Valor de uso anual de equipos' . sort cases by form. ** Generate an output file with ID code of household and consumption value. aggregate outfile 'c:\mecovi\salman\gasto5.sav'/presort/break form/ v_equipo !Use value of durable goods! = sum(v.equipo). 97 AS. Durables Consumption Subcomponent: Stata Code for Kyrgyz Republic * * * Durables consumption * * * use fall96\sectl2c, clear collapse (sum) v12c04, by(hhid) * Assuming a i=10% to attribute a consumption flow to stock of durables gen durables = O.l*v12c04 recode durables .=O label var durables "Annual durables consumption" k-eep =;d uabe sort hhid durables save results\durables, renlace 98 A6. Housing Consumption Subcomponent: Stata Code for South Africa #delimit * The calculation of the housing cost is obtained using the following measurements: .. . _ T .L± /1s1t ata' U.L _,;;LUCe V~.U VaCIu L.the _cJ ofU X LLI ren Aai p1X L.L__ or _1 ar ~.. ____ stiat _L _ 11-L Li of L.U t _ eWX the rental value of the house if it is provided for free by sombodv else. 2) Estimate of the rental value based on the ratio of property value and rental value in the same area for all the people that report the resale value of their homes. 3) Estimate of the value of the homes for all the poeple that do not provide the cost of rent nor the value of their homes, so as to use the same ratio to estimate the rental value.; version 4.0; clear; log using results\clcexpO4,replace; set log linesize 200; * Name : CLCEXPO4.DO V : 01 * Date lAUGUST 5, 1994 * Infile : S4_HSV1,STRATA2 * Outfile : HHEXPO4 * * * OBJECTIVE: Calculate Actual and Inputed Housing * Expenditure * * set more 1; ** Get the files use data\s4_hdef; keep hhid; sort hhid; m.erge ;=hd using hsvl; Gaa\s tab _merge; drop __merge; sort hhid; merge hhid using data\strata2; tab _merge; drop merge; sort ILI.LU, gen clustnum=int(hhid/1000); *** ACTUAL OR ESTIMATES RENTAL EXPENDITURE (use values above R10) ***; gen rentexp=rent_a if rent_a>10; replace rentexp=rent_m if rent_m>10 & rentexp==.; lab var rentexp "Actual Rental Expenses"; gen int markerO4=0; lab var markerO4 "Marker"; replace .marker04=1 if rent-A3mexp>0 & rent-P- & ?-t_a>l0; *Have arctua1 rent- replace markerO4=2 if rentexp>0 & rentexp-=. & rent_m>10; *Have market rent replace markerO4=3 if markerO4==0 & sale>0 & sale-=.; *Have Value; replace rooms_to=. if rooms coO & mdroom-=.; sort hhid; save stexOl,replace; ** Get the median value by cluster **; gen valroom=sale_val/rooms_to; egen mdvalrm=median(valroom) if valroom>O, by(clust); collapse mdvalrm, max(mdvalrm) by(clust); des; sum; 8Ort- clu8t"; save stexO2, replace; ** By New province metro and race **; use stexOl; gen valroom=sale_val/rooms_to; egen mdvalrm2=median(valroom) if valroom>O, by(newp metro race); col"lapse md-Va 'L-JI, ,max(,,dt%A'vaClrrLJ[L,Q ro % by %neA-W-- -1,.et 0rac-C e);i des; sum; sort newp metro race; save stexO3,replace; ** Put the median values back in the file **; use stexOl; keep hhid clust markerO4 rooms to newp metro race; sort clust: merge clust using stexO2; tab _merge; drop _merge; sort newp metro race; merge newp metro race using stexO3; tab _merge; do-,. mA_- gen mdval=mdvalrm*rooms_to; replace mdval=mdvalrm2*rooms_to if mdval==.; des; Sum; keep if markerO4==O; sort hhid; 00.Vs - a = > ^_i use stexOl; merge hhid usinq stexo4; tab _merge; drop merge; replace sale=mdval if markerO4-=O; tab newpro if markerO4==O, sum(sale); ta"-b newJ*ro if markerO4==;, u CLe); tab newpro if markerO4==2, sum(sale); tab newnro if markerQ4==3. sum(!ale1! replace markerO4=4 if markerO4-=0 & sale>O & sale-=.; lab def mar 0 "Miss" 1 "Rent a" 2 "Rent mi" .3 11Val 4 "No Re/Val" 5 " T.mr-.ute lab val markerO4 mar; I100 save stexOl, replace; *** Check the ratio: value to rental by province metro and race **; use stexOl; egen valmed = median(sale_val) if sale_val>O , by(newpr metro race); egen rentmed= median(rentexp) if rentexpO , by(newpr metro race); egen numrent= count(rentexp) if rentexp>O , by(newpr metro race); egen numval = count(sale_val) if sale val>O , by(newpr metro race); collapse rentmed valmea r.uiimrert nmme1 max(rentmed valmed numrent numval) by(newpr metro race); gen ratio = rent*1200/val if rent>O & val>O; egen mdratio=median(ratio), by(metro race); collapse mdratio , max(mdratio) by(metro race); des; list; 1 savt.exa5,ra e ea_ , *** CALCULATE IMPUTED VALUE OF RENT USING REPORTED AND ESTIMATED SALE VALUE OF THE PROPERTY AND RENTAL RATIO BY LOCATION AND RACE; use stexOl; sort metro race; merge metro race using stexO5; drop _merge; aen rentimp=sale*mdratio/1200 replace rentimp=. if markero4==1 I markerO4==2; lab var rentimp "Imputed Rental Expenses"; *** REPLACE REMANING VALUES WITH CLUSTER MEDIANS - In three clusters they are stiL'l' m.8Ss|ng because ,obody has a valu ot in which they are, because everybody else is renting. gen rentroom=rentexn/room_ t: egen mdrtrom=median(rentroom), by(clust race); replace mdrtrom = 20 if clust==40 & mdrtrom==. * Median for 2 Coloured in African area; gen mdrt=mdrtrom*rooms_t; replace markerO4=5 if markerO4==0 & mdrt>o & mdrt-=.; replare rentim-d="mr* if marke-r4==S ; ** SAVE THE RESULTS IN A FILE **; keep hhid rentexp rentimp markerO4; lab data "Rental Expenditure"; egen mxtrent=rsum(rentimp rentexp); replace mxtrent=. if markerO4==O; lab va-r mxtrent "Total Housini-=irig E tnditre"; sort hhid; des; sum; save results\hhexpO4,replace; ** DELETE UNECESSARY FILES **; stex0l.dtJa; ddell Idel stexO2.dta; Idel stexO3.dta; Idel stexO4.dta; Idel stexO5.dta; log close; iOi A 17 1F-.Ancin nkntrnnf g 40 f n. -- imn * * * OBJECTIVE: calculate rents * * * * This p-rogra.. i_rp-ut-es rents. hue.aoiyo epelv 14- n their * own dwelling (94%) and only 17 out of 4800 households rent their dwlling * from private persons. The value of housing consumption taken to be * 3% of the current value of the house * The housing value is predicted with a regression of housing value on • various housing characteristics. version 4.0 clear set matsize 150 set axuobs 500u0 use data\sectO6 *-----region & location variables *----commune number used to distinguish urban and rural areas, specific * cities and maior reaions gen cum=round((int(hid/100)/2) 1) replace cum=68 if cum==151 label variable cum "Commune number" * ---- Tmmv variahles for H4anoi & qaignn gen hanoi=cum>123 & cum<127 gen saigon=cum>138 & cum<145 gen byte urban=O if l<=cum&cum<=120 replace urban=-l i 1if 1 -cr.u-c-1 =15 gen int region=l if (cum>=1&cum<=12)1(cum>=22&cum<=28) replace region=l if (cum>=121&cum<=123)jcum==127 replace region=2 if (cum>=13&cum<=21)1(cum>=29&cum<=51) replace regio=2' i4 c4..>=1 244.cum..=52&cum<=69 replace region=3 if cum==131|cum==132 replace region=4 if (cum>=70&cum<=79&cum-=73)1(cum>=82&cum<=84) replace region=4 if cum>=133&cum<=137 replace region=5 if I cum==73 cu ==8cum==81 cum==85 replace region=6 if (cum>=86&cum<=89)1(cum>=92&cum<=97) replace region=6 if cum>=139&cum<=145 replace region=7 if cum==901cum==911(cum>=98&cum<=120) replace region=7 if cum==138j(cum>=146&cum<=150) label define region 1 "NU" 2 "RR" 3 "NC" 4 "MNC 5 "CH" 6 "SE" 7 "MD" 1abeol vauAes rperion rpriegon label define urban 0 'Rural" 1 "Urban" label values urban urban tab region, gen(region) * ----Housing characteristics n2 10 '.J II gen electrcy=light==l tab bultyear, gen(dwelage) tab dwater, gen(dh2osrc) tab walls, gen(walls) tab floor. gen(floor) tab roof, gen(roof) *-----dummy variables for repair condition of dwelling egen repair=group(dwlwcond dwlfcond dwlrcond) ALd~elir-mit; label define repair 1 "A113" 2 "W+F" 3 "W+R" 4 "Wall" 5 "F+R" 6 "Floor" 7 "Roof" 8 "AllOkl- #delimit cr label values repair repair *----recode some categories to create dummy variables replace light=5 if light==2 replace window=5 if window==3 tab door, gen(door) tab window, gen(window) tab toilet, gen(toilet) tab repair, gen(repair) gen roomgp=rooms rpnlanp ronnmon=' if roonms> label variable roomgp "Room groups: > 5=5" tab roomgp, gen(roomgp) gen loghval=log(saleval) #delimit ; 4 stepwi-se n,o L a lgvldelg2eae6r-omgp2-roo.m.gp5 xr 1 1_ --- -io 1 - -- =n - -wn -n C nnn) nmn elect-rcy la dh2osrcl-dh2osrc7 wallsl-walls8 floorl-floor7 roofl-roof7 toilet2-toilet5 window2-window5 door2-door4 repairl-repair7 region2-region7 urban hanoi saigon uar lar, backward; #delimit cr predict lnhvalht h-) gen housenval=exp1(an1rVhalInt replace houseval = saleval if hid == 27815 /* house with fifteen rooms */ label variable houseval "Predicted house value" * estimated rental expenditures - two scenarios: 2 and 3 percent (annually) * of predicted sale value of dwelling - multiplied by 1000 because sale * value info in millions of dongs. For the consumption aggregate the 3% * wLL AJe used. gen rentexo2=0.02*houseval*1000 gen rentexp3=0.03*houseval*1000 label variable rentexp2 "Imputed rent - interest rate=2%d" label variable rentexp3 "Imputed rent - interest rate=3%" sum rentexp*, d keep hid rentexp* saleval houseval region urban cum rentby vrentc rentuc replace vrentc = vrentc * 2 if rentuc == 7 replace vrentc = vrentc * 4 if rentuc == 6 replace vrentc = vrentc * 12 if rentuc == 5 gen ratio_rs = vrentc/(1000 * saleval) if rentby == 3 103 label variable ratio_rs "Rent/Sale if rented from private agency" tab ratio_rs drop rentby vrentc rentuc ratio_rs sort hid save results\rentexp, replace 104 LSMS Worldng Papers No. TITLE AUTHOR i fving StandarasSurveys in Developing Countries IChanderiGrootaerltryatt 2 IPoverty and Living Standards in Asia: An Overview of the Main Ivisaria Iesults andLessons ofSelected HouseholdSurveys I iAA'Pne rina Lpvpl'v nf Livinag in Latin A,norirne An flVPrViPw of ITtnit.Id Natintns Rtntictit' ain Problems |Office 4 Toa 1.3 JoreAW 17fr_S4-V AI-e ofJ L of _u LiVing, -4 1n Io.' AnA-1d.'Ch.dUe fReview of Work of the United Nations StatisticalOffice (UNSO) 1 ieu to Stalu.6jc; of JLeve' oj L- wig 5 Conducting Surveys in Developing Countries:Practical Scott/de Andre/Chander Irrobiems and Experience in Brazil, Malaysia, and Tne fPhilippines 6 jHouseholdSurvey Experience in Africa Booker/Singh/Savane 7 I1a#nctuironant nf Wplfnro, Thonr:7and Prn,'ti,Ic71 4u.iA4li,ia. 'Deatnin 8 ,Employmen't Datafor the Measurement of Living Standards "ehran 9 fncome and ExpenditureSurveys in Developing Countries: Wahab iVRnnlple Deign and Exer1ution 10 Ftl7ectionsof the LSMS GroupMeeting Saunders/Grootaert 11 IThree Essays on a Sri Lanka HouseholdSurvey Deaton 12 IThe ECItEL Study oXf H-ousehold Income and Co.nsu.tnhpion i,n, 13 Urban Latin America: An Analytical History YL5U11 and'""IF U,j(4 hJlS I vtNva r 'Mus grv,e L"vIVII [vifLc of the Standardof Living in Developing Countries i4 Chnia acnooning and the Measurement of Living Stanaards Bndsai 15 IAeasuringHealth as a Component ofLiving Standards Ho 16 proceduresfor Collecting andAnalyzing MortalityData in Sullivan/Cochrane/Kalsbeek 7The LaborMarket and SocialAccounting: A Frameworkof DataGrootaert 17 Presentation 18 Time Use Data andthe Living StandardsMeasurementStudy Acharya 19 The ConceptualBasis ofMeasures of Household Welfare and |Grootaert Experimentationfor Household Surveys: Two Case 20 |tatistical Grootaert/Cheurg/Fung/Tam pruazeS Oy nc/rig &ung 21 The Collection ofPriceDatafor the Measurementof Living od/Knight S'tandaras 22 HouseholdExpenditure Surveys: Some MethodologicalIssues Grootaert/Cheung 23 CollectingPanel Datain Developing Countries:Does It Make |Ashenfelter/Deaton/Solon .Vr,nee? 24 feasuringandAnalyzing Levels ofLiving in Developing |Grootaert 25 |TheDemandfor UrbanHousing in the Ivory Coast |Grootaert/Dubois LSMS Working Papers No. TITLE AUTHOR ,- in an6- T_ - 2 t-. Iine Cule a -~ ivoireLiving Standards Survey. Design ana -iisworiiviunoz mplementation (English-French) 27 phe Role of Employment and Earningsin Analyzing Levels of (Urootaert Living: A GeneralMethodology with Applications to Malaysia Iand Thailand I 28 Analysis of Household Expenditures IDeaton/Case 29 |The distribution of Welfare in C6te d'Ivoire in 1985 (English- |Glewwe 30 Quality, Quantity, and Spatial Variation of Price:Estimating tDeaton Price Elastlciliesform Cross-SectUonalData 31 Financingthe Health Sector in Peru ISuarez-Berenguela 32 JrnformalSector, Labor Markets, andReturns to Education in |Suarez-Berenguela [Peru 33 |Wage Determinants in C6te d'Ivoire Van der Gaag/Vijverberg 34 (JuidelinesjorAdapting the LSMS Living Standards IAinsworthVan der Gaag Questionnairesto Local Conditions I The DemandforMedical Care in Developing Countries: r 3QuantityRationing in Rural Cote d'Ivoire 1 vorNan des Gaag 36 jaborMarketActivity in C6te d'Ivoire andPeru Fewma 37 |Health CareFinancingand the DemandforMedical Care DGelerNiocaydSanderson g ~~~~~~~~~~~Dor/Van gder Gaag 38 Wage Determinants and School Attainment among Men in Peru Stelcner/Arriagada/Moock - IThe Allocation of Goods within the Household. Adults- Children-L J 9and Gender 'IDeaton_ Th.e Pfforfce nfMnvP,h1useo.ld a,nd ummnnuit3nharac.li,c o,, 40 Nutrition of PreschoolChildren: Evidencefrom Rural C6te Strauss 41 Public-Private Sector Wage Differentials in Peru, 1985-86 Stelcner/an der Gaag/ 4_ 1 Public-Private vijverDerg 42 1The Distributionof Welfare in Peru in 1985-86 [Glewwe 43 |ProfitsfromSelf-Employment: A class Study of Cote d'Ivoire |Vijverberg 44 1t ne Living StandardsSurvey andPricePolicy ReJorm: A Study leatonBenjaniin jof Cocoa and Coffee Productionin C6te d'Ivoire 45 geasuringthe Willingness to Payfor Social Services in GertlerNan der Gaag Developing Countries t 46 gVonagriculturalFamily Enterprises in C6te d'Ivoire: A Vijverberg Developing Analysis 47 The PoorduringAdjustment: A Case Study of C6te d'Ivoire |Glewwe/de Tray 48 1nfonrontngPovety in Developing Countries: Definitions, GlewweNan der Gaag 48Jnformation, and Policies Gew/andsGg Smple DDesignsfor the Living Standards Surveys in Ghana and 4 auntania (English-French) ICO enuvegbe 50 kood Subsidies:A Case Study of PriceReform in Morocco [arald LSMS Working Papers No. TITLE AUTHOR j(L-ngu.ah-Frnncnj 5 |ChildAnthropometty in C6te d'Ivoire:Estimates from Two Srauss/Mebra Surveys, 1895-86 i Public-PrivateSector Wag'e ConmnarLvvn. and Anonlia'htino in tV5n dir D |DevelopingCountries: Evidencefrom Cote d'voire and Peru |Gaag/StelcnerNijverberg 5L3 coueconoumk Determinantsof Fertilityin Cote a .ivoire VAnsworth 4 |The Willingness to PayforEducation in Developing Countries: ....... [Evidencefrom ruralPeru Rigidite des salaires:Donnees microeconomiques et 55 |macroeconomiquessur l'ajustement du marche du travaildans leLevy/Newrnan secteur moderne (French only) I 56 7The Poorin Latin America duringAdjustment: A Case Study of |G1ewwe/de Tra [Peru IGewdeTa 57 The substitutability ofPublic andPrivateHealth Carefor the Alderman/Gertler 58 Identiying the Poor: Is "Headship" a Useful Concept? Rosenhouse 59 jaborMarket Performanceas a Determinantof Migration Vijverberg 60 llMe Rel&;V E0Cr&'eeso ,.vt n ulcShos Smez/Cox 6 ~I r & Evidencefrom Two Developing _V VI ).1 (JJ I- IZ Countries L i.(fU JF(JL l M5W 61 eargeSample Distrioutionoj aeveraiinequaiityMeasures: With akwani Application to Cote d'Ivoire 62 [testingforSignificance ofPoverty Differences: With Application akan Ito C6te d'Ivoire I 63 overny and Economic Growth: With Application to C6te iakwani 6 'Ivoire 6Education and Earningsin Peru'sInformal Nonfarm Family oock/M us/Stl IEnterDrises 65 ormal andInformal Sector Wage Determination in Urban Low- Alderman/Kozel 65 ri.nPa'nAe liuhnmrhinL in Testingfor Labor Market Duality: The Private Wage Sector in 66 dg'voire nCte ivjverberg/Van der Gaag 67 |Does EducationPay in the Labor Market? The LaborForce {n 'aramcipation, Occupation, andEarnings of Peruvian Women 68 1The Composition andDistribution ofIncome in C6te d'Ivoire Kozel 69 triceElasticitiesfrom Survey Data:Extensions and Indonesian Ecient Allocation of Transfers to the Poor: The Problem of |G1ewe 71 Investigating the Determinants ofHousehold Welfare in C6te Glewwe 7The Selectivity ofFertilityand the Determinantsof Human IC-apitai Investments: Parametricand SemiparametricEstimates ., 2 hadow Wages and PeasantFamily Labor Supply: An r J 'JEconometric Application to the Peruvian Sierra I LSMS Working Papers No. TITLE AUTHOR 74The Action of uman ResourceI" ar Poverty on. Oe Ar.or: a What we have yet to learn 75 1The Distributionof Welfare in Ghana, 1987-88 Glewwe/Twum-Baah 76 LSchooling. Skills, and the Returns to Government Investment in I_ |Education:An Exploration Using Datafrom Ghana 'lewwe M Iw_L-1. 12- 4 rmn - V .3 '1IC. y L--------- -4 VLT 1T--. fAU- 78 IDualSelection Criteriawith Multiple Alternatives: Migration, I Viiverhercr Work Status, and Wages . 79 GenderDifferences in HouseholdResource Allocations Thomas 80 |The HouseholdSurvey as a Toolfor Policy Change: Lessons 80oSr, IV.-- JGrioshflgI*tr;Ofk T 81 |PatternsofAging in Thailand and Cote d'Ivoire leaton/Paxson 82 |Does UndernutritionRespond to Incomes andPrices? Ravallion ominance Testsfor Indonesia ~Growth andRedistribution Components of Changes in Poverty 83 Measure: A Decomposition with Applications to Brazil and India Ravallion/Datt in the 1980s laDneeiri,ner Tpinmpn, fr.,b P"vntilit Prtarnw.icav withd Xfl,,,,eahnl 84 as.r. ncm,rmFml nerte .n.h..ueod.....................Vv [urveys L 85 jVemana Analysis anda ax Reform in Pakistan 86 |Poverty andInequality during UnorthodoxAdjustment: The °Case ofPeru, 1985-90 (English-Spanish) rifI---we-'-aiT Iueaton/Unmard IFamilv Productivity. Labor SuDDIV. and Welfare in a Low- O/ , ,,, L ,. z~~~~~~~~~~evmntiw(ertier Income Country 00 Dr VF L_IrSI W) ._)_A. .11 '_J14&(W LU IGUF&a Uri" IVIUWCU V4111U11 89 Public Policy andAnthropometric Outcomes in C6te d'Ivoire Thomas/Lavy/Strauss 90 1feasuring the Impact of FatalAdult Illness in Sub-Saharan ers oAinohn IAfriin An Annntnted ! oiehild nlti Unnai.Ue Estimatingthe Determinantsof CognitiveAchievement in Low- 91 o Counries: Th Cae of G a_ Glewwe/Jacoby 92 EconomicAspects of ChildFosteringin CMte d'Ivoire Ainsworth 93 nvestment in Human Capital:SchoolingSupply Constraintsin Lav IRural Ghana 94 |WillingnesstoPayfor the Quality and Intensity ofMedical Care:f /Qu.l 11.nw-fritcomp UnoivAnol n Grhan¢1 a/uge 95 Mleasurement ofReturns to Adult Health: Morbidity Effects on Schultz/Tansel 'Wage Rates ienCte d'Iao.,e and Ghana Welfare Implications ofFemale Headship in Jamaican 96 -r ehods iLouant/GroshNVan der Gaag 97 |HouseholdSize in Cote dIvoire: Sampling Bias in the CILSS |Coulombe/Demery Delayed PrimarySchool Enrollment and Childhood 9 nIA.lnutritinn in (rhannn An Frnpnnmni Anauci .. lewwe/Jacoby Wr.L'In Papen LSMSw, No. TITLE AUTHOR 99 Poverty Reduction through Geographic Targeting: How Well Baker/Grosh IDoes It Work? l OOfncome Gainsforthe Poorfrom Public Works Employment: a vidence from Two Indian Village1sI)a on iAssessing the Quality ofAnthropometric Data:Background and 10ln1.~, at C Sn :> .. ,k Kostermans 102 iow Well Does the Social Safety Net Work? The Incidence of 102__O n_ _r._ * r__ nJIjS rnrungury, IY0/-0Y Itsn DR5 e n... k .. de ..... - ._ wauetKavaiiontunum 103 IDeterminants ofFertility and Child Mortality in Cote d 'Ivoire Ren/fh,i1t, and (ihana _ 104 Children'sHealth andAchievement in School IBehnnan/Lavy |Quality and Cost in Health Care Choice in Developing The Impact of the Quality of Health Care on Children'sNutrition~aySrusronsd 106 Su,vi-ial in vhaa v'reyer chool 1 Quality, Achievement Bias, and DropoutBehavior in 1 R tgypt jContraceptive Use and the Quality, Price,and Availability of Family Planningin Nigeria . L;^*.sebn/ _ 109kContraceptiveChoice Fertility, and Public Policv in Zimbabwe [Thomnns/Maluecn The Impact ofFemale Schooling on Fertility and Contraceptive 11 OsTTe A S-d..y o SCoreer'.L ILwor BeeogleMyanmte L'v. Ai &JtLuy UJ V- UU~L, ,uu-&Juiururi W-uU4ngri Contraceptive Use in Ghana: The Role of Service Availability, IOliv& 1 11 EJ..~ I-0i Quaiiy, anaPrice 112The Tradeoffbetween Numbers of Children and Child Schooling: Evidencefrom C6te d'Ivoireand Ghana 113 1Sector Particivation Decisions in Labor SuDDIV Models Pra&an 114The Quality andAvailability ofFamily PlanningServices and Beegle 1 14,,ce.-.v s in"' Tarnegl ChangingPatternsof Illiteracy in Morocco:Assessment Law/Soratt/Leboucher IMethoa's Compared 11 IQuality of MedicalFacilities,Health, andLaborForce -Partcipation in Jamaica 117 Who is Most Vulnerable to MacroeconomicShocks? Hypothesis O . Tests Using Panel Datafrom Peru roxy Means Tests: Simulationsand Speculation for Social P18 [Programs UrOs/Kae lWomen 'svSoonlinrg, Sple-tiive FPrtilitV, and Child Mortality in 19ub-SaharanAfrica 12A Gid to Livin.g.tandardsM.,easurementStudy Surve, |Groshb/Gw I Teir Data Sets e w 121 V!nfrastructureand Poverty in VietNam Ivan de Walle 1226lomDaraisonsde la Pauvrete: Concents et Methndpes hvtfiJin 123 |TheDemandforMedicalCare: Evidencefrom Urban Areas in V OXXV U7...J. LJOIVSM TV UIr'I £5a35o1 No. TITLE AUTHOR Bjolivia I 14onstrucingan indicatorof Consumption for tne Analysis of tHentsdheML=j0uw ,P0verty: Principlesand llustrationswith Reference to Ecuador 12 The Contribution ofIncome Components to income inequaifty i-n icibradt/Woolard/Woolard ,South Afica: A Decomposable Gini Analysis 12 61A ManualforPlanningand Implementing the LSMS Survey 'Gr unoz I(English, Spanish andRussian) I |UnconditionalDemandforHealth Carein C6te d'lvoire: Does ow on Health Status Matter? _elec_in ow Does Schooling of Mothers Improve ChildHealth: 129 1Making Poverty . Comparisons "jL. Deign: Hruw und viny Taking Into Account Survey Survel__ and anjouw (Jean) owes a odel Living StandardsMeasurementStudy Survey _ i3ul uesuonnairefortheCountriesoftheFormerSovietunion (English andRussian) 131 |ChronicIllness and Retirement in Jamaica V dvcr jHanda and Ncitzeot 133|PovertyLines in Theory and Practice ,avullion Scial Assistance in Albania: Decentralizationand Targeted ld n 134 13 GuidelinesforConstructing Consumption Agregatesfor Dcaton and Zaidi AralysAs Welfare (Ers;-h and --- DI Zaidi THE WORLD BANK 1818 H Street, N.W. R Washington, D.C. 20433, USA Tplepnhnnpe 22-477-1 0O0 Facsimile: 202-477-6391 InLerr.et: -www.worluuank.oig, E-mail: feedback(worldbank.org | Edward A. StrudwIck 00803 ISN I MC C3-301 WASHINGT,DCT iii ~ ~ V1 4990 ISBN 0-8213i499012 11111111 |ISBN 0-8213-4990-2l