Pangolins have been exploited throughout history but evidence points to population declines across parts of their ranges since the 1960s, especially in Asia. This is the result of overexploitation for local use and international trade and trafficking of their derivatives. The prevalence of the consumption of pangolin products has been estimated for different localities in Viet Nam but, considering that national legislation prohibits the purchase of pangolin products, previous research has not accounted for the potential for biased responses. In this study, we treat pangolin consumption as a sensitive behaviour and estimate consumption prevalence of pangolin meat, scales and wine (a whole pangolin or pangolin parts or fluids soaked or mixed in rice wine) in Ho Chi Minh City using a specialized questioning method, the unmatched count technique. We also characterize the demographics of consumers. Our results suggest there is active consumption of all three pangolin products, with a best-estimate prevalence of 7% of a representative sample of Ho Chi Minh City residents for pangolin meat, 10% for scales and 6% for wine. Our prevalence estimates exceed estimates from direct questions, providing evidence for the sensitivity of pangolin consumption. We compared our analysis of consumer characteristics with existing profiles of pangolin consumers and found substantial differences, suggesting that consumption occurs among broader demographic groups than previously described. Our findings suggest that efforts to reduce demand for pangolin consumption in Viet Nam should focus on a broader range of consumers than previously identified.

Hunting is a primary driver of biodiversity loss across South-east Asia. Within Cambodia, the use of wire snares to capture wildlife is a severe threat in protected areas but there have been few studies of the behaviour of hunters from local communities. Here, we combine the unmatched count technique with direct questioning to estimate the prevalence of hunting behaviours and wildlife consumption amongst 705 households living within Keo Seima Wildlife Sanctuary, Cambodia. We assessed respondents’ knowledge of rules, and their perceptions of patrols responsible for enforcing rules. Estimates of hunting behaviour were variable: results from the unmatched count technique were inconclusive, and direct questioning revealed 9% of households hunted, and 20% set snares around farms to prevent wildlife eating crops. Hunting with domestic dogs was the method most commonly used to catch wildlife (87% of households owned dogs). Wild meat was consumed by 84% of households, and was most frequently bought or caught, but also gifted. We detected a high awareness of conservation rules, but low awareness of punishments and penalties, with wildlife depletion, rather than the risk of being caught by patrols, causing the greatest reduction in hunting. Our findings demonstrate the challenges associated with reliably estimating rule-breaking behaviour and highlight the need to incorporate careful triangulation into study design.

Measurement error threatens the validity of survey research, especially when studying sensitive questions. Although list experiments can help discourage deliberate misreporting, they may also suffer from nonstrategic measurement error due to flawed implementation and respondents’ inattention. Such error runs against the assumptions of the standard maximum likelihood regression (MLreg) estimator for list experiments and can result in misleading inferences, especially when the underlying sensitive trait is rare. We address this problem by providing new tools for diagnosing and mitigating measurement error in list experiments. First, we demonstrate that the nonlinear least squares regression (NLSreg) estimator proposed in Imai (2011) is robust to nonstrategic measurement error. Second, we offer a general model misspecification test to gauge the divergence of the MLreg and NLSreg estimates. Third, we show how to model measurement error directly, proposing new estimators that preserve the statistical efficiency of MLreg while improving robustness. Last, we revisit empirical studies shown to exhibit nonstrategic measurement error, and demonstrate that our tools readily diagnose and mitigate the bias. We conclude this article with a number of practical recommendations for applied researchers. The proposed methods are implemented through an open-source software package.