Do Development Interventions Confer Adaptive Capacity? Insights from Rural India

World Development Vol. xx, pp. xxx–xxx, 2017 0305-750X/Ó 2017 Elsevier Ltd. All rights reserved. www.elsevier.com/locate/worlddev

http://dx.doi.org/10.1016/j.worlddev.2017.04.017

Do Development Interventions Confer Adaptive Capacity? Insights from Rural India UNMESH PATNAIK a and PRASUN KUMAR DAS b,* a Tata Institute of Social Sciences, Mumbai, India b APRACA, FAO, Bangkok, Thailand Summary. — Risks due to the occurrence of climatic aberrations pose an impediment to the economic growth of the households in vulnerable regions. The frequency of these events is projected to increase in the foreseeable future, with developing countries being the worst sufferers. Dealing with this appears to be an additional burden on the resources of such countries, a large part of which is already devoted to providing better living standards for its inhabitants. Does this imply that developmental interventions should be discontinued? Is there a link between these programs and adaptation to environmental shocks? In an effort to answer such questions, the paper examines the impact of the developmental schemes on the livelihood of the households in Western Odisha, India and investigates whether they augment post disaster coping and adaptation as well. The results indicate that overall the programs have made an impact concerning their intended goals but the diffusion of benefits across beneficiary groups is heterogeneous. Additionally, the programs have contributed in post disaster coping, but only in the regions where either they performed well or their penetration was extensive. Activities promoting livelihood diversification, food security, and poverty reduction also tacitly facilitate improvements in the resilience of the individuals and communities thereby enhancing their capacity to deal with climatic risks. Policy implication advocates the continuation of developmental interventions. However, realigning their framework to incorporate actions intended toward disaster risk reduction and management would result in more inclusive impacts. Ó 2017 Elsevier Ltd. All rights reserved. Key words — climatic aberration, impact evaluation, adaptation, disaster coping, India

1. INTRODUCTION

on the resources already committed to issues like poverty eradication, infant mortality reduction, rural development, provision of access to basic needs etc. The paper examines the role of developmental interventions in reducing the vulnerability of the poor in a rural Indian setting. It empirically tests first, whether developmental interventions have resulted in income enhancement for beneficiaries, and, second, if this has also augmented the coping/adaptive capacity of the households to deal with the impacts of climatic variations. In doing so, the impact of the Western Orissa Rural Livelihoods Project (WORLP), a developmental inter-

Risks arising out of climatic aberrations and extremes (like droughts, deficient rainfall spells, and floods) disrupt the livelihoods of people and pose a threat to their economic growth. The fifth assessment report of the International Governmental Panel on Climate Change (IPCC) has restored the findings of the earlier versions of the report that stated that the warming of the earth’s climate system is unequivocal and, since the 1950s, several observed changes have been unprecedented over the millennium (IPCC, 2014). Additionally, the special report on extreme events stated that the frequency and severity of these events will increase in the future (IPCC, 2012), with developing countries being the worst affected in terms of damages suffered (IPCC, 2012; Botzen & Van den Bergh, 2009; Bouwer, Crompton, Faust, Hoppe, & Pielke, 2007; Mirza, 2003). Equivocation of impacts involve deployment of mitigation measures (address causes) or initiating adaptation processes or coping (tackle effects). While adaptation refers to the longer process of adjusting to experienced and expected change, coping denotes the response to endured impacts (shorter term). Coping capacity can be increased with adaptation measures whereas adaptive capacity incorporates possible adaptation in addition to coping and cannot be increased beyond a certain point. However, both adaptation and coping endeavor to build resilience 1 and reduce vulnerability to climate variability and extremes, and include actions that reduce or avoid the risk. The benefits of adaptation on local or regional scale not only include enhancement in capacity to cope with impacts of climatic aberrations and extremes but also potentially contributes toward providing better living standards for the population. This assumes importance particularly in the context of the developing countries where tackling the climatic impacts appears as an additional burden

* The research was possible by a research grant from South Asian Network for Development and Environmental Economics (SANDEE), Nepal [SANDEE/2013-06]. We are thankful to Jean-Marie Baland for his constructive suggestions and for mentoring the research. We also express our gratitude to SANDEE advisers for their helpful comments and to SANDEE staff for their support. We have benefited from discussions with Sarmila Banerjee, K. Narayanan, T. Jayaraman, Chandra Sekhar Bahinipati and Subash S. We acknowledge the support from Onkar Nath Tripathi who along with his team coordinated the household surveys. We are also thankful to Bijoy Satpathy, staff of Odisha Watershed Development Mission and the Project Implementing Agencies of Western Orissa Rural Livelihoods Program in Bolangir for facilitating the research. Last but not the least we are grateful to the participants of household survey. The research grant was hosted at M. S. Swaminathan Research Foundation, Chennai, India where both the authors were working at time of receipt of grant. We are thankful to the anonymous reviewers and editor for their suggestions. All views, interpretations, recommendations, and conclusions expressed in this paper are those of the authors and not necessarily those of the supporting or collaborating institutions. Final revision accepted: April 15, 2017. 1

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

2

WORLD DEVELOPMENT

vention that was operational in four districts of western Odisha (prior to 2011, the state’s name was spelled as Orissa 2), India, is examined while controlling for benefits from other ongoing schemes. The results are with reference to the district of Bolangir 3 in Odisha, where the WORLP interventions were first introduced. This region is highly vulnerable to climateinduced natural disasters like droughts, deficient rainfall spells and flash floods. The contribution of the paper is in demonstrating that interventions aimed at reducing poverty and income enhancement also tacitly enhance the capacity to deal with climatic risks (through building resilience and augmenting the ability to cope) and hence are mutually inclusive. The nature and extent of this symbiotic relationship has not received much attention in literature. On the policy front, the findings endorse the continuation of such interventions by emphasizing their role as strategies for pro-active risk management and means for increasing the resilience of human and natural systems by either diminishing and/or spreading the shocks due to climatic aberrations and extremes 4. The remainder of this paper is organized as follows. Section 2 presents the anatomy of the outcome of developmental interventions and their role in facilitating adaptive/coping capacity of the households to deal with climatic shocks. The interventions under enquiry are described here, with the empirical strategy being conceptualized in the concluding part. While Section 3 depicts the study area and research design, Section 4 analyzes the impact of the interventions on household income, reasons for the same and outcome of these on adaptation/coping. Finally, Section 5 presents the summary and conclusions. 2. COPING WITH CLIMATIC ABERRATIONS AND EXTREMES AND DEVELOPMENTAL INTERVENTIONS In India, approximately 70% of the population resides in the rural areas. The direct impacts of climate-induced aberration and extreme events aggravate the vulnerability of existing livelihoods 5 due to: (i) higher dependence for income on climate sensitive sectors like agriculture and (ii) climatic aberrations and extremes being the principal source of risk to agriculture (Mall, Singh, Gupta, Srinivasan, & Rathore, 2006; Sathaye, Shukla, & Ravindranath, 2006; NATCOM, 2004). Simultaneously, the soaring population, higher incidence of poverty, large economic inequality, and rudimentary state of infrastructure amplify exposure and vulnerability (Patnaik, Das, & Bahinipati 2013; Patnaik & Narayanan, 2005). Among the vulnerable, the poorer sections bear the greatest risk of detrimental impacts due to their limited coping capacities and less favorable economic, social and institutional conditions (De Haen & Hemrich, 2007; Benson & Clay, 2004; Wisner et al., 1994). The indirect effects (a consequence of the primary random shocks) are of longer term, with additional implications on livelihoods, and, yet again, the poorest struggle most with the outcomes that can trap them in an impoverished state from which they cannot escape (Carter, Little, Mogues, & Negatu, 2007; Carter & May, 1999; Dercon, 2004). Although the risks faced by the households due to these events are covariate over smaller geographical regions like blocks, (administrative division within districts) the consequences differ across households and are determined by their ability and capacity to withstand these shocks and hence are related to their resilience (Arouri, Nguyen, & Youssef, 2015; Briguglio, Cordina, Farrugia, & Vella, 2009; Cannon, 2008;

Rose, 2004; Perrings, 2001; Holling, 1973). For instance, a drought may result in an immediate reduction in income and consumption for some, increases in food insecurity for others and longer term impacts (like falling into poverty traps) for a few. Likewise, the idea of securing livelihood includes the notion of coping with and recovery from external stresses so as to maintain or enhance existing capabilities and assets (Agrawal & Perrin, 2009). Dercon (2002) observes that risks faced by the households are crucial in determining the level of assets and endowments maintained by them while resilience is conditional upon the past and current exposure level and the socio-economic and institutional set-up witnessed by them. Important sources for increasing the resilience of the households to shocks include interventions for improving assets, providing alternative livelihood mechanisms, access to credit and public transfers (Davies, Be´ne´, Arnall, Tanner, Newsham, & Coirolo, 2013; Bruneau et al., 2003). In the light of this, the role of developmental interventions assumes significance as the focus of these in India are on issues like poverty eradication, infant mortality reduction, rural development, provision of access to basic needs etc. that are drivers of both resilience and coping capacity. A key developmental intervention in the state of Odisha, India was the WORLP that was funded by the United Kingdom’s Department for International Development (DFID) and implemented over a period of ten years (2000–10) by the Odisha Watershed Development Mission (OWDM), an autonomous agency of the Government of Odisha (GoO). The overall goal was to reduce poverty in rain-fed areas while the intermediate objective was to provide and promote sustainable livelihoods for the poorest in the pre-selected districts where human development indicators are very low and comparable to sub-Saharan Africa (WORLP, 1999). The total project outlay was Rs. 2.3 billion spread over the ten year period (2000– 10) and designed to cover 1,180 villages over 677 watersheds 6 spread across four districts of western Odisha (Bolangir, Nuapada, Bargarh and Kalahandi). On the upper slopes, communities planted orchards and other trees to reduce run-off. At the bottom of the slopes, ponds and embankments were dug to slow the water rushing off the hills. On the lower lands, watershed groups dug water storage ponds and irrigation channels to irrigate crops at the end of the monsoon and other crops in the summer. Sometimes, small concrete structures like sluice gates were also built here. The watersheds were selected through a three-stage selection process, beginning with short listing of watersheds using primary and secondary information. Subsequently, these got ranked as per the parameters developed by the OWDM, encompassing central and state government guidelines and suggestions of consultants associated with watershed development. In the second stage, quick assessments were conducted for examining attributes like unity of the villages, ability of the community to participate and availability of traditional institutions like the Pani Panchayat, Vana Surakhshya Samiti, Village Development Committee etc. Villages exhibiting these traits moved to stage three which was a probation of six months for demonstrating the creation of community institution-building work, worth Rs. 50,000 through people’s contribution in labor or cash and with the help of a Project Implementing Agency (PIA) assigned to the village (for more details see WORLP, 1999). Although an identical institutional structure was followed in the implementation across all watersheds and regions, the project impacts varied across different watersheds depending on the types of activities chosen, management practices followed and the vision of the PIA (Routray, 2015; ICAI, 2013; CRD, 2010).

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

DO DEVELOPMENT INTERVENTIONS CONFER ADAPTIVE CAPACITY? INSIGHTS FROM RURAL INDIA

(a) Linking the WORLP interventions to poverty reduction, resilience and coping The interventions in the WORLP pertained to: (i) land and water management initiatives comprising activities for developing infrastructure like the construction of embankments, water storage ponds and irrigation channels, (ii) economic support for the poorest, encompassing activities such as providing loans and grants for microenterprises and microcredit and (iii) a capacity-building component focusing on the empowerment of the communities, through the adoption of better practices for natural resources management and livelihoods promotion. Based on a new design called watershed plus approach, 7 the additional focus was on people’s livelihoods and it provided a range of livelihood support services to the poor while encouraging an enabling environment (Sharma, Reddy, & Sahu, 2014). Of the total project outlay, Rs. 1.4 billion (60%) was available as financial aid for implementing watershed and watershed plus activities. The rest was allocated for technical support and project management. The program outcomes concentrated on increasing livestock and crop productivity, soil and water conservation, improved management of natural resources, financial services (including savings, credit, insurance and money transfers) and institutional strengthening. The realization of these intended outcomes would not only lead to the increase in the resilience of the households (though indirectly, through securing livelihoods) but would also directly reduce their vulnerability to deal with climatic stress by reducing risk (Grossi & Kunreuther, 2005; Adger, 1999). The state of poverty is both the condition as well as a determinant of vulnerability (Carter & Barrett, 2006)—thus, poverty reduction is imperative for augmenting resilience. Reduction in the incidence of poverty was also the overall goal of the WORLP in its operational areas. Figure 1 describes the logic model for the interventions. It can be observed from Figure 1 and as described in the preceding paragraph that the major inputs in the WORLP were land and water management, and capacity building. Resulting from these inputs were the outcomes corresponding to: (i) irrigation and soil conservation, (ii) provision and availability of inputs for agriculture, (iii) higher crop production, (iv)

3

avenues for diversification of income and (v) facilitating the emergence of social networks. The impacts of these translate to increase in income, poverty reduction, decrease in migration, and housing and food security for the beneficiaries. Moreover, a combination of improvements in each of these makes improvement in the resilience of households and communities highly probable, thus augmenting coping capacity/ adaptation to shocks and stresses. The watershed plus component while addressing poverty reduction also focused on building the existing assets of the poor. This provides a way for managing the vulnerability context (presence of the exogenous external climatic aberrations and extremes dragging the households back below the poverty line levels) by assisting resilience augmentation and taking advantage of the addition to their assets (natural, financial, social and human capital). Therefore it is hypothesized that the interventions, while serving their goal of poverty reduction, simultaneously improve the ability to withstand climatic aberrations and extremes. Hagen, Phukan, and Honore (2004) emphasizes that integrated watershed management including sustainable livelihoods exerts a strong influence on the adaptive capacities of the communities. (b) Role of other interventions Schemes aiming poverty reduction, livelihood diversification and enhancement of income sources are mechanisms for improving entitlements. A range of such interventions have been undertaken in India for addressing long term developmental goals. Although tacitly, but these also enhance the ability of the households to cope with disaster impacts and are classified as generic adaptation measures (Bahinipati & Patnaik, 2015; Sharma & Patwardhan 2008). Notable among the schemes operational in India is the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) that provides income security to the households in rural India by providing at least a hundred days of guaranteed wage employment. Esteves et al. (2013) and Tiwari et al. (2011) find that development-based activities (like water conservation and harvesting, irrigation provisioning and improvement, renovation of traditional water bodies, land development, drought proofing, flood control, etc.)

Figure 1. The Impact of WORLP interventions on Income and Adaptive/Coping Capacity of the Beneficiaries.

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

4

WORLD DEVELOPMENT

undertaken through the MGNREGS, reduce the vulnerability of the poor households to current climatic risks in rural India. Additionally, there also exist interventions that endow the households with housing and food security. For instance, the Indira Awas Yojana (IAY), Rajiv Awas Yojana (RAY), Biju Awas Yojana (BAY), and Mo Kudia scheme intend to provide affordable dwelling to poor households on a mutually contributory basis. Likewise, the Antyodaya Anna Yojana (AAY) is a food security plan of the government which is carried out through the Public Distribution System (PDS). Here the beneficiary households are entitled to a specific quota of food items per month which includes cereals along with sugar, kerosene and cooking oil. These are directly implemented by the national, state and local governments and, occasionally, also run in partnership with the non-governmental organizations and donor agencies. Given the operation of multiple schemes targeting cross-cutting outcomes, the efficacy of these interventions becomes critical. Especially important is to examine whether the households are benefitting from these interventions in terms of: (i) the greater goal of poverty reduction and (ii) their capacity to cope with the impact of climatic aberrations and extremes.

To analyze the impact of interventions on coping/adaptive capacity of the households, two outcomes are evaluated for the villages in the treatment and control group. The assumption here is that the any improvement in the resilience of the households will translate to higher capacity to cope with the impact of climatic stress (both ex-ante and ex-post). It is to be noted here that income is employed as a proxy to quantify overall improvements in rural livelihoods which consecutively explains coping capacity or resilience. In other words, it is the melioration in agricultural production, food and housing security, income, and reduced risk of facing climatic stress that are responsible for enhancement in the resilience and coping/ adaptive capacity of the households. Hence as a measure of coping/adaptive capacity, we identify how households respond to climatic risks, and take into account the variability in the choices made by the households, similar to the approach of Brown, Agrawal, Sass, Wang, Hua, & Xie (2013). Hence the outcome variables are: (i) number of coping instruments used by the households as a coping mechanism during the previous incidence of droughts and (ii) funds generated through each of these measure respectively. This is depicted in Eqn. (3).

(c) Empirical strategy

As before, Ti is the treatment dummy, X is a vector of household-specific characteristics and Z represents participation in other interventions. The nature of incidence of extreme events is such that they are highly uncertain and exogenous hence it is plausible to assume that the funds devoted by the households for post disaster and coping are not endogenous. The details of the instruments adopted by the households are described in the subsequent section. The DID framework outlined in Eqn. (1) is used to examine the impact of WORLP and other interventions on the income of beneficiary households. The single difference approach presented in Eqns. (2) and (3) are further used to examine the determinants for the observed changes in income between WORLP beneficiaries and non beneficiaries.

The difference-in-difference method (DID) is popular for non-experimental evaluations. It estimates the difference in the outcome during the post-intervention period between a treatment group and a comparison group relative to a preintervention baseline. Adopting the foundation of DID, here the program and non-program villages are compared to analyze the program level impacts. The DID estimator allows for unobserved heterogeneity (the unobserved difference in mean counterfactual outcomes between treated and untreated units) that may lead to selection bias. For example, one may want to account for unobserved factors, such as differences in innate ability or personality across treated and control regions or the effects of non-random program placement at the policy-making level. DID assumes that this unobserved heterogeneity is time invariant, so the bias cancels out through differencing (Khandker, Koolwal, and Samad, 2010). The operationalization of DID is achieved by using the model described in equation 1. Instead of a comparison between years, program and non-program villages are compared, and the treatment effects are observed for the beneficiary and non-beneficiary sample (Khandker et al., 2010; Pattanayak, 2009). DY i ¼ a þ bT i þ cX i þ UZ i þ i

ð1Þ

In Eqn. (1), the outcome variable measures the change in income of the ith household to that at baseline, Ti is the treatment dummy, X is a vector of household-specific characteristics and Z represents the vector denoting benefits from other interventions. Subsequent analysis explores the reasons underlying the behavior of the outcome variable in Eqn. (1). In doing so, different outcomes are compared for the control and treated villages using a single difference approach. Keeping in the mind the nature of interventions under the WORLP, five outcomes have been analyzed: (i) value of agricultural production, (ii) total input cost of production, (iii) irrigation and labor cost, (iv) diversification practices, and (v) migration. The functional form for this analysis is represented by equation 2. W i ¼ q þ /T i þ wX i þ ti

ð2Þ

Here, Wi depicts the five outcomes, Ti is again the treatment dummy and X is a vector of household-specific characteristics.

AC i ¼ g þ rT i þ wX i þ kZ i þ li

ð3Þ

3. STUDY AREA AND RESEARCH DESIGN Bolangir is a constituent district of the erstwhile KBK (Kala handi–Bolangir–Koraput) region of Odisha, one of the poorest and underdeveloped regions in India. Bolangir is also one of the districts in Odisha most vulnerable to droughts (Swain & Swain, 2011; Roy, Selvarajan, & Mohanty, 2004). During the last decade, droughts were witnessed in Bolangir during the years 1996, 1998, 2000, 2002, 2009, and 2011 (DDMP, 2015). Bolangir is classified as a region where the mean temperatures are rising and where its vulnerability profile places it among the highest risk areas in India to climatic aberrations and extreme events (Satyanarayana, Singha, Das, & Lopamudra., 2009). The recurrent drought phenomenon has been cited as one of the reasons for the chronic backwardness of the district (Pattnaik, 2012, 1998). The intensity and frequency of drought episodes are increasing with evidence of widening in variability of rainfall and declining long term normal rainfall (Swain, 2010; Mallik & Meher, 1999; Sainath, 1996). The HDI value for the district is 0.546 and it ranks 21st among the 30 districts in Odisha (Economic Survey, 2013). Four blocks from Bolangir, where the WORLP interventions were carried out during the initial phase, have been chosen for the present analysis. These are: (i) Agalpur, (ii) Bongamunda, (iii) Gudvela, and (iv) Patnagarh. The map of the study area and the blocks are shown in Figure 2. Agalpur is geographically located on the northern part of

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

DO DEVELOPMENT INTERVENTIONS CONFER ADAPTIVE CAPACITY? INSIGHTS FROM RURAL INDIA

5

Figure 2. Map of the study area (Left: Location of District Bolangir in Odisha; Right: Survey Blocks in Bolangir District).

the district while Bongamunda is on the southern part. Gudvela and Patnagarh lie in the eastern and western part of Bolangir respectively. The study uses data from secondary sources and household surveys. The secondary sources are published reports of the Odisha Watershed Development Mission, Govt. of Odisha, and research reports (both published and unpublished) of governmental and non-governmental agencies. (a) Sampling design, baseline and survey instrument The study adopts two stage stratified random sampling, with the first stage being the performance of the watersheds. Accordingly, blocks are categorized as the ones where: (i) better performance of watersheds was recorded under the WORLP (Agalpur and Patnagarh) and (ii) performance was not so good (Bongamunda and Gudvela) after discussion with the PIAs and OWDM, and based on ICAI (2013) and WORLP (2011). In the second stage, villages identified as per the above criteria and segregated to the ones: (i) inside the command area of the watersheds and (ii) outside the command area of the watersheds. All the households residing in the villages falling under the command area are WORLP beneficiaries while those outside are the non-beneficiaries and also the counterfactual. In the sample, the beneficiaries are the households surveyed randomly from villages falling under the command area and the control is characterized by random households surveyed from neighboring villages but lying outside the command area of the watersheds. Because of geographical proximity, adjoining villages have comparable socioeconomic and biophysical conditions, except the benefits from the watershed projects. Sample villages for the surveys are identified after checking the availability of baseline data for the households (pre-WORLP). The baseline data was collected from the PIAs for the WORLP beneficiaries and from the Below Poverty Line (BPL) census of the Govt. of Odisha for the non-beneficiaries with the baseline reference year being 2010 for both the samples. Baseline information includes a unique household identifier with the name of the head of the household that was used to track the household and administer the current survey instrument. The data also had information about the age and gender details of the head along with the occupation, farm type and income level of the household. The questionnaire was finalized after pilot testing with 15 households (at least one from each identified village). The survey instrument consisted of the following nine sections: (i)

household details, (ii) migration status, (iii) housing conditions, (iv) land, crop and livestock details, (v) awareness and access to other govt. interventions, (v) consumption details, (vi) health and food security, (vii) household assets, (viii) loan, credit and savings and (ix) impact of climatic aberrations and coping/adaptation (farm and non-farm based). Most questions were close-ended and responses from the head of the household were recorded as nominal, ordinal or scale variables and surveyed based on their availability to participate in the survey. The total sample size is 800, of which 600 are beneficiaries and 200 non-beneficiaries. The survey was conducted during June–October, 2014, accounting for activities during the previous agricultural year. Although the sample size is restrictive with only 200 non-beneficiaries, the distribution of income is not skewed, with both the treatment and the control group not having much difference in the standard deviation (the details are presented in Table 2). However, considering that our interest is in comparing the average effects between the two groups, it would not pose as a significant problem. (b) Data and variables The study employs proxies to capture the coping practices of the households to deal with the impacts of previous droughts. Socioeconomic characteristics of the households are depicted through variables like households living below the poverty line, human development factors like caste, age, education, housing, health, etc. The existing livelihood practices are measured through agricultural production, input cost for crops, cropping pattern, income and consumption, sources of income, assets and migration. Table 1 lists the variables used in the analysis, explains the definition and presents the summary statistics. The independent variables depict information regarding the beneficiary status and performance of the region in the WORLP, socioeconomic characteristics, current and baseline income, participation in other governmental interventions, land and livestock details, and access to social networks at the household level. In the sample, 75% households are WORLP beneficiaries of which 37% reside in regions where the performance was good. YB is the baseline income and stands at Rs. 9,800 while the current income (YC) is Rs. 41,700 for the sample. Household-specific characteristics are reflected in the variables SIZE, AGE and EDU. While SIZE refers to the number of household members, AGE indicates

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

6

WORLD DEVELOPMENT Table 1. Summary and descriptive statistics of variables used in the analysis Variable

Definition of the Variable

WORLP PERF

Program Treatment (Dummy equals 1 if the household resides in the Treatment Village, 0 otherwise) Interactive Dummy of Treatment and Performance of WORLP (Dummy equals 1 if beneficiary and performance is good, 0 otherwise) Baseline Income of the household in Rs. during 2001 Baseline Income in Natural log Number of Members in the Household Age of the Head of the Household Number of years of Education of the Head of the Household Access to Social Networks (Dummy equals 1 if the household has membership in Self Help Groups, else 0) Number of Livestock owned by the household Number of big ruminants owned by the household Number of small ruminants owned by the household Land in Acres of the Household Marginal Farmers (Dummy equals 1 of the household owns up to 2.47 Acres of land, else 0) Small Farmer (Dummy equals 1 of the household owns between 2.47 and 4.94 Acres of land, else 0) Semi-Medium Farmer (Dummy equals 1 of the household owns between 4.94 and 9.88 Acres of land, else 0) Employment Benefit (MGNREGS Participation Dummy equals 1 if the household has participated, else 0) Housing Benefit (Dummy equals 1 if the household has benefited from govt. housing schemes, else 0) Food Security (Dummy equals 1 if the household has benefited from govt. food security schemes, else 0) Total Income of the household in Rs. in constant prices during 2014 Total Income in Natural log Difference between Current Income and Baseline Income (Rs.) Difference between Current Income and baseline Income in natural log Value of Total Crop Production in Rs. Value of Total Crop Production in natural log Total Input Cost for cultivation in Rs. Total Input Cost for cultivation in natural log Irrigation Charges for all crops in Rs. Value of Total Vegetable Production in Rs. Share of Vegetable Production of the Total Production Value of Own Labor for cultivation in Rs. Value of Hired Labor for cultivation in Rs. Value of Total Labor for cultivation in Rs. Presence of migrant members in the household Remittances from Migrant Members in Rs. Remittances from Migrant Members in Natural log Number of coping instruments adopted by the household during previous droughts Money generated from the use of coping instruments during previous droughts

YB lnYB SIZE AGE EDU SHG LVST RUMB RUMS LAND FMARG FSMAL FSMED SUPY SUPH SUPF YC lnYC DY D lnY PRODT ln(PROD) TC ln(TC) CIrri PRODV SHRV LCO LCH LCTot MIG REMIT ln(REMIT) COPI COPF

Mean

S.D.

0.75 0.37

0.43 0.48

9,871.87 9.04 5.22 51.46 3.26 0.76 3.23 1.09 2.18 1.6 0.76 0.17 0.06 0.67 0.17 0.14 41,748. 6 10.56 31,876.72 10.24 30,811.95 10.13 11,804.36 9.15 284.19 5,020.22 13.11 2,469.94 2,244.02 4,713.97 0.403 6,369.50 2.67 1.2 2,678.32

5,554.33 0.59 2.76 12.15 3.41 0.43 5.3 1.38 4.55 1.98 0.43 0.38 0.23 0.38 0.38 0.34 16,049.4 0.43 15,131.58 0.55 22,105.95 0.65 8,410.15 0.69 612.76 8,820.53 20.65 2,017.22 2,675.41 3,931.96 0.783 12,577.61 4.41 0.78 3,189.01

Note: N = 800 except for agriculture related variables where N = 544 (Due to households non participation in farming).

the age of the head of the household and EDU represents his/ her number of years of education. Average family size is five members per household; mean age of the household head is 51 years with three years of education. Land ownership is measured by the variable LAND and LVST refers to the number of livestock owned. In the sample, the households own an average of 1.6 acres of land and three livestock (either big or small ruminants or a combination of both) respectively. Decomposing these further, two sets of dummies are created. The first set breaks down livestock ownership into big and small ruminants represented by the variables RUMB and RUMS respectively. The second set grades household on land ownership and creates three dummies: FMARG, FSMAL, FSMED that show marginal, small and semimedium farmers respectively, with the reference category being medium and large farmers (those with land ownership more than 9.88 acres). Membership in social networks is depicted by the dummy SHG which shows whether a household is affiliated with any self-help group in their village. In the sample, approximately 76% have membership in these groups. In conjunction with the WORLP, other developmental interventions of the govt. are also ongoing in the study area.

The variable SUPY depicts the participation in the MGNREGS scheme, providing opportunity for income diversification. The penetration of this program is noticeable as 67% households in the sample had participated in this scheme. While the dummy SUPH denotes receiving support for the construction of dwelling structures, SUPF refers to benefit from food security programs for the households below poverty line. The diffusion of these is limited in the sample, with approximately 17% benefitting from the former and even lesser (14%) having access to the latter. The outcome variables are classified as those: (i) related to income, (ii) linked to agriculture and migration and (iii) associated with coping practices of the households. DY measures the difference in the income of the household (current and baseline), with an average increase of 15% per annum observed for the sample. While PRODT measures the total value of the agricultural production, TC shows the total input cost of production for all crops during the previous year. Approximately, the households produce Rs. 31,000 worth of different crop produce in a year while their input cost for the same is Rs. 12,000. The variable CIrri refers to the cost of irrigation and stands at Rs. 300 per year for all the crops grown. While PRODV measures the value of total vegetables pro-

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

DO DEVELOPMENT INTERVENTIONS CONFER ADAPTIVE CAPACITY? INSIGHTS FROM RURAL INDIA

duced in the entire season, SHRV depicts the share of production of vegetables to total crops produced. Households produce vegetables amounting to Rs. 5,000 per year, representing 13% of their total agricultural production. The variable LCO, LCH and LCT measure the cost for own, hired and total labor respectively. The households contribute around Rs. 2,500 worth of own labor and Rs. 2,200 is spent on hiring, amounting to Rs. 4,700 in total annual labor cost. Migration is computed through the number of migrants present (MIG) and remittances from migrant members (REMIT). In the sample, a migrant member is present for every three households approximately, with Rs. 5,400 accruing from remittances. Coping with droughts and climatic extremes is captured by the variable COPI which measures the number of instruments (non-farm based) used for coping during the previous drought spell. At the household level, the following coping options are identified as the ones used in the study area to cope during the previous incidence of droughts: (i) selling of livestock, household assets and land, (ii) use of loans and credit, (iii) use of govt. relief, (iv) interest free transfers from friend and relatives, (v) use of past savings and (vi) remittances from migrant members. Similarly, COPF depicts the funds generated by the household through these instruments. It is observed that households have used combinations of more than one instrument as a coping mechanism, translating to approximately Rs. 2,700 funds generated. 4. GAUGING THE LONGER TERM OUTCOMES OF DEVELOPMENTAL INTERVENTIONS The end term evaluation of the WORLP states that significant increase in natural capital is observed primarily owing to activities like leasing of water bodies and land, better health of livestock etc. (WORLP, 2011). Additionally, Sharma et al. (2014) conclude that it improved the production of food and incomes while reducing the vulnerability to climatic variability. As a result, incomes have increased and livelihoods have become more secure. The project provided a platform to bring together communities to access financial services through the creation of SHGs that function as semi-formal financial intermediary institutions. The members of SHGs engage in regular collection of savings (micro deposits) which is used as capital to tender small loan support within the group. The groups are also supported by formal financial institutions such as banks, with bulk credit for livelihood support activities. The findings from our sample are also in line with the findings of these studies; we differ slightly from them in terms of the scale of observed impacts. ICAI (2013) also echoes a notion similar to us as it reports compatibility with the findings of WORLP (2011) on two out of seven impacts and broad compatibility with another two.

7

In the present sample, no significant difference is observed between the income of the control and the treatment groups before the implementation of the WORLP. The average total income for the households in the treatment sample is Rs. 9,847 while it is marginally higher for the control sample at Rs, 9,947. However, a significant difference is observed between the current income of beneficiaries and non-beneficiaries. The total income for the beneficiary sample is Rs. 42,667 while that for the control one is Rs. 38,998. Similarly, the difference between current and baseline income is approximately Rs. 32,819 for the beneficiaries while for the non-beneficiaries it stands at Rs. 29,050. These correspond to an annual increase in income of approximately 24% for the beneficiaries compared to 21% for the non-beneficiaries. Table 2 shows the results obtained from the comparison of means across the two samples. (a) The impact of interventions on income The ultimate goal of the WORLP was to reduce poverty. Hence the impact of the program on the income of the households is examined using four sets of DID estimations, employing linear and semi-log specifications as outlined in equation 1. The outcome variable is the difference in income between the current and baseline period. Different sets of predictors are introduced in the alternative specifications and results are presented in Table 3. The income of the beneficiaries is higher by Rs. 6,800 than non-beneficiaries of the WORLP which corresponds to 12% enhancement in income for the participating households (columns 2 and 3 of Table 3). This is also in line with the findings of WORLP (2011) that the decrease in poverty in the intervention areas is around 10%. To account for the relative performance of the watersheds, a multiplicative dummy is added that depicts the interactions between the beneficiaries and performance of the interventions with the base category representing either non-beneficiary or relatively poor performance. The results are presented in columns 4 and 5 of Table 3. The results indicate no significant impact of performance on the overall increases in income of the households considering only these two indicators. The role of socioeconomic characteristics in explaining the observed increase in income is examined in models 5 and 6 depicted in Table 3. Among the indicators considered, household size, ownership of land, and presence of livestock have positively contributed to income enhancement. While household size and ownership of land are responsible for 7% and 8% growth respectively, the presence of livestock explains approximately 1% augmentation in income. The inclusion of socioeconomic characteristics changes the impact of the WORLP interventions, stressing the role of the mediating factors. Now it is found that the performance of the watersheds is significant in explaining the surge in income of the households. So, in the regions where

Table 2. Comparison between income of WORLP beneficiaries and non beneficiaries Variables

YB YC DY lnYB lnYC D lnY

Beneficiary (N = 600)

Non Beneficiary (N = 200)

Mean Difference

Mean

S.D.

Mean

S.D.

9,846.67 42,665.63 32,818.96 9.04 10.58 10.27

5,390.42 16,292.71 15,340.54 0.578 0.438 0.550

9,947.5 38,997.51 29,050.94 9.04 10.49 10.15

6,033.07 15,001.65 14,150.68 0.596 0.411 0.528

100.83 3,668.11*** 3,768.94*** 0 0.08*** 0.12***

Note: *** indicates significance p < 0.01.

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

8

WORLD DEVELOPMENT Table 3. Difference in difference (DID) estimation results for impacts on income Variables (1)

Model 1 (2)

Model 2 (3)

Model 3 (4)

Model 4 (5)

Model 5 (6)

Model 6 (7)

Model 7 (8)

Model 8 (9)

Model 9 (10)

Model 10 (11)

WORLP

6,768.94*** (1,179.51) –

0.118*** (0.037) –

YB

–

–

3,095.21** (1,358.28) 1,347.47 (1,252.65) –

0.085* (0.049) 0.065 (0.044) –

SIZE

–

–

–

–

AGE

–

–

–

–

EDU

–

–

–

–

SHG

–

–

–

–

LAND

–

–

–

–

–

–

–

–

–

–

–

–

-

-

SUPH

–

–

–

–

–

–

–

–

SUPF

–

–

–

–

–

–

–

–

RUMB

–

–

–

–

–

–

0.034 (0.049) 0.091** (0.042) 0.056 (0.035) 0.067*** (0.006) 0.001 (0.001) 0.004 (0.005) 0.044 (0.042) 0.064*** (0.015) 0.008*** (0.003) 0.079*** (0.04) 0.051 (0.042) 0.093* (0.055) –

–

SUPY

123.66 (1,323.2) 2,186.75** (1,193.39) 0.295*** (0.105) 1,761.38*** (199.52) 38.55 (43.55) 223.54 (149.81) 314.62 (1,188.73) 1,913.89*** (465.38) 244.83** (105.2) 2,286.46** (1,131.11) 56.98 (1,222.15) 3,609.55** (1,635.13) –

0.017 (0.048) 0.096*** (0.041) 0.063* (0.035) 0.069*** (0.006) 0.001 (0.001) 0.005 (0.005) 0.073* (0.041) -

–

0.02 (0.048) 0.098*** (0.041) 0.061* (0.036) 0.071*** (0.006) 0.001 (0.001) 0.006 (0.005) 0.054 (0.042) 0.06*** (0.015) 0.01*** (0.003) -

660.21 (1,289.44) 1,950.51* (1,423.02) 0.33*** (0.095) 1,830.1*** (181.99) 12.95 (42.14) 247.75* (148.02) 1,080.71 (1,162.8) -

LVST

511.52 (1,314.54) 2,116.01** (1,146.27) 0.321*** (0.104) 1,854.83*** (195.55) 28.006 (42.79) 266.474* (148.86) 442.83 (1,204.16) 1,846.33*** (469.37) 294.83*** (108.79) -

RUMS

–

–

–

–

–

–

–

–

FMARG

–

–

–

–

–

–

–

–

FSMAL

–

–

–

–

–

–

–

–

FSMED

–

–

–

–

–

–

–

–

29,050.01*** (999.35)

10.15*** (0.037)

29,050.01*** (999.98)

10.15*** (0.037)

20,034.32*** (3,156.74)

10.41*** (0.309)

17,967.38** (3,274.14)

10.29*** (0.314)

318.99 (384.41) 316.11*** (131.59) 2,185.57 (3,940.02) 11,380.89*** (4,018.26) 18,289.02*** (4,404.69) 19,619.16*** (5,106.2)

0.018 (0.014) 0.01*** (0.004) 0.087 (0.137) 0.394*** (0.139) 0.565*** (0.146) 10.39*** (0.355)

10.21*** 0.012 DY 800

7.33*** 0.008 D lnY 800

5.83*** 0.013 DY 800

4.90*** 0.011 D lnY 800

17.55*** 0.213 DY 800

19.51*** 0.224 D lnY 800

14.75*** 0.224 DY 800

15.85*** 0.234 D lnY 800

22.08*** 0.255 DY 800

23.33*** 0.255 D lnY 800

PERF

Constant f R2 Outcome Variable N Note:

***

p < 0.01;

**

p < 0.05; *p < 0.10; Robust S.E. in parentheses.

the performance of the WORLP was better, the income of the households is higher by Rs. 2,100 (10%) compared to the nonbeneficiaries or locations where the performance was not so good. Hence, the governance of the watershed associations created through the WORLP has significantly affected the way benefits percolated to the beneficiaries. The benefits from other ongoing interventions are controlled through the inclusion of respective dummies depicting participation of households in these. Three schemes have been analyzed for which penetration has been more than 10% in the sample. First is the case of employment guarantee through the MGNREGS (SUPY). For households who have benefitted from this scheme, the increase in income is 7% (Rs. 2,290) compared to the non-participants. It also emerges that the role of food security schemes (SUPF) is significant. The subsidized food available to the beneficiaries is responsible for higher income and represents approximately 9% increase in income compared to the non-beneficiaries (column 8 and 9 of Table 3). Rahman (2016) also concludes that the food assistance scheme

has resulted in the improvement in the household nutritional intake and that the diet quality proportion of the households consuming below the recommended dietary allowance of calorie, fats and protein, has declined significantly in this region post the intervention. The variable YB which depicts the income of the household at the baseline is negative across all specifications, implying that the households in the lower strata are catching up with the richer households over a period of time. This confirms the neoclassical phenomenon of convergence in a microeconomic framework as also found by Carter et al. (2007). Enquiring into the nature of heterogeneous effects, the case of livestock and land ownership is analyzed and the results are presented in columns 10 and 11. It is found that the presence of small ruminants has contributed to the observed increase in income by about 1%. Although the scale of enlargement is small, nonetheless, it is significant. Similarly, it also appears that the benefit of interventions have accrued only to the farmers in the small and semi-medium category and not to the

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

DO DEVELOPMENT INTERVENTIONS CONFER ADAPTIVE CAPACITY? INSIGHTS FROM RURAL INDIA

marginal ones. Although the enhancement in income of all these groups is more than the reference category (medium and large farmers), notably significant improvement is observed only for the small (39%) and semi-medium farmers (56%), with the estimated results being more parsimonious and other variables exhibiting attributes as before. While the observed enhancement in income is mostly spent on securing consumption and agriculture for the subsequent season, there is also evidence of this being invested in microenterprises or used for scaling up the existing traditional enterprises. (b) Reasons for enhancement in income The interventions in the WORLP consisted of: first, the physical land and water management initiatives representing activities like improving the management of land and water, creation of infrastructure such as embankments, water storage ponds and irrigation channels; second, the capacity building and economic support measures like providing loans and grants for microenterprises, microcredit and sustainable livelihoods promotion. These are directly responsible for enhancing farm output and productivity, changing land use, increasing irrigation availability, reducing migration and poverty, and aiding human capital; and, indirectly, facilitating food security, access to resources and institutions, and diversification of livelihood. In the view of this, the rationale for the observed improvement in income is analyzed by using a set of outcome variables that quantify these aspects through the functional form outlined in equation 2. The results are presented in detail in Tables 4 and 5. It is observed that the rise in income due to the WORLP could be attributed to: (i) change in agricultural output and modification in cropping practices, (ii) diminution in the cost

9

of inputs and amplification in the availability of a few factors of production. The interventions exhibit a positive impact on crop production, with the beneficiaries reporting an increase in revenue to the tune of Rs. 5,000. The increase in the value of crop production in the treatment area is approximately 16% more than in the control regions (columns 2 and 3 of Table 4). Accounting for the performance of the interventions, it is observed that the regions where the performance of the WORLP was good, the crop revenue of farmers was even higher (Rs. 5,700) than the non-beneficiaries and locations were the outcome was relatively poor. The addition of this dimension reduces the scale of diffusion, as revenue from crop production for the beneficiaries is 10% higher now than the control sample (column 4 and 5 of Table 4). Implications regarding the input cost of cultivation are analyzed next by using total input cost of cultivation as the outcome variable. The results show that the input cost of cultivation has declined for the beneficiaries by almost 18%. The households which were covered under the WORLP reported that input cost were lower by approximately Rs. 2,500. Subsequently, the inclusion of other inputs for cultivation, socioeconomic characteristics of the households and access to social networks in the framework further establishes the above results. From columns 6 and 7 of Table 4, it is observed that increase in land ownership further augments production. An additional acre of land raises production by Rs. 8,000 or results in 20% more of agricultural produce. Similarly, access to social networks enhances agricultural output by approximately 8%. This is important as one of the objectives of the WORLP was the creation of social capital and inclusion under the livelihood plus component of the design. The self-help groups created during the program offered savings and credit schemes and developed microenterprises with

Table 4. Single difference estimation results for determinants of production value and input cost in agriculture Variables (1)

Model 1 (2)

Model 2 (3)

Model 3 (4)

Model 4 (5)

Model 5 (6)

Model 6 (7)

Model 7 (8)

Model 8 (9)

Model 9 (10)

Model 10 (11)

WORLP

4,913.227** (1,791.48)

0.159*** (0.067) –

2,207.21 (1,778.30) 5,602.38*** (2,364.34)

0.129** (0.069) 0.061 (0.063)

6667.09*** (1441.81) 556.71

0.237*** (0.061) 0.091

2455.75*** (800.484) 4,814.532*** (828.843)

0.18*** (0.077) 0.372*** (0.064)

897.61 (622.1) 2,452.39*** (507.11)

0.082 (0.068) 0.198*** (0.049)

YB

–

–

–

–

–

–

–

–

–

–

–

AGE

–

–

–

–

–

–

EDU

–

–

–

–

–

–

LAND

–

–

–

–

–

–

LVST

–

–

–

–

–

–

SHG

–

–

–

–

–

–

27,090.91*** (1,357.92)

10.01*** (0.059)

27,090.91*** (1,359.17)

10.01*** (0.059)

(0.048) 0.067 (0.043) 0.008 (0.008) 0.0004 (0.001) 0.0009 (0.006) 0.2*** (0.025) 0.006 (0.005) 0.086* (0.045) 8.79*** (0.379)

–

SIZE

(1,486.89) 0.229 (0.153) 426.69 (291.125) 28.55 (55.30) 78.15 (190.01) 8,147.79*** (1,048.71) 160.49 (157.93) 3,679.14*** (4,655.90) 765.01 (4,665.90)

11,903.03*** (675.515)

9.15*** (0.064)

0.141*** (0.478) 26.15 (108.59) 11.43 (21.17) 32.48 (69.39) 2,934.35*** (289.99) 15.77 (59.16) 735.98 (534.97) 1,816.03 (1,853.03)

0.125*** (0.041) 0.005 (0.009) 0.001 (0.002) 0.006 (0.006) 0.201*** (0.025) 0.002 (0.005) 0.015 (0.049) 7.41*** (0.366)

7.52*** 0.009 OUTT 544

5.67*** 0.011 ln(OUT) 544

3.06*** 0.012 OUTT 544

3.06*** 0.012 ln(OUT) 544

22.62*** 0.606 OUTT 544

13*** 0.431 ln(OUT) 544

18.01*** 0.062 TC 544

16.66*** 0.054 ln(TC) 544

25.81*** 0.593 TC 544

15.29*** 0.463 ln(TC) 544

PERF

Constant f R2 Outcome Variable N Note:

***

p < 0.01;

**

p < 0.05; *p < 0.10; Robust S.E. in parentheses.

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

10

WORLD DEVELOPMENT Table 5. Single difference estimation results for determinants of other input costs and migration. Variables (1)

Model 1 (2)

Model 2 (3)

Model 3 (4)

Model 4 (5)

Model 5 (6)

Model 6 (7)

Model 7 (8)

Model 8 (9)

Model 9 (10)

WORLP

136.10*** (67.25) 54.67 (54.43) 0.0008 (0.004) 7.89 (9.86) 3.76* (2.17) 7.44 (8.43) 69.26*** (23.21) 5.86 (5.15) 19.92 (56.67) 379.58** (175.07)

7,134.01*** (836.65) 1,201.46 (886.36) 0.172*** (0.064) 194.37* (121.20) 8.69 (33.55) 167.39 (105.76) 1,178.04*** (273.10) 160.10* (97.35) 4,050.24*** (827.59) 8,013.67*** (2,272.92)

19.48*** (2.12) 5.68*** (2.16) 0.0001 (0.0001) 0.349 (0.313) 0.096 (0.079) 0.546** (0.259) 0.288 (0.397) 0.327 (0.235) 10.59*** (1.93) 15.83*** (5.58)

882.84*** (199.306) 683.10*** (172.70) 0.015 (0.013) 99.15*** (35.54) 10.86 (7.16) 31.75 (22.90) 362.02*** (106.05) 2.44 (18.78) 473.98*** (181.71) 1,662.32*** (559.67)

325.08 (218.614) 1,059.31*** (180.10) 0.034** (0.016) 38.02 (41.08) 9.61 (7.89) 44.63* (25.49) 812.66*** (143.83) 7.50 (21.23) 628.87*** (213.87) 1,517.4*** (625.06)

557.83* (299.13) 1,743.31*** (274.90) 0.049** (0.023) 61.13 (59.607) 1.25 (11.85) 12.88 (35.87) 1,174.68*** (182.86) 5.06 (27.79) 1,102.85*** (302.12) 144.92 (926.01)

0.354*** (0.068) 0.389*** (0.065) – 0.065*** (0.012) 0.002 (0.002) 0.028*** (0.007) 0.048*** (0.013) 0.007 (0.005) 0.053 (0.065) 0.223 (0.158)

4,549.06*** (1,112.23) 5,113. 95*** (1,042.98) 0.078 (0.095) 892.91*** (184.41) 20.58 (39.07) 325.09*** (125.23) 699.68*** (245.41) 98.22 (92.05) 40.38 (1,094.53) 3,033.5 (3,236.78)

1.79*** (0.431) 2.47*** (0.358) 0.026 (0.269) 0.296*** (0.057) 0.018 (0.013) 0.135*** (0.044) 0.224*** (0.082) 0.041 (0.031) 0.097 (0.372) 2.26 (2.47)

2.70*** 0.078 CIrri 544

12.02*** 0.208 PRODV 544

11.38*** 0.142 SHRV 544

9.88*** 0.246 LCO 544

13.97*** 0.495 LCH 544

15.6*** 0.492 LCTot 544

11.49*** 0.144 MIG 800

8.23*** 0.096 REMIT 800

13.32*** 0.125 ln(REMIT) 800

PERF YB SIZE AGE EDU LAND LVST SHG Constant f R2 Outcome Variable N Note:

***

p < 0.01;

**

p < 0.05; *p < 0.10; Robust S.E. in parentheses.

the members being mainly the women and the poorest. The number of crops cultivated has increased among the beneficiaries with most of the farmers cultivating around 2–3 crops and some big farmers cultivating a maximum of 4 crops per year. The results also accentuate this as the regions where the performance of the WORLP was good, the input cost is higher by Rs. 4,800. The increase in intensity of cultivation for the beneficiaries and regions where the performance of the WORLP was better has translated to higher input cost of cultivation (37% additional). Among the other variables, land ownership is significant and so also is income at baseline. It can be inferred that higher land deployed for cropping increases input cost. Taking up an additional acre of land for cultivation moves the input cost upward by Rs. 3,000, which translates to a profit of Rs. 5,000 per acre. The impact of the interventions on other input costs is also analyzed using equation 2. The outcome variables are: (i) cost of irrigation, (ii) production and share of vegetables in total production, (iii) cost of labor (own, hired and total), and (iv) migration and remittances from migrant members. The results are depicted in Table 5. The irrigation cost is lower by Rs. 130 for the beneficiaries (column 2 of Table 5). This has improved the resilience to the increasingly variable monsoon rain, prolonged dry spells, and drought. Interventions have also had marked effects on the groundwater tables, which have been raised in the range of 2–4 m (Sharma et al., 2014). This has changed the land use patterns, permitting a second crop during the summer season diversification of cropping. Examining changes in the cropping systems, it emerges that beneficiary farmers are cultivating more vegetables. They produce approximately Rs. 7,000 worth of more vegetables, displaying better crop diversification and, likewise, the share of vegetables in total production is almost 20% higher than for the non-beneficiaries. Richer farmers are cultivating more vegetables and, as before, membership in social networks

augments diversification in production. One of the activities under the livelihood plus support of the WORLP (capacity building and augmentation of human capital) was also to provide seeds and technical support for practicing agriculture to the farmers. This has played a constructive role as well since the results demonstrate that education contributes in deciding the cropping system to be adopted, with farmers with higher levels of education investing in the production of vegetables (column 3 and 4 of Table 5). Analyzing the role of labor in cultivation, the results reveal that beneficiary farmers use less of both family and total labor. However in the regions where the performance of the WORLP has been good, the usage of labor is higher owing to intensive crop cultivation. Similarly, richer households employ more of hired labor and total labor for production and households with higher education also resorting to greater usage of hired labor (column 5, 6, and 7 of Table 5). Migration has increased in larger sized households and among the beneficiaries resulting in withdrawal of family labor for agricultural activities. The results presented in columns 8, 9, and 10 of Table 5 summarize that the number of migrants has increased and so also has the remittances from migrant members. However, migration has diminished by almost 37% in the regions where the WORLP has performed well as also observed by WORLP (2011). Importantly, the results also confirm that there is a decrease in migration, with increase in education levels and enhancement in land ownership. (c) Outcome on resilience, coping and adaptive capacity The incidence of drought and rainfall gaps is reported in the study area during both cropping seasons, with floods also being witnessed lately during the monsoon crop. The direct impact of these events is the resultant crop loss either due to the unavailability of water in the growing seasons or because

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

DO DEVELOPMENT INTERVENTIONS CONFER ADAPTIVE CAPACITY? INSIGHTS FROM RURAL INDIA

of the crops being washed away and the land getting inundated due to the floods. The study blocks lie within a district and hence are not likely to have faced an event on a varying scale. However, what differs is the resilience, which depicts the capacity to anticipate, cope with, resist, and recover from the impact. Farm-level adaptation mechanisms like facilitating ground water recharge, creation of water sources, cropping system diversification, use of drought tolerant varieties of crops etc. were promulgated through the WORLP and, hence, by design, the beneficiaries are assumed to be using a mixture of them. Using Eqn. (3), the contribution of these factors has been examined. The outcome variables used in the analysis are: (i) the number of coping instruments used by the households to cope with the impact of drought and (ii) the funds generated through these instruments with reference to the previous incidence of drought during the year 2010. The results are presented in Table 6. It is observed that more instruments are available to the beneficiaries residing in the regions where the performance

11

of the WORLP interventions was good. Likewise, the funds generated through the use of these mechanisms also stands higher by Rs. 1,100 in the treatment than either nonbeneficiaries or regions where the execution was relatively poor (column 2 and 3 of Table 6). Including socioeconomic characteristics indicates that those with higher land ownership and those involved in rearing of livestock, use more instruments, indicating better diversification opportunities. Similarly, a household where the head is more educated or one having access to social networks, uses less of the formal instruments to cope with the drought impacts. Also, the beneficiaries employ fewer of these formal coping mechanisms, further indicating that the vulnerability could have declined. Analyzing the role of funds generated through these instruments, similar results emerge with the exception that household size is significant, implying that larger sized families utilize more funds for coping with the impacts (column 4 and 5 of Table 6). The benefits from other government interventions are included in subsequent functional forms. The results are

Table 6. Single difference estimation results for determinants of coping with drought impacts Variables (1)

Model 1 (2)

Model 2 (3)

Model 3 (4)

Model 4 (5)

Model 5 (6)

Model 6 (7)

Model 7 (8)

Model 8 (9)

WORLP

0.095 (0.075) 0.31*** (0.062) –

173.0 (316.061) 1,138.87*** (242.617) –

3.69 (310.17) 819.54*** (246.16) –

0.301*** (0.071) 0.379*** (0.059) –

–

–

AGE

–

–

EDU

–

–

SHG

–

–

LAND

–

–

0.005 (0.009) 0.002 (0.002) 0.017*** (0.007) 0.321*** (0.068) –

–

–

–

–

SUPY

–

–

–

–

SUPH

–

–

–

–

–

–

SUPF

–

–

–

–

–

–

RUMB

–

–

–

–

113.59*** (43.41) 15.75* (8.51) 30.59 (33.08) 276.62 (251.79) 531.35*** (92.44) 37.09** (16.52) 526.47*** (214.57) 330.59 (296.73) 179.19 (358.21) –

124.787 (308.65) 832.06*** (240.87) 0.062*** (0.019) 127.26*** (43.69) 14.62* (8.61) 11.62 (32.04) 85.72 (247.04) –

LVST

130.17*** (42.59) 13.38 (8.44) 21.91 (33.19) 188.61 (248.99) 510.49*** (92.32) 29.98* (16.52) –

0.282*** (0.071) 0.315*** (0.058) 0.003 (0.044) 0.003 (0.009) 0.001 (0.002) 0.013* (0.007) 0.266*** (0.069) 0.073*** (0.016) 0.017*** (0.007) 0.259*** (0.058) 0.241*** (0.074) 0.074 (0.403) –

52.06 (322.46) 666. 36*** (263.89) –

SIZE

0.278*** (0.071) 0.377*** (0.059) 0.012 (0.044) 0.005 (0.009) 0.002 (0.002) 0.019*** (0.007) 0.312*** (0.071) 0.061*** (0.015) 0.022*** (0.007) –

RUMS

–

–

–

–

–

–

FMARG

–

–

–

–

–

–

FSMAL

–

–

–

–

–

–

FSMED

–

–

–

–

–

–

Constant

1.15*** (0.055)

2,381.0*** (256.88)

1.61** (0.4)

64.52 (638.95)

1.27*** (0.404)

438.66 (637.56)

0.094*** (0.026) 0.009 (0.008) 0.188 (0.18) 0.034 (0.185) 0.098 (0.203) 1.708*** (0.257)

167.37* (91.54) 68.81*** (21.13) 9,453.38*** (2,198.32) 8,755.61*** (2,216.33) 9,042.38*** (2,233.34) 9,512.02*** (2,212.83)

f R2 Outcome Variable N

14.17*** 0.031 COPI 800

12.47*** 0.027 COPF 800

14.55*** 0.121 COPI 800

10.31*** 0.148 COPF 800

12.92*** 0.16 COPI 800

9.30*** 0.155 COPF 800

12.70*** 0.127 COPI 800

9.31*** 0.171 COPF 800

PERF YB

Note:

***

p < 0.01;

**

p < 0.05; *p < 0.10; Robust S.E. in parentheses.

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

12

WORLD DEVELOPMENT

similar to the previous cases and additionally it is found that two government-led interventions play a positive role in facilitating the adoption of the coping instruments. Households participating in the MGNREGS and benefitting from housing schemes are also deploying more instruments and generating more funds through their use for post-drought coping (column 6 and 7 of Table 6). Examining heterogeneous effects of the interventions, it is observed that the presence of big ruminants in the household facilitates access to more instruments and also more funds, suggesting possible collateralizing of these resources. Further, it surfaces that small ruminants contribute in building coping capacity. Households owning them devoted fewer funds for drought coping through the usage of formal instruments, implying small ruminants act as a supplementary source of revenue. It also emerges that marginal, small and semi-medium farmers have devoted lesser funds for postdrought coping compared to medium and large farmers. This could probably be either due to the non-availability of coping instruments for these groups or due to their lower exposure necessitating lesser funds (column 8 and 9 of Table 6). Likewise, richer households have devoted more funds for postdrought coping, again reiterating higher exposure or easier access to coping instruments and highlighting the role of economic, social, and institutional factors in defining resilience and adaptive capacity.tpb .5 5. SUMMARY AND CONCLUSIONS There is a growing recognition that the impacts of climatic aberrations and extremes are going to increase in the future due to climate change. Although the threats may not be new, the possibility of amplification exists. This assumes significance due to the developmental and sustainability challenges faced in the developing nations. The paper examined the role of developmental interventions in augmenting the income of the households in a rural setting. Subsequently, it also enquired whether this has enhanced the adaptive/coping capacity of the beneficiaries to deal with climatic aberrations and extremes. The results reveal that the activities undertaken through the WORLP in Bolangir has increased the income of the beneficiaries in the range of 8–11%. This is due to the improvement in agricultural production, adoption of diversified cropping systems and improved water availability for agriculture. This is complemented by the reduction in input costs on a relative scale, with the most notable decrease being observed for cost of irrigation, followed by labor costs. A noticeable decline in migration (around 37%) is observed where the performance of the WORLP has been good, which has improved the availability of hired labor for cultivation. In the regions of superior WORLP performance, the beneficiaries are resorting to intensive cropping supported by family labor. Cropping system diversification among the beneficiaries has resulted in higher cultivation of vegetables, both in absolute terms and as a share of total production. Access to social networks

facilitated crop diversification through a demonstration effect and aided the availability of labor for farming. Apart from the WORLP, employment guarantee and access to food security schemes have contributed in revenue generation. On the other hand, it also emerges that the benefits of WORLP have trickled down only to the small and medium farmers. Since a substantial share of the interventions was devoted to land and water management, the landless are excluded from the benefits by design. Furthermore, the results also indicate that marginal farmers in the study regions have not significantly benefited from these interventions. The creation of an infrastructure for water and farm management has bolstered the resilience of the beneficiaries to deal with climate variability. The availability of non-farm coping instruments is higher for the beneficiaries and so also are the funds generated through these (approximately Rs. 600 or 9% more) although only in the regions where the performance of the WORLP was good. Also crucial is access to social networks as the households having membership in these, adopt less formal coping instruments (at the same time not negating the use of non-formal ones). Possessing livestock assets and land enhances coping capacity and adaptation. Adaptive/coping capacity being determined by factors such as assets, income levels and access to education and resources, improvements in these due to the WORLP also contributed toward increasing the resilience to climatic shocks. Other interventions like employment guarantee programs and affordable housing schemes in rural areas are equally important for increasing the resilience of the vulnerable. Hence it could be concluded that interventions aiming at reducing poverty, diversification of livelihood and income are mechanisms to build the resilience of the households as well, thereby enhancing the capacity for them to cope with the impacts of climatic variations. From a policy perspective, the results advocate for the continuation of developmental interventions, focusing on providing better living standards in the developing countries. However, there is a need to concurrently address concerns regarding the impact of increasing incidence of climatic aberrations and extremes on the vulnerable poor. The two goals could be addressed simultaneously by introducing suitable adjustments within the scope of the ongoing developmental interventions. For instance, activities promoting livelihood diversification, food security and poverty reduction also facilitate improvements in the resilience of the communities and households augmenting their coping/adaptive capacity. Given the resource constraints, the key is to realign and incorporate activities within the ongoing and planned interventions that contribute to the intended outcomes and, in addition, reduce the exposure and encourage ex-ante risk management associated with climatic aberrations and extremes. From an up scaling angle, future programs in general and those being designed on foundations similar to the WORLP, should aim to integrate these outcomes and devise interventions that percolate to a diverse stratum of population within their targeted regions.

NOTES 1. By resilience the reference is to the capacity of a system, community or society potentially exposed to hazards, to adopt, by resisting or changing, in order to reach and maintain an acceptable level of functioning and structure as defined by the United Nation’s International Strategy for Disaster Reduction (UNISDR, 2006).

2. The state of Odisha is geographically located in the eastern coast of India, bound by the Bay of Bengal in the East and the states West Bengal, Jharkhand, Chhattisgarh, and Andhra Pradesh in the North, West, and South respectively.

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

DO DEVELOPMENT INTERVENTIONS CONFER ADAPTIVE CAPACITY? INSIGHTS FROM RURAL INDIA 3. Also spelled as Balangir.

13

adequate consumption of goods and services, health, status, achievement, and security (Squire, 1991).

4. India’s National Action Plan on Climate Change (NAPCC) and the associated missions and submissions gained significance post 2005 and outline specifically existing and future policies and programs for climate mitigation and adaptation. 5. Following Chambers and Conway (1992), livelihoods are ways in which people satisfy their needs or gain a living. It is defined as a set of flows of income from a combination of sources that improve well-being for a household that includes workers and dependents (Ahmed & Lipton, 1999). Again, well-being is the product of a range of factors, including

6. A watershed is an area of land that drains all the streams and rainfall to a common outlet such as the outflow of a reservoir, mouth of a bay, or any point along a stream channel. The word watershed is sometimes used interchangeably with drainage basin or catchment. The size of a watershed can vary from a few hectares to large river basins. The regions where the WORLP was implemented consisted of hilly terrain and hence the typical size of the watersheds was approximately 500 hectares. 7. This term was coined during the inception of the WORLP project.

REFERENCES Adger, W. N. (1999). Social vulnerability to climate change and extremes in coastal Vietnam. World Development, 27(2), 249–269. Agrawal, A., & Perrin, N. (2009). Climate adaptation, local institutions and rural livelihoods. In W. N. Adger, I. Lorenzoni, & K. L. O’Brien (Eds.), Adapting to climate change: Thresholds, values, governance. 9780-511-59626-1 (pp. 350–367). New York: Cambridge University Press. Ahmed, I. I., & Lipton, M. (1999). Impact of structural adjustment on sustainable rural livelihoods: A review of the literature, IDS Working Paper 62. Sussex: Institute of Development Studies. Arouri, M., Nguyen, C., & Youssef, A. B. (2015). Natural disasters, household welfare, and resilience: Evidence from rural Vietnam. World Development, 70, 59–77. Bahinipati, C. S., & Patnaik, U. (2015). The damages from climatic extremes in India: Do disaster specific and generic adaptation measures matter?. Environmental Economics and Policy Studies, 17(1), 157–177. http://dx.doi.org/10.1007/s10018-014-0094-x. Benson, C., & Clay, E. J. (2004). Understanding the economic and financial impacts of natural disasters. World Bank, Washington DC: Disaster Risk Management Series. Botzen, W. J. W., & Van den Bergh, J. C. J. M. (2009). Managing natural disaster risks in a changing climate. Environmental Hazards, 8(3), 209–225. Bouwer, L. M., Crompton, R. P., Faust, E., Hoppe, P., & Pielke, R. A. Jr., (2007). Confronting disaster losses. Science, 318, 753. Briguglio, L., Cordina, G., Farrugia, N., & Vella, S. (2009). Economic vulnerability and resilience: Concepts and measurements. Oxford Development Studies, 37(3), 229–247. Brown, D. G., Agrawal, A., Sass, D. A., Wang, J., Hua, J., & Xie, Y. (2013). Responses to climate and economic risks and opportunities across national and ecological boundaries: Changing household strategies on the Mongolian plateau. Environmental Research Letters, 8(4), 045011. http://dx.doi.org/10.1088/1748-9326/8/4/045011. Bruneau, M., Chang, S., Eguchi, R., Lee, G., O’Rourke, T., Reinhorn, A., ... von Winterfelt, D. (2003). A framework to quantitatively assess and enhance seismic resilience of communities. Earthquake Spectra, 19(4), 733–752. Cannon, T. (2008). Reducing people’s vulnerability to natural hazards: Communities and resilience, Research Paper No. 2008/34. UNU World Institute for Development Economics Research (WIDER). Carter, M. R., Little, P. D., Mogues, T., & Negatu, W. (2007). Poverty traps and natural disasters in Ethiopia and Honduras. World Development, 35(5), 835–856. Carter, M. R., & Barrett, C. B. (2006). The economics of poverty traps and persistent poverty: An asset-based approach. Journal of Development Studies, 42(2), 178–199. Carter, M. R., & May, J. (1999). Poverty, livelihood and class in rural South Africa. World Development, 27(1), 1–20. Chambers, R., & Conway, R. (1992). Sustainable rural livelihoods: Practical concepts for the 21st century, IDS Discussion Paper 296. Sussex: Institute of Development Studies. CRD (2010). Impact assessment of externally aided project interventions on livelihood of the poor and marginalized in KBK districts of Orissa. Study report. Centre for Rural Development, Bhubaneswar India, co-sponsored by Planning Commission, Government of India, New Delhi. Davies, M., Be´ne´, C., Arnall, A., Tanner, T., Newsham, A., & Coirolo, C. (2013). Promoting resilient livelihoods through adaptive social

protection: Lessons from 124 programmes in South Asia. Development Policy Review, 31(1), 27–58. DDMP (2015). District disaster management plan for Balangir. Bhubaneswar: District Disaster Management Authority, Government of Odisha. De Haen, H., & Hemrich, G. (2007). The economics of natural disasters: Implications and challenges for food security. Agricultural Economics, 37(S1), 31–45. Dercon, S. (2004). Growth and shocks: Evidence from rural Ethiopia. Journal of Development Economics, 74(2), 309–329. Dercon, S. (2002). Income risk, coping strategies and safety nets. World Bank Research Observer, 17(2), 141–166. Economic Survey (2013). Odisha economic survey. Bhubaneswar: Planning and Coordination Department, Government of Odisha. Esteves, T., Rao, K. V., Sinha, B., Roy, S. S., Rao, B., Jha, S., ... Ravindranath, N. H. (2013). Agricultural and livelihood vulnerability reduction through the MGNREGA. Economic and Political Weekly, XLVIII(52), 94–103. Grossi, P., & Kunreuther, H. (Eds.) (2005). Catastrophe modeling: A new approach to managing risk. New York: Springer. Hagen, N., Phukan, I., & Honore, G. (2004). Watershed development: A tool for climate change adaptation. Discussion paper on sustainable natural resources management in India. Indo German bilateral project ‘Watershed Management’, New Delhi. Holling, C. (1973). Resiliency and stability of ecological systems. Annual Review of Ecological Systems, 4, 1–24. http://dx.doi.org/10.1146/ annurev.es.04.110173.000245. ICAI (2013). DFID’s livelihoods work in western Odisha. Independent Commission for Aid Impact, Report, 18 February 2013, London. https://icai.independent.gov.uk/wp-content/uploads/ICAI-ReportDFIDs-Livelihoods-Work-in-Western-Odisha.pdf (last accessed in November, 2016). IPCC (2014). Climate change 2014: Synthesis report. In L. A. Pachauri, & Meyer (Eds.), Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Core Writing Team. Geneva, Switzerland: IPCC, p. 151. IPCC (2012). Summary for policymakers. In C. B. Field, V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, & K. L. Ebi et al. (Eds.), Managing the risks of extreme events and disasters to advance climate change adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (pp. 1–19). Cambridge, UK, and New York, NY, USA: Cambridge University Press. Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2010). Handbook on impact evaluation: Quantitative methods and practices. Washington DC: The International Bank for Reconstruction and Development/The World Bank, doi: 10.1596/978-0-8213-8028-4. Mall, R., Singh, K. R., Gupta, A., Srinivasan, G., & Rathore, L. S. (2006). Impact of climate change on Indian agriculture: A review. Climatic Change, 78(2), 445–478. Mallik, R. M., & Meher, R. K. (1999). Impact of IRDP on KBK districts of Odisha. Bhubaneswar, Odisha: Nabakrushna Choudhury Centre for Development Studies. Mirza, M. M. Q. (2003). Climate change and extreme weather events: Can developing countries adapt?. Climate Policy, 3(3), 233–248. NATCOM (2004). India’s initial national communication to the United Nations framework convention on climate change. Government of India, New Delhi: Ministry of Environment and Forests.

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

14

WORLD DEVELOPMENT

Pattanayak, S. K. (2009). Rough guide to impact evaluation of environmental and development programs. SANDEE Working Paper No. 40, ISSN: 1893–1891. Pattnaik, C. R. (1998). Why drought in Bolangir is a man-made phenomenon. Exchange, 22(September). Pattnaik, I. (2012). Livelihood pattern and coping mechanisms during drought: A study of two villages in Odisha, RULNR Working Paper No. 17. Hyderabad: Centre for Economic and Social Studies. Patnaik, U., Das, P. K., & Bahinipati, C. (2013). Analyzing vulnerability to climatic variability and extremes in the coastal districts of Odisha, India. Review of Development and Change, 18(2), 173–189, ISSN: 09722661. Patnaik, U., & Narayanan, K. (2005). Vulnerability and climate change: An analysis of the eastern coastal districts of India. Paper presented at the International Workshop of Human Security and Climate Change, Holmen Fjord Hotel, Asker, Oslo, 21–23 June 2005. Perrings, C. (2001). Resilience and sustainability. In H. Folmer, H. L. Gabel, S. Gerking, & A. Rose (Eds.), Frontiers of environmental economics. Cheltenham: Edward Elgar. Rahman, A. (2016). Universal food security program and nutritional intake: Evidence from the hunger prone KBK districts in Odisha. Food Policy, 63, 73–86. Rose, A. (2004). Defining and measuring economic resilience to disasters. Disaster Prevention and Management, 13(4), 307–314. Routray, S. (2015). Local appropriations of developmental imperatives: Notes from a watershed project in Odisha, eastern India. European Journal of Development Research, 27(5), 672–685. http://dx.doi.org/ 10.1057/ejdr.2014.51. Roy, B. C., Selvarajan, S., & Mohanty, C. (2004). Vulnerability and adaptation to climate change in Odisha: A livelihood analysis, Working Paper No. 6. Delhi: National Centre for Agricultural Economics and Policy Research. Sainath, P. (1996). Everybody loves a good drought. New Delhi: Penguin Books. Sathaye, J., Shukla, P. R., & Ravindranath, N. H. (2006). Climate change, sustainable development and India: Global and national concerns. Current Science, 90(3), 314–325. Satyanarayana, M., Singha, A., Das, B., & Lopamudra (2009). Effect of climate change in western Odisha, WORLP working paper 69. Bhubaneswar: Odisha Watershed Development Mission, Government of Odisha.

Sharma, U., & Patwardhan, A. (2008). An empirical approach to assessing generic adaptive capacity to tropical cyclone risk in coastal districts of India. Mitigation and adaptation strategies for global change, 13(8), 819–831. Sharma, V., Reddy, B., & Sahu, N. (2014). Sustainable rural livelihoods approach for climate change adaptation in western Odisha, eastern India. Development in Practice, 24(4), 591–604. http://dx.doi.org/ 10.1080/09614524.2014.911817. Squire, L. (1991). Introduction: Poverty and adjustment in the 1980s. The World Bank Economic Review, 5(2), 177–185. Swain, M., & Swain, M. (2011). Drought vulnerability, coping capacity and residual risk: Evidence from Bolangir district in Orissa, India. Asian Journal of Environment and Disaster Management, 3(4), 453–473. http://dx.doi.org/10.3850/S1793924011001027. Swain, M. (2010). Drought vulnerability and livelihoods: Impacts and adaptation strategies in western Odisha. Saarbrucken, India: VDM Verlag. Tiwari, R., Somashekhar, H. I., Parama, V. R. R., Murthy, I. K., Kumar, M. S. M., Kumar, B. K. M., ... Ravindranath, N. H. (2011). MGNREGA for environmental service enhancement and vulnerability reduction: Rapid appraisal in Chitradurga district, Karnataka. Economic and Political Weekly, XLVI(20), 39–47. UNISDR (2006). Terminology: Basic terms of disaster risk reduction. https://www.unisdr.org/we/inform/terminology (last accessed in November 2016). Wisner, B., Blaikie, P., Cannon, T., & Davis, I. (1994). At risk: Natural hazards, people’s vulnerability and disasters. 978-0-4152-5216-4. Routledge. WORLP (2011). Impact assessment report, western Odisha rural livelihoods project. New Delhi: Sambodhi Research and Communications and Winrock International. WORLP (1999). Project memorandum. Odisha Watershed Development Mission, Government of Odisha, Bhubaneswar: Western Odisha rural livelihoods project.

APPENDIX 1. Table 7

Table 7. Differences between beneficiaries and non beneficiaries of WORLP across different variables Variables Household Size (Nos.) Age of the head of the household (Years) Education of the head of the household (No. of years of education) Total number of livestock (Nos.) Number of big ruminants (Nos.) Number of small ruminants (Nos.) Total land in Acres Dummy for marginal farmers Dummy for small farmers Dummy for semi medium farmers Dummy for employment benefits from other interventions

Mean for WORLP Beneficiaries

Mean for Non Beneficiaries

Mean Difference

5.400 (2.805) 51.632 (2.805) 3.070 (3.391) 3.615 (5.811) 1.208 (1.462) 2.447 (4.996) 1.604 (2.019) 0.767 (0.423) 0.163 (0.369) 0.058 (0.235) 0.707 (0.456)

4.670 (2.564) 50.945 (2.563) 3.815 (3.424) 2.095 (3.076) 0.730 (1.021) 1.390 (2.705) 1.602 (1.855) 0.740 (0.439) 0.205 (0.405) 0.05 (0.218) 0.545 (0.499)

0.730*** 0.687 0.745*** 1.520*** 0.478*** 1.057*** 0.003 0.027 0.042 0.008 0.162*** (continued on next page)

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017

DO DEVELOPMENT INTERVENTIONS CONFER ADAPTIVE CAPACITY? INSIGHTS FROM RURAL INDIA

15

Table 7 (continued) Variables

Mean for WORLP Beneficiaries

Mean for Non Beneficiaries

Mean Difference

0.188 (0.391) 0.167 (0.372) 0.453 (0.846) 7,051 (13,362) 1.215 (0.774) 2,777 (3,023) 0.568 (0.599) 5,396 (6,774) 32,000 (23,704) 11,772.74 (8,614.19) 243.689 (576.92) 6,247.57 (9,463.51) 16.275 (21.738) 2,302.79 (2,027) 2,371.97 (2,805.95) 4,674.76 (4,135.60) 16.43 (21.87)

0.125 (0.331) 0.045 (0.208) 0.255 (0.531) 4,325 (9,599) 1.155 (0.784) 2,381 (3,635) 0.535 (0.566) 4,940 (4,772) 27,000 (15,632) 11,903.03 (7,769.13) 410.606 (700.63) 1,189.39 (4,689.01) 3.249 (12.504) 2,991.67 (1,901.11) 1,844.70 (2,180.67) 4,836.36 (3,225.55) 20.09 (32.04)

0.063**

Dummy for housing benefits from other interventions Dummy for food security benefits from other interventions Number of migrants present in the household Remittances received from migrant members during past one year (Rs.) Number of coping instruments used to deal with drought during the year 2011 Funds generated through these instruments during the year 2011 (Rs.) Number of coping instruments used to deal with drought during the year 2009 Funds generated through these instruments during the year 2009 (Rs.) Monetary value of total agricultural production during the previous year (Rs.) Total input cost for agriculture during the previous year (Rs.) Cost of irrigation for agriculture during the previous year (Rs.) Monetary value of vegetable production during the previous year (Rs.) Share of production of vegetables to total agricultural production (%) Own labor cost for agriculture during the previous year (Rs.) Hired labor cost for agriculture during the previous year (Rs.) Total labor cost for agriculture during the previous year (Rs.) Percentage of crop loss

0.122*** 0.198*** 2,726*** 0.06 396 0.033 457 5,000** 130.288 166.917*** 5,058.179*** 13.026*** 688.875*** 527.269** 161.606 3.658*

Note: N = 600 for WORLP beneficiaries and 200 for Non beneficiaries except for agriculture related variables where N = 412 for WORLP beneficiaries and 132 for Non beneficiaries (Due to households non participation in farming); Standard Deviation in parentheses; *** indicates significance p < 0.01, ** indicates significance p < 0.05 and * indicates significance p < 0.10.

Available online at www.sciencedirect.com

ScienceDirect

Please cite this article in press as: Patnaik, U., & Das, P. K. Do Development Interventions Confer Adaptive Capacity? Insights from Rural India, World Development (2017), http://dx.doi.org/10.1016/j.worlddev.2017.04.017