How responsive is domestic labor outsourcing to 

an income shock? Evidence from the Alaska 

Permanent Fund Dividend

Elena Pojman, MA

The Pennsylvania State University

Department of Sociology & Criminology

July 20, 2023

2023 CE Microdata 

Users’ Workshop
Project context

 Housework and childcare can be burdensome and time-consuming, yet not 

everyone outsources their domestic labor

 Women still do most of the unpaid domestic labor

 Outsourcing: cleaning & laundry, childcare & daycare/preschool, dining out, gardening 

& lawn, home maintenance

 Would extra money encourage different-gender couples to outsource more? 

Would there be differences by type of task? (Whose labor would be replaced? i.e., 

women’s work or men’s work)
Project context

 Quasi-natural experiment: Alaska Permanent Fund Dividend (PFD)

 Cash transfer paid yearly to individuals since 1980s

 Paid in October

 High eligibility and take-up rate

 No work requirements or age limits

 PFD paid to the household → couple + children

 I impute the total dividend received by each family { (family size) * (PFD amount) }
Data used

 1996 – 2015 Consumer Expenditure Survey, interview survey

 FMLI – household/couple info

 state, urban, marital status, household size, home ownership, total 

expenditures previous and current quarter

 MEMI – individual/partner info

 education, sex, household composition (children, 

multigenerational), occupation

 MTBI – monthly amount spent on outsourcing

 cost of outsourcing cleaning & laundry, childcare & 

daycare/preschool, dining out, gardening & lawn, home 

maintenance

 XPA – quarterly spending on dining out

 quarterly cost of dining out & quarterly food budget
Problems 

encountered 

with CE data

 Looking at income or expenditures across time

 Income variable inconsistent across survey years and quarters, 

problems with pre-/post-tax data

 Total expenditures of previous/current quarter unclear

 Requires lots of data wrangling

 Duplicate observations, Q1 vs Q1x, merging potentially dozens or 

hundreds of files

 Harmonizing across time – 2003 & 2013 stand out

 Ex. Race & Hispanic ethnicity

 Inconsistencies with data at the household-level and individuallevel

 Ex. marital status 
Weighting 

data in 

analyses

 I use Stata to conduct all analyses

 Have traditionally used robust SEs clustered at the household level 

& weighted using the “finlwt21” variable

 CE replicate weights → balanced repeated replications (brr)

 cl(rfamid) vs svy brr:
Research 

design

 Research questions

1. How does a positive and exogenous income shock affect the 

outsourcing behaviors of different-sex couples, including their 

likelihood and amount spent to outsource domestic labor? 

2. Does this vary by gender-typed task? 

 Event study design akin to differences-in-differences

 Month (October), location (Alaska), amount (PFD)

 Supplementary analyses: (1) counterfactual snow-burdened states, 

(2) expenditures triple-diff, (3) use 2016-2021

 Controls

 Marital status, her age, age difference, education, race, home 

ownership, urbanicity, multigenerational, age of youngest child
Results

-.00004

-.00002

0

.00002

.00004

aprmayjun jul augsepoctnovdecjan febmar

Month

all

-.00004

-.00002

0

.00002

.00004

apr-jun jul-sep oct-dec jan-mar

Quarter

proportion of food budget

dining out

-.00004

-.00002

0

.00002

.00004

aprmayjun jul augsepoctnovdecjan febmar

Month

households with children

childcare

-.00004

-.00002

0

.00002

.00004

aprmayjun jul augsepoctnovdecjan febmar

Month

cleaning

-.00004

-.00002

0

.00002

.00004

aprmayjun jul augsepoctnovdecjan febmar

Month

gardening

-.00004

-.00002

0

.00002

.00004

aprmayjun jul augsepoctnovdecjan febmar

Month

Selected preliminary results.

maintenance

I find minimal effects of the 

PFD on probability of 

outsourcing, with possible 

slight decreases by Alaskans. 
Limitations

 Unable to match participants to time-use data such as ATUS or 

PSID, or other data sources

 Hard to harmonize some variables (race & ethnicity) across time

 Would love for the data to be easily downloadable like on 

IPUMS.org
Thank you!

empojman@psu.edu