Hidden Truths: Are Underreported Benefits Distorting Economic Research?

The data imperfections discussed are primarily underreporting of government benefits and imputation of missing data. Underreporting occurs when households either intentionally or unintentionally provide inaccurate information about the benefits they receive. Imputation involves estimating missing benefit amounts using statistical models. Both issues compromise the accuracy of economic research by introducing biases, especially when instrumental variable (IV) methods are employed. These imperfections can lead to overestimation of program impacts, thus undermining the validity of research findings and potentially misguiding policy decisions.

Underreporting of government benefits weakens the correlation between the instrumental variable and the actual benefit amount, a critical assumption for IV methods. This weakening leads to attenuation bias, reducing the observed variation in benefit amounts. This, in turn, artificially inflates the IV estimates, making programs appear more effective than they are. For instance, if a study is evaluating the impact of Social Security income and some benefits are underreported, the instrumental variable used to assess the impact of the Social Security will be less strongly correlated with the actual benefits received which results in an overestimation of the impact.

There are three key biases: Attenuation Bias, Imputation Bias, and Ratio Bias. Attenuation Bias occurs because underreporting reduces the observed variation in benefit amounts, weakening the relationship between the instrument and the benefit. Imputation Bias happens when the imputation model doesn't fully capture the factors influencing benefit receipt, causing imputed values to systematically differ from true values. Ratio Bias arises in IV estimates calculated as a ratio of two coefficients; when the denominator (the effect of the instrument on the benefit) is biased downwards due to underreporting, the overall IV estimate is biased upwards. An example would be a study of Social Security benefits where benefits are imputed, and the instrument (policy change) is less correlated with actual benefits, overestimating the impact of Social Security.

Using imputed data introduces potential biases, because the imputation methods may not fully capture the complexities of benefit distribution. This can lead to inaccurate conclusions about the program's true effectiveness. If the imputation model does not accurately reflect the factors affecting benefit receipt, the imputed values will systematically deviate from the true values. This is particularly problematic when instrumental variable (IV) methods are used, as the correlation between the instrument and the benefit is weakened, resulting in biased estimates and potentially misinformed policy decisions. Consider a scenario in which the impact of the Child Benefit is studied and if the benefits are imputed using a model that does not fully account for all the variables impacting child benefit receipt, results will be misleading.

Researchers must employ rigorous data handling and methodologies to mitigate biases. This involves being vigilant in identifying and addressing data imperfections. Specific strategies include a thorough understanding of the underlying data-generating processes, employing methods to correct for measurement error, and using sensitivity analyses to assess the robustness of findings to different imputation scenarios. Researchers should be transparent about data limitations and consider alternative estimation strategies or sensitivity analyses to account for these biases. Also, it is important to acknowledge the limitations of the data and their potential impact on the conclusions, striving for more informed and effective policy decisions.