Do grassroots interventions relax behavioral constraints ... grassroots interventions relax...IFPRI...

IFPRI Discussion Paper 01839

May 2019

Do grassroots interventions relax behavioral constraints to the adoption of nutrition-sensitive food production systems?

Muzna Alvi

Patrick Ward

Simrin Makhija

David J. Spielman

Environment and Production Technology Division

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE The International Food Policy Research Institute (IFPRI), established in 1975, provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition. IFPRI’s strategic research aims to foster a climate-resilient and sustainable food supply; promote healthy diets and nutrition for all; build inclusive and efficient markets, trade systems, and food industries; transform agricultural and rural economies; and strengthen institutions and governance. Gender is integrated in all the Institute’s work. Partnerships, communications, capacity strengthening, and data and knowledge management are essential components to translate IFPRI’s research from action to impact. The Institute’s regional and country programs play a critical role in responding to demand for food policy research and in delivering holistic support for country-led development. IFPRI collaborates with partners around the world.

AUTHORS Muzna Fatima Alvi ([email protected]) is an Associate Research Fellow in the Environment and Production Technology Division at the International Food Policy Research Institute (IFPRI), New Delhi.

Patrick Ward ([email protected]) is an assistant professor of environmental economics and policy in the iMEP program at Duke Kunshan University. He was formerly a Research Fellow in the Environment and Production Technology Division at IFPRI, Washington DC.

Simrin Makhija ([email protected]) is a Research Analyst in the Environment and Production Technology Division at IFPRI, Washington DC.

David Spielman ([email protected]) is a Senior Research Fellow in the Environment and Production Technology Division at IFPRI, Washington DC.

Notices 1 IFPRI Discussion Papers contain preliminary material and research results and are circulated in order to stimulate discussion and critical comment. They have not been subject to a formal external review via IFPRI’s Publications Review Committee. Any opinions stated herein are those of the author(s) and are not necessarily representative of or endorsed by IFPRI. 2 The boundaries and names shown and the designations used on the map(s) herein do not imply official endorsement or acceptance by the International Food Policy Research Institute (IFPRI) or its partners and contributors. 3 Copyright remains with the authors. The authors are free to proceed, without further IFPRI permission, to publish this paper, or any revised version of it, in outlets such as journals, books, and other publications.

mailto:[email protected]




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Do grassroots interventions relax behavioral constraints to the adoption of nutrition-sensitive food production systems?1

Muzna Alvi1, Patrick Ward2, Simrin Makhija3, David J. Spielman3

1International Food Policy Research Institute, New Delhi, India

2Duke Kunshan University, Kunshan, China 3International Food Policy Research Institute, Washington, DC, USA

May 2, 2019

Abstract

In many developing countries, agricultural policies and programs are often designed in a way to promote productivity growth with modern inputs and technologies, and with limited reference to the nutrition gains that gain be made through production diversification. We test whether grassroots programs can relax behavioral constraints inhibiting the adoption of diversified nutrition-sensitive production systems. We use a series of lab-in-field experiments and survey instruments in Odisha, India to elicit male and female farmers’ preferences for risk, aversion to loss, empowerment and aspirations for one’s self and children. We find that respondents in villages where grassroots interventions were promoted showed significantly lower levels of risk aversion, higher levels of loss aversion and higher aspirations for themselves and their children, along with improvements in production and consumption diversity. Insights into the prevalence of behavioral constraints and interventions that relax such constraints fills an important knowledge gap in how to design programs that promote more nutrition-sensitive food production systems.

Keywords: Agri-food systems, laboratory-in-field experiments, prospect theory, public goods games, India

1 Corresponding author: National Agricultural Sciences Complex, Pusa Institutional Area, New Delhi 110012, [email protected]. This work was supported by funding from the CGIAR Collaborative Research Program on Agriculture for Nutrition and Health (A4NH) and the Bill and Melinda Gates Foundation through Technical Assistance and Research for Indian Nutrition and Agriculture (TARINA). All remaining errors are our own. Declarations of interest: none.

mailto:[email protected].

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1 Introduction

Malnutrition in India is a pernicious problem, despite rapid gains in agricultural productivity arising

from the Green Revolution and rapid economic growth resulting from industrialization and

urbanization. Recent estimates indicate that roughly 15 percent of India’s population are under-

nourished, and nearly 40 percent of children under the age of 5 suffer from stunting (GHI 2018). As

of 2018, India’s Global Hunger Index (GHI) score—comprised of national measurements for

undernourishment in the population, prevalence of wasting in children under 5 years of age,

prevalence of stunting in children under 5 years of age, and child mortality—was on par with some

of the poorest countries in the world, signaling a serious hunger situation in the country.

While it is tempting to believe that merely increasing food grain production and increasing the

caloric availability would be enough to ameliorate these deficiencies, nutritionists have long

argued that food security entails much more than caloric security. A growing consensus has

emerged among members of the research, development, and international donor communities

regarding the promotion of more nutrition-sensitive food systems, improving both the access to

and affordability of more nutrient-dense and diversified food baskets.

However, agricultural policies and programs are often designed to promote the production

of certain crops at the expense of more crop-neutral policies that encourage diversification (Pingali,

2015). Meanwhile, interventions designed to encourage nutrition-sensitive agricultural production

systems are often at odds with conventional practices in many rural communities. In some cases,

interventions promoting diversified production and consumption bundles in such communities fail

to gain traction as farmers prefer to simply maintain the status quo and eschew changes in behaviors

or practices, even at the expense of enhanced nutrition. In other cases, farmers are confronted with

thin input or output markets, therein restricting their ability to alter their behaviors or practices in

line with the intervention. In still other cases, the desired change in behaviors or practices is

conditional on the management of common pool resources, such as grazing land use-rights or

irrigation infrastructure, requiring strategic coordination among individuals and households that

can be difficult to secure or sustain. These constraints can pose serious challenges for civil society

organizations and government agencies that aim to advance the nutrition-sensitive development of

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local food systems within these communities. But without understanding the nature of these

constraints, it is difficult to gain traction on the problem of malnutrition. Studying the determinants

of, or constraints to, changes in behaviors and practices can provide considerable insight into how

programs, investments, and policies – beyond conventional efforts to increase food grain production

and caloric security – can effect change in this context.

Our objective in this study is to explore the social and economic viability of production

system diversification in support of greater dietary diversity and nutrient-sensitivity in local food

systems. Diversified production can be beneficial for food security in several ways (Haddad, 2000).

First, by diversifying production and increasing the production of higher value crops, farmers may

ultimately have higher incomes from the sale of marketable surpluses (Koppmair, et al, 2017),

allowing them to consume diverse and potentially more expensive foods such as meats and fruits

(Pellegrini & Tasciotti, 2014). Additionally, to the extent that these food items are also used for home

consumption, farm households may increase consumption of more nutritious food items (Ayenew,

et al. 2018).

We do this in the context of two large agriculture-nutrition projects: Pathways and TARINA,

both implemented by CARE-India, a non-governmental organization (NGO), with the aim of

improving livelihoods and agricultural productivity in the Indian state of Odisha, with a particular

focus on women. The interventions aimed not only to improve access to agricultural inputs, markets

and technologies but also to change farmers’ practices through targeted behavior change

communication (BCC) activities implemented largely through village, farmer, and self-help groups.

With these project objectives in mind, we focus on two questions. The first is the overarching

question of whether CARE’s activities have led to more nutrition-sensitive local food systems.

Specifically, we study whether villages that have previously benefitted from CARE’s activities are

more likely to diversify their production practices, particularly towards more nutrient-dense foods

such as pulses, and more likely to diversify their consumption practices. The second is the deeper

question of whether CARE’s activities have led to changes in behaviors and preferences. To this end,

we conducted a series of framed- and survey-based experiments to gain greater insight into farmers’

decision-making processes and their ability to coordinate with members of their community to

provide public benefits commensurate with CARE’s goals. In short, we study the mechanisms that

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lead to behavioral change in local food systems by not only assessing the program’s direct impact on

production and consumption diversification, but also exploring its indirect impact on relaxing

behavioral constraints, improving aspirations and increasing empowerment.

2 Background on TARINA and Pathways

In late 2015, the Bill and Melinda Gates Foundation funded a consortium of researchers,

development practitioners, and NGOs to explore solutions to the problem of hunger and

malnutrition in India though a “food systems” lens (Pingali, 2015). This consortium, known as

Technical Assistance and Research for Indian Nutrition and Agriculture (TARINA), is led by the

Tata-Cornell Initiative (TCi) and links the evidence-generation capabilities and grassroots-level

implementation capacity of various partner organizations.2

Operating in the states of Bihar, Odisha, and Uttar Pradesh, TARINA aims to redirect

agricultural policy from an overt bias towards staple grains and toward a broader food system focus

that considers the important linkages between agricultural production and household nutrition

(TARINA, 2018). Importantly, TARINA focuses on agricultural pathways for enhancing the rural

poor’s year-round access to affordable, diverse, and high-quality foods that are rich in

micronutrients3.

In Odisha, CARE India’s contribution to TARINA builds on prior work conducted under a

project entitled Pathways to Empowerment (Pathways for short), also supported by the Bill and

Melinda Gates Foundation between 2012 and 2016. Pathways worked in two districts in Odisha to

promote more productive and equitable participation of scheduled caste (SC) and scheduled tribe

(ST) women farmers in sustainable agriculture. To that end, Pathways worked with existing self-

help groups to strengthen solidarity among SC and ST women farmers, promote sustainable and

intensified agricultural practices to increase agricultural productivity and diversify livelihood

2 International Food Policy Research Institute (IFPRI), Tata Institute of Social Sciences (TISS), Emory University, Cornell University, BAIF, CARE-India, and Grameen Development Services in India are the organizations part of this consortium. 3 The project’s main objectives are to (a) provide technical assistance in redesigning agricultural projects to ensure nutrition outcomes at scale, (b) provide assistance and evidence for policy reform that enhances diet quality at affordable prices, and (c) build capacity to design and implement nutrition-sensitive agricultural programs and policies.

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strategies, enhance women’s access to markets and services using a value chain approach, and

improve women’s access to information, including information on farming techniques, markets,

public and private sector services, nutrition, and other development opportunities to diversify

livelihoods (CARE, 2018).

Under TARINA, CARE’s activities leverage many of the institutions and interventions that

were introduced under Pathways. For example, by working through women’s self-help groups that

participated in Pathways activities, TARINA has sought to introduce and promote legume

(specifically, pulse) rotations into the rice production system; engaged in nutrition education and

nutrition awareness building; improved goal husbandry, feed management, and value-chain

linkages; intensification systems for homestead/kitchen gardens; women’s empowerment through

gender-based dialogue, awareness building and behavior change; and storage technologies for

reducing post-harvest losses.

At times, both Pathways and TARINA have confronted entrenched beliefs among farmers—

beliefs related to both production and consumption practices that are often difficult to change. While

efforts to change those beliefs may take years, even decades, of engagement, education, and

conversation, far too little is known about the underlying and often unobservable constraints to

change. Our research is meant to inform the sorts of behavioral or institutional constraints that are

perhaps most entrenched, or at least most likely to act as a binding constraint to systems-level

transformations and the community level, and to test how CARE’s interventions have relaxed these

behavioral constraints. Our experiments, focused on risk aversion, loss aversion, present bias,

cooperation, collective action, empowerment, and aspirations. In this paper we present the results

from risk and loss experiments, described in detail below, as well as the findings from the modules

on empowerment and aspirations.

3 Theoretical background

In this section we introduce the underlying theories that guide the experiments and modules we

implemented in the field, along with rationale behind their inclusion in this study. By and large,

researchers working in the behavioral sciences and behavioral economics are familiar with the

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theories and experiments described below. However, in developing countries, the implementation

of some of these experiments is relatively uncommon but gathering traction.

3.1 Prospect Theory

In developing-country agriculture, one of the most important behavioral changes entails the

adoption of new agricultural technologies or practices. There is, by now, a rich literature studying

the process of technology adoption. The myriad factors contributing or inhibiting technology

adoption include tenurial arrangements, farm size, education, credit constraints, social networks

and social learning, and risk. Traditionally, some authors have preferred to estimate the effects of

risk econometrically, often estimating a production function along the lines of those proposed by

Just (1975).

Starting with Binswanger (1980), many researchers have sought to elicit these otherwise un-

observable behavioral characteristics through carefully constructed field experiments. Many of these

have assumed that preferences conform to expected utility theory (EUT). In their seminal paper,

Kahneman and Tversky (1979) highlighted choice problems in which respondent preferences

systematically deviated from what would otherwise be predicted if the respondents strictly adhered

to the behavioral axioms of EUT, concluding that EUT is too restrictive to adequately describe

decision-making under risk. Among the key features of Kahneman and Tversky’s Prospect Theory

(PT) are that individuals tend to prefer certainty over doubt (when comparing potential gains),

dislike losses (and would risk large potential losses to avoid smaller, certain losses), evaluate changes

based on reference levels rather than the end result, and overweight the likelihood of low probability

events and underweight the probability of high probability events (see also Tversky and Kahneman,

(1992), who introduce a cumulative representation of uncertainty, appropriately dubbed Cumulative

Prospect Theory, CPT). These systematic features of decision-making under uncertainty result in a

value function that is generally concave for gains and convex for losses. As an alternative to EUT,

CPT therefore allows for more flexibility in characterizing responses to risky situations. CPT does

not reject EUT outright, but rather is a general model of decision-making under uncertainty within

which EUT is a specific case.

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Unlike EUT, where only an individual’s risk aversion coefficient characterizes decision-

making under uncertainty, in CPT, three important parameters characterize individual behavior. In

this paper we study two of these parameters that pertain to risk and loss aversion. The first

parameter (σ) dictates the curvature of the prospect value function and can be thought of as a

measure of risk aversion. The second parameter (λ) characterizes loss aversion. The third parameter

(α), which we do not consider in this paper, captures the degree to which low probability outcomes

are disproportionately weighted when valuing risky prospects. Together, these three parameters

jointly characterize the valuation of risky prospects4.

3.2 Empowerment and aspirations

Conventional studies that evaluate agricultural development interventions tend to focus entirely on

readily measurable outcomes that can be quantified such as adoption of a new technology or

practice, or increases in farm output, yield, and income. However, such interventions may also affect

less tangible outcomes such as attitudes toward risk and cooperative behavior that, in turn, may lead

to changes in aspirations and personal agency. These outcomes tend to get ignored when measuring

programmatic success, but are nonetheless important, and perhaps especially so for a project like

the one we are studying, which is aimed towards empowering individuals to make changes in their

established practices of food cultivation and consumption. These outcomes are even more pertinent

when focusing not just on the household as the unit of analysis, but on women within the household

who, in their multiple roles in managing both on-farm production and consumption decisions, are

important agents of change, and thus central to both TARINA’s and Pathways’ theory of change and

the design of CARE’s interventions.

One important avenue through which CARE has aimed to change practices is by encouraging

improved household nutrition through increasing women’s empowerment. There is a growing body

of literature that demonstrates how increasing women’s empowerment can lead to various positive

household outcomes, including more equitable distribution of household resources, more nutritious

household food consumption (Malapit & Quisumbing, 2015), and better health and educational

4 Details of the prospect theory experiment are available in the Appendix.

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outcomes for children, especially girls (Glewwe, 1999; Thomas, 1994). In addition, women’s

empowerment can be directly associated with better agricultural outcomes and productivity gains

(Diiro, et al. 2018; Seymour, 2017), and the adoption of improved agricultural practices and

technologies (Cornish, 2018). Empowering women is thus an important pathway towards changing

both attitudes and practices away from production of staple grains, and towards more nutrition

sensitive production systems.

An important, and related, behavioral response to increased livelihood opportunities is an

increase in aspirations. There has been considerable recent research on what determines the

formation of an individual’s hopes and beliefs about her future, and how this in turn affects

investments towards realizing her beliefs. Genicot and Ray’s (2017) pioneering work introduces the

concept of aspirations-based poverty traps, wherein individuals with low levels of aspirations invest

less effort than those with higher levels of aspirations, which in turn leads to self-fulfilling low

realization of wealth in the next period. In their conceptualization, it is not actual aspiration that

matters, rather the gap between current and aspirational status that determines behavior. The model

is unique as it accounts for the possibility that aspirations could be too high or too low, thus causing

frustration and subsequent underinvestment. Janzen et al. (2017) also find strong evidence

supporting the inverse-U theory of aspirations failure, wherein very high or very low educational

(for own child) and financial aspirations lead to low education and financial investment. Increased

aspirations could thus be an important development outcome, not only in itself, but also as a

determinant of investments in improving one’s future outcomes. If programs such as CARE’s can

increase individual’s aspirations for themselves and their families, they can be an important catalyst

in the adoption of, and investment in, new agricultural practices and technologies, as a pathway

towards the realization of these increased aspirations. In this paper we focus on absolute aspiration

levels and its determinants, rather than aspiration gaps. This is in the spirit of work done by Kosec

and Mo (2017) and Bernard and Taffesse (2014) using the aspiration index.

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4 Sampling strategy, survey design, and experimental protocols

4.1 Sampling strategy

To accurately measure program impacts on production, consumption, and behavior, we need a valid

counterfactual against which to ascribe program impacts. Since CARE had already been active in

these areas for several years, we were not able to randomize program treatments. Our sampling

strategy in this study aimed to create a counterfactual group that was similar enough to the treated

villages that actual observed treatment status could be perceived as effectively random. More

specifically, we used village-level data for the districts of Kandhamal and Kalahandi in the state of

Odisha from the 2011 Census of India (prior to the initiation of CARE’s activities in the state) to

identify villages that were similar in all attributes apart from the subsequent presence of CARE

activities. The following steps were carried out to identify a valid counterfactual for CARE’s

intervention villages and determine the sampling frame for the study.

First, we used data from the 2011 census of India to identify all villages from the same

administrative blocks (sub-district administrative units) within which CARE implemented Pathways

and/or TARINA activities. From this list, villages were divided into two groups: those where CARE

had subsequently implemented Pathways and/or TARINA activities (these were classified as

treatment villages), and those where CARE had no active or past programs as of the time of our

study (these were classified as control villages).

We then estimated propensity scores (predicted probability of treatment by CARE’s

interventions) for each village using a probit model and a fairly comprehensive list of relevant

variables.5 Using these propensity scores, we used nearest neighbor matching to pair each treatment

village with a control village which had the closest propensity score. This gave us 119 pairs of

treatment and control villages. From these, 42 pairs of villages were randomly selected to get an

5 The variables used for propensity score matching included population density, sex-ratio, proportion of Scheduled Caste (SC) households, proportion of Scheduled Tribe (ST) households, number of schools, access to postal service, presence of pucca [tarmac/asphalt] roads, distance to agricultural credit societies, and net sown area. In addition, we matched the villages on availability of healthcare facilities, ground and surface water irrigation, landline phones, mobile phone coverage, bus and taxi services, tractors, nutritional service, daily newspaper, assembly polling station and power supply for domestic used. See appendix Figure A1 for distribution of propensity scores between the two groups of villages.

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equal number of treatment and control villages, for a total sample of 84 villages.

In each village, the survey team identified all households actively engaged in agricultural cultivation

with the help of the sarpanch (a type of local leader or administrator), and from this list 12

households were randomly selected for participation in our study. One adult male and one adult

female were interviewed from each household for a total sample of 1,960 respondents (980 males

and 980 females). Each interview contained questions on household agricultural practices,

consumption, assets and household composition which was answered by the main decision maker

in the household. The next set of questions which included the experiments on risk, loss, ambiguity,

and time preference, as well as questions on decision making, aspirations and empowerment, were

answered by both the respondents.6 Later that day, or the next day, all the respondents participated

in a group game with other respondents in the village.7

We recognize that the village-level pairwise matching procedure described here can only

account for observable differences between villages, leaving open the possibility of non-observable

heterogeneity. We also recognize that the data used in this analysis are cross-sectional in nature and

were collected after the end of Pathways and after the first two years of TARINA activities. As a

result, treated and control villages and households may differ on the basis of omitted observable

variables, unobservable characteristics, or due to intentional targeting or placement bias

attributable to CARE, household or individual self-selection into CARE activities, and other factors.

Having said that, the matching strategies employed here were the best method available for

constructing a counterfactual given the evaluation timeline and the secondary data available.

4.2 Experimental protocol

4.2.1 Risk and loss aversion

We use a series of lottery-based experiments to elicit the latent behavioral characteristics related to

6 In this paper we focus only on risk and loss aversion as these were fully incentive compatible and played with real money that was handed out after the survey. The experiments on time preference and ambiguity aversion did not involve any monetary reward, were not incentive compatible, hence are not part of this paper. 7 The results from the group game are the subject of another paper on cooperation, collective action and altruism.

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risk, and potential losses. The experiments used in this study were modified from Tanaka et al.

(2010), Liu (2013), and Ward and Singh (2015) in their studies of in their studies of Vietnamese

households, Chinese cotton farmers, and Indian rice farmers, respectively. The experimental design,

which essentially takes the form of a Multiple Price List (Holt and Laury, 2002), has already been

tested among individuals in several different countries, including in several states in India, and is

thus simple enough for illiterate or innumerate participants to understand. Our experiments

maintain the general design of the Tanaka et al. (2010) and Liu (2013) experiments, with specifically

calibrated payouts as well as some additional simplifications to increase the probability of

comprehension. A notable difference in our approach and the approaches of Tanaka et al. (2010) or

Liu (2013) is that, like Ward and Singh (2015), most of our experiments involve choices between a

certain (riskless) payment and a risky prospect. This simplifies the choices participants face, as well

as simplifying the estimation of risk premia. The exact experiment protocol is available in the

appendix.

4.2.2 Aspirations and Empowerment

To measure women’s empowerment and agency in effecting change in agricultural production and

technology adoption, we construct an index that is a composite measure of decision-making power,

access to credit, and group membership, where each of the components is given equal weight8.

We also include a detailed module on aspirations regarding education, assets, income and

social status. These aspirations are measured both for the respondents as well as for their children.

We construct an aspirations index, weighting each aspect using self-reported weights. The index is

modelled on the index created by Bernard and Taffesse (2014) and subsequently used by Kosec and

Mo (2017). The respondents are asked questions about their current and aspirational status with

regard to four dimensions: income, education, assets and social status. Income and assets are

measured in Indian rupees whereas education is measured as years of schooling. Social status is

measured on a progressive ten-point scale. We then gave the respondents twenty beads and asked

them to weight each of the four dimensions based on their perception of its importance in their

8 If the woman makes at least some decisions regarding agricultural production and credit, she receives a score of 1 on decision making and access to credit respectively. Similarly, membership in at least one village groups gets a score of 1. The scores from all three components are added and divided by 3 to give a score that ranges from a minimum of 0 to a maximum of 1 in increments of 0.33.

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overall well-being9. The distribution of beads was used as weights for each of the four components,

while constructing the aspirations index.

The index is formulated as follows:

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐼𝐼𝐴𝐴𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 = � �𝐴𝐴𝑖𝑖𝑘𝑘 − 𝜇𝜇𝑘𝑘

𝜎𝜎𝑘𝑘� . 𝜔𝜔𝑖𝑖

𝑘𝑘4

𝑘𝑘=1

Where 𝑘𝑘 is one of the four dimensions mentioned above, 𝐴𝐴𝑖𝑖𝑘𝑘 is respondent 𝐴𝐴’s aspiration on

dimension 𝑘𝑘 and 𝜇𝜇𝑘𝑘and 𝜎𝜎𝑘𝑘measure the sample mean and standard deviation of the response in the

sample, and 𝜔𝜔𝑖𝑖𝑘𝑘 is the weight attached to dimension 𝑘𝑘.

The other outcome we use to measure aspirations are respondents’ hopes for their children’s

future. We ask the respondents what they want their children to be when they grow up, and a parent

is considered to have aspirations for their children if they hope for their children to be in a profession

other than a homemaker, farmer, or laborer. We ask this question separately for the oldest son and

oldest daughter of the respondent, though in the present manuscript we report results for daughters

only, since the response for sons had little to variation. We then asked respondents about what they

believe their son and daughter will become in the future. We frame this as their belief about their

child’s future, as opposed to their hope for the child, which is captured by aspirations. The measure

is binary and is coded as was done for aspirations (above), but again we report results only for

daughters owning to very low variation in the responses for sons.

5 Data

Over 98 percent of the households in our sample are male-headed, which is consistent with the

largely patriarchal culture and low rates of migration in the area. Around 88 percent of the

respondents in our sample report being household heads or spouses of household heads, and the

remaining are predominantly children or children-in-law of the household head. Our survey was

conducted in a predominantly tribal area, with households identifying as being members of

9 Details of the questions used to calculate the aspirations index can be found in the Appendix.

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Scheduled Tribes (ST) making up 58 percent of the total population. Together with households

identifying as being members of Scheduled Castes (SC), they make up 79 percent of our sample. Of

the two districts where we collected primary data, Kalahandi had a more heterogenous population,

with non-SC/ST making up 30 percent of the population, compared to 8.9 percent for Kandhamal.

<Table 1 here>

Literacy rates in Odisha are among the lowest in India, and within Odisha, Kalahandi and

Kandhamal are among the most educationally-disadvantaged districts. The self-reported literacy

rate in our sample was 69 percent overall and 55 percent for women. The average household size in

the sample is 5.3 persons with a low overall dependency ratio. The average cultivated area per

household is 2.8 acres, with 83 percent of respondent households indicating that they cultivated

land that they owned, with the remainder cultivating leased land.

6 Results

We study our results in three stages. In the first stage we consider the effect of the program on

measures of behavior such as risk and loss aversion. We hypothesize that this is the first step towards

bringing about a change in other, more direct program outcomes. In the second step we consider the

effect of the program in changing indirect outcomes such as aspirations, empowerment and personal

agency. Finally, we consider the effect of the program on direct outcomes that the intervention

targeted, such as diversification of production practices and improvements in consumption

diversity. The first two sets of outcomes are analyzed at the individual level, while the set of direct

outcomes are analyzed at the household level.

6.1 Risk and loss preference

The experiments described above are specifically designed to disambiguate various dimensions of

attitudes about uncertainty, including disambiguating risk aversion and loss aversion, as well as

the extent to which perceived probabilities deviate from objective realities when farmers evaluate

risky decisions. Here, we study the effect of the program on relaxing risk and loss aversion. By

comparing the average willingness of farmers to take on risk between villages that have previously

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been beneficiaries of the intervention and those that have not, we find that there are, indeed,

systematic differences in farmers’ attitudes toward risk, with farmers in villages that have received

the intervention being less sensitive to risk and therefore more willing to undertake risky activities.

We divide our risk preference parameter, which is measured as the curvature of the value function,

into two categories- high risk aversion, defined as 0 ≤ 𝜎𝜎 < 1.5 and low risk aversion, 𝜎𝜎 ≥ 1.5.

Similarly, loss aversion is divided into two bins corresponding to low and high loss aversion10. The

distribution of the two parameters is given in Table 2. On average the value function curvature of

those classified as being less risk averse (or more risk loving) is 1.5. Similarly, the loss aversion

parameter for high loss averse individuals is 11.7 on average.

<Table 2 here> We use linear probability models to estimate the effect of treatment on risk and loss

aversion11. Table 3 shows that treatment has a significant negative effect on the probability of being

highly risk averse, implying that farmers in treatment villages are much less likely to be in the high

risk aversion category. If we assume that these preferences extend beyond monetary payoffs and

into other domains, such as agricultural production, then this evidence would largely support the

assertion that CARE’s programming was successful in relaxing constraints imposed by aversion to

risk, and we might therefore expect to see farmers that have benefitted from CARE’s activities being

more willing to branch out, try new agricultural technologies, or undertake practices that are

perhaps a little countercultural.

<Table 3 here>

On the other hand, we find that respondents in treatment villages were also more likely to be

especially sensitive to potential losses. This implies that the disutility suffered from a loss exceeds

the utility that would be enjoyed from a gain of an equivalent magnitude. We thus find that while

the program has relaxed aversion to risk, respondents in treated villages are more likely to be

particularly averse to downside risks. Increased loss aversion could lead to conservative investment

10 Those who were among the top 30th percentile of the risk aversion parameter were categorized as having low risk aversion. The bottom 70 percentile were similarly categorized as being highly risk averse. Analogously, those in the top 30th percentile of the loss aversion parameter were categorized as being highly loss averse and the bottom 70th percentile were defined as having low loss aversion. Our findings are robust to different definitions of low and high risk (and loss) aversion. 11 Our results are robust to using other binomial regression specifications.

15

behavior, leading to lower investment in potentially remunerative activities that have even a small

risk of negative returns. Future programming could focus on bundling loss mitigating insurance

products along with the pre-existing package of extension and services offered to offset the impact

of higher aversion to loss among treatment farmers.

Lowering aversion to risk is important because farmers in general operate in an environment

of risk. This is especially true among farmers in developing countries, and even more so among

farmers in rainfed production systems who must rely on the vagaries of increasingly uncertain

weather. In such risky environments, farmers are often observed to behave in a very conservative

fashion, foregoing investments, technologies, and practices that could result in higher productivity

or profits. As a result, they may become trapped in a low-level development equilibrium. It stands

to reason, therefore, that relaxing these constraints and encouraging the adoption of higher risk

higher returning technologies and practices could be an important avenue by which development

practitioners might achieve their objective of encouraging agricultural transformations and the

adoption of more nutrition-sensitive food production.

6.2 Aspirations and Empowerment

CARE’s programs have aimed to improve household nutrition outcomes by educating

women about the importance of dietary diversity and empowering them to make decisions about the

quality of their family’s diet. To assess the viability of this impact pathway, we wanted to evaluate

the extent to which CARE’s interventions improved the level of women’s empowerment. To do so,

we created a composite measure that can serve as a proxy for empowerment. This index can be

concurrently used for both women and men and is a composite measure of decision-making power,

access to credit and group membership, with each component weighted equally. Thus, a woman who

is adequate on all three parameters will score a 1, and another who is inadequate on all 3 parameters

will score a zero. We find that respondents in CARE villages score higher on the index compared to

control villages (Table 4), and this is especially true for the women-only sample (Table 8). This is

perhaps not surprising, since many of CARE’s activities are targeted towards empowering women

and increasing their agency over productive resources. We also find that respondents in treatment

villages were more likely to be members of at least one village group, where village groups are

16

defined as a credit group, self-help group or forest user group. It is to be noted that the probability

of being a member in at least one village group is higher even for males in the treated villages,

indicating that CARE’s promotion of collective action through village groups had positive spillovers

for men12.

One potential impact of increasing women’s empowerment could be on enhancing household

members’ hopes for their own futures and the futures of their family members. These aspirations

have increasingly been recognized as an important developmental indicator, as they arguably reflect

individuals’ hopes for achieving the life they have a reason to value. The aspirations index used in

the present study is based on four indicators: education, assets, social status, and income. The index

is created based on current and aspirational status, and each indicator is weighted based on self-

reported weights. The index was then standardized to facilitate easy interpretation.

<Table 4 here>

We find that respondents in treatment villages had a significantly higher value of the

aspirations index compared to the control group, which is both statistically and qualitatively

significant. In addition to aspirations for themselves, we also tested how parents’ aspirations for

children differed between the control and CARE villages. We define an individual being aspirational

for their child if they aspired for their child to be in a profession other than farming, manual labor

or being a homemaker. We then ask them what they believed their child will end up becoming, which

focusses on their perception of what will happen rather than on what they wish would happen.13

<Table 5 here>

We find that in treatment villages, respondents did have higher aspirations for their

daughters, and compared to the control they also had stronger positive beliefs about their daughter’s

future profession. We also test whether the aspired level of education differed between treatment

and control villages, and find that for both boys and girls, respondents in treatment villages had a

higher level of aspirations for their children’s years of education.

12 Results for male sub-sample available on request. 13 Belief, like aspiration is a binary variable equal to one if the respondent believes their daughter will be in a profession other than farming or manual labor.

17

6.3 Production and consumption diversity The primary aim of CARE has been to improve diversity in agricultural production as a

means to eventually diversify consumption and diets. In this section we examine how effective the

interventions have been in changing these direct program outcomes. In the previous section

behavioral outcomes and preferences were analyzed at the individual level, however, since

production and consumption decisions are made by the household, we consider the household as

the unit of analysis for this section.

<Table 6 here>

Due to lack of irrigation and winter rain, farmers in Orissa- our area of study- widely practice

Rabi fallows, cultivating rice in the monsoon season, and leaving their field fallow for the Rabi

(winter) cropping season. One of CARE’s primary focus has been to promote the production of

legumes and vegetables in Rabi. However, we find that households in treated villages are no more

likely to cultivate in Rabi compared to households in control villages. However, among the subset of

farmers who do cultivate a crop in Rabi, farmers in treated villages are 20 percentage points more

likely to grow pulses. We find that even in Kharif, the main cropping season, farmers in villages

where CARE is active, are more likely to have a slightly larger proportion of their land dedicated to

pulses indicating a small but significant diversification away from staple grains, towards nutrient

dense crops. We find positive impacts of CARE’s interventions on consumption diversity as well. In

treatment villages, household dietary diversity was higher (0.27 food group) compared to the

control14.

7 Heterogenous effects Since the intervention we study was focused largely towards women, we test whether the

treatment was effective in changing outcomes for women by looking at the women only subsample.

Hitherto, there has been wide agreement in the literature that women are more risk averse than men

14 Dietary diversity is measured on a 12-food group scale using the FAO guidelines on household dietary diversity using a 24-hour recall. Detailed methodology available at http://www.fao.org/3/i1983e/i1983e00.pdf

http://www.fao.org/3/i1983e/i1983e00.pdf

18

when confronted with decisions under uncertainty. In economics, for instance, Eckel and Grossman

(2008) and Croson and Gneezy (2009) find evidence supporting this along several dimensions, such

as characteristics of the subject pool, strength of incentives, comparing gain and loss domains, and

abstract versus contextual frameworks. Charness and Gneezy (2012) focus on a single elicitation

method, the Investment Game, and find strong evidence that females are less willing to take risk. In

psychology, Byrnes et al. (1999) using a broad definition of risk, from smoking to driving and

gambling, analyze self-reported, incentivized as well as observed choices in a meta-analysis of 150

studies, find that males take more risks than females in most of the risk categories.

There are however, some recent studies that disagree with this, previously established,

finding. A recent study by Filippin and Crosetto (2016) finds that gender differences in risk

preferences are more of an exception than a rule. Using the Holt and Larry risk elicitation method,

similar to the method used in our analysis, they find that the likelihood of observing gender

differences crucially depends on the features of the task used to elicit risk preferences and in

particular, on the availability of a safe option and fixed probabilities. Likewise, Fischer and Yao

(2017) find that gender differences in financial risk tolerance result from differences in the

relationship between several independent variables – economic characteristics, demographic

characteristics, and expectations – and risk tolerance for men and women, rather than gender itself.

They argue that these variables serve as moderating variables in the relationship between gender

and risk tolerance. Nelson (2016) also argues that widespread acceptance of statements like ‘women

are more risk averse than men’ appear to be rooted more in confirmation bias than in reality. As

Table 3 shows, in our study as well, we find no difference in the risk and loss preference between

men and women, both by itself and when controlling for other confounding variables.

<Table 7 here>

However, we find that the program has been very successful in relaxing risk aversion among

women. Table 7 shows that women in the treatment group have much lower probability of being in

the high risk aversion category compared to having low levels of risk aversion. However, we also find

that treated women are more likely to have higher levels of aversion to loss – showing strong

preference for non-negative outcomes relative to the control.

The program also has a strong positive impact on aspirations. We find that women in treated

19

villages had a significantly higher aspiration index z-score compared to the control and were also

more likely to score higher on the empowerment index. The probability of women in the treatment

group being active members of at least one village group was also 37% higher compared to control.

<Table 8 here>

In addition to outcomes for themselves, we also test whether the treatment had an impact on

women’s aspirations for their children. We find that women in the treated group were likely to aspire

to more years of schooling for their sons and daughters, and more likely to believe that their daughter

would be in a non-farm, non-manual labor profession.

<Table 9 here>

8 Policy Implications and Conclusion This paper studies the impact of a large agriculture program that aimed to improve

livelihoods and nutrition through the promotion of production diversification away from staple

grains and towards more nutrient dense crops such as pulses and vegetables. A large part of the

program was targeted towards women by using behavior change communication disseminated

through village and self-help groups. We first study the impact of the program on relaxing behavioral

constraints around risk and loss aversion using field experiments and survey-based diagnostics. We

also study the impact of the program on indirect outcome measures such empowerment and

aspiration. We find that respondents in treatment villages are much more willing to take on risk,

thus offering evidence that the program was able to relax aversion to risk among the treated

individuals. However, we find that farmers in treated villages were also more likely to be averse to

loss, showing strong preference for non-negative outcomes.

Overall, we see significant positive impacts of the interventions on women’s empowerment

and aspirations, which in turn translated to higher aspirations for girls. Higher aspirations are the

first step towards better outcomes, and future monitoring should focus on measuring how these

aspirations convert into action as the program matures. As the result of the combined effects of

increasing farmers’ willingness to accept risks, increasing community engagement, and increasing

women’s empowerment, the evidence suggests that the intervention has had broader positive effects,

such as in increasing active participation in village groups. In intervention villages, both men and

20

women were much more likely to be active participants in community groups. This is encouraging

since the intervention has focused extensively on group-based activities and messaging towards

promoting collective action.

Translating these findings into scalable programs in challenging, but the findings of our

study offer some guidelines to designing interventions that aim to change fundamental behavioral

preferences. Most importantly, our results show that social development and grassroots approaches

may be necessary to induce a shift towards nutrition-sensitive production systems. A recommended

programmatic first step is to build upon areas where community social development programming

already exists. As an example, recent research points towards the relative success of leveraging self-

help groups formed under India’s National Rural Livelihoods Mission (NRLM) in increasing

knowledge about nutrition and agriculture (Raghunathan, Kannan, & Quisumbing, 2018)

Insights into the prevalence of behavioral constraints and interventions that relax such

constraints fills an important knowledge gap in how to design programs that promote more

nutrition-sensitive food production systems. Our experiments also help identify heterogeneous

differences in behavior across various social groups, such as between men and women, that can

guide more targeted interventions. We find strong positive effects of CARE’s interventions on

ancillary measures, such as empowerment and aspirations, especially for women. This is suggestive

of the broad reaching impact of the project, beyond proximate outputs. Overall, our results indicate

that changing behavioral preferences and collective action norms may be a necessary pre-condition

for improving food systems.

21

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24

Tables

Table 1: Balance test

Entire sample Treated Control Difference Male head of household 0.985 0.980 0.990 -0.010

(0.123) (0.141) (0.101) (0.008) Head of Household 0.483 0.484 0.483 0.001

(0.500) (0.500) (0.500) (0.008) Spouse of head 0.404 0.408 0.400 0.008

(0.491) (0.492) (0.490) (0.014) Female 0.500 0.500 0.500 0.000

(0.500) (0.500) (0.500) (0.000) Scheduled Caste or Scheduled Tribe 0.787 0.834 0.739 0.095

(0.410) (0.372) (0.440) (0.059) Age 41.677 42.019 41.328 0.691

(14.021) (12.750) (15.204) (0.949) Literate 0/1 0.687 0.704 0.670 0.035

(0.464) (0.457) (0.471) (0.039) Married 0.951 0.959 0.942 0.016

(0.217) (0.200) (0.233) (0.014) Dependency ratio 0.122 0.121 0.122 -0.001

(0.100) (0.102) (0.098) (0.008) Household Size 5.339 5.300 5.379 -0.079

(2.080) (1.960) (2.196) (0.135) Total plot area 2.832 2.961 2.701 0.259

(12.956) (18.041) (2.794) (0.870) Fraction of plot area owned 0.831 0.815 0.847 -0.032

(0.351) (0.369) (0.332) (0.037) Household wealth index 0.000 0.024 -0.025 0.049

(0.885) (0.866) (0.905) (0.107) N 1,960 988 972 1,960

Source: Authors’ calculations. Note: Columns (1) – (3) present means and standard deviations of the entire sample, treated sample, and control sample, respectively. Figures in column (4) are coefficient estimates and their associated standard errors (clustered at the village level) from linear regressions of the form xij = α + βTi + εij , where xij is the characteristic over which balance is being tested (i.e., the variable described in the row header) and Ti is an indicator variable capturing the difference in random assignment between treatment and control groups. Statistical significance of these differences was based on a t-test of the estimated coefficient β for each household and individual characteristic.

25

Table 2: Summary of behavioral parameters

Low High N

Risk aversion 1.50 0.55 1741 Loss aversion 4.84 11.73 1741

Source: Authors’ calculations. Note: Rows (1) and (2) correspond to the values of the value function curvature and loss aversion parameter respectively. Higher value of loss aversion parameter implies greater level of loss aversion. Higher value of value function curvature (risk aversion parameter) correspond to lower levels of risk aversion.

26

Table 3: Risk and loss aversion

(1) (2) High Risk Aversion High Loss Aversion Treated -0.333*** 0.356*** (0.0271) (0.0218) Female 0.058 -0.010 (0.0756) (0.0654) Scheduled Caste 0.013 0.008 (0.0459) (0.0472) Scheduled Tribe 0.034 -0.004 (0.0500) (0.0462) Married=1 -0.046 0.063 (0.0695) (0.0656) Literate=1 0.021 -0.029 (0.0290) (0.0281) Age -0.013** 0.004 (0.0057) (0.0060) Age Squared 0.000** -0.000 (0.0001) (0.0001) Head of household 0.080 -0.023 (0.0759) (0.0626) Spouse of household head 0.115*** -0.034 (0.0409) (0.0425) Dependency ratio 0.016 0.179 (0.1559) (0.1613) Household Size -0.007 -0.001 (0.0075) (0.0073) Household Wealth Index 0.015 0.015 (0.0173) (0.0172) Plot area owned and operated -0.009* -0.001 (0.0047) (0.0044) Constant 0.718*** 0.232 (0.1766) (0.1584) Village Fixed effect Yes Yes Observations 1739 1739

Source: Authors’ calculations. Note: The results report coefficients from a linear probability model. Columns 1 presents probability of being highly risk averse (1) versus having low risk aversion (0). Similarly, column (2) present probability of being highly loss averse (1) versus having low loss aversion (0). Standard errors (in parenthesis) are clustered at village level. * p <0.1, ** p <0.05, *** p<0.01.

27

Table 4: Aspirations and Empowerment

(1) (2) (3) Aspiration Index Empowerment

Index Member of Village

Group (0/1) Treated 3.02*** 0.19*** 0.40*** (0.498) (0.015) (0.023) Female -3.47** 0.06 0.11** (1.348) (0.035) (0.056) Scheduled Caste 1.63* 0.01 0.05 (0.885) (0.025) (0.049) Scheduled Tribe -1.05 0.02 0.04 (0.706) (0.024) (0.039) Married=1 -0.96 0.07** 0.05 (1.191) (0.033) (0.049) Literate=1 4.90*** 0.07*** 0.10*** (0.552) (0.018) (0.027) Age -0.27*** 0.02*** 0.03*** (0.102) (0.004) (0.006) Age Squared 0.00 -0.00*** -0.00*** (0.001) (0.000) (0.000) Head of household -2.47* -0.00 -0.06 (1.404) (0.039) (0.063) Spouse of household head -1.02 0.06** 0.10** (0.813) (0.028) (0.047) Dependency ratio 2.59 0.04 -0.05 (2.609) (0.099) (0.153) Household Size -0.08 0.00 0.01 (0.115) (0.005) (0.007) Household Wealth Index 2.53*** 0.00 0.02 (0.358) (0.010) (0.015) Plot area owned and operated 0.72*** 0.00 0.00 (0.083) (0.003) (0.006) Constant 5.94** -0.17** -0.72*** (2.627) (0.084) (0.126) Village Fixed effect Yes Yes Yes Observations 1925 1957 1957

Source: Authors’ calculations. Note: The results report coefficients from an ordinary least squares regression. Aspirations index is measured as detailed in section xxx. The empowerment index takes values between 0-1 and is composite measure of adequacy in decision making about productive assets (0/1), access to credit (0/1) and membership in village groups (0/1), with each of three components weighted equally. Standard errors (in parenthesis) are clustered at village level. * p <0.1, ** p <0.05, *** p<0.01.

28

Table 5: Aspirations for Children

(1) (2) (3) (4) Aspirational for

Daughter (0/1) Belief for

Daughter (0/1) Aspired Years of Education (Son)

Aspired Years of Education (Daughter)

Treated 0.16*** 0.20*** 1.89*** 2.96*** (0.026) (0.039) (0.137) (0.172) Female 0.07 -0.09 0.17 -0.39 (0.051) (0.081) (0.402) (0.556) Scheduled Caste 0.12*** 0.07 0.56*** 0.64** (0.033) (0.048) (0.184) (0.300) Scheduled Tribe 0.05* 0.08 0.44** 0.25 (0.030) (0.049) (0.195) (0.284) Married=1 0.22** 0.14 0.51 0.21 (0.107) (0.112) (0.370) (0.620) Literate=1 0.10*** 0.13*** 0.32** 0.86*** (0.031) (0.038) (0.130) (0.192) Age 0.00 -0.01 0.02 0.07* (0.006) (0.009) (0.034) (0.043) Age Squared -0.00 -0.00 -0.00 -0.00*** (0.000) (0.000) (0.000) (0.000) Head of household 0.03 -0.06 -0.12 -0.41 (0.063) (0.097) (0.397) (0.547) Spouse of household head

-0.04 -0.07 -0.02 0.25

(0.051) (0.069) (0.255) (0.347) Dependency ratio -0.04 0.53*** 0.44 -0.86 (0.152) (0.201) (0.749) (0.990) Household Size -0.01 -0.03** -0.07** -0.03 (0.009) (0.012) (0.037) (0.054) Household Wealth Index

0.05*** 0.06** 0.27*** 0.51***

(0.018) (0.022) (0.089) (0.125) Plot area owned and operated

0.00 0.01 0.08*** 0.12***

(0.006) (0.007) (0.022) (0.039) Constant 0.50** 0.97*** 12.24*** 10.50*** (0.196) (0.233) (1.068) (1.324) Village Fixed effect Yes Yes Yes Yes Observations 1253 1253 1473 1232

Source: Authors’ calculations. Note: The results report coefficients from an ordinary least square regression linear probability model for column 1 and 2). Aspiration (belief) is coded as 1 if parent aspires (believes) for the daughter to have a profession other than farmer, laborer or homemaker. Standard errors (in parenthesis) are clustered at village level. * p <0.1, ** p <0.05, *** p<0.01.

29

Table 6: Production and Consumption Diversity

(1) (2) (3) (4) Cultivated in

Rabi (0/1) Cultivated

pulses in Rabi (0/1)

Fraction of plot area for pulses

in Kharif

Household dietary diversity

score Treated 0.04 0.20*** 0.03* 0.27** (0.050) (0.064) (0.019) (0.108) Scheduled Caste -0.04 0.03 0.02 -0.25* (0.061) (0.065) (0.017) (0.141) Scheduled Tribe 0.01 0.13* -0.00 -0.18 (0.036) (0.071) (0.013) (0.136) Household head married=1

0.14*** -0.00 -0.02 0.13

(0.053) (0.108) (0.022) (0.226) Head Literate=1 0.00 0.06 0.02 0.37*** (0.039) (0.066) (0.015) (0.139) Age of Head 0.02** -0.01 0.00 -0.00 (0.007) (0.016) (0.003) (0.021) Age of Head squared -0.00** 0.00 -0.00 0.00 (0.000) (0.000) (0.000) (0.000) Dependency ratio -0.20 -0.07 -0.05 -0.20 (0.299) (0.252) (0.059) (0.587) Household Wealth Index 0.07*** -0.08** -0.02*** 0.48*** (0.024) (0.032) (0.006) (0.058) Total plot area owned and operated

0.02*** 0.02* 0.00** 0.06***

(0.005) (0.008) (0.002) (0.015) Household Size 0.00 0.01 -0.00 0.03 (0.014) (0.014) (0.003) (0.025) Constant -0.03 (0.070) Block Fixed effect Yes Yes Yes Yes Observations 979 261 979 979

Source: Authors’ calculations. Note: Columns (1) and (2) report marginal effects from a logit regression. Column (3) reports coefficients from an ordinary least squares regression. Column (4) reports marginal effects from a Poisson regression. For (2) we do a Heckman sample selection test and find no selection bias. Dietary diversity (4) is measured on a 12-food group scale using the FAO guidelines on household dietary diversity using a 24-hour recall.

30

Table 7: Risk and Loss Aversion- Female sub-sample

(1) (2) High Risk Aversion High Loss Aversion Treated -0.523*** 0.693*** (0.0360) (0.0355) Scheduled Caste -0.039 0.075 (0.0641) (0.0662) Scheduled Tribe 0.020 0.045 (0.0697) (0.0631) Married=1 -0.109 0.115 (0.0835) (0.0829) Literate=1 0.037 -0.026 (0.0449) (0.0370) Age -0.007 -0.008 (0.0082) (0.0088) Age Squared 0.000 0.000 (0.0001) (0.0001) Head of household -0.079 -0.025 (0.1101) (0.1111) Spouse of household head 0.054 0.006 (0.0501) (0.0509) Dependency ratio 0.303 0.150 (0.2119) (0.2413) Household Size -0.016 0.002 (0.0114) (0.0103) Household Wealth Index 0.030 -0.001 (0.0252) (0.0233) Plot area owned and operated -0.009 0.002 (0.0079) (0.0078) Constant 0.960*** 0.035 (0.1985) (0.1990) Village Fixed effect Yes Yes Observations 875 875

Source: Authors’ calculations. Note: The results report coefficients from a linear probability model. Columns 1 presents probability of being highly risk averse (1) versus having low risk aversion (0). Similarly, column 2 present probability of having high level of loss aversion (1) versus low loss aversion (0). Standard errors (in parenthesis) are clustered at village level. * p <0.1, ** p <0.05, *** p<0.01.

31

Table 8: Aspirations and Empowerment- Female sub-sample

(1) (2) (3) Aspiration Index Empowerment

Index Member of Village

Group (0/1) Treated 6.63*** 0.12*** 0.37*** (0.563) (0.020) (0.032) Scheduled Caste 1.29 0.04 0.13* (1.058) (0.033) (0.075) Scheduled Tribe -1.75** 0.04 0.09 (0.765) (0.032) (0.062) Married=1 0.05 0.12** 0.10 (1.226) (0.046) (0.081) Literate=1 5.32*** 0.08*** 0.11*** (0.697) (0.026) (0.041) Age -0.39*** 0.03*** 0.04*** (0.139) (0.005) (0.009) Age Squared 0.00* -0.00*** -0.00*** (0.002) (0.000) (0.000) Head of household -4.88* 0.13 -0.00 (2.866) (0.085) (0.141) Spouse of household head -1.64* 0.07** 0.12* (0.890) (0.033) (0.063) Dependency ratio 3.88 -0.08 -0.08 (3.458) (0.125) (0.201) Household Size -0.29** 0.01 0.02* (0.146) (0.006) (0.010) Household Wealth Index 1.51*** -0.00 0.02 (0.336) (0.014) (0.024) Plot area owned and operated

0.54*** -0.01 -0.01*

(0.109) (0.004) (0.007) Constant 3.62 -0.31*** -0.93*** (2.916) (0.111) (0.178) Village Fixed effect Yes Yes Yes Observations 970 978 978

Source: Authors’ calculations. Note: The results report coefficients from ordinary least squares regressions. Aspirations index is measured as detailed in section xxx. The empowerment index takes values between 0-1 and is composite measure of adequacy in decision making about productive assets (0/1), access to credit (0/1) and membership in village groups (0/1), with each of three components weighted equally. Standard errors (in parenthesis) are clustered at village level. * p <0.1, ** p <0.05, *** p<0.01.

32

Table 9: Aspirations for Children- Female Sub-sample

(1) (2) (3) (4) Aspirational for

Daughter (0/1) Belief for

Daughter (0/1) Aspired Years of Education (Son)

Aspired Years of Education (Daughter)

Treated 0.18*** 0.22*** 0.67*** 2.62*** (0.041) (0.055) (0.153) (0.272) Scheduled Caste 0.08 0.07 0.32 0.46 (0.057) (0.063) (0.196) (0.350) Scheduled Tribe 0.01 0.09 0.25 0.16 (0.056) (0.066) (0.207) (0.377) Married=1 0.02 0.06 0.25 -0.22 (0.088) (0.123) (0.428) (0.856) Literate=1 0.12** 0.10* 0.52*** 1.05*** (0.045) (0.054) (0.161) (0.253) Age -0.00 -0.02* 0.01 0.13** (0.010) (0.012) (0.047) (0.062) Age Squared -0.00 0.00 -0.00 -0.00*** (0.000) (0.000) (0.001) (0.001) Head of household 0.07 -0.10 -0.37 -1.00 (0.060) (0.173) (0.464) (0.857) Spouse of household head 0.00 -0.03 0.03 0.12 (0.060) (0.085) (0.297) (0.422) Dependency ratio -0.23 0.43 0.21 -0.58 (0.212) (0.258) (0.954) (1.197) Household Size -0.01 -0.02 -0.09* -0.06 (0.011) (0.016) (0.049) (0.064) Household Wealth Index 0.03 0.05 0.17* 0.48*** (0.023) (0.029) (0.095) (0.153) Plot area owned and operated

0.01 -0.00 0.05** 0.12***

(0.009) (0.009) (0.025) (0.040) Constant 0.88*** 1.03*** 14.24*** 10.19*** (0.229) (0.278) (1.097) (1.500) Village Fixed effect Yes Yes Yes Yes Observations 637 637 741 633

Source: Authors’ calculations. Note: The results report coefficients from an ordinary least square regression. Aspiration (belief) is coded as 1 if parent aspires (believes) for the daughter to have a profession other than farmer, laborer or homemaker. Standard errors (in parenthesis) are clustered at village level. * p <0.1, ** p <0.05, *** p<0.01.

33

Appendix

Prospect Theory Experiments

In this section we describe in detail the experiments used to measure risk and loss aversion as well

as the theory underpinning our estimation. Consider the simple case of a risky prospect with two

outcomes, x and y, occurring with probabilities p and q = 1 − p, respectively. The value of the

prospect can be written as

𝑈𝑈(𝐼𝐼, 𝑦𝑦: 𝐴𝐴, 𝑞𝑞,𝜎𝜎,𝛼𝛼, λ) = �𝑣𝑣(𝑦𝑦) + 𝑤𝑤(𝐴𝐴)[𝑣𝑣(𝐼𝐼) − 𝑣𝑣(𝑦𝑦)] 𝑓𝑓𝐴𝐴𝐴𝐴 |𝐼𝐼| > |𝑦𝑦| > 0𝑤𝑤(𝐴𝐴)𝑣𝑣(𝐼𝐼) + 𝑤𝑤(𝑞𝑞)𝑣𝑣(𝑦𝑦) 𝑓𝑓𝐴𝐴𝐴𝐴 𝐼𝐼 < 0 < 𝑦𝑦 (1)

where

𝑣𝑣(𝐼𝐼) = �𝐼𝐼𝜎𝜎 𝑓𝑓𝐴𝐴𝐴𝐴 𝐼𝐼 > 0−𝜆𝜆(−𝐼𝐼)𝜎𝜎 𝑓𝑓𝐴𝐴𝐴𝐴 𝐼𝐼 < 0 (2)

and w(p) is a probability weighting function. We follow Tanaka et al. (2010) and Liu (2013)

by using the axiomatically derived weighting function presented in Prelec (1998). This weighting

function is written as w(p) as w(p) = exp [−(− ln p) α], for 0 < α ≤ 1. Note that this functional form

is flexible enough to allow for decisions to be consistent with EUT; all that must hold for such a case

is α = 1 and λ = 1. In that case, the only parameter that defines preferences is σ, the degree of value

function curvature. In this paper we focus on the parameters of risk (σ) and loss aversion (λ).

Risk Aversion

For a choice between two prospects x > 0 and y > 0 (i.e., one in which there are no possible

losses), there are only two parameters that characterize prospect valuation: the probability

weighting parameter (α) and the parameter describing value function curvature (σ). These two

parameters are jointly determined, and so cannot be uniquely identified from a single experimental

observation. We thus rely upon two distinct experiments to estimate these two parameters. In each

of these two experiments, respondents are presented with 14 choice scenarios and are asked to

choose between two options. Option (A) is a riskless option while Option (B) is a risky option with

both a “winning” and a “losing” draw. In these experiments, chips numbered 1-10 are divided into

“winning” and “losing” chips, such that chips 1 − p are winning chips, while chips (p + 1) − 10 are

“losing” chips, and p varies from one experiment to the other. In the first of these two experiments,

the riskless option always pays INR 50, while the risky option pays a “losing” award of INR 20

with a probability of 0.9 and a “winning” award of increasing magnitude with probability of 0.1.

The second experiment has a similar structure, but with a higher riskless payout (INR 100), a

higher probability of “winning” the risky draw (0.7), and a more gradual increase in the “winning”

payouts with subsequent rounds. The payoff schedules for these two experiments are shown in

34

Tables 1 and 2.

Table A1: Risk Aversion Experiment Payoffs I

Round

Option A: Certain payment

Option B: Lottery “Winning” Lottery

Payment “Losing” Lottery Payment 1 INR 50 INR 142

if chip is INR 20

if chip is , , , , , , , , or

2 INR 50 INR 157 if chip is

INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or


INR 20 if chip is , , , , , , , , or

Since both the riskless payoff and the payoff arising from the “losing” draw are fixed, and since

the payoffs from the “winning” draw are monotonically increasing in each successive round,

respondents should be observed to switch their preferences from the riskless option to the risky

option at most one time.15 In their earlier studies, Tanaka et al. (2010) and Ward and Singh (2015)

capture information only on the switching round in each experiment, thus essentially enforcing

monotonic preferences.

15 It should be noted, however, that respondents were not forced to switch. In conducting the experiments, several examples were given prior to eliciting responses. In one of these examples, the riskless option is chosen throughout, while in another example, the risky option is preferred in all rounds. This is meant to illustrate to participants their potential options at these two extremes.

35

Table A2: Risk Aversion Experiment Payoffs II

Round

Option A: Certain payment

Option B: Lottery

“Winning” Lottery Payment “Losing” Lottery Payment

15 INR 100 INR 135 if chip is , , , , , , or

INR 20 if chip is , , or



























The switching rounds are useful in identifying the underlying behavioral parameters, since

the switching round provides insight into the range of values for which the participant is

indifferent between the riskless and risky options. For example, when a participant switches between

preferring the riskless option (A) to the lottery (B) in round 6 in the first experiment (Table 1), we

know the

following inequalities should be satisfied:

50δ > 20δ + exp [−(− ln 0.1)α] (231δ − 20δ)

50δ < 20δ + exp [−(− ln 0.1)α] (270δ − 20δ)

36

Similarly, if a participant switches between preferring the riskless option (A) to the lottery (B)

in round 20 in the second experiment (Table 2), we know these two inequalities should be

satisfied:

100δ > 20δ + exp [−(− ln 0.7)α] (158δ − 20δ)

100δ < 20δ + exp [−(− ln 0.7)α] (166δ − 20δ)

Since the observed choices in these two experiments come from the same individual, the α and

δ values in equations (3) and (4) should be the same. Thus, the four inequalities represented in

equations (3) and (4) must be solved simultaneously.

Because the two parameters are simultaneously determined, a given switching round in each of

the experiments is potentially consistent with several different combinations of behavioral

parameters. As such, these two experiments are carefully designed so that the pair of switching

rounds from the two experiments (in tandem) uniquely identifies the lower and upper bounds for

this range of parameter tuples for which the respondents’ choices are consistent under CPT.16

Following Liu (2013), we calculated the midpoint of this range and use this as an approximation

of the individuals’ CPT parameter values. In the case illustrated above, therefore, an individual

switching in the sixth round in each of the two experiments would be assigned a parameter tuple of

(δ, α) = (0.915, 0.715). Tanaka et al. (2010) report the approximate values of these parameters for

different combinations of switching rows in the two experiments.

Loss aversion

A third experiment is used to estimate the loss aversion (λ) parameter. In this experiment,

respondents are presented with seven choice scenarios, each comprised of two lotteries. In each

lottery, there is a “winning” draw with a positive payout and a “losing” draw with a negative

payout. The payoff schedule for this experiment is presented in Table A3.

16 When switching rows take extreme values {0, N }, the parameters cannot be uniquely identified. There is a potentially an infinite number of parameter combinations for which some of the switching rows are consistent. If we assume 0 < σ < 1.5 and 0 < α ≤ 1, then we can approximate parameter combinations for these as the mean of a truncated range of possible values for σ and α

37

Table A3: Loss Aversion Experiment Payoffs Round Option A Option B

“Winning” Lottery

Payment “Losing” Lottery

Payment “Winning” Lottery

Payment “Losing” Lottery

Payment 29 You win INR 50

if chip is , , , , or

You lose INR 8 if chip is , , , ,

or

You win INR 60 if chip is , , , ,

or


or 30 You win INR 8



or


or


or 31 You win INR 2



or


or


or 32 You win INR 2



or


or


or 33 You win INR 2



or


or


or 34 You win INR 2



or


or


or 35 You win INR 2



or


or


or

The “winning” and “losing” payouts vary from round to round but are specified in such a way

that enables estimation of a range of possible loss aversion coefficients for each respondent. For

each of the two lotteries, the probabilities of “winning” and “losing” are equivalent, so the

probability weighting function applied to both “winning” and “losing” payouts in each of the

lotteries is the same, and further drops out of consideration when estimating the loss aversion

parameter for a particular switching round. For example, suppose an individual switched from

Option A to Option B in round j. Then we assume that the utility derived from Option A in round

j is the same as the utility derived from Option B in round j. It can be shown that λj (the loss

aversion parameter consistent with switching from Option B to Option A in round j) is a function

of σ. There is a range of values of λj for a particular value of σ, so an estimate for the upper bound

of λj(σ) is given by

𝜆𝜆𝑗𝑗(𝜎𝜎) =𝐼𝐼𝑗𝑗,𝐴𝐴𝜎𝜎 − 𝐼𝐼𝑗𝑗,𝐵𝐵

𝜎𝜎

�−𝑦𝑦𝑗𝑗,𝐴𝐴�𝜎𝜎 − �−𝑦𝑦𝑗𝑗,𝐵𝐵�

𝜎𝜎

where 𝐼𝐼𝑗𝑗,𝐴𝐴𝜎𝜎

and 𝐼𝐼𝑗𝑗,𝐵𝐵𝜎𝜎 are the “winning” payoffs in round j corresponding to Options A and B,

38

respectively, and 𝑦𝑦𝑗𝑗,𝐴𝐴 and 𝑦𝑦𝑗𝑗,𝐵𝐵 are the corresponding “losing” payoffs.

Aspirations Questions

• Assets: When we talk about assets we mean non-productive assets such as fridge, TV, furniture etc. Please

give value for your entire household. Give us an estimate of the total value of such assets in Rupees.

1. What is the value of assets you have at present?

2. What is the value of assets you would like to achieve?

• Social status: In this section we would like to know about your social status. Please answer on

a scale of 1 to 10, where 1 means "no social status- no one asks me for my advice on any

significant decision, in my community", 5 means "half the people in my community ask for my

advice on important decisions" and 10 means "No important decision in the community is taken

without consulting me"

1. What is the level of social status you have at present?

2. What is the level of social status that you would like to achieve?

• Education: Please answer in terms of level of education completed.

1. What is your current level of education?

2. What level of education you would like to (or wanted to) achieve?

3. What is the level of education that your oldest daughter currently has?

4. What is the level of education that your oldest son currently has?

5. What is the level of education that you would like your oldest daughter to achieve?

6. What is the level of education that you would like your oldest son to achieve?

• No schooling/Illiterate

• Nursery or KG

• Class 1

• Class 2

• Class 3

• Class 4

• Class 5

• Class 6

• Class 7

• Class 8

• Class 9

• Class 10

39

• Class 11

• Class 12

• Completed college at least or higher (Masters, PhD, Doctor, engineering etc.)

• Don’t know/Too young

• Income: Annual income is the amount of CASH income you earn from all agricultural and non-

agricultural activities, and money from any other government assistance programs

1. What is the amount of household annual income you have at present?

a. What is the amount of household annual income that you would like to

achieve?

The index for individual i is defined as:

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐼𝐼𝐴𝐴𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 = � �𝐴𝐴𝑖𝑖𝑘𝑘 − 𝜇𝜇𝑘𝑘

𝜎𝜎𝑘𝑘� . 𝜔𝜔𝑖𝑖

𝑘𝑘4

𝑘𝑘=1

Where 𝑘𝑘 is one of the four dimensions mentioned above, 𝐴𝐴𝑖𝑖𝑘𝑘 is respondent 𝐴𝐴’s aspiration

on dimension 𝑘𝑘 and 𝜇𝜇𝑘𝑘and 𝜎𝜎𝑘𝑘measure the sample mean and standard deviation of the response

in the respondent’s community, and 𝜔𝜔𝑘𝑘 is the weight attached to component 𝑘𝑘.

Robustness check Since our sample of treated and control villages was identified well after the treatment had

been administered, we consider some additional analyses to demonstrate the robustness of our

results and to demonstrate that the above outcomes are not an artifact of our identification strategy.

Rather than relying on our above described method for constructing a counterfactual based on

propensity scores from the 2011 Census, we instead rely on matching from the individual-level data

collected through face-to-face interviews. Specifically, we re-estimate regressions for the behavioral

preference parameters and indirect outcomes estimated above using nearest neighbor matching. We

first estimate propensity scores for each individual based on time and treatment invariant

characteristics from the household survey17 because baseline data for the study sample is not

available. We then match each respondent in the treatment group with a respondent in the control

group with whom the respondent’s propensity score is most closely matched and drop those

respondents for whom no matches are found. Table A1 shows the results from nearest neighbor

17 These variables are: male headed household, respondent is head of the household, respondent is spouse of household head, female, scheduled caste or scheduled tribe, age of household head, literate household head, married, dependency ratio of household, household size, plot area cultivate (in acres), fraction of cultivated area owned.

40

propensity score matching. Several of the findings from our main estimation and identification

strategy are corroborated by the results shown in Table A1. We do see some attenuation of the

treatment effects, which is expected with multi-step estimators. We are also constrained by the lack

of a rich set of treatment invariant variables. It is widely accepted that propensity score matching

requires a large sample of baseline data for a robust match. The matching techniques employed here

are limited as baseline data were not available at the time of matching. The next best approach –

using time and treatment invariant characteristics collected ex post – was thus adopted.

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Table A1: Treatment effect—propensity score matching

Treatment Effect

S.E p-value

Value function curvature 0.043 0.0397 0.279

Loss aversion parameter 0.324 0.2903 0.264

Household dietary diversity 0.355 0.0844 0.000

Aspiration index 1.069 0.6079 0.078

Daughter aspirations 0.027 0.0293 0.359

Daughter beliefs -0.0063 0.0392 0.872

Boy education years 0.413 0.1568 0.008

Girl education years 0.174 0.1982 0.381

Member of Village Group 0.126 0.0281 0.000

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Figure A1: Propensity score distribution of treatment and control villages

02

46

810

P-Sc

ore

0 .2 .4 .6

Control Treated

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