Formal finance and informal safety nets of the poor: Evidence from a savings field experiment

Accepted Manuscript Formal finance and informal safety nets of the poor: Evidence from a savings field experiment Jeffrey A. Flory PII:

S0304-3878(18)30445-0

DOI:

10.1016/j.jdeveco.2018.07.015

Reference:

DEVEC 2274

To appear in:

Journal of Development Economics

Received Date: 7 September 2016 Revised Date:

17 July 2018

Accepted Date: 31 July 2018

Please cite this article as: Flory, J.A., Formal finance and informal safety nets of the poor: Evidence from a savings field experiment, Journal of Development Economics (2018), doi: 10.1016/ j.jdeveco.2018.07.015. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Formal Finance and Informal Safety Nets of the Poor: Evidence from a Savings Field Experiment

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By JEFFREY A. FLORY*

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Using a field experiment on financial service information delivery in over 320 villages in Malawi, I find that formal savings increases agricultural investments and crop income among adopters, and raises their private transfers to other households during the hungry season. This causes large rises in transfer receipts, improved food consumption, and better health, among non-savers. The worst-off experience a nearly threefold increase in informal aid. Findings show financial markets expansion has immediate effects beyond service-users, with large and surprising impacts on informal support systems. Results also support the effectiveness of a novel method to spur service uptake and accelerate financial deepening.

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*Flory: University of Chicago Department of Economics, 5757 S University Ave, Chicago, IL 60637, [email protected] (Visiting); Robert Day School of Economics and Finance, Claremont McKenna College, 500 E. 9th St., Claremont, CA, 91711, [email protected].

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Keywords: Formal savings, Spillover effects, Safety nets, Informal institutions, Financial access

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Formal Finance and Informal Safety Nets of the Poor: Evidence from a Savings Field Experiment

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THIS VERSION: 16 JUL, 2018

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Using a field experiment on financial service information delivery in over 320 villages in Malawi, I find that formal savings increases agricultural investments and crop income among adopters, and raises private transfers to other households during the hungry season. This causes large rises in transfer receipts, improved food consumption, and better health, among nonsavers. The worst-off experience particularly strong increases in informal aid. Findings show financial markets expansion has immediate effects beyond service-users, with large and surprising impacts on informal support systems. Results also support the effectiveness of a novel method to spur service uptake and accelerate financial deepening. Households with higher education adopt accounts and experience rises in savings and income, while low education households do not. However, the benefits to adopters are used to help insure the rest of the village – in contrast to suggestions elsewhere in the literature that savings may negatively impact sharing through networks.

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Keywords: Formal savings, Spillover effects, Safety nets, Informal institutions, Financial access Improving savings access for the poor has become one of the most prominent (and promising) recent ideas for reducing poverty, having gained enormous traction in the last decade. A rich and fast-growing literature examines formal savings demand, impacts of accounts on

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users, and the channels of effects (e.g. Dupas and Robinson, 2013a, 2013b; Ashraf et al., 2006; Ashraf et al., 2010; Brune et al., 2016; Prina, 2015; Callen et al., 2014). The importance of the insights emerging from this burgeoning set of studies is underscored by the large and growing

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amount of resources devoted to savings expansion in developing countries. More recently, a few studies have begun to question whether formal savings expansion (and the spread of formal finance more generally) may have negative spillover effects on households connected to service adopters, through social networks.1 This study uses a field experiment that boosts rates of formals savings use, and finds that formal accounts can increase private transfers to the poor in times of need, with large positive impacts on non formal-savers. 1

See, for example, Dupas and Robinson (2013a, 2013b), Callen et al. (2014), Chandrasekhar et al. (2013), Binzel et al. (2013), and Banerjee et al. (2015).

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Since most of the world’s poorest remain uncovered by formal safety nets, private help from friends and relatives often serves a crucial function in times of need.2 While the importance of informal institutions is widely recognized (e.g. Cox, 1987; Arnott and Stiglitz, 1991; Coate and Ravallion, 1993; Townsend, 1994; Udry, 1994; Fafchamps and Lund, 2003), how such

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institutions respond to financial market integration is not yet well understood. This question takes on particular urgency for the poorest, among whom even modest changes in safety nets can have large consequences. While the push for financial access expansion continues across the developing world, many of the critical changes this is bringing to the lives of the very poor

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remain unclear.

To shed light on this area, I use a randomly assigned information treatment that boosts

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formal savings rates in rural Malawi to examine the effects of account use on inter-household transfers. The setting of the experiment is an innovative approach to increase access to financial services through a drivable bank on wheels, which makes daily trips to more remote areas. The treatment consists of an information intervention designed to increase service use in areas served by the bank, through periodic village visits.

My first set of results sheds light on the effectiveness of this method for increasing

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savings deposits as banks move deeper into isolated areas. Estimates indicate these visits increased savings rates by 5 percentage points (32-38%) overall, and by a larger 11-13 percentage points (45-54%) among households likely to open an account. There are many open questions regarding the determinants of financial service use.3 The results reported here help

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advance our understanding of ways to accelerate financial deepening, showing that informational visits can be an effective tool to recruit new clients and integrate rural areas into modern

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financial markets.

The second set of results yields new evidence on the direct impacts of savings accounts

on adopters. Recent work (e.g. Dupas and Robinson, 2013b) has shown that formal savings adoption can dramatically boost investments in small businesses by micro-entrepreneurs, with

2 Despite expansions in coverage over the last decade, about 870 million people (71%) living under $1.25/day still lack access to social assistance. For further discussion of social protection in development policy, and recent statistics on costs and coverage, see Gentilini et al. (2014), Hanlon et al. (2013), and Barrientos (2013). 3 Mobarak and Rosenzweig (2012) examine the effects of informal insurance on demand for formal insurance services in rural India, Bertrand et al. (2010) show in South Africa that marketing and presentation can be as important as interest rates in recruiting formal borrowers, Schaner (2016) examines the role of temporary promotional interest rates on savings adoption in rural Kenya, and Duflo and Saez (2002) find strong peer effects on savings decisions in developed countries.

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large effects on expenditures.4 My findings help extend this literature to effects on agricultural production among the poor, complementing recent results in Brune et al. (2016). Using the randomly assigned information intervention as a source of exogenous variation, I find that savings uptake can have large positive effects on investments in farming and crop income.

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The third major set of results exploits the exogenous boost in account uptake to identify causal impacts of formal savings expansion on private transfers, shedding new light on the interaction of financial markets and informal local safety nets. First, I find that local savings adoption has positive effects on transfers out of households during a period of high vulnerability:

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rises in account uptake cause large rises in probability, quantity, and size of transfers to other households during the pre-harvest hungry season. Turning to transfer receipts, I find that living

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in areas where savings-use was randomly boosted raises the likelihood of receiving private cash transfers during the hungry season by 10 percentage points (35%) – with large effects on nonsavers. Restricting to the worst-off households, the proportion receiving private transfers in savings-boosted areas rises to 13-16 percentage points (65-79%) above that in control villages. My final set of results shows that the increase in private aid receipts is linked to key household welfare improvements. For example, food consumption significantly improves among

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non-savers in savings-boosted areas – with the strongest effects concentrated among the worstoff households. This group also experiences significant improvements in short-term health indicators relative to comparable households in the control villages. The rest of the paper is organized as follows. The next section provides a brief overview

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of safety nets for the poor, the role of formal finance, and an emerging set of studies on the interaction of formal and informal systems. In section III, I describe the field experiment, setting,

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and the data. Section IV analyzes the effects of a novel approach to spur savings uptake, while section V examines direct effects of account-adoption. Section VI estimates the effects of formal savings on private transfers between households, and section VII examines their impact on key household welfare indicators. Section VIII concludes. II. Safety Nets and Formal Finance

Much of the developing world is vulnerable to critically low consumption, with effects ranging from lower human capital to physical stunting, crippling health problems, and higher 4

See also Callen et al. (2014), Schaner (2016), and section V for further details on findings from this line of research.

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death rates (see Dercon, 2005 for a review). To help eliminate the worst effects of poverty and vulnerability, a steady proliferation of social assistance programs has spread across developing countries over the last 10-15 years. However, the extreme poor remain largely exposed, with 870 million people living under $1.25 a day uncovered by social safety nets (Gentilini et al., 2014).

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While formal assistance is often lacking, the poor also use informal methods to help prevent low consumption and welfare outcomes. A rich literature documents, for example, the critical role of social networks, with gifts and loans from friends and relatives helping households address shortfalls in income (Townsend, 1995; Udry, 1994; Fafchamps and Lund,

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2003). Often interpreted as informal insurance (Coate and Ravallion, 1993), or as informal local taxes on the better-off (Jakiela and Ozier, 2015), these private transfers provide integral support

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to consumption among the extreme poor.

A growing body of work on financial services for the poor explores whether formal services can improve on existing informal options, to reduce vulnerability and improve welfare outcomes among service users.5 This study explores two complementary questions that are gaining increased attention from researchers, as awareness of their significance for poverty outcomes grows: First, how does the spread of formal financial services affect informal safety

services?

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nets? Second, what are the resulting effects on households that do not (or cannot) use formal

While there is a surge of new activity in working papers and recently published studies in this area, the evidence to date from this ongoing work is mixed. Possible reasons include the fact

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that different financial services may have different effects, the availability of experimental variation, and the importance of field context – all of which this study helps address. Regarding

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the first, if effects of financial services differ by service type, studies which examine general access to financial services may yield conflicting findings depending on the composition of service use. For example, while Binzel et al. (2013) report that bank expansion in rural India weakens risk-sharing capacity and lowers resource-sharing in dictator games in the lab, Kinnan and Townsend (2012) find evidence from Thai villages that having relatives and friends connected to the formal financial system can help households smooth consumption shocks. 5 See Karlan and Morduch (2009) for a review. Also see Mobarak and Rosenzweig (2012) for an interesting example in this literature that examines the interaction of formal and informal financial systems, but from the perspective of the effects of informal networks on formal insurance demand, and on the benefits of using formal insurance.

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Variation in financial service use is also observational in both studies, rather than experimentally induced, adding another potential cause for conflicting findings. Addressing both issues from the perspective of formal credit, Banerjee et al. (2015) find experimental evidence that microcredit can crowd out informal loans and weaken informal

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connections. Feigenberg et al. (2013), on the other hand, find that the intra-group bonds fostered by banks using group-lending may help offset negative impacts of this sort by strengthening informal financial networks within the group itself. An insightful lab experiment that tackles this from the savings side is Chandrasekhar et al. (2013), which finds that transfers between certain

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player types in risk-sharing games are lower when a savings tool is simulated in the lab. While one of the virtues of the lab is that it enables greater control of the environment, removing field

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context can also sometimes eliminate factors that turn out to be central to certain questions of interest. For example, losing the field context of an unwell or struggling neighbor asking for help during the hungry season may affect transfer rates. Or losing the possibility for impacts of accounts on production and income that manifest via pathways and time-horizons not present in the lab may affect the scope for the role that formal savings can play in prevalence, frequency, and amounts of transfers.

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In the context of this mixed set of results, the present study offers clear causal evidence from the field of the impacts of formal savings expansion in particular. This is important because it disentangles the effects of savings from other financial services and thereby avoids the conundrum of possible countervailing effects from different types of service. It is also important

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because savings are often the first service be expanded to new clients, a common point of entry to the formal financial system for new users, and often the only financial service that some users

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can access – especially at first.

The study that comes closest to providing clear answers about the impacts of formal

savings expansion on transfers in the field is a working paper by Comola and Prina (2015), which finds experimental evidence that giving formal savings access to female headed households in Nepalese slums can raise informal credit flows between households within the slum.6 The study provides intriguing initial insights on possible roles of formal savings, but it 6 Comola and Prina (2015) develop a method to evaluate treatment effects when interventions alter social networks. To illustrate, they use the impact of women’s savings adoption on prevalence of local transfers to or from fellow villagers. While the study’s main focus is accounting for network changes in impact estimates, their findings provide intriguing early insights into formal-informal interactions in the field.

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also leaves open several questions. For example, the extent to which the impacts depend on the fact that only women were offered accounts (which they hypothesize as the driver), or depend on the urban setting of the slums. A more urgent open question, however, especially as the evidence grows on positive direct effects of savings, is what happens to non-savers in the same areas as

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adopters. Given the large resources and attention that savings services for the poor is now drawing, it is imperative we have a clearer understanding of the spillover effects of savings expansion onto the safety nets and well-being of non-adopters – particularly the poorest, since they will be the last to (or may never) adopt – and a stronger understanding of the causal links

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between formal savings and changes in household transfers.

This paper, which takes place in rural agricultural communities, does not restrict to

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female household heads, and focuses attention specifically on the spillover effects, helps answer these questions and fill these important gaps. It advances this new and growing body of research by zeroing in on the effects of savings adoption on transfers out of the household, by shedding light on the causal channels through which these effects occur, and by assessing the spillover impacts onto transfer receipts and household welfare among non-adopters and the extreme poor during periods of high vulnerability.

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Ex-ante it is unclear whether we should expect formal savings to strengthen or weaken informal networks in the field. On the one hand, large positive effects on service-users may propagate through networks to other households to whom they are socially connected. Several studies over the last few years have found dramatic benefits from the adoption of simple formal

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savings accounts on a range of indicators such as income, expenditures, and total assets.7 Indeed, effects have been large enough in some cases that they have been referred to as the “magic

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income effect” (Callen et al., 2014), and their causal channel remains an intriguing puzzle in its own right. Positive effects of savings adoption on household wealth and income, coupled with what we already know about widespread use of private transfers to prevent the worst effects of poverty, suggest broad scope for positive spillovers that may strengthen informal safety nets. On the other hand, it has been suggested (e.g. Dupas and Robinson, 2013a, 2013b) that

one channel of the benefits from formal accounts may be that they help users deny requests for aid from others in their sharing network. This could weaken informal safety nets, causing 7

See for example Schaner (2016), Callen et al. (2014), Dupas and Robinson (2013b). See Karlan et al. (2014) for a review.

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negative spillovers onto non-users. The findings by Chandrasekhar et al. (2013), that introducing a wealth storage tool in the lab lowers sharing between players, lend support to this concern. This paper uses a randomly assigned positive shock to savings uptake in the field to test the effects of

impacts on non-savers and the very poor. III. Data and Experimental Design A. Background and Sampling

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accounts on private transfers during a high-vulnerability period, and measures the resulting

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To test the empirical effects of expanding formal savings markets on informal safety nets, I use a field experiment in rural Malawi – one of the poorest regions in the world, with low financial market penetration but robust use of inter-household transfers.8 In late 2007, a

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microfinance bank expanded savings access to rural areas of the three largest districts in the country’s central region through a mobile bank on wheels. The bank drove out from the capital city of Lilongwe to make regular weekly stops at six different trading centers along the highway, two of them in small townships serving as the district capital. Similar to other recent studies on savings, the accounts the bank offered had positive nominal, but negative real, interest rates.9 The data consist of a two-year household panel. The baseline data were collected

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February-April of 2008, during the pre-harvest “hungry” season, when household resources are often scarce and food-stocks running low.10 This was prior to any measurable use of the bank’s services.11 The second round was collected over the same period in 2010, after an information intervention to encourage use of the bank.

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Community sampling followed a matched-pair design. Each pair consisted of two villageclusters (enumeration areas defined by the National Statistics Office that typically include 2-4

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villages). Clusters were first categorized based on distance from the bank stop, and then further split into two population categories – high and low. Two clusters were then randomly sampled 8 For example, prior to the savings-boost, 6.0% of sampled households had a formal loan, and 11.6% had a formal savings account. On the other hand, 23.6% of the sample reported having at least one informal loan from a friend or relative. See Appendix 1 for discussion of the nature and strength of local social ties, as well as types of relationships that underlie household safety nets, in the research area. 9 The nominal interest rate of the accounts for customers accessing the bus-bank was 2%, while inflation over the study period averaged 8.6% (International Monetary Fund, 2015). An exception to positive nominal interest is Dupas and Robinson (2013b), who instead pay the minimum balance for adopters. (While this may suggest some positive return to adoption, they show the overall de facto interest is negative.) 10 Most farming households in Malawi receive the majority of their annual income during a single harvest period, which in Central Malawi usually lasts from late April into June. 11 Though the bus-bank began operating in late 2007, focus-group discussions from February and March of 2008 confirm awareness of its existence was still extremely low. (Even the few households with baseline accounts at the bank that expanded access through the bus report nearest known account access at distances (40 km on average) that indicate they are unaware of the far closer branch provided by the bus-bank.)

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from each population-distance group to form a pair. Finally, within each pair, one of the clusters was randomly selected to receive the information intervention. From each cluster, 20-23 households were sampled. The final panel contains 56 pairs, or 112 village-clusters (about 325 villages), with a total of 2,011 households. Villages are located at radial distances to the bank

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stop ranging from 0 to 14 kilometers.

B. The Experiment: A Financial Services “Extension Worker”

Adoption of newly available financial services may be correlated with important unobservables. To introduce exogenous variation in take-up rates, I therefore use a formal

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savings encouragement that was randomly assigned at the community-level. This also allows me to test the effectiveness of a novel method for promoting service-adoption and the expansion of

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financial markets.

Drawing from qualitative interviews on ways people obtain information they trust from sources outside the village, an information intervention was designed to mimic pre-existing methods of disseminating knowledge about new services or technologies. It was structured around the model of regular local visits by outside extension workers (such as agricultural, health, or nutrition extension workers). The rationale for this delivery method was twofold. First,

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it was the preferred method suggested by community members themselves for providing financial services information. Second, it has the added advantage of fitting with pre-existing practices in these areas for information and education about new technologies and services, thereby minimizing the introduction of any new factors besides the information.

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Eighteen individuals were trained to work as promotional assistants, traveling to treated villages along dirt paths and roads by foot or bicycle, bringing information on the bank’s

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services. Assistants were introduced by bank managers to local leaders and village chiefs, who then introduced them to the local community at village-wide gatherings. The assistants then met and spoke with village residents during door-to-door visits and occasional group meetings, once every 2-3 weeks for up to a few hours per village. Their role was to provide basic information on savings accounts and services at the bank, explain how the services might be useful, answer any questions potential clients might have, and in general serve as the community’s own local information source on the services just recently made available in their area. They explained, for example, details of the account designed for bus-bank customers – minimum balance, 8

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withdrawal fees, interest rate, cost and function of ATM card, and lack of monthly fee. Village visits were focused during the harvest months and shortly after, to catch potential service-users at a time when they are comparatively flush with cash and more likely to find improved savings options attractive. They began mid-April of 2008, after the baseline survey.

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Prior to the bank’s introduction, many in the area reported little to no experience with banking services and little or no understanding of these basic features of accounts. Many also expressed strong suspicion of organizations that want to collect households’ savings. Dupas et al. (2014) similarly find mistrust of banks is one of the top barriers to uptake and use of formal

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savings in rural Kenya. A consistent personalized link to basic information in such a setting may have a significant impact on willingness to try a new service.

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In addition to the information intervention delivered just to treatment areas, general blanket advertising through traditional media such as newspaper and radio also took place, and was accessible in all areas (control and treated). Information on the bank’s products, services and fees, days and hours of operation, and weekly location also likely spread to some extent by word of mouth. Effects of the targeted information intervention are therefore best understood as the impact of a personalized information boost over and above information available through

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traditional advertising and word of mouth transmission.

C. Descriptive Statistics and Balance-Check Table 1 reports household characteristics of the baseline sample. The first column reports the sample mean and standard deviation of each variable. The second reports coefficient

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estimates (and standard errors) of the difference between the baseline means of the treatment and control groups, for each variable. Errors are clustered by village-cluster (the level of

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randomization). The variables HFIAP and HDDS are food-security measures: for HIAP (ranging from 1 to 4) higher values indicate less food-security, while for the more continuous HDDS higher scores reflect more food diversity, higher household per capita consumption, and greater per capita caloric availability.12 Educated Head is an indicator for whether the household head has a primary education or is literate in English (the official language).

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HFIAP (Household Food Insecurity Access Prevalence) classifies households by 4 levels of food security, and is from the USAID Household Food Insecurity Access Scale for Measurement of Food Access (see Coates, Swindale, and Bilinsky, 2007). HDDS (Household Dietary Diversity Score) ranges from 1 to 12 and is used as an indicator of dietary diversity, caloric intake, protein adequacy, and food consumption. For more on HDDS and its ability to measure different aspects of food consumption, see Swindale and Bilinsky (2006) and Hoddinott and Yohannes (2002).

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The majority of households (85%) are male-headed, heads are on average 41 years old, roughly a quarter of them have at least a primary education, and just under a third are literate in English. Average household size is just over 5 people, about 27% have a non-agricultural business and 16% have a member with a salaried job. While 12% of the sample has a formal

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savings account, 6% have formal loans. Average food-security scores are low: an HFIAP score of 1 means a household is food-secure while a score of 4 means it is severely food insecure; the average score in the sample is 3.2. The average distance from the bank stop is about 8 km.

Estimates of differences between the treated and control clusters show the two groups are

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well-balanced, with no more significant differences than should be expected. Table 1 reports 26 coefficients, and only 1 is significantly different from zero (at the 5 percent level). Table A.1

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shows the summary statistics and balance check among just the attriters, confirming the balance among this group as well (among 25 coefficients, only 1 is significant, at p=0.04). IV. Results: Formal Savings Use Since access to credit was not available to most communities, the information intervention served as an encouragement to open a formal savings account.13 If it raised general awareness of financial services, however, it might also induce service uptake at other

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organizations. I therefore test for changes in use of savings and credit at any financial institution. Before examining the results of the experiment, a simple observational analysis yields some valuable insights regarding the predictors of account ownership. Table 2 shows estimates from regressions of whether a household has a formal account on key variables we expect may

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play a role. These include baseline education (as measured by head’s education), distance from nearest the bank, and two baseline indicators of wealth – assets and food security. While it is

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important to bear in mind this analysis is correlative, the findings are instructive. First, they show a very strong link between savings uptake and the head’s education (English literacy and primary school degree). We also see that shorter distance from banks, higher total assets, and stronger food security, all predict account ownership. These estimates are also used to complement the results on the direct effects of formal savings that follow. The Probit model feeds into a propensity score for having an account, and analyses included in Tables 3-5 use these probability estimates as an alternative approach to confirm results from the main specifications used to 13

Access to credit from the bank expanded very slowly, village-by-village, and targeted areas of high economic activity close to the bank stop.

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estimate the direct effects of formal savings. While the tables report estimates for having a high propensity, Figures A.1-A.4 also illustrate the patterns in a flexible non-parametric manner that helps visualize impacts on the outcomes of interest as predicted probabilities of account use rise. Turning to the first set of results from the experiment, Table 3 shows the effect of the

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information intervention on use of savings accounts. Column 1 reports results from a simple linear regression of whether a household has formal savings on a dummy variable representing the information intervention, with standard errors clustered at the village-cluster level. The dependent variable is a {0,1} indicator for whether the household has a savings account in 2010.

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Column 2 shows results with fixed effects at the cluster-pair level.14

The estimated impact on formal savings use across the entire sample is a 4.5 (p=0.008) to 5.2 (p=0.055) percentage point boost, or a 32-38% increase over formal savings rates in the

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controls.15 However, this represents the broadest possible approach, supposing homogeneous effects of the encouragement. It is unlikely that all households would be equally capable of, and interested in, opening a savings account. As discussed above, Table 2 shows a critical predictor of whether a household has formal savings is education, as measured by the head's completion of primary school and proficiency in the country’s official language. The importance of education

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may stem from several reasons. On the one hand, higher numeracy and stronger ability to digest information may help households assess the costs and benefits of using a newly available financial product in order to better determine its value. Higher education may also facilitate a more accurate understanding of the bank’s motivations, and may lower perceived risks to saving

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via accounts, to help overcome any existing mistrust of organizations that want to collect deposits from households (Dupas et al., 2014). More accurately understanding the value and the

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risks may raise adoption rates. On the other hand, education and English literacy may proxy for other factors, such as income, wealth, better access to markets, or familiarity with other modern services – all of which may also increase baseline likelihood to take up financial services as they become increasingly available. Already closer to the margin and more likely to adopt, households with higher education are also those we would expect to be most responsive to an informational encouragement. As short-hand, households with heads that have at least a primary 14 Pair-level effects account for differential responses to expanded access by pair. Villages in pairs close to the bank-stop, for example, are more likely to start service-use, whether or not they receive the information treatment. 15 A simple ߯ ଶ test also shows the 5.2 percentage point difference is significant at p=0.002.

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education or are literate in English are referred to as “educated”. This group represents about a third of the baseline sample.16 Columns 3 and 4 of Table 3 add an indicator for education level and its interaction with the information treatment, to test for heterogeneous impacts (the omitted category is educated).

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As the coefficient for Information shows, among educated households there is a 10.6 (p=0.004) to 11.1 (p=0.017) point rise in the percentage with formal accounts (a 45-47% increase) when moving from the control to information-treated villages. Appendix Table A.15 shows that when the sample is split by education level, estimated impacts on the educated subsample are as high

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as 12.7 (p=0.000) percentage points – a 54% rise in the proportion with accounts. We also see the coefficient on the interaction term is significant and large enough to eliminate the effect of

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the information treatment when switching to the non-educated. The bottom of the table reports the sum of the coefficients for Information and Information×Non-Educated, with F-test p-values in brackets. The estimates confirm there is no significant impact among households with noneducated heads: the overall rise appears driven almost entirely by the impact of the information treatment on the educated group. Appendix Table A.3 confirms these impacts are limited to formal savings: estimates show the information intervention had no significant effect on use of

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formal credit – overall, among educated households, or among the non-educated. The above findings lead to my first main experimental result. Result 1: Informational visits can significantly increase overall formal savings rates, with effects varying by household type. The proportion of households with formal accounts was 4.5 to 5.2

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percentage points (32-38%) higher overall in information-treated villages, and 10.6 to 12.7 percentage points (45-54%) higher among the more savings-prone (households with educated

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heads).

Further analysis shows use of accounts is quite robust. For example, households have

substantial balances at the time of the survey interview, averaging about MK 15,000 (recalling that this is pre-harvest time, when savings are expected to be lower than normal). Also, about 40% have used their account at least once in the last 30 days for deposits (median value MK 1,000) or withdrawals (median value MK 7,000). There is also little evidence that induced 16 Appendix Table A.2 reports summary statistics on this group and confirms the balance of its characteristics across savings-encouraged and control villages.

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adopters use accounts any less.17 Mean balances are similar across account-holders in treated and control areas, whether looking at all households (MK 14,000 in treated; MK 16,000 in controls), or the educated (MK 16,000 in treated; MK 18,500 in controls). As Appendix Table A.4 shows, there are also no significant differences by treatment status across a broad range of account use

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measures (average balance, frequency of use, deposit, or withdrawal, and amounts of deposits and withdrawals), among households affected by the information treatment. There are also no significant differences in account opening month, whether among all formal savers, or the educated (Wilcoxon rank-sum tests, p>0.56 and linear regressions, p>0.42), with the mean

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month falling in June across all savers, and in May for the educated.

The last two columns of Table 3 show estimates for the alternative approach to

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identifying households with high savings demand, used in the next two sections to complement the main results. The estimates show the information treatment raises adoption rates by about 910 percentage points among the most uptake-prone third of the sample based on propensity scores from the specification in Table 2, and has no effect on the other two-thirds of the sample. V. Results: Direct Effects of Savings Accounts on Crop Production A number of recent studies have found strong positive effects from formal savings. For

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example, Callen et. al. (2014) find large boosts to household income among micro-business owners and daily wage-workers in Sri Lanka, Dupas and Robinson (2013b) find large effects on business investment and private expenditures by female microentrepreneurs in Kenya, Prina (2015) shows that savings accounts increase total assets and investments in health and education

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in urban slums of Nepal, and Schaner (2016) finds that even short-term formal savings uptake in Kenya dramatically raised business assets and incomes, and increased investments in household

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public goods.18 This section tests for evidence of effects of formal accounts in a different production setting – farming in low-income rural areas.19 The findings complement and extend

17 In other settings (e.g. Dupas and Robinson 2013b), it has been found that many households would adopt accounts, without subsequent use. This may be due to the different encouragement mechanisms. In Dupas in Robinson (2013b), opening costs for an account were covered, while in the present study adopters had to cover these costs, increasing the likelihood that only those who thought they would use the account would adopt. 18 See Karlan et al. (2014) for a more thorough review of existing studies on the impacts of formal accounts. 19 In a related vein, Beaman et al. (2014) use an intriguing intervention designed to leverage pre-existing informal savings methods, and find that improving group-based savings methods increased agricultural output and food security for informal savers in Mali. These findings are consistent with the effects of improved formal savings access reported here.

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those of Brune et al. (2016), which examines the impact of switching direct deposits of crop proceeds from group bank accounts to individual accounts among tobacco farming clubs.20 Recent evidence suggests that savings accounts may help the poor protect income from themselves (e.g. if they have present-biased preferences), as well as from others they feel

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pressured to share with, until such time as it can be invested in production-related activities (e.g. Dupas and Robinson, 2013b). In farming, particularly in areas with a single growing season such as Malawi, there is often a substantial lag between the time income is received (harvest season) and the time it may be reinvested in production (planting season). This suggests improved

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savings technologies may help raise investments in crop production.

A. Impacts on Crop Production: Intention to Treat

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Table 4 reports estimates from linear regressions of two key outcomes on exposure to the savings encouragement, and on instrumented adoption, to examine evidence for any such effects and their impact on farm income. Panel A shows cross-sectional estimates of the reduced form effect of the savings encouragement (the ITT estimate for account adoption). Regressions in the first three columns examine effects on average fertilizer expenditure as one important indicator of changes in crop investments. (Fertilizer is a critical input for crop production – for example

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Duflo et al., 2008, find income rises on the order of 30-60% from increased fertilizer use in Kenya.) Columns 4-8 report estimated average effects on crop income the following harvest. Regressions include pair fixed effects and inference is based on cluster-robust standard errors.21 As columns 1 and 4 show, estimates across all households are positive (significant for

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fertilizer, but not for crop income). However, as columns 2 and 5 show, when adding the indicator for education and its interaction with the information treatment, we see that the effect is

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limited to the households whose adoption is affected by the information intervention – for them the impacts on both crop production variables are large and significant. Among the educated (the omitted category) average fertilizer expenditure is an estimated MK 7,137 higher in savings encouraged villages, a 48% increase over the control village mean of MK 15,025, indicating a large rise in crop investments. Average cash incomes from crops the following harvest are MK 11,544 higher, a 38% increase over the control village mean of MK 29,992. Column 6, which 20

Schaner (2016) also tests for the impacts of formal savings access in a rural setting where about 40% of the sample consists of subsistence farmers, and finds some evidence of positive impacts on livestock holdings. Appendix Table A.10 shows the results change little when excluding pair effects.

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adds household controls for crop production, suggests the intention to treat impacts on the educated are even higher, raising average crop sales an estimated MK 14,516 (48%) over control villages.22 Nonparametric tests also show highly significant differences among the educated households between the savings-encouraged and non-encouraged for each variable.23

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Turning to the coefficient on the interaction terms, we see they are significant, negative, and large enough to drive the reduced form effects to zero for households who are not induced by the encouragement to adopt accounts. As the bottom of panel A shows, intention to treat estimates for crop inputs and output among the non-educated are negative in sign and not

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significantly different from zero.

B. Impacts on Crop Production: Treatment on the Treated

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Panel B shows two-stage least squares estimates for the effect of formal accounts on crop production, instrumenting for formal savings use with the information treatment. As columns 1 and 4 show, estimates across all households are positive but rather noisy. However, when accounting for education level and its interaction with the instrument, the estimates become much more precise. The IV point estimates in columns 2, 5, and 6 indicate formal savings raises fertilizer expenditures by an average of MK 68,248 and income from crop sales the following

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harvest by an estimated MK 105,425 to MK 122,402. Splitting the sample by household education level and running the regressions independently on the educated and non-educated show similar results, with IV estimates for effects of savings uptake among the educated of a MK 62,721 (p=0.001) rise in fertilizer expenditures and a MK 118,102 (p=0.001) to MK 127,404

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(p=0.000) rise in crop incomes, as well as stronger reduced form effects on the educated (p<0.01 for all specifications) and no reduced form effects on the non-educated. (See Appendix Table

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A.16.) Finally, columns 3, 7, and 8 show similar reduced form effects when using the propensity score approach to identify households with high savings demand, and two-stage least squares estimates that are higher in magnitude, though less precise. These point estimates for the average effects of account adoption are quite large. On the

one hand, this suggests high farming productivity gains from savings uptake – perhaps due to helping individuals shelter income from the pressure of sharing it with others (and spending it 22 23

Controls include household size, head age and gender, dummies for cash crop production and farming as main occupation, and distance. Wilcoxon rank-sum tests: fertilizer expenditures (p=0.006), crop income (p=0.005).

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themselves) until such time as it can be productively invested. Surprisingly large estimates for the impacts of savings accounts are not uncommon in this literature. Dupas and Robinson (2013b), for example, find in a sample of 250 microbusiness owners that account adoption raises business investment 143%, private expenditures 93%, and food expenditures 32%, within just

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the first 4-6 months of use.24 When restricting to the subsample driving their results, 170 female market vendors, the estimated impact of adoption climbs to a 170% rise in business investment and a 44% rise in food expenditures. Schaner (2016) also reports strikingly large impacts on income and assets. Indeed, the persistent finding of surprisingly large benefits of savings

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adoption (the “magic income effect”) across a number of studies has inspired research designs to uncover the source of these dramatic impacts on income and assets (e.g. Callen et al., 2014).

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The findings above suggest that, among farmers, protecting income for crop investments such as fertilizer is one important channel. While fertilizer is one of the most salient and flexible inputs, Appendix Table A.5 shows results from additional regressions indicating that other crop inputs, such as seedlings and land cultivated, are also significantly increased by savings adoption.25 These positive effects on other inputs also help explain the large point estimates for average impacts of accounts on crop output.26

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On the other hand, point estimates must be interpreted with care, and with a view to plausible magnitudes. Standard errors for the above estimates imply a broad range of possible values for the true average impact of accounts on crop production. More moderate effect sizes toward the lower end of confidence intervals may be more in line with effects reported in Kenya

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by Duflo et al. (2008) on the 30-60% rise in crop incomes from higher fertilizer use. While the account impacts on other inputs beyond fertilizer suggest we should see a stronger effect in the

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present setting, more plausible magnitudes may still be in the lower range of intervals. Finally, instrumental variables estimates should also be interpreted with an eye to

possible exclusion restriction violations. Great care was taken to design a savings encouragement that mimicked pre-existing practices in order to minimize the possibility of introducing elements to treated villages not already present in control villages, besides the increased probability of adopting formal savings. We have also already seen that there is no evidence of differences in 24 25 26

They also note the large magnitudes they find are confirmed by qualitative interviews with business owners. (See Dupas and Robinson, 2013b.) See Appendix Table A.5 for regression estimates, and Flory (2018) for more on the effects of savings uptake on the full range of farming inputs Brune et al. (2016) also report large rises in land cultivated due to a savings intervention of a different type, among borrower groups in Malawi.

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account use, after take-up, across treated and control areas (e.g. balances, and frequencies and amounts of deposits and withdrawals do not differ by treatment status). However, one might still wonder whether the information treatment could have helped households save more of their harvest income, even without adopting an account, and thereby increase their farm inputs and the

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following cycle’s crop income. It is not inconceivable that a “culture of saving” may have been transmitted through the information treatment, independent of account adoption. It is also possible that greater local use of accounts may have been used as cover even by non-savers to refuse transfer requests during the high-income harvest period. (Not having an account does not

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prevent one from dishonestly saying his or her cash is tied up at the bank). While the results above strongly point to accounts per se as the underlying driver, these other channels cannot be

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completely ruled out as possible contributors to the reduced form effects.

Importantly, to the extent that either of these channels might play a role, they would still represent effects of financial market penetration. One of the advantages of this particular instrument is that it is, by definition, an extension of the bank into previously isolated areas – through its own representatives. As savings services move into new communities, our primary interest tends to be on the effects of service use per se. However, service expansion will also

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cause local residents to increasingly encounter messages encouraging them to save (as banks try to attract clients and mobilize capital), and indications that others in their community are using accounts. It is conceivable this could by itself affect behavior. Thus, were the exclusion restriction to be violated by having either of the above factors affect savings habits of some

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households that did not adopt accounts, the ITT estimates would still be capturing the effects of what happens when formal savings expands – either through the primary channel of service

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uptake or these secondary channels that might not be restricted to formal savers per se. However, this would also mean the instrumental-variables estimates for the impacts of

savings adoption would contain some positive bias. While the pattern of reduced form effects on crop production (no effects among those insensitive to the savings encouragement and strong effects among those induced to adopt accounts) provides evidence against exclusion restriction violations, they are difficult to completely eliminate. I therefore present both the ITT and instrumented estimates, and urge some caution when interpreting the magnitudes of the latter.

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Even with the above caveats, the evidence strongly points to a significant boost in crop inputs and income from account adoption per se. Taken together, the findings from regressions in Table 4, along with those in Table A.5, lead to my next main experimental result:

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Result 2: Formal savings expansion can significantly increase farming investments and output. The exogenous boost to formal savings rates substantially raised investments in fertilizer, and subsequent income from crops.

In a similar setting, also in Malawi, Brune et al. (2016) find that, among cash cropping

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clubs with tobacco-loans, use of savings accounts sharply increases farm investments and subsequent crop output.27 The results reported here complement their findings, showing they

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extend to a more general population (only a quarter of the educated group grows tobacco, and about a tenth have a formal loan). In particular, they show the positive effects of savings access for the poor documented through other channels can also manifest via boosts to agricultural production – the main income source for much of the world’s poor. It should be noted that the educated households are not representative of the entire population, and that it is possible the effects of savings adoption on farm production would be less pronounced among those with

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lower education. While the impacts on crop production are significant in their own right, their primary importance for the present study is that they create scope for spillovers onto non-savers. VI. Results: Impacts on Inter-Household Transfers

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Private transfers are often a vital source of aid for the poor – particularly if social safety nets are weak or incomplete. To examine whether informal assistance is affected by expanded savings use, information was collected in the endline survey on all cash aid of at least 50 MK

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(US $.33) to or from friends and relatives, made over a 90-day recall period.28 Since the data were gathered during the pre-harvest hungry season, when there are few sources of income and savings and food-stocks run low among the worst-off, this enables us to examine effects on household transfers at a time of year when they are likely to be most critical for household 27

Tobacco is the main cash crop of Malawi. Cash aid includes gifts of money and any other cash help covering expenses (food, medicines, clinic visits, etc.). Enumerators were intensively trained to include only cash help without expectation of anything given in return. Regardless of conscious intent, however, researchers often see gifts as part of a system of reciprocal obligations. In the present setting, it is possible that future reciprocation of gifts may still occur even when respondents state otherwise – in the form of respect, favors, cash, material goods, etc. What motivates givers and whether they expect to receive something unspecified in return is an intriguing question in its own right, one which represents an interesting area for further research. 28

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welfare. About 40% of the sample gave cash aid to others one or more times during this period, and 33% received aid one or more times, the vast majority from within the local community.29 A. Direct Effects on Transfers Out

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It has been shown elsewhere (e.g. Aneglucci and DeGiorgi, 2009) that, in rural areas of the developing world, increases in household income can have positive spillover effects onto neighboring households mediated through inter-household transfers. A rise in household income that stems from savings adoption may therefore increase private transfers to others in the local community.30 Table 5 reports results from linear regressions that examine the effect of formal

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savings on transfers out of the household. Columns 1-3 examine impacts on the decision to provide cash aid to another household at least once, columns 4-6 report estimated effects on

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monetary amounts given (largest transfer), and columns 7-9 examine effects on the total number of transfers made.31 Panel A shows intention to treat estimates – first across all households, and then accounting for differential effects by sensitivity of adoption to the savings encouragement (with educated as the omitted category). The estimates show significant effects across all households for the three measures of cash assistance to others (columns 1, 4, and 7), rising sharply when restricting attention to the households responsive to the savings encouragement

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(columns 2, 5, and 8). In particular, in column 2 we see that among the educated the savings encouragement leads to an estimated 12.1 percentage point increase (p=0.001) in the likelihood of providing at least one transfer (a 26% rise over the control villages). In column 5, we see the intention to treat also raises the average amount of the largest transfer by an estimated MK 346

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(p=0.003), a 154% increase over the mean amount given by these households in the controls (MK 225), and column 8 shows the intention to treat causes an estimated 0.47 (p=0.016) rise in

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the number of transfers out (a 40% rise over the mean transfer quantity by this group in control

For 80% of the transfers, the total time-cost of requesting the cash assistance, including round-trip travel time and time spent on location, was less than 30 minutes (implying one-way trips of 1-15 minutes). As walking is the common method of transportation in these areas, this means that most of these transfers are between households in the same village or villages very nearby. 30 Note the transfers considered here occur after cash-intensive farm investments have already been made, and are also likely to be more urgent and harder to deny than at other times. Transfers out in this period should thus not be taken as evidence against the hypothesis advanced by others that the income effect stems partly from increased ability to shelter money from sharing networks until it is invested in production. See conclusion for further discussion. 31 Data was gathered on the value of both the largest and the most recent cash assistance given to a friend or relative. Findings are very similar whether using the most recent value or the largest value. Results available upon request.

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villages of 1.17). Non-parametric tests on the treatment-control difference for all three measures of transfers out by the educated are also highly significant.32 For the interaction between the encouragement and non-educated, the coefficient is once again negative and large, pushing the ITT estimate down toward zero for the non-educated.

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Interestingly, however, here there is some evidence that even the non-educated may also experience some rises in transfers out. For example, living in a savings encouraged village raises the likelihood a non-educated household gives cash aid by an estimated 4.4 percentage points, significant at the .10-level. This suggests the possibility that spillovers onto one household may

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induce further spillovers from that household onto a third. The evidence suggests two plausible channels. First, a cash transfer received by one household may be shared with others, or relax

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budgets constraints, such that one transfer begets secondary transfers. Nearly half of all noneducated households that receive cash help also give cash help (45%), and amounts given are far lower than amounts received – consistent with sharing.33 Second, spillovers from savings uptake may also occur through local markets. As mentioned above, savings adoption raised demand for several crop inputs. A closer look shows the encouragement increased the proportion of noneducated renting out assets such as farmland, track animals, and tools by 3 percentage points

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(p<0.01) with median rental incomes at MK 3,000. It also raised mean casual labor income in this group over the last 30 days by MK 240-290 (see Table A.9). Non-transfer spillovers may thus put some of the non-educated in a better position to give cash aid as well. Turning to panel B of Table 5, the results show instrumental variables estimates from

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two-stage least squares regressions in which the local take-up rate of formal savings is instrumented by the savings encouragement. Columns 1, 4, and 7 show average effects across all

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households. The estimates indicate a 10 percentage point rise in adoption by the educated increases the likelihood that local households provide a transfer during the hungry season by 5.3 percentage points, and raises average amounts given by about MK 120 and average quantity of 32

The proportion of educated households making at least one transfer is 12.9 percentage points higher in the savings-encouraged than the nonencouraged areas (߯ ଶ -test, p=0.001). The distribution of total number of cash transfers out of educated households is also significantly higher in the savings-encouraged areas (rank-sum test, p=0.003), as is the distribution of their values (p=0.002). Non-parametric tests on cluster aggregates yield similar results: the distribution of percentages of educated households in each cluster that make at least one transfer is significantly higher in savings encouraged areas than control areas (rank-sum test, p=0.001); the distribution of cluster means for number of times cash aid is given by educated households is also higher in savings-encouraged areas (rank-sum, p=0.021); and the distribution of cluster means for value of cash help given by this group of households is also higher in savings-encouraged areas than in the controls (rank-sum, p=0.005). 33 For the non-educated households that received and gave cash aid, mean largest values received and given were MK 746 and MK 343. For the households in this group that received aid once and gave aid once (so data on transfer values is complete), mean values received and given were MK 479 and MK 196. Note also that sharing may not be explicit: a transfer may relax a household’s budget constraint enough that it can help a third household if asked. This is also consistent with the smaller transfers out than in by the non-educated.

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transfers out by about 0.2. Columns 2, 5, and 8, which allow the impacts to differ by household type, show the effects are driven almost entirely by the educated households, with a 10 percentage point rise in their adoption rates causing a 10.4 percentage point rise in the likelihood they give aid during the hungry season, a MK 300 rise in the average amount they give, and a

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rise in the average number of transfers they give by 0.4. While effects of savings uptake on transfers out are likely driven mostly by own-adoption, as the above discussion of possible spillovers from the spillovers suggests, some of them may also be driven by non-adopters who received cash from formal savers earlier in the hungry season or even before it. (Appendix Table

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A.11 shows impacts for the non-educated on likelihood of making a transfer lose significance when excluding pair effects, while results change little for the educated.) Columns 3, 6, and 9

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show estimates when allowing effects to differ between high and low propensity savers, instead of the educated and non-educated groups, with similar reduced form effects and IV estimates that are also very close, though somewhat more noisy.

Taken together, the findings above inform my third main experimental result: Result 3: Formal savings can lead to significantly higher transfers out of a household. An exogenous rise in local use of accounts significantly raised (i) the probability of making a

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transfer to another household, (ii) the average total number of transfers made, and (iii) the average value of transfers given, during the pre-harvest period. These findings are striking. They show that formal savings can sharply stimulate transfers

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out of households to others in the community during a period of high need. B. Indirect Effects: Transfers In Among Non-Savers

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The large impacts of account uptake on transfers out indicate strong spillover effects from formal savings onto other households. Appendix 1 presents evidence that the vast majority of households have safety nets within their own villages. We can therefore also use transfer receipts among nearby households to shed light on the magnitude and importance of these formal savings spillovers from another perspective. A simple comparison shows that while 28.3% of all households living in control villages received cash help from another household at least once,

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38.3% of those in savings-boosted villages did. This 10 percentage point (35%) rise in probability of receiving cash assistance is both large and highly significant (p=0.000).34 In assessing the indirect effects of savings adoption, impacts on non-users are of particular interest. Such effects occur even if a household itself does not start savings use. They

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would also be completely missed in standard program evaluations that focus on impacts experienced by program participants (service-users). Since the savings encouragement has nonuniform effects on uptake across different household types, outcomes across all non-adopters in control areas do not provide an appropriate counterfactual for outcomes of non-adopters in

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savings-boosted areas.35 To get a clean estimate of the indirect effects of savings uptake on nonsavers, I therefore use the non-educated group of households. Rates of formal savings among this

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group are already comparatively low. Moreover, since their savings use is not affected by the savings encouragement, non-savers among this group in control areas should remain a good counterfactual group for non-savers in savings-boosted areas. Appendix Table A.7 confirms the balance of this group’s baseline characteristics across savings-boosted and control villages. Looking first among all the non-educated, 25.8% in control villages received cash help from other households, compared to 33.3% in the savings-boosted villages. This 7.5 percentage

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point (29%) rise is highly significant.36 Restricting to the non-educated without accounts confirms this difference is driven by transfer receipts among non-savers: 24.4% of this group in control villages ever received cash aid, compared to 34.5% in savings-boosted villages. This 10.1 percentage point (41%) rise is also highly significant.37

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Table 6 reports estimates from regressions that more closely examine these spillover effects from savings uptake onto transfer receipts by non-savers, using the savings

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encouragement and non-educated group to identify impacts. Panel A shows reduced form effects, and panel B shows two-stage least squares estimates which instrument for the local adoption rate with the information treatment. Attention is focused on the impacts of adoption rates by the educated – those whose adoption choice is heavily driven by the randomly assigned savings 34

Pearson’s ߯ ଶ test, p=0.000. Estimates from Probit and linear regressions with clustered errors, p=0.000. See Appendix Table A.6, columns 1-3 for regression results from Probit and linear specifications (with and without pair level effects). 35 We have seen, for example, that the savings encouragement raised adoption rates particularly among the more educated. This could alter baseline characteristics among non-savers in savings-boosted villages (e.g. lower education) such that the non-adopters in control areas no longer provide an unbiased estimate of their counterfactual outcome. 36 Pearson’s ߯ ଶ test, p=0.002. Estimates from Probit and linear regressions (with and without fixed effects) using cluster-robust errors, p<0.01. (See Appendix Table A.6, columns 4-6). 37 Pearson’s ߯ ଶ test, p=0.000. Estimates from Probit and linear regressions (with and without fixed effects) using cluster-robust errors, p=0.000. (See Appendix Table A.6, columns 7-9).

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encouragement.38 Columns 1 and 5 show estimates across all households, with pair level effects.39 As panel A shows, the rise in savings uptake causes an estimated 10.5 percentage point rise in likelihood of receiving cash aid, and a rise in the average quantity of transfers received by an estimated 0.25. In panel B, we see that a 10 percentage point increase in savings uptake

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among the educated raises the percentage of nearby households that receive a cash transfer by an estimated 7.6 percentage points and the number of cash aid receipts by an estimated 0.18.

In order to test for spillovers onto non-savers, columns 2 and 6 allow effects to differ by household type. The omitted category for these regressions is now non-educated, so that the first

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row of estimates shows impacts on transfer receipts by households not induced by the encouragement to adopt accounts. As panel A shows, the rise in local savings use causes an

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estimated 8.1 percentage point (31%) increase in probability a non-educated household receives cash aid, and an estimated 0.16 rise in the average quantity of transfers received by the noneducated (a 33% increase over the mean number received in control villages). Panel B shows that a 10 percentage point rise in the local adoption rate raises the probability of receiving cash aid by an estimated 5.6 percentage points among the non-educated, and increases the average total number of transfers they receive by an estimated 0.11. Non-parametric tests also show that the

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proportions of non-educated receiving transfers are much higher in the savings-boosted villages, as are the quantities of transfers received by this group.40 These estimates suggest sharp positive spillover effects onto populations that do not take up accounts. To confirm these impacts are indeed driven by transfer receipts among non-savers,

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results are also shown when restricting to households without accounts (columns 3 and 7). As the first row of estimates show, impacts on the non-educated are in fact even higher when excluding

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any formal savers in this group. We see for example in panel A that the estimated reduced form effect of the encouragement rises from 8.1 to 11.0 percentage points for probability of transfer receipt (45% above the control probability of 24.4), and from 0.16 to 0.20 for average number of transfers received (44% higher than the control mean of 0.45). Panel B shows that a 10

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Two-stage least squares estimates for the effect of the overall local adoption rate yield similar findings. Estimates are less precise (since the first stage is less strong), though still comfortably significant at conventional levels (ranging from p<0.10 to p<0.01). 39 There is a slight decrease in the sample to 1,954 households since data on adoption rates of the educated are not available for two clusters. Despite this minor sample size reduction, as the estimates in Table 6 show, the first stage remains highly significant: the savings encouragement raises local adoption rates in each cluster by an estimated average 14 percentage points (p<0.01, column 9). 40 Tests of treatment-control differences: proportion receiving cash aid, p=0.003 (household level, ߯ ଶ ) and p=0.016 (cluster aggregates, ranksum); number of transfers received, rank-sum p=0.004 (household level) and p=0.046 (cluster aggregates).

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percentage point increase in local savings uptake raises the percentage of non-educated that receive a cash transfer by an estimated 7.9 percentage points, and raises the average number of transfers received by an estimated 0.15, when restricting to non-savers. Non-parametric tests also remain highly significant.41

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Finally, columns 4 and 8 of Table 6 show the effects by household type when instead using the propensity score-based approach to identify those not induced by the encouragement to adopt accounts. Results for the low-propensity group are about the same as for the non-educated, though the estimates are all a bit higher. Appendix Table A.14 shows results when excluding

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formal savers for this specification, as well as for the simple overall regressions in columns 1 and 3. Appendix Table A.12 shows all results without pair effects, with little change in the estimates.

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Changes in access to informal aid can be particularly important for the extreme poor – not only due to their already low welfare states but also because their informal aid networks are often weaker. Among the worst-off quartile of the sample by income and assets, looking first at control areas, we see that 20.6% received cash help, compared to 30.8% of households in the top three quartiles.42 This 10.2 percentage point (50%) higher probability of transfer receipts among the better-off suggests access to private transfers and sharing networks is substantially weaker

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for the poorest compared to their less poor neighbors. In the savings-boosted villages, however, the proportion of the worst-off quartile receiving cash help from other households rose from 20.6% to 34.0%. The 13.4 percentage point (65%) rise in probability of transfer receipt by this group when moving from control to savings-boosted villages is large and highly significant.43

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Table 7 shows results from regressions similar to those in Table 6, but which instead allow the effects of local savings uptake to differ between the bottom quartile and the top three

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quartiles. Panel A, column 1 confirms the large and significant reduced form effect on the proportion of the extreme poor receiving transfers – an estimated 16.0 percentage point (p=0.000) rise, a 78% increase over the proportion of this group that received cash aid in the controls.44 In panel B, we see in column 1 that a 10 percentage point increase in savings uptake

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Tests of treatment-control differences among non-educated without accounts: proportion receiving cash aid, p=0.000 (household level, ߯ ଶ ) and p=0.001 (cluster-level, rank-sum); number of transfers received, rank-sum p=0.000 (household level) and p=0.009 (cluster-level). 42 The bottom quartile consists of households that are in the bottom two quintiles of both total crop income and physical assets. A total of 550 households fall into both categories, comprising 24% of the baseline sample. Appendix Table A.8 shows household characteristics for this group and confirms balance across treated and control villages. 43 Pearson’s ߯ ଶ test, p=0.001. Estimates from Probit and linear regressions (with and without fixed effects) using cluster-robust errors, p<0.01. 44 Estimates in Table 7 are based on a slightly smaller sample, since the 2 clusters missing uptake rates for the educated are omitted.

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among the educated raises the percentage of nearby bottom quartile households that receive a cash transfer by an estimated 13.0 (p=0.013) percentage points. As columns 4 shows, the exogenous rise in local adoption rates also increases number of transfers received: panel A shows reduced form estimates of a 0.29 (p=0.007) increase in the quantity of cash aid receipts (a 79%

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rise over control villages), and panel B shows a 10 percentage point rise in local adoption leads to an estimated 0.23 (p=0.035) rise in the average number of transfers received. Columns 2 and 4 confirm these results are not somehow driven by the handful of households in this category that have formal accounts: excluding formal savers from the regressions in fact increases estimated

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effects on the bottom quartile. Non-parametric tests also confirm the proportion of the extreme poor receiving transfers, as well as the average quantity they receive, are significantly higher in

with little to no change in estimates.

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the savings-boosted villages (p<0.01). Appendix Table A.13 shows results without pair effects,

Taken together, these findings on aid receipt impacts inform my fourth major result. Result 4: Expanding formal savings can increase private transfers to non-savers in times of need. An exogenous rise in local account adoption rates significantly raised cash aid receipts by non-savers in the hungry season – with particularly strong impacts on the poorest.

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The focus here on indirect effects of savings adoption on non-savers stems from the importance of capturing spillovers onto the supposedly non-treated. Savers may also benefit from higher access to transfers, however. As the coefficients on the interaction terms in Table 6

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show, the savings encouragement increases transfer receipts by the educated group as well. VII. Results: Effects of Increased Access to Private Transfers

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The rises in private transfers identified above show large increases in informal cash assistance to non-savers from friends and relatives during a period of high need. This section tests for evidence of whether this had effects on consumption and welfare outcomes. A. Food Security

The average local price of maize (the primary food staple in the region) at the time of the

endline survey was MK 30 per kg.45 This suggests the mean value of transfer receipts by non45

Based on data from the 2010-2011 World Bank LSMS/Malawi Integrated Household Survey, using prices from approximately 100 enumeration areas in the districts comprising the research area for this study.

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educated households in treated areas (about MK 500 across all savings-encouraged clusters) could buy about 17 kg of maize – enough to cover the consumption requirements for one adult for over 55 days, or a household of 5 for over two weeks.46 This would be a substantial amount of food supplementation – enough that a household running low on food stocks may be able to

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avoid having individuals go days without eating, skip meals, cut portions, or otherwise restrict diets and food intake. Given the low measures of food security in the baseline data, this suggests broad scope for possible impacts of transfer receipts on food consumption indicators.

To test for evidence of this, we first see that the distribution of values for the HDDS

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measure of food security among non-educated households is significantly higher in savingsencouraged villages than in the controls – whether looking across all non-educated (Wilcoxon

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rank-sum, p=0.002) or restricting to those without accounts (p=0.000). Results in Table 8 also show significant impacts on the mean HDDS score – for all non-educated (column 3) and those without accounts (column 4).47 The estimates in panel A suggest the rise in transfer receipts from living in savings-boosted villages improved food consumption among this group by an estimated 0.37-0.47 points in the HDDS measure – a 6-8% improvement over the control villages. Turning to the bottom quartile (columns 1-2), the impacts are larger, and once again

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appear driven by those without accounts. Estimates in Panel A suggest the rise in transfer receipts from living in treated villages improved food intake by 0.60-0.76 points, an 11-14% improvement over values in control areas. Non-parametric tests also show a highly significant rise in the distribution of HDDS values in moving from control to savings-boosted villages (all

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bottom quartile: p=0.002; those without accounts: p=0.000). Restricting attention to the bottom quartile that are also non-educated (a very high vulnerability group), impacts grow stronger still,

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raising average HDDS scores by an estimated 0.67-0.78 points (p<0.01), a 12-15% rise over mean values in control villages. (Appendix Table A.17, panel A.) Taken together, these findings inform my next result:

46

The Malawi Ministry of Agriculture and World Bank estimate average per capita consumption requirements of maize at 0.30 kg per day for adults, 0.15 for children (Jayne et al., 2010). This means that a transfer receipt of 500 MK could translate to 56 days’ worth of maize for adults at this rate (or 112 days’ worth, for children) – roughly 16 full days of staple food for a household of 2 adults and 3 children. 47 All 112 clusters included. Estimates are similar if the 2 clusters that do not have educated households in the data sample are dropped.

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Result 5: The increased transfer receipts by non-savers improves food-intake: food-security scores rose by as much as 8% among non-savers and those unlikely to adopt, and by as much as

B. Health Indicators

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15% among the extreme poor in savings boosted villages.

Besides helping people buy food, one of the other top reasons indicated for giving cash aid to others is helping them address health problems, such as malaria (for which the peak months fall during the pre-harvest season). Malawi has one of the highest malaria infection rates

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in the world.48 Fortunately, international aid programs have helped make anti-malarial medicines accessible to the poor. The average price in the research area for a full course of Fansidar to treat

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malaria, for example, was about 80 MK during the lean season of 2010.49 This means that 500 MK could potentially treat as many as 6 cases of malaria. Ailments unrelated to malaria can also be treated by other locally available medicines.50 For example, at an average price of MK 7-10 per pill of Aspirin or Acetaminophen in the three districts of the research area, a transfer receipt of 500 MK would be enough to cover 50-70 pills. Indeed, according to Malawi’s 2010 Integrated Household Survey, nearly 40% of households bought non-prescription medicines (antimalarials,

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pain-killers, or others) within just 30 days prior to the survey interview. This suggests that improved access among non-savers to private transfers may also help households address health shocks and shorten the duration of illness.51 To test this, I examine a simple self-reported health measure – incidence of illness or injury during the 14 days preceding

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the interview.52 When looking across all non-educated households, 17.4% report that no members are unwell (82.6% report at least one unwell individual), with no significant difference

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between control and treated areas (columns 7-8 of Table 8). Turning to the extreme poor, however, in control areas only 13.8% of those without savings report no unwell members, compared to 21.6% in treated areas (Pearson’s ߯ ଶ , p=0.036). This 7.8 percentage-point rise

48 According to the World Health Organization, in 2010 there were 6.85 million probable and confirmed cases of malaria, in a country with 14.8 million people (https://knoema.com/WHOWMS2014/who-world-malaria-statistics-2015?location=1000270-malawi). 49 Estimated from the 2010-2011 World Bank LSMS/IHS3 survey data. 50 While almost three-quarters of all illnesses reported in the World Bank Malawi LSMS/IHS3 survey data are attributed to malaria or entail other symptoms that may be caused by malaria, other common symptoms include stomachaches, headaches, and flu. 51 Informal health-insurance based on inter-household transfers have been documented elsewhere. Dercon et al. (2008), for example, find that households in Ethiopia provide each other assistance for medical expenses and other observable components of health-related shocks. 52 World Bank data suggests the vast majority of these cases are illness-related. The 2010-2011 World Bank LSMS/IHS3 survey asks an almost identical question to measure incidence, but with more details classifying type of illness/injury, and about 95% of cases fall under illness.

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represents a 57% improvement. Regression results reported in columns 5 and 6 confirm that living in a savings-boosted village significantly raises the proportion of the extreme poor with no unwell members, by an estimated 6.1-7.7 percentage points. Restricting attention to the noneducated among this group, impacts grow stronger still: living in a savings-encouraged village

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raises the proportion with no unwell individuals by 8.4 (p=0.035) to 9.5 (p=0.022) percentage points (68-76%). (Appendix Table A.17, panel A.) These findings inform my final main result: Result 6: Increased access to private transfers from savings expansion improves simple health-

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indicators among the extreme poor: the probability that no one is unwell rises 6.1-9.5 percentage points in savings-boosted areas (rising as much as 76% for the highly vulnerable).

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Results 5 and 6 suggest the rise in private cash transfers represents a global improvement in sources of consumption support. The strong effects on such key welfare indicators underscore how sensitive welfare outcomes can be to changes in informal support systems. It should be noted that, for the impacts on the worst-off quartile, the large effects on food-security and health likely derive in part from low baseline levels, due to the extreme poverty of this group. It is also possible the impacts of savings expansion on welfare indicators among non-

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savers are not driven entirely by increases in pure transfers alone. For example, casual employment opportunities for odd jobs or on the farm (ganyu, or “piecework”) are sometimes provided to neighbors in need as a way to help them (Ravallion and Lokshin, 2010). In addition, positive spillovers in the form of within-village expenditures by account adopters (for example

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higher use of local crop inputs or patronage of microenterprises) may also be playing a role. As mentioned in Section VI, there was an uptick among the non-educated in renting out farmland

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and other assets. Appendix Table A.9 looks closer at possible non-transfers spillovers of this sort. There is no significant evidence among the bottom quartile – those for whom the welfare impacts are sharpest. Across all non-educated households as a whole, though, there is evidence that transfers via the channel of casual labor, as well as moderate rises in annual rental income, may also be playing a role in the positive impacts on food consumption. However, these additional spillovers are limited to the better-off non-savers, and do not extend to the extreme poor. For the worst-off group, the results suggest increased receipts of pure transfers are the main driver of improvements in food security and health indicators. 28

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VIII. Conclusion Over $300 billion a year is spent in the developing world on social safety nets, and yet enormous holes remain: nearly a billion of the world’s poorest lack access to formal social assistance. Strengthening local support systems represents a potential cost-effective method to

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help address these gaps.

Using a unique field experiment, I find that formal financial markets can improve the ability of informal safety nets to help prevent some of the worst consequences of extreme poverty. An exogenous boost to savings adoption raises crop investments and incomes of

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adopters, increases transfers out during a time of high need, and expands transfer receipts among non-adopters. The rise in informal aid receipts significantly improves key indicators of

benefit from savings expansion.

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household welfare – particularly among the poorest. This constitutes a large unanticipated

These findings contribute to current efforts to shed light on a critical question that has come to the fore in research on poverty. While expansion of access to financial markets steadily marches forward, the effects on informal safety nets, and those who rely on them, are not yet well understood. Emerging evidence, due possibly to differing effects across service type, is

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mixed – some studies suggesting financial market integration may weaken informal support systems, others suggesting it may strengthen them. Through a field experiment that uses a randomly assigned positive shock to account adoption, I show that formal savings expansion can improve access to private transfers during a period of widespread high vulnerability. I also find

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compelling evidence for the underlying mechanism: savings expansion has positive impacts on crop production and farming income of adopters – similar in scale to impacts found elsewhere on

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microentrepreneurs, though perhaps even larger. The data strongly indicate formal accounts per se as the driver. However, it cannot completely rule out that some of these effects may stem partly from other aspects of the encouragement. Importantly, the strongest candidates for any alternative channels represent other effects of savings services penetration into new areas. Further study will help enhance the precision of estimates for effects of account adoption per se on farming outcomes. The results also contribute to an important set of studies on the indirect effects of aid programs on non-beneficiaries often (falsely) assumed to be “untreated”. The large impacts I find of savings uptake on non-users reveals externalities and spillovers onto non-participants can be 29

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just as significant for aid programs that expand financial services as for deworming (Miguel and Kremer, 2004) and social assistance (Angelucci and De Giorgi, 2009) programs. This implies accurate evaluation of financial access programs requires including non-service users, and careful selection of control groups socially distant enough from the treated to be unaffected.

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In closing, some words of caution are in order regarding the broader implications of these results. First, as with any empirical study, the macroeconomic environment and its cycles should be kept in mind. These large positive impacts were observed after a moderately good crop year. The next five years were all roughly similar or better. This suggests this harvest was not

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particularly unusual, but there is no guarantee the same effects would be observed in periods of broadly shared negative shocks or widespread crop failure. Knowing how the effects found in

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this study might change under widespread negative agricultural shocks is an important open question that must be answered before drawing definitive conclusions about likely impacts in such circumstances.

Second, it is important to consider the timing of the private transfers examined in this study – particularly in light of evidence in some places in the literature that account adoption may instead decrease transfers (e.g. Dupas and Robinson 2013b). One of the reasons proposed

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for the benefits of savings-adoption is that it may help users refuse requests for cash aid from members of their sharing network, enabling them to better channel income to production investments instead.53 Since the transfers examined here occur in a period after cash-intensive production investments have already been made, this possible use of accounts as a tool to shield

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income from sharing networks for investment is likely to play a smaller role. These transfers also occur during the hungry season, when many requesting help are in dire need, which likely raises the probability of assistance.54 For both of these reasons, it is possible the effects of account-

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adoption on transfers in other periods are less strong, or even run in the other direction.55 Impacts on aid receipts during the hungry season are of paramount importance for poverty alleviation and

53 Indeed, this hypothesized channel for positive direct effects on account adopters has helped fuel the recent concerns that savings-adoption might instead weaken informal safety nets and cause negative spillovers onto non-users. 54 The extent of need during this period is often readily observable by givers. In qualitative interviews, better-off individuals state they verify a person really needs help before giving it, by visiting a person’s home, inquiring about the person among others in the village, etc. 55 Appendix 2 discusses several pieces of evidence from the data suggesting access to critically needed transfers was no lower in treated areas at other times of the year. While detailed data on transfers is limited to the lean season, the appendix highlights comparisons of treated and control villages outside this period pointing to similar levels of access to transfers to address shocks, similar perceived access to cash help from others in the village in emergencies, and no evidence that the non-educated had less access to any critical consumption support that may have been needed earlier in the year. However, it is possible that access to household transfers for less urgent needs is lower. See Appendix 2 for details.

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prevention of severe negative household outcomes. However, the effect of formal savings on transfer behavior at other times of the year is also of interest, and remains an open question.56 Third, this study finds that informal assistance is highly responsive to local changes in the use of one particular type of financial service – savings. The focus on savings reflects a common

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practical constraint when banks expand financial access – offering only savings services to many clients. Isolating the effects of savings also has the virtue of disentangling its impact from other financial services. However, other types of financial services may well have different impacts on informal safety nets – as in Banerjee et al. (2015), for example, who find evidence of negative

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effects from formal credit. Understanding the full range of effects on informal safety nets from different types of financial service uptake, the net effects of adopting multiple services, and the

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channels through which these occur, remain a rich area for future research.

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As an important piece of qualitative information that helps shed light on this question, one of the most commonly stated reasons for opening an account is to avoid money slipping away to friends, relatives, or household members – with over a third of adopters citing this among their top 2 or 3 reasons for having an account. This suggests formal savings may help shield income from transfers at certain times of the year (times of plenty) so that it can be productively invested at the appropriate time, raising total incomes, and ultimately strengthening local informal safety nets. This represents an intriguing open question for future research.

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Prina, Silvia. 2015. "Banking the poor via savings accounts: Evidence from a field experiment." Journal of Development Economics 115: 16-31. Ravallion, Martin, and Michael Lokshin. 2010. "Who cares about relative deprivation?" Journal

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Temporary Savings Subsidies for the Poor." Working Paper, Dartmouth College. Swindale, Anne, and Paula Bilinsky. 2006. "Household Dietary Diversity Score (HDDS) for Measurement of Household Food Access: Indicator Guide." Food and Nutrition Technical Assistance Project, Washington D.C. Townsend, Robert M. 1994. "Risk and Insurance in Village India." Econometrica 62 (3): 539– 91.

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Investigation in Northern Nigeria," Review of Economic Studies 61 (3): 495–526.

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TABLES TABLE 1 – HOUSEHOLD LEVEL SUMMARY STATISTICS AND BALANCE CHECK

Head's Age (Years) Household Size (People) Head has Primary Education Head Literate in English Bank-Stop Distance (km) HFIAP Category (1-4)

One or More Members Literate in Chichewa Has Business Has Member with Salaried Job Crop Income Last Harvest (Kwacha) Per Cap Crop Income Last Harvest (Kwacha/Person)

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Salary Income Last 30 Days (Kwacha) Per Cap Salary Income Last 30 Days (Kwacha/Person) Non-Ag Business Income Last 30 Days (Kwacha) Physical Assets (Kwacha)

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Formal + Informal Account Balances (Kwacha) Livestock Value (Kwacha)

Land and Buildings Value (Kwacha)

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Amount of Land (Acres) Has Formal Savings Has Formal Loan Educated Head

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HDDS Score (1-12)

0.85 (0.36) 41.00 (13.84) 5.13 (1.98) 0.26 (0.44) 0.30 (0.46) 7.92 (3.38) 3.22 (0.89) 7.14 (2.60) 0.86 (0.35) 0.27 (0.44) 0.16 (0.36) 36,682 (152,786) 7,369 (25,764) 1,170 (4,927) 253 (1,191) 1,211 (18,065) 27,595 (146,818) 2,949 (27,281) 17,765 (70,447) 108,450 (313,660) 2.62 (1.85) 0.12 (0.32) 0.06 (0.24) 0.32 (0.47)

Obsv. 2,335

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Head is Male

Coefficient (std. errors) on Treatment Dummy 0.026 (0.018) -0.09 (0.68) 0.208 (0.103)** 0.030 (0.035) 0.016 (0.035) 0.14 (0.64) 0.02 (0.06) 0.29 (0.24) -0.003 (0.022) 0.01 (0.03) 0.02 (0.04) 10,070 (7,321) 1,549 (1,329) 134.0 (459.6) 28 (107) 1,167 (733.6) 4,111 (8,230) 1,337 (1,583) 4,453 (3,984) 12,763 (15,619) 0.02 (0.12) 0.03 (0.03) 0.00 (0.01) 0.019 (0.035)

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Sample Mean (Std. Dev.)

Demographic Characteristics

2,283 2,335 2,331 2,335 2,335 2,335 2,335 2,335 2,334 2,335 2,335 2,335

2,300 2,298 2,322 2,335 2,335 2,335 2,335 2,174 2,329 2,332

2,333

Extreme Poor

0.24 (0.42)

-0.01 (0.03)

2,335

Attrition

0.14 (0.35)

-0.002 (0.033)

2,335

Notes: Exchange rate was approximately 140 Malawi Kwacha to US $1 during the 2008 survey period. The above table reports descriptive statistics for households in the 2008 cross-section. Except where indicated in parentheses, units are proportions. Standard errors clustered at the village-cluster level *** p<0.01, ** p<0.05, * p<0.1.

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TABLE 2 – PREDICTORS OF ACCOUNT OWNERSHIP – OBSERVATIONAL ANALYSIS

Radial Distance from Bank Stop (km) HFIAP Food Insecurity Level (1-4) Constant

2,003

2,003

Observations

(2) Linear Has Formal Savings 0.157*** (0.0217) 0.00116** (0.000502) -0.00803** (0.00387) -0.0541*** (0.0102) 0.338*** (0.0432)

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Assets (Ten Thousands of Kwacha)

(2) Probit – Marginal Effects Has Formal Savings 0.154*** (0.0213) 0.000816** (0.000376) -0.00699* (0.00372) -0.0474*** (0.00857)

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Head is Educated

(1) Probit Has Formal Savings 0.603*** (0.0788) 0.00355** (0.00163) -0.0304* (0.0165) -0.206*** (0.0377) -0.383** (0.155)

2,003

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Notes: Estimates from Probit and linear regressions of formal savings account ownership on baseline variables. Column 1 shows Probit coefficients, Column 2 shows estimated marginal effects, and Column 3 shows linear estimates. Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

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TABLE 3 – EFFECTS OF INFORMATION INTERVENTION ON FORMAL SAVINGS (2) Has Formal Savings

(3) Has Formal Savings

0.0522* (0.0270)

0.0450*** (0.0167)

0.111** (0.0461) -0.138*** (0.0312) -0.0936** (0.0468)

Non-Educated

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Information x Non-Educated

Information x Low Propensity Saver Constant

0.139*** (0.0164)

Y

[F-Test p-value] Information on Low Propensity Saver [F-Test p-value] Observations

2,011

0.106*** (0.0357) -0.119*** (0.0290) -0.0932** (0.0416)

0.236*** (0.0310)

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Pair Fixed Effects Information on Non-Educated

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Low Propensity Saver

(4) Has Formal Savings

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Panel A: All Households Information

(1) Has Formal Savings

2,011

0.0178 [0.470]

2,008

(5) Has Formal Savings

(6) Has Formal Savings

0.0954** (0.0454)

0.0875** (0.0361)

-0.160*** (0.0300) -0.0724 (0.0466) 0.248*** (0.0289)

-0.140*** (0.0280) -0.0684 (0.0422)

Y 0.0124 [0.477]

2,008

Y

0.0230 [0.340] 2,003

0.0191 [0.270] 2,003

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Notes: Estimates from linear regressions of formal savings account ownership. The dependent variable is an indicator equal to 1 if the household has a formal account in 2010. Sample size differs slightly across specifications due to incomplete data for education and assets (households with missing values omitted). Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

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TABLE 4 – EFFECTS OF FORMAL SAVINGS ON CROP INVESTMENTS AND INCOME Fertilizer (MK) (2)

(1)

(3)

(4)

(5)

Crop Sales Income (MK) (6) (7)

(8)

Panel A: Intention to Treat 7,137***

8,760***

2,689

11,544**

14,516***

11,798**

15,656***

(1,091)

(2,604)

(2,099)

(5,302) -8,505**

(5,047) -5,043

(5,578)

(5,554)

Non-Educated

(2,562) -3,290**

Information x Non-Educated

(1,560) -7,356**

(3,920) -13,220**

(3,712) -15,535**

(6,537)

(6,525) -15,546***

-11,708***

(3,569) -14,141**

(3,722) -17,771**

(6,761)

(6,853)

Y

Y Y

-2,343 [0.344]

-2,114 [0.384]

Information x Low Propensity Saver

(1,722) -9,997***

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-4,586***

SC

(3,052) Low Propensity Saver

RI PT

2,186**

Information

(3,158)

Controls Pair Effects

Y

Y

Information on Non-Educated

-218.7 [0.863]

[F-Test p-value]

Observations Panel B: Treatment on the Treated Has Savings

1,959 53,614*

Controls Observations

AC C

Pair Effects

Y Y

-1,676 [0.527]

-1,019 [0.709]

1,954

1,980

1,977

1,927

1,975

1,927

68,248**

93,329**

61,950

105,425**

122,402***

115,614*

142,040**

(28,221) 3,898

(40,817)

(46,291)

(50,755) 1,637

(45,386) 4,680

(62,222)

(56,503)

(7,780)

(6,481) -3,232 (9,619)

374.7 (7,990)

(4,085)

Low Propensity Saver

Y

1,956

EP

(29,834) Non-Educated

Y

-1,237 [0.332]

TE D

Information on Low Propensity Saver [F-Test p-value]

Y

5,919 (6,300)

Y

Y

Y

Y

Y

Y

Y

Y

1,959

1,956

1,954

1,980

1,977

Y 1,927

1,975

Y 1,927

Notes: Estimates from linear regressions of fertilizer expenditures (columns 1-3) and crop sales (columns 4-8). Exchange rate was approximately 150 Malawi Kwacha to US $1 during the survey period in 2010. Households with missing crop production values omitted. Regressions include pair fixed effects. (See Appendix Table A.10 for results excluding pair effects.) Cluster-robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

40

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TABLE 5 – EFFECTS OF FORMAL SAVINGS ON CASH TRANSFERS OUT DURING PRE-HARVEST SEASON

Non-Educated Information x Non-Educated Low Propensity Saver Information x Low Propensity Saver Pair Effects Information on Non-Educated

0.121*** 0.113*** (0.0356) (0.0351) -0.146*** (0.0309) -0.0759* (0.0448) -0.150*** (0.0311) -0.0651 (0.0434)

Y

Y 0.044 [0.065]

[F-Test p-value] Information on Low Propensity Saver [F-Test p-value] Observations Panel B: Two-Stage Least Squares Adoption Rate of Savings Prone (Educated)

Non-Educated Adoption Rate of Savings Prone (High Propensity)

0.527*** (0.176)

1.040*** (0.385) -0.729* (0.421) 0.0246 (0.119)

Y

AC C

Pair Effects Adoption Rate on Non-Educated

1,942

EP

Adoption Rate of Savings Prone x Low-Propensity Low-Propensity

1,945

0.047 [0.035] 1,961

[F-Test p-value] Adoption Rate on Low Propensity Saver [F-Test p-value] Observations

1,945

160.1*** 346.2*** 269.0*** (42.29) (112.9) (100.5) -97.26*** (36.11) -282.6** (126.2) -134.2*** (38.42) -209.7* (105.9) Y

1,944

Y 63.58 [0.113] 1,941

1.244** (0.518) -0.914* (0.530) 0.0816 (0.149)

Y 0.311 [0.092] 1,942

Y 59.28 [0.053] 1,960

1,160*** 2,982*** (374.4) (1,129) -2,530** (1,213) 471.0 (290.3)

TE D

Adoption Rate of Savings Prone x Non-Educated

Y

Amt. of Largest Transfer (MK) (4) (5) (6)

RI PT

0.0727*** (0.0197)

(3)

SC

Panel A: Reduced Form Estimates Information

Give Transfer (2)

M AN U

(1)

Y 0.329 [0.102] 1,961

(7) 0.297*** (0.0744)

No. Transfers (8) 0.468** (0.191) -0.564*** (0.131) -0.274 (0.223)

1,944

Y 452.1 [0.176] 1,941

Y 394.1 [0.172] 1,960

0.400** (0.172)

-0.597*** (0.132) -0.191 (0.202) Y

Y 0.194 [0.015]

1,943

1,940

2.149*** (0.657)

4.021** (1.823) -2.676 (1.982) 0.0715 (0.530)

2,863** (1,275) -2,469* (1,322) 456.3 (333.5) Y

(9)

Y 0.209 [0.006] 1,959

4.434** (2.126) -2.983 (2.238) 0.180 (0.610) Y

1,943

Y 1.345 [0.037] 1,940

Y 1.451 [0.043] 1,959

Notes: Dependent variables are an indicator for whether the household recently gave a transfer to a friend or relative (columns 1-3), value of transfers given (columns 4-6), and total number of transfers given (columns 7-9). Panel A shows estimates of the reduced form effect of the savings encouragement. Panel B shows two-stage least squares estimates for the effect of account uptake rates on transfers out. (First stage estimates reported in Table 6, columns 9-10.) Households with missing data on transfers omitted. Regressions include pair fixed effects. (See Appendix Table A.11 for results excluding pair effects.) Cluster-robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

41

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TABLE 6. EFFECT OF LOCAL SAVINGS ADOPTION ON TRANSFER RECEIPTS IN PRE-HARVEST SEASON Receive Transfer (2)

(3)

Panel A:Reduced Form Estimates Information Treatment

(4)

(5)

(7)

(8)

0.251*** 0.158*** 0.200*** 0.176*** (0.0571) (0.0581) (0.0595) (0.0582) 0.171** 0.175** (0.0754) (0.0856) 0.266** 0.166 (0.126) (0.140) 0.244*** (0.0726) 0.148 (0.121) Y 0.424 0.366 [0.000] [0.006] 0.324 [0.004] 1,954 1,951 1,625 1,970

Educated High Prop (9) (10) 0.137*** (0.0315)

0.119*** (0.0360)

1,954

1,975

1.829*** (0.458)

1.095** (0.425) 2.603** (1.302) -0.444 (0.331)

1.457*** (0.482) 2.166 (1.515) -0.197 (0.305) 1.226*** (0.471) 2.484 (1.566) -0.413 (0.404)

AC C

EP

TE D

M AN U

SC

0.105*** 0.0808*** 0.110*** 0.0866*** (0.0175) (0.0236) (0.0260) (0.0232) Educated 0.0855*** 0.100** (0.0321) (0.0437) Information x Educated 0.0677 0.0274 (0.0474) (0.0627) High Propensity Saver 0.0968*** (0.0322) Information x High Propensity Saver 0.0306 (0.0437) Excludes Savers Y Information on Educated 0.149 0.137 [F-Test p-value] [0.000] [0.007] Information on High Propensity Saver 0.117 [0.000] [F-Test p-value] Observations 1,954 1,951 1,625 1,970 Panel B: Two-Stage Least Squares Adoption Rate of Savings Prone (Educated) 0.764*** 0.558*** 0.787*** (0.172) (0.178) (0.221) Adoption Rate of Savings Prone x Educated 0.731 0.541 (0.447) (0.623) Educated -0.0967 0.00427 (0.120) (0.134) Adoption Rate of Savings Prone (High Propensity) 0.612*** (0.203) Adoption Rate of Savings Prone x High-Propensity 0.749 (0.573) High-Propensity -0.115 (0.155) Excludes Savers Y Adoption Rate on Educated 1.289 1.328 [F-Test p-value] [0.003] [0.026] Adoption Rate on High Propensity Saver 1.361 [0.017] [F-Test p-value] Observations 1,954 1,951 1,625 1,970

(6)

RI PT

(1)

2SLS First Stage Local Adoption Rate

No. Transfers Received

3.698 [0.003]

1,954

1,951

Y 3.623 [0.018]

1,625

3.710 [0.015] 1,970

Notes: Dependent variables are an indicator for receiving any cash aid during the hungry season (columns 1-4) and the number of times cash aid was received (columns 5-8). Panel A reports linear estimates of the reduced-form effect of the savings encouragement. Panel B reports two-stage least squares estimates of the effect of the local savings uptake rate among the adoption prone (first stage estimates in columns 9-10). Regressions include pair fixed effects. (See Appendix Table A.12 for results excluding pair effects.) Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

42

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TABLE 7. EFFECT OF LOCAL SAVINGS ADOPTION ON TRANSFER RECEIPTS BY EXTREME POOR

Information x Top Three Quartiles

Excludes Savers Pair Effects Information on Top Three Quartiles [F-Test p-value]

EP

Adoption Rate of Savings Prone x Top Three Quartiles

AC C

Top Three Quartiles

Adoption Rate Impact on Top Three Quartiles

[F-Test p-value]

Observations

Y

Y Y

0.0884 [0.000]

(4)

(5)

0.291*** (0.107) 0.143 (0.0875) -0.0539 (0.113)

0.296*** (0.111) 0.138 (0.0928) -0.0710 (0.125)

0.137*** (0.0315)

Y

Y Y

0.0997 [0.000] 1,628

0.237 [0.000] 1,954

0.225 [0.001] 1,628

1.296** (0.523) -0.637 (0.503) 0.194* (0.116)

1.345** (0.554) -0.584 (0.534) 0.179 (0.114)

2.250** (1.066) -0.514 (1.060) 0.213 (0.262)

2.379** (1.174) -0.679 (1.179) 0.239 (0.267)

Y

Y Y

Y

Y Y

1,954

Panel B: Two-Stage Least Squares Adoption Rate of Savings Prone (Educated)

Excludes Savers Pair Effects

0.163*** (0.0455) 0.0877** (0.0351) -0.0632 (0.0532)

TE D

Observations

0.160*** (0.0440) 0.0893*** (0.0333) -0.0712 (0.0485)

2SLS First Stage Local Adoption Rate

(3)

SC

Top Three Quartiles

(2)

M AN U

Panel A:Reduced Form Estimates Information Treatment

(1)

No. Transfers Received

RI PT

Receive Transfer

0.658 [0.000] 1,954

0.761 [0.000] 1,628

1.736 [0.000] 1,954

1,954

1.700 [0.002] 1,628

Notes: Dependent variables are an indicator for receiving any cash aid during the hungry season (columns 1-2) and the number of times cash aid was received (columns 3-4). Panel A reports linear estimates of the reduced-form effect of the savings encouragement. Panel B reports two-stage least squares estimates of the effect of the local savings uptake rate among the educated (first stage estimate in column 5). Regressions include pair fixed effects. (See Appendix Table A.13 for results excluding pair effects.) Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

43

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TABLE 8. EFFECTS OF INCREASED TRANSFER RECEIPTS ON FOOD-CONSUMPTION AND HEALTH INDICATORS HDDS Food-Access Score

Information x Top Three Quartiles

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.597** (0.276) 1.495*** (0.196) -0.280 (0.304)

0.759*** (0.277) 1.417*** (0.198) -0.504 (0.312)

0.366** (0.141)

0.469*** (0.140)

0.0611* (0.0349) 0.0616** (0.0270) -0.131*** (0.0421)

0.0730** (0.0352) 0.0717** (0.0293) -0.148*** (0.0447)

-0.0210 (0.0203)

-0.0234 (0.0213)

0.0466* (0.0276) -0.0635 (0.0395)

0.0370 (0.0270) -0.0551 (0.0401)

Educated

1.408*** (0.199) 0.0246 (0.248)

Top Three Quartiles Information x Top Three Quartiles

Y 0.255 [0.102]

1,970

1,646

0.646** (0.279) 1.736*** (0.198) -0.361 (0.310)

0.796*** (0.288) 1.561*** (0.207) -0.536 (0.330)

EP

Educated

5.689*** (0.188)

5.554*** (0.190)

0.284 [0.232]

0.261 [0.278]

AC C

Information x Educated Constant

0.317 [0.038]

TE D

Excludes Savers Information Impact on Top Three Quartiles [F-Test p-value] Information Impact on Educated [F-Test p-value] Observations Panel B:Excluding pair effects Information Treatment

Excludes Savers Information Impact on Top Three Quartiles [F-Test p-value] Information Impact on Educated [F-Test p-value] Observations

1,970

1.407*** (0.210) -0.294 (0.278)

M AN U

Information x Educated

RI PT

Top Three Quartiles

(1)

SC

Panel A:With pair effects Information Treatment

No One in Household is Unwell

Y

0.391 [0.102] 1,967

0.176 [0.516] 1,643

0.373* (0.215)

0.486** (0.216)

1.686*** (0.250) -0.0140 (0.297) 6.501*** (0.161)

1.611*** (0.237) -0.264 (0.301) 6.275*** (0.156)

Y

1,646

-0.0697 [0.003] 2,008

1,675

0.0679* (0.0372) 0.0714** (0.0274) -0.134*** (0.0415)

0.0773** (0.0372) 0.0821*** (0.0293) -0.147*** (0.0434)

0.147*** (0.0227)

0.138*** (0.0222)

-0.0664 [0.0184]

-0.0701 [0.0179]

Y

0.359 [0.235] 1,967

0.221 [0.467] 1,643

Y -0.0746 [0.002]

Y -0.0844 [0.023] 2,005

-0.0785 [0.034] 1,672

-0.0148 (0.0255)

-0.0202 (0.0270)

0.0684** (0.0327) -0.0717* (0.0427) 0.181*** (0.0178)

0.0488* (0.0288) -0.0479 (0.0414) 0.186*** (0.0189)

Y

2,008

1,675

Y

-0.0865 [0.042] 2,005

-0.0681 [0.096] 1,672

Notes: Dependent variables are endline food consumption as measured by the Household Dietary Diversity Score (HDDS) – a food security metric running from 0 to 12 – and an indicator for whether the household reported that all members were well (no individuals unwell) at the time of the endline survey. Both panels report linear estimates of the reduced-form effect of the savings encouragement, panel A with pair effects, panel B without. Sample slightly smaller in columns 1-4 due to missing values for HDDS. Cluster-robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

44

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Formal Finance and Informal Safety Nets of the Poor: Evidence from a Savings Field Experiment By J EFFREY A. FLORY

RI PT

ONLINE APPENDICES

.1

.2

.3 .4 Estimated propensity score

lpoly smooth

lpoly smooth: fsaverT

.1

.2

.3 .4 Estimated propensity score

lpoly smooth

.5

lpoly smooth: fertmkT

Figure A.2

Crop Income by P-Score

Private Transfers Out by P-Score

C=Blue, T=Red

C=Blue, T=Red

.2

.3 .4 Estimated propensity score

lpoly smooth

kernel = epanechnikov, degree = 0, bandwidth = .04

Figure A.3

0

AC C

0

.1

C=Blue, T=Red

kernel = epanechnikov, degree = 0, bandwidth = .04

EP

C r o p S a le s ( T h o u s a n d s o f M K ) 10 20 30 40

Figure A.1

.52

TE D

kernel = epanechnikov, degree = 0, bandwidth = .03

Fertilizer Expenditure by P-Score

M AN U

0

C=Blue, T=Red

P c t H H s G iv in g C a s h A id .2 .4 .6 .8

P c t H H s w it h F o r m a l S a v in g s .2 .4 .6 .8

Savings Adoption by P-Score

SC

S p e n t o n F e r t. ( T h o u s a n d s o f M K ) 0 5 10 15 20 25

FIGURES

.5

lpoly smooth: cropsalesT

.1

.2

.3 .4 Estimated propensity score

lpoly smooth

.5

lpoly smooth: givecashT

kernel = epanechnikov, degree = 0, bandwidth = .03

Figure A.4

Notes: Local polynomial kernel regressions with epanechnikov kernel and degree 0, extreme outliers omitted.

45

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APPENDIX 1. SOCIAL TIES AND INFORMAL SAFETY NETS WITHIN THE VILLAGE Villages in this area are organized in large part around kinship, with many village residents related to each other. Since it is common throughout the study region for individuals to

RI PT

settle in their own natal village, many individuals live in the same village as their parents, siblings, cousins, aunts, uncles, children, grandchildren, etc., who also stayed to settle in the village. According to the World Bank Living Standards Measurement Survey/Malawi National Statistics Office Integrated Household Survey from 2010-2011, 49% of household heads in Malawi live in the village they were born in, while 50% of all adults (those over the age of 18),

SC

live in their natal village.

For further information on the general nature and strength of within village ties in the

M AN U

data sample, I make use of variables from the baseline data intended to gauge the household’s beliefs and sentiments about the strength of relationships within the village community in general. The data show that almost 60% of households in the baseline believe people in the village generally trust each other in matters of lending and borrowing; 60% believe that there is less conflict in their village compared to other villages and only 30% believe there is more; and 76% worry only a little or not at all about the security of their things if they need to be away

TE D

from their home for extended periods compared to 33% who worry a lot. Another variable gives more specific information about the type of social ties that undergird the informal safety nets to which households have access. Nearly all households in the baseline sample (92%) state that, if they had an income shortfall or unexpected expense, they

EP

have someone in the village they could turn to for help: 71% say they could turn to family outside the household, 47% indicate friends, 15% indicate neighbors, and 11% indicate

AC C

community leaders. (Households could list up to three sources of assistance they could turn to; these are the top 4 categories mentioned.) Combining all categories besides relatives, I find that 68% of households indicate someone in the village besides a relative. Turning specifically to the 1,579 baseline non-educated – those households for whom the

rises in transfer receipts are cleanly identified as spillover effects – the relative importance of each type of social tie is similar: 92% say they could turn to at least one person in the village for aid, 71% state they could turn to family, 46% to friends, 14% to neighbors, and 11% to leaders in the village. Also, 66% of all non-educated include someone in the village who is not a relative among those they say they can turn to for help. These percentages are almost identical when 46

ACCEPTED MANUSCRIPT

restricting to the non-educated without formal accounts in the endline, and they are very similar when restricting to the bottom quartile households (92% indicating at least one person, 72% indicating family, 47% friends, 15% neighbors, 14% local leaders, and 67% stating reliance on at least one non-relative). The high percentage of households indicating relatives as a source of aid

RI PT

suggests on the one hand that kinship networks form an important part of informal safety nets (for households overall as well as the non-educated and the bottom quartile). On the other hand, the fact that other community members besides relatives are cited almost as often as a source of help when in financial distress suggests non-family members are also an important part of one’s

SC

safety net.

With regard to the specific types of social ties linked to the cash transfers observed in this

M AN U

study, while information on these details was not collected for specific transfers, several insights can be gleaned through a more indirect approach. Returning to the previous analysis and restricting to those who recently received transfers provides an indication of the nature of the link between the recipient and the provider of the transfer. While admittedly the question used for this analysis was hypothetical, and was not asked with specific reference to the transfer recently received, this question was asked shortly after the series of questions about transfer

TE D

receipts and it is likely that whoever provided recently received transfers would be salient for the respondent and would strongly affect their response to this question. I find that 73% of recent transfer recipients say they could turn to relatives outside the household, 36% to friends, 11% to neighbors, and 7% to community leaders. I also find that 52% indicate they can turn to someone

EP

in the village other than family members for help. Restricting to the non-educated that recently received cash transfers, the percentages are very similar to those among all recent transfer

AC C

recipients taken together: 73% say they can turn to relatives for financial help, 35% say they can turn to friends, 9% say they can turn to neighbors, 8% say they can turn to local community leaders. Moreover, 50% say they can turn to someone in the village who is not a relative. Thus, among those reporting recent cash aid receipts in the endline data, there is a somewhat lower emphasis of friends, neighbors, local leaders, and other non-relatives in the village, suggesting a somewhat larger role played by kinship in safety nets. However, with the difference between relatives and non-relatives being 73% versus 52%, non-familial network members also seem to be very important.

47

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Indicators therefore suggest relationships in the communities of the data sample are such that trust of others is relatively strong (though not complete), fear of theft relatively low (though not absent), and perceptions of social cohesion/stability relatively high (though some signs of conflict exist). This relatively stable and cohesive picture is perhaps not surprising given that

RI PT

villages are structured in large part around kinship, as is the case in many areas of sub-Saharan Africa. Finally, the data indicates that social ties with non-relatives as well as relatives form the basis of households’ local informal safety nets. It also suggests that both relatives and non-

AC C

EP

TE D

M AN U

SC

relatives play an important role in the spillover effects of savings expansion on transfer receipts.

48

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APPENDIX 2. INTER-H OUSEHOLD TRANSFERS AT OTHER TIMES OF THE YEAR One important question is whether the benefits to households found during the hungry season might somehow be negated by other changes in transfer patterns that may occur at other

RI PT

times of the year. Suppose for example that non-savers receive fewer transfers during, and just after, the harvest period as a result of the rise in formal savings use. If so, and if the transfers to which they lose access are for critical consumption smoothing (e.g. food, medicine), this would imply a reduction in access to vital informal support at these other times of the year. One

SC

possible implication is that food consumption (or health indicators) may suffer during these times as a result.

This does not seem particularly likely, since there tend to be more income opportunities

M AN U

in general during this time of year, and even the extreme poor often have enough resources to meet critical needs at this time – since even they have access to land and harvest crops. The problem tends to be their crop output is lower, which can cause them to run out of resources earlier in the lean season compared to other households. But this low point in resources usually does not happen in the months during and just after harvest. For example, endline median and mean crop output values are MK 46,000 and MK 69,000 among the non-educated households,

TE D

and MK 31,000 and MK 38,000 among the extreme poor – enough to cover basic necessities for at least a few months. This makes it seem more likely that, if access to household transfers decreased during and just after harvest, it would not have had a particularly large impact on

EP

consumption of critical necessities. Indeed, the lack of income-generating opportunities during the lean season, and the prevalence and intensity of deprivation at this time, suggests transfers driven by urgent need are more likely to occur in the lean season period.

AC C

Several pieces of evidence in the data appear to confirm that, even if availability of inter-

household transfers during and after harvest dropped due to increased savings, access to at least the most critically needed transfers was not part of this reduction. First, supposing diminished access to critical support just after harvest, to the extent that this causes sharp negative consequences to those who would have otherwise had access to the transfers, there should be some evidence of this even in the hungry season. Since a lack of access to urgently needed help earlier in the year would likely prompt more severe emergency measures and sharp falls in own resources (selling food, other possessions, liquidating wealth stored in durables, etc.), this should

49

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cause households that experienced this to be in an even worse state by the time the lean season arrives. However, the data instead shows the opposite – particularly for the poorest, who are the most likely to exhibit this type of effect: among the bottom quartile, household welfare measures are significantly better in the savings encouraged areas.

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In addition, the data contain an indicator for perceived local informal safety net strength from a question which asks whether there is anyone in the village who the household could turn to for help in the event of an income shortfall or unexpected expense. Since it is a hypothetical, it is not bound to any particular time frame or even necessarily to any specific transfers received.

SC

However, if there were reductions in access to critically needed transfers at times of the year outside of the hungry season, we would expect this to be reflected to at least some degree in this

M AN U

variable on people’s expectations of their ability to rely on others in their village when help is needed. When restricting to households that did not recently receive cash help (to eliminate affirmative answers that are based on cash assistance received during the lean season), I find that 75.0% of households in savings-encouraged villages, and 73.0% of those in controls, report having access to financial help from someone in the village. Importantly, these percentages change little when looking at the non-educated (71.9% state access to help in savings-encouraged

TE D

villages, 71.8% in the controls) or the non-educated without accounts (70.3% in savingsencouraged, 70.6% in controls). This suggests that perceived access to informal safety nets did not weaken in savings-boosted villages – even among those who did not recently receive transfers. If these non-savers had lost access to critical sources of private aid earlier in the year,

EP

and then also not obtained a transfer more recently during the lean season, we would expect them to state lower levels of access to help from others in their community than this variable indicates.

AC C

Turning to another piece of evidence, there is also no less aid (in the form of gifts or loans) from friends and relatives in savings encouraged villages to help households cope with negative shocks over the year preceding the survey interview. This is true whether during or outside of the hungry season. Among households experiencing a shock, 10.6% in control villages and 10.9% in savings-encouraged received help from another household. Restricting to the months outside the lean season, 5.6% in control villages and 7.7% in savings-encouraged received help from another household. Restricting to just the non-educated, 10.4% of those that had negative shocks in control villages, and 10.9% in savings-encouraged, got help from other households; and when restricting to the months outside the lean season 5.9% in control villages, 50

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and 8.3% in savings-encouraged, got help from other households. Looking at the non-educated without formal savings, this becomes 10.4% in the controls and 11.6% in the savingsencouraged; and 5.3% in the controls compared to 9.2% in the savings-encouraged. Finally, had there been a noteworthy reduction of access to urgently needed transfers

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during the harvest and post-harvest periods (rather than a reduction in transfers that might have helped with less essential expenditures, for example), we would expect some evidence of this in amounts invested in crops at planting time. If households in information-treated areas had less access earlier in the year to transfers to help with critical necessities, they likely would have had

SC

to use more of their own resources to cover those needs. This would leave less available for investment in their main income-activity at planting time. However, as Tables 4 and A.10 show,

M AN U

there is no treatment-control difference in amount spent on fertilizer by the non-educated (the median is identical across treated and control villages, and there is less than 0.2% difference in the means, p=0.99). That is, there is no evidence that the budget constraints of non-educated households were made tighter during the harvest and post-harvest period in savings-boosted villages such that it caused them to have less income available for crop inputs during the planting season.

TE D

The above evidence therefore strongly suggests that any impacts on transfers outside of the time period observable in this data do not negate the benefits from inter-household transfers to non-savers during the period observed in the data. If there was a reduction in transfers during the harvest/post-harvest period, this evidence suggests it was a reduction in transfers facilitating

EP

less critical consumption. However, this remains an important and interesting question that

AC C

would benefit from further research before drawing definitive conclusions.

51

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APPENDIX TABLES APPENDIX TABLE A.1 SUMMARY STATISTICS AND BALANCE CHECK AMONG ATTRITERS

Head's Age (Years) Household Size (People) Head has Primary Education Head Literate in English

HFIAP Category (1-4) HDDS Score (1-12) One or More Members Literate in Chichewa Has Business No. Members with Salaried Job (People) Crop Income Last Harvest (Kwacha)

TE D

Per Cap Crop Income Last Harvest (Kwacha/Person)

M AN U

Bank-Stop Distance (km)

Salary Income Last 30 Days (Kwacha)

Per Cap Salary Income Last 30 Days (Kwacha/Person) Non-Ag Business Income Last 30 Days (Kwacha)

EP

Physical Assets (Kwacha)

Formal + Informal Account Balances (Kwacha)

AC C

Livestock Value (Kwacha)

Land and Buildings Value (Kwacha) Amount of Land (Acres) Has Formal Savings

0.84 (0.36) 37.2 (12.6) 4.79 (1.98) 0.32 (0.47) 0.35 (0.48) 7.16 (4.15) 3.11 (0.98) 7.40 (2.78) 0.87 (0.33) 0.33 (0.47) 0.25 (0.53) 29,360 (54,220) 6,145 (9,474) 2,541 (9,962) 606 (2,606) 1,479 (7,434) 39,492 (170,000) 7,680 (42,335) 14,423 (52,594) 101,900 (311,659) 2.39 (1.59) 0.18 (0.38)

Obsv.

RI PT

Head is Male

Coefficient (std. errors) on Treatment Dummy 0.0307 (0.0477) -0.188 (1.600) -0.0429 (0.268) -0.0411 (0.0874) -0.0429 (0.0804) 0.842 (1.364) 0.0307 (0.149) -0.0307 (0.503) -0.0429 (0.0436) 0.0245 (0.0750) -0.110 (0.0896) -2,596 (7,327) -892.5 (1,242) -914.1 (1,582) -226 (414) 1,092 (759.2) 12,339 (29,087) 4,102 (7,296) -6,002 (7,563) 34,352 (34,270) 0.0317 (0.254) -0.0163 (0.0649)

SC

Sample Mean (Std. Dev.)

Demographic Characteristics

326 322 326 325

326 326 326 326 326 326 326 326 326 322 322 323 326 326 326 326 280 324

Has Formal Loan

0.06 (0.23)

0.0552** (0.0263)

326

Educated Head

0.38 (0.49) 0.29 (0.46)

-0.0407 (0.0837) 0.085 (0.055)

325

Extreme Poor

326

Notes: Exchange rate was approximately 140 Malawi Kwacha to US $1 during the 2008 survey period. The above table reports descriptive statistics in the 2008 cross-section among households that attrited from the sample. Except where indicated in parentheses, units are proportions. Standard errors clustered at the village-cluster level *** p<0.01, ** p<0.05, * p<0.1.

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APPENDIX TABLE A.2 SUMMARY STATISTICS AND BALANCE CHECK AMONG EDUCATED HOUSEHOLDS

Head's Age (Years) Household Size (People) Head has Primary Education Head Literate in English Bank-Stop Distance (km) HFIAP Category (1-4)

One or More Members Literate in Chichewa Has Business Has Member with Salaried Job Crop Income Last Harvest (Kwacha) Per Cap Crop Income Last Harvest (Kwacha/Person) Salary Income Last 30 Days (Kwacha)

TE D

Per Cap Salary Income Last 30 Days (Kwacha/Person) Non-Ag Business Income Last 30 Days (Kwacha) Physical Assets (Kwacha)

Formal + Informal Account Balances (Kwacha)

EP

Livestock Value (Kwacha)

Land and Buildings Value (Kwacha)

AC C

Amount of Land (Acres) Has Formal Savings Has Formal Loan Extreme Poor Attrition

M AN U

HDDS Score (1-12)

0.94 (0.24) 38.7 (13.0) 5.15 (2.04) 0.79 (0.41) .92 (.28) 7.36 (3.81) 2.95 (0.99) 8.26 (2.56) 0.99 (0.04) 0.28 (0.45) 0.31 (0.54) 51,479 (253,446) 10,243 (42,945) 2,816 (7,996) 615 (1,978) 2,794 (31,154) 52,393 (242,740) 7,384 (46,386) 23,766 (102,650) 116,059 (269,226) 2.65 (2.12) 0.22 (0.41) 0.09 (0.28) 0.13 (0.34) 0.16 (0.37)

SC

Head is Male

Coefficient (std. errors) on Treatment Dummy -0.00 (0.02) 1.01 (0.93) 0.241 (0.147) 0.05 (0.04) 0.00 (0.03) 0.58 (0.93) -0.007 (0.121) 0.419 (0.341) 0.00 (0.00) -0.01 (0.04) -0.01 (0.08) 22,265 (19,085) 4,254 (3,445) -179 (1,216) -12.32 (284.1) 2,961 (2,074) 7,562 (21,919) 4,999 (4,382) 10,395 (8,908) 18,564 (22,572) 0.097 (0.170) 0.08 (0.05) 0.031 (0.024) -0.017 (0.026) -0.029 (0. 048)

Obsv. 757 747

RI PT

Sample Mean (Std. Dev.)

Demographic Characteristics

757 757 757 757 757 757 757 756 757 757 757 757

739 757 757 757 757 757 682 755 755 757 757

Notes: Exchange rate was approximately 140 Malawi Kwacha to US $1 during the 2008 survey period. The above table reports descriptive statistics for the educated households in the 2008 cross-section. Except where indicated in parentheses, units are proportions. Standard errors clustered at the village-cluster level *** p<0.01, ** p<0.05, * p<0.1.

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APPENDIX TABLE A.3 INFORMATION INTERVENTION EFFECTS ON FINANCIAL SERVICE USE – CREDIT (2) Formal Credit

(3) Formal Credit

(4) Formal Credit

(5) Formal Credit

(6) Formal Credit

0.00289 (0.0201)

0.00113 (0.0146)

0.0468 (0.0401) -0.0779*** (0.0289) -0.0684 (0.0428)

0.0445 (0.0310) -0.0750*** (0.0264) -0.0663* (0.0389)

0.0248 (0.0380)

0.0197 (0.0294)

-0.0970*** (0.0278) -0.0361 (0.0412) 0.204*** (0.0245)

-0.0889*** (0.0253) -0.0296 (0.0372)

SC

Non-Educated

Low Propensity Saver Information x Low Propensity Saver 0.138*** (0.0141)

0.193*** (0.0266)

TE D

Constant

M AN U

Information x Non-Educated

Pair Effects Information on Non-Educated

Y

[F-Test p-value] [F-Test p-value]

EP

Information on Low Propensity Saver Observations

RI PT

Panel A: All Households Information

(1) Formal Credit

2,011

2,011

-0.022 [0.270]

2,008

Y -0.022 [0.222]

2,008

Y

-0.011 [0.558] 2,003

-0.010 [0.581] 2,003

AC C

Notes: Estimates from linear regressions of formal credit use. The dependent variable is an indicator equal to 1 if the household has had a formal loan. Sample size differs slightly across specifications due to incomplete data for education and assets (households with missing values omitted). Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

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APPENDIX TABLE A.4 ACCOUNT USE AMONG FORMAL SAVERS BY TREATMENT STATUS

Observations

Observations

Deposit Amounts (MK)

Opening Month

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

-1,853

2,364

0.0414

0.0684

0.0582

0.0356

-117.4

-235.8

0.0184

0.0391

3,366*

3,663*

-0.271

-0.426

(4,736)

(6,008)

(2,309)

(2,217)

(1,794)

(1,917)

(0.503)

(0.520)

15,801

(0.0639) (0.0460) (0.0525) (0.0344) 0.381

Y

0.158 Y

Y

328

328

333

333

333

-2,674

-5,677

0.0484

-0.005

0.0678

(7,122)

(9,065)

18,606

0.408

182

0.225 Y

187

187

0.281

Y

621.7 Y

6.2 Y

Y

333

333

333

333

333

333

214

214

0.0794

-1,117

-929.4

0.0341

-0.0442

4,784

6,923

-0.00522

-0.102

(4,305)

(6,202)

(2,922)

(4,410)

(0.672)

(0.883)

6,385

Y

187

(0.0625) (0.0468)

333

(0.0809) (0.0667) (0.0844) (0.0621)

Y

182

3,804

TE D

Mean in Control Villages Pair Fixed Effects

Any Deposit

(1)

Panel B: Savers Among

Head-Educated Information

Withdrawal Amounts (MK)

SC

Mean in Control Villages Pair Fixed Effects

Any Withdrawal

M AN U

Panel A: All Formal Savers Information

Any Use

RI PT

Account Balance (MK)

187

(0.0712) (0.0649) 0.268

Y

187

187

897.3 Y

187

187

5.6 Y

187

187

Y

121

121

AC C

EP

Notes: Estimates from linear regressions of seven different measures of account use on community treatment status. Dependent variables are a {0,1} indicator for columns 3-6 and 9-10, and amounts in Malawi Kwacha for columns 1-2, 5-6, and 7-8. Observations differ across regression models due to missing values. Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

55

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TABLE A.5. ACCOUNT IMPACTS ON BROADER RANGE OF CROP INPUTS – INTENTION TO TREAT

Information Treatment Mean in control villages Pair Effects Observations

(2)

(3)

0.4655** 0.5529*** (0.2171) (0.1530) 2.610 Y 567

1.822 (1.174) 3.7

(4) 1.903** (0.810) Y

567

618

618

Land Rental Costs (MK) (5) 621.8* (364.4) 946.3

Seedlings (MK)

RI PT

(1)

Land Rented In (Acres)

(7)

(8)

628.4*** (232.6)

743.4** (353.3) 1,111

822.5*** (280.7)

Y

618

Fertilizer Amount (kg)

(6)

SC

Own Land Cultivated (Acres)

618

603

Y 603

(9)

(10)

66.91** 66.43*** (25.59) (17.22) 131.4 Y 605

605

AC C

EP

TE D

M AN U

Notes: Linear regressions. Sample restricted to educated households. Number of observations differs across models due to missing values. Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

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APPENDIX TABLE A.6 REDUCED-FORM EFFECT OF SAVINGS ENCOURAGEMENT ON TRANSFER RECEIPTS All Households (2)

(3)

(4)

(5)

(6)

Probit

Linear

Linear

Probit

Linear

Linear

0.0990*** 0.0990*** 0.102*** (0.0251)

(0.0171)

0.283

0.0753*** 0.0753*** 0.0795*** (0.0279) 0.258

Pair Effects

Y 1,994

1,994

1,994

(8)

(0.0210)

Probit

Linear

Linear

0.101*** 0.101*** 0.107*** (0.0309)

(0.0309)

(0.0232)

0.244

Y

1,365

(9)

1,365

1,365

Y 1,219

1,219

1,219

M AN U

Observations

(0.0279)

(7)

RI PT

(1)

(0.0251) Mean in control areas

Non-Educated, No Account

SC

Information Treatment

Non-Educated

AC C

EP

TE D

Notes: Dependent variable is an indicator for whether a household received a recent cash transfer from friends or relatives. Pair fixed effects are included for regressions reported in columns 3, 6, and 9. Cluster-robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

57

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APPENDIX TABLE A.7 SUMMARY STATISTICS AND BALANCE CHECK AMONG NON-EDUCATED

Household Size (People) Head has Primary Education Head Literate in English Bank-Stop Distance (km) HFIAP Category (1-4) HDDS Score (1-12) One or More Members Literate in Chichewa Has Business Has Member with Salaried Job Crop Income Last Harvest (Kwacha) Per Cap Crop Income Last Harvest (Kwacha/Person) Salary Income Last 30 Days (Kwacha)

TE D

Per Cap Salary Income Last 30 Days (Kwacha/Person) Non-Ag Business Income Last 30 Days (Kwacha) Physical Assets (Kwacha)

Formal + Informal Account Balances (Kwacha)

EP

Livestock Value (Kwacha)

Land and Buildings Value (Kwacha)

AC C

Amount of Land (Acres) Has Formal Savings Has Formal Loan Extreme Poor Attrition

Obsv. 1,578 1,536

RI PT

Head's Age (Years)

0.81 (0.39) 42.12 (14.11) 5.12 (1.95) 0 (0) 0 (0) 8.18 (3.12) 3.35 (0.81) 6.61 (2.45) 0.79 (0.41) 0.26 (0.44) 0.11 (0.33) 29,584 (60,001) 5,991 (9,615) 389 (1,897) 82 (385) 455 (4,201) 15,698 (56,735) 821 (7,484) 14,886 (47,625) 104,799 (332,901) 2.61 ( 1.71) 0.07 (0.25) 0.05 (0.22) 0.28 (0.45) 0.13 (0.33)

M AN U

Head is Male

Coefficient (std. errors) on Treatment Dummy 0.0364 (0.0242) -0.553 (0.844) 0.191 (0.123) 0 (0) 0 (0) -0.0542 (0.592) 0.0421 (0.0503) 0.179 (0.203) -0.0108 (0.0268) 0.0204 (0.0307) 0.0257 (0.0211) 3,654 (4,180) 141.3 (675.1) 232* (125) 36.22 (27.72) 249.2 (213.0) 1,493 (3,420) -590.0 (361.0) 1,374 (2,777) 9,694 (18,535) -0.0108 (0.138) 0.00486 (0.0201) -0.0157 (0.0155) -0.005 (0.030) 0.0108 (0.0326)

SC

Sample Mean (Std. Dev.)

Demographic Characteristics

1,578 1,574 1,578 1,578 1,578 1,578 1,578 1,578 1,578 1,578 1,578 1,559 1,559 1,572 1,578 1,578 1,578 1,578 1,492 1,576 1,577 1,579 1,578

Notes: Exchange rate was approximately 140 Malawi Kwacha to US $1 during the 2008 survey period. The above table reports descriptive statistics for the low-education households in the 2008 cross-section. Except where indicated in parentheses, units are proportions. Standard errors clustered at the village-cluster level *** p<0.01, ** p<0.05, * p<0.1.

58

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APPENDIX TABLE A.8 SUMMARY STATISTICS AND BALANCE CHECK AMONG WORST-OFF QUARTILE

Household Size (People) Head has Primary Education Head Literate in English Bank-Stop Distance (km) HFIAP Category (1-4) HDDS Score (1-12) One or More Members Literate in Chichewa Has Business Has Member with Salaried Job Crop Income Last Harvest (Kwacha) Per Cap Crop Income Last Harvest (Kwacha/Person) Salary Income Last 30 Days (Kwacha)

TE D

Per Cap Salary Income Last 30 Days (Kwacha/Person) Non-Ag Business Income Last 30 Days (Kwacha) Physical Assets (Kwacha)

Formal + Informal Account Balances (Kwacha)

EP

Livestock Value (Kwacha)

Land and Buildings Value (Kwacha)

AC C

Amount of Land (Acres) Has Formal Savings Has Formal Loan Attrition

Obsv. 548 531

RI PT

Head's Age (Years)

0.73 (0.45) 39.45 (14.56) 4.49 (1.81) 0.14 (0.35) 0.16 (0.36) 8.01 (3.26) 3.56 (0.69) 5.68 (2.24) 0.69 (0.46) 0.19 (0.39) 0.13 (0.35) 7,321 (3,632) 1,910 (1,399) 533 (2,668) 124 (550) 266 (933) 1,920 (1,168) 122 (1,317) 2,645 (7,549) 93,660 (480,613) 2.14 (1.52) 0.016 (0.127) 0.036 (0.189) 0.18 (0.38)

M AN U

Head is Male

Coefficient (std. errors) on Treatment Dummy 0.0703* (0.0417) -0.663 (1.401) 0.193 (0.161) 0.0307 (0.0334) -0.0162 (0.0354) 0.0896 (0.678) -0.0453 (0.0625) 0.0395 (0.257) 0.00700 (0.0460) -0.0369 (0.0346) -0.0283 (0.0369) -368.2 (336.8) -90.69 (140.6) 187.5 (260.2) -2.255 (56.86) 27.39 (86.30) 12.44 (114.2) 159.0 (112.4) 305.2 (661.8) -9,900 (42,062) 0.0723 (0.199) 0.00432 (0.0117) -0.00584 (0.0223) 0.0575 (0.0436)

SC

Sample Mean (Std. Dev.)

Demographic Characteristics

548

548 548 548 548 548 548 548 548 548 548 543 543 547 548 548 548 548 512 547 547 548

Notes: Exchange rate was approximately 140 Malawi Kwacha to US $1 during the 2008 survey period. The above table reports descriptive statistics for households in the bottom quartile by physical assets and crop income in the 2008 cross-section. Except where indicated in parentheses, units are proportions. Standard errors clustered at the village-cluster level *** p<0.01, ** p<0.05, * p<0.1.

59

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Business Income (MK) Last 30 Days

Information x Top Three Quartiles

(3)

(4)

(5)

(6)

(7)

(8)

1,766 (3,117) 2,619 (2,918) 1,166 (3,450)

-591.9 (3,019) 215.6 (2,551) 3,461 (3,312)

-0.531 (0.911) -3.169*** (0.821) 0.829 (1.003)

-0.515 (0.936) -2.812*** (0.855) 1.169 (1.083)

-127.0 (203.9) -777.3*** (181.2) 287.3 (233.8)

-90.19 (207.5) -684.1*** (187.6) 369.7 (251.6)

142.7 (168.2) 300.9** (133.0) 325.6 (256.2)

156.2 (140.5) 189.4* (110.4) 239.6 (199.2)

Excludes Savers

Mean in Controls Excludes Savers

Observations

1,541 (2,266) 4,858 (3,258) 4,106 (4,909)

329

404

Y

1,191 (1,608) 1,939 (2,728) 4,842 (3,895)

Y

1,597

1,936

1,609

1,944

1,627

0.554 (0.557) -1.188* (0.699) -1.285 (0.865)

0.711 (0.599) -0.847 (0.776) -1.148 (1.050)

235.7** (106.2) -213.0 (136.7) -396.6** (176.1)

288.7** (114.6) -114.9 (155.4) -330.2 (214.0)

315.8** (130.8) 246.9 (180.1) 251.6 (376.3)

269.7*** (86.14) 103.0 (159.9) 252.3 (344.0)

Y

326

Y

1,916

TE D

407

EP

Information x Educated

Y

AC C

Educated

Rental Income (MK) Last 12 Months

(2)

Mean in Controls

Observations Panel B: ITT Effects on Non-Educated Information Treatment

Casual Labor Income (MK) Last 30 Days

(1)

SC

Top Three Quartiles

Casual Labor Days Worked Last 30 Days

M AN U

Panel A: ITT Effects on Bottom Quartile Information Treatment

RI PT

APPENDIX TABLE A.9 TESTING FOR SAVINGS SPILLOVERS ONTO RECEIPTS BY LOCAL NON-SAVERS VIA OTHER CHANNELS

Y

1,913

1,594

Y

1,933

1,606

Y

1,941

1,624

Notes: Estimates from linear regressions of non-agricultural business income the last 30 days (columns 1-2), total number of days of casual labor worked out of the last 30 days (columns 3-4), total income from casual labor the last 30 days (columns 5-6), and total rental income over the last 12 months (columns 7-8). Sample in columns 1-2 restricted to households with non-agricultural enterprises. Extreme outliers omitted. Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

60

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APPENDIX TABLE A.10 EFFECTS OF FORMAL SAVINGS ON CROP INVESTMENTS AND INCOME – EXCLUDING PAIR EFFECTS

2,589 (1,586)

Non-Educated Information x Non-Educated

-5,299*** (1,852) -10,418*** (3,439) 12,068*** 15,025*** 15,718*** (933.1) (1,376) (1,371)

Information x Low Propensity Saver Constant

Controls Information on Non-Educated

Low Propensity Saver Constant

Controls Observations

TE D

AC C

Non-Educated

1,959

1,956

51,836 (36,758)

67,917* (36,652) 4,001 (5,874)

EP

[F-Test p-value]

Crop Sales Income (MK) (6) (7)

(4)

(5)

3,687 (3,534)

13,628** (6,527) -7,507* (4,007) -14,936** (6,831)

24,706*** (2,343)

-18.41 [0.990]

[F-Test p-value] Information on Low Propensity Saver Observations Panel B: Treatment on the Treated Has Savings

9,335*** (3,170)

M AN U

Low Propensity Saver

7,759** (3,128) -4,193** (1,773) -7,777** (3,326)

(3)

RI PT

Panel A: Intention to Treat Information

Fertilizer (MK) (2)

SC

(1)

15,343** (5,967) -4,992 (3,778) -16,708** (6,689)

29,992*** (3,490)

-2,234 (5,869)

-1,308 [0.715]

Y -1,365 [0.655]

-1,083 [0.462] 1,954

1,980

1,977

1,927

86,310* (50,471)

74,454 (67,138)

117,867* (71,286) 5,940 (12,105)

126,420** (60,861) 6,536 (9,228)

4,961 (5,661)

-417.0 (9,460)

5,726 (8,822) -4,486 (13,556)

1,959

1,956

1,954

(8)

12,890* (6,949)

15,637** (6,240)

-13,538*** (3,978) -14,597** (7,092) 34,020*** (3,561)

-11,748*** (3,748) -17,713** (6,903) -2,213 (5,715) Y

-1,707 [0.608] 1,975

-2,076 [0.467] 1,927

120,042 (85,933)

139,777* (73,320)

1,790 (11,240) -28,847* (16,975) Y 1,927

14,447 (10,909)

3,287 (19,477)

-29,028* (15,494)

1,876 (15,357) 5,765 (23,860)

1,980

1,977

Y 1,927

1,975

Notes: Estimates from linear regressions of fertilizer expenditures (columns 1-3) and crop sales (columns 4-8), without pair-level effects. Exchange rate was approximately 150 Malawi Kwacha to US $1 during the survey period in 2010. Households with missing crop production values omitted. Cluster-robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

61

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APPENDIX TABLE A.11 EFFECTS OF FORMAL SAVINGS ON CASH TRANSFERS OUT DURING PRE-HARVEST SEASON – EXCLUDING PAIR EFFECTS Give Transfer (3)

(4)

(5)

(6)

(7)

(8)

(9)

0.0799*** (0.0292)

0.129*** (0.0426) -0.157*** (0.0303) -0.0819* (0.0441)

0.124*** (0.0419)

153.9*** (55.36)

326.0** (136.0) -125.6*** (41.20) -266.8** (133.2)

249.8** (121.0)

0.309*** (0.112)

0.482** (0.225) -0.530*** (0.130) -0.285 (0.218)

0.419** (0.210)

Information x Non-Educated Low Propensity Saver Information x Low Propensity Saver

[F-Test p-value] Information on Low Propensity Saver [F-Test p-value] Observations Panel B: Two-Stage Least Squares Adoption Rate of Savings Prone (Educated) Adoption Rate of Savings Prone x Non-Educated Non-Educated Adoption Rate of Savings Prone (High Propensity)

Low-Propensity

1,942

0.579** (0.250)

1.159** (0.562) -0.838 (0.528) 0.0625 (0.154)

0.248*** (0.0656) N

AC C

Constant Pair Effects Adoption Rate on Non-Educated

1,945

EP

Adoption Rate of Savings Prone x Low-Propensity

[F-Test p-value] Adoption Rate on Low Propensity Saver [F-Test p-value] Observations

137.0*** (16.85) N

0.0475 [0.105] 1,961

1,945

0.193 (0.164) N 0.322 [0.138] 1,942

1.309* (0.680) -0.958 (0.633) 0.109 (0.186) 0.139 (0.198) N 0.352 [0.147] 1,961

224.9*** (39.22) N 59.17 [0.070]

1,944

1,941

1,114** (486.4)

2,908* (1,557) -2,506* (1,489) 488.6 (389.0)

TE D

Pair Effects Information on Non-Educated

0.465*** (0.0267) N 0.0473 [0.110]

M AN U

0.355*** (0.0195) N

-0.154*** (0.0300) -0.0769* (0.0436) 0.460*** (0.0262) N

RI PT

(2)

Non-Educated

Constant

No. Transfers

(1)

SC

Panel A: Reduced Form Estimates Information

Amt. of Largest Transfer (MK)

-67.88 (107.5) N

1,944

-454.7 (405.8) N 402.2 [0.100] 1,941

-166.8*** (43.04) -194.6* (114.9) 251.5*** (39.89) N 55.20 [0.028] 1,960

2,584 (1,591) -2,176 (1,503) 394.8 (406.1) -378.4 (424.2) N 408.1 [0.0785] 1,960

0.798*** (0.0582) N

1.168*** (0.123) N 0.197 [0.040]

1,943

1,940

2.246** (0.940)

4.336* (2.534) -2.999 (2.404) 0.269 (0.687)

0.385* (0.232) N

1,943

0.152 (0.719) N 1.336 [0.0605] 1,940

-0.555*** (0.130) -0.194 (0.206) 1.178*** (0.118) N 0.225 [0.021] 1,959

4.427 (2.759) -2.760 (2.611) 0.252 (0.751) 0.0914 (0.785) N 1.667 [0.0587] 1,959

Notes: Dependent variables are an indicator for whether the household recently gave a transfer to a friend or relative (columns 1-3), value of transfers given (columns 4-6), and total number of transfers given (columns 7-9). Panel A shows estimates of the reduced form effect of the savings encouragement. Panel B shows two-stage least squares estimates for the effect of account uptake rates on transfers out. (First stage estimates reported in Table A.12, columns 9-10.) Households with missing data on transfers omitted. Pair effects omitted. Cluster-robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

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APPENDIX TABLE A.12 EFFECT OF LOCAL SAVINGS ADOPTION ON TRANSFER RECEIPTS IN PRE-HARVEST SEASON – EXCLUDING PAIR EFFECTS Receive Transfer

0.0737** (0.0289) 0.0858*** (0.0304) 0.0722 (0.0466)

0.283*** (0.0146)

0.258*** (0.0163)

Educated Information x Educated High Propensity Saver Information x High Propensity Saver

Excludes Savers Information on Educated [F-Test p-value] Information on High Propensity Saver [F-Test p-value] Observations 1,954 Panel B: Two-Stage Least Squares Adoption Rate of Savings Prone (Educated) 0.739*** (0.257) Adoption Rate of Savings Prone x Educated

Adoption Rate of Savings Prone (High Propensity)

High-Propensity Constant Excludes Savers Adoption Rate on Educated

0.509** (0.216) 0.824 (0.599) -0.145 (0.169)

AC C

Adoption Rate of Savings Prone x High-Propensity

1,951

[F-Test p-value] Adoption Rate on High Propensity Saver [F-Test p-value] Observations

0.713*** (0.272) 0.858 (0.783) -0.0602 (0.167)

0.146** (0.0645)

0.174*** (0.0505) 1.332 [0.041]

1,954

1,951

(5)

(6)

(7)

(8)

0.239*** 0.132* 0.163** 0.169** (0.0824) (0.0774) (0.0783) (0.0760) 0.140* 0.143* (0.0759) (0.0836) 0.306** 0.224 (0.134) (0.141) 0.223*** (0.0725) 0.165 (0.129) 0.519*** 0.476*** 0.452*** (0.0447) (0.0487) (0.0464) Y 0.439 0.387 [0.002] [0.006] 0.334 [0.015] 1,954 1,951 1,625 1,970 1.764** (0.704)

0.663** (0.282) 0.584 (0.658) -0.0964 (0.187) 0.140** (0.0688)

EP

Educated

0.146 [0.000]

0.100*** 0.0856*** (0.0319) (0.0301) 0.105** (0.0410) 0.0412 (0.0593) 0.0984*** (0.0303) 0.0290 (0.0451) 0.255*** (0.0165) Y 0.141 [0.008] 0.115 [0.002] 1,625 1,970

TE D

Constant

(4)

RI PT

0.100*** (0.0256)

(3)

0.190 (0.173)

Y 1.570 [0.062] 1,625

0.914* (0.516) 3.093* (1.861) -0.656 (0.513)

0.136*** (0.0432)

0.118** (0.0457)

0.186*** (0.0287)

0.198*** (0.0275)

1,954

1,975

1,954

1.161** (0.575) 3.144 (2.057) -0.436 (0.427) 1.309** (0.652) 2.323 (1.935) -0.453 (0.543) 0.225 (0.165)

0.326** (0.127) 4.006 [0.044]

1.246 [0.084] 1,970

Educated High Prop (9) (10)

SC

(2)

2SLS First Stage

M AN U

Panel A: Reduced Form Estimates Information Treatment

(1)

No. Transfers Received

1,951

Y 4.305 [0.052] 1,625

3.632 [0.090] 1,970

Notes: Dependent variables are an indicator for receiving any cash aid during the hungry season (columns 1-4) and the number of times cash aid was received (columns 5-8). Panel A reports linear estimates of the reduced-form effect of the savings encouragement. Panel B reports two-stage least squares estimates of the effect of the local savings uptake rate among the adoption prone (first stage estimates in columns 9-10). Pair effects omitted. Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

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APPENDIX TABLE A.13 EFFECT OF LOCAL SAVINGS ADOPTION ON TRANSFER RECEIPTS BY EXTREME POOR – EXCLUDING PAIR EFFECTS

(1) Panel A: Reduced Form Estimates Information Treatment

SC

M AN U

Information x Top Three Quartiles Constant Excludes Savers Pair Effects Information on Top Three Quartiles

N

EP

Adoption Rate of Savings Prone x Top Three Quartiles

TE D

[F-Test p-value] Observations Panel B: Two-Stage Least Squares Adoption Rate of Savings Prone (Educated)

Top Three Quartiles

AC C

Constant Excludes Savers Pair Effects Adoption Rate Impact on Top Three Quartiles Observations

(2)

0.156*** 0.158*** (0.0493) (0.0528) 0.102*** 0.103*** (0.0317) (0.0340) -0.0749 -0.0650 (0.0488) (0.0540) 0.206*** 0.196*** (0.0269) (0.0292)

Top Three Quartiles

[F-Test p-value]

0.081 [0.002]

No. Transfers Received

RI PT

Receive Transfer

Y N

(3)

(4)

0.288** 0.267** (0.125) (0.128) 0.196** 0.173* (0.0821) (0.0875) -0.0701 -0.0616 (0.122) (0.135) 0.370*** 0.362*** (0.0706) (0.0757) N

1,954

1,954

1,954

0.206 [0.014] 1,628

1.415** (0.643) -0.839 (0.576) 0.230* (0.125) -0.0333 (0.139)

1.477** (0.671) -0.776 (0.593) 0.223* (0.122) -0.0388 (0.136)

2.611** (1.328) -1.065 (1.185) 0.340 (0.274) -0.0713 (0.296)

2.494* (1.301) -0.949 (1.225) 0.318 (0.270) -0.0355 (0.276)

N

Y N

N

Y N

1,954

1.546 [0.026] 1,954

0.136*** (0.0432)

N

0.218 [0.013]

0.702 [0.015] 1,628

(5)

Y N

0.093 [0.002] 1,628

0.577 [0.013]

2SLS First Stage Local Adoption Rate

1.546 [0.036] 1,628

Notes: Dependent variables are an indicator for receiving any cash aid during the hungry season (columns 1-2) and the number of times cash aid was received (columns 3-4). Panel A reports linear estimates of the reduced-form effect of the savings encouragement. Panel B reports two-stage least squares estimates of the effect of the local savings uptake rate among the educated (first stage estimate in column 5). Pair effects omitted. Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

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APPENDIX TABLE A.14 EFFECT OF LOCAL SAVINGS ADOPTION ON TRANSFER RECEIPTS IN PRE-HARVEST SEASON – EXCLUDING SAVERS Receive Transfer (2)

(3)

(4)

0.115*** (0.0202)

0.110*** (0.0260) 0.100** (0.0437) 0.0274 (0.0627)

0.111*** (0.0254)

0.242*** (0.0582)

Educated Information x Educated High Propensity Saver

0.102** (0.0435) 0.00476 (0.0585)

Excludes Savers Information on Educated

Y

Y 0.137 [0.007]

[F-Test p-value] Information on High Propensity Saver [F-Test p-value] 1,628

1,625

0.868*** (0.218)

0.787*** (0.221) 0.541 (0.623) 0.00427 (0.134)

Adoption Rate of Savings Prone x Educated

High-Propensity Excludes Savers Adoption Rate on Educated

AC C

Adoption Rate of Savings Prone x High-Propensity

EP

Educated Adoption Rate of Savings Prone (High Propensity)

Y

[F-Test p-value] Adoption Rate on High Propensity Saver

Y 1.328 [0.026]

[F-Test p-value] Observations

Y

0.116 [0.015] 1,645

TE D

Observations Panel B: Two-Stage Least Squares Adoption Rate of Savings Prone (Educated)

M AN U

Information x High Propensity Saver

1,628

1,625

(5) 0.200*** (0.0595) 0.175** (0.0856) 0.166 (0.140)

Y

Y 0.366 [0.006]

1,628

1,625

1.825*** (0.532)

1.457*** (0.482) 2.166 (1.515) -0.197 (0.305)

0.854*** (0.277) 1.146 (1.239) -0.128 (0.257) Y 1.999 [0.113] 1,645

(6)

RI PT

(1)

SC

Panel A: Reduced Form Estimates Information Treatment

No. Transfers Received

0.206*** (0.0594)

2SLS First Stage Local Adoption Rate Educated High Prop (7) (8) 0.133*** (0.0311)

0.112*** (0.0357)

1,628

1,649

0.237*** (0.0871) 0.0718 (0.139) Y 0.278 [0.034] 1,645

1.586*** (0.586) 3.242 (2.954) -0.396 (0.600) Y

1,628

Y 3.623 [0.018] 1,625

Y 4.828 [0.107] 1,645

Notes: Sample restricted to households without formal savings. Dependent variables are an indicator for receiving any cash aid during the hungry season (columns 1-3) and number of times cash aid was received (columns 4-6). Regressions include pair fixed effects. Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

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APPENDIX TABLE A.15 EFFECTS OF INFORMATION INTERVENTION ON FORMAL SAVINGS – SUBSAMPLE REGRESSIONS

Mean in Control Villages

(2) Has Formal Savings

0.111** (0.0461)

0.127*** (0.0305)

0.236

Pair Fixed Effects Observations Panel B:Non- Educated Head Information

635

SC

0.0178 (0.0246)

RI PT

Panel A: Educated Head Information

(1) Has Formal Savings

0.0976

Pair Fixed Effects Observations

1,373

M AN U

Mean in Control Villages

Y 635

0.0151 (0.0150)

Y 1,373

AC C

EP

TE D

Notes: Estimates from linear regressions of formal savings account ownership. The dependent variable is an indicator equal to 1 if the household has a formal account in 2010. Panel A includes only households with educated heads and panel B includes only households without educated heads. Cluster-robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

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APPENDIX TABLE A.16 EFFECTS OF FORMAL SAVINGS ON CROP INVESTMENTS AND INCOME – SUBSAMPLE REGRESSIONS

Panel A: Intention to Treat, Educated Information Treatment Mean in Control Villages Controls Observations

7,790*** (2,282) 15,025

Mean in Control Villages Controls Observations

-396.0 (1,106) 10,832

Panel C: Treatment on the Treated: Educated Has Savings Controls Observations

613

M AN U

1,354

14,430*** (4,438) 29,992

-1,758 (2,274) 22,485

SC

605

Panel B: Intention to Treat, Non-Educated Information Treatment

Crop Sales Income (MK) (2) (3) 18,961*** (4,549)

RI PT

Fertilizer (MK) (1)

1,367

62,721*** (19,349)

118,102*** (36,388)

605

613

Y 603

-995.1 (2,052) Y 1,327

127,404*** (29,718) Y 603

AC C

EP

TE D

Notes: Estimates from linear regressions of fertilizer expenditures (column 1) and crop sales (columns 2-3). Exchange rate was approximately 150 Malawi Kwacha to US $1 during the survey period in 2010. Households with missing crop production values omitted. For column 3, households missing values for controls also omitted. Panels A and C restrict the sample to households with educated heads and panel B restricts the sample to households without educated heads. Cluster-robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

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TABLE A.17 TRANSFER RECEIPT EFFECTS ON FOOD-CONSUMPTION AND HEALTH – SUBSAMPLE REGRESSIONS HDDS Food-Access Score

(2)

(8)

(9)

0.673*** (0.258) 5.47

0.781*** (0.272) 5.36 Y 333

0.0843** (0.0394) 0.128

0.0945** (0.0407) .124 Y 336

347

Panel B: Bottom Quartile Information

0.639*** (0.235) 5.69

Mean in Controls Excludes Savers Observations Panel C: Non-Educated Information

M AN U

431

0.784*** (0.243) 5.55 Y 408

RI PT

Mean in Controls Excludes Savers Observations

(1)

0.384*** (0.128) 6.50

Mean in Controls Excludes Savers Observations

1,317

350

0.0777** (0.0345) 0.147

SC

Panel A: Bottom Quartile and Non-Educated Information

No One in Household is Unwell

0.454*** (0.125) 6.28 Y 1,174

436

-0.0224 (0.0195) 0.181 1,334

0.0883** (0.0352) 0.138 Y 412 -0.0274 (0.0215) 0.186 Y 1,190

AC C

EP

TE D

Notes: Dependent variables are endline food consumption as measured by the Household Dietary Diversity Score (HDDS) – a food security metric running from 0 to 12 – and an indicator for whether the household reported that all members were well (no individuals unwell) at the time of the endline survey. All panels report linear estimates of the reduced-form effect of the village-level savings encouragement. Sample slightly smaller in columns 1-2 due to a few missing values for HDDS. Cluster-robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

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Highlights Informational village visits increase formal savings adoption.

•

Savings expansion raises household transfers out by adopters and in by non-adopters.

•

Evidence points to crop production increases among adopters as the mechanism.

•

The largest spillover benefits of more access to transfers accrue to the worst-off.

•

Spillovers are linked to better food intake and health indicators of the very poor.

AC C

EP

TE D

M AN U

SC

RI PT

•