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The financial well-being (FWB) of individuals is a topic that is becoming increasingly important across a multitude of disciplines. In this study, we use the 2016 National Financial Well-Being Survey administered by the Consumer Financial Protection Bureau to assess the determinants of an individual's FWB. We identify 144 potential covariates that could explain variation in the FWB score of individuals. The statistical methodology of choice is the Bayesian LASSO, which is a covariate selection algorithm that also allows for the importance ranking of covariates. Out of the 144 potential covariates, we find that 26 have 95% credible intervals that do not contain zero. Broadly speaking, the results show that objective measures of financial competency and psychological and sociological factors contribute the bulk of the explanatory power that help explain an individual's FWB score., INTRODUCTION Financial well-being (FWB), often known as financial freedom, is the ability to make decisions that allow individuals to live a fulfilling life while still being able to satisfy their [...]

Personal finance research often utilizes Likert-type items and Likert scales as dependent variables, frequently employing standard probit and ordered probit models. If inappropriately modeled, the "neutral" category of discrete dependent variables can bias estimates of the remaining categories. Through the utilization of hierarchical models, this paper demonstrates a methodology that accounts for the econometric issues of the neutral category. We then analyze the technique through an empirical exercise relevant to personal finance research using data from the National Financial Capability Study. We demonstrate that ignoring the "neutral" category bias can lead to incorrect inferences, hindering the progression of personal finance research. Our findings underscore the importance of refining statistical modeling techniques when dealing with Likert-type data. By accounting for the neutral category, we can enhance the reliability of personal finance research outcomes, fostering improved decision-relevant insights. [ABSTRACT FROM AUTHOR]

There is a large literature estimating the effect of economic freedom on economic growth or income levels. Most studies examine the relationship between economic freedom and growth or income levels for countries, while a few examine the relationship for U.S. states. Absent in the state-level literature is consideration of the presence of spatial spillovers affecting the freedom-income relationship. Neglecting to account for spatial autocorrelation can bias estimation results and therefore inferences drawn. We find evidence of a spatial pattern in real per-capita gross state product (GSP) that affects nonspatial estimates of the freedom-income relationship. Taking into account the direct and indirect effects of economic freedom on real per-capita GSP, we find a 10% increase in economic freedom is associated with a 5% increase in real per-capita GSP. {JEL E02, 047, R11), I. INTRODUCTIONThe role that market-oriented institutions play in determining long-run economic prosperity has recently come to the forefront of the economics profession. In recent years the development of cross-country measures [...]

While markets for agricultural commodities are often global, markets for cropland can be highly localized. Soil makeup and climate also tend to make farmland rental markets localized. This study compares several econometric models to measure the extent of spatial spillovers within these markets. A fully dynamic spatial model estimated on cropland transactions shows that sales are highly localized in the short term but face greater neighboring spillovers in the long term. Accounting for longer-term spatial interdependence of farmland markets can help to improve land value forecasts. [ABSTRACT FROM AUTHOR]

A study on the way the four states that did not participate in the 1998 Master Settlement Agreement (MSA) have fared on to their independence is presented. The findings show that two non-participating states Minnesota and Mississippi might have fared substantially worse if they would be included in the MSA.

This study examines residential solar photovoltaic (PV) adoption in mid-Atlantic counties over the 2005–2016 period. Using a spatial Durbin panel model, we find significant spatial dependence in residential solar PV adoption at the county level. In the presence of spatial dependence, major determinants of the solar adoptions are electricity rates and solar-related policy regulation. The combined direct and indirect effect of electricity rates on solar PV capacity is 5.37 percent increase for a 1 percent increase in electricity price. This spatial coefficient estimate is about twice as large as corresponding panel estimates. We also find that the regulation and incentives significantly boost solar PV demand, which explains about 38 percent variation in residential solar capacity. [ABSTRACT FROM AUTHOR]