Selection corrections for panel data models under conditional mean independence assumptions

doi:10.1016/0304-4076(94)01645-G

Journal of Econometrics

Volume 68, Issue 1, July 1995, Pages 115-132

https://doi.org/10.1016/0304-4076(94)01645-G Get rights and content

Abstract

Some new methods for testing and correcting for sample selection bias in panel data models are proposed. The assumptions allow the unobserved effects in both the regression and selection equations to be correlated with the observed variables; the error distribution in the regression equation is unspecified; arbitrary serial dependence in the idiosyncratic errors of both equations is allowed; and all idiosyncratic errors can be heterogeneously distributed. Compared with maximum likelihood and other estimators derived under fully parametric assumptions, the new estimators are much more robust and have significant computational advantages.

References (15)

G. Chamberlain
Multivariate regression models for panel data
Journal of Econometrics
(1982)
N.M. Kiefer
Estimation of fixed effect models for time series of cross-sections with arbitrary intertemporal covariance
Journal of Econometrics
(1980)
W.K. Newey
A method of moments interpretation of sequential estimators
Economics Letters
(1984)
J.E. Zabel
Estimating fixed and random effects with selectivity
Economics Letters
(1992)
G. Chamberlain
Analysis of covariance with qualitative data
Review of Economic Studies
(1980)
J. Heckman
The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models
Annals of Economic and Social Measurement
(1976)
G.S. Maddala
Limited dependent and qualitative variables in econometrics
(1983)

There are more references available in the full text version of this article.

Cited by (524)

Impact of environmental, social and governance initiatives on firm value: Analysis using AI-based ESG scores for Japanese listed firms
2024, Research in International Business and Finance
This paper is the first study to examine the relationship between ESG initiatives and firm value for almost all Japanese listed firms. By constructing our unique AI-based ESG (Environmental Social Governance) scores that have higher coverage than existing ESG scores, we score and analyze firms that have not previously been given ESG scores and provide more robust evidence for the impact of ESG initiatives on firm value. Our unique AI-based ESG scores are classified as ESG disclosure scores. We empirically show that Japanese listed firms engaging in ESG activities have a significantly higher Tobin’s Q and higher volatility. We use the COVID-19 pandemic to eliminate reverse causality in our analysis.
Innovation Strategies and Technical Efficiency
2024, European Research on Management and Business Economics
In the innovation literature, little attention has been paid to technical efficiency as a measure of performance, despite the fact that technical efficiency is key to explaining firm productivity. In this study we analyze the influence of internal R&D, external R&D and R&D cooperation on the technical efficiency of the firm. The data used come from the Panel de Innovación Tecnológica 2016 for Spanish companies, from whose database we have selected the set of innovative manufacturing firms. To estimate the coefficients of interest we have constructed a knowledge production function, estimated using Stochastic Frontier Analysis (SFA) and the Heckman correction. The results indicate that both internal R&D and R&D cooperation have a positive and significant influence on technical efficiency, while external R&D has a significant negative relationship. Knowing this type of information in advance is very important for business managers and policy makers, since it facilitates decision-making on innovation strategies, as well as the formulation of public policies to support innovation leading to an efficient allocation of public resources.
Knowledge agglomeration and public subsidies to business R&D: Evidence for Spanish firms
2024, Structural Change and Economic Dynamics
In this paper, we study the role played by different dimensions of knowledge agglomeration as determinants of public subsidies to business R&D. For the analysis, we use firm-level information from the Spanish Panel of Technological Innovation (PITEC) and combine these data with information from other sources at the sectoral–regional level, obtaining an unbalanced panel of 28,082 observations of Spanish firms. Our results suggest that knowledge agglomeration affects both the probability of participation in subsidy programs and the subsidy amount awarded to supported firms. This effect is heterogeneous depending on the dimension of knowledge agglomeration considered (technological relatedness, regional specialization, intrasectoral or upstream knowledge pool, technological cooperation) and the level (regional, national, or European) of governance of public agencies.
The economic and environmental dividends of the digital development strategy: Evidence from Chinese cities
2024, Journal of Cleaner Production
Digital development is critical for achieving coordinated regional economic growth, energy conservation, and emissions reduction, which makes it a viable method for achieving carbon neutrality and peaks. This study investigates the impact of the digital economy on urban total factor productivity and carbon emissions, as well as its mechanism, from 2011 to 2019 by developing a digital economy evaluation index system. We find that the growth of the digital economy can increase urban total factor productivity while lowering carbon emissions and also has a significant spatial spillover effect on regional economic growth, energy conservation, and emissions reduction. The main influencing mechanism is that the digital economy accelerates marketization by encouraging industrial structure upgrades and technological progress, which results in higher total factor productivity and lower energy consumption. In terms of the spatial distribution, the digital economy promotes total factor productivity more in the central and western regions and large cities and promotes carbon emission reduction less in the eastern regions and smaller cities. The findings extend the benefits of the digital economy beyond economic and social welfare benefits and serve as a model for achieving a win–win situation in terms of both economic growth and carbon emissions reduction.
The impact of CAP subsidies on the productivity of cereal farms in six European countries: A historical perspective (2008–2018)
2023, Food Policy
Productivity is an always-hotly-debated issue within the EU agricultural policymaking, particularly concerning the effect of subsidies distributed via the Common Agricultural Policy (CAP). The cereal sector is under particular political scrutiny nowadays, with the Ukraine conflict and environmental issues menacing food security levels for both developing and developed countries.
Using individual farm data from 2008 to 2018 for France, Germany, Italy, Poland, Spain, and the United Kingdom, we investigate the effects of different subsidies on the cereal farms’ productivity, accounting for the intrinsic farms’ productivity levels (low, medium and high productive farms). The System-GMM estimator, combined with Inverse Mills Ratios, is applied within a three-step strategy to overcome endogeneity problems. Results highlight how CAP subsidies affect farms’ Total Factor Productivity (TFP), and to what extent they differ according to the nature of the subsidy, the country, and, interestingly, the intrinsic farm’s productivity level. The analysis reveals that TFP embeds an autoregressive behaviour that should always be accounted for when assessing the relationship with subsidies. Most of the CAP subsidies entail a negative-to-unsignificant effect on TFP, except for agri-environmental subsidies, which can increase farms’ productivity. Interestingly, the negative role of direct payments seems more recurrent in the low productive farms, accelerating their exit from the market.
Autonomous vehicle impacts on travel-based activity and activity-based travel
2023, Transportation Research Part C: Emerging Technologies
This paper undertakes a deep dive into the kinds of activities that individuals are likely to pursue when freed from the task of driving in the fully automated vehicle (AV) era. We refer to such activities as travel-based activities (TBAs) and examine the potential effects of TBA participation on activity-based travel (ABT). Two aggregate ABT characteristics are considered: additional local area travel (ALT) and additional long distance travel (ALDT). TBAs and the two ABTs are jointly modeled in a parsimonious fashion using psycho-social latent constructs, individual characteristics, and built environment (BE) attributes. The data used in this study is drawn from a 2019 “emerging mobility” survey administered in the Austin, Texas metropolitan area by the research group. Our study indicates that “productive use of time” is not necessarily always tied with activities such as work and study; rather, being able to partake in relatively chill activities (such as sleeping, relaxing, and gazing out the window) is also considered as good use of time. This suggests caution in the interpretation of what are traditionally referred to as “productive” activities and also a need for scholarly restraint in the use of the label “multitasking” to exclusively refer to non-passive activities. We suggest that the field move away from subjective/ambiguous terms such as multitasking and “productive” activities, and adopt the more neutral label of “travel-based activity”. The results also support the notion that the option of opening up travel to pursue work/study activities may itself be engendering stress in individuals; that is, as the option to pursue “non-chill” activities increases in an AV environment, that itself may produce angst in individuals and lead to less enjoyment in travel. This also highlights a need to examine TBAs in the broader context of emotional well-being and quality of life. Indeed, AVs may further erode into our time of tuning-out from the “chatter” of routine life and make it less possible to partake in “calm and mindless” activities. Finally, our study cautions against the use of simple and uniform (across individuals) value of travel time savings (VTTS) factor modifications to study AV impacts on ABT.

View all citing articles on Scopus

^☆: Tom Mroz, Peter Schmidt, Jeff Zabel, and two anonymous referees provided very helpful comments. Financial support from a Sloan Foundation Research Fellowship is gratefully acknowledged.

View full text

Panel dataSelection corrections for panel data models under conditional mean independence assumptions☆

Abstract

Journal of Econometrics

Journal of Econometrics

Economics Letters

Economics Letters

Analysis of covariance with qualitative data

Review of Economic Studies

The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models

Annals of Economic and Social Measurement

Limited dependent and qualitative variables in econometrics

Panel data
Selection corrections for panel data models under conditional mean independence assumptions☆