IntroductionDespite the growing challenges associated with household debt, research on factors influencing its relationship with psychological well-being remains limited. This study investigates the role of financial literacy in the nexus between household indebtedness and mental health, addressing a significant gap in the literature.MethodsUsing data from the China Family Panel Studies (CFPS) 2014 wave, a nationally representative dataset, we analyze how financial literacy interacts with household debt and mental health outcomes. Multiple model specifications are employed to assess the moderating and mediating effects of financial literacy.ResultsOur findings reveal two key roles of financial literacy: (1) it improves mental health by reducing household indebtedness, and (2) it moderates the negative relationship between debt and mental health. Notably, basic financial literacy is a critical factor, particularly in explaining the effects of non-housing debt (as opposed to housing debt).DiscussionThe study highlights the dual function of financial literacy in mitigating the adverse psychological effects of household debt. Policymakers and financial educators should consider promoting financial literacy as a tool to enhance mental health, especially in contexts of high indebtedness. Future research could explore additional mediators and cultural variations in this relationship.

Monetary policy is generally regarded as a central element in the attempts of policy makers to attenuate business-cycle fluctuations. According to the New Keynesian paradigm, central banks are able to stimulate or depress aggregate demand in the short run by adjusting their nominal interest rate targets. The effects of interest rate changes on aggregate consumption, the largest component of aggregate demand, are well understood in the context of this paradigm, on which the canonical "workhorse'' model used in monetary policy analysis is grounded. A key feature of the model is that aggregate consumption is fully described by the amount of goods consumed by a representative household. A decline in the policy rate for instance implies that the real interest rate declines, the representative household saves less and hence increase its demand for consumption. At the same time, general equilibrium effects let labour income grow causing consumption to increase further. However, the mechanism outlined above ignores a considerable amount of empirically-observed heterogeneity among households. For example, households with a higher earnings elasticity to interest rate changes benefit more from a rate cut than those with a lower elasticity; households with large debt positions are at a relative advantage over households with large bond holdings; and households with low exposure to inflation are relatively better off than those holding a sizeable amount of nominal assets. As a result, the contribution to the aggregate consumption response differs substantially across households, implying that monetary expansions and tightenings produce relative "winners'' and relative "losers''. The aim of the project laid out in this proposal is to give a disaggregated account of the heterogeneous effects of monetary-policy induced interest rate changes on household consumption and a detailed analysis of the channels underlying them. Additionally, it seeks to draw conclusions about the determinants of the strength of the transmission mechanism of monetary policy. To do so, it relies on a large panel comprising detailed data from the universe of all households residing in Norway between 1993 and 2015 supplemented with additional micro-data provided by the European Commission. I will be assisted by two project partners, Pascal Paul who is a member of the Research Department of the Federal Reserve Bank of San Francisco and Martin Holm who is affiliated with the Research Unit of Statistics Norway and the University of Oslo. In addition, I would like to collaborate with and help train a doctoral student based at the University of Lausanne on this project. Existing empirical studies of the consumption response to monetary policy at the micro level rely on survey data. Therefore, they are subject to a number of severe data limitations. The surveys employed typically have either no or only a short panel dimension, suffer from attrition, include only limited information on income and wealth, are top-coded, and contain a significant amount of measurement error. The administrative data set provided to us by Statistics Norway suffers from none of these issues, implying that we are in a unique position to evaluate the household-level effects of policy rate changes. In a first step, we use forecasts published by the Norwegian central bank to derive monetary policy shocks that are robust to the simultaneity problem inherent in the identification of the effects of monetary policy following Romer and Romer (2004). We then confront the micro-data with the estimated shocks to study the consumption response along different segments of the income and wealth distribution and to test the importance of heterogeneity in labour earnings, financial income, liquid assets, inflation exposure and interest rate exposure among others. The findings will be of high relevance as they will not only allow us to evaluate channels hypothesised in the analytical literature, improve our understanding of the monetary policy transmission mechanism and its distributional consequences but also serve as a benchmark for structural models built both by theorists and practitioners.

Abstract

The Monitoring of the Effects of the Economic Deterioration on Refugee Households dataset is a Phone survey of Syrian and non-Syrian households to monitor the changes over time in key areas in the context of the deteriorating economic situation in Lebanon. This wave focuses on livelihoods, economic vulnerability, living conditions, access to health services, food and livelihood coping strategies, covid-19. This dataset includes only the non-Syrian refugees' cases.

Geographic coverage

National coverage

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

A nationally representative sample was extracted from the UNHCR database in Lebanon using a simple random sampling approach. Two nationally representative samples were extracted: (1) Syrian refugees, and (2) Non-Syrian refugees. Data collected through the call center via Phone survey. To accounting for non-response rate, 1,000 Syrian and 1,000 non-Syrian cases were sampled. NB: Please note that while comparison is usually made to VARON/VASYR, methodologies completely differ and as such comparisons should be approached with caution and not interpreted to the dot. Result: High non-response rate for non-Syrians (close to 65%). Therefore, results might not be representative of the non-Syrian population and therefore should be treated with caution. 353 interviews completed.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire included key indicators on household demographics, shelter, expenditures, livelihoods, debt, coping strategies, food security and consumption, and health.

Abstract

The Monitoring of the Effects of the Economic Deterioration on Refugee Households dataset is a Phone survey of Syrian and non-Syrian households to monitor the changes over time in key areas in the context of the deteriorating economic situation in Lebanon.The UNHCR call center was used to conduct the two waves of data collection: 20-28 February (Wave I) and 17 April-15 May (Wave II). Several call attempts were made at different times of the day to reach the largest possible number of households.

After the Wave I of the survey, which was collected before the first case of Covid 19 was reported in Lebanon, the Wave II was conducted to account for the impacts of the spread of the Covid 19 virus on refugees, the level of awarness among them and their accessibility to hygiene items and health care services. This dataset includes only Wave1 for the non-Syrian refugees cases.

Geographic coverage

National coverage

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to facilitate regular monitoring of the change for households nationally, this monitoring approach employed a phone survey approach conducted on a quarterly basis with wave 1 starting in February 2020.

To achieve this, a simple random sampling approach was used, extracted from UNHCR database in Lebanon. Two nationally representative samples were extracted: (1) Syrian refugees, and (2) Non-Syrian refugees. Each sample is estimated at 500 (total 1,000) refugee households.

To account for non-response rate, the data samples were enlarged: 1,000 Syrians and 1,000 non-syrians.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire included key indicators on household demographics, shelter, expenditures, livelihoods, debt, coping strategies, food security and consumption, and health.

Response rate

The response rate in wave 1 was 51% for Syrians (513 completed surveys) .

We employ a discrete choice experiment to elicit demand and supply side preferences for insurance-linked credit and explore heterogeneity in these preferences using primary data from smallholder farmers and managers of financial institutions combined with household socio-economic survey data in Kenya. Bundling insurance with credit has emerged as a promising market-based tool for both managing agricultural weather risks and providing access to credit to farmers. However, to develop a suitable bundled credit product it is essential to tailor the product to the needs and preferences of both smallholder farmers and insurance and credit providers. We analyse the choice data using multinomial logit and Hierarchical Bayes estimation of mixed logit model. We find that farmers prefer credit for both seasons, credit term to be one year or longer, no or partial collateral for loan, lower risk premium, and loans to be used for any purpose. Supply side results suggest that managers of financial institutions prefer the risk premium to be added with loan amount, loans to be repaid after harvest, credit available for both seasons, credit term to be shorter than one year, loans to be used only for agricultural purpose, and loans to be fully or partially collateralised. We also analyse willingness to purchase and willingness to offer for farmers and suppliers, respectively for risk premium at different attributes and their levels. Identifying the preferred attributes and levels for both farmers and financial institutions can guide optimal packaging of insurance and credit providing market participation and adoption motivation for insurance-bundled credit product.Farm households in Africa must cope with bad conditions as to soil quality, weather and infrastructure. The variability of rainfall causes yields to vary strongly from one year to the next. With yields already low (due to poor soil condition) these variations can be life threatening. Meanwhile, inadequate infrastructure makes it difficult to help the households with access to financial services, insurance and inputs that could stabilize their access to resources, and enhance yields. Solving a single aspect, say bringing inputs to the farm, will not be sufficient as credit is also needed. But credit can only be provided if sufficient likelihood exists that loans will be repaid. Here, insurance can help. If insurance of the loan makes it attractive enough for the lender, a package can be composed of inputs, with credit and insurance, that solves all these problems with one bundle. Yet, the households will remain exposed to some risks as insuring against all is prohibitively expensive. What is the appropriate degree of insurance in such bundles? That is the core question addressed in this research. It aims at supplying inputs to farmers on credit, with insurance, in such a way that a good balance is found between the benefits and risks to the farmers and the profits and risks to the credit provider. We investigate the possibilities for such a balanced approach in Kenya and Ethiopia in collaboration with a large insurance provider and a farmers organisation. Together with them we collect information on the costs, benefits and risks involved in using the inputs, the alternatives open to them, and the costs and benefits involved in providing credit to finance the purchase of inputs, with and without an insurance against crop failure. With all this information, we go and talk to the stakeholders concerned to find out how they would respond if more or less insurance would be provided. Will credit suppliers lower their prices, if repayment of loan is more likely because the crop is insured? Will households decide to take higher yielding (but more risky) crops if part of the downside risk is insured? We establish this for the parties concerned in Kenya and Ethiopia, but also in other African countries. Having established how these stakeholders respond to changes in insurance, we can proceed to derive what the best degree of insurance might be. And this is then finally tested in a field experiment. With this knowledge we can help other suppliers of insurance and credit, and farm organisations to establish similar packages that are adapted to the local conditions for input supply, and financial services. Our research team came up with nine attributes for the choice experiment that are thought to be the most important characteristics that a consumer and a supplier would look for. The attributes are insurance cost, insurance payment, insured risk coverage, credit term, collateral requirement, loan repayment flexibility, loan use flexibility, preferred season for loan, and rainfall measurement. Insurance cost or risk premium was included to allow for estimation of money metric measure of willingness to purchase for farmers and willingness to offer for finance providers. We specified four premium levels in our choice sets based on actually fair premium pricing. Regarding insurance payment attribute, actuarial design team and bank and insurance company representatives highlighted the option of premium to be added to loan amount and paying premium separately. Insurance risk coverage is directly related to cost of insurance, we define low coverage as insurance providing payout once in every 20 years, medium coverage as providing payout in every 10 years, and high coverage as covering frequent risk allowing payout in every 4 years. Credit terms are defined as six months (maize being a six-month crop in the area), one year and more than one year. As collateral is very important component of any credit lending in Kenya, we included partial, full and no collateral options. Loan repayment option of monthly and after harvest came clearly during our focus group discussions. Regarding loan use term, two options were included, loans can be used for any purpose versus loans can only be used for agricultural production. Since the area have two distinct seasons with bimodal rainfall pattern we included long, short and both seasons options. Finally, we included an option to elicit opinions about rainfall measurement for pricing and payout decisions. The rainfall calculation should be based on total rainfall shortage in season or shortage at each growth cycle of maize crop. This variable indirectly captures spatial basis risk option where rainfall shortage at crop growth cycle will have much lower basis risk compared to rainfall shortage in a season.