About

Miriam Barnum is an assistant professor in the Department of Political Science at Purdue University and a faculty member in the Cornerstone Integrated Liberal Arts program. She previously served as a Stanton Nuclear Security postdoctoral fellow at the Center for International Security and Cooperation at Stanford University. Dr. Barnum received her Ph.D. in political science and international relations from the University of Southern California in 2022. Her research focuses on how and why states make particular arming choices, the relationship between economic factors and security outcomes such as arming and conflict, and the innovations in measurements necessary to advance the study of these relationships. Her ongoing book project explores how internal and external security threats influence states' decisions to pursue chemical and biological weapons. Additionally, her work involves projects related to arming investment, international conflict, and nonproliferation and arms control, with an emphasis on applying computational measurement models to deepen understanding of these substantive areas.

Research topics

• Political Science
• Sociology
• Economics
• Mathematics
• Statistics
• Law and economics
• Demography
• Philosophy
• Epistemology
• Macroeconomics
• Econometrics
• Law

Selected publications

• De jure powersharing 1975–2019: Updating the Inclusion, Dispersion, and Constraints Dataset

  Journal of Peace Research · 2024-11-10 · 1 citations

  article

  Abstract Powersharing institutions are often prescribed to enhance civil peace, democratic survival, and the equitable provision of public services, and these institutions have become more prevalent over time. Nonetheless, the past decade has seen a rise in democratic backsliding and competitive authoritarianism, raising questions about how the relationship between powersharing, democracy, and civil peace may be evolving. This article introduces an update to the Inclusion, Dispersion, and Constraints (IDC) powersharing dataset that adds nine years of data, up through 2019. These new data include enhanced intercoder reliability checks, a significant reduction in missing values, and the documentation and correction of some coding errors in the original data. We also employ latent variable models to estimate each of three types of powersharing, allowing scholars to account for measurement uncertainty in analyses of the causes and consequences of powersharing. This dataset allows scholars to address urgent questions about whether previously observed relationships between powersharing and democracy and powersharing and civil peace still hold in this new era, and in what contexts powersharing institutions remain advisable.

  PublisherDOI

• Measuring Arms: Introducing the Global Military Spending Dataset

  Journal of Conflict Resolution · 2024 · 10 citations

  1st authorCorresponding
  • Political Science
  • Political Science
  • Economics

  Military spending data measure key international relations concepts such as balancing, arms races, the distribution of power, and the severity of military burdens. Unfortunately, missing values and measurement error threaten the validity of existing findings. Addressing this challenge, we introduce the Global Military Spending Dataset (GMSD). GMSD collates new and existing expenditure variables from a comprehensive collection of sources, expands data coverage, and employs a latent variable model to estimate missing values and quantify measurement error. We validate the data and present new findings. First, correlations between economic surplus and military spending are currently higher than at any point in the last two-hundred years. Second, updating DiGiuseppe and Poast’s (2018) analysis, we find larger substantive effects. Specifically, we find that the (negative) effect of a democratic ally on military spending is three times larger, and the (positive) effect of an increase in GDP is five times larger than previously estimated.

  PublisherDOI

• The International Political Economy Data Resource: Dyadic

  Harvard Dataverse · 2023-02-28

  datasetOpen access

  Quantitative scholars in international relations and international political economy often draw on the same sources of country-year data across a diverse range of projects. The IPE Data Resource: Dyadic version seeks to provide a public good to the field by standardizing and merging together variables from 27 IPE data sources into a single bilateral (directed-dyad) dataset, increasing efficiency and reducing the risk of data management errors. Easier access to data both encourages researchers to perform more robustness checks than they otherwise might and makes it easier for teachers of quantitative research methods to assign realistic exercises to their students. The unit of analysis in this dataset is the directed dyad-year, with unique observations identified by Gleditsch-Ward number (gwno) and year (Gleditsch and Ward 1999). Countries not present in the Gleditsch-Ward system are excluded from this dataset. This resource will be updated and expanded annually and is available via the Harvard Dataverse Network.

  PublisherDOI

• Nuclear Proliferation

  International Relations · 2023-03-20

  reference-entrySenior author

  This bibliography reviews the scholarly literature on the meaning, causes, and consequences of nuclear proliferation. Specifically, the bibliography focuses on “horizontal” nuclear weapons proliferation, which can be defined as the acquisition of nuclear weapons by states and other political entities that did not previously have them. Overshadowed by the superpower nuclear arms race during the Cold War, nuclear proliferation has become a major field of interdisciplinary international relations (IR) research since the 1990s. Much nuclear proliferation research has revolved around the empirical puzzle of why so many states that could build nuclear weapons have refrained from doing so. Despite proliferation’s surprisingly slow pace, however, the number of de facto nuclear-weapon states has gradually grown larger over time, and a slow pace in the past does not guarantee a slow pace in the future. On the other side of the coin, proliferation reversal, also known as nuclear renunciation, is also possible. In addition to studying the causes of nuclear proliferation, scholars have also investigated the effects of nuclear proliferation on numerous dimensions of policy and politics. But the literature on the consequences of proliferation remains much thinner than the literature on its causes.

  PublisherDOI

• Global Military Spending Dataset

  Harvard Dataverse · 2022-11-04

  datasetOpen access1st authorCorresponding

  The world has become much more peaceful, and yet, even after adjusting for inflation, global military spending is now three times greater than at the height of the Cold War. These developments have motivated a renewed interest from both policy makers and scholars about the drivers of military spending and the implications that follow. Existing findings on the relationship between threat and arming and arms races and war hinge on the completeness and accuracy of existing military spending data. Moreover, data on military spending is used to measure important concepts from international relations such as the distribution of power, balancing, the severity of states’ military burdens, and arms races. Everything we know about which states are most powerful, whether nations are balancing, and whether military burdens and arms races are growing more or less severe rests on the accuracy of existing military spending estimates.

  PublisherDOI

• De Jure Powersharing 1975-2019: Updating the Inclusion, Dispersion and Constraints Dataset

  Harvard Dataverse · 2022-03-03

  datasetOpen access

  Powersharing institutions are often prescribed to enhance civil peace, democratic survival, and the equitable provision of public services, and these institutions have become more prevalent over time. Nonetheless, the past decade has seen a rise in democratic backsliding and competitive authoritarianism, raising questions about how the relationship between democracy and powersharing may be evolving. This paper introduces an update to the Inclusion, Dispersion, and Constraints (IDC) powersharing data that adds nine years of data, up through 2019. These new data also include enhanced intercoder reliability checks, a significant reduction in missing values, and the documentation and correction of some coding errors in the original data. Our new data show that, during the past decade, constraining and dispersive institutions have increasingly been adopted in non-democratic states. These data allow scholars to address urgent questions about whether previously observed relationships between powersharing and democracy and powersharing and civil peace still hold in this new era, and in what contexts powersharing institutions remain advisable.

  PublisherDOI

• Latent Estimates of Historic Gross Domestic Product, GDP per capita, Surplus Domestic Product, and Population Data Version 1

  Harvard Dataverse · 2022-02-18 · 3 citations

  datasetOpen accessSenior author

  Gross Domestic Product (GDP), GDP per capita, and population are central to the study of politics and economics broadly, and conflict processes in particular. Despite the prominence of these variables in empirical research, existing data lack historical coverage and are assumed to be measured without error. We develop a latent variable modeling framework that expands data coverage (1500 A.D--2018 A.D) and, by making use of multiple indicators for each variable, provides a principled framework to estimate uncertainty for values for all country-year variables relative to one another. Expanded temporal coverage of estimates provides new insights about the relationship between development and democracy, conflict, repression, and health. We also demonstrate how to incorporate uncertainty in observational models. Results show that the relationship between repression and development is weaker than models that do not incorporate uncertainty suggest. Future extensions of the latent variable model can address other forms of systematic measurement error with new data, new measurement theory, or both.

  PublisherDOI

• New Estimates of Over 500 Years of Historic GDP and Population Data

  Journal of Conflict Resolution · 2022 · 80 citations

  Senior authorCorresponding
  • Sociology
  • Econometrics
  • Economics

  Gross domestic product (GDP), GDP per capita, and population are central to the study of politics and economics broadly, and conflict processes in particular. Despite the prominence of these variables in empirical research, existing data lack historical coverage and are assumed to be measured without error. We develop a latent variable modeling framework that expands data coverage (1500 AD–2018 AD) and, by making use of multiple indicators for each variable, provides a principled framework to estimate uncertainty for values for all country-year variables relative to one another. Expanded temporal coverage of estimates provides new insights about the relationship between development and democracy, conflict, repression, and health. We also demonstrate how to incorporate uncertainty in observational models. Results show that the relationship between repression and development is weaker than models that do not incorporate uncertainty suggest. Future extensions of the latent variable model can address other forms of systematic measurement error with new data, new measurement theory, or both.

  PublisherDOI

• Dealing with missing and incomplete data

  Edward Elgar Publishing eBooks · 2022-08-05

  book-chapter1st authorCorresponding

  Missing data is an issue frequently encountered by international relations researchers, and there's no single "right answer" to any missing data problem. Rather, there are numerous options, each with its own costs and benefits, and each of which relies on a set of assumptions about the data. This chapter starts by suggesting a general roadmap for thinking about incomplete data and choosing missing data handling approaches. Next, it introduces different types of missing data - in terms of the location of missing values and their relationship to the observed data. It then reviews deletion, single imputation, multiple imputation, and maximum likelihood estimation approaches to handling missing data, discussing the assumptions they rely on their potential costs and benefits. Finally, it highlights how thinking carefully about missingness can provide insight into other research design considerations, such as scope and generalizability, causal inference, and measurement error.

  PublisherDOI

• New Estimates of Over 500 Years of Historic GDP and Population Data

  2021-12-25 · 27 citations

  preprintOpen accessSenior author

  Gross Domestic Product (GDP), GDP per capita, and population are central to the study of politics and economics broadly, and conflict processes in particular. Despite the prominence of these variables in empirical research, existing data lack historical coverage and are assumed to be measured without error. We develop a latent variable modeling framework that expands data coverage (1500 A.D--2018 A.D) and, by making use of multiple indicators for each variable, provides a principled framework to estimate uncertainty for values for all country-year variables relative to one another. Expanded temporal coverage of estimates provides new insights about the relationship between development and democracy, conflict, repression, and health. We also demonstrate how to incorporate uncertainty in observational models. Results show that the relationship between repression and development is weaker than models that do not incorporate uncertainty suggest. Future extensions of the latent variable model can address other forms of systematic measurement error with new data, new measurement theory, or both.

  PublisherOA PDFDOI

Frequent coauthors

• Christopher J. Fariss

  University of Michigan–Ann Arbor

  14 shared

• Jonathan N. Markowitz

  University of Southern California

  12 shared

• Therese Anders

  University of Southern California

  11 shared

• Benjamin A.T. Graham

  4 shared

• Gaea Morales

  University of Southern California

  3 shared

• Marie Zaragoza

  University of Southern California

  3 shared

• Nicole Jao

  University of Southern California

  3 shared

• Jasmine Chu

  University of Southern California

  3 shared

Awards & honors

• Alumni Awards