The Developing World Is Poorer Than We Thought, But No Less Successful in the Fight against Poverty

Transcription

1 Publc Dsclosure Authorzed Pol c y Re s e a rc h Wo r k n g Pa p e r 4703 WPS4703 Publc Dsclosure Authorzed Publc Dsclosure Authorzed The Developng World Is Poorer Than We Thought, But No Less Successful n the Fght aganst Poverty Shaohua Chen Martn Ravallon Publc Dsclosure Authorzed The World Bank Development Research Group August 2008

2 Polcy Research Workng Paper 4703 Abstract The paper presents a major overhaul to the World Bank s past estmates of global poverty, ncorporatng new and better data. Extreme poverty as judged by what poverty means n the world s poorest countres s found to be more pervasve than we thought. Yet the data also provde robust evdence of contnually declnng poverty ncdence and depth snce the early 1980s. For 2005 we estmate that 1.4 bllon people, or one quarter of the populaton of the developng world, lved below our nternatonal lne of $1.25 a day n 2005 prces; 25 years earler there were 1.9 bllon poor, or one half of the populaton. Progress was uneven across regons. The poverty rate n East Asa fell from almost 80 percent to under 20 percent over ths perod. By contrast t stayed at around 50 percent n Sub-Saharan Afrca, though wth sgns of progress snce the md 1990s. Because of lags n survey data avalablty, these estmates do not yet reflect the sharp rse n food prces snce Ths paper a product of the Development Research Group s part of a larger effort n the department to montor the developng world's progress aganst absolute poverty. Polcy Research Workng Papers are also posted on the Web at econ.worldbank.org. The authors may be contacted at schen@worldbank.org or mravallon@worldbank.org. The Polcy Research Workng Paper Seres dssemnates the fndngs of work n progress to encourage the exchange of deas about development ssues. An objectve of the seres s to get the fndngs out quckly, even f the presentatons are less than fully polshed. The papers carry the names of the authors and should be cted accordngly. The fndngs, nterpretatons, and conclusons expressed n ths paper are entrely those of the authors. They do not necessarly represent the vews of the Internatonal Bank for Reconstructon and Development/World Bank and ts afflated organzatons, or those of the Executve Drectors of the World Bank or the governments they represent. Produced by the Research Support Team

3 The Developng World s Poorer than we Thought, But no Less Successful n the Fght Aganst Poverty Shaohua Chen and Martn Ravallon 1 Development Research Group, World Bank 1818 H Street NW, Washngton DC, 20433, USA 1 Ths the August 2009 revsed verson of the workng paper under the same ttle frst ssued n August In response to comments receved, the revsed verson ncludes new senstvty tests and a fully explanaton of the dfferences wth past estmates. The man results are unchanged. A great many colleagues at the World Bank have helped us n obtanng the necessary data for ths paper and answered our many questons. An mportant acknowledgement goes to the staff of over 100 governmental statstcs offces who collected the prmary household and prce survey data. Our thanks go to and Prem Sangraula, Yan Ba and Xaoyang L for ther nvaluable help n settng up the data sets we have used here. The Bank s Development Data Group has helped us wth our many questons concernng the 2005 ICP and other data ssues; we are partcularly grateful to Yur Dkhanov, Olver Duprez and Qnghua Zhao for ther help. We have also benefted from the comments of Francos Bourgugnon, Gaurav Datt, Angus Deaton, Massoud Karshenas, Aart Kraay, Peter Lanjouw, Rnku Murga, Ana Revenga, Lus Serven, Merrell Tuck, Domnque van de Walle and Kavta Watsa. These are our vews and should not be attrbuted to the World Bank or any afflated organzaton. Addresses: schen@worldbank.org and mravallon@worldbank.org.

4 1. Introducton In assessng the extent of poverty n a gven country one naturally uses a poverty lne that s consdered approprate for that country. However, the purchasng power of natonal poverty lnes vares across countres, wth rcher countres tendng to adopt hgher lnes. For the purposes of measurng poverty n the world as a whole, the World Bank s $1 a day measures have amed to apply a common standard, such that any two people wth the same purchasng power over commodtes are treated the same way both are ether poor or not poor, even f they lve n dfferent countres. A delberately conservatve standard has been used by the Bank, anchored to what poverty means n the world s poorest countres. By focusng on how poverty s defned n the poorest countres, the $1 a day lne gves the global poverty measure a salence n focusng on the world s poorest, though t s recognzed that hgher lnes also need to be consdered to obtan a complete pcture of the dstrbuton of lvng standards. Followng ths approach, Ravallon, Datt and van de Walle (RDV) (1991), n research for the 1990 World Development Report (World Bank, 1990), compled data on natonal poverty lnes across 33 countres and proposed a poverty lne of $1 per day at 1985 Purchasng Power Party (PPP) as beng typcal of low-ncome countres. 2 They estmated that one thrd of the populaton of the developng world n 1985 lved below ths lne. 3 Snce then the Bank s researchers have updated the orgnal RDV estmates of global poverty measures n the lght of new and often better data. The estmates done for the 2000/01 World Development Report (World Bank, 2000) used an nternatonal poverty lne of $1.08 a day, at 1993 PPP, based on the orgnal set of natonal poverty lnes n RDV (Chen and Ravallon, 2001). In 2004, about one n fve people n the developng world slghtly less than one bllon people were deemed to be poor by ths standard (Chen and Ravallon, 2007). Ths paper reports on the most extensve revson yet of these past estmates of poverty measures for the developng world. In the lght of a great deal of new data and under varous assumptons pertanng to the key methodologcal choces, we estmate the global poverty count for 2005 and update all our past estmates back to Ths puts our knowledge of the extent of poverty n the world on a frmer footng. 2 RDV also used a lower lne of $0.75 per day, whch was the predcted lne n the poorest country n ther data set, Somala, though t also happened to concde wth Inda s lne at the tme. 3 By the developng world we mean all low and mddle ncome countres essentally the Part 2 member countres of the World Bank (based on Gross Natonal Income per capta n 2005). 2

5 New data from three sources make the need for ths revson compellng. The frst new data source s the 2005 Internatonal Comparson Program (ICP). The prce surveys done by the ICP have been the man data source for estmatng PPPs. Ths started n 1968 wth PPP estmates for just 10 countres, based on rather crude prce surveys. 4 Pror to the present paper, our most recent global poverty measures had been anchored to the 1993 round of the ICP. An ndependent evaluaton (known as the Ryten Report; see UN, 1998) of the ICP rounds dentfed a number of methodologcal and operatonal concerns, ncludng lack of clear standards n defnng nternatonally comparable commodtes. Ths s a serous concern when comparng the cost of lvng between poor countres and rch ones, gven that there s lkely to be an economc gradent n the qualty of commodtes consumed; wthout strct standards n defnng the products to be prced, there s a rsk that one wll underestmate the cost of lvng n poor countres by confusng qualty dfferences wth prce dfferences. PPPs wll be underestmated n poor countres. Ths hghlghts the dffculty of dong prce surveys for the purposes of nternatonal comparsons. The exstence of non-traded goods s (on the one hand) the man reason why we need to use a PPP rather than market exchange rate, but (on the other hand) non-traded goods are harder to compare between countres. The only way to deal wth ths s through detaled product lstngs and descrptons, whch add sgnfcantly to the cost of the data collecton. A better funded round of the ICP n 2005, managed by the World Bank, has taken consderable effort to address ths problem as well as ntroducng other mprovements n the data and estmaton methods for PPPs (World Bank, 2008a,b). 5 A number of methodologcal and operatonal mprovements were mplemented by the 2005 ICP. The new ICP data mply some dramatc revsons to past estmates, consstent wth the vew that the old ICP data had under-estmated the cost-of-lvng n poor countres. The second data source s a new complaton of poverty lnes for developng countres provded by Ravallon, Chen and Sangraula (RCS) (2008). Based on these data, we mplement 4 The ICP started as a jont project of the UN and the Unversty of Pennsylvana, wth support from the Ford Foundaton and the World Bank. Pror to 2000, the Penn World Tables (PWT; see Summers and Heston, 1991) were the man source of the PPPs for consumpton used n the World Bank s global poverty measures. In 2000 we swtched to the PPPs estmated by the Bank s Development Data Group. There are methodologcal dfferences between the PWT and the Bank s PPPs, as dscussed n Ackland et al. (2006) and World Bank (2008, Appendx G). 5 Whle we do not know of any cost comparsons, there can be lttle doubt that the 2005 ICP entaled a far hgher cost than prevous rounds; as the Ryten Report had also dscussed, fxng the problems wth the ICP data would nevtably come at a cost. 3

6 an updated nternatonal poverty lne and test robustness to that choce. Recognzng that the new PPPs also change the $US value of natonal poverty lnes n the poorest countres, our nternatonal poverty lne of $1.25 per day n 2005 s delberately lower than the 2005 value n the US of our old nternatonal lne. The new lne s the mean of the natonal poverty lnes for the poorest 15 countres n terms of consumpton per capta. To test robustness of our man qualtatve results to the choce of poverty lne we also gve results for a range of lnes spannng $1.00 to $2.50 per day n 2005 prces. The lower bound (not to be confused wth the old $1-aday lne n 1993 prces) s very close to the natonal poverty lne used by Inda, whle the upper bound s the medan of the poverty lnes for all countres except the poorest 15. The $2.00 lne s the medan poverty lne found amongst developng countres as a whole. The thrd data source s the large number of new household surveys now avalable. We draw on 675 surveys, spannng and 115 countres. (By contrast, the orgnal RDV estmates used 22 surveys, one per country; Chen and Ravallon, 2004, used 450 surveys.) Our methods of analyzng these data follow Chen and Ravallon (2001, 2004, 2007). The nternatonal poverty lne s converted to local currences n the ICP benchmark year and s then converted to the prces prevalng at the tme of the relevant household survey usng the best avalable Consumer Prce Index (CPI) for that country. (Equvalently, the survey data on household consumpton or ncome for the survey year are expressed n the prces of the ICP base year, and then converted to PPP $ s.) Then the poverty rate s calculated from that survey. All nter-temporal comparsons are real, as assessed usng the country-specfc CPI. We make estmates at three-year ntervals over Interpolaton/extrapolaton methods are used to lne up the survey-based estmates wth these reference years, ncludng We also present a new method of mxng survey data wth natonal accounts (NAS) data to try to reduce surveycomparablty problems. For ths purpose, we treat the natonal accounts data on consumpton as a Bayesan pror for the survey mean and the actual survey as the new nformaton. We show that, under log-normalty wth a common varance, the mxed posteror estmator s the geometrc mean of the survey mean and ts predcted value based on the NAS. Based on these new data and methods we show that the ncdence of poverty n the world s hgher, or at least no lower, than past estmates have suggested. However, we also fnd that the poverty profle across regons of the developng world and the overall rate of progress aganst absolute poverty are farly smlar to past estmates. 4

7 2. The 2005 ICP round and ts mplcatons for global poverty measures Internatonal comparsons of economc aggregates have long recognzed that market exchange rates are deceptve, gven that some commodtes are not traded; ths ncludes servces but also many goods, ncludng some food staples. Furthermore, there s lkely to be a systematc effect, stemmng from the fact that low real wages n developng countres ental that laborntensve non-traded goods tend to be relatvely cheap. In the lterature, ths s known as the Balassa-Samuelson effect, 6 and s the now wdely-accepted explanaton for an emprcal fndng known as the Penn effect that GDP comparsons based on market exchange rates tend to understate the real ncomes of developng countres. 7 Smlarly, market exchange rates overstate the extent of poverty n the world when judged relatve to a gven $US poverty lne. Global economc measurement, ncludng poverty measurement, has reled nstead on PPPs, whch gve converson rates for a gven currency wth the am of assurng party n terms of purchasng power over commodtes, both nternatonally traded and non-traded. World Bank (2008a) s the source of our PPP s based on the 2005 ICP round. Here we only pont to some salent features relevant to measurng poverty n the developng world. 8 The 2005 ICP s the most complete and thorough assessment to date of how the cost of lvng vares across countres. The world was dvded nto sx regons wth dfferent product lsts for each. All regons partcpated, although the partcpaton rate was lower for Latn Amerca. The ICP collected prmary data on the prces for (dependng on the regon) goods and servces grouped under 155 basc headngs deemed to be comparable across countres; 110 of these relate to household consumpton. The prces were typcally obtaned from a large sample of outlets n each country. The prce surveys were done by the government statstcs offces n each country, under supervson from regonal authortes. The number of countres partcpatng n the 2005 ICP s larger than n 1993, the last ICP round we used for global poverty measurement; 146 countres partcpated, as compared to 117 n Ths was the frst tme that a number of countres, ncludng Chna, partcpated n the ICP. And the surveys were mplemented on a more scentfc bass. The 2005 ICP used strcter standards n defnng nternatonally 6 7 See Balassa (1964) and Samuelson (1964). The term Penn effect stems from Penn World Tables (Summers and Heston, 1991), whch provded the prce level ndces across countres that were frst used to establsh ths effect emprcally. 8 Broader dscussons on PPP methodology can be found n Ackland et al. (2006), World Bank (2008a) and Deaton and Heston (2009). 5

8 comparable qualtes of the goods dentfed n the ICP prce surveys. Regonal-specfc product lsts were derved, whch amed to balance the twn objectves of beng nternatonally comparable goods wthn the regon and beng representatve of the consumpton bundles found n each country. Creatng the product lstngs took about two years, as t nvolved extensve collaboraton amongst the countres and the relevant regonal ICP offce. Rng comparsons were used to lnkng the regonal PPP estmates through a common set of global prces; these comparsons were done for 18 countres n all a marked mprovement over past ICP rounds. 9 As n the past, the Bank uses a multlateral extenson of the blateral Fsher prce ndex known as the EKS method. 10 The changes n the methods of product lstng and prcng are of partcular relevance to global poverty measurement. The 2005 ICP appled more rgorous standards of specfyng nternatonally comparable commodtes for lnkng across countres (World Bank, 2008b). In comparson to 2005, t s lkely that the 1993 ICP would have used lower qualtes of goods n poor countres than would have been found n (say) the US market. 11 The goods prced by the 1993 ICP tended to be more typcal of the tems avalable n local markets. The 1993 ICP round also over-valued the servces derved from government n developng countres. RCS show that a szable underestmaton of the 1993 PPP s mpled by the new PPP data and the data on rates of nflaton. Furthermore, the extent of ths underestmaton tends to be greater for poorer countres. Gven the changes n data and methodology, PPPs for dfferent benchmark years cannot be easly compared, and cannot be expected to be consstent wth natonal data sources (Dalgaard and Sørensen, 2002; World Bank, 2008b). We follow common practce n lettng the natonal data overrde the ICP data for nter-temporal comparsons; ths s the most reasonable poston to take gven the changes n methodology between dfferent ICP rounds (World Bank, 2008b). Thus the PPP converson s only done once for a gven country, and all estmates are revsed back n tme consstently wth the data for that country. So the PPPs serve the role of locatng the 9 There were other dfferences, less relevant to global poverty measurement. New methods were used for measurng government compensaton and housng. Adjustments were also made for the lower average productvty of publc sector workers n developng countres (lowerng the mputed value of the servces derved from publc admnstraton, educaton and health). 10 On the advantages of ths method over the alternatve (Geary-Khams) method see Ackland et al. (2006). In the 2005 ICP the Afrca regon chose a dfferent aggregaton method (Afrcan Development Bank, 2007); World Bank (2008b) descrbes ths as a mnor dfference to the EKS method. 11 There were a number of problems n the mplementaton of the 1993 ICP round, as dscussed n Ahmed (2003). 6

9 resdents of each country n the global dstrbuton, but we do not mx the new PPPs wth those from prevous ICP rounds. We wll, however, dscuss the salent dfferences between the new results reported here usng the 2005 ICP and our past estmates. Some dramatc revsons to past PPPs are mpled, not least for the two most populous developng countres, Chna and Inda (nether of whch had actually partcpated n the 1993 ICP). For example, the 1993 consumpton PPP used for Chna was 1.42 Yuan to the $US n 1993 (updatng an earler estmate by Ruoen and Chen, 1995), whle the new estmate based on the 2005 ICP s 3.46 Yuan (4.09 f one excludes government consumpton). The correspondng prce level ndex (PPP dvded by market exchange rate; US=100) went from 25% n 1993 to 52% n So the Penn effect s stll evdent, but the sze of ths effect has declned markedly, wth a new PPP at about half the market exchange rate rather than one quarter. Adjustng solely for the dfferental nflaton rates n the US and Chna one would have expected the 2005 PPP to be 1.80 Yuan not Smlarly, Inda's 1993 consumpton PPP was Rs 7.0, whle the 2005 PPP s Rs 16, and the prce level ndex went from 23% to 35%. If one updated the 1993 PPP for nflaton one would have obtaned a 2005 PPP of Rs 11 rather than Rs 16. The results for Chna have naturally attracted much attenton, gven that they suggest that Chna s GDP n 2005 s much smaller than we all thought; wth the PPP revsons, Chna s GDP per capta at PPP for 2005 falls from $6,760 to $4,091 (World Bank, 2008b). Kedel (2007) clamed that the new PPP for Chna adds 300 mllon to the count of that country s poor. Some observers have gone further to clam that the new PPPs also cast doubt on the extent of Chna s and (hence) the world s progress over tme aganst poverty. For example, the Bretton Woods Project (an NGO) clams that the new PPPs undermne the much-trumpeted clams that globalzaton has reduced the number of people lvng n extreme poverty. 12 Ths would be surprsng f t were true, gven that rates of economc growth at the country level are not altered by changng the PPP benchmark; wth Chna s (remarkable) growth rates ntact one must expect that progress over tme wll be smlar usng the new PPP, even f the poverty rate s hgher (by nternatonal standards) at all dates. Whle there were many mprovements n the 2005 ICP, the new PPPs stll have some lmtatons. Makng the commodty bundles more comparable across countres (wthn a gven regon) nvarably entals that some of the reference commodtes are not typcally consumed n 12 See 7

10 certan countres, generatng ether mssng values or prces drawn from unusual outlets; for example, Deaton and Heston (2009) pont out that rce s hard to fnd n Ethopa and teff (the staple n Ethopa) s hard to fnd n Thaland, say. One could avod ths problem by choosng more representatve country-specfc bundles, but ths would re-ntroduce the qualty bas dscussed above, whch has plagued past ICP rounds. There s also a problem of urban bas n the ICP prce surveys for some countes; the next secton descrbes our methods of addressng ths problem. As was argued n Ravallon et al. (1991), a further concern s that the weghts attached to dfferent commodtes n the conventonal PPP rate may not be approprate for the poor; secton 6 examnes the senstvty of our results to the use of alternatve PPPs for the poor avalable for a subset of countres from Deaton and Duprez (2009). Another lmtaton s that the PPP s a natonal average. Just as the cost of lvng tends to be lower n poorer countres, one expects t to be lower n poorer regons wthn one country, especally n rural areas. Ravallon et al. (2007) have allowed for urban-rural cost of lvng dfferences facng the poor, and provded an urban-rural breakdown of our pror global poverty measures usng the 1993 PPP. We plan to update these estmates n future work. Gven that the bulk of the PPPs have rsen for developng countres, the poverty count wll tend to rse at any gven poverty lne n $PPPs. However, the same changes n the PPPs also alter the (endogenous) nternatonal poverty lne, gven that t s anchored to the natonal poverty lnes n the poorest countres. Next we turn to the poverty lnes. 3. Natonal and nternatonal poverty lnes In settng an nternatonal poverty lne usng the 2005 ICP we have amed to follow the same defnton used n our past work, namely that the lne should be representatve of the natonal lnes found n the poorest countres n the sprt of the orgnal $1 a day lne (RDV; World Bank, 1990). For ths purpose, RCS have compled a new set of natonal poverty lnes for developng countres drawn from the World Bank s country-specfc Poverty Assessments and the Poverty Reducton Strategy Papers (PRSP) done by the governments of the countres concerned. These documents provde a rch source of data on poverty at the country level, and almost all nclude estmates of natonal poverty lnes. The RCS dataset was compled from the most recent poverty assessments and PRSPs over In the source documents, each poverty lne s gven n the prces for a specfc survey year (for whch the subsequent poverty 8

11 measures are calculated). In most cases, the poverty lne was also calculated from the same survey (though there are some exceptons, for whch pre-exstng natonal poverty lnes were updated usng the consumer prce ndex). About 80 percent of these reports used a verson of the cost of basc needs method n whch the food component of the poverty lne s the expendture needed to purchase a food bundle specfc to each country (or regon) that yelds a stpulated food energy requrement. 13 To ths amount an allowance s added for nonfood spendng, whch s typcally anchored to the nonfood spendng of people whose food spendng (or sometmes total spendng) s near the food poverty lne. Whle there are smlartes across countres n how poverty lnes are set, there s much scope for dscreton. The stpulated food-energy requrements are smlar, but the food bundles that yeld a gven food energy ntake can vary enormously (such as n the share of calores from starchy staples and the share from meat). The nonfood component wll also vary. The judgments made n settng the varous parameters of a poverty lne are lkely to reflect prevalng notons of what poverty means n each country settng, wth more frugal lnes n poorer countres. There are some notable dfferences between the old (RDV) and new (RCS) data sets on natonal poverty lnes. The RDV data were drawn from sources for the 1980s (wth a mean year of 1984) whle the new and larger complaton n RCS s post-1990 (mean of 1999); n no case do the proxmate sources overlap. The RCS data cover 75 developng countres whle the earler data ncluded only 22 countres (plus 11 developed countres). The RDV data set used rural poverty lnes when there was a choce, whle the RCS data set estmated natonal average lnes. And the RDV data set was unrepresentatve of the poorest regon, Sub-Saharan Afrca (SSA), wth only four countres from that regon (Burund, South Afrca, Tanzana and Zamba), whle the RCS data set has a good spread across regons, ncludng 23 countres n SSA. The sample bas n the RDV data set was unavodable at the tme (1990) but t can now be corrected. Fgure 1 plots the poverty lnes compled by RCS n 2005 $PPPs per month aganst log household consumpton per capta also at 2005 PPP; there are 74 countres wth complete data. The Fgure also gves a nonparametrc regresson of the natonal poverty lnes aganst log mean consumpton. Above a certan pont, the poverty lne rses wth mean consumpton. The overall elastcty of the poverty lne to mean consumpton s about 0.7. However, the slope s essentally 13 Ths method, and alternatves, are dscussed n detal n Ravallon (1994, 2008a). 9

12 zero amongst the poorest 20 or so countres, where absolute poverty clearly domnates. The economc gradent n natonal poverty lnes evdent n Fgure 1 s drven more by the gradent n the non-food component of the poverty lnes (whch accounts for about 60% of the overall elastcty) than the food component, although there s stll an apprecable share attrbutable to the gradent n food poverty lnes (RCS). Our nternatonal poverty lne s $1.25 a day for 2005, whch s the mean of the lnes found n a reference group of countres defned as those wth consumpton per capta at 2005 PPP below $60.00 per month; the RCS sample has 15 countres n ths group namely Malaw, Mal, Ethopa, Serra Leone, Nger, Uganda, Gamba, Rwanda, Gunea-Bssau, Tanzana, Tajkstan, Mozambque, Chad, Nepal and Ghana. (Ther medan poverty lne s $1.27 per day.) Consumpton per capta for ths group ranges from $1.03 to $1.87 per day wth a mean of $1.40 per day. The level of ths poverty lne s qute robust to the choce of the poorest 15 countres (takng plus or mnus fve countres ranked by consumpton per capta). However, t makes sense to focus on the poorest 15 snce the econometrc tests reported n RCS mply that natonal poverty lnes tend to rse wth consumpton per person when t exceeds about $2 per day, whch s near the upper bound of the consumpton levels found amongst these 15 countres. 14 Of course, there s stll a varance n the natonal poverty lnes at any gven level of mean consumpton, ncludng amongst the poorest countres. The poverty lnes found amongst the poorest 15 countres vary from $0.70 to $1.90 per day and RCS estmate the robust standard error of the $1.25 lne to be $0.10 per day. To assess the robustness of qualtatve comparsons to the choce of poverty lne, we provde estmates for fve lnes at 2005 PPP namely: () $1.00 a day, whch s very close to the natonal poverty lne used by the Government of Inda; 15 () $1.25, whch s the mean poverty lne for the poorest 15 countres, as proposed by RCS; () $1.45, as obtaned by updatng the 1993 $1.08 lne for nflaton n the US; () $2.00, whch s the medan of the RCS sample of natonal poverty lnes for developng and transton economes and s also approxmately the lne 14 RCS use a sutably constraned verson of Hansen s (2002) method for estmatng a pece-wse lnear ( threshold ) model. (The constrant s that the slope of the lower lnear segment must be zero and there s no potental dscontnuty at the threshold.) Ths method gave an absolute poverty lne of $1.23 (t=6.36) and a threshold level of consumpton (above whch the poverty lne rses lnearly) very close to the $60 per month fgure used to defne the reference group. 15 Inda s offcal poverty lnes for 2004/05 were Rs and Rs per day for urban and rural areas. Usng our urban and rural PPPs for 2005 (descrbed below) these represent $1.03 per day. 10

13 obtaned by updatng the $1.45 lne at 1993 PPP for nflaton n the US; and (v) $2.50, twce the $1.25 lne, whch s also the medan poverty lne of all except the poorest 15 of countres n the RCS data set of natonal poverty lnes. The range $1.00 to $1.45 s roughly the 95% confdence nterval for our estmate of the mean poverty lne for the poorest 15 countres. To test the robustness of qualtatve comparsons, we also estmate the cumulatve dstrbuton functons up to a maxmum poverty lne, whch we set at $13 per day, whch s about the offcal poverty lne for the US (at average household sze and composton). 16 We use the same PPPs to convert the nternatonal lnes to local currency unts (LCUs). Three countres were treated dfferently, Chna, Inda and Indonesa. In all three we used separate urban and rural dstrbutons. For Chna, the ICP survey was confned to 11 ctes. 17 We treat the ICP s PPP as an urban PPP and use the rato of urban to rural natonal poverty lnes to derve the correspondng rural poverty lne n local currency unts. For Inda the ICP ncluded rural areas, but they were underrepresented. We derved urban and rural poverty lnes consstent wth both the urban-rural dfferental n the natonal poverty lnes and the relevant features of the desgn of the ICP samples for Inda; further detals can be found n Ravallon (2008b). For Indonesa, we converted the nternatonal poverty lne to LCUs usng the offcal consumpton PPP from the 2005 ICP. We then unpack that poverty lne to derve mplct urban and rural lnes that are consstent wth both the rato of the natonal urban-to-rural lnes for Indonesa and the fact that the natonal PPP from the ICP s based on expendture-weghted prces. Comparson wth our old nternatonal poverty lne. Recall that the nternatonal poverty lne for 1993 proposed by Chen and Ravallon (2001) was $1.08 a day ($32.74 per month). If one adjusts only for nflaton n the US one obtans $1.45 a day at 2005 prces, whch s well above the average poverty lne for the poorest countes n Fgure 1. However, t wll be recalled that the $1.08 fgure at 1993 PPP was based on the RDV data set of natonal poverty lnes for the 1980s. If we use the new data set on natonal poverty lnes provded by RCS but evaluated at 1993 prces and converted to $ s usng the 1993 PPPs we would obtan a consderably hgher poverty lne. Fgure 2 plots both the new (RCS) and old (RDV) data on natonal poverty lnes, both at 1993 PPP. The relatonshp between the RCS natonal poverty lnes and consumpton per capta 16 See We used the poverty lne for a four member household at 2005, whch s $416 per person per month. 17 Although the survey ncluded some surroundng rural areas, t cannot be consdered representatve of rural Chna; evdence on ths pont s provded by Chen and Ravallon (2008a). 11

14 (at 1993 PPP) looks smlar to Fgure 1. But the RDV lnes are notably lower; the gap dmnshes as consumpton falls, but stll perssts amongst the poorest countres. For the poorest 15 countres ranked by consumpton per capta at 1993 PPP, the mean poverty lne n the RCS data set s $44.19 ($1.45 a day 18 ) versus $33.51 ($1.10 a day) usng the old (RDV) seres for eght countres wth consumpton below upper bound of consumpton for those 15 countres. Ths mght suggest that there was an upward drft n the natonal poverty lnes of poor countres between the 1980s and the 1990s and 2000s. However, ths seems unlkely, gven that t appears to be qute rare for developng countres to ncrease the real value of ther poverty lnes over tme. The more plausble explanaton les wth the aforementoned dfferences between the two samples of natonal poverty lnes. There are two salent dfferences. Frst, recall that the RDV sample used rural poverty lnes, whch are nvarably lower than urban lnes (Ravallon et al., 2007); ths wll lower the schedule of poverty lnes at gven natonal mean consumpton. Second, the orgnal RDV sample under-represented SSA. Three of the eght countres n the RDV sample that qualfy to be n our reference group of countres (wth consumpton per capta less than $60 per month) are n SSA, yet 22 of the 29 countres wth consumpton less than $60 per month n the set of 115 countres for whch we measure poverty are n SSA. In other words, the proporton of SSA countres n the RDV sample s about half what t should be to be consdered representatve of poor countres. Ths s not such a problem for the RCS sample; as can be seen from the lst of countres above used to set the $1.25 a day poverty lne, 13 out of the 15 countres are n SSA; f anythng ths appears to slghtly over-represent SSA. Ths dfference between the RDV and RCS samples s relevant to understandng why the RCS lnes are hgher at gven consumpton, as seen n Fgure 2. Natonal poverty lnes at 1993 PPP tend to be hgher n SSA than other regons at gven consumpton. The dfference between SSA and non-ssa poverty lnes amongst the reference group of countres cannot be relably estmated gven that there are so few non-ssa countres n the reference group. In other words there s very lttle common support at very low consumpton levels. A reasonable defnton of the regon of common support s the 37 countres wth consumpton over $50 and under $250 per month at 1993 PPP. (Only one country n the sample, Maurtus, has consumpton $250, and t s well above t at $426.) On regressng the poverty lne at 1993 PPP on consumpton per capta for the sample n the regon of common support, wth a dummy varable for SSA countres, one 18 Note that ths s at 1993 PPP; $1.45 n 1993 prces represents $1.96 a day at 2005 US prces. 12

15 fnds that the dfference between SSA and non-ssa poverty lnes was $0.41 a day (wth a standard error of $0.28). 19 Amongst the samples used to calculate the above poverty lnes $1.45 a day at 1993 PPP usng the RCS sample versus $1.10 a day usng the RDV sample the share from SSA went from 37.5% (three countres out of eght) to 87.7% (13/15). Assumng that SSA poverty lnes are $0.41 a day hgher than non-ssa lnes, the change n the sample would add about $0.20 per day to the poverty lne closng slghtly more than half the gap between the $1.45 a day and $1.10 lnes. Whle the precson of ths estmate s clearly rather low, gven the sample szes, but t s at least suggestve that f the RDV sample had better represented Afrcan countres then the old nternatonal lne would have been apprecably hgher. Implcatons for global poverty measures. The mpact of these varous data revsons on the global poverty count s theoretcally ambguous. On the one hand, the new PPPs mply that the cost-of-lvng s hgher n developng countres, whch wll put upward pressure on the poverty count, but (on the other hand) the new PPPs wll put downward pressure on the nternatonal poverty lne. The key factor s whether the PPP revsons tend to be larger n the countres used to set the nternatonal poverty lne. RCS fnd that ths s the case. For any fxed set of natonal poverty lnes, the poverty rates n the sub-group of the poorest countres used to set the nternatonal lne wll be unchanged, whle the poverty rates n the less poor countres wll fall, snce they wll have relatvely hgher purchasng power. The Appendx provdes a complete analyss, for whch the followng result emerges as an nterestng specal case: Proposton 1: If the nternatonal poverty lne s that of the poorest country, whch also has the largest upward revson to ts PPP, then the aggregate poverty rate wll fall. Intutvely, gven that the nternatonal lne s anchored to the natonal lnes n the poorest countres, the poverty count for those countres s roughly constant, whle that for the other (less poor) developng countres tends to fall, snce the PPP revsons are smaller for those countres, whch translates nto a lower local currency equvalent of the nternatonal lne. Ths result s only of expostory nterest, gven that our nternatonal poverty lne s not n fact for the poorest country but rather for the poorest 15 countres; then there s a potentally confoundng effect of the dfferences amongst those countres. A further confoundng effect s 19 It made neglgble dfference f we also allow the slope parameter on consumpton to be dfferent for SSA countres and evaluate the mean dfference n poverty lnes at the medan or mean of consumpton n the regon of common support; ths also gave a mean dfference of $0.35 (s.e.=0.22) at the medan and $0.39 (0.29) at the mean. Nor dd addng a squared term n consumpton have much effect. 13

16 that there s (of course) a varance n the PPP revson at a gven level of consumpton per capta. (For example, Chna, s a not one of the countres used to set the nternatonal poverty lne but t had one of the hghest PPP revsons.) Nonetheless, Proposton 1 s suggestve that the pure effect of the PPP revsons, for a gven set of natonal lnes, wll be tend to reduce the aggregate poverty count. As we wll see, ths s confrmed by our emprcal results n secton 5. Aganst ths effect, the upward shft n the natonal poverty lnes mpled by the RCS data wll tend to ncrease the poverty count. The balance of these effects s an emprcal queston. 4. Household surveys and poverty measures We have estmated all poverty measures ourselves from the prmary sample survey data rather than relyng on pre-exstng poverty or nequalty measures. The prmary data come n varous forms, rangng from mcro data (the most common) to specally desgned grouped tabulatons from the raw data, constructed followng our gudelnes. All our prevous estmates have been updated to assure nternal consstency. We draw on 675 surveys for 115 countres; a full lstng s found n Chen and Ravallon (2008b). 20 The surveys are natonally representatve. Takng the most recent survey for each country, about 1.23 mllon households were ntervewed n the surveys used for our 2005 estmate. The surveys were mostly done by governmental statstcs offces as part of ther routne operatons. Not all avalable surveys were ncluded; a survey was dropped f there were known to be serous comparablty problems wth the rest of the data set. 21 Poverty measures. Followng past practce, poverty s assessed usng household per capta expendture on consumpton or household ncome per capta as measured from the natonal sample surveys. 22 Households are ranked by consumpton (or ncome) per person. The dstrbutons are weghted by household sze and sample expanson factors. Thus our poverty counts gve the number of people lvng n households wth per capta consumpton or ncome below the nternatonal poverty lne. 20 We had data for 116 countres but Zmbabwe had to be dropped drop due to the dffculty n convertng local currency to PPP due to the hgh nflaton rate. 21 Also, we have not used surveys for 2006 or 2007 when we already have a survey for 2005 the latest year for whch we provde estmates n ths paper. 22 The use of a per capta normalzaton s standard n the lterature on developng countres. Ths stems from the general presumpton that there s rather lttle scope for economes of sze n consumpton for poor people. However, that assumpton can be questoned; see Lanjouw and Ravallon (1995). 14

17 When there s a choce we use consumpton n preference to ncome, on the grounds that consumpton s lkely to be the better measure of current welfare on both theoretcal and practcal grounds. 23 Of the 675 surveys, 417 allow us to estmate the dstrbuton of consumpton; ths s true of all the surveys used n the Mddle East and North Afrca, South Asa and Sub-Saharan Afrca, though ncome surveys are more common n Latn Amerca. 24 Gven that savngs and credt can be used to smooth consumpton from ncome shocks, one expects hgher nequalty for ncomes than consumptons, for the same place and data. The measures of consumpton (or ncome, when consumpton s unavalable) n our survey data set are reasonably comprehensve, ncludng both cash spendng and mputed values for consumpton from own producton. But we acknowledge that even the best consumpton data need not adequately reflect certan non-market dmensons of welfare, such as access to certan publc servces, or ntra-household nequaltes. For these reasons, our poverty measures need to be supplemented by other data, such as on nfant and chld mortalty, to obtan a more complete pcture of how lvng standards are evolvng. We use standard poverty measures for whch the aggregate measure s the (populatonweghted) sum of ndvdual measures. In ths paper we report three such poverty measures. 25 The frst measure s the headcount ndex gven by the percentage of the populaton lvng n households wth consumpton or ncome per person below the poverty lne. We also gve estmates of the number of poor, as obtaned by applyng the estmated headcount ndex to the populaton of each regon under the assumpton that the countres wthout surveys are a random sub-sample of the regon. Our thrd measure s the poverty gap ndex, whch s the mean dstance below the poverty lne as a proporton of the lne where the mean s taken over the whole populaton, countng the non-poor as havng zero poverty gaps. 23 Consumpton requres fewer mputatons and assumptons, s lkely to be reported more accurately and s arguably a better measure of current economc welfare than ncome. For further dscusson see Ravallon (1994, 2003) and Deaton and Zad (2002). It has also been argued that consumpton s a better welfare ndcator n developed countres; see Slesnck (1998). 24 For a few cases we do not have consumpton dstrbutons but we stll have survey-based estmates of mean consumpton. Then we replace the ncome mean by the consumpton mean leavng the Lorenz curve the same (.e., all ncomes are scaled up by the rato of the consumpton mean to the ncome mean). There s, however, no obvous bass for adjustng the Lorenz curve. 25 PovcalNet provdes a wder range of measures, drawn from the lterature on poverty measurement. See 15

18 Havng converted the nternatonal poverty lne at PPP to local currency n 2005 we convert t to the prces prevalng at each survey date usng the country-specfc offcal Consumer Prce Index (CPI). 26 The weghts n ths ndex may or may not accord well wth consumer budget shares at the poverty lne. In perods of relatve prce shfts, ths wll bas our comparsons of the ncdence of poverty over tme, dependng on the extent of (utltycompensated) substtuton possbltes for people at the poverty lne. We started the seres n 1981 and made estmates at three yearly ntervals, up to For the 115 countres, 14 have only one survey; 17 have two surveys; 14 have three; whle 70 have four or more surveys over the perod, of whch 23 have 10 or more surveys. If there s only one survey for a country then we estmate measures for each reference year by applyng the growth rate n real prvate consumpton per person from the NAS to the survey mean assumng that the Lorenz curve for that country does not change. 27 Ths seems the best opton for dealng wth ths problem, though there can be no guarantee that the Lorenz curve would not have shfted or that a survey-based measure of consumpton would have grown at the same rate as prvate consumpton n the NAS. For example, growth n the latter mght reflect growth n the spendng by non-proft organzatons whch are not separated from households n the NAS for most developng countres rather than household spendng (Ravallon, 2003). Our benchmark estmates only use the annual NAS data for nterpolaton purposes gven the rregular spacng of surveys; Chen and Ravallon (2004, 2008b) descrbe our nterpolaton methods. However, we provde senstvty tests to the use of a Bayesan mxed method, combnng the surveys and natonal accounts n estmatng the mean, as a means of addressng the lkely heterogenety amongst surveys; we dscuss ths further below. In the aggregate, 90% of the populaton of the developng world s represented by surveys wthn two years of Survey coverage by regon vares from 74% of the populaton of the Mddle East and North Afrca (MENA) to 98% of the populaton of South Asa. Naturally, the further back we go, the fewer the number of surveys reflectng the 26 Note that the same poverty lne s generally used for urban and rural areas. There are three exceptons, Chna, Inda and Indonesa, where we estmate poverty measures separately for urban and rural areas and use sector-specfc CPIs. 27 For a few SSA countres, prvate consumpton per capta s mssng from the World Bank s Development Data Platform; we use the seres from Afrca Development Indcators Some countres have graduated from the set of developng countres; we apply the same defnton over tme to avod selecton bas. In ths paper our defnton s anchored to

19 expanson n household survey data collecton for developng countres snce the 1980s. And coverage deterorates n the last year or two of the seres, gven the lags n survey processng. Two gudes to the relablty of our estmates are to count the number of surveys by year and to measure the coverage rate. Fgure 3 gves the number of surveys; we gve the three-year movng average centered on each year (gven that havng a survey last year or next year can help greatly n estmatng poverty ths year). For comparson purposes, we also gve the numbers of surveys used by Chen and Ravallon (2004). By ths measure, our estmates around the md 1990s onwards are clearly the most relable whle our estmate for 1981 s the least relable. We have a cumulatve total of only 18 surveys up to 1983, though the number doubles by By contrast we have a total of 480 surveys after Naturally the number of surveys drops off n the last year or so, gven the lags n avalablty; there has been a marked mprovement n the coverage of recent surveys, though ths partly reflects our unwllngness to make an estmate yet for 2006 (as we stll only have seven surveys for that year, at the tme of wrtng). Most regons are qute well covered from the latter half of the 1980s (East and South Asa beng well covered from 1981 onwards). 29 Unsurprsngly, we have weak coverage n Eastern Europe and Central Asa (EECA) for the 1980s; many of these countres dd not offcally exst then. More worryng s the weak coverage for Sub-Saharan Afrca n the 1980s; ndeed, our estmates for the early 1980s rely heavly on projectons based on dstrbutons around Table 1 gves the average survey year by regon for each reference year. By comparng Table 1 wth the correspondng table n Chen and Ravallon (2004) we can see how much the lags n survey data avalablty have fallen. Lke the present paper, Chen and Ravallon (2004) reported results for a reference year that was three years pror to the tme of wrtng (namely 2001, versus 2005). Table 2 gves the average lag by regon (where zero means no lag for the latest reference year). The overall mean has fallen by one year (1.6 to 0.6 years); for East Asa (the lowest mean lag for 2001), the average lag s down to almost zero; for SSA (the hghest lag n 2001), the lag has also fallen apprecably, from 4.0 to 1.5 years, and MENA s now the regon wth the hghest mean lag. Note that the lags n Table 2 reflect both the frequency of surveys and our access to the data. Based on our observatons n assemblng the data base for ths study, we would conjecture 29 Chna s survey data for the early 1980s are probably less relable than later years, as dscussed n Chen and Ravallon (2004) where we also descrbe our methods of adjustng for certan comparablty problems n the Chna data, ncludng changes n valuaton methods. 17

20 that the large lag for MENA s due more to access to exstng surveys than to the frequency of those surveys, whle for SSA t s due more to nfrequent producton of adequate surveys. The second ndcator s the percentage of the populaton covered by household surveys. Table 3 gves the coverage rate by regon and for each reference year; a country s defned as beng covered f there was a survey (n our data base) wthn two years of the reference date (a fve-year wndow). Note that our method only strctly requres one survey per country, though we have almost sx surveys per country on average. Naturally, the more surveys we have for a gven country the more confdent we are about the estmates. The weak coverage for EECA, MENA and SSA n the 1980s s evdent n Table 3. Our estmates for these regons n the 1980s are heavly dependent on the extrapolatons from NAS data. We wll dscuss the lkely bases. Note that there s a hole n coverage for South Asa n Ths reflects a problem n Inda s Natonal Sample Survey (NSS) for 1999/ We dropped that NSS survey round gven that we now have a new survey for 2004/05 that we consder to be reasonably comparable to the round of 1993/94. We use only the 5-yearly rounds of the NSS, whch have larger samples and more detaled and more comparable consumpton modules (asde from the 1999/00 round). Unfortunately, ths leaves a 10-year gap n our survey coverage for Inda; the estmates for Inda over the ntervenng perod use our nterpolaton method. Includng all avalable survey rounds for Inda adds to the varablty n the seres but does not change the trend. 31 Gven the lags n survey data, our estmates do not nclude the mpacts of the recent rse n food and fuel prces and the global fnancal crss (GFC). Ex ante projectons of the welfare mpacts of the rse n food prces for a set of nne low-ncome countres by Ivanc and Martn (2008) predct that, on balance, the rse n food prces over wll have been povertyncreasng. Elsewhere we have argued that the same s lkely of the mpact of the GFC on the ncdence of poverty n the developng world as a whole n 2009; n Chen and Ravallon (2009) we estmate that the GFC added about 1% pont to the headcount ndex for $1.25 a day n Heterogenety n surveys. As n past work, we have tred to elmnate obvous comparablty problems, ether by re-estmatng the consumpton/ncome aggregates or the more Further dscusson and references can be found n Datt and Ravallon (2002). If one uses the 1999/2000 survey for Inda one obtans a sharp fall n that year, and a subsequent rse n poverty ncdence to However, ths s clearly spurous, beng drven by the fact that the 1999/2000 survey over-estmates level of consumpton relatve to other survey rounds. 18