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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.
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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.
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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
3
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
4
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.
5
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
6
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.
8
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.
9
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.
11
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.
12
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.
13
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
14
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
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
References Ayenew, H. Y., Biadgilign, S., Schickramm, L., Abate-Kassa, G., & Sauer, J. (2018). Production
diversification, dietary diversity and consumption seasonality: panel data evidence from Nigeria. BMC Public Health, 18(1), 988. https://doi.org/10.1186/s12889-018-5887-6
Bereby-Meyer, Y. and A. E. Roth (2006). The speed of learning in noisy games: Partial reinforce- ment and the sustainability of cooperation. American Economic Review 96 (4), 1029–1042.
Bernard, T., & Seyoum Taffesse, A. (2014). Aspirations: An Approach to Measurement with Validation Using Ethiopian Data. Journal of African Economies, 23(2), 189–224. https://doi.org/10.1093/jae/ejt030
Binswanger, H. P. (1980). Attitudes toward risk: Experimental measurement in rural India. American Journal of Agricultural Economics 62 (179), 395–407.
Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125(3), 367–383. https://doi.org/10.1037/0033-2909.125.3.367
C´ardenas, J.-C., M. A. Janssen, M. Ale, R. Bastakoti, A. Bernal, J. Chalermphol, Y. Gong, H. Shin, G. Shivakoti, Y. Wang, and J. M. Anderies (2017). Fragility of the provision of local public goods to private and collective risks. Proceedings of the National Academy of Sciences 114 (5), 201614892.
CARE. (2018). India | Pathways to Secure Livelihoods. Retrieved October 31, 2018, from http://www.carepathwaystoempowerment.org/portfolio-view/india/
Charness, G., & Gneezy, U. (2012). Strong Evidence for Gender Differences in Risk Taking. Journal of Economic Behavior & Organization, 83(1), 50–58. https://doi.org/10.1016/J.JEBO.2011.06.007
Coller, M. and M. B. Williams (1999). Eliciting individual discount rates. Experimental Eco- nomics 2 (2), 107–127.
Cornish, L. (2018). How empowering women leads to innovative agricultural practices | Devex. Retrieved November 1, 2018, from https://www.devex.com/news/how-empowering-women-leads-to-innovative-agricultural-practices-92236
Croson, R. T. (2008). Public goods experiments. In S. N. Durlauf and L. E. Blume (Eds.), The New Palgrave Dictionary of Economics (2 ed.). New York: Palgrave Macmillan.
Croson, R., & Gneezy, U. (2009). Gender Differences in Preferences. Journal of Economic Literature, 47(2), 448–474. https://doi.org/10.1257/jel.47.2.448
Diiro, G. M., Seymour, G., Kassie, M., Muricho, G., & Muriithi, B. W. (2018). Women’s empowerment in agriculture and agricultural productivity: Evidence from rural maize farmer households in western Kenya. PloS One, 13(5), e0197995. https://doi.org/10.1371/journal.pone.0197995
Eckel, C. C., & Grossman, P. J. (2002). Sex differences and statistical stereotyping in attitudes toward financial risk. Evolution and Human Behavior, 23(4), 281–295. https://doi.org/10.1016/S1090-5138(02)00097-1
Filippin, A., & Crosetto, P. (2016). A Reconsideration of Gender Differences in Risk Attitudes. Management Science, 62(11), 3138–3160. https://doi.org/10.1287/mnsc.2015.2294
22
Fisher, P. J., & Yao, R. (2017). Gender differences in financial risk tolerance. Journal of Economic Psychology, 61, 191–202. https://doi.org/10.1016/J.JOEP.2017.03.006
Genicot, G., & Ray, D. (2017). Aspirations and Inequality. Econometrica, 85(2), 489–519. https://doi.org/10.3982/ECTA13865
GHI. (2018). India - Global Hunger Index. Retrieved May 21, 2019, from https://www.globalhungerindex.org/india.html
Glewwe, P. (1999). Why Does Mother’s Schooling Raise Child Health in Developing Countries? Evidence from Morocco. The Journal of Human Resources, 34(1), 124. https://doi.org/10.2307/146305
Haddad, L. (2000). A Conceptual Framework for Assessing Agriculture–Nutrition Linkages. Food and Nutrition Bulletin, 21(4), 367–373. https://doi.org/10.1177/156482650002100405
Holt, C. and S. Laury (2002). Risk aversion and incentive effects. American Economic Review 92 (5), 1644–1655.
Isaac, R. M., J. M. Walker, and S. H. Thomas (1984). Divergent expectations on free riding: An experimental examination of possible explanations. Public Choice 43, 113–149.
Janzen, S. A., Magnan, N., Sharma, S., & Thompson, W. M. (2017). Aspirations failure and formation in rural Nepal. Journal of Economic Behavior & Organization, 139, 1–25. https://doi.org/10.1016/J.JEBO.2017.04.003
Just, R. E. (1975). Risk Aversion under Profit Maximization. American Journal of Agricultural Economics, 57(2), 347. https://doi.org/10.2307/1238513
Kahneman, D. and A. Tversky (1979). Prospect theory: An analysis of decision under risk. Econo- metrica 47 (2), 263–292.
Koppmair, S., Kassie, M., & Qaim, M. (2017). Farm production, market access and dietary diversity in Malawi. Public Health Nutrition, 20(2), 325–335. https://doi.org/10.1017/S1368980016002135
Kosec, K., & Mo, C. H. (2017). Aspirations and the Role of Social Protection: Evidence from a Natural Disaster in Rural Pakistan. World Development, 97, 49–66. https://doi.org/10.1016/J.WORLDDEV.2017.03.039
Liu, E. M. (2013). Time to change what to sow: Risk preferences and technology adoption decisions of cotton farmers in China. Review of Economics and Statistics 95 (4), 1386–1403.
Malapit, H. J. L., & Quisumbing, A. R. (2015). What dimensions of women’s empowerment in agriculture matter for nutrition in Ghana? Food Policy, 52, 54–63. https://doi.org/10.1016/J.FOODPOL.2015.02.003
Nelson, J. A. (2016). Not-So-Strong Evidence for Gender Differences in Risk Taking. Feminist Economics, 22(2), 114–142. https://doi.org/10.1080/13545701.2015.1057609
Pellegrini, L., & Tasciotti, L. (2014). Crop diversification, dietary diversity and agricultural income: empirical evidence from eight developing countries. Canadian Journal of Development Studies / Revue Canadienne d’études Du Développement, 35(2), 211–227. https://doi.org/10.1080/02255189.2014.898580
Pender, J. L. (1996). Discount rates and credit markets: Theory and evidence from rural India. Journal of Development Economics 50 (2), 257–296.
23
Pingali, P. (2015). Agricultural policy and nutrition outcomes – getting beyond the preoccupation with staple grains. Food Security, 7(3), 583–591. https://doi.org/10.1007/s12571-015-0461-x
Prelec, D. (1998). The probability weighting function. Econometrica 66 (3), 497–527.
Raghunathan, K., Kannan, S., & Quisumbing, A. R. (2018). Women’s self-help groups, decision-making, and improved agricultural practices in India: From extension to practice (IFPRI Discussion Paper No. 1735). Washington, DC. https://doi.org/10.2499/1046080777
Samuelson, P. (1937). A note on measurement of utility. Review of Economic Studies 4 (2), 155–161.
Seymour, G. (2017). Women’s empowerment in agriculture: Implications for technical efficiency in rural Bangladesh. Agricultural Economics, 48(4), 513–522. https://doi.org/10.1111/agec.12352
Tanaka, T., C. F. Camerer, and Q. Nguyen (2010). Risk and time preferences: {Linking experimental and household survey data from Vietnam}. American Economic Review 100 (1), 557–571.
TARINA. (2018). Food Systems Approach – Technical Assistance and Research for Indian Nutrition and Agriculture. Retrieved October 31, 2018, from https://tarina.tci.cornell.edu/about-us/food-systems-approach/
Thaler, R. (1981). Some empirical evidence on dynamic inconsistency. Economics Letters 8 (3), 201–207.
Thomas, D. (1994). Like Father, like Son; Like Mother, like Daughter: Parental Resources and Child Height. The Journal of Human Resources, 29(4), 950. https://doi.org/10.2307/146131
Tversky, A. and D. Kahneman (1992). Advances in Prospect Theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5 (4), 297–323.
Ubfal, D. (2016). How general are time preferences? eliciting good-specific discount rates. Journal of Development Economics 118, 150–170.
Ward, P. S. and V. Singh (2015). Using field experiments to elicit risk and ambiguity preferences: Behavioural factors and the adoption of new agricultural technologies in rural India. Journal of Development Studies 51 (6), 707–724.
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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
3 INR 50 INR 177 if chip is
INR 20 if chip is , , , , , , , , or
4 INR 50 INR 201 if chip is
INR 20 if chip is , , , , , , , , or
5 INR 50 INR 231 if chip is
INR 20 if chip is , , , , , , , , or
6 INR 50 INR 270 if chip is
INR 20 if chip is , , , , , , , , or
7 INR 50 INR 321 if chip is
INR 20 if chip is , , , , , , , , or
8 INR 50 INR 370 if chip is
INR 20 if chip is , , , , , , , , or
9 INR 50 INR 439 if chip is
INR 20 if chip is , , , , , , , , or
10 INR 50 INR 536 if chip is
INR 20 if chip is , , , , , , , , or
11 INR 50 INR 719 if chip is
INR 20 if chip is , , , , , , , , or
12 INR 50 INR 1050 if chip is
INR 20 if chip is , , , , , , , , or
13 INR 50 INR 1633 if chip is
INR 20 if chip is , , , , , , , , or
14 INR 50 INR 2729 if chip is
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
16 INR 100 INR 139 if chip is , , , , , , or
INR 20 if chip is , , or
17 INR 100 INR 145 if chip is , , , , , , or
INR 20 if chip is , , or
18 INR 100 INR 151 if chip is , , , , , , or
INR 20 if chip is , , or
19 INR 100 INR 158 if chip is , , , , , , or
INR 20 if chip is , , or
20 INR 100 INR 166 if chip is , , , , , , or
INR 20 if chip is , , or
21 INR 100 INR 176 if chip is , , , , , , or
INR 20 if chip is , , or
22 INR 100 INR 183 if chip is , , , , , , or
INR 20 if chip is , , or
23 INR 100 INR 192 if chip is , , , , , , or
INR 20 if chip is , , or
24 INR 100 INR 202 if chip is , , , , , , or
INR 20 if chip is , , or
25 INR 100 INR 215 if chip is , , , , , , or
INR 20 if chip is , , or
26 INR 100 INR 231 if chip is , , , , , , or
INR 20 if chip is , , or
27 INR 100 INR 252 if chip is , , , , , , or
INR 20 if chip is , , or
28 INR 100 INR 279 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
You lose INR 42 if chip is , , , ,
or 30 You win INR 8
if chip is , , , , or
You lose INR 8 if chip is , , , ,
or
You win INR 60 if chip is , , , ,
or
You lose INR 42 if chip is , , , ,
or 31 You win INR 2
if chip is , , , , or
You lose INR 8 if chip is , , , ,
or
You win INR 60 if chip is , , , ,
or
You lose INR 42 if chip is , , , ,
or 32 You win INR 2
if chip is , , , , or
You lose INR 8 if chip is , , , ,
or
You win INR 60 if chip is , , , ,
or
You lose INR 32 if chip is , , , ,
or 33 You win INR 2
if chip is , , , , or
You lose INR 16 if chip is , , , ,
or
You win INR 60 if chip is , , , ,
or
You lose INR 32 if chip is , , , ,
or 34 You win INR 2
if chip is , , , , or
You lose INR 16 if chip is , , , ,
or
You win INR 60 if chip is , , , ,
or
You lose INR 28 if chip is , , , ,
or 35 You win INR 2
if chip is , , , , or
You lose INR 16 if chip is , , , ,
or
You win INR 60 if chip is , , , ,
or
You lose INR 22 if chip is , , , ,
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.
41
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
42
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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