Real Stats Using Econometrics For Political Science And Public Policy

Running Regressions on Policy Data Is Not the Same as Understanding Policy

I spent three years trying to get a clean estimate on how campaign spending actually affects voter turnout in local elections. The data looked fine on paper. The county-level variables were there, the time series made sense, the sample size was reasonable. Then I realized the incumbent had been redistricting their own districts between election cycles, which meant the unit of analysis was not stable across years. Random effects models fell apart because the grouping variable was actively shifting. Fixed effects would have eaten the entire variation I needed. I ended up using a regression discontinuity design around the closest district boundary lines and matched it with event study diagnostics to check for pre-trends. It took four months to sort out. This is the part nobody tells you about applied econometrics in political science. Your research design has to survive the actual messy reality of how governments collect data, not just the sanitized version in the codebook.

Real Stats Using Econometrics For Political Science And Public Policy

The field sits at the intersection of statistical method and political question, which sounds straightforward until you sit down with a dataset and realize most political science data was never designed for causal inference. You are working with observational data where the treatment assignment is almost never random. That means your job is not to run the prettiest model. It is to find a situation where the treatment looks as close to randomly assigned as possible, and then build your identification strategy around that moment. The tools themselves are not complicated. Stata, R, Python with statsmodels or linearmodels will all handle standard fixed effects, instrumental variables, difference-in-differences, regression discontinuity, and matching. The hard part is knowing which one applies to your specific problem and then diagnosing whether your assumptions actually hold.

Identification Strategies That Actually Work in Practice

Difference-in-differences remains the most widely used approach in public policy evaluation because it is relatively intuitive and the parallel trends assumption is testable. You compare how outcomes change over time in a treated group versus a control group. The trick is finding a control group that genuinely would have followed the same trajectory without treatment. I have seen people use states that adopted similar policies at slightly different times as controls, which violates the exclusion restriction if those policies are correlated with other unobserved state-level shocks. You end up attributing the effect of policy A to policy B. Instrumental variables require an instrument that satisfies relevance and exclusion. Relevance is easy to check with first-stage F-statistics. Exclusion is where everything falls apart. In a study I worked on about the effect of public education spending on local economic growth, the instrument was state-level education mandates. The first stage was strong. The exclusion restriction was impossible to defend because those mandates came with unfunded requirements that changed local budget priorities in ways unrelated to the spending increase itself. I dropped the IV approach and switched to a synthetic control method instead. Regression discontinuity designs give you the cleanest causal identification when a cutoff rule determines treatment assignment. The local randomization around the threshold means you can compare units just above and just below the cutoff as if they were randomized. The downside is that your estimate is local. It only tells you the effect for units near the threshold, which may not generalize. A voting age law study estimating the effect of turning 18 on voter registration gives you a number that applies to 18-year-olds, not to the entire population. That matters when a policy brief wants a headline number.

Common Pitfalls That Ruin Papers Before They Get Reviewed

Clustering standard errors at the wrong level is probably the single most common mistake I see. People cluster at the individual level when the treatment is applied at the state or district level. This inflates your effective sample size and makes confidence intervals too narrow. If your treatment varies at the district level, cluster at the district level. If multiple observations come from the same district and share a treatment, clustering at the individual level gives you biased standard errors every time. Another issue is over-controlling. When you add too many covariates to a fixed effects model, especially in difference-in-differences, you can absorb part of the treatment effect itself. If the treatment influences some of the controls you include, you are blocking the mechanism you are trying to measure. This is particularly common in political science where policy interventions change institutions, which then change the behavior of the variables you want to control for. Lag the controls. Or don't include them at all and let the fixed effects handle it. Selection on observables is treated like a solved problem in introductory courses. In practice it is almost never solved. Propensity score matching reduces dimensionality but does not address unobserved confounding. Covariate balance after matching is necessary but not sufficient. I once reviewed a paper where the standardized mean differences were below 0.1 across all covariates after matching, but the treatment group had systematically different trends before the intervention. The matching removed the level differences but not the time-varying confounding. The DiD estimate was biased because parallel trends did not hold even though the baseline characteristics looked identical.

Practical Workflow for a Typical Analysis

Start with the research question and work backward to the identification strategy. Do not start with a dataset and try to force a method onto it. That produces fragile results that collapse under scrutiny. Once you have a strategy, map out the assumptions explicitly. Write them down. Then spend more time on diagnostics than on model estimation. For fixed effects models in panel data, always check for serial correlation in the residuals. Political and economic outcomes are highly autocorrelated, and ignoring that will make your standard errors unreliable. Use Driscoll-Kraay standard errors or cluster-robust SEs with time clustering if your panel has both individual and time dimensions. In Stata, the xtccvr command handles this. In R, the fixest package with clustered SEs at the appropriate level does the same thing. When working with spatial data, which is unavoidable in political science, standard OLS assumptions break down because observations are not independent across geographic units. Neighboring districts influence each other. Spatial autocorrelation bias is real and often ignored. The Moran I test is the default check. If your test statistic is significant, consider a spatial lag or spatial error model, or use spatial HAC standard errors. The spdep package in R covers most of this. In Stata, spregress handles spatial lag models adequately.

For event study specifications in difference-in-differences, plot the coefficients for each period before and after treatment. If the pre-treatment coefficients show a pattern, your parallel trends assumption is violated and the model is invalid. I cannot stress this enough. A formal statistical test for parallel trends exists but has low power. The graph is more informative. Report both.

Software Choices and What They Actually Do Well

Stata is still the dominant tool in political science departments because the command syntax maps directly to econometric methods and the documentation is exhaustive. Commands like reghdfe for high-dimensional fixed effects, ivreg2 for instrumental variables diagnostics, and rdrobust for regression discontinuity are production-quality. The learning curve is steep but the payoff is that most things you need have a well-tested command waiting for you. R is better for custom diagnostics, visualization, and when you need to combine econometric methods with machine learning preprocessing. The fixest package rivals Stata's fixed effects commands in speed and convenience. plm handles panel data well. ivreg covers instrumental variables. rdrobust in R mirrors the Stata command. The advantage of R is that you can pipe cleaning, transformation, and modeling into a single reproducible workflow. The disadvantage is that packages conflict with each other constantly and dependency management can consume more time than the actual analysis. Python is useful when your data pipeline is large or when you need to integrate with web scraping or natural language processing. The linearmodels package covers panel data, IV, and SUR models. statsmodels handles GMM and basic regression diagnostics. But the ecosystem for causal inference in Python is fragmented. There is no single package that covers the range of methods that Stata or R handle out of the box. If your work stays within standard econometric methods, Python adds complexity without much benefit. If you are already processing text or network data, it saves a translation step.

When Econometric Methods Fail Completely

There are situations where no amount of methodological sophistication will rescue your identification. If the treatment and control groups are fundamentally different in ways that cannot be observed or controlled for, your estimate is just a number with a p-value. This happens frequently in comparative politics where you are trying to estimate the effect of regime type, institutional design, or cultural factors. These are country-level or civilization-level variables with very few units. Fixed effects eat your degrees of freedom. Matching is meaningless with twenty units. Instrumental variables require instruments that simply do not exist at this level of aggregation. Small-N studies require different tools. Process tracing, structured focused comparison, and qualitative comparative analysis are not inferior methods. They are adapted to the data you actually have. Forcing a regression onto a dataset with thirty observations and twelve predictors will produce results that look precise but are statistically meaningless. The confidence intervals will be wide, the R-squared will be unstable, and any coefficient could flip sign with the addition of a single case. Another failure mode is when the treatment effect is heterogeneous and your method assumes homogeneity. Average treatment effects hide variation that matters for policy. A welfare reform might help some groups while harming others. ADiD will give you a single number. Quantile treatment effects or interaction terms with group dummies can reveal the distribution, but only if your sample is large enough to support that decomposition. In policy evaluation, reporting the average effect without discussing heterogeneity is misleading at best and dishonest at worst.

Data Sources That Are More Useful Than You Expect

The Comparative Political Data Set provides harmonized indicators across countries and years, which saves you from cleaning twelve different national datasets. The Database of Political Institutions covers legislative constraints, executive constraints, and party system characteristics. The Varieties of Democracy dataset is extremely detailed on democratic attributes, though the aggregation methods are sometimes controversial. For U.S.-specific work, the Congressiona Research Service reports are publicly available and contain raw data on legislation, appropriations, and agency activities that is not easily found elsewhere. State-level policy data comes from the Statesman's Yearbook and various state government open data portals, but the quality varies enormously. Some states publish quarterly fiscal data with clear definitions. Others publish annual summaries that change categorization between years. Always check the documentation for each variable in each year. I learned this the hard way when I used expenditure categories that were reclassified mid-series, making my time series appear to have a structural break that was entirely artificial. Microdata from national surveys like the General Social Survey, the American National Election Studies, and the European Social Survey are freely available after registration. These are valuable for individual-level analysis but require complex survey weights to use correctly. Ignoring the weights produces biased estimates because the survey design oversampled certain groups. Apply the appropriate weight variable and use the survey commands in your software. In R, the survey package handles this. In Stata, the svy prefix does the same work.

What Reviewers Actually Look For

A robustness section that tests five different model specifications is worth more than a theoretically perfect model that has never been stress-tested. Change the functional form. Add polynomial trends. Try alternative clustering levels. Exclude outlier periods. Use a different identification strategy if one is available. If your main result survives these changes, your paper is stronger. If it disappears under any single modification, figure out why before the reviewer does. Pre-registration is increasingly expected in top journals. It prevents specification searching, which is the practice of trying different model specifications until you get a significant result. If you preregister your hypothesis, your identification strategy, and your robustness checks, you signal that your findings are not the product of data mining. Even if your journal does not require it, pre-registering on OSF takes less than an hour and protects you from accusations of p-hacking. Replication data and code should be deposited alongside your manuscript. Many journals now require this. The process of making your code reproducible usually exposes bugs you did not know existed. A variable mislabeled as a string instead of a float will silently produce wrong results. A missing observation in a merge will reduce your sample without warning. Running your entire analysis from a clean data folder to the final output table is the only way to be confident that your results are correct.

The Trade-Off Between Internal and External Validity

The most rigorously identified estimate in political science is still a single number from a single setting. A regression discontinuity estimate of the effect of incumbency advantage in U.S. congressional elections tells you something precise about U.S. elections. It does not tell you about parliamentary systems, or about local elections, or about countries without competitive party systems. Internal validity comes at the cost of external validity. The tighter your identification, the more specific your result. This trade-off is often framed as a hierarchy where causal inference sits at the top and descriptive statistics sit below it. That framing is inaccurate. A well-executed descriptive analysis of institutional change in post-Soviet states can be more useful for policy than a causal estimate from a narrow regression discontinuity. The question is whether your methods match your goals. If you want to know whether a policy caused an outcome, use causal methods. If you want to understand how a system works, use descriptive and comparative methods. Mixing the two without clarifying which you are doing produces confused arguments. Econometrics in political science is a set of tools for answering specific questions with imperfect data. The tools work when you understand their assumptions and limitations. They produce misleading results when you treat them as black boxes that convert data into truth. Most of the work is not in the estimation. It is in figuring out whether the question you are asking can be answered with the data you have, and being honest about what the answer actually means.