Cumulative Knowledge in the Social Sciences: The Case of Improving Voters’ Information

Abstract

Two (non-exhaustive) conditions are necessary for knowledge accumulation: unbiasedness and comparability. Research designs should be unbiased so that researchers obtain correct estimates of an underlying quantity. Empirical specifications should permit comparability so that researchers measure the same quantity across distinct studies. The first condition is covered by the causal revolution, the second is the object of this paper. Using the example of interventions providing additional information to voters, we highlight the difficulty to obtain comparability even after removing all concerns linked to external validity, all statistical noise, and all sources of bias. Commonly used specifications reach comparability only under specific, non-testable conditions. We propose several recommendations to restore comparability.

Keywords: electoral accountability, accumulation of knowledge, comparability, bias, theoretical implications of empirical models

JEL Classification: D72, D80, C99, C81, H40