Abstract
Measuring variables that accurately reflect the desired effect is part of designing successful statistical and econometric studies. This chapter discusses challenges related to data compilation and how inadequate proxies, selection bias and measurement error can undermine empirical projects. Two examples illustrate how mismeasurement has been successfully exploited in recent studies in economic history.
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Notes
- 1.
Needless to mention, there is also a need to assess the magnitude of bias. If the magnitude of a bias is small or negligible, then Bodenhorn et al.’s (2017) arguments only add an academic discussion to an empirical study.
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Blum, M. (2018). Measurement and Metrics. In: Blum, M., Colvin, C. (eds) An Economist’s Guide to Economic History. Palgrave Studies in Economic History. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-96568-0_44
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DOI: https://doi.org/10.1007/978-3-319-96568-0_44
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