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Identifying BPL Households: A Comparison of Competing Approaches

Two approaches are available for identifying households below the poverty line - the score-based ranking approach proposed by the N C Saxena Committee and an alternative proposed by Jean Dreze and Reetika Khera. Comparing these approaches for Udupi district in Karnataka shows that the former does a fair job of revealing how households are placed on the economic spectrum but excludes more deserving households than the latter. Also, when it comes to implementation, the latter is transparent and relatively much faster.

Identifying BPL Households: A Comparison of Competing Approaches

M R Sharan

of Karnataka 2008: 5), and has a high literacy rate of 81.25% overall (ibid: 3). Its rural literacy is also high, ranking second in the state behind neighbouring Dakshina Kannada. Five villages in Kundapura Taluk were chosen by a simpl e random draw from among 100 villages. Kundapura is a heterogeneous region comprising villages with widely varying socio-economic

Two approaches are available for identifying households below the poverty line – the score-based ranking approach proposed by the N C Saxena Committee and an alternative proposed by Jean Dreze and Reetika Khera. Comparing these approaches for Udupi district in Karnataka shows that the former does a fair job of revealing how households are placed on the economic spectrum but excludes more deserving households than the latter. Also, when it comes to implementation, the latter is transparent and relatively much faster.

I am grateful to Sameeksha Trust for funding my study through the Krishna Raj Fellowship Programme at the Delhi School of Economics in 2010 and especially to J V Meenaskhi, my teacher-guide, Himanshu, Jean Dreze and Reetika Khera for invaluable discussions, suggestions and research tools. Special thanks to Dipa Sinha for ideas and constant guidance.

M R Sharan ( is currently with a project of the International Growth Centre, London School of Economics.

he primary objective of this study is to compare competing approaches for identifying households below the poverty line (BPL). The two methodologies that will be compared are the scorebased ranking (SBR) approach proposed by the NC Saxena Committee (2009) and an alternative proposed by Jean Dreze and Reetika Khera (2010).

The BPL Census will be carried out in 2011 and the ideal method to adopt is still to be decided. The debate between various approaches is still raging, and the Saxena Committee’s approach (henceforth SCA) and the Dreze and Khera approach (DKA) are at the centre of it. The ideal approach needs to satisfy a range of criteria, of which two are pivotal – identification and implementation. Identification involves ensuring inclusion of the most deserving households in the BPL list. Implementation implies ensuring that the process of identification is done with as little cost – in terms of labour and time – as possible.

There has been little field-testing of these approaches, hence the motivation for the current study. This paper is an attempt to address concerns, question certain elements of the proposed underlying hypotheses and wherever possible, provide pointers for action.

The findings of this paper are based on a study of five villages, and 469 households, in Udupi district in Karnataka. The results presented here are the outcome of two separate field trips – the first from late April to early June 2010, and a subsequent visit to two villages some months later.

Udupi district is amongst the better-off districts in Karnataka. Formed fairly recently – a little over a decade ago – the district ranks 6th among 27 districts in Karnataka in terms of per capita income (Government characteristics.

1 Introduction to the Methodologies

The SCA’s SBR system resembles earlier BPL census methodologies with some improvements. It considers 11 household characteristics that are indicative of the socioeconomic status of a household. For each of these characteristics, a certain number of points are awarded. The higher the number of points awarded for a characteristic, the stronger is the correlation with household poverty. These points are aggregated over all the characteristics of a household to obtain its total score.

Points are awarded on the basis of occupational categories. For example, casual labour households get two points. Points are also awarded if households belong to marginalised sections of the society, for instance, scheduled caste (SC) and scheduled tribe (ST) households get three points, or are suffering from illnesses or headed by a member above the age of 60.

Each gram panchayat is given an exogenously determined quota of households to be classified as poor. Based on the quota, a cut-off score is set and every household having a score above this cutoff is classified as poor.

The DKA is vastly different. It specifies certain inclusion and exclusion criteria on the basis of households which are either included in the BPL list or excluded from it. For example, if owning a landline phone is a component of the exclusion criteria, then every household that owns a landline phone is excluded from the list of the poor. Similarly, if landless households are a component of the inclusion criteria, then a household that owns no agricultural land is included in the BPL list.1 What if a household both owns a landline and has no agricultural land? In order to solve

june 25, 2011 vol xlvi nos 26 & 27

Economic & Political Weekly


this conundrum, they prescribe four distinct sub-approaches for combining these exclusion and inclusion criteria.

Exclusion Approach: Reject a household from the list of poor if and only if it satisfies any of the exclusion criteria.

Inclusion Approach: Include a household if and only if it satisfies any of the inclusion criteria.

Play-safe Approach: Reject a household only if it satisfies any of the exclusion criteria and does not satisfy any of the inclusion criteria.

Restrictive Approach: Include a household only if it satisfies any of the inclusion criteria and does not satisfy any of the exclusion criteria.

Clearly the exclusion and the restrictive approaches give primacy to exclusion criteria, i e, they do not select households that are excluded, irrespective of whether they satisfy any of the inclusion criteria. The inclusion and play-safe approaches give primacy to inclusion criteria in drawing up the BPL list.

This paper is divided into six sections. The introductory section described the two methodologies and the sampling strategies. The following Section 2 examines the DKA and scrutinises their inclusion and exclusion criteria. Section 3 looks at the SBR system proposed by the Saxena Committee and how it orders different households. Section 4 provides a more direct comparison of the two approaches focusing on intersection sets and exclusion sets – that is, households both methodologies include and both exclude from the list of the poor – and compares them with the results from a more qualitative analysis. Section 5 compares feasibility of implementation for the two approaches and looks at an alternative method somewhere half-way between the two approaches proposed here that looks to address the problems inherent in both. Section 6 comprises concluding remarks.

A main finding of this paper is that while neither approach is ideal, the DKA is far easier to implement and even the most exacting of its approaches excludes fewer deserving households. There are, however, issues with the DKA, in particular, the questionability of some of its inclusion and exclusion criteria and the apparent tension between long-term indicators and short-term causes of poverty, a concern that affects even the SCA.

2 Dreze-Khera Approach

Dreze and Khera (2010), as mentioned above, describe four ways to count the number of poor in a village. Table 1 (p 258) presents the figures for those thus counted as being BPL out of a total of 469 households surveyed.

The overall number of poor in the sample swings quite dramatically from 26% with the rather strict restrictive criteria, to 73.3% when the far more lenient play-safe approach is adopted. The fact that the number of poor nearly doubles if one moves from an exclusion-based approach to an inclusion-based approach indicates that a considerable proportion are both asset-owning and socially or economically disadvantaged.

To explore this further, I critically examine some of the inclusion and exclusion criteria. In the context of Udupi, the inclusion criterion of single woman headed households (SWHH) is problematic. These are included directly because “households headed by a single woman suffer many economic and social disadvantages that may not be captured in asset-based indicators” and because of “economic vulnerability of widows in general” (Dreze and Khera 2010: 58). The Saxena Committee Report (2009) also backs their automatic inclusion in the BPL list. However, in Udupi, a considerable proportion of households are traditionally matriarchal, thriving economically, and have multiple breadwinners, in contrast to the situation commonly associated with single mothers/widows struggling to make ends meet.

Numerically, SWHHs make up nearly 20% of the total households surveyed, about half of whom are headed by women over the age of 60. In terms of assets owned, 52% of all SWHHs own a television (TV), compared to 57% for the complement set. For most other assets as well, except for scooter, there is no statistically significant difference between SWHH and other households at the 5% level (see Table A.1 in the Appendix, p 262).

Several of the exclusion criteria are similarly debatable, some in the context of the study, some more generally.

Colour TV as an exclusion criterion presents an interesting conundrum. More than one in two households own a colour TV. The corresponding numbers for both

2010 Krishna Raj Fellowship Programme

Delhi School of Economics

Under the 2010 Krishna Raj Fellowship programme at the Delhi School of Economics, nine groups of students from the Departments of Economics and Sociology of the DSE were provided financial assistance to carry out projects during the summer of 2010. The projects were selected from a set of 21 applications and the work was supervised by the faculty of the DSE.

The projects were:

  • 1. Evaluation of Ladli Yojana
  • 2. Living In Bawana: Childhood Experiences of Girls
  • 3. Cost of Human Wildlife Conflict and Its Compensation in Kuno Wildlife Sanctuary
  • 4. Identifying BPL Households: A Comparison of Competing Approaches
  • 5. Van Panchayats in Three Villages: Dependency and Access
  • 6. Solid Waste Management: A Case Study of Delhi
  • 7. How Does the Existing Bamboo Policy Affect the Livelihood Options and Commercial Prospects of the Economically Disadvantaged in the North Eastern States of Mizoram and Arunachal Pradesh?
  • 8. The Role of NGOs in Social Welfare of Poor People
  • 9. Interface between Religion, Politics and Tourism in Dharamshala
  • This article is based on one of the projects completed in 2010.

    The Krishna Raj Fellowship programme is funded by the Sameeksha Trust.

    june 25, 2011 vol xlvi nos 26 & 27

    Table 1: Number and Percentage of BPL Households Using the DKA that access to piped water is definitely a

    Exclusion approach Inclusion approach Play-Safe Approach Restrictive Approach Number % Number % Number % Number %

    165 35.18 302 64.39 343 73.13 125 26.65

    Source: Author’s survey.

    Table 2: Number of Households in Each Social Group Possessing Certain Assets

    Type Fridge Scooter Landline Colour TV Electricity Total

    Casual labourers 4 (2.41) 20(12.05) 33(19.88) 64 (38.55) 138 (83.13) 167

    privilege of the rich. Table 3 looks at two sets of households – those that have access to piped water and those that do not – and compares the two across occupational/social categories and asset ownership. Perversely, those households that

    Landless agricultural labourers 0(0) 4(4.25) 14(3.19) 33(35.11) 73(77.66) 94 have access to piped water seem to be

    Figures in brackets are percentages.

    Table 3: A Comparison of Households With and Without Access to Piped Water (%)

    Households With Households Without

    Landless agricultural
    labourers 21.62 5.27
    SC/ST 17.56 6.58
    Car-owning 6.75 7.36
    Fridge-owning 10.81 13.23
    Landline-owning 29.73 37.40
    Scooter-owning 22.97 27.27
    Colour TV-owning 52.70 57.50

    Sample size is 469 households.

    SC/ST houses and landless households are about one in three – a large number, by any standards. However, the proportion of TV owners among the most disadvantaged classes is significantly lower than in their complement sets (Table A.1). Given the high number of TV owners among even the most backward of classes (Table 2), using this as an exclusion criterion would lead to leaving otherwise deserving households out of the list, something that Dreze and Khera are themselves keen on limiting.2

    Landline phones as an exclusion criterion pose a different problem. In the village of Hosadu, an ex-panchayat official, a woman of the business Bunt caste, told me an interesting story. Apparently, the villagers got wind of the fact that there were some government officials coming to allocate BPL cards to the needy. Furthermore, word went around that houses with landline phones would not be given a card. Several villagers, with little hesitation, discarded/disconnected their landlines in order to ensure continuing access to cheap government resources. With mobile phones being so easily available, people have lost the need for landlines.

    Landline phones are, nevertheless, an exclusive asset. Nearly all socially/ economically deprived groups – casual workers, landless agricultural labour households, SCs and STs – have significantly lower proportion of landline phones compared to their complement sets (Table 2). While the proportion is a little over one-third overall, this number drops to one in nine for all disadvantaged groups taken together. While these phones are only owned by a minority, this does not necessarily invalidate the issues raised previously. Landline phones as an exclusion criterion could prove ineffective ex-post, as the example of Hosadu village shows.

    “Amenities” consist of electricity, flush and piped water. Any household owning all three is automatically excluded. In Udupi, piped water as an exclusion criterio n is probably not meaningful. Udupi has an extended monsoon; in fact, Agumbe

    – barely 45 minutes from Udupi – ranks consistently amongst the top five towns for rainfall in the country. Water is plentiful. Rivers, streams and other such surface water sources aside, groundwater levels are also “very high” (Central Ground Water Board 2008: 12).

    The sample corroborates this – 55% of the houses own a well and therefore, do not need external piped water supply. This ownership cuts across classes, both economic and social. Furthermore, there is very little in the numbers to suggest are landless labourers or SC/STs. Also, those with access to piped water own fewer assets than those without. This could be due to the fact that certain water-related government schemes are in place to benefit the poorest of the poor. Insufficient data prevents us from exploring this further.

    What emerges quite clearly however, is that access to piped water is not a good basis on which houses should be excluded, especially in areas such as the one sampled. While it may be difficult to generalise this claim given the specific geographical setting of Udupi, it does raise the important question of whether one can employ one set of inclusion and exclusion criteria for the whole country.

    Similar geographical issues crop up if one looks at ownership of a pucca house as an exclusion criterion. If it rains six months a year, one cannot cope with a kaccha roof. This coupled with the presence of several government schemes that provide houses and function fairly efficiently ensures near-universality of pucca houses in the study area. Any exclusion-based approach that includes pucca house as a criterion would result in misclassification errors.3

    A slightly better exclusion criterion is a multi-roomed pucca house that is

    Table 4: Comparison of the Sensitivity of DKA to Alternate Inclusion/Exclusion Criteria

    Exclusion Approach Inclusion Approach Play-safe Approach Restrictive Approach
    Type Number % Number % Number % Number %
    Exclusion criteria 1 165 35.18 302 64.39 343 73.13 125 26.65
    Exclusion criteria 2* 39 15.6 152 61.04 157 63.05 34 13.65
    TV-less analysis 1 212 45.20 302 64.39 367 78.25 182 38.81
    TV-less analysis 2* 54 21.69 152 61.04 161 64.66 45 18.07

    Exclusion Criteria 1: Exclude a Household if it owns any of the baseline assets. These numbers are the same as Table 1. Exclusion Criteria 2: Exclude on the basis of ownership of any of the baseline assets or a multiroom pucca house that is self-constructed. The numbers, expectedly, drop across the approaches. TV-less analysis (1 and 2): The numbers are recalculated here after doing away with the TV as an exclusion criterion. Here, the numbers rise because TVs are a fairly common asset. *Sample size is 249 households.

    Table 5: Percentage from Disadvantaged Groups Excluded under Various Exclusion Criteria

    SC/ST Landless Agricultural Labourers Illiterate Adults Casual Labourers
    Exclusion criteria 1 33.33 37.65 30.50 46.71
    Exclusion criteria 2* 46.15 58.82 62.50 64.07
    Exclusion criteria 1 without TV 20.51 18.82 32.07 28.14
    Exclusion criteria 1 and 2 are as defined previously.
    *Sample size is 249 households.
    june 25, 2011 vol xlvi nos 26 & 27 Economic & Political Weekly


    self-constructed.4 This, as Table 4 (p 258) shows, halves the number of poor if one uses primarily exclusion-based approaches, but maintains the hierarchy in numbers. Moreover, as Table 5 (p 258) shows, any exclusion-based approach would lead to leaving out significant number of deserving households from the disadvantaged classes.

    Recalculating the numbers without using TV as an exclusion criterion leads to a significant increase in the number of poor, especially amongst those approaches that use exclusion criteria as the primary means of separating the rich

    Data on amount of land owned was collected for three villages – a total of 249 houses. As Figure A3 (p 262) in the Appendix shows, the strong negative correlation between a household’s total points and land owned is once again striking. There is first a slight increase in the average amount of land owned, between points 0 to 1, and then the graph slopes downward in an almost linear fashion. Those with 0 points represent shopowners, goldsmiths, bank employees and hotel owners, several of whom have sold off their land and have turned to their professions full-time. This might explain the marginally lower

    Table 6: Mean Scores and Standard Deviations for Socio-economic Groups in the Sample

    Mean Score Standard Deviation Minimum Maximum

    4 Comparing the SCA and DKA

    The above sections have analysed the two methodologies, and contrasted their approaches to tackling the same problem. This section attempts a more direct comparison. This is done in the following manne r

    – first, I define an arbitrary cap on the number of households based on a cut-off score and then proceed to draw a comparison between households that are classified as poor under the SCA and the DKA.5 This entails looking at the intersection set (set of households classified as poor under both methodologies) and excluded sets (households included by one and excluded by another) from both the approaches.6 Finally, I compare results

    from both methods with the communities’

    Illiterate adults 4.18 2.25 1 11

    Landless agricultural labour households 6.16 1.62 4 11 own identification of poor households,

    SWHH 3.10 2.34 0 8 obtained in a subsequent visit.

    SC/ST 6.85 1.97 3 11 Of the four combinatorial approaches
    Casual labourers 4.40 1.79 2 9 that Dreze and Khera propose, I choose
    Overall 2.72 Sample size is 469 households. 2.36 0 11 the restrictive approach for comparison purposes. The households included under
    from the poor. A significantly smaller qu antum of land owned per h ousehold on the restrictive approach are, by design,
    proportion of the visibly needy classes av erage in that category. On the whole, those that are included under all four
    are excluded under exclusion-based crio wnership of lan d too broadly reaffirms approaches. These houses are, in a sense,
    teria (Table 5, row 3). Still, one in five SC/ th e ability of the points system to separate the poorest households under this meth-
    ST households and nearly the same proth e better-off fro m the poorer sections. odology. In the given sample, this amounts
    portion of landless agricultural labourers As Table 6 sho ws, scores on the higher to about 26% (122 households). Impor
    are still excluded. en d of the distri bution are dominated by tantly, there is some external validity for
    th e clearly depriv ed classes – S Cs and STs, this approach – this figure is very much
    3 Saxena Committee Approach la ndless agricultu ral labourers and in line with the 28% that the Directorate houses
    Fundamental to the application of SBR w ith illiterate ad ults. Of cour se, there is of Economics and Statistics, Udupi dis
    is the definition of caps on the number of an obvious case of endogeniety here trict, estimates as the total number of
    households that are classified as poor. su ch households get points for belonging poor in Kundapura Taluk (Government of
    Defining these caps for the area of to these particul ar categories . A similar Karnataka 2008: 48). Under the Saxena
    study is beyond the scope of this paper, tr end can be ob served for occupationa l Committee’s approach, we will look at
    but this analysis profiles households pr ofiles of house holds across the distribu households with a score strictly greater
    at various intervals on the distribution ti on. The gradua l transition fr om than three,7 a total of 130 households in agricul
    and sees whether asset-based or other tu rists, business men, goldsmiths, doctors the sample. Again, this cut-off has no nor
    socio-economic indicators match these an d teachers at t he lower end to coolies, mative basis but for the fact that this fig
    distributions. band players, beedi cutters, basket ure corresponds closely with the number
    Since asset ownership is the principal w eavers and agr icultural wor kers of poor in the sample as per the DKA higher
    source of economic (and indirectly, social) up is clear indic ation of the correlation restrictive approach.
    well-being, a comparison of assets and be tween occupat ional characteristics and The intersection set comprises 73
    points is a simple method to check the th e points system. households – about 15.5% of the total
    usefulness of the points system. As shown Based on the se numbers alone, one sampled (Table 7, p 260). These are those
    in Figures A1 and A2 in the Appendix w ould be tempt ed to conclu de that the households that are considered extremely
    (p 262), there is a systematic downward Sa xena Committe e’s approach does a good poor under both the methodologies.
    trend in terms of asset ownership – across jo b at identifying the poor. The devil, how- These households are, on an average, at
    all assets – as one moves from those with ev er, lies in the details. SWHHs are auto least doubly disadvantaged, are by design
    fewer points (the less disadvantaged) to m atically include d; as argued above, this assetless and comprise nearly 70% of the
    those with higher points. Explicitly asset w ould result in gross inclus ion errors. SC/ST households and over 65% of the lan
    ownership has no role to play in the for-T here are other , more general, issues , dless agricultural labourers – the two
    mulation of the SBR system. di scusse d in the next section. classes that come off the worst under

    june 25, 2011 vol xlvi nos 26 & 27

    whatever indicator one chooses. Only Table 7: A Comparison of Exclusion and Intersection Sets between the Restrictive Approach and the SCA

    eight such houses own agricultural land. Similarly 290 households are classified as being non-poor by both approaches.

    The exclusion sets are less straightforward. Of the 57 excluded under the restrictive approach (Table 7), 22 are

    DKA Poor (Restrictive Approach) DKA Non-Poor (Restrictive Approach)

    SCA poor (points>3) 73


    SCA, non-poor (points <=3) 49


    The second and the third cells give the exclusion sets – those households whose classification is in conflict. The fourth cell comprises all those households that both the methodologies classify as not poor. Sample size is 469 households.

    Table 8: Households Revisited, Classification and Exclusion Sets

    excluded solely on the basis of ownership Total number of Households Number of QP Number of Poor by the Number with Scores Number with Scores Restrictive Score above in Two Villages Restrictive Approach above Four above Three Approach, Three, SCA

    of a colour TV – an exclusion criterion

    Excluded Excluded

    that, as previously mentioned, is question

    147 60 52 31 47 18

    able. Consequently, DKA comes across as too harsh on some kinds of households – of those excluded, 33 are at least doubly disadvantaged and six are landless and triply disadvantaged.8

    On the other hand, there is a significant drop in the percentage of both landless agricultural labourers and casual workers in this excluded set (when compared to the intersection set) and the percentage of SC/ST households almost exactly halves. The average points per household, consequently, reduce from 6.5 to about

    4.9. These factors indicate that perhaps indeed these houses are slightly better off than those in the intersection set.

    The SCA, on the other hand, consistently misses out on asset-poor marginal farmers who own plots of land of barely half an acre. Agriculture as a means of income is growing increasingly infeasible in the district and could perhaps be even heading for a “total collapse” (Government of Karnataka 2008: 38). The hardest hit are the marginal farmers.

    These figures were checked in a subsequent visit using intensive discussions with previously sampled respondents and key informants (including anganwadi and panchayat workers) and personal revisits to reconcile, if possible, the households that were incongruously classified by the two approaches, and also to identify deprived households that escaped the notice of both approaches. This was done in two villages, resurveying 147 of the 185 households in these villages. The result was a list of what may be termed the “qualitative poor” (QP); that is households that a majority of my respondents classified as poor or abjectly poor.9

    The number of QP households is higher than those given by all other methodologies (Table 8). Indeed, this accounts for over 40% of the overall number of houses revisited. I would venture to suggest that there might be some upward bias in these numbers; when I personally visited households with conflicting status, I usually found that such households were not as poorly off as they were made out to be.

    Table 8 also looks at households excluded in each approach vis-à-vis those that were classified as QP. A cut-off score of three under the SCA still results in over 40% of QP households being excluded. The restrictive approach excludes fewer QP.

    The most glaring exclusion of QP housholds under SCA is Shambhu Naik. He is a marginal farmer whose plot of land is


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    negligible (0.5 acres). He lives with his sister in a thatched hut as his governmentprovided Ashraya house is still being constructed. His house has no electricity; he spoke of going days with little or no food when the monsoon failed. Every single respondent categorised him in the “abjectly poor” category. Yet, the SCA awards him a mere two points and he slips under the radar of the methodology.

    The restrictive approach did not make conspicuous errors, at least as far as this sample is concerned. There are few unanimously poor households that the methodology leaves out. Furthermore, there is no


    june 25, 2011 vol xlvi nos 26 & 27

    Economic & Political Weekly


    common thread that binds the excluded households.

    The revisit also drove home a few ground realities. Separating households that were, quite literally, sitting on the poverty line proved quite difficult. In the end, some will always be left out, irrespective of how accurate an approach one uses. Certain persons – for example, the lower-level staff in a panchayat office, or anganwadi teachers – seemed to have a clear grasp of local conditions. My own analytical findings were not always accurate, but these persons could well tell me, with consummate ease and quite some precision, where most people stood in the income spectrum. Their access to specialised knowledge was beyond any methodology’s or researcher’s scope.

    Indeed, of all the QP households in the two villages, only one did not have a BPL card. This suggests, counter-intuitively, that the existing methodology worked pretty well. The BPL list was, in fact, drawn up by a process that could best be described as intuitive. The panchayat secretary and his assistant, ignoring official guidelines, would sit with the voter list, discuss, debate, visit houses and conjure up a list of poor. However, only 73% of the households revisited owned a BPL/Antyodaya Anna Yojana (AAY) card. This points to two lessons. One, if one wants to ensure that all poor households have access to a card, then overall BPL coverag e must be fairly high. Two, the importance of local knowledge cannot be exaggerated.

    5 Ease of Implementation

    During the course of the survey, it became amply clear that with respect to time taken to implement the survey, the SCA may be an improvement over practices currently in place, but the DKA is quite some distance ahead. As a surveyor, I found that the time taken for completion of the DKA questionnaire was much lower. On quite a few occasions, the inclusion and exclusion criteria were so evident that I need not have asked the questions. For example, in Ajri, I surveyed a household in the ST hamlet. Since STs are automatically included, that took care of all the inclusion-related questions. I was then ushered into a living room that had a TV.

    If I was only interested in identifying households based on the DKA, I might as well have thanked the household head and walked out.

    In terms of ease of understanding the criteria too, the SCA does not fare as well. Under the DKA, it is typically a matter of common knowledge whether a household has a baseline asset or satisfies an exclusion criterion. While outsiders might not exactly know all the assets a particular household owns or indeed, which inclusion criteria it satisfies, most of them would easily be able to identify one of each from a list of many.

    In contrast, I found it rather difficult to explain to even well-read villagers the intricacies of the points system, and the different weights for distinct characteristics. For Hosadu, the final village I evaluated, I managed to arrange for someone else to carry out the study, so that I could mitigate any inherent surveyor’s bias. While he seemed quite at ease asking either of the two sets of questions, I found his DKA-related sheets to be more reliably filled.

    Alternative Approach: Recognising that their method might be “too rigid, insofar as it does not allow for weighing and aggregation of different criteria, as in the scoring method” (2010: 61), Dreze and Khera propose an alternative binary scoring method. The essence of this method lies in awarding a single point for each of the inclusion criteria. Therefore, if a household satisfies only one of the inclusion criteria – say, it is an SC household – then it is assigned a score of

    1. If a household is doubly disadvantaged

    – say, it is an SC household and owns no agricultural land – it is assigned a score of two and so on.

    The single, and significant, point of deviation from the usual binary scoring method is that it includes an additional asset-based criterion. If a household does not own any of the baseline assets as defined previously, it is awarded a point that adds to its overall tally. This is in recognition of the fact that the lack of ownership of assets significantly curtails a household’s freedoms.

    Any household that has a binary score of at least two – i e, is doubly disadvantaged or worse – is included in the BPL list. Incorporating some form of aggregation, this proves advantageous since it is flexibl e and that inclusion [or exclusion] is not determined solely by a single characteristic.

    Under this methodology, slightly fewer than 35% of the households are poor in the given sample. This number is comparable to other independent estimates. The Tendulkar Committee’s Report puts the figure for rural poverty in Karnataka at 37.5%. The Udupi Human Development Report puts the figure at 24% for the district and at 28% for Kundapura Taluk for the 1990s (Government of Karnataka 2008: 48).

    This methodology also excludes fewer of the visibly needy. Of those excluded, none are landless agricultural labourers, only three are either SCs or STs and over 90% have at least one adult educated beyond class five.

    6 Conclusions

    The simplicity of the DKA is at the same time its greatest strength and weakness. Excluding or including households on the basis of a single characteristic in some cases requires such attributes to be very closely linked with poverty. While some exclusion criteria, like cars or fridges, are almost exclusively owned by the rich and therefore share a tight link with poverty, these are also quite narrow, excluding only a few who are well above the poverty line. As shown in this study, any attempt to broaden the criteria – by the addition of, say, TVs to the list

    – would result in considerable dilution of this link and exclusion of arguably deserving households.

    One would expect the obvious limitations of the SCA with regard to implementation to be overcome by its more comprehensive depiction of household indicators. That, however, does not occur. The SCA does no better and if anything, a little worse at identification and excludes a higher number of arguably deserving households than even the restrictive approach.

    In purely comparative terms, the DKA seems to have an edge – while it does no worse in ranking households, it is far simpler to implement. To pick one of the four combinatorial approaches from the DKA is problematic and contextual. Any approach

    june 25, 2011 vol xlvi nos 26 & 27


    Central Ground Water Board (2008): “Groundwater Information Booklet”, Government of Karnataka, Bengaluru.

    Dreze, J and R Khera (2010): “The BPL Census and a Possible Alternative”, Economic & Political Weekly 45(9): 54-63.

    Government of Karnataka (2008): Udupi Human Development Report (Bengaluru: Planning, Programme Monitoring and Statistics Department).

    Appendix Table A.1: Differences in Ownership of Key Assets

    Saxena, N C, Raghav Gaiha, N J Kurian, P Sainath, Harsh Mander, S E Seddey, M N Roy, K Raju, Manoj Kumar Singh, G S Sandhu, A K Misra, Amar Singh, Arun Kumar, Wilfred Lakka, Aruna Roy, S C Gautam and K L Datta (2009): “Report of the Expert Group on the Methodology for BPL Census”, Ministry of Rural Development, New Delhi. Accessed 20 June 2011: ReportofExpertGroupChaired-Dr.N.C.Saxena. pdf

    Car Fridge Landline Scooter Colour TV
    SWHH 0.2811 0.0732* 0.0677* 0.0043*** 0.1850
    Uneducated adults 0.0133** 0.0006*** 0.0002*** 0.0000*** 0.0000***
    Landless persons 0.3629 0.2769 0.0004*** 0.0003*** 0.0108***
    Landless agricultural labourers 0.0084*** 0.0002*** 0.0000*** 0.0000*** 0.0000***
    Casual labourers 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000***
    Artisan/fisherfolk/self-employed 0.1530 0.0802 0.0151** 0.1775 0.0886
    SC/ST 0.0340** 0.0061*** 0.0002*** 0.0080*** 0.0003***

    that gives primacy to exclusion criteria could potentially end up excluding deserving households; one that gives primacy to inclusion criteria would result in including at least 65% of the population, a fiscally burdensome proposition. The alternative approach looks promising, but has to be tested further.

    Finally, the causes for the poverty of several households were occurrences in the immediate past – for example, advent of illness for the primary breadwinner or a poor monsoon or two. The indicators used to characterise their economic status do

    not always capture this. Indeed, education of adult members above the age of 30, belonging to historically marginalised sec tion s or even asset ownership, especially land ownership, need not always reflect current economic status. This tension between proximate and distant causes of poverty is difficult to reconcile even theoretically, let alone in implementation.


    1 Dreze and Khera (2010) prefer the term Social Assistance Base (SAB) list to BPL, but I use these terms interchangeably.

    2 As the authors state, “[u]ltimately, both exclusion and inclusion errors may be important, but the main concern here is squarely with exclusion errors” (Dreze and Khera 2010:55).

    3 Dreze and Khera recognise this fact: “In some areas, living in a pucca house is no indication of economic prosperity, e g, because stone houses are easy to build or the weather makes it hard to survive in a kaccha house” (2010: 56).

    4 Using only a multiroom pucca house (and not the if clause that follows) as an exclusion criterion is not feasible since the bulk of government schemes in the region provide for two-roomed houses. Thus, several deserving poor would be eliminated. Dreze and Khera again anticipate this predicament: “some poor households live in pucca houses as beneficiaries of the Indira Awaas Yojana (IAY), a national programme for housing subsidies. In principle, one should consider ‘pucca houses other than IAY building’ as an exclusion criterion” (2010:6).

    5 The cap is arbitrary in the sense that it is not based on any normative criteria and is to be seen only as a tool for exposition and comparison.

    6 The analysis that follows treats SWHH like other households.

    7 Thus implying that any household with points strictly greater than three is included in the BPL list.

    8 Here, we do not, in the light of the previous analysis, consider SWHH as disadvantaged households.

    9 The respondents were asked to classify original participants into four categories: (1) the abjectly poor (roughly coinciding with the bottom 15%),

    (2) the poor (15-30%), (3) average households (30-50%), and (4) those above average (50% and above). In case of conflicting reports as to where a particular household stood, a simple majority rule was used to determine actual status. In nearly every case, respondents could easily point out those who were abjectly poor and those that were above average; those in between, however, were not as straightforward.


    In the test of proportions, we test for whetherthe advantaged groups own a higher proportion (P) of an asset than those that


    are disadvantaged (P), as prescribed under the two methodologies. For each such group, its complement set is taken to be the


    advantaged group. Null Hypothesis: H: P=P; H:P>P. ; The numbers presented in the table are all p-values; Sample size is 469


    households. *** 1 % level of significance. ** 5 % level of significance. * 10% level of significance.

    Figure A1: Number of Households at a Given Score under the SCA






    0 0 1 2 3 4 5 6 7 8 9 10 11

    X axis represents the score, and the Y axis represents the number of households with a given score.

    Figure A2: Score Curves for Various Assets


    0.8 07







    0 -0.1

    Y axis represents the proportion of households owning the asset and the X axis represents the score.

    0 2 4 6 8 10 12 Landline Car Fridge Scooter Colour TV

    Figure A3: Average Amount of Land Owned by Score


    2.5 2

    1..5 1

    0.5 0

    Average acres of land Owned
    0 1 2 3 4 5 6 7 8 9 10
    X axis represents the score, and the Y axis represents the average number of acres of land.
    june 25, 2011 vol xlvi nos 26 & 27 Economic & Political Weekly

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