Understanding the magnitude and source of measurement biases in self-reported data is critical to effective economic policy research. This paper examines the role of anchoring bias in self-reports of objective and subjective outcomes under recall. The research exploits a unique panel survey data set collected over a three-year period from four countries in Central America. It assesses whether respondents use their reported value of specific measures from the most recent survey period as a cognitive heuristic when recalling the value from a previous period, while controlling for the value they reported earlier. We find strong evidence of sizable anchoring bias in self-reported retrospective indicators for both objective measures (household and per capita income, wages, and hours spent on the household’s main activity) and subjective measures (reports of happiness, health, stress, and well-being). In general, we also observe a larger bias in response to negative changes for objective indicators and a larger bias in response to positive changes for subjective indicators.

Self-reported retrospective survey data is widely used in empirical work but may be subject to cognitive biases, even over relatively short recall periods. This paper examines the role of anchoring bias in self-reports of objective and subjective outcomes under recall. We use a unique panel-survey dataset of smallholder farmers from four countries in Central America collected over a period of three years. We exploit differences between recalled and concurrent responses to quantify the degree of mental anchoring in survey recall data. We assess whether respondents use their reported value for the most recent period as a cognitive heuristic when recalling the value from a previous period, while controlling for the value they reported earlier. The results show strong evidence of sizeable anchoring bias in self-reported retrospective indicators for both objective measures (income, wages, and working hours) and subjective measures (reports of happiness, health, stress, and well-being). We also generally observe a larger bias in response to negative changes for objective indicators and a larger bias in response to positive changes for subjective indicators.

Recall biases in retrospective survey data are widely considered to be pervasive and have important implications for effective agricultural research. In this paper, we leverage the survey design literature and test three strategies to attenuate mental anchoring in retrospective data collection: question order effects, retrieval cues, and aggregate (community) anchoring. We embed a survey design experiment in a longitudinal survey of smallholder farmers in Malawi and focus on anchoring bias in maize production and happiness exploiting differences between recalled and concurrent responses. We find that asking for retrospective data before concurrent data reduces recall bias by approximately 34% for maize production, a meaningful improvement with no increase in survey data collection costs. Retrieval cues are less successful in reducing the bias for maize reports and involve more data collection time, while community anchors can exacerbate the bias. Reversing the order of questions and retrieval cues do not help to ease the bias for happiness reports.

Thirty-five chapters describe various judgmental heuristics and the biases they produce, not only in laboratory experiments, but in important social, medical, and political situations as well. Most review multiple studies or entire subareas rather than describing single experimental studies.

Survey research involves asking people to remember past events. Four biases occur when one asks people to recall past events in the presence of current information: hindsight bias, telescoping bias, anchoring and adjustment bias and implicit theory of change bias. This paper conducts a narrative review summarizing empirical research findings on the four sources of bias in self-reporting. The review provides an up-to-date summary of this research from 54 studies. It shows that the four biases exist and have fairly long research histories, the biases can be moderated by certain variables, they have negative effects and the clarification of the processes underlying each bias occurred gradually and continues. The review also shows the importance of using an integrative framework in the development of methods to minimize the effects of the four biases as each bias never occurs alone.