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A feed for public administration and public policy research and discussion. For academics and people who want to follow scholarship, not general discussion/rants/etc. Ask @andrew.heiss.phd to be added. Tag posts with "PAsky" or 🏛️ or "policysky"

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Andrew Heiss
@andrew.heiss.phd
5 months ago
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/sno… SocArXiv: doi.org/10.31235/osf...
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
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
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Andrew Heiss
@andrew.heiss.phd
8 months ago
Woohoo two big names in public admin/nonprofits just got here! @professajay.bsky.social and @rkchristensen.bsky.social #pasky #nonprofitsky
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Andrew Heiss
@andrew.heiss.phd
9 months ago
This post by @donmoyn.bsky.social is an excellent, sobering outline of the next few years of the federal bureaucracy and public administration in general #pasky
What Happens Next?

open.substack.com

What Happens Next?

The administrative state under a second Trump term

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Andrew Heiss
@andrew.heiss.phd
10 months ago
This new AJPS paper by Alexander Sahn is a methodological tour de force, using tens of thousands of comments to show how public hearing commenters are older, white, NIMBY homeowners who exert extreme influence on zoning decisions at public meetings doi.org/10.1111/ajps... #polisky #PAsky
Public comment and public policy
Alexander Sahn

Abstract  Is public policy responsive to demographically and ideologically unrepresentative comments given at public meetings? I investigate this possibility using a novel data set of over 40,000 comments made at the San Francisco Planning Commission between 1998 and 2021, matched to information about proposed developments discussed in hearings and administrative data on commenters. I document four stylized facts: First, commenters at public meetings are unrepresentative of the public along racial, gender, age, and homeownership lines; second, distance to the proposed development predicts commenting behavior, but only among those in opposition; third, commission votes are correlated with commenters’ preferences; finally, the alignment of White commenters (vs. other racial groups) and neighborhood group representatives and the general public (vs. other interest groups) better predict project approvals.
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Andrew Heiss
@andrew.heiss.phd
11 months ago
This new paper by Johanna Thoma is an incredible exploration of how big social scientific indicators like GDP, national happiness, etc threaten democracy, & provides some helpful possible solutions. Essential reading for anyone doing policy-focused quantitative work policysky doi.org/10.1111/papa...
JOHANNA THOMA 
Social Science, Policy and Democracy
Can social science provide policy-guidance without undermining some basic democratic values? It would clearly be devastating if the answer was “no”: Most people are deeply committed both to democracy, as well as to the idea that policy decisions should be informed by the best available sci- ence, including the best available social science. Accordingly, the many philosophers who have worried about potential tensions between science and democracy have come out arguing that, if done right, good science and democracy mutually support rather than undermine each other, John Dewey and Philip Kitcher being paradigmatic examples.1
This article argues that there is an under-appreciated democratic chal- lenge for policy-relevant science, which I will articulate specifically in the context of value-laden social scientific indicators. Value-ladenness has long been acknowledged to pose an obstacle for reconciling science and democracy: It creates
Consequently, solutions to this challenge have either def- ended the value-free ideal,2 or stressed the need to, in one way or another, democratically align the values entering science, in a way that is parallel to how democratic legitimacy is given to public decision-making more generally. The nature of many social scientific indicators makes the value-free ideal wholly unworkable, lest we give up the entire project of aiming to measure poverty, inequality, or wellbeing. And so only the sec- ond common type of response seems to be available in their case. But, I will argue, this response misses a significant part of the challenge value- ladenness poses to democracy. The solution, I will argue, is greater value pluralism rather than democratic alignment.
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Andrew Heiss
@andrew.heiss.phd
almost 2 years ago
policysky PAsky
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Andrew Heiss
@andrew.heiss.phd
almost 2 years ago
policysky
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Andrew Heiss
@andrew.heiss.phd
almost 2 years ago
Do you study public policy / administration? Follow and join the PAsky / policysky feed! Like and pin the feed to follow posts. If you want your posts to appear in the feed, lmk and I'll add you. Anything you post with "PAsky" or 🏛️ or "policysky" will show up, like a type of protected hashtag.
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