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Jason Coupet

@professajay.bsky.social

Towards a big, efficient public sector that serves the least of us. Associate Professor of Public Management and Policy at the Andrew Young School. United Campus Workers of Georgia. Father of 3. Chicago southsider. Proud 〽️ichigan alum. 🏀 nut.

6,625 Followers  |  1,801 Following  |  998 Posts  |  Joined: 05.07.2023
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Posts by Jason Coupet (@professajay.bsky.social)

Lolll

02.03.2026 12:50 — 👍 7    🔁 0    💬 1    📌 0
This essay provides an overview of statistical methods in public policy, focused primarily on the United States. I trace the historical development of quantitative approaches in policy research, from early ad hoc applications through the 19th and early 20th centuries, to the full institutionalization of statistical analysis in federal, state, local, and nonprofit agencies by the late 20th century. I then outline three core methodological approaches to policy-centered statistical research across social science disciplines: description, explanation, and prediction, framing each in terms of the focus of the analysis. In descriptive work, researchers explore what exists and examine any variable of interest to understand their different distributions and relationships. In explanatory work, researchers ask why does it exist and how can it be influenced. The focus of the analysis is on explanatory variables (X) to either (1) accurately estimate their relationship with an outcome variable (Y), or (2) causally attribute the effect of specific explanatory variables on outcomes. In predictive work, researchers as what will happen next and focus on the outcome variable (Y) and on generating accurate forecasts, classifications, and predictions from new data. For each approach, I examine key techniques, their applications in policy contexts, and important methodological considerations. I then consider critical perspectives on quantitative policy analysis framed around issues related to a three-part “data imperative” where governments are driven to count, gather, and learn from data. Each of these imperatives entail substantial issues related to privacy, accountability, democratic participation, and epistemic inequalities—issues at odds with public sector values of transparency and openness. I conclude by identifying some emerging trends in public sector-focused data science, inclusive ethical guidelines, open research practices, and future directions for the field.

This essay provides an overview of statistical methods in public policy, focused primarily on the United States. I trace the historical development of quantitative approaches in policy research, from early ad hoc applications through the 19th and early 20th centuries, to the full institutionalization of statistical analysis in federal, state, local, and nonprofit agencies by the late 20th century. I then outline three core methodological approaches to policy-centered statistical research across social science disciplines: description, explanation, and prediction, framing each in terms of the focus of the analysis. In descriptive work, researchers explore what exists and examine any variable of interest to understand their different distributions and relationships. In explanatory work, researchers ask why does it exist and how can it be influenced. The focus of the analysis is on explanatory variables (X) to either (1) accurately estimate their relationship with an outcome variable (Y), or (2) causally attribute the effect of specific explanatory variables on outcomes. In predictive work, researchers as what will happen next and focus on the outcome variable (Y) and on generating accurate forecasts, classifications, and predictions from new data. For each approach, I examine key techniques, their applications in policy contexts, and important methodological considerations. I then consider critical perspectives on quantitative policy analysis framed around issues related to a three-part “data imperative” where governments are driven to count, gather, and learn from data. Each of these imperatives entail substantial issues related to privacy, accountability, democratic participation, and epistemic inequalities—issues at odds with public sector values of transparency and openness. I conclude by identifying some emerging trends in public sector-focused data science, inclusive ethical guidelines, open research practices, and future directions for the field.

	Description	Explanation	Prediction
General question	What exists?	Why does it exist? How can it be influenced?	What will happen next?
Focus of analysis	Focus is on any variable—understanding different variables and their distributions and relationships	Focus is on X —understanding the relationship between X and Y, often with an emphasis on causality	Focus is on Y —forecasting or estimating the value of Y based on X, often without concern for causal mechanisms
Names for variable of interest	—		Explanatory variable
	Independent variable
	Predictor variable
	Covariate		Outcome variable
	Dependent variable
	Response variable
Goal of analysis	Summarize and explore data to identify patterns, trends, and relationships	Estimation: Test hypotheses or theories and make inferences about the relationship between one or more X variables and Y
 
Causal attribution: A special form of estimating—make inferences about the causal relationship between a single X of interest and Y through credible causal assumptions and identification strategies	Generate accurate predictions; maximize the amount of explainable variation in Y while minimizing prediction error
Evaluation criteria	—	Confidence/credible intervals, coefficient significance, effect sizes, and theoretical consistency	Metrics like root mean square error (RMSE) and R^2; out-of-sample performance
Typical approaches	Univariate summary statistics like the mean, median, variance, and standard deviation; multivariate summary statistics like correlations and cross-tabulations	t-tests, proportion tests, multivariate regression models; for causal attribution, careful identification through experiments, quasi-experiments, and other methods with observational data	Multivariate regression models; more complex black-box approaches like machine learning and ensemble models

Description Explanation Prediction General question What exists? Why does it exist? How can it be influenced? What will happen next? Focus of analysis Focus is on any variable—understanding different variables and their distributions and relationships Focus is on X —understanding the relationship between X and Y, often with an emphasis on causality Focus is on Y —forecasting or estimating the value of Y based on X, often without concern for causal mechanisms Names for variable of interest — Explanatory variable Independent variable Predictor variable Covariate Outcome variable Dependent variable Response variable Goal of analysis Summarize and explore data to identify patterns, trends, and relationships Estimation: Test hypotheses or theories and make inferences about the relationship between one or more X variables and Y Causal attribution: A special form of estimating—make inferences about the causal relationship between a single X of interest and Y through credible causal assumptions and identification strategies Generate accurate predictions; maximize the amount of explainable variation in Y while minimizing prediction error Evaluation criteria — Confidence/credible intervals, coefficient significance, effect sizes, and theoretical consistency Metrics like root mean square error (RMSE) and R^2; out-of-sample performance Typical approaches Univariate summary statistics like the mean, median, variance, and standard deviation; multivariate summary statistics like correlations and cross-tabulations t-tests, proportion tests, multivariate regression models; for causal attribution, careful identification through experiments, quasi-experiments, and other methods with observational data Multivariate regression models; more complex black-box approaches like machine learning and ensemble models

Table of contents
Introduction
Brief history of statistics in public policy
Core methodological approaches
Description
Explanation
Prediction
The pitfalls of counting, gathering, and learning from public data
Future directions
References

Table of contents Introduction Brief history of statistics in public policy Core methodological approaches Description Explanation Prediction The pitfalls of counting, gathering, and learning from public data Future directions References

New preprint! A general overview of stats in public policy research with this (oversimplified but still helpful) separation of methods into description, explanation, and prediction #policysky

HTML/PDF: stats.andrewheiss.com/snoopy-spring/
SocArXiv: doi.org/10.31235/osf...

12.03.2025 17:36 — 👍 146    🔁 28    💬 4    📌 4

These mfs are abusing kids, on purpose spreading diseases, and insider trading on mass murder and klan kidnappings. If by some miracle there is an election that democrats win and a transfer of power they all need to be tried. It’s also clear there’s a wing of the democrats don’t want that.

01.03.2026 11:39 — 👍 9    🔁 0    💬 0    📌 0

When this ICE shit started, a mutual I’d never met asked how to help. I sent him to a friend’s kid’s preschool that was struggling to find patrollers. He’s been there almost daily, even in brutal cold, at a school where he didn’t know anyone, to keep folks safe. We finally met and got beers tonight.

01.03.2026 04:55 — 👍 6833    🔁 581    💬 24    📌 38
I hope you aren’t drunk and took your staff’s advice, Rashida and I don’t know this man and feel confident he didn’t care about us. Please restrain from drinking too much as you have been warned from your staff and stay off social media when you are drunk. I pray in his holy month you find peace and respect for your self,

I hope you aren’t drunk and took your staff’s advice, Rashida and I don’t know this man and feel confident he didn’t care about us. Please restrain from drinking too much as you have been warned from your staff and stay off social media when you are drunk. I pray in his holy month you find peace and respect for your self,

uhhh oh my god

01.03.2026 08:39 — 👍 26008    🔁 4728    💬 575    📌 485

This is our “opposition” party. Christ we are in trouble man

28.02.2026 20:49 — 👍 10    🔁 0    💬 0    📌 0

Clown ass mf. I’ve had dozens of students from the armed services in my classroom and they’ve been among the best students I’ve had. And they’ve been among the ones most likely to reach out after and tell me how much they’ve learned and loved the learning environment.

Shame on you you Klan clown

28.02.2026 15:25 — 👍 9    🔁 4    💬 0    📌 0

Call me a filthy commie but I feel like the best way to "support our troops" is NOT sending them to kill and die in imperialist wars for corporate profit.

28.02.2026 14:43 — 👍 278    🔁 53    💬 5    📌 1

all the the mighty and morally superior “West” knows how to do anymore is murder children

28.02.2026 14:29 — 👍 2188    🔁 526    💬 5    📌 7

I was the biggest doomsday person ever, EVER, when this clown ass motherfucker won again. And somehow I underestimated how bad this would be.

Mass disenfranchisement, death troops, genocide, inflation, child abuse schemes, the death of science, federal kidnappings,concentration camps, nuclear war.

28.02.2026 14:38 — 👍 19    🔁 0    💬 0    📌 0
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No doubt Tulsi Gabbard is drafting her resignation letter as we speak.

28.02.2026 10:42 — 👍 4864    🔁 1192    💬 154    📌 59

Our media is so bad

28.02.2026 14:24 — 👍 5    🔁 0    💬 0    📌 0

lol he is such a 🤡

28.02.2026 13:54 — 👍 3    🔁 0    💬 0    📌 0

I can’t officially agree with you since technically I work for the man but I’ve heard that sentiment before I shall say

28.02.2026 03:20 — 👍 1    🔁 0    💬 0    📌 0

It’s unreal to me that some liberals hold these dudes are some kind of paragon of Republican backbone and decency. Yall don’t love down here. We are the vanguard of Jim Crow.

28.02.2026 02:49 — 👍 5    🔁 0    💬 2    📌 0
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The Mississippi Free Press is looking for new Voices writers. Columns should be between 700-1,200 words and sources fact-checking the included information. Submit opinion columns to the MFP at voices@mississippifreepress.org.

27.02.2026 17:05 — 👍 8    🔁 8    💬 0    📌 1

As a dialectical materialist, culture takes a backseat to material well being in my analysis. This little black history assembly I’m enjoying really puts into context how in America the two can be connected. We gotta see each other as human to band together. Thats why the Nazis hate this shit.

27.02.2026 14:09 — 👍 9    🔁 0    💬 0    📌 0

They also learn to see black and Hispanic folk as human. Learn that taking direction from black and Hispanic leadership and teachers and neighbors is normal. They see equal black and Hispanic talent around them as normal. That shit erodes the superiority complex.

27.02.2026 14:09 — 👍 8    🔁 0    💬 1    📌 0

Honestly, it’s the white kids here that benefit that most on average, and that’s why they are flocking to this place. All the higher wealth and shit stays put and these kids get to download all this cultural competence. Learn to navigate and respect Mexican and and Black culture and food and shit.

27.02.2026 14:09 — 👍 6    🔁 0    💬 1    📌 0

It’s Atlanta of course so they also performed a trap version of “Little Einsteins” in English and Spanish to “launch” the celebration. I just saw a group of 6yo Mexican kids duwop to Duke Ellington. How insecure do you have to be to hate this? My 10yo is *fully* conversational in Mexican Spanish.

27.02.2026 14:09 — 👍 5    🔁 0    💬 1    📌 0

I guess my kids’ school is what these Nazis are scared of and I love it. Their traditional public elementary school is basically a third black, white and Hispanic. Today is the black history month celebration and this shit is awesome. These kids performing “Still I Rise” together makes me emotional.

27.02.2026 14:09 — 👍 27    🔁 0    💬 1    📌 0

Zohran is going to be reelected with Saddam Hussein level vote shares.

26.02.2026 20:33 — 👍 12    🔁 1    💬 1    📌 0

Black Mecca! Unless you are working class of course.

26.02.2026 15:22 — 👍 5    🔁 0    💬 0    📌 0

💯

26.02.2026 14:53 — 👍 7    🔁 2    💬 0    📌 0

stop calling us nazis just because we are invalidating IDs and rounding people up based on their skin color and putting them in concentration camps and sending them off to foreign gulags and using all that nazi imagery and banning books and dont forget about all the pedophilia. so just stop alright

26.02.2026 12:05 — 👍 12062    🔁 3485    💬 78    📌 40

Bro this is wild

26.02.2026 12:50 — 👍 4    🔁 0    💬 0    📌 0

Good question! I can’t recall anyone ever making anything of it, but I don’t write about race super centrally. I too am working on a book project though on black street gangs where this is going to come up and dunno how to handle it. Keep me posted?

26.02.2026 01:27 — 👍 1    🔁 0    💬 0    📌 0
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Timeline cleanse!

My 5yo’s baseball coach fucketh not around lol. Ball got through those little legs because your glove wasn’t down? Push-ups. Lazy throw? Sprints.

No wonder this team beat the shit outa us last year. He’s on mine extra hard too because he knows he can handle it. I’m here for it!

26.02.2026 01:07 — 👍 9    🔁 0    💬 3    📌 0
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I really appreciate this TikToker with Tourette syndrome, Shay, for taking the time to educate so many of us (including me) about her disability and coprolalia in light of what happened at the BAFTAs with John Davidson when Michael B. Jordan and Delroy Lindo were on stage.

I learned a lot.

23.02.2026 19:10 — 👍 5108    🔁 1722    💬 74    📌 304

Welcome online MPA!

23.02.2026 23:53 — 👍 1    🔁 0    💬 1    📌 0