1. Christopher Dougherty EC220 - Introduction to econometrics (chapter 5) Slideshow: two sets of dummy variables Original citation: Dougherty, C. (2012) EC220 - Introduction to econometrics (chapter 5). [Teaching Resource] © 2012 The Author This version available at: http://learningresources.lse.ac.uk/131/http://learningresources.lse.ac.uk/131/ Available in LSE Learning Resources Online: May 2012 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This license allows the user to remix, tweak, and build upon the work even for commercial purposes, as long as the user credits the author and licenses their new creations under the identical terms. http://creativecommons.org/licenses/by-sa/3.0/ http://creativecommons.org/licenses/by-sa/3.0/ http://learningresources.lse.ac.uk/

2. 1 TWO SETS OF DUMMY VARIABLES The explanatory variables in a regression model may include multiple sets of dummy variables. This sequence provides an example of a model with two types. COST =  1  +   OCC +   RES +  2 N + u

3. 2 TWO SETS OF DUMMY VARIABLES We will continue with the school cost function model and extend it to take account of the fact that some of the schools are residential. COST =  1  +   OCC +   RES +  2 N + u

4. 3 TWO SETS OF DUMMY VARIABLES To model the higher overhead costs of residential schools, we introduce a dummy variable RES which is equal to 1 for them and 0 for nonresidential schools.  is the extra annual overhead cost of a residential school, relative to that of a nonresidential one. COST =  1  +   OCC +   RES +  2 N + u

5. 4 TWO SETS OF DUMMY VARIABLES We will also make a distinction between occupational and regular schools, using the dummy variable OCC defined in the first sequence. (It would be better to use the four-category school classification, and in practice we would, but it would complicate the graphics.) COST =  1  +   OCC +   RES +  2 N + u

6. 5 TWO SETS OF DUMMY VARIABLES If a school has a regular curriculum and is nonresidential, both dummy variables are 0 and the cost function simplifies to its basic components. COST =  1  +   OCC +   RES +  2 N + u Regular, nonresidentialCOST =  1  +  2 N + u (OCC = RES = 0)

7. 6 TWO SETS OF DUMMY VARIABLES For a residential regular school, RES is equal to 1 and the intercept increases by an amount . COST =  1  +   OCC +   RES +  2 N + u Regular, nonresidentialCOST =  1  +  2 N + u (OCC = RES = 0) Regular, residentialCOST = (  1  +   ) +  2 N + u (OCC = 0; RES = 1)

8. 7 TWO SETS OF DUMMY VARIABLES In the case of a nonresidential occupational school, RES is 0 and OCC is 1, so the overhead cost increases by . If the school is both occupational and residential, it increases by (  +  ). COST =  1  +   OCC +   RES +  2 N + u Regular, nonresidentialCOST =  1  +  2 N + u (OCC = RES = 0) Regular, residentialCOST = (  1  +   ) +  2 N + u (OCC = 0; RES = 1) Occupational, nonresidentialCOST = (  1  +   ) +  2 N + u (OCC = 1; RES = 0) Occupational, residentialCOST = (  1  +  +   ) +  2 N + u (OCC = RES = 1)

9. COST N  1 +  +  1+1+ 1+1+ 11 Occupational, residential Regular, nonresidential    +  8  Occupational, nonresidential Regular, residential TWO SETS OF DUMMY VARIABLES The diagram illustrates the model graphically. Note that the effects of the different components of the model are assumed to be separate and additive in this specification.

10. COST N  1 +  +  1+1+ 1+1+ 11 Occupational, residential Regular, nonresidential    +   Occupational, nonresidential Regular, residential TWO SETS OF DUMMY VARIABLES 9 In particular, we are assuming that the extra overhead cost of a residential school is the same for regular and occupational schools.

11. TWO SETS OF DUMMY VARIABLES Here are the data for the first 10 schools. Note how the values of the dummy variables vary according to the characteristics of the school. 10 School Type Residential?COST N OCCRES 1OccupationalNo345,00062310 2Occupational Yes537,00065311 3Regular No170,00040000 4Occupational Yes526.00066311 5RegularNo100,00056300 6Regular No28,00023600 7Regular Yes160,00030701 8Occupational No45,00017310 9Occupational No120,00014610 10 OccupationalNo61,0009910

12. TWO SETS OF DUMMY VARIABLES Here is a scatter diagram showing the four types of school. 11

13. . reg COST N OCC RES Source | SS df MS Number of obs = 74 ---------+------------------------------ F( 3, 70) = 40.43 Model | 9.3297e+11 3 3.1099e+11 Prob > F = 0.0000 Residual | 5.3838e+11 70 7.6911e+09 R-squared = 0.6341 ---------+------------------------------ Adj R-squared = 0.6184 Total | 1.4713e+12 73 2.0155e+10 Root MSE = 87699 ------------------------------------------------------------------------------ COST | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------+-------------------------------------------------------------------- N | 321.833 39.40225 8.168 0.000 243.2477 400.4183 OCC | 109564.6 24039.58 4.558 0.000 61619.15 157510 RES | 57909.01 30821.31 1.879 0.064 -3562.137 119380.2 _cons | -29045.27 23291.54 -1.247 0.217 -75498.78 17408.25 ------------------------------------------------------------------------------ TWO SETS OF DUMMY VARIABLES Here is the Stata output for the regression. We will start by interpreting the regression coefficients. The coefficient of N indicates that the marginal cost per student is 322 yuan per year. 12

14. TWO SETS OF DUMMY VARIABLES The constant provides an estimate of the annual overhead cost of the reference category, nonresidential regular schools. It is still negative, which does not make any sense. 13. reg COST N OCC RES Source | SS df MS Number of obs = 74 ---------+------------------------------ F( 3, 70) = 40.43 Model | 9.3297e+11 3 3.1099e+11 Prob > F = 0.0000 Residual | 5.3838e+11 70 7.6911e+09 R-squared = 0.6341 ---------+------------------------------ Adj R-squared = 0.6184 Total | 1.4713e+12 73 2.0155e+10 Root MSE = 87699 ------------------------------------------------------------------------------ COST | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------+-------------------------------------------------------------------- N | 321.833 39.40225 8.168 0.000 243.2477 400.4183 OCC | 109564.6 24039.58 4.558 0.000 61619.15 157510 RES | 57909.01 30821.31 1.879 0.064 -3562.137 119380.2 _cons | -29045.27 23291.54 -1.247 0.217 -75498.78 17408.25 ------------------------------------------------------------------------------

15. TWO SETS OF DUMMY VARIABLES The coefficient of OCC indicates that the annual overhead costs of occupational schools are 110,000 yuan more than those of regular schools. 14. reg COST N OCC RES Source | SS df MS Number of obs = 74 ---------+------------------------------ F( 3, 70) = 40.43 Model | 9.3297e+11 3 3.1099e+11 Prob > F = 0.0000 Residual | 5.3838e+11 70 7.6911e+09 R-squared = 0.6341 ---------+------------------------------ Adj R-squared = 0.6184 Total | 1.4713e+12 73 2.0155e+10 Root MSE = 87699 ------------------------------------------------------------------------------ COST | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------+-------------------------------------------------------------------- N | 321.833 39.40225 8.168 0.000 243.2477 400.4183 OCC | 109564.6 24039.58 4.558 0.000 61619.15 157510 RES | 57909.01 30821.31 1.879 0.064 -3562.137 119380.2 _cons | -29045.27 23291.54 -1.247 0.217 -75498.78 17408.25 ------------------------------------------------------------------------------

16. TWO SETS OF DUMMY VARIABLES The coefficient of RES indicates that the annual overhead costs of residential schools are 58,000 yuan greater than those of nonresidential schools. 15. reg COST N OCC RES Source | SS df MS Number of obs = 74 ---------+------------------------------ F( 3, 70) = 40.43 Model | 9.3297e+11 3 3.1099e+11 Prob > F = 0.0000 Residual | 5.3838e+11 70 7.6911e+09 R-squared = 0.6341 ---------+------------------------------ Adj R-squared = 0.6184 Total | 1.4713e+12 73 2.0155e+10 Root MSE = 87699 ------------------------------------------------------------------------------ COST | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------+-------------------------------------------------------------------- N | 321.833 39.40225 8.168 0.000 243.2477 400.4183 OCC | 109564.6 24039.58 4.558 0.000 61619.15 157510 RES | 57909.01 30821.31 1.879 0.064 -3562.137 119380.2 _cons | -29045.27 23291.54 -1.247 0.217 -75498.78 17408.25 ------------------------------------------------------------------------------

17. TWO SETS OF DUMMY VARIABLES The regression result is shown at the top in equation form. Putting both dummy variables equal to 0, we obtain the implicit cost function for nonresidential regular schools. 16 ^ ^ COST = –29,000 + 110,000OCC + 58,000RES + 322N Regular, nonresidentialCOST= –29,000 + 322N (OCC = RES = 0)

18. TWO SETS OF DUMMY VARIABLES Putting RES equal to 1, but keeping OCC at 0, we obtain the cost function for residential regular schools. 17 ^ ^ ^ COST = –29,000 + 110,000OCC + 58,000RES + 322N Regular, nonresidentialCOST= –29,000 + 322N (OCC = RES = 0) Regular, residentialCOST= –29,000 + 58,000 + 322N (OCC = 0; RES = 1) = 29,000 + 322N

19. TWO SETS OF DUMMY VARIABLES And similarly the cost functions for nonresidential and residential occupational schools are derived by putting OCC equal to 1 and RES equal to 0 and 1, respectively. 18 COST = –29,000 + 110,000OCC + 58,000RES + 322N Regular, nonresidentialCOST= –29,000 + 322N (OCC = RES = 0) Regular, residentialCOST= –29,000 + 58,000 + 322N (OCC = 0; RES = 1) = 29,000 + 322N Occupational, nonresidentialCOST= –29,000 + 110,000 + 322N (OCC = 1; RES = 0) = 81,000 + 322N Occupational, residentialCOST= –29,000 + 110,000 + 58,000 + 322N (OCC = RES = 1) = 139,000 + 322N ^ ^ ^ ^ ^

20. TWO SETS OF DUMMY VARIABLES Here is the scatter diagram with the four cost functions implicit in the regression result. 19

21. TWO SETS OF DUMMY VARIABLES t tests and F tests can be performed in the usual way. The coefficient of the occupational school dummy variable is significantly different from 0 at the 0.1% significance level. 20. reg COST N OCC RES Source | SS df MS Number of obs = 74 ---------+------------------------------ F( 3, 70) = 40.43 Model | 9.3297e+11 3 3.1099e+11 Prob > F = 0.0000 Residual | 5.3838e+11 70 7.6911e+09 R-squared = 0.6341 ---------+------------------------------ Adj R-squared = 0.6184 Total | 1.4713e+12 73 2.0155e+10 Root MSE = 87699 ------------------------------------------------------------------------------ COST | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------+-------------------------------------------------------------------- N | 321.833 39.40225 8.168 0.000 243.2477 400.4183 OCC | 109564.6 24039.58 4.558 0.000 61619.15 157510 RES | 57909.01 30821.31 1.879 0.064 -3562.137 119380.2 _cons | -29045.27 23291.54 -1.247 0.217 -75498.78 17408.25 ------------------------------------------------------------------------------

22. TWO SETS OF DUMMY VARIABLES However, the t ratio for the coefficient of RES is only 1.87. Fortunately we may perform a one-sided test (why?), so it is significantly different from 0 at the 5% level (but not the 1% level). 21. reg COST N OCC RES Source | SS df MS Number of obs = 74 ---------+------------------------------ F( 3, 70) = 40.43 Model | 9.3297e+11 3 3.1099e+11 Prob > F = 0.0000 Residual | 5.3838e+11 70 7.6911e+09 R-squared = 0.6341 ---------+------------------------------ Adj R-squared = 0.6184 Total | 1.4713e+12 73 2.0155e+10 Root MSE = 87699 ------------------------------------------------------------------------------ COST | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------+-------------------------------------------------------------------- N | 321.833 39.40225 8.168 0.000 243.2477 400.4183 OCC | 109564.6 24039.58 4.558 0.000 61619.15 157510 RES | 57909.01 30821.31 1.879 0.064 -3562.137 119380.2 _cons | -29045.27 23291.54 -1.247 0.217 -75498.78 17408.25 ------------------------------------------------------------------------------

23. Copyright Christopher Dougherty 2011. 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 http://www.oup.com/uk/orc/bin/9780199567089/http://www.oup.com/uk/orc/bin/9780199567089/. Individuals studying econometrics on their own and 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 http://www2.lse.ac.uk/study/summerSchools/summerSchool/Home.aspx http://www2.lse.ac.uk/study/summerSchools/summerSchool/Home.aspx or the University of London International Programmes distance learning course 20 Elements of Econometrics www.londoninternational.ac.uk/lsewww.londoninternational.ac.uk/lse. 11.07.25