Self-published Studies
132 resultsAccounting for Limited Commitment between Spouses when Estimating Labor-Supply Elasticities
The Frisch elasticity of labor supply can be estimated by regressing hours worked on
the hourly wage rate, controlling for consumption of the individual worker. However,
most household panel surveys contain consumption information only at the household
level. We show that proxying individual consumption by household consumption biases
estimated Frisch elasticities downward as limited commitment in the household induces
individual consumption to behave differently from household consumption. We develop
an improved estimation approach that eliminates this bias by exploiting information on
the composition of household consumption to infer its distribution. Using PSID data, we
estimate Frisch elasticities of about 0.65 for men and 0.8 for women.
the hourly wage rate, controlling for consumption of the individual worker. However,
most household panel surveys contain consumption information only at the household
level. We show that proxying individual consumption by household consumption biases
estimated Frisch elasticities downward as limited commitment in the household induces
individual consumption to behave differently from household consumption. We develop
an improved estimation approach that eliminates this bias by exploiting information on
the composition of household consumption to infer its distribution. Using PSID data, we
estimate Frisch elasticities of about 0.65 for men and 0.8 for women.
Arocho, 2019 Emerging Adulthood Changing Expectations
Final analyses file used in Arocho, 2019, Emerging Adulthood article "Changes in expectations to marry and to divorce across the transition to adulthood". Data are from the PSID Transition into Adulthood 2005-2015 surveys, supplemented with marital history files of individuals and parents. Data have been imputed with multiple imputation and analyses variables have been demeaned.
CDS-2020 time diary weights are provided
as “User Generated” Data through OpenICPSR. Due to a low response rate and
small sample size for the CDS-2020 time diaries, these weights are considered unofficial and
being made available to researchers who understand the limitations (and
potential uses) of these data. CDS-2020 was
a follow-up data collection in the Fall of 2020 during
the Covid-19 pandemic for children who participated in the 2019 wave of CDS. Children’s time diaries
in CDS-2020 were
collected for a random week day and a random weekend day.
This study helps identify factors that contributed to changes in donations during the COVID-19 pandemic by examining the financial and health hardships families experienced. Using the Panel Study of Income Dynamics (PSID) and Philanthropy Panel Study (PPS), this study explores trends in charitable giving across three time points (2016, 2018, and 2020) for groups of respondents. A quasi-experimental double pretest design and multi-group path modeling were used to explore changes in charitable giving.
Class exercise: Predicting income mobility in PSID
This repository contains data for a data science class exercise.
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Students: This exercise is about income mobility over three generations: grandparents (g1), parents (g2), and children (g3). Your task is to predict log income in generation 3 using data on log incomes in generations 1 and 2. Additional predictors available include education in each generation, race as reported by the grandparent (g1), and sex of the respondent in g3.
The data you will use are in for_students.zip.
Here are some details about the variables in the data. All cases are from the cross-sectional Survey Research Sample of the PSID. In each generation, we took each respondent's annual income over several surveys from age 30 to 45, adjusted to 2022 dollars, and took the average. We truncated the data to the range from $5,000 to $448,501.10, where the bottom code is arbitrary and the top code is what we believe to be the lowest PSID top code over the series (in 1978), converted to 2022 dollars. Education is the first report at ages 30-45, coded as less than high school, high school, some college, or 4+ years of college. We merged the data together across generations using the PSID Family Identification Mapping System 3-generation prospective linkage file. See for_replication.zip for code to produce these data as well as a log file noting sample restrictions.
We are trusting the students to not open the instructor data, which contains the outcomes you are trying to predict. You could peek of course, but that would be no fun! We are trusting you not to peek.
Instructors: The file for_instructors.zip contains the true holdout outcomes in holdout_private.csv. You can use these to evaluate students' predictive performance (as long as you trust that they have not peeked).
For those replicating: The file for_replication.zip contains the directory structure and code that produced this exercise from raw files downloaded from the PSID.
The data you will use are in for_students.zip.
- learning.csv contains 1,365 observations for which the outcome g3_log_income is recorded
- holdout_public.csv contains 1,365 observations for which the outcome g3_log_income is NA
Here are some details about the variables in the data. All cases are from the cross-sectional Survey Research Sample of the PSID. In each generation, we took each respondent's annual income over several surveys from age 30 to 45, adjusted to 2022 dollars, and took the average. We truncated the data to the range from $5,000 to $448,501.10, where the bottom code is arbitrary and the top code is what we believe to be the lowest PSID top code over the series (in 1978), converted to 2022 dollars. Education is the first report at ages 30-45, coded as less than high school, high school, some college, or 4+ years of college. We merged the data together across generations using the PSID Family Identification Mapping System 3-generation prospective linkage file. See for_replication.zip for code to produce these data as well as a log file noting sample restrictions.
We are trusting the students to not open the instructor data, which contains the outcomes you are trying to predict. You could peek of course, but that would be no fun! We are trusting you not to peek.
Instructors: The file for_instructors.zip contains the true holdout outcomes in holdout_private.csv. You can use these to evaluate students' predictive performance (as long as you trust that they have not peeked).
For those replicating: The file for_replication.zip contains the directory structure and code that produced this exercise from raw files downloaded from the PSID.