A first estimate of LCD by gender
(Uruguay)
Marisa Bucheli
Cecilia González
dECON, FCS, Udelar
• In Uruguay we are doing NTA by SES
• We have estimations of labor income, consumption, LCD
and public transfers
• We have preliminary estimations of RA and private
transfers
• We recently began to think of doing estimations by
gender
• We have not worked on unpaid activities
NTA by SES groups
• To my best knowledge, in the Latin American team we used
(at least at the beginning) different procedures for estimation
• In our case, we have estimations basead on two differente
procedures (the one we used at the beginning and the
proposed late by CELADE)
• But we have not compared the sensitivity of the results to the
procedures
First procedure
• We estimate the profiles (mean and smooth mean) as usual but for
each SES group separately
• Note that all members of the hh belong to the same group so the
only challenge is define groups with a “good” size in all ages
• We estimate the aggregated value (AV) of each group (g) and age
(a), where P is the population and XS is the smooth microdata
value:
• The Total AV is the sum of the Total AV of groups
First procedure
• In order to calculate the formula, we need to know the
population of each age-group
• We estimate it using its proportion in the survey
• Note: in the definition of the classification, we took into
account (¿?) the size of the age-group population in the
survey
Second procedure
• We estimate the total AV of each group using the weight of the
group in the microdata
• We estimate the total AV of the age-group using the weight of the
age-group in the microdata (X is the mean value in the microdata):
• We estimate the mean value as AV of the age-group / Population in
the age-group
• In the analysis of the data we work with five-year-age group
Up to now…
• We have a complete NTA estimation (though a preliminary
version particularly of private transfers and RA) following the
first procedure (using the “educational level of the hh adults”
as the proxy of SES)
• Many challenges: 1) ¿inter-hh transfers?; 2) public RA; ….
• We have estimations of labor income, consumption and
public transfers following the second procedure (using the
“educational level of the hh head 2” as the proxy of SES)
NTA by gender
• Our first idea was to follow the first procedure to estimate
NTA by gender
• Two differences:
• In the gender classification we know the population of each
age-group. We used it
• In the SES classification, all the members of a hh belong to the
same group. In the classification by sex, it is possible that
members of the same hh belong to a different group
• This issue is not important in the estimation of accounts for
which we have individual information in the surveys: labor
income, some components of private consumption, public
inflows and some public outflows
Labor income (smooth mean)
4,E+05
3,E+05
3,E+05
2,E+05
women
2,E+05
men
1,E+05
5,E+04
0,E+00
0 3 6 9 121518212427303336394245485154576063666972757881848790
Labor income (aggregate value)
7,E+06
6,E+06
5,E+06
4,E+06
women
3,E+06
men
2,E+06
1,E+06
0,E+00
0 3 6 9 121518212427303336394245485154576063666972757881848790
Public consumption (smooth mean)
4,E+04
3,E+04
3,E+04
2,E+04
women
2,E+04
men
1,E+04
5,E+03
0,E+00
0 3 6 9 121518212427303336394245485154576063666972757881848790
Public consumption (aggregate value)
1,E+06
9,E+05
8,E+05
7,E+05
6,E+05
5,E+05
women
4,E+05
men
3,E+05
2,E+05
1,E+05
0,E+00
0 3 6 9 121518212427303336394245485154576063666972757881848790
Public health (smooth mean)
12.000
10.000
8.000
6.000
women
men
4.000
2.000
0
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89
Public education (smooth mean)
3,E+04
2,E+04
2,E+04
women
1,E+04
men
5,E+03
0,E+00
0
2
4
6
8 10 12 14 16 18 20 22 24 26 28 30 32 34
But if the information is given at householdlevel …
• Private education: we follow exactly the same procedure
than in NTA:
– In the survey, we identify the students (and their level
of education) that attend private school. We assign to
each one the amount of the tuitions paid by the hh.
– In the case other spending (books, courses of
language, computation, etc.) we use the method
proposed by NTA
• We classify the persons by age and sex in order to
calculate mean and smooth mean values
The difference is due to spending
not related to attendance
(apparently, to “enseñanza no
curricular” -language,
computation, special teachers,
etc.-)
¿Is it the method? ¿Should we
take into account the sexcomposition of the hh when we
have to assign spending informed
at hh level?
But if the information is given at householdlevel …
• This is the case of most of the private consumption and
indirect taxes
But if the information is given at householdlevel …
• Private health: we follow exactly the same procedure
than in NTA:
– In the survey, we identify the persons who were ill.
We assign to each one the amount of the spending
related to be ill.
– In the case other spending we use the method
proposed by NTA
• We classify the persons by age and sex in order to
calculate mean and smooth mean values
Private health (smooth mean)
We do not know which
components explain
the increasing gap
30.000
25.000
20.000
15.000
women
men
10.000
5.000
0
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89
Health (smooth mean)
40.000
35.000
30.000
25.000
20.000
women
15.000
men
10.000
5.000
0
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89
We should explore if it
is due to a component
assigned to an
individual through an
indirect method (not
an ill-related
component)
But if the information is given at householdlevel …
• Rest of private consumption: we follow exactly the
same procedure than in NTA:
– We used an equivalence scale to calculate the rest
of private consumption per hh member
– We assigned to each individual of the hh the same
amount
• We classify the persons by age and sex in order to
calculate mean and smooth mean values
Consumption (smooth mean)
1,E+05
1,E+05
1,E+05
8,E+04
women
6,E+04
men
4,E+04
2,E+04
0,E+00
0 3 6 9 121518212427303336394245485154576063666972757881848790
Consumption (aggregate value)
3,E+06
3,E+06
2,E+06
2,E+06
women
men
1,E+06
5,E+05
0,E+00
0 3 6 9 121518212427303336394245485154576063666972757881848790
Some questions
• We would like to know more about the gender difference in
the private health and private education.
• Are they sensitive to the method of allocation of spending
informed at household level?
• If there is a gender difference in private / health education,
should we use the traditional method of imputation of the
rest of private consumption?
• Another challenge: private transfers
Unpaid work
• We have not worked in this issue in the last year
• In the past, we performed some estimations of the value of
unpaid activities in which we imputed a wage to unpaid work:
– Results quite sensitive to use the opportunity cost criteria or
replacement criteria
– Also sensitive to consider specialist / non-specialist wage in the
replacement criteria
• There is a new survey (2009) but we had not worked with it
yet