# Do Advisors' Status and Identity Shape Adherence to Advice? *

Lata Gangadharan, Pushkar Maitra, Joseph Vecci, Chellattan Veettil, Marie Claire Villeval

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Lata Gangadharan, Pushkar Maitra, Joseph Vecci, Chellattan Veettil, Marie Claire Villeval. Do Advisors' Status and Identity Shape Adherence to Advice? *. 2025. hal-05378062

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# Do Advisors' Status and Identity Shape Adherence to Advice?*

Lata Gangadharan,† Pushkar Maitra,‡ Joseph Vecci§
Prakashan Chellattan Veettil¶ and Marie Claire Villeval||

November 2025

## Abstract

This study examines whether adherence to advice depends on an advisor's identity and status beyond message content. Using a survey experiment with over 3000 farmers in India, we find that individuals are more likely to follow advice in a social dilemma game when it comes from high-status or in-group advisors, even when the advice diverges from prevailing norms. Admired role models can attenuate the influence of status and identity, though their beneficial effect is not universal. Our experimental findings align with evidence from an agricultural advisory program involving the same participant sample, highlighting the broader real-world relevance of these patterns.

**Keywords:** Advice, Social learning, Status, Group identity, Survey experiment

**JEL Codes:** C93, D83, D91, O13, Q16

<footnote>*We have benefitted from conversations with and comments from Ritwik Banerjee, Sebastiano Della Lena, Marco Lambrecht, and conference and seminar participants at the Australian Development Economics Workshop, Indian Institute of Management, Kolkata, Indira Gandhi Institute of Development Research, Indian Institute of Technology, Mumbai, Indian Institute of Management, Bangalore, the ESA World meetings, SEEDEC, and Monash University. Funding was provided by iFREE. Additional support was provided by the CGIAR Initiatives Excellence in Agronomy, and we thank funders supporting research through contributions to the CGIAR Trust Fund. Both the agro-advisory experiment and the additional survey were pre-registered (AEARCTR-0008659 and AEARCTR-0014538, respectively). The usual caveat applies.</footnote>

<footnote>†Department of Economics, Monash University. Lata.Gangadharan@monash.edu</footnote>

<footnote>‡Department of Economics, Monash University. Pushkar.Maitra@monash.edu</footnote>

<footnote>§Department of Economics, University of Gothenburg, Gothenburg, Sweden.
joseph.vecci@economics.gu.se</footnote>

<footnote>¶Department of Sustainable Impact, International Rice Research Institute (IRRI), NAS Complex, Pusa Campus, New Delhi. pc.veettil@cgiar.org</footnote>

<footnote>||CNRS, Université Lumière Lyon 2, Université Jean-Monnet Saint-Etienne, Emlyon Business School, GATE, 35 rue Raulin, F-69007, Lyon, France. villeval@gate.cnrs.fr</footnote>
# 1 Introduction

One of the most common policy tools used by governments and civil society is advice campaigns, which aim to educate, inform, and provide advice to citizens. The World Bank, for example, often allocates a significant part of a program's budget to Information, Education, and Communication activities (World Bank Group, 2021). In India, the government spends an estimated 5 billion USD annually on agricultural "extension" programs aimed at advising farmers about improved farming practices (Gulati et al., 2018). Despite the prevalence of these programs, a growing body of evidence suggests these programs can be ineffective at changing behaviour (e.g., Gine and Yang, 2007, Fafchamps and Minten, 2012, McIntosh et al., 2013).<fnref n="1" /> For instance, a large-scale systematic review of farmer field schools (an advice program implemented with over 10 million farmers) found no evidence that the program improved outcomes over the long term or once scaled (Waddington and White, 2014). Similar challenges are observed in OECD countries, where public advice campaigns in domains such as environmental sustainability and health often struggle to compete with private-sector messaging for consumer attention, limiting their impact (OECD, 2008, 2017). This raises a broader question: why do interventions designed to provide efficiency-enhancing advice fail to achieve widespread adoption or exhibit strong effects?

We argue that the effectiveness of advice-based interventions depends not just on the message itself, but also on the messenger – that is, both the content of the advice and the attributes of the advisor may matter. Motivated by this, we design a survey experiment (Alesina et al., 2023, Stantcheva, 2023) to explore two research questions. First, are individuals more likely to follow advice when it is delivered by someone of higher social status or with shared group identity? Building on prior work which shows that agent attributes—particularly status and identity—can significantly influence decision making (e.g., Chen and Li, 2009, Gangadharan et al., 2016, Chen et al., 2017), we focus on the impact of advisor attributes on advice adherence. Second, we ask: how do these advisor attributes influence the likelihood of following advice that is not socially optimal vs. socially optimal? In many real-world settings, individuals receive advice from multiple, sometimes conflicting sources – including peers, neighbours in networks, local leaders, and government agents – some of which may reinforce inefficient norms or misinformation. Understanding how the

<footnote n="1">¹Innovative approaches, such as peer selection, self-help groups, network-based interventions, and using well-connected agents, aim to improve information and advice flow in contexts with asymmetric or incomplete information (Banerjee, 2013, Banerjee et al., 2015, 2013, 2019, Maitra et al., 2017, 2024). While some have shown promise, their effects remain uneven.</footnote>
characteristics of an advisor interact with the quality of the advice itself is essential for de-
signing interventions that effectively shift behaviour in a socially beneficial way, especially
in contexts where socially suboptimal local advice competes with policy guidance.

Our survey experiment was conducted in Bihar, India, in collaboration with the Inter-
national Rice Research Institute. It was embedded as a part of an endline survey of an
agro-advisory project and involved 3153 farmers. It contained a vignette describing a game
in which mutual cooperation increases efficiency, but whose Nash equilibrium is to not
cooperate. Our focus on this game and on the decision to cooperate is motivated by the
observation that the adoption of new agricultural or community-level practices often hinges
on collective action, as the tension between private incentives and public benefits can affect
both individual adoption rates and broader community-level spillovers. In this hypothetical
scenario, participants were asked to assume that they are part of a two-person agricultural
production team, where their earnings would depend on their choice of effort as well as
the choice of effort of their business partner. Putting in high effort incurs higher costs
compared to low effort, but it results in greater total revenue for the team. Decisions were
made independently, simultaneously, and only once. The Nash equilibrium of this one-shot
Prisoner's Dilemma game is for both players to choose low effort – the non-cooperative out-
come. However, previous literature has established that many players deviate from Nash
equilibrium in prisoner's dilemma games by choosing to cooperate (e.g., Mengel, 2018).
Specifically, as they face strategic uncertainty, participants may use their everyday life ex-
periences, such as who they interact with, to decide whether to cooperate or not in the
game. Further, advice may help them resolve the dilemma.

The vignette indicated that the respondent had received advice from a similar person in a
nearby village, with prior experience in the game. Participants were randomly assigned to
treatment conditions in which the advisor's profile varied along the following dimensions:
identity, as defined by gender (male or female) and caste (Brahmin or Dalit), and the nature
of the advice (High effort or Low effort).<fnref n="2" /> Men and Brahmins are considered to have higher
status in this setting. A control group received no advice.

In our study, both gender and caste simultaneously shape identity and status relationships.

<footnote n="2">²Caste is a rigid and hierarchical class structure that divides Hindus into groups. One's caste is assigned at birth and is generally immutable. Traditional roles or professions are often tied to caste identity. Castes are arranged in a hierarchical order, often justified by religious or cultural ideologies, and marriage is restricted within the caste. Dalit is a Sanskrit term, meaning 'oppressed' or 'downtrodden'. Since the 1970s, it has been increasingly adopted as a self-designation by those at the bottom of the caste hierarchy in India. Brahmins, on the other hand, are the "learned" class and sit at the top of the caste hierarchy.</footnote>
We define a status effect as occurring when a respondent is more likely to follow advice from a high-status advisor who does not share their group identity. In contrast, we define a group identity effect as occurring when a respondent is more likely to follow advice from an advisor who shares their group identity but not their status. We can most clearly distinguish between status and identity effects when the respondent is from a low-status group (e.g., women or disadvantaged castes). Only in these cases can the advisor be either high-status and out-group, thereby isolating the status effect, or low-status and in-group, thereby cleanly identify isolating the identity effect.

As part of the survey, respondents were also asked whether they would advise future participants in another village to choose high or low effort in the same game. We randomly varied the profile (gender and caste) of the advice recipient across respondents. We use this to examine advice-giving and patterns of intergenerational transmission of advice.

Compared to much of the literature, our vignette-based experiment offers several advantages. It enabled us to present respondents with varied scenarios that would be challenging to implement through other methods. Specifically, we exogenously varied the advice content, allowing us to study the impact of both cooperative and non-cooperative advice.<fnref n="3" /> Another advantage of using a vignette experiment is that it allows us to easily randomize the advisor’s characteristics—such as identity and status—which helps us avoid the endogeneity bias that typically affects field studies of advice. In natural settings, individuals are more likely to choose their advisors (see, e.g., Copeland et al., 2007), which can bias estimates of the effect of advice.

We developed a theoretical framework to understand how advice influences decision-making in our experiment. The advisee receives advice and chooses whether to follow it based on whether doing so maximizes their expected utility. Advice serves as a signal of the appropriate action to undertake, considering its alignment with prevailing social norms. Advice from an in-group member or a high-status individual can be perceived as more credible, while following advice from an out-group or low-status advisor may incur a psychological cost. In this setting, individuals are more likely to follow advice from high-status or in-group advisors. Moreover, in case the advice conflicts with the prevailing empirical norm,

<footnote n="3">³Delivering both cooperative and non-cooperative advice in real-world settings, especially to farmers, may be ethically and practically problematic, as it might discourage the adoption of efficiency-enhancing technologies. Consequently, studying the impact of socially suboptimal advice in a natural setting is difficult. In contrast, the use of vignettes framed as a strategic game enabled a realistic and ethically acceptable examination of non-cooperative advice.</footnote>
identity and status may play a pivotal and potentially negative role: individuals may be more willing to act against norms if the advice comes from an in-group member or someone of high status. Finally, the framework explores the impact of admired figures or role models, in terms of gender or caste, on adherence to advice. We hypothesize that a sufficiently respected (female or Dalit) role model can mitigate the adherence to advice that challenges social norms, potentially eliminating or reducing the perverse influence of identity or status in such cases.

To inform the hypotheses derived from this framework, we combined data from our survey experiment with administrative records on state-level elections in India and a second survey from the same sample. This allowed us to construct a dataset that includes information on current and former political leaders who could serve as role models in our setting. Our approach enables us to define exposure to female and disadvantaged caste leaders, as role models, at two levels: the state and the local village level. At the state level, political leadership is captured by elected members of the State Legislative Assembly, who serve as representatives of their constituencies. At the village level, leadership is defined by locally elected members of the village council. In this case we took advantage of a unique feature of Indian elections: an affirmative action reservation policy that randomly reserves leadership positions for disadvantaged caste and female citizens. This policy creates random variation in the presence of female and Dalit leaders across areas, enabling us to examine whether exposure to such role models causally influences the likelihood of advice adherence among residents in these areas.

Our findings highlight that who delivers advice matters as much as what advice is delivered, and can be summarized more precisely as follows. *First*, the same advice can have different effects depending on the identity and the status of the advisor. Individuals are more likely to adhere to advice when it is provided by high-status or in-group advisors.

*Second*, respondents exhibit greater adherence to advice that aligns with the prevailing empirical norm of cooperation, as captured in our data. In the case of caste, respondents are more likely to follow advisors who are in-group or of higher status when the advice is norm-consistent. In the case of gender, on the other hand, neither status nor shared identity plays a role when advice is norm-consistent. Advice that deviates from the cooperative norm can influence behaviour if it originates from advisors with high status or shared group identity. Specifically, female respondents are more likely to follow norm-inconsistent, Low effort advice when it is delivered by male advisors, who are generally perceived to hold high status in society: for women, status matters more than their own group identity.
Disadvantaged caste respondents are more likely to follow in-group advisors even when they provide Low effort advice: for these advisees, their own group identity matters more than status.

Our *third* key finding is that, holding group identity constant and exogenously increasing the status of women and disadvantaged caste individuals, influences the effect that status and identity have on advice adherence. Consistent with our theoretical framework, we find that when the role model is prominent, female respondents exposed to female leaders (elected in their constituency or electoral district of residence) are significantly less likely to follow norm-inconsistent, Low effort advice from men in the experiment. When role models are less prominent (lower-profile village-level leaders), the negative influence of male status, while not entirely eliminated, is reduced. However, this beneficial effect of role models is not universal. Indeed, in the context of caste, exposure to disadvantaged caste prominent role models makes caste salient and has a polarizing effect along caste lines: individuals become more likely to follow the norm-inconsistent advice from advisors with whom they share an identity. Interestingly, these results are consistent with the caste dynamics that are playing out in modern India. Dalit identity has been increasing and asserting itself more prominently over the years, both socially and politically. Simultaneously (and possibly as a consequence), there is also evidence of this leading to backlash from other castes, which has taken the form of stronger identity-based resistance, increased anti-reservation rhetoric, and covert forms of exclusions (Mendelsohn and Vicziany, 1998, Gorringe, 2013, Abraham and Janarthanan, 2024). When role models are less prominent, these local role models do not exert a consistent effect. Taken together, these findings suggest that exposure to role models with increased status in real-world settings plays an important role in shaping how individuals evaluate and act upon advice.

Our *fourth* result is that when leaving advice for future participants, respondents’ advice is consistent with their own actions, regardless of the advice received. Respondents do not condition their advice on the caste of the recipient of the advice, but at the margin, men are more likely than women to recommend the norm-consistent cooperative strategy to future male participants. Our analysis also reveals that providing cooperative advice can have a crowding-out effect independent of status and group identity effects. The decision to cooperate when individuals do not receive any advice may result from a self-image concern that may weaken once cooperation follows advice to cooperate.

To complement our experimental data and attest to their external validity, we used two additional sources of data. In addition to collecting data on local political leaders, the
second survey, which was conducted on the same sample of respondents, enabled us to
examine whether advice seeking and adherence are common behaviours among respondents.
We found that 52% of individuals in our sample regularly seek advice and tend to follow it
when offered, primarily because of trust in the advisor. Respondents consistently reported
relying on a small, influential circle of individuals for guidance, suggesting that the quality
and credibility of just a few advisors can have substantial consequences for community-
level outcomes. This pattern also highlights the potential for negative effects of advice
adherence: the presence of one or two poorly informed advisors can push the community
toward a suboptimal equilibrium. Concerns about misinformation are prominent, with at
least 20 percent of respondents explicitly identifying it as a serious issue. These findings
underscore that advice adherence and, critically, decisions about whose advice to follow,
play a central role in shaping behaviour and outcomes in these communities. Our survey
experiment was part of an agro-advisory Randomized Controlled Trial. We found that the
respondents who adhere to advice in the experiment are significantly more likely to adopt
the agricultural advice in the RCT and have a higher willingness to register for future advice
in subsequent seasons. This suggests that our survey experiment meaningfully captures
real-world behaviour.

Overall, these findings carry important implications for the design and implementation of advice-based policy interventions. Governments and NGOs often assume that providing accurate, efficiency-enhancing advice is sufficient to shift behaviour. However, our results suggest that the effectiveness of such interventions depends critically on the existing social landscape. In many situations, individuals receive guidance from key local figures, and when this local advice is sub-optimal in ways that can potentially hinder efficiency or innovation, it can undermine the goals of the policy. Our findings suggest that effective policy design must thus consider not only the content of the advice being delivered, but also who is influencing behaviour on the ground, and whether their messages reinforce or conflict with official guidance. They also help reconcile the mixed evidence on the effectiveness of interventions, which generally do not account for the identity of the advice giver.

Our study makes several contributions. We advance the economic literature on social sta-
tus and group identity and their role in decision-making (e.g., Akerlof and Kranton, 2000,
Chen and Li, 2009) by examining their causal impact on adherence to advice. We con-
tribute to the literature on social learning (e.g., Foster and Rosenzweig, 1995, Bandiera
and Rasul, 2006, BenYishay and Mobarak, 2019, Conley and Udry, 2010, Rogers, 2010),
which shows that advice influences behaviour, is often followed, but can also be discounted.
Most of these studies, however, focus on passive learning from peers or neighbours. They assume frictionless information flow, and lack causal identification of advisor characteristics.<fnref n="4" /> Our survey experiment, conducted with farmers in a developing country, in contrast, directly varies advisor attributes and provides active advice, both of which are varied randomly. Our design allows us to isolate the effects of advisor status and group identity on advice adherence.<fnref n="5" /> Moreover, unlike much of the existing literature, we examine not only norm-consistent advice but also socially suboptimal advice, reflecting the reality that individuals often receive guidance from friends, family, and fellow villagers, which makes them potentially vulnerable to misinformation or information that is not optimal for society. Misinformation has been shown to spread significantly farther, faster, deeper, and more broadly than true information (e.g., Vosoughi et al., 2018).

We also build on studies on inter-generational advice (Schotter, 2003, Schotter and Sopher, 2003, Chaudhuri et al., 2006, Çelen et al., 2010, Schotter and Sopher, 2007), which show that advice can reinforce beneficial norms. Our study, in contrast, explores whether advice from individuals of varying social status and group identity undermines socially beneficial learning. This has implications for how behaviours persist or change.

Finally, we contribute to the literature on role models (see Serra, 2025). Prior studies show that exposure to role models can shift norms and behaviours across domains such as education, labour market, and civic life (e.g., Jensen and Oster, 2009, Mahmud et al., 2025, Riley, 2024, Chattopadhyay and Duflo, 2004, Ahmed et al., 2024, Vecci and Želinský, 2019, Beaman et al., 2012). We extend this research by examining how role models influence advice adherence in a domain where outcomes are more immediate. Identifying immediate changes can be important because they are easier to measure and attribute causally, and

<footnote n="4">⁴An exception is BenYishay and Mobarak (2019) who examine the role of homophily in information diffusion and show that technology uptake depends on the social identity of the agent who communicates. We differ from this paper as we focus on advice, including socially sub-optimal advice.</footnote>

<footnote n="5">⁵Relatedly, previous studies examined the influence of leaders’ gender on followers’ behaviour in coordination and public good games (Gangadharan et al., 2016, Reuben and Timko, 2018, Gangadharan et al., 2019, Heursen et al., 2020); in contrast, in our study adherence to advice cannot depend on how individuals form beliefs about how others react to the gender of the advisor, and advisors’ payoffs do not depend on advice adherence. Abel et al. (2024) observed no evidence of ex ante gender discrimination in adherence to financial advice; similarly, Brandts and Rott (2021) found no effect of gender matching on advice giving or following; Bundi et al. (2024) found that female scientific experts are not considered less credible by citizens than male experts and Sievertsen and Smith (2025) also found no gender difference when the credentials of experts in economics were not common knowledge. In Ethiopia, Ayalew et al. (2021) showed that people follow advice from female leaders less often than from males, unless given information on ability. In contrast to previous studies, we examine advice that can be socially inefficient and, by varying two advisor attributes—gender and caste—and manipulating identity matching, our design allows for a clearer identification of the distinct effects of group identity and social status on advice adherence.</footnote>
also because they may serve as early indicators of deeper, longer-term shifts. Our findings
offer policy-relevant evidence on the conditions under which role models affect the impact
of advice givers, depending on their identity and context.

2 Research Design

Our research strategy builds on multiple and complementary data sources, including our main survey experiment, administrative records on political leaders, a randomized controlled trial, and a supplementary survey.

**2.1 Sample**

The study was conducted in Bihar, a state in Eastern India (see Figure A1a in the Appendix).<sup>6</sup> The survey experiment was conducted in 220 villages in 9 districts. Figure A1b in the Appendix shows the survey districts. A total of 3153 individuals (50.49% female) participated in the survey experiment. Due to missing demographic data for 47 individuals, our final estimation sample consists of 3106 individuals. The sample for this study comes from two groups of farmers: women farmers registered to the Bihar Rural Livelihoods Project (or “JEEViKA”) network and farmers enrolled in the BAU (Bihar Agricultural University) advisory network. We collected data from both male and female primary decision makers in the household.

Our participant pool of farmers constitutes an important population to study for several reasons. *First*, identifying the quality of advice is far from straightforward, particularly when individuals are confronted with multiple external agricultural risks and face constraints such as bounded rationality, limited cognitive capacity, or conflicting signals. *Second*, these communities are often socially segmented, where group identity and social status play an important role in shaping behaviour. *Third*, farmers are often reluctant to change their habits and choose to remain with the status quo even when more beneficial options are available. Despite the significant benefits, agricultural productivity in developing countries continues to be hindered by the persistently low adoption of improved practices and innovations (McIntosh et al., 2013, Gine and Yang, 2007, Banerjee et al., 2006).<sup>7</sup> *Fourth*, the

<footnote n="6"><sup>6</sup>Bihar is India's third most populous state. With a population of nearly 120 million, it is equivalent to the world's 10<sup>th</sup> most-populous country, yet it is only ranked 178<sup>th</sup> in terms of GDP per capita (PPP).</footnote>

<footnote n="7"><sup>7</sup>Fertilizer use is a commonly cited example and in particular, African and Indian farmers apply fertil-</footnote>
rapid growth of social media use in developing countries, including India, facilitates the
rapid spread of both helpful and misleading information.

Table 1 presents descriptive statistics for our sample. The sample is balanced in terms of gender (50% females) and a large majority (97%) of the participants are Hindus. In terms of caste 27.4% of households belong to General Castes while the remaining 72.6% belong to the disadvantaged castes (comprising of Scheduled Castes, Scheduled Tribes and Other Backward Castes). Mobile phone usage is common, with 63% of respondents reporting that they use (own or have access to) a mobile phone. About 49.5% of our participants report agriculture as the primary income source of their household, followed by non-agricultural labour (16.2%), self-employment or business (13.9%), other (10.6%), and salaried jobs (9.7%). Mobile phone usage is common, with 63% of respondents reporting that they use (own or have access to) a mobile phone.

Table A2 in the Appendix presents the averages by the sample assigned to the different profiles in our survey experiment. The p-values from the Kruskal-Wallis (KW) test, reported in column 10, show that we cannot reject the null hypothesis that participants across treatment conditions are drawn from the same population, suggesting that randomization was successful.

## 2.2 Procedures

Our survey experiment was conducted at the end of a bigger survey administered face-to-face and privately in the participant's house. Female respondents were interviewed by females, and male respondents by males. The survey was conducted in Hindi, the main language of the area. Our survey experiment typically took about 20 minutes to complete. Preceding the survey experiment, participants completed modules relating to agricultural production and household consumption, a cognitive ability module that included a Math task and a Raven's Matrix task and a task to elicit risk preferences (Eckel and Grossman (2002)). These modules were unrelated to identity or advice. The cognitive ability and risk measures are included as control variables in our regression analysis to assess robustness of our results.

The enumerator read the experimental instructions aloud, pausing frequently to ensure the
respondent understood. To clarify payoffs, the enumerator also read aloud and presented
a visual payoff table. When introducing the advisor, the enumerator read the advisor's

<footnote>izers at significantly lower rates than farmers elsewhere (Emerick et al., 2016).</footnote>
<table><caption>Table 1: Sample Characteristics</caption><thead><tr><th></th><th>Mean (SD)</th></tr></thead><tbody><tr><td>Female</td><td>0.5000<br>(0.5001)</td></tr><tr><td>General Caste</td><td>0.2737<br>(0.4459)</td></tr><tr><td>Disadvantaged Caste</td><td>0.7263<br>(0.4459)</td></tr><tr><td>Hindu</td><td>0.9704<br>(0.1696)</td></tr><tr><td>Mobile Phone Use</td><td>0.6304<br>(0.4828)</td></tr><tr><td colspan="2"><em>Primary Source of Income for the Household</em></td></tr><tr><td>Self Employment / Business</td><td>0.1391<br>(0.3461)</td></tr><tr><td>Salaried Job</td><td>0.0972<br>(0.2963)</td></tr><tr><td>Agriculture</td><td>0.4952<br>(0.5001)</td></tr><tr><td>Non Agricultural Labour</td><td>0.1623<br>(0.3688)</td></tr><tr><td>Other Occupation</td><td>0.1062<br>(0.3082)</td></tr><tr><td>Sample Size</td><td>3106</td></tr></tbody></table>

Notes: A total of 3153 individuals participated in the survey experiment. However, we do not have complete demographic characteristics for 47 individuals. Disadvantaged Caste consists of Scheduled Caste (SC), Scheduled Tribe (ST) and Other Backward Caste (OBC). Standard deviations are presented in parenthesis.

characteristics aloud.

## 2.3 Survey Experiment

To measure the impact of advisors' identity and status on the willingness to follow advice, participants in the survey experiment were provided with a vignette. They were asked to consider a hypothetical scenario where they were part of a two-person production team and their earnings depended on their choice of effort, as well as the choice of effort of the business partner.<fnref n="8" /> Decisions had to be made independently and only once. Participants were also

<footnote n="8">⁸Previous studies (Duflo et al., 2007, Hainmueller et al., 2015, Kuziemko et al., 2015, Stantcheva, 2023) have provided empirical validation of survey experiments, showing that hypothetical responses align with real-world choices and that when implemented with high standards of scientific rigour, can yield insights comparable to, and supplement those obtained from, incentivized experiments.</footnote>
told that their partner lived in the same area as them. The hypothetical payoff matrix of this scenario, modeled as a Prisoner's Dilemma, is presented in Table 2. We implemented a Prisoner's Dilemma game because it highlights the tension between individual and collective benefits in a strategic context, unlike games such as the Dictator game. This setting is also likely to be familiar to our respondents, as many community projects in their villages require collective effort. Moreover, the strategic nature of the game makes the advice more relevant.

<table><caption>Table 2: Choice of Effort and Associated Payoffs in Experimental Task</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Business Partner</th></tr><tr><th>High effort</th><th>Low effort</th></tr></thead><tbody><tr><th rowspan="2">You</th><th>High effort</th><td>(100, 100)</td><td>(0, 150)</td></tr><tr><th>Low effort</th><td>(150, 0)</td><td>(25, 25)</td></tr></tbody></table>

**Notes:** The first number in each cell (in blue) denotes the respondent's payoff and the second number (in red) denotes the other business partner's payoff. All payoffs are expressed in Indian Rupees (Rs.), with Rs. 100 = $1.22 US at the time the survey experiment was conducted.

To enhance comprehension and relevance for participants, the choices in the game were framed as either 'High effort' (working hard) or 'Low effort' (working less hard). Participants were also informed that putting in high effort incurs higher costs compared to low effort, but it results in greater total revenue for the team. After we explained to them the consequences of each possible action of the two team members on their payoffs, as per Table 2, participants were asked to choose their level of effort (*Low* or *High*).

Given the payoff matrix, the Nash equilibrium of the game is for both players (Respondent and the Business Partner) to choose Low effort. However, previous literature has established that many players deviate from the Nash equilibrium in one-shot Prisoner's Dilemma games by choosing to cooperate (e.g., Mengel, 2018). Indeed, there are two points of attraction in this game: the equilibrium, with mutual defection, and the social optimum, with mutual cooperation. Players had to choose whether to maximize their own expected payoff by choosing Low effort or try to maximize joint payoffs by choosing High effort. Faced with
strategic uncertainty, players may draw on their everyday life experiences to decide whether or not to cooperate in the game. If a cooperation norm exists in the community, participants may be more likely to choose High rather than Low effort.

Before making their decision, participants were also told that someone from another village in the same area had faced a similar scenario and had offered them advice. They were informed of the gender and caste of the advice-giver, as well as the content of the advice—whether to provide High or Low effort. We randomly varied across participants both the advisor's profile in terms of gender and caste, and the advice provided, while two other characteristics were kept constant. The advice could come from a male or a female, either a Dalit (someone from a disadvantaged caste group in India) or a Brahmin (an individual from the “learned” caste group, traditionally situated at the top of the caste hierarchy). Exogenously varying the characteristics helps mitigate endogeneity bias, as, in natural non-experimental settings, individuals are more likely to exhibit homophily and seek advice from advisors who share similar characteristics. In addition, one randomly selected group received no advice before choosing between High and Low effort. The nine different advisor profiles are summarized in Table 3 and described in more detail in Table A1 in the Appendix.

<table><caption>Table 3: Identity of Advisor and Suggested Effort (Advice)</caption><thead><tr><th>Profile</th><th>Condition</th><th>Advisor's Gender</th><th>Advisor's Caste</th><th>Suggested Effort</th></tr></thead><tbody><tr><td>1</td><td>MDH</td><td>Male</td><td>Dalit</td><td>High</td></tr><tr><td>2</td><td>FDH</td><td>Female</td><td>Dalit</td><td>High</td></tr><tr><td>3</td><td>MDL</td><td>Male</td><td>Dalit</td><td>Low</td></tr><tr><td>4</td><td>FDL</td><td>Female</td><td>Dalit</td><td>Low</td></tr><tr><td>5</td><td>MBH</td><td>Male</td><td>Brahmin</td><td>High</td></tr><tr><td>6</td><td>FBH</td><td>Female</td><td>Brahmin</td><td>High</td></tr><tr><td>7</td><td>MBL</td><td>Male</td><td>Brahmin</td><td>Low</td></tr><tr><td>8</td><td>FBL</td><td>Female</td><td>Brahmin</td><td>Low</td></tr><tr><td>9</td><td>No Advice</td><td></td><td></td><td></td></tr></tbody></table>

Notes: The word Dalit means oppressed or downtrodden. Dalits include the disadvantaged caste groups in India (Scheduled Caste, Scheduled Tribes and Other Backward Castes). Brahmins are the “learned” class and sit at the top of the caste hierarchy. See Table A1 in the Appendix for details of the different profiles.

We aggregate the different conditions shown in Table 3 (column 1) to consider three broad categories: (i) advisor is male or female (gender treatments); (ii) advisor is Brahmin or
Dalit (caste treatments); (iii) No advice is provided (No Advice treatment). In our analysis, we do not consider variations in the caste (gender) of the advisor within the gender (caste) treatments. Combining the gender and caste treatments yields four advisor categories: Male Brahmin, Male Dalit, Female Brahmin, Female Dalit. While it may be reasonable to consider Male Brahmins and Female Dalits as representing the extremes of the social hier- archy, it is more difficult to unambiguously rank Male Dalits relative to Female Brahmin.

After respondents made their decision, they were asked what advice they would give to another respondent, with the profile of the intended recipient randomly varied by gender and caste. Respondents were then asked several questions relating to their decisions. Their beliefs about their partner's effort choice were elicited, along with whether they trusted the advice received. They also reported the likelihood of making the same or a different decision without the advice, and how this likelihood might have changed if the advisor had been from their own caste.

## 2.4 Data from the Agro-advisory RCT

Our sample of experimental subjects was part of a large RCT that examined the impact of different forms of agro-advisory information on the behavior of smallholder farmers. More details of the Agro-advisory RCT are presented in Appendix A1. As noted earlier, our survey experiment was incorporated into the endline survey, which also included modules on household income, agricultural production, household characteristics, behavioral measures, and general attitudes. We use data from this RCT to examine whether individuals who follow advice in the RCT also follow advice in the survey experiment, providing a measure of the generalizability of our findings (see Section 6).

## 2.5 Administrative Records on Political Leaders

The administrative records on political leaders come from data made available by the Election Commission of India (ECI).<fnref n="9" /> The ECI data has information, at the constituency level, on several electoral variables, including vote shares and party affiliation of the candidates, voter turnout rates, reservation status of the constituency, and the total number of contestants. We use data from state assembly elections conducted in Bihar in 2010, 2015, and 2020, that is, the last three elections prior to the endline survey. We matched each village in our sample to the assembly constituency where it is located. This allowed us to

<footnote n="9">⁹We make use of the compiled election dataset from Trivedi Centre for Political Data (TCPD), Ashoka University.</footnote>
investigate how exposure to political role models affects the extent to which an advisor's
status and group identity influence adherence to their advice in the survey experiment.

## 2.6 Additional Survey

In 2024, we conducted a telephone survey and contacted participants from the original sample. All households were contacted by phone, with 1611 total observations collected, including at least one household from every village in our original sample.

The survey collected information on village governance, with a focus on the recent village council (Gram Panchayat or GP) elections. Specifically, it collected information on the gender, caste, and reservation status of the head of the village council.<fnref n="10" /> Because administrative data on the head of the village (the gender, caste, and reservation status of the position) are not publicly available, we collected this information through this additional survey. This allowed us to examine whether exposure to local role models in their community impacts the relationship between advisor identity or status and advice adherence.

The survey also collected data on broader attitudes and sources of advice, such as how often advice is given and followed, its typical domains (e.g., agricultural, financial, family matters), and the perceived impact of misinformation within the village. Additional questions collected information on social capital, including trust, cooperation, and community engagement. We use this information to motivate and contextualise the study setting.

# 3 Theoretical Framework

We develop a simple theoretical framework to guide our intuition and motivate the empirical hypotheses. Let us consider an *Advisee*, who receives advice from an *Advisor* and makes a decision. In our experiment, the advice is exogenously provided, which allows us to examine the causal effect of the content of the advice on advice adherence. The Advisor can differ

<footnote n="10">¹⁰Representation by disadvantaged groups and women in village governance was mandated by the 73rd amendment to the Indian constitution. Mandated representation was implemented through a reservation policy. At least 33% of all positions of the head of the village council in the state were to be reserved for women. Orthogonally, the head's position was also reserved for the disadvantaged caste groups in proportion to the share of these groups at the district level. Positions to be reserved were randomly selected before every village council election. Reservations for women and disadvantaged caste groups operate independently without interference. Only candidates from the relevant reserved category could stand for election to the seat, but voting was open to all.</footnote>
from the Advisee along two dimensions: (i) identity, where the Advisor is in-group (shares the same group identity) or out-group relative to the Advisee; and (ii) status, where the Advisor occupies a high or low status within the social hierarchy.

Let $\Theta \in \{0, 1\}$ denote the action that the Advisee must choose. The Advisee receives advice, denoted by $\sigma \in \{0, 1\}$, regarding $\Theta$. If the advice is informative, $P(\sigma = \Theta|\Theta) = p > 0.5$. Upon receiving the advice $\sigma$, the Advisee updates her belief about $\Theta$, taking into account not only the content of the signal but also the Advisor's identity ($I$) and status ($S$). The Advisee's posterior belief can be written as follows:

$$P(\Theta = 1|\sigma, I, S) = \frac{\alpha_{I,S}P(\sigma|\Theta = 1)P(\Theta = 1)}{\alpha_{I,S}P(\sigma|\Theta = 1) + (1 - \alpha_{I,S})P(\sigma|\Theta = 0)P(\Theta = 0)}$$

$\alpha_{I,S} \in (0, 1]$ can be defined as the credibility weight of the advice, depending on the perceived credibility of the Advisor, with (i) $\alpha_{in-group} > \alpha_{out-group}$; and (ii) $\alpha_{high-status} > \alpha_{low-status}$.

The decision maker chooses whether to follow the advice based on whether it maximizes her expected utility:

$$U(a) = \mathbf{E}(u(a, \Theta)) - \lambda C(I, S)$$

where $a \in \{0, 1\}$ denotes the action taken by the Advisee. $u(a, \Theta) = 1$ if $a = \Theta$ and 0, otherwise. $C(I, S)$ denotes the psychological costs associated with following the advice, which is increasing when (a) the Advisor is from an out-group (i.e., the Advisor and the Advisee do not share a group identity); and (b) the Advisor has a low status. Finally, $\lambda$ is a weighting factor for the cost, and represents the Advisee's sensitivity to the Advisor's identity and status.

This results in the following testable hypothesis:

**Hypothesis 1** (a) The same advice can yield different adherence rates depending on the Advisor's identity and status.

(b) Advisees are more likely to adhere to the advice from high-status Advisors and from those Advisors with whom they share an identity.
## Impact of Norms

While Hypothesis 1 focuses on the characteristics of the Advisor, another important factor influencing advice adherence is the content of the advice itself. Specifically, whether the advice aligns with prevailing community norms can affect both beliefs and social cost, thereby affecting the Advisee's final decision. Assume that the signal, advice, ($\sigma$) itself is characterized by its norm consistency, denoted by $N \in \{\text{Norm Consistent, Norm Inconsistent}\}$. The perceived credibility of the signal ($\sigma$) now depends not only on the Advisor's identity and status but also on whether the advice aligns with the empirical social norms, i.e.:

$$ \tilde{\alpha}_{I,S,N} = \alpha_{I,S} + \psi \mathbf{I}(N = \text{Norm consistent}) $$

where $\alpha_{I,S}$ is defined as before and is higher for advice from an in-group or high-status Advisor, and $\psi$ is the additional weight associated with a norm-consistent signal. A norm-consistent signal may have a higher weight due to possible confirmation bias or social anchoring, its consistency with most frequently observed behaviour in reality, or the perceived risk of social sanctions for deviating from prevailing norms.

The posterior belief after receiving signal $\sigma$ can now be written as:

$$ P(\Theta = 1 | \sigma, I, S, N) = \frac{\tilde{\alpha}_{I,S,N} P(\sigma | \Theta = 1) P(\Theta = 1)}{\tilde{\alpha}_{I,S,N} P(\sigma | \Theta = 1) + (1 - \tilde{\alpha}_{I,S,N}) P(\sigma | \Theta = 0) P(\Theta = 0)} $$

If $N$ is norm-inconsistent, then $\tilde{\alpha}_{I,S,N}$ is lower, compared to a norm-consistent signal, as is $P(\Theta = \sigma | \sigma, I, S, N)$ (as $\frac{\partial P(\Theta = \sigma | \sigma, I, S, N)}{\partial \tilde{\alpha}_{I,S,N}} > 0$).

Suppose that acting on advice that violates the prevailing norm incurs added psychological costs:

$$ C(I, S, N) = C_0(I, S) + \eta \mathbf{I}(N = \text{Norm inconsistent}) $$

where $C_0(I, S)$ is the baseline cost of adhering to advice from the out-group or low status Advisor, and $\eta > 0$ denotes the penalty for norm deviation, which may be strong in tightly knit rural communities.

The decision rule is now revised. The Advisee follows advice if:

$$ P(\Theta = \sigma | \sigma, I, S, N) > 1 - \lambda C(I, S, N) $$
The left hand side is the posterior belief influenced by identity, status, and whether the advice aligns with norms, while the right hand side is the psychological cost threshold, which is higher if the advice is norm-inconsistent but may be reduced if advice is provided by someone the individual respects more (i.e., an Advisor who shares the same identity or has a higher status).

This leads to the following hypothesis:

**Hypothesis 2** (a) Advice is adhered to when it is consistent with dominant community norms.

(b) Advisees may adhere to advice even if it challenges prevailing community norms when it is delivered by in-group or high-status advisors.

## Impact of Role Models

Next, we introduce the impact of Role Models on advice adherence. Role Models can attenuate the weight of the Advisor's group identity or status on the Advisees' adherence to the signals they receive. Specifically, the role model may enhance the credibility of advice even when the Advisor belongs to an out-group or holds lower status. We first examine the role model effect in the baseline framework. Building on the baseline credibility weight $\alpha_{I,S}$, we can define a role model adjusted credibility weight as follows:

$$ \tilde{\alpha}_{I,S,R} = \alpha_{I,S} + \xi R(S, I) $$

where $R(S, I)$ can be viewed as a role model effect when the Advisor is “admired”, $R \in \{0, 1\}$ where $R = 1$ implies that the Advisor is admired. $\xi$ is the strength of the role model effect. The role model effect is increasing in the level of admiration of the Advisor. It is possible that $R(S, I) > 0$ even for an out-group or low-status Advisor, if the Advisor is perceived to be sufficiently inspirational. The stronger the role model effect, the more likely it offsets the psychological costs associated with going against the advice of an in-group or a high-status Advisor ($C(I, S)$).

The modified belief updating rule can be written as:

$$ P(\Theta = 1 | \sigma, I, S, R) = \frac{\tilde{\alpha}_{I,S,R} P(\sigma | \Theta = 1) P(\Theta = 1)}{\tilde{\alpha}_{I,S,R} P(\sigma | \Theta = 1) + (1 - \tilde{\alpha}_{I,S,R}) P(\sigma | \Theta = 0) P(\Theta = 0)} $$
Advisors who are perceived as role models have an increased influence on posterior beliefs. While an in-group and high-status Advisor is seen as more credible, role models may compensate for low status and offset the out-group bias.<fnref n="11" /> Finally, the role model effect reduces the perceived psychological cost of following the advice from an out-group or low-status Advisor.

The updated decision rule can be written as follows. The Advisee adheres to the advice if:

$$P(\Theta = \sigma | \sigma, I, S, R) > 1 - \lambda C(I, S)$$

$P(\Theta = \sigma | \sigma, I, S, R)$ $u(\sigma, \Theta)$ is the posterior belief that advice is correct, while $1 - \lambda C(I, S)$ is the threshold adjusted for psychological cost. When $R = 1$, $\tilde{\alpha}$ increases, thus raising the posterior belief. The advice from otherwise discounted sources (out-group or low-status Advisor) may now exceed the threshold for advice adherence. Additionally, the perceived psychological cost of adhering to advice from an out-group or low-status Advisor falls.

We next consider the impact of Role Models and norms together. As before, the signal $\sigma$ might be norm-consistent if it aligns with widely held community expectations. Belief in the accuracy of signal $\sigma$ is influenced by whether it confirms or contradicts norms:

$$\tilde{\alpha}_{I,S,N,R} = \alpha_{I,S} + \xi R + \psi I(N = \text{Norm consistent})$$

where $\psi$ denotes the strength of conformity bias or confirmation bias. If the signal $\sigma$ is norm-inconsistent, the receiver discounts its credibility further, i.e.,

$$P(\Theta = \sigma | \sigma, I, S, R, N(\text{Norm inconsistent}) < P(\Theta = \sigma | \sigma, I, S, R, N(\text{Norm consistent}))$$

We assume that acting against community norms imposes psychological costs:

$$C(I, S, R, N) = C_0(I, S) + \eta I(N = \text{Norm inconsistent})$$

where $\eta > 0$, i.e., there is a cost for violating the norm.

<footnote n="11">¹¹Role Models from the in-group are likely to have the strongest impact, but Role Models from the out-group could still have a positive impact as their leadership position provides authority relative to the context of no Role Models.</footnote>
We can revise the updated decision rule. The Advisee follows the advice if:

$$P(\Theta = \sigma | \sigma, I, S, R, N) > 1 - \lambda C(I, S, R, N)$$

Both sides of the inequality are influenced by norm (in)consistency. *First*, beliefs are weakened (left side decreases) when advice contradicts norms, and *second*, the psychological cost of taking counter-normative action rises (the right side increases). Role Models can potentially reduce the perverse influence of identity or status in cases where the Advisee follows norm-inconsistent advice.

This leads us to our final hypothesis:

**Hypothesis 3** A sufficiently admired Role Model attenuates the adherence to advice that challenges prevailing community norms provided by an in-group or high-status Advisor.

# 4 Results

We begin with a brief discussion of respondents' effort choices in response to High and Low effort advice (Section 4.1). Next, we explore the empirical social norm expressed by respondents (Section 4.2). The core of our analysis focuses on patterns of advice adherence, our primary outcome of interest, which we present in Section 4.3. Subsequently, in Section 5, we report the effects of the exposure to role models on respondents' likelihood of following advice.

## 4.1 Effort Choice by Advice Received

Figure 1a presents the proportion of respondents who choose High effort by the type of advice they receive (Low or High effort). The proportions are averaged over caste and the gender of the advisor. Respondents are significantly more likely to choose High effort (0.63 vs. 0.57, $p-value = 0.001$) when they receive High effort advice. The patterns are the same when we split the sample by the respondents' gender or caste (Figures A2a and A2b respectively in the Appendix): The likelihood of choosing High effort actions increases when respondents receive High effort advice, regardless of whether the respondent is male, female, from a Disadvantaged caste, or a General Caste.
<figure><img src="image_2.png" /><figcaption>Figure 1: Effort Choice and Advice Adherence by Advice Received</figcaption></figure>

**Notes:** In Figure 1a, the bars denote the proportion of respondents who choose High effort in the Low and High effort advice treatments. In Figure 1b, the sample is restricted to participants who received advice. The bars denote the average proportion of survey respondents who follow advice (choose High (Low) effort when they received High (Low) effort advice), by type of advice received. The sample is aggregated over the gender and caste of the advisors.

## 4.2 Empirical Norm

We investigate the prevailing empirical norm in two ways. *First*, we examine the majority of respondents’ behaviour when *no advice* is available. While the Nash equilibrium of our game is for both players to choose Low effort, in the absence of any advice, 72% of participants choose High effort (Figure 2a).<fnref n="12" />

*Second*, in response to the question: *what decision do you think your partner would choose?*, Figure 2b shows that a large majority (65%) of the respondents believe that their partner would have chosen High effort. The beliefs are similar across the types of advice received. The data from our survey experiment suggests that the empirical norm — *i.e.*, the expectation of most participants — is to choose High effort.

While the empirical norm is one of social cooperation, the likelihood of choosing High effort falls in the Advice treatments relative to the No Advice treatment: overall, 59.8% of the participants choose High effort in the two advice treatments (57% when they receive Low effort advice and 63% when they receive High effort advice). The difference is statistically significant ($p = 0.001$). Unsurprisingly, Low effort advice decreases the proportion of players choosing High effort compared to the No Advice treatment even more (0.57 vs.

<footnote n="12">¹²Figure A3a presents the proportion of participants who chose High effort under the different conditions (Profiles). This is a disaggregated version of Figure 2a. The likelihood of choosing High effort is the highest in the No Advice treatment.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 2: Empirical Norm</figcaption></figure>

Notes: The bars denote the average proportion of survey respondents who choose High effort, with and without advice (Figure 2a) and the proportion of respondents who believe that their business partner will choose High effort, for each advice type (Low, High, None).

0.72, *p* = 0.000). Such advice may make the risk and monetary consequences of one-sided cooperation more salient, and it also facilitates self-serving motivated reasoning by providing the defectors with an excuse to free ride on their partner’s effort. However, participants are also more likely to play the equilibrium strategy of exerting low effort after receiving High effort advice than when no advice is provided (0.63 *vs*. 0.72, *p* = 0.001). This is surprising since such advice is consistent with the empirical norm and the magnitude of the difference is quite large. One possible interpretation is that, in the absence of advice, individuals may choose to cooperate in order to signal a positive self-image. This psychological motivation may disappear when the cooperation decision is prompted by a recommendation. In other words, even cooperative advice may crowd out intrinsic motivation to cooperate.

## 4.3 Adherence to Advice

### 4.3.1 Aggregate Analysis

We next turn to the analysis of our key variable of interest: adherence to advice. We define *Follow advice* as a dummy variable = 1 if the respondent *i* follows the advice (s)he received and 0 otherwise. Figure 1b presents the average proportion of respondents who choose to follow advice, by type of advice received. Advice adherence is significantly higher when receiving High effort advice (63%) than when receiving Low effort advice (43%) (*p* = 0.000). Figure A4 in the Appendix shows that the higher advice adherence when High effort advice
is received holds when we stratify the sample by the gender of the respondent (male vs. female, Figure A4a) or by caste of the respondent (Disadvantaged vs. General Caste, Figure A4b).

To further investigate the patterns of advice adherence, we consider a multivariate regression framework. We control for individual and household characteristics (the respondent's gender and caste, religion, primary source of income for the household) and mobile phone usage.<fnref n="13" /> Standard errors are clustered at the district level to account for potential intra-district correlation. The regression results presented in column 1 of Table 4 corroborate the results presented in Figure 1b. They indicate that receiving High effort advice increases the likelihood of advice adherence by 19.7 percentage points, corresponding to a 46% increase compared to receiving a Low effort advice.

Columns 2–10 show that the effect of High effort advice remains robust even after including additional controls, beyond the demographic and socio-economic controls listed in Table 1. These additional controls include belief about partner’s choice, trust in the information provided by the advice, risk preferences, cognitive ability based on the Raven’s score, participation in community activities, belief that advice will provide the highest payoff, likelihood that the respondent would have made the same decision without the advice, and likelihood that the respondent would have made the same decision if the person giving advice was from the same caste as the respondent. These variables were included one by one in different specifications, as reported in columns 2–9, and then altogether in column 10. The main result that High effort advice results in a large and statistically significant increase in the likelihood of following advice is unchanged. The coefficient estimate of the High effort advice dummy is remarkably stable.

The results reported in Table 4, together with our finding that the prevailing empirical norm in this setting is to exert High effort, provide support for Hypothesis 2(a). Specifically, advice is significantly more likely to be followed when it is consistent with the dominant social norm.

<footnote n="13">¹³We control for mobile phone usage because we expect that the use of mobile phones can facilitate the dissemination of advice in the real world.</footnote>
<table><caption>Table 4: Choosing to Follow Advice</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>High effort advice received</td><td>0.197***<br>(0.045)</td><td>0.197***<br>(0.045)</td><td>0.197***<br>(0.044)</td><td>0.196***<br>(0.044)</td><td>0.197***<br>(0.045)</td><td>0.196***<br>(0.044)</td><td>0.197***<br>(0.045)</td><td>0.196***<br>(0.045)</td><td>0.197***<br>(0.044)</td><td>0.197***<br>(0.044)</td></tr><tr><td>Constant</td><td>0.450***<br>(0.099)</td><td>0.437***<br>(0.103)</td><td>0.422***<br>(0.097)</td><td>0.434***<br>(0.112)</td><td>0.477***<br>(0.103)</td><td>0.426***<br>(0.114)</td><td>0.476***<br>(0.092)</td><td>0.440***<br>(0.104)</td><td>0.448***<br>(0.099)</td><td>0.439***<br>(0.115)</td></tr><tr><td>Sample Size</td><td>2,759</td><td>2,759</td><td>2,759</td><td>2,759</td><td>2,759</td><td>2,759</td><td>2,759</td><td>2,759</td><td>2,759</td><td>2,759</td></tr><tr><td>R-squared</td><td>0.047</td><td>0.048</td><td>0.049</td><td>0.047</td><td>0.049</td><td>0.048</td><td>0.048</td><td>0.048</td><td>0.048</td><td>0.052</td></tr><tr><td colspan="11">Additional Variables</td></tr><tr><td>Demographic controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Belief about partner's choice</td><td>X</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>✓</td></tr><tr><td>Belief advice will provide highest payoff</td><td>X</td><td>✓</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>✓</td></tr><tr><td>Same decision without advice</td><td>X</td><td>X</td><td>X</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>✓</td></tr><tr><td>Trust Advice</td><td>X</td><td>X</td><td>X</td><td>X</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>X</td><td>✓</td></tr><tr><td>Same decision if advisor different caste</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>✓</td></tr><tr><td>Risk attitudes</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>✓</td><td>X</td><td>X</td><td>✓</td></tr><tr><td>Raven score</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>✓</td><td>X</td><td>✓</td></tr><tr><td>Participate in Community Groups</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>✓</td><td>✓</td></tr></tbody></table>

**Notes:** Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. OLS regression results presented. Sample restricted to advice received sessions. Additional demographic controls include gender and caste of the respondent, religion, primary source of income for the household, and mobile phone use (see Table 1). District-clustered standard errors are presented in parentheses. Significance: *** $p < 0.01$; ** $p < 0.05$; * $p < 0.1$.
### 4.3.2 Does the Advisor's Identity or Status Matter?

**All Respondents:** To investigate the roles of the advisor's identity and status on the decision to follow advice, we introduce the following regression specifications:

$$ \text{Follow advice}_i = \alpha_0 + \alpha_1 \text{ Male advisor} + \alpha_2 \text{ High effort advice} \\ + \alpha_3 \text{ Male advisor} \times \text{ High effort advice} + \zeta \mathbf{Z}_i + \varepsilon_i \quad (1) $$

$$ \text{Follow advice}_i = \beta_0 + \beta_1 \text{ Brahmin advisor} + \beta_2 \text{ High effort advice} \\ + \beta_3 \text{ Brahmin advisor} \times \text{ High effort advice} + \gamma \mathbf{Z}_i + \epsilon_i \quad (2) $$

The dependent variable Follow Advice$_i$ is as defined above. Male advisor is equal to 1 if the respondent received advice from a male advisor (conditions MDH, MDL, MBH or MBL) and 0 otherwise. Correspondingly, Brahmin advisor is equal to 1 if the respondent received advice from a Brahmin advisor (conditions MBH, FBH, MBL or FBL), and 0 otherwise. Finally, High effort advice is equal to 1 if the respondent was assigned to treatments MDH, FDH, MBH or FBH, and 0 otherwise. Z includes a set of individual and household characteristics of the respondent (see Table 1). In our analysis, we restrict the sample to the advice treatments and run separate regressions for male and female advisors (equation (1)) and Brahmin and Dalit advisors (equation (2)).<fnref n="14" /> Standard errors are clustered at the district level.<fnref n="15" />

Table 5 presents the results from the estimates of equations (1) and (2). This analysis allows us to test Hypothesis 1(a). Consistent with this hypothesis, we find that identical advice can lead to varying adherence rates depending on the advisor's characteristics. Respondents are significantly more likely to follow High effort advice when it is provided by a Brahmin advisor. Additionally, they are more likely to adhere to Low effort advice when it comes from a male advisor compared to a female advisor, and from a Dalit advisor compared to a Brahmin advisor.

**Effect of Shared Identity and Status on Adherence to Advice:** We now turn to the results from estimating equations (1) and (2) separately by the gender and caste of

<footnote n="14">¹⁴Regression (1) controls for the caste of the respondent but not the caste of the advisor. Regression (2), on the other hand, controls for the gender of the respondent but not the gender of the advisor. See page 14 for an explanation.</footnote>

<footnote n="15">¹⁵We examine the robustness of our regression results by clustering the standard errors at different levels (household, village, and sub-district). The results are unchanged. See Section 4.4 and Table A4 in the Appendix.</footnote>
<table><caption>Table 5: Adherence to Advice. All Respondents</caption><thead><tr><th></th><th colspan="2">Gender of advisor</th><th colspan="2">Caste of advisor</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Male advisor</td><td>0.048**<br>(0.019)</td><td>0.048**<br>(0.020)</td><td></td><td></td></tr><tr><td>High effort advice received</td><td>0.231***<br>(0.049)</td><td>0.227***<br>(0.049)</td><td>0.126***<br>(0.036)</td><td>0.126***<br>(0.036)</td></tr><tr><td>Male advisor × High effort advice</td><td>-0.072<br>(0.043)</td><td>-0.064<br>(0.044)</td><td></td><td></td></tr><tr><td>Brahmin advisor</td><td></td><td></td><td>-0.056***<br>(0.015)</td><td>-0.058***<br>(0.017)</td></tr><tr><td>Brahmin advisor × High effort advice</td><td></td><td></td><td>0.141***<br>(0.026)</td><td>0.141***<br>(0.027)</td></tr><tr><td>Constant</td><td>0.410***<br>(0.029)</td><td>0.460***<br>(0.071)</td><td>0.462***<br>(0.028)</td><td>0.512***<br>(0.074)</td></tr><tr><td>Additional controls</td><td><strong>x</strong></td><td><strong>✓</strong></td><td><strong>x</strong></td><td><strong>✓</strong></td></tr><tr><td>Observations</td><td>2,803</td><td>2,803</td><td>2,803</td><td>2,803</td></tr><tr><td>R-squared</td><td>0.039</td><td>0.048</td><td>0.043</td><td>0.052</td></tr></tbody></table><p><em>Difference estimates: Additional effect of male advisor</em></p><table><tbody><tr><td>Low effort advice</td><td>0.048**<br>(0.019)</td><td>0.048**<br>(0.020)</td><td></td><td></td></tr><tr><td>High effort advice</td><td>-0.024<br>(0.036)</td><td>-0.016<br>(0.035)</td><td></td><td></td></tr></tbody></table><p><em>Difference estimates: Additional effect of Brahmin advisor</em></p><table><tbody><tr><td>Low effort advice</td><td></td><td></td><td>-0.056***<br>(0.015)</td><td>-0.058***<br>(0.017)</td></tr><tr><td>High effort advice</td><td></td><td></td><td>0.085**<br>(0.026)</td><td>0.083**<br>(0.026)</td></tr></tbody></table>

**Notes:** Dependent variable *Follow Advice* = 1 if respondent follows advice and 0, otherwise. Additional effect of Male advisor is the difference estimate from equation (1) (Male advisor – Female advisor); additional effect of Brahmin advisor is the difference estimate from equation (2) (Brahmin advisor – Dalit advisor). The sample is restricted to advice received sessions. Additional demographic controls include gender and caste of the respondent, religion, primary source of income for the household, and mobile phone use. Standard errors clustered at the district level are presented in parentheses. Significance: ***p < 0.01; **p < 0.05; *p < 0.1.
<table><caption>Table 6: Effect of Identity and Status on Adherence to Advice. Additional Effect of Male Advisor</caption><thead><tr><th></th><th>Low effort advice<br>(1)</th><th>High effort advice<br>(2)</th></tr></thead><tbody><tr><td colspan="3"><strong>Panel A: Full sample effect by gender</strong></td></tr><tr><td>Male respondent</td><td>-0.044<br>(0.045)</td><td>0.001<br>(0.037)</td></tr><tr><td>Female respondent</td><td>0.138***<br>(0.031)</td><td>-0.039<br>(0.047)</td></tr><tr><td colspan="3"><strong>Panel B: Role model effect. Exposure to female MLAs</strong></td></tr><tr><td colspan="3"><strong>Male respondent</strong></td></tr><tr><td>Male MLA constituency</td><td>-0.049<br>(0.046)</td><td>0.014<br>(0.040)</td></tr><tr><td>Female MLA constituency</td><td>0.012<br>(0.402)</td><td>-0.364<br>(0.250)</td></tr><tr><td colspan="3"><strong>Female respondent</strong></td></tr><tr><td>Male MLA constituency</td><td>0.139***<br>(0.036)</td><td>-0.053<br>(0.047)</td></tr><tr><td>Female MLA constituency</td><td>-0.0808<br>(0.206)</td><td>0.404<br>(0.236)</td></tr><tr><td colspan="3"><strong>Panel C: Role model effect. Female head of village council</strong></td></tr><tr><td colspan="3"><strong>Male respondent</strong></td></tr><tr><td>Male Village Head</td><td>-0.290***<br>(0.098)</td><td>0.044<br>(0.095)</td></tr><tr><td>Female Village Head</td><td>0.045<br>(0.059)</td><td>-0.019<br>(0.051)</td></tr><tr><td colspan="3"><strong>Female respondent</strong></td></tr><tr><td>Male Village Head</td><td>0.220***<br>(0.258)</td><td>-0.003<br>(0.454)</td></tr><tr><td>Female Village Head</td><td>0.108**<br>(0.045)</td><td>-0.053<br>(0.054)</td></tr></tbody></table>

Notes: Dependent variable *Follow Advice* = 1 if respondent follows advice and 0, otherwise. OLS regression results presented in Panels A and B. Estimating equation is given by equation (1). In Panel B, Female MLA constituency denotes that the village is in a constituency that has elected at least one female MLA in the last three state assembly elections; Male MLA constituency if not. In Panel C Female Village Head denotes at least one head of the village council following the last three village council elections is a woman; Male Village Head if otherwise. An additional effect of Male advisor is the difference estimate Male advisor – Female advisor. IV regressions presented in Panel C. Female reserved and Female reserved × Female advisor are used as instruments for Female Village Head and Female advisor × Female Village Head, respectively. The sample is restricted to advice received sessions. Additional demographic controls include gender and caste of the respondent, religion, primary source of income for the household, and mobile phone use. Standard errors clustered at the district level are presented in parentheses. Significance: ***p < 0.01; **p < 0.05; *p < 0.1. Figures B1a, B2a and B3a present the graphical versions of the results presented in Panels A, B and C respectively.
the respondent. This allows us to identify the effect of the advisor's identity (shared vs. not) and status (high vs. low) on the likelihood of adhering to advice, depending on the attributes of the advisor and the respondent. Of particular interest are the difference-in-means estimates, which help us distinguish between two mechanisms: status effect and identity effect. We define a status effect as occurring when a respondent is more likely to follow advice from a high-status advisor who does not share their group identity—for example, when a female respondent is more likely to follow advice from a male advisor than from a female advisor, since the male advisor is perceived to hold higher social status. In contrast, we define an identity effect as occurring when a respondent is more likely to follow advice from an advisor who shares their identity—for example, when a female respondent follows advice from a female advisor more than from a male one. We can most clearly distinguish between status and identity effects when the respondent is from a low-status group (e.g., women or disadvantaged castes). Only in these cases can the advisor be either high-status and out-group (cleanly identify the status effect) or low-status and in-group (cleanly identify the identity effect).

Panel A of Tables 6 (for the additional effect of male advisors) and 7 (for the additional effect of Brahmin advisors) present the difference estimates thus computed, by advice type. The full set of regression results is presented in Table A3 in the Appendix.

Panel A of Table 6 shows that the gender of the advisor does not have a statistically significant effect when the advice is High effort and is, therefore, consistent with the empirical norm. In these cases, both male and female respondents are equally likely to follow the advice, regardless of the advisor's gender, consistent with Hypothesis 2(a). In contrast, when the advice is norm-inconsistent (Low effort), female respondents are 13.8 percentage points (or 34.5%) more likely to follow a male advisor than a female advisor ($p = 0.002$).<fnref n="16" /> Male respondents, however, show no differential response based on the advisor's gender. Taken together, these findings suggest the presence of a status effect but no identity effect for female respondents. These results are consistent with Hypothesis 2(b).

Turning to the results on caste, Panel A of Table 7 shows that when they receive High effort advice, both General and Disadvantaged Caste respondents are more likely to follow a Brahmin advisor: the effects are large and statistically significant at 21.8 and 6.15 percentage points (33% and 6.5%) for General Caste and Disadvantaged caste respondents

<footnote n="16">¹⁶On average, likelihood of female respondents adhering to a Low effort advice from a Female advisor is 0.4. So $(0.138/0.4) \times 100 = 34.5\%$.</footnote>
<table><caption>Table 7: Effect of Identity and Status on Adherence to Advice. Additional Effect of Brahmin Advisor</caption><thead><tr><th></th><th>Low effort advice<br>(1)</th><th>High effort advice<br>(2)</th></tr></thead><tbody><tr><td colspan="3"><strong>Panel A: Full sample effect by caste</strong></td></tr><tr><td>General Caste respondent</td><td>-0.052<br>(0.051)</td><td>0.218***<br>(0.038)</td></tr><tr><td>Disadvantaged caste respondent</td><td>-0.063**<br>(0.023)</td><td>0.061*<br>(0.032)</td></tr><tr><td colspan="3"><strong>Panel B: Role model effect. Exposure to SC/ST MLAs</strong></td></tr><tr><td colspan="3"><strong>General Caste respondent</strong></td></tr><tr><td>Non-reserved constituency</td><td>-0.0543<br>(0.070)</td><td>0.210***<br>(0.051)</td></tr><tr><td>Reserved constituency</td><td>1.305***<br>(0.288)</td><td>0.276<br>(0.174)</td></tr><tr><td colspan="3"><strong>Disadvantaged caste respondent</strong></td></tr><tr><td>Non-reserved constituency</td><td>-0.062**<br>(0.023)</td><td>0.062*<br>(0.033)</td></tr><tr><td>Reserved constituency</td><td>-0.202*<br>(0.0974)</td><td>0.022<br>(0.145)</td></tr><tr><td colspan="3"><strong>Panel C: Role model effect. Head of village council belongs to Disadvantaged caste</strong></td></tr><tr><td colspan="3"><strong>General Caste respondent</strong></td></tr><tr><td>General Caste Village Head</td><td>0.272<br>(0.258)</td><td>-0.118<br>(0.454)</td></tr><tr><td>Disadvantaged Caste Village Head</td><td>-0.136<br>(0.114)</td><td>0.279**<br>(0.130)</td></tr><tr><td colspan="3"><strong>Disadvantaged caste respondent</strong></td></tr><tr><td>General Caste Village Head</td><td>-0.456<br>(0.388)</td><td>0.208<br>(0.271)</td></tr><tr><td>Disadvantaged Caste Village Head</td><td>-0.030<br>(0.024)</td><td>0.024<br>(0.052)</td></tr></tbody></table>

Notes: Dependent variable *Follow advice* = 1 if respondent follows advice and 0, otherwise. OLS regression results presented in Panels A and B. Estimating equation is given by equation (2). In Panel B, *Reserved constituency* denotes the village is in a reserved constituency (at the state assembly level); *Non-reserved constituency* if in an open constituency. In Panel C, *Disadvantaged caste village head* denotes at least one head of the village council following the last three village council elections belongs to a Disadvantaged Caste; General caste village head if otherwise. Additional effect of Brahmin advisor is the difference estimate Brahmin advisor – Dalit advisor. IV regressions presented in Panel C: Caste reserved and Caste reserved × Dalit advisor are used as instruments for Reserved village and Dalit advisor × Reserved village respectively. The sample is restricted to advice received sessions. Additional demographic controls include gender and caste of the respondent, religion, primary source of income for the household, and mobile phone use. Standard errors clustered at the district level are presented in parentheses. Significance: ***p < 0.01;**p < 0.05;*p < 0.1. Figures B1b, B2b and B3b present the graphical versions of the results presented in Panels A, B and C respectively.
$(p = 0.000$ and $p = 0.091)$, respectively. This result is indicative of a marginally significant status effect for the Disadvantaged caste respondents and a strong identity effect for the General Caste respondents.<fnref n="17" />

On the other hand, when the advice received is Low effort, we find that the Disadvantaged
caste respondents are significantly less likely to follow the advice of a Brahmin advisor
relative to that of a Dalit advisor ($p = 0.029$). This is indicative of an identity effect for
the Disadvantaged caste respondents. We therefore find that identity matters more in the
context of caste than in the context of gender.

To summarize the analysis in this section, our results are consistent with Hypothesis 2. *First*, as Table 4 shows, respondents are significantly more likely, by almost 20 percentage points, to adhere to High effort advice. This suggests that advice aligned with the dominant social norm is more likely to be followed, in line with Hypothesis 2(a). *Second*, Table 6, Panel A, indicates that female respondents are significantly more likely to adhere to Low effort advice, despite its inconsistency with the prevailing community norm, when it is delivered by a male (high status) advisor. The response is suggestive of a *status effect*. In contrast, Panel A of Table 7 shows that Disadvantaged caste respondents are significantly more likely to follow Low effort advice when the advisor shares their caste identity, consistent with an *identity effect*. These findings support Hypothesis 2(b). *Finally*, the results presented in Panel A of Tables 6 and 7 also align with Hypothesis 1(b): advisees are more likely to adhere to advice from advisors of higher social status and from those with whom they share a social identity.

## 4.4 Robustness and Heterogeneity

We conducted several robustness tests that support our previous analyses.

**Alternative Clustering:** In the main regressions, the standard errors were clustered at the district level. In Table A4 in the Appendix, we present the results with clustering at alternative levels (sub-district, village, household and district, using wild bootstrap p-values to account for the small number of districts). The results are largely unaffected as mentioned briefly in footnote 15.

<footnote n="17">¹⁷The finding that General Caste respondents are 33% more likely to follow a Brahmin advisor could potentially be driven by both identity and status effects. However, the identity effect appears stronger in this case, since General Caste respondents include Brahmins, and a shared caste is more likely to trigger identity rather than status considerations.</footnote>
**District Fixed Effects:** To account for the possibility of district-level observed and unobserved differences, we re-ran the previous analysis using district fixed effects. These regression results are presented in Table A5 in the Appendix. The regression results with district fixed effects are quantitatively similar to the baseline regression results (Tables 6 and 7).

**Including Additional Controls:** Our results in Tables 6 and 7 are also robust to including a large set of controls. These controls are the same as in the regression reported in column 10, Table 4, and include cognitive ability, beliefs about the behaviour of one's partner, risk preferences, and whether the respondent participates in community groups (see Table A6).

**Women's Empowerment Effects:** Recall that our sample of households consists of some JEEViKA farmers. The JEEViKA program aims to socially and economically empower rural women. This allows us to explore whether women who are JEEViKA members react differently to advice from high-status (male) advisors and from in-group advisors. To examine this, Table A7 in the Appendix presents the regression results for Equation (1), estimated separately for women who are not JEEViKA members (column 1) and those who are (column 2). Women who are not JEEViKA members exhibit a strong status effect: they are significantly more likely to follow the Low effort advice from a male advisor relative to a female advisor. There is no evidence of such a pattern of behaviour for women who are JEEViKA members. This suggests that participation in the JEEViKA program influences advice adherence, although it is not possible to make causal claims since assignment to JEEViKA is not random.

# 5 Role Models and Adherence to Advice

This section examines whether respondents' willingness to follow advice from female or Dalit advisors is influenced by role models, as proxied by prior exposure to female or disadvantaged caste leaders. This analysis isolates the effect of status, holding group identity constant, allowing us to assess whether elevating the status of women or Dalits affects the likelihood of the respondent adhering to advice based on the advisor's gender or caste.
## 5.1 Measures of Exposure to Role Models

To that purpose, we defined exposure to female and disadvantaged caste leaders using two distinct approaches. The first considers political leadership at the state level, specifically Members of the Legislative Assembly (MLAs) who represent respondents in the state legislature. The second focuses on local leadership, measured by elected officials in village councils (Gram Panchayats). These two definitions allow us to capture broader political representation and exposure to more admired role models vs. more immediate, community-level role models, in line with our theoretical framework.

**Exposure to Female / Disadvantaged caste MLAs:** We operationalized the first approach in the following way. Using administrative data made available by the Election Commission of India (see Section 2.5), we matched each village in our sample to its corresponding State Legislative Assembly constituency (or electoral district). To capture exposure to female leaders, we used the election data to construct a binary indicator variable *Female MLA constituency* = 1 if at least one elected member of the Legislative Assembly (MLA) from that constituency during the period 2010–2020 (last 3 elections, in 2010, 2015 and 2020) was a woman; 0 otherwise (we refer to such a constituency as a *Male MLA constituency*). 214 villages out of our 220 sample villages have not been exposed to a female MLA. Specifically, 112 respondents in our sample had been exposed to a female MLA.

To capture exposure to disadvantaged caste leaders, we constructed a binary indicator *Reserved constituency* = 1 if the village falls within a reserved constituency, i.e., one reserved for candidates from the disadvantaged castes. The reservation status of constituencies has remained unchanged since 2008. The village falls in a *Non-reserved constituency* if *Reserved constituency* = 0.

There are no corresponding institutional reservations for women at the state level. Exposure to disadvantaged caste leaders can be considered to be plausibly exogenous, while exposure to female leaders may be endogenous. For instance, constituencies with more pro-female attitudes among voters may be more likely to elect a female MLA. However, it could be argued that any endogeneity concerns are largely mitigated by the fact that our respondents represent only a tiny fraction of the electorate: the average constituency has 305,152 registered voters, while our sample of 3106 respondents spans 18 such constituencies. It is therefore unlikely that our respondents had any meaningful influence on electoral outcomes and that they would have moved residence based on the gender or caste of the
political leader.

**Exposure to Female / Disadvantaged Caste Village Head:** Our second definition of leaders focuses on the local level, specifically the village chief. Under India's reservation policy, the position of the head of the village council is randomly reserved by gender and caste before each village council election (see Chattopadhyay and Duflo, 2004, for details). This policy introduces exogenous variation in exposure to female and disadvantaged caste leadership, allowing us to assess whether such exposure to role models at the local level influences advice adherence.

In the additional survey (see Section 2.6), we collected data on the gender and caste of the elected head in the last three village council elections (2011, 2016, and 2021), as well as the reservation status of the position in each election. Using this data we define a *Female Headed Village* = 1 if at least one village council head following the last 3 village council elections is a woman. A *Male Headed Village* = 1 if otherwise. Similarly a *Disadvantaged Caste Headed Village* is one where at least one village council head following the last 3 village council elections belonged to a disadvantaged caste.<fnref n="18" />

## 5.2 Results: State Level Role Models

We begin with the first definition of role models (*i.e.*, the state level) and start by examining the effects of female role models.

Panel B of Table 6 presents the difference estimates, *i.e.*, the additional likelihood of following advice from a male vs. a female advisor. Results are stratified by respondent gender (male or female), advice type (Low effort or High effort), and exposure to female leaders (none or at least one elected female MLA during the period 2010–2020).<fnref n="19" />

Male respondents exhibit no differential adherence based on the advisor's gender, regardless of the advice type or exposure. In contrast, female respondents display a markedly different pattern. Recall from Panel A of Table 6 that women are nearly 14 percentage points more likely to follow Low effort advice from male advisors – advice that contradicts the empirical norm. Panel B reveals that this effect is driven entirely by women who have not been exposed to female leaders. In this group, the absence of female role models is linked to greater adherence to Low effort advice from men. This status effect disappears among

<footnote n="18">¹⁸In additional regressions we also consider the effect of intensity of exposure to female / disadvantaged caste village head defined by the number of female / disadvantaged caste village heads in the last 3 elections.</footnote>

<footnote n="19">¹⁹The full set of regression results are presented in Table A8 in the Appendix.</footnote>
women exposed to female leaders, who are equally likely to follow Low effort advice from either gender. Thus, holding group identity constant, increasing the perceived status of women through exposure to female leaders counters the status-driven tendency to follow Low effort advice from male advisors, consistent with Hypothesis 3.

Panel B of Table 7 presents parallel estimates for caste. We assess the additional likelihood of following advice from a Brahmin vs. a Dalit advisor, stratified by respondent caste (General or Disadvantaged Caste), advice type (Low effort or High effort), and exposure to Dalit leaders (village is in a reserved or open constituency).<fnref n="20" />

When advice aligns with the prevailing community norm (High effort), a status effect among Disadvantaged Caste respondents emerges in non-reserved constituencies: they are 6.25 percentage points more likely to follow the advice when it comes from a Brahmin advisor (only marginally significant at the 10% level). In contrast, among General Caste respondents, a stronger identity effect is observed: they are 21 percentage points more likely to follow High effort advice from a Brahmin advisor. However, in reserved constituencies, neither group exhibits status or identity effects. This aligns with Hypothesis 3, suggesting that exposure to role models mitigates bias when advice aligns with community norms.

In open (non-reserved) constituencies, where respondents are likely to have limited or no exposure to Dalit leaders, Disadvantaged caste respondents are 6.18 percentage points more likely to follow the Low effort advice of a Dalit advisor. This is suggestive of an identity effect, which strengthens to 20.2 percentage points in reserved constituencies. Among General Caste respondents, a similar intensification emerges: in reserved constituencies, they are significantly more likely to follow Low effort advice from Brahmin advisors. Thus, identity effects are amplified in reserved constituencies, with both caste groups favouring Low effort advice from in-group advisors, suggesting increased polarization under caste-based political reservations.<fnref n="21" />

This is consistent with evidence that over the years, Dalit identity has become increasingly visible, assertive, and politicized in both social and political spheres in India. This transformation has been marked by the rise of Dalit political power, cultural expression, and media presence (Gorringe, 2013). However, this growing assertion has also triggered

<footnote n="20">²⁰The full set of regression results are presented in Table A9 in the Appendix.</footnote>

<footnote n="21">²¹This pattern aligns with the notion of affective polarization and group antagonism (Iyengar et al., 2012, Lena et al., 2025), where emotional attachment to one's own group and hostility toward the opposing one (rather than ideological differences), create an “us vs. them” mentality that could hinder communication and understanding.</footnote>
backlash from other castes, manifested in anti-reservation rhetoric, covert forms of exclusion, and the strengthening of identity-based resistance (Mendelsohn and Vicziany, 1998; Abraham and Janarthanan, 2024). This polarization has been further intensified by the phenomenon of *jobless growth* that has characterized the Indian economy over the last two decades (see Ghose and Kumar, 2021), which has increased competition for limited employment opportunities. In this context, caste identity has strengthened, and conflicts, particularly surrounding reservations, have become more pronounced.<fnref n="22" />

## 5.3 Results: Village Level Role Models

Next, we investigate the role model effect using our second definition (i.e., at the village level). While only a woman (individual belonging to a Disadvantaged caste) can serve as the village head in a gender (caste) reserved village, such individuals can, in principle, also be elected in an unreserved village. We therefore employ an instrumental variable (IV) approach, using the randomly assigned gender or caste reservation status of the head position as an instrument for the gender/caste of the head.<fnref n="23" /> Panel C of Table 6 presents the additional likelihood of following the advice of a male advisor relative to a female advisor, by type of advice received (Low/High effort), gender of the respondent, and the gender of the Village Head.

In villages with male village heads, female respondents continue to exhibit a significant *status* effect, as they are more likely to follow Low effort advice when it comes from male advisors compared to female advisors (Panel C of Table 6). This effect is, however, substantially weakened, by more than 50% (from 0.220 to 0.108), when respondents have been exposed to a village female role model (at least one head of the village council following the last three village council elections is a woman). These results are consistent with Hypothesis 3.<fnref n="24" /> Unexpectedly, male respondents are significantly more likely to follow the advice of a female advisor in a male-headed village.

<footnote n="22">²²The strengthening of caste identities and polarization we observe also echoes Dixit and Nair (2024) who show that the *Dalit Bandhu* (Friend of the Dalit) program in India, which provides Dalit families with substantial financial support to address historical injustices, improved Dalit asset ownership and confidence in engaging dominant castes but left segregation norms intact and reduced support among dominant castes for affirmative action and subsidies, indicating a policy backlash.</footnote>

<footnote n="23">²³The regression results are presented in Table A10 in the Appendix.</footnote>

<footnote n="24">²⁴Figure A7a in the Appendix presents the OLS regression results for the additional likelihood of following the advice of a male advisor relative to a female advisor, by type of advice received (Low/High effort), gender of the respondent, and the number of female village heads following the last 3 village council elections. Female respondents are significantly more likely to follow a Low effort advice from a male advisor (vs female advisor) when they have had limited exposure to female village council heads (0 or 1 female village head). This *status* effect is however eliminated with increased exposure to female village heads.</footnote>
Turning to the results on following the advice from Brahmin *vs*. Dalit advisors (Panel C of Table 7), we find that among General caste respondents receiving High effort advice, the *identity* effect is amplified in Disadvantaged caste headed villages (at least one head of the village council following the last three village council elections belongs to a disadvantaged caste): they are significantly more likely to follow advice from a Brahmin advisor in these villages, whereas this pattern is not observed in General caste headed villages.<fnref n="25" />

To summarize, using both role model approaches, our results on advice and gender role models are consistent with Hypothesis 3: holding the effect of group identity constant, elevating the status of females reduces the influence of gender on the likelihood of following the norm-inconsistent, Low effort advice. The effect is more pronounced if the role model is more prominent. In the context of advice and caste role models, the results differ by the prominence of the role model. We found evidence of increased *polarization* in reserved constituencies when the advice is norm-inconsistent. This pattern is not observed when role models are less prominent. Overall, there is less support for Hypothesis 3 in the context of caste.

# 6 A Randomized Control Trial as a Test of External Validity

In this section, we examine the external validity of our survey experiment's key measure (following advice). Farmers in our sample participated in an agricultural advisory program conducted by the International Rice Research Institute. To understand whether those who follow advice in our survey experiment are more likely to follow agricultural advice in their occupation, we asked two questions to all survey participants. *First*, we asked if they adopted the agricultural advice. We created a dummy variable equal to one if they adopted the advice and zero otherwise. *Second*, we asked whether they registered for further advice in subsequent seasons. Again, a dummy variable was created for those who registered. We posit that individuals who adhered to the advice or registered for further advice serve as a reliable proxy for real-life adherence. Subsequently, we examined the

<footnote n="25">²⁵The regression results are presented in Table A11 in the Appendix. Figure A7b in the Appendix presents the OLS regression results for the additional likelihood of following the advice of a Brahmin advisor relative to a Dalit advisor, by type of advice received (Low/High effort), caste of the respondent, and the number of disadvantaged village heads following the last 3 village council elections. There is no systematic patterns for either general caste or disadvantaged caste respondents.</footnote>
<table><caption>Table 8: External Validity. Following Advice in the Survey Experiment and Adoption of Advice in the RCT</caption><thead><tr><th></th><th>Full Sample</th><th>Full Sample</th><th>Low effort advice</th><th>High effort advice</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A: Adopt advice in RCT</strong></td></tr><tr><td>Follow advice (survey experiment)</td><td>0.130***<br>(0.024)</td><td>0.048**<br>(0.015)</td><td>0.031**<br>(0.012)</td><td>-0.007<br>(0.036)</td></tr><tr><td>Constant</td><td>0.184***<br>(0.035)</td><td>0.052***<br>(0.015)</td><td>0.332***<br>(0.075)</td><td>0.360***<br>(0.056)</td></tr><tr><td>Additional Controls</td><td><strong>X</strong></td><td><strong>✓</strong></td><td><strong>✓</strong></td><td><strong>✓</strong></td></tr><tr><td>Observations</td><td>3,724</td><td>3,724</td><td>1,381</td><td>1,422</td></tr><tr><td>R-squared</td><td>0.022</td><td>0.094</td><td>0.024</td><td>0.017</td></tr><tr><td colspan="5"><strong>Panel B: Registered for further advice</strong></td></tr><tr><td>Follow advice (survey experiment)</td><td>0.171***<br>(0.036)</td><td>0.072***<br>(0.021)</td><td>0.062<br>(0.043)</td><td>-0.010<br>(0.031)</td></tr><tr><td>Constant</td><td>0.221***<br>(0.043)</td><td>0.064**<br>(0.021)</td><td>0.413***<br>(0.080)</td><td>0.456***<br>(0.081)</td></tr><tr><td>Additional Controls</td><td><strong>X</strong></td><td><strong>✓</strong></td><td><strong>✓</strong></td><td><strong>✓</strong></td></tr><tr><td>Observations</td><td>3,724</td><td>3,724</td><td>1,381</td><td>1,422</td></tr><tr><td>R-squared</td><td>0.034</td><td>0.108</td><td>0.016</td><td>0.015</td></tr></tbody></table>

Notes: OLS regressions presented. In Panel A, the dependent variable is whether the farmer adopted agricultural advice in the RCT. In Panel B, the dependent variable is whether the farmer registered for further advice in the RCT. Standard errors clustered at the district level are presented in parentheses. Significance: ***p < 0.01,**p < 0.05,*p < 0.1.

correlation between these variables and the same individuals' compliance with the advice in our survey experiment.

Results are reported in Table 8. In Panel A, the dependent variable is whether the farmer who was treated in this RCT adopted agricultural advice. In Panel B, the dependent variable is whether the farmer registered for further advice in subsequent seasons. In column 3 (in Panels A and B), we restricted the sample to those that received High effort advice in the survey experiment, and in column 4 (Panels A and B), to those that received Low effort advice in this experiment.

The regression analysis provides strong evidence that the farmers who follow the advice in our survey experiment are also more likely to both adopt agricultural advice and register for receiving further advice in the RCT. This relationship seems to be strongest for those who received High effort advice in the survey experiment. While this is correlational evidence, it is suggestive that the survey measure has external validity: those who follow the advice in
our survey experimental context are also more likely to follow advice in more consequential
settings.

7 Giving Advice

We now turn to the question of intergenerational transmission of advice to future players: what advice do these respondents give? As part of the survey experiment, after receiving advice and making a decision, participants were asked to advise others. Specifically, they were told that someone in another village would also participate in this survey and answer these questions. They were asked to respond to the following question: *What advice would you give them: Do you advise them to select high or low effort?* The profile of who would receive the advice was varied randomly. Participants may have to advise a male or a female and either a Disadvantaged Caste or a General Caste individual. Participants were exposed to only one condition. We examine whether the advice given varies by the gender/caste of the advised, focusing on (i) whether the respondent's identity influences the advice they give and (ii) whether their advice is influenced by the advice they receive or the actions they undertake.

Figure A5 in the Appendix examines whether advice received and effort choices made in Stage 1 affect advice given in Stage 2. Figure A5a displays the proportion of respondents who suggest choosing High effort by the advice they received in Stage 1 (Low effort, High effort, or No advice). The proportion recommending High effort choice to future players is not different by the advice received (the difference p-values are never statistically significant). Figure A5b presents the proportion of respondents who recommend a High effort choice by the choice they made in Stage 1. Respondents’ advice is consistent with their actions: those who chose High effort in Stage 1 are significantly more likely to recommend a High effort choice compared to respondents who chose Low effort in Stage 1 (0.71 vs. 0.45, difference *p* = 0.000).

Finally, Figure A6 in the Appendix presents the proportion of respondents who recommend
High effort choice disaggregated by gender (Figure A6a) and caste (Figure A6b) of both
the advisee and the respondent. While there is some evidence that male respondents are
more likely to leave High effort advice to men, this is only significant at the 10% level. No
difference reaches standard levels of significance.
# 8 Conclusion

Many of the decisions we make are significantly shaped by advice from others. Despite this, limited research has explored whose advice we are more inclined to follow and how this influences social outcomes in a field setting. In this paper, we investigate how the identity and the status of the advisor influence decision-making, using a survey experiment with around 3000 farmers in India. Our study provides new insights into the ways in which the advisor's advice, identity, and social status shape behaviour.

Information frictions and knowledge constraints often hamper economic development. Information provision in the form of advice can help mitigate these problems. Who provides advice and what kind of advice is provided are both crucial factors that can help explain why some information or advice interventions fail to be effective. We find that providing cooperative advice can have a crowding-out effect, underscoring the importance of message content. The decision to cooperate when individuals do not receive any advice may result from a self-image concern, a psychological effect that may weaken once cooperation follows external advice to cooperate. Moreover, advice that is incongruent with a prevailing cooperative social norm has a particularly detrimental effect on cooperation, even if such a message is less likely to be followed than a norm-congruent message.

Who the messenger is matters as well, even when their characteristics are unrelated to the quality of the message. Similar advice yields different adherence rates, depending on the advisor's status and group identity. Moreover, there are interconnections between the content of the message and the characteristics of the messenger. For example, adherence rates are higher when the advisor is of a higher status or the advisor shares the same identity with the respondent, even if the message contradicts the prevailing social norm.

Our results also show that role models can influence the importance attached to criteria that are irrelevant to the content of the message, such as advisor characteristics in terms of their identity and status. We expected that policies promoting equal access to leadership across gender or other observable characteristics like caste could have the additional effect of reducing status biases in following advice that might be detrimental for social welfare. In our study, we found that exposure to female role models reduced women's tendency to follow non-cooperative advice from male advisors, without affecting their adherence to norm-consistent advice—an indirect but often overlooked benefit of such policies. The benefits of such policies when applied to caste are, however, much less clear, as we found
evidence of polarization on the basis of identity. Thus, the political consequences of role model effects warrant a more nuanced interpretation.

Our study also sheds light on potential negative consequences of advice adherence that might be particularly problematic when poorly informed advisors influence decisions. These findings suggest that interventions aimed at improving decision-making should not only focus on providing better information but also consider the social dynamics and identity-based biases that influence advice reception. Beyond this, our research highlights some of the concerns relating to the spread of misinformation and the role of social media. This is a particularly acute problem in countries like India, where the spread of unverified claims through WhatsApp messaging groups (called the “WhatsApp University”, e.g., Arun (2019)) is increasingly common. These social media groups often share misleading information on topics like health, politics, and current events, which then gets widely circulated and believed by group members. In cases where misinformation can be socially inefficient, individuals need to think carefully about whose advice to follow. These concerns are not limited to the context of developing countries. For example, many public health messages from medical experts are disregarded by their intended audience who would rather follow the advice of peers and influencers on social media despite their lack of expertise, which can be extremely harmful (e.g., Engel et al., 2024, Denniss and Lindberg, 2025). This suggests that policymakers have an important role to play in raising awareness, choosing the right messengers for communicating advice, educating individuals on how to evaluate advice and helping them detect misinformation, particularly when it comes from individuals of a higher status or from within the individuals’ in-group.

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# Appendix

## A1 The Agro Advisory Experiment

To test the generalizability of our experimental findings, we also used data from a random-
ized controlled trial about agricultural advice to farmers, conducted by the International
Rice Research Institute. Indeed, our sample of experimental subjects was part of a large
RCT that examined the impact of different forms of agro-advisory information on the be-
havior of smallholder male and female farmers. This RCT provided agricultural advice to
female farmers via SMS and through demonstration plots in their village. Indeed, female
farmers frequently encounter exclusion from conventional agricultural extension services,
largely due to entrenched social norms and gender disparities in land access. This advi-
sory program took place at the start of the agricultural season. The household survey and
survey-experiment were conducted at the end of the season, immediately following harvest.

The experiment involved a random assignment of male and female farmers to the following control and treatments. In the Control, farmers did not receive agricultural advice. In the SMS/Voice Advice treatment, the randomly selected farmers received agricultural advice via SMS and pre-recorded voice calls. The Targeted SMS/Voice treatment was identical to the previous one, except that information was tailored to farmers in the local area. The Demo Plot treatment was the same as the SMS/Voice Advice treatment, except that farmers also participated in a demo plot, meaning that they also observed other demonstration farmers in their area. Finally, the Targeted Demo Plot treatment was the combination of the two previous treatments.

Treatment farmers received generic advice on four specific areas: timely sowing and variety, irrigation, weed management, and balanced fertilizer application. The information was generic in the sense that it was not directly tailored to the individual farm but to the standard farmer in the state. In the demo plot treatments, local farmers were selected to represent typical farms in the area. Over the crop cycle, four visits were made to the demo plot, during which within-village discussions were held with the broader village sample. These visits allowed the team to monitor progress, offer guidance, and document the practices adopted by the demo farmers. Before harvest, a seminar was held with farmers and local stakeholders.
A2 Additional Tables and Figures

<table><caption>Table A1: Profile of Advisor</caption><thead><tr><th></th><th>Profile 1</th><th>Profile 2</th><th>Profile 3</th><th>Profile 4</th><th>Profile 5</th><th>Profile 6</th><th>Profile 7</th><th>Profile 8</th><th>Profile 9</th></tr></thead><tbody><tr><td>Height<br>ऊंचाई</td><td>Normal height<br>सामान्य<br>ऊंचाई</td><td>Normal height<br>सामान्य<br>ऊंचाई</td><td>Normal height<br>सामान्य<br>ऊंचाई</td><td>Normal height<br>सामान्य<br>ऊंचाई</td><td>Normal height<br>सामान्य<br>ऊंचाई</td><td>Normal height<br>सामान्य<br>ऊंचाई</td><td>Normal height<br>सामान्य<br>ऊंचाई</td><td>Normal height<br>सामान्य<br>ऊंचाई जी</td><td>No Advice</td></tr><tr><td>Gender<br>लिंग</td><td>Male<br>पुरुष</td><td>Female<br>महिला</td><td>Male<br>पुरुष</td><td>Female<br>महिला</td><td>Male<br>पुरुष</td><td>Female<br>महिला</td><td>Male<br>पुरुष</td><td>Female<br>महिला</td><td></td></tr><tr><td>Hair colour<br>बालों का रंग</td><td>Black<br>काली</td><td>Black<br>काली</td><td>Black<br>काली</td><td>Black<br>काली</td><td>Black<br>काली</td><td>Black<br>काली</td><td>Black<br>काली</td><td>Black<br>काली</td><td></td></tr><tr><td>Caste<br>जाति</td><td>Dalit<br>दलितों</td><td>Dalit<br>दलितों</td><td>Dalit<br>दलितों</td><td>Dalit<br>दलितों</td><td>Brahmin<br>ब्राह्मण</td><td>Brahmin<br>ब्राह्मण</td><td>Brahmin<br>ब्राह्मण</td><td>Brahmin<br>ब्राह्मण</td><td></td></tr><tr><td>Advice<br>सलाह</td><td>High Effort<br>उच्च प्रयास</td><td>High Effort<br>उच्च प्रयास</td><td>Low Effort<br>कम प्रयास</td><td>Low Effort<br>कम प्रयास</td><td>High Effort<br>उच्च प्रयास</td><td>High Effort<br>उच्च प्रयास</td><td>Low Effort<br>कम प्रयास</td><td>Low Effort<br>कम प्रयास</td><td></td></tr></tbody></table>

Notes: Five characteristics of advisors provided (in profiles 1-8). Three were randomly varied across participants; the other two were kept constant. Profile 9 corresponds to the *No Advice* Treatment.
<figure><img src="image_4.png" /><figcaption>Figure A1: Location and Survey Districts</figcaption></figure>

Notes: The darker shaded state in Figure A1 shows the location of Bihar; darker shaded districts in Figure A1b denote the survey districts. In Panel Figure A1b, the blue dot denotes the state capital, Patna.
<caption>Figure A2: High Effort Choice: by Gender and Caste</caption>

<figure><img src="image_5.png" /><figcaption>(a) High Effort Choice. Advice and Gen- (b) High Effort Choice. Advice and Caste
der of Respondent of Respondent</figcaption></figure>

Notes: The bars denote the average proportion of survey respondents by gender (Figure A2a) and caste (Figure A2b) who choose High effort, by type of advice received.

<figure><img src="image_6.png" /><figcaption>Figure A3: High Effort Choice and Follow Advice: Disaggregated Treatments</figcaption></figure>

Notes: Figure A3a presents the proportion choosing High effort by treatment. Figure A3b presents the proportion that follow advice by treatment. In Figure A3b, the sample excludes data from the No Advice treatment.
<figure><img src="image_7.png" /><figcaption>Figure A4: Advice Adherence by Gender and Caste</figcaption></figure>

**Notes:** The bars denote the average proportion of survey respondents — by gender (Figure A4a) and caste (Figure A4b) — who follow advice, by type of advice received.
<table><caption>Table A2: Sample Characteristics, by Treatment and Conditions</caption><thead><tr><th rowspan="3"></th><th colspan="10">Conditions of the Advice treatment</th><th rowspan="3">KW<br>(p-value)</th></tr><tr><th>MDH</th><th>FDH</th><th>MDL</th><th>FDL</th><th>MBH</th><th>FBH</th><th>MBL</th><th>FBL</th><th>No Advice</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>Female</td><td>0.4626<br>(0.4993)</td><td>0.5028<br>(0.5007)</td><td>0.5439<br>(0.4988)</td><td>0.4638<br>(0.4994)</td><td>0.4675<br>(0.4997)</td><td>0.5513<br>(0.4981)</td><td>0.4919<br>(0.5006)</td><td>0.5129<br>(0.5006)</td><td>0.5072<br>(0.5007)</td><td>0.3506</td></tr><tr><td>General Caste</td><td>0.2960<br>(0.4571)</td><td>0.2869<br>(0.4530)</td><td>0.2807<br>(0.4500)</td><td>0.2708<br>(0.4450)</td><td>0.2724<br>(0.4459)</td><td>0.2727<br>(0.4460)</td><td>0.2649<br>(0.4419)</td><td>0.2419<br>(0.4289)</td><td>0.2738<br>(0.4465)</td><td>0.9868</td></tr><tr><td>Disadvantaged Caste</td><td>0.7040<br>(0.4571)</td><td>0.7131<br>(0.4530)</td><td>0.7193<br>(0.4500)</td><td>0.7292<br>(0.4450)</td><td>0.7276<br>(0.4459)</td><td>0.7273<br>(0.4460)</td><td>0.7351<br>(0.4419)</td><td>0.7581<br>(0.4289)</td><td>0.7262<br>(0.4465)</td><td>0.9868</td></tr><tr><td>Hindu</td><td>0.9828<br>(0.1304)</td><td>0.9801<br>(0.1398)</td><td>0.9444<br>(0.2294)</td><td>0.9786<br>(0.1451)</td><td>0.9814<br>(0.1352)</td><td>0.9677<br>(0.1769)</td><td>0.9622<br>(0.1911)</td><td>0.9613<br>(0.1932)</td><td>0.9741<br>(0.1592)</td><td>0.9954</td></tr><tr><td>Mobile Phone Use</td><td>0.6638<br>(0.4731)</td><td>0.6136<br>(0.4876)</td><td>0.6140<br>(0.4875)</td><td>0.6810<br>(0.4667)</td><td>0.6471<br>(0.4786)</td><td>0.5836<br>(0.4937)</td><td>0.6459<br>(0.4789)</td><td>0.6032<br>(0.4900)</td><td>0.6138<br>(0.4876)</td><td>0.4004</td></tr><tr><td colspan="11"><em>Primary Source of Income for the Household</em></td></tr><tr><td>Self Employment / Business</td><td>0.1552<br>(0.3626)</td><td>0.1534<br>(0.3609)</td><td>0.1374<br>(0.3448)</td><td>0.1287<br>(0.3353)</td><td>0.1269<br>(0.3334)</td><td>0.1026<br>(0.3039)</td><td>0.1486<br>(0.3562)</td><td>0.1419<br>(0.3495)</td><td>0.1556<br>(0.3630)</td><td>0.9615</td></tr><tr><td>Salaried Job</td><td>0.1236<br>(0.3296)</td><td>0.0909<br>(0.2879)</td><td>0.1140<br>(0.3183)</td><td>0.1019<br>(0.3029)</td><td>0.0867<br>(0.2818)</td><td>0.1026<br>(0.3039)</td><td>0.0973<br>(0.2968)</td><td>0.0742<br>(0.2625)</td><td>0.0807<br>(0.2728)</td><td>0.9812</td></tr><tr><td>Agriculture</td><td>0.4914<br>(0.5006)</td><td>0.4602<br>(0.4991)</td><td>0.4766<br>(0.5002)</td><td>0.5121<br>(0.5005)</td><td>0.5325<br>(0.4997)</td><td>0.5132<br>(0.5006)</td><td>0.4757<br>(0.5001)</td><td>0.5129<br>(0.5006)</td><td>0.4870<br>(0.5006)</td><td>0.8152</td></tr><tr><td>Non Agricultural Labour</td><td>0.1552<br>(0.3626)</td><td>0.1790<br>(0.3839)</td><td>0.1667<br>(0.3732)</td><td>0.1448<br>(0.3523)</td><td>0.1486<br>(0.3563)</td><td>0.1584<br>(0.3656)</td><td>0.1730<br>(0.3787)</td><td>0.1613<br>(0.3684)</td><td>0.1729<br>(0.3787)</td><td>0.9971</td></tr><tr><td>Other Occupation</td><td>0.0747<br>(0.2633)</td><td>0.1165<br>(0.3213)</td><td>0.1053<br>(0.3073)</td><td>0.1126<br>(0.3165)</td><td>0.1053<br>(0.3074)</td><td>0.1232<br>(0.3291)</td><td>0.1054<br>(0.3075)</td><td>0.1097<br>(0.3130)</td><td>0.1037<br>(0.3054)</td><td>0.9926</td></tr><tr><td>Sample Size</td><td>348</td><td>352</td><td>342</td><td>373</td><td>323</td><td>341</td><td>370</td><td>310</td><td>347</td><td></td></tr></tbody></table>

Notes: The conditions are defined as in Table 3. Column 10 presents the p-values from a Kruskal-Wallis test. Standard Deviations in parenthesis.
<table><caption>Table A3: Following Advice by Type of Advice Received</caption><thead><tr><th rowspan="3"></th><th colspan="4">Gender of the respondent</th><th colspan="4">Caste of the respondent</th></tr><tr><th colspan="4">Male</th><th colspan="4">Female</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>High effort advice received</td><td>0.188**<br>(0.069)</td><td>0.185**<br>(0.069)</td><td>0.270***<br>(0.078)</td><td>0.269***<br>(0.076)</td><td>0.144***<br>(0.035)</td><td>0.145***<br>(0.034)</td><td>0.014<br>(0.127)</td><td>0.001<br>(0.141)</td></tr><tr><td>Male advisor</td><td>-0.041<br>(0.044)</td><td>-0.045<br>(0.045)</td><td>0.132***<br>(0.034)</td><td>0.137***<br>(0.033)</td><td></td><td></td><td></td><td></td></tr><tr><td>Male advisor × High effort advice</td><td>0.038<br>(0.057)</td><td>0.046<br>(0.057)</td><td>-0.172**<br>(0.060)</td><td>-0.174**<br>(0.062)</td><td></td><td></td><td></td><td></td></tr><tr><td>Brahmin advisor</td><td></td><td></td><td></td><td></td><td>-0.062**<br>(0.023)</td><td>-0.063**<br>(0.024)</td><td>-0.021<br>(0.055)</td><td>-0.043<br>(0.059)</td></tr><tr><td>Brahmin advisor × High effort advice</td><td></td><td></td><td></td><td></td><td>0.127***<br>(0.036)</td><td>0.124***<br>(0.037)</td><td>0.233***<br>(0.053)</td><td>0.262***<br>(0.068)</td></tr><tr><td>Constant</td><td>0.423***<br>(0.033)</td><td>0.468***<br>(0.057)</td><td>0.397***<br>(0.056)</td><td>0.515***<br>(0.110)</td><td>0.454***<br>(0.025)</td><td>0.494***<br>(0.081)</td><td>0.516***<br>(0.086)</td><td>0.592***<br>(0.144)</td></tr><tr><td>Additional controls</td><td colspan="4">X</td><td colspan="4">X</td></tr><tr><td>Observations</td><td>1,390</td><td>1,390</td><td>1,413</td><td>1,413</td><td>2,420</td><td>2,420</td><td>383</td><td>383</td></tr><tr><td>R-squared</td><td>0.044</td><td>0.047</td><td>0.044</td><td>0.056</td><td>0.046</td><td>0.054</td><td>0.040</td><td>0.056</td></tr><tr><td colspan="9">Difference estimates: Additional effect of male advisor</td></tr><tr><td>Low effort advice</td><td>-0.0410<br>(0.044)</td><td>-0.0445<br>(0.045)</td><td>0.132***<br>(0.034)</td><td>0.137***<br>(0.033)</td><td></td><td></td><td></td><td></td></tr><tr><td>High effort advice</td><td>-0.003<br>(0.037)</td><td>0.002<br>(0.036)</td><td>-0.040<br>(0.044)</td><td>-0.037<br>(0.046)</td><td></td><td></td><td></td><td></td></tr><tr><td colspan="9">Difference estimates: Additional effect of Brahmin advisor</td></tr><tr><td>Low effort advice</td><td></td><td></td><td></td><td></td><td>-0.062**<br>(0.023)</td><td>-0.063**<br>(0.024)</td><td>-0.021<br>(0.055)</td><td>-0.043<br>(0.059)</td></tr><tr><td>High effort advice</td><td></td><td></td><td></td><td></td><td>0.065*<br>(0.032)</td><td>0.062*<br>(0.032)</td><td>0.212***<br>(0.035)</td><td>0.218***<br>(0.043)</td></tr></tbody></table>

Notes: Dependent variable Follow Advice = 1 if respondent follows Brahmin advice and 0, otherwise. OLS regression results presented. Sample restricted to advice received sessions. Additional effect of Male (Brahmin adviser) is the difference in the likelihood of following the advice of a male advisor relative to a female advisor (Brahmin advisor relative to a Dalit advisor). Additional demographic controls include gender and caste of the respondent, religion, primary source of income for the household, and mobile phone use (see Table 1). Standard errors clustered at the district level are presented in parentheses. Significance: ***p < 0.01; **p < 0.05; *p < 0.1.
<table><caption>Table A4: Robustness. Follow Advice by Type of Advice Received. Alternative Clustering</caption><thead><tr><th></th><th colspan="2">Gender of respondent</th><th colspan="2">Caste of respondent</th></tr><tr><th></th><th>Male<br>(1)</th><th>Female<br>(2)</th><th>Disadvantaged<br>(3)</th><th>General<br>(4)</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A: Cluster Block</strong></td></tr><tr><td>High effort advice received</td><td>0.185***<br>(0.044)</td><td>0.269***<br>(0.043)</td><td>0.145***<br>(0.030)</td><td>0.001<br>(0.092)</td></tr><tr><td>Male advisor</td><td>-0.045<br>(0.040)</td><td>0.137***<br>(0.039)</td><td></td><td></td></tr><tr><td>Male advisor × High effort advice</td><td>0.046<br>(0.057)</td><td>-0.174***<br>(0.054)</td><td></td><td></td></tr><tr><td>Brahmin advisor</td><td></td><td></td><td>-0.063**<br>(0.027)</td><td>-0.043<br>(0.071)</td></tr><tr><td>Brahmin advisor × High effort advice</td><td></td><td></td><td>0.124***<br>(0.037)</td><td>0.262***<br>(0.094)</td></tr><tr><td>Constant</td><td>0.468***<br>(0.069)</td><td>0.515***<br>(0.076)</td><td>0.494***<br>(0.044)</td><td>0.592***<br>(0.138)</td></tr><tr><td>Observations</td><td>1,390</td><td>1,413</td><td>2,420</td><td>383</td></tr><tr><td>R-squared</td><td>0.047</td><td>0.056</td><td>0.054</td><td>0.056</td></tr><tr><td colspan="5"><em>Difference estimates: Additional effect of male advisor</em></td></tr><tr><td>Low effort advice</td><td>-0.045<br>(0.041)</td><td>0.137***<br>(0.039)</td><td></td><td></td></tr><tr><td>High effort advice</td><td>0.002<br>(0.034)</td><td>-0.037<br>(0.037)</td><td></td><td></td></tr><tr><td colspan="5"><em>Difference estimates: Additional effect of Brahmin advisor</em></td></tr><tr><td>Low effort advice</td><td></td><td></td><td>-0.063**<br>(0.027)</td><td>-0.043<br>(0.071)</td></tr><tr><td>High effort advice</td><td></td><td></td><td>0.062**<br>(0.028)</td><td>0.218***<br>(0.060)</td></tr><tr><td colspan="5"><strong>Panel B: Cluster Village</strong></td></tr><tr><td>High effort advice received</td><td>0.185***<br>(0.041)</td><td>0.269***<br>(0.040)</td><td>0.145***<br>(0.029)</td><td>0.001<br>(0.087)</td></tr><tr><td>Male advisor</td><td>-0.045<br>(0.036)</td><td>0.137***<br>(0.037)</td><td></td><td></td></tr><tr><td>Male advisor × High effort advice</td><td>0.046<br>(0.057)</td><td>-0.174***<br>(0.054)</td><td></td><td></td></tr><tr><td>Brahmin advisor</td><td></td><td></td><td>-0.063**<br>(0.027)</td><td>-0.043<br>(0.073)</td></tr><tr><td>Brahmin advisor × High effort advice</td><td></td><td></td><td>0.124***<br>(0.039)</td><td>0.262**<br>(0.101)</td></tr><tr><td>Constant</td><td>0.468***<br>(0.068)</td><td>0.515***<br>(0.078)</td><td>0.494***<br>(0.047)</td><td>0.592***<br>(0.140)</td></tr><tr><td>Observations</td><td>1,390</td><td>1,413</td><td>2,420</td><td>383</td></tr><tr><td>R-squared</td><td>0.047</td><td>0.056</td><td>0.054</td><td>0.056</td></tr><tr><td colspan="5"><em>Difference estimates: Additional effect of male advisor</em></td></tr><tr><td>Low effort advice</td><td>-0.045<br>(0.036)</td><td>0.137***<br>(0.037)</td><td></td><td></td></tr><tr><td>High effort advice</td><td>0.002<br>(0.038)</td><td>-0.037<br>(0.037)</td><td></td><td></td></tr></tbody></table>

Continued ...
Robustness. Follow Advice by Type of Advice Received. Alternative Clustering
(Continued)

<table><thead><tr><th></th><th colspan="2">Gender of respondent</th><th colspan="2">Caste of respondent</th></tr><tr><th></th><th>Male<br>(1)</th><th>Female<br>(2)</th><th>Disadvantaged<br>(3)</th><th>General<br>(4)</th></tr></thead><tbody><tr><td colspan="5"><em>Difference estimates: Additional effect of Brahmin advisor</em></td></tr><tr><td>Low effort advice</td><td></td><td></td><td>-0.063**<br>(0.027)</td><td>-0.043<br>(0.074)</td></tr><tr><td>High effort advice</td><td></td><td></td><td>0.062**<br>(0.027)</td><td>0.218***<br>(0.061)</td></tr><tr><td colspan="5">[</td></tr><tr><td colspan="5"><strong>Panel C: Cluster Household</strong></td></tr><tr><td>High effort advice received</td><td>0.183***<br>(0.038)</td><td>0.269***<br>(0.036)</td><td>0.144***<br>(0.028)</td><td>0.001<br>(0.073)</td></tr><tr><td>Male advisor</td><td>-0.045<br>(0.037)</td><td>0.137***<br>(0.037)</td><td></td><td></td></tr><tr><td>Male advisor × High effort advice</td><td>0.048<br>(0.053)</td><td>-0.174***<br>(0.052)</td><td></td><td></td></tr><tr><td>Brahmin advisor</td><td></td><td></td><td>-0.063**<br>(0.028)</td><td>-0.043<br>(0.074)</td></tr><tr><td>Brahmin advisor × High effort advice</td><td></td><td></td><td>0.125***<br>(0.041)</td><td>0.262**<br>(0.101)</td></tr><tr><td>Constant</td><td>0.469***<br>(0.063)</td><td>0.515***<br>(0.084)</td><td>0.494***<br>(0.043)</td><td>0.592***<br>(0.141)</td></tr><tr><td>Observations</td><td>1,389</td><td>1,413</td><td>2,419</td><td>383</td></tr><tr><td>R-squared</td><td>0.047</td><td>0.056</td><td>0.053</td><td>0.056</td></tr><tr><td colspan="5"><em>Difference estimates: Additional effect of male advisor</em></td></tr><tr><td>Low Effort</td><td>-0.045<br>(0.037)</td><td>0.137***<br>(0.037)</td><td></td><td></td></tr><tr><td>High Effort</td><td>0.003<br>(0.038)</td><td>-0.037<br>(0.037)</td><td></td><td></td></tr><tr><td colspan="5"><em>Difference estimates: Additional effect of Brahmin advisor</em></td></tr><tr><td>Low Effort</td><td></td><td></td><td>-0.063**<br>(0.028)</td><td>-0.043<br>(0.074)</td></tr><tr><td>High Effort</td><td></td><td></td><td>0.063**<br>(0.028)</td><td>0.218***<br>(0.069)</td></tr><tr><td colspan="5"><strong>Panel D: Cluster District (Wild Bootstrap CI)</strong></td></tr><tr><td>High effort advice received</td><td>0.185<br>[-0.002, 0.323]</td><td>0.269<br>[0.015, 0.426]</td><td>0.145<br>[0.076, 0.213]</td><td>0.002<br>[-0.335, 0.398]</td></tr><tr><td>Male advisor</td><td>-0.045<br>[-.013, 0.082]</td><td>0.137<br>[0.067, 0.206]</td><td></td><td></td></tr><tr><td>Male advisor × High effort advice</td><td>0.046<br>[-0.089, 0.195]</td><td>-0.174<br>[-0.302, 0.017]</td><td></td><td></td></tr><tr><td>Brahmin advisor</td><td></td><td></td><td>-0.063<br>[-0.118, -0.006]</td><td>-0.043<br>[-0.334, 0.398]</td></tr><tr><td>Brahmin advisor × High effort advice</td><td></td><td></td><td>0.124<br>[0.021, 0.186]</td><td>0.262<br>[.099, 0.546]</td></tr><tr><td colspan="5"><em>Difference estimates: Additional effect of male advisor<sup>†</sup></em></td></tr><tr><td>Low effort advice</td><td>-0.045</td><td>0.137</td><td></td><td></td></tr></tbody></table>

Continued . . .
Robustness. Follow Advice by Type of Advice Received. Alternative Clustering
(Continued)

<table><thead><tr><th rowspan="2"></th><th colspan="2">Gender of respondent</th><th colspan="2">Caste of respondent</th></tr><tr><th>Male<br>(1)</th><th>Female<br>(2)</th><th>Disadvantaged<br>(3)</th><th>General<br>(4)</th></tr></thead><tbody><tr><td>High effort advice</td><td>[-0.125, 0.043]<br>0.002<br>[-0.067, 0.070]</td><td>[0.079, 0.200]<br>-0.037<br>[-0.132, 0.057]</td><td></td><td></td></tr><tr><td colspan="5"><em>Difference estimates: Additional effect of Brahmin advisor<sup>†</sup></em></td></tr><tr><td>Low effort advice</td><td></td><td></td><td>-.063<br>[-0.112, -0.014]</td><td>-0.043<br>[-0.146, 0.089]</td></tr><tr><td>High effort advice</td><td></td><td></td><td>0.062<br>[-0.009, 0.118]</td><td>0.218<br>[0.121, 0.321]</td></tr></tbody></table>

Notes: Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. OLS regression results presented. Sample restricted to advice received. Additional demographic controls include gender and caste of the respondent, religion, primary source of income for the household, and mobile phone use (see Table 1). In Panels A, B and C, significance: ***p < 0.01; **p < 0.05; *p < 0.1. In Panel D, the square brackets are the 95% confidence intervals using Wildbootstrap, 1000 iterations. †: 90% confidence intervals using Wildbootstrap, 1000 iterations.
<table><caption>Table A5: Follow Advice by Type of Advice Received. District Fixed Effects</caption><thead><tr><th rowspan="2"></th><th colspan="2">Gender of Respondent</th><th colspan="2">Caste</th></tr><tr><th>Male</th><th>Female</th><th>Disadvantaged</th><th>General</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>High effort advice received</td><td>0.183**<br>(0.070)</td><td>0.268***<br>(0.076)</td><td>0.144***<br>(0.034)</td><td>0.024<br>(0.147)</td></tr><tr><td>Male advisor</td><td>-0.049<br>(0.045)</td><td>0.139***<br>(0.034)</td><td></td><td></td></tr><tr><td>Male advisor × High effort advice</td><td>0.049<br>(0.057)</td><td>-0.174**<br>(0.063)</td><td></td><td></td></tr><tr><td>Brahmin advisor</td><td></td><td></td><td>-0.062**<br>(0.024)</td><td>-0.011<br>(0.059)</td></tr><tr><td>Brahmin advisor × High effort advice</td><td></td><td></td><td>0.126***<br>(0.036)</td><td>0.232***<br>(0.066)</td></tr><tr><td>Constant</td><td>0.479***<br>(0.055)</td><td>0.523***<br>(0.088)</td><td>0.494***<br>(0.075)</td><td>0.554***<br>(0.134)</td></tr><tr><td>Observations</td><td>1,390</td><td>1,413</td><td>2,420</td><td>383</td></tr><tr><td>R-squared</td><td>0.051</td><td>0.063</td><td>0.057</td><td>0.078</td></tr><tr><td colspan="5"><em>Difference estimates: Male advisor - Female advisor</em></td></tr><tr><td>Low effort</td><td>-0.0487<br>(0.0451)</td><td>0.139***<br>(0.0339)</td><td></td><td></td></tr><tr><td>High effort</td><td>0.0000<br>(0.0360)</td><td>-0.0350<br>(0.0467)</td><td></td><td></td></tr><tr><td colspan="5"><em>Difference estimates: difference</em></td></tr><tr><td colspan="5">Estimates: Brahmin advisor - Dalit advisor</td></tr><tr><td>Low effort</td><td></td><td></td><td>-0.0618**<br>(0.0242)</td><td>-0.0113<br>(0.0587)</td></tr><tr><td>High effort</td><td></td><td></td><td>0.0644*<br>(0.0315)</td><td>0.221***<br>(0.0399)</td></tr></tbody></table>

Notes: Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. Sample restricted to respondents in advice received sessions. OLS regression results with district fixed effects presented. Additional demographic controls include gender and caste of the respondent, religion, primary source of income for the household, and mobile phone use (see Table 1). Standard errors clustered at the district level are presented in parentheses. Significance: ***p < 0.01; **p < 0.05; *p < 0.1.
<table><caption>Table A6: Follow Advice by Type of Advice Received. Including Additional Controls</caption><thead><tr><th rowspan="3"></th><th colspan="2">Gender of Respondent</th><th colspan="2">Caste of Respondent</th></tr><tr><th>Male</th><th>Female</th><th>Disadvantaged Caste</th><th>General Caste</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>High effort advice received</td><td>0.188**<br>(0.068)</td><td>0.267***<br>(0.075)</td><td>0.141***<br>(0.034)</td><td>0.012<br>(0.140)</td></tr><tr><td>Male advisor</td><td>-0.045<br>(0.044)</td><td>0.140***<br>(0.035)</td><td></td><td></td></tr><tr><td>Male advisor × High effort advice</td><td>0.045<br>(0.056)</td><td>-0.179**<br>(0.062)</td><td></td><td></td></tr><tr><td>Brahmin advisor</td><td></td><td></td><td>-0.068**<br>(0.022)</td><td>-0.062<br>(0.055)</td></tr><tr><td>Brahmin advisor × High effort advice</td><td></td><td></td><td>0.132**<br>(0.040)</td><td>0.254***<br>(0.061)</td></tr><tr><td>Constant</td><td>0.557***<br>(0.086)</td><td>0.666***<br>(0.159)</td><td>0.584***<br>(0.127)</td><td>0.770***<br>(0.178)</td></tr><tr><td>Observations</td><td>1,390</td><td>1,413</td><td>2,420</td><td>383</td></tr><tr><td>R-squared</td><td>0.050</td><td>0.082</td><td>0.058</td><td>0.090</td></tr><tr><td colspan="5"><em>Difference Estimates: Additional effect of male advisor</em></td></tr><tr><td>Low effort advice</td><td>-0.045<br>(0.044)</td><td>0.140***<br>(0.035)</td><td></td><td></td></tr><tr><td>High effort advice</td><td>0.0003<br>(0.035)</td><td>-0.038<br>(0.046)</td><td></td><td></td></tr><tr><td colspan="5"><em>Difference estimates: Additional effect of Brahmin advisor</em></td></tr><tr><td>Low effort</td><td></td><td></td><td>-0.068**<br>(0.022)</td><td>-0.062<br>(0.055)</td></tr><tr><td>High effort</td><td></td><td></td><td>0.065*<br>(0.034)</td><td>0.192***<br>(0.038)</td></tr></tbody></table>

**Notes:** Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. Sample restricted to respondents in advice received sessions. OLS regression results with district fixed effects presented. Demographic controls include gender and caste of the respondent, religion, primary source of income for the household, and mobile phone use (see Table 1). Additional controls include Belief about partner's choice, Belief advice will provide highest payoff, Same decision without Advice, Trust Advice, Same Decision if Advisor Different Caste, Risk Attitudes, Raven Score, Participate in Community Groups. Standard errors clustered at the district level are presented in parentheses. Significance: ***p < 0.01; **p < 0.05; *p < 0.1.
<table><caption>Table A7: Likelihood of Following Advice. Women’s Empowerment Effects</caption><thead><tr><th></th><th colspan="2" style="text-align: center;">JEEVIKA Member</th></tr><tr><th></th><th style="text-align: center;">No</th><th style="text-align: center;">Yes</th></tr><tr><th></th><th style="text-align: center;">(1)</th><th style="text-align: center;">(2)</th></tr></thead><tbody><tr><td>Male advisor</td><td style="text-align: center;">0.135**<br>(0.041)</td><td style="text-align: center;">0.101<br>(0.057)</td></tr><tr><td>High effort advice received</td><td style="text-align: center;">0.282**<br>(0.087)</td><td style="text-align: center;">0.157<br>(0.077)</td></tr><tr><td>Male advisor × High effort advice</td><td style="text-align: center;">-0.189**<br>(0.069)</td><td style="text-align: center;">-0.021<br>(0.167)</td></tr><tr><td>Constant</td><td style="text-align: center;">0.536***<br>(0.119)</td><td style="text-align: center;">0.355*<br>(0.143)</td></tr><tr><td>Observations</td><td style="text-align: center;">1,214</td><td style="text-align: center;">199</td></tr><tr><td>R-squared</td><td style="text-align: center;">0.060</td><td style="text-align: center;">0.075</td></tr></tbody></table><p><em>Difference estimates: Additional effect of male advisor</em></p><table><tbody><tr><td>Low effort advice</td><td style="text-align: center;">0.135**<br>(0.0412)</td><td style="text-align: center;">0.101<br>(0.0569)</td></tr><tr><td>High effort advice</td><td style="text-align: center;">-0.0545<br>(0.0465)</td><td style="text-align: center;">0.0803<br>(0.110)</td></tr></tbody></table>

**Notes:** Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. Sample restricted to women respondents in advice received sessions. Sample stratified whether woman is a member of JEEVIKA. OLS regression results presented. Additional effect of Male is the difference in the likelihood of following the advice of a male advisor relative to a female advisor. Additional demographic controls include gender and caste of the respondent, religion, primary source of income for the household, and mobile phone use (see Table 1). Standard errors clustered at the district level are presented in parentheses. Significance: ***p < 0.01; **p < 0.05; *p < 0.1.
<table><caption>Table A8: Follow Advice. Role Model Effect: Exposure to Female Leaders</caption><thead><tr><th></th><th colspan="8">Female MLA 2010—2020:</th></tr><tr><th></th><th colspan="4">None</th><th colspan="4">At least one</th></tr><tr><th></th><th>Male</th><th>Male</th><th>Female</th><th>Female</th><th>Male</th><th>Male</th><th>Female</th><th>Female</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>High effort advice Received</td><td>0.183**<br>(0.069)</td><td>0.179**<br>(0.070)</td><td>0.275***<br>(0.079)</td><td>0.273***<br>(0.077)</td><td>0.333<br>(0.228)</td><td>0.320<br>(0.215)</td><td>0.102<br>(0.285)</td><td>0.042<br>(0.292)</td></tr><tr><td>Male advisor</td><td>-0.045<br>(0.045)</td><td>-0.049<br>(0.046)</td><td>0.135***<br>(0.037)</td><td>0.139***<br>(0.036)</td><td>0.083<br>(0.334)</td><td>0.012<br>(0.402)</td><td>0.032<br>(0.076)</td><td>-0.081<br>(0.206)</td></tr><tr><td>Male advisor × High effort advice</td><td>0.055<br>(0.059)</td><td>0.063<br>(0.059)</td><td>-0.192**<br>(0.063)</td><td>-0.192**<br>(0.065)</td><td>-0.405<br>(0.586)</td><td>-0.376<br>(0.613)</td><td>0.423<br>(0.285)</td><td>0.484<br>(0.395)</td></tr><tr><td>Constant</td><td>0.423***<br>(0.032)</td><td>0.482***<br>(0.052)</td><td>0.399***<br>(0.057)</td><td>0.514***<br>(0.109)</td><td>0.417*<br>(0.169)</td><td>0.312<br>(0.366)</td><td>0.353**<br>(0.122)</td><td>0.494**<br>(0.184)</td></tr><tr><td>Observations</td><td>1,342</td><td>1,342</td><td>1,361</td><td>1,361</td><td>48</td><td>48</td><td>52</td><td>52</td></tr><tr><td>R-squared</td><td>0.045</td><td>0.049</td><td>0.043</td><td>0.054</td><td>0.074</td><td>0.268</td><td>0.185</td><td>0.276</td></tr><tr><td>Additional controls</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td></tr><tr><td colspan="9">Difference estimates: Male advisor – Female advisor</td></tr><tr><td>Low effort advice</td><td>-0.0448<br>(0.0452)</td><td>-0.0489<br>(0.0461)</td><td>0.135***<br>(0.0370)</td><td>0.139***<br>(0.0364)</td><td>0.0833<br>(0.334)</td><td>0.0120<br>(0.402)</td><td>0.0317<br>(0.0761)</td><td>-0.0808<br>(0.206)</td></tr><tr><td>High effort advice</td><td>0.00993<br>(0.0416)</td><td>0.0139<br>(0.0403)</td><td>-0.0567<br>(0.0457)</td><td>-0.0528<br>(0.0466)</td><td>-0.321<br>(0.276)</td><td>-0.364<br>(0.250)</td><td>0.455<br>(0.228)</td><td>0.404<br>(0.236)</td></tr></tbody></table>

**Notes:** Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. OLS regression results presented. Estimating equation given by equation (1). Sample restricted to advice received sessions. Additional demographic controls include gender/caste of respondent, religion, primary source of income and mobile phone use (see Table 1). District clustered standard errors presented in parenthesis. Significance: ***p < 0.01; **p < 0.05; *p < 0.1.
<table><caption>Table A9: Follow Advice. Role Model Effect: Exposure to Dalit Leaders</caption><thead><tr><th rowspan="3"></th><th colspan="4">Non-Reserved</th><th colspan="4">Reserved</th></tr><tr><th colspan="2">Disadvantaged caste</th><th colspan="2">General Caste</th><th colspan="2">Disadvantaged caste</th><th colspan="2">General Caste</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Brahmin advisor</td><td>-0.062**<br>(0.022)</td><td>-0.062**<br>(0.023)</td><td>-0.033<br>(0.059)</td><td>-0.054<br>(0.070)</td><td>-0.083<br>(0.174)</td><td>-0.202*<br>(0.097)</td><td>0.500**<br>(0.194)</td><td>1.305***<br>(0.288)</td></tr><tr><td>High effort advice received</td><td>0.143***<br>(0.033)</td><td>0.145***<br>(0.032)</td><td>0.017<br>(0.133)</td><td>0.006<br>(0.149)</td><td>0.167<br>(0.327)</td><td>0.091<br>(0.239)</td><td>-0.000<br>(0.306)</td><td>-0.021<br>(0.239)</td></tr><tr><td>Brahmin advisor × High effort advice</td><td>0.126***<br>(0.035)</td><td>0.124***<br>(0.037)</td><td>0.240***<br>(0.062)</td><td>0.265**<br>(0.082)</td><td>0.144<br>(0.294)</td><td>0.224<br>(0.209)</td><td>-0.250<br>(0.332)</td><td>-1.029**<br>(0.396)</td></tr><tr><td>Constant</td><td>0.453***<br>(0.023)</td><td>0.492***<br>(0.081)</td><td>0.517***<br>(0.092)</td><td>0.604***<br>(0.149)</td><td>0.500*<br>(0.198)</td><td>0.142<br>(0.318)</td><td>0.500**<br>(0.194)</td><td>-0.305<br>(0.288)</td></tr><tr><td>Observations</td><td colspan="2">2,371</td><td colspan="2">355</td><td colspan="2">49</td><td colspan="2">28</td></tr><tr><td>R-squared</td><td colspan="2">0.046</td><td colspan="2">0.053</td><td colspan="2">0.060</td><td colspan="2">0.210</td></tr><tr><td>Additional controls</td><td colspan="2">X</td><td colspan="2">✓</td><td colspan="2">X</td><td colspan="2">✓</td></tr><tr><td colspan="9"><em>Difference estimates: Brahmin advisor – Dalit advisor</em></td></tr><tr><td>Low effort advice</td><td>-0.062**<br>(0.022)</td><td>-0.062**<br>(0.023)</td><td>-0.033<br>(0.059)</td><td>-0.054<br>(0.070)</td><td>-0.083<br>(0.174)</td><td>-0.202*<br>(0.097)</td><td>0.500**<br>(0.194)</td><td>1.305***<br>(0.288)</td></tr><tr><td>High effort advice</td><td>0.065*<br>(0.032)</td><td>0.0625*<br>(0.033)</td><td>0.207***<br>(0.040)</td><td>0.210***<br>(0.051)</td><td>0.061<br>(0.158)</td><td>0.022<br>(0.145)</td><td>0.250<br>(0.169)</td><td>0.276<br>(0.174)</td></tr></tbody></table>

**Notes:** Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. OLS regression results presented. Estimating equation given by equation (2). Sample restricted to advice received sessions. Additional demographic controls include gender/caste of respondent, religion, occupation and mobile phone use (see Table 1). Reserved denotes reserved for SC/ST seat. District clustered standard errors presented in parenthesis. Significance: ***p < 0.01; **p < 0.05; *p < 0.1.
<table><caption>Table A10: Follow Advice. Role Model Effect: Exposure to any Female head of village council in last 3 village council elections</caption><thead><tr><th rowspan="3"></th><th colspan="4">Low effort advice</th><th colspan="4">High effort advice</th></tr><tr><th colspan="2">Male</th><th colspan="2">Female</th><th colspan="2">Male</th><th colspan="2">Female</th></tr><tr><th>OLS</th><th>IV</th><th>OLS</th><th>IV</th><th>OLS</th><th>IV</th><th>OLS</th><th>IV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Female advisor</td><td>0.215**<br>(0.077)</td><td>0.290***<br>(0.098)</td><td>-0.189*<br>(0.086)</td><td>-0.220**<br>(0.103)</td><td>0.048<br>(0.043)</td><td>-0.044<br>(0.095)</td><td>0.059<br>(0.091)</td><td>0.003<br>(0.147)</td></tr><tr><td>Female village council head (any)</td><td>0.168***<br>(0.044)</td><td>0.193***<br>(0.064)</td><td>-0.024<br>(0.051)</td><td>-0.042<br>(0.114)</td><td>-0.036<br>(0.057)</td><td>-0.102<br>(0.086)</td><td>0.018<br>(0.053)</td><td>-0.092<br>(0.111)</td></tr><tr><td>Female advisor × Female village council head</td><td>-0.230**<br>(0.090)</td><td>-0.335***<br>(0.126)</td><td>0.069<br>(0.105)</td><td>0.112<br>(0.131)</td><td>-0.068<br>(0.078)</td><td>0.063<br>(0.122)</td><td>-0.028<br>(0.085)</td><td>0.050<br>(0.176)</td></tr><tr><td>Constant</td><td>0.383***<br>(0.097)</td><td>0.373***<br>(0.125)</td><td>0.659***<br>(0.123)</td><td>0.675***<br>(0.128)</td><td>0.606***<br>(0.102)</td><td>0.648***<br>(0.095)</td><td>0.743***<br>(0.194)</td><td>0.811***<br>(0.177)</td></tr><tr><td>Observations</td><td colspan="2">700</td><td colspan="2">717</td><td colspan="2">687</td><td colspan="2">692</td></tr><tr><td>R-squared</td><td colspan="2">0.030</td><td colspan="2">0.027</td><td colspan="2">0.049</td><td colspan="2">0.014</td></tr><tr><td>Additional Controls</td><td colspan="2">✓</td><td colspan="2">✓</td><td colspan="2">✓</td><td colspan="2">✓</td></tr><tr><td colspan="9"><em>Difference Estimate: Male Advisor - Female Advisor</em></td></tr><tr><td>Male Village Council Head</td><td>-0.215**<br>(0.0769)</td><td>-0.290***<br>(0.0977)</td><td>0.189**<br>(0.0860)</td><td>0.220**<br>(0.103)</td><td>-0.0483<br>(0.0429)</td><td>0.0437<br>(0.0947)</td><td>-0.0589<br>(0.0908)</td><td>-0.00254<br>(0.147)</td></tr><tr><td>Female Village Council Head</td><td>0.015<br>(0.0556)</td><td>0.0454<br>(0.0592)</td><td>0.120**<br>(0.0413)</td><td>0.108**<br>(0.0448)</td><td>0.0196<br>(0.0558)</td><td>-0.0191<br>(0.0505)</td><td>-0.0305<br>(0.0462)</td><td>-0.0528<br>(0.0540)</td></tr><tr><td colspan="9">First Stage F</td></tr><tr><td>female village council head (any)</td><td colspan="2">35.04<br>[0.000]</td><td colspan="2">156.04<br>[0.000]</td><td colspan="2">176.21<br>[0.000]</td><td colspan="2">54.21<br>[0.000]</td></tr><tr><td>Female advisor × Female village council head</td><td colspan="2">43.26<br>[0.000]</td><td colspan="2">63.52<br>[0.000]</td><td colspan="2">47.18<br>[0.000]</td><td colspan="2">100.06<br>[0.000]</td></tr></tbody></table>

Notes: Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. Female village head (any) = 1 if at least one village head following the last 3 village council elections is a woman. Male village head if otherwise. Sample restricted to advice received sessions. Female reserved and Female reserved × Female Advisor are used as instruments for Female Village Head and Female Advisor × Female Village Head respectively. Additional demographic controls include gender/caste of respondent, religion, occupation, and mobile phone use (see Table 1). District clustered standard errors are presented in parentheses. Significance: ***p < 0.01; **p < 0.05; *p < 0.1.
<table><caption>Table A11: Follow Advice. Role Model Effect: Exposure to any Disadvantaged caste head of village council in last 3 village council elections</caption><thead><tr><th rowspan="3"></th><th colspan="6">Low Effort Advice</th><th colspan="2">High Effort Advice</th></tr><tr><th colspan="2">Disadvantaged caste</th><th colspan="2">General Caste</th><th colspan="2">Disadvantaged caste</th><th rowspan="2">OLS</th><th rowspan="2">IV</th></tr><tr><th>OLS</th><th>IV</th><th>OLS</th><th>IV</th><th>OLS</th><th>IV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Dalit advisor</td><td>-0.035<br>(0.061)</td><td>0.456<br>(0.388)</td><td>-0.079<br>(0.071)</td><td>-0.272<br>(0.258)</td><td>-0.249***<br>(0.057)</td><td>-0.208<br>(0.271)</td><td>-0.307*<br>(0.156)</td><td>0.118<br>(0.454)</td></tr><tr><td>Disadvantaged Caste Village Council Head (any)</td><td>-0.058<br>(0.053)</td><td>0.121<br>(0.191)</td><td>0.075<br>(0.131)</td><td>-0.003<br>(0.230)</td><td>-0.173***<br>(0.045)</td><td>-0.374**<br>(0.150)</td><td>0.058<br>(0.073)</td><td>0.205*<br>(0.111)</td></tr><tr><td>Dalit advisor × Disadvantaged village council head</td><td>0.110<br>(0.065)</td><td>-0.426<br>(0.402)</td><td>0.168<br>(0.108)</td><td>0.407<br>(0.340)</td><td>0.226**<br>(0.079)</td><td>0.183<br>(0.315)</td><td>0.155<br>(0.116)</td><td>-0.398<br>(0.480)</td></tr><tr><td>Constant</td><td>0.536***<br>(0.061)</td><td>0.373**<br>(0.150)</td><td>0.415**<br>(0.165)</td><td>0.474**<br>(0.238)</td><td>0.775***<br>(0.112)</td><td>0.956***<br>(0.214)</td><td>0.672**<br>(0.222)</td><td>0.579***<br>(0.223)</td></tr><tr><td>Observations</td><td>1,229</td><td>1,229</td><td>188</td><td>188</td><td>1,186</td><td>1,186</td><td>193</td><td>193</td></tr><tr><td>R-squared</td><td>0.022</td><td>-0.003</td><td>0.082</td><td>0.072</td><td>0.017</td><td>0.000</td><td>0.057</td><td>0.016</td></tr><tr><td>Additional Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td colspan="9"><em>Brahmin Advisor - Dalit Advisor</em></td></tr><tr><td>General Caste Village Council Head</td><td>0.0346<br>(0.0610)</td><td>-0.456<br>(0.388)</td><td>0.0793<br>(0.0706)</td><td>0.272<br>(0.258)</td><td>0.249***<br>(0.0565)</td><td>0.208<br>(0.271)</td><td>0.307*<br>(0.156)</td><td>-0.118<br>(0.454)</td></tr><tr><td>Disadvantaged Caste Village Council Head</td><td>-0.0759**<br>(0.0271)</td><td>-0.0298<br>(0.0243)</td><td>-0.0889<br>(0.0792)</td><td>-0.136<br>(0.114)</td><td>0.0233<br>(0.0359)</td><td>0.0244<br>(0.0520)</td><td>0.152*<br>(0.0790)</td><td>0.279**<br>(0.130)</td></tr><tr><td colspan="9"><em>First Stage F</em></td></tr><tr><td>Disadvantaged Caste Village Council Head (any)</td><td></td><td>5.97<br>[0.015]</td><td></td><td>15.2<br>[0.000]</td><td></td><td>10.86<br>[0.001]</td><td></td><td>25.84<br>[0.000]</td></tr><tr><td>Dalit advisor × Disadvantaged village council head</td><td></td><td>6.01<br>[0.014]</td><td></td><td>17.83<br>[0.000]</td><td></td><td>10.28<br>[0.001]</td><td></td><td>22.64<br>[0.000]</td></tr></tbody></table>

**Notes:** Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. Sample restricted to advice received sessions. Disadvantaged caste village head (any) = 1 if at least one village head following the last 3 village council elections is from a disadvantaged caste. General caste village head if otherwise. Disadvantaged caste reserved and Disadvantaged caste reserved × Dalit advisor are used as instruments for Disadvantaged caste Village Head and Dalit advisor × Disadvantaged caste Village Head respectively. Additional demographic controls include gender/caste of respondent, religion, occupation, and mobile phone use (see Table 1). District clustered standard errors are presented in parentheses. Significance: ***p < 0.01; **p < 0.05; *p < 0.1.
<caption>Figure A5: High Effort Advice Given by Advice Received and Effort Choice</caption>

<figure><img src="image_8.png" /><figcaption>(a) High Effort Advice Given by Advice (b) High Effort Advice Given by Effort Received Choice</figcaption></figure>

Notes: The bars denote the proportion of subjects suggesting High Effort Choice in Stage 2 of the task, by advice received and effort choice in Stage 1 of the task.

<caption>Figure A6: High Effort Advice Given by Gender and Caste of Advisor and Advisee</caption>

<figure><img src="image_9.png" /><figcaption>(b) Caste</figcaption></figure>

<figure><img src="image_10.png" /></figure>

Notes: Male (Female) advisee denotes the advise recipient is a Male (Female). Brahmin (Dalit) advisee denotes the advise recipient is a Male (Female).
<figure><img src="image_11.png" /><figcaption>Figure A7: Follow Advice. Role Model Effect. Intensity of exposure to female / disadvantaged caste head of village council in last 3 village council elections</figcaption></figure>

**Notes:** OLS regression results and 90% confidence intervals presented. Dependent variable Follow Advice = 1 if respondent follows advice and 0, otherwise. Sample restricted to advice received sessions. Number of female heads/ number of disadvantaged caste village heads following the last 3 village council elections. Additional demographic controls include gender/caste of respondent, religion, occupation, and mobile phone use (see Table 1). Standard errors are clustered at the district level.
<caption>Figure B1: Following Advice by Type of Advice Received</caption>

<figure><img src="image_12.png" /><figcaption>(b) Full Sample Effect by Caste of Respondent: Additional Effect of a Brahmin Advisor</figcaption></figure>

<figure><img src="image_13.png" /></figure>

Notes: Figure B1a presents the results in Panel A of Table 6. Figure B1b presents the results in Panel A of Table 7.
Figure B2: Following Advice by Type of Advice Received. Role Model Effect. MLA

<figure><img src="image_14.png" /><figcaption>(b) Role Model Effect. Exposure to SC/ST MLAs: Additional Effect of a Brahmin Advisor</figcaption></figure>

<figure><img src="image_15.png" /></figure>

Notes: Figure B2a presents the results in Panel B of Table 6. Figure B2b presents the results in Panel B of Table 7.
<caption>Figure B3: Following Advice by Type of Advice Received. Role Model Effect. Village Head</caption>

<figure><img src="image_16.png" /><figcaption>(b) Role Model Effect. Exposure to Disadvantaged Caste Village Head: Additional Effect of a Brahmin Advisor</figcaption></figure>

<figure><img src="image_17.png" /></figure>

Notes: Figure B3a presents the results in Panel C of Table 6. Figure B3b presents the results in Panel C of Table 7.
# Online Appendix: Survey

## 7.5 Vignette

For this part of the survey, we will give you a scenario where you will have to make some choices. This scenario is a hypothetical, and won't involve real money, just imagined money. Suppose you are working in a small business such as a tool making business or stitching and tailoring business with another person, who you don't know but lives in the same area as you. The two of you are the only people working in the business and you both get half of the business revenue.

सर्वेक्षण के इस भाग के लिए, हम आपको एक परिदृश्य देंगे जहां आपको कुछ विकल्प चुनने होंगे। यह परिदृश्य एक काल्पनिक है, और इसमें वास्तविक धन शामिल नहीं होगा, केवल काल्पनिक धन। मान लीजिए कि आप एक छोटे से व्यवसाय में काम कर रहे हैं जैसे कि उपकरण बनाने का व्यवसाय या किसी अन्य व्यक्ति के साथ सिलाई और दर्जी का काम का व्यवसाय, जिसे आप नहीं जानते हैं लेकिन उसी क्षेत्र में रहते हैं जहाँ आप रहते हैं। आप दोनों ही व्यवसाय में काम करने वाले अकेले लोग हैं और आप दोनों को व्यवसाय के राजस्व का आधा हिस्सा मिलता है।

## Your Decisions

In this scenario how much you earn depends on your choice as well as the choice of the other employee. Both of you will have to make your decisions independently.

We will explain all the possible alternatives that can happen. Please, take a look at the following table

### आपके निर्णय

इस परिदृश्य में आप कितना कमाते हैं यह आपकी पसंद के साथ-साथ दूसरे कर्मचारी की पसंद पर भी निर्भर करता है। आप दोनों को अपने निर्णय स्वतंत्र रूप से लेने होंगे।

हम उन सभी संभावित विकल्पों की व्याख्या करेंगे जो हो सकते हैं। कृपया, निम्न तालिका पर एक नज़र डालें

<table><caption>Table 1: Payoff Decisions</caption><thead><tr><th colspan="2"></th><th colspan="2">Other Employee (business partner)<br>अन्य कर्मचारी (व्यापार भागीदार)</th></tr><tr><th colspan="2"></th><th>High Effort<br>उच्च प्रयास</th><th>Low Effort<br>कम प्रयास</th></tr></thead><tbody><tr><th rowspan="2">YOU (Respondent)<br>आप (उत्तरदाता)</th><th>High Effort<br>उच्च प्रयास</th><td>100, 100</td><td>0, 150</td></tr><tr><th>Low Effort<br>कम प्रयास</th><td>150, 0</td><td>25, 25</td></tr></tbody></table>

Your choices and payoffs from those choices are highlighted in blue colour, and the other employee's is highlighted in red. Notice, you can make one of two decisions – either **put in High Effort** (work hard) or **Low Effort (not work as hard)**. It costs you more to put in high effort than low effort, however, total revenue is
Supplementary Appendix Material: Survey

greater when employees put in high effort. All amounts are in Indian Rupees. You won't be working actual hours or putting in actual effort, this is a hypothetical situation.

उन विकल्पों में से आपकी पसंद और भुगतान को नीले रंग में हाइलाइट किया गया है, और दूसरे कर्मचारी को लाल रंग में हाइलाइट किया गया है। ध्यान दें, आप दो में से एक निर्णय ले सकते हैं - या तो उच्च प्रयास (कड़ी मेहनत) या कम प्रयास (कड़ी मेहनत नहीं) में लगाएं। कम प्रयास की तुलना में उच्च प्रयास करने के लिए आपको अधिक खर्च करना पड़ता है, हालांकि, कर्मचारियों द्वारा उच्च प्रयास करने पर कुल राजस्व अधिक होता है। सभी राशियां भारतीय रुपए में हैं। आप वास्तविक घंटे काम नहीं कर रहे होंगे या वास्तविक प्रयास नहीं कर रहे होंगे, यह एक काल्पनिक स्थिति है।

If you work hard (put in High Effort), and the other employee also works hard (puts in High Effort) then your TOTAL revenue will be 500, however, putting in high effort costs you 150 each, this means you and the other employee will both get 100 INR. This is shown in the table. Think of this as your earnings from the business. However, if you choose High Effort, and the other employee puts in Low Effort then your TOTAL revenue will be 300, split equally between the two of you. You will earn 0 INR since high effort costs you 150 INR, the other employee will get 150 INR since low effort costs zero. This is like if you work hard and spend long hours and money your costs are higher but the other employee who does not put in these costs can earn more.

यदि आप कड़ी मेहनत करते हैं (उच्च प्रयास में), और दूसरा कर्मचारी भी कड़ी मेहनत करता है (उच्च प्रयास करता है) तो आपका कुल राजस्व 500 होगा, हालांकि, उच्च प्रयास करने पर आपको 150 प्रत्येक का खर्च आता है, इसका मतलब है कि आप और अन्य कर्मचारी दोनों को 100 रुपये मिलेंगे। यह तालिका में दिखाया गया है। इसे व्यवसाय से आपकी कमाई के रूप में सोचें। हालांकि, यदि आप उच्च प्रयास चुनते हैं, और दूसरा कर्मचारी कम प्रयास करता है तो आपका कुल राजस्व 300 होगा, आप दोनों के बीच समान रुप से विभाजित। आप 0 INR कमाएंगे क्योंकि उच्च प्रयास की लागत आपको 150 INR है, अन्य कर्मचारी को 150 INR मिलेगा क्योंकि कम प्रयास लागत शून्य है। यह ऐसा है जैसे यदि आप कड़ी मेहनत करते हैं और लंबे घंटे और पैसा खर्च करते हैं तो आपकी लागत अधिक होती है लेकिन दूसरा कर्मचारी जो इन लागतों को नहीं लगाता है वह अधिक कमा सकता है।

If you put in Low Effort, and the employee puts in High Effort then total revenue is 300, since your costs are zero your earnings are 150 INR but the other employees earnings are zero after accounting for their costs of 150 from high effort. If you select Low Effort, and the employee puts in Low Effort then total revenue will be 50 since it costs zero to put in low effort, will receive 25 INR and the other employee will receive 25 INR.

यदि आप कम प्रयास करते हैं, और कर्मचारी उच्च प्रयास करता है तो कुल राजस्व 300 है, क्योंकि आपकी लागत शून्य है, आपकी कमाई 150 आईएनआर है, लेकिन अन्य कर्मचारियों की कमाई उच्च प्रयास से 150 की लागत के हिसाब से शून्य है। यदि आप कम प्रयास का चयन करें, और कर्मचारी कम प्रयास करता है तो कुल राजस्व 50 होगा क्योंकि कम प्रयास में शून्य खर्च होता है, 25 रुपये प्राप्त होंगे और अन्य कर्मचारी को 25 रुपये प्राप्त होंगे।

**Will you select high or low effort?**

क्या आप उच्च या निम्न प्रयास का चयन करेंगे?

Before you make your decision, we would like to mention that someone in another village given a similar scenario has offered you advice. You can see the profile of this participant below and the advice that they have provided you.
# Supplementary Appendix Material: Survey

इससे पहले कि आप अपना निर्णय लें, हम यह उल्लेख करना चाहेंगे कि इसी तरह के परिदृश्य को देखते हुए दूसरे गाँव में किसी ने आपको सलाह दी है। आप नीचे इस प्रतिभागी की प्रोफाइल और उनके द्वारा आपको दी गई सलाह को देख सकते हैं।

## Profile 1

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height<br/>सामान्य ऊंचाई</td></tr><tr><td>Gender<br/>लिंग</td><td>Male<br/>पुरुष</td></tr><tr><td>Hair colour<br/>बालों का रंग</td><td>Black<br/>काली</td></tr></table>

<table><tr><td>Caste<br/>जाति</td><td>Dalit<br/>दलितों</td></tr><tr><td>Advice<br/>सलाह</td><td>High Effort<br/>उच्च प्रयास</td></tr></table>

## Profile 2

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height<br/>सामान्य ऊंचाई</td></tr><tr><td>Gender<br/>लिंग</td><td>Male<br/>पुरुष</td></tr><tr><td>Hair colour<br/>बालों का रंग</td><td>Black<br/>काली</td></tr><tr><td>Caste<br/>जाति</td><td>Dalit<br/>दलितों</td></tr><tr><td>Advice<br/>सलाह</td><td>High Effort<br/>उच्च प्रयास</td></tr></table>

## Profile 3

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height<br/>सामान्य ऊंचाई</td></tr><tr><td>Gender<br/>लिंग</td><td>Male<br/>पुरुष</td></tr></table>
Supplementary Appendix Material: Survey

<table><tr><td>Hair colour<br>बालों का रंग</td><td>Black<br>काली</td></tr><tr><td>Caste<br>जाति</td><td>Dalit<br>दलितों</td></tr><tr><td>Advice<br>सलाह</td><td>Low Effort<br>कम प्रयास</td></tr></table>

Profile 4

<table><tr><td>Height<br>ऊंचाई</td><td>Normal height<br>सामान्य ऊंचाई</td></tr><tr><td>Gender<br>लिंग</td><td>Male<br>पुरुष</td></tr><tr><td>Hair colour<br>बालों का रंग</td><td>Black<br>काली</td></tr><tr><td>Caste<br>जाति</td><td>Dalit<br>दलितों</td></tr><tr><td>Advice<br>सलाह</td><td>Low Effort<br>कम प्रयास</td></tr></table>

Profile 5

<table><tr><td>Height<br>ऊंचाई</td><td>Normal height<br>सामान्य ऊंचाई</td></tr><tr><td>Gender<br>लिंग</td><td>Male<br>पुरुष</td></tr><tr><td>Hair colour<br>बालों का रंग</td><td>Black<br>काली</td></tr><tr><td>Caste<br>जाति</td><td>Brahmin<br>ब्राह्मण</td></tr><tr><td>Advice<br>सलाह</td><td>High Effort<br>उच्च प्रयास</td></tr></table>

Profile 6
Supplementary Appendix Material: Survey

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height<br/>सामान्य ऊंचाई</td></tr><tr><td>Gender<br/>लिंग</td><td>Female<br/>महिला</td></tr><tr><td>Hair colour<br/>बालों का रंग</td><td>Black<br/>काली</td></tr></table>

<table><tr><td>Caste<br/>जाति</td><td>Brahmin<br/>ब्राह्मण</td></tr><tr><td>Advice<br/>सलाह</td><td>High Effort<br/>उच्च प्रयास</td></tr></table>

Profile 7

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height<br/>सामान्य ऊंचाई</td></tr><tr><td>Gender<br/>लिंग</td><td>Male<br/>पुरुष</td></tr><tr><td>Hair colour<br/>बालों का रंग</td><td>Black<br/>काली</td></tr><tr><td>Caste<br/>जाति</td><td>Brahmin<br/>ब्राह्मण</td></tr><tr><td>Advice<br/>सलाह</td><td>Low Effort<br/>कम प्रयास</td></tr></table>

Profile 8

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height g<br/>सामान्य ऊंचाई जी</td></tr><tr><td>Gender<br/>लिंग</td><td>Female<br/>महिला</td></tr><tr><td>Hair colour<br/>बालों का रंग</td><td>Black<br/>काली</td></tr></table>
Supplementary Appendix Material: Survey

<table><tr><td>Caste<br/>जाति</td><td>Brahmin<br/>ब्राह्मण</td></tr><tr><td>Advice<br/>सलाह</td><td>Low Effort<br/>कम प्रयास</td></tr></table>

## Profile 9 – No profile

This version differs from the others as subjects are not shown the profiles or the text "Before you make your decision, we would like to mention that others have previously answered this survey and these questions. You can see the profile of one of the previous participants below and the advice that they have provided you". Instead they are simply asked to make their decision as either high effort or low effort.

यह संस्करण दूसरों से अलग है क्योंकि विषयों को प्रोफाइल या टेक्स्ट नहीं दिखाया जाता है "इससे पहले कि आप अपना निर्णय लें, हम यह उल्लेख करना चाहेंगे कि अन्य लोगों ने पहले इस सर्वेक्षण और इन सवालों का जवाब दिया है। आप नीचे पिछले प्रतिभागियों में से एक की प्रोफाइल और उनके द्वारा आपको दी गई सलाह देख सकते हैं" इसके बजाय उन्हें केवल उच्च प्रयास या कम प्रयास के रूप में अपना निर्णय लेने के लिए कहा जाता है।

### Will you select high or low effort?

क्या आप उच्च या निम्न प्रयास का चयन करेंगे?

<table><tr><td>Q.1 Please select either high or low effort</td><td>1) High effort; 2) Low effort</td></tr><tr><td>कृपया उच्च या निम्न प्रयास चुनें</td><td>1) उच्च प्रयास; 2) कम प्रयास</td></tr></table>

## Part 2

Someone in another village will also participate in this survey and answer these questions. What advice would you give them? Do you advise them to select high or low effort? Please see below the profile of the person you will be giving advice to

दूसरे गांव का कोई व्यक्ति भी इस सर्वेक्षण में भाग लेगा और इन सवालों के जवाब देगा। आप उन्हें क्या सलाह देंगे?
क्या आप उन्हें उच्च या निम्न प्रयास का चयन करने की सलाह देते हैं? कृपया उस व्यक्ति की प्रोफाइल नीचे देखें जिसे आप सलाह देंगे

### Profile 1

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height<br/>सामान्य ऊंचाई</td></tr><tr><td>Gender</td><td>Male</td></tr></table>
Supplementary Appendix Material: Survey

<table><tr><td>लिंग</td><td>पुरुष</td></tr><tr><td>Hair colour<br/>बालों का रंग</td><td>Black<br/>काली</td></tr><tr><td>Caste<br/>जाति</td><td>Dalit<br/>दलितों</td></tr></table>

Profile 2

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height<br/>सामान्य ऊंचाई</td></tr><tr><td>Gender<br/>लिंग</td><td>Female<br/>महिला</td></tr><tr><td>Hair colour<br/>बालों का रंग</td><td>Black<br/>काली</td></tr><tr><td>Caste<br/>जाति</td><td>Dalit<br/>दलितों</td></tr></table>

Profile 3

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height<br/>सामान्य ऊंचाई</td></tr><tr><td>Gender<br/>लिंग</td><td>Male<br/>पुरुष</td></tr><tr><td>Hair colour<br/>बालों का रंग</td><td>Black<br/>काली</td></tr><tr><td>Caste<br/>जाति</td><td>Brahmin<br/>ब्राह्मण</td></tr></table>

Profile 4

<table><tr><td>Height<br/>ऊंचाई</td><td>Normal height<br/>सामान्य ऊंचाई</td></tr></table>
Supplementary Appendix Material: Survey

<table><tr><td>Gender<br/>लिंग</td><td>Female<br/>महिला</td></tr><tr><td>Hair colour<br/>बालों का रंग</td><td>Black<br/>काली</td></tr><tr><td>Caste<br/>जाति</td><td>Brahmin<br/>ब्राह्मण</td></tr></table>

What is your advice?

आपकी क्या सलाह है?

<table><tr><td>Q.2 Please select either high or low effort कृपया उच्च या निम्न प्रयास चुनें</td><td>High effort उच्च प्रयास<br/>Low effort कम प्रयास</td></tr></table>

<table><thead><tr><td>Q No.</td><td>Question</td><td>Response</td></tr></thead><tbody><tr><td>1</td><td>In part 1, what decision do you think the other employee was going to choose?<br/>भाग 1 में, आपको क्या लगता है कि दूसरा कर्मचारी किस निर्णय को चुनने वाला है?</td><td>High effort<br/>Low effort<br/>उच्च प्रयास<br/>कम प्रयास</td></tr><tr><td>2</td><td>In part 1, did you trust the information from the person sending the advice?<br/>भाग 1 में, क्या आपको सलाह भेजने वाले व्यक्ति की जानकारी पर भरोसा था?</td><td>Trust a lot बहुत भरोसा है<br/>Trust a little थोड़ा विश्वास है<br/>Don't trust भरोसा नहीं है</td></tr><tr><td>3</td><td>In part 1, do you believe the advice will provide you with the highest payoff?<br/>भाग 1 में, क्या आपको विश्वास है कि सलाह आपको उच्चतम भुगतान प्रदान करेगी?</td><td>Highest payoff<br/>Not highest payoff<br/>उच्चतम अदायगी<br/>उच्चतम भुगतान नहीं</td></tr><tr><td>4</td><td>In part 1, would you have made the same decision without the advice?<br/>भाग 1 में, क्या आपने सलाह के बिना वही निर्णय लिया होगा?</td><td>Same Decision वही निर्णय<br/>Different Decision अलग निर्णय<br/>If answer is different decision then Why?<br/>अगर उत्तर अलग निर्णय है तो क्यों?<br/>Options (can select more than one): विकल्प (एक से अधिक चुन सकते हैं):<br/>Related to trust भरोसा से संबंधित<br/>Related to payoffs अदायगी से संबंधित<br/>Related to gender लिंग से संबंधित</td></tr></tbody></table>
Supplementary Appendix Material: Survey

<table><tr><td></td><td></td><td>Related to caste<br/>जाति से संबंधित</td></tr><tr><td>5</td><td>In part 1, would you have made the same decision if the person giving the advice was the same caste as you?</td><td>Same Decision<br/>Different Decision<br/>वही निर्णय<br/>अलग निर्णय</td></tr></table>