Zikai Li
@tzkli.bsky.social
360 followers 110 following 18 posts
PhD student in political science at the University of Chicago
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Reposted by Zikai Li
yiqingxu.bsky.social
Thrilled to share that **fect** has won the 2025 Best Statistical Software Award from the Society of Political Methodology. We're honored!
polmeth.org/statistical-...

To celebrate, we've just released fect v2.0.5 on CRAN & Github 🎉
2025 Best Statistical Software Award
tzkli.bsky.social
Bottom line: "Deliverism" itself may not help the incumbent politician/party in today's America. Targeting investments at left-behind communities ≠ winning votes. Politics around place-based (climate) politics seem to be messier than advocates hope 9/9
Full paper: tiny.cc/energycommun....
OSF
tiny.cc
tzkli.bsky.social
I refined a two-dimensional RDD method with bootstrap aggregation + delta bootstrap CIs. Simulations show less bias, higher statistical efficiency. 8/9
tzkli.bsky.social
The finding: The bonus decreased the vote share for Harris by 0.39 percentage points in 2024. (95% CI: -0.78 pp to -0.02 pp) 7/9
tzkli.bsky.social
There are two sharp cutoffs for eligibility. Areas just above vs. just below these thresholds are "alike" - except one happened to have been eligible for the bonus, one didn't. Great setup for a quasi-experiment! 6/9
tzkli.bsky.social
So which story wins? I approached this with a focus on Energy Community Tax Credit Bonus: 1/3 extra tax credit for renewable projects in areas with high unemployment + (past or present) fossil fuel jobs. 5/9
tzkli.bsky.social
But the argument can also go the other way. Maybe renewables threaten local identity. Maybe projects drive up prices in the short term. Maybe benefits are too invisible to matter. The evidence has been mixed 4/9
tzkli.bsky.social
The "deliverism" theory: Channel green money into struggling towns → ease transition fears → create grateful voters → more support. Sounds reasonable, right? 3/9
tzkli.bsky.social
The Biden bet: help "left behind" communities → win voters for both the party and further climate action. Such “place-based” policies have become increasingly popular 🔋➡️🗳️2/9
tzkli.bsky.social
Did the Biden administration's targeted industrial policies help lift the Democratic Party in "left-behind" America? Well, no, and it might've shifted the targeted areas further away from the party, at least in the case of one place-based green subsidy. New working paper 🧵 1/9
tzkli.bsky.social
Bottom line: "Deliverism" itself may not help the incumbent politician/party in today's America. Targeting investments at left-behind communities ≠ winning votes. Politics around place-based (climate) politics seem to be messier than advocates hope. 9/9

Full paper: tiny.cc/energycommunities
OSF
osf.io
tzkli.bsky.social
The finding: The bonus decreased the vote share (of the two-party vote) for Harris by 0.39 percentage points in 2024. (95% CI: -0.78 pp to -0.02 pp) 7/9
tzkli.bsky.social
There are two sharp administrative cutoffs for eligibility. Areas that just met vs. just missed these thresholds are "alike" - except the former happened to have been eligible for the bonus while the latter didn't. Great setup for a quasi-experiment! 6/9
tzkli.bsky.social
So which story wins? I approached this with a focus on the Energy Community Tax Credit Bonus: 1/3 increase over baseline tax credits for renewable projects in areas with high unemployment + (past or present) fossil fuel jobs. 5/9
tzkli.bsky.social
But the argument can also go the other way. Maybe renewables threaten local identity. Projects can drive up prices in the short term. Perhaps benefits are too invisible to matter. And empirical evidence has been mixed 4/9
tzkli.bsky.social
The "deliverism" theory: Channel (green) investments into struggling towns → ease transition fears → create grateful voters → more support. Sounds reasonable, right? 3/9
tzkli.bsky.social
The Biden bet: help "left behind" communities (yellow areas in the map) → win voters for both the party and further climate action. Such "place-based" policies have become increasingly popular 2/9
Reposted by Zikai Li
astrezh.bsky.social
New working paper! - zikai.li and I look at the problem of assessing pre-trends using one of the "new" DiD methods - fixed effects regression imputation. tl;dr - don't use the same regression you used to impute post-treatment to also impute pre-treatment. osf.io/preprints/so... (1/14)
"Benchmarking parallel trends violations in regression imputation difference-in-differences" https://osf.io/preprints/socarxiv/ngr3d_v1
tzkli.bsky.social
Thanks for your interest! I have the data pipeline ready but haven't got the county-level returns yet. Will send the report your way once I have preliminary results!
tzkli.bsky.social
Thanks, Bit!