China s (Uneven) Progress Against Poverty

Transcription

1 China s (Uneven) Progress Agains Povery Marin Ravallion and Shaohua Chen 1 Developmen Research Group, World Bank 1818 H Sree NW, Washingon DC, 20433, USA While he incidence of exreme povery in China fell dramaically over , progress was uneven over ime and across provinces. Rural areas accouned for he bulk of he gains o he poor, hough migraion o urban areas helped. The paern of growh maered; rural economic growh was far more imporan o naional povery reducion han urban economic growh; agriculure played a far more imporan role han he secondary or eriary sources of GDP. Rising inequaliy wihin he rural secor grealy slowed povery reducion. Provinces saring wih relaively high inequaliy saw slower progress agains povery, due boh o lower growh and a lower growh elasiciy of povery reducion. Taxaion of farmers and inflaion hur he poor; exernal rade had lile shor-erm impac. Keywords: China, povery, inequaliy, economic growh, policies JEL: O15, O53, P36 World Bank Policy Research Working Paper 3408, Sepember 2004 The Policy Research Working Paper Series disseminaes he findings of work in progress o encourage he exchange of ideas abou developmen issues. An objecive of he series is o ge he findings ou quickly, even if he presenaions are less han fully polished. The papers carry he names of he auhors and should be cied accordingly. The findings, inerpreaions, and conclusions expressed in his paper are enirely hose of he auhors. They do no necessarily represen he view of he World Bank, is Execuive Direcors, or he counries hey represen. Policy Research Working Papers are available online a hp://econ.worldbank.org. 1 The auhors are graeful o he saff of he Rural and Urban Household Survey Divisions of China s Naional Bureau of Saisics for heir invaluable help in assembling he daa base we draw on in his paper. Helpful commens were received from David Dollar, Tamar Manuelyan Ainc, Jusin Lin, Will Marin, Thomas Pikey, Sco Rozelle, Dominique van de Walle, Shuilin Wang, Alan Winers and seminar/conference paricipans a he Naional Bureau of Saisics, Beijing, he McArhur Foundaion Nework on Inequaliy, he Cener for Economic Research, Beijing Universiy, he Ausralian Naional Universiy and he World Bank. Addresses for correspondence: mravallion@worldbank.org and schen@worldbank.org.

2 1. Inroducion This paper aims o documen and explain China s record agains povery over he wo decades following Deng Xiaoping s iniiaion of pro-marke reforms in We apply new povery lines o newly assembled disribuional daa much of which has no previously been analyzed and we address some of he daa problems ha have clouded pas esimaes. We hus offer he longes and mos inernally consisen series of naional povery and inequaliy measures, spanning While daa are less complee a he provincial level, we can esimae rends since he mid-1980s. Armed wih hese new measures, we address some long-sanding quesions in developmen economics. How much did poor people share in he gains from growh? Did he secoral and geographic paern of growh maer? Wha role was played by urbanizaion of he populaion? How did iniial disribuion influence subsequen raes of growh and povery reducion? Wha role was played by economic policies? Our principal findings are as follows: Finding 1: China has made huge overall progress agains povery, bu i has been uneven progress. In he 20 year period afer 1981, he proporion of he populaion living below our new povery lines fell from 53% o 8%. However, here were many sebacks for he poor. Povery reducion salled in he lae 1980s and early 1990s, recovered pace in he mid-1990s, bu salled again in he lae 1990s. Half of he decline in povery came in he firs few years of he 1980s. Some provinces saw far more rapid progress agains povery han ohers. Finding 2: Inequaliy has been rising, hough no coninuously and more so in some periods and provinces. In marked conras o mos developing counries, relaive inequaliy is higher in China s rural areas han in urban areas. However, here has been convergence over 2

3 ime wih a seeper increase in inequaliy in urban areas. Relaive inequaliy beween urban and rural areas has no shown a rend increase once one allows for he higher rae of increase in he urban cos of living. Absolue inequaliy has increased appreciably over ime beween and wihin boh urban and rural areas, and absolue inequaliy is higher in urban areas. Finding 3: The paern of growh maers. While migraion o urban areas has helped reduce povery naionally, he bulk of he reducion in povery came from wihin rural areas. Growh in he primary secor (primarily agriculure) did more o reduce povery and inequaliy han eiher he secondary or eriary secors. Saring in 1981, if he same aggregae growh rae had been balanced across secors, i would have aken 10 years o bring he povery rae down o 8%, raher han 20 years. The geographic composiion of growh also maered. While provinces wih higher rural income growh ended o have higher povery reducion, growh did no end o be higher in he provinces where i would have had he mos impac on povery naionally. The paern of growh maered o he evoluion of overall inequaliy. Rural and (in paricular) agriculural growh brough inequaliy down. Rural economic growh reduced inequaliy in boh urban and rural areas, as well as beween hem. Finding 4: Inequaliy has emerged as a concern for boh growh and povery reducion. Wih he same growh rae and no rise in inequaliy in rural areas alone, he number of poor in China would be less han one-quarer of is acual value (a povery rae in 2001 of 1.5% raher han 8%). This calculaion would be decepive if he rise in inequaliy was he price of high economic growh, which did help reduce povery. However, we find no evidence of such an aggregae rade off. The periods of more rapid growh did no bring more rapid increases in inequaliy. Nor did provinces wih more rapid rural income growh experience a seeper increase in inequaliy. Thus he provinces ha saw a more rapid rise in inequaliy saw less progress 3

4 agains povery, no more. Over ime, povery has also become far more responsive o he (coninuing) increase in inequaliy. A he ouse of China s curren ransiion period, levels of povery were so high ha inequaliy was no an imporan concern. Tha has changed. Furhermore, even wihou a furher rise in inequaliy, he hisorical evidence suggess ha more unequal provinces will face a double handicap in fuure povery reducion; hey will have lower growh and povery will respond less o ha growh. 2. Daa on income povery and inequaliy in China We draw on he Rural Household Surveys (RHS) and he Urban Household Surveys (UHS) of China s Naional Bureau of Saisics (NBS). 2 NBS ceased doing surveys during he Culural Revoluion in and saed afresh in 1980 (for rural areas) and 1981 (urban). While virually all provinces were included from he ouse, 30% had sample sizes in he early surveys ha NBS considered oo small for esimaing disribuional saisics (hough sill adequae for he mean). However, his does no appear o be a source of bias; we could no rejec he null hypohesis ha he firs available esimaes of our povery measures were he same for hese small sample provinces as he res. 3 While sample sizes for he early surveys were smaller, hey are sill adequae for measuring povery; 16,000 households were inerviewed for he 1980 RHS and abou 9,000 for he 1981 UHS. Since 1985, he surveys have had naionally represenaive samples of abou 70,000 in rural areas and 30-40,000 in urban areas. An unusual feaure of hese surveys is ha heir sample frames are based on China s regisraion sysem raher han he populaion census. This means ha someone wih rural 2 On he hisory and design of hese surveys see Chen and Ravallion (1996) and Bramall (2001). 3 Included provinces had a povery rae by our main povery lines ha was 1.9% poins higher, bu his is no significanly differen from zero (-raio=0.32). This held for all oher povery measures. 4

5 regisraion who has moved o an urban area is effecively missing from he sample frame. Migrans from rural areas gain from higher earnings (he remiances back home are capured in he RHS), bu are probably poorer on average han regisered urban residens. Agains his likely source of downward bias in povery esimaes from he UHS, he UHS income aggregaes do no capure fully he value of he various enilemens and subsidies received exclusively by urban residens, hough hese appear o be of declining imporance over ime. While NBS has selecively made he micro daa (for some provinces and years) available o ouside researchers, he complee micro daa are no available o us for any year. Insead we use abulaions of he disribuion of income. The majoriy of hese abulaed daa are unpublished and were provided by NBS. 4 The income aggregaes include impued values for income from own-producion, bu exclude impued rens for owner-occupied housing. (Impuaion is difficul, given he hinness of housing markes.) The usual limiaions of income as a welfare indicaor remain. For example, our measures of inequaliy beween urban and rural residens may no adequaely reflec oher inequaliies, such as in access o public services (healh, educaion, waer and saniaion all of which end o be beer provided in urban areas). There was a change in he mehods of valuaion for consumpion of own-farm producion in he RHS in 1990 when public procuremen prices were replaced by local selling prices. 5 To help us correc for his problem, NBS provided abulaions of he disribuion in 1990 by boh mehods, allowing us o esimae wha he income disribuions for he lae 1980s would have 4 There are a number of abulaions in he NBS Saisical Yearbook, bu hey only provide he percenages of households in each income class; wihou he mean income for each income class and mean household size hese abulaions are unlikely o give accurae esimaes of he Lorenz curve. Some of hese daa are available in he Provincial Saisical Yearbooks or he Household Survey Yearbooks. 5 Pas esimaes have used he old prices for he 1980s and he new prices for 1990 onwards, ignoring he change. Chen and Ravallion (1996) creaed a consisen series for for he micro daa for a few provinces. However, his is no feasible wihou he complee micro daa. 5

6 looked like if NBS had used he new valuaion mehod. The Appendix describes he correcion mehod in deail. Our correcions enail lower povery measures in he lae 1980s. In measuring povery from hese surveys, we use wo povery lines. One is he longsanding official povery line for rural areas of 300 Yuan per person per year a 1990 prices. (There is no comparable urban povery line.) I has been argued by many observers ha his line is oo low o properly reflec prevailing views abou wha consiues povery in China. I can hardly be surprising ha in such a rapidly growing economy, percepions of wha income is needed o no be considered poor will rise over ime. 6 In collaboraion wih he auhors, NBS has been developing a new se of povery lines ha appears o beer reflec curren condiions. Region-specific food bundles are used, wih separae food bundles for urban and rural areas, valued a median uni values by province. The food bundles are based on he acual consumpion bundles of hose beween he poores 15 h percenile and he 25 h percenile naionally. These bundles are hen scaled o reach 2100 calories per person per day, wih 75% of he calories from foodgrains. 7 Allowance for non-food consumpion are based on he nonfood spending of households in a neighborhood of he poin a which oal spending equaled he food povery line in each province (and separaely for urban and rural areas). The mehods closely follow Chen and Ravallion (1996) and Ravallion (1994). For measuring povery naionally we have simply used he means of hese regional lines. Wih a lile rounding off, we chose povery lines of 850 Yuan per year for rural areas and 1200 Yuan for urban areas, boh in 2002 prices. (Ideally one would build up all naional povery 6 Povery lines across counries end o be higher he higher he mean income of he counry, hough wih an iniially low elasiciy a low income (Ravallion, 1994). 7 Wihou he laer condiion, he rural food bundles were deemed o be nuriionally inadequae (in erms of proein and oher nuriens) while he urban bundles were considered o be preferable. The condiion was binding on boh urban and rural bundles. 6

7 measures by applying he regional povery lines o he provincial disribuions and hen aggregaing. However, his would enail a subsanial loss of informaion given ha we have only years of rural daa a province level.) We use he 2002 differenial beween he urban and rural lines o calculae an urban equivalen o he 300 Yuan rural line. Finally, we conver o prices a each dae using he rural and urban Consumer Price Indices produced by NBS. We also use hese urban and rural povery lines as deflaors for urban-rural cos-of-living (COL) adjusmens in forming aggregae inequaliy measures and for measuring inequaliy beween urban and rural areas. Pas work in he lieraure on inequaliy in China has ignored he COL difference beween urban and rural areas, and we will see ha his does maer. However, our COL adjusmens are no ideal, in ha a common deflaor is applied o all levels of income. We provide hree povery measures: The headcoun index (H) is he percenage of he populaion living in households wih income per person below he povery line. The povery gap index (PG) gives he mean disance below he povery line as a proporion of he povery line (where he mean is aken over he whole populaion, couning he non-poor as having zero povery gaps.) The hird measure is he squared povery gap index (SPG), in which he individual povery gaps are weighed by he gaps hemselves, so as o reflec inequaliy amongs he poor (Foser e al., 1984). For all hree, he aggregae measure is he populaion-weighed mean of he measures found across any complee pariion of he populaion ino subgroups. Da and Ravallion (1992) describe our mehods for esimaing he Lorenz curves and calculaing hese povery measures from he grouped daa provided by he NBS abulaions. 3. Povery measures for China I can be seen from Table 1 ha he (census-based) urban populaion share rose from 19% in 1980 o 39% in This may be a surprisingly high pace of urbanizaion, given ha here 7

8 were governmenal resricions on migraion (hough less so since he mid-1990s). For example, in India (wih no such resricions) he share of he populaion living in urban areas increased from 23% o 28% over he same period. We do no know how much his semmed from urban expansion ino rural areas versus rural-urban migraion. The cos-of-living differenial in Table 1 rises over ime, from 19% o 41% in This reflecs he fac ha he urban inflaion rae is higher han he rural rae; he index a base 1980 (=100) had risen o 438 in urban areas by 2001 versus 368 in rural areas. 8 This divergence beween urban and rural inflaions raes sared in he mid-1980s and undoubedly reflecs he rising coss of urban goods ha had been subsidized in he pre-reform economy. Table 1 also gives our esimaes of mean income for rural and urban areas. The large dispariies in mean incomes beween urban and rural areas echo a well-known feaure of he Chinese economy, hough our COL adjusmen narrows he differenial considerably. 9 We will reurn in secion 5 o discuss he implicaions for urban-rural inequaliy. Table 2 gives our rural povery measures. Table 3 gives our esimaes for urban areas, and Table 4 gives he naional aggregaes. Figure 1 plos he naional headcoun indices for boh povery lines. By he new lines, he headcoun index falls from 53% in 1981 o 8% in Conservaively assuming he 1981 urban number for 1980, he naional index was 62% in For all years and all measures, rural povery incidence exceeds urban povery, and by a wide margin. Rural povery measures show a srong downward rend, hough wih some reversals, noably in he lae 1980s, early 1990s and in he las wo years of our series. The urban measures also show a rend decline, hough wih even greaer volailiy. 8 While here is a high correlaion beween he urban populaion share and he urban-rural COL differenial, his appears o be spurious; here is no correlaion beween he changes over ime. 9 Since he laer adjusmen is based on he povery lines, i may no be appropriae for he mean (a leas oward he end of he period). Bu i is our bes available opion. 8

9 There was more progress in some periods han ohers. There was a dramaic decline in povery in he firs few years of he 1980s, coming from rural areas. By our new povery line, he rural povery rae fell from 76% in 1980 o 23% in The lae 1980s and early 1990s were a difficul period for China s poor. Progress was resored around he mid-1990s, hough he lae 1990s saw a marked deceleraion, wih signs of rising povery in rural areas. 10 We can decompose he change in naional povery ino a populaion shif effec and a wihin secor effec. 11 Leing P denoe he povery measure for dae, while i P is he measure for secor i=u,r (urban, rural), wih corresponding populaion shares exac decomposiion of he change in povery beween =1981 and =2001 as: r r r u u u u r u u (1) P P = n ( P P ) + n ( P P )] + [( P P )( n n )] [ Wihin-secor effec Populaion shif effec i n, we can wrie an The wihin-secor effec is he change in povery weighed by final year populaion shares while he populaion shif effec measures he conribuion of urbanizaion, weighed by he iniial urban-rural difference in povery measures. The populaion shif effec should be inerpreed as he parial effec of urban-rural migraion, in ha i does no allow for any effecs of migraion on povery levels wihin urban and rural areas. Table 5 gives his decomposiion. We find ha he naional headcoun index fell by 45% poins, of which 35% poins were accounable o he wihin-secor erm; wihin his, 33% poins was due o falling povery wihin rural areas while only 2% was due o urban areas. The populaion shif from rural o urban areas accouned for 10% poins. The oher povery measures ell a very similar sory, hough he rural share is slighly higher for SPG han PG, and lowes for 10 Using differen measures and daa sources, Benjamin e al., (2003) also find signs of falling living sandards amongs he poores in rural China in he lae 1990s. 11 This is one of he decomposiions for povery measures proposed by Ravallion and Huppi (1991). 9

10 H. As can be seen from he lower panel of Table 5, he paern is also similar for he period , he main difference being ha he wihin-urban share falls o zero using he old povery line, wih he rural share rising o around 80%. So we find hen ha 75-80% of he drop in naional povery incidence is accounable for povery reducion wihin he rural secor; mos of he res is aribuable o urbanizaion. Undersanding wha has driven rural povery reducion is clearly of firs-order imporance o undersanding he counry s overall success agains povery. 4. Povery reducion and economic growh The rae of economic growh is a key proximae deerminan of China s diverse performance over ime agains povery. The regression coefficien of he log naional headcoun index on he log naional mean is 1.43, wih a -raio of However, his regression is decepive, given ha boh series are nonsaionary; he residuals show srong serial dependence (he Durbin-Wason saisics is 0.62). Differencing he series deals wih his problem. 12 Table 6 gives regressions of he log difference in each povery measure agains he log difference in mean income per capia. (All growh raes in his paper are annualized log differences.) There is a possible upward bias in he OLS esimaes semming from common measuremen errors in he dependen and independen variable; when he mean is overesimaed he povery measure will be underesimaed. Following Ravallion (2001) we use he GDP growh rae as he insrumen for he growh rae in mean income from he surveys, under he assumpion ha measuremen errors in he wo daa sources are uncorrelaed. (China s naional accouns have been based 12 The correlograms of he firs differences of he hree log povery measures shows no significan auocorrelaions. While he firs difference of he log mean sill shows mild posiive serial correlaion, he residuals of he regression of he log difference of he povery measure on he on he log difference of he mean shows no sign of serial correlaion. 10

11 largely on adminisraive daa.) Boh he OLS and IVE resuls in Table 6 confirm sudies for oher counries indicaing ha periods of higher economic growh ended o be associaed wih higher raes of povery reducion. 13 The implied elasiciy of povery reducion o growh is over hree for he headcoun index and around four for he povery gap measures. The IVE elasiciy is similar o ha for OLS, suggesing ha he aforemenioned problem of correlaed measuremen errors is no a serious source of bias. Noice ha he inerceps are posiive and significan in Table 6. Our OLS resuls imply ha a zero growh, he headcoun index would have risen a 11% per year (16% for PG and 19% for SPG). So falling povery in China has been he ne oucome of wo srong bu opposing forced: rising inequaliy and posiive growh. We also give regressions in Table 6 ha include he rae of change in inequaliy. I is unsurprising ha his has a srong posiive effec on povery. (The regression can be viewed as a log-linear approximaion of he underlying mahemaical relaionship beween a povery measure and he mean and disribuion on which ha measure is based.) Wha is more ineresing is ha here is evidence of a srong ime rend in he impac of inequaliy, as indicaed by he posiive ineracion effec beween ime and he change in inequaliy (Table 6). Povery in China has become more responsive o inequaliy over his period. Indeed, he size of he ineracion effec in Table 6 suggess ha he elasiciy of povery o inequaliy was virually zero around 1980, bu he elasiciy rose o 3.7 in 2001 for he headcoun index and 5-6 for he povery gap measures. While China s economic growh has clearly played an imporan role in he counry s long-erm success agains absolue povery, he daa sugges ha he secoral composiion of 13 Evidence on his poin for oher counries can be found in Ravallion (2001). 11

12 growh has maered. 14 This can be seen clearly if we decompose he growh raes by income componens. Consider firs he urban-rural decomposiion for he survey mean. The overall mean a dae is r r u u µ = n µ + n µ where i µ is he mean for secor i=r,u for rural and urban areas. I is readily verified ha he growh rae in he overall mean can be wrien as r r u u r u r u ln µ = s ln µ + s ln µ + [ s s ( n / n )] ln n where s = n µ / µ (for i=r,u) is he income share. We can hus wrie down he following regression for esing wheher he composiion of growh maers: r r r u u u n r u n r (2) ln P = η 0 + η s ln µ + η s ln µ + η ( s s. ) ln n u + ε n whereε is a whie-noise error erm. The moivaion for wriing he regression his way is r i i r i i eviden when one noes ha if he η (i=r,u,n) parameers are he same hen equaion (2) collapses o a simple regression of he rae of povery reducion on he rae of growh ( ln µ ). i Tesing H 0 : η = η for all i ells us wheher he urban-rural composiion of growh maers. Noe ha his regression decomposiion is based on somewha differen assumpions o ha used in equaion (1). In paricular, any sysemaic wihin-secor disribuional effecs of urbanizaion would now change he measured conribuion o povery. i Table 7 gives he resuls for all hree povery measures. The null hypohesis ha η = η for all i is convincingly rejeced in all hree cases. Furhermore, we canno rejec he null ha only he growh rae of rural incomes maers. 14 The lieraure has ofen emphasized he imporance of he secoral composiion of growh o povery reducion; for an overview of he argumens and evidence see Lipon and Ravallion (1995). The following analysis follows he mehods inroduced in Ravallion and Da (1996), which found ha he composiion of growh maered o povery reducion in India. 12

13 A second decomposiion is possible for GDP per capia which we can divide ino n sources o esimae a es equaion of he following form: (3) ln P = π 0 + π isi lnyi + ε n i=1 where Y i is GDP per capia from source i, i s = Y / Y is he source s share, and ε is a whienoise error erm. In he special case in which π i = π for i=1,..,n, equaion (3) collapses o a simple regression of he rae of povery reducion on he rae of GDP growh ( lny ). i Wih only 21 observaions over ime here are limis on how far we can decompose GDP. We used a sandard classificaion of is origins, namely primary (mainly agriculure), secondary (manufacuring and consrucion) and eriary (services and rade). Figure 2 shows how he shares of hese secors evolved over ime. The primary secor s share fell from 30% in 1980 o 15% in 2001, hough no mononically. Almos all of his decline was made up for by an increase in he eriary-secor share. However, i should no be forgoen ha hese are highly aggregaed GDP componens; he near saionariy of he secondary secor share reflecs he ne effec of boh conracing and expanding manufacuring secors. Table 8 gives he esimaed es equaions for H and PG, while Table 9 gives he resuls for SPG (for which a slighly differen specificaion is called for, as we will see). We find ha he secoral composiion of growh maers o he rae of povery reducion. The primary secor has far higher impac (by a facor of abou four) han eiher he secondary or eriary secors. The impacs of he laer wo secors are similar (and we canno rejec he null ha hey have he same impac). For SPG we canno rejec he null hypohesis ha only he primary secor maers and Table 9 gives he resriced model for his case. Our finding ha he secoral composiion of 13

14 growh maers echoes he findings of Ravallion and Da (1996) for India, hough eriary secor growh was relaively more imporan in India han we find for China. These aggregae resuls do no ell us abou he source of he povery-reducing impac of primary secor growh. Wih a relaively equiable disribuion of access o agriculural land and higher incidence and deph of povery in rural areas i is plausible ha agriculural growh will bring large gains o he poor. There is evidence for China ha his may also involve exernal effecs a he farm-household level. One imporan source of exernaliies in rural developmen is he composiion of economic aciviy locally. In poor areas of souhwes China, Ravallion (2004) finds ha he composiion of local economic aciviy has non-negligible impacs on consumpion growh a he household level. There are significan posiive effecs of local economic aciviy in a given secor on income growh from ha secor. And here are a number of significan cross-effecs, noably from farming o cerain nonfarm aciviies. The secor ha maers mos as a generaor of posiive exernaliies urns ou o be agriculure (Ravallion, 2004). A naural counerfacual for measuring he conribuion of he secoral composiion of growh is he rae of povery reducion if all hree secors had grown a he same rae. We call his balanced growh. Then he secor shares of GDP in 1981 would have remained consan over ime, wih 32% of GDP originaing from he primary secor. From Table 8, he expeced rae of change in he headcoun index, condiional on he overall GDP growh rae, would hen have been lny (where 4.039=0.32 x x 2.245, based on Table 8). For he same GDP growh rae, he mean rae of povery reducion would hen have been 16.3% per year, raher han 9.5%. Insead of 20 years o bring he headcoun index down from 53% o 8% i would have aken abou 10 years. 14

15 This assumes ha he same overall growh rae would have been possible wih balanced growh. There may well be a rade off, arising from limied subsiuion possibiliies in producion and rigidiies in some aggregae facor supplies; or he rade-off could sem from aggregae fiscal consrains facing he governmen in supplying key public infrasrucure inpus o privae producion. I is suggesive in his respec ha here is a correlaion of beween he wo growh componens idenified from Table 8, s1 lny1 and s2 lny2 + s3 lny3. However, his correlaion is only significan a he 6% level, and i is clear ha here were subperiods ( , and ) in which boh primary secor growh and combined growh in he secondary and eriary secors were boh above average. We have seen ha growh accouns for a sizeable share of he variance in raes of povery reducion. When measured by survey means, growh accouns for abou half of he variance. When measured from he naional accouns, growh accoun for one fifh of he variance, hough he share of variance explained is doubled when we allow for he secoral composiion of growh, wih he primary secor emerging as far more imporan han he secondary or eriary secors (hough again here may well be heerogeneiy wihin hese broad secors). 5. Inequaliy and growh The lieraure has provided numerous parial picures of inequaliy in China, focusing on sub-periods (he longes we know of is for , in Kanbur and Zhang, 1999) and/or subsecors or seleced provinces. As we will see, hese parial picures can be decepive. We begin by considering inequaliy beween urban and rural secors; hen wihin secors and in he aggregae. Finally we urn o he relaionship beween inequaliy and growh. Has inequaliy risen beween urban and rural areas? Figure 3 gives he raio of he urban mean income o he rural mean. Wihou our adjusmen for he cos-of-living difference, here is 15

16 a significan posiive rend in he raio of urban o rural mean income. The regression coefficien of he raio of means on ime is 0.047, wih a -raio of 3.12 (his is correced for serial correlaion in he error erm). However, when using he COL adjused means he coefficien drops o and is no significanly differen from zero a he 5% level (=1.79). Noice also ha here are sill some relaively long sub-period rends in which he raio of he urban o he rural mean was rising. This includes he period 1986 o 1994 sudied by Yang (1999) who argued ha here was a rising urban-rural dispariy in mean incomes in pos-reform China. However, his is clearly no a general feaure of he pos-reform period. Indeed, he raio of means fell sharply in he mid-1990s, hough re-bounding in he lae 1990s. There is a rend increase in absolue inequaliy beween urban and rural areas. This can be measured by he absolue difference beween he urban and rural means, as given in Figure 4 (normalized by he 1990 naional mean). The rend in he absolue difference (again calculaed as he regression coefficien on ime) is per year, wih a -raio of 3.40 (again correced for serial correlaion in he error erm). However, here oo here were periods ha wen agains he rend, including in he early 1980s and mid-1990s. Turning o inequaliy wihin urban and rural areas, we find rend increases, hough rural inequaliy fell in he early 1980s and again in he mid-1990s (Table 10). In marked conras o mos developing counries, relaive income inequaliy is higher in rural areas, hough he rae of increase in inequaliy is higher in urban areas; i looks likely ha he paern in oher developing counries will emerge in China in he near fuure. Noice also ha here appears o be a common facor in he changes in urban and rural inequaliy; here is a correlaion of 0.69 beween he firs difference in he log rural Gini index and ha in he log urban index. We will reurn o his. 16

17 In forming he naional Gini index in Table 10 we have incorporaed our urban-rural cos of living adjusmen. The able also gives he unadjused esimaes (as found in pas work). As one would expec, naional inequaliy is higher han inequaliy wihin eiher urban or rural areas. And allowing for he higher cos-of-living in urban areas reduces measured inequaliy. By 2001, he COL adjusmen brings he overall Gini index down by over five percenage poins. While a rend increase in naional inequaliy is eviden (Figure 5), he increase is no found in all subperiods: inequaliy fell in he early 1980s and he mid-1990s. The rise in inequaliy is even more pronounced. Figure 6 gives he absolue Gini index, in which income differences are normalized by a fixed mean (for which we use he 1990 naional mean). (The absolue Gini calculaed his way is no bounded above by uniy.) I is also noable ha while relaive inequaliy is higher in rural areas han urban areas, his reverses for absolue inequaliy, which is higher in urban areas a all daes. Higher inequaliy grealy dampened he impac of growh on povery. On re-calculaing our povery measures using he 2001 rural mean applied o he 1981 Lorenz curve, we find ha he incidence of povery in rural areas (by our upper line) would have fallen o 2.04% in 2001, insead of 12.5%. The rural PG would have fallen o 0.70% (insead of 3.32%) while he SPG would have been 0.16 (insead of 1.21). Repeaing he same calculaions for urban areas, povery would have virually vanished. Bu even wih he same urban povery measures for 2001 (so leing inequaliy wihin urban areas rise as i acually did), he naional incidence of povery would have fallen o 1.5% wihou he rise in rural inequaliy. This begs he quesion of wheher he same growh rae would have been possible wihou he rise in inequaliy. If de-conrolling China s economy ineviably pu upward pressure on inequaliy hen we would be underesimaing he level of povery in 2001 ha would have been 17

18 observed wihou he rise in rural inequaliy, because he lower inequaliy would have come wih a lower mean. Inequaliy has cerainly risen over ime, in line wih mean income. The regression coefficien of he Gini index on GDP per capia has a -raio of 9.22 (a correlaion coefficien of 0.90). Bu his correlaion is probably spurious; he Durbin-Wason saisic is 0.45, indicaing srong residual auo-correlaion. This is no surprising since boh inequaliy and mean income have srong rends, hough possibly associaed wih differen causaive facors. A beer es is o compare he growh raes wih changes in inequaliy over ime. 15 Then i becomes far less clear ha higher inequaliy has been he price of China s growh. The correlaion beween he growh rae of GDP and log difference in he Gini index is Now he regression coefficien has a -raio of 0.22 (and a Durbin-Wason of 1.75). This es does no sugges ha higher growh per se mean a seeper rise in inequaliy. The same conclusion is reached if insead of using annual daa we divide he series ino four sub-periods according o wheher inequaliy was rising or falling a naional level, as in Table 11. If here was an aggregae growh-equiy rade-off hen we would expec o see higher growh in he period in which inequaliy was rising. This is no he case; indeed; he wo periods wih highes growh were when inequaliy was falling. These calculaions do no reveal any sign of a shor-erm rade off beween growh and equiy. Possibly hese ime periods are oo shor o capure he effec. Anoher es is o see wheher he provinces ha had higher growh raes saw higher increases in inequaliy; we reurn o ha quesion in secion There is sill posiive firs-order serial correlaion of 0.48 in he firs difference of log GDP hough he regression of he firs difference of log Gini on log GDP shows no sign of serial correlaion in he residuals. So he differenced specificaion is appropriae. 18

19 Wha role has he secoral composiion of growh played in he evoluion of inequaliy? 16 Repeaing our es based on equaion (2) bu his ime using changes in he log Gini index as he dependen variable we find srong evidence ha he urban-rural composiion of growh maers o he evoluion of he Gini index: (4) ln G r r u = s ln µ s ln µ 0.366[ s s ( n / n )] ln n + ˆ ε (1.285) ( 4.399) (2.651) u ( 0.208) R 2 =0.622; n=20 There is no sign of a populaion shif effec on aggregae inequaliy and he rural and urban r + u coefficiens add up o abou zero. The join resricions η η = 0 and η = 0 (borrowing he noaion of equaion 2) pass comforably, giving he rae of change in inequaliy as an increasing funcion of he difference in (share-weighed) growh raes beween urban and rural areas: r u r n u r G (5) u u ln G = ( s ln µ s ln µ ) + ˆ ε R 2 =0.619; n=20 (2.507) (5.405) r r G If insead one looks a he componens of China s GDP by origin, one finds ha primary secor growh has been associaed wih lower inequaliy overall, while here is no correlaion wih growh in eiher he secondary or eriary secors. This can be seen from Table 12. I is clear ha an imporan channel hrough which primary secor growh has been inequaliy reducing is is effec on he urban-rural income dispariy. There is a negaive correlaion beween primary secor growh and he changes in he (log) raio of urban o rural mean income; he correlaion is sronges if one lags primary secor growh by one period, giving he following OLS regression for he log of he raio of urban mean ( Y ) o rural mean ( Y ): u r (6) u r ln( Y / Y ) = lny1 1 (2.657) ( 3.802) + εˆ Y R 2 =0.437; n=20 16 The lieraure on inequaliy and developmen has emphasized he imporance of he secoral composiion of growh (see, for example, Bourguignon and Morrison, 1998). 19

20 Primary secor growh has also brough lower inequaliy wihin rural areas. A he same ime, secondary secor growh has been inequaliy increasing wihin rural areas: 17 (7) r ln G = ( lny lny2 (1.892) ( 4.516) 1 r ) + εˆ R 2 =0.346; n=21 Boh secondary and eriary secor growh were inequaliy increasing in urban areas, bu here is no sign of an effec from primary secor growh (he bulk of which sems from rural areas). The secondary and eriary effec is sronges wih a one year lag, bu is no differen beween he wo secors when share-weighed, giving a simple regression for he rae of change in urban inequaliy: 18 (8) u ln G = ( s2 1 lny2 1 + s3 1 lny3 1 ( 2.078) (2.989) ) + εˆ u R 2 =0.396; n=21 An alernaive perspecive on he paern of growh is found in he survey means. Table 13 gives regressions of he log difference of he Gini index by urban and rural areas on he growh raes (log differences) of boh rural and urban mean incomes. We find ha growh in rural incomes is inequaliy reducing naionally, and his is so in boh urban and rural areas. However, here is a srong and roughly offseing lagged effec in rural areas, suggesing ha i is he posiive (negaive) shocks o rural incomes ha reduce (increase) inequaliy. Growh in urban incomes is inequaliy increasing in he aggregae and wihin urban areas, bu no rural areas. This echoes he resuls of Ravallion and Da (1996) for India. Wha hen is driving he co-movemen of inequaliy beween urban and rural areas? The answer appears o lie in he role of rural incomes. As we have seen, for boh urban and rural areas, he firs differences in he log Gini index are negaively correlaed wih rural income 17 The homogeneiy resricion in he following regression passes comforably; if one adds lny 2 o his regression is coefficien has a -raio of There is (negaive) firs-order serial correlaion in he residuals of his regression. Correcing for his, he slope coefficien falls o 0.974, hough he sandard error falls more (giving a -raio of 3.942). 20

21 growh. The regression residuals for he changes in rural inequaliy in Table 13 show no significan correlaion wih hose for urban inequaliy Economy-wide policies and income disribuion The early 1980s saw high growh in primary secor oupu and rapid rural povery reducion in he wake of de-collecivizaion and he privaizaion of land-use righs under he household responsibiliy sysem. (Agriculural land had previously been farmed by organized brigades, in which all members shared he oupu more-or-less equally.) The lieraure has poined o he imporance of hese reforms in simulaing rural economic growh a he early sages of China s ransiion (Fan, 1991; Lin, 1992; Chow, 2002). Since his was a one-off even, we canno es is explanaory power agains alernaives. However, i would appear reasonable o aribue he bulk of rural povery reducion beween 1981 and 1985 o his se of agrarian reforms. The rural headcoun index fell from 64.7% in 1981 o 22.7% in 1985 (Table 2). Afer weighing by he rural populaion shares, his accouns for 77% of he decline in he naional povery rae beween 1981 and Even if oher facors accouned for (say) one hird of he drop in rural povery over , we are lef wih he conclusion ha China s agrarian reforms in he early 1980s accouned for half of he oal decline in povery over his 20 year period. Agriculural pricing policies have also played a role. Unil recenly, he governmen has operaed a domesic foodgrain procuremen policy by which farmers are obliged o sell fixed quoas o he governmen a prices ha are ypically below he local marke price. For some farmers his is an infra-marginal ax, given ha hey produce more foodgrains han heir assigned 19 Rural economic growh as measured from he surveys does a beer job in accouning for he correlaion beween changes in urban and rural Gini indices han does primary secor GDP growh. 21

22 quoa; for ohers i will affec producion decisions a he margin. I has clearly been unpopular wih farmers (see, for example, Kung s, 1995, survey of Chinese farmers aiudes.) Reducing his ax by raising procuremen prices simulaed primary secor GDP. We find a srong correlaion beween he growh rae of primary secor oupu and he real procuremen price of foodgrains (nominal price deflaed by he rural CPI); see Figure 7. There is boh a curren and lagged effec; an OLS regression of he growh rae in primary secor GDP on he curren and lagged raes of change in he real procuremen price (PP) gives: (9) lny = ln PP ln PP + 1 εˆ 1 R 2 =0.590; D-W=2.60; n=19 (5.937) (2.152) (3.154) I is no hen surprising ha we find a srong negaive correlaion beween he changes in he governmen s procuremen price and changes in inequaliy; Figure 8 plos he wo series (lagging he procuremen price change by one year); he simple correlaion coefficien is Cuing his ax has hus been an effecive shor-erm policy agains povery. The regression coefficien of ln H on ln PP 1 is (-raio=3.043). The channel for his effec was hrough agriculural incomes, which (as we have seen) responded posiively o higher procuremen prices. (The regression coefficien changes lile if one adds conrols for secondary and eriary secor growh.) The elasiciies of naional povery o procuremen price changes are even higher for he povery gap indices; for PG he regression coefficien (of log differences on log differences) is (=2.929) and for SPG i is even higher a (=3.134). Two oher ypes of economy-wide policies have been idenified as relevan o povery in he lieraure, namely macroeconomic sabilizaion and rade reform. A number of sudies in oher developing counries have found evidence ha inflaion hurs he poor, including Easerly and Fischer (2001) and Dollar and Kraay (2002) boh using cross-counry daa, and Da and Ravallion (1998) using daa for India. There were wo inflaionary periods in China,

23 and Povery rose in he former period and fell in he laer. However, when one conrols for procuremen price changes we find an adverse effec of lagged changes in he rae of inflaion for all hree povery measures; for he headcoun index: 2 (10) ln H = ln PP ln CPI 1 + εˆ ( 3.058) ( 3.688) 1 (2.493) R 2 =0.491; D-W=1.86; n=19 where CPI is he rural CPI. The regression was similar for he oher povery measures. There are also srong (pro-poor) disribuional effecs of procuremen and inflaionary shocks as can be seen by he fac ha boh regressors in (10) remain significan if one conrols for he log difference in overall mean income: 2 (11) ln H = ln PP ln CPI lny ˆ ε 1 + ˆ ν (3.791) ( 8.049) (4.651) ( 9.843) ( 3.775) R 2 =0.907; D-W=2.28; n=18 I has also been claimed ha China s rade reforms helped reduce povery (World Bank, 2002; Dollar, 2004). However, he iming does no sugges ha hey are a plausible candidae for explaining China s progress agains povery. Graned, rade reforms had sared in he early 1980s as par of Deng Xiaoping s Open-Door Policy mainly enailing favorable exchange rae and ax reamen for exporers and creaion of he firs special-economic zone, Shenzhen, near Hong Kong. However, he bulk of he rade reforms did no occur in he early 1980s, when povery was falling so rapidly, bu were laer, noably wih he exension of he special-economic zone principle o he whole counry (in 1986) and from he mid-1990s, in he lead up o China s accession o he World Trade Organizaion (WTO); Table 14 shows ha mean ariff raes fell only slighly in he 1980s and non-ariff barriers acually increased. And some of he rade policies of his early period were unlikely o have been good for eiher equiy or efficiency For example, a wo-ier price sysem allowed exporers o purchase commodiies a a low planning price and hen expor hem a a profi. For his reason, oil was a huge expor iem unil

24 Nor does he imes series on rade volume (he raio of expors and impors o GDP) sugges ha rade was povery reducing, a leas in he shor erm; he correlaion beween changes in rade volume and changes in he log headcoun index is 0.00! Nor are changes in rade volume (curren and lagged wo-years) significan when added o eiher equaions (10) or (11). Trade volume may well be endogenous in his es, hough i is no clear ha correcing for he bias would imply ha i played a more imporan role agains povery. This would require ha rade volume is posiively correlaed wih he omied variables. However, one would probably be more inclined o argue ha rade volume is negaively correlaed wih he residuals; oher (omied) growh-promoing policies simulaneously increased rade and reduced povery. Oher evidence, using differen daa and mehods, also suggess ha rade reform had had lile impac on povery or inequaliy. Chen and Ravallion (2004b) sudied he household level impacs of he ariff changes from 1995 onwards (in he lead up o accession o he WTO). (The induced price and wage changes were esimaed by Ianchovichina and Marin, 2004, using a CGE model.) There was a posiive impac of hese rade reforms on mean household income, bu virually no change in aggregae inequaliy and only slighly lower aggregae povery in he shor erm. 7. Povery a provincial level So far we have focused solely on he naional ime series. We now urn o he less complee daa available a province level. We focus solely on rural povery. (Urban povery incidence is so low in a number of provinces ha i becomes hard o measure and explain rends.) The series on mean rural incomes from NBS is complee from However, here are only years of provincial disribuions available. Table 15 gives summary saisics on he iniial values of he mean, povery and inequaliy. For he mean, he firs observaion is for 24

25 1980; for he disribuional measures he firs available year is 1983 in wo-hirds of cases and 1988 for almos all he res. There are marked differences in saring condiions. Even for inequaliy, he Gini index around he mid 1980s varied from 18% o 33% (Table 15). Table 16 gives he rends based on he OLS esimaes of log X = α + β + ν for i X i X i X i variable X and province i. We assume an AR(1) error erm for mean income; for he (incomplee, disconinuous) disribuional daa we have lile pracical choice bu o rea he error erm as whie noise. Trend growh raes in mean income vary from 1% per year (in Xinjiang) o almos 7% per year (in Anhui). Trends in he Gini index vary from near zero (Guangdong) o 3% (Beijing). Guangdong had an asonishing rend rae of decline in H of 29% per year. A he oher exreme here are six provinces for which he rend was no significanly differen from zero, namely Beijing, Tianjin, Shanghai, Yunnan, Ningxia, Xinjiang, hough he firs hree of hese sared he period wih very low povery raes (Table 15). The lieraure has poined o divergence beween he coasal and inland provinces. 21 This has been linked o he governmen s regional policies, which have favored coasal provinces hrough differenial ax reamen and public invesmen. We confirm expecaions ha coasal provinces had significanly higher rend raes of povery reducion. 22 The mean rend rae of decline in he headcoun index was 8.43% per year for inland provinces (=4.14) versus 16.55% for he coasal provinces (=5.02); he -saisic for he difference in rends is Povery and growh a he provincial level The associaion beween rural income growh and povery reducion is confirmed in he provincial daa. Figure 9 plos he rend rae of change in he headcoun index agains he rend 21 See Chen and Fleisher (1996), Jian e al. (1996), Sun and Dua (1997), and Raiser (1998). 22 The cosal provinces are Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shangdong and Guangdong; following convenion, we do no classify Guangxi as coasal hough i has a cosal area. 25

26 rae of growh in mean rural income across provinces. The figure also idenifies he hree observaions wih lowes iniial povery measures, for which here was also an increase (hough saisically insignifican) in povery over ime, namely Beijing, Shanghai and Tianjin. The regression coefficien of he rend in he headcoun index on he rend in rural income is 1.58, which is significan a he 5% level ( = 2.05). The 95% confidence inerval for he impac of a 3% growh rae on he headcoun index is abou (0, 9%). However, if one drops Beijing, Shanghai and Tianjin hen he relaionship is seeper and more precisely esimaed. The regression coefficien is hen 2.43 (=4.29). The 95% confidence inerval for he impac of a 3% growh rae is hen abou (4%,10%). While higher growh mean a seeper decline in povery, we see in Figure 9 considerable dispersion in he impac of a given rae of growh on povery. This is also eviden if we calculae he growh elasiciy of povery reducion as he raio of he rend in he headcoun index o he rend in he mean. This varies from 6.6 o 1.0, wih a mean of 2.3. Wha explains hese diverse impacs of a given rae of growh on povery? If inequaliy did no change hen he elasiciy will depend on he parameers of he iniial disribuion, roughly inerpreable as he mean and inequaliy. More generally, wih changing disribuion, he elasiciy will also depend on he rend in inequaliy. On imposing daa consisen parameer resricions, he following regression is easily inerpreed: 23 H Y R R G (12) βi / βi = ( y i )(1 G i ) β ˆ i + ε ( 4.487) (2.560) (2.392) R 2 =0.386; n=29 23 This specificaion is a variaion on Ravallion (1997). Saring from an unresriced regression of β H / β M R R on G 83, y, G R. y R G 83 and β a join F-es does no rejec he null hypohesis (wih prob.=0.17) ha he join resricions hold ha are needed o obain (12) as he resriced form. 26