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Dani Sandler

@dismalscientist86.bsky.social
6K followers 1.9K following 529 posts

Economist at the Census Bureau studying women's employment, post-prison outcomes, and low-income housing. Living in Davis, CA.

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Dani Sandler @dismalscientist86.bsky.social · 6h
And the entire suite of data from LEHD: lehd.ces.census.gov/data/ Quarterly Workforce Indicators (QWI), LEHD Origin-Destination Employment Statistics (LODES), Job-to-Job Flows (J2J), Post-Secondary Employment Outcomes (PSEO), Veteran Employment Outcomes (VEO)
lehd.ces.census.gov
LEHD Data Products for Workforce Analytics | U.S. Census Bureau
Download LEHD data to explore employment, worker mobility, commuting patterns, and workforce trends with LODES, QWI, J2J, PSEO, and VEO statistics
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Dani Sandler @dismalscientist86.bsky.social · 6h
Some worth highlighting: Business Dynamics Statistics (BDS) & Offshoots: www.census.gov/programs-sur... Mobility, Opportunity, and Volatility Statistics (MOVS): www.census.gov/programs-sur... Gridded Environmental Impacts Frame (Gridded EIF): www.census.gov/data/experim...
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Dani Sandler @dismalscientist86.bsky.social · 7h
Some additional Census datasets can be found on the Census Experimental Data Products page: www.census.gov/data/experim... And data tools page: www.census.gov/programs-sur...
census.gov
What are Experimental Data Products?
Innovative statistical products created using new data sources or methodologies that benefit data users in the absence of other relevant products.
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Todd Jones @toddrjones.bsky.social · 8h
🚨 Public good alert: I put together a list of 100+ free datasets for social science research, spanning a large number of topics. I hope it's useful for students, researchers, and anyone looking for interesting data to explore. It's on GitHub - contributions welcome! github.com/toddrjones/d...
github.com
GitHub - toddrjones/datasets: A collection of free datasets useful for use in applied microeconomics, data visualization, etc.
A collection of free datasets useful for use in applied microeconomics, data visualization, etc. - toddrjones/datasets
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Paul Hünermund @p-hunermund.com · 12/10/2026
Congrats to Hall and Woodford www.nobelprize.org/prizes/econo...
nobelprize.org
Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2026
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2026 was awarded to Robert E. Hall and Michael Woodford and “for microeconomic foundations of macroeconomic stabilisation.”
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Flo 🔶 @faz.ms · 10/10/2026
This goes surprisingly hard youtu.be/3TNpOD6bov8?...
youtu.be
LGTM (Looks Good to Me) - Claude Opus 5.5 music video
YouTube video by Degenerative Pixels
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Dani Sandler @dismalscientist86.bsky.social · 12/10/2026
Since I'm the editor of a Census working paper series, that's just about all I post.
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Dani Sandler @dismalscientist86.bsky.social · 08/10/2026
This paper examines linkages between the National Longitudinal Survey of Youth and the Decennial Census, focusing on the match rate and the reasons for not matching.
In this paper, we examine linkages between the National Longitudinal Survey of Youth (NLSY) 1979 and 1997 sample members and the restricted version of the 2020 Decennial Census (DC), focusing on the match rate and the reasons for not matching. Match rates between the NLSYs and the 2020 DC differ by sex and race/ethnicity. Men as well as Black and Hispanic individuals are less likely to match to the DC. Differences in match rate are explained in part by differences in rates of death, in the likelihood of being assigned a linking identifier, and in the likelihood of being enumerated in the DC. This is part of a larger project to compare information about NLSY sample members who have missed several rounds with those who respond regularly. The representativeness of the matched samples affects the capacity of the linked data for evaluating nonresponse bias in the NLSY79 and NLSY97. Table 4. Percent of NLSY and 2020 Decennial Census Matched and Not Matched, by Reason

Note: GQ—Group Quarters. Noise injection was applied to the counts before rates were computed. Rates are rounded to two significant digits. Because of rounding, rates in a row may not add to 100. 

Source: NLSY79, NLSY97, 2020 Census, and authors’ calculations. DRB release number CBDRB-FY26-063.
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Dani Sandler @dismalscientist86.bsky.social · 08/10/2026
New Census Working Paper: "Matching the National Longitudinal Survey of Youth to the Decennial Census: Match Rates and Reasons for Not Matching" by Alison Aughinbaugh, Jeffrey A. Groen, and Donna S. Rothstein www.census.gov/library/work...
census.gov
Matching the National Longitudinal Survey of Youth to the Decennial Census: Match Rates and Reasons for Not Matching
We examine linkages between the National Longitudinal Survey of Youth and the Decennial Census, focusing on the match rate and the reasons for not matching.
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Dani Sandler @dismalscientist86.bsky.social · 08/10/2026
This paper reconciles macro and micro facts about healthcare expenditures and argues that rising incomes drive up the share of spending going to healthcare.
This paper studies variation in healthcare expenditures across time and space using a model of structural transformation tailored to the healthcare sector. It addresses a key tension: crosscountry and time-series data show the health spending share rising with overall consumption, suggesting an aggregate income elasticity above one, yet within countries health spending is flat across household incomes, and quasi-experimental spending responses to oil-driven income shocks rise less than one-for-one with income. The calibrated model reconciles this tension in two ways: First, health spending is equalized across incomes by the government or insurers. Second, with an own-price elasticity greater than one in absolute value, positive shocks outside healthcare such as oil income shocks generate price effects that drive the health share down. I find that rising incomes are indeed the dominant driver of healthcare spending, and that reforms that raise the productivity of the healthcare sector will drive its spending share up. Figure 3: OECD Panel: Model vs Data 
Notes: This figure compares health spending in the full panel of OECD countries in the real and model-simulated data. Model observations are squares and data observations are circles.
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Dani Sandler @dismalscientist86.bsky.social · 08/10/2026
New Census Working Paper: "Structural Transformation Towards Healthcare" by Fil Babalievsky www.census.gov/library/work...
census.gov
Structural Transformation Towards Healthcare
This paper reconciles macro and micro facts about healthcare expenditures and argues that rising incomes drive up the share of spending going to healthcare.
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kottke.org @kottke.org · 08/10/2026
“Margaret Hamilton, who led the 100-person team that wrote the onboard software for the Apollo missions, guiding Neil Armstrong and Buzz Aldrin to a safe landing on the moon, died Sept. 30 in Cambridge, Mass. She was 90.” [nytimes.com]
nytimes.com
Margaret Hamilton, Whose Software Guided the Apollo Missions, Has Died
Margaret Hamilton, the first female programmer hired to work on the space project at M.I.T., was 90.
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samuel mehr @mehr.nz · 02/10/2026
early-career academics, if you don't have a website, make yourself one. yes it's annoying but just do it it can be plain text on a white background, or google sites, or even a free Wix one with ads. nobody cares we just need to be able to look you up, quickly, bc there's like 100 other apps here
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Dani Sandler @dismalscientist86.bsky.social · 30/09/2026
AI exposure predicts firm adoption, but imperfectly, revealing a complex mapping that matters for interpreting exposure-based estimates of AI’s economic impact.
Measures of exposure to artificial intelligence (AI) are widely used to study where AI is likely to affect firm and worker outcomes, yet limited evidence exists on how closely exposure relates to realized AI adoption at the firm level. We examine this relationship by linking 14 firm-level exposure measures, constructed from occupational exposure estimates in the literature and occupational employment shares from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program, to direct measures of firm AI adoption from the Census Bureau’s Business Trends and Outlook Survey. Exposure is positively and statistically significantly associated with adoption, but explains only a modest share of its variation. The strength of the relationship varies considerably across standardized exposure measures: a one-standarddeviation increase in firm-level exposure is associated with a 4–11 percentage point higher firm adoption probability, falling to 1–8 percentage points after controlling for year and sub-sector fixed effects. The exposure–adoption relationship is also heterogeneous across firm-size classes and sectors. Exposure is thus an informative but incomplete signal of adoption. More broadly, the results have implications for the interpretation of exposure-based measures in studies of the economic effects of AI. Figure 2. Regression estimates, all exposure measures
Note: The figure plots coefficient estimates and 95% confidence intervals from regressing AI adoption on AI exposure. Panel A reports coefficients from a bivariate regression without any controls, and Panel B reports coefficients with year and NAICS-3 fixed effects. The sample and weights match those of Table 3. The adjusted R2 for each regression is reported to the right.
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Dani Sandler @dismalscientist86.bsky.social · 30/09/2026
New Census Working Paper: "AI Exposure and Adoption Among U.S. Firms" by Matthew Dey, Emin Dinlersoz, Lucia Foster, Cheryl Grim, John Haltiwanger, Peter B. Meyer, Sabrina Wulff Pabilonia, Aditya Pande, Martha Stinson, Sean Wang, and Zoltan Wolf www.census.gov/library/work...
census.gov
AI Exposure and Adoption Among U.S. Firms
AI exposure predicts firm adoption, but imperfectly, revealing a complex mapping that matters for interpreting exposure-based estimates of AI’s economic impact.
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Anna Gifty @itsafronomics.blacksky.app · 29/09/2026
I believe that Dr. Ellora Derenoncourt is the first Black woman economist EVER to be named a MacArthur Genius. My heart is bursting. I am OVERJOYED.
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Jake Grumbach @jakemgrumbach.bsky.social · 29/09/2026
Shoutout to the 🐐GOAT🐐, respect greatness www.macfound.org/videos/2026-...
macfound.org
Ellora Derenoncourt - 2026 MacArthur Fellow
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Dani Sandler @dismalscientist86.bsky.social · 29/09/2026
Women earn about 5% more for double majoring in college, while men see no return, consistent with double majors serving as a signal in the labor market.
Over 15 percent of college graduates in the United States graduate with more than one major. While much research exists on the returns to different individual majors, less is known about the causal effects of double majoring. This study provides novel estimates on the returns to those who choose to double major as an undergraduate. We improve on previous studies that rely on controls for observable characteristics by addressing selection concerns in two notable ways. First, we control for institution-specific differences that may influence both the decision to double major and subsequent earnings using proprietary data by including institution fixed effects. Second, we adopt a partial identification approach to address non-random selection into double majoring within institutions. Results broadly align with estimates from prior studies at the aggregate level, but reveal notable gender differences not previously detected. Women experience an earnings return to double majoring of approximately 5 percent, while the return for men is statistically negligible. Our analysis suggests that this discrepancy is consistent with double majors serving as a signaling effect in the labor market, which may help offset the gender pay gap. Table 3: Main Results
FSRDC Project Number 2985; Disclosure Clearance Numbers CBDRB-FY24-P2985-R11339 and CBDRB-FY24-P2985R11713. Results rounded according to Census disclosure rules. Each italicized row header indicates a separate regression. Job match column restricted to individuals whose highest education is a single BA given that the survey question asks bp about the highest degree. r¯ (0) is the selection on unobservables percentage, relative to selection on observables, required D ∗ to overturn the empirical finding implied by the corresponding estimate in the table. Standard errors in parentheses. p< 0.05, ∗∗ p< 0.01, ∗∗∗ p< 0.001
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Dani Sandler @dismalscientist86.bsky.social · 29/09/2026
New Census Working Paper: "Gender Differences in the Returns to Double Majoring: A Case of Signaling?" by Andrew Smith, Alfonso Flores-Lagunes, and Maria Zhu www.census.gov/library/work...
census.gov
Gender Differences in the Returns to Double Majoring: A Case of Signaling?
Women earn about 5% more for double majoring in college, while men see no return, consistent with double majors serving as a signal in the labor market.
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Dani Sandler @dismalscientist86.bsky.social · 29/09/2026
Retailer profitability effect of store inventory depends on e-commerce intensity, product mix and firm size.
Problem definition: Retailers increasingly sell both in physical stores and via e-commerce channels. In this setting, a central yet underexplored strategic decision concerns store inventory positioning (SIP)—the share of total inventory held in stores rather than in upstream facilities— and its effect on firm performance. While classical inventory theory highlights a cost– responsiveness tradeoff associated with SIP, little empirical evidence exists on how SIP affects retailer profitability in a multi-channel context. This study investigates how SIP relates to operating margins and how this relationship varies with retailers’ e-commerce intensity (ECI), the share of total sales facilitated via online channels. We also explore whether these effects are contingent on factors like retailer size and type. Methodology/results: We use restricted-access firm-level microdata from the U.S. Census Bureau’s Annual Retail Trade Survey covering approximately 42,500 U.S. retailers and 190,000 firm-year observations from 2006 to 2019. The data uniquely allows us to observe both the physical location of inventory and e-commerce sales shares. Using two-way fixed effects models complemented by robustness analyses, we find that higher SIP is, on average, negatively associated with operating margins. However, this negative relationship is significantly attenuated as ECI increases. Importantly, the moderating effect of ECI is economically large and statistically significant only for large retailers and is strongest for specialty-oriented retailers relative to convenience-oriented ones. Managerial implications: Our findings show that SIP is not universally beneficial; its profitability implications depend critically on a retailer’s channel mix, scale, and type. Accordingly, managers should align inventory placement decisions with their ECI, size, and type. For large retailers with substantial online sales, greater store-based inventory can enhance operating margins, whereas smaller or co…Table 2 Two-way fixed effects regression results (N = 190,000) 
Notes: DV = OpMgn; robust standard errors are reported in parentheses. Source: FSRDC Project Number 1871. Disclosure clearance number CBDRB-FY26-P1871-R12847; CBDRB-FY26-P1871R12808. † p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
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Dani Sandler @dismalscientist86.bsky.social · 29/09/2026
New Census Working Paper: "Store Inventory Positioning, E-Commerce Intensity, and Retailer Profitability: Evidence From U.S. Census Microdata" by Cuneyt Eroglu, Christian Hofer, and Adriana Rossiter Hofer www.census.gov/library/work...
census.gov
Store Inventory Positioning, E-Commerce Intensity, and Retailer Profitability: Evidence From U.S. Census Microdata
Retailer profitability effect of store inventory depends on e-commerce intensity, product mix and firm size.
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Dani Sandler @dismalscientist86.bsky.social · 29/09/2026
Natural disasters leave lasting employment scars, but SBA disaster loans help firms preserve jobs, avoid closure, and support recovery in affected communities.
We show that natural disasters generate persistent declines in U.S. county employment and net job creation. We then examine whether emergency credit can mitigate these effects. Linking the universe of SBA disaster-loan applications to Census Bureau records, we exploit a credit-score screening rule introduced in 2016 and implement a fuzzy regression discontinuity design around the score cutoff. Firms just above the threshold are substantially more likely to receive government-backed loans, and approval increases employment relative to declined applicants, with effects persisting over subsequent years. The adjustment operates primarily through lower job destruction, while reduced firm exit and stronger job creation also contribute. Overall, SBA lending preserved an estimated 9.8% of the jobs that would otherwise have been lost in disaster affected counties. Figure 6: Dynamic effects of loan approval on employment.
Notes: Coefficients are estimated using robust bias-corrected local polynomial regressions around the normalized FICO cutoff (set equal to 0). The specification fts first-order polynomials separately on each side of the cutoff. For each semester i, the dependent variable is the change in the applicant’s employment between semester i and the loan-decision semester 0, i.e. Li − L0. Estimates in this figure were disclosed under CBDRB-FY26-0317.
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Dani Sandler @dismalscientist86.bsky.social · 29/09/2026
New Census Working Paper: "Resilience After Climate Shocks: Evidence From SBA Disaster Loans" by Federico Esposito and Saverio Simonelli www.census.gov/library/work...
census.gov
Resilience After Climate Shocks: Evidence From SBA Disaster Loans
Natural disasters leave lasting employment scars, but SBA disaster loans help firms preserve jobs, avoid closure, and support recovery in affected communities.
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Dani Sandler @dismalscientist86.bsky.social · 29/09/2026
Using U.S. population data on twins, this paper shows that social interactions can lead standard twin designs to overstate the heritability of earnings.
We use data on 44,000 twin pairs observed as adolescents in the 2000 Census, linked to their earnings 20 years later, to study the heritability of labor market outcomes. We extend the Classical Twins Design (CTD), which identifies heritability from contrasts between monozygotic and dizygotic twins, to settings where an analyst does not observe zygosity but can measure outcomes for same-sex and opposite-sex twins. We also allow for the presence of a family- and sex-specific component affecting same-sex siblings but only partly shared by opposite-sex siblings. Our extended model identifies heritability from the difference-in-differences of samesex vs. opposite-sex twins vs. siblings. The estimates indicate strong heritability (h2 » 0.36) of log earnings. Using an AKM (Abowd et al., 1999) decomposition that separates the person-specific component of earnings from employer-specific pay premiums, we find that both components are heritable. The heritability of employer pay premiums, however, is mainly driven by same-sex twins who work at the same firm, suggesting that social interactions may lead to an over-estimate of the genetic component in earnings. Excluding siblings who work together leads to estimates of the heritability of log earnings that are 15% lower. Similar biases may be present in CTD-based estimates of heritability for other social outcomes like education. Figure 1. Between-sibling and between-twin correla6ons, same-sex (SS) and opposite-sex (OS)
Notes: Horizontal lines represent the 95% confidence intervals. Log earnings and its components, presented in the first six panels, are computed over the sample of twin and sibling pairs who are both stably employed, as described in the data sec6on. Employment outcomes, in the final three panels, are computed over all twin pairs, including those who are not both stably employed.
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Dani Sandler @dismalscientist86.bsky.social · 29/09/2026
New Census Working Paper: "Heritability and Social Interaction in Labor Market Outcomes: Evidence From the Population of Twins in the United States" by David Card, Jesse Rothstein, and Moises Yi www.census.gov/library/work...
census.gov
Heritability and Social Interaction in Labor Market Outcomes: Evidence From the Population of Twins in the United States
Using U.S. population data on twins, we show that social interactions can lead standard twin designs to overstate the heritability of earnings.
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The Quarterly Journal of Economics @qjeharvard.bsky.social · 27/09/2026
Recently accepted by #QJE: “Busing to Opportunity? The Impacts of the METCO Voluntary School Desegregation Program on Urban Students of Color” by Elizabeth Setren (@elizabeth-setren.bsky.social): doi.org/10.1093/qje/...
doi.org
Busing to Opportunity? The Impacts of the METCO Voluntary School Desegregation Program on Urban Students of Color
Abstract. School assignment policies are a key lever for expanding access to high-performing schools and promoting racial and socioeconomic integration. Fo
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Dani Sandler @dismalscientist86.bsky.social · 26/09/2026
AI doing things the humans didn't mean for them to do is not good in general, but the Census part (at least based on the text of the article) sounds like it used an API key to fetch public use data. Which seems ... fine. Maybe even exactly why the data exists in that format.
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Daniel Zhao @danielzhao.bsky.social · 24/09/2026
BLS employee tenure data shows that younger workers have slightly increased their median tenure at a job over the last 20 years while mid-career workers age 35–64 have decreased their median tenure. Source: www.bls.gov/news.release...
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U.S. Bureau of Labor Statistics @usbls.bsky.social · 24/09/2026
Time spent providing primary care to household children in 2025 #BLSData www.bls.gov/opub/ted/202...
Stacked Column Chart Showing Average Hours Per Day Adults Spent Caring For Household Children As A Primary Activity By Age Of Youngest Household Child, 2025 Annual Averages
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Aaron Sojourner @aaronsojourner.org · 24/09/2026
Administration gags public servants from serving public talkingpointsmemo.com/news/exclusi...
talkingpointsmemo.com
Exclusive: Census Bureau Directs Staff to Keep Quiet During Comment Period for Controversial New Rule
Leadership at the Census Bureau has ordered staff to halt all external...
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Louis Henderson @hendersonlouis.bsky.social · 23/09/2026
Happy to announce that “Valuing five centuries of unpaid breastfeeding in England”, with Jane Humphries, is published in PNAS: www.pnas.org/doi/10.1073/pnas.2600844123. Breastfeeding benefits both children and mothers, but its economic value often escapes notice because it is unpaid labour (1/8)
pnas.org
Valuing five centuries of unpaid breastfeeding in England | PNAS
Breastfeeding is vital and valuable labor, contributing to the physical and cognitive health of children throughout their lives, yet it is rarely c...
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Aaron Sojourner @aaronsojourner.org · 18/09/2026
Public comment on proposed regs is a way rule-making incorporates public voice. Agencies must consider & respond to comments. Others can read them & learn. Scholars can put evidence & expertise into the policymaking processes by these channels. scholars.my.salesforce-sites.com/event/home/s...
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Melissa Gentry @melissadgentry.bsky.social · 16/09/2026
Are you a disability researcher working in the FSRDC wondering where the 1980s disability variable in the CPS went? I've written a CES Technical Note describing my process of bringing in those missing data! ideas.repec.org/p/cen/tnotes...
ideas.repec.org
Recovering Work-Limiting Disability Variables in the 1980s C
Disability status has been elicited systematically in United States surveys as far back as the 1970s and was included in the March Supplement of the Current Population Survey starting in 1981. Unfortu
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Dr Lindsay Tedds @lindsaytedds.bsky.social · 15/09/2026
Don’t forget get that voting in Fat Bear Week starts next week explore.org/fat-bear-week
explore.org
Fat Bear Week 2026 - Explore.org
Explore.org is the world's leading philanthropic live nature cam network and documentary film channel. Our mission is to champion the selfless acts of others, create a portal into the soul of humanity...
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Dani Sandler @dismalscientist86.bsky.social · 14/09/2026
The most AI-exposed new college grads have initial employment declines of 5pp and earnings declines of 13%, partially driven by shifts to lower-paying sectors.
How are new college graduates affected by the rise of artificial intelligence, and what can this tell us about the mechanisms behind AI's overall labor market effects? We use administrative records on college graduates to observe how economic outcomes among college majors with differing levels of labor market AI exposure have evolved since large language models became available. We find that post-graduation employment, earnings, and job switching patterns among the most AI-exposed college majors began to diverge immediately following the introduction of ChatGPT in late 2022. In regression-adjusted estimates, the most AI-exposed decile of college majors saw their likelihood of initial employment decline by five percentage points, while full-quarter initial earnings declined by thirteen percent. This earnings decline is comparable in magnitude to the earnings losses associated with graduating into a large recession. Roughly half of the decline in earnings is attributable to lower earnings within the industry sectors that employ these graduates, with the remainder resulting from a shift in the industry mix into lower-wage sectors such as restaurants and retail. The effects attenuate as graduates move further from labor market entry but remain substantial for the most exposed majors. Figure 3: Share employed one-quarter post-graduation 
Notes: Employment share calculated as the fraction of graduates with non-zero earnings for the quarter. Estimates from a regression with institution-major, institution-quarter, and major-quarter of year fixed effects. Standard errors are clustered by college major. Error bars denote 95% confidence intervals. Quarter denotes observation period. November 2022, corresponding to the release of ChatGPT-3.5, marked with a red line. The gray shaded region identifies cohorts who graduated after the release of ChatGPT-3.5. Number of observations: 5,685,000. All results were approved for release by the U.S. Census Bureau, authorization number CBDRB-FY26-CES011-011.
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Dani Sandler @dismalscientist86.bsky.social · 14/09/2026
New Census Working Paper: "Graduating into Disruption: Labor Market Outcomes for AI-Exposed College Majors" by Cody Orr, Lee C. Tucker, and Lawrence Warren www.census.gov/library/work...
census.gov
Graduating into Disruption: Labor Market Outcomes for AI-Exposed College Majors
The most AI-exposed new college grads have initial employment declines of 5pp and earnings declines of 13%, partially driven by shifts to lower-paying sectors.
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Dani Sandler @dismalscientist86.bsky.social · 13/09/2026
Double cat nap #Caturday
Dark grey and white cat sleeping back to back with grey and brow cat
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Dani Sandler @dismalscientist86.bsky.social · 09/09/2026
Firm boundaries shape shock transmission: natural disasters abroad reduce US imports less for MNC vs non-MNC buyers and for intra-firm vs arm's-length trade.
Do the boundaries of the multinational firm shape how shocks propagate through global production networks? We link U.S. Bill of Lading microdata, geocoded natural disaster records, and cross-border ownership data to trace the transmission of exogenous supply disruptions from foreign suppliers to U.S. importers. Exploiting the quasi-random timing and location of natural disasters in a staggered event-study design, we document that disasters abroad generate sharp, persistent export declines at affected suppliers—shocks that propagate downstream, reducing the growth rate of U.S. importers’ total purchases by up to 25 percentage points. Transmission, however, is far from uniform: non-MNC buyers contract roughly twice as much as importers belonging to global corporations, and a comparable gap separates arm’s-length from intra-firm trade. These patterns point to the internal networks of multinational firms as shock absorbers that attenuate and redirect supply-side disruptions—suggesting that firm boundaries are not merely organizational choices, but also determinants of macroeconomic resilience. Figure 5: Supplier Export Disruptions and Downstream Propagation to U.S. Importers
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Dani Sandler @dismalscientist86.bsky.social · 09/09/2026
New Census Working Paper: "How Shocks Travel: The Cross-Border Impact of Natural Disasters in Firm Networks" by Vanessa Alviarez, Brian C. Fujiy, Angel Espinoza E., and Tomasz Swiecki www.census.gov/library/work...
census.gov
How Shocks Travel: The Cross-Border Impact of Natural Disasters in Firm Networks
Firm boundaries shape shock transmission: natural disasters abroad reduce US imports less for MNC vs non-MNC buyers and for intra-firm vs arm's-length trade.
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Rebecca Sear @rebeccasear.bsky.social · 08/09/2026
Period measures of fertility, like TFR, are less stable than cohort measures and are currently overstating fertility decline. New paper out on The Long-Term Trends in Cohort Fertility: Global, Regional and National Estimates and Projections for Cohorts Born in 1935–2055 doi.org/10.1111/padr...
Graph from paper comparing period and cohort measures of fertility since 1950 projected to 2100. Lines are similar until around 1990 when period drops below cohort measure and stays below cohort measure
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ProPublica @propublica.org · 06/09/2026
ProPublica spoke to dozens of people who had lived in homeless encampments when cities cleared them out in “sweeps.” We distributed notecards so people could tell us about the toll in their own words. ➡️ This is what they wrote:
Handwritten notecard. Prompt at the top asks: What object was most devastating for you to lose, and how have you been coping? Answer below: Dentures. Were thrown away and coping isn't somthing I can do. It’s made me feel ugly, unworthy, can’t go get a job with no teeth. So how can get off the streets until another pair can be made?
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Dani Sandler @dismalscientist86.bsky.social · 03/09/2026
Without saying your age, what is your favorite movie that came out when you were 20 years old?
static.klipy.com
Ben Stiller & Dum Dum: You Give Me Gum-Gum
Alt: Ben Stiller & Dum Dum: You Give Me Gum-Gum
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Dani Sandler @dismalscientist86.bsky.social · 02/09/2026
Sunrise from the train
Blue and gold sunrise over the water with a dock in the foreground
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Erika McEntarfer @erikamcentarfer.bsky.social · 01/09/2026
This change grants Commerce political appointees veto power over data and research releases by scientists and statisticians at NOAA, NIST, Census, and BEA.
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Karen Benjamin Guzzo @karenguzzo.bsky.social · 31/08/2026
This week in my grad Soc of Family, we're covering Relationship Formation. First up, a quick data viz by @joannapepin.bsky.social & @philipncohen.com documenting shifts in young people's expectations of marriage & being a spouse. 1/ journals.sagepub.com/doi/full/10....
Figure 1. The figure shows data from the 1976 to 2022 Monitoring the Future Public-Use Cross-Sectional Datasets, a nationally representative annual survey of U.S. high school seniors. (A) Young adults’ assessment of how good a spouse they expect to be, by gender (“very good,” “fairly good,” “good,” “not so good,” or “poor”), by gender. (B) Young adults’ expectation of marriage (“get married,” “not get married,” or “no idea”), also by gender. The bottom panels show the percentage point change over the 10 years from 2012 to 2022 for how good a spouse they will be (C) and their expectation of marriage (D). Over the last 10 years of the survey, the percentage of high school seniors who think they will make very good spouses has dropped, as has the percentage expecting to marry. Data access and replication code are available at https://github.com/jrpepin/MTF_Marriage.
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Dani Sandler @dismalscientist86.bsky.social · 31/08/2026
After taking most of August off, I will be posting reading notes again starting tomorrow morning: danisreadingnotes.substack.com/p/danis-read...
danisreadingnotes.substack.com
Dani’s Reading Notes
A brief introduction to me and this blog/substack: I’m Dani Sandler, a Principal Economist at the U.S.
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Dani Sandler @dismalscientist86.bsky.social · 31/08/2026
This paper links American Housing Survey data to administrative records to analyze the long term effects of homeownership.
We measure the effects of housing quality and living in renter- versus owner-occupied housing as a child on later life outcomes. Homeownership is the primary source of most US households’ wealth, and homeownership and housing quality potentially have large impacts on children, yet we know relatively little about any long-term effects. Ownership and housing quality are correlated with income, neighborhood characteristics, and many other socioeconomic characteristics, making causal estimation difficult. Using the US Census Bureau’s data linkage infrastructure, we create a new data linkage between the American Housing Survey (1995-2002) and administrative tax data (2016-2019), to investigate adult outcomes such as earnings and having a mortgage or owning a home, while comprehensively controlling for a wide variety of variables, including housing quality. We find strong positive correlations with growing up in a parent-owned home, approximately $2,000 higher incomes and a 6 percentage point increased likelihood of ownership. However, we find no evidence of causal effects: unobservables that are significantly less correlated with outcome and treatment than observables would drive the coefficients of interest to zero, and family fixed effects models that compare siblings to each other show no effect of years of childhood spent in an owned home or being born into an owned home. Table 3: Wages and Total Income 
Robust standard errors clustered at county level in brackets. Columns one to three use W2 wages as the outcome, and columns four to six use AGI. Beta and Delta are Oster (2019) statistics calculated using the treatment coefficient Parents Own.
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Dani Sandler @dismalscientist86.bsky.social · 31/08/2026
New Census Working Paper: "The Long-Run Effects of Parental Homeownership and Housing Quality on Later Life Outcomes" by Garret Christensen and John Voorheis www.census.gov/library/work...
census.gov
The Long-Run Effects of Parental Homeownership and Housing Quality on Later Life Outcomes
This paper links American Housing Survey data to administrative records to analyze the long term effects of homeownership.
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Dani Sandler @dismalscientist86.bsky.social · 31/08/2026
Also posted as an NBER Working Paper: www.nber.org/papers/w35675
nber.org
The Distribution and Consequences of Natural Disasters: Evidence from U.S. Wildfire Victims
Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...
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Dani Sandler @dismalscientist86.bsky.social · 31/08/2026
This paper uses linked administrative data to study the individual level economic impact of wildfires.
The economic burden of natural disasters is shaped by who is exposed, who experiences losses, how those losses alter individual economic trajectories, and how affected communities evolve in the aftermath. We link property-level damage data from over 100 U.S. wildfires to administrative tax and Census data to understand individual level relationships between disaster damage and economic outcomes. Residents of burned areas are wealthier than the U.S. population on average, but within these wildfire perimeters the likelihood that a home burns is negatively correlated with pre-fire income. Home destruction imposes costs beyond rebuilding the property: occupants of destroyed homes experience lower income for three years post-fire, producing cumulative income losses equal to 26 percent of pre-fire annual income. We do not detect income or employment effects for residents without direct property damage. Community recovery is slow and accompanied by changes in composition. Four years after a fire, half of destroyed addresses remain unoccupied and reoccupied addresses house residents with substantially higher incomes than before. Figure 4: Effect of Wildfires on Income by Damage
Notes: Figure documents the effect of a wildfire event on income, split by whether the individual’s home was destroyed or not. Panel A reports local projection estimates of the effect on log Adjusted Gross Income for Destroyed and Survived groups relative to the Adjacent (10–25 km) group in years before and after the wildfire and Panel B reports similar local projection estimates for the inverse hyperbolic sine of wages.
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