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 [email protected] or [email protected]. 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: [email protected] and [email protected].

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

21 radcal step of droppng a survey. However, there are problems we cannot fx. It s known that surveys dffer between countres, ncludng how the questons are asked (such as recall perods), survey response rates, whether the surveys are used to measure consumpton or ncome, and n what gets ncluded n the survey s aggregate for consumpton or ncome. These dfferences are known to matter to the statstcs calculated from surveys, ncludng poverty measures. The lterature on measurng global poverty has dealt wth such survey comparablty problems n two man ways. The frst makes some effort to ron out such problems usng the mcro data, but essentally gnores the problem beyond that, n the expectaton that aggregaton across surveys wll reduce the problem consderably. Followng ths method, our past estmates have appled an nternatonal poverty lne, converted to local currency at PPP, to the dstrbutons from the surveys. Ths follows the practce n natonal poverty measurement; essentally the dfference s that for global poverty measures the poverty lne s fxed n real terms across countres rather than beng country specfc. However, t s acknowledged that when applyng a common lne to dfferent natonal surveys one wll obtan dfferent poverty measures even f the same measure would be obtaned usng the same survey nstrument n each country. The second approach re-scales the survey means to be consstent wth the natonal accounts (NAS), whch are assumed to be comparable and accurate. In one verson of ths method, Bhalla (2002) replaces the survey mean by consumpton from the NAS, but keeps the survey-based dstrbuton. (In other words, he re-scales all survey-based consumpton (or ncome) levels by the rato of NAS consumpton to the survey mean.) Bourgugnon and Morrsson (2002) and Sala--Martn (2006) also re-scale the mean, although they anchor ther measures to GDP per capta rather than to consumpton. 32 It s clamed by the proponents of the second method that t corrects for survey mssmeasurement. It s argued that NAS consumpton captures thngs that are sometmes mssng from surveys (such as mputed rents for owner-occuped housng) and that NAS methods are more standardzed across countres, snce all countres are supposed to follow the UN Statstcal Dvson s System of Natonal Accounts (SNA), although, n practce, complance wth SNA gudelnes appears to be qute uneven across developng countres. 32 Gven the tme span of ther study, Bourgugnon and Morrsson (2002) had no choce. Sala-- Martn dd have a choce, though computatonally convenence appears to have dctated hs use of natonal accounts data, combned wth aggregate summary tabulatons of the relatve dstrbuton. 19

22 Proponents of the frst class of methods do not clam that the surveys are accurate, but argue that there s no justfcaton for assumng that the dscrepancy between the survey mean and NAS consumpton per capta s solely due to underestmaton n the surveys or that the survey measurement errors are dstrbuton neutral. The dscrepancy between the two data sources reflects a number of factors, ncludng dfferences n what s beng ncluded, 33 and selectve complance n surveys. Arguments can be made for and aganst both methods. For example, there s also lkely to be under-reportng or selectve complance wth the randomzed assgnment n a survey, but t would seem unlkely that ths would only affect the mean and not the measure of nequalty; more plausbly, the underestmaton of the mean by surveys due to selectve complance comes wth an underestmaton of the extent of nequalty. 34 To gve a counter-example, suppose that the surveys exclude mputed rent for owner-occuped housng (practces are uneven n how ths s treated), and that ths s a constant proporton of expendture. Then the surveys get nequalty rght and the mean wrong. In our case, t s also mportant to note that the underlyng natonal poverty lnes were calbrated to the surveys. By the most common methods of settng poverty lnes, underestmaton of non-food spendng n the surveys wll lead to an under-estmaton of the poverty lne, whch s anchored to the spendng (as measured n the surveys) of sampled households lvng near the food poverty lne (or wth food-energy ntakes near the recommended norms). Correctng for under-estmaton of non-food spendng n surveys would then requre hgher poverty lnes. Ths provdes a further justfcaton for testng robustness to a range of poverty lnes. Arguably the more mportant concern s the heterogenety of surveys gven that the level of the poverty lne s always somewhat arbtrary. In an nterestng varaton on the re-scalng method, Karshenas (2003, 2004) replaces the survey mean by ts predcted value from a regresson on consumpton per capta from the NAS. So nstead of usng NAS consumpton, Karshenas uses a stable lnear functon of NAS consumpton, wth mean equal to the overall mean of the survey means. As n Bhalla s method, ths assumes that natonal accounts 33 For example, NAS prvate consumpton ncludes mputed rents for owner-occuped housng, mputed servces from fnancal ntermedares and the expendtures of non-proft organzatons; none of these are ncluded n consumpton aggregates from standard household surveys. Surveys, on the other hand, are undoubtedly better at pckng up consumpton from nformal-sector actvtes. For further dscusson see Ravallon (2003) and Deaton (2005). 34 Kornek et al. (2006) examne the mplcatons of selectve complance and Kornek et al (2007) provde an econometrc method for correctng survey data for ths problem. 20

23 consumpton data are comparable and gnores the country-specfc nformaton on the levels n surveys. As noted above, that s a questonable assumpton. However, unlke all the other examples of ths second class of methods, Karshenas assumes that the surveys are correct on average and focuses nstead on the problem of survey comparablty, for whch purpose the poverty measures are anchored to the natonal accounts data. Where we depart from the Karshenas method s that we do not gnore the countryspecfc survey means. When one has two measures of roughly the same thng, but nether s deal, t s natural to combne the two measures. For almost all developng countres, surveys are less frequent and more recent than NAS data. It s natural then to thnk of the NAS consumpton seres as beng the bass for settng a Bayesan pror for average consumpton, whle treatng the survey as new, posteror, data. But how should we combne the two n a mxed-method? A result from Bayesan statstcs provdes an nterpretaton of a mxng parameter under the assumpton that consumpton s log normally dstrbuted wth a common varance n the pror dstrbuton as n the new survey data. In partcular, the Appendx proves the followng clam: Proposton 2: Suppose that the pror s the expected value of the survey mean, condtonal on natonal accounts consumpton, and consumpton s log normally dstrbuted wth a common varance. Then the posteror estmate s the geometrc mean of the survey mean and ts expected value. Over tme, the relevant growth rate s the arthmetc mean of the growth rates from the two data sources. These assumptons can certanly be questoned. As noted, t s unlkely that the pror based on the NAS would have the same relatve dstrbuton as the survey. However, ths proposton does at least offer a clear foundaton for a senstvty test, gven the lkely heterogenety n surveys. 5. Benchmark estmates We report aggregate results over for the regons of the developng world and (gven ther populatons) Chna and Inda. Jontly wth ths paper, we have updated the webste PovcalNet to provde publc access to the underlyng country-level data set, so that users to replcate these results and try dfferent assumptons, ncludng dfferent poverty measures, poverty lnes and country groupngs, ncludng dervng estmates for ndvdual countres See The process of updatng the PovcalNet web ste to ncorporate the 2005 PPPs wll be complete by September

24 Aggregate measures. The top row of Table 4, panel (a), reproduces our past estmates (from Chen and Ravallon, 2007) of the aggregate headcount ndces usng the $1.08 lne at 1993 PPP for at three-year ntervals. 36 We then gve our new estmates for the same reference years usng the 2005 PPPs and for the range of lnes from $1.00 to $2.50 n 2005 prces. Table 5 gves the correspondng counts of the number of poor. We calculate the global aggregates under the assumpton that the countres wthout surveys have the poverty rate of ther regon. The bulk of the followng dscusson wll focus on the $1.25 lne, though we test the robustness of our qualtatve poverty comparsons to that choce. Our new global poverty count s apprecable hgher than our past estmates suggested. Both the $1.25 and $1.45 lnes ndcate a substantally hgher poverty count n 2005 than obtaned usng our old $1.08 lne n 1993 prces; 1.7 bllon people are found to lve below the $1.45 lne, and 1.4 bllon lve below the $1.25 lne. Focusng on the $1.25 lne, we fnd that 25% of the developng world s populaton n 2005 s poor, versus 17% usng the old lne at 1993 PPP representng an extra 400 mllon people lvng n poverty. It s notable that the concluson that the global poverty count has rsen s also confrmed f one does not update the old 1993 poverty lne. Usng the $1.08 lne for 2005 one obtans an aggregate poverty rate of 19% (1026 mllon people) for The 2005 lne that gves the same headcount ndex for 2005 as the $1.08 lne at 1993 PPP turns out to lower, at $1.03 a day. Whle the adjustment for US nflaton clearly gves a poverty lne for 2005 that s too hgh, the 2005 lne must exceed the 1993 lne to have comparable purchasng power. So the qualtatve result that the new ICP round mples a hgher global poverty count s robust. Holdng constant the real value n the US of the 1993 poverty lne of $1.08 per day, but revsng the PPPs, the poverty rate for the developng world n 2005 rses from 17% to 32% (the latter fgure corresponds to the $1.45 lne). However, ths does not allow for the fact that the same PPP revsons mean that the $US value of the poverty lne at PPP was also overestmated. Ths effect brngs the poverty rate down from 32% to 25%, gvng the net ncrease of 8% ponts We have updated the 2004 estmate n Chen and Ravallon (2007) to 2005 consstently wth the data sued n that paper. 37 Note that the dfference between the 25% and 17% numbers reflects other updates to the data base, besdes the new PPPs. When we use the new data base for 2005 to estmate the poverty rate based on the 1993 PPPs we get a slghtly hgher fgure, namely 17.6% (957.4 mllon people). 22

25 The pure PPP effect on the poverty count can only be properly solated by usng the same set of natonal poverty lnes. For ths purpose, we need to focus nstead on the $1.45 a day lne at 1993 PPP, rather than the $1.08 lne, whch was based on the old RDV complaton of poverty lnes. (Recall that, usng the new RCS data set on natonal poverty lnes, the mean for the poorest 15 countres s $1.45 a day at 1993 PPP.) The 2005 poverty rate usng ths lne, and the 1993 PPPs, s 29.0%. Thus the pure effect of the PPP revsons s to brng the poverty rate down from 29% to 25%. Followng the dscusson n secton 3, the fact that the PPP revsons on ther own brng down the overall poverty count s not surprsng, gven that the poverty lne s set at the mean of lnes for the poorest countres and that the proportonate revsons to the PPPs tend to be greater for poorer countres. Workng aganst ths downward effect of the new PPPs, there s an upward adjustment to the overall poverty count comng from the new data on natonal poverty lnes, whch (as we have seen) tend to be hgher for the poorest countres than those used by RDV for the 1980s. The updatng of the data on natonal poverty lnes moved the global poverty rate from 17% to 29%, whle the PPP revsons brought t back down to 25%. Over the 25 year perod, we fnd that the percentage of the populaton of the developng world lvng below $1.25 per day was halved, fallng from 52% to 25%. (Expressed as a proporton of the populaton of the world, the declne s from 42% to 21%; ths assumes that there s nobody lvng below $1.25 per day n the developed countres. 38 ) The number of poor fell by slghtly over 500 mllon, from 1.9 bllon to 1.4 bllon over (Table 5). The trend rate of declne n the $1.25 a day poverty rate over was 1% pont per year; regressng the poverty rate on tme the estmated trend s -0.99% per year wth a standard error of 0.06% (R 2 =0.97). Ths s slghtly hgher than the trend we had obtaned usng the 1993 PPPs, whch was -0.83% per year (standard error=0.11%). Smply projectng ths trend forward to 2015, the estmated headcount ndex for that year s 16.6% (standard error of 1.5%). Gven that the 1990 poverty rate was 41.6%, the new estmates ndcate that the developng world as a whole s on track to achevng the Mllennum Development Goal (MDG) of halvng the 1990 poverty rate by The 1% pont per year rate of declne n the poverty rate also holds f one focuses on the perod snce 1990 (not just because ths s the base year for the MDG but also recallng that the data for the 1980s s weaker). The $1.25 poverty rate fell 38 The populaton of the developng world n 2005 was 5453 mllon, representng 84.4% of the world s total populaton; n 1981, t was 3663 mllon or 81.3% of the total. 23

26 10% ponts n the 10 years of the 1980s (from 52% to 42%), and a further 17% ponts n the 16 years from 1990 to It s notable that suggests a hgher (absolute and proportonate) drop n the poverty rate than other perods. Gven that lags n survey data avalablty mean that our 2005 estmate s more heavly dependent on non-survey data (notably the extrapolatons based on NAS consumpton growth rates) there s a concern that ths mght be exaggerated. However, that does not seem lkely. The bulk of the declne s n fact drven by countres for whch survey data are avalable close to The regon for whch non-survey data have played the bggest role for 2005 s Sub-Saharan Afrca. If nstead we assume that there was n fact no declne n the poverty rate over n SSA then the total headcount ndex (for all developng countres) for the $1.25 lne n 2005 s 26.2% stll suggestng a szeable declne relatve to Chna s success aganst absolute poverty has clearly played a major role n ths overall progress. Panel (b) n Tables 4 and 5 repeats the calculatons excludng Chna. The $1.25 a day poverty rate falls from 40% to 28% over , wth a rate of declne that s less than half the trend ncludng Chna; the regresson estmate of the trend falls to -0.43% per year (standard error of 0.03%; R 2 =0.96), whch s almost dentcal to the rate of declne for the non-chna developng world that we had obtaned usng the 1993 PPPs (whch gave a trend of -0.44% per year, standard error=0.01%). Based on our new estmates, the projected value for 2015 s 25.1% (standard error=0.8%), whch s well over half the 1990 value of 35% (Table 4). So the developng world outsde Chna s not on track to reachng the MDG for poverty reducton. Our estmates suggest less progress (n absolute and proportonate terms) n gettng above the $2 per day lne than the $1.25 lne. The poverty rate by ths hgher standard has fallen from 70% n 1981 to 47% n 2005 (Table 4). The trend s about 0.8% per year (a regresson coeffcent on tme of -0.84; standard error=0.08); excludng Chna, the trend s only 0.3% per year (a regresson coeffcent of -0.26; standard error=0.05%). Ths has not been suffcent to brng down the number of people lvng below $2 per day, whch was about 2.5 bllon n both 1981 and 2005 (Table 5). Thus the number of people lvng between $1.25 and $2 a day has rsen sharply over these 25 years, from about 600 mllon to 1.2 bllon. Ths marked bunchng up of people just above the $1.25 lne suggests that the poverty rate accordng to that lne could rse sharply wth aggregate economc contracton (ncludng real contracton due to hgher prces). 24

27 The qualtatve concluson that poverty measures have fallen over the perod as a whole, and between 1990 and 2005, are robust to the choce of poverty lne over a wde range (and robust to the choce of poverty measure wthn a broad class of measures). 39 Fgure 4 gves the cumulatve dstrbuton functons up to $13 per day, whch s the average offcal poverty lne n the US n Frst order domnance s ndcated. In 2005, 95.7% of the populaton of the developng world lved below the US poverty lne; 25 years earler t was 96.7%. Regonal dfferences. Table 6 gves the estmates over for four lnes, $1.00, $1.25, $2.00 and $2.50. There have been notable changes n regonal poverty rankngs over ths perod. Lookng back to 1981, East Asa had the hghest ncdence of poverty, wth 78% of the populaton lvng below $1.25 per day and 93% below the $2 lne. South Asa had the next hghest poverty rate, followed by SSA, LAC, MENA and lastly, EECA. Twenty years later, SSA had swapped places wth East Asa where the $1.25 headcount ndex had fallen to 17%, wth South Asa stayng n second place. EECA had overtaken MENA. The regonal rankngs are not robust to the poverty lne. Two changes are notable. At lower lnes (under $2 per day) SSA has the hghest ncdence of poverty, but ths swtches to South Asa at hgher lnes. (Intutvely, ths dfference reflects the hgher nequalty found n Afrca than South Asa.) Second, MENA s poverty rate exceeds LAC s at $2 or hgher, but the rankng reverses at lower lnes. The composton of world poverty has changed notceably over tme. The number of poor has fallen sharply n East Asa, but rsen elsewhere. For East Asa, the MDG of halvng the 1990 $1 per day poverty rate by 2015 was already reached a lttle after Agan, Chna s progress aganst absolute poverty was a key factor; lookng back to 1981, Chna s ncdence of poverty (measured by the percentage below $1.25 per day) was roughly twce that for the rest of the developng world; by the md-1990s, the Chnese poverty rate had fallen well below average. There were over 600 mllon fewer people lvng under $1.25 per day n Chna n 2005 than 25 years earler. Progress was uneven over tme, wth setbacks n some perods (the late 1980s) and more rapd progress n others (the early 1980s and md 1990s); Ravallon and Chen (2007) dentfy a number of factors (ncludng polces) that account for ths uneven progress aganst poverty over tme (and space) n Chna. 39 Frst order domnance up to a poverty lne of Z max mples that all standard (addtvely separable) poverty measures rank the dstrbutons dentcally for all poverty lnes up to Z max ; see Atknson (1987). 25

28 Over , the $1.25 poverty rate n South Asa fell from almost 60% to 40%, whch was not suffcent to brng down the number of poor (Table 7). If the trend over ths perod n South Asa were to contnue untl 2015 the poverty rate would fall to 32.5% (standard error=1.2%), whch s more than half ts 1990 value. So South Asa s not on track to attanng the MDG wthout a hgher trend rate of poverty reducton. Note, however, ths concluson s not robust to the choce of the poverty lne. If nstead we use a lower lne of $1.00 per day at 2005 prces then the poverty rate would fall to 15.7% (standard error=1.3%) by 2015, whch s less than half the 1990 value of 34.0%. Not surprsngly (gven ts populaton weght), the same observatons hold for Inda, whch s not on track for attanng the MDG usng the $1.25 lne but s on track usng the $1.00 lne (whch s also closer to the natonal poverty lne n Inda). 40 The extent of the bunchng up that has occurred between $1.25 and $2 per day s partcularly strkng n both East and South Asa, where we fnd a total of about 900 mllon people lvng between these two lnes, roughly equally splt between the two sdes of Asa. Whle ths ponts agan to the vulnerablty of the poor, by the same token t also suggests that substantal further mpacts on poverty can be expected from economc growth, provded that t does not come wth substantally hgher nequalty. We fnd a trend declnng n the poverty rate n LAC, by both lnes, but not suffcent to reduce the count of the number of poor over the perod as a whole, though wth more encouragng sgns of progress snce The MENA regon has experenced a farly steady declne n the poverty rate, though (agan) not suffcent to avod a rsng count n the number of poor n that regon. We fnd a generally rsng poverty n EECA usng the lower lnes ($1.00 and $1.25 a day) though there are very people are poor by ths standard n EECA. The $2.50 a day lne s more representatve of the poverty lnes found n the relatvely poorer countres of EECA. By ths standard, the poverty rate n EECA has shown lttle clear trend over tme n ether drecton, though there are encouragng sgns of a declne n poverty snce the late 1990s. The paucty of survey data for EECA n the 1980s should also be recalled. Thus our estmates are heavly based on extrapolatons, whch do not allow for any changes n dstrbuton. One would expect that 40 The correspondng poverty rates for the $1.00 lne n Inda are 42.1 (1981), 37.6, 35.7, 33.3, 31.1, 28.6, 27.0, 26.3, 24.3 (2005). 26

29 dstrbuton was better from the pont of vew of the poor n EECA n the 1980s, n whch case poverty would have been even lower than we estmate and the ncrease over tme even larger. The ncdence of poverty n Sub-Saharan Afrca s vrtually unchanged at slghtly over 50% n both 1981 and Wthn ths perod, there was an ncrease untl the md 1990s, and there has been an encouragng downward trend snce then. The number of poor by our new $1.25 a day standard has almost doubled n SSA over , from 214 mllon to over 390 mllon. The share of the world s poor by ths measure lvng n Afrca has rsen from 11% n 1981 to 28% n The trend ncrease n SSA s share of poverty s 0.67% ponts per year (standard error=0.04% ponts), mplyng that one thrd of the world s poor wll lve n ths regon by 2015 (more precsely, the projected poverty rate for that year s 33.7%, wth a standard error of 0.8%). However, there are sgns of progress snce the md-1990s. The $1.25 a day poverty rate for SSA fell from 59% n 1996 to 51% n The declne s proportonately hgher the lower the poverty lne; for the $1 a day lne, the poverty rate n 2005 s 16% lower than ts 1996 value. Poverty gaps. Table 8 gves the PG ndces for $1.25 and $2.00 a day. The aggregate PG for 2005 s 7.6% for the $1.25 lne and 18.6% for the $2 lne. To put these n perspectve, the GDP per capta of the developng world was $11.30 per day n 2005 (at 2005 PPP). The aggregate poverty gap for the $1.25 lne s 0.84% of GDP, whle t s 3.29% for the $2 lne. World (ncludng the OECD countres) GDP per capta was $24.58 per day, mplyng that the global aggregate PG was 0.33% of global GDP usng the $1.25 lne and 1.28% usng $2. 41 Comparng Tables 6 and 8, t can be seen that the regonal rankngs n terms of the poverty gap ndex are smlar as those for the headcount ndex, and the changes over tme follow smlar patterns. What the PG measures do s magnfy the nter-regonal dfferences seen n the headcount ndces. The most strkng feature of the results n Table 6 s the depth of poverty n Afrca, wth a $1.25 per day poverty gap ndex of almost 21% roughly twce the next poorest regon by ths measure (South Asa). For the $1.25 lne, Afrca s aggregate poverty gap represents 3.2% of the regon s GDP; for the $2 lne t s 9.0% Ths assumes that nobody lves below our nternatonal poverty lne n the OECD countres. Under ths assumpton, the aggregate poverty gap as a % of global GDP s PG.( Z / Y ).( N / NW ) where PG s the poverty gap ndex (n %), Z s the poverty lne, Y s global GDP per capta, N s the populaton of the developng world and NW s world populaton. 42 The GDP per capta of SSA n 2005, at 2005 PPP, was $8.13 per day. 27

30 Table 9 gves the mean consumpton of the poor. 43 For 2005, those lvng below the $1.25 a day lne had a mean consumpton of $0.87 (about 3.5% of global GDP per capta). The overall mean consumpton of the poor tended to rse over tme, from $0.74 per day n 1981 to $0.87 n 2005 by the $1.25 lne, and from $0.94 to $1.21 for the $2 lne. Poverty has become shallower n the world as a whole. The mean consumpton of Afrca s poor s not only lower than for other regons, t has shown very lttle ncrease over tme (Table 8). The mean consumpton of those lvng under $1.25 per day n SSA was $0.72 per person per day n 1981 and was almost unchanged at $0.73 n For the $2 lne, the mean consumpton of Afrca s poor remaned roughly constant. The same persstence n the depth of poverty s evdent n MENA and LAC, though the poor have slghtly hgher average levels of lvng n both regons. The mean consumpton of EECA s poor has actually fallen snce the 1990s, even though the overall poverty rate was fallng. 6. Senstvty to methodologcal choces We have already seen how much mpact the choce of poverty lne makes, though we have also seen that the qualtatve comparsons as over tme are robust to the choce of lne. In ths secton we consder senstvty to two further aspects of our methodology: the frst s our use of the PPP for aggregate household consumpton and the second s our relance on surveys for measurng average lvng standards. Alternatve PPPs. The benchmark analyss has reled solely on the ndvdual consumpton PPPs ( P3s ) from the ICP. One defcency of these PPPs s that they are desgned for natonal accountng purposes not poverty measurement. Deaton and Duprez (DD) (2009) have estmated PPPs for the poor (P4s) for a subset of countres wth the requred data. 44 Constructng P4s requres re-weghtng the prces to accord wth consumpton patterns of those lvng near the poverty lne. Notce that there s a smultanety n ths problem, n that one cannot do the re-weghtng untl one knows the poverty lne, whch requres the re-weghted PPPs. 43 The mean consumpton of the poor s (1-PG/H)Z where PG s the poverty gap ndex, H s the headcount ndex and Z s the poverty lne. 44 The Asan Development Bank (2008) has taken a further step of mplementng specal prce surveys for Asan countres to collect prces on explctly lower qualtes of selected tems than those dentfed n the standard ICP. Usng lower qualty goods essentally entals lowerng the poverty lne. In terms of the mpact on the poverty counts for Asa n 2005, the ADB s method s equvalent to usng a poverty lne of about $1.20 a day by our methods; ths calculaton s based on a log lnear nterpolaton between the relevant poverty lnes. 28

31 Deaton and Duprez (2009) mplement an teratve soluton to derve nternally consstent P4s. 45 They do ths for three prce ndex methods, namely the Country Product Dummy (CPD) method and both Fsher and Törnqvst versons of the EKS method used by the ICP. The Deaton-Duprez P4s cannot be calculated for all countres and they cannot cover the same consumpton space as the P3s from the ICP. The lmtaton on country coverage stems from the fact that P4s requre sutable household surveys, namely mcro data from consumpton expendture surveys that can be mapped nto the ICP basc headng categores for prces; the DD P4s are avalable for 60 countres, whch s about half of our sample. The 60-country sample s clearly not representatve of the developng world as a whole and n some specfc regons, notably EECA where the populaton share covered by surveys n the 60-country sample s only 8%, whle overall coverage rate s 79%. As we wll see, the 60-country sample s poorer, n terms of the aggregate (populaton-weghted) poverty count. Also, some of the 110 basc headngs for consumpton n the ICP were dropped by DD n calculatng ther P4s. These ncluded expendtures made on behalf of households by governments and non-governmental organzatons (such as on educaton and health care). Gven that such expendtures are not ncluded n household surveys they cannot be ncluded n DD s P4s. DD also preferred to exclude housng rentals from ther calculatons on the grounds that ths was hard to measure and that dfferent practces for mputng rental for owner-occuped housng had been used by the offcal ICP n dfferent countres. There are other (seemngly more mnor) dfferences n how DD calculated ther P4s and the methods used by the ICP. Usng the P4s at country level kndly provded by Deaton and Duprez, we have recalculated our global poverty measures. In all cases we recalculate the nternatonal poverty lne under the new PPPs, as well as (of course) the poverty measures. Table 10 gves the results by regon for 2005, whle Fgure 5 plots the estmates by year. In both cases we gve our benchmark estmates for the offcal ICP PPP for consumpton usng all 110 basc headngs for consumpton and results for the 102 basc headngs comprsng those that can be matched to surveys less the extra few categores that DD chose not to nclude. Snce the 60 countres used by DD dd not nclude one of the 15 countres n our reference group, the poverty lne s recalculated for In general there s no guarantee that there s a unque soluton for ths method, although DD provde a seemngly plausble restrcton on the Engel curves that assure unqueness. They also use an exact, one-step soluton, for the Törnqvst ndex under a specfc parametrc Engel curve. 29

32 countres, gvng a lne of $1.23 a day ($37.41 per month). Wth the help of the World Bank s ICP team we also recalculated the offcal P3s for consumpton usng the set of basc headngs chosen by DD. Column (1) reproduces the estmates from Table 6, whle the column (2) gves the correspondng estmates for the full sample of countres usng P3s calbrated to the 102 basc headngs used by DD. Columns (3) and (4) gves the correspondng results to (1) and (2) usng the 60 country sub-sample used by DD. Columns (5)-(7) gven our estmates of the poverty measures usng the P4s from DD, for each of ther three methods. We gve (populatonweghted) aggregate results for the sample countres. 46 It can be seen from Table 10 that the swtch from 110 to 102 basc headngs reduces the aggregate poverty measures by about three percentage ponts, whle swtchng from the 115 country sample to the 60 country sample has the opposte effect, addng three ponts. The pure effect of swtchng from P3 to P4 s ndcated by comparng column (4) wth columns (5)-(7). Ths change has only a small mpact usng the EKS method (for ether the Fscher or Törnqvst ndces) though t has a slghtly larger effect usng the CPD method. On balance, the aggregate poverty count turns out to be qute smlar between the P4s and our man estmates usng standard P3s on the full sample. If one assumes that the countres wthout household surveys have the regonal average poverty rate then the Fsher P4 gves a count of 1402 mllon for the number of poor, whle the CPD and Törnqvst P4s gve counts of 1454 and 1359 respectvely, as compared to 1377 mllon usng standard P3s. The regonal profle s also farly robust, the man dfference beng lower poverty rates n EECA usng P4s, although the poor representaton of EECA countres n the 60-country sample sued by DD s clearly playng a role here. The reducton n coverage of consumpton tems makes a bgger dfference, wth a hgher poverty count n the aggregate (28% for these 60 countres usng the standard PPP, versus 25% usng the PPP excludng housng), due manly to hgher poverty rates n East and South Asa when all 110 basc headngs for consumpton are ncluded. The trends over tme are also very smlar (Fgure 5). Ths s not surprsng gven that, followng the usual practce of dong the PPP converson at only the benchmark year and then usng natonal data sources over tme, the real growth rates and dstrbutons at country level are unaffected. 46 Note that ths s a slghtly dfferent aggregaton method to our earler results, whch assumed that the sample was representatve at regonal level. That s clearly not plausble for the 60-country sample used by DD. We have re-calculated the aggregates for the 115 country sample under the same bass as for the 60-country sample. 30

33 Mxng natonal accounts and surveys. Next we test senstvty to usng nstead the geometrc mean of the survey mean and ts expected value gven NAS consumpton; as noted n secton 4, ths can be gven a Bayesan nterpretaton. Table 11 gves the estmates mpled by the geometrc mean; n all other respects we follow the benchmark methodology. The expected value was formed by a separate regresson at each reference year; a very good ft was obtaned usng a log-log specfcaton (addng squared and cubed values of tke log of NAS consumpton per capta dd lttle or nothng to ncrease the adjusted R 2 ). In the aggregate for most years, and most regons, the level of poverty s lower usng the mxed method than the survey-means only method. In the aggregate the 2005 poverty rate s 18.6% (1017 mllon people) usng the geometrc mean versus 25.2% (1374 mllon) usng unadjusted survey means. Nonetheless, the mxed method stll gves a hgher poverty rate for 2005 than mpled by the 1993 PPPs. Usng the $2.00 lne, the 2005 poverty rate falls from 47.0% to 41.0%. Fgure 6 compares the aggregate headcount ndces for $1.25 a day between the benchmark and mxed method. The trend rate of poverty reducton s almost dentcal between the two, at about 1% pont per year. (Usng the mxed method, the OLS trend s -0.98% ponts per year, wth a standard error of 0.04%, versus -0.99% wth a standard error of 0.06% usng only the survey means.) The lnear projecton to 2015 mples a poverty rate of 9.95% (s.e.=1.02%), less than one thrd of ts 1990 value. The mxed method gves a hgher poverty rate for LAC and MENA and makes neglgble dfference for SSA. Other regons see a lower poverty rate. The $1.25 a day poverty rate for East Asa n 2005 falls from 17% to 12%. The largest change s for South Asa, where by 2005 the poverty rate for Inda falls to about 20% usng the mxed method versus 42% usng the unadjusted survey means; the proportonate gap was consderable lower n 1981 (42% usng the mxed method versus 60% usng the survey mean alone). Inda accounts for a large share of the dscrepances between the levels of poverty between the benchmark and the mxed method, reflectng both the country s populaton weght and the large gap that has emerged n recent tmes between the NSS and NAS consumpton aggregates for Inda (Ravallon, 2003). Fgure 6 also gves the complete seres for $1.25 a day excludng Inda; t can be seen that the gap between the two methods narrows over tme. If we focus on the poverty rates for the developng world excludng Inda then the dfference between 31

34 the mxed method and the benchmark narrows consderably, from 21.1% (918 mllon people) to 18.2% (794 mllon) n (The $2.00 poverty rates are 39.8% and 36.7% respectvely.) In 2005, about two-thrds of the drop n the count of the number of people lvng under $1.25 a day n movng from the benchmark to the mxed method s due to Inda. 7. Conclusons Global poverty measurement combnes data from vrtually all branches of the statstcal system. The measures reported here brng together natonal poverty lnes, household surveys, census data, natonal accounts and both natonal and nternatonal prce data. Inevtably there are comparablty and consstency problems when combnng data from such dverse sources. Prce ndces for cross-country comparsons do not always accord well wth those used for ntertemporal comparsons wthn countres. In some countres, the surveys gve a dfferent pcture of average lvng standards to the natonal accounts, and the methods used n both surveys and natonal accounts dffer across countres. However, thanks to the efforts and support of governmental statstcs offces and nternatonal agences, and mproved technologes, the avalable data on the three key ngredents n nternatonal poverty measurement natonal poverty lnes, representatve samples of household consumpton expendtures (or ncomes) and data on prces have mproved greatly snce global poverty montorng began. The expanson of country-level poverty assessments snce the early 1990s has greatly ncreased the data avalable on natonal poverty lnes. Sde-bysde wth ths, the country coverage of credble household survey data, sutable for measurng poverty, has mproved markedly, the frequency of data has ncreased, publc access to these data has mproved, and the lags n data avalablty have been reduced apprecably. And wth the substantal global effort that went nto the 2005 Internatonal Comparson Program we are also n a better poston to assure that the poverty lnes used n dfferent countres have smlar purchasng power, so that two people lvng n dfferent countres but wth the same real standard of lvng are treated the same way. The results of the 2005 ICP mply a hgher cost of lvng n developng countres than past ICP data have ndcated; the Penn effect s stll evdent, but t has been over-stated. We have combned the new data on prces from the 2005 ICP and household surveys wth a new complaton of natonal poverty lnes, whch substantally updates the old lnes for the 32

35 1980s prevously used for the $1-a-day global poverty counts. Importantly, the new complaton of natonal lnes s more representatve of low-ncome countres, gven that the sample sze s larger and t corrects the sample bases n the old data set. The pure effect of the PPP revsons s to brng the poverty count down but ths s outweghed by the hgher level of the natonal poverty lnes n the poorest countres, as used to determne the nternatonal lne. Our new calculatons usng the 2005 ICP and new nternatonal poverty lne of $1.25 a day mply that 25% of the populaton of the developng world, 1.4 bllon people, were poor n 2005, whch s 400 mllon more for that year 2005 than mpled by our old nternatonal poverty lne based on natonal lnes for the 1980s and the 1993 ICP. In Chna alone, whch had not prevously partcpated offcally n the ICP, the new PPP mples that an extra 10% of the populaton s lvng below our nternatonal poverty lne. But the mpact s not confned to Chna; there are upward revsons to our past estmates for all regons, consstent wth the hgher cost of lvng n developng countres mpled by the results of the 2005 ICP. The hgher global count s n no small measure the result of correctng the sample bas n the orgnal complaton of natonal poverty lnes used to set the old $1-a-day lne. Although there are a number of data and methodologcal ssues that cauton comparsons across dfferent sets of PPPs, t s notable that our poverty count for 2005 s qute robust to usng alternatve PPPs anchored to the consumpton patterns of those lvng near the poverty lne. Of course, dfferent methods of determnng the nternatonal poverty lne gve dfferent poverty counts. If we use a lne of $1.00 a day at 2005 PPP (almost exactly Inda s offcal poverty lne) then we get a poverty rate of 16% slghtly under 900 mllon people whle f we use the medan poverty lne for all developng countres n our poverty-lne sample, namely $2.00 a day, then the poverty rate rses to 50%, slghtly more than two bllon people. There s greater somewhat greater senstvty to mxng natonal accounts consumpton wth survey means. We have proposed a smple Bayesan mxed method, n whch the survey mean s replaced by the geometrc mean of the survey mean and ts predcted value based on pror natonal accounts data. Ths s only justfed under certan assumptons, notably that consumpton s dentcally log-normally dstrbuted between the (natonal-accounts-based) pror and the surveys. These assumptons can be questoned, but they do at least provde a clear bass for an alternatve hybrd estmator. Ths gves a lower poverty count for 2005, namely 19% lvng below $1.25 a day rather than 25%. A large share of ths gap two thrds of the drop n 33

36 the count of the number of poor n swtchng to the mxed method s due to Inda s (unusually large) dscrepancy between consumpton measured n the natonal accounts and that measured by surveys. Explanng ths gap should be a hgh prorty. Whle the new data suggest that the developng world s poorer than we thought, t has been no less successful n reducng the ncdence of absolute poverty snce the early 1980s. Indeed, the overall rate of progress aganst poverty s farly smlar to past estmates and robust to our varous changes n methodology. The trend rate of global poverty reducton of 1% pont per year turns out to be slghtly hgher than we had estmated prevously, due manly to the hgher weght on Chna s remarkable pace of poverty reducton. The trend s even hgher f we use our Bayesan mxed-method. The developng world as a whole s clearly stll on track to attanng the frst Mllennum Development Goal of halvng the 1990s extreme poverty rate by Chna attaned the MDG early n the mllennum, almost 15 years ahead of the target date. However, the developng world outsde Chna wll not attan the MDG wthout a hgher rate of poverty reducton than we have seen over The persstently hgh ncdence and depth of poverty n Sub-Saharan Afrca are partcularly notable. There are encouragng sgns of progress n ths regon snce the late 1990s, although lags n survey data avalablty and problems of comparablty and coverage leave us unsure about how robust ths wll prove to be. The marked bunchng up n the global dstrbuton of consumpton just above our nternatonal poverty lne s also notable. There are a great many people who have reached the frugal $1.25 standard, but are stll very poor, and clearly vulnerable to downsde shocks. Two such shocks have been experenced snce The frst s the steep rse n nternatonal food and fuel prces and the second s the global fnancal crss that emerged n late Despte the progress n reducng the lags n survey data avalablty, t wll probably not be untl 2010 that we can make a reasonably confdent assessment of the ex post mpacts of these events on the world s poor. Untl then, ex ante assessments wll be requred, based on pre-crss data and economc assumptons. Such assessments suggest that at least a few years of the progress reported here have been eroded snce

37 Appendx Proof of Proposton 1: Let H = F Z ) denote the poverty rate (headcount ndex) n country, wth dstrbuton functon F, ( Z = Z. PPP s the nternatonal poverty lne n local currency unts (LCU) whle Z s the nternatonal lne n $PPP and the PPP rate s aggregate poverty rate s PPP. The H N = = 1 n F ( Z. PPP ) (1) where n s the populaton share of country. It s convenent to rank countres by consumpton per capta, so that =1 s the poorest. The proportonate mpact of the PPP revsons on the aggregate poverty rate can be wrtten as: where s N d ln H = = s η ( d ln PPP + d ln Z) (2) 1 = n H H s the poverty share of country and η = ln H / ln Z s the elastcty of / the dstrbuton functon at the poverty lne. The nternatonal poverty lne can be represented as a (non-negatvely) weghted mean of the natonal poverty lnes: Z N = = 1 w Z LCU / PPP (3) The natonal poverty lnes n LCU are data and so can be treated as fxed. Thus: N 1 d ln Z w v d ln PPP (4) = = where LCU v ( Z / PPP ) / Z. Thus we can re-wrte (2) as: where d ln H N = = s η ( d ln PPP γ ) (5) 1 γ N = 1 w v d ln PPP The sgn of (5) s ambguous based on the assumptons so far. Consder the specal case n Proposton 1, whereby: () the nternatonal poverty lne s that of the poorest country ( w, 0 mplyng that v1 = 1and hence γ d ln PPP1 ); () the PPP revson s largest for 1 = 1 w 1 = the poorest country ( d ln PPP1 > d ln PPP 1 ). Then Proposton 1 follows mmedately. Proof of Proposton 2: Let the pror estmate of the mean be denoted by M 0, and assume that ths s combned wth new nformaton from the survey, wth mean M, to obtan a posteror 35

38 estmate of the mean, * M. Let m 0 denote the mean of the pror dstrbuton of the logs of M 0, 2 wth varance σ 0. The survey entals takng a random sample of n households, and the mean of ths sample dstrbuton of log consumpton s the mean of the posteror dstrbuton s: 47 m wth varance 2 1 * 0 = αm0 + (1 α m where = ( σ 0 ) + ( σ m / n ) m ) σ 2 m. It can then be shown that ( σ ) α (6) (The varance of the posteror dstrbuton s [( σ ) + ( σ m / n) ].) The mxng parameter can then be nterpreted as the relatve precson of the pror relatve to the survey data. Next note that ln M 0 = m0 + I 0, where I 0 s the mean log devaton measure of nequalty (a member of the Generalzed Entropy calls of nequalty measures), and (smlarly) ln M = m + I and m ln M = m + I n obvous notaton. 48 One can then derve: * where * * ln M = α ln M 0 + (1 α ) ln M + ν * * ν = I α I + (1 α ) I ] (8) [ 0 m Under the assumpton that the dstrbuton of relatve consumptons s dentcal n the pror and 2 2 survey dstrbuton we have σ = n, mplyng that α = 0. 5, and ν.e., = 0. Then: 0 σ m / * 0 m (7) I = I = I, mplyng * ln M = (ln M 0 + ln M ) / 2 (9) * M s the geometrc mean of M 0 and M as clamed n Proposton For a proof of ths result see, for example, Thel (1971, pp ). Note that m 0 and m are the means of the log, not log of the means. 36

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41 , 2008b. A Global Perspectve on Poverty n Inda, Economc and Poltcal Weekly 43 (43): Ravallon, Martn and Shaohua Chen, 2007, Chna s (Uneven) Progress Aganst Poverty. Journal of Development Economcs, 82(1): Ravallon, Martn, Shaohua Chen and Prem Sangraula, 2007, New Evdence on the Urbanzaton of Global Poverty, Populaton and Development Revew, 33(4): Ravallon, Martn, Shaohua Chen and Prem Sangraula, 2008, Dollar a Day Revsted, Polcy Research Workng Paper 4620, Washngton DC, World Bank. Ravallon, Martn, Gaurav Datt and Domnque van de Walle, 1991, Quantfyng Absolute Poverty n the Developng World, Revew of Income and Wealth 37: Ruoen, Ren and Ka Chen, 1995, Chna s GDP n US Dollars based on Purchasng Power Party, Polcy Research Workng Paper 1415, Washngton DC, World Bank. Sala--Martn, Xaver, 2006, The World Dstrbuton of Income: Fallng Poverty and Convergence. Perod, Quarterly Journal of Economcs, CXXI (2): Samuelson, Paul, 1964, Theoretcal Notes on Trade Problems, Revew of Economcs and Statstcs 46(2): Slesnck, Danel, Emprcal Approaches to Measurng Welfare, Journal of Economc Lterature 36: Summers, Robert and Alan Heston, 1991, The Penn World Table (Mark 5): An Extended Set of Internatonal Comparsons, , Quarterly Journal of Economcs 106: Thel, Henr, 1971, Prncples of Econometrcs, Amsterdam: North-Holland. Unted Natons, 1998, Evaluaton of the Internatonal Comparson Programme. World Bank, 1990, World Development Report: Poverty, New York: Oxford Unversty Press., 2008a, Global Purchasng Power Partes and Real Expendtures Internatonal Comparson Program, Washngton DC: World Bank., 2008b, Comparsons of New 2005 PPPs wth Prevous Estmates. (Revsed Appendx G to World Bank, 2008a). Washngton DC: World Bank. 39

42 Table 1: Average date of the surveys used for each reference year Average date of the surveys used for each reference year Regon East Asa-Pacfc (EAP) Of whch Chna Eastern Europe and Central Asa (EECA) Latn Amerca and Carbbean (LAC) Mddle East and North Afrca (MENA) South Asa (SA) Of whch Inda Sub-Saharan Afrca (SSA) Total Table 2: Average lag n survey data avalablty for the latest reference year by regon Regon 2001 (Chen and Ravallon, 2004) 2005 (Present paper) East Asa Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Sub-Saharan Afrca Total Table 3: Proporton of the populaton represented by household surveys wthn two years Coverage rate: % of pop. represented by a survey two years ether sde of each year Regon EAP Chna EECA LAC MENA SA Inda SSA Total

43 Table 4: Headcount ndces of poverty (% below each lne) (a) Aggregate for developng world Old estmates usng 1993 ICP $1.08 (1993) New estmates usng 2005 ICP $ $ $ $ $ (b) Excludng Chna Old estmates usng 1993 ICP $1.08 (1993) New estmates usng 2005 PPP $ $ $ $ $ Note: The headcount ndex s the percentage of the relevant populaton lvng n households wth consumpton per person below the poverty lne. Table 5: Numbers of poor (mllons) (a) Aggregate for developng world Old estmates usng 1993 ICP $1.08 (1993) New estmates usng 2005 ICP (number n mllons below each lne at 2005 PPP) $ $ $ $ $ (b) Excludng Chna Old estmates usng 1993 ICP $1.08 (1993) New estmates at 2005 ICP (number n mllons below each lne at 2005 PPP) $ $ $ $ $

44 Table 6: Regonal breakdown of headcount ndex for nternatonal poverty lnes of $1.00-$2.50 a day over (a) % lvng below $1.00 a day Regon East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total (b) % lvng below $1.25 a day Regon East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total

45 Table 6 cont., (c) % lvng below $2.00 a day Regon East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total (d) % lvng below $2.50 a day East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total

46 Table 7: Regonal breakdown of number of poor (mllons) for nternatonal poverty lnes of $1.00-$2.50 a day over (a) Number lvng below $1.00 a day Regon East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total (b) Number lvng below $1.25 a day East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total

47 Table 7 cont., (c) Number lvng below $2.00 a day Regon East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total (c) Number lvng below $2.50 a day East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total

48 Table 8: Poverty gap ndex (x100) by regon over (a) $1.25 Regon East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total (b) $2.00 Regon East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total Note: The poverty gap ndex 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. 46

49 Table 9: Mean consumpton of the poor ($ per day) by regon over (a) $1.25 Regon East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total (b) $2.00 Regon East Asa and Pacfc Of whch Chna Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total

50 Table 10: Aggregate poverty rate and regonal profle for 2005 under alternatve PPPs (1) (2) (3) (4) (5) (6) (7) PPP P3 P3 P3 P3 P4: CPD P4: Fsher P4: Törnqvst No. countres for poverty measures No. countres for poverty lne Poverty lne (per month) $38.00 $33.34 $37.41 $32.88 Rs Rs Rs No. basc headngs Poverty rate (% of populaton) East Asa and Pacfc Eastern Europe and Central Asa Latn Amerca and Carbbean Mddle East and North Afrca South Asa Sub-Saharan Afrca Total (for sampled countres) Note: The Deaton-Duprez P4 calculatons are only possble for about half the countres n the full sample, gven that consumpton expendture surveys are requred. Also one country drops out of the reference group for calculatng the poverty lne. The lne for column P4s are Deaton- Duprez world rupees. 48

51 Table 11: Headcount ndex for usng Bayesan mxed method (%) (a) $1.25 a day East Asa & Pacfc Of whch Chna Europe & Central Asa Latn Amerca & Carbbean Mddle East & North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total (b) $2.00 a day East Asa & Pacfc Of whch Chna Europe & Central Asa Latn Amerca & Carbbean Mddle East & North Afrca South Asa Of whch Inda Sub-Saharan Afrca Total

52 Fgure 1: Natonal poverty lnes plotted aganst mean consumpton 10 Poverty lne at 2005 PPP ($/day) Log consumpton per person at 2005 PPP Note: Bold symbols are ftted values from a nonparametrc regresson Fgure 2: Comparson of new and old natonal poverty lnes at 1993 PPP New poverty lnes ($/day) Old poverty lnes ($/day) Log consumpton per person at 1993 PPP Note: Bold symbols are ftted values from a nonparametrc regresson

53 Fgure 3: Number of surveys by year 50 Number of surveys used (3-year movng average) Fgure 4: Cumulatve dstrbutons for the developng world 100 Cumulatve dstrbuton of consumpton (% below each poverty lne) Poverty lne ($ per day at 2005 PPP) 51

54 Fgure 5: Aggregate poverty rates over tme for alternatve PPPs 60 Poverty rate (% below poverty lne) Benchmark: $1.25 a day; standard P3 and full sample Standard P3 but for the 60 countres used for P4s 10 P3 for 102 basc headngs and 60 countres Deaton-Durpez P4 (CPD) Deaton-Duprez P4 (Fsher) Deaton-Duprez P4 (Tornqvst) Fgure 6: Aggregate poverty rates over tme for benchmark and mxed method Poverty rate (% below $1.25 a day) Benchmark Mxed method Benchmark less Inda Mxed method less Inda