(e.g. consumption aggregate or wealth index, shocks, household head gender, education, age), plot characteristics (e.g. crop loss, land area, irrigation).

There are some marked differences in household characteristics and agricultural outcomes between the Malawi IHS4 and IHPS 2016/17 samples. This may be the result of several factors. First, the IHPS is a panel whose sample households have been interviewed several times before since the first round of the IHPS in 2010. Respondents are likely familiar with the survey process and the survey instrument and may have become better at responding to questions. At the same time, those respondents may act strategically and take shortcuts in answering in order to reduce the time of the interview, as they are familiar with the questionnaire structure. It is also likely that the sample of agricultural households in the panel survey that we select for this analysis have been active in farming for an extended period of time, having been followed since 2010. Second, data on agricultural outcomes were collected in two rather than in one visit. This means, on the one hand, that the recall length for many agricultural outcomes of interest is cut in half. It may further lead to differences in recorded agricultural outcomes beyond what the recall length can explain. Unfortunately, we cannot distinguish between the recall effect, the effect of forming part of a long-running panel, and the effect of a post-planting visit beyond recall length with the IHS4 and IHPS 2016/17 data sets, though these issues would make for interesting future research.

# Empirical strategy

We use the following specification to assess the effect of the recall length on outcomes of interest: with Y� the outcome variable of household _i_ (or plot _i_ depending on the level of analysis), T� explanatory variable of interest (planting or harvest recall length in months, depending on the outcome of interest), X� a vector of control variables, γ an indicator for region, μ an enumerator fixed effect, and ε the error clustered at the EA level. Provided X� captures all relevant confounding factors, the length of the recall period, T�, should not be correlated with the outcome variables if farmers accurately recall them. Therefore, if β�, the coefficient on T�, is significantly different from zero, this is evidence of systematic measurement error.

We estimate equation 3 with OLS when assessing the uncensored continuous outcome variables of interest, maize production, error in self-reporting, labor input in person-days. The number of plots listed and cultivated is a count variable, so we asses this outcome with both OLS and Poisson. For the binary outcome variables use of hired labor, use of organic and of inorganic fertilizer, we make use of Probit. Finally, the quantity of inorganic fertilizer applied per plot is truncated at zero. On the considerable share of plots to which no fertilizer is applied, the fertilizer quantity variable takes the value zero, while it is continuous and positive for the share of plots to which fertilizer was applied. We deal with this case by running both a Tobit model on the truncated variable as well two separate regressions, a Probit model on the binary choice whether inorganic fertilizer is used at all and OLS on the continuous quantity of application conditional on use.

To verify that the linear baseline specification in equation 3 accurately captures the relationship between recall length and the outcomes of interest, we run a set of regressions with added polynomial terms 11