1. Cohort analysis using LIS data

2. The Lexis table US - us86 us91 us94 us00 us04 us10
FR - fr84 fr89 fr94 fr00 fr05 fr10
Age 25-65
5X5 years Lexis table
Variables - nhhmem Educlev year5 pweight iso2 female partner immigr page ter leqdhi
File name: $mydata/prosoc/us_fr_APC.dta

3. Variables Iso2 – Country code
Year5 – period (5 years interval based on the actual time of the wave)
Page – age (5 years interval)
Pweight – weights
Female – dummy variable for sex (male omitted)
Ter – tertiary education dummy (based on Educlev)
Leqdhi – (log) ppp adjusted DHI divided by the square root of HH members

4. APCD Cohort bumps in leqdhi (deviation from the overall trend)
Age 25 to 55
For each country separately

5. Lissy syntax (stata)
use $mydata/prosoc/us_fr_APC.dta , clear levelsof iso2, local(cnty) foreach l of local cnty { capt noi di "`l'" apcd leqdhi [w=pw] if page>=25 & page<=55 & iso2=="`l'" , age(page) period (year5) }

6. The output

7. Easier to plot output
use $mydata/prosoc/us_fr_APC.dta , clear levelsof iso2, local(cnty) foreach l of local cnty { capt noi di "`l'" capture apcd leqdhi [w=pw] if page>=25 & page<=55 & iso2=="`l'" , age(page) period (year5) capture estout , cells(b ci_u ci_l) matlist r(coefs) }

8. The output

9. Plotting the output
Copy the entire output (including everything) and paste it into a .txt file
Open “output to graph.do”
In line 1, change the folder name to the folder in which your .txt file is
In line 2, change the file name to your .txt file name (don’t forget the .txt suffix!)

10. Inter-cohort income inequalities – the US and France

11. APCtlag Cohort trend in leqdhi (deviation from the overall trend)
Age 25 to 55
For each country separately
First, with no controls, next with controls for partner, female and tertiary education

12. Syntax (no control varaibles)
use $mydata/prosoc/us_fr_APC.dta , clear levelsof iso2, local(cnty) foreach l of local cnty { capt noi di "`l'" apctlag leqdhi [w=pw] if page>=25 & page<=55 & iso2=="`l'" , age(page) period (year5) rep(2) }

13. Results (using the same .do file as before)

14. Syntax (with control variables)
apctlag leqdhi partner female ter [w=pw] if page>=25 & page<=55 & iso2=="`l'" , age(page) period (year5) rep(2)

15. Results

16. APCGO
apcgo leqdhi partner female [w=pw] if page>=25 & page<=55 & iso2=="`l'" , age(page) period (year5) gap(ter) rep(2)

17. Output

18. Output to graph
We use a different .do file: output to graph (apcgo).do
Again, we need change the folder and file names

19. Results

20. APCTlag of the Gini coeffcient
We calculate the Gini for each 5X5 cell
We give each case the value of Gini of its cell
APCTlag of the new varaible

21. Syntax
gen gini=. foreach l of local cnty { capt noi di "`l'" qui { forvalues ii=1985(5)2010 { forvalues jj=25(5)55{ fastgini leqdhi [w =pw] if year5==`ii' & page==`jj' & iso2=="`l’” replace gini=r(gini) if year5==`ii' & page==`jj' & iso2=="`l'"

22. Syntax (.cont)
} apctlag gini [w=pw] if page>=25 & page<=55 & iso2=="`l'" , age(page) period (year5) rep(2)

23. Results

24. Exercise
$mydata/prosoc/APC_WS.dta is similar dataset (Lexis table) for more countries:
ca de es fi fr it no dk lu nl us il au
Using only two countries, estimate:
APCD and APCTlag models of leqdhi
APCGO model of gender gap in leqdhi
APCTlag model of Gini inequality