1. Market-based NTA Labor Income and Consumption by Gender Gretchen Donehower Day 4, Session 1, NTA Time Use and Gender Workshop Thursday, May 24, 2012 Institute for Labor, Science and Social Affairs (ILSSA) Hanoi, Vietnam

2. Outline 1.Introduction, single-sex NTA review 2.How to add gender? 3.Labor income 4.Consumption 5.Adjustment for consistency with single-sex NTA

3. Introduction If you have already computed NTA age profiles of consumption and production, NTA by gender is MUCH simpler than NTTA by gender Overall strategy: 1.Apply the usual NTA method 2.Instead of age-specific means, calculate age- and sex-specific means instead 3.Adjust the age- and sex- profiles so they are consistent with the single-sex profiles

4. Review single-sex estimation strategy In single sex NTA, we use different estimation strategies depending on data source, level of availability, and type of age profile: – Data source: household surveys For individual-level data, compute age means directly For household-level data, allocate to household members – Use “equivalent adult consumer” (EAC) weights for non-health, non- education private consumption – Use regression method or iterative method for education and health care if utilization measures are available – Allocate total amount to household head if assets are involved or for interhousehold transfers – Data source: administrative data (government reports) Take age-means from government sources – Profiles based on imputation/assumption

5. How to add gender? Data source: household surveys – For individual-level data, compute age and sex means directly – For household-level data, allocate to household members Use “equivalent adult consumer” (EAC) weights for non-health, non-education private consumption, using the same weights for males and females of the same age Use regression method or iterative method for education and health care if utilization measures are available, adding sex to the regression equations Allocate total amount to household head if assets are involved or for interhousehold transfers, treating male and female heads the same Data source: administrative data (government reports) – Take age- and sex-means from government sources Profiles based on imputation/assumption (use same imputation/assumption for both sexes)

6. Additional concerns Same EAC weights by gender may be bad assumption – Research suggests unitary sharing model in households is not accurate – Sensitivity testing Make different assumptions about relative EAC weights Experiment with data-driven estimates like regression – Captures any correlation between household composition by gender and consumption Could be interesting research projects on their own – numeric model to assess potential consumption impacts of unequal power in the household For data-driven methods, many ways to add gender to the regression equation, so how to choose? – Current methodology: “Kitchen sink” approach Where single-sex regression uses a term for age, make it age by sex We are not concerned with statistical significance so okay to have terms in a regression equation that don’t add much fit – But, using goodness of fit tests to get the most parsimonious model may be better for some research question

7. Labor Income Labor income is the sum of wages and salaries, self- employment income, and supplements to wages and income (fringe benefits) Wages and supplements usually available for each individual, so measure age- and sex-specific means directly – Many countries do not have data for supplements, so assume same age- and sex-distribution as wages Self-employment income measured directly or with data-driven methods, so add sex to the method you use Q: what data and methods are you currently using to calculate labor income?

8. Consumption Education and health (public and private) – If using regression or iterative method, add sex into regression equations or iteration procedure – If using administrative data, get by age and sex from public reports or request special tabulations Other – Public: same per capita consumption for males and females – Private: use same EAC weights for males and females

9. Adjustment for consistency with single-sex NTA Single-sex NTA is our best estimate – Keeping sexes together means larger sample size – Averaging both sexes over age makes some potential errors in gender assumptions cancel out – Single-sex NTA profiles are adjusted to macro controls Want gender-specific profiles to be consistent with single-sex Adjust each age of gender-specific profiles for consistency – Adjustment is different at each age, but the same for both sexes within an age group – Adjust smoothed profiles to be consistent with smoothed profiles; unsmoothed with unsmoothed

10. Calculating adjustment factors N(a) :Population age a N(a,g) :Population, age a, sex g :Single-sex profile, adjusted to control x(a,g) :Sex-specific profile Adjustment Factor: Adjusted Profiles :

11. Final notes on adjustment Adjusting this way makes the gender profiles consistent with single sex profiles and macro controls in one step Save the schedule of adjustment factors and plot them for review – Factors should be similar size to the control adjustment factor for single-sex NTA – If gender adjustment factors are very different, there may be a mistake in the calculations – If factors have an age pattern, there may be a problem with the data not measuring the concept well

12. Sensitivity Tests Talked about some at beginning of presentation – Try data-driven methods for allocations by gender, instead of assuming equality Changing assumption about headship – Does not affect consumption or labor income profiles, but for transfers and asset-based reallocations there is a big impact

13. Lab Session Start modifying single-sex NTA programs to produce sex-specific age profiles Start with labor earnings (wages and salaries) – Plot single-sex and sex-specific age profiles – Plot age schedule of adjustment factors to single- sex profile – Unsmoothed is fine for now, or smooth it if you have time and compare any patterns in adjustment factors for smoothed versus unsmoothed