Santiago Olivella

Santiago Olivella

Associate Professor

UNC at Chapel Hill

About Me

I am an associate professor of Political Science at the University of North Carolina, Chapel Hill. My research focuses on the use and development of novel computational methods for quantitative political research (particularly Bayesian and Machine Learning probabilistic models), the inferential study of networks, the measurement of latent traits, and the political consequences of electoral and legislative institutions. You can download my CV here.

Academic Positions

Associate Professor
2021 - Present · University of North Carolina at Chapel Hill
Assistant Professor
2017 - 2021 · University of North Carolina at Chapel Hill
Visiting Assistant Professor
2019 - 2020 · Harvard University
Visiting Associate Research Scholar
2015 - 2017 · Princeton University
Assistant Professor
2013 - 2015 · University of Miami (Miami, FL)


Ph.D. in Political Science
2007 - 2013 · Washington University in St. Louis
M.A. in Political Science
2007 - 2009 · Washington University in St. Louis
B.A. in Political Science
2001 - 2006 · Universidad de los Andes (Bogotá, Colombia)


Journal Articles

2023. Shiraito, Y., Lo, J., and Olivella, S. A Nonparametric Bayesian Model for Detecting Differential Item Functioning: An Application to Political Representation in the US. Political Analysis (forthcoming).

2022. Rosenman, E., McCartan, C., and Olivella, S. Recalibration of Predicted Probabilities Using the 'Logit Shift': Why does it work, and when can it be expected to work well?. Political Analysis (forthcoming).

2022. Imai, K., Olivella, S., and Rosenman, E. Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements. Science Advances 8(eadc9824): 1-10.

2022. Olivella, S., and Pratt, T., and Imai, K. . Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts. Journal of the American Statistical Association (forthcoming).

2018. Olivella, S., and Montgomery, J. B. Tree Based Methods for Political Science. American Journal of Political Science 62(3): 729–744.

2017. Uscinski, J. and Olivella, S. . The Conditional Effect of Conspiracy Thinking on Attitudes toward Climate Change. Research & Politics October-December 2017: 1–9.

2017. Olivella, S., Kanthak, K. and Crisp, B. ...And Keep Your Enemies Closer: Cosponsorship Patterns as Reputation Building. Electoral Studies 46(1): 75-–86.

2015. Montgomery, J., Olivella, S., Potter, J., and Crisp, B. . An Informed Forensics Approach to Detecting Vote Irregularities. Political Analysis, 23(4): 488–505.

2015. Potter, J. and Olivella, S. Electoral strategy in geographic space: Accounting for spatial proximity in district-level party competition. Electoral Studies, 40(1): 76–86.

2014. Atkinson, M., Mann, C., Olivella, S., Simon, A. and Uscinski. J. (Where) Do Campaigns Matter? The Impact of National Party Convention Location. The Journal of Politics, 76(4): 1045–1058.

2014. Crisp, B., Olivella, S., Potter, J. and Mishler, W. . Elections as Instruments for Punishing Bad Representatives and Selecting Good Ones. Electoral Studies, 31(1): 1–15.

2013. Olivella, S. and Tavits, M. Legislative Effects of Electoral Mandates. British Journal of Political Science, 44(2): 301–321.

2013. Crisp, B., Olivella, S., Malecki, M., and Sher, M. Vote-Earning Strategies in Flexible List Systems: Seats at the Price of Unity. Electoral Studies, 32(4): 658–669.

2013. Crisp, B., Olivella, S., and Potter, J. Party System Nationalization and the Scope of Public Policy. Comparative Political Studies, 46(4): 431–456.

2012. Crisp, B., Olivella, S., and Potter, J. Electoral contexts that impede voter coordination. Electoral Studies, 31(1): 143–158.

2006. Olivella, S. and Vélez, C. Will Uribe’s coalition survive?” (in Spanish). Colombia Internacional, 64(2): 194–205.


2020. Crisp, B. F., Olivella, S. and Rosas, G. The Chain of Representation: Preferences, Institutions, and Policy in Latin America’s Presidential Systems. Cambridge University Press.

Book Chapters

2020. Shoub, K., and Olivella, S. . 'Machine Learning in Political Science' (in The SAGE Handbook of Research Methods in Political Science and International Relations ) . SAGE Publications.

2013. Crisp, B., Olivella, S., and Potter, J. 'A Comparison of different indicators of party system consolidation' (in Spanish) in Torcal, M. (Ed.) Sistemas de partidos en América Latina. Causas y consecuencias de su equilibrio inestable. Editorial Anthropos.

2011. Olivella, S. 'Cross-Tabular Analysis.' in Badie B. et al. (Eds.) International Encyclopedia of Political Science. SAGE Publications.

2009. Olivella, S. and Rodríguez-Raga, J. C. . 'What is spatial is special: Proximity voting in Colombia' (in Spanish). in Botero, F. (Ed.) Juntos pero no revueltos? Partidos, candidatos y campañas en las elecciones legislativas de 2006 en Colombia. Uniandes - CESO.


wru (R package)
Who are You? Bayesian Prediction of Racial Category Using Surname, First Name, Middle Name, and Geolocation.
NetMix (R package)
Variational EM estimation of mixed-membership stochastic blockmodel for networks, incorporating node-level predictors of mixed-membership vectors, as well as dyad-level predictors.
poisbinom (R package)
Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution.

Work in progress

Representing communities of economic interest in the U.S. House (w/ S. Treul and N. Pinnell)
Communities of shared interest can easily fail to coincide with congressional dis-tricts. When areas that demarcate these communities span multiple congressionaldistricts, do representatives shift their efforts to reflect the interests of a broader set ofcitizens, or do they maintain a focus on the local interests of their own constituents?Using journey-to-work data for millions of Americans, we measure the extent to whichone such community — the labor market area — crosses congressional district bound-aries, and evaluate the effect of these shared economic interests on the representationalefforts of House members. We find that members coming from a common labor marketarea tend to collaborate more often, and that members representing districts embed-ded in larger labor market areas sponsor more legislation on national issues. Thesepatterns may exacerbate polarization in Congress and hurt representation of urbanminority populations, even in the absence of malapportionment.
Electoral System Incentives for Interparty and Intraparty Politics: A Computational Approach (w/ P. Cuhna, B. Crisp, and G. Rosas)
Electoral systems are sets of formal rules that create incentives for strategic behavior on the part of voters, (pre-)candidates, party elites, and elected representatives. All electoral systems are shaped by the same set of component rules, and their combinations define specific systems with the potential to provide incentives for both interparty and intraparty politics. In this book-length manuscript, we use extensive computational tools (including a vast number of simulations and machine learning predictive models), combine them with the most comprehensive dataset on electoral system types around the world, and explore whether a vast array of electoral rule combinagtions relate to interparty politics — the effective number of parties, parties’ locations in the policy space, congruence between citizens’ preferences and policy — and intraparty politics — the content of campaigns, the amount of constituency service provided, the shape of legislative institutions, levels of party discipline, and the balance struck between programmatic policy and pork barrel politics.