SNP and SML Estimation of Univariate and Bivariate Binary-Choice Models

Luca, Giuseppe De

doi:10.1177/1536867x0800800203

Cited by 72 publications

(57 citation statements)

References 19 publications

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“…By relaxing assumptions on the joint distribution of e and v, such that the form of FðÁ; ÁÞ is unknown, the approximation methodology from Stewart (2004) and De Luca (2008) can be generalized to deal with the case here. This is the semiparametric estimation of De Luca and Perotti (2011).…”

Section: Model Specification Estimation and Test: Sample Selectionmentioning

confidence: 99%

The Impact of Social Capital on Wages of Rural Migrants and its Gender Difference in China

Wang

Zhou

Zhang

2014

Journal of Macromarketing

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The objective of this article is to study the impact of social capital on the wages of Chinese rural migrants and the gender difference of the effect. The empirical results show that both ''bridging'' and ''bonding'' social capital have positive effects on wages, but ''bridging'' has a more significant effect. Friendly relationships between migrants and local workers can help the migrants increase the probability of earning upper middle to high wages by 6.4%. Migrants whose jobs are introduced by friends or relatives have over 1.6% higher probabilities of earning a upper middle to high wage. In addition, gender differences exist in the social capital effect on wage. The ''bridging'' effect on male migrants' wage is significantly larger than the effect on female migrants' wage, while the ''bonding'' effect on female's wage is larger than the effect on male's wage. Some policy implications are discussed.

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Section: Model Specification Estimation and Test: Sample Selectionmentioning

confidence: 99%

The Impact of Social Capital on Wages of Rural Migrants and its Gender Difference in China

Wang

Zhou

Zhang

2014

Journal of Macromarketing

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“…While originally it was used in research areas other than agricultural economics, it is now quickly entering this field as well. Recent "agriculture-oriented" studies that use this approach include Hess and von Cramon-Taubadel (2007, 2008, Gallet (2007Gallet ( , 2010, Johnston and Duke (2009) and Lagerkvist and Hess (2011).…”

Section: Empirical Approachmentioning

confidence: 99%

Does Market Structure Influence Price Transmission in the Agro‐food Sector? A Meta‐analysis Perspective

Bakucs¹,

Fałkowski²,

Fertő³

2013

J Agricultural Economics

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There exists a large literature on price transmission in agro‐food sectors. However, a great majority of empirical studies focus on the existence of asymmetry and, by and large, do not investigate the reason for its presence or absence. This is in sharp contrast to the theoretical literature that provides a number of explanations of why we should expect (a)symmetry. In response, this paper investigates the reasons for asymmetric price transmission in the agro‐food chain, using meta‐analysis of existing studies. Our focus is on the organizational and institutional characteristics of the agro‐food supply chain. Our findings suggest that asymmetric price transmission in farm–retail relationships is more likely to occur in sectors/countries with more fragmented farm structure, higher governmental support and more restrictive regulations on price controls in the retail sector. On the other hand, more restrictive regulations on entry barriers in the retail sector and the relative importance of the sector tend to promote symmetric farm–retail price transmission. The latter is also more likely in the presence of a strong processing industry.

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“…The latter have the advantage of not requiring the true parametric model to be specified by the researcher. However, while theoretically possible, 26 the intercept is typically not identified in these models, and so this approach is not suitable for estimating population means based on binary outcomes, such as HIV prevalence. Semi-parametric approaches that allow for the estimation of the intercept require additional assumptions and have only been developed for the case of continuous outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…However, if the true distribution of the error terms is not bivariate normal, then the estimates are likely to be both inconsistent and inefficient. 26 Simulation studies have indicated that HIV prevalence estimates from selection models may indeed be sensitive to violations of this assumption. 27 …”

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confidence: 99%

On the Assumption of Bivariate Normality in Selection Models

McGovern¹,

Bärnighausen

Marra

et al. 2015

Epidemiology

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Background Heckman-type selection models have been used to control HIV prevalence estimates for selection bias, when participation in HIV testing and HIV status are correlated after controlling for observed variables. These models typically rely on the strong assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal. Methods We introduce a novel approach for relaxing the bivariate normality assumption in selection models using non-linear copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambian Demographic and Health Survey (DHS), using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status). Results We show in a simulation study that selection models can generate biased results when the bivariate normality assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% = CI 16% to 25%), compared with 12% (11% to 13%) among those who consented to be tested; for women, the corresponding figures are 19% (13% to 24%) and 16% (15% to 17%). Conclusions Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology, and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.

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SNP and SML Estimation of Univariate and Bivariate Binary-Choice Models

Cited by 72 publications

References 19 publications

The Impact of Social Capital on Wages of Rural Migrants and its Gender Difference in China

The Impact of Social Capital on Wages of Rural Migrants and its Gender Difference in China

Does Market Structure Influence Price Transmission in the Agro‐food Sector? A Meta‐analysis Perspective

On the Assumption of Bivariate Normality in Selection Models

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