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Economic and sociological research that touches upon the determinants of alcohol consumption is mostly centered on the traditional factors of human capital (e.g., education). While much attention is given to education as a valid instrument to reduce alcohol misuse, less is given to the impact of non-cognitive skills. Data are collected from a nationally representative Russian panel survey, 2016–2018. We estimate a random-effects probit model for the probability of abstinence and a random-effects tobit model with a Heckman correction for the volume of alcohol consumption. Non-cognitive skills are consistent predictors of drinking in Russia. In both genders, conscientiousness and extraversion have strong connections to the probability and the volume of alcohol consumption, while openness to experience and neuroticism only affect the volume. The estimates for education differ substantially when the Big Five variables are excluded from the model, which suggests that a major part of the effect of education on alcohol consumption patterns may be mediated through non-cognitive skills. Although educational interventions are often seen as a method of solving excessive drinking problems, introducing personality traits into the analysis raises the question of the effectiveness of such interventions.

Young children face the risk of being infected by a variety of diseases; this makes it necessary to resort to vaccination as a failsafe for ensuring healthy life of the child. However the success of the immunisation drive is conditioned by prevailing conditions. Some of these also determine whether family members opt for full, partial or no immunisation. This paper attempts to identify and estimate the impacts of various socio-economic factors on childhood immunisation coverage in the East district of Sikkim. The study is based on primary data consisting of 204 respondents collected through household survey. The ordered probit and Heckman probit models have been utilised to estimate the significant change in childhood immunisation coverage. The study found that the birth order of child (BOC), mother’s age, the standard of living, caste and religion have a significant impact on childhood immunisation coverage. With regard to the extent of immunisation, the important elements are mother’s education, mother’s age, gender of the household head and distance from hospital.

The VIX index and the spread between long- and short-term Treasury bond yields co-move in counterclockwise cycles that align with the business cycle. Based on this empirical fact, I predict U.S. recessions using an indicator of the economy’s location on the VIX-yield-curve cycle. The proposed indicator significantly outperforms the yield-curve spread in predicting U.S. recessions from 1990–2021 both in- and out- of-sample using both static and dynamic probit models. VIX-yield-curve cycles also contain predictive power above and beyond other leading economic indicators.

The COVID-19 pandemic has reignited interest in trust in public institutions as the efficacy of public health interventions is predicated on trust in the institutions (different levels of government and health authorities). Using a unique micro-level dataset, I assess trust of Canadians in public institutions and whether it might contribute to containment of the COVID-19 pandemic. I find that on average Canadians trust their public institutions and this trust is primarily driven by social trust. However, trust in institutions is not uniform. It is strongly positively related to level of education, age, and sex. More educated, older, female Canadians are more likely to trust public institutions. However, there are significant differences across provinces as well. I provide positive evidence relating trust and flattening of the COVID-19 incidence curve. I also find that higher level of trust in public institutions significantly increases the likelihood of taking vaccines when they become available. This initial assessment of trust in containing COVID-19 indicates that policymakers should take the initiative to maintain and enhance trust to effectively implement public initiatives to curb future waves of this and other pandemics, which have significant consequences for the Canadian economy.

Problem gambling is becoming a growing concern in the United States because of the proliferation of, and state support for, gambling opportunities. The economic cost along with the physical and mental health problems associated with problem gambling make it necessary to study how problem gambling can be reduced. Our study examines whether financial literacy could be a means to reducing gambling frequency in the United States. We use data from the Preference Parameter Study of Osaka University, Japan, and apply instrumental variable probit regression models. The results show that, generally, financial literacy does not have a relationship with gambling frequency, but the relationship is significant in the states where electronic gambling machines (EGMs) are available. The results imply that gamblers are irrational and fail to assess the risks of gambling as well as the probabilities that maximize expected payoffs. It appears that gamblers' psychological gain from gambling outweighs the negative expected utility when there is easy access to gambling. Thus, rationality with regard to gambling decisions does not work unless the easy access to EGMs is controlled. Our results further show that males, older people, people with higher household income, and people who have easy access to gambling are likely to be frequent gamblers.

The study documented in this paper utilises a probit regression analysis to empirically investigate the key macroeconomic factors that influence credit risk in the peer-to-peer (P2P) lending market. By aggregating the United States (US) state-level data with LendingClub’s loan book covering the period from 2008–2019, this study examines multiple factors related to default risks of loans issued by P2P lending platforms. Our results show that a higher interest rate and inflation increase the probability of default in the P2P lending market. We also find that the impact of interest rate on the probability of default is significantly higher for loans with lower ratings. The study’s outcomes, by paving the way to future market best practices, are applicable to P2P lending platforms and investors in their default estimation of loans.

Data mining is a technique used to explore the big data from the different sources to obtain the hidden patterns through statistical technique and machine learning methods. Due to implication in the development of a variety of applications, data mining algorithms are applied to a healthcare domain for the prediction of disease at an earlier stage. The various data mining applications have been useful for medical experts to diagnose disease in an accurate method is a challenging one. To improve the accuracy in heart disease prediction using Artificial Neuro Probit Regressed Associative Frequent Pattern Mining (ANPRFPM) model is introduced. Initially, the number of attributes and the data are collected from dataset in the ANPRFPM model (i.e. patterns). The ANPRFPM model trained by the input layer, hidden layer, and the output layer using large set of attributes and patterns. Input layer contains set of attributes and patterns are given as input to the network. In hidden layer of the proposed ANPRFPM model contains probit regressed associative rule mining concept to identify the frequent or non-frequent itemsets based on the support value. Continued by, the confidence is measured between the training and testing patterns of disease and conveyed into the output layer. Finally, soft sign activation function at the output layer for analyzing the confidence value and providing the two classification outcomes as normal or abnormal with higher accuracy in ANPRFPM model. To revolve, the error in the disease prediction gets minimized at the output layer. Using a heart disease dataset with different performance metrics an experimentation is carried out such as prediction accuracy, false-positive rate and prediction time with respect to a number of patterns. In an ANPRFPM model achieves higher prediction accuracy with minimum time as well as the false positive rate than the state-of-the-art methods proves an observed result.

This article investigated the impact of Igbo Apprenticeship System (IAS) on informal sector businesses in Nigeria. IAS is an innovative indigenous informal sector financing approach among the Igbo people in Nigeria. The study conceptualized the capital sharing model of IAS in comparison with venture capital and modern incubation system. Using a household survey data collected from about 400 informal sector businesses randomly selected from Onitsha (Anambra state) and Aba (Abia states) in South-Eastern Nigeria, the study examined the effect of capital sharing model on small firm growth in Nigeria. The estimation technique was based on ordinary least square (OLS) method while the Probit regression was used as a robust check on the OLS estimates. Findings revealed that the businesses of apprentice-graduates, i.e. those who underwent IAS grew significantly in any of the three measures of growth used; unlike the businesses whose owners did not undergo IAS. Therefore, this study provided an empirical validation to the use of the capital sharing model of the Igbo Apprenticeship System as an innovative informal sector financing model in Nigeria. The study thus recommends a policy framework that supports this innovative informal sector financing in Nigeria. Also, the venture capital and modern incubation system can be extended to incorporate features of the capital sharing model of IAS. This would however require strengthening the informal sector business environment.

This paper proposes that country-pairs sharing homogeneity in domestic institutions tend to sign preferential trade agreements (PTAs) interdependently, and such an interdependence effect decreases with institutional differential. Using spatial econometrics, we take democracy and economic freedom as fundamental components of the spatial weight matrix. The baseline results support our hypothesis from both the panel data over 1996 to 2017 through a probit model and the cross-sectional data through a spatial autoregressive probit model. We also employ a compound spatial weight matrix to account for the institutional differential and geographical distance simultaneously, which confirms the role of domestic institutions in the evolution of PTAs. Our findings are robust to the correction of potential endogeneity in the spatial weight matrix, and we provide not only novel evidence for the spatial effect of institutional differential on the domino-like spread of PTAs, but also some insights on the relationship between regionalism and multilateralism.

This study investigates the effect of grandparental childcare on the second birth in China when considering the mediating role of maternal labor participation, drawing on the China Migrants Dynamic Survey in 2016. By controlling sample selection bias and endogeneity with an endogenous switching Probit regression model, our analysis finds that grandparental childcare of the first child has a positive and significant effect on the second birth regardless of whether the mother participates in the labor market or not. To further understand how the positive effect comes to be, we make a mediation analysis which demonstrates the mediating effect of maternal labor force participation. It indicates a significant and negative effect of grandparenting on the second birth through promoting maternal employment. However, due to the positive, direct effect of the grandparental childcare on the second birth, the total effect is still positive.

I establish the equivalence between the two-way fixed effects (TWFE) estimator and an estimator obtained from a pooled ordinary least squares regression that includes unit-specific time averages and time-period specific cross-sectional averages, which I call the two-way Mundlak (TWM) regression. This equivalence furthers our understanding of the anatomy of TWFE, and has several applications. The equivalence between TWFE and TWM implies that various estimators used for intervention analysis – with a common entry time into treatment or staggered entry, with or without covariates – can be computed using TWFE or pooled OLS regressions that control for time-constant treatment intensities, covariates, and interactions between them. The approach allows considerable heterogeneity in treatment effects across treatment intensity, calendar time, and covariates. The equivalence implies that standard strategies for heterogeneous trends are available to relax the common trends assumption. Further, the two-way Mundlak regression is easily adapted to nonlinear models such as exponential models and logit and probit models.

This study analyzes the relationship between entrepreneurship intention and personal characteristics and skills by using the surveys we conducted in Turkey on 1465 senior university students. We use a modified version of the Entrepreneurial Orientation (EO) scale and the Political Skills Inventory to measure some personal characteristics and skills. We also use the nine sub-dimensions of these two scales. Probit model and wavelet coherence analysis results show that proactivity, entrepreneurship, and networking sub-dimensions of the scales are related to entrepreneurship intention. We also find that gender, the number of siblings, the grade point average (GPA) of the students, their family’s education level, the parent’ ownership of an enterprise, and the number of non-governmental organizations (NGO) that they are a member of are also related to entrepreneurship intention. Results may be useful to understand and enhance entrepreneurship potential.

This article builds off work by Devine and Kopko (2021) and Lacy and Burden (1999) who estimate a probit model of candidate choice from nationally representative survey data to determine the second choice of third-party voters. Using this model on 2020 election data, we show that the Libertarian candidate Jo Jorgenson probably cost Donald Trump victory in at least two state – AZ and GA. Additionally, the popular vote margin enjoyed by Joe Biden could have been between 260,000 and 525,000 votes less, using conservative estimates. The motivation of this paper is to give contrary evidence for two main misconceptions. First, that third-party candidates are “spoiling” elections for the Democrats. Our evidence clearly shows that third-parties have the potential to hurt either of the two main parties, but in 2020, it was Donald Trump who was hurt most; though not consequentially. Second, some reformers believe that Rank Choice Voting benefits the Democrats; again, we show that, all else being equal, in the presidential election of 2020, it was the Republicans that would have benefited by the change in rules, since the majority of third-party votes are going to the Libertarian candidate, and whose voters prefer Republicans over Democrats 60% to 32%.

Comparative marketing communication has emerged as an important area of managerial and scholarly inquiry. In the pharmaceutical industry, it has been practiced in detailing, the personal selling to physicians. With physician-level panel data of detailing and prescriptions, we examine the effectiveness of comparative detailing versus noncomparative detailing. In particular, we investigate whether a brand’s comparative detailing directly damages competing brands or provides them free exposure to physicians. The Bayesian hierarchical probit model with reduced-form detailing policy functions allows us to examine comparative detailing at both aggregate level and individual physician level, while controlling for possible simultaneity issues in noncomparative detailing and comparative detailing. We find that (1) comparative detailing is less or equally effective than noncomparative detailing at the aggregate level, but there is strong heterogeneity across individual physicians; (2) some brands strategically focus their comparative detailing efforts on physicians who are less responsive to noncomparative detailing but more responsive to comparative detailing than others; (3) the market leader faces a denigrating loss if underdog brands compare against it in their detailing visits, but a generic brand enjoys a free-exposure benefit when other brands in the category compare against it in their detailing visits. Our estimates provide rich managerial implications by identifying physicians who are more profitable for comparative detailing. Finally, our policy simulation shows that a ban of comparative detailing can reduce the market share of generic drugs by 1.76% in statin market.

Public transport and attending social events are becoming a stumbling block to maintain a safe social distancing in the fight against COVID-19. We developed a quasi-experiment that involves a tradeoff between economic outcomes over social distancing in some natural settings. The results show that about 58% of subjects prefer to break the social distancing rule and choose the most unsafe public transport. As a result, we further tried to understand the determinant factors of practicing social distancing using a binary probit model. The results show that a relatively lower income, and lower education level are significant and positively influence the decision to practice the social distancing rule which is contrary to the existed literature. The second experiment was devoted to understanding the effect of framing media message on subject’s decision to take part in a social event. The results show that there is no statistically significant difference in decisions between the treatment (those who received a loss framed message) and control (those who received the gain COVD related media message) groups. However, we find a strong association between social participation and decision to take part in social events.

The incessant corporate scandals engulfing the corporate world today calls for great concern, also even the so-called big four recently have had their fair share of this blame too. Against this backdrop, we investigated to investigate the impact of board characteristics on audit quality of listed manufacturing firms in Nigeria. The study was driven by the positivist research philosophy and a deductive research approach using a multi-method quantitative research design. Descriptive and inferential statistics were employed to summarize the data and to draw inference on the population studied. We employed the Binary Probit Regression in testing the hypotheses stated. Findings revealed that board size had a positive and significant relationship on audit quality. The study found no evidence on the relationship between board independence, female gender on audit quality. The study also found no evidence on the moderating effect of the presence of a female on the board on the nexus between board independence and audit quality. The study concluded that board characteristics do not affect audit quality. Hence, we recommended that the Non-executive director on the board should be reduced in manufacturing firms to improve audit quality.

The Altman Z Score (AZS) does not predict bankruptcy. The false positive rate of the AZS is 98%-99%. The AZS fails as a predictive model because it does not incorporate market evidence bearing on bankruptcy probability, specifically, returns, debt to an approximation of market value of assets, and stock price. In a probit model, AZS is statistically insignificant in the presence of these market variables.

The use of structural models for decision-making under risk and uncertainty in applied economics is scarce compared to reduced form approaches. This is unfortunate, as structural models have clear connections to theory and permit direct tests of hypotheses and exploration of potential causal mechanisms. In this paper, we derive and estimate a structural model of decision making in the presence of natural hazard risk. Utilizing a unique data set that includes information on risk preferences, subjective likelihood of hurricane strike, and expectations of damage, we estimate several variants of a subjective expected utility model for coastal households’ decisions to purchase flood insurance. We benchmark our model’s out of sample accuracy by comparing it against both reduced-form and machine learning estimates. Overall, we find our structural models perform about as well as a reduced-form probit model in predicting out of sample behavior, but offer increased external validity and insight into theory and mechanisms.

Equity crowdfunding provides a new channel for enterprise project financing. I found that most equity crowdfunding projects in China have adopted the leader-follower investor paradigm, which is considered as a way to alleviate information asymmetry. To explore the impact of the lead investor on financing performance, based on the data of the equity crowdfunding platform from 2014 to 2020, I used OLS regression to conduct an empirical study. The results show that the existence of lead investors promotes equity crowdfunding performance, and the attention to projects plays a mediating role. Project risk weakens the positive effect of lead investors on financing performance. The study further found that if the lead investors are individuals instead of institutions, the financing performance is better. The lead investment amount and the information completeness of the leading investors have a positive effect on financing performance, while whether the leading investors use real names has no effect on financing results. Structural social capital, relational social capital, and regional social capital all play a positive role in promoting financing level, and the lead investment amount negatively regulates the positive effect of social capital on financing performance. Finally, I chose the propensity score matching (PSM) method to avoid endogenous problems, and then replaced the dependent variable and used logistic and probit models to ensure the robustness of the conclusions.

The present pitch paper intends to pitch the research idea, its methodology, and possible outcomes by following Faff (2015) pitch my research template The rese

Peek and Rosengren (American Economic Review 2005; 95) suggested the mechanism of ``unnatural selection,'' where low-capitalized Japanese banks increase credit to low-quality firms because of their motivation to pursue balance sheet cosmetics. In this study, we replicate their estimation results, using loan-level data from 1994 to 1999, and thereby reexamine this mechanism in terms of the interaction effect in a nonlinear specification of bank lending. We demonstrate that their results imply that Japanese banks allocated lending from viable firms to unviable ones regardless of the degree of bank capitalization, being not consistent with the balance sheet cosmetics hypothesis.

Telecommuting is one of the most popular flexible work arrangement nowadays. It can give employees the autonomy to decide when, where, and how to work and, as a consequence, employees have a lot of unstructured time to work and think. This boosts the creativity of employees and might result in higher innovation activity. On the other hand, casual interactions in the cafeteria or corridors of the firms, not only between employees of the same department, but especially between those of all departments, lead to an information exchange that is relevant for the creation of new innovations. Due to the fact that telecommuting prevents casual interactions and the respective information exchange, it might hamper innovation. Using individual-level data from 15,542 German employees, this paper analyzes the relationship between telecommuting and innovation in services. The analysis is based on a probit model and an instrumental variable regression. The results reveal that telecommuters are more likely to be innovative in services. The instrumental variable regression confirms this result and suggests that the causality runs from telecommuting to service innovation.

By employing Alkire and Foster (2011) counting approach and Cerioli & Zani (1990)s’ unequal weighting method, this study estimates the non-monetary multidimensional poverty in Ethiopia for three waves with fourteen country-specific indicators and four dimensions in its first part; and tried to identify the presence of state dependence with unobserved heterogeneity using dynamic random effect probit model with unobserved heterogeneity estimation technique in its second part. The data for the three waves are from Demographic and Health Survey (DHS) of Ethiopia for the year 2005, 2011 and 2016. The panel estimation of non-monetary multidimensional poverty gives a higher level of multidimensional poverty compared to the reports from international organizations, mainly from OPHI and UNDP, in respective years. This is mainly due to the additional indicators and the weighting method used. Housing, cooking fuel, toilet and sanitation and electricity played a significant role compared with the other indicators for the high non-monetary multidimensional poverty in the country. It is also found that there is a substantial and positive impact of the previous poverty status of households for their future experience in poverty. Households living in rural areas are more prone to experience poverty and have low chance to exit from poverty compared to urban households. In addition to focus on infrastructure development in fighting poverty, the governments’ development policies and strategies should target households who are experiencing poverty trap mainly those who are living in rural areas.

In this paper, we develop a general method for heterogeneous variable selection in Bayesian nonlinear panel data models. Heterogeneous variable selection refers to the possibility that subsets of units are unaffected by certain variables. It may be present in applications as diverse as health treatments, consumer choice-making, macroeconomics, and operations research. Our method additionally allows for other forms of cross-sectional heterogeneity. We consider a two-group approach for the model's unit-specific parameters: each unit-specific parameter is either equal to zero (heterogeneous variable selection) or comes from a Dirichlet process (DP) mixture of multivariate normals (other cross-sectional heterogeneity). We develop our approach for general nonlinear panel data models, encompassing multinomial logit and probit models, poisson and negative binomial count models, exponential models, among many others. For inference, we develop an efficient Bayesian MCMC sampler. In a Monte Carlo study, we find that our approach is able to capture heterogeneous variable selection whereas a "standard'' DP mixture is not. In an empirical application, we find that accounting for heterogeneous variable selection and non-normality of the continuous heterogeneity leads to an improved in-sample and out-of-sample performance and interesting insights. These findings illustrate the usefulness of our approach.

The objective of this paper was to measure the impact of owning a website and an email address on the exports of companies in Mali. It is based on panel data collected by the World Bank for the years 2003, 2007, 2010 and 2016. First, the study uses a probit model made it possible to analyze the determinants of the decision to export. for a company. As such, it appears that the technological variables are not significant in terms of the choice to export. A second linear regression analyzed the factors that explain the level of export volumes. It reveals that owning a website increases the volume of a company's exports, while owning an email address decreases the volume of exports. The results can be used to fuel public policies aimed at boosting the country's exports.

Rating transition matrices have become a workhorse of the IFRS 9 expected credit loss and ICAAP stress test modelling. The standard method to stress a through-the-cycle transition matrix is based on a single factor Gaussian model with a correlation parameter that is usually estimated on the level of a product pool. The goal of the paper is to generalize the model allowing for more general distributional assumptions and to test empirically the sensitivity of the results with respect to these assumptions and different possible approaches to the correlation parameter estimation. We are not aware of any such empirical study in the literature. The results show that this dependence is very strong with the standard approach underestimating the results, as we argue, of a more precise calculation many times. Therefore, there is a significant model risk that needs to be taken into account in ICAAP/IFRS 9 implementation and dealt with in further research.

The Transatlantic Trade and Investment Partnership (TTIP) generated an unprecedented public contestation across Europe. In this paper, we focus on the sources of such backlash in European public opinion. Previous studies of this issue have analysed opinions on free trade and the specific agreement separately. However, not accounting for their correlated character could lead to biased conclusions about their determinants. To address this, we apply an innovative empirical approach and construct a set of bi-variate probit models to calculate joint probabilities for the different configurations of support and opposition. We validate that attitudes toward free trade and the TTIP have similar but not identical foundations. Inconsistent preferences are rooted in individual values, EU attitudes, and political cues, as well as treaty partner heuristics. Our innovative empirical approach offers an improved understanding of trade attitudes within EU’s multilevel context.

Benchmark bonds help to improve market efficiency. They seem to arise spontaneously in deep and liquid markets. Can governments help to create them where markets are too small? This paper examines three emerging markets in Asia where authorities have tried: they have designated specific bonds as benchmarks and fostered their liquidity. We identify exactly which bonds were the designated benchmarks. We then propose rank-order measures of liquidity and determine the extent to which these de jure benchmarks end up as de facto benchmarks in the sense of being the most liquid bonds in their maturity segments. We find that this occurs in close to 60% of months in our sample, covering a range of maturities for Indonesia, Malaysia and Thailand. We identify three factors that make success more likely: (a) choosing already liquid bonds; (b) choosing bonds that have previously served as de jure benchmarks; and (c) choosing bonds that will be issued during the month.

This study contributes to the literature by examining how socio-economic and behavioral characteristics such as neighborhood income inequality and peer effects determine voluntary pension contributions for self-employed workers in Chile. We use a survey representative of the Chilean household sector and we run five different probit regressions to elucidate voluntary contributions to the pension system for self-employed workers. Additionally, we run probit models with a heteroscedastic structure (hetprobit models). The results show that the driving forces of the decision to contribute for retirement are significantly related to age, income, sex, area of residence, education, belonging to or from indigenous descent, multidimensional poverty, job formality, cash transfers, having a current account and credit card. Exceptionally, this study finds that behavioral aspects, that where not considered in depth so far both in research and public policy, are significant in savings contribution. Our research concludes that neighborhood income inequality and peer effects should be further analyzed as part of the context for which public policies are designed.

Online retailers are facing an increasing variety of product choices and diversified consumer decision journeys. To improve many operations decisions for online retailers, such as demand forecasting and price management, an important first step is to achieve a realistic understanding of the substitution patterns among a large number of products offered in the complex online environment. Although classic choice models offer an elegant framework for estimating substitution patterns among competing options, they have very limited applicability and performance in complex settings with many products. We provide a solution by developing a high-dimensional choice model that not only scales up more easily but also allows for flexible substitution patterns. We leverage consumer click-stream data, and combine econometric and machine learning (graphical lasso, in particular) methods to learn the substitution patterns among a large number of products. We show our model offers significantly better in- and out-of-sample demand forecasts in various synthetic datasets and in a real-world empirical setting. For example, in both settings, our model reduces the out-of-sample mean absolute percentage error (MAPE) by approximately 60% compared to classical models (i.e., the IID and the random coefficient Probit models). Our model can be used to enhance many business decisions such as assortment planning, inventory management and pricing decisions. In particular, we illustrate with a counter-factual pricing experiment that our model recommend better price points which increase total profit or revenue by 10% or above compared to classical models.

Based on monthly data covering the period from 1987 to 2019, we analyse whether cross-sectional moments of stock market returns may provide information about the future position of the German business cycle. We apply in-sample forecasting regressions with and without leading indicators as control variables, pseudo-out-of-sample exercises, Probit models, and Autoregressive Distributed Lag Models. We find in-sample predictive power of the first and third cross-section moments for the future growth of industrial production, even if one controls for well-established leading indicators for the German business cycle. In addition, out-of-sample tests show that these variables reduce the relative Mean Squared Error compared to benchmark models. The results for the second moment are less promising. Also, we do not observe a long-run relation between the moment series and industrial production.

A 1900-2016 time series model predicts former Vice President Joe Biden will win 55.44 percent of the 2020 US presidential popular vote. A change in party control of the U.S. House of Representatives two years before a presidential election has a significant coefficient to explain the popular vote that has not been explored in the past 25 years. A cross-section 50 state Electoral College probit model projects President Trump’s re-election, winning 26 states with 279 electoral votes while Biden receives 259. These popular vote and electoral vote forecasts surely have wide discrepancies. Biden is forecast to win the Electoral College vote under several circumstances. If he wins Wisconsin’s 10 electoral votes, which are predicted for Trump, the candidates could tie with 269 electoral votes and the House of Representatives would select the president. Biden would be elected if he wins Wisconsin and the popular vote in one Congressional district in Nebraska or both districts in Maine. Biden could win with victories in several states where he is forecast to lose close contests. Numbers of COVID-19 virus cases and deaths are not highly correlated with states where Trump is projected to win. The effects of factors such as the President’s health and candidates’ closing campaign behavior cannot be measured. Forecasting US presidential outcomes in the 21st century has become a highly risky activity.

In this article, we are testing the effects of an inverted Yield curve, as a result of the relationship between the short and long-term interest rates of the US Treasury with constant maturities. Our aim is to illustrate and spot cycles that created the US recession in 2008 based on Estrella and Mishkin, (1996), spread definition. In our model, the recession probability is calculated by using a 99% confidence level of the standard normal cumulative distribution function. Then, we will apply a probit model to measure the relationship between a binary variable strength such as prediction of a recession over a number of other variables such as the logarithmic monthly returns of the Federal funds effective rates, and the logarithmic monthly returns of the seasonally adjusted money supply, (M2). The data that we have used are monthly returns starting from 01/10/1993 to 01/01/2013, which total to 231 observations. The data was obtained from the Federal Reserve Statistical Release Department and the symbols of the series are H.6, and H.15.

This paper explored the reliability of self-reported health and the impact of the social position in determining the inconsistent health response, in late life. The study uses nationally representative unit level data from National Sample Survey of India. Using bi-variate probit model, joint estimation of reported health and inconsistent health responses are determined for all factors that manifest social injustice and constitutes individual characteristics. The study has two major findings: firstly, in the privileged socioeconomic segment, reported health underestimates the presence of morbidity might because it can be controlled by the treatment due to greater access to healthcare services, economic resources and health awareness; and secondly, among the deprived elderly the reported health suppress the redundancy of chronicle ailment. Therefore, the factors, responsible for social justice like social caste, economic status, and attainment of education have significant impact on reporting inconsistent health responses. Results from bi-variate probit model offer deeper understandings about reliability of self-reported health and provide further insights to improve policy design designed to mitigate the health inequality among the elders. The study identifies, the type of inconsistent health responses, and the factors responsible for the inconsistency of self-reported health with existence of chronicle morbidity among the elderly population.

This paper examines the relationship between individuals’ migration decisions and risk attitudes. We use the Two-Sample Two-Stage Instrumental Variable (TS2SIV) technique to measure relative risk aversion and its impact on migration. Using the probit model, we find that more risk-averse people are less likely to migrate. The results also indicate that the impact of risk attitudes on migration varies by other demographic characteristics such as age, sex, etc. In addition, we test whether there is any network effect on migration and how risk attitudes vary with the network effect. We consider two ethnic groups; Asian and Hispanic, and find that the size of one’s own ethnic group in a source location has a significant effect on the migration decisions. In addition, we also find that risk attitudes vary with the network effect.

Autonomous Vehicles (AVs) will soon be a reality on our roads. Up to now, their standard behaviour is predetermined since it is the result of programmed algorithms. Under risky traffic situations, however, they will face dilemmas such as, deciding between affecting the passenger or affecting others. Thus, in this experiment, we investigate how people solve a reframed version of the well-known Trolley-problem (Foot, 1967) under two conditions: when subjects actually drive a vehicle simulator, versus when they solve the same dilemma by programming a hypothetical AV. In both settings the participants’ decisions have real monetary consequences, which affect others and themselves. Our Probit models indicate that subjects who program an AV and who are more cautious in terms of speed are less (more) likely to sacrifice a pedestrian (themselves) compared to those who actually drive and prefer a higher driving speed. Moreover, we find that the subjects’ choices are associated with risk aversion but not with moral beliefs or loss aversion. Implications of the driving vs programming discrepancies for the design of AVs algorithms are discussed.

This paper presents non-take-up rates of benefits from the German Income Support for Job Seekers scheme, called Unemployment Benefit II (Arbeitslosengeld II). Eligibility to these benefits is simulated by applying a microsimulation model based on data from the Socio-economic Panel for the years 2005 to 2014. To ensure the quality of the results, feasible upper and lower bounds of nontake-up are shown for different simulation assumptions. By employing a binary choice framework, determinants of the decision (not) to take-up benefits are studied by means of a cross-sectional probit regression and a fixed effects linear probability model. The findings of this study indicate that rates of non-take-up are substantial and time-stable in the decade after the Hartz IV reform of 2005. On average, the share of households that do not claim benefits despite being eligible, amounts to 55.7 percent of all eligible households in that period. The issue of non-take-up has further important implications for the determination of the standard benefit rate. Since the legally defined calculation procedure does not account for non-take-up households in the reference group, the approximated consumption expenditure that is considered as necessary for a dignified life is calculated too low. The results of this study suggest that the legally defined monthly adult lump sum amount in the year 2014 would have been twelve euros higher if the issue of non-take-up was accounted for in the methodology. Based on the findings, the paper aims to give policy recommendations.

The preservation of cultural heritage is entailing excessive cost (paid by people through taxation) while is a source of additional income for both, the State and the people, due to tourism. Since the evaluation of this good cannot be in market terms, we apply a modified version of the Contingent Valuation Method (CVM), which is frequently used in Experimental Economics, in order to investigate the significance that people put on this good and how much they might be willing to pay for supporting activities concerning the preservation of such antiquities, that also improve the urban environment. For this purpose, we have developed a methodological framework under the form of an algorithmic procedure with 21 activity stages and 4 decision nodes, and proved its functionality by implementing it in the case of the Cononian Walls surrounding the peninsula of Piraeus, the main port of Athens, Greece. The results indicate that people are in favor of overturning the devaluation of the monument, by participating in relevant activities, including financial support. Nevertheless, a part of the interviewees disagrees with any financial contribution, judging that this expenditure should be covered exclusively by the State. All statistical processing of answers, obtained through a properly designed/circulated questionnaire, was carried out by Logit and Probit model regression analysis. Both models gave significant (at 0.05 level) dependence of willingness to pay (WTP) on preferred course of action (i.e., leaving the ruins situation as is, performing only the necessary remediation, proceeding with radical restoration).

After a long debate on wine import tariffs, the Italian Parliament rejected the Spanish-Italian trade agreement on 17 December 1905. This decision left Spain and Italy without a bilateral trade treaty for an entire decade. In the literature, broader political issues and local interests are alternatively indicated as the main drivers of the rejection. Based on a new database which collects economic and political variables (including MPs personal features) and using a probit model, this paper provides a quantitative analysis of the vote. Results show that constituency interests had a role in determining the result of the vote on the trade treaty. Moreover, constituency interests were also important for the “vote switchers”, i.e. those MPs that supported the overall government policy stance in the first round, but opposed the Spanish-Italian trade agreement in the second.

While institutions are said to be poor in China in cross-country comparison, recent research indicates that at the provincial level, institutional quality plays in fact an important role for the economic success of a province in China. Our paper aims to add further arguments to this discussion by focusing on the concept of club convergence. In particular, we analyze whether institutional quality in low-income provinces converges to the level experienced by relatively highly developed provinces or whether there exist multiple institutional clubs over the period 1997-2007 by using the log t test proposed by Phillips and Sul (2007). Our findings indicate that there exist multiple institutional clubs within China, three rather small clubs which follow an above-average high institutional quality path and two clubs which find themselves on a relatively low institutional quality path and which together account for the majority of provinces. Using the same methodology, we find that various provinces of the poor institutional clubs are additionally caught in a low-income trap. In a next step, we analyze the causal relationship between poor institutional traps and low-income traps in China. Using a recursive bi-variate probit model enables us to control for the problem of endogeneity in the institutions-income relationship. We find evidence that institutional traps are important determinants of income traps, giving rise to the recently identified phenomenon of a ‘double trap’. Finally, our findings indicate that human capital and urbanization are additional important determinants of income traps, while globalization is decisive for avoiding poor institutional traps.

This paper studies the determinants of repeated use of Fund-supported programs in a large sample covering virtually all General Resources Account (GRA) arrangements that were approved between 1952 and 2012. Generally, the revolving nature of the IMF’s resources calls for the temporary sup-port of member countries to address balance of payments problems while repeated use has often been viewed as program failure. First, using probit models we show that a small number of country-specific variables such as growth, the current account balance, the international reserves position, and the institutional framework play a significant role in explaining repeated use. Second, we discuss the role of IMF-specific and program-specific variables and find evidence that a country’s track record with the Fund is a good predictor of repeated use. Finally, we conduct an out-of-sample forecasting exer-cise. While our approach has predictive power for repeated use, exact forecasting remains challenging. From a policy perspective, the results could prove useful to assess the risk IMF programs pose to the revolving nature of the Fund’s financial resources.

Ambiguity in literature about how pensions influence retirement decision can be a challenge to policy formulation unless further inquiry is done. This ambiguity could be attributed to a number of factors including the study scope, different pension designs and differing study methodologies. Using Uganda’s public service pension scheme, this study delves into exploring whether the scheme’s design influences timing of retirement. Using a probit model, analysis of the effect of pension o timing of retirement is done using pensioners’ data set from the pension department, ministry of public service. Results from the experiment show that the scheme significantly influences timing of retirement. Key implication from this is that the investigated pension scheme variables (age, experience and pension benefits) can be used as fiscal tools for attaining socioeconomic targets.

Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals., La modélisation et l’estimation de données discrètes persistantes peuvent s’avérer difficiles. Dans cette étude, nous utilisons un modèle probit autorégressif avec données de panel où l’autocorrélation de la variable discrète dépend de l’autocorrélation de la variable latente. Dans ce type de modèle non linéaire, l’autocorrélation d’une variable non observée entraîne une vraisemblance incalculable contenant des intégrales de haute dimension.

The Korean wave has become a global phenomenon whose effect has been widely studied in Asia, Europe and the US. However the presumption of cultural distance makes it appear unlikely that the Korean wave could gain traction among African consumers of cultural products. As such, a dearth of evidence exists on the effects of the wave in Africa. This paper examines the effect of the wave in East African countries employing both descriptive and Probit model analyses. The results show that, contrary to conventional beliefs, most Africans surveyed perceive value proximity with Korea through the values conveyed in Korean dramas, movies and music. Confucius values, such as filial piety, family love and respect for the elderly are the most appealing to the East African audience. Importantly, contact with Korean wave contents contributes to the respondents’ disposition to form favorable attitude towards Korea. The African consumers of Korea’s cultural products are equally likely to purchase other Korean commercial products. These results remotely suggest that Hallyu may be a tool for advancing Korea’s soft power towards Africa and could generate positive economic externalities.

Does the length of a worker’s prior experience as an employee later determine their choice of self-employment? If so, to what extent is this type of mobility attributable to genuine duration-dependence implied by specific capital accumulation? And then, what theoretical basis is the presence of specific capital attributable? To answers these questions, this paper uses longitudinal HILDA survey data of Australian workers to estimate a dynamic random-effects panel probit model of the probability of self-employment and isolate the impact of genuine duration-dependence on the likelihood that employees increasingly choose self-employment with increasing job-tenure. In doing so, this paper makes a departure from the heterogeneity-based explanations for self-employment common in the entrepreneurship literature and, instead, expands upon the well-established rationales for labour mobility and job-turnover in labour economics. While employee job-change and labour market mobility has long attracted the attention of labour economists, existing models ignore self-employment as the outside option. Of particular interest, therefore, is whether duration-dependence influences the probability of employees becoming self-employed either because the relative appeal of outside self-employment opportunities increase as ‘better’ employee job-offers from external firms diminish (as implied by Jovanovic’s theory of job-matching), or because the prospective opportunities in self-employment become increasingly valuable (as implied by Becker’s human-capital theory). This paper finds compelling evidence that the choice of self-employment is driven by the same economic forces that determine job-change and labour mobility more broadly. But, rather than strengthening worker-firm relationships, duration-dependence attributable to specific capital accumulation acts as a ‘double-edged sword’ increasing the probability of employees becoming self-employed with prolonged tenure.

This paper investigates how households have been adapting to climate change through the use of two technologies important for thermal comfort, air conditioning and thermal insulation. Merging a global gridded dataset of historical temperatures with the 2011 OECD EPIC survey, we study the determinants of installing air conditioning or adopting thermal insulation in response to a warmer climate in eight countries. After controlling for a set of demographic, socio-economic and attitudinal variables, we apply a binary probit model and find that exposure to a warmer climate influences only air conditioning adoption whereas, climatic conditions seem not to affect thermal insulation decisions which, instead, mainly depends on household wealth, dwelling characteristics, age, household size and propensity to energy-saving behaviours. This study does not find any evidence of a possible joint decision for the two technologies.

This paper brings new evidence of predicting the U.S. recessions through Markovian models. The Markovian models, including the Hidden Markov and Markov models, incorporate the temporal autocorrelation of binary recession indicators in a more traditional and natural way. Considering interest rates and spreads, stock prices, monetary aggregates, and output as the candidate predictors, we examine the out-of-sample performance of the Markovian models in predicting the recessions one to twelve months ahead. The Markovian models are superior to the probit models in detecting a recession and capturing the recession duration. We find the "one-month lag phenomenon" that the best Markovian model supported by statistical model selection procedures can always recognize the onset of a recession one month after it starts. In addition, the yield spread continues to serve as the most e cient predictor variable in explaining business cycles.

We employ probit regression analysis to compare the adult voting activity of students who participated in the Milwaukee Parental Choice Program (MPCP) to their matched public school counterparts. We use a sophisticated matching algorithm to create a traditional public school student comparison group using data from the state-mandated evaluation of the MPCP. By the time the students are 19-26 years old, we do not find evidence that private school voucher students are more or less likely to vote in 2012 or 2016 than students educated in public schools. These results are robust to all models and are consistent for all subgroups.

This study applies a probit model to secondary data to investigate the factor influencing demands for health insurance in Uganda. The results reveal that wealth, level of education, access to information and area of residence are significantly associated with demand for health insurance. However, age, marital status and health status as proxied by smoking are insignificant. Results further reveal that health insurance is more pronounced among wealthier, educated and well-informed individuals who reside in urban areas. The study therefore recommends for the policies geared towards poverty reduction, investing in education both at primary and secondary levels, increased public awareness about benefits of health insurance and establishment of a National Social Health insurance scheme since such variables were highly associated with demand for health insurance.

Wage adjustments for employees are a reaction mechanism to changing market conditions and form a significant part of pay policy. Though various attempts to explore wage levels and wage differentials have been made, wage adjustment policies remain an understudied topic. This paper analyses the determinants of wage adjustments based on data from Russian enterprises 2015–17. The analysis is based on detailed data from an employer survey which covers more than 5,000 firms in both the public and private sector. The study adopts probit models to identify the reasons which determine wage revisions, depending on internal employer characteristics and external labour market conditions. The results are in line with previous research on the topic (Bayo-Moriones et al., 2016) and suggest that both internal and external factors influence wage adjustments. A wage adjustment is a reflection of the ability to pay meaning that revisions are often made by successful firms with high employee turnover. Institutional frameworks, especially trade union activity, affects the firm’s decision to adjust wages despite the general opinion on the insignificance of unions in Russia. This study contributes to the limited literature by analysing the determinants of wage policies depending on the firm’s characteristics. This is the first study of its kind based on extensive Russian data.