Moving from the Household to the Individual: Multidimensional Poverty Analysis

World Development Vol. 59, pp. 70–81, 2014� 2014 Elsevier Ltd. All rights reserved.

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www.elsevier.com/locate/worlddevhttp://dx.doi.org/10.1016/j.worlddev.2014.01.029

Moving from the Household to the Individual: Multidimensional

Poverty Analysis

RAMYA M. VIJAYARichard Stockton College of New Jersey, USA

RAHUL LAHOTI and HEMA SWAMINATHAN *

Indian Institute of Management Bangalore, India

Summary. — Due to data limitations, current multidimensional measures of poverty use household rather than the individual as the unitof analysis. Household measures are unable to capture intrahousehold differences in resource allocation which differ along gender lines.This paper uses data from the Karnataka Household Asset Survey to construct an individual level multidimensional poverty measure forKarnataka, India. Our results show that individual level measures can identify substantial gender differences in poverty that are maskedwhen using household measures. Thus, gender-disaggregated data and analysis can provide a useful complement to household level pov-erty measures.� 2014 Elsevier Ltd. All rights reserved.

Key words — Intrahousehold, multidimensional poverty, deprivation, assets, Karnataka, India

* This paper is from a larger research study, the Gender Asset Gap Pro-

ject, funded by the Dutch Ministry of Foreign Affairs under their MDG3

Fund for gender equality. We thank project team members, Cheryl Doss,

Suchitra JY, and Abena Oduro, as well as two anonymous reviewers for

their comments. All errors remain the responsibility of the authors. Final

1. INTRODUCTION

A pluralist conception of poverty recognizes that depriva-tions are multi-faceted and not limited to only income consid-erations. Narayan and Petesch (2007) cogently argue that“lack of money is just one of many disadvantages of beingpoor and one of the many obstacles to escaping a life ofimpoverishment” (p. 2). The concern that monetary measuresdo not permit a nuanced yet complex understanding of pov-erty experiences was a key motivation behind the UNDP-Mul-tidimensional Poverty Index (MPI) introduced in the 2010Human Development Report, (Alkire & Santos, 2010). TheUNDP-MPI evaluates poverty based on a household’s depri-vation in three basic dimensions—education, health, and liv-ing standards. Various indicators are used to measure eachof the dimensions and they represent a mix of commoditiesand actual functionings. While the multidimensional measuretakes cognizance of the interconnectedness of deprivations,one of the shortcomings is the use of the household as the unitof analysis. That is, the UNDP-MPI is derived from house-hold level averages rather than individual level information.This is mainly due to a data constraint as individual levelinformation is not routinely collected for all dimensions in-cluded in the poverty measure (for example, health and nutri-tion data are typically available only for certain age and sexcategories in the Demographic and Health Surveys on whichthe UNDP-MPI is based).

Given the evidence on intrahousehold inequalities, equatingthe household with the individual is problematic, especially ifone is interested in the gendered experience of poverty. Boththeoretical and empirical literature are in agreement that gen-der is an important axis of differentiation within householdswith men and boys often privileged over women and girls(Agarwal, 1997; Doss, 2006; Duflo, 2003; Quisumbing,2003). Such differences are rendered invisible when povertyand deprivation are defined by household averages. Thus,while the multidimensional measures help in unpacking the

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range of deprivations faced by a household, much can alsobe learnt from considering individual experiences of poverty.

Incorporating a perspective on how poverty may be differen-tially experienced by household members can aid policymakers in the design and evaluation of anti-poverty and live-lihoods creation programs. Since individuals withinhouseholds can experience different kinds of deprivations,household level multidimensional measures do not giveenough information about the interventions that might bemost suitable for individuals based on gender, age, etc. Moreimportantly, an individual level analysis allows an identifica-tion of individuals, both men and women, who might be expe-riencing deprivations even within “non-poor” households.

Using data from the Karnataka Household Asset Survey(KHAS), we construct a multidimensional measure of povertyfor individuals within households and use a gender lens to siftthrough the results. While there are other studies that haveused individual data to construct multidimensional measuresof child poverty and women’s poverty (Batana, 2013; Roche,2013; Trani, Biggeri, & Mauro, 2013), this is the first studyto conduct a gendered analysis using such data.

The results indicate that individual experiences of povertydiffer substantially along gender lines within households.Gender differentials in poverty that are almost non-existentin the household level analysis become prominent in individualanalysis. When individuals are assigned their household’spoverty status, there is only a one percentage point differencebetween male and female poverty rates. But when individuallevel information is used to define poverty, there is a 34 per-centage point difference between male and female poverty

revision accepted: January 25, 2014.

http://dx.doi.org/10.1016/j.worlddev.2014.01.029

MOVING FROM THE HOUSEHOLD TO THE INDIVIDUAL: MULTIDIMENSIONAL POVERTY ANALYSIS 71

rates. These results draw attention to the difference in under-standing of gender patterns in poverty that emerges dependingupon the unit of analysis. Finally, we find that the experienceof poverty is not the same for everyone and differs accordingto individual characteristics. These differences, which do notcome to light at the household level, have important policyimplications.

The rest of the paper is organized as follows. The nextsection provides a background to multidimensional poverty.Section 3 describes the data and presents the components ofour multidimensional poverty measure. Section 4 presentsthe results and Section 5 concludes.

2. MULTIDIMENSIONAL POVERTY

The UNDP-MPI represents an ambitious effort to evaluatepoverty based on a household’s deprivation in education,health, and living standards. Various indicators are used tomeasure each of the dimensions and they represent a mix ofcommodities and actual functionings. The three dimensionsare equally weighted and a household’s total deprivation scoreis compared to an established poverty cutoff. Since the MPIfocuses on information from each household, it is possibleto consider the multiple and interconnected deprivations forthe household, enabling identification of not only the povertyheadcount ratio but also the intensity of poverty.

In India, for over a decade, the policy discourse hasacknowledged the multiple deprivations faced by poor individ-uals. The identification of the poor, referred to as “below pov-erty line” (BPL) households, since 2002, is based on 13parameters including size of land, type of house, food security,clothing, sanitation, literacy, means of livelihood, and indebt-edness. More recently, an Index of Deprivation (using theUNDP-MPI methodology) was constructed from the thirdwave of the National Family and Health Survey (NFHS-3)data covering nine dimensions and 11 indicators (Alkire &Seth, 2008). The dimensions included were living standards,health, water and sanitation, air quality, assets, education,livelihoods, child status, and women’s empowerment. 1

Operationalization of the multidimensional measures hasbecome feasible due to the availability of detailed householdlevel surveys. But the paucity of individual level data in thesesurveys has been limiting. Although poverty is an individualexperience, all poverty measures typically use the householdas the unit of analysis where per capita values of householdmembers are derived from household aggregates. There is noway to evaluate inequalities or differences in poverty experi-ence within households. If one is interested in the question“are women more likely to be poor in comparison to men”then the solution within this framework is to deconstruct pov-erty measures for male-headed and female-headed householdsseparately. Female-headed households, thus, become proxiesfor all women. Medeiros and Costa (2010) emphasize thatpoverty among women is a distinct concept from povertyamong female-headed households. The former is based onthe sex of the individual whereas the latter is concerned withthe sex of the household head. The headship approach oftenpresents an inaccurate picture of gender differences by ignor-ing women and men in male-headed households. Deere,Alvarado, and Twyman (2012) using data from Latin Americaand Caribbean show that for certain categories of assets,gender inequality is overestimated as headship-based analysisignores women in male-headed households. A similar concernof using headship-based analysis is echoed by Peterman,Quisumbing, Behrman, and Nkonya (2011), who find in

Uganda that using the sex of the household head as a genderindicator underestimates the differences in agricultural pro-ductivity between male-owned and female-owned plots.

The assumption that household level information is adequateto understand poverty has been discredited by research onintrahousehold issues which has established that resource allo-cation within households is uneven (see Haddad, Hoddinott, &Alderman, 1997 for a collection of papers). In an early paper,Haddad and Kanbur (1990) note that neglect of intrahouseholdinequality may understate the levels of poverty and inequality.A large body of this research has focused on differences betweenmen and women and boys and girls although other forms of dis-crimination also exist. The empirical evidence suggests that dif-ferences in resource control has implications for one’s statuswithin the household and has influence on decisions regardingallocation of resources for the education and nutrition of maleversus female members of the household (Doss, 2006; Duflo,2003; Quisumbing, 2003; Quisumbing & Maluccio, 2003).Duflo (2011) argues that even if discrimination along genderlines is absent during normalcy, households that are in the gripof poverty and face challenging circumstances will make choicesthat are detrimental for women and girls.

The differences in the disbursement of resources amonghousehold members points to a key conceptual problemhighlighted by Amartya Sen in his capabilities approach tohuman development. Income or money represents the meansto better living conditions but it is not the better living conditionin itself. A movement out of poverty should represent a reduc-tion in deprivations and an actual improvement in living condi-tions or functionings that people can achieve. While incomerepresents the ability to purchase commodities that help achievesome functionings, the conversion of commodities into functi-onings is not precise. Individuals and households differ in theirability to convert commodities into functionings due to variousfactors such as age, gender, or physical ability. In most cultures,women have lower achievements in dimensions of health, edu-cation, nutrition, etc. when they have limited influence on theallocation and disbursement of income within the household.

Commodities also often have to be supported by publicgoods and services (for example having access to schoolingis necessary to achieve education) in order to achieve thefunctioning (Alkire, 2002; Sen, 1999). Moreover not all functi-onings are derived from commodities. For instance, one of theimportant basic human functionings in Sen’s approach is thefreedom to choose or exercise one’s individual agency (Sen,1988). Even for the wealthy, individual agency is often circum-scribed by gender among other factors.

3. A MULTIDIMENSIONAL POVERTY MEASURE FORKARNATAKA

Ultimately poverty analysis can only incorporate intra-household differences if it moves beyond the household tothe individual. This requires within household, sex-disaggre-gated data that are often not readily available. This analysisuses sex-disaggregated data from KHAS to construct anindividual level multidimensional poverty measure. KHAS isa state-representative survey of Karnataka State, located insouthwest India. Karnataka is categorized into four agro-climatic zones; the northern and southern plateaus, the coastalareas, and the mountainous Western Ghats. A stratifiedrandom sampling method was used with the survey coveringeight districts across the four agro-ecological zones.

A household and an individual questionnaire were adminis-tered by the survey. The household questionnaire, in addition

72 WORLD DEVELOPMENT

to the standard socio-demographic information, included an as-set inventory to capture ownership details, and valuation data.Further to the convention of recording if the household ownedan asset or not, the IDs of all owners were also recorded on thequestionnaire which permits an individual level analysis of assetdistribution. The individual questionnaire, among other things,obtained information on mode of acquisition, transaction rightsover assets (if the respondent was an asset owner), financialassets, and decision-making processes within the household.Both household and individual questionnaires were adminis-tered to a primary respondent defined as the household memberwho had most knowledge of the economic circumstances of thehousehold. If the primary was married, then his or her spousewas interviewed as the second respondent. If the primary wasnot married or the spouse was not available, a second respon-dent was chosen according to a predetermined set of proce-dures. The final sample comprising 4088 households isrepresentative of both rural and urban areas (see Swaminathan,Suchitra, & Lahoti, 2011 for further details on sampling).

The starting point for the multidimensional poverty measurefor Karnataka (hereafter referred to as the KHAS-MPI) is theUNDP–MPI which considers deprivations along health, edu-cation, and standard of living. The KHAS-MPI, on the otherhand, incorporates four dimensions with some overlap withthe UNDP measure. The four dimensions are education, stan-dard of living, ownership of land and house, and empower-ment. We first present the rationale for choosing thesedimensions followed by a discussion of the specific indicatorsrepresenting them.

The KHAS-MPI does not include a health dimension as thedata do not have the relevant information. While undoubtedlyan important component of the experience of poverty, we ar-gue that the lack of the health dimension does not compromisethe goal of demonstrating that considerable differences inoverall functionings exist within a single household. In fact,the well documented gender disparities in health status implythat the deprivation gap between men and women reportedin the paper is likely an underestimate of the true gap, under-scoring the point that household poverty experiences cloakinequalities in individual differences.

(a) Education and standard of living

There is universal acceptance about the relevance of educa-tion and basic standard of living dimensions in categorizingand understanding poverty (Alkire & Santos, 2010). The edu-cation dimension is also crucial from a gender perspectivesince intrahousehold differences in resource allocation havebeen shown to result in boys’ education being favored overgirls’ (Doss, 2006). It is well recognized that closing the gendergap in primary education is one of the key pathways topromote gender equality and empowerment of women. Simi-larly standard of living indicators such as access to safe drink-ing water, basic sanitation, and safe cooking fuels areattributes, which if inaccessible to households and individualscan have a profound impact on their current and future healthand wellbeing (United Nations, 2003).

(b) Assets

Assets provide insights into a household’s economic activityand security in a way that is not possible using income orconsumption data. Asset portfolios reflect both past and fu-ture income-generation opportunities through their contribu-tion to livelihood choices, and the potential for participatingin financial markets, generating rents, interests on savings,

and profits from business. The characteristics of assets impactthe experience of poverty by providing a safety net duringtimes of economic crises, through their sale or pawning tocope with an income shortfall. There is growing recognitionthat the composition of the asset basket can be a powerfulforce in mediating the experience of poverty. Households withfew or no assets are typically more vulnerable to long term orchronic poverty than households that possess some level ofthese assets but experience income fluctuations (Carter &Barrett, 2006).

It is well established that assets are not equitably distributedwithin a household, especially among men and women (Deere& Doss, 2006). Ownership of assets can affect an individual’sbargaining power through enhancing their economic securityand thus, strengthening fallback options. This has implica-tions for women’s participation in household decisions, theirability to avail of human capital investments, and for overallhousehold welfare. Ownership of assets has been shown to im-prove women’s ability to influence household decision-makingsuch as control over income and expenditure, schooling, andhealth decisions for themselves and for children (Allendorf,2007; Doss, 2006; Katz & Chamorro, 2003; Swaminathan,Lahoti, & Suchitra, 2012) while reducing their vulnerabilityto violence (Bhattacharyya, Bedi, & Chhachhi, 2011;Friedemann-Sanchez, 2006; Panda & Agarwal, 2005).Further, it is also beneficial for children’s schooling and healthoutcomes (Allendorf, 2007; Katz & Chamorro, 2003).Ownership of assets is therefore, a key indicator of intrahouse-hold differences in deprivations affecting both women and chil-dren.

One of the concerns with the inclusions of assets is that assetownership by itself does not specifically describe an individualor a household’s current state of living as some of the livingstandards indicators or education indicators do. In essence,it does not constitute living standards but is a determinantof living standards and is inappropriate to combine an assetdimension with the other constituent dimensions in evaluatingmultidimensional deprivation. However, we take the standthat the security of having a buffer against shocks in itselfcan create a sense of wellbeing and control over life circum-stances and is a critical ingredient for improving one’s qualityof life. Further, the inclusion of ownership of assets criteria isa well-established part of the discourse regarding identificationof poor households in India.

(c) Empowerment

Any conceptualization of the multiple aspects of poverty isincomplete without a discussion of empowerment. Kabeer(2001) defines empowerment in terms of the ability to makechoices that could have an important impact on one’s life,which would therefore be closely related to the overall experi-ence of poverty. Control over resources is another frequentlyoccurring theme in this literature wherein ownership ofassets is sometimes considered a proxy for empowerment(Garikipati, 2009). However, this is problematic as it equatesresources as being both necessary and sufficient conditionfor empowerment. Malhotra, Schuler, and Boender (2002)argue that while resources can act as a catalyst, women’sagency is required to effectively utilize the resources foradvancing their goals and interests. One of the constraintswith including empowerment in poverty analysis is that it iscontext specific and does not lend itself to an easy set of met-rics. The empowerment dimension was not included in theUNDP-MPI due to lack of internationally comparable set ofindicators.


Using the four dimensions—education, living standards,ownership of assets, and empowerment—this study developsan individual multidimensional poverty measure for all adults(18 years and older). For comparison, we also calculate ahousehold level measure. Table 1 presents the indicators cho-sen to represent the dimensions and the cut offs established foreach indicator. 2 There are 10 indicators for the householdmeasure and nine for the individual measure. While the dimen-sions remain the same across the measures, a few indicatorsare varied to capture intrahousehold differences. In order toensure comparability, the individual and the householdmeasures are calculated for the same sample of households.

(i) EducationThe first indicator is based on the idea of proximate literacy,

discussed by Basu and Foster (1998) where the presence of oneliterate individual provides positive externalities for the entirehousehold. Thus, a household with one member who has com-pleted primary education is better off in comparison to house-holds with no members with primary schooling. This measureof proximate literacy is also included in the UNDP-MPI. Thisconcept however does not extend to the individual level as theymay have differing access to the literate member. Moreover,differences in literacy between members of a household couldimpact the power dynamics within the household. It is con-ceivable that the bargaining capacities of an illiterate husbandand illiterate wife might be more equal than those of a literatehusband and an illiterate wife with consequences for house-hold dynamics and resource allocation. Therefore, in the

Table 1. KHAS-MPI dim

Dimension Indicator

Education Schooling No adult member hat least primary edui.e., 5 years of scho

Child Enrollment A child in the age gnot attending schoo

Living standards Electricity No electricity

Floor Floor is earth/mudSanitation No toilet or has toWater Water is not from p

well or closed/openCooking fuel Cooking fuel is not

Biogas (it is wood,Consumer durables Owns less than two

cycle, refrigerator, atwo-wheeler; and dofour-wheeler

Assets Land and house Does not own at le

Empowerment Allowed to travel toa. Marketb. Health facilityc. Natal homed. Outside village/community/area

Household values aon individual respo1–4: Not allowed to

Decision to access health servicesfor own needs

Decision made by wpermission or by so

individual measure of multidimensional poverty, we consideronly the individual’s own level of education.

In the household level analysis we also include the childenrollment indicator. Universal primary education is recog-nized as a key component of a country’s investment in futurehuman capital. This indicator also reflects the generationaltrend in education. If a household currently has no adultliterate member but the school age children are enrolled inprimary education, there is the potential for future literacyfor the household. In addition, enrollment rates in Karnatakahave reached well above the 90% level in recent years (ASER,2011). Given this trend, the inability to send all primary schoolage children to school can indicate acute distress and povertyfor a household. If a household has no children in that agecohort, however, they are non-deprived in this indicator.

(ii) Living standardsThere are both conceptual and empirical challenges in the

construction of individual deprivations for the standard of liv-ing dimension. Certain goods are semi-public in nature, that is,they are non-excludable. For instance availability of a toiletcan be classified as a semi-public good that all individualswithin the household can use and derive well-being from.Moreover there is no empirical way to determine specificindividual ownership of the toilet. In such cases the house-holds and the individual’s deprivation (or lack of deprivation)would be identical.

We follow Alkire and Santos (2010) in selecting and derivingthe deprivation cut off for the living standard indicators. In the

ensions and indicators

Deprivation

Household Individual

as completedcation,

oling

If he/she has not completed at leastprimary education

roup 5–10 isl

Not included

Same as household deprivation forall living standard indicators

share a toiletiped source, borewellElectricity, LPG orcharcoal, dung, etc.)of either fan, TV, cell phone,ndes not own a car or other

ast one of the two assets Household does not own at least oneof the assetsIndividual does not own (individuallyor jointly) at least one of the assets

re basednses

Same as household deprivation for allempowerment measures

travel alone

omen withmeone else

All women in household are attributedthe deprivation score of the womanrespondent. Men are assumed tobe non-deprived


measure of individual poverty, we treat living standards as apublic good accessible equally by all individuals in the house-holds. Each individual was therefore assigned the values oftheir household’s living standards indicators in our individualpoverty measure. If a household has electricity, access tosanitation and proper flooring, the benefits would automati-cally be available to all members. Indeed it would be difficultto establish how an individual member might be excluded fromthe benefit of these amenities. A similar approach is adopted forconsumer durables even though differential access is possible. 3

However this is hard to measure, since there are no clearownership documents for most consumer durables. In fact,most households considered consumer durables to be jointlyowned by all members of the household (Swaminathan et al.,2011).

It can be argued that given the gendered nature of roles andresponsibilities, lack of clean cooking fuel and access to safedrinking water will likely impact women more than men.Recent evidence shows that indoor air pollution from contam-inated cooking fuel has a disproportionately large impact onwomen’s respiratory health (Duflo, Greenstone, & Hanna,2008). Similarly, the lack of a dependable water source oraccess to piped water will certainly increase women’s workburden while also contributing to time poverty. However, inthe absence of a detailed time use module it is impossible topinpoint which woman in the household is most affected. Itcan be argued that deprivation in water and cooking fuel willhave some secondary impact on all members even if they donot directly participate in the activity. This can take the formof some degree of air pollution from cooking fuel and anexperience of water shortage for personal use.

(iii) AssetsAsset dimension at the household level is evaluated on the

basis of a household’s ownership of assets. Although the datacontain information on the full range of physical assets, this pa-per focuses on two key assets, house and agricultural land. Theprimacy of land for livelihoods, particularly in developingcountries is not debatable where being landless in ruralcommunities is often a very clear indication of the poor eco-nomic status of the household. It is often the last asset to be dis-posed of in times of crises and can make a significant differenceto a household’s poverty profile (Krishna, 2006). The use ofhousing as a criterion in poverty targeting has focused exclu-sively on the quality of housing and its associated amenities.Without denying the role of amenities, we contend that homeownership is an equally critical measure of well being, giventhe vulnerabilities associated with lack of tenure security, par-ticularly for informal settlements in urban spaces. Land andhome ownership have to be understood in the larger contextof social relations beyond the economic benefits attached tothem. Ownership confers status and prestige within one’s com-munity and can also be empowering due to the potential tocontrol one’s immediate environment (Datta, 2006).

A household is deprived if it does not own at least one ofthese two assets. For the individual measure, in addition tothe household deprivation, we also include an individualdeprivation. If the individual is not the owner (individuallyor joint) of either the house or land then she is considereddeprived. The quantity of land owned and the type of houseare not taken into consideration under the assumption thatowning any asset, no matter how small, can be empoweringfor women. We recognize that ownership can be divorcedfrom control over assets, especially for women. While infor-mation on transaction rights (ability to sell, rent, collateralize,or bequeath) can help, these data are available in KHAS only

for the respondent owners. Incorporating this informationwould truncate the sample.

(iv) EmpowermentThis paper uses mobility indicators which provide an insight

into women’s freedom of movement outside their householdand community. These indicators are culturally specific andare often used as a proxy for empowerment in the South Asiancontext (Alkire & Seth, 2008; Hashemi, Schuler, & Riley, 1996;Jejeebhoy, 2000). They relate to women’s ability to travel inde-pendently to places outside their home. Based on field testing,mobility constraints were not found to be relevant for men andtherefore these questions were posed only to women respon-dents. We assume that all adult females in the household havethe same level of mobility as the female respondent. 4 We alsoinclude an additional question on women’s ability to accesshealth care services for themselves as it is a decision that hasdirect implications for their well-being. Since women’sempowerment has positive externalities for the household asa whole, a household is assigned the values of its women mem-bers for each of the above indicators. At the individual level allmen are considered non-deprived in these indicators.

This paper follows the UNDP-MPI methodology in adopt-ing an equal weighting approach. The issue of weighting inmultidimensional poverty measures has been much discussed(Decancq & Lugo, 2008) but given that the focus of this studyis on demonstrating the usefulness of an individual level pov-erty measure and not on the robustness of the measure to dif-ferent weighting schemes per se, we opt for the simplestapproach. All four dimensions are weighted equally and with-in each dimension, all indicators are assigned equal weights.The household and individual measure weights differ due tothe variation in the number of indicators within each dimen-sion. For example, at the household level, education receivesa weight of 0.25 while each indicator (schooling and childenrollment) is weighted at 0.125. For the individual measure,since the education dimension has only one indicator, itreceives a weight of 0.25. Conversely, for the assets dimension,the household measure has one indicator (asset ownership)while the individual measure has two indicators (householdownership and individual ownership of assets).

We aggregate the total number of weighted deprivations foreach household and individual with the identification of poorbased on a poverty cut-off of 33% as per the methodology ofthe UNDP-MPI. For example, an individual deprived in theindividual asset ownership indicator (weight of 0.125) andthe education indicator (weight of 0.25) would be consideredpoor since these two deprivations together constitute 37%.The KHAS-MPI is calculated as the product of the head countor the percentage of poor households (or individuals) and theaverage intensity of deprivation among the poor which is cal-culated as the average of the weighted deprivation score ofeach household (individual) poor. As a robustness check, wealso conduct a dominance analysis comparing results acrossa range of poverty cut-offs and differing dimensions. Theresults are invariant to changes in the poverty cut off anddimension specifications (Appendix Figures A1–A4).

4. RESULTS

This section presents the multidimensional poverty measureboth at the household and the individual level. After account-ing for missing variables, the final sample used in this study is3400 households. When evaluated at the household level, theKHAS-MPI for Karnataka is 0.10 with approximately 25%


of the households classified as being multidimensionally poor(Table 2). 5 On assigning the multidimensional poverty valueof their household to individual members, about 22% of allindividuals are identified as multidimensionally poor withthe poverty rate (or the head count) similar for men (21%)and women (22%). Since men and women within a householdhave the same deprivation scores, this similarity in the povertyrate seems to suggest that men and women are fairly evenlydistributed across poor and non-poor households and thatthere is no major gender difference in poverty.

The analysis by male- and female-headed households 6 alsosuggests that the gender differences in poverty are not compel-ling. Male-headed households show only a slightly higher pov-erty rate (25%) and KHAS-MPI value (0.10) than female-headed ones (23% and 0.09, respectively). When individualsare assigned the same poverty score as their households, thepoverty rate is slightly higher for women in both male-headedand female-headed households.

The individual level analysis, however, paints a dramaticallydifferent picture of the gender difference in poverty. While theindividual and household poverty measures are not directlycomparable we can compare the gender picture that emergesfrom the two measures. Large gender differences are high-lighted using the individual level analysis (second and thirdcolumn of Table 3). At 64%, the poverty rate among womenis more than double the poverty rate among men (30%) withthe consequence that the majority of the poor are women(71%). This steep difference in the poverty rates of men andwomen is completely masked when poverty is conceptualizedat the household level (Table 2). Poor women also experiencegreater intensity of deprivation on an average (51% in compar-ison to 42% for men) and therefore have a much higherKHAS-MPI value of 0.321 (compared to 0.123 for men). Sinceall men are non-deprived in the empowerment dimensions, it ispossible that the difference between the poverty rates is biasedagainst women. We therefore estimate the KHAS-MPIwithout the empowerment dimension while maintaining the

Table 2. Household MPI and pover

A

KHAS-MPI 0.Average intensity of deprivation among the poor (%) 40

Head count (%)

Households 24Individuals 21Women 22Men 21Total number of households 34

Differences in headcount ratio between male- and female-headed households a

Table 3. Individual multidimensional poverty ind

All dimension

Total Women

Number of poor individuals 5219 3633(71.0%)

KHAS-MPI 0.225 0.321Average intensity of deprivation (%) 48.5 51.4Head count (%) 47.0 63.8Total number of individuals 11,092 5691Mean age of individuals in sample 39.2 39.2

Differences in headcount ratio and intensity of deprivation between men and

equal weighting approach. Thus, the weight of the empower-ment dimension is redistributed equally among the remainingthree dimensions.

The individual KHAS-MPI and the poverty headcount rateare both higher when empowerment is excluded. This is likelydue to the increased weight on the education indicator. With-out empowerment, the remaining three dimensions receive aweight of 0.33 each. Since there is only one educationindicator, anyone deprived in education is automaticallymultidimensionally poor which also increases the number ofindividuals classified as poor. What is notable though, is thatdespite the change in specification, substantial genderdifferences in poverty persists. The majority of the poor(59%) are still women, the poverty rate for women continuesto be substantially higher (65% compared to 47% for men)and the female KHAS-MPI value (0.361) continues to behigher than the male KHAS-MPI (0.233). The empowermentdimension is clearly not the only contributor to the genderdifference in poverty; deprivations in the other dimensionsare just as critical to understanding why and men andwomen experience poverty differentially. At the same time,empowerment and individual agency constitute an importantaspect of multidimensional poverty and its contribution togender differences should not be ignored. Therefore, allfurther analyses are based on the KHAS-MPI that includesempowerment.

To further test the robustness of the gender differenceshighlighted in the individual measure, we estimate separateKHAS-MPIs for urban and rural regions. In Table 4 it is seenthat overall, rural poverty rate (53%) is substantially greaterthan urban poverty rate (36%). To see what is driving the dif-ference is the urban and rural poverty rate we can look at thedifferences in the deprivation rates for each of our indicators.Rural poverty is greater due to higher levels of deprivation inrural areas in the education and living standards indicators. Inour data, 43% of individuals in rural areas are deprived in theeducation indicator. In comparison, the education deprivation

ty rate (poverty cut off = 33%)

ll Female-headed Male-headed

10 0.09 0.10.2 41.0 40.0

.9 23.0 25.4

.8 20.6 22.0

.3 21.9 22.4

.2 18.4 21.700 699 2701

re not statistically significant.

ex and poverty rate (poverty cut off = 33%)

s Without empowerment

Men Total Women Men

1586(29.0%)

6221 3690(59.3%)

2531(40.7%)

0.123 0.298 0.361 0.23341.9 52.6 54.8 48.329.4 56.2 64.9 46.85401 11,092 5691 540139.1

women are statistically significant at 1% levels.

Table 4. Individual multidimensional poverty index and poverty rate disaggregated by region (poverty cut off = 33%)

Urban Rural

Total Women Men Total Women Men

Number of poor individuals 1374 956(69.6%)

418(30.4%)

3845 2677(69.6%)

1168(30.4%)

KHAS-MPI 0.156 0.222 0.087 0.272 0.390 0.149Average intensity of deprivation (%) 46.9 49.0 41.8 49.1 52.2 41.9Head count (%) 35.5 48.1 22.2 53.2 72.2 33.2Number of individuals 3868 1986 1882 7224 3705 3519

Differences in headcount ratio and intensity of deprivation between rural and urban areas are statistically significant at 1% levels.


rate among urban individuals in 21%. Similarly while ruraldeprivation rate in sanitation 72% the urban rate is 40%.

In Table 4 we continue to see significant and substantialgender differences in both urban and rural areas. Women con-stitute 70% of the total number of poor individuals in bothurban and rural areas. This difference is also reflected in thehigher KHAS-MPI values and poverty rates for women ascompared to men in both locations. In fact the poverty rateamong urban women is greater than the poverty rate amongrural men even though the overall urban poverty rate is lowerthan the rural poverty rate. 7

We can therefore conclude that a substantial differenceemerges in the comparison between the household level andthe individual level analysis of poverty. Gender differencescan be seen at the individual level but not at the householdlevel. This suggests that real differences exist in the intrahouse-hold distribution of resources (physical and human capital inthis case) as well as in the ability to participate in decisionsthat are of importance. It also points to gender as animportant axis of differentiation. To further disentangle thefactors driving these differences, we examine deprivation ratesamong individuals and households for each indicator. Forease of exposition, individual poor refers to those who havebeen classified as such based on the individual level analysis.

Among households, the deprivation rates in the empower-ment dimension are high as also in access to some of thebasic amenities like cooking fuel and sanitation (Table 5).Deprivation in education even among poor households islow which is in sharp contrast to the experience of individuals,

Table 5. Deprivation rate for the mu

Indicator All households (%)

Schooling 5.8Child enrollment 0.8Consumer durables 31.3Floor 21.1Water 11.8Cooking 77.4Sanitation 62.6Electricity 9.6Household owns land/house 17.3Individual owns land/house –Travel to market 35.2Travel to health facility 43.2Travel to natal home 36.8Travel outside village/community/area 44.1Access health services for own needs 1.5Average age –Total number of observations 3400

signaling differing education attainments among householdmembers. Substantial improvements in school enrollmentrates in Karnataka have been reported in recent years (ASER,2011). However the enrollment efforts have focused on theyoung school going age-group. Older adults thereforecontinue to be deprived in education with women showing lar-ger deprivations in education (44% compared to 26% for men),signaling that boys were prioritized over girls with respect toschooling in the recent past.

We also see substantive differences in the asset ownershipindicator. Approximately 84% of all women are deprived inthe individual ownership of assets indicator. In comparison,the deprivation rate in the assets category for households isonly about 18%. Even among poor households the deprivationrate in assets is relatively lower at 49%. Among men, the depri-vation rate for the individual ownership indicator (56%) isconsiderably smaller than women’s deprivation rate. Thus,there are substantial differences in the distribution of economicresources among individuals within households. At a broaderlevel, this divergence between the household and the individualdeprivations explains why the gender difference is much higherwhen poverty is evaluated at the individual level.

The interesting question to ask then, is, how are poor peopledistributed across poor and non-poor households? Given thatthe poverty rate from the household level measure is only 22%,it is expected that a majority of the poor individuals from theindividual poverty measure will belong to the non-poorhouseholds. However, here the interest lies in examining thegender patterns of poor people residing in poor and non-poor

ltidimensional poverty indicators

All men (%) All women (%) Poor households (%)

25.9 44.2 15.4– – 2.2

26.8 28.5 53.118.6 19.5 36.311.8 13.6 9.278.5 78.4 84.861.1 60.3 81.87.4 8.3 19.413.9 14.4 49.056.2 84.0 –

0 36.7 61.60 44.4 72.90 38.8 66.20 44.9 74.70 1.6 2.8

39.1 38.1 –5401 5691 847

Table 8. Distribution of poor individuals across poor and non-poorhouseholds, by headship status (poverty cut off = 33%)

Household type Poor men (%) Poor women (%)

Male headed

Poor 34.6 29.8Non-poor 48.3 58.1

Female headed

Poor 7.5 4.1Non-poor 9.6 7.8Number of individual poor 1586 3633

Differences in household poverty status within poor individuals by sex andheadship status are statistically significant at 5% levels.


households (Table 6). We find that a greater proportion ofboth poor men (58%) and poor women (66%) belong tonon-poor households. On the other hand, only 9% of individ-uals are non-poor in poor households and this figure is dom-inated by men.

Based on the poverty status of its members, households canbe classified as those where only women are poor, only menare poor, both men and women are poor, and neither is poor. 8

On examining this distribution by the poverty status of thehousehold, we find the expected result that households whereboth men and women are poor (61%) is the dominant categoryamong poor households (Table 7). This is followed by thosehouseholds where only women are poor (37%) and neither ispoor (2%). What is surprising is that more than a third ofnon-poor households are in the category where only its womenmembers are poor. In contrast, households with only poormen are less than 5% of non-poor households. In essence thistable indicates that there are many households, both poor andnon-poor, where only women are in poverty whereas there arenot many households where only men are in poverty.

From a policy perspective, the presence of individuals withdifferent poverty status within the same household is worri-some. Many social protection schemes and subsidies targethouseholds based on household-level aggregates which wouldexclude many poor individuals who are present in non-poorhouseholds. Not only are poor men and women more likelyto reside in non-poor households, these are also more likelyto be in male-headed households (Table 8). About 48% ofpoor men and 55% of poor women are in non-poor house-holds headed by a man. These results highlight the flaw ofusing headship as a proxy for understanding the gendered im-pact of poverty. Since male-headed households are the major-ity, more poor individuals whether men or women will in facttend to be in male-headed households. Therefore intrahouse-hold dynamics have an impact on both men and women.

We are also able to identify important differences in thecontribution of each dimension to multidimensional povertyamong different groups. The contribution of each dimensioncan be calculated by multiplying the percentage of individualswho are poor and deprived in each indicator by the indicatorweight and then dividing by the multidimensional povertyscore. 9 Since so many poor women are in non-poorhouseholds we first begin by looking at what it means to bepoor in a non-poor household versus a poor household. Forpoor women in non-poor households, at 36%, educationcontributes slightly more than one-third to multidimensional

Table 6. Distribution of poor/non-poor individuals acros

Household type I

Poor

Total Men W

Poor 36.5 42.1Non-poor 63.5 57.9

Differences in household poverty status within poor and non-poor individuals

Table 7. Intra-household differences in

Household type % Households whe

Only women are poor Only men are poor Both men

Poor 36.5 0.0Non-poor 36.8 3.5

poverty (Table 9). Lack of asset ownership (23%) is the secondcontributor to poverty for this group even though theyactually belong to households that do own some assets (contri-bution of household assets indicator is zero). Women in poorhouseholds on the other hand are more multidimensionallypoor, with all the four dimensions making somewhat equalcontribution to their poverty status. Here the household depri-vation in land or house ownership also contributes to theKHAS-MPI.

The differences in the poverty experience of the two groupshave important policy consequences. In poor households thereis a clear need for more monetary support to improve livingstandards, and encourage asset accumulation. In non-poorhouseholds on the other hand, a mere increase in householdwealth does not necessarily improve the position of women.Their relative position in terms of bargaining power withinthe household might even decline if they are shut out of theownership of assets and if household resources are not allo-cated to educate women.

We also examine the poverty experience of poor womenbased on their marital status as it is one of the factors that af-fect women’s position and access to resources within a house-hold. In comparison to married and single women, povertyamong widowed women is less a factor of lack of empower-ment or lack of ownership of assets. This is not unexpectedas they are likely to have inherited assets from their spouseand also enjoy greater decision-making powers and fewerrestrictions on their mobility which presumably reflects thefact that widowed women are older and are often the headsof their household. The biggest influence is lack of educationcontributing 44% to their poverty score. For never married

s poor/non-poor households (poverty cut off = 33%)

ndividual level analysis (%)

Non-poor

omen Total Men Women

34.0 8.7 12.6 1.566.0 91.3 87.4 98.5

by sex are statistically significant at 1% levels.

poverty (poverty cut off = 33%)

re Total number of households

and women are poor Neither are poor

61.2 2.0 84725.7 34.0 2553

Table 9. Contribution of dimensions/indicators to KHAS-MPI among poor women (poverty cut off = 33%)

Dimensions/indicators Household poverty status Marital status*

Poor Non-poor Never married Currently married Widowed

Living standards 21.2 20.5 23.0 20.6 20.2Education 23.7 36.1 12.0 31.4 44.1Household owns land/housing 10.0 0.4 5.0 4.0 3.3Individual owns land/housing 20.4 23.4 29.0 23.4 15.1Empowerment 25.5 21.2 31.0 22.9 18.7Number of individuals 1236 2397 314 2560 678

* Deserted women are not included in this category since they might have different circumstances from widows in terms of inheriting the assets of thespouse.


women, on the other hand, education contributes only 12% totheir poverty. This is most likely a generational effect with thegovernment efforts to increase enrollment rates mainly benefit-ting younger women. The average age of the women in thenever married group is 23 years while for the currentlymarried and widowed groups it is 41 and 60 years, respec-tively. The empowerment dimension and the lack of ownershipof assets are the main contributors to the poverty status ofnever married women. This seems plausible as they may nothave had an opportunity to acquire assets either through theirearnings or as inheritance. There are also greater social restric-tions on traveling alone by young unmarried women. Theseresults highlight the importance of not treating women as ahomogenous group in poverty analysis.

5. CONCLUSION

This paper constructs an individual level multidimensionalpoverty measure which highlights important shortcomings inthe current poverty discourse We find that a majority of bothpoor men and poor women belong to non-poor households.These individuals would be misclassified as non-poor in ahousehold level poverty analysis. Women, in particular, arecompletely overlooked in the traditional approach. Currentgender analysis relies on using female heads as a proxy forall women while ignoring those who reside in male-headedhouseholds. Since several studies including this one has foundthat female heads are not necessarily worse off than male-headed ones, it has resulted in the erroneous conclusion thatgender differences in poverty do not exist. However, this studyalso demonstrates that when poverty is calculated at the indi-vidual level, a substantial majority of the poor are women andthe poverty experienced by them is more intense in terms ofthe number of deprivations they face in comparison to menor the average household. In fact, women contribute 91% ofthe total individual KHAS-MPI.

Furthermore, the individual level analysis adds to theliterature supporting a multidimensional approach ratherthan the traditional approach to poverty. Deprivation in thematerial space as measured through income and consumption

are inadequate to describe poverty even for households with-out accounting for individuals differences. Individual experi-ences of what it means to live in poverty are varied andbased on the specific kinds of deprivations they experience.For example, it is shown that the poverty of poor women innon-poor households is primarily caused by deprivation ineducation and lack of individual ownership of assets evenwhen the households are mostly non-deprived in ownershipof assets.

These differences in the experience of poverty among the di-verse groups of poor women highlight the role of individualagency, a crucial component of the capabilities approach topoverty. The ability to control life circumstances can haveimportant implications for individual’s ability to avoid chronicdeprivations. From a gender perspective, women’s empower-ment and ability to have a greater voice in household decisionshas been shown to have many positive implications for the liv-ing conditions of both individuals and households.

These results are important in the policy discourse. Policymakers are moving away from a narrow conceptualizationof poverty to a comprehensive understanding of multipledeprivations. However, the fact that households and individu-als cannot be equated in poverty analysis is still far from beingaccepted universally. Certainly, there are conceptual, method-ological, and data collection challenges in moving from thehousehold to the individual. But there are compelling reasonsto take on such an exercise. Poor individuals in non-poorhouseholds would be completely excluded from any policyintervention that targets only poor households. Even withinpoor households, men and women experience different setsof deprivations. Women are typically deprived in the empow-erment, asset ownership, and education dimensions. Merelyincreasing the material wealth of the households where the wo-men reside, therefore will not necessarily translate into fewerdeprivations for them. As household wealth increases, the bar-gaining position of these women might even decline due tochanges in relative wealth positions. If the goal of povertyreduction is a serious consideration, then the assumptionsof relying solely on household aggregates need revisitingwhile also attempting to grapple with the complexities of anindividual level approach.

NOTES

1. For detailed discussion, see Alkire and Seth (2008).

2. The assets indicator (ownership of radio, TV, telephone, bike,motorbike, car, or tractor) in the UNDP-MPI is relabeled in theKHAS-MPI as consumer durables. The assets dimension in this paperrefers to ownership of land or house.

3. The cutoff for the consumer durables indicator is based on the UNDP-MPI albeit with a slight variation to reflect the consumer durablesownership patterns in our dataset. The UNDP-MPI list of consumerdurables includes a radio. However, radio ownership rate among theKHAS households is very low (13%). Several households that own someof the other more valuable consumer durables do not own radios. It seems


it is not a product that households aspire to or find very useful anymore.We therefore did not include radio in our list of consumer durables. Wereplaced it with the fan, which has a much wider rate of ownership amongthe KHAS households (43% for rural households and 79% for urbanhouseholds).

4. It is possible though that some female members may have greater orlesser mobility than the respondent depending upon age and positionwithin the household. A daughter-in-law, for example, may face greaterrestrictions than her mother-in-law.

5. We also calculate the household poverty measure excluding the childschool enrollment indicator to ensure comparability between the individ-ual and household level analysis. About 26% per cent of households areclassified as poor when we exclude child enrollment indicator vs. 25% per

cent when we include it. This indicates that including child enrollment inthe household measure does not bias the comparison between thehousehold and individual level analysis.

6. The survey requested households to identify a primary respondent tomove away from the traditional headship concept. However, in manyhouseholds, the primary respondent coincided with the head and thus, isused as a proxy for headship.

7. We also estimated KHAS-MPI by caste groups. While differences inthe overall poverty rates among castes follow the traditional castehierarchy, gender differences in poverty persist within each caste group.

8. We thank an anonymous reviewer for suggesting this table.

9. For the detailed methodology, see Alkire and Santos (2010).

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0 102030405060708090

100

100 90 80 70 60

Pove

rty

Hea

d Co

unt

%

Povert

HC-Women

Figure A1. Poverty head count by

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 20 40 6

KHA

S-M

PI

Poverty He

KHAS-MPI-Men

Figure A2. KHAS-MPI by sex

0

20

40

60

80

100

120

100 90 80 70 60

Pove

rty

Hea

d Co

unt

%

Poverty

HC-Women

Figure A3. Poverty head count by sex for differ

APPENDIX A. DOMINANCE ANALYSIS

We calculate gender disaggregated KHAS-MPI for 10 differ-ent poverty cutoffs ranging from 10% to 100% deprivation, tosee if our result of higher poverty rates among women holdsacross different poverty lines. Poverty head count is uniformlygreater for women across the different cutoffs (Figure A1) andthe KHAS-MPI for women dominates the KHAS-MPI formen across the different poverty cut-offs (Figure A2). Thisshows that poverty among women is higher than men irrespec-tive of the deprivation poverty cutoff chosen.

The analysis is repeated without the empowerment dimen-sion without any change in the results (Figures A3 and A4).

50 40 30 20 10

y Cut Off %

HC-Men

sex for different poverty cutoffs.

0 80 100 120

ad Count (%)

KHAS-MPI-Women

for different poverty cutoffs.

50 40 30 20 10

Cut Off %

HC-Men

ent poverty cutoffs excluding empowerment.






















0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 20 40 60 80 100 120

KHA

S-M

PI

Poverty Head Count %

KHAS-MPI-Men KHAS-MPI-Women

Figure A4. KHAS-MPI by sex for different poverty cutoffs excluding empowerment.


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Moving from the Household to the Individual: Multidimensional Poverty Analysis