A household-based Human Development Index. Kenneth Harttgen and Stephan Klasen Göttingen University, Germany

Transcription

1 A household-based Human Development Index Kenneth Harttgen and Stephan Klasen Göttngen Unversty, Germany

2 Introducton Motvaton HDI tres to operatonalze capablty approach at cross-natonal level. HDI measures the average achevement n a country n three basc dmensons of human development: A long and healthy lfe (L) Educaton (E) Standard of lvng (Y) HDI = 1/3 L + 1/3 E + 1/3 Y HDI s today wdely used n academa, the meda and n polcy crcles to measure and compare progress n human development between countres and over tme. The 2010 Human Development Report wll revst the concept and measurement of human development and the HDI n partcular 2

3 Introducton Transparent and smple, but also some major drawbacks: 1. It neglects several other dmensons of human well-beng (e.g. human rghts, securty and poltcal partcpaton, see e.g. Anand and Sen (1992), Rans, Stewart and Samman (2006)); 2. It mples substtuton possbltes between the three dmenson ndces and t uses an arbtrary weghtng scheme (see e.g. Kelley (1991), Srnvasan (1994) and Ravallon (1997)); 3. It only looks at average achevements (see e.g. Sagar and Najam (1998). It does not take nto account nequaltes between socoeconomc subgroups wthn countres (Foster et al. 2003; Grmm et al. (2009); Harttgen and Klasen (2009)); 4. So far, there have only been ad hoc ways to measure nequalty n human development (ether nequalty tself, e.g. Hcks 1997; Foster et al. 2003) ; or along some other dmenson of human development, e.g. Grmm et al. (2008, 2009), Harttgen and Klasen (2009), but t always remans an aggregate (not householdlevel) analyss. 3

4 Introducton A household specfc HDI would allow a broad range of new analyses, ncludng the exstng ad hoc analyses, but much more than that! 4

5 Introducton Man challenge: overcome data problems usng household survey data No avalablty of mortalty, educaton and ncome n one survey Lfe expectancy and aggregate statstcs: dffcult to be measured at the household level Educaton ndcators wth lots of nose dependng on presence and age of chldren Avalablty of ncome data 5

6 Outlne Introducton Methodology Results Outlook and further steps 6

7 Methodology: calculatng GDP component Rely on data where nformaton of mortalty and educaton are avalable Demographc and Health Surveys (DHS) Problem: The DHS do not contan nformaton on ncome or expendture Approach: combne an asset ndex based approach wth an ncome smulaton approach (Flmer and Prtchett (2001), Harttgen and Vollmer (2010)) Frst step: Calculate an asset ndex Identfcaton of a set of household assets: rado, TV, frdge, bcycle, motorzed vehcle, housng characterstcs,... Aggregaton nto one sngle metrc ndex for each household usng prncpal component analyss Derve the log normal dstrbuton Assumpton: Ownershp of assets s a good proxy for ncome (e.g. Stewart and Smalne (2005, Flmer and Scott (2008)) 7

8 Methodology: calculatng GDP component Fourth step: Calculate the household specfc GDP component Converson of the smulated household ncome per capta y calculated from the DHS n USD PPP Rescalng y, PPP s GDPPC y PPP, PPP = y PPP, PPP log y log(100) Y = s = 1, K, K. log(40,000) log(100) 8

9 Methodology: calculatng educaton component Problems 1. Mssng data on enrolment n households wthout chldren 2. Problem: Enrolment strongly depends on age of chldren Possble solutons: 1. Use only nformaton on lteracy to calculate the educaton component of the HDI Advantage: no mputaton of mssng values necessary Shortcomngs: overstates educaton ndex because lteracy rate are often hgher than enrolment rate loss of one educaton sub-component 2. Use a regresson based approach to predct mssng values (sngle determnstc and stochastc regresson) Advantage: no loss of mssng values Shortcomngs: strong assumptons on ft of model ssue of nterpretaton: generates HDI not for partcular household (e.g. household number ) but for a hypothetcal household wth those characterstcs) 9

10 Methodology: calculatng educaton component Impute mssng values Regresson based approach: use stochastc regresson method to mpute mssng values (e.g. Landerman et al. 1997, Allson 2007) mssng values are replaced by a regresson-predcted score based on a set of covarates plus a resdual error term ~ x = a + X b + u *, =1,... K. x =value of mputed value (enrolment rate) of household X =vector of covarates b=vector of regresson coeffcents u* =estmated resdual from the regresson of x on X Major drawback: all sngle mputaton methods suffer from a possble underestmaton of standard errors Possble soluton: multple mputaton A more sophstcated way to mpute the mssng values s multple mputaton (Rubn, 1998; Schäfer, 1997, Allson, 2007) 10

11 0 1 0 = a A = g G.,, 1 K K = Lteracy ndex (adults above the age 15) Educaton ndex.,, 1 K K =.,, 1 K K = Gross enrolment ndex (youth 5-23) = A A A A HDR s = G G G G HDR s s s G A E + = Methodology: calculatng educaton component

12 Methodology: calculatng lfe expectancy component Combnng nformaton on chld mortalty wth model lfe tables. Calculatng under fve chld mortalty rates for each household, q 0, and for the whole sample. Problems: hghly 1. Calculaton of household specfc mortalty rate (means); mortalty s a dscrete event at the household level; mortalty rate wll be dscontnuous 2. Households wthout chldren Approach: Regress chld mortalty on socoeconomc characterstcs and calculate the mean household mortalty rates based on the predcted rates (regresson-based mputaton) Impute chld household specfc chld mortalty rates for all households based on the regresson predcton Regresson approach for chld mortalty: hazard model to control for censorng Usng q 5 and modfed model lfe tables to compute household specfc lfe expectancy e 0 (Murray et al. 2003). 12

13 Methodology: calculatng lfe expectancy component Modfed logt lfe table system (Murray et al. 2003) Logt( l x ) = α + β * Logt( l s x ) + γ 1 x Logt( l Logt( l 5 s 5 ) + θ x 1 ) Logt( l Logt( l 60 s 60 ) ) α, β, l l l x γ x, θ x 5, 60 Age specfc Standard Lfe Table Country specfc Survval probablty from 0 to x,5,60 = 1, K, K. for every household teratve method to estmate l Q 5 and lq 60, the lfe table l x (x=1,,85), whch best fts to 5 q 0 and 45 q 15 Calculatng the household specfc lfe expectancy ndex: eˆ0 25 L = = 1, K, K L s = L L L HDR 13

14 Methodology Calculatng the household specfc HDI Sample of countres Illustrate the approach for 15 countres usng Demographc and Health Survyes (DHS) Armena (2005) Ncaragua (2000) Burkna Faso (2003) Ngera (2003) Bolva (2003) Pakstan (2007) Egypt (2007) Peru (2005) Ethopa (2005) Senegal (2005) Inda (2005) Vetnam (2002) Indonesa (2003) Zamba (2002) Kyrgyz Republc (1997) 14

15 Results Household specfc Human Development Index Educaton Educaton Educaton ndex ndex GDP Lfe ndex (regresson (years of Country HDI HDI 2 HDI 3 ndex ndex (only lteracy) appraoch) educaton) Armena (2005) Egypt (2007) Peru (2005) Indonesa (2003) Vetnam (2002) Kyrgyz Republc (1997) Inda (2005) Ncaragua (2000) Bolva (2003) Pakstan (2007) Zamba (2002) Ngera (2003) Senegal (2005) Ethopa (2005) Burkna Faso (2003) Source: Demographc and Health Surveys; own calculatons. Note: HDI 1 s based only on lteracy; HDI 2 s based on the regresson based approach for lteracy and enrolment. HDI 3 s based on years of educaton and mputed enrolment. 15

16 Results Household specfc Human Development Index by HDI decles HDI Rato Rato Rato 10:med medan: Country Year Total 10:1 an 1 Armena Egypt Peru Indonesa Vetnam Kyrgyz Republc Inda Ncaragua Bolva Pakstan Zamba Ngera Senegal Ehtopa Burkna Faso

17 Results Household specfc Human Development Index by ncome decles By ncome decles Rato Rato Rato Country Year Total 10:1 10:medan medan:1 Armena Egypt Peru Indonesa Vetnam Kyrgyz Republc Inda Ncaragua Bolva Pakstan Zamba Ngera Senegal Ehtopa Burkna Faso Source: Demographc and Health Surveys; own calculatons. 17

18 Results Household specfc Human Development Index by urban and rural areas HDI Rato rural / Country Year Urban Rural Total urban Armena Egypt Peru Vetnam Indonesa Kyrgyz Republc Inda Ncaragua Bolva Pakstan Zamba Ngera Senegal Ehtopa Burkna Faso Source: Demographc and Health Surveys; own calculatons. 18

19 Results Household specfc Human Development Index by educaton of the household head No HDI Rato hger/ Country Year educaton Prmary Secondary Hgher Total no educaton Armena Egypt Peru Indonesa Vetnam Kyrgyz Republc Inda Ncaragua Bolva Pakstan Zamba Ngera Senegal Ehtopa Burkna Faso Source: Demographc and Health Surveys; own calculatons. 19

20 Results Household specfc Human Development Index by sex of household head HDI Rato female / Country Year Male Female Total male Armena Egypt Peru Vetnam Indonesa Kyrgyz Republc Inda Ncaragua Bolva Pakstan Zamba Ngera Senegal Ehtopa Burkna Faso Source: Demographc and Health Surveys; own calculatons. 20

21 Results Inequalty n Human Development between countres and subgroups Gn ndex GDP ndex Gn Educ. Lfe GDP Income (wthout log (PovcalNet) Country HDI ndex ndex ndex (uncapped) transform.) (%) Armena (2005) Kyrgyz Republc (1997) Vetnam (2002) Indonesa (2003) Egypt (2007) Inda (2005) Peru (2005) Pakstan (2007) Ncaragua (2000) Bolva (2003) Zamba (2002) Ethopa (2005) Senegal (2005) Burkna Faso (2003) Ngera (2003) Source: Demographc and Health Surveys; own calculatons. Note: HDI s based on the regresson based approach for lteracy and enrolment., The Gn for the global HDI s based on the Gray Purser dataset. 21

22 Results Wthn and between nequalty n human development by country and sub-group By ncome decles By urban and rural areas By Educaton of household head Thel Index of lfe HDI Thel Index of lfe HDI Thel Index of lfe HDI Wthn Between Wthn Between Wthn Between Total group group Total group group Total group group Armena (2005) Burkna Faso Bolva Egypt (2007) Ethopa (2005) Inda (2005) Indonesa (2003) Kyrgyz Republc (1997) Ncaragua (2000) Ngera (2003) Pakstan (2007) Peru (2005) Senegal (2005) Vetnam (2002) Zamba (2002) Source: Human Development Reports, PovcalNet, World Development Indcators. 22

23 Results Summary Sgnfcant dfferences between two alternatves to calculate educaton ndex Large dfferences n human development across ncome groups Human development n urban areas hgher than n rural areas; substantal dfferences n Afrca Age and educaton of household head matters No clear pcture for headshp and household sze (.e. small dfferentals) Gn lower for HDI than for ncome Inequalty wthn populaton subgroups always hgher than between groups 23

24 Concluson We provde a method and llustraton for calculatng the HDI at the household level Despte the assumptons that have to be made, we thnk that the calculaton of a household specfc HDI has a sgnfcant value added for the HDR. Allows a large range of prevously unavalable analyses Transparent and ntutve measure of the level and nequalty of deprvaton n multple dmensons wthn and across populaton subgroups and over tme The results can provde new nsghts wth respect to dfferences n the levels human development by populaton subgroups ( e.g. nequalty n HDI for dfferent subgroups s drven by dfferent sub-components) The man problem of calculatng a household-based HDI s data lmtaton However strong assumptons can be justfed by applyng reasonable approaches to overcome data problems Due to regresson-based predctons: Not HDI for partcular households n survey, but HDI for households wth certan characterstcs (those that are used n regresson); but ths s what should be of nterest to polcy-makers. Despte ts shortcomngs, ths approach hopefully enhances the dscusson of measurement ssues concernng the HDI 24

25 Concluson Possble to Implement n rch countres such as Germany? Problems: Also not a sngle survey that cover health, educaton, and ncomes well (possbly based on Mkrozensus?); Dffcult to mpute dfferentals n lfe expectancy based on nfant mortalty rates Need to study soco-economc determnants of health drectly (own research project); Possbly better to adjust HDI rather than calculate household-based verson based on strong assumptons. E.g. use subjectve questons on health rather than lfe expectancy. 25

26 Appendx Egypt (2007) Inequalty n Human Development Inda (2005) Cumulatve HDI Proporton Cumulatve HDI Proporton Cumulatve Populaton Proporton urban rural Cumulatve Populaton Proporton urban rural Cumulatve HDI Proporton Ngera (2003) Cumulatve HDI Proporton Peru (2005) Cumulatve Populaton Proporton Cumulatve Populaton Proporton urban rural urban rural 26

27 Appendx: calculatng educaton component Illustraton of the means of socoeconomc characterstcs for households wth and wthout chldren n school gong age (Zamba 2002) Means for household wth chldren n school gong age Varable N Mean Std. Dev. Age (head) Female headed household Urban Household sze Asset ndex Sex (1=male) Head has no educaton Means for household wthout chldren n school gong age Age (head) Female headed household Urban Household sze Asset ndex Sex (1=male) Head has no educaton Source: Demographc and Health Surveys; own calculatons. 27

28 Appendx: calculatng educaton component Illustraton of the regresson results for enrolment (Zamba 2002) Enrolment Rate Coeffcent Std. Err. P> z Age (head) Age^2 (head) Age^3 (head) Urban Female headed household (=1) Household Sze Number of chldren at home Asset ndex Sex (female=1) Head has no educaton (=1) Interactons Sex X urban Sex X age Urban X age Cluster means Mean lteracy per cluster Mean enrolment rate per cluster Mean Asset ndex value per cluster Mean years of educaton per cluster Constant N R^ Note: Controlled for regon dummes. Source: Demographc and Health Survey; own calculatons. 28

29 Methodology: calculatng GDP component Second step: Estmate household ncome per capta dstrbuton The natonal ncome dstrbutons wll be modeled by a log-normal dstrbuton: LN( μ, σ ) The densty of the log-normal dstrbuton s gven by: The mean and varance are gven by: 1 2 (log( x) μ ) ( ;, ) 2 / 2σ f x μ σ = e, x > 0 xσ 2π E( Y ) = e μ +σ 2 / 2 Var( Y ) = ( e 1) e 2 σ 2μ + σ 2 The Gn coeffcent G of LN(μ, σ) s gven by: G = 2Φ( σ / 2) 1 The parameters μ and σ of LN(μ, σ) can be determned from the average ncome E(Y) and the Gn coeffcent G: 1 G σ = 2Φ μ = log( E( Y )) σ / 2 2 Data on the Gn and on the mean ncome are taken from PovcalNet. 29

30 Methodology: calculatng GDP component Thrd step: Smulate household ncome per capta based on the asset ndex 1. Estmate LN A (μ, σ) based on the asset ndex μ and σ are estmated usng ML 2. Estmate LN I (μ, σ) of the ncome dstrbuton μ and σ are estmated based on the Gn and the mean ncome 3. Attach to each quantum of the asset ndex LN A the respectve ncome value from the LN I Approach s valdated wth LSMS data where both ncome and assets are avalable (Harttgen and Vollmer 2010) Promsng frst results 30

31 Incorporatng nequalty n dmensons and across people

32 Appendx Household specfc Educaton Index by HDI decles Edu ndex Rato Rato Rato 10:med medan: Country Year Total 10:1 an 1 Armena Kyrgyz Republc Peru Vetnam Indonesa Egypt Bolva Zamba Ncaragua Ngera Inda Pakstan Senegal Ehtopa Burkna Faso Source: Demographc and Health Surveys; own calculatons. 32

33 Appendx Household specfc Lfe expectancy Index by HDI decles Lfe ndex Rato Rato Country Year Total Armena Vetnam Inda Egypt Indonesa Ncaragua Peru Kyrgyz Republc Bolva Pakstan Senegal Zamba Burkna Faso Ngera Ehtopa Source: Demographc and Health Surveys; own calculatons. Rato 10:1 10:med an medan: 1 33

34 Results Household specfc Human Development Index by age of household head HDI Rato oldest/ Country Year Total youngest Armena Egypt Peru Indonesa Vetnam Kyrgyz Republc Inda Ncaragua Bolva Pakstan Zamba Ngera Senegal Ehtopa Burkna Faso Source: Demographc and Health Surveys; own calculatons. 34