Charlotte Ambrozek
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cambrozek.bsky.social
Charlotte Ambrozek
@cambrozek.bsky.social
3.8K followers 640 following 38 posts
Assistant Prof, Applied Econ, UMN
Food, econ, and data
she/her
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cambrozek.bsky.social
Lastly (for now): IV hotel by Phillipa Gowdy-Jaehnig
January 3, 2025 at 7:50 PM
cambrozek.bsky.social
Next, can't argue with fixed effects, by Michael Pliscott:
The "woman yelling at a cat" meme format. Above the woman, it says "`You will never be able to isolate the relationship between those variables, there are too many other variables!'" Above the cat, it says "Me using fixed effects in a simple linear regression"
January 3, 2025 at 7:50 PM
cambrozek.bsky.social
First up, losing sleep over your code, by Havy Nguyen:
A four panel comic. In the top left panel, a brain has a speech bubble that says "are you going to sleep?". In the top right panel, a woman lying in bed has a speech bubble that says "Yes I am. Now shut up." In the bottom left panel, the brain has a speech bubble that says "did you code your standard errors to be heteroskedasticity-robust?" In the bottom right panel, we see the woman, lying in the dark, with eyes wide open in horror.
January 3, 2025 at 7:50 PM
cambrozek.bsky.social
Farmworkers could be seasonally attached to the safety net because:
(a) their eligibility really changes, or
(b) because they don't have the time or bandwidth to re-cert during harvest

We find suggestive evidence for (b) ➡️ outreach and admin changes could help farmworkers use SNAP! 2/3
Panel a of a figure showing the number of non-farmworker households in Fresno County churning (re-entering SNAP after being off the program for between 1 and 3 months) and participating in SNAP in every month from April 2016 to February 2020. Churn varies sort of randomly between 600 and 1,000 households per month. Participation is declining pretty smoothly from 95,000 households at the start to 80,000 households in June 2019, then picking back up to 85,000ish households and staying pretty constant for the rest of the sample. The spike in participation is caused by the end of SSI cash-out in California. Churn increases in the first 6 months of the sample as the number of households regularly observed stabilizes. Panel b of a figure showing the number of farmworker households in Fresno County churning (re-entering SNAP after being off the program for between 1 and 3 months) and participating in SNAP in every month from April 2016 to February 2020. Churn varies very seasonally, with lows in February of 125 households per month and highs in November of 250ish households per month. Participation is declining over time (from 11,000 households at start of sample to 8,500 at end), but also has seasonal variation, with highs around January ~7% higher than lows around August.
December 10, 2024 at 8:30 PM
cambrozek.bsky.social
🚨 new policy brief 🧵

poverty.ucdavis.edu/post/snap-pa...

Farmworkers have high food insecurity AND low SNAP participation (even if eligible).

We show big correlations between harvest timing and farmworkers leaving then re-entering SNAP.

This pattern hurts both households and state budgets. 1/3
Screenshot of the title, author list, and abstract of a policy brief from the UC Davis Center for Poverty and Inequality Research December 2024 issue. Title: "SNAP Participation Among Agricultural Workers Impacted by Seasonal Employment" Authors: Casey J. McNichols and Charlotte E. Ambrozek, University of Minnesota; and Timothy K. M. Beatty, UC Davis The text of the abstract reads: "The Supplemental Nutrition Assistance Program (SNAP) serves as a safety net for more than 41 million low-income families, but only about 80 percent of eligible individuals participate. Among SNAP-eligible agricultural workers, take-up is likely even lower. In a recent study, we explored the seasonality of agricultural employment and the extent to which its associated administrative burdens impact households’ SNAP eligibility and participation. To measure households’ attachment to SNAP, we used ‘churn’—exit and subsequent re-entry into SNAP—among Fresno County, CA households. We found a significant negative correlation between SNAP churn and agricultural employment seasonality. Understanding the relationship between SNAP churn and the seasonality of agricultural employment would likely improve access to the safety net for a vulnerable population, as well as reduce SNAP implementation costs for both households and the state."
December 10, 2024 at 8:30 PM
cambrozek.bsky.social
Sharing another UMN APEC #EconJMP!

Jiuchen Deng is a labor economist studying how children’s personality traits & cognitive skills develop (mediated by kid’s environment) and effects on labor market outcomes.

Jiuchen’s JMP finds that the Big 5 and cognitive skills matter for adult earnings.
Screenshot of the title page of Jiuchen Deng's econ job market paper. The paper is titled "The Development of Personality Traits and Cognitive Skills in Adolescence: Evidence from a Skill Formation Model and a Control Function Approach." This version of the paper was posted on October 24, 2024. The abstract reads: "Despite its great importance in child development, education, intra-household allocation, and labor market outcomes, the development of personality traits remains under- studied. I use a skill formation model and a control function approach to investigate the formation of the Big Five personality traits and cognitive skills in adolescence. I find evidence of cross-effects between personality traits and cognitive skills, which are consistent with the findings in neuroscience research. I find strong self-productivity of personality traits and cognitive skills. Based on the control function approach, I uncover a positive impact of family income that fully offsets the negative effect of maternal hours worked on the development of adolescents’ personality traits and cognitive skills. Both personality traits and cognitive skills are important for adult earnings. Results from the skill formation model and the control function approach are internally consistent, and consistent with other studies." The paper is available at: https://sites.google.com/view/jiuchendeng/research
November 18, 2024 at 3:05 PM
cambrozek.bsky.social
Check out @yanxulong.bsky.social's #EconJMP!

Yanxu shows that rural poverty alleviation in China increases forest cover, with benefits from carbon storage 5x the cost of the policy. The main mechanism? People move away from rural areas as poverty falls.

Find out more at yanxulong.github.io
Figure 3 from Yanxu Long's job market paper "Rural Poverty Alleviation and Forest Conservation." The figure presents event-study results on the impact of rural poverty alleviation on forest share, providing suggestive evidence for the parallel trends assumption: across estimators, coefficients for the semesters prior to the poverty alleviation implementation in 2011 are consistently close to zero, indicating no discernible pre-trends. Additionally, the figure highlights treatment effect dynamics, with four difference-in-diffences estimators robust to staggered adoption showing an increase in treatment effects over time in the post-treatment period (2011-2020). This increase may be due to (i) rising poverty alleviation spending and (ii) natural tree growth.
November 4, 2024 at 9:02 PM
cambrozek.bsky.social
🙋‍♀️
Picture of me and my husband smiling and holding our ballots while walking to the mailbox. There is a bright red maple tree in the background and a gray autumn sky.
November 4, 2024 at 6:53 PM
cambrozek.bsky.social
Exciting work from Monique Davis - an applied micro & labor economist focusing on the economics of race (UMN APEC PhD, Minneapolis Fed RA). Her #EconJMP shows that race-neutral school policies do not reduce racial gaps in discipline, and worsen them in majority Black schools.

www.moniqueedavis.com
Screenshot of the title page of Monique Davis' job market paper. Title: Safety for Whom? How Law Enforcement and School Resource Officer Training Impacts Racial Gaps in School Exclusionary Discipline Abstract: Do race-neutral school safety policies have race-neutral impacts? In this paper, I present novel findings on the effects of statewide law enforcement credential and special training requirements for school resource officers (SROs) on Black-White gaps in suspensions, expulsions, law enforce- ment referrals, and school-related arrests (i.e., school exclusion). I answer these questions using data from the U.S. Department of Education’s Office for Civil Rights’ 2013-14 through 2020- 21 Civil Rights Data Collection school-level surveys and supplementary sources. The study leverages state and time variation in adopting SRO credential and training statutes between 2014-15 and 2020-21. The analysis compares Black-White school exclusion gaps in majority and minority Black middle and high schools with SROs between treated and untreated schools. I estimate average treatment effects on the treated using an advanced difference-in-difference method, clustered by state. The results indicate that requiring SROs to hold sworn law enforce- ment credentials more than doubles racial gaps in suspensions and law enforcement referrals in majority Black schools but not in minority Black schools and that SRO training policies have no significant relationship with racial school exclusion gaps. I then evaluate whether racial differences in school exclusion outcomes result from individual behaviors and cultural norms or structural factors like systemic discrimination—the first study to assess whether individualist or structuralist explanations underlying the relationship between SROs and racial disparities in school discipline better explain the findings. The findings suggest the need for structural, race-conscious policy changes to address racial disparities in school discipline.
October 24, 2024 at 6:03 PM
cambrozek.bsky.social
My two favorite memes from this semester's Intro to Applied Econometrics take-home final: the exclusion restriction is John Cena and always takers.

John Cena by Lars Rostad; always takers by Eduardo Sedano.
An image of John Cena, overlaid with text that says “Adds IV” and “Exclusion Restriction.” Caption reads: “The picture above is of actor John Cena, whose signature phrase in WWE was `you can’t see me!’ When you add an instrumental variable, it is necessary to meet the following assumptions: relevance, exclusion restriction, and monotonicity. The exclusion restriction refers to the fact that the instrumental variable can only affect the outcome through the exogenous treatment. Because the exclusion restriction is saying something about the relationship with the error term, it is never observable. Thus, `you can’t see me!’” A two panel comic. The first panel shows a hand hovering between two red buttons, both labeled “do treatment.” The second panel shows a sweating man labeled “Always Takers.”
December 22, 2023 at 7:39 PM