This article addresses the important issue of anchoring in contingent valuation surveys that use the double-bounded elicitation format. Anchoring occurs when responses to the follow-up dichotomous choice valuation question are influenced by the bid presented in the initial dichotomous choice question. Specifically, we adapt a theory from psychology to characterize respondents as those who are likely to anchor and those who are not. Using a model developed by Herriges and Shogren (1996), our method appears successful in discriminating between those who anchor and those who did not. An important result is that when controlling for anchoring ? and allowing the degree of anchoring to differ between respondent groups ? the efficiency of the double-bounded welfare estimate is greater than for the initial dichotomous choice question. This contrasts with earlier research that finds that the potential efficiency gain from the double-bounded questions is lost when anchoring is controlled for and that we are better off not asking follow-up questions. JEL Classification: Q26, C81, D71.

In this paper, we study starting-point bias in double-bounded contingent valuation surveys. This phenomenon arises in applications that use multiple valuation questions. Indeed, response to follow-up valuation questions may be influenced by the bid proposed in the initial valuation question. Previous researches have been conducted in order to control for such an effect. However, they find that efficiency gains are lost when we control for undesirable response effects, relative to a single dichotomous choice question. Contrary to these results, we propose a way to control for starting-point bias in double-bounded questions with gains in efficiency.

In the presence of heteroskedasticity of unknown form, the Ordinary Least Squares parameter estimator becomes inefficient, and its covariance matrix estimator inconsistent. Eicker (1963) and White (1980) were the first to propose a robust consistent covariance matrix estimator, that permits asymptotically correct inference. This estimator is widely used in practice. Cragg (1983) proposed a more efficient estimator, but concluded that tests basd on it are unreliable. Thus, this last estimator has not been used in practice. This article is concerned with finite sample properties of tests robust to heteroskedasticity of unknown form. Our results suggest that reliable and more efficient tests can be obtained with the Cragg estimators in small samples.

In this paper, I investigate the finite sample performance of a test robust to heteroskedasticity of unknown form, based on the consistent covariance matrix estimator proposed in Eicker (1963) and White (1980). The simulation results suggest that, as often used in practice, this test could be unreliable and inefficient, even if the sample size is large. They suggest that reliable and more efficient inference can be obtained if a heteroskedasticity-robust test is computed with the restricted residuals and an appropriate bootstrap method.