scholarly journals Estimating Grouped Data Models with a Binary-Dependent Variable and Fixed Effects via a Logit versus a Linear Probability Model: The Impact of Dropped Units

2019 ◽  
Vol 28 (1) ◽  
pp. 139-145 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Fixed Effects ◽  
Probability Model ◽  
Grouped Data ◽  
Linear Probability Model ◽  
Simple Question ◽  
Binary Dependent Variable ◽  
The Difference ◽  
Linear Probability ◽  
The Impact ◽  
Correct Comparison

This letter deals with a very simple question: if we have grouped data with a binary-dependent variable and want to include fixed effects in the specification, can we meaningfully compare results using a linear model to those estimated with a logit? The reason to doubt such a comparison is that the linear specification appears to keep all observations, whereas the logit drops the groups where the dependent variable is either all zeros or all ones. This letter demonstrates that a linear specification averages the estimates for all the homogeneous outcome groups (which, by definition, all have slope coefficients of zero) with the slope coefficients for the groups with a mix of zeros and ones. The correct comparison of the linear to logit form is to only look at groups with some variation in the dependent variable. Researchers using the linear specification are urged to report results for all groups and for the subset of groups where the dependent variable varies. The interpretation of the difference between these two results depends upon assumptions which cannot be empirically assessed.

scholarly journals The Soviet Informal Economy as a Factor of Post-Soviet Development

2021 ◽  
Vol 18 (4) ◽  
pp. 559-569
Author(s):  
A. A. Lipanov ◽  
◽  
E.N. Kalmychkova ◽  
Keyword(s):  
Soviet Union ◽  
Market Economy ◽  
Informal Economy ◽  
Fixed Effects ◽  
Least Squares Method ◽  
Positive Impact ◽  
Probability Model ◽  
Linear Probability Model ◽  
The Soviet Union ◽  
Linear Probability

The article analyzes the quantitative relationship between the informal economy in the Soviet republics of the 1980s and the characteristics of the market economy in these republics after the collapse of the Soviet Union. Methodologically, the study relies on the logit, linear probability model and least-squares method. The logit and linear probability model are used to quantify the fixed effects affecting the attitudes of households in different countries in the 2000s to the market economy in comparison with the planned economy. The authors compare the obtained fixed effects with the size of the informal economy in Soviet republics of the 1980s using the least-squares method. The study shows a direct relationship between people’s involvement in the Soviet informal sector and their subsequent adaptability to the new conditions of the market economy after the collapse of the Soviet Union. Thus, the possible positive impact of the informal economy on the adaptation of the population to the market economy is empirically proved. The authors conclude that the Soviet informal economy helped facilitate households’ transition to the market economy and in the medium term had a positive impact on post-Soviet economic development.

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.

scholarly journals A Crook is a Crook . . . But is He Still a Crook Abroad? On the Effect of Immigration on Destination-Country Corruption

2015 ◽  
Vol 16 (4) ◽  
pp. 464-489 ◽  
Author(s):  
Eugen Dimant ◽  
Margarete Redlin ◽  
Tim Krieger
Keyword(s):  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Destination Country ◽  
Estimation Methods ◽  
Econometric Methodology ◽  
Migration Flows ◽  
The Difference ◽  
Method Of Moments Estimator ◽  
The Impact

AbstractThis paper analyzes the impact of migration on destination-country corruption levels. Capitalizing on a comprehensive dataset consisting of annual immigration stocks of OECD countries from 207 countries of origin for the period 1984-2008, we explore different channels through which corruption might migrate. We employ different estimation methods using fixed effects and Tobit regressions in order to validate our findings. Moreover, we also address the issue of endogeneity by using the Difference- Generalized Method of Moments estimator. Independent of the econometric methodology, we consistently find that while general migration has an insignificant effect on the destination country’s corruption level, immigration from corruption-ridden origin countries boosts corruption in the destination country. Our findings provide a more profound understanding of the socioeconomic implications associated with migration flows.

scholarly journals Estimating the Effects of Individual Nurses on ICU Outcomes

2020 ◽  
Author(s):  
Stephen E. Brossette ◽  
Ning Zheng ◽  
Daisy Y. Wong ◽  
Patrick A. Hymel
Keyword(s):  
Fixed Effects ◽  
Unobserved Heterogeneity ◽  
Probability Model ◽  
Kidney Injury ◽  
Fine Grained ◽  
High Side ◽  
Work Shifts ◽  
Near Term ◽  
Linear Probability ◽  
Mimic Iii

AbstractA better understanding of the effects of nursing on clinical outcomes could be used to improve the safety, efficacy, and efficiency of inpatient care. However, measuring the performance of individual nurses is complicated by the non-random assignment of nurses to patients, a process that is confounded by unobserved patient, management, workforce, and institutional factors. Using the MIMIC-III ICU database, we estimate the effects of individual registered nurses (RNs) on the probability of acute kidney injury (AKI) in the ICU. We control for significant unobserved heterogeneity by exploiting panel data with 12-hour fixed effects, and use a linear probability model to estimate the near-term marginal effects of individual RN assignments. Among 270 ICU RNs, we find 15 excess high-side outliers, and 4 excess low-side outliers. We estimate that in one twelve-hour work shift, each high-side RN outlier increases the probability of AKI by about 4 percentage points, and in 25 work shifts, causes about one additional AKI. Conversely, each low-side outlier prevents about one AKI in 50 work shifts. Given the fine-grained nature of the fixed effects employed, we believe that the estimated individual nursing effects are approximately causal. We discuss our contribution to the literature and identify potential use cases for clinical deployment.

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 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.

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