Rob Alessie

Bio: Rob Alessie is an academic researcher from University of Groningen. The author has contributed to research in topics: Financial literacy & Consumption (economics). The author has an hindex of 36, co-authored 182 publications receiving 8497 citations. Previous affiliations of Rob Alessie include Tinbergen Institute & Tilburg University.

Financial Literacy and Stock Market Participation

Abstract: Individuals are increasingly put in charge of their financial security after retirement. Moreover, the supply of complex financial products has increased considerably over the years. However, we still have little or no information about whether individuals have the financial knowledge and skills to navigate this new financial environment. To better understand financial literacy and its relation to financial decision-making, we have devised two special modules for the DNB Household Survey. We have designed questions to measure numeracy and basic knowledge related to the working of inflation and interest rates, as well as questions to measure more advanced financial knowledge related to financial market instruments (stocks, bonds, and mutual funds). We evaluate the importance of financial literacy by studying its relation to the stock market: Are more financially knowledgeable individuals more likely to hold stocks? To assess the direction of causality, we make use of questions measuring financial knowledge before investing in the stock market. We find that, while the understanding of basic economic concepts related to inflation and interest rate compounding is far from perfect, it outperforms the limited knowledge of stocks and bonds, the concept of risk diversification, and the working of financial markets. We also find that the measurement of financial literacy is very sensitive to the wording of survey questions. This provides additional evidence for limited financial knowledge. Finally, we report evidence of an independent effect of financial literacy on stock market participation: Those who have low financial literacy are significantly less likely to invest in stocks.

Financial Literacy, Retirement Planning, and Household Wealth

Abstract: There is ample empirical evidence documenting widespread financial illiteracy and limited pension knowledge At the same time, the distribution of wealth is widely dispersed and many workers arrive on the verge of retirement with few or no personal assets In this paper, we investigate the relationship between financial literacy and household net worth, relying on comprehensive measures of financial knowledge designed for a special module of the DNB (De Nederlandsche Bank) Household Survey Our findings provide evidence of a strong positive association between financial literacy and net worth, even after controlling for many determinants of wealth Moreover, we discuss two channels through which financial literacy might facilitate wealth accumulation First, financial knowledge increases the likelihood of investing in the stock market, allowing individuals to benefit from the equity premium Second, financial literacy is positively related to retirement planning, and the development of a savings plan has been shown to boost wealth Overall, financial literacy, both directly and indirectly, is found to have a strong link to household wealth

Financial literacy and stock market participation

Abstract: Individuals are increasingly put in charge of their financial security after retirement. Moreover, the supply of complex financial products has increased considerably over the years. However, we still have little or no information about whether individuals have the financial knowledge and skills to navigate this new financial environment. To better understand financial literacy and its relation to financial decision-making, we have devised two special modules for the DNB Household Survey. We have designed questions to measure numeracy and basic knowledge related to the working of inflation and interest rates, as well as questions to measure more advanced financial knowledge related to financial market instruments (stocks, bonds, and mutual funds). We evaluate the importance of financial literacy by studying its relation to the stock market: Are more financially knowledgeable individuals more likely to hold stocks? To assess the direction of causality, we make use of questions measuring financial knowledge before investing in the stock market. We find that, while the understanding of basic economic concepts related to inflation and interest rate compounding is far from perfect, it outperforms the limited knowledge of stocks and bonds, the concept of risk diversification, and the working of financial markets. We also find that the measurement of financial literacy is very sensitive to the wording of survey questions. This provides additional evidence for limited financial knowledge. Finally, we report evidence of an independent effect of financial literacy on stock market participation: Those who have low financial literacy are significantly less likely to invest in stocks.

Financial Literacy, Retirement Planning and Household Wealth†

Abstract: Relying on comprehensive measures of financial knowledge, we provide evidence of a strong positive association between financial literacy and net worth, even after controlling for many determinants of wealth. We discuss two channels through which financial literacy might facilitate wealth accumulation. First, financial knowledge increases the likelihood of investing in the stock market, allowing individuals to benefit from the equity premium. Second, financial literacy is positively related to retirement planning and the development of a savings plan has been shown to boost wealth.

Econometric Analysis of Panel Data

Abstract: Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

Identification of Endogenous Social Effects: The Reflection Problem

Abstract: This paper examines the reflection problem that arises when a researcher observing the distribution of behaviour in a population tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group. It is found that inference is not possible unless the researcher has prior information specifying the compisition of reference groups. If this information is available, the prospects for inference depend critically on the population relationship between the variables defining reference groups and those directly affecting outcomes. Inference is difficult to implossible if these variables are functionally dependent or are statistically independent. The prospects are better if the variables defining reference groups and those directly affecting outcomes are moderately related in the population.

Classification and diagnosis of diabetes

Abstract: 1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)