Ranjit Lall

selected publications

1. AJPS

   Making Global Governance Accountable: Civil Society, States, and the Politics of Reform

   Lall, Ranjit

   American Journal of Political Science 2023

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   Recent decades have witnessed the adoption of unprecedentedly broad and inclusive accountability mechanisms by many major international institutions, from grievance redress systems to transparency policies. What explains the establishment of these mechanisms — and why have only some institutions embraced them? I argue that adoption is more likely when member states, in particular the most powerful, face “bottom-up” pressures for accountability from dense transnational civil society networks — networks with the capacity to build leverage through agenda setting, coalition building, and advocacy strategies — and when institutions perform governance tasks that are costly to monitor. Analysis of a rich new dataset shows that adoption is positively related to the density of international nongovernmental organizations in an institution’s issue area — including only those based in powerful member countries — and that this relationship is stronger when governance tasks entail high monitoring costs. Statistical tests are complemented by qualitative evidence from interviews and other primary sources.

   @article{lall2023ajps,
     abbr = {AJPS},
     bibtex_show = {true},
     title = {Making Global Governance Accountable: Civil Society, States, and the Politics of Reform},
     author = {Lall, Ranjit},
     journal = {American Journal of Political Science},
     pages = {1--22},
     year = {2023},
     doi = {10.1111/ajps.12824},
     url = {https://onlinelibrary.wiley.com/doi/10.1111/ajps.12824},
     html = {https://onlinelibrary.wiley.com/doi/10.1111/ajps.12824},
     pdf = {Lall 2023 AJPS Online.pdf},
     publisher = {Wiley Online Library},
     selected = {true}
   }

2. JSS

   Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS

   Lall, Ranjit, and Robinson, Thomas

   Journal of Statistical Software 2023

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   This paper introduces software packages for efficiently imputing missing data using deep learning methods in Python (MIDASpy) and R (rMIDAS). The packages implement a recently developed approach to multiple imputation known as MIDAS, which involves introducing additional missing values into the dataset, attempting to reconstruct these values with a type of unsupervised neural network known as a denoising autoencoder, and using the resulting model to draw imputations of originally missing data. These steps are executed by a fast and flexible algorithm that expands both the quantity and the range of data that can be analyzed with multiple imputation. To help users optimize the algorithm for their particular application, MIDASpy and rMIDAS offer a host of user-friendly tools for calibrating and validating the imputation model. We provide a detailed guide to these functionalities and demonstrate their usage on a large real dataset.

   @article{lall2023jss,
     abbr = {JSS},
     bibtex_show = {true},
     title = {Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS},
     author = {Lall, Ranjit and Robinson, Thomas},
     journal = {Journal of Statistical Software},
     pages = {1--38},
     year = {2023},
     volume = {107},
     number = {9},
     doi = {10.18637/jss.v107.i09},
     url = {https://www.jstatsoft.org/article/view/v107i09},
     html = {https://www.jstatsoft.org/article/view/v107i09},
     pdf = {Lall and Robinson 2023 JSS.pdf},
     selected = {true}
   }