# A first estimate of LCD by gender (Uruguay) Marisa Bucheli Cecilia González dECON, FCS, Udelar.

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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

But if the information is given at household- level … 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 sex- composition of the hh when we have to assign spending informed at hh level?

But if the information is given at household- level … This is the case of most of the private consumption and indirect taxes

But if the information is given at household- level … 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

We do not know which components explain the increasing gap 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 household- level … 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

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

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