Economists often take datasets “as given,” without considering data quality and its implications for analysis and policy recommendations.

There are two major reasons for this: Some do not understand the painstaking process involved in collecting good quality primary data despite (seemingly) knowing the econometric implications of “poor data.”

While classical measurement error in continuous dependent variables in a regression-based framework does not bias the parameter estimates, there is the potential for introducing a significant bias when the measurement error lies in independent variables.

Others may understand data challenges but might not find the marginal investment in trying to collect better data worth their blood and sweat.

After all, being able to collect primary data involves a combination of skills – submitting funding proposals (and obtaining them), designing robust samples, preparing questionnaires and other survey tools.