metadata: authors: 0: author: Miller,Margaret J.
1: author: Reichelstein,Julia Elaine
2: author: Salas Pauliac,Christian Henri
3: author: Zia,Bilal Husnain
count: World
docna: 0: docna: Can you help someone become financially
            capable ? a meta-analysis of the literature
docty: Policy Research Working Paper
subtopic: Pensions & Retirement Systems,Educational Sciences,Financial Sector Policy,Financial Literacy
teratopic: Education,Finance and Financial Sector Development
abstracts: cdata!: This paper presents a systematic and
            comprehensive meta-analysis of the literature on financial
            education interventions. The analysis focuses on financial
            education studies designed to strengthen the financial
            knowledge and behaviors of consumers. The analysis
            identifies 188 papers and articles that present impact
            results of interventions designed to increase
            consumers' financial knowledge (financial literacy) or
            skills, attitudes, and behaviors (financial capability).
            These papers are diverse across a number of dimensions,
            including objectives of the program intervention, expected
            outcomes, intensity and duration of the intervention,
            delivery channel used, and type of population targeted.
            However, there are a few key outcome indicators where a
            subset of papers are comparable, including those that
            address savings behavior, defaults on loans, and financial
            skills, such as record keeping. The results from the meta
            analysis indicate that financial literacy and capability
            interventions can have a positive impact in some areas
            (increasing savings and promoting financial skills such as
            record keeping) but not in others (credit default).
repnme: repnme: Can you help someone become financially
            capable ? a meta-analysis of the literature
display_title: Can you help someone become
            financially capable a meta-analysis of the literature
listing_relative_url: /research/2014/01/18807418/can-help-someone-financially-capable-meta-analysis-literature
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Education m1327048 470,Financial Capability m1327248 1255,Retirement m1504007 2896,Financial Literacy m1504979 3391
publishtoextweb_dt: 2014-01-01T00:00:00Z
historic_topic: Education,Finance and Financial Sector Development
no_of_pages: 77
author_keys: 0: author_key: 95543
1: author_key: 1252753
2: author_key: 1152208
3: author_key: 642766
model_extractions: WPS6745 Policy Research Working Paper 6745 Background Paper to the 2014 Global Financial Development Report Can You Help Someone Become Financially Capable?

A Meta-Analysis of the Literature _Margaret Miller Julia Reichelstein Christian Salas Bilal Zia_ The World Bank Development Research Group Finance and Private Sector Development Team & Financial and Private Sector Development Financial Inclusion and Infrastructure Practice January 2014
Policy Research Working Paper 6745

# **Abstract**

This paper presents a systematic and comprehensive meta-analysis of the literature on financial education interventions. The analysis focuses on financial education studies designed to strengthen the financial knowledge and behaviors of consumers. The analysis identifies 188 papers and articles that present impact results of interventions designed to increase consumers’ financial knowledge (financial literacy) or skills, attitudes, and behaviors (financial capability). These papers are diverse across a number of dimensions, including objectives of the program intervention, expected outcomes, intensity and duration of the intervention, delivery channel used, and type of population targeted. However, there are a few key outcome indicators where a subset of papers are comparable, including those that address savings behavior, defaults on loans, and financial skills, such as record keeping. The results from the meta analysis indicate that financial literacy and capability interventions can have a positive impact in some areas (increasing savings and promoting financial skills such as record keeping) but not in others (credit default).

This paper is a product of the Finance and Private Sector Development Team, Development Research Group; and the Financial Inclusion and Infrastructure Practice, Financial and Private Sector Development. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding authors may be contacted at mmiller5@worldbank.org and bzia@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Can You Help Someone Become Financially Capable? A Meta-Analysis of the Literature**<sup>**1**</sup>

Margaret Miller Julia Reichelstein Christian Salas Bilal Zia<sup>2</sup>

**JEL Codes:** D12, D14, I22, O12 **Sector Board:** FSE

> 1 This paper is part of the background research conducted for the 2014 World Bank _Global Financial Development Report_ on Financial Inclusion. The authors would like to acknowledge comments received on this document from Martin Cihak, from peer reviewers Annamaria Lusardi, David McKenzie, Florentina Mulaj, Valeria Perotti, Oya Pinar and Siegfried Zottel and from Caio Piza and other participants at a May 15 GFDR seminar on this topic. Research assistance was also provided by Hendrick Chan, Meghan Conway, and Anisha Mudaliar.

> 2 All authors are from the World Bank. Corresponding authors: Margaret Miller (mmiller5@worldbank.org) and Bilal Zia (bzia@worldbank.org)
# **I. Introduction**

A decade ago there was limited interest in the topic of financial literacy. Now this issue is at the top of the policy agenda for national regulators, international organizations, researchers, and private financial institutions. An important reason for the increased attention to financial literacy is the global financial crisis which highlighted the importance of financial knowledge and skills for consumers. Anecdotal evidence from the crisis immediately suggested that people had taken on financial products – and risks – that they did not fully understand. Later empirical studies confirmed this relationship, including Klapper et al. (2012) with data from Russia and Gerardi et al. (2010) who link outcomes in the subprime housing market in the U.S. with consumers’ financial knowledge and skills. Furthermore, Lusardi and Mitchell (2012) present evidence from around the world suggesting that individuals even in developed countries have a difficult time understanding basic financial concepts.

While financial literacy can clearly be a factor in avoiding financial risks, it can also be important for taking advantage of financial opportunities. Studies have shown that financial knowledge is linked to higher levels of retirement planning and savings (Behrman et al., 2012; Alessie, et al., 2011; BucherKoenen and Lusardi, 2011; Lusardi and Mitchell, 2011, 2007); investment decisions such as diversification (Abreu and Mendes, 2010) and investments in equities (Van Rooij, et al., 2011; Christelis, et al., 2010); credit management and satisfaction (Akin et al., 2012); and mortgage performance (Gerardi, et al., 2010; Quercia and Spader, 2008; Ding, et al., 2008).

At the lower end of the financial market where consumers are seeking their first access to financial products and services such as opening basic accounts or borrowing small sums of money, there is also evidence that financial literacy may be important. Cole et al. (2011) find that use of insurance in India and bank accounts in Indonesia are both linked to higher levels of financial literacy. Using data from _Finscope_ in Africa, Honohan and King (2009) likewise find a positive relationship between financial knowledge and use of financial products and services.

There is also ample evidence that financial literacy levels are relatively low across a wide range of countries and negatively correlated with per capita income,<sup>3</sup> hence suggesting that while financial literacy is important for engaging in financial markets, it remains at low levels for many consumers. With this context, it is not surprising that a recent informal global poll of country officials and financial sector experts – the Financial Development Barometer<sup>4</sup> – undertaken for the 2014 World Bank _Global Financial_

> 3 The 2014 World Bank _Global Financial Development Report_ (GFDR) compares national survey data on responses to three common questions used to evaluate financial knowledge – calculation of compound interest, understanding inflation, and diversification of risk. Based on data from more than 30 countries, average response rates were 56% correct for the question on compound interest, 63% for the question on inflation, and 48% for the question on risk diversification. Lower income countries scored significantly lower than high income countries - when higher income OECD countries are evaluated separately from the rest of the sample, the difference between the percent correct on compound interest is more than 20 percentage points (OECD at 65.5%, lower income countries at 44.5%).

> 4 The Financial Development Barometer is an informal poll available on-line through the website for the Global Financial Development Report. In total, officials from 21 developed and 54 developing economies participated in the survey. From 265 polled, 161 responded, a 61 percent response rate.

2
_Development Report_ (GFDR) identifies financial education as the leading response to the question “What is the most effective policy to improve access to finance among low-income borrowers?” (See Figure 1.) What still remains to be proven is whether this faith in financial education is substantiated by evidence of impact. Are there approaches to teaching financial skills or modifying financial behaviors through educational programs, training, or other outreach activities which have reliable, positive results? The objective of this paper is to analyze the evidence of impact for financial literacy and capability interventions through a systematic review of the evidence. The review includes the use of meta-analysis, a statistical technique that pools data from different studies to test for significance in the enlarged sample of observations this creates. This paper is also different from most previous narrative reviews in focusing exclusively on research that analyzes the impact of financial education interventions. Key characteristics of 188 papers are coded to create a rich data set with the characteristics of the interventions as well as statistical information on the impact of programs on outcome variables such as general savings, retirement savings, and credit performance. This data set is then used for a descriptive analysis of the literature and for empirical tests using meta-analysis.

More than 140 of the 188 studies identified through this review indicate that financial education can be helpful in improving financial outcomes, although it is important to note that most of these employ nonrigorous empirical methods and may suffer from selection bias or other econometric concerns. Some of the more recent studies employ rigorous analytical tools such as randomized control trials (RCTs), and the impacts reported across these papers are more reliable. Examples of positive impacts come from Cai et al. (2013) who find that financial education sessions for rural farmers increase take-up rates for insurance in China. In South Africa, financial messages delivered through a popular soap opera are shown to improve desirable financial behaviors such as borrowing from formal financial institutions rather than from higher cost options such as retailers (Berg and Zia, 2013). In India, Sarr et al. (2012) find that financial education increases the use of a no-frills savings account even months after the intervention ended. In the U.S. a non-RCT study of the Money Smart financial education curriculum by the FDIC (2007) finds that participants are more likely to open deposit accounts, save money, and adhere to a budget.

However, our review of the literature also finds numerous papers (approximately 40 in our sample) citing either no impact or only a modest impact from the intervention which, may not justify the cost of financial education. Cole et al. (2011) for example, find that a financial education intervention among unbanked consumers in Indonesia is less effective at stimulating savings accounts than a small monetary incentive. Likewise, results from a media intervention in Kenya that included comics with financial literacy messages find no significant impact on key variables including savings rates (Eissa et al., 2013). In the U.S., Cole et al. (2013) evaluate mandated personal finance courses in high schools and find that they have no effect on financial outcomes, while training in mathematics is shown to benefit students through greater levels of financial market participation, more investment income, and better management of debt. Similarly, Hung and Yoong (2010) study retirement savings behaviors of adult populations in the U.S. and find that unsolicited financial advice has no impact on savings and investment decisions.

3
Pooling and systematically studying these varied impacts through meta-analysis has the potential to provide valuable policy insight on what works in financial education, as well as help identify where the research gaps lie. Yet, the diversity of the research in this field thus far makes such comparison – and drawing conclusions on effectiveness – difficult even if it is a logical response to the varied and constantly evolving needs in this area.<sup>5</sup> Variation in context, purpose and duration of training, target populations, and outcome measures is evident in Appendix 1, which presents brief information on each study and in the descriptive statistics for the 188 papers, which are presented in Section III. Nevertheless, our meta-analysis presents some key insights after controlling for observable differences across studies. Importantly, we find that financial education can consistently improve outcomes such as savings and record keeping, but does less well in preventing outcomes such as loan default. These results suggest a role for financial education in improving behaviors where individuals have the ability or slack to exert greater control. Arguably, loan default is imposed by external agencies (banks or other financial providers) and hence can only be avoided secondarily or over the long term if financial education leads to more prudent borrowing decisions. Savings and record-keeping, in contrast, are immediate and primary decisions that can be acted upon by targeted consumers.

The only other paper which uses meta-analysis to evaluate the literature on financial literacy and capability (Fernandes et al., 2013) discusses another potential source of variance in the results for financial education interventions – omitted variables related to psychological traits such as impulse control, delayed gratification, and self-efficacy. They perform meta-analysis on both financial literacy and capability interventions, which they term “manipulated financial literacy” and on observational literature that links levels of financial knowledge or literacy to outcomes (termed “measured financial literacy” by the authors). Their findings indicate that measured financial literacy has a greater impact on financial outcomes than does manipulated financial literacy. They posit that omitted variables are a source of the different results because people with certain psychometric profiles are more likely to engage in activities that increase their financial literacy levels and improve their financial outcomes, but these behaviors (self-control for example) are not the typical focus of financial education interventions which focus on imparting financial knowledge. Yet, while psychometric measures may indeed explain selection into desirable financial behaviors and choices, they are unlikely to be the only omitted category – moreover, it is difficult to identify empirically whether such measures are the only (or even primary) omitted driver of financial choices.

Our paper does not take a stand on the source of statistical bias in observational studies. Instead, we carefully classify and separate observational studies from impact studies, and more than double the number of financial education impact studies which are available for meta-analysis from slightly more than 80 in the case of Fernandes et al. to 188 -- all papers cited in Fernandes et al. are also included in this paper. Another key difference lies in the choice of variables and statistical rigor in the meta-analysis. As indicated previously, there is great diversity in the sample of studies which makes it impossible to

> 5 Over the life cycle there are a number of different financial skills and behaviors that are needed, innovation in financial markets can quickly create demand for new skills or make others irrelevant (such as writing checks) and new technologies are creating new delivery channels for training. Skills required by employed workers in high income countries, such as investment abilities and pension planning, are irrelevant for low income consumers in developing countries, further adding to the diversity of interventions present in a global review.

4
calculate an effect size that is meaningful and comparable across the entire literature, yet Fernandes et al. estimate such an effect size which includes interventions that are fundamentally different across many characteristics such as how outcomes are measured (binary or continuous), targeted populations, and mode of delivery. We take a more conservative approach and carefully screen and compare studies with similar outcome measures and intervention characteristics. This greater precision comes at a cost, however, as we are only able to compare binary outcomes for a subset of studies that pass our comparability screening. However, this careful analysis allows us to classify interventions among key topics such as savings, record keeping, and debt management, yielding more nuanced results in terms of the evidence of impact. Investigating the details of interventions and outcome measures in this way additionally allows us to present important stylized facts about financial literacy programs.

This paper proceeds as follows. Section II describes the systematic review process including a brief discussion of meta-analysis and the rationale for using this tool for such a diverse body of literature. In this context, we look at previous narrative reviews and the literature they cover to help understand why their findings on the impact of financial education have not been consistent. Section II also discusses the search approach used to identify relevant studies in journals, working papers, and other publications, and the inclusion and exclusion criteria. Section III provides information about the data set including both the program descriptions and outcome variables that were identified. Section IV presents the results of the meta-analysis, its potential unique contribution, and limitations. Section V concludes. Appendices 1 and 2 provide supplemental materials on the meta-analysis, and Appendix 3 summarizes ways to strengthen the research protocols of financial literacy studies going forward.

# **II. The Systematic Review Process Including Meta-Analysis**

This paper uses a systematic review process, including meta-analysis, to compare and contrast the findings of a large body of literature on the impact of financial literacy and capability interventions. The systematic review includes five steps:

- i) Hypothesis

- ii) Search approach and inclusion / exclusion criteria

- iii) Collection and coding of data

- iv) Statistical analysis (meta-analysis)

- v) Conclusions

A systematic review of a diverse body of literature helps to identify patterns in the data and develop insights on the nature and quality of divergent evidence. The descriptive statistics that are presented in Section III based on the coded data from the 188 studies of financial education interventions provide valuable insights on what has been studied thus far, the research methods which have been used, and initial insights on the evidence. However, there are limits to what can be gleaned from these types of rough comparisons.

Meta-analysis was developed to facilitate a statistically rigorous comparison of data across independent studies. By pooling data and statistical information across studies, papers which may individually point 5
to inconclusive or contradictory results may together yield a statistically significant finding. If there are adequate data available on means and standard deviations in the individual research papers, it may also be possible to identify an effect size which indicates the magnitude of change that may be expected.

There are two general types of meta-analysis models: fixed effects and random effects. Fixed effects models assume that the studies are all roughly equivalent in terms of the intervention studied and thus are estimating the same outcomes. This means that data can be pooled and that larger studies with more observations are more highly weighted than smaller ones. Medical drug trials are common forms of fixed effects meta-analysis since the interventions are identical (provision of a drug or pill) and the outcomes are similar, observable, and uniformly measurable.

Random effects models estimate the mean of a distribution of true effects but assume that each study is measuring a different effect size as the interventions and / or populations are not equivalent. Weights in the random effects models are more balanced across studies and those with large sample sizes do not dominate the results as they would in a fixed effects model. There is only one source of error in a fixed effects model – random error – and as the sample size grows this tends toward zero. In the random effects model, however, there are two sources of error – random error within populations and error in estimation of the true effect size across studies. This means that a large number of observations in a study address only the first kind of error – within population – and not the estimation error across studies, which requires a robust number of studies to increase the precision of the estimate.

The diverse nature of the underlying studies on financial literacy and capability clearly leads to the use of a random effects model. When looking at the available studies and data for this exercise, it is reasonable to assume that there are still substantial sources of error from both limited population sizes for specific interventions and inadequate data on the scope of interventions and studies which are available for analysis. The effect sizes that are estimated in the regression analysis in Section IV, therefore, should not be taken as a true measure of effect size for financial literacy and capability interventions. This is not to say that the measures of effect size contain no information. They provide an aggregate measure of impact and are indicative of the state of evidence, but at the same time should not be seen as definitive proof for or against the null hypothesis (defined below) on financial education activities. More technical details on the estimation model used for this study are available in Appendix 2.

**Hypothesis** – The null hypothesis for this study assumes that financial literacy and capability interventions do not affect the financial knowledge and/or financial outcomes of people who are subject to the treatment.

**Search approach and inclusion / exclusion criteria** – For this paper we undertook a comprehensive search of a particular segment of the literature on financial literacy and capability – papers which evaluate the impact of interventions designed to strengthen financial knowledge and behaviors. A broad definition of “financial literacy and capability interventions” was used for this review, which included any kind of intervention (intentional or not) which would impact financial knowledge, attitudes and/or behaviors for individuals.

6
We identified papers meeting the criteria through several sources:

- 1) Search of peer-reviewed papers in Econlit under the broad terms _financial awareness, financial capability, financial competence, financial education, financial knowledge_ and _financial literacy_ between January 2000 and September 2013.

- 2) Search of papers included in previous literature reviews (starting with literature reviews published in 2007 – see table below).

- 3) Recent studies completed within the World Bank, many of which are listed in the website finlitedu.org.

- 4) Websites that are likely to include relevant studies, including the OECD, World Bank, Global Partnership for Financial Inclusion, and Alliance for Financial Inclusion (AFI).

The process of evaluating studies for inclusion or exclusion was performed initially through a review of paper abstracts in the case of both papers identified through Econlit and those cited in previous literature reviews. In the case of World Bank studies and research found on websites the papers themselves were typically available and reviewed directly. All papers that reported on an intervention are included in Appendix 1, even those which lacked sufficient rigor or statistical results for use in the meta-analysis portion of this paper. Descriptive information from these papers is included, where possible, in the descriptive statistics presented in the following section of the paper.

In order to reduce the number of studies to review to a manageable size and to focus the evidence on research results which had been screened for quality, only articles from peer reviewed journals were included from Econlit, for the period January 2000 to September 2013. The reasons for exclusion for papers located through Econlit searches are presented in Table 1.

|**Reasons for Exclu**<br>|**Reasons for Ex**<br>**sion**<br>**Papers Included**<br>**Measurement of Fi**<br>**Financial Ca**<br>**Sur**<br>**Exclusions**<br><br><br>|**Table 1**<br>**clusion of Research Papers Identified Through Econlit**<br>**nancial Literacy /**<br>**pability**<br>**vey data linking financial literacy**<br>**to an outcome (no intervention)**<br>**Survey data linking financial literacy**<br>**to retirement (no intervention)**<br>**[This is a subset of previous column]**<br>**Business training**<br>**Descriptive or Policy Paper**<br>**Theoretical Paper**<br>**Evaluation Published Previously or**<br>**Survey Article**<br>**Not About Financial Literacy or**<br>**Capability**<br>**Other**<br>**TOTAL Articles**<br>**for Econlit Searches(January 2000 to September 2013)**<br><br><br><br><br><br><br><br>|
|---|---|---|
|**Financial Attitudes**|**6**<br>**1**<br>**5**|**0**<br>**0**<br>**0**<br>**0**<br>**0**<br>**49**<br>**2**<br>**63**|
|**Financial Awareness**|**0**<br>**0**<br>**5**|**0**<br>**0**<br>**8**<br>**0**<br>**0**<br>**16**<br>**0**<br>**29**|
|**Financial Capability**|**2**<br>**3**<br>**6**|**1**<br>**0**<br>**1**<br>**1**<br>**1**<br>**62**<br>**0**<br>**77**|
|**Financial Competence**|**0**<br>**0**<br>**2**|**0**<br>**0**<br>**3**<br>**0**<br>**0**<br>**15**<br>**0**<br>**20**|
|**Financial Education**|**34**<br>**11**<br>**17**|**2**<br>**7**<br>**14**<br>**14**<br>**8**<br>**229**<br>**5**<br>**341**|
|**Financial Knowledge**|**17**<br>**14**<br>**22**|**9**<br>**4**<br>**5**<br>**4**<br>**1**<br>**161**<br>**1**<br>**238**|
|**Financial Literacy**|**14**<br>**13**<br>**47**|**17**<br>**2**<br>**26**<br>**6**<br>**7**<br>**5**<br>**12**<br>**149**|

Many of the excluded papers analyzed the importance of financial literacy or knowledge on various financial and non-financial outcomes. Using the terminology in Fernandes et al. (2013), these are 7
“measured financial literacy” papers. They were excluded because they did not test an intervention but they do provide valuable insights on the importance of financial literacy and knowledge on outcomes. Studies were included when they discussed any program, educational outreach, media intervention or other type of communication or training for consumers which could either strengthen knowledge (financial literacy) or modify attitudes and behaviors (financial capability).

One of the insights from the Econlit search on these key terms is that a relatively small percentage of the literature involves an evaluation of some kind of financial literacy or capability intervention or program. Even for the terms “financial education” and “financial literacy” only about 10% of the studies in these categories evaluated the effectiveness of an intervention. Searches of “financial awareness” and “financial competence” yielded no impact evaluations and even the widely used term “financial capability” produced only two papers for the database out of a total number of 77 citations. With the exception of the search for “financial literacy,” a vast majority of the papers listed under the searches conducted on Econlit are not about personal financial literacy or capability.

The review of nine previously published narrative reviews identified more than 500 unique references which included more than half of the 188 papers that are listed in Appendix 1. The references cited by these nine narrative literature surveys, however, have relatively little overlap. This can be seen with greater precision in Table 2 below which presents the Pearson correlation coefficients and associated R<sup>2</sup> terms for reference lists across the literature survey papers.<sup>6</sup> In only two instances do the survey papers in Table 2 have a correlation coefficient above 0.2 (Martin 2007 with Hathaway and Khatiwada 2008 and Collins and O’Rourke 2010 with Agarwal et.al. 2010), which is already a relatively low level of correlation. The R<sup>2</sup> terms in parentheses (which is simply the squared term of the correlation coefficient) are sometimes called the coefficient of determination and provide another indication of the strength of the relationship among the variables. Approximately 55% of the R<sup>2</sup> figures are below .01 indicating a very weak relationship and relatively little overlap across studies.

> 6 A matrix was created based upon the references in each of these papers, for a total of 536 unique citations. For each of the nine literature reviews, a “1” or “0” was entered to indicate whether the reference was cited in the review. This matrix was then used to calculate the Pearson correlation coefficients and r<sup>2</sup> terms.

8
# **Table 2**

**Evaluating similarities between recent literature surveys on financial education (Pearson Correlation Table)**

|**Referen**|**ces**|**Martin 2007**<br>**Hatha**|**way and Khatiw**<br>|**ada 2008**<br>**McCormick 2009**<br>**A**|<br>**garwal et al 201**<br>**Colli**|**0**<br>**ns and O’Rourke 2010**<br>**Gale and Levine 2010**<br>**Hastings, Madrian, Skimmyhorn 2012**<br>**Xu and Zia 2012**<br>**Lusardi and Mitchell 2013**|
|---|---|---|---|---|---|---|
|**Martin 2007**|1.000||||||
||(1.000)||||||
|**Hathaway and**|0.416|1.000|||||
|**Khatiwada 2008**|(0.173)|(1.000)|||||
|**McCormick 2009**|-0.067|0.001|1.000||||
||(0.004)|(0.000)|(1.000)||||
|**Aarwal et al 2010**|0.118|0.097|-0.017|1.000|||
|**g**|(0.014)|(0.009)|(0.000)|(1.000)|||
|**Collins and**|0.161|0.104|0.026|0.425|1.000||
|**O’Rourke 2010**|(0.026)|(0.011)|(0.001)|(0.181)|(1.000)||
|**Gale and Levine**|0.171|0.190|-0.088|0.174|0.047|1.000|
|**2010**|(0.029)|(0.036)|(0.008)|(0.030)|(0.002)|(1.000)|
|**Hastings, Madrian,**|-0.057|0.009|-0.060|0.075|0.036|0.084<br>1.000|
|**Skimmyhorn 2012**|(0.003)|(0.000)|(0.004)|(0.006)|(0.001)|(0.007)<br>(1.000)|
|**X d Zi 2012**|-0.077|-0.099|-0.065|-0.088|-0.100|-0.083<br>-0.175<br>1.000|
|**u an a**|(0.006)|(0.010)|(0.004)|(0.008)|(0.010)|(0.007)<br>(0.031)<br>(1.000)|
|**Lusardi and**|-0.104|-0.038|-0.155|-0.105|-0.167|0.039<br>-0.002<br>-0.217<br>1.000|
|**Mitchell 2013**|(0.011)|(0.001)|(0.024)|(0.011)|(0.028)|(0.002)<br>(0.000)<br>(0.047)<br>(1.000)|
|**Total Number of**|||||||
|**References**|44|35|45|82|57|61<br>100<br>132<br>187|

The limited overlap in terms of the literature they are reviewing helps to explain the variety of findings in these narrative literature reviews. Martin (2007), at the Federal Reserve Bank of Richmond, provides one of the earliest reviews and has a limited number of references (44). He assesses the evidence positively, writing “Generally, we can conclude from this literature review that there is a need for financial education and that many existing approaches are effective. More specific conclusions might be best described as tentative given the current scarcity of research in some areas.”<sup>7</sup> Just one year later another literature review published by the Federal Reserve Bank of Cleveland (Hathaway and Khatiwada 2008) came to a much less positive conclusion using many of the same research papers. Hathaway and Khatiwada (2008) state, “Unfortunately, we do not find conclusive evidence that, in general, financial education programs do lead to greater financial knowledge, and ultimately, to better financial behavior. However, this is not the same as saying that they do not or could not – it is just that current studies, while at times illustrating some success, leave us with an unclear feeling about whether we can grant a blanket application of these results specifically, to financial education programs more generally.”<sup>8</sup>

> 7 Martin (2007), page 22.

> 8 Hathaway and Khatiwada (2008), page 19.

9
Several years later, Gale and Levine 2010 also find little evidence of impact, stating, “None of the four traditional approaches to financial literacy – employer-based, school-based, credit counseling, or community-based – has generated strong evidence that financial literacy efforts have had positive and substantial impacts.”<sup>9</sup> Still, Gale and Levine 2010 recommend further work in this area, since they find financial planning to be beneficial as well as simplification initiatives to facilitate decision making which public policy can support. Lusardi and Mitchell 2013 provide the most comprehensive and recent of the narrative literature reviews with nearly 200 references. They offer a cautious endorsement of financial education activities but also refer to the issues of endogeneity and omitted variables, “Research on efforts to enhance financial literacy suggest that some interventions work well, but additional experimental work is needed to explore endogeneity and establish causality.”<sup>10</sup> While the search on Econlit was limited to papers that had appeared in peer reviewed journals, this exclusion criteria was not used for the many articles identified through the survey papers or from other key sources (websites, World Bank research) in order to increase the coverage of available research on this topic. Many of the papers included in the analysis have been written in the past few years and thus many (about one-third) are still in working paper format (see Figure 2). Apart from universities, the NBER and the World Bank are the main sources of working papers cited here.

<!-- Start of picture text --> Figure 2<br>Type of Publication<br>(n=188)<br>38%<br>34%<br>28%<br>Peer-Reviewed Journal Academic Working Paper Non-Academic Publications<br>& Books<br><!-- End of picture text --> There are also numerous papers, briefs, and reports which are released by the many public, private, and non-profit organizations involved in this topic but which are not published through the academic press and subject to peer review. Known as “gray literature” these documents include research reports written by academics and researchers in think-tanks evaluating specific curricula using statistical methods and are thus also included in this literature search. However, since the financial education

> 9 Gale and Levine (2010), page 2.

> 10 Lusardi and Mitchell (2013), page 49.

10
provider may often also be the funder for the evaluation, these papers are identified separately from working papers published through academic institutions where such conflicts of interest are less likely to occur.

**Publication Bias –** The first step in any systematic review involves the identification of relevant research so that conclusions are based on a comprehensive body of evidence which is not biased in some important way. If researchers or journal editors have a tendency to publish studies with a certain type of result (for example, in this case studies which show that financial education programs are effective in changing behavior, rather than studies showing no impact) this can result in one form of publication bias. The fact that many studies in the financial education literature (and in social science research more generally) involve little-known research projects with small sample sizes further exacerbates this problem since the broader community of practice may not be aware of instances where research results are not published or widely disseminated. By contrast, medical research trials which involve large public grants and large sample sizes are under more scrutiny and pressure to share results. Including studies from the “gray literature” which have not been published in journals can help to reduce the impact of this type of publication bias.

In addition to including gray literature in the systematic review, this paper reviews all of the impact evaluations cited in previous narrative literature surveys as well as all interventions in the recently released meta-analysis by Fernandes et al. (2013). Further, papers were identified through searches of broad terms in Econlit, and on websites and through World Bank research. As such, this paper encompasses a broader, and more comprehensive, analysis of the literature than previous reviews, which reduces the likelihood of this type of publication bias. Appendix 2 also presents evidence from an econometric test (metafunnel in Stata) for publication bias to help show the extent to which the review may be affected by missing studies.

Failing to publish the results from research is perhaps the most well-known type of publication bias, but it is not the only one. Other types of publication bias (or dissemination bias) which can negatively impact a systematic review include language bias (including only research in a certain language); availability bias (limiting consideration to research which is readily available to the researcher); cost bias (including only research papers that are available for free or at a low cost); familiarity bias (limiting the research to papers in one’s own discipline); and outcome bias (reporting selectively on the empirical data in papers to highlight certain types of results) (Rothstein, Sutton and Borenstein 2005). These various types of publication bias are not considered significant issues for this review. In terms of language bias, the vast majority of research on this topic internationally has been done in the United States (perhaps due to the greater level of consumer lending and less heavily regulated financial markets) and in the English language. Further, the search process did not use language as a criterion for inclusion and where nonEnglish language research was identified, efforts were made to include these studies. In terms of availability, studies on financial literacy and capability are generally publicly available through online databases available to academic researchers or on websites of relevant NGOs, public agencies or multilateral organizations. No study was eliminated due to a cost consideration and in some cases authors were contacted directly to request copies of articles not readily available on-line (in a few instances of older research articles). Also, this review extends beyond typical economics journals to 11
include research published by academics in consumer and family sciences, education, sociology and psychology. All empirical findings are reported where a financial intervention was an explanatory variable in a regression model explaining a financial outcome or behavior, including financial knowledge gains.

# **III. A Systematic Review of Financial Literacy and Capability Interventions Using Meta-Analysis**

The extensive literature search described in the previous section resulted in 188 journal articles, reports, and other publications that analyze the effectiveness of financial education interventions. The rigor required for meta-analysis of this literature resulted in a unique database that enables us to more precisely describe the types of financial education programs or interventions which have been evaluated as well as their reported impacts.

The main relationship that is tested through meta-analysis in this paper is whether financial education interventions have an impact on outcome variables of interest such as general savings levels, retirement savings, record keeping, and credit performance. If data were available outcome variables could extend to other economic indicators of well-being such as total assets, consumption, or income; or on the other hand, to measures of vulnerability such as declaring bankruptcy or losing major assets such as a home or land.

The key characteristics of financial education interventions are also recorded. In the context of metaanalysis these data are referred to as moderator variables because they help to explain the strength of the relationship between the predictor variable (in this case financial education) and the outcome. Moderator variables which are collected for this paper include information on the financial topic which is addressed (i.e. savings, credit, etc.), the type of delivery channel used (classroom instruction, mass media, etc.), the country where the intervention occurred, the location of the intervention (school, workplace, etc.), the number of hours of instruction, whether teacher training was involved, and whether the intervention focused narrowly on the individual or extended to the family or broader social setting. Another important data point which was collected through this exercise concerned the evaluation methods used. In particular, the dataset distinguishes whether studies used a randomized controlled trial (RCT) design.

# **The Dataset – Moderator Variables**

Until 2008 the vast majority (98%) of studies on financial education interventions which were identified through this search focused on the United States. This started to change after 2008, when the financial crisis focused attention on the importance of this topic not only in the US but around the world (See Figure 3). The World Bank also contributed to the growth of literature on the impact of financial education interventions in developing country markets with the institution’s researchers contributing to more than 20 papers on this topic since 2008. As shown in Figure 4, Africa, Asia, and Latin America are the regions outside the United States with the largest shares of studies in our sample.

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<!-- Start of picture text --> 54%<br>88 of 90<br>53 of 98<br>studies<br><!-- End of picture text --> <!-- Start of picture text --> Figure 4<br>Region in which Intervention Conducted<br>(n=188)<br>Africa<br>2% 6%<br>Asia<br>9%<br>2% Europe<br>6% Latin America<br>USA<br>Other<br>75%<br><!-- End of picture text --> There is a wide range of financial topics covered by financial education interventions in the literature which were grouped into six categories in this paper (Figure 5). Interventions which provide information on a variety of financial issues, coded as “mixed” interventions in the data, are the most common type and represent more than 40% of the total sample. Financial literacy programs in the U.S. are most likely to involve either multiple messages (mixed) or focus on savings and retirement. In developing countries 13
courses that include business management are among the most common in our sample together with those involving multiple messages with each comprising about one-quarter of the sample, with savings and retirement messages a close third.

<!-- Start of picture text --> Figure 5<br>Topic of Financial Education<br>(n=188)<br>50%<br>43%<br>40%<br>30%<br>30%<br>20%<br>9% 7%<br>10%<br>5% 6%<br>0%<br>Mortgage Credit Counseling Business Savings/Retirement Mixed Other<br>Management<br><!-- End of picture text --> As is evident in Figure 6, the majority of interventions in this sample involve direct contact with an instructor / advisor typically in a classroom or seminar setting, with a significantly smaller share (9%) involving individualized counseling. In terms of the cost per person reached, these kinds of interventions are likely to be the most expensive. Most interventions studied in the literature also focus on programs using only one delivery channel – classroom, video, phone advice, etc. – although there is widespread awareness in the communications field that a more comprehensive approach using multiple types of media (often referred to as a transmedia or 360<sup>o</sup> strategy) is more effective. A relatively small number of interventions in the evaluation literature look at use of mass media. These include use of TV soap operas in Africa and in the U.S., both of which show positive results in increasing awareness and shifting attitudes.

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<!-- Start of picture text --> Figure 6<br>Intervention Channel<br>(n=188)<br>60%<br>54%<br>50%<br>40%<br>30%<br>18%<br>20%<br>9%<br>10% 5% 6%<br>3% 3% 3%<br>0%<br>Course Individual Mass Media Phone Print Online Mixed Other<br>taught in Counseling<br>person<br><!-- End of picture text --> While financial education interventions are often associated with youth and programs taught in schools, these are only a relatively small share of the interventions which have been rigorously studied (18% including both high school and university programs). See Figure 7 for locations where financial education programs are provided.

<!-- Start of picture text --> 24%<br>19%<br>14% 14%<br>11%<br>8%<br>4% 5%<br><!-- End of picture text --> 15
Most programs which have been evaluated are provided through community organizations or in the workplace. Workplace programs almost always relate to a “teachable moment” when employees have an opportunity to immediately use the information they are gaining, such as signing up for benefits or to save part of their salary. Many community based interventions also focus on teachable moments, sometimes related to helping people through challenging financial moments (such as credit counseling for people finding themselves overwhelmed by debt or mortgage counseling for prospective home buyers). Community interventions are almost exclusively focused on low income populations or other groups that are seen as financially vulnerable (battered women for example).

Most of the financial education interventions which have been evaluated are relatively short in terms both of the number of hours of exposure (see Figure 8) and in terms of the period of time during which the exposure occurred (see Figure 9). As can be seen in Figure 8, slightly more than one-third of the interventions (38%) last 10 hours or less and 16% last just 2 hours or less. These shares are probably understated, as many of the interventions labeled as “varied” in terms of hours of exposure likely fit within the 10 hour or less categories.

<!-- Start of picture text --> Figure 8<br>Intensity of Intervention (hours)<br>50% (n=148)<br>43%<br>40%<br>30%<br>20%<br>16%<br>11%<br>11% 11%<br>10%<br>6%<br>2%<br>0%<br>≤2 3-6 7-10 11-20 21-50 >50 Varied<br><!-- End of picture text --> Figure 9 presents information on the duration of interventions and shows that more than one-third are delivered within one week or less and more than one-half of interventions are delivered within 6 months or less. Just as with the classification of intensity, these shares are likely to be underestimates as many of the interventions listed as “varied” are likely to be of relatively short duration (6 months or less). Since repetition is a key element of learning, the limited amount of time typically devoted to interventions may be a factor working against stronger impact results.

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<!-- Start of picture text --> Figure 9<br>Duration of Intervention<br>(n=156)<br>40% 33% 32%<br>30%<br>17%<br>20%<br>7%<br>10% 5% 5%<br>0%<br><!-- End of picture text --> Overall, there has been a noticeable evolution in the literature on financial education interventions over the past five years. The increase in research beyond the United States, mentioned previously, has been accompanied by an increase in more rigorous research methods such as randomized controlled trials (RCTs). As can be noted in Figure 10, RCTs have gone from being relatively rare to frequently used, with more than 40% of papers since 2008 using these rigorous methods. In order to be as comprehensive as possible in reviewing the literature, studies were included which used a variety of research methodologies, not only RCTs.

<!-- Start of picture text --> Figure 10<br>Share of Studies that were RCTs<br>(n=188)<br>45% 43%<br>40%<br>35%<br>42 of 98<br>studies<br>30%<br>25%<br>20%<br>14%<br>15%<br>10% 13 of 90<br>studies<br>5%<br>0%<br>Up to and Including 2008 Post 2008<br><!-- End of picture text --> 17
# **IV. Results from Meta-Analysis**

This section presents the results from the application of meta-analysis statistical techniques on the data assembled on impact evaluations of financial education interventions. Before discussing the specific results it is important to place them in context. The diversity of the literature on financial education interventions means that even where there were common outcome variables (such as likelihood to save) the underlying interventions were each unique, making a direct comparison impossible. The limited number of studies available for evaluation in each of the categories (no more than ten, often closer to five) also reduces the strength of the statistical results. Despite these limitations, it is still useful to systematically and quantitatively evaluate the diverse body of evidence on financial education interventions to provide policy makers with an indication of what is working in financial education and to provide researchers insights on where knowledge gaps lie.

# **Identifying the Issues That Could Be Tested Using Meta-analysis**

The majority of the 188 papers identified through the search as presenting results of financial literacy or capability interventions include potentially useful statistical data that could be used for meta-analysis. Many papers report on multiple regressions which are of interest and have multiple outcome variables or results from empirical tests on population subsets based on criteria such as educational attainment, income level, or score on a financial literacy test. In some cases, there are several types of explanatory variables related to the intervention. There are a total of 839 observations from the 188 papers; however, not all contain usable statistical information.

For the meta-analysis of this paper, outcome (dependent) variables were coded among several major categories based on a review of the variable descriptions (savings, borrowing, financial literacy test scores, account opening, and record keeping). Within these categories there was further disaggregation to ensure comparability across studies for the meta-analysis. For example category “S1” corresponds to the savings category and is a binary variable with a value of “1” if the individual reported having saved, while “S4” is a continuous variable indicating total reported savings during a particular period of time. No effort was made to compare these types of disparate data through meta-analysis. Rather, we used the coding of outcome variables to identify those instances where existing studies had similar impact measurements and thus could be reasonably combined in a meta-analysis. The list of coded outcome variables is provided in Appendix 1.

Once the outcome variables were categorized, the data were analyzed to determine the number of studies using this variable and to further identify those which reported adequate statistical information to be able to be included in the meta-analysis. We selected five studies as a minimum number for the meta-analysis.<sup>11</sup> Further, each study had to provide the coefficient on the financial literacy / capability intervention and the standard error (or other statistical data from which to construct the standard error) and studies had to have comparable measurement criteria for the outcome variables. For example, studies that reported on a change in a financial literacy score were not used because the studies were based on potentially very different tests and scales for measuring financial literacy and thus did not

> 11 In the case of one of the outcome variables – loan defaults – the number of eligible papers dropped to just four after further review due to the exclusion of a study (Quercia and Spader 2008) which used a logit model instead of one employing linear probability.

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represent a true comparison across similar results. This rigorous screening exercise produced just four outcome variables which were used in five or more separate studies.

The four qualifying binary variables (listed below) were analyzed using meta-analysis.

S1 = savings reported in past period (1 if positive) – binary S11 = contributes to retirement savings (1 if positive) -- binary B6 = defaulted on a loan (1 if positive) – binary R1 = keeps financial records / budgets (1 if positive) – binary In addition to the development of forest plots with the 4 outcome variables mentioned above, metaregression analysis was used to determine if specific characteristics of the interventions (the explanatory variables) made significant contributions to results. Due to the small sample sizes these explanatory variables were tested individually on each outcome variable. The intensity of the intervention, measured in terms of number of hours of instruction or exposure, was the only continuous explanatory variable which also was widely reported on (with only one or two exceptions) among the studies used in the meta-analysis. The results from the forest plots and regressions looking at the intensity of the intervention are presented in the next section. Both meta-analysis and meta-regressions are conducted on study-level summary data because individual observations from all studies are not available.

The meta-analysis performed is a two-stage process involving the estimation of a relevant summary statistic for each of a set of studies followed by the calculation of a weighted average of these statistics across the studies (Deeks, Altman, and Bradburn, 2001). In this case, pre-calculated effect estimates and their standard errors from each study are pooled. The meta-analysis includes a forest plot, in which results from each study are displayed as a square and a horizontal line, representing the intervention effect estimate together with its confidence interval. The area of the square reflects the weight the study contributes to the meta-analysis. The combined-effect estimate and its confidence interval are represented by a diamond.

# **Meta-Analysis Results**

The most common single-issue topic for financial education interventions was savings and retirement. The greatest number of comparable papers also addresses this issue. The forest plot from the research papers that address the impact of interventions on savings are shown below in Figure 11. A forest plot graphically displays the treatment effects across multiple studies with a solid line representing the null hypothesis of no effect from the intervention – in this case from exposure to financial education outreach / training. Intensity refers to the number of hours of exposure. Channel refers to the type of intervention / content delivery channel and is coded as follows (1= course taught in person; 2= individual consulting; 3= mass media; 4=phone; 5=print; 6=online; 7=mixed; 8=other).

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# Figure 11

|Papers testing|sa|vings be|<br>havior after financial education inte|**%**<br><br>rvention|
|---|---|---|---|---|
|Authors|Date|Country|Intensity<br>Channel<br>**ES (95% CI)**|**Weight**|
|Cole Sampson Zia|2011|Indonesia|2<br>1<br>**0.01 (-0.07, 0.09)**|**11.54**|
|Drexler Fischer Schoar|2011|Dominican Rep|15<br>1<br>**0.14 (-0.02, 0.30)**|**3.73**|
|Bruhn Ibarra McKenzie|2012|Mexico|4<br>7<br>**0.03 (-0.01, 0.06)**|**24.33**|
|Doi McKenzie Zia|2012|Indonesia|13<br>1<br>**0.10 (0.00, 0.20)**|**8.29**|
|Berg & Zia|2013|South Africa|26<br>3<br>**-0.03 (-0.09, 0.03)**|**15.98**|
|Bruhn Leão et al|2013|Brazil|36<br>1<br>**0.05 (0.04, 0.06)**|**36.13**|
|Overall (I-squared = 54.|5%, p =|0.051)|**0.03 (0.00, 0.06)**|**100.00**|
|NOTE: Weights are from|random|effects analysis|||
||||**0**<br>-.297<br>.297||

The results are presented with the statistical output from the meta-analysis as well as with data on important characteristics of the interventions. The weight assigned to each of the six studies is provided in the far-right column and is based on the reported strength of the statistical result in the various studies. The size of the shaded squares for each study reflects their weight in the meta-analysis. The horizontal line through the shaded square is the confidence interval for the reported results. When this line intersects the vertical line which represents the null hypothesis (no effect from financial education) then the null cannot be rejected. In this particular meta-analysis, only two of the six papers reject the null hypothesis and indicate a positive impact on savings from financial education while four cannot reject the null hypothesis. Taken together, however, the meta-analysis finds that these papers do provide evidence of impact for financial education at the 95% confidence interval. The confidence interval for the pooled data across the six papers is represented by the diamond at the bottom of the graph and is on the right hand side of the null hypothesis, rejecting a finding that financial education has no impact on savings behavior. We lack the data on control group means to determine the true effect size for this finding. The meta-analysis in Figure 11 was based only on data from RCT studies to increase the quality of the data and thus to strengthen the cross-study analysis.

The I-squared statistic, which is also presented in the forest plots, describes the percentage of variation across studies that is due to heterogeneity rather than chance. Thus, the I-squared is useful for determining the consistency of results between studies in a meta-analysis. A lower I-squared would indicate more consistency underlying the results of the sample studies while a higher I-squared would 20
indicate more heterogeneity in this regard. The p-value also provides a way to test for heterogeneity by examining the null hypothesis that all studies in the meta-analysis are evaluating the same effect. A p- value lower than 0.05 would reject this hypothesis at the 5% significance level and indicate that the results of the studies are more heterogeneous.

As seen in Figure 11 (savings behavior), the I-squared was 54.5%, which is not generally considered a low number (indicating consistency) but which is lower than the I-squared terms for the other outcome variables, indicating _relatively_ more consistency among the results for savings. The other meta-analysis results (Figure 12 - retirement savings behavior, Figure 13 - loan default behavior, and Figure 14 - record-keeping behavior) reveal higher I-squared statistics than Figure 11, indicating more heterogeneity between the studies involved in their respective samples.

Meta-analysis to evaluate the impact of financial education interventions on retirement savings produced weaker results and was unable to reject the null hypothesis of no effect on retirement savings at the 95% confidence interval, but it could reject the null when the confidence interval was relaxed to 90% (p=0.077). Five studies were included in this analysis, all based on programs in the U.S., none using randomized controlled trials and only two using exclusively classroom methods. Results are shown in Figure 12.

# Figure 12

|Papers testing|retirem|ent sav|ings behavior after|interventio|n|
|---|---|---|---|---|---|
||||||**%**|
|Authors|Date<br>Country|Intensity|Channel|**ES (90% CI)**|**Weight**|
|Bernheim & Garret|2003<br>USA|Varied|7|**0.12 (0.07, 0.18)**|**22.23**|
|Muller|2003<br>USA|Varied|1|**0.09 (-0.06, 0.24)**|**12.98**|
|Lusardi and Mitchell - ALP article|2007<br>USA|Seminars|NA|**0.23 (0.13, 0.33)**|**17.60**|
|Hung Yoong|2010<br>USA|Varied|2|**0.06 (0.01, 0.11)**|**22.98**|
|Beshears, Choi, Laibson, Madrian, Milkman|2011<br>USA|One letter sent|5|**-0.04 (-0.07, -0.01)**|**24.21**|
|Overall (I-squared = 87.7%, p = 0.000)||||**0.08 (0.01, 0.16)**|**100.00**|
|NOTE: Weights are from random effects analysis||||||
||||**0**<br>-.327|.327||

Care should be taken with the interpretation of these results – the sample diversity and small sample size in both the meta-analysis of general savings and retirement savings require that the results are seen as indicative rather than definitive in terms of the impact of financial education on savings. Still, they 21
provide another piece of information to policy makers interested in the use of financial education for increasing saving, whether for general purposes or for retirement / old age.

In addition to the topic of savings, numerous studies focused on credit and borrowing and used information on loan defaults after interventions as an outcome variable. The meta-analysis of these studies found the least evidence of impact from financial education interventions. Figure 13 presents this analysis in a forest plot. Even at the 90% confidence interval, the null hypothesis of no impact from the intervention cannot be discarded.

Figure 13 Papers testing loan defaults after intervention <!-- Start of picture text --> %<br>Authors Date Country Intensity Channel ES (90% CI) Weight<br>Agarwal et al 2010 USA 35 1 -0.09 (-0.12, -0.06) 27.54<br>Gine Mansuri 2011 Pakistan 46 1,2 -0.02 (-0.04, -0.00) 28.82<br>Bruhn Ibarra McKenzie 2012 Mexico 4 7 0.02 (-0.01, 0.05) 26.81<br>Berg & Zia 2013 South Africa 26 3 0.04 (-0.04, 0.11) 16.83<br>Overall (I-squared = 87.6%, p = 0.000) -0.02 (-0.06, 0.02) 100.00<br>NOTE: Weights are from random effects analysis<br>-.115 0 .115<br><!-- End of picture text --> While the results from the meta-analysis in figure 13 are significantly heterogeneous and do not show a significant overall effect size, they nevertheless provide interesting insights. Two substantive points of difference between the studies that did and did not show impact lie in whether the education intervention was targeted around a specific topic and whether there was an associated immediate opportunity to use the newly-acquired information.

The two studies in particular that showed positive impacts on loan behavior were Agarwal et al (2010) and Gine and Mansuri (2011). The study population in Agarwal et al (2010) consisted of low-income households with lower credit scores who voluntarily participated in a counseling program provided by a non-profit organization. In this sense, the participants in the financial education intervention gained targeted information (specific to home ownership) and had the immediate opportunity to apply their 22
learning in a relevant context (purchase of a home). In this same light, the study participants in Gine and Mansuri (2011) were microfinance clients who could choose to participate in a business training course and loan lottery and then apply their learning directly to their microenterprises and loan behavior. On the other hand, Bruhn, Ibarra and McKenzie (2012) studied the effects of financial education programs that covered a broad range of financial topics in which the population did not experience an immediate circumstance like the purchase of a home or the financing of a business in which they could apply their learning. Bruhn, Ibarra and McKenzie (2012) evaluated a broad-based financial education program in Mexico, and among other factors, the diverse variety of topics covered and lack of a “teachable moment” to apply the learning may have contributed to the lower reported effectiveness of their program on loan default behavior. Similarly, while loan default was an outcome measured in Berg and Zia (2013), the topic was not central to their intervention which focused on source of borrowing (formal vs. retail credit) and gambling behavior.

The final meta-analysis relates to the impact of financial education on record-keeping. Record keeping and tracking expenditures are often cited as critical elements of gaining control of one’s finances, much the way that many fitness and diet programs focus on recording eating and exercise habits to control weight and improve health. This is a behavior that is fully under the control of the individual as compared to decisions to default or save money which may be influenced by factors outside one’s control such as unexpected illness (and medical fees), loss of a job or other problems that leads to financial distress.

The forest plot and regression results (Figure 14) combine data for record keeping related to both personal finance and to microenterprise or business records. This is necessary due to the small sample size but there are also reasons to believe that effects may be similar across these two types of interventions. Microenterprises frequently co-mingle finances between the business and individual owner so record keeping for a business is likely to include some common elements with personal financial management facilitating comparisons between these two types of interventions.

The meta-analysis presented in Figure 14 indicates that financial education may positively encourage record keeping behaviors although the meta-analysis results are not quite significant at the 90% confidence interval (p=0.134). The effects are weighted fairly evenly across the studies which involve an unusually diverse set of country cases (Ghana/Kenya, Vietnam, Australia / New Zealand, Indonesia and Mexico) but which use, with one exception, classroom methods.

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## Figure 14

# Papers testing record keeping behaviors after intervention

<!-- Start of picture text --> %<br>Authors Date Country Intensity Channel ES (90% CI) Weight<br>Mano Akoten Otsuka Sonobe 2010 Ghana / Kenya 45 1 0.18 (0.05, 0.31) 9.16<br>Sonobe Suzuki Otsuka Nam 2011 Vietnam NA 1 0.06 (-0.00, 0.12) 21.11<br>Gibson McKenzie Zia 2012 Australia/NZ 2 1 -0.05 (-0.11, 0.02) 19.07<br>Doi Mckenzie Zia 2012 Indonesia 13 1 0.09 (0.04, 0.14) 23.49<br>Bruhn Ibarra McKenzie 2012 Mexico 4 7 0.01 (-0.03, 0.04) 27.17<br>Overall (I-squared = 69.1%, p = 0.012) 0.04 (-0.00, 0.09) 100.00<br>NOTE: Weights are from random effects analysis<br>-.314 0 .314<br><!-- End of picture text --> Utilizing the meta regression framework, we were also able to analyze the relationship between the intensity of the treatment (in hours of instruction / exposure to the intervention) and the outcome of financial behaviors for savings (Figure 15), loan default (Figure 16), and recordkeeping (Figure 17).<sup>12</sup> While the results were not statistically significant in part due to the small number of observations, they nevertheless provide a sense of the directional relationship between each outcome variable and the intensity of the associated treatment intervention.

In Figure 15, the overall trend line suggests no direct relationship between intensity of treatment exposure and impact on savings behavior. However, on closer inspection of the individual data points, we can see that there is a positive relationship between hours of treatment exposure and impact on savings up to about 15 hours, whereafter impact declines. This result suggests that more instructional time may be important for increased positive impact, but there may also be a point at which diminishing returns set in. Exploring this relationship would be a good candidate for deeper investigation and further research.

> 12 Sufficient data on intensity of intervention was not available for the retirement savings behavior variable.

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Figure 15: Relationship Between Intensity of Exposure and Savings Behavior <!-- Start of picture text --> 0 10 20 30 40<br>Intensity<br>.15<br>.1<br>beta .05<br>0<br>-.05<br><!-- End of picture text --> Figure 16 suggests a negative relationship between intensity of exposure and the loan default outcome. Increased exposure to the treatment intervention may reduce the likelihood of loan default, again providing an indication that more instructional time can lead to greater treatment impacts.

Figure 16: Relationship between Intensity of Exposure and Loan Default Behavior Figure 17: Relationship between Intensity of Exposure and Record Keeping <!-- Start of picture text --> 0 10 20 30 40 50<br>Intensity 0 10 20 30 40 50<br>Intensity<br>.05<br>.3<br>.2<br>0<br>beta .1<br>beta<br>-.05<br>0<br>-.1<br>-.1<br><!-- End of picture text --> Figure 17 shows that the intensity of exposure to a financial capability intervention is associated with improved record-keeping behavior. While this finding does not rise to the level of causality, the data here seem to be the most supportive of a relationship between these two variables when compared with the previous regression results (shown in Figures 15 and 16). It may be the case that additional instructional time is especially valuable for learning record keeping skills. Another possibility is that regardless of one’s financial situation, record keeping 25
can be implemented whereas savings and loan repayment are more dependent on financial standing. Further research to understand these initial findings would help to focus efforts in financial literacy.

# **V. Conclusion**

The 188 papers that were identified for this meta-analysis represent an incredible level of diversity across numerous study characteristics including, importantly, outcome measures. The diversity in both general outcomes (savings, credit performance, record keeping, financial knowledge, etc.) and in how they are measured severely reduces the number of comparable studies for the meta-analysis and metaregressions. Further, effect sizes are not comparable as typically data on control group means are not available for this estimation from the published works. Where randomized controlled trials have been done, the results of impact appear limited at best, indicating perhaps omitted variables or publication bias may be present for studies not employing these rigorous methods. With these caveats, some insights do emerge.

Our meta-analysis suggests that financial education can impact some financial behaviors, including savings and record keeping. These are both considered fundamental to good personal financial management and are potentially behaviors where individuals can exert greater control than in the case of other outcomes such as loan default. Meta-analysis was unable to provide insights regarding the importance of program characteristics on impact due to the nature of the sample and lack of direct comparability. While the intensity (number of hours of exposure to the intervention) was weakly significant in the case of record keeping it was not significant in the other specifications of the model. Other characteristics which could reasonably be expected to influence the effectiveness of interventions (delivery mechanism, duration or period of time during which subjects were exposed to the treatment, location of the intervention such as school, community, workplace) were not shown to be significant but the small sample size limits the power of the statistical analysis.

The difficulty in evaluating this literature for meta-analysis highlights the importance of strengthening and expanding the use of rigorous evaluation methods. Researchers should take care to report more complete statistical information so that others can better understand the strength of the findings and the limitations of the results as well as possibly use the findings in future applications of meta-analysis to this topic. There is also a potential value from defining outcome variables in common terms, including measurement and reporting of dependent variables, to facilitate comparisons across studies. To increase the likelihood that these variables could be used in a large number of studies they should employ simple and replicable definitions and measurement criteria. The use of common questions and/or survey instruments, such as those developed at the World Bank, DfiD, and OECD in the past few years, to measure financial literacy and capability in a target population is a step in the right direction and will help increase the availability of comparable data on what is working in financial education. Finally, new and ongoing research should make use of social science impact evaluation registries undertaken by the American Economic Association and 3ie, to provide a common repository of research in this area for future meta-analyses, as well as avoid issues related to publication bias. Appendix 3 26
details some key steps that could strengthen research protocols in financial literacy studies going forward.

Finally, a surprising and yet common omission among virtually all studies is the analysis of costs and benefits of financial literacy interventions. Likewise, we did not find any discussion of potential alternative methods to achieve the desired outcomes across the 188 articles in this literature, which could help benchmark both costs and benefits. Such analyses could be simple to perform in individual studies, and certainly worthwhile to assess returns to development investments.

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# **References**

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**Figure 1**

# **“What is the most effective policy to improve access to finance among low-income borrowers?”**

<!-- Start of picture text --> Financial<br>Other education<br>33% 32%<br>Promote<br>new<br>Better lending<br>laws techs<br>18% 17%<br><!-- End of picture text --> Responses from the 2012 Survey of the Financial Development Barometer Source: 2 **0** 14 Global Financial Development Report 41