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Intrinsic Incentives: A Field Experiment on Leveraging Intrinsic
Motivation in Public Service Delivery
Scott S. Lee∗
JOB MARKET PAPER
January 5, 2018
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Abstract Although extrinsic and intrinsic motivation likely jointly explain the effort of many agents engaged in public service delivery, canonical models of incentives in firms focus on the former. In the context of a rural health worker program in India, I develop and test a novel mobile phone app designed to increase agents’ intrinsic returns to effort. At one year of follow-up, the self-tracking app leads to a 24% increase in performance as measured by the main job task (home visits). Moreover, the app is most effective when it leverages pre-existing intrinsic motivation: it produces a 41% increase in performance in the top tercile of intrinsically motivated workers, but no improvement in the bottom tercile. This treatment effect persists over time for the most intrinsically motivated workers, whereas early improvements decay among the least motivated workers. Supplementary evidence suggests that the treatment effect on performance is mediated primarily by making effort more intrinsically rewarding, and not by other mechanisms such as the provision of implicit extrinsic incentives. Despite these effects on worker performance, I find no effect on health outcomes. ∗Harvard Medical School and Brigham and Women’s Hospital, [email protected]. I thank the Chief Medical Office of Kaushambi District, Dr. Nandini Sharma/Maulana Azad Medical College, and Dimagi, Inc. for hosting this research; Brian DeRenzi, Andrew Ellner, and Neal Lesh for ongoing collaboration as co-Investigators; and Sapana Gandhi, Sangya Kaphle, Sugandha Nagpal, Robert Racadio, and Jeremy Wacksman for excellent research assistance. I am grateful to Nava Ashraf, Oriana Bandiera, Iqbal Dhaliwal, Paul Farmer, Rema Hanna, Michael Kremer, Matthew Rabin, Andrew Weiss, and seminar participants at the Northeastern Universities Development Consortium, Harvard Business School, Harvard Department of Economics, and Harvard Medical School for helpful comments. Generous financial support has been provided by the Massachusetts General Hospital Consortium for Affordable Medical Technologies, Child Relief International, and the Harvard Business School Doctoral Program.
1 Introduction
Public services such as governance, education, health care, and national defense rely on agents for their provision. In developing countries, the effort of these agents is often a binding constraint, prevailing over other factors such as the agents’ ability and market demand (Das & Hammer, 2007, 2014; Leonard et al., 2013; Maestad et al., 2010). The standard agency model offers both an explanation and a solution to this problem: agents dislike effort, and they can be persuaded with incentives to exert it. In line with this view, recent field experiments have shown that both monetary and non-monetary incentives can improve the performance of agents engaged in public service delivery (Ashraf et al., 2014; Basinga et al., 2011; Duflo et al., 2012; Miller et al., 2012; Muralidharan & Sundararaman, 2011).^1 What this perspective neglects, however, is the converse scenario: agents who exert effort despite having little extrinsic incentive to do so. In settings in which forty percent of health workers are absent from their posts on any given day (Chaudhury et al., 2006), what explains the presence of the remaining sixty percent? While other extrinsic factors (e.g., monitoring, social pressure, status-seeking) likely contribute, the persistence of effort in the face of weak incentives—and the decision to select into pro-social jobs in the first place—suggests a role for intrinsic motivation.^2 That agents can be intrinsically motivated is not new in economics (Benabou & Tirole, 2003; Fehr & Schmidt, 1999). But this motivation is typically taken as given—a fixed trait that or- ganizations may wish to select for but cannot influence (Besley & Ghatak, 2005). If it can be influenced, it is only for the worse, as proposed by theories of motivational crowd-out (Deci et al., 1999; Benabou & Tirole, 2006).^3 In contrast, the business literature has long posited that managers can leverage intrinsic motivation by manipulating job attributes such as autonomy (Deci & Ryan, 1985), purpose (Weick, 1995), and organizational culture (Schein, 1985).^4 But these attributes are (^1) For evidence challenging the effectiveness of financial incentives in public service delivery, see Banerjee et al. (2008), Glewwe et al. (2010), and, in the US context, Fryer (2013). For theoretical contributions on why financial incentives in public service delivery may fail, see Benabou & Tirole (2003, 2006). 2 For empirical evidence that supports the hypothesis that those with pro-social preferences select into pro-social jobs, see Kolstad & Lindkvist (2013) and Lagarde & Blaauw (2013). 3 A small but notable exception is theoretical and empirical work on the role of delegation and empowerment in enhancing intrinsic motivation. See, e.g., Aghion & Tirole (1997); Rasul & Rogger (2015). 4 For more recent evidence from laboratory experiments, see Grant (2007) and Ariely et al. (2008).
motivation are complements. Moreover, those with high intrinsic motivation exhibit a durable response to the self-tracking app, whereas those who are least intrinsically motivated respond initially but eventually perform no better than their counterparts in the control group. Fourth, despite these gains in client visits, no aggregate impact on the health of the pregnant clients and their children is observed, which, while not desirable from a welfare standpoint, supports the notion that the increased effort is elicited primarily for intrinsic reasons. To support this, supplementary evidence suggests that the treatment effects of the self-tracking app are not mediated by implicit extrinsic incentives or effects on the production function. Rather, they appear to increase effort by making effort more intrinsically rewarding. This paper contributes to two nascent literatures. First, recent empirical work has evaluated information incentives as a tool for motivating prosocial behavior. In particular, various forms of relative performance feedback have been found to be effective in improving home energy conser- vation (Allcott, 2011), student learning (Tran & Zeckhauser, 2012; Azmat & Iriberri, 2010), and physician quality (Kolstad, 2013). Information incentives, however, can function as extrinsic or intrinsic rewards, and when the targeted task (such as energy conservation or school performance) confers financial benefits, an information incentive that increases effort in the task is likely to be operating at least in part via an extrinsic channel.^7 In contrast, the self-tracking app studied in this experiment is designed to increase only intrinsic returns to effort. In this regard, this intrinsic in- formation incentive conceptually shares more in common with other intrinsic rewards, such as task meaning and organizational mission, than with other information incentives such as performance feedback. The second literature examines the interaction between incentives and psychological traits such as intelligence and personality in prosocial behavior, in the context, for example, of civil servants (Dal Bo et al., 2013), health agents (Ashraf et al., 2014, 2015), and taxpayers (Dwenger et al., 2014). In particular, Callen et al. (2015) find that personality traits predict job performance among health (^7) Kolstad (2013) finds that cardiac surgeons respond to physician report cards in ways that cannot be explained solely by profit maximization. He defines this reduced-form residual as an “intrinsic incentive” effect, but is silent about what in the physicians’ utility function drives this effect. As such, the results are consistent with physicians behind motivated not only intrinsically, but also by non-financial extrinsic preferences such as those for prestige, recognition, and career promotion.
officials in Pakistan, and that the experimental response to a novel monitoring technology varies with these personality traits. I extend this approach by (a) testing an intrinsic rather than extrinsic incentive, (b) testing a specific, theoretically guided interaction—that between intrinsic incentives and intrinsic motivation—and (c) elucidating psychological mechanisms. The paper is organized as follows. Section 2 presents a simple principal-agent framework in which the principal can offer extrinsic and intrinsic incentives, and agents have extrinsic and intrinsic preferences. Section 3 describes the policy and program context. Sections 4 and 5 present the experimental design and results, respectively. Section 6 concludes.
2 Framework
In this section, I develop a simple framework for analyzing the potential role of intrinsic incentives in public service delivery. I take extrinsic incentives as given and instead focus on the principal’s ability to stimulate effort by increasing intrinsic—i.e., direct hedonic—returns to effort. The framework assumes that an agent may find an effort task tasteful or distasteful for intrinsic reasons. Modifying or augmenting some aspect of the task may make it more tasteful, thereby increasing its marginal intrinsic returns and, by extension, equilibrium effort. The possibility that the principal may be able to control this modification paves the way for intrinsic incentives. I show that, like an extrinsic incentive, an intrinsic incentive can be high- or low-powered, and the incentive interacts with the agent’s intrinsic motivation to determine the marginal intrinsic utility of effort. This interaction generates testable implications that I then take to the experimental data.
2.1 Principal’s choice
Consider a single-principal, single-agent framework. The principal (e.g., a government, nonprofit organization, hospital) is invested in the production of a social good Y (e.g., population health). The agent can contribute to the production of Y with effort ei ≥ 0. Assume that the production function Y ( ei ) is monotonically increasing in ei.
the only cost incurred by the principal is βY , but, as is made explicit below, Y is affected by ψ ( j ) via the latter term’s effect on optimal effort. Let the principal’s utility be V ( Y − βY ), where, given risk neutrality, V ′^ > 0 and V ′′^ = 0. The principal chooses { e, β, j } to maximize her expected utility, subject to the agents’ individual rationality and incentive compatibility constraints.
2.2 Agent’s choice
I now turn to the agent’s choice of e. A risk-neutral agent i with preferences ( θEi , θIi ) chooses effort ei to maximize his expected utility:
Uij = θEi [ α + βY ( ei )] + θIi ( γi + ψj ei ) − e (^2) i 2 (1)
Equation 1 has three terms: an extrinsic payoff term, an intrinsic payoff term, and an effort cost function. The two payoff functions are weighted by extrinsic ( θEi ) and intrinsic ( θIi ) preference parameters, respectively. Assume θEi and θIi are independent and that θEi is distributed over [0 , 1]. In contrast, assume θIi is distributed over [− 1 , 1]. Similar to the discussion of ψ (·) above, whereas preferences for extrinsic rewards can logically only be weakly positive, intrinsic payoff can be positive or negative depending on the tastes of the agent toward the effort task. The convex cost function, e 22^ i , represents an intrinsic cost of effort encompassing quantities such as time, leisure, and caloric expenditure. The first term—the extrinsic payoff function—is simply the principal’s payment contract con- sisting of a fixed wage α and a piece rate βY weighted by the agent’s preference for extrinsic rewards, θiE. For simplicity, assume α = 0. As with the payment contract, the intrinsic payoff term has two components—one that is effort-independent and another that is effort-dependent. The effort-independent parameter, γi , can be thought of as an endowment of intrinsic (dis)utility—e.g., the warm glow experienced from having a prosocial job, or the effort-independent disutility of hav- ing a job that runs counter to one’s tastes. Since this endowment affects equilibrium utility but is explained in more detail in Section 4.1, but in brief, because j only contains objective information, it does not rely on scarcity for its value, whereas recognition, praise, or positive feedback does.
not effort choice, for simplicity, assume γi = 0. The second component of the intrinsic payoff term, ψj ei , captures the practical intuition that, in some circumstances (e.g., volunteering), even when there is no extrinsic payoff ( α = β = 0), agents are still willing to supply e ∗ i > 0 , implying that, over the interval [0 , e ∗ i ), marginal utility of effort is positive. In the absence of an intrinsic incentive (i.e., ψ = 0), the agent derives no intrinsic returns to effort, even if he has intrinsic taste θEi > 0 for the effort task. In other words, only inasmuch as the effort task is observable to the agent can he derive hedonic benefit from it. With the high-powered intrinsic incentive ( j = h = 1), marginal intrinsic benefit is positive if θIi > 0 , negative if θIi < 0 , and 0 if θIi = 0. The agent maximizes Equation 1 with respect to ei , such that:
e ∗ ij = θiE β + θIi ψj (2) This simple first-order condition illustrates the complementarity between intrinsic incentives and intrinsic motivation that this experiment tests. It implies that intrinsic incentives will lead to higher effort for intrinsically motivated agents, but may have no effect or even reduce effort for those who are intrinsically unmotivated. As such, the aggregate effect of an intrinsic incentive depends on the distribution of intrinsic motivation in the agent population, which implies that the principal can increase her utility both by selecting for agents whose intrinsic preferences align with the effort task and by providing such agents with strong intrinsic incentives.
3 Context
3.1 Program context
In 2005, the Government of India launched the Accredited Social Health Activist (ASHA) program, a nationwide effort to improve health services at the community level, especially in rural regions.^11 ASHAs are female community health workers who are selected by local village councils to provide for the health needs of the villages in which they reside.^12 Each ASHA is assigned a discrete (^11) As of 2014, 828,000 ASHAs had been recruited across IndiaGovernment of India (2015). (^12) Job qualifications include: female gender; married, widowed, or divorced status (due to patrilocality); grade 8 education or higher; age between 25 to 45 years; and preferably, literacy.
with polio immunization campaigns, and mobilizing men and women to undergo sterilization.^16 ASHAs’ chief source of income arises from a federally sponsored conditional cash transfer scheme designed to encourage institutional delivery, called Janani Suraksha Yojana (JSY). In this scheme, pregnant women are paid INR 1,400 (USD 23 at 2015 exchange rates) for delivering in an accredited public or private health facility. In addition, an ASHA who accompanies the woman to the hospital for delivery is paid INR 600 (USD 10). ASHAs are not paid for visits to the client during the antenatal period. Thus, in the absence of intrinsic motivation or other non-pecuniary preferences, antenatal visits are rational only inasmuch as they increase the probability of institutional delivery.
3.2 Program site
With a population of 1.6 million, Kaushambi District is one of 19 (out of 70 total) districts in Uttar Pradesh designated by the state government as “high-focus” in view of its poor development indicators. Its maternal mortality ratio of 442 deaths per 100,000 live births is nearly twice the national average and 30% higher than the state average (Government of India, 2011a); the dis- trict’s neonatal mortality rate is twice the national average; and the district has the second-highest scheduled caste population share in the state (Government of India, 2011a).^17 This experiment takes place in Mooratganj, one of eight sub-districts in Kaushambi, with a population of 193, (Government of India, 2011b). At the time of the experiment launch in 2014, Mooratganj had 145 ASHAs, all of whom had been recruited and trained in 2006-2007 when the ASHA program was rolled out in the district. In 2012, a nongovernmental organization (NGO) established a partnership with the Kaushambi district health office to equip the ASHAs with mobile phones to facilitate their work. The phones contain a software application called CommCare, through which the ASHAs register clients and doc- ument home visits as described above.^18 These records, which are synchronized with a cloud-based server, provide the data on which the self-tracking app functions. By the time of the experiment (^16) For example, if an infant registered by the ASHA completes his or her complete course of routine immunizations, the ASHA receives a payment of INR 150 (USD 2.50). 17 Scheduled castes are castes designated in the Constitution of India as historically disadvantaged. Nationwide, 17% of the Indian population belongs to a scheduled caste; in Kaushambi, the proportion is 36%. 18 The application is developed by a US-based company called Dimagi, Inc.
launch, the ASHAs had been using CommCare for 15 months. As all of the ASHAs in the exper- iment were trained to use CommCare, this experiment is not designed to evaluate the underlying work technology. Table 1, Panel A shows descriptive statistics for the performance of the 72 ASHAs in the control group over the pre- and post-intervention periods. Despite the expectation that ASHAs should visit all clients on a monthly basis, the average control ASHA visits 46% of her 13.1 pregnant client s in the average month. Panel B shows descriptive statistics for a limited set of client characteristics and health outcomes as reported by ASHAs. Of note, the 77% institutional delivery rate is substantially higher than the 22% rate for rural Uttar Pradesh reported in the most recent (2005-2006) wave of the National Family Health Survey (International Institute for Population Sciences & Macro International, 2007); though possibly overstated due to selection (both in registration of pregnant women and in reporting of outcomes), this statistic is consistent with a broad increase in institu- tional delivery that has been observed across India since the introduction of the JSY conditional cash transfer scheme in 2005 (Lim et al., 2010).
4 Experimental design
4.1 Experimental interventions
I create a novel mobile phone-based “self-tracking app” designed to enhance the intrinsic utility that ASHAs derive from providing care to pregnant women.^19 In this paper, I mean “intrinsic” both in the sense used in the psychological literature (motivation to do a task for its own sake—effort as its own reward) and in the sense of prosocial preferences (motivation to do a task for its positive social externalities). Both types of motivation stand in contrast to extrinsic motivation, which may be rooted in individualistic (income, job security, career advancement) or social (status, reputation, recognition) concerns, but which, in either case, regards effort as palatable only for the extrinsic rewards it earns. (^19) Credit for the technological development of the app is due to Brian DeRenzi.
Figure 1: Self-tracking app: menu page (Hindi sample and English mock-up).
Menu (Figure 1). The menu page serves as the entry point for accessing the tool. It is accessed by pressing a pre-specified button on the phone. The menu displays a date interval spanning the first day of the current month to the current day. The calendar month is the primary performance interval for ASHAs; the official ASHA program guideline is that ASHAs should visit all of their pregnant clients at least once per month. Below the date interval are three rows that link to the three other pages, which the ASHA can select using the phone’s navigation buttons.
Figure 2: Self-tracking app: relative performance page.
Relative performance (Figure 2). This page features an ordered bar graph of the number of unique pregnant clients visited in the current month, for the ASHA and 15 other ASHAs. The peer ASHAs are chosen randomly from among the 73 ASHAs who belong to the self-tracking treatment condition. The ASHAs are not told the identities of the peers. Anonymity is important for both ethical and theoretical reasons—ethically, to avoid harmful repercussions that may result from publicizing individual ASHAs’ performance, and theoretically, to unlink the informational effect of social comparison from external peer effects, such as these very repercussions. Each ASHA’s peer
group is randomly chosen, such that all peer groups are asymptotically identical but individually unique.^22 In order to avoid serial correlation effects, all peer groups are redrawn at the beginning of each month. The graph updates in response to changes in the ego ASHA’s performance, as well as the performance of peer ASHAs. The relative performance graph is designed to provide an anonymous social benchmark that makes the information signal about performance more useful.^23 Conceptually, the relationship between social comparison and intrinsic and extrinsic motivation is nuanced. On one hand, pub- licly identifiable social comparison would be expected to interact with externally-oriented social preferences such as tastes for status and recognition. Private social comparison, however, does not engage these external preferences; status, for example, cannot be conferred on an anonymous entity.^24 On the other hand, private social comparison can interact with internally-oriented social preferences, such as competitiveness and taste for winning, or preferences that may not involve so- cial interaction but may be mediated by social information, such as social norms regarding private behavior. If these preferences are in relation to an abstract reference group and do not implicate social interaction, they more closely align with standard definitions of intrinsic rather than extrinsic motivation.^25 (^22) In consequence, each peer group has, in expectation, the same average performance, but any given peer group may be higher- or lower-performing than average (and this may fluctuate on a day-to-day basis). 23 In a different study population (such as students taking an exam), analogous information could be conveyed by disclosing only the group mean, which is also anonymous. In the ASHA population, piloting exercises revealed that the concept of “average” was difficult to convey to many ASHAs, and thus the visual display format was adopted. 24 In the absence of peers, motivation around managing one’s self-image could affect effort—e.g., an ASHA who is motivated to do ASHA work because she self-identifies as a prosocial type. However, whereas social image motivation (in which an agent has preferences over how others attribute her behavior) implicates social preferences, self-image motivation can exist in the absence of social preferences, and thus I classify this as a type of intrinsic, not extrinsic, motivation, consistent with Benabou & Tirole (2006); Bénabou & Tirole (2011). 25 To illustrate, consider a competitive athlete. While preferences for status and recognition may play a role in driving the athlete’s effort, there may also be a private component—a desire to excel—that is intrinsic. The athlete might set a performance goal that is absolute (running a mile in less than five minutes), relative to her own performance (setting a personal record), or relative to others’ performance (setting a world record). In each case, good performance can be an end in itself, not an instrument for attaining other rewards. Relative social benchmarks, a type of social norm, have been shown to exert substantial influence on behavior even when the behavior is private and does not entail social interaction, such as the case of home energy consumption (Allcott, 2011; Schultz et al., 2007).
of visits conducted in the current month, number of clients visited monthly over the previous six months, and visit performance relative to an anonymous peer group. All of these elements are meant to make the effort task and its prosocial impact more psychologically salient to the ASHA, thereby, in theory, amplifying the marginal intrinsic utility of effort.
4.1.2 Low-powered intrinsic incentive: Generic encouragement
One counterfactual to the self-tracking app could be to continue the status quo in which ASHAs are not given access to any additional app on their phones. However, this would introduce a mechanism confound; any treatment effect could be due to the interactive nature of the app, rather than its information content.The intent of the experiment is to focus on the incentive effect of information itself. To this end, I develop a counterfactual intervention that preserves the technological interface of the self-tracking app, but provides generic information that is putatively less effective at making the effort task and its prosocial impact salient. The low-powered app also has four pages; sample pages are shown in Figure 5. The content includes generic encouragement messages. A different set of pages is generated on a daily basis, out of an inventory of 52 sets. The menu page points to three further pages:“Responsibilities of an ASHA,” “Advice for Healthy Mothers & Babies,” and “Inspiring Quotes.” Each page includes a statement accompanied by a picture that illustrates the statement. Content for the first two sections is drawn from ASHA training materials. The “Inspiring Quotes” section contains quotes drawn from Hindi-language websites, with many attributed to well-known South Asian cultural figures such as Gandhi and Mother Theresa.
4.1.3 Audio service
In the study sample, 28% of the ASHAs are illiterate.^27 To ensure that the interventions would be useful to these ASHAs, the research team developed an automated audio version of each interven- tion. Analogous to the act of accessing the app on the phone, an ASHA calls a designated phone (^27) Each ASHA was asked to read a Hindi sentence which stated, “The woman went to the market to buy vegetables.” Twenty-eight percent were able to read no words or only a few words; the remainder were able to read all of thewords and are classified as literate.
number from her work phone. An automated recording then reads aloud the information contained in the ASHA’s visual app. There is no limit on how often the ASHA can utilize the audio service. While there are differences in the user experience of the audio and the visual software interface (e.g., navigation is possible only in the latter), the underlying information content is the same. Thus, the theoretical framework is unaffected, and in the empirical analysis, unless otherwise stated, I combine usage of the visual and audio systems to yield a composite measure of take-up.
4.2 Randomization, implementation, and data sources
The 145 ASHAs in Mooratganj were randomly assigned to one of the two intrinsic incentive treat- ments: self-tracking and generic encouragement. Randomization was conducted in May 2014, one month before the launch of the experiment, and was stratified by six variables: Hindi literacy, total client visits conducted over the prior 4 and 12 months, respectively, and scores on three psy- chometric scales for extrinsic, intrinsic, and prosocial motivation, respectively.28,29^ In June 2014, the self-tracking and generic encouragement apps were installed on ASHAs’ phones, and all 145 ASHAs were trained in their use. All ASHAs were told that two different phone-based tools were being piloted, and that the eventual plan was to make both available if so desired by ASHAs. No complaints were raised to the research team or the implementing NGO during the training or at any point thereafter regarding the randomization. Once the experiment launched, care was taken to preserve the intent of the intervention: to alter the intrinsic utility of effort without altering real or perceived extrinsic returns to effort.To avoid a potential monitoring effect, no efforts were made to affect demand for the apps through, e.g., routine follow-up visits, marketing, or interactions during other program activities such as trainings.^30 (^28) Literacy was directly measured during the baseline survey by asking the ASHAs to read a Hindi sentence. The two visit measures were included to capture both short- and medium-term baseline work performance. The psychometric scales are described in detail in Section A. 29 I used the “T-min-max” method with 1,000 draws to carry out the randomization. For a discussion of this randomization method, see Bruhn & Mckenzie (2009). 30 The one partial exception was an automated SMS system created by the research team, in which a text message is sent to each ASHA every Monday stating either (for self-tracking app users), “Your visits information is available. Please press the shortcut button to access your information,” or (for encouragement app users), “Your advice and encouragement is available. Please press the shortcut button to access your encouragement.” ASHAs were told that these messages were sent automatically to all ASHAs. SMS use is low overall in this setting, and we find no significant Monday fixed effect for app usage.
corroborate rates self-reported by ASHAs (cf. 1; e.g., 77% vs. 70% institutional delivery rate in data collected from ASHA reports and the household survey, respectively). Some of this difference is likely attributable to selection—that is, ASHAs submit data only for registered clients, whereas the household survey samples all pregnancies. In addition, I run t -tests for differences in means between the two treatment conditions for all 263 numeric variables in the baseline ASHA survey. Of these, 4.9% of the differences are significant at the 10% level; 1.9% at the 5% level; and 0.4% at the 1% level. Repeating this exercise for daily home visits for all 455 days in the pre-experimental period, t -tests reveal that 6.9%, 2.5%, and 0.5% of daily visit counts are significantly different at the 10%, 5%, and 1% levels, respectively. Taken together, these results indicate failure to reject the null hypothesis that the two treatment groups are identical on observables. In the analysis that follows, to the extent possible, baseline differences are controlled for, either with explicit covariates or with fixed-effect estimators.
5.2 Empirical strategy and average treatment effects
To identify causal effects, I exploit the fact that performance is observed at the daily (or, in the case of earnings, monthly) level. This allows for panel analysis with ASHA fixed effects that control for all time-invariant ASHA characteristics. In addition, the availability of pre-experimental data allows for differencing out time-variant, ASHA-specific trends. I estimate the general equation:
yit = β 0 + β 1 At + β 2 At ∗ Ti + αi + Zmγm + uit (3)
where yit is an outcome of interest for ASHA i at time t ; At is an indicator for whether time t is after the launch of the experiment; Ti is an indicator for treatment assignment that takes value 0 in the generic encouragement condition and 1 in the self-tracking condition; αi is a vector of ASHA fixed effects; Zm is a vector of month-year fixed effects; and uit is the error term. I assume that errors are serially correlated and thus present standard errors clustered at the ASHA level throughout.
Randomization ensures, in expectation, that Ti ⊥ uit , and that trends in Yit between the two treatment groups, in the absence of treatment, would have been parallel. Furthermore, spillovers between treatments are unlikely given that ASHAs live in different villages and cover defined, non-overlapping catchment areas and thus are unlikely to experience spillovers via either market demand or through direct interactions. Finally, there is minimal attrition during the follow-up period of the experiment. Of the 145 ASHAs, all participate in app training, and at twelve months of follow-up, only 3 ASHAs—one in the generic encouragement condition and two in the self- tracking condition—are no longer working (all three due to out-migration). Under these identifying assumptions, β 2 measures the average causal effect of the self-tracking app relative to the generic encouragement app.
5.2.1 Average treatment effects on take-up and client visits
Table 4 presents average treatment effects on take-up of the interventions, on client visits, and on earnings. Columns 1-3 show results for take-up of the main visual software, the complementary audio service, and total use of the two modalities combined. All estimates are in units of sessions per day. Three findings are noteworthy. First, despite an experimental protocol that provides little explicit encouragement to use the interventions, demand for both apps is high. Over the course of the one-year experimental period, the average ASHA in the self-tracking (generic encouragement) treatment uses her app once every 3.89 (3.22) days. To contextualize this, client visits occur every 3.78 and 4.67 days in the self-tracking and generic encouragement conditions, respectively, during the experimental period. Thus, in both conditions, app usage is higher than visit frequency, suggesting that use of the phone for visits is not exclusively driving use of the apps.^31 Second, the fact that take-up of the two apps is similar (with the point estimate favoring the generic encouragement app) suggests that any treatment effects favoring the self-tracking app cannot be explained purely by differences in how often each app is used (e.g., a learning-by-doing effect that makes frequent phone users more efficient at filling out forms). (^31) That take-up of the the visual software is higher for the generic encouragement app than for the self-tracking app is likely because the encouragement app provides new content daily, whereas the self-tracking app is informationally static in the absence of visits, which occur less than daily for all ASHAs.
