I study technology, innovation, investment allocation, and the role of money, trade, and other resources in political competition.
Political Competition in Dynamic Economies. Presented at the University of Oxford Political Economy of Finance Conference (2018), the Atlanta Conference on Science and Innovation Policy (2017), in the Princeton University Political Economy Colloquium (2017), and the Princeton University International Relations Colloquium (2016).
Does Globalization Bring Peace? A Study of Trade and Interstate War, 1845-1905. Presented in the Princeton University Political Economy Colloquium (2018), at the Midwest Political Science Association Annual Meeting (2018) and the Princeton University International Relations Colloquium (2017).
Abstract: Testing the link between trade and peace, both between and within countries, has so far been difficult due to the lack of a good instrument for trade. I leverage a novel instrument for trade – the change in effective shipping distances due to the invention and adoption of steam technology (Pascali 2017) – to study the relationship between increases in trade and countries’ propensity for conflict. I find that as countries trade more overall, the probability that they go to war goes down, in a relationship not driven by bilateral effects, and not present for civil war. This has implications both for our understanding of the first wave of globalization, and the one we are experiencing today: I provide evidence in favor of a “commercial peace” that is general, and not within or between particular states.
Technology Diffusion and the International System, with Helen V. Milner.
The Soil of Politics: Resources, Competition, and the Consequences of Ethnic Inequality, with Rachael McLellan. Presented at the 4th Annual Conference of the History and Politics Network (2017), the Princeton Political Economy Colloquium (2017), and APSA (2016).
Abstract: We demonstrate the importance of the distribution of land between ethnic groups in shaping political development. We argue and show that the concentration of resources, here as measured by the cumulative agricultural potential of ethnic homelands – which we call soil power – makes it easier to form stable ruling coalitions. Where political resources are highly fragmented between groups, coalitions are less secure. Ruling elites therefore have a greater incentive to impose narrow, repressive institutions to retain power and are more likely to face armed challenges to their rule. To test our argument, we leverage the natural experiment of the African borders drawn at the Berlin Conference. We find that higher concentration of power leads to better quality institutions, less civil war and higher levels of economic development. We find similar relationships globally, and that African countries are low-concentration outliers.
The US Patent Data Set (1976-2015).
Data Set of all patents granted in the United States between 1976 and 2015, with information on patent class, country and city of inventor, and number of times cited by other patents. Includes 5.9 million patents and 31.6 million associated citations.
The Military Technology Data Set
- Country-year adoption rate of 33 military technologies.
Coverage – an R package for seeing what you are missing. Available on my Github.
The coverage package and associated function provides you with a summary of your time and unit coverage, producing compact tables and a visual representation. This is important for any analysis conducted with row-wise deletion in the presence of missing data, especially if one suspect that patterns of missingness are non-random with respect to variables of interest. Analysis which use row-wise deletion include standard regression analysis and most implementations of maximum likelihood. See my Github for examples and details.
The “out-of-the-box” visual output below shows country and year coverage for a technology-country-year analysis, with darker blue indicating more observations, and grey indicating years in which the unit in question did not exist as an independent country:
QuickReg – Easy OLS with options in R. Available on my Github.
The QuickReg package and associated function provides an easy interface for linear regression in R. This includes the option to request robust and clustered standard errors, automatic labeling, an easy way to specify multiple regression specifications simultaneously, and a compact html or latex output (relying on the widely used “stargazer” package).
QuickReg also includes several functionalities to speed up OLS computation, including a special implementation of the method of alternating projections. This method reduces calculation time drastically (>60 percent in tests) for analysis with a large number of fixed effects, with a performance gain that is increasing in the number of regression specifications passed to the function simultaneously.
QuickCoefPlot – Easy OLS coefficient plots with options in R. Available on my Github.
The QuickCoefPlot package turns base R’s linear-model objects into easy-to-read coefficient plots, complete with model statistics, labeling and heteroskedasticity robust standard errors (by default). Includes a range of options, including the opportunity to request default or clustered standard errors, the option to calculate and report bootstrapped confidence intervals and/or estimates, and change text-size, plot title, which model statistics or coefficients to report, and so on.
# Use the QuickCoefPlot to produce a regression table model <- lm(log_gdppc_mad ~ polity2 + tropical + desert + soil + near_coast + year, data = mydata) QuickCoefPlot(model)
seeAI – Visualizations of Machines Learning in R. Available on my Github.
The seeAI package aims to visualize common machine learning purposes. Visuals are dynamically generated based on supplied models so as to be useful for both teachers and practitioners. It currently supports the popular glmnet implementation of cross-validated lasso regression.