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Insurance Through Savings Accounts Evidence from a Randomized Field Experiment among Low-Income Micro-Entrepreneurs in Chile∗ PRELIMINARY. DO NOT CITE OR CIRCULATE. Ronald Abraham† Felipe Kast‡ Dina Pomeranz§ October 2011 Abstract Poverty is often characterized not only by low average income, but also by highly variable income and expenditures, and a lack of access to insurance services that can help smooth consumption. We investigate whether access to a formal savings account can provide a vehicle for self-insurance, by allowing participants to build a buﬀer stock of precautionary savings. In a randomized ﬁeld experiment in Chile, about 3000 low-income micro-entrepreneurs are provided access to a formal savings account with no minimum balance or maintenance fees. Evaluating the impact after one year, we ﬁnd that access to such accounts helps participants alleviate the burden of economic shocks, both objectively and subjectively. Participants with access to a savings account have less informal debt, fewer outstanding payments, and less often need to reduce consumption due to economic diﬃculties. Subjectively, they report being less worried about their ﬁnancial future, and evaluate their recent economic situation as less severe. We therefore conclude that formal savings accounts can be an eﬀective vehicle to provide a means for consumption smoothing in contexts where many other forms of insurance are lacking. ∗ We are grateful to Fondo Esperanza, Banco Credichile and Microdatos for outstanding collaboration in the implemen- tation process. We thank Shawn Cole, David Cutler, Michael Kremer, Nicola Fuchs-Schuendeln, Edward Glaeser, Jessica Goldberg, Daniel Hojman, Lakshmi Iyer, Sandy Jencks, David Laibson, Dean Karlan, Lawrence Katz, Dan Levy, Jeﬀrey Liebman, Stephan Litschig, Brigitte Madrian, Sendhil Mullainathan, Rohini Pande, Richard Zeckhauser and participants at the Harvard Development Lunch for helpful comments and discussions. This project would not have been possible without the generous support by the following institutions: the Ford Foundation, the Lab for Economic Applications and Policy (LEAP) at Harvard University and the Woman and Public Policy Program at the Harvard Kennedy School, the David Rockefeller Center for Latin American Studies, and the Russell Sage Foundation Small Grants Program. The experiment was carried out in accordance with Harvard GSAS IRB approval. † Intellectual Capital Advisory Services, Road No. 12, Banjara Hills, Hyderabad, AP 500034, India ‡ Pontiﬁcia Universidad Catlica de Chile § Harvard Business School, Rock Center 213, Soldiers Field, Boston, MA 02163; email@example.com. 1 1 Introduction At low levels of income, economic shocks can have devastating eﬀects. Resources may fall below subsistence levels, which leads to potentially dire impacts, or costly measures to avoid them. The severity of this issue is compounded by the fact that poor people are often faced with highly variable income streams and expenditure shocks. Correspondingly, worry and anxiety about their economic future often mark the lives of the poor (e.g. Collins et al., 2009). Building a buﬀer stock of precautionary savings that facilitates smoothing of economics shocks therefore has potentially large beneﬁts on their wellbeing. While the literature on precautionary savings and buﬀer stocks has long acknowledged that incomplete markets may lead to borrowing constraints and an absence of contingent assets or insurance services, access to a riskless bond or a formal savings account to build such precautionary savings is usually taken as given (e.g. Zame, 1993). An emerging literature shows, however, that many individuals in developing countries may not only be credit constrained, but also savings constrained (e.g. Burgess and Pande, 2005, Ashraf et al., 2006; Brune et al., 2011; Atkinson et al., 2010; Dupas and Robinson, 2011). This paper provides what is to our knowledge the ﬁrst micro-empirical evidence show- ing that access to a savings vehicle can facilitate building of precautionary savings, and reduce participants’ vulnerability to economic shocks, as well as their use of other, po- tentially more costly smoothing mechanisms. We conduct a randomized ﬁeld experiment among around 3000 low-income micro-entrepreneurs in Chile. We ﬁnd that participants with access to a formal savings account are less worried about their ﬁnancial future, evaluate their recent economic situation as less severe and less often have to cut back consumption due to economic diﬃculties. In addition, they report lower levels of informal debt and fewer arrears on outstanding payments. Our exceptional access to detailed savings data of participants also allows us to 2 track the timing of all deposits and withdrawals. During the follow-up survey, we elicit information about whether participants experienced any of several categories of economic shocks or important life events, and if so in which month. Matching this information to the savings data reveals that participants’ deposit patterns closely match the timing of these shocks, while no signiﬁcant correlation is detected with withdrawals. This paper contributes both to the growing literature on savings and on (mi- cro)insurance in developing countries. The literature on savings in developing countries has followed two strands: a ﬁrst series of papers studies mechanisms aimed at helping individuals increase their savings (e.g. Ashraf et al., 2006a Ashraf et al., 2006b; Atkinson, et al., 2010; Brune et al., 2011; Kast, Meier and Pomeranz, 2011). A second strand of the literature aims at assessing the impacts of increasing savings or of having access to a formal savings account on outcomes such as reduction in poverty (Pande and Burgess, 2005), increase in investment and income (Brune et al., 2011; Dupas and Robinson, 2011) and female empowerment in intra-household bargaining (Ashraf et al., 2010). Several of the savings vehicles analyzed in these studies have a withdrawal commitment compo- nent, which impedes discretionary withdrawals in times of need, and may therefore not be appropriate for building a buﬀer stock for general precautionary savings. This paper, in contrast, analyzes a savings account that is fully liquid on the withdrawal margin, in order to study the potential of formal savings accounts to provide insurance through precautionary savings. Much of the literature on insurance in developing countries has focused on analyz- ing to what degree individuals are able to smooth shocks even without access to formal insurance products, for example through loans from their social network (e.g. Townsend, 1994; Murdoch, 1995; Kinnan and Townsend, 2010), and on understanding the reasons why there are very few functioning formal insurance markets in developing countries, even for risks that seem to present relatively little problems of moral hazard or adverse 3 selection, such as weather risks (Cole et al., 2009.) Given the limitations of informal insur- ance mechanisms, and the challenges faced by formal insurance products, self-insurance through savings has the potential to play an important role. While the precautionary sav- ings motive has been long established as a key determinant of savings behavior (Browning and Lusardi, 1996), this paper provides what is to our knowledge the ﬁrst empirical evi- dence establishing the potential of savings to reduce the impact of economic shocks and improve the wellbeing of the poor. The remainder of the paper is organized as follows: Section 2 provides information about background, data and the study design; Section 3 presents our main empirical ﬁndings; and Section 4 concludes. 2 Background, Data and Study Design 2.1 Background and Data This paper is based on a randomized ﬁeld experiment with around 3000 low-income micro- entrepreneurs in Chile. The study was conducted in collaboration with Fondo Esperanza (FE), a Chilean microﬁnance institution, and Banco Credichile (BC), a large commercial bank. FE’s members are self-employed micro-entrepreneurs (e.g. street vendors, cosmetic saleswomen), many of whom work in the informal sector. About 90% of FE members are women, and most live and work in urban areas. FE provides micro-loans to its clients in 3-month cycles, repayment of which is monitored in weekly or biweekly group meetings. The credit disbursement and repayment is on a very rigid schedule, and consequently cannot be used as insurance for emergencies or for unexpected income or expenditure shocks. In focus groups, many participants therefore expressed the desire for a savings vehicle that would allow them to build a buﬀer stock of precautionary savings. The savings 4 accounts oﬀered to FE’s clients as part of the intervention are with the commercial Bank Banco Credichile, because FE is not legally licensed to hold savings deposits. We draw on three diﬀerent sources of data. Data on deposits and withdrawals of those who took up savings accounts is directly obtained from BC, the bank holding the accounts. It includes information on which participants open an account, whether they use the account after opening it, and the dates and amounts of all withdrawals and deposits made after opening the account. The second source of data comes from FE’s administrative ﬁles, which includes information on each participant’s estimated household size and income and years of education. Finally, we complement these two sources of administrative data with an extensive baseline and follow-up survey. The baseline survey was conducted prior to the introduction of the savings accounts in April-May 2008 during one of the group meetings. The follow- up survey, in June-July 2009, was administrated at individuals’ home or work place, so that those participants who had left FE in the meantime could still be reached. The surveys include detailed questions about participants’ savings and debt, their economic situation, recent economic diﬃculties and consumption patterns, as well as subjective measures such as participants’ worry about their ﬁnancial future, regret about not having saved more, being intimidated by banks, risk aversion and time preferences. Centro de Microdatos from the University of Chile administered all surveys. 2.2 Experimental Design and Experimental Speciﬁcation Prior to any intervention, the baseline survey was conducted in the 196 groups of the micro-credit organization, Fondo Esperanza (FE). The universe of study participants consists of the 4175 members who were present in the meeting when the baseline survey was administered. Two third of the groups were subsequently randomly elected to be 5 oﬀered a savings account. The accounts were introduced during group meetings in the weeks following the baseline survey. The randomization was conducted at the group level, such that all members of each group received the same treatment. While take-up of the account was completely vol- untary (and ended up being about 50%), interested participants were also oﬀered the opportunity to go open the savings account together with their peers, to overcome the frequently expressed sentiment of feeling intimidated by entering into a bank. The standard savings accounts oﬀered to participants have a real interest rate of 0.3% (similar to the highest available alternative in the Chilean market). The accounts are particularly suited for the low-income population of this study in that they have no maintenance fees, no minimum balance, and only a two-dollar minimum opening deposit. Savings in the accounts are fully liquid for withdrawal at any time. These conditions were guaranteed for a minimum of two years. A subgroup of one quarter of participating groups was randomly assigned to receive a preferential interest rate of 5%, and in half of the groups, the savings accounts were accompanied by a peer group commitment mechanism, (see Kast, Meier, and Pomeranz 2011 for an evaluation of the diﬀerential savings behavior resulting from these diﬀerent sub-treatments). The 5% higher interest rate only very modestly aﬀected savings, if at all, while the peer group commitment device had a strong positive eﬀect on the number of deposits and the amounts saved in the accounts. Due to sample size limitations, analyzing the impacts of the individual treatments is relatively noisy. The main analysis of this paper therefore focuses on the average impact of having access to a savings account with any of these characteristics. In Section 3.4 below, we aim to disentangle the diﬀerential eﬀects for those with and without the peer group support. About one year after opening of the accounts, the follow-up survey was conducted 6 to collect data on the impacts of the accounts. To analyze the eﬀect of having a savings account on various outcomes of interest, we estimate a simple diﬀerence in diﬀerence speciﬁcation comparing those in the treatment group to those in the control group at the time of the baseline and the follow-up survey: Yit = α + βAccountit + γi + µt + it (1) where Yit is the outcome variable of interest. Accountit is a dummy variable that takes on the value 1 if individual i is in the treatment group and period t is the treatment period. Individual and time ﬁxed eﬀects are represented by γi and µt respectively, while it is the error term. We use this speciﬁcation to analyze the impact of savings accounts on a series of out- comes, such as participants’ economic diﬃculties and worry about their ﬁnancial future, their need to cut back their consumption as well as their borrowing and lending behavior. We then analyze whether the savings behavior of individuals who opened an account with Banco Credichile is correlated to events in their lives that trigger economic shocks. In the follow-up survey, we elicit information about whether participants experienced any of a series of possible life events (e.g. loss of a job, birth of a child, etc.), and if so in which month, and we match this information with data about the account use. We use panel regressions to estimate whether deposits and withdrawals were aﬀected in the month of the life event: Yit = α + LifeEventit β + γi + ηt + it (2) where Yit is a dummy variable indicating whether participant i made a deposit or a withdrawal in month t. Lif eEventit is a vector of life events for individual i in month t. Individual and month ﬁxed eﬀects are represented by γi and ηt respectively, while it is 7 the error term. 2.3 Baseline Summary Statistics, Balance of Randomization and Use of the Accounts Table 1 presents baseline summary statistics for the 4175 study participants. As expected, given the random assignment, characteristics in the treatment group are not statistically signiﬁcantly diﬀerent from the control group. In the control group, participants are on average 43.3 years old and have 9.8 years of schooling. Approximately 31% already had a savings account prior to the study. The mean monthly income per capita is 116,140 Chilean pesos (about 230 USD), total formal and informal savings is 63,302 pesos (about 130 USD), and total debt, including the micro-loan from FE, is 454,872 pesos (about 900 USD). While income is expressed in per capita terms (household income divided by number of household members, which is 4.3 on average), savings and debt may be the combined for several household members, including participants’ children. The larger amounts of debt compared to savings is not surprising given that participants are entrepreneurs and most of their debt is backed up by inventories and future sales. On a question on whether participants are worried about their ﬁnancial future, the mean score was 2.9 out of 4, and evaluating their recent economic diﬃculty, participants indicate on average around 5 on a scale of 10. Among those oﬀered access to a savings account, the take-up rate was 53%. One year after opening the account, at the time of the impact evaluation, the average savings balance by those who had opened an account was about 14,000 pesos. The average number of deposits in the course of the year was a little over 2, and the average number of withdrawals was only 0.7. 8 3 Results 3.1 Impact on worry and self-assessed economic diﬃculties The ﬁrst set of impacts that we analyze is whether participants experienced an overall insurance eﬀect, both forward and backward looking. We measure the backward looking eﬀect by asking study participants how much they agreed with the statement that they were worried about their economic future. Participants chose from four options ranging from 1 for ’very much disagree’ to 4 for ’very much agree’. One year after receiving a savings account, participants in the treatment group were scored 0.11 points less worried than those in the treatment group (see Table 2). This diﬀerence is statistically signiﬁcant at the 10% level. Given that this is in response to a broad question on economic wellbeing, something aﬀected by a host of factors other than having access to precautionary savings, this result is striking. The lower levels of worry about their future might be a result of participants’ expe- rience of the recent past. In order to capture the retrospective assessment of economic diﬃculties, we asked participants, ”In sum, thinking about all economic diﬃculties in the last 3 months, how diﬃcult was the situation for you?” The answers ranged from 1 stand- ing for ’not diﬃcult at all’, to 10 for ’very diﬃcult’. One year after receiving an account, the treatment group scored 0.24 points lower than the control group. This diﬀerence is statistically signiﬁcant at the 10% level as well. Therefore the individuals in the treatment group have a qualitatively better assessment about their economic past. We therefore conclude that the treatment increased participants’ economic well-being, both in terms of improving their self-assessment about their recent economic diﬃculties and their outlook to the future. Reducing such economic diﬃculties and increasing peace of mind about the future are some of the central beneﬁts that one would expect of an 9 eﬀective insurance mechanism, so it seems that the savings accounts are fulﬁlling this important function. We now test for direct evidence of a reduction in ﬂuctuations of consumption due to economic diﬃculties for those who were oﬀered a savings account. In order to be able to test for this, we asked participants whether in the last three months they had to cut back certain forms of consumption due to economic diﬃculties. We asked in turn about the following categories of consumption: meals, meat, medicines, school supplies, clothing, school snacks, public transport and eating out. This provides us with a series of binary outcome variables indicating whether the participant had to cut back on consumption for the category in question. Table 4 reports these results. Each represents a diﬀerent item of consumption, as indicated by the column heading. The direction of almost all the coeﬃcients on Accountit is negative, indicating lesser need to cut back consumption for those who have access to an account. However, only 2 coeﬃcients out of 8 are statistically signiﬁcant at the 10% level. A F-test for joint signiﬁcance of these coeﬃcients provides a p-value of 0.22. Hence, while there is some evidence of a reduction in consumption variation, the results of all possible consumption categories taken together is not statistically signiﬁcant at the 10% level. 3.2 Impact on lending and borrowing practices One way in which precautionary savings may aﬀect the way individuals deal with economic shocks is to replace other, potentially more costly sources to smoothen consumption. In this sector, we therefore look whether access to a formal savings account aﬀects partici- pants lending and borrowing practices. First, we look at whether participants report less outstanding debt. We capture two 10 categories of such debt: informal borrowing from relatives and friends, and arrears in payments to institutions such as utility providers and educational institutions.1 The re- gression results in Table 3 show that individuals in the treatment group are 6.3 percentage points less likely to owe money to their relatives or friends (signiﬁcant at the 1% level) and 4.2 percentage points less likely to owe money to institutions than the control group (signiﬁcant at the 10% level). The direction of both these coeﬃcients makes intuitive sense. People often resort to small informal loans from relatives and friends when they are cash constrained or when they need money urgently. Those who have access to a savings account will be able to meet some of these contingencies by dipping into their savings and not having to undertake an informal loan. Similarly having a pool of savings reduces the likelihood of arrears in payments to institutions. Having a savings account may not only aﬀect participants’ debt, but also their lending behavior to others. On the one hand, it could lead participants to become less generous in providing loans to their social network, since they now depend less on loans from their network for insurance purposes. In addition, an important barrier for savings in low- income communities is that individuals are not able to refuse loans or gifts to relatives and friends who ask for small help to manage their economic needs (e.g. Dupas and Robinson, 2011; Brune, Gine, Goldberg and Yang, 2011). The use of a savings account may allow individuals to hide their savings and therefore reduce exposure to such requests. On the other hand, if having a savings account increases total savings or makes the savings more visible to the social network, participants with access to a savings account may be better able or more pressured to extend small loans to others. 1 Since more speciﬁc questions tend to increase the precision of survey responses, these aggregate outcome variables were each constructed based on a series of more speciﬁc underlying questions of the following format: ”Do you owe money to X? If yes, how much?” with ’X’ successively being replaced by your sister, brother, father, aunt, etc. We aggregated these responses into a dummy variable indicating whether the participants had outstanding debt to anyone in one of the two categories. 11 To test for this question, we asked each participant whether they were owed money by their relatives, friends or business contacts.2 Table 3 shows a slightly negative but not statistically signiﬁcant eﬀect. So, consistent with the ﬁndings of Chandrasekhar et al. (2010), we do not ﬁnd evidence that access to a savings account crowds out lending to others. It is, however, conceivable that the results would have been diﬀerent in the long run in a context where participants could be conﬁdent that they would be able to continue to rely on savings in the future. The savings accounts in this study were only guaranteed for two years. 3.3 Life Events and Savings Behavior In this section we explore whether life events - such as the birth of a child or a sudden loss of a job - are reﬂected in individuals’ savings behavior, in terms of changes in deposit or withdrawal patterns. This can give us a better understanding of how participants use their accounts and also allows us to see whether economic diﬃculties are reﬂected in real behavior. If this is the case, we are more conﬁdent that the self-reported indicators reﬂect real changes in participants’ economic situation. In order to measure how life events are reﬂected in savings behavior, in our follow-up survey we asked a series of questions on whether X life event happened in the individual’s life in the past 6 months. ’X’ stood for a series of events, such as loss of a job, business downturn, accident, signiﬁcant theft, birth of a child, marriage of self or a child, increase in household size, partnership breakup or death within nuclear family. Whenever the participant responded in the aﬃrmative for a particular life event, we asked what month the event happened in. Thereafter this data was merged with the banking data on monthly 2 Similar to the questions on participants debt, we asked a series of questions, such as, ”Have you lent money to X? If yes, how much?” with ’X’ replaced by ’sister’, ’brother’, etc which were subsequently aggregated into the categories of relatives and friends, or business partners. 12 deposits and withdrawals. We only have this data for 1219 individuals since the sample is restricted to those within the treatment group who opened an account. Table 5 shows that life events are highly correlated with deposits by participants but not with withdrawal patterns. For examples in the months where individuals lose their job, they are 10.4 percentage points less likely to make a deposit (statistically signiﬁcant at the 1% level). This eﬀect is very large in magnitude given that in a month with no ”life event”, the likelihood of an individual making a deposit in that month is 22.1%. Similarly, a business downturn, an increase in household size and a death within the nuclear family brings the likelihood of deposit down by 16.4, 12.7 and 13.6 percentage points respectively (all coeﬃcients are statistically signiﬁcant at the 1% level). The only life events that do not have a statistically signiﬁcant eﬀect on the deposit patterns are the birth of a child and breakup with a partner. We also run a speciﬁcation where Lif eEventit is not a vector but a dummy variable that takes on the value 1 if any life event has taken place for individual i in month t. We ﬁnd that the average eﬀect of any life event is a decrease in likelihood to make a deposit by 11.3 percentage points. However, for withdrawals we do not see signiﬁcant correlations with these life events. None of the coeﬃcients on speciﬁc life events returned a statistically signiﬁcant result when withdrawal was the outcome variable. Perhaps this is related to the fact that withdrawals overall are relatively rare events: the average withdrawal rate in a month, when there is no life event, is 5.1 percent, considerably lower than the average rate of deposits. One possible explanation of these ﬁndings may be that participants use savings accounts for long-term savings rather than for short-term management of economic shocks. This is consistent with the ﬁndings of Dupas and Robinson (2011), who ﬁnd that individuals manage to save at home for short periods of time but not over the long run (a few weeks or months). Therefore, when individuals face a shock, they may dip into their within-household savings and only rarely draw from their formal savings accounts. Perhaps having a formal savings 13 account, which ensures long-term security of funds, is an incentive to save small amounts at home as individuals are aware that they can accumulate the small amounts and deposit them within the bank. 3.4 Diﬀerential eﬀects by types of accounts As discussed above, half of the sample received a savings with a peer group savings commitment device, which has been found by Kast et al. (2011) to signiﬁcantly increase savings in the study account. It is therefore of interest to understand whether the results we are ﬁnding in this paper are mainly driven by the subgroup who received the peer group support, or whether they also apply to those who have received an account without group support. Table 6 addresses this question. Looking at the subgroups with and without peer group support reduces the sample size by half and correspondingly introduces more noise in the analysis. Nevertheless, Table 6, which reports treatment eﬀects on the treated, shows some interesting suggestive results. Column 1 shows that the reduction in worry is concentrated among those in the peer group treatment, and is statistically signiﬁcantly lower for those with access to a savings account without the peer group component. On the other hand, the necessity to cut back on consumption due to economics diﬃculty is reduced more for those with a savings account without the peer group treatment. The reduction is signiﬁcant for those without the peer group treatment, and smaller and insigniﬁcant for those with the peer group treatment. However, this diﬀerence between the two is not statistically signiﬁcant. Finally, overall economic diﬃculties and credit to or from others do not respond diﬀerently for the diﬀerent sub-treatments. These results have several implications. First, they show that the impacts found in this paper are not generally concentrated on those with the peer group support. The 14 ﬁndings therefore seem to hold more generally everyone who received access to a savings account in the scope of this study. Due to the noisiness of the results at the subgroup level, we cannot make very strong interpretations beyond that, but one possible interpretation of the result that the eﬀect on worry seems to be stronger for those with peer group support and the eﬀect on consumption cutback weaker is that those with the peer group treatment save more, and are therefore less worried about the future, while those without the peer group support use more money for consumption smoothing during the crisis. This would be consistent with the ﬁnding of how transactions track participants’ life events, where we see that deposits are reduced signiﬁcantly at times of economic shocks, but the number of withdrawals does not seem to respond. If participants save small amounts at home before depositing them into the bank, it is conceivable that during a time of crisis, those without the peer group treatment use these small savings to smooth consumption and those with the peer group support continue to save. 3.5 Robustness checks In the following section, we will analyze possible threats to validity of the analysis. First, we investigate whether the results might be driven by demand eﬀects, and second, we discuss attrition in the follow-up survey. Demand eﬀects refer to changes in behavior by experimental subjects due to cues about what constitutes appropriate behavior (Zizzo, 2010). In the context of this study, one concern is that participants who received access to a savings account through Fondo Esperanza might report more positive answers in the follow up survey than the control a group, out of gratitude or a sense of indebtedness vis-`-vis the organization. A reason for why this is not very likely to happen is that participants do not know that the survey is related to the savings account. Nevertheless, in order to test for possible demand eﬀects, 15 we included two questions in the survey speciﬁcally designed to capture possible demand eﬀects. These are questions whose answer we expect would be aﬀected by demand eﬀects, but are not directly aﬀected by the treatment. The ﬁrst question asks participants how complicated they found the process of scheduling this interview, and the second asks them how satisﬁed they are with Fondo Esperanza. Table 7 shows that neither of these questions respond to the treatment. This indicates that it is very unlikely that the other self-reported eﬀects we are ﬁnding in this paper are driven by demand eﬀects. The second series of robustness checks that we are looking at are dealing with the issue of attrition in the follow-up survey. The baseline survey was conducted in meetings of FE and by deﬁnition had 100% compliance, since we constructed the universe to consist of all those FE members who were present on the day of the survey. The follow-up survey, one year later, happened at participants’ home or work place, to ensure we also included those individuals who were no longer members of FE. Even though we had taken great care to keep attrition low,3 12.4% of participants could not be found for the follow-up survey (see Table 8). Since we do not have information for these participants, we are only able to include individuals in this study who could be reached for the follow-up survey. When comparing attrition rates across the treatment and control groups, we ﬁnd that attrition is 2.81 percentage points higher in the treatment group. This type of diﬀerential attrition is a potential concern, as the characteristics of the attritors tend to diﬀer from those who stay in the panel. We control for any permanent diﬀerences by using individual ﬁxed eﬀects. However, we cannot rule out that the trend over time is diﬀerent for attritors compared to those who stay in the sample. 3 During the baseline survey, we asked participants not only about their own contact information, but also about the contact details of a close relative or friend. In addition, we chose to work with the survey agency Microdatos, who has special expertise in following participants for panel studies. 16 Using the baseline survey, we can check whether characteristics of attritors and stayers are indeed diﬀerent. We ﬁnd that younger persons have a higher propensity not to be present in the follow-up survey. Similarly, attritors tend to have a lower amount of previous savings (see Table 8). This makes intuitive sense: young people are more likely to move for college, marriage or professional reasons, and are therefore more diﬃcult to follow in a panel study. They also tend to have lower savings. One way to adjust for the potential bias caused by non-balanced attrition is to apply inverse probability weights (Wooldridge, 2002 and Wooldridge, 2007), which works as follows. First, we predict the probability that an individual surveyed during the baseline will be in the follow-up survey. The calculated probability for each individual forms the propensity score - i.e. the propensity of an individual being in the follow-up survey. Thereafter, we weigh each individual with the inverse of this propensity score. Therefore, those who are less likely to be part of the follow-up survey receive a higher weight as compared to the other participants. This weighing scheme allows us to weigh more heavily the individuals who are underrepresented in the follow-up survey. All results remain qualitatively unchanged when applying inverse probability weights. In Appendix A1, we present results from section 3.1 once again, both with and without attrition weights. The data reveals no signiﬁcant diﬀerences between both types of mea- surements. For example on the question of the participant’s assessment on their recent economic diﬃculty, treatment improved the average score by 0.240, while the correspond- ing coeﬃcient when adjusting with attrition weights is 0.249. Appendix A2 similarly shows the results on lending and borrowing by participants. Once again, the diﬀerences between results with and without attrition weights were minor. For example, without attrition weights, being in the treatment group reduces the likelihood of owing money to relatives and friends by 6.3 percentage points. With attrition weights, the corresponding coeﬃcient is 6.2 percentage points. 17 4 Conclusion This paper investigates the eﬀectiveness of precautionary savings through formal savings accounts as a means to insure against economic shocks, among a low-income population in Chile that works in the informal sector and has little access to other formal insur- ance mechanisms to smooth consumption through the frequent economic shocks. We ﬁnd that access to a savings account, coupled with deposit commitment support, leads to a signiﬁcant reduction in one of the overbearing elements that often characterize poverty: worry and anxiety about one’s economic future. This reduction in worry is based on con- crete economic improvements in participants’ ability to smooth shocks: they experience recent economic diﬃculties as less severe, less frequently need to cut back certain types of consumption due to economic shocks, and have lower outstanding debt to their informal network or fewer arrears on bills. These ﬁndings add to the growing of evidence showing the on the beneﬁts of savings accounts. While the microﬁnance movement includes micro-credit, micro-insurance and micro-savings, the latter has traditionally received less attention, possibly because its potential to generate proﬁts to the microﬁnance institution is lower. Recent evidence seems to suggest, however, that microcredit may have relatively limited impacts (Banerjee et al., 2010), and micro-insurance faces some strong challenges in generating demand, partly because it requires the client to trust the microﬁnance institution to fulﬁll on their future commitment to pay out in case of a bad state of the world (Cole et al., 2009). Combined with the other recent ﬁndings of positive impacts of access to various forms of savings vehicles, discussed in the introduction, the ﬁndings of this paper therefore suggests that increasing access to micro-savings vehicles may be a powerful, if less proﬁtable, intervention for increasing welfare of the poor. These ﬁndings have important policy implications. First, they suggest that subsidiz- 18 ing savings accounts, coupled with deposit commitment support, may have large social returns. Private banks often do not ﬁnd it in their interest to host savings accounts for very low account, and correspondingly charge such accounts with large administrative hurdles, minimum balance requirements, or maintenance fees. Such costs often result in very large negative interest rates in formal savings accounts for the poor, which can result in savings constraints. This paper suggests that such constraints can have large welfare implications. While there has been a large focus in recent years on expanding access to (expensive) credit options for the poor, this paper points to the potentially high returns to improving access to savings vehicles. Free basic current accounts, such as recently introduced by the Chilean government, for example, can be expected to generate signiﬁcant beneﬁts. Similarly, policies that facilitate deposits into savings accounts, such as the recent policy adopted by the Chilean government to deposit welfare payments into government provided savings accounts, rather than dispersing them in cash, have potentially large beneﬁts. The ﬁndings of this paper also raise a number of interesting questions for future research: If returns to savings are so high, why aren’t individuals building a buﬀer stock for precautionary savings at home? Do they have self-control issues? ”Other-control issues” of having to keep demands by their social network at bay (Brune et al., 2011)? Concerns about the safety from theft at home? Knowing where the source of the savings constraint lies will also help determining what characteristics of the savings accounts have the highest potential to increase welfare. In future analysis for this paper, analyzing heterogeneous treatment eﬀects between individuals with diﬀerent degrees of self-reported self-control and other-control problems and security concerns, we hope to shed more light on this question. 19 5 References Ashraf, Nava, Dean Karlan, and Wesley Yin, ”Tying Odysseus to the Mast: Ev- idence from a Commitment Savings Product in the Philippines,” Quarterly Journal of Economics, 2006, 121 (1), 635-672. Ashraf, Nava, Dean Karlan, and Wesley Yin, ”Deposit Collectors.” Art. 5. Special Issue on Field Experiments. Advances in Economic Analysis and Policy 6, 2006, no. 2. Ashraf, Nava, Dean Karlan, and Wesley Yin, ”Female Empowerment: Impact of a Commitment Savings Product in the Philippines,” World Development, 2010, 38 (3), 333-344. Atkinson, Jesse, Alain de Janvry, Craig McIntosh, and Elisabeth Sadoulet, ”Creating incentives to save among microﬁnance Borrowers: A Behavioral experiment from Guatemala,” 2010, Working Paper. Banerjee, Abhijit, Esther Duﬂo, Rachel Glennerster and Cynthia Kinnan, ”The miracle of microﬁnance? Evidence from a randomized evaluation,” 2010, Working Paper. Browning, Martin and Annamaria Lusardi, ”Household Saving: Micro Theories and Micro Facts,” Journal of Economic Literature, 1996, XXXIV (December), 1797-1855. Brune, Lasse, Xavier Gin, Jessica Goldberg, and Dean Yang, ”Commitments to Save: a Field Experiment in Rural Malawi,” 2011, Working Paper. Burgess, Robin, and Rohini Pande, ”Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment,” American Economic Review, 2005, 95 (3), 780- 795. Chandrasekhar, Arun, Cynthia Kinnan, Cynthia and Horacio Larreguy, ”Do Savings Crowd Out Informal Insurance? Evidence from a Lab Experiment in the Field”, 2010, Working Paper. Cole, Shawn A., Xavier Gine, Jeremy Tobacman, Petia Topalova, Robert Townsend, and James Vickery, ”Barriers to Household Risk Management: Evi- dence from India.”, Harvard Business School Working Paper, 2009, No. 09-116. Collins, Daryl, Jonathan Morduch Stuart Rutherford, and Orlanda Ruthven, Portfolios of the Poor: How the World’s Poor Live on $2 a Day, 2009, Princeton University Press. Dupas, Pascaline, and Jonathan Robinson, ”Savings Constraint and Microenterprise Development: Evidence from a Field Experiment in Kenya,” 2011, Working Paper. Kast, Felipe, Stephan Meier, and Dina Pomeranz, ”Under-Savers Anonymous: Evidence on Self-Help Groups and Peer Pressure as a Savings Commitment Device”, 2011, Working Paper. 20 Kinnan, Cynthia and Robert Townsend, ”Kinship and Financial Networks, Formal Financial Access and Risk Reduction,” 2011, Working Paper. Rosenzweig, Mark and Hans Binswanger, ”Wealth, Weather Risk, and the Compo- sition and Proﬁtability of Agricultural Investments,” Economic Journal, 1993, 103 (January), 56-78. Wooldridge, Jeﬀrey, ”Inverse probability weighted M-estimators for sample selection, attrition, and stratiﬁcation,” Portuguese Economic Journal, 2002, 1, 117-139. Wooldridge, Jeﬀrey, ”Inverse probability weighted estimation for general missing data problems,” Journal of Econometrics, 2007, 141 (2007), 1281-1301. Zame, William, ”Eﬃciency and the Role of Default When Security Markets are Incom- plete,” American Economic Review, 1993, 83 (1), 1142-1164. Zizzo, Daniel, ”Experimenter demand eﬀects in economic experiments”, Experimental Economics, 2010, 12(1), 75-98. 21 6 Tables Table 1: Baseline Summary Statistics and Balance of Randomization Control Account Variable (1) (2) Education in years 9.815 -0.158 (3.111) (0.156) Age 43.29 0.101 (11.596) (0.444) Income per capita (monthly) 116,117 4,008 (80,582) (4,247) Household size 4.273 0.0565 (1.732) (0.0661) Has prior savings account 0.312 0.00309 (0.464) (0.0176) Amount of other savings 63260 5,720 (241,301) (8,482) Amount of former debt 454,659 73,964 (1659202) (76,757) Worry 2.899 0.0413 (0.968) (0.0428) Economic diﬃculty 5.026 0.142 (14.115) (0.12) Group size 14.79 0.0139 (4.322) (0.508) Observations 1,488 2,687 Notes: Standard deviation reported in parentheses be- low control means in Column (1). Robust standard er- rors reported in parentheses in Column (2), clustered at the group level. Monetary amounts in Chilean Pesos. 500 Pesos = approximately 1 USD. The variables worry and economic diﬃculty range from 1 to 4 and 1 to 10 respectively. 22 Table 2: Eﬀect of Being in Treatment Group on Self-reported Economic Wellbeing Worry Recent Economic Diﬃculty Intention Treatment on Intention Treatment on to Treat the Treated to Treat the Treated (1) (2) (3) (4) Account -0.109* -0.205** -0.240* -0.456** (0.058) (0.080) (0.142) (0.214) Constant 2.930*** 2.930*** 5.113*** 5.113*** (0.013) (0.015) (0.034) (0.039) Individual ﬁxed eﬀects Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Observations 7,101 7,101 7,097 7,097 R2 0.02 0.01 Notes: Robust standard errors in parenthesis for all regressions; errors clustered at group level for ITT regressions. Outcome variable ”worry” in Columns (1)-(2) ranges from 1 to 4; it indicates how worried participants are about their economic future with higher numbers signifying more worry. Outcome variable ”recent economic diﬃculty” in Columns (3)-(4) ranges from 1 to 10; it indicates participants’ assessment of their economic diﬃculties in the recent past with higher numbers signifying a more diﬃcult past. Level of signiﬁcance: *** p<0.01, ** p<0.05, * p<0.1. 23 Table 3: Informal Lending and Borrowing Practices Owes to Owes to Owed by Owed by relative and friends institutions relatives and friends business contacts Panel A (Intention to Treat) Account -0.063*** -0.042* -0.019 -0.014 (0.021) (0.023) (0.024) (0.023) Constant 0.237*** 0.357*** 0.377*** 0.480*** (0.005) (0.006) (0.006) (0.006) Individual Fixed Eﬀects Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes 24 R 2 0.047 0.05 0.052 0.017 Panel B (Treatment on the Treated) Account -0.118*** -0.078** -0.037 -0.027 (0.032) (0.037) (0.037) (0.038) Constant 0.236*** 0.357*** 0.377*** 0.480*** (0.006) (0.007) (0.007) (0.007) Individual Fixed Eﬀects Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Observations 7,086 7,087 7,094 7,110 Notes: Robust standard errors in parenthesis for all regressions; errors clustered at group level for ITT regres- sions. Level of signiﬁcance: *** p<0.01, ** p<0.05, * p<0.1 Table 4: Consumption Variation in Response to Shocks Meals Meat Medicine School Clothing School Public Eating Overall Supplies Snack Transport Out Index (1) (2) (3) (4) (5) (6) (7) (8) (9) Panel A (Intention to Treat) Account -0.014 -0.048* -0.025 -0.008 0.005 -0.004 -0.051* -0.021 -0.166 (0.016) (0.027) (0.022) (0.018) (0.032) (0.013) (0.028) (0.03) (0.131) Constant 0.107*** 0.408*** 0.262*** 0.202*** 0.513*** 0.085*** 0.317*** 0.416*** 2.311*** (0.004) (0.006) (0.005) (0.004) (0.007) (0.003) (0.007) (0.007) (0.030) Individual Fixed Eﬀects Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes R2 0.000 0.002 0.011 0.043 0.002 0.001 0.004 0.003 0.008 25 Panel B (Treatment on the Treated) Account -0.026 -0.090** -0.047 -0.014 0.010 -0.008 -0.096** -0.039 -0.311* (0.024) (0.040) (0.034) (0.030) (0.041) (0.022) (0.039) (0.041) (0.161) Constant 0.107*** 0.408*** 0.262*** 0.202*** 0.513*** 0.085*** 0.317*** 0.416*** 2.310*** (0.004) (0.007) (0.006) (0.005) (0.007) (0.004) (0.007) (0.007) (0.029) Individual Fixed Eﬀects Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 7,164 7,164 7,164 7,164 7,164 7,164 7,164 7,164 7,164 Notes: Robust standard errors in parenthesis for all regressions; errors clustered at group level for ITT regressions. Outcome variables in Columns (1)-(8) are dummy variables indicating whether there was a consumption cut back in that particular item. Index in Column (9) is an additive score of the number of items that an individual cut back on and hence ranges from 0 to 8. Level of signiﬁcance: *** p<0.01, ** p<0.05, * p<0.1 Table 5: Live Events and Savings Behavior Life Event Deposits Withdrawals Deposits Withdrawals Loss of Job -0.104*** 0.014 (0.017) (0.014) Business Downturn -0.164*** 0.005 (0.025) (0.012) Accident -0.064* -0.013 (0.037) (0.012) Signiﬁcant Theft -0.087* 0.005 (0.048) (0.034) Birth of Child 0.104 0.079 (0.122) (0.075) Marriage of Self or Child -0.214** 0.095 (0.105) (0.078) Increase in Household Size -0.127*** -0.012 (0.038) (0.020) Partnership Break-up -0.008 0.021 (0.087) (0.088) Deaths in Nuclear Family -0.136*** -0.018 (0.049) (0.026) Any Life Event -0.113*** 0.004 (0.015) (0.006) Constant 0.221*** 0.051*** 0.221*** 0.051*** (0.001) (0.000) (0.001) (0.000) Yes Yes Yes Yes Individual Fixed Eﬀects Year Dummy Yes Yes Yes Yes Observations 1,219 1,219 1,219 1,219 R2 0.000 0.000 0.000 0.000 Notes: Robust standard errors in parenthesis, clustered at group level. Level of signiﬁ- cance: *** p<0.01, ** p<0.05, * p<0.1 26 Table 6: Diﬀerential Eﬀects of Treatment on the Treated Worry Recent Econ. Owes to Owes to Owed by Owed by Consumption Diﬃculty relative institutions family business cutback and friends and friends contacts index Account -0.083 -0.519* -0.142*** -0.048 -0.061 -0.028 -0.477** (0.099) (0.266) (0.040) (0.046) (0.046) (0.048) (0.199) Peer Group Treatment -0.218** 0.112 0.041 -0.055 0.044 0.002 0.298 27 (0.098) (0.262) (0.040) (0.045) (0.046) (0.047) (0.196) Constant 2.930*** 5.113*** 0.236*** 0.356*** 0.377*** 0.480*** 2.310*** (0.015) (0.039) (0.006) (0.007) (0.007) (0.007) (0.029) Individual Fixed Eﬀects Yes Yes Yes Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Yes Yes Yes Observations 7,101 7,097 7,086 7,087 7,094 7,110 7,164 Notes: Robust standard errors in parenthesis. Level of signiﬁcance: *** p<0.01, ** p<0.05, * p<0.1 Table 7: Checking for Possible Demand Eﬀects Survey Process Satisfaction with FE Account 0.040 -0.013 (0.043) (0.052) Constant 2.45*** 6.38*** (0.033) (0.042) Observations 3,365 3,572 R2 0.001 0.000 Notes: Robust standard errors in parenthesis, clustered at group level. Level of signiﬁcance: *** p<0.01, ** p<0.05, * p<0.1 28 Table 8: Checking for the Eﬀect of Attrition Attrition Income Has Prior in Treatment Education Age per capita Household Savings vs. Control (monthly) size Account Account 0.028** (0.014) Attrition 0.210 -1.179** 3,775 -0.160** -0.006 (0.138) (0.494) (5,720) (0.079) (0.021) Constant 0.124*** 9.683*** 43.520*** 84,456*** 4.332*** 0.315*** (0.009) (0.076) (0.229) (2,796) (0.035) (0.009) Observations 4,175 4,175 4,175 4,129 4,175 4,175 29 R2 0.001 0.001 0.001 0.000 0.001 0.000 Amount of Amount of Group Size Take-up Number Number Other Savings Formal Debt of Account of Deposits of Withdrawals Attrition 32,687* -74,682 -0.396 -0.053* -0.008 0.004 (18,417) (107,532) (0.278) (0.030) (0.013) (0.006) Constant 62,306*** 512,852*** 14.86*** 0.533*** 0.059*** 0.012*** (3,914) (48,546) (0.235) (0.018) (0.008) (0.002) Observations 4,175 4,175 4,175 2,687 4,175 4,175 R2 0.002 0.000 0.001 0.001 0.000 0.000 Notes: Robust standard errors in parenthesis, clustered at group level. Level of signiﬁcance: *** p<0.01, ** p<0.05, * p<0.1 A Appendix Table A1: Eﬀect of being in Treatment Group on Self-reported Economic Wellbeing Worry Recent econ. diﬃculty Independent Variable (1) (2) Panel A: No Weights Account -0.109* 0.240* (0.058) (0.142) Constant 2.930*** 5.113*** (0.013) (0.034) Individual ﬁxed eﬀects Yes Yes Year dummy Yes Yes R2 0.020 0.010 Panel B: With Attrition Weights Account -0.107* -0.249* (0.057) (0.141) Constant 2.929*** 5.110*** (0.013) (0.034) Individual ﬁxed eﬀects Yes Yes Year dummy Yes Yes R2 0.020 0.010 Observations 7,101 7,097 Notes: Robust standard errors in parenthesis, clustered at group level. Level of signiﬁcance: *** p<0.01, ** p<0.05, * p<0.1. Outcome variable in Column (1) ranges from 1 to 4 and in Column (2) from 1 to 10. 30 Table A2: Informal Lending Owes to Owes to Owed by Owed by relative and friends institutions relative and friends business contacts Panel A: No Weights Account -0.063*** -0.042* -0.019 -0.014 (0.021) (0.023) (0.024) (0.023) Constant 0.237*** 0.357*** 0.377*** 0.480*** (0.005) (0.006) (0.006) (0.006) Individual Fixed Eﬀects Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes R2 0.047 0.05 0.052 0.017 31 Panel B: With Attrition Weights Account -0.062*** -0.040* -0.019 -0.014 (0.021) (0.023) (0.024) (0.023) Constant 0.237*** 0.355*** 0.378*** 0.480*** (0.005) (0.006) (0.006) (0.006) Individual Fixed Eﬀects Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes R2 0.048 0.05 0.051 0.017 Observations 7086 7087 7094 7110 Notes: Robust standard errors in parenthesis, clustered at group level. Level of signiﬁcance: *** p<0.01, ** p<0.05, * p<0.1.
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