scholarly journals Why do households participate in the loan moratorium in Hungary? Theoretical and empirical considerations

2021 ◽  
Vol 71 (S1) ◽  
pp. 119-140
Keyword(s):  
Survey Data ◽  
Probability Model ◽  
Borrowing Constraints ◽  
Economic Effects ◽  
Driving Factors ◽  
Linear Probability Model ◽  
The Past ◽  
Credit Demand ◽  
Linear Probability

Abstract In order to mitigate the economic effects from the COVID-19 epidemic, a moratorium on loan repayments was introduced in several countries, including Hungary. Essentially, a loan moratorium provides additional finance for participants, allowing theories of both credit demand and consumption to be tested on debtors’ decisions as to whether or not they participate in the programme. In this paper, we use a linear probability model on the Hungarian survey data to examine the driving factors behind the households’ decision to participate in the scheme. Our results show that the younger debtors and those with more children are more likely to utilise the programme. Stretched financial situations, i.e., lower incomes, lower savings and higher payment-to-income ratios, increase the probability of continued participation as well. The chance of participating in the scheme also increases significantly when a household has faced borrowing constraints over the past two years, i.e., it has not been or only partially been able to satisfy its credit demand.

scholarly journals The Millennial Generation

SAGE Open ◽  
2017 ◽  
Vol 7 (1) ◽  
pp. 215824401769715 ◽  
Author(s):  
Pasi Pyöriä ◽  
Satu Ojala ◽  
Tiina Saari ◽  
Katri-Maria Järvinen
Keyword(s):  
Family Life ◽  
Probability Model ◽  
Quality Of Work Life ◽  
Work Commitment ◽  
Linear Probability Model ◽  
Quality Of Work ◽  
Older Employees ◽  
The Past ◽  
Linear Probability

This article puts to the test the notion that younger generations, most notably the Millennials, value work less than older generations do. The analysis, deploying a linear probability model, is based on Statistics Finland’s Quality of Work Life Surveys, 1984 to 2013. Focusing on labour market entrants aged 15 to 29, we address two main themes: the value given to work, leisure and family life, and work commitment. Regardless of age, the value given to work has remained consistently high for the past three decades. At the same time, leisure and family life have gained increasing importance, not only among the Millennials but also among older generations. The Millennials are more prepared to change to a different occupational field than older employees, but this is not a new tendency, and therefore the generational gap remains unaffected. The evidence does not support the argument that the Millennials are less work-oriented than older generations.

Sibling Models, Categorical Outcomes, and the Intra-Class Correlation

2021 ◽  
Author(s):  
Richard Breen ◽  
John Ermisch
Keyword(s):  
Random Effects ◽  
Probability Model ◽  
Intraclass Correlation ◽  
Linear Probability Model ◽  
Intra Class Correlation ◽  
Sibling Differences ◽  
Sibling Models ◽  
Alternative Measures ◽  
Linear Probability ◽  
Parametric Estimate

Abstract In sibling models with categorical outcomes the question arises of how best to calculate the intraclass correlation, ICC. We show that, for this purpose, the random effects linear probability model is preferable to a random effects non-linear probability model, such as a logit or probit. This is because, for a binary outcome, the ICC derived from a random effects linear probability model is a non-parametric estimate of the ICC, equivalent to a statistic called Cohen’s κ. Furthermore, because κ can be calculated when the outcome has more than two categories, we can use the random effects linear probability model to compute a single ICC in cases with more than two outcome categories. Lastly, ICCs are often compared between groups to show the degree to which sibling differences vary between groups: we show that when the outcome is categorical these comparisons are invalid. We suggest alternative measures for this purpose.

Charged with Instability: Are Paternal Arrests and Criminal Charges Enough to Shake the Marital Stability of American Couples?

2020 ◽  
Vol 41 (12) ◽  
pp. 2423-2447
Author(s):  
Antonius D. Skipper ◽  
Douglas S. Bates ◽  
Zachary D. Blizard ◽  
Richard G. Moye
Keyword(s):  
Justice System ◽  
Probability Model ◽  
Well Being ◽  
Marital Stability ◽  
Fragile Families ◽  
Linear Probability Model ◽  
Paternal Incarceration ◽  
Criminal Charges ◽  
Linear Probability ◽  
The Relationship

With the growing rate of divorce, increasing efforts are being made to identify the factors that contribute to relationship dissolution for many American couples. One commonly noted, and particularly concerning, factor toward relationship instability is the incarceration of husbands and fathers. Although paternal incarceration and familial stability have been studied, little is known about the relationship between criminal charges and divorce. The current study utilized data from the Fragile Families and Child Wellbeing Study to understand the effect of paternal criminal charges on divorce for 725 families. Utilizing a logistic regression and two-stage least squares linear probability model, results show that, even without incarceration, being charged with a crime as a husband significantly increases the likelihood that a couple will get divorced. These findings have significant implications for understanding how encounters with the criminal justice system affect familial well-being and stability.

scholarly journals Pengaruh Faktor Sosial Ekonomi terhadap Perkawinan Muslim di Indonesia

2020 ◽  
Vol 6 (8) ◽  
pp. 1674
Author(s):  
Fauzia Aqilla Fadhil ◽  
Ilmiawan Auwalin
Keyword(s):  
Family Life ◽  
Regression Models ◽  
Probability Model ◽  
Muslim Women ◽  
Sample Population ◽  
Probit Regression ◽  
Linear Probability Model ◽  
Logit Regression ◽  
Indonesian Family Life Survey ◽  
Linear Probability

This study aims to find out what factors that affect a Muslim's decision to get married. This study uses the data from the Indonesian Family Life Survey (IFLS) with a quantitative approach using 83% of the sample population in Indonesia covering approximately 30,000 people taken in 13 of the 27 provinces in Indonesia. This study was analyzed using Linear Probability Model (LPM) regression, Logit regression and Probit regression. The data in this study were processed using STATA MP software. According to the results of data using three regression models, the factors that affect the decision of each individual in Indonesia in general to marry are gender, religion, age, education and occupation. The factors that affect each individual Muslim in Indonesia to make a decision to marry are gender, age, education and occupation. Then, for women in Indonesia in general, the factors that affect the decision to get married are religion, age, and occupation. Last but not least, for Muslim women, the factors that affect the decision to marry is age and occupation.Keywords: Socio-Economy, Muslim Marriage, Marital Decision

Musculoskeletal Pain and Associated Sociodemographic Factors (Linear Probability Model)

2007 ◽  
Vol 7 (4) ◽  
pp. 620-624
Author(s):  
Nevin Uzgoren ◽  
Ali Cimbiz . ◽  
Cihan Caner Aksoy . ◽  
Sultan Ozturk . ◽  
Emel Elem .
Keyword(s):  
Musculoskeletal Pain ◽  
Probability Model ◽  
Sociodemographic Factors ◽  
Linear Probability Model ◽  
Linear Probability

scholarly journals Binary Response Models: Logits, Probits and Semiparametrics

2001 ◽  
Vol 15 (4) ◽  
pp. 43-56 ◽  
Author(s):  
Joel L Horowitz ◽  
N.E Savin
Keyword(s):  
Probability Model ◽  
Binary Response ◽  
Linear Probability Model ◽  
Response Models ◽  
Explanatory Variables ◽  
Nonparametric Models ◽  
Binary Response Model ◽  
Unrealistic Assumption ◽  
Binary Response Models ◽  
Linear Probability

A binary-response model is a mean-regression model in which the dependent variable takes only the values zero and one. This paper describes and illustrates the estimation of logit and probit binary-response models. The linear probability model is also discussed. Reasons for not using this model in applied research are explained and illustrated with data. Semiparametric and nonparametric models are also described. In contrast to logit and probit models, semi- and nonparametric models avoid the restrictive and unrealistic assumption that the analyst knows the functional form of the relation between the dependent variable and the explanatory variables.

scholarly journals Comparing linear probability model coefficients across groups

2014 ◽  
Vol 49 (5) ◽  
pp. 1823-1834 ◽  
Author(s):  
Anders Holm ◽  
Mette Ejrnæs ◽  
Kristian Karlson
Keyword(s):  
Probability Model ◽  
Linear Probability Model ◽  
Linear Probability
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