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In I. Theodossiou’s article from the journal of health economics, regressors such as low pay and unemployment are measured against psychological well-being. The dependent variable, psychological well-being, are numerically quantified using feelings such as dissatisfaction, unhappiness, and low self-esteem. These individual feelings are rated on a integer scale. In the article, dummy variables are brought up as a possible measure to help the author draw conclusions about the data. In OLS regressions, dummy variables are used to illustrate the absence or presence of some categorical effect that may be expected to shift the outcome. A dummy variable takes on a value of 0 or 1. The significance of a regression using dummy variables is it now becomes binary and the coefficient in front of the slope is defined as the treatment effect. The treatment effect is this predicted difference among the two groups in the regression.

In MLC’s post on “Unemployment on Psychological Well-Being”, the author highlights the process of collecting categorical data and quantifying this data somehow. One example of a rating scale the author used that was present in the article is the following: not at all (1), no more than usual (2), rather more than usual (3), much more than usual (4). My only issue with this type of analysis is the difficulty in labeling these distinct categories. In OLS regression, I prefer using continuous variables instead of discrete.