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Stata Technical Bulletin

. list id activity age birth year period

	id	activity	age	birth	year	period
1.	1	nonactiv	22	54	76	-90
2.	1	selfemp	27	54	81	-90
3.	1	selfemp	39	54	90	-90
4.	1	selfemp	39	54	91	[90-91
ε.	1	selfemp	39	54	93	[91-
6.	2	nonactiv	19	74	90	-90
7.	2	nonactiv	19	74	91	[90-91
S.	2	nonactiv	19	74	93	[91-
9.	3	nonactiv	19	65	84	-90
10.	3	selfemp	22	65	87	-90
11.	3	waged	28	65	90	-90
12.	3	waged	28	65	91	[90-91
13.	3	waged	28	65	93	[91-
14.	4	nonactiv	7	70	77	-90
ιε.	4	nonactiv	20	70	90	-90
16.	4	selfemp	23	70	91	[90-91
17.	4	selfemp	23	70	93	[91-
18.	ε	nonactiv	4	68	72	-90
19.	ε	waged	11	68	79	-90
20.	ε	nonactiv	22	68	90	-90
21.	ε	waged	26	68	91	[90-91
22.	ε	waged	26	68	93	[91-
23.	6	nonactiv	12	66	78	-90
24.	6	nonactiv	24	66	90	-90
28.	6	waged	27	66	91	[90-91
26.	6	waged	27	66	93	[91-
27.	7	nonactiv	24	69	90	-90
28.	7	nonactiv	24	69	91	[90-91
29.	7	nonactiv	24	69	93	[91-
30.	8	nonactiv	16	54	70	-90
31.	8	nonactiv	39	54	90	-90
32.	8	nonactiv	39	54	91	[90-91
33.	8	nonactiv	39	54	93	[91-

Since every individual is observed as late as 1993 and none is observed in both 1990 and 1991, slicing the data on the
1990-91 interval affects the observations for each individual. For instance, slice adds two observations for individual ‘1’, one
for 1990, and one for 1991. The values of all the variables except year are copied down from the observation where year==93,
the first observation following the 1990-91 interval. As a consequence, the age is incorrect in the added observations. This
problem is easily corrected.

. replace age = year - birth
(13 real changes made)

Now we can examine activity in 1991 (period==l):

. tabulate activity sex if period==l
I Sex

Activityl	Male	Female I	Total
			—
nonactiV I	0	3 I	3
waged I	1	2 I	3
selfemp I	2	0 I	2
			—
Total I	3	5 I	8

Apart from descriptive analysis, the slice command is most useful in Cox regression, when we use time-varying regressors
to test the effect of passing through different calendar periods or age groups. For example, we can slice the time into three
periods: from the earliest year through 1980, from 1981 through 1990, and from 1991 until the time at survey (1993):

. use example, clear
. slice year, tvid(id) interval(80,90) saving(new) generate(period)

-80 + 8

[80-90 + 5

[90- --------

13 records added to 20

Note that age has to be corrected again, before running the regression:

. replace age = year - birth
(13 real changes made)

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