THE DUMMY VARIABLE TRAP 1 Suppose that you have a regression model with Y depending on a set of ordinary variables X 2,..., X k and a qualitative variable.

2 Suppose that the qualitative variable has s categories. We choose one of them as the omitted category (without loss of generality, category 1) and define dummy variables D 2,..., D s for the rest. THE DUMMY VARIABLE TRAP

3 What would happen if we did not drop the reference category? Suppose we defined a dummy variable D 1 for it and included it in the specification. What would happen then? THE DUMMY VARIABLE TRAP

4 We would fall into the dummy variable trap. It would be impossible to fit the model as specified. THE DUMMY VARIABLE TRAP

5 We will start with an intuitive explanation. The coefficient of each dummy variable represents the increase in the intercept relative to that for the basic category. But there is no basic category for such a comparison. THE DUMMY VARIABLE TRAP

6  1 represents the fixed component of Y for the basic category. But again, there is no basic category. Thus the model does not have any logical interpretation. THE DUMMY VARIABLE TRAP

7 Mathematically, we have a special case of exact multicollinearity. If there is no omitted category, there is an exact linear relationship between X 1 and the dummy variables. The table gives an example where there are 4 categories. THE DUMMY VARIABLE TRAP Observation CategoryX 1 D 1 D 2 D 3 D

8 X 1 is the variable whose coefficient is  1. It is equal to 1 in all observations. Usually we do not write it explicitly because there is no need to do so. THE DUMMY VARIABLE TRAP Observation CategoryX 1 D 1 D 2 D 3 D

9 If there is an exact linear relationship among a set of the variables, it is impossible in principle to estimate the separate coefficients of those variables. To understand this properly, one needs to use linear algebra. THE DUMMY VARIABLE TRAP Observation CategoryX 1 D 1 D 2 D 3 D

10 If you tried to run the regression anyway, the regression application should detect the problem and do one of two things. It may simply refuse to run the regression. THE DUMMY VARIABLE TRAP Observation CategoryX 1 D 1 D 2 D 3 D

11 Alternatively, it may run it, dropping one of the variables in the linear relationship, effectively defining the omitted category by itself. THE DUMMY VARIABLE TRAP Observation CategoryX 1 D 1 D 2 D 3 D

12 There is another way of avoiding the dummy variable trap. That is to drop the intercept (and X 1 ). There is no longer a problem because there is no longer an exact linear relationship linking the variables. THE DUMMY VARIABLE TRAP Observation CategoryX 1 D 1 D 2 D 3 D

13 The  parameters are now the intercepts in the relationship for the individual categories. For example, if the observation relates to category 2, all the dummy variables except D 2 will be equal to 0. D 2 = 1, and hence the relationship for that observation has intercept  2. Observation CategoryX 1 D 1 D 2 D 3 D THE DUMMY VARIABLE TRAP

Copyright Christopher Dougherty These slideshows may be downloaded by anyone, anywhere for personal use. Subject to respect for copyright and, where appropriate, attribution, they may be used as a resource for teaching an econometrics course. There is no need to refer to the author. The content of this slideshow comes from Section 5.2 of C. Dougherty, Introduction to Econometrics, fourth edition 2011, Oxford University Press. Additional (free) resources for both students and instructors may be downloaded from the OUP Online Resource Centre Individuals studying econometrics on their own who feel that they might benefit from participation in a formal course should consider the London School of Economics summer school course EC212 Introduction to Econometrics or the University of London International Programmes distance learning course EC2020 Elements of Econometrics