Dealing with Internal Inconsistency in Double-Bounded Dichotomous Choice: An Application to Community-Based Health Insurance
Contingent valuation method is commonly used in the field of health economics in an attempt to help policy maker in taking decisions. The use of the double-bounded dichotomous choice format results in a substantial gain in statistical efficiency over the single bounded dichotomous choice format. Yet, this efficiency gain comes at the cost of biasness known as internal inconsistency. This paper aims at reducing this internal inconsistency in double-bounded dichotomous choice by using the certainty calibration technique in a community-based health insurance study. Findings confirm the internal inconsistency between the initial and the follow-up responses and the statistical efficiency gains of the double-bounded dichotomous choice over the single-bounded dichotomous choice. Furthermore, the use of certainty calibration reduces this internal inconsistent pattern in responses and still maintains efficiency gain. We further discuss the policy implications.
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