A competing risks analysis of firms’ exit

Esteve‐Pérez, Silviano; Sanchis, Amparo; Sanchís-Llopis, Juan A.

doi:10.1007/s00181-009-0266-x

Cited by 46 publications

(31 citation statements)

References 53 publications

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“…(), Honjo and Harada (), Esteve‐Pérez et al . (), Fackler et al . (), Lee and Mukoyama (), and industry‐specific datasets, e.g.…”

Section: Literaturementioning

confidence: 95%

“…There is also a large literature on the estimation of exit probabilities using reduced form models with both multi-industry datasets, e.g. Dunne et al (1988), Disney et al (2003), Honjo and Harada (2006), Esteve-P erez et al (2010), Fackler et al (2013), Lee and Mukoyama (2015), and industry-specific datasets, e.g. Airlines (Joskow et al (1994)) and grocery retailing (Hosken et al (2016)).…”

Section: Literaturementioning

confidence: 99%

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Entry into exit: insolvency in English professional football

Szymanski

2017

Scottish J Political Eco

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This study uses a unique database of financial accounts for English football clubs between 1974 and 2010 to examine the process by which firms fail, which in this context means entering insolvency proceedings. From the data it is possible to estimate shocks to demand and productivity and to show that failing firms typically experience a series of negative shocks. This is consistent with the standard IO theory models of exit.

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“…(), Honjo and Harada (), Esteve‐Pérez et al . (), Fackler et al . (), Lee and Mukoyama (), and industry‐specific datasets, e.g.…”

Section: Literaturementioning

confidence: 95%

Section: Literaturementioning

confidence: 99%

Entry into exit: insolvency in English professional football

Szymanski

2017

Scottish J Political Eco

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“…Among the non‐survivors, the KFS distinguishes temporary from permanent exits, and, among the latter, those exiting due to merger/acquisition from exits due to failure. The latter distinction is critical because exits due to merger/acquisition are fundamentally different from exits due to failure, and may even be a sign of the venture's viability (Esteve‐Perez, Sanchis‐Llopis, and Sanchis‐Llopis ).…”

Section: Data and Measurementmentioning

confidence: 99%

“…Table describes the composition of the start‐ups included in the study group . Exits due to merger or acquisition are not included due to the ambiguity of whether this constitutes success or failure (Esteve‐Perez, Sanchis‐Llopis, and Sanchis‐Llopis ). Firms that could not be contacted or otherwise refused to participate in the follow‐up surveys are not included in the study group.…”

Section: Data and Measurementmentioning

confidence: 99%

Externalities or Experience? Localization Economies and Start‐up Business Survival

Renski¹

2015

Growth and Change

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This study examines whether spatial variations in the human capital of business owners help explain why the failure rates of new businesses tend to be lower in areas where similar businesses are concentrated. More specifically, I test whether the prior industry experience of the firm's owner/founder has a mediating influence on the relationship between industrial localization and new business failure for six industry sectors. Localization is found to have either a beneficial or a damaging relationship to new firm survival, depending on the industry in question. Localization reduces the likelihood of failure in two sectors, while competition effects prevail in two others. When industry experience is added to the models, the benefits of localization diminish, but only for entrants in the business, professional, and information services sector and only by a small amount. This suggests that at least some of the observed benefits of localization are due to regional differences in embodied human capital, but localization economies remain significant. Further study is needed to see if such mediating effects are common among a wider array of more narrowly defined industries and for other forms of entrepreneurial human capital.

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“…Applications of CR models in economics include analyzing unemployment duration (Flinn and Heckman, ; Katz and Meyer, ; Tysse and Vaage, ; Alba‐Ramírez et al, , ), PhD completion (Booth and Satchell, ), teacher turnover (Dolton and van der Klaauw, ), studies of age at marriage or cohabitation (Berrington and Diamond, ), mortgage termination (Deng et al, , ), school dropout decisions (Jakobsen and Rosholm, ), and manufacturing firms’ exits from the market (Esteve‐Pérez et al, , ). A comprehensive overview is given in Van den Berg ().…”

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confidence: 99%

A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity

Burda

Harding

Hausman

2014

J of Applied Econometrics

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This paper generalizes existing econometric models for censored competing risks by introducing a new flexible specification based on a piecewise linear baseline hazard, time‐varying regressors, and unobserved individual heterogeneity distributed as an infinite mixture of generalized inverse Gaussian (GIG) densities, nesting the gamma kernel as a special case. A common correlated latent time effect induces dependence among risks. Our model is based on underlying latent exit decisions in continuous time while only a time interval containing the exit time is observed, as is common in economic data. We do not make the simplifying assumption of discretizing exit decisions—our competing risk model setup allows for latent exit times of different risk types to be realized within the same time period. In this setting, we derive a tractable likelihood based on scaled GIG Laplace transforms and their higher‐order derivatives. We apply our approach to analyzing the determinants of unemployment duration with exits to jobs in the same industry or a different industry among unemployment insurance recipients on nationally representative individual‐level survey data from the US Department of Labor. Our approach allows us to conduct a counterfactual policy experiment by changing the replacement rate: we find that the impact of its change on the probability of exit from unemployment is inelastic. Copyright © 2014 John Wiley & Sons, Ltd.

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A competing risks analysis of firms’ exit

Abstract: Exit, Liquidation, Acquisition, Competing-risks hazards model, L1, C41, L60,

Cited by 46 publications

References 53 publications

Entry into exit: insolvency in English professional football

Entry into exit: insolvency in English professional football

Externalities or Experience? Localization Economies and Start‐up Business Survival

A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity

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