Estimation of sample selection bias models

Nawata, Kan; Nagase, Nobuko

doi:10.1080/07474939608800363

Cited by 78 publications

(44 citation statements)

References 13 publications

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“…Nawata and Nagase (1996), Yu (1996, 2000), Puhani (2000). The advantage of MLE in terms of efficiency is not always confirmed in finite samples: Heckman's procedure may turn to be more efficient than MLE (see Leung and Yu, 2000).…”

Section: Modelsmentioning

confidence: 99%

The Effect of Protest Votes on the Estimates of Willingness to Pay for Use Values of Recreational Sites

et al. 2001

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in SUMMARYSelectivity bias caused by protest responses in Contingent Valuation studies can be detected and corrected by means of sample selection models. This paper compares two methods: the Heckman 2-steps method and the full ML, applied to data on forest recreation -where WTP is elicited as a continuous variable. Either method has its own drawback: computational complexity for the ML method, susceptibility to collinearity problems for the 2-steps method. The latter problem is observed in our best fitting specification, with the ML estimator outperforming the 2-steps. In this application, overlooking the effect of protest responses would cause an upwards bias of the final estimates of WTP.Keywords: Contingent valuation, protest responses, sample selection, MLE, two-steps method JEL: C35, C51, C81, D60, H41, Q26 NON TECHNICAL SUMMARYContingent Valuation studies are often characterized by a considerable amount of protest responses, which may have an important effect on the final estimates if the protest responses are not randomly distributed across the sample. If the standard procedure of censoring protest responses is adopted, the estimates may be biased. Sample selection models can detect and -if necessary-correct selectivity bias. We apply a sample selection model to data on valuation of forest resources for recreational use, where WTP responses are obtained through a mixed dichotomous choice-open ended elicitation method. Dealing with continuous data for WTP allows us to apply the Heckman 2-steps method, and compare it to the full ML estimator. Either method has its own drawback: computational complexity for the ML method, susceptibility to collinearity problems for the 2-steps method. The latter is observed in our model. The results show that censoring protest responses in this study would lead to overestimates of the willingness to pay. CONTENTS

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Section: Modelsmentioning

confidence: 99%

The Effect of Protest Votes on the Estimates of Willingness to Pay for Use Values of Recreational Sites

et al. 2001

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“…For treatment-effects model estimation, we implement a full information maximum likelihood (FIML) approach (Greene, 2003;Nawata and Nagase, 1996), which is more efficient than the Heckman two-step estimator. We use agricultural information constraint and knowledge of a TC nursery as instruments, which are correlated with TC adoption but uncorrelated with all outcome variables.…”

Section: Model Specificationmentioning

confidence: 99%

Impact of tissue culture banana technology on farm household income and food security in Kenya

Kabunga¹,

Dubois

Qaim

2014

Food Policy

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“…In addition, convergence problems usually appear when it is necessary to estimate a large set of parameters (Nawata and Nagase, 1996).…”

Section: Discussionmentioning

confidence: 99%

Do Spanish Informal Caregivers Come to the Rescue of Dependent People with Formal Care Unmet Needs?

Jiménez-Martín

Prieto

2014

Applied Eco Perspectives Pol

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This paper analyses the effect of unmet formal care needs on informal caregiving hours in Spain using the two waves of the Informal Support Survey (1994, 2004). Testing for double sample selection from formal care receipt and the emergence of unmet needs provides evidence that the omission of either one of these two variables would cause underestimation of the number of informal caregiving hours. After controlling for these two factors the number of hours of care increases with both the degree of dependency and unmet needs. In the presence of unmet needs, the number of informal caregiving hours increases when some formal care is received. This result refutes the substitution model and supports complementarity or task specificity between both types of care. For the same combination of formal care and unmet needs, informal caregiving hours increased between 1994 and 2004. Finally, in the model for 2004, the selection term associated with the unmet needs equation is larger than that of the formal care equation, suggesting that using the number of formal care recipients as an indicator of the goodness of the long-term care system may be confounding, if we do not complete this information with other quality indicators.

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Estimation of sample selection bias models

Cited by 78 publications

References 13 publications

The Effect of Protest Votes on the Estimates of Willingness to Pay for Use Values of Recreational Sites

The Effect of Protest Votes on the Estimates of Willingness to Pay for Use Values of Recreational Sites

Impact of tissue culture banana technology on farm household income and food security in Kenya

Do Spanish Informal Caregivers Come to the Rescue of Dependent People with Formal Care Unmet Needs?

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