West Virginia University Development Economics Paper

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I want to share with you some papers in the field of development economics. Today, as our world is more interconnected, organizations, particularly non-profit, have risen to the challenge to address inequality (economic, political, social) through innovative policies and interventions. These organizations have found economists and economic thinking useful in the monitoring and evaluation of these interventions.

The readings this week feature examples of the kind of economic research that was recognized in this year's Nobel Prize in Economics. Winners, Michael Kremer, Abhijit Banerjee, Esther Duflo, have worked for years, combining techniques from the hard sciences, like randomized controlled trials, and economic analysis to better understand how we can encourage community development around the world.

Things to remember:

  • This semester, we've seen how the major figures in the history of economics have produced their greatest works in partnership with governments or organizations. Smith and Keynes are good examples.
  • Dean Karlan, one of the economists we're reading this week, works in partnership with Bill Gates and Warren Buffet to effectively implement large-scale development initiatives around the world. His organization, Innovations for Poverty Action, represents an organization dedicated to an evidence-based approach to understanding the impact of foreign aid around the world.
  • For my PhD, I am currently applying the same methods in my own research. I started a non-profit organization in Uganda, Embrace It Africa (www.embraceitafrica.org), where we operate a rural bank, out-patient health clinic, and a primary school and orphanage. If you'd like to know more about what we do or how economic thinking contributes to our mission, drop me a note!

Discussion Posts (min 500 – max 515 word count) – Each week, all students will be responsible for posting to our discussion board and responding to at least two other students. As this is an online course, posting to the discussion board is crucial for the facilitation of class conversation and the understanding of course materials. I'll download the students response later and it should be 3 to 5 sentences. Please make the students response in a different file

I downloaded the syllabus for the class

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World Development Vol. 38, No. 3, pp. 333–344, 2010 Ó 2009 Elsevier Ltd. All rights reserved 0305-750X/$ - see front matter www.elsevier.com/locate/worlddev doi:10.1016/j.worlddev.2009.05.010 Female Empowerment: Impact of a Commitment Savings Product in the Philippinesq NAVA ASHRAF Harvard Business School, Boston, MA, USA DEAN KARLAN Yale University, New Haven, CT, USA and WESLEY YIN * University of Chicago, IL, USA Summary. — Female “empowerment” has increasingly become a policy goal, both as an end to itself and as a means to achieving other development goals. Microfinance in particular has often been argued, but not without controversy, to be a tool for empowering women. Here, using a randomized controlled trial, we examine whether access to and marketing of an individually held commitment savings product lead to an increase in female decision-making power within the household. We find positive impacts, particularly for women who have below median decision-making power in the baseline, and we find this leads to a shift toward female-oriented durables goods purchased in the household. Ó 2009 Elsevier Ltd. All rights reserved. Key words — savings, microfinance, female empowerment, household decision making, commitment or training which encourage separate assets. In theory, such interventions could be unwound by adjustments to the control over other assets in the household. Nevertheless, it is unknown whether simply expanding access to products and training that can directly impact financial control, and thus in turn affect overall household power of women. Using a randomized control trial, we implemented a program which provided a financial savings account whose use was controlled by an individual and/or provided direct marketing to facilitate personalized savings goals. This program did not necessarily increase income in the household (in fact, we have no evidence that it did so); rather it offered individuals a savings vehicle over which only the account holder has control. Specifically, we designed and implemented a commitment savings product with the Green Bank of Caraga, a rural bank in the Philippines. Current bank clients were randomly chosen to either (a) “savings commitment treatment” (SEED): receive 1. INTRODUCTION Female “empowerment” has increasingly become a policy goal, both as an end to itself and as a means to achieving other development goals. 1 A growing literature on intra-household bargaining finds that exogenous increases in female share of income, interpreted as providing the female more power in the household, lead to an allocation of resources that better reflect preferences of the woman (Duflo, 2003; Rangel, 2005). This often leads to greater investment in education, housing, and nutrition for children (Thomas, 1990, 1994; Thomas & Strauss, 1995; Duflo, 2003). Many development interventions have thus focused on transferring income as a way of inducing empowerment (Adato, de la Brière, Mindek, & Quisumbing, 2000). However, it is not clear in theory that transfers of income alone to women can improve their status in the household. Marginal increases in income given to women may be bargained over in the same way as the existing income, and are therefore not guaranteed to lead to gains in bargaining power. 2 On a policy level, microfinance proponents often argue that these empowerment mechanisms justify increased attention and financing to microfinance institutions, and perhaps even subsidies (Hashemi, Schuler, & Riley, 1996; Kabeer, 1999). However, there is little rigorous evidence that expanding financial access and usage can promote female empowerment. What may be more important than providing access to additional sources of income, or simply expanding access to finance, is giving control and property rights over allocated money. 3 Household power could be increased directly by interventions which lead women to have more control over the existing assets. This could be done explicitly through financial accounts in her and only her name, or through marketing q This paper was formerly titled “Tying Husbands to the Mast: Impact of a Commitment Savings Product in the Philippines.” * We thank the Green Bank of Caraga for cooperation throughout this experiment, John Owens and the USAID/Philippines Microenterprise Access to Banking Services Program team for helping to get the project started, Chona Echavez for collaborating on the field work, Robin Burgess, Pascaline Dupas, Larry Katz, Sendhil Mullainathan and Chris Udry for comments, and Nathalie Gons, Tomoko Harigaya, Karen Lyons and Lauren Smith for excellent research and field assistance. We thank the National Science Foundation (SGER SES-0313877, CAREER SES-0547898), Innovations for Poverty Action, Russell Sage Foundation and the Social Science Research Council for funding. All views, opinions, and errors are our own. Final revision accepted: May 28, 2009. 333 334 WORLD DEVELOPMENT an offer to open a “commitment” account accessible only by them, and which does not mature until a pre-specified goal is reached, 4 (b) “marketing treatment”: receive one-on-one marketing about the importance of saving for a goal, or (c) control: no household visit. The savings commitment device could benefit those with self-control, but could also benefit those with familial or spousal control issues. Indeed, the literature on household savings, and on informal savings devices in particular, has emphasized motivations for both reasons (Anderson & Baland, 2002; Gugerty, 2007). Those who choose to open such accounts are likely fundamentally (and un-observably) different from those who do not open such accounts, and thus a comparison of accountholders to non-account-holders would be plagued by a selection bias. By using a randomized control trial, and comparing those who were offered the account to those who were not, we are able to draw causal inference about the impact of the account itself (i.e., and not a self-selection bias in which impact estimates are confounded by account openers being motivated to save) on household dynamics. We reported earlier (Ashraf, Karlan, & Yin, 2006) that after one year individuals who were offered the product increased their savings by 81% relative to a control group, and that in accordance with the theoretical literature on hyperbolic preferences (Laibson, 1997; O’Donoghue and Rabin, 1999) and dual-self models (Fudenberg & Levine, 2005; Gul & Pesendorfer, 2001, 2004), time-inconsistent individuals were the ones most likely to demonstrate a preference for this commitment. Using two new sources of data, a follow-up survey collected after one year and administrative bank data collected after two and a half years, we examine here the impact of this commitment savings product on both self-reported decision-making processes within the household and the subsequent household allocation of resources. We find positive impacts, particularly for women who have below median decision-making power in the baseline, and we find this leads to a shift toward femaleoriented durables goods purchased in the household. This paper proceeds as follows. Section 2 describes the commitment savings product and the experimental design. Section 3 presents the empirical results on household decision making and self-perception of savings behavior. Section 4 concludes with a discussion of the theoretical mechanisms through which this impact may have occurred. 2. INTERVENTION AND EXPERIMENTAL DESIGN (a) The SEED account We designed and implemented a commitment savings product called a SEED (Save, Earn, Enjoy Deposits) account with the Green Bank of Caraga, a small rural bank in Mindanao, Philippines. The SEED account requires that clients commit not to withdraw funds that are in the account until they reach a goal date or amount but does not explicitly commit the client to deposit funds after opening the account. The SEED accounts are individual accounts, even if the participants were married. There are three critical design features to the account, one regarding withdrawals and two regarding deposits. First, individuals restricted their rights to withdraw funds until they reached a specific goal. Clients could restrict withdrawals until a specified month when large expenditures were expected, for example, the beginning of school, Christmas, a particular celebration, or when business needs arose. Alternatively, clients could set a goal amount and only have access to the funds once that goal was reached (e.g., saving a quantity of money Table 1. Clients’ specific savings goals Frequency Percent 97 42 21 20 8 48.0 20.8 10.4 9.9 4.0 4 4 3 2 1 202 2.0 2.0 1.5 1.0 0.5 100.0 Date-based goals Amount-based goals Total 140 62 202 69.3 30.7 100.0 Bought Ganansiya box Did not buy Ganansiya box Total 167 35 202 82.7 17.3 100.0 Christmas/birthday/celebration/graduation Education House/lot construction and purchase Capital for business Purchase or maintenance of machine/ automobile/appliance Agricultural financing/investing/maintenance Vacation/travel Personal needs/future expenses Did not report reason for saving Medical Total known to be needed for a new roof). The clients had complete flexibility to choose which of these restrictions they would like on their account. Once the client had made the decision they could neither change it, nor could they withdraw from the account until they met their chosen goal amount or date. 5 After the goal is reached, the SEED client, not his or her spouse, could withdraw the funds. All clients, regardless of the type of restriction they chose, were encouraged to set a specific savings goal as the purpose of their SEED savings account. SEED marketers insisted that the client herself or himself, and not another household member, set the goal. 6 Table 1 shows a list of the savings goals selected broken down by percentage of the group that selected them. The savings goal was written on the SEED form used to open the account, as well as on a “Commitment Savings Certificate” that was given to the client to keep. Forty-eight percent of clients reported wanting to save for a celebration, such as Christmas, birthday, or fiesta. 7 Twenty-one percent of clients chose to save for tuition and education expenses, while 20% of clients chose business and home investments as their specific goals. The bank offered each SEED client a locked box (called a “ganansiya” box) for a small fee in order to encourage deposits. This locked box is similar to a piggy bank: it has a small opening to deposit money and a lock to prevent the client from opening it. In our setup, only the bank, and not the client, had a key to open the lock. Thus, in order to make a deposit, clients need to bring the box to the bank periodically. Of the 202 clients who opened SEED accounts, 167 opted for this box. This feature can be thought of as a mental account with a small, physical barrier; the box is merely a mechanism that provides individuals a way to save their small change. Individuals put loose change or bills on an occasional basis, hence making “deposits” that normally would be too small to warrant a trip to the bank. These small daily “deposits” keep cash out of one’s (and others’) pocket; eventually, once enough money accumulates in the box, the client deposits the funds at the bank. The barrier, however, is largely psychological; the box is easy to break and hence is a weak physical commitment at best. 8 Other than providing a possible commitment savings device, no further benefit accrued to individuals with this account. The interest rate paid on the SEED account was identical to the interest paid on a normal savings account (4% per annum). FEMALE EMPOWERMENT (b) The experimental design and data collection Our sample for the field experiment consists of 4,001 adult Green Bank clients who have savings accounts in one of two bank branches in the greater Butuan City area, and who have identifiable addresses. We randomly chose 3,125 of 4,001 bank clients to interview for our baseline survey. We then performed a second randomization to assign these individuals to three groups: commitment-treatment (T), marketing-treatment (M), and control (C) groups. One-half the sample was randomly assigned to T, and a quarter of the sample each were randomly assigned to groups M and C. We verified at the time of the randomization that the three groups were not statistically different in terms of preexisting financial and demographic data. Of the 3,125, 1,776 were located by the survey team and then completed a survey. Table 2 provides summary statistics, broken down by treatment and control groups. See Ashraf et al. (2006) for analysis that shows that the treatment and control groups were observably statistically similar at the time of the baseline. 335 Next, we trained a team of marketers hired by the partnering bank to go to the homes and/or businesses of the clients in the commitment-treatment group, to stress the importance of savings to them—a process which included eliciting the clients’ motivations for savings and emphasizing to the client that even small amounts of saving make a difference—and then to offer them the SEED product. We were concerned, however, that this special (and unusual) face-to-face visit might in and of itself inspire higher savings. 9 To address this concern, we created a second treatment, the “marketing” treatment. We used the same exact script for both the commitment-treatment group and the marketing-treatment group, up to the point when the client was offered the SEED savings account. For instance, members of both treatment groups were asked to set specific savings goals for themselves, write those savings goals into a specific “encouragement” savings certificate, and talk with the marketers about how to reach those goals. However, members of the marketing-treatment group were neither offered nor allowed to open the SEED account. The bank staff was trained to Table 2. Summary statistics Total Completed baseline survey Completed follow-up survey Baseline Female, proportion Married, proportion Household decision-making power index 1 Household decision-making power index 2 Household decision-making power index 1 (married female) Household decision-making power index 2 (married female) Total savings at Green Bank, MIS Total household savings Total household informal savings Savings in shared accounts (client is not the principal user) Formal savings of other household members Followup Household decision-making power index 1 Household decision-making power index 2 Household decision-making power index 1 (married female) Household decision-making power index 2 (married female) All (1) Control (2) Treatment (3) Marketing (4) 3,125 1,776 1,629 803 469 428 1,553 842 771 769 465 430 0.595 0.773 1.209 (0.422) 0.004 (0.812) 1.264 (0.401) 0.026 (0.799) 509.974 (506.408) 5,428.758 (15,781.820) 967.125 (4,641.664) 211.739 (2,784.990) 1,212.963 (7,365.828) 0.624 0.806 1.225 (0.423) 0.024 (0.799) 1.288 (0.385) 0.091 (0.739) 536.489 (515.373) 5,894.524 (16,279.700) 968.960 (5,697.623) 335.801 (3,533.014) 1,143.356 (7,212.905) 0.601 0.767 1.220 (0.416) 0.019 (0.808) 1.271 (0.399) 0.036 (0.803) 504.440 (500.692) 5,764.304 (18,305.750) 1,078.983 (4,988.806) 202.528 (2,885.735) 1,445.227 (8,639.445) 0.558 0.753 1.171 (0.432) 0.045 (0.834) 1.220 (0.424) 0.076 (0.856) 493.505 (507.773) 4,363.517 (8,852.169) 764.733 (2,171.288) 104.767 (1,426.876) 865.791 (4,462.855) 1.103 (0.286) 0.001 (0.775) 1.168 (0.273) 0.079 (0.779) 1.090 (0.289) 0.048 (0.799) 1.140 (0.266) 0.003 (0.773) 1.117 (0.285) 0.040 (0.766) 1.193 (0.270) 0.159 (0.771) 1.093 (0.282) 0.027 (0.763) 1.152 (0.284) 0.017 (0.789) F statistic (5) 0.136 0.151 0.190 0.480 0.275 0.167 0.423 0.262 0.531 0.475 0.415 0.270 0.203 0.068 0.036 Standard deviations are reported in the parentheses. Household decision making power indices are composed from answers to “Who decides” on the following nine domains: what to buy at the market, expensive purchases, giving assistance to family members, family purchases, recreational use of the money, personal use of the money, number of children, schooling of children, and use of family planning. The value for each item takes zero if the decision making is done by spouse, one if the decision making is done by the couple, and two if decision making is done by the respondent. Index 1 is the equally weighted mean of an individual’s responses across the nine decision categories; index 2 is the first factor of an individual’s responses across the nine categories. The factor index (2) is created only for those who have no missing response to the nine questions on household decision-making power, and thus removes all individuals without children. Analytical results throughout do not change if index 1 is calculated with the same sample restriction as index 2. 336 WORLD DEVELOPMENT refuse SEED accounts to members of the marketing-treatment and control groups, and to offer a “lottery” explanation: clients were chosen at random through a lottery for a special trial period of the product, after which time it would be available for all bank clients. Green Bank reported that this happened on fewer than ten occurrences. 10 After one year, we conducted a follow-up survey on each of the participants. We completed follow-up surveys on 92% of those in the baseline. Those in the treatment group were equally likely to complete a follow-up survey as those in the marketing or control group. This survey contained three sections: (1) inventory of assets, in order to measure whether the impact on savings represented a net increase in savings or merely a crowd-out of other assets, whose results are reported in a separate paper (Ashraf, Karlan, & Yin, 2008); (2) impact on household decision making and savings attitudes; and (3) impact on economic decisions, such as the purchase of durable goods, health, and consumption. Table 3. Impact on the aggregate household decision-making power. Sample: Individuals who have children and whose spouses/partners live in the same household Index 1 (mean) Level (1) Panel A: All Treatment Marketing Constant Observations R-squared Panel B: Female Treatment Marketing 3. IMPACT ON HOUSEHOLD DECISION MAKING AND SELF-PERCEPTION OF SAVINGS BEHAVIOR (a) Household decision-making power We first examine whether being offered the SEED account changed the decision-making roles in the household. In the follow-up survey, we ask questions regarding family planning, financial, and consumption decisions in order to ascertain the structure of spousal or familial control within married households. For each decision category, we record whether the principle decision-maker is the respondent, the spouse, or both. Responses are assigned values of two, zero, and one, respectively. We construct two decision-making indices from the nine decision categories: (1) equally weighted mean of each response given, and (2) a linear combination, determined through a factor analysis, of the individual responses to each question (Pitt, Khandker, & Cartwright, 2006). The nine categories refer to decisions on what to buy at the market, expensive purchases, giving assistance to family members, family purchases, recreational use of the money, personal use of the money, number of children, schooling of children, and use of family planning. 11 Table 3 shows the impact of treatment assignment on household decision making. Household decision making comprises control over the following decisions: what to buy at the market, purchase of expensive items, giving assistance to family members, family purchases, recreational use of the money, personal use of the money, number of children, schooling of children, and use of family planning. Panel A provides the results for the full sample, Panel B for married women and Panel C for married men. 12 The strongest results are for married women. 13 We find that assignment to the treatment group leads to a 0.14 standard deviation increase in the first (equally weighted) decision-making index (Table 3, Panel B, Column 1), and a 0.25 standard deviation increase in the second (factor-analysis) decision-making index (Table 3, Panel B, Column 3). 14 In Table 4, we separately analyze the impact on women who began the year below (above) the median decision-making power. We find that the average effect is largely driven by increases in decision-making ability for women who were below the baseline median (comparing Panels A and B in Table 4)—a fact consistent with initially less-empowered women experienced the largest gains in decision-making ability through increased financial savings and control over committed assets. In contrast, we find no such Constant Observations R-squared Panel C: Male Treatment Marketing Constant Observations R-squared Change (2) Index 2 (factor) Level (3) Change (4) 0.029 (0.018) 0.012 (0.021) 0.778*** (0.028) 1,184 0.14 0.040 (0.028) 0.052 (0.033) 0.138*** (0.021) 1,184 0.00 0.107** (0.053) 0.054 (0.061) 0.061 (0.043) 1,114 0.12 0.124* (0.064) 0.102 (0.076) 0.080 (0.050) 1,114 0.00 0.056** (0.023) 0.023 (0.027) 0.793*** (0.040) 643 0.16 0.073** (0.034) 0.071* (0.042) 0.147*** (0.025) 643 0.01 0.198*** (0.069) 0.087 (0.085) 0.032 (0.054) 600 0.15 0.241*** (0.080) 0.192* (0.103) 0.090 (0.060) 600 0.01 0.001 (0.029) 0.018 (0.032) 0.791*** (0.039) 541 0.10 0.002 (0.047) 0.030 (0.052) 0.125*** (0.037) 541 0.00 0.006 (0.083) 0.041 (0.091) 0.105 (0.069) 514 0.09 0.019 (0.103) 0.012 (0.115) 0.068 (0.084) 514 0.00 Robust standard errors in parentheses. Dependent variable: index of household decision-making power on what to buy at the market, expensive purchases, giving assistance to family members, family purchases, recreational use of the money, personal use of the money, number of children, schooling of children, and use of family planning. The value for each item takes zero if the decision making is done by spouse, one if the decision making is done by the couple, and two if decision making is done by the respondent. See notes under Table 1 for the exact definition of each index. Regressions in columns (1) and (3) control for the household decision-making power in the baseline (August 2003). * Significant at 10%. ** Significant at 5%. *** Significant at 1%. treatment effect for married men (Table 4, Panel A, Columns 5–8). We find that marketing has a smaller, but still significant, effect on changes in decision-making indices, suggesting that the encouragement of savings alone had a positive effect on self-reported decision-making power of women in the household. 15 Next, we examine whether the increased reported decision making led to a difference in the types of goods purchased for the household. By increasing the assets available for lumpy purchases, the mere presence of the SEED account may increase female decision-making power in the household and hence increase the likelihood that the household acquires female-oriented durables. Naturally, if the account is held in the women’s name this effect should be even stronger. We use three categories for expenditures: house repair, female-oriented durables 16 (washing machines, sewing machines, electric irons, kitchen appliances, air-conditioning units, fans, and stoves), and other durables (vehicles/motorcycles, entertainment, FEMALE EMPOWERMENT 337 Table 4. Impact on aggregate household decision-making power, by gender. Sample: Individuals who have children and whose spouses/partners live in the same household Female Index 1 (mean) Level (1) Change (2) Male Index 2 (factor) Level (3) Index 1 (mean) Index 2 (factor) Change (4) Level (5) Change (6) Level (7) Change (8) Panel A: Household decision-making power below median in baseline 0.094** 0.291*** Treatment 0.089*** (0.032) (0.039) (0.097) Marketing 0.023 0.061 0.123 (0.040) (0.050) (0.117) 0.075** 0.124 Constant 0.800*** (0.068) (0.030) (0.090) Observations 322 322 303 R-squared 0.08 0.02 0.07 0.341*** (0.102) 0.223* (0.131) 0.233*** (0.080) 303 0.03 0.018 (0.036) 0.051 (0.040) 0.751*** (0.056) 296 0.06 0.021 (0.047) 0.075 (0.051) 0.105*** (0.037) 296 0.01 0.041 (0.102) 0.133 (0.117) 0.128 (0.101) 284 0.07 0.025 (0.115) 0.132 (0.128) 0.296*** (0.095) 284 0.00 Panel B: Household decision-making power above median in baseline Treatment 0.026 0.022 0.111 (0.032) (0.037) (0.098) Marketing 0.027 0.019 0.068 (0.037) (0.048) (0.120) 0.342*** 0.115 Constant 0.879*** (0.103) (0.027) (0.096) Observations 321 321 297 R-squared 0.04 0.00 0.03 0.109 (0.103) 0.045 (0.137) 0.380*** (0.078) 297 0.00 0.027 (0.049) 0.030 (0.053) 0.954*** (0.137) 245 0.01 0.015 (0.058) 0.027 (0.062) 0.440*** (0.047) 245 0.00 0.061 (0.137) 0.092 (0.145) 0.123 (0.139) 230 0.00 0.004 (0.149) 0.027 (0.157) 0.579*** (0.122) 230 0.00 Robust standard errors in parentheses. Dependent variable: index of household decision-making power on what to buy at the market, expensive purchases, giving assistance to family members, family purchases, recreational use of the money, personal use of the money, number of children, schooling of children, and use of family planning. The value for each item takes zero if the decision making is done by spouse, one if the decision making is done by the couple, and two if decision making is done by the respondent. See notes under Table 1 for the exact definition of each index. Regressions in columns (1) and (3) control for the household decision-making power in the baseline (August 2003). * Significant at 10%. ** Significant at 5%. *** Significant at 1%. and recreational goods). Table 5 finds no significant impacts on the choice and/or quantity of durables purchased in the household in aggregate, nor broken down by gender. Table 6 analyzes the same dependent variables, but separately for those above and below the median in terms of household decision-making power at the baseline. We find that both the number of items purchased and the total expenditures of consumer durables traditionally associated with female use in the Philippines increase for married women who were below the median in the pre-existing bargaining power. This effect is smaller, and not statistically significant, for married women above the median. This finding is consistent with the impact on decision-making ability for the purchases of personal items and durable goods. We do not, however, find that married households where the women are below the median in decision-making ability increase expenditures on other non-female specific durables. Likewise, we do not find any effect for men offered SEED, either in aggregate (Table 3, Panel C) or for those above or below the median in household decision-making power (Table 4, Columns 5–8, Panels A and B). Taken together, the presence of both direct impact on selfreported decision-making measures, and a greater composition of female-oriented durables, suggest that women who were offered the commitment savings product indeed increased their power within their household. In Tables A2 and A3 we evaluate the additional effect of the commitment savings product above and beyond the marketing treatment for both self-reported decision-making measures and household purchases. Indeed, the results suggest that for women the SEED product increased both measures of empowerment above and beyond the marketing treatment, however the differences are not statistically significant. (b) Self-perception of savings behavior In the follow-up survey, we included several qualitative questions about personal savings habits and attitudes. In earlier research we found that time-inconsistent women were more likely than time-consistent women to take up the SEED product, but that no such differential was found for men. 17 Here we examine whether there are heterogeneous treatment effects on savings attitudes and practices for men versus women and time-inconsistent versus time-consistent clients. Table 7 presents four outcomes, using an ordered probit specification. For each outcome, the respondent was asked whether they strongly agree, agree, are neutral, disagree or strongly disagree with a specific statement. First, we ask about savings practices: (1) (Columns 1 and 2) “Although my income is low, I am a disciplined saver,” (2) (Columns 3 and 4) “I never save,” and (3) (Columns 5 and 6) “When I have a little cash, I spend it rather than save it.” We find no aggregate effect, although we do find that timeinconsistent women who were offered the SEED account report being more likely to be a disciplined saver, less likely to never save, and less likely to report spending rather than saving extra cash. This indicates that at least in their perception, the SEED account helped them overcome their self-control problem and led to improved savings practices (in earlier research, we do not find that the time-inconsistent women actually save more than the time-consistent women). In addition, the marketing condition may have had an independent effect on women’s perceptions of their efficacy in financial decisions (Column 5, Panel B). The final statement (Columns 7 and 8) is “I often find that I regret spending money. I wish that when I had cash, I was better disciplined and saved it rather than spent it.” Being 338 WORLD DEVELOPMENT Table 5. Impact on consumer durables. OLS, probit. Sample framework: Those whose spouses are living in the same house. House repair Panel A: All Treatment Marketing Panel B: Females Treatment Marketing Probit (1) Total number (2) Cost (3) Probit (4) Total number (5) Cost (6) 0.007 (0.033) 0.018 (0.038) 172.201 (1,611.810) 1,393.116 (1,648.315) 7,615.907*** (1,299.894) 1,181 0.00 0.019 (0.032) 0.035 (0.036) 0.009 (0.062) 0.017 (0.072) 0.495*** (0.047) ,183 0.00 48.293 (312.882) 144.558 (475.376) 1,997.997*** (242.252) 1,183 0.00 0.015 (0.030) 0.011 (0.034) 0.006 (0.042) 0.024 (0.047) 0.305*** (0.034) 1,183 0.00 2,293.060 (1,529.312) 2,493.613 (1,543.340) 6,095.462*** (1,344.654) 1,183 0.00 2,758.632 (1,960.731) 1,133.261 (1,875.305) 6,761.989*** (1,289.453) 641 0.01 0.023 (0.043) 0.023 (0.051) 0.086 (0.086) 0.038 (0.104) 0.489*** (0.060) 642 0.00 504.622 (433.285) 56.553 (508.971) 1,947.878*** (297.011) 642 0.00 0.002 (0.040) 0.029 (0.048) 0.050 (0.052) 0.043 (0.058) 0.261*** (0.036) 642 0.00 2,146.550 (2,340.491) 1,731.438 (2,401.692) 6,230.154*** (2,032.658) 642 0.00 3,137.328 (2,759.733) 2,010.130 (2,942.709) 8,796.324*** (2,534.068) 540 0.00 0.012 (0.049) 0.043 (0.052) 0.086 (0.090) 0.071 (0.103) 0.504*** (0.077) 541 0.00 519.682 (456.142) 315.665 (805.930) 2,066.774*** (406.126) 541 0.00 0.032 (0.044) 0.055 (0.047) 0.080 (0.071) 0.107 (0.077) 0.365*** (0.062) 541 0.00 2,453.800 (1,739.883) 3,165.144* (1,764.869) 5,910.628*** (1,555.118) 541 0.01 1,181 0.026 (0.045) 0.020 (0.053) Constant Observations R-squared Panel C: Males Treatment Marketing 641 0.016 (0.051) 0.016 (0.056) Constant Observations R-squared Other durables Cost (2) Constant Observations R-squared Female-oriented durables Probit (1) 540 1,183 642 541 1,183 642 541 Robust standard errors in parentheses. Female-oriented durables consist of washing machines, sewing machines, electric iron, kitchen appliances, air conditioners, fans, and stoves. Other durables include vehicles, motorcycles, and entertainment items (i.e., CD players, TV, and radio). Marginal effects reported for probit specifications. * Significant at 10%. *** Significant at 1%. assigned to treatment makes individuals more likely to report feeling regret over their spending and savings decisions. 18 Note that only 28% of those offered SEED took up, and of those only about one-third regularly used the account. Hence it follows that although SEED helped 10% of the treatment group save more (and generate an overall positive intent-totreat effect), the mere offer of the SEED account generated, on average, a feeling of remorse. Perhaps those who did not take up and use felt remorse, and those who did take up and use did not feel remorse, but the average effect is an increase in remorse because of the relative size of these two groups. Perhaps a second marketing would have been more successful than the first, if the first offer made individuals more aware of their inability to save as much as they would like. 4. CONCLUSION Even when husbands appropriate their wives’ loans, microcredit is thought to empower women in household decisionmaking processes (Mizan, 1993). Policymakers frequently cite these arguments as a key motivation for targeting microfinance and microsavings interventions to women. On the other side, some have argued that microfinance usage and the subsequent need to repay (e.g., in order to protect her reputation amongst her peers) may subjugate women to the power of their spouses, hence potentially increasing domestic violence (Rahman, 1999). Evidence (albeit weak) points both ways, and naturally may depend largely on the region-specific economic and social setting. 19 The effects of microcredit and, more generally, microfinance, which includes savings and/or insurance products, on female empowerment remain unclear, in large part because studies of it tend to suffer from a pronounced selection bias in the type of women who access microcredit (Pitt et al., 2006). Using a randomized controlled trial, we evaluate the impact of a commitment micro-savings account. We find that the commitment product positively impacts both household decision-making power for women (i.e., the household is more likely to buy female-oriented durables), self-perception of savings behavior (time-inconsistent females report being more disciplined savers), as well as actual consumption decisions regarding durables goods. The offering of the commitment savings product could change household dynamics through several mechanisms. First, the commitment product could have affected bargaining power through the various forms of control (both legal and normative/psychological) over decisions to withdraw and to roll-over balances. A second person may still apply pressure to influence withdrawal decisions, or exert pressure on other margins in response to the account, and unwind the control gained by the account. Nonetheless, in restricting legal control to one individual, the product creates a formal barrier to second persons that the account holder can use in bargaining. 20 FEMALE EMPOWERMENT 339 Table 6. Impact on consumer durables. OLS, Probit. Sample Framework: Those whose spouses are living in the same house. House repair Probit (1) Cost (2) Female-Oriented Durables Total number (3) Other Durables Cost (4) Total number (5) Cost (6) Panel A: Females with household decision-making power below median in baseline Treatment 0.027 2,480.870 0.192* (0.063) (2,133.872) (0.108) Marketing 0.081 1,149.406 0.126 (0.075) (1,676.488) (0.142) 0.386*** Constant 5,206.818*** (1,276.748) (0.069) Observations 322 322 322 R-squared 0.01 0.01 1,456.938** (654.295) 600.512 (786.664) 1,518.750*** (359.206) 322 0.01 0.006 (0.073) 0.052 (0.088) 0.273*** (0.058) 322 0.00 3,887.597 (4,109.914) 4,446.125 (3,691.585) 8,037.500** (3,550.889) 322 0.01 Panel B: Females with household decision-making power above median in baseline Treatment 0.080 3,247.131 0.008 (0.063) (3,231.059) (0.131) Marketing 0.048 625.615 0.036 (0.077) (3,433.478) (0.148) 0.580*** Constant 8,130.540*** (2,145.179) (0.094) Observations 319 319 320 R-squared 0.00 0.00 403.082 (552.084) 702.348 (586.010) 2,325.510*** (458.549) 320 0.00 0.092 (0.075) 0.029 (0.077) 0.250*** (0.046) 320 0.00 623.256 (2,436.893) 926.486 (3,346.618) 4,639.690** (2,202.953) 320 0.00 Panel C: Males with household decision-making power below median in baseline Treatment 0.006 4,114.137 0.080 (0.066) (4,284.529) (0.122) Marketing 0.052 3,657.542 0.014 (0.072) (4,618.274) (0.148) 0.468*** Constant 9,718.987** (4,083.798) (0.105) Observations 296 296 296 R-squared 0.01 0.00 741.921 (619.640) 841.101 (1,316.247) 2,072.152*** (569.847) 296 0.01 0.092 (0.103) 0.212** (0.102) 0.405*** (0.089) 296 0.02 2,878.840 (2,561.748) 4,822.457** (2,415.286) 6,301.975*** (2,352.200) 296 0.02 Panel D: Males with household decision-making power above median in baseline Treatment 0.030 1,795.457 0.100 (0.079) (2,829.019) (0.132) Marketing 0.093 104.123 0.177 (0.087) (2,980.016) (0.143) 0.552*** Constant 7,517.544*** (2,156.450) (0.113) Observations 244 244 245 R-squared 0.00 0.01 259.666 (666.850) 288.920 (836.159) 2,059.448*** (568.124) 245 0.00 0.058 (0.094) 0.023 (0.114) 0.310*** (0.082) 245 0.00 1,881.499 (2,182.161) 1,172.725 (2,466.193) 5,377.586*** (1,813.668) 245 0.00 Robust standard errors in parentheses. Female-oriented durables consist of washing machines, sewing machines, electric iron, kitchen appliances, air conditioners, fans, and stoves. Other durables include vehicles, motorcycles, and entertainment items (i.e., CD players, TV, and radio). * Significant at 10%. ** Significant at 5%. *** Significant at 1%. Second, a commitment savings account could establish a norm within the household that the funds are to be used for certain purposes. Any norms created by the commitment savings account might not be unwound by ex-post reallocation of resources. Duflo and Udry (2003) find that crop revenues in Cote d’Ivoire are labeled as either male, female, or family, and shocks to one “mental account” remain in that account and are not reallocated fully ex-post. The mere labeling of this account as the wife’s provided her with additional power to allocate those funds, which did not in turn crowd-out the allocation of other funds. Third, it may also be the case that the woman actually got more control of liquid funds. Many who took up the savings product made use of a lock-box. These individuals were thus able to keep small amounts aside, giving the person the power to make decisions about the accumulated savings. Particularly given the small amount of individual deposits, it is possible that accumulations in this account were generated without other household members being aware of the amount being saved (although note that the treatment effect on savings volume was not stronger for women than it was for men). Fourth, the commitment savings treatment (or the marketing treatment, which had a positive but insignificant statistically impact on savings (Ashraf et al., 2006)), could have encouraged savings in general. The increased savings by woman could signal her outside option in case of a breakdown of marriage. Female savings in this setting functions as the female wage rate in previous cooperative bargaining models (Pollak, 2005). Although plausible in theory, note that the savings amounts here were small enough such that this theory is likely only true for marriages on the margin of breakdown. Greater savings or the opening of a non-joint savings account raises the threat point in bargaining, representing what could be earned in a non-cooperative outcome. 340 WORLD DEVELOPMENT Table 7. Impact on savings attitude. Ordered probit. Dependent variable Panel A: All Treatment Marketing Although my income is low, I’m a disciplined saver (3) (4) (5) (6) (7) (8) 0.025 (0.069) 0.057 (0.078) 0.053 (0.080) 0.073 (0.091) 0.147 (0.126) 0.300* (0.156) 0.050 (0.175) 1,626 0.104 (0.072) 0.105 (0.085) 0.021 (0.083) 0.064 (0.098) 0.252* (0.138) 0.303* (0.165) 0.152 (0.195) 1,626 0.095 (0.065) 0.084 (0.075) 0.051 (0.077) 0.105 (0.090) 0.109 (0.115) 0.163 (0.146) 0.064 (0.161) 1,626 0.181*** (0.066) 0.070 (0.074) 0.160** (0.078) 0.102 (0.088) 0.043 (0.120) 0.082 (0.149) 0.102 (0.161) 1,626 0.136 (0.103) 0.160 (0.123) 0.310** (0.158) 0.395** (0.196) 0.040 (0.225) 968 0.049 (0.093) 0.148 (0.112) 0.069 (0.107) 0.082 (0.132) 0.308* (0.173) 0.389* (0.209) 0.209 (0.246) 968 0.104 (0.081) 0.214** (0.099) 0.005 (0.097) 0.209* (0.123) 0.216 (0.136) 0.339* (0.180) 0.018 (0.199) 968 0.130 (0.084) 0.118 (0.096) 0.065 (0.128) 0.007 (0.135) 0.128 (0.213) 0.133 (0.263) 0.249 (0.283) 658 0.199* (0.116) 0.077 (0.131) 0.155 (0.133) 0.066 (0.148) 0.196 (0.222) 0.200 (0.266) 0.080 (0.312) 658 0.084 (0.110) 0.073 (0.118) 0.123 (0.126) 0.000 (0.134) 0.118 (0.212) 0.168 (0.255) 0.285 (0.279) 658 0.257** (0.109) 0.010 (0.117) Marketing  time inconsistent, baseline Marketing 1,629 0.021 (0.088) 0.176* (0.103) Time inconsistent, baseline Treatment  time inconsistent, baseline Marketing  time inconsistent, baseline Observations Panel C: Male Treatment Marketing 970 0.105 (0.112) 0.066 (0.118) Time inconsistent, baseline Treatment  time inconsistent, baseline Marketing  time inconsistent, baseline Observations I often regret spending, I wish I was more disciplined to save (2) Treatment  time inconsistent, baseline Panel B: Female Treatment When I have a little cash, I spend it rather than save (1) Time inconsistent, baseline Observations I never save 659 1,629 970 659 1,629 970 659 1,629 970 659 0.153 (0.101) 0.184 (0.118) 0.069 (0.140) 0.072 (0.180) 0.216 (0.203) 968 0.170 (0.121) 0.001 (0.134) 0.014 (0.241) 0.344 (0.277) 0.066 (0.288) 658 Robust standard errors in parentheses. Dependent variables are categorical, indicating how strongly the respondent agrees to each statement. The variable equals one if the respondent strongly disagree, two if somewhat disagree, three if neutral, four if somewhat agree, and five if strongly agree. * Significant at 10%. ** Significant at 5%. *** Significant at 1%. Finally, even in the absence of an actual increase in savings, the simple act of having a bank staff member come to one’s door and encourage one to set savings goals could in itself have increased a sense of “locus of control.” The presence of the bank staff member may offer an external social reinforcement of the account holder’s preferences for how deposits are to be spent. This is akin to the second mechanism detailed above, but works through the marketing process, not the design features of the savings product itself. Our results suggest that both the marketing process and control over the asset through the product design seem important—although the product design effect is somewhat larger, we do not have the sample size to distinguish well between the two treatments. We do find, however, that the package of increased control over assets and direct encouragement via marketing to take control of goal-setting and savings caused a significant increase in empowerment for women, compared to a control group that did not receive any special asset or marketing. Through continued experimentation, we can learn more about the factors that drive savings decisions in the household and thus also how to best design savings products that help individuals reach goals such as asset building and consumption smoothing. We also need continued measurement of how products impact household decision making, and how household decision-making affects the efficacy of different savings products. The results here suggest that commitment features, in particular loss of liquidity combined with sole control of the FEMALE EMPOWERMENT account, appeal to those with self-control and have positive impacts on female decision-making power. These are not contradictory findings, but rather point out that a simple design feature such as a restriction on withdrawals or encourag- 341 ing savings through marketing or door-to-door deposits, can benefit both those in search of self control devices as well as those who desire to have more decision-making power in the household. NOTES 1. See, for example, Engendering Development (World Bank, 2001). By “female empowerment” we mean increasing the bargaining power of the woman within the household, manifested through increased influence in household decisions and through household outcomes that greater reflect her preferences. 2. See Garikipati (2008) as an example of other work posing a similar question with respect to credit. In that work, the author finds that women with longer durations in a lending program do not experience higher levels of empowerment. Further work to separate selection and tenure effects from the impact of credit would help to link those findings to ours to understand whether the results are inconsistent or not. 3. Anderson and Eswaran (in press) find that income needs to be in the control of women – not just generated by them – in order to impact their bargaining power in the household. The relevant threat point in their context, as in ours where divorce is uncommon, is non-cooperative behavior. 4. The commitment savings product also incorporated the option to keep a locked box (for which only the bank had the key) into which cash and coins could be deposited. 5. Exceptions are allowed for medical emergency, in which case a hospital bill is required, for death in the family, requiring a death certificate, or relocating outside the bank’s geographic area, requiring documentation from the area government official. The clients who signed up for the SEED product signed a contract with the bank agreeing to these strict requirements. After six months of the project, no instances occurred of someone exercising these options. For the amount-based goals, the money remains in the account until either the goal is reached or the funds withdrawn or the funds are requested under an emergency. 6. SEED marketers reported instances of household visits in which the husband tried to influence the goal-setting process. Typically the marketers then asked that only the wife give her goal and this was recorded, but at no point did the marketer make an issue out of the goal setting process. Green Bank prohibits spouses from being able to withdraw from each others’ accounts, unless the account was explicitly opened as a joint account. No SEED accounts were opened as joint accounts. 7. Fiestas are large local celebrations that happen at different dates during the year for each barangay (smallest political unit and defined community, on average containing 1,000 individuals) in this region. Families are expected to host large parties, with substantial food, when it is their barangay’s fiesta date. Families often pay for this annual party through loans from local high-interest-rate money-lenders. 8. To facilitate deposits, clients also were offered automatic transfers from a primary checking or savings account into the SEED account. This feature was not popular. Many clients reported not using their checking or savings account regularly enough for this option to be meaningful. Even though preliminary focus groups indicated demand for this feature, only 2 of the 202 clients opted for automated transfers. 9. Because individuals were randomly selected, marketers were trained to ask only for that person and ensure that the individual was the one setting goals and, in the case of SEED, opening the account (i.e., the privilege went to the individual, not to their spouse or others in the household, even if they wanted to be the ones setting the goals (as happened in the case of a few husbands). 10. In only one instance an individual in the control group opened a SEED account. This individual is a family member of the owners of the bank and hence was erroneously included in the sample frame. Due to the family relationship, the individual was dropped from all analysis. 11. See Pitt, Khandker, and Cartwight (2006) for a discussion of alternative constructions of a household decision making index. Our results are robust to summing across the measures, and to specifications that measure changes, rather than controlling for baseline levels as we report in the text. Furthermore, since the factor analysis drops observations for which any answer is missing, we also examine the first measure of equal weights but omit all observations for which any one answer is missing. Results for the equally weighted mean index do not change on this smaller sample of individuals. 12. This applies to married women whose spouses live at home with them. Fifty-three of 696 married women had no spouse in the house in both baseline and follow-up; 24 of 541 married men had no spouse during both surveys. These married individuals were not included in our analysis. 13. If we were to examine each question individually, we would find positive impacts of the SEED treatment for women on 8 of the 9 variables in the decision making index, two of which were significant at the 90% level. The significant results are from survey questions asking who makes the primary decision on expensive purchases in the household and the number of children to have. Positive results were also found for the use of family planning, giving assistance to other family members, buying items for personal use, spending money on personal recreational (movies, liquor, gambling), family purchases, working outside the household, and schooling for children. 14. The treatment effect in terms of standard deviations is calculated by dividing the point estimates on the coefficient of interest (0.056 and 0.198 from Table 2) by the standard deviation for the dependent variable of each index for married women, as found in Table 1. 15. In Table A2 we test the impact for married women for each of the nine household decision categories that comprise the indices used in Table 2. 16. These goods were classified as female-oriented durables after consultations with qualitative and quantitative social science researchers at the Research Institute for Mindanao Culture (RIMCU) at Xavier University, and conversations in focus group discussions. 17. Individuals defined as present-biased time-inconsistent when in hypothetical time preference questions in the survey, they revealed a higher discount rate for tradeoffs between now and 30 days than tradeoffs between 6 months and 7 months. We measured this by posing questions about two hypothetical situations involving winning a raffle cash prize. In the first, respondents are asked whether they would like to receive the winnings now or a larger amount of money in 30 days. In the second situation, respondents are asked to choose between receiving the winnings in 6 months or a larger amount in 7 months. 342 WORLD DEVELOPMENT 18. Interestingly, agreeing with this statement is also correlated with being time-inconsistent when answering hypothetical time preference questions. 19. Recent evidence from a randomized controlled trial in South Africa finds no impact from access to credit on household decision making (Karlan & Zinman, 2007). See Chapter 7 of Armendariz de Aghion and Morduch (2005) for more discussion on this. 20. Particularly, the threat of roll-overs, combined with illiquidity, may enhance bargaining power, even in the absence of any positive savings impact. REFERENCES Adato, M., de la Brière, B., Mindek, D., & Quisumbing, A. (2000). The impact of PROGRESA on women’s status and intrahousehold relations. IFPRI working paper. Anderson, S., & Baland, J. M. (2002). The economics of roscas and intrahousehold resource allocation. Quarterly Journal of Economics, 117(3), 963–995. Anderson, S., & Eswaran, M. (in press). What determines female autonomy? Evidence from Banglades. 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Like father, like son; like mother, like daughter: Parental resources and child health. Journal of Human Resources, 29(4), 950–989. Thomas, D., & Strauss, J. (1995). Human resources: Empirical models of household decisions. In Behrman J.R., & T. N. Srinivasan (Eds.), Handbook of Development Economics (Vol. IIIA, pp. 1885–2023). Amsterdam: North Holland. World Bank (2001). Engendering development: Through gender equality in rights, resources and voice. A World Bank Policy Research Report. Washington, DC and New York: World Bank and Oxford University Press. APPENDIX See Tables A1–A4. Table A1. Qualitative feedback from SEED account holders Frequency Those that did not withdraw: reason for not withdrawing Argued with spouse Bad bank service/bank is far Could not save Damaged passbook Destroyed ganansiya box Did not need money Did not like terms/low interest Forgot about it Inconvenience Money stolen (7)/lost (1) Never joined/not a member Nobody collected Not interested Not to term Rolled over Total 1 3 43 1 2 1 3 13 8 9 5 2 1 51 3 149 Those that withdrew: spent SEED money on Fiesta Children’s schooling Other/did not say Add to capital of business/sari-sar Birthday (own, child, grandchild, missus, etc) Child is giving birth Children’s graduation Christmas Contruction of house/repair of kitchen Everyday needs/necessities/groceries Medical treatment Reached time goal (3 months) Refrigerator Supplement mothers budget Total 7 6 4 2 5 1 2 3 2 4 2 1 1 2 42 Spent money on original goal Spent money on different goal from original 26 14 FEMALE EMPOWERMENT 343 Table A2. Impact on household decision making, components. Ordered probits. Sample: Women whose spouses/partners are living in the same house Dependent variable What to buy in market (1) Personal use (6) Recreation (4) Assist family members (5) 0.023 (0.110) 0.117 (0.131) 641 0.143 (0.113) 0.046 (0.125) 642 decision-making power below median in baseline 0.175 0.010 0.409** (0.162) (0.164) (0.162) 0.148 0.165 0.192 (0.181) (0.182) (0.187) 321 321 321 Panel C: Females with household decision-making power above median in baseline 0.033 Treatment 0.005 0.037 0.297* (0.171) (0.148) (0.159) (0.151) Marketing 0.169 0.020 0.178 0.048 (0.205) (0.184) (0.207) (0.186) Observations 321 321 318 320 Panel A: Female Treatment Marketing Observations 0.004 (0.117) 0.026 (0.134) 641 Panel B: Females with household Treatment 0.005 (0.162) Marketing 0.154 (0.182) Observations 320 Expensive purchases (2) Number of children (3) Family planning 0.203* (0.109) 0.060 (0.128) 642 0.217* (0.114) 0.139 (0.137) 639 (7) Family purchase (8) Schooling for children (9) 0.013 (0.118) 0.124 (0.137) 643 0.112 (0.107) 0.062 (0.120) 642 0.174 (0.111) 0.115 (0.138) 641 0.162 (0.125) 0.220 (0.151) 609 0.323** (0.158) 0.316* (0.174) 321 0.243 (0.167) 0.238 (0.183) 322 0.229 (0.152) 0.282* (0.171) 321 0.237 (0.164) 0.150 (0.191) 320 0.065 (0.199) 0.123 (0.228) 306 0.002 (0.160) 0.174 (0.179) 321 0.222 (0.170) 0.130 (0.213) 321 0.022 (0.152) 0.143 (0.169) 321 0.136 (0.155) 0.127 (0.197) 321 0.328* (0.168) 0.509** (0.210) 303 Robust standard errors in parentheses. All regressions in this table control for the initial household decision-making power in the baseline. The value for each item takes zero if the decision making is done by husband, one if the decision making is done by the couple, and two if decision making is done by wife. * Significant at 10%. ** Significant at 5%. Table A3. Impact on the aggregate household decision-making power (marketing and treatment groups only). Sample: Individuals who have children and whose spouses/partners live in the same household Index 1 (mean) Panel A: All Treatment Constant Observations R-squared Panel B: Female Treatment Constant Observations R-squared Panel C: Male Treatment Constant Observations R-squared Index 3 (factor) Level (1) Change (2) Level (5) Change (6) 0.022 (0.020) 0.822*** (0.034) 813 0.12 0.005 (0.031) 0.091*** (0.025) 813 0.00 0.055 (0.054) 0.008 (0.044) 809 0.10 0.022 (0.070) 0.022 (0.057) 809 0.00 0.040 (0.027) 0.865*** (0.051) 430 0.13 0.002 (0.042) 0.070** (0.036) 430 0.00 0.115 (0.078) 0.052 (0.066) 427 0.12 0.049 (0.098) 0.102 (0.083) 427 0.00 0.012 (0.028) 0.827*** (0.044) 383 0.08 0.018 (0.046) 0.110*** (0.036) 383 0.00 0.036 (0.075) 0.064 (0.059) 382 0.08 0.030 (0.098) 0.057 (0.078) 382 0.00 Robust standard errors in parentheses. Dependent variable: Index of household decision-making power on what to buy at the market, expensive purchases, giving assistance to family members, family purchases, recreational use of the money, personal use of the money, number of children, schooling of children, and use of family planning. The value for each item takes zero if the decision making is done by spouse, one if the decision making is done by the couple, and two if decision making is done by the respondent. See notes under Table 1 for the exact definition of each index. ** Significant at 5%. *** Significant at 1%. 344 WORLD DEVELOPMENT Table A4. Impact on consumer durables (marketing and treatment groups only). Sample framework: Those whose spouses are living in the same house House repair Panel A: All Treatment Panel B: Females Treatment Binary (3) Total number (4) Cost (5) Binary (6) Total number (7) Cost (8) 0.011 (0.034) 1,565.317 (1,391.052) 6,222.791*** (1,013.413) 857 0.00 0.016 (0.033) 0.026 (0.067) 0.479*** (0.054) 858 0.00 96.265 (454.382) 2,142.554*** (408.977) 858 0.00 0.003 (0.030) 0.019 (0.041) 0.281*** (0.032) 858 0.00 200.554 (1,050.847) 3,601.848*** (757.422) 858 0.00 3,891.893* (2,008.677) 5,628.728*** (1,361.465) 453 0.01 0.001 (0.047) 0.048 (0.105) 0.527*** (0.085) 454 0.00 561.176 (519.888) 1,891.324*** (413.268) 454 0.00 0.031 (0.044) 0.006 (0.059) 0.304*** (0.046) 454 0.00 415.112 (1,726.796) 4,498.716*** (1,279.057) 454 0.00 1,127.198 (1,852.180) 6,786.194*** (1,495.551) 404 0.00 0.032 (0.046) 0.015 (0.083) 0.432*** (0.069) 404 0.00 835.347 (726.221) 2,382.439*** (695.914) 404 0.00 0.024 (0.043) 0.027 (0.058) 0.258*** (0.046) 404 0.00 711.343 (1,142.098) 2,745.484*** (834.237) 404 0.00 857 0.005 (0.048) Constant Observations R-squared Panel C: Males Treatment 453 0.032 (0.049) Constant Observations R-squared Other durables Cost (2) Constant Observations R-squared Female-oriented durables Binary (1) 404 858 454 404 858 454 404 Robust standard errors in parentheses. Female-oriented durables consist of washing machines, sewing machines, electric iron, kitchen appliances, air conditioners, fans, and stoves. Other durables include vehicles, motorcycles, and entertainment items (i.e., CD players, TV, and radio). * Significant at 10%. *** Significant at 1%. Available online at www.sciencedirect.com American Economic Journal: Applied Economics 2016, 8(2): 35–64 http://dx.doi.org/10.1257/app.20150023 The Returns to Microenterprise Support among the Ultrapoor: A Field Experiment in Postwar Uganda† By Christopher Blattman, Eric P. Green, Julian Jamison, M. Christian Lehmann, and Jeannie Annan* We show that extremely poor, war-affected women in northern Uganda have high returns to a package of $150 cash, five days of business skills training, and ongoing supervision. Sixteen months after grants, participants doubled their microenterprise ownership and incomes, mainly from petty trading. We also show these ultrapoor have too little social capital, but that group bonds, informal insurance, and cooperative activities could be induced and had positive returns. When the control group received cash and training 20 months later, we varied supervision, which represented half of the program costs. A year later, supervision increased business survival but not consumption. (JEL I38, J16, J23, J24, L26, O15, Z13) T he World Bank, the United Nations, and the United States government have made the eradication of extreme poverty by 2030 a central development goal.1 Since the world’s poor often live in economies with few firms, anti-poverty programs often try to foster self-employment. This includes farm enterprises such as raising livestock for sale, and nonfarm enterprises such as trading or retail. But can the extreme poor be expected to start and sustain such microenterprises? And what constraints hold them back? * Blattman: Columbia University School of International and Public Affairs (SIPA) and National Bureau of Economic Research (NBER), 420 W 118 Street, Suite 819, New York, NY 10027 (e-mail: chrisblattman@columbia. edu); Green: Duke Global Health Institute, Box 90519, Durham, NC 27708 (e-mail: eric.green@duke.edu); Jamison: Global INsights Initiative, The World Bank, 1818 H Street NW, Washington, DC 20433 (e-mail: julison@gmail. com); Lehmann: University of Brasilia, Department of Economics, Campus Universitário Darcy Ribeiro, Brasília, DF, 70910-900, Brazil (e-mail: christianlehmann0@gmail.com); Annan: International Rescue Committee, 122 East 42nd Street, Suite 1407, New York, NY 10168 (e-mail: jeannie.annan@rescue.org). Association of Volunteers in International Service (AVSI) implemented the program and we thank Jackie Aldrette, Fabio Beltramini, Ezio Castelli, Filippo Ciantia, Francesco Frigerio, John Makoha, Francesca Oliva, Federico Riccio, Samuele Rizzo, and Massimo Zucca for collaboration. For comments we thank Abhijit Banerjee, Theresa Betancourt, Gustavo Bobonis, Nathan Fiala, Don Green, Nathan Hansen, Dean Karlan, Bentley MacLeod, David McKenzie, several anonymous referees, and seminar participants at George Washington University (GWU), Harvard University, Massachusetts Institute of Technology (MIT), United States Agency for National Development (USAID), the World Bank, and Yale University. A Vanguard charitable trust and the World Bank’s Learning on Gender and Conflict in Africa (LOGiCA) trust fund funded the research. This article does not necessarily represent the views of the World Bank, the Consumer Financial Protection Bureau, or the US government. For research assistance we thank Filder Aryemo, Natalie Carlson, Samantha DeMartino, Mathilde Emeriau, Sara Lowes, Lucy Martin, Godfrey Okot, Richard Peck, Alex Segura, Xing Xia, and Adam Xu through Innovations for Poverty Action. † Go to http://dx.doi.org/10.1257/app.20150023 to visit the article page for additional materials and author disclosure statement(s) or to comment in the online discussion forum. 1 “Extreme poverty” refers to earning less than the $1.25 per day international poverty line. See Burt, Hughes, and Milante (2014) for a discussion of the goals. 35 36 American Economic Journal: applied economics April 2016 Two in five of the world’s extreme poor are projected to live in fragile and c­onflict-affected states by 2030, yet rigorous evidence on what works in these ­settings is sparse.2 To help fill this gap, this paper studies a relatively common approach to relieving extreme poverty—transfers of human and physical capital— but to a postwar population: the most marginalized people living in small villages in northern Uganda, following a 20-year war. A humanitarian organization, the Association of Volunteers in International Service (AVSI), identified 1,800 poor people, mostly women, in 120 war-affected villages, and tried to help them start very small but sustainable retail and trading enterprises. AVSI’s Women’s INncome Generating Support (WINGS) program provided people grants of $150 (about $375 in purchasing power parity, or PPP, terms), along with five days of business skills training and planning, plus ongoing supervision to help implement the plan. The grant was 30 times larger than the beneficiaries’ baseline monthly earnings. An abundance of evidence argues that the average poor person has high returns to capital and is held back in part by poor access to credit and insurance, and that capital transfers and insurance products help grow microenterprises and incomes.3 Most of this evidence, however, comes from people who already have businesses or were selected for their business aptitude.4 It’s not clear if it applies to the most marginalized and “ultrapoor”—the people with the lowest incomes, no capital, and limited social networks—especially after war.5 The WINGS program has parallels to “graduation” style programs delivered to hundreds of thousands of ultrapoor households globally. Graduation programs give a bundle of temporary income support, livestock, livestock training, access to microfinance, supervision, and life-skills education. On balance, these programs have been successful: several studies show substantial shifts from casual labor to farm self-employment, and 10 to 40 percent increases in household consumption or earnings compared to control groups, lasting at least two to four years (Bandiera et al. 2013; Banerjee et al. 2015). The WINGS program differed from these other ultrapoor programs in several dimensions, however, including: the postwar setting; fewer program components; the focus on petty business; and providing cash rather than livestock.6 WINGS was also focused on young women. 2 See Burt, Hughes, and Milante (2014) for population projections. For reviews of the evidence see Blattman and Miguel (2010) and Puri et al. (2014). 3 For example, see Udry and Anagol (2006); de Mel, McKenzie, and Woodruff (2008); Banerjee and Duflo (2011); Karlan, Knight, and Udry (2015); Fafchamps et al. (2014); Blattman, Fiala, and Martinez (2014). 4 For example, Blattman, Fiala, and Martinez (2014) see high returns to a group-based cash transfer in northern Uganda. But the program targeted young adults with much higher levels of education and existing business plans for relatively high-skill microenterprises. That program also excluded the two most conflict-affected districts, where WINGS was implemented. 5 On the one hand, returns to capital or other inputs could be greater on the extensive margin than the intensive one. Indeed there is growing evidence that poor households use cash to start new enterprises and earn high returns, although little of this evidence comes from the poorest of the poor (Macours, Premand, and Vakis 2012; Gertler, Martinez, and Rubio-Codina 2012; Blattman, Fiala, and Martinez 2014; Bianchi and Bobba 2013). Returns could also be high in a newly stable political equilibrium, as neoclassical models of growth predict (Blattman and Miguel 2010). On the other hand, the ultrapoor could have low returns to capital, for instance because they lack crucial inputs such as education or business experience, or because they are vulnerable to expropriation within or outside the family. 6 Many in the aid community fear cash can be seized, wasted, or cause harm. They could be right. Besides the lack of other important inputs, extreme poverty has also been associated with cognitive deficits that impede Vol. 8 No. 2 Blattman et al.: Microenterprise Support For Ugandan UltraPoor 37 We evaluated WINGS by assigning the targeted people to either receive the program immediately or a year-and-a-half later, randomizing at the village level. Given the extreme setting, AVSI was reluctant to have a permanent control group—a common concern in humanitarian settings, and one reason humanitarian evaluations are rare. Thus, our design evaluates impacts a few months before the 60 control villages entered the program. We also tested the role of social capital in business success: could social capital be fostered, and would it increase the returns to grants? In poor rural villages, social networks are a main source of business advice, cooperation, and informal finance.7 For instance, in microcredit, growing evidence suggests that group lending is helpful not because of joint liability, but rather because it builds social capital and promotes risk-pooling.8 To test this, in half of the treatment villages, AVSI returned a couple of months after the grants (after individual businesses had already been started) to encourage the participants to form self-help groups, and offered three additional days of training in working together. The curriculum focused on developing organizational structures, decision-making processes, leadership, and helping them form a rotating savings and credit association (ROSCA). Sixteen months after grants, the standard WINGS program (without group encouragement) led to large changes in occupation and incomes. Thirty-nine percent of the control group had a nonfarm business, and this rose to 80 percent among WINGS participants. Employment rose from 15 to 24 hours per week, and cash earnings rose about PPP $1 a day. Since the average person in the control group earned less than $1 a day, the program doubled earnings. As a result, a conservative estimate of household consumption rose by almost a third, to roughly PPP $1.25 per day. Annualized, this impact corresponds to a PPP $465 increase in nondurable consumption—about a quarter of the PPP $1,946 standard program cost. For program participants, the gains were mainly economic. There was little evidence of changes in physical health, mental health, financial autonomy, or domestic violence. Outside the household, however, the program increased self-reported social support and community participation. Participants also reported an increase in resentment and verbal abuse from some neighbors, however, perhaps due to jealousy, or because they posed competition for preexisting traders. The group encouragement, meanwhile, increased the frequency and intensity of group activities. We see no impact on consumption after 16 months, but program participants who received group encouragement reported double the earnings of those that did not. Interestingly, this was not because their petty trading businesses were larger, more likely to survive, or more profitable. Rather, the evidence suggests that groups spurred informal finance as well as labor-sharing and cooperative cash i­nvestment and raise the risk of temptation spending (e.g., Bertrand, Mullainathan, and Shafir 2004). Among the poorest women, moreover, traditional norms could pressure them to share cash, make it easy to expropriate them, or hinder their business growth (Field, Jayachandran, and Pande 2010; Duflo 2012). This is the fundamental reason that AVSI designed WINGS to include training and supervision. 7 See Fafchamps (1992), Foster and Rosenzweig (1995), Murgai et al. (2002). 8 Feigenberg, Field, and Pande (2013) show that encouraging social interaction via group meetings reduces default on individual loans in India. Giné and Karlan (2014) also show individual liability has little impact on default in the Philippines. 38 American Economic Journal: applied economics April 2016 cropping. Group formation also seems to have mitigated the resentment and abuse from neighbors. Ideally, we could have randomly varied all program components and measured their returns. This was not possible. But, following the main evaluation, we used the entry of the control group into the program to investigate the marginal effect of the most expensive component: supervision. The supervisory visits provided substantive advice as well as pressure to implement the business plan, but were more than twice as costly as the grant. When the control group received WINGS after 20 months, we randomized them individually to receive the business training and grant plus: no supervisory visits; two visits (to provide commitment to invest); or five visits for both commitment and substantive business advice. A month after grants, but before any potential visits, expecting a visit had an ambiguous effect on business investment: those assigned to supervision increased investment by some measures and decreased by others. A year later, the effect of supervision on incomes is also ambiguous: nondurable consumption is marginally lower among those assigned to visits, earnings are marginally higher, but neither effect is statistically significant. Supervision, however, did increase business survival. Altogether, these results come with caveats. First, the control group knew they were on the waitlist, and so anticipation of treatment could affect their behavior. Second, all measures are self-reported. Experimenter demand is a risk, but given the size of impacts (and the absence of noneconomic impacts, where we might expect experimenter demand) the bias seems unlikely to drive our results. Third, there is mild randomization imbalance and attrition. Treatment effects, however, are robust to corrections and to missing data scenarios. Finally, these are 16-month impacts and given the fact that the control group entered the program, we cannot say whether they persist. Nonetheless, WINGS illustrates that the poorest may be able to start and sustain small enterprises, even in very small, fairly poor communities. Moreover, the 16-month consumption impacts of WINGS are almost identical to the one-year or ­two-year impacts of livestock-based ultrapoor programs, although WINGS was about half as costly. So far the livestock programs have longer term evidence in their favor, and the sustainability of cash-centric programs to the very poorest is an open question.9 Even so, studies of cash transfers to the non-extreme poor show sustained or growing impacts after four to six years (Blattman, Fiala, and Martinez 2014; de Mel, McKenzie, and Woodruff 2012b). Cash should be much cheaper and easier to deliver than livestock or capital goods, so if it stimulates employment as well as the accumulation of income-generating assets it could affect how ultrapoor programs are designed and scaled. This warrants more investigation, especially in humanitarian settings where cash is becoming more common as it can be difficult to provide in-kind capital. Also important to investigate are cost-effective forms of supervision and training. 9 Livestock programs sustain gains after two to four years, while ultrapoor cash transfer studies have 1 to 2 years of evidence so far (e.g., Haushofer and Shapiro 2013; Macours, Premand, and Vakis 2012). Vol. 8 No. 2 Blattman et al.: Microenterprise Support For Ugandan UltraPoor 39 Finally, the results support the view that social interactions encourage cooperation, and that such social capital delivers economic returns. Most social capital is endogenously formed, and it’s unusual to have experimental variation in local bonds. Echoing Feigenberg, Field, and Pande (2013) on microcredit, we see that a program that simply encourages group and ROSCA formation can increase social interactions, enhance social capital, increase risk-pooling and cooperation, and perhaps even raise incomes. What’s striking is that these profitable social bonds did not form in the absence of encouragement, and yet were provoked by a relatively short training. It implies the poor may be social capital constrained as well as credit constrained, and external intervention seems to help overcome barriers to collective action. I. Setting and Study Participants Uganda as a whole is a poor but stable and growing country. National income grew roughly 6.5 percent per year for the two decades prior to this study (Government of Uganda 2007). A long-running, low-level insurgency in northern Uganda, however, meant that most of the north was left out. From 1987 to 2006, small bands of rebels conscripted, abused, and stole from civilians in northern Uganda, especially the Kitgum and Gulu districts. Equally devastating was the Ugandan government’s decision to fight the insurgency by forcibly moving nearly the entire rural population of Kitgum and Gulu—about two million people—into dozens of displacement camps. The camps were often no more than a few miles from people’s rural homes, but people generally could not access their farmland during the war. Most households lost everything—livestock, homes, savings, and household durables—as a result. By 2006 the rebels were mostly defeated or pushed out of the country, and by 2007 the government permitted displaced people to return home and rebuild. The north’s economy began growing quickly, aided by an increase in demand from a newly peaceful Sudan. The government started a number of large-scale development programs to help the north catch up to the rest of the country. Even so, northern Uganda had some of the lowest standards of living in the world. By 2007, two-thirds of households were unable to meet basic needs and lived mainly on food aid (Government of Uganda 2007). By 2009, when this study began, most people had rebuilt their homes and had begun farming again. Food distribution and other emergency relief had ended. Most rural villagers, however, were still desperately impoverished. A. Study Sites and Participants AVSI identified 120 villages in the two most war-affected districts, Kitgum and Gulu. Most villages ranged in size from 350 to 1,000 people, with an average ­population of 699 (about 100 households). The study villages represented about a quarter of the population of the six rural subcounties where AVSI worked.10 10 AVSI actively worked in six subcounties—Odek, Lakwana, and Lalogi in Gulu and Omiya Anyima, Namokora, and Orom in Kitgum. These have 252 total villages: 84 in Gulu; 168 in Kitgum. AVSI excluded from 40 American Economic Journal: applied economics April 2016 AVSI held community meetings to describe the program and asked communities to nominate 20 marginalized villagers, asking that three-quarters be women aged 14 to 30. AVSI staff interviewed all nominees and selected 10 to 17 participants per village, excluding relatives of leaders and the least poor. Table 1 describes the 120 villages and all 1,800 study participants, based on a baseline survey of participants and each community leader. Twenty-six percent of villages had a weekly market, and while on average there were three shops or kiosks selling goods, the median village had none. Most goods were imported from the district capital and retailed by a handful of shop owners. Outside of traditional occupations (e.g., subsistence agriculture and some casual labor), main work opportunities came from petty trade and retail, cottage production (e.g., bricks, charcoal), livestock rearing, and cash crops. These farm and nonfarm microenterprises required few new skills or education, but they were capital-intesive and had fixed costs of entry. The average participant in the program was female, 27 years old, and had 2.8 years of education. Half were married or partnered. They reported an average of 15 hours of work a week in the past month, mainly in their own agriculture. Just 3 percent did any petty trade or business. In general they were poor with no access to finance. Average cash earnings in the month before the survey were 8,940 Ugandan shillings (UGX) ($4.47 at 2009 market exchange rates). Formal insurance was unknown, and almost no formal lenders were present in the north at the outset of this study in 2008. Only 9 percent of the sample were members of a village savings and loans group. On average they had UGX 4,860 ($2.42) in cash savings and a nearly equal amount in debts, usually from family and friends. Just 4 percent said they could get a loan of $50, which is unsurprising because of high transaction costs and the near absence of informal or formal lending institutions. Formal loan terms seldom extended beyond three months, moreover, with annual interest rates of 100 to 200 percent. Because of high fees, real interest rates on savings were typically negative. Given the startup costs of microenterprise, this absence of credit and insurance was a major barrier to entry. Effects of the War.—The war affected and displaced everyone in the study sample, impoverishing all. Until about a year before the program, everyone in the village had lived in a nearby displacement camp for at least three years, with no access to farmland, during which their lands became overgrown and their houses destroyed. At the time of the WINGS program, households were reestablishing agriculture for the first time since at least 2003. One in five people in our sample were abducted into the armed group at some point, usually only for a few days to carry looted goods. Long stays with the armed group were less common, and only 5 percent of the sample became fighters or were forced to marry a rebel commander. Abduction and conscription, however, were not the sample ­villages with fewer than 80 households. AVSI then chose program villages proportional to parish population, whereby more populous parishes would have a higher number of program villages (A parish is an administrative unit within the subcountry with five to ten villages). Official population figures did not exist and estimates were based on 2008 data from AVSI and the United Nations. We estimate participant households in treatment villages were less than 2 percent of households in the subcounty. Vol. 8 No. 2 Blattman et al.: Microenterprise Support For Ugandan UltraPoor 41 Table 1—Descriptive Statistics and Randomization Balance for Select Covariates Variable Means, full sample Treatment Control (Observations = 896) (Observations = 904) Balance test Difference p-value (1) (2) (3) (4) Demographics Age Female Married or living with partner Single-headed household Highest grade reached at school Forcibly recruited into rebel group Carried gun within rebel group Forcibly married within rebel group 27.02 0.86 0.46 0.51 2.82 0.20 0.03 0.03 27.63 0.86 0.50 0.47 2.75 0.25 0.04 0.03 −0.62 −0.01 −0.05 0.04 0.07 −0.05 −0.01 −0.00 0.17 0.72 0.26 0.17 0.70 0.03 0.45 0.63 Lagged outcomes Any nonfarm self-employment Average work hours per week Agricultural Nonagricultural Average hours of chores per week No employment hours in past month Monthly cash earnings, 000s UGX Durable assets (consumption), z-score Durable assets (production), z-score Number of community groups Member of a savings group Total savings, 000s UGX Total debts, 000s UGX Activities of daily life, z-score Symptoms of distress, z-score Quality of family relationships, z-score Any community maltreatment, past year 0.03 14.57 11.27 3.29 34.88 0.23 8.54 −0.67 −0.53 0.48 0.08 4.24 4.24 −0.06 0.09 −0.09 0.19 0.04 16.19 13.36 2.83 34.25 0.18 9.32 −0.59 −0.50 0.58 0.11 5.47 4.08 −0.04 −0.09 0.09 0.16 −0.01 −1.62 −2.09 0.46 0.63 0.05 −0.78 −0.07 −0.02 −0.10 −0.03 −1.23 0.15 −0.02 0.18 −0.19 0.03 0.17 0.12 0.02 0.25 0.68 0.07 0.26 0.05 0.48 0.04 0.07 0.20 0.82 0.75 0.02 0.00 0.11 Village-level covariates (Observations = 120) Village population Inverse distance to all villages Inverse distance to treatment villages Distance to capital (km) Accessible by bus Village has a market Number of shops in village Total NGOs in village 749.62 0.51 0.56 46.21 0.98 0.18 1.23 7.13 649.05 0.58 0.47 44.72 0.91 0.34 1.75 7.42 100.58 −0.07 0.09 1.48 0.08 −0.16 −0.52 −0.29 0.34 0.34 0.43 0.58 0.05 0.05 0.30 0.68 p-value from joint significance of 76 covariates < 0.01 Notes: All variables denominated in UGX and hours were top-censored at the nintey-ninth percentile to contain outliers. The durable asset indexes (z-scores) are calculated so that they have mean zero and unit standard deviation for the full sample over all survey waves, and hence the sign is negative at baseline. The differences in columns 3 and 4 come from OLS regressions of baseline covariates on an indicator for treatment plus a district fixed effect, with robust standard errors clustered by village. necessarily a source of relative poverty. Annan et al. (2011) used exogenous variation in conscription to identify the long-term effects. Social acceptance of former ­conscripts was high, and most people were psychologically resilient.11 These findings held even for the longest-serving females and those who were forcibly married 11 Psychological distress is commonplace, but serious symptoms are concentrated in the minority exposed to the most violence and with the least social support. 42 American Economic Journal: applied economics April 2016 or bore children. Conscription also had little effect on women’s schooling and labor market outcomes. Women’s options outside the armed group were not much better than inside the armed group, since most would not have been schooled or accumulated capital. Conscripted men, however, were well behind their peers after the war, because they missed out on opportunities to accumulate physical and human capital. In short, the war was so destructive that few young people emerged with any assets or schooling. At the time of the WINGS program, they were rebuilding their livelihoods from almost nothing. B. Comparison to Nonparticipating Villagers In general, the program successfully targeted the villages’ poorest, but it’s worth noting that almost all villagers were very poor by any measure. We do not have data on nonparticipants at baseline. Twenty months after the start of the program, however, we surveyed 2,836 nonparticipant households in treatment and control villages (about 25 from each village), and sought to interview two working age adults per household, in order to measure spillovers.12 Table 2 reports summary statistics for participants and nonparticipants in the control villages only, in order to compare people in the absence of direct treatment effects. We distinguish between households that were and were not traders at baseline. If we look at similar-aged adults in “nonparticipant” households, participants have similar cash earnings, but 24 percent lower consumption, 0.63 standard deviations fewer durable consumption assets (e.g., house quality, furniture, and household items), and 0.22 standard deviations fewer production assets (e.g., livestock or farm tools). Participants also have about half the education and 63 percent of ­nonparticipants’ work hours. About a third of nonparticipant households have at least one adult engaged in trading at baseline, and these tend to be wealthier than average. II. Intervention, Experimental Design, and Data The WINGS program had four components: Basic Skills Training.— Participants received five days of business skills training, ending with the preparation of a simple business plan. Training was designed for the illiterate and focused on business planning, sales, marketing, record-keeping, and budgeting (see online Appendix A for the curriculum). Trainers reviewed plans with the participants and returned unsatisfactory plans for revision. They encouraged participants to consider high cash flow activities that would diversify their income sources, especially petty trading and retailing. 12 Shortly before Phase 2 disbursement, we created village household lists, randomly sampled 25 nonparticipant households from each village (excluding the roughly 15 participant households), and sought to interview two working age adults per household on their economic activities, plus household data on assets and expenditures. We also collected village prices on a variety of goods. Nonresponse to the survey was only 5.5 percent. Vol. 8 No. 2 Blattman et al.: Microenterprise Support For Ugandan UltraPoor 43 Table 2—Participants versus Nonparticipants (control villages at Phase 1 endline) Nonparticipants ages 17–40, control villages Covariate Age Years of education Average weekly work hours Agricultural weekly hours Nonagricultural weekly hours Reports any hours in petty business Monthly cash earnings, 000s of UGX Monthly household consumption, 000s of UGX Durable assets (consumption), z-score Metal roof Number of goats Number of bicycles Number of mobile phones Durable assets (production), z-score Observations Participants (1) Traders (2) Non-traders (3) All (4) 28.10 2.81 15.02 9.68 5.47 0.16 15.76 108.38 −0.45 0.00 0.97 0.39 0.14 −0.21 29.35 5.58 31.08 21.11 9.98 0.26 23.45 175.05 0.64 0.03 1.62 0.77 0.58 0.30 28.55 4.48 21.93 16.80 5.13 0.07 10.14 134.04 0.06 0.00 1.22 0.60 0.35 −0.07 28.71 4.70 23.78 17.67 6.11 0.11 12.82 142.30 0.18 0.01 1.30 0.63 0.39 0.01 917 360 1,427 1,787 Notes: For work hours and income, we report household averages in nonparticipant households, restricting data to adults aged 17–40. A household is coded as a trading household if at least one household respondent says he or she regularly sold an item a year ago, and did not obtain that item from his or her own production, for any items in a list of 35. Individual-level covariates come from a self-reported survey of all respondents. All variables denominated in UGX and hours were top-censored at the ninety-nineth percentile to contain outliers. Cash.—Once a plan was approved, the participant received a grant of 300,000 UGX or $150 at 2009 market exchange rates. The grant was framed as funds to implement the business plan. AVSI delivered cash in two equal installments about two and six weeks after training. Supervision.—AVSI trainers traveled four to five times to the villages in the six months after the grant to provide one-on-one advising and supervision. Group Formation.—About two months after grants were disbursed, AVSI also offered a three-day group dynamics training that encouraged participants in the village to form self-help groups that would exchange ideas for improving their petty business and agriculture, organize savings and credit, and (to a lesser extent) collaborate or cooperate in activities such as marketing their produce or buying inputs. The vast majority of the three days focused on providing information and skills related to working effectively as a group, including: leader selection, group ­decision making, communication and listening skills, and conflict resolution methods. It applied these skills to the same topics that were the focus of the five-day business skills training: business planning, saving, and record-keeping. The difference is that the group dynamics training mostly focused on how they could adapt these skills when working as a group. The final day focused on how to organize a savings group, including best practices and record-keeping. Other forms of business cooperation, such as joint purchasing and collaborative marketing, were mentioned in passing as advantages of working in groups, but these production economies of scale received very little attention. Indeed, AVSI deliberately did not encourage 44 American Economic Journal: applied economics April 2016 participants to form firms or cooperatives. This is one reason AVSI offered the group training some weeks after the individual business plans, grants, and initial investment decisions. Groups decided on their own aims and organization, however, and at the end of the training AVSI helped groups write a constitution that formalized the aims, leadership, and decision-making structure of the group. Online Appendix A describes the curriculum. On average, WINGS cost $860 per person at market exchange rates: $150 for grants; $125 for targeting and disbursement; $124 for business training; $82 for group dynamics training; $348 for five supervisory visits; and $31 in other costs. This is equivalent to PPP $2,150. A. Phase 1 Research Design In Phase 1, we randomized 60 of the 120 villages to receive the WINGS program. The other 60 were randomized to a waitlist group (Phase 2) to be treated at least 18 months later. The participants in the waitlist villages were aware of this treatment status. Within these 60 treatment villages in Phase 1, we randomized 30 to receive the group dynamics training and 30 to no group encouragement. Participants in the 60 control villages were told they would receive the intervention in about 18 to 24 months, called Phase 2. Figure 1 illustrates the sample, design, and timing. We randomized by public draw, to ensure village buy-in and transparency.13 The draw resulted in a slight imbalance in baseline covariates, reported in Table 1.14 Treatment participants were slightly worse off, with lower durable assets, employment, literacy, savings group memberships, participation in armed groups, and family and community support. The villages they lived in were also less likely to have a market. A test of joint significance of all covariates has a p-value of < 0.001. If anything, this may lead us to understate treatment effects. To account for possible bias, we will control for these covariates in all treatment effects estimates and show robustness to difference-in-differences measures. To evaluate Phase 1, we attempted to survey all 1,800 participants 20 months after baseline, 16 months after the first grant (at the median). Attrition was minimal; we tracked migrants to their current location and found 96.3 percent (not including three who died). Attrition is generally not significantly correlated with treatment or baseline covariates (see online Appendix B).15 13 We gathered village leaders in each district. They drew village names without replacement to be assigned to Phase 1 or 2. The authors were present for the draw to ensure its validity. We randomized group dynamics training via computer. 14 See online Appendix B for all 76 covariates, as well as balance tests for the group dynamics randomization. In total, 24 percent of the (nonindependent) covariates have p < 0.10. In general, the group dynamics randomization was balanced. 15 In addition to these survey data, we collected formal qualitative data to better understand program experiences, constraints, and mechanisms. Two Ugandan research assistants interviewed 32 randomly selected participants in eight villages three times during and after the program. They followed semi-scripted questionnaires and recorded, transcribed, and translated all interviews and notes. Vol. 8 No. 2 Blattman et al.: Microenterprise Support For Ugandan UltraPoor 02/09: Selected 120 villages (60 per district) and communities nominated ~2,300 persons 03/09–04/09: Registered 1,800 clients in 120 villages 45 Persons deemed not “vulnerable” excluded from study 04/09–06/09: Baseline survey of 1,800 clients (100%) 06/09: 60 villages (896 clients) randomized to receive training (06/09–08/09), cash (08/09–10/09), and follow-up (10/09–10/10). 06/09: 60 villages (904 clients) randomized to waitlist treatments 02/09–03/09: 30 villages also receive group dynamics training 30 villages receive core program only 11/10–02/11: Surveyed 861 (96%) of clients (0 deaths, and 0 villages, and 35 people unfound) 01/11: 57 clients no longer in village replaced 11/10–02/11: Surveyed 870 (96%) of clients (3 deaths, and 0 villages, and 31 people unfound) 904 clients (847 original Phase 2 clients and 57 replacements) receive training (03/11–05/11) and cash grants (08/11–09/11) 318 clients: No follow-up 300 clients: 1 to 2 followups 286 clients: 3 to 5 followups 09/11–10/11: Surveyed 858 (95%) of clients (3 deaths, and 0 villages, and 35 people unfound) Performed by implementer (AVSI) Performed by researchers 09/11 to 06/12: Follow-ups performed (94% adherence rate) 06/12–08/12: Surveyed 868 (96%) of clients (1 death, and 0 villages, and 35 people unfound) Figure 1. Description of the Study Sample and Experimental Design B. Phase 2 Research Design In Phase 2, participants in the 60 control villages received the WINGS program.16 We used this as an opportunity to evaluate the marginal impact of the highest-cost component, supervision.17 The first supervisory visit or two was mainly to hold grantees accountable for implementing their business plan. The later visits were primarily to provide advice. Of the 904 people in Phase 2, we randomly assigned 16 Program changes were minor. AVSI increased grants to 360,000 UGX to account for inflation, and disbursed grants in a single tranche for efficiency. 17 Of the 60 villages, 30 were also randomized to have spouses included in the training, and present at the grant disbursement, described in a companion paper (Green et al. 2015). 46 American Economic Journal: applied economics April 2016 individuals to receive the cash and basic training with one of three treatments: no supervisory visits; one to two supervisory visits, focused principally on commitment to invest; or all five supervisory visits, to provide commitment but also substantive advice on business management and household bargaining.18 To evaluate impacts, we first surveyed Phase 2 participants about a month after the grant, shortly before the first follow-up. We intended this short-run survey to assess how participants’ actions and investments varied with expectations of any supervision. We surveyed them again about a year after the grants to study the impacts of actual supervision. Again, attrition was low; we found 95 percent at the one-month survey and 96 percent at the one-year survey. III. Empirical Strategy We estimate intent-to-treat (ITT) effects via the ordinary least squares (OLS) regression: (1) ​​Y​ij​​​ = θ​​Tj​​​​ + ​​δT​ ​​​​​D​ jT​  ​​ + ​​δ​T​–​​​​​​D​ jA​  ​​ + ​​Xi​j​​​β + ​​εi​j​​​, where Y is an outcome for participant i in village j, T is an indicato...
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Outline
The paper is all about discussion on the class reading with covered three main titles which are
"Female Empowerment: Impact of a Commitment Savings Product in the
Philippines"
"The Returns to Microenterprise Support among the Ultrapoor"
"The Experimental Approach to Development Economics"


Running Head: DEVELOPMENT ECONOMICS

Development Economics
Student’s Name
Institution Affiliation

1

2

DEVELOPMENT ECONOMICS
Development Economics
According to our last class discussion, we were majoring in the field of economic

components being explored in the present. Development commercial aims around improving the
economic parts of countries by considering variables, as an instance, looking at facts like the
health, education, and working situations. This area is quite not the same as the economic fields
we have currently found out about as it relies upon more on subject analyses and trying out for
explicit countries in place of building general financial theories.
Our first reading and discussion were on t female Empowerment: Impact of a Dedication
Saving Product in the Philippines by Nava Ashraf, Dean Karlan, and Wesley Yin. It talks about
several...


Anonymous
Awesome! Perfect study aid.

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