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Jose Pina-Sánchez, Johan Koskinen and Ian Plewis


We use work histories retrospectively reported and matched to register data from the Swedish unemployment office to assess: 1) the prevalence of measurement error in reported spells of unemployment; 2) the impact of using such spells as the response variable of an exponential model; and 3) strategies for the adjustment of the measurement error. Due to the omission or misclassification of spells in work histories we cannot carry out typical adjustments for memory failures based on multiplicative models. Instead we suggest an adjustment method based on a mixture Bayesian model capable of differentiating between misdated spells and those for which the observed and true durations are unrelated. This adjustment is applied in two manners, one assuming access to a validation subsample and another relying on a strong prior for the mixture mechanism. Both solutions demonstrate a substantial reduction in the vast biases observed in the regression coefficients of the exponential model when survey data is used.