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Safety Net Programmes: Outreach and Effectiveness

The safety net programmes, which are designed with three main purposes, protection (ex post), insurance (ex ante) and poverty alleviation, offer help to households during a period of crisis. This article evaluates the efficiency, awareness, participation, targeting and distributive outcomes of these programmes, based on household/village-level surveys conducted in Orissa, Madhya Pradesh and Karnataka. In addition, the article pays special attention to the functioning of village-level institutions and social capital. Besides giving an overview of the risks and shocks faced by households in these states, the article shows that the current safety net programmes do not seriously address the health risk. , , ,

Special articles

Safety Net Programmes: Outreach and Effectiveness

The safety net programmes, which are designed with three main purposes, protection (ex post), insurance (ex ante) and poverty alleviation, offer help to households during a period of crisis. This article evaluates the efficiency, awareness, participation, targeting and distributive outcomes of these programmes, based on household/village-level surveys conducted in Orissa, Madhya Pradesh and Karnataka. In addition, the article pays special attention to the functioning of village-level institutions and social capital. Besides giving an overview of the risks and shocks faced by households in these states, the article shows that the current safety net programmes do not seriously address the health risk.

S MAHENDRA DEV, K SUBBARAO, S GALAB, C RAVI

S
afety nets are non-contributory transfer programmes tar- geted at the poor and those vulnerable to poverty and shocks. These programmes help households manage risks. Within this overall role of safety nets, programmes are usually designed with three main purposes: protection (ex post), insurance (ex ante) and other considerations like poverty alleviation (targeting the transient poor and chronically poor), income redistribution and aiding economic growth [Subbarao 2003]. These motivations are complementary, and often overlap. For example, a programme with protection motivation could also alleviate poverty. In addition, the recent theory and evidence “offers a new perspective on social protection policies in poor countries, suggesting that there is scope for using these policies to compensate for the market failures that perpetuate poverty, particularly in high-inequality settings” [Ravallion 2003, WDR 2006]. Moreover, well-designed safety nets may also promote high risk/high return private investments by the households, and prevent adverse welfare outcomes (such as pulling children out of school) during periods of acute shocks/crises.

The state has a role to play in the design, financing and execution of safety net programmes in all countries, both developing and developed, though the extent of state involvement may vary with the level of development of a country and the degree of uninsured risk faced by households [Subbarao et al 1997]. In India, providing some measure of income security and ensuring a minimum level of well-being to the poor has been a central plank of public policy since independence. Towards this end a number of safety net programmes, known popularly as anti-poverty programmes, have been launched. The number and diversity of anti-poverty programmes has grown enormously, with the state governments often introducing their own programmes in addition to those sponsored and funded by the central govern ment. India spends annually about 2 per cent of the gross domestic product (GDP) on such programmes financed by the central government.

This paper examines some aspects of safety net programmes in three states, viz, Orissa, Madhya Pradesh and Karnataka based on household (hh) and village surveys. A special household survey was conducted in these three districts in 2005-06. Two blocks from each district and, five villages from each two blocks were selected. Thirty households per village (12 hhs participating in programmes and 18 hhs randomly) were selected. Thus we have a total of 1,356 sample households in nine districts covering three states. Focus group discussions in the village (separately for men and women) include interviews with block development officers and NGOs and, case studies. The collected data at the household level enabled us to construct indexes on asset, social capital and women’s participation.

The study has relevance in the context of the common minimum programmes’ (CMP) emphasis on effective implementation of safety net programmes. This has been restated and reinforced in the approach paper of the 11th Five-Year Plan [GoI 2006]. This study contributes to the existing literature by dealing simultaneously with four aspects reflecting on programme efficiency: awareness, participation, targeting and distributive outcomes (benefit incidence). While past research has focused on one or two of the above-mentioned aspects of programme efficiency, few studies have dealt with all aspects. In addition, this study also complements a quantitative analysis with a brief qualitative analysis based on the focus group discussions and household-level as well as village-level analysis paying particular attention to the functioning of village-level institutions and social capital. We have also taken the opportunity to elicit information on household risks and shocks.

It is important to state the limitations of the study at the outset. First, due to time and resource constraints, only a small household questionnaire could be canvassed, which did not include a consumption module. However, detailed questions were canvassed on assets so we could construct a wealth/ asset index which formed the basis for our distributive-share analysis. Second, not all programmes could be analysed. The programmes analysed depended on the extent and intensity of their operation which varied a great deal across the three states. Third, the quantitative analysis was limited to those aspects where there was a critical minimum sample size. Because of these limitations, the study does not purport to offer a ing health shocks is high for the bottom two quartiles (and it dominates all other idiosyncratic shocks), but unlike in Orissa, it falls to less than 17 per cent for the richest two quartiles. Also a much higher proportion of households experienced a drought risk in Madhya Pradesh than in Orissa, and that proportion rises sharply for the richest two quartiles.

In Karnataka, in the poorest quartile, a much higher percentage of households (50 per cent) reported drought risk than in the other two states; the proportion falls for the upper quartiles though still quite high. Like in Orissa and Madhya Pradesh, a high proportion of households in the poorest quartile reported sudden health shock as a major risk factor.

We show the relative importance of various risks in the three states and for all states combined (Figures 1, 2, 3 and 4). In the relatively more developed state like Karnataka, the incidence of health risk is about one half of the incidence of drought (which is not surprising because Karnataka has a large proportion of arid zone) whereas in a relatively poorer state like Orissa health risk dominates (which is also not surprising given the preponderance of malaria) alongside covariate risks. Madhya Pradesh is somewhere in between – health risk is about two-thirds of weather-induced covariate risks. Another interesting difference is that in Orissa not only is health risk hitting humans, but it is also hitting livestock – highest proportion of households experienced epidemics of livestock in Orissa in comparison with the other two states.

two quartiles (Table 1 columns 16-20, row 1). As for health risk, the proportion reporting is substantially higher for the poorer two quartiles compared with the top two quartiles. For the poorer two quartiles, drought and health risks are followed by death of a family member or livestock epidemic. For the richer two quartiles, the percentage of households reporting cyclone/ flood and pest attack is also high. When all states are taken together, it is interesting that the proportion reporting “robbery and violence” is small, but the proportion, though small, is twice as large for the poorest two quartiles compared with the top two quartiles – a clear reflection that failure to maintain law and order hurts the poor more than the non-poor.

Risk patterns vary by states. In the case of Orissa, the proportion of households reporting sudden health risk is more or less similar across quartiles, and it dominates all other risks for the bottom two quartiles. The proportion reporting weather risks is somewhat lower for the poorest quartile as compared to rich largely because they own less (or no) land asset.

In Madhya Pradesh too, the proportion of households report-

When all idiosyncratic risks for all states are considered 0.00 together, sudden health problem dominates as the principal risk for all quartiles (Table 1). Under covariate shocks, drought dominates other risks followed by cyclone/flood for all quartiles. The percentage of households reporting drought risks is about the same for the bottom two quartiles but increases for the top comprehensive treatment of all safety net programmes operating in the three states.

The paper is organised as follows. First section provides an overview of risks and shocks faced by the households in the three states. Awareness of anti-poverty programmes is discussed in Section II, followed by participation (Section III), targeting and benefit incidence (Section IV) and qualitative analysis (Section V). The last Section (VI) provides conclusions and draws some inferences for policy.

I Household Risks and Coping Strategies

idiosyncratic and covariate risks experienced by households. The results show that the wealth index has a significant negative relationship with idiosyncratic shocks (Table 2), confirming its dominance for all poor households. Scheduled castes and other backward castes (OBCs) have a significant positive relationship with idiosyncratic risks. Moreover, the probability of a household experiencing an idiosyncratic shock is higher if that household happens to be located in Orissa than in other states.

It is not surprising that wealth index and landownership have a positive relationship with covariate risks, reflecting the fact that the richer landowning classes have higher probability of drought or flood risk as compared to poorer households most of whom own no land or very little land. However, the schedule castes (most of whom are engaged in agricultural labour activity) have a significant positive relationship with covariate risks. The probability of a household experiencing a covariate risk is higher if located in Madhya Pradesh than in other states.

II Awareness of Safety Net Programmes

In the household questionnaire, the questions regarding awareness and participation are: (a) Are you aware about the following programmes? (b) If yes, did any member of the household participate in this programme during the last three

Using a logit model, we examined the factors that determine

Figure 2: Percentage of Households Reporting Different Risk Events (Madhya Pradesh)

35.00

30.00

25.00

20.00

15.00

10.00

5.00

0.00

Figure 1: Percentage of Households Reporting Different Risk Events (Orissa)

30.00

25.00

20.00

15.00

10.00

5.00

Others

Others

DroughtSudden health

problemCyclone/Drought

flood/hailstormSudden health Pest attack

problem

Pest attackLivestock

epidemicFire accidentDeath of other

family membersBad seed qualityDeath of HoHH

Bad seed quality Family division/Fire accident

divorceRobbery/violence

Death of HoHHFamily division/

Robbery/

violenceHuman epidemic

Figure 3: Percentage of Households Reporting Different Risk Figure 4: Percentage of Households Reporting Different Risk

Events (Karnataka) Events (All States)

35.00

40.00

35.00

30.00

30.00

25.00

25.00

20.00

20.00

15.00

15.00

10.00

10.00

5.00

5.00

0.00

0.00

Drought

Sudden healthproblemPest attackDeath of HoHHDeath of other

family membersLivestock epidemicHuman epidemicCyclone/flood/hailstormBad seed qualityFire accidentRobbery/violenceFamily division/

divorceOthers

DroughtSudden healthproblemCyclone/flood/hailstormPest attackLivestock epidemicDeath of other

family membersDeath of HoHHFire accidentBad seed qualityFamily division/

divorce

Human epidemicRobbery/violence

Others

years? (c) Who in the household participates/participated in these quartiles as compared to the bottom two quartiles (Table 3). programmes? The survey covers 25 safety net programmes; For example, a slightly lower percentage of households in the most of them being centrally-sponsored and funded schemes. poorest quartile are aware of the education-related programmes The 25 programmes cover four broad categories, viz, cash than the richer ones but the difference is not large. Most of transfer, in-kind transfer, work fare and subsidy based livelihood the differences (means) are not statistically significant. What programmes. However, only 13 programmes are covered for this suggests is that if the average awareness is high in the analysis because of limitations of sample size. We distinguish village for a specific programme, the awareness for all quartiles targeted and universal programmes among these. is likely to be high for that programme.

Across quartiles, differences in awareness are small for most Differences in awareness between villages: Our survey covers programmes. In general, for majority of the programmes, the 45 villages. We constructed an awareness index for all proawareness percentage is only slightly higher for the top two grammes at the village level, and used village-level factors as

Table 1: Percentage of Households Reporting Risk Events by the Type of Risk Events and by Quartiles

State Orissa Madhya Pradesh Q1 Q2 Q3 Q4 Total Q1 Q2 Q3 Q4 Total 1 2 3 4 5 6 7 8 9 10

Drought 12.39 33.93 30.36 34.82 27.84 22.03 16.22 40.35 50.00 32.17 Cyclone/flood/hailstorm 5.31 14.29 11.61 13.39 11.14 11.86 16.22 35.09 38.60 25.38 Pest attack 1.77 6.25 20.54 16.96 11.36 0.85 6.31 21.93 26.32 13.79 Bad seed quality 0.00 0.00 1.79 3.57 1.34 0.00 1.80 6.14 3.51 2.84 Livestock epidemic 6.19 13.39 11.61 10.71 10.47 0.00 7.21 6.14 7.02 5.03 Fire accident 0.00 2.68 0.00 0.89 0.89 3.39 0.90 6.14 3.51 3.50 Robbery/violence 0.88 0.89 0.89 0.89 0.89 1.69 0.90 0.88 0.88 1.09 Human epidemic 0.00 0.89 0.00 0.89 0.45 0.00 1.80 0.00 0.00 0.44 Death of HoHH 3.54 2.68 3.57 3.57 3.34 0.85 3.60 0.88 0.88 1.53 Death of other family members 4.42 5.36 7.14 3.57 5.12 1.69 2.70 3.51 4.39 3.06 Sudden health problem 27.43 29.46 25.89 29.46 28.06 27.97 24.32 17.54 11.40 20.35 Family division/divorce 0.88 0.89 1.79 0.00 0.89 2.54 2.70 2.63 1.75 2.41 Others 0.00 0.00 0.00 0.00 0.00 0.85 0.00 0.00 0.00 0.22

State Karnataka Total

Q1 Q2 Q3 Q4 Total Q1 Q2 Q3 Q4 Total 11 12 13 14 15 16 17 18 19 20

Drought 49.56 28.32 33.04 33.93 36.22 27.91 26.19 34.62 39.64 32.08 Cyclone/flood/hailstorm 0.00 0.88 2.68 3.57 1.78 5.81 10.42 16.57 18.64 12.83 Pest attack 2.65 0.88 8.04 15.18 6.67 1.74 4.46 16.86 19.53 10.62 Bad seed quality 0.00 0.00 0.89 2.68 0.89 0.00 0.60 2.96 3.25 1.70 Livestock epidemic 1.77 3.54 1.79 3.57 2.67 2.62 8.04 6.51 7.10 6.05 Fire accident 0.88 0.00 0.00 2.68 0.89 1.45 1.19 2.07 2.37 1.77 Robbery/violence 1.77 0.88 0.00 0.00 0.67 1.45 0.89 0.59 0.59 0.88 Human epidemic 2.65 1.77 1.79 2.68 2.22 0.87 1.49 0.59 1.18 1.03 Death of HoHH 4.42 3.54 4.46 2.68 3.78 2.91 3.27 2.96 2.37 2.88 Death of other family members 4.42 0.88 4.46 2.68 3.11 3.49 2.98 5.03 3.55 3.76 Sudden health problem 30.97 17.70 12.50 15.18 19.11 28.78 23.81 18.64 18.64 22.49 Family division/divorce 0.00 0.00 1.79 0.00 0.44 1.16 1.19 2.07 0.59 1.25 Others 0.00 0.88 0.00 0.00 0.22 0.29 0.30 0.00 0.00 0.15

Notes: Idiosyncratic risks include: fire accident, robbery/violence, death of head of household, death of others in family, sudden health problem, long-term health problems, family division/divorce. Covariate risks include: drought, cyclone/flood/hailstorm, pest attack, bad seed quality, livestock epidemic, human epidemic.

independent variables to see if these factors influence aware-Karnataka and Orissa, the corresponding participation rate is ness at the village level. The logit model results are presented more than 60 per cent. in Table 4. Factors (combined for all states) such as better Factors determining participation: Participation in the safety functioning of the panchayat raj institutions (PRIs), status nets depends on several factors such as ownership of assets, of women in the household, presence of an NGO in the vil-social characteristics like caste, occupation of the household,

lage and high (overall) level of education in the village have contributed positively and significantly to creating awareness of safety net programmes in sample villages. However, when disaggregated state-level PRI functioning indices are used as explanatory variables, relative to Karnataka, the coefficients for Orissa and Madhya Pradesh are either non-significant or have a negative sign. It is not clear why in Orissa (negatively related to PRI) and Madhya Pradesh PRI functioning has not led to better awareness and participation – we pursued this issue in our qualitative analysis (Section V). An interesting finding is that awareness of safety net programmes is low in wealthier villages. Understandably, the concern (and probably the demand) for safety net programmes seems to be high in relatively poorer villages. At the village level, awareness overall is better in Orissa and Madhya Pradesh than in Karnataka.

III Programme Participation: A Household Level Analysis

Awareness of the programme is a precondition for participation but it does not guarantee participation. This section looks at participation rates at the household level by quartiles based on wealth/assets and by social groups. All households (all states): The participation rates are more than 60 per cent for in-kind programmes like public distribution system (PDS), Integrated Child Development Scheme (ICDS), mid-day meal and free textbooks. The PDS showed the highest participation rate (70 per cent) among the 13 programmes considered in the study (Table 5). As expected, the participation rates of PDS are higher for below poverty line (BPL) households than for above poverty line (APL) households. On the other hand, the higher awareness rates notwithstanding, the participation rate is less than 10 per cent for programmes like the Antyodaya Anna Yojana (AAY), Sampoorna Grameen Rozgar Yojana (SGRY), food for work and Swarnajayanti Gram Swarojgar Yojana (SGSY). For pension schemes it is around 30 per cent. As for differences between the states, participation rates for all households are higher in eight programmes in Orissa than in Madhya Pradesh and Karnataka. It is a source of concern that only 32 per cent of BPL households participated in the PDS in Madhya Pradesh despite higher awareness. In

Table 2: Results of a Logit Model of Determinants of Risks Experienced by Households

Variables Idiosyncratic Covariate
B SE Sig B SE Sig
Landowned 0.0031 0.0121 0.7956 0.0240 0.0150 0.1098
Wealth index -0.0052 0.0015 0.0005 0.0067 0.0015 0.0000
Caste (Ref: Others)
Scheduled castes 0.4334 0.2244 0.0535 0.6634 0.2038 0.0011
Scheduled tribes -0.1139 0.2175 0.6004 -0.1709 0.1938 0.3777
Other backward
castes 0.5837 0.2066 0.0047 0.4451 0.1834 0.0152
States
Orissa 0.5223 0.1532 0.0007 0.1731 0.1441 0.2296
Madhya Pradesh 0.0424 0.1554 0.7851 0.4350 0.1425 0.0023
Constant -0.4515 0.3310 0.1726 -1.8499 0.3218 0.0000

Table 3: Awareness about Programmes: All States (Per cent)

Quartiles
1 2 3 4 All
Cash transfer programmes
Targeted
Indira Awas Yojana 68.02 71.13 68.93 63.61 67.92
National Old Age Pension Scheme 59.59 62.8 57.1 54.73 58.55
Widow/disable pension 60.76 63.99 58.58 54.73 59.51
Universal
Rural education scholarship 29.65 29.46 28.11 33.43 30.16
In-kind transfer programmes
Targeted
Public distribution system 86.05 90.48 92.9 94.97 91.08
Antyodaya Anna Yojana 34.01 33.33 32.84 31.36 32.89
Universal
Integrated Child Development
Services 26.74 33.63 36.98 37.28 33.63
National Mid-Day Meal Scheme 67.73 77.38 78.11 68.93 73.01
Free textbook 60.17 67.56 71.6 68.64 66.96
Free hostel 18.02 30.95 31.07 33.14 28.24
Free uniform 51.16 59.82 62.72 61.54 58.78
Workfare programmes (self-targeted)
Sampoorna Grameen Rozgar
Yojana 25 33.33 30.18 29.59 29.5
Food for work 28.49 29.76 25.74 26.04 27.51
Subsidy based livelihood programmes
Targeted
Swarnajayanti Gram Swarojgar
Yojana 10.17 16.96 18.93 21.3 16.81
Table 4: Village Level Determinants of Awareness
of Programmes
Dependent Variable: Awareness of All Programmes
Coefficients(a)
Model Beta T Sig
1 (Constant) -0.128 0.900
PRI functioning index 0.261 1.820 0.087
Social infrastructure -0.019 -0.122 0.905
Economic infrastructure 0.022 0.146 0.886
HH structural social capital -0.758 -1.454 0.165
Trust PRI -0.034 -0.140 0.891
Trust officials 0.204 0.781 0.446
Trust groups -0.029 -0.179 0.860
Total female participation (elec, meet, etc) 0.246 1.368 0.190
Empowerment 0.548 2.863 0.011
Control on assets -0.073 -0.487 0.633
Average index -0.411 -1.754 0.098
Ratio of female and male literacy -0.126 -0.429 0.673
Per cent of female literacy 0.219 0.595 0.560
Migrated 0.009 0.069 0.946
Presence of NGO in village 0.291 1.907 0.075
Social composition of village (Herfindal) 0.080 0.573 0.575
Per cent of landless -0.057 -0.150 0.882
Per cent of small farmers 0.036 0.120 0.906
Per cent of HH with at least one educated 0.342 1.817 0.088
Orissa dummy 2.198 2.477 0.025
Madhya Pradesh dummy 1.671 2.255 0.039
Orissa – social capital 0.409 0.988 0.338
MP – social capital 0.597 0.855 0.405
Orissa – women’s autonomy and
decision-making -0.949 -1.376 0.188
MP – women’s autonomy and
decision-making -1.231 -2.351 0.032
Orissa – PRI functioning -0.957 -2.039 0.058
MP – PRI functioning -0.510 -1.040 0.314

literacy, household size, sex of the head of the household, social capital of households, women’s autonomy and participation, village level characteristics like social and economic infrastructure, functioning of local councils like PRIs. The factors determining participation of households in the programmes are examined using multivariate analysis. We have selected 13 programmes, for which the sample size is sufficiently large. For each of these programmes we have estimated a logistic regression model to determine the factors explaining the participation of households:

P = f(H, V)

where P takes value 1, if any member of the household participates in the programme and zero otherwise. H and V are household and village level variables. At household level, we have considered the following variables:

  • (1) Wealth index: It is an index computed based on the household asset base. For the multivariate analysis, we used quartiles formed with the wealth index. The use of quartiles in the regression is found to give better fit than when wealth index is used directly. If the participation of household into any programme is pro-poor, the wealth effect on participation should be negative.
  • (2) Caste groups: Caste is an important factor in explaining the household participation in programmes. With low levels of social and economic attainment, we expect higher probability of participation among SC and ST households.
  • (3) Household size: Ceteris paribus, large households may be expected to be poorer than small households, and so large households may be expected to seek and participate in any safety net programme.
  • (4) Sex of the head of the household: In general, the femaleheaded households may be expected to participate in safety net programmes more than the male-headed households.
  • (5) Percentage of literates in the household: Households with a larger number of literates may be more likely to participate in child/education centred programmes and less likely to participate in the workfare/livelihood programmes.
  • (6) Occupation of head of the household: We expect higher probability of participation for households predominantly engaged in labour-based activities than self-employed households.
  • (7) Household experiencing shocks/risks: In order to examine the interaction between household experiencing shocks and its participation in the programme, we included variables indicating shocks and risks. We included two types of shocks, idiosyncratic and covariate. It is worth stressing that while the incidence of risks/shocks is for the previous three years, the programme participation is for the current year (of the survey).
  • (8) Household structural social capital: The social capital of the household is computed as the participation of household members in a number of social/political groups. The social capital index indicates the networking ability of the household. We expect that the household with higher social capital is to have higher probability of participating in the programme.
  • (9) Women’s autonomy and decision-making: This is an index based on the women’s decision-making on various household issues (like child education, marriages, health, savings, investments, etc) and ability of the women to do a few things on her own independently. We expect households with higher women’s autonomy to be more likely to participate in the programme.
  • (10) Women’s participation in meetings and elections: This index is based on the women’s participation and raising issues (any) in the meetings of village councils/education committees/
  • water user associations and women’s participation in the elections (village/block/district/state council and national elections). We expect similar participation in the safety net programme to increase with women’s participation index.

    We used age of the head of the household (HoHH) and average land size as controls. At the village level we used the following controls:

  • (1) Social/economic infrastructure: Two separate indices have been constructed to capture the level of village development with respect to social and economic infrastructure. These are used as controls. The households located in the higher social/ economic infrastructure may be expected to have higher probability of participating in programmes.
  • (2) Functioning of PRIs: The index of functioning of PRI is based on the regularity of the village council meetings and active participation of SCs and STs. The households in the villages with better functioning of PRIs may have higher probability of participation in programmes.
  • (3) Average wage rate in the village: It is an important determinant of participation in case of workfare programmes and SGSY.
  • A summary of the variables with their coefficients and statistical significance levels are given for 13 programmes in Table 6. Which factors are generally important for participation in the safety net programmes? The results from Table 6 are summarised below.
  • (a) Quartiles: The quartiles based on assets do not seem to have a significant relationship with the participation in the majority of the programmes. For example, the lowest quartile is significantly positive only for three programmes, viz, widow pensions, mid-day meals (MDM) and free textbooks. In fact, the third quartile is positively significant for three programmes viz, Indira Awas Yojana (IAY), PDS under BPL and mid-day meals.
  • (b) Social groups: On the other hand, the households belonging to socially disadvantaged sections have a significant positive
  • Table 5: Participation Rates for All HHs: All States and Individual States (Per cent)

    All States Orissa MP Karnataka
    Cash transfer programmes
    Targeted
    Indira Awas Yojana (IAY) 10.57 12.98 10.35 8.91
    National Old Age Pension Scheme
    (NOAP) 31.04 43.76 32.48
    Widow/disable pension 24.94 29.37 26.16
    Universal
    Rural education scholarship 32.66 44.73 24.47
    In-kind transfer programmes
    Targeted
    Public distribution system (APL) 20.82 12.02 42.81 2.96
    Public distribution system (BPL) 50.1 60.24 32.34 62.11
    Public distribution system (PDS) 69.59 69.04 74.37 64.62
    Antyodaya Anna Yojana (AAY) 4.61 7.94 4.16
    Universal
    Integrated Child Development
    Services (ICDS) 64.88 77.54 51.64 50.47
    National Mid-Day Meal Scheme 68.4 68 72.72 63.79
    Free textbook 67.77 70.07 78.26 56.91
    Free uniform 51.88 48.61 62.52 46.94
    Workfare programmes (self-targeted)
    Sampoorna Grameen Rozgar
    Yojana (SGRY) 8.23 22.93 4.04
    Food for work (FFW) 9.73 6.27 17.76
    Subsidy-based livelihood programmes
    Targeted
    Swarnajayanti Gram Swarojgar
    Yojana (SGSY) 3.29 10

    relationship with participation in many programmes. For example, the SCs have significant and positive coefficient for eight out of 13 programmes, viz, IAY, rural scholarship, AAY, MDM, free text, free uniform, SGRY and food for work programmes. In comparison with scheduled castes, the households belonging to scheduled tribes have probability of participation in only fewer (four) programmes.

  • (c) Household size: It has a significant and positive influence on household participation of seven programmes, viz, IAY, National Old Age Pension (NOAP), rural education scholarship, ICDS, MDM, free text and free uniform. Female-headed households do not show significant relationship for most of the programmes. Similarly, with few exceptions, occupation groups do not seem to significantly influence the participation.
  • (d) Percentage of literates: The percentage of literates in the household is positively related to four education-related programmes and negatively with seven poverty-centred programmes. These results are in the expected direction.
  • (e) Size of landholding: It has a negative relationship for four programmes (NOAP, PDS under BPL, free uniforms and food for work). In other programmes, land variable is not significant.
  • (f) Covariate risks and idiosyncratic risks: In case of IAY, ICDS and MDM the covariate risk is positively associated with the participation in the programmes, whereas in the case of SGRY, it is negatively associated. Idiosyncratic risk is significant in case of only three programmes, rural education scholarships,
  • free uniforms and SGSY. However, it is negatively associated with free uniforms and SGSY. It is worth stressing that the relationship bet ween risks and programme participations can be better explained only with panel data, which is beyond the scope of this study.

  • (g) Village infrastructure: It does not have much influence on the households participation in many safety programmes. Social infrastructure has a positive influence on participation in two programmes while economic infrastructure has negative coefficient in only one programme. It is interesting that economic infrastructure does not influence programme participation generally.
  • (h) PRIs: It is also interesting to note the functioning of PRIs also do not positively influence on the participation in safety net programmes. In fact, it has a negative (counter-intuitive?) relationship on participation in three programmes, viz, widow pensions, rural scholarships and the AAY.
  • (i) Social capital: One interesting finding is that social capital has a positively significant relationship with participation in nine out of 13 programmes. This is an important finding of this study. It shows that household’s networking ability goes a long way in promoting the household participation in safety net programmes. Accessing programmes: One of the factors often mentioned in the literature on anti-poverty programmes is that the poor often take the help of various functionaries to access programmes
  • Table 6: Significance Levels of the Variables in 13 Safety Net Programmes

    Variables Dependent Variables
    IAY NOAP Widows’ Rural Edu PDS AAY ICDS Mid-day Free SGRY FFW SGSY
    Pensions Scholarships (BPL) Meals Uniforms
    Wealth index (Ref: 4th quartile)
    1st quartile 0.603 -0.914 1.056 * -0.989 * 0.310 0.637 0.554 0.888 ** 0.127 -0.179 -0.595 -0.133
    2nd quartile 0.366 -0.802 1.217 ** -0.663 0.174 0.875 -0.097 0.484 * 0.120 0.458 -0.559 0.232
    3rd quartile 0.606 * -0.996 * 0.373 -0.492 0.462 ** 0.993 -0.517 0.399 * 0.340 0.222 -0.289 0.052
    Social group (Ref: others)
    SC 0.687 * -0.599 -2.237 ** 1.099 ** 0.130 1.663 0.456 0.629 ** 1.253 ** 1.146 ** 1.331 * -1.098 *
    ST 0.491 -0.825 -0.976 * 0.485 0.260 1.965 * 0.063 0.286 1.322 ** 1.288 ** 1.913 ** -0.700
    OBC -0.053 -0.366 0.082 -0.364 -0.204 1.779 * 0.632 0.410 -0.128 1.380 ** 1.137 * -0.291
    Household size 0.109 ** 0.231 ** -0.017 0.339 ** 0.028 0.008 0.444 ** 0.513 ** 0.214 ** 0.050 0.114 * -0.034
    Sex of HoHH (Ref: male)
    Female 0.575 * 0.417 2.763 ** 1.602 ** 0.285 0.858 * -0.079 0.397 0.059 0.144 1.289 ** 1.008 *
    Percentage of literates in HH -0.005 * -0.033 ** -0.021 ** 0.018 ** -0.003 -0.012 * -0.021 ** 0.014 ** 0.020 ** -0.013 ** -0.010** -0.006
    Occupation of HoH (Ref: non-worker)
    Self-employed in agriculture 0.650 -1.033 ** 0.162 0.126 -0.016 -0.846 -0.169 0.152 -1.186 0.861 2.529 ** 0.605
    Agricultural labour 0.522 -1.418 ** -0.372 1.318 0.261 0.588 0.183 0.177 -0.941 0.741 2.445 ** -0.582
    Self-employed in non-agriculture 0.923 * -1.667 ** 1.517 ** -0.536 0.075 0.318 0.551 -0.096 -1.060 0.438 1.592 0.099
    Non-agriculture labour 0.376 -2.299 ** 0.138 1.055 0.182 0.041 -0.222 0.017 -1.174 1.108 * 1.457 -0.627
    Others -0.318 -1.353 -0.224 -1.472 -0.349 -17.962 -0.689 -1.025 ** -1.814 ** 0.376 0.720 -1.009
    HH experiencing any
    idiosyncratic risks (Ref: none) -0.173 -0.267 0.493 0.641 ** 0.186 0.231 -0.143 0.098 -1.076 ** -0.391 0.024 -0.958 **
    HH experiencing any covariate
    risks (Ref: none) 0.327 * -0.268 0.263 -0.015 -0.191 0.308 0.727 ** 0.298 * -0.067 -0.436 * -0.463 0.300
    Age of HoHH -0.007 ** -0.008 -0.031 ** 0.013 ** 0.022 * -0.018 * -0.031 ** -0.008 -0.001 0.004 -0.008
    Land possessed -0.052 -0.147 * 0.004 -0.005 -0.027 0.006 0.003 0.017 -0.060 * -0.025 -0.118 * 0.023
    Social infrastructure -0.208 -0.226 3.991 ** 1.120 0.228 0.123 -0.751 0.160 0.884 3.452 ** 2.984 ** -5.475 **
    Economic infrastructure 0.668 1.340 0.950 0.130 0.362 -1.235 -0.429 -0.404 0.540 -1.425 -1.457 1.147
    Functioning of PRIs 0.890 -0.415 -2.468 ** -1.810 ** 0.324 -2.570 ** -0.171 0.464 0.424 0.865 0.982 -0.379
    Household structural social
    capital 0.807 ** 0.036 0.494 0.692 * 0.676 ** 0.254 0.293 0.687 ** 0.435 0.833 ** 0.738 ** 1.712 **
    Women autonomy and
    decision-making score -0.145 0.099 -0.128 1.593 ** -0.193 -0.071 1.004 * 0.240 0.090 -0.656 -0.285 -0.121
    Women participation in
    meetings and elections 0.161 1.095 -1.213 0.251 0.350 0.826 1.116 1.502 ** -1.123 2.693 ** 0.695 0.049
    Average wage in village 0.004 -0.020 * 0.031 **
    State (Ref: Karnataka)
    Orissa 0.387 1.341 ** -0.384 -2.066 ** -0.357 ** 0.704 1.755 ** 0.562 ** 0.125 2.468 ** 0.334 4.598 **
    Madhya Pradesh -0.058 1.210 * -1.028 * 1.164 ** -1.619 ** 1.376 ** 0.718 0.432 * 0.824 * 0.462 1.410 ** 1.547
    Constant -4.363 ** -0.476 -1.792 -3.667 ** -0.728 -5.371 ** -2.583 ** -4.407 ** -1.883 -7.329 ** -6.734 ** -6.003 **
    Notes: * Significant at 1 per cent level.
    ** Significant at 5 per cent level.
    3560 Economic and Political Weekly September 1, 2007

    including such things as obtaining a BPL card. Ideally, the poor Table 7: Factors Determining the Contacts with Middle Men

    should be able to gain access to BPL cards and other programmes without anybody’s help, but typically that did not appear to be case in villages we surveyed. So we collected the village-level information on the role of middlemen (or other “contacts”) in accessing the programmes, and computed a village-level index, and used various village-level factors to assess what factors influence the dependence of poor households on middlemen to access programmes. Results are reported in Table 7.

    Female literacy and women’s general status in the household and households “trust” in public institutions significantly reduce the dependence on middlemen. The dependence on middlemen is higher in the relatively poorer state of Orissa than in the other two states.

    IV Pro-Poor, Pro-Rich or Pro-All? Targeting and Benefit Incidence

    By their very definition, the anti-poverty programmes are supposed to be targeted to poor households, i e, the households below the poverty line. We have divided all households into four quartiles in terms of the asset/wealth index. In order to gain insights into targeting efficiency, we look at the distribution of beneficiary households by quartiles. This is shown in Table 8. One would expect the beneficiary households to belong to the poorest quartile or the bottom two quartiles. We see a different picture. For IAY, widows pension, AAY, SGRY, FFW and rural education scholarship, the bottom three quartiles account for the bulk of participants. The beneficiaries are about equally distri buted across all quartiles for PDS, ICDS, MDM, free uniforms and free textbooks. A high proportion of beneficiaries of credit-based micro finance programme (SGSY) belong to the richest quartile. It is clear that with few exceptions, most anti-poverty programmes are reaching the poorest quartile as well as not-so-poor quartiles – thus confirming the widely acknowledged fact of poor targeting.

    The distribution of beneficiary households by quartiles does not inform us about the actual benefits (kilos of grain, for example). We have tried to quantify the benefits and estimated distributive shares across quartiles for major safety net programmes. The results are discussed below. Public distribution system (PDS): The PDS benefits targeted to BPL families are supposed to go to the lowest two quartiles (particularly to the bottom quartile). Table 9 shows that the lowest quartile for all states gets around 30 per cent for rice and wheat and 36 per cent and 40 per cent for sugar and kerosene, respectively. The second quartile also gets more than 25 per cent for all the commodities. However, the share of the third quartile is high for wheat at 30 per cent and 25 per cent for rice. The richest quartile also gets 17 per cent for rice and less than 15 per cent for other commodities. The results can be interpreted in two ways. The programme is reasonably targeted to the poor as their share is higher than the top two quartiles. However, there is substantial leakage of benefits to the non-poor.

    Statewise details show that the programme effectiveness, in terms of higher quantity of benefits reaching the poor, in Orissa and Madhya Pradesh is far better than Karnataka (Table 9). In Karnataka, the second and third quartiles receive most of the benefits; even the fourth quartile receives substantial benefits. By contrast, in Madhya Pradesh the benefits accruing to richest quartile are negligible for all commodities. Antyodaya Anna Yojana: As one would expect, this programme

    Dependent Variable: Contacting Middle Man (All Programmes)
    Coefficients(a)
    Model Standar- t Sig
    dised
    Unstandardised Coeffi-
    Coefficients cients
    Beta Std Error Beta
    1 (Constant) -0.122 0.132 -0.925 0.369
    PRI functioning index -0.048 0.033 -0.255 -1.483 0.158
    Social infrastructure 0.002 0.050 0.006 0.030 0.976
    Economic infrastructure 0.027 0.039 0.124 0.696 0.497
    HH structural social capital 0.091 0.076 0.743 1.190 0.251
    Trust PRI -0.030 0.068 -0.131 -0.445 0.662
    Trust officials -0.064 0.107 -0.187 -0.597 0.559
    Trust groups 0.129 0.046 0.540 2.796 0.013
    Total female participation
    (elec, meet, etc) 0.047 0.079 0.129 0.596 0.559
    Empowerment 0.126 0.101 0.286 1.247 0.230
    Control on assets -0.377 0.247 -0.275 -1.524 0.147
    Average index 0.000 0.000 0.083 0.295 0.772
    Ratio of female and
    male literacy 0.183 0.084 0.769 2.183 0.044
    Percentage of female
    literacy -0.244 0.103 -1.048 -2.371 0.031
    Migrated 0.000 0.001 -0.139 -0.884 0.390
    Presence of NGO in
    village 0.006 0.021 0.050 0.274 0.788
    Social composition of
    village (Herfindal) -0.037 0.034 -0.181 -1.077 0.298
    Percentage of landless 0.130 0.104 0.569 1.249 0.230
    Percentage of small
    farmers 0.113 0.078 0.519 1.455 0.165
    Percentage of HH with
    at least one educated 0.067 0.047 0.326 1.444 0.168
    Orissa dummy 0.296 0.084 3.766 3.541 0.003
    Madhya Pradesh dummy -0.019 0.071 -0.240 -0.270 0.791
    Orissa – social capital -0.117 0.099 -0.587 -1.184 0.254
    MP – social capital -0.064 0.083 -0.651 -0.777 0.448
    Orissa – women’s autonomy
    and decision-making -0.586 0.209 -2.319 -2.803 0.013
    MP – women’s autonomy
    and decision-making -0.090 0.150 -0.375 -0.597 0.559
    Orissa – PRI functioning -0.105 0.080 -0.745 -1.323 0.205
    MP – PRI functioning 0.113 0.062 1.075 1.828 0.086

    Table 8: Distribution of Participants by Quartiles: All States (Per cent)

    Quartiles
    1 2 3 4 Total
    Cash transfer programmes
    Targeted
    Indira Awas Yojana 33.61 26.03 29.08 11.27 100
    National Old Age Pension Scheme 29.01 27.6 13.63 29.76 100
    Widow/disable pension 37.01 34.43 18.25 10.31 100
    Universal
    Rural education scholarship 33.05 23.38 25 18.57 100
    In-kind transfer programmes
    Targeted
    Public distribution system (BPL) 31.49 24.71 25.78 18.02 100
    Antyodaya Anna Yojana 38.1 29.08 26.4 6.43 100
    Universal
    Integrated Child Development
    Services 25.41 22.06 26.2 26.33 100
    National Mid-Day Meal Scheme 27.94 24.16 26.29 21.62 100
    Free textbook 28.83 23.12 26.98 21.07 100
    Free uniform 29.54 24 26.62 19.84 100
    Workfare programmes (self-targeted)
    Sampoorna Grameen Rozgar
    Yojana 29.1 31.81 23.8 15.29 100
    Food for work 42.14 26.74 19.02 12.09 100
    Subsidy-based livelihood programmes
    Targeted
    Swarnajayanti Gram Swarojgar
    Yojana 21.93 24.26 21.92 31.89 100

    fares better in terms of benefit incidence (Table 10). The lowest quartile for all states gets around 36 per cent to 44 per cent of the total. In the case of wheat, second and third quartiles get higher than the first quartile. The share of top quartile was less than 5 per cent for all commodities except rice. At the state level, the performance is the best in Karnataka followed by Orissa. In Orissa, all commodities except kerosene are well-targeted to the bottom two quartiles. In Madhya Pradesh, the third quartile also gets substantial benefits from all commodities. SGRY: Since wage employment may be expected to be selftargeted, the bottom two quartiles should participate disproportionately in the programme. We disaggregated the information also by gender (Table 11). The wage employment generated by SGRY for all states shows that the benefits are going more to the second quartile as compared to the first quartile. More than 80 per cent of the males in SGRY belonged to the second quartile. Interestingly, the targeting seems to be more effective for females than for males. Around 59 per cent of the females working in SGRY belong to the lowest quartile. Among the seasons, works during rabi and summer seem to be more pro-poor for females as seen by the higher share of lowest quartile. In the case of males, the share of the second quartile dominates in all the seasons. Clearly the programme is not attracting the richer quartiles, thus confirming some degree of self-selection. Food for work: The employment generated under food for work seems to be even more pro-poor than SGRY. The lowest quartile has a share of 43 per cent in the total employment generated under food for work (Table 12). In all the seasons, the share of the lowest quartile is the highest. Particularly, the share of the first quartile in summer season (when unemployment rates are high and the opportunity cost of labour is low), FFW employment is 68 per cent. About the benefits to males and females, the results are similar to those of SGRY. However, we have a counter-intuitive result: 28 per cent of the employment is generated by the households belonging to the rich quartile. The reasons for participation by the rich are not known.

    V Findings from Qualitative Analysis

    To supplement the quantitative analysis, we conducted focus group discussions and also collected brief family histories of selected poor households who either attempted but failed or actually participated and benefited from safety net programmes. Ideally, a qualitative analysis should throw light on those aspects in which the quantitative analysis is either inadequate or throws some puzzles. For example, we found that in two important dimensions, awareness of programmes and the pathways in which households access programmes, the contribution of PRI institutions has been minimal except in Karnataka. In order to unpack some of these puzzles, we organised focus group discussions across the different stakeholders that included the target communities, lower level bureaucracy, NGOs and political leaders.

    The respondents from the communities across the three states generally agreed that the functioning of safety net programmes depended a great deal on the institutions implementing the programmes including the local governing bodies (PRIs), elected representatives and grassroot level bureaucracy, and the relevant institutions/agencies channelling the funds, as well as “activeness” of target communities themselves. The latter depended on households’ literacy levels, cohesiveness among women, and the extent of caste, religious and class discrimination practices prevalent in the communities.

    An important factor noted by respondents is the role caste and religion plays in the functioning of local governing bodies including PRIs. Significant discrimination against SC/ST and minorities is noted as one pervasive feature of local bodies. For example, there is hardly any representation of tribals in local bodies. The awareness levels of tribal households are very low in all the states. In some villages, the failure to access major safety net programmes is evident among minorities. The village-level institutions functioned poorly due to bureaucratic interference in Madhya Pradesh relative to Orissa and Karnataka. In particular, interventions by politicians in conjunction with caste-based discriminations led to poor governance of

    Table 9: Percentage Distribution Quantities of Important Commodities Purchased in PDS (BPL) by Quartiles

    State/Quartiles Commodity
    Rice Wheat Kerosene Sugar
    Orissa
    Q1 29.31 31.98 39.73 32.76
    Q2 24.02 17.27 19.40 24.69
    Q3 25.49 31.01 22.18 22.05
    Q4 21.18 19.73 18.69 20.49
    All 100.00 100.00 100.00 100.00
    Madhya Pradesh
    Q1 47.44 36.87 56.43 48.75
    Q2 27.61 23.32 24.84 26.00
    Q3 20.27 31.21 13.90 16.57
    Q4 4.68 8.60 4.83 8.68
    All 100.00 100.00 100.00 100.00
    Karnataka
    Q1 25.98 24.13 24.95 24.44
    Q2 29.87 28.35 31.41 30.20
    Q3 25.33 27.14 26.14 26.77
    Q4 18.81 20.38 17.50 18.60
    All 100.00 100.00 100.00 100.00
    All States
    Q1 30.25 31.92 40.05 36.28
    Q2 27.81 25.10 26.36 27.64
    Q3 24.60 29.66 20.65 21.65
    Q4 17.33 13.32 12.94 14.43
    All 100.00 100.00 100.00 100.00

    Table 10: Percentage Distribution (Quantities) of Important Commodities Purchased in PDS (AAY) by Quartiles

    State/Quartiles Commodity
    Rice Wheat Kerosene Sugar
    Orissa
    Q1 43.78 3.04 21.90 31.92
    Q2 16.44 95.10 16.90 48.62
    Q3 21.70 0.00 52.68 0.00
    Q4 18.07 1.85 8.52 19.46
    All 100.00 100.00 100.00 100.00
    Madhya Pradesh
    Q1 25.72 19.48 53.05 29.14
    Q2 15.80 34.94 17.97 28.09
    Q3 58.48 45.58 28.97 42.77
    Q4 0.00 0.00 0.00 0.00
    All 100.00 100.00 100.00 100.00
    Karnataka
    Q1 50.31 53.11 38.24 53.70
    Q2 40.58 37.14 53.59 38.95
    Q3 0.00 0.00 0.00 0.00
    Q4 9.12 9.75 8.17 7.35
    All 100.00 100.00 100.00 100.00
    All States
    Q1 41.98 22.71 44.00 36.18
    Q2 23.02 40.09 25.56 32.89
    Q3 23.03 35.74 27.06 27.19
    Q4 11.96 1.47 3.38 3.74
    All 100.00 100.00 100.00 100.00

    local bodies. Representatives of NGOs were of the view that political interference and bureaucratic hassles are the main hindrances for the effective implementation of programmes. Between Orissa and Karnataka states, focus group discussions revealed that in Karnataka, largely due to better functioning of PRI institutions and the grassroot level bureaucracy, targeted communities manage to access programmes better than in other states. However, the respondents from the sample communities of Karnataka have reported that the PRIs and the bureaucracy have neglected the poorest of the poor in providing access to the safety net programmes. This perhaps explains the lack of relationships of PRI functioning and access to programmes that are targeted to the poorest of the poor (such as IAY).

    One important finding that emerged from focus group discussions is that better functioning of PRIs can lead to functioning of the grassroot level bureaucracy – as is the case in Karnataka. Respondents from bureaucracy across the states also have expressed this perspective. The relatively better status of decentralisation was evident from better empowerment of PRIs in Karnataka as compared to Orissa and Madhya Pradesh. From what one gathers from respondents in Madhya Pradesh is that the mere fact of decentralisation to empower the PRIs may not work in favour of vulnerable communities given the pervasive social discrimination practised.

    The respondents have also reflected on the role of alternative institutions, viz, NGOs and women’s self-help groups in implementing the safety net programmes. The respondents from Karnataka state are not totally in favour of NGOs. They have preferred PRIs to NGOs. This indicates the trust of the targeted communities in PRIs. Moreover, this also indicates that the NGOs are crowded out if the PRI institutions function well. The NGOs are preferred in Orissa and Madhya Pradesh. Women’s self-help groups are preferred as alternatives to improve the implementation process of safety net programmes in all the states, by and large.

    VI Conclusions

    This study is based on household-level and village-level surveys on the profile of household risks and the functioning of safety net (anti-poverty) programmes in the states of Orissa, Madhya Pradesh and Karnataka. The survey instrument itself is concise and does not include a consumption module. In all 13 programmes covered in the study, which fall into four broad categories, viz, cash transfer, in-kind transfer, work fare and subsidy-based livelihood programme. The conclusions are summarised as follows:

    (1) The evidence in three states reveals that sudden health risk dominates all idiosyncratic risks. Risk patterns vary by states, quartiles and social classes. The proportion of households that experience a health risk is much higher among the poorest quartile than for other quartiles, higher among scheduled castes and tribes than among other castes, and higher in Orissa than in other states. Weather-induced covariate shocks are present in all states and among quartiles, but the incidence is varied. The proportion of households affected is larger in Karnataka (which has a large percentage of dry land) than in other states. In general, a larger proportion of households in the upper two quartiles experienced weather risks, whereas a larger proportion of households in the poorest two quartiles experienced sudden health risks. The risk of livestock epidemic is largest in Orissa.

    (2) Awareness is high for some of the in-kind transfer programmes like PDS and mid-day meals and cash transfer programmes like IAY and pension schemes as compared to workfare and subsidy-based livelihood programme. The PDS has the highest awareness, followed by mid-day meal and IAY. Differences in awareness across quartiles are small for most programmes. However, differences in awareness across social groups are significant for a few programmes. In particular, a significantly lower proportion of tribal households is aware of three critical programmes which include, surprisingly PDS, and also education-related safety net programmes. Awareness of programmes is better in Orissa than in other states. At the village level, factors such as status of women in the household, presence of NGO in the village, and high (overall) level of education in the village have contributed positively and significantly to creating awareness of safety net programmes in sample villages. As compared to Karnataka, the PRIs in the other two states are not playing an important role in creating awareness of programmes. An interesting finding is that awareness of safety net programmes is low in wealthier villages; presumably the demand for safety net programmes is high in relatively poorer villages.

    Table 11: Distribution of Wage Employment Generated by SGRY by Quartiles

    All States
    Q1 Q2 Q3 Q4 Total
    Head of household
    Kharif 27.76 51.36 10.37 10.51 100.00
    Rabi 33.54 46.93 10.87 8.66 100.00
    Summer 4.95 29.52 61.53 4.00 100.00
    All 17.27 39.23 36.72 6.78 100.00
    Adult male
    Kharif 10.47 84.03 0.00 5.50 100.00
    Rabi 20.01 56.26 6.34 17.40 100.00
    Summer 0.00 86.45 6.02 7.52 100.00
    All 4.90 81.52 4.96 8.62 100.00
    Adult female
    Kharif 23.44 48.51 17.54 10.52 100.00
    Rabi 48.96 27.67 9.14 14.24 100.00
    Summer 70.05 9.66 9.62 10.67 100.00
    All 58.78 19.13 10.79 11.30 100.00
    All members
    Kharif 23.96 56.83 9.61 9.60 100.00
    Rabi 34.49 44.40 9.69 11.41 100.00
    Summer 20.26 39.23 33.89 6.61 100.00
    All 23.81 44.26 23.72 8.21 100.00

    Table 12: Distribution of Employment Generated by Food for Work by Quartiles

    All states
    1 2 3 4 Total
    Head of household
    Kharif 48.30 23.58 14.58 13.53 100.00
    Rabi 54.14 1.22 16.52 28.13 100.00
    Summer 62.03 9.78 11.88 16.31 100.00
    All 50.83 19.43 14.39 15.35 100.00
    Adult male
    Kharif 13.92 43.77 12.74 29.57 100.00
    Rabi 0.00 24.96 75.04 0.00 100.00
    Summer 15.94 52.17 15.94 15.94 100.00
    All 13.64 43.63 14.52 28.22 100.00
    Adult female
    Kharif 27.00 37.32 21.60 14.07 100.00
    Rabi 30.61 0.00 16.33 53.06 100.00
    Summer 77.50 13.97 3.28 5.24 100.00
    All 51.34 24.24 12.57 11.86 100.00
    All members
    Kharif 36.37 30.76 15.09 17.77 100.00
    Rabi 46.53 2.77 20.79 29.90 100.00
    Summer 68.32 13.87 7.45 10.36 100.00
    All 43.35 25.42 14.02 17.21 100.00
  • (3) There is much variation in the participation rates across programmes, quartile groups and as among states. Across programmes, more than 60 per cent of households are aware of in-kind programmes like PDS, ICDS, mid-day meal and free textbooks. Participation rate is less than 10 per cent for programmes like AAY, SGRY, food for work and SGSY. Across states, participation rates for all households are higher in eight out of 13 programmes in Orissa, six out of 13 programmes in Madhya Pradesh. Across quartile groups, the participation rate for PDS is above 70 per cent in all the four quartiles. Participation rates are higher for the lowest quartile in nine out of 13 as compared to the top two quartiles. Relatively high participation rates for the top two quartiles in PDS, ICDS, mid-day meal, free textbook, free uniform, SGRY, etc, indicate that substantial sections of non-poor are participating in these programmes. In Orissa, the proportion of tribal households participating in the programmes is lowest.
  • (4) Among the factors determining participation of households in various programmes, the coefficient of social capital is positive and statistically significant in nine out of 13 programmes. It indicates that households use their networking ability to gain entry into safety net programmes. Another interesting finding is that the literacy in the household influences positively in the households’ participation in all child-related and educationrelated programmes and negatively related to participation in the workfare programmes. Women’s empowerment is also significant in child-related safety net programmes. In most programmes,
  • functioning of PRI institutions and village level infrastructure are not statistically significant except in Karnataka. In half of the programmes, the probability of participation is higher in Orissa than in Madhya Pradesh and Karnataka.

  • (5) Targeting in terms of distribution of beneficiaries by quartiles shows that the beneficiaries are about equally distributed across all quartiles for PDS, ICDS, mid-day meals, free uniforms and free textbooks. A high proportion of beneficiaries of creditbased micro finance programme (SGSY) belongs to the richest quartile. We see a different picture for IAP, widows’ pension, AAY, SGRY, FFW and rural education scholarship. In these programmes, the bottom three quartiles account for the bulk of participants.
  • (6) Data constraints limited the analysis of benefit incidence to only four programmes: PDS, AAY, SGRY and FFW. The analysis shows that the poor benefits disproportionately from AAY, SGRY and FFW in which the share of the richer quartiles is lowest. In PDS, however, the share of the poorest quartile is only slightly higher than 25 per cent whereas the shares of richer two quartiles are not insubstantial. Households in the third quartile, and even in the fourth quartile, seem to benefit substantially in all four commodities (rice, wheat, kerosene and sugar) from PDS. Such leakage of benefits of PDS to the upper two quartiles is highest in Orissa and Karnataka, but lowest in Madhya Pradesh. On the other hand, the distributive shares of benefits from the two wage employment programmes seem to favour the bottom two quartiles and women among them disproportionately, reflecting some degree of self-selection.
  • (7) Taken together, the results generally confirm the poor targeting outcomes of the oldest anti-poverty programme (PDS) that
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    Category – 2: Salary Protected Fellowship: One Fellowship is available under this category. A person who is employed in the teaching and research institutes is eligible for this category of fellowship and his/her entire salary will be protected. In addition, a contingency grant of Rs. 12000/- per annum will also be paid. The duration of the salary-protected fellowship will, in no case, be more than two years.

    Age: The upper age limit is below 35 years as on 31 July 2007 (relaxable by 5 years in case of SC/ST). For teachers and professional staff of research institutes holding regular posts (for salary-protected fellowship), the upper age limit would be 40 years. In exceptional cases, the age limit may be relaxed marginally at the discretion of the selection committee. Reservation for SC & ST will be followed as per the UGC guidelines.

    Interested candidates can download the application from the ICSSR website www.icssr.org and the completed applications should be sent to the following address on or before 14th September 2007.

    The Programme Coordinator Centre for Economic and Social Studies Nizamiah Observatory Campus, Begumpet Hyderabad – 500 016 Email: research@cess.ac.in

    accounts for the bulk of expenditure on safety nets in India. In general, the newly introduced cash-based, low budget programmes and workfare programmes seem to be better in terms of targeting outcomes reaching the poorest quartiles disproportionately, than the largest food-based and expensive programmes like the PDS.

    (8) The qualitative analysis strongly complements the findings from the quantitative analysis. In particular, it reveals the role of caste discrimination in gaining access to programmes. In both Orissa and Madhya Pradesh, caste discrimination seems to be a major factor lowering the participation rates. Households of disadvantaged social classes do not seem to have much faith in the functioning of PRI institutions in Orissa and Madhya Pradesh. Particularly discriminated are tribal households who simply do not seem to avail of PRI institutions to air their grievances. It also explains why poor households in all three states seem to be using their networking ability to gain access to programmes. The qualitative analysis also shows that in places where caste discrimination is pervasive in PRI functioning, poor households seem to use NGOs and SHGs to access and benefit from programmes. Where PRIs are doing reasonably well, as in Karnataka, poor households do not seem to favour NGOs.

    Some Policy Implications

    The study points to one gaping hole in the safety net policy/ programme framework. The prevailing safety net programmes do not seem to address the most dominant and pervasive risk of poor households, viz, exposure to serious health risk. Considering that the poor resort to coping strategies of borrowing and working extended hours by women (and possibly also children), episodes of serious illness in the household are the most likely cause of perpetual indebtedness and possibly also to poverty trap situations.

    Communitywide weather-induced risks (drought, cyclone and/ or flood) are also experienced by households in all quartiles, but their direct incidence is highest among households in the relatively richer quartiles who happen to own land. Unlike in the case of health risks, there are workfare programmes (SGRY, FFW) in place to provide consumption-smoothing in the wake of such weather-induced risks. However, participation rates in these programmes is low – either because of inappropriate timing of programmes or due to sporadic or untimely release of funds for these programmes.

    The risks that are best covered by programmes are those relating to household food security. PDS, MDM, and ICDS are all reaching a large proportion of poor households. But the problem is: they are also reaching a high proportion of households in the upper two quartiles – an immediate result of weak targeting enforced by BPL lists.

    Homelessness is a risk that attracted policy attention several years ago when the programme of housing subsidy (IAY) was launched. But gaining “entry” into the programme would require households to possess enough “social capital”, i e, networking ability. Not surprisingly, the programme (which has a very large cash transfer) is attracting households in the upper quartiles. Qualitative analysis had shown substantive abuses and corruption in the actual implementation of this programme.

    Recent programmes have begun to cover other risks, viz, the risk of old age poverty, poverty among widows and the risk of children dropping out school. The two pension programmes

    – NOAP and widows’ pensions – seem to be reaching poor households, especially widows’ pension is strongly correlated with participation by widows from the poorest quartiles. The education programmes (free uniforms, textbooks, etc) on the other hand, show mixed results. These programmes seem to work well in Madhya Pradesh, but not in the other two states. The fact that the presence of educated individuals in the household is an important factor promoting children’s participation. It suggests that children belonging to parents with no education, especially belonging to scheduled tribes, have not been able to take advantage of this safety net. On the positive side, women’s empowerment seems to strongly influence participation in child-related safety nets. To the extent women are empowered by programmes such as self-help groups, children of deprived communities should be able to take advantage of these programmes.

    The study has provided some important insights into the functioning of institutions and social capital. Karnataka clearly stands apart from the other two states in the functioning of PRI institutions, and their positive role in promoting awareness and participation of the poor. Where caste discrimination is pervasive, households do not seem to trust PRI institutions. Instead, they seem to rely on their social capital and caste networks to gain entry into programmes (and even in obtaining BPL cards). As the stranglehold of caste is eased over time, the PRI institutions may be expected to do better in reaching out to the poor.

    Looking into the future, immediate policy initiatives need to focus on improving the productivity of existing programmes by encouraging PRI institutions at the village level to promote awareness of programmes among poor households and especially correct the observed serious exclusion errors (as evidenced by low participation of households belonging to scheduled tribes). At the same time the outreach of programmes to the poor have to be tightened via improvements in approaches to targeting (so as to avoid inclusion errors), launch new programmes to cover uncovered risks (especially health risks). Most importantly the PRI institutions should be made accountable for better functioning of safety net programmes with possible external oversight, and work towards synergy between programmes and policies launched by the centre and states so as to avoid duplication of efforts.

    EPW

    Email: profmahendra@gmail.com

    [For comments and suggestions on earlier drafts, we are grateful to the participants of a seminar at CESS and to Philip O’Keefe, Mansoora Rashid and Puja Dutta. The views expressed herein are those of the authors and do not necessarily reflect the opinions or policies of the organisations to which they belong.]

    References

    GoI (2006): ‘Towards a Faster and More Inclusive Growth: An Approach

    to the 11th Five-Year Plan’, Planning Commission, Government of

    India, June 14. Ravallion, M (2003): ‘The Debate on Globalisation, Poverty and Inequal

    ity: Why Measurement Matters’, World Bank Policy Research Working

    Paper 3038. Subbarao, K (2003): ‘Systematic Shocks and Social Protection: The Role

    and Effectiveness of Public Works Programme’, Social Protection

    Discussion Paper No 0302, World Bank, Washington DC. Subbarao, K et al (1997): ‘Safety Net Programmes and Poverty Reduction:

    Lessons from Cross-Country Experience’, World Bank, Washington

    DC. World Development Report (2006): ‘Equity and Development’, published

    for the World Bank, Oxford University Press.

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