Capturing year effects using interaction with year dummies

[Konstantina Boutsioukou]

Hi everyone,

I seek to examine whether selection into highly selective university programmes in Greece changed following the recession of 2008, and if so, whether the socio-economic gradient of entry to elite university departments became steeper or flatter. I utilise two linear probability models (LPM) with year effects that have the following specification forms;

Elitei=β0+β1*Yeari + β2*ParOccupi + β3 *(ParEdui × Yeari) +β4 Xi+ εi
Elitei=β0+β1*Yeari + β2*ParEdui + β3 *(ParOccupi × Yeari) +β4 Xi+ εi
where the dependent variable Elite_i is a dummy variable that takes the value 1 if student[IMG]file:////Users/konstantinaboutsioukou/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image006.png[/IMG]is admitted to an elite university department and 0 if he is admitted to a non-elite university programme. The independent variables in the model are Year_i, representing the years between 2004 and 2016, ParEdu_i, a categorical variable representing different parental education levels and ParOccup_i, a categorical variable representing different parental occupational groups. ParEdu_i×Year_i is the interaction term between parental education and years and ParOccup_i×Year_i isthe interaction term between parental occupation and years. By examining the interaction between years and parental education and occupation, I aim to capture the recession’s impact on access to elite university departments. Finally, xistands for some controls, namely, student’s gender and nationality. I estimate paternal and maternal effects separately.

I received the following comment:

The estimation strategy is really a dynamic/event-study difference-in-differences and should be referred to as such. It would also be helpful to re-estimate everything with 2007 as the base year and comment on issues such as pre-trends and related DiD issues. If there are diverging pre-trends it might also be useful to consider recent sensitivity analysis such as Rambachan/Roth (2023, A more credible approach to parallel trends. Review of Economic Studies forthcoming). To address this I was thinking to take 2007 as the base year as it is the last year before the crisis. Also, to include both education and the interaction of education and year in the equation. Any further recommendations would be more than welcome about how to addrss these issues.

An example of my dataset follows below:

[CODE]
* Example generated by -dataex-. To install: ssc install dataex
clear
input double(elite yeard1) float(occup_f occup_m edu_f edu_m)
1 18 1 1 1 2
0 10 2 1 1 1
0 10 1 1 1 1
0 22 1 2 1 2
0 13 1 1 1 1
0 15 1 1 1 1
0 14 2 2 1 2
0 21 1 2 1 2
0 17 2 1 1 1
1 14 1 1 1 1
0 20 1 4 1 1
0 21 4 2 1 2
0 19 4 4 1 2
0 22 4 4 1 4
0 11 1 4 1 2
0 10 1 2 1 1
0 22 2 4 1 2
0 12 3 3 1 2
0 11 1 1 1 1
0 15 1 1 1 1
0 20 4 2 1 1
0 22 2 2 1 4
1 21 1 1 1 1
1 21 1 2 1 3
0 10 1 1 1 1
0 16 1 1 1 2
0 19 2 2 1 2
0 16 1 2 1 2
0 11 1 3 1 1
0 13 1 2 1 1
0 19 1 1 1 1
0 20 4 3 1 4
0 16 1 3 1 1
1 22 2 1 1 1
1 14 2 2 1 2
0 20 4 4 1 4
0 11 2 2 1 2
1 12 1 2 1 2
0 12 1 1 1 1
0 15 1 2 1 2
0 15 1 2 1 2
0 19 1 4 1 1
0 20 2 1 1 1
0 16 1 3 1 4
0 10 1 1 1 1
0 10 1 1 1 1
1 15 1 1 1 2
0 16 1 2 1 2
0 15 1 2 1 2
0 14 2 3 1 2
1 13 1 2 1 2
0 20 1 1 1 1
1 10 1 2 1 3
0 18 1 2 1 2
0 14 1 2 1 1
0 18 1 2 1 2
0 13 4 4 1 2
0 15 1 2 1 2
0 15 1 4 1 2
0 20 4 1 1 1
0 16 1 1 1 1
0 10 1 2 1 2
0 17 1 1 1 1
0 16 1 1 1 2
0 13 2 2 1 1
0 20 1 2 1 2
0 16 2 1 1 2
0 19 1 2 1 2
0 17 4 1 1 1
0 16 1 1 1 1
0 11 2 2 1 1
0 10 1 2 1 3
0 19 4 2 1 1
0 17 2 2 1 1
0 13 1 2 1 2
0 12 1 2 1 2
0 17 1 1 1 1
0 16 1 4 1 2
0 10 1 1 1 1
0 11 1 1 1 1
0 18 1 2 1 2
0 20 1 1 1 1
0 10 2 1 1 1
0 10 2 2 1 2
0 18 1 1 1 2
0 15 3 4 1 2
0 16 2 2 1 2
0 12 1 3 1 4
1 20 1 1 1 1
0 13 1 1 1 1
0 16 1 1 1 1
0 19 1 1 1 2
0 21 1 1 1 2
0 14 1 3 1 4
0 20 2 2 1 2
0 19 3 2 1 2
0 12 2 4 1 1
1 13 1 1 1 1
0 16 3 3 1 4
0 10 1 4 1 2
end


Thank you very much in advance for any input.

Best,
Konstantina