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on Financial Literacy and Education |
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Issue of 2019‒09‒02
ten papers chosen by |
| By: | Seng, Kimty |
| Abstract: | This article analyses the effects of financial inclusion on poverty in terms of household income per capita in Cambodia, with data from the FinScope Survey carried out in 2015. The analysis describes the effects via financial literacy, accounting for endogenous selection bias resulting from unobserved confounders and for structural differences between users and non-users of financial services in terms of income functions. The findings suggest that the use of financial services is very likely to make a great contribution to reducing household budget deficit and poverty if the users, female in particular, have at least basic financial knowledge. |
| Keywords: | Poverty, financial inclusion, financial literacy, endogenous, Cambodia |
| JEL: | O1 O12 |
| Date: | 2019–08–26 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95726&r=all |
| By: | Gopi Shah Goda; Matthew R. Levy; Colleen Flaherty Manchester; Aaron Sojourner; Joshua Tasoff |
| Abstract: | Defaults have been shown to have a powerful effect on retirement saving behavior yet there is limited research on who is most affected by defaults and whether this varies based on features of the choice environment. Using administrative data on employer-sponsored retirement accounts linked to survey data, we estimate the relationship between retirement saving choices and individual characteristics – long-term discounting, present bias, financial literacy, and exponential growth bias – under two distinct choice environments: an opt-in regime and an auto-enrollment regime. Consistent with our conceptual model, we find that the determinants of following the default and contribution behavior are regime-specific. Under the opt-in regime, financial literacy plays an important role in predicting total contributions, active saving choices, and maxing out contributions in the tax-preferred account. In contrast, under the auto-enrollment regime, present bias is the most significant behavioral predictor of contribution behavior. A causal interpretation of the estimates suggests that auto-enrollment increases saving primarily among those with low financial literacy. |
| Keywords: | present bias, exponential-growth bias, household finance, retirement savings decisions, choice architecture, defaults, financial literacy, procrastination |
| JEL: | D91 J26 G11 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:hka:wpaper:2019-050&r=all |
| By: | Bernd Hayo (University of Marburg); Ken Iwatsubo (Kobe University) |
| Abstract: | We use a 2018 survey of FX margin traders in Japan to investigate which key factors influence their performance: socio-demographic and economic situation, investment strategy and trading behaviour, and/or financial literacy. First, the data show that variables from all three groups are significant predictors of traders’ performance. Second, we find that older traders and those without a specific trading strategy demonstrate lower performance. Performance is higher for those who trade greater amounts, rely more on fundamental analysis, and report having profitable FX trade skills. Third, respondents’ subjectively stated claim of having FX trade skills is based on a more advanced understanding of FX trading and a reliance on professional advice. Neither objective financial knowledge nor over/underconfidence play a noteworthy role in the performance of margin traders. |
| Keywords: | Foreign exchange margin trading, investor survey, foreign exchange trading profits, financial literacy, Japan |
| JEL: | F31 G11 G28 |
| Date: | 2019–08–20 |
| URL: | http://d.repec.org/n?u=RePEc:cth:wpaper:gru_2019_026&r=all |
| By: | Gopi Shah Goda; Matthew R. Levy; Colleen Flaherty Manchester; Aaron Sojourner; Joshua Tasoff |
| Abstract: | Defaults have been shown to have a powerful effect on retirement saving behavior yet there is limited research on who is most affected by defaults and whether this varies based on features of the choice environment. Using administrative data on employer-sponsored retirement accounts linked to survey data, we estimate the relationship between retirement saving choices and individual characteristics – long-term discounting, present bias, financial literacy, and exponential growth bias – under two distinct choice environments: an opt-in regime and an auto-enrollment regime. Consistent with our conceptual model, we find that the determinants of following the default and contribution behavior are regime-specific. Under the opt-in regime, financial literacy plays an important role in predicting total contributions, active saving choices, and maxing out contributions in the tax-preferred account. In contrast, under the auto-enrollment regime, present bias is the most significant behavioral predictor of contribution behavior. A causal interpretation of the estimates suggests that auto-enrollment increases saving primarily among those with low financial literacy. |
| JEL: | J32 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26078&r=all |
| By: | Mahmoud, Zeinab |
| Abstract: | Mobile Money (MM) services are growing very fast in the developing countries as an important tool for the financial inclusion. Many critical challenges surrounds mobile money services adoption with the most important challenges to improve the service quality, attract and retain more customers, and reduce dealing with cash. Overcoming all these challenges would allow all citizens to have full access to financial services or in other words being financially included. Recently, Financial Inclusion has received higher priority to improve financial existing and reduce poverty on large scale. It is clear that due to the large scale of mobile phone access and the existing network, mobile money on of the most key enabler to financial inclusion. However, the differences of mobile money countries, characteristics, economy, adoption variables. This Paper analyzes the mobile money success factors from seven developing countries (Egypt, Kenya, Ghana, Tanzania, Uganda, Zimbabwe, and Rwanda) where there has been successful penetration of mobile money services in order to extract the determinants of mobile money services adoption. Mobile money adoption is affected by several factors that includes country specific characteristics, regulatory considerations, and service provision characteristics as a result nine independent variables were selected to be included in this research. Two dependent variables are chosen to present the mobile money adoption, these are registered subscribers ratio and active subscribers ratio. The analysis is based on the data collected from the central banks published statistics in each country of the above-mentioned seven countries. The analysis is achieved using panel data analysis for a sample of seven African countries for the period from 2013 to 2017. Data is analyzed using the linear regression model for each dependent variable of the mobile money adoption using nine explanatory variables. The statistical analysis is done using Eviews and least square (LS) estimation techniques are used to provide further strength for the results. The paper aims to define a model for measuring mobile money adoption and defining the impact of each of the mobile money adoption determinants on the adoption level. This could be used to define recommendations or strategic decisions for policy makers or mobile money service providers in Egypt to improve mobile money adoption. |
| Keywords: | Financial Inclusion,Mobile Money (MM),Mobile Money Services,Mobile Money Determinants,Registered subscribers ratio,Active subscribers ratio |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itsm19:201742&r=all |
| By: | G. Gulsun Akin (Department of Economics, Bogazici University); Ahmet Faruk Aysan (Department of Economics, Istanbul Sehir University); Sezgim Dasdogen (Department of Economics, Istanbul Sehir University); Levent Yildiran (4 Department of Economics, Bogazici University) |
| Abstract: | This paper attempts to assess whether the driving factor behind the rising credit card indebtedness of consumers in Turkey is financial illiteracy. Using the results of a nationwide survey, the authors conclude that even though credit card borrowing frequency and debt amount are affected by components of financial literacy, being credit-constrained has a very pronounced impact. An exploratory analysis finds that the probability of irrational credit card borrowing is increased by being credit-constrained but not affected by financial literacy. These findings suggest that credit card debt is at least as much a result of necessity as nescience. |
| Date: | 2019–08–21 |
| URL: | http://d.repec.org/n?u=RePEc:erg:wpaper:1315&r=all |
| By: | Christian Ewerhart; Robertas Zubrickas |
| Abstract: | We model the financial cooperative as an optimal institution sharing liquidity risks among agents with social preference and group identity. Stronger social concerns imply objectively better (worse) conditions for borrowers (depositors). Testing the model, we find that, indeed, deposit and loan rates offered by U.S. credit unions between 1995 and 2014 co-moved with (i) the number of members, and (ii) the common bond. Our theory explains how cooperatives coexist with banks, and why they have tended to be more resilient. However, the analysis also suggests that financial inclusion and advantages in resilience might quickly evaporate as membership requirements get diluted. |
| Keywords: | Social preferences, group identity, liquidity insurance, cooperative banking, credit union, common bond, bank competition, resilience |
| JEL: | G21 D91 L31 G28 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:zur:econwp:332&r=all |
| By: | Anya Samek; Arie Kapteyn; Andre Gray |
| Abstract: | Evidence shows that people have difficulty understanding complex aspects of retirement planning, which leads them to under-utilize annuities and claim Social Security benefits earlier than is optimal. To target this problem, we developed vignettes about the consequences of different annuitization and claiming decisions. We evaluated our vignettes using an experiment with a representative online panel of nearly 2,000 Americans. In our experiment, respondents were either assigned to a control group with no vignette, to a written vignette, or to a video vignette. They were then asked to give advice to hypothetical persons on annuitization or Social Security claiming, and were asked factual questions about these concepts. We found evidence that being exposed to vignettes led respondents to give better advice. For example, the gap between advised claim age for a relatively healthy person versus a relatively sick person was larger by nearly a year in the vignette treatments versus the control group. Further, the vignettes increased financial literacy related to these concepts by 10-15 percentage points. Interestingly, the mode of communication did not have a significant impact – the video and written vignettes were equally effective. |
| JEL: | H3 J26 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26176&r=all |
| By: | Mark Weber; Giacomo Domeniconi; Jie Chen; Daniel Karl I. Weidele; Claudio Bellei; Tom Robinson; Charles E. Leiserson |
| Abstract: | Anti-money laundering (AML) regulations play a critical role in safeguarding financial systems, but bear high costs for institutions and drive financial exclusion for those on the socioeconomic and international margins. The advent of cryptocurrency has introduced an intriguing paradox: pseudonymity allows criminals to hide in plain sight, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. Meanwhile advances in learning algorithms show great promise for the AML toolkit. In this workshop tutorial, we motivate the opportunity to reconcile the cause of safety with that of financial inclusion. We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency. We share results from a binary classification task predicting illicit transactions using variations of Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN), with GCN being of special interest as an emergent new method for capturing relational information. The results show the superiority of Random Forest (RF), but also invite algorithmic work to combine the respective powers of RF and graph methods. Lastly, we consider visualization for analysis and explainability, which is difficult given the size and dynamism of real-world transaction graphs, and we offer a simple prototype capable of navigating the graph and observing model performance on illicit activity over time. With this tutorial and data set, we hope to a) invite feedback in support of our ongoing inquiry, and b) inspire others to work on this societally important challenge. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.02591&r=all |
| By: | Said, Ahmed |
| Abstract: | The huge rapid growth of using internet and technology has been affecting all economies whether emerging or developed across the world. The financial sector is one of the sectors that has been directly influenced by technology due to the growth of electronic commerce and electronic payments. The emergence of digital currencies such as Bitcoin and the underlying blockchain as well as the distribution ledger technology have attracted significant interest. These developments have raised the possibility of considerable impacts on the financial system and perhaps the wider economy. The huge price leaps that happened to Bitcoin towards the end of 2017 until it reached its highest ever price, (19000 USD) since the beginning of its trading, followed by the significant fall that took place afterwards till it fell under the level of 4000 USD in 2018, made the Central banks more worried about the future of this market. In addition to that, the increase of developing new cryptocurrencies as well as the lack of control over it, made the central banks very alert to the futuristic view of this sector keeping their eyes wide open to this rapid growth. As a result, over the past few years, public authorities and central banks around the world have been monitoring developments of digital currencies and studying their implications. A question that has been raised frequently is whether central banks themselves should issue digital currency that could be used by the general public or not. The legal status of cryptocurrencies was always in question. Some administrations have banned them and other had implicit bans. In many other countries they are still under study and only official warnings from using and investing in cryptocurrencies were announced. The idea of issuing the central bank cryptocurrencies or Digital Fiat currencies has been studied by central banks in order to offer a formal/legal substitute for the consumer that is trusted and protected by central banks. Transitioning from private Cryptocurrencies to a legally issued digital currency will enhance the suite of financial inclusion tools that are already in place, offer "cash"-only households a leap into digital transactions, and increase the consumer choices of how to manage their household income and expenditures. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itsm19:201744&r=all |