Robert Kubinec

Robert Kubinec

Assistant Professor of Political Science

New York University Abu Dhabi

About

This website serves mainly as a blog in which I present ideas on political-economic topics I study and issues related to data science with R/Rstudio and causal inference. My R-related posts are syndicated with RBloggers, a consortium of R bloggers.

My research centers on political-economic issues such as corruption, economic development, and business-state relations in developing countries, and in particular the Middle East and North Africa. I am also involved in the development of Bayesian statistical models with Stan for hard-to-study subjects like corruption, polarization, and other latent social constructs. To see a list of my current working papers and access the PDFs, see my Google Scholar page.

Recent Posts

Alt-APSA 2021 Online Panel List

To submit your panel Zoom session, please use this form (submissions are moderated): https://docs.google.com/forms/d/e/1FAIpQLSfF_Edqb5ssiS9kTUtm87-T44VLofKuiTdObZgoWdICbXuilg/viewform?usp=sf_link

Which Religious Groups Have the Most Sex?

There has been plenty of discussion about declining fertility rates and patterns of marriage among people in the United States following the news that the US birth rate declined to its lowest since the Great Depression.

An Overview of Data for COVID Analysis

The COVID-19 pandemic has led to a wealth of research studies examining various aspects of the pandemic. In this post, I will discuss some of the available datasets for doing aggregate level analysis, i.

How to Estimate Models with Measurement Error for our COVID-19 Indices

This tutorial gives an overview of the COVID-19 policy indexes just released by the CoronaNet project of which I am a part and the Oxford Government Response Tracker.

Tornadoes Have Side Effects: A Response to Victor Chernozhukov

Recently I had a spirited conversation with Victor Chernozhukov, a leading econometrician whose work spans both traditional subjects like panel data modeling and newer application of machine learning models to causal inference.

Contact

  • Abu Dhabi,