Andrew Q. Philips

My Picture

Welcome! I am an Assistant Professor in the Department of Political Science at the University of Colorado Boulder. I received my Ph.D in Political Science from Texas A&M University in 2017.

I specialize in political economy, methodology, and distributive politics. My current research interests focus on the manipulation of budgets during elections, approaches to modeling compositional data (e.g., budgets or party vote support), and developing and implementing models for time series and cross-sectional time series data.

On this website you can find my CV, links to some of my current work, software and a variety of other downloads for both Stata and R, and course materials. You can also contact me or check out my Google Scholar page.

Recent Publications

Jordan, Soren and Andrew Q. Philips. 2018. "Cointegration testing and dynamic simulations of autoregressive distributed lag models." Stata Journal: 18(4): 902-923.

In this article, we introduce dynamac, a suite of commands designed to assist users in modeling and visualizing the effects of autoregressive distributed lag models and in testing for cointegration. We discuss the bounds cointegration test proposed by Pesaran, Shin, and Smith (2001, Journal of Applied Econometrics 16: 289–326), which we have adapted into a command. Because the resulting models can be dynamically complex, we follow the advice of Philips (2018, American Journal of Political Science 62: 230–244) by introducing a flexible command designed to dynamically simulate and plot a variety of types of autoregressive distributed lag models, including error-correction models.

Funk, Kendall D. and Andrew Q. Philips. 2018. "Gendered budgeting: Women chief executives and budget allocations in local governments." Political Research Quarterly: 1-15.

One potential consequence of increasing women's numeric representation is that women elected officials will behave differently than their men counterparts and improve women's substantive representation. This study examines whether electing women to local offices changes how local government expenditures are allocated in ways that benefit women. Using compositional expenditure data from more than 5,400 Brazilian municipalities over eight years, we find significant differences in the ways men and women mayors allocate government expenditures. Our findings indicate that women mayors spend more on traditionally feminine issues, and less on traditionally masculine issues, relative to men mayors. In regard to specific policy areas, we find that women spend more on women's issues, including education, health care, and social assistance, and less on masculine issues, including transportation and urban development, relative to men mayors. We further find that women's legislative representation significantly influences the allocation of expenditures as a larger percentage of women councilors increases spending on traditionally feminine issues, as well as education, health care, and social assistance, relative to other policy issues. These findings indicate that women local elected officials improve women's substantive representation by allocating a larger percentage of expenditures to issues that have historically and continue to concern women in Brazil.

Philips, Andrew Q. 2018. "Have your cake and eat it too? Cointegration and dynamic inference from autoregressive distributed lag models." American Journal of Political Science: 62(1): 230-244.

Although recent articles have stressed the importance of testing for unit-roots and cointegration in time series analysis, practitioners have been left without a straightforward procedure to implement this advice. I propose using the autoregressive distributed lag model and bounds cointegration test as an approach to dealing with some the most commonly encountered issues in time series analysis. Through Monte Carlo experiments I show that this procedure performs better than existing cointegration tests under a variety of situations. I illustrate how to implement this strategy with two step-by-step replication examples. To further aid users, I have designed software programs in order to test and dynamically model the results from this approach.

Lipsmeyer, Christine S., Andrew Q. Philips, Amanda Rutherford, and Guy D. Whitten. 2017. "Comparing dynamic pies: A strategy for modeling compositional variables in time and space." Political Science Research and Methods: 1-18.

Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross-sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationships into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the US states.

Lipsmeyer, Christine S., Andrew Q. Philips, and Guy D. Whitten. 2017. "The effects of immigration and integration on European budgetary trade-offs." Journal of European Public Policy: 24(6): 912-930.

What affects government policy-making continues to be an important question for researchers interested in political competition and policy priorities. In this contribution, we bring together a theoretical framework that focuses on the influence of globalizing forces on government policy decisions with a methodological emphasis on explaining dynamic budgetary trade-offs. While comparative public policy and budgetary scholars typically have focused on entire budgets or amounts spent on specific policies, we look at the political priorities embedded in budgets by modeling the budget as a pie. Then, we theorize about how governments respond to external shocks by altering the allocations to the various policy areas. Using this approach, we find that governments of different ideological types react to external shocks by altering their different policy priorities.

Philips, Andrew Q. 2016. "Seeing the forest through the trees: A meta-analysis of political budget cycles." Public Choice 168(3): 313-341.

Despite a vast number of articles, the political budget cycle literature contains many conflicting theories and empirical results. I conduct the first ever meta-analysis of this literature in order to establish whether a link between elections and government budgets exists. Using data on 1198 estimates across 88 studies published between 2000 and 2015, I find evidence of a statistically significant---yet substantively small---increase in government expenditures and public debt around elections, and reductions in revenues and fiscal balance. Using meta-regression analysis combined with Bayesian model averaging, I find support for some of the context-conditional theories in the literature. Although the findings of political budget cycles are robust to publication bias as well as some of the methodological- and study-specific choices authors are forced to make, they also shed light on how certain decisions may affect a study's findings. This has implications for current and future research on political budget cycles.


You can find my most recent curriculum vitae here.


Feel free to contact me at andrew[dot]philips[at]colorado[dot]edu. You can also find me on GitHub.