arXiv subjects

Pulak Ghosh

Publications and source records attributed to Pulak Ghosh.

5 recordsLinked to original sources

arXiv2026

Political Power-Sharing, Firm Entry, and Economic Growth: Evidence from Multiple Elected Representatives

We examine the effect of political power-sharing on local economic activity. This effect depends on the relative importance of the risks associated with unchecked power and the potential efficiency gains or losses arising from checks and balances. Our research design exploits a geographic discontinuity design due to the haphazard overlap of electoral and administrative boundaries that generates quasi-random variation in the number of politicians governing adjacent regions. We supplement this design using an episode of electoral delimitation that allows us to exploit within-region variation in the number of politicians. We find increasing the number of politicians governing an area can lead to new firm creation, lower unemployment, and greater real economic activity. Our results suggest that non-aligned multiple politicians enhance state efficiency by imposing checks and balances on each other, leading to lower regulatory obstacles, less cronyism, and improved provision of public infrastructure, creating an economically favorable environment for firm creation.

econ.GN↗

arXiv2025

Multivariate Zero-Inflated Causal Model for Regional Mobility Restriction Effects on Consumer Spending

The COVID-19 pandemic presents challenges to both public health and the economy. Our objective is to examine how household expenditure, a significant component of private demand, reacts to changes in mobility. This investigation is crucial for developing policies that balance public health and the economic and social impacts. We utilize extensive scanner data from a major retail chain in India and Google mobility data to address this important question. However, there are a few challenges, including outcomes with excessive zeros and complicated correlations, time-varying confounding, and irregular observation times. We propose incorporating a multiplicative structural nested mean model with inverse intensity weighting techniques to tackle these challenges. Our framework allows semiparametric/nonparametric estimation for nuisance functions. The resulting rate doubly robust estimator enables the use of a conventional sandwich variance estimator without taking into account the variability introduced by these flexible estimation methods. We demonstrate the properties of our method theoretically and further validate it through simulation studies. Using the Indian consumer spending data and Google mobility data, our method reveals that the substantial reduction in mobility has a significant impact on consumers' fresh food expenditure.

stat.ME↗

arXiv2022

On Learning and Testing of Counterfactual Fairness through Data Preprocessing

Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. In this paper, we develop the Fair Learning through dAta Preprocessing (FLAP) algorithm to learn counterfactually fair decisions from biased training data and formalize the conditions where different data preprocessing procedures should be used to guarantee counterfactual fairness. We also show that Counterfactual Fairness is equivalent to the conditional independence of the decisions and the sensitive attributes given the processed non-sensitive attributes, which enables us to detect discrimination in the original decision using the processed data. The performance of our algorithm is illustrated using simulated data and real-world applications.

stat.ML↗

arXiv2021

Non-Gaussian Normal Diffusion in Low Dimensional Systems

Brownian particles suspended in disordered crowded environments often exhibit non-Gaussian normal diffusion (NGND), whereby their displacements grow with mean square proportional to the observation time and non-Gaussian statistics. Their distributions appear to decay almost exponentially according to "universal" laws largely insensitive to the observation time. This effect is generically attributed to slow environmental fluctuations, which perturb the local configuration of the suspension medium. To investigate the microscopic mechanisms responsible for the NGND phenomenon, we study Brownian diffusion in low dimensional systems, like the free diffusion of ellipsoidal and active particles, the diffusion of colloidal particles in fluctuating corrugated channels and Brownian motion in arrays of planar convective rolls. NGND appears to be a transient effect related to the time modulation of the instantaneous particle's diffusivity, which can occur even under equilibrium conditions. Consequently, we propose to generalize the definition of NGND to include transient displacement distributions which vary continuously with the observation time. To this purpose, we provide a heuristic one-parameter function, which fits all time-dependent transient displacement distributions corresponding to the same diffusion constant. Moreover, we reveal the existence of low dimensional systems where the NGND distributions are not leptokurtic (fat exponential tails), as often reported in the literature, but platykurtic (thin sub-Gaussian tails), i.e., with negative excess kurtosis. The actual nature of the NGND transients is related to the specific microscopic dynamics of the diffusing particle.

cond-mat.stat-mech↗

arXiv2015

Dirichlet Process Hidden Markov Multiple Change-point Model

This paper proposes a new Bayesian multiple change-point model which is based on the hidden Markov approach. The Dirichlet process hidden Markov model does not require the specification of the number of change-points a priori. Hence our model is robust to model specification in contrast to the fully parametric Bayesian model. We propose a general Markov chain Monte Carlo algorithm which only needs to sample the states around change-points. Simulations for a normal mean-shift model with known and unknown variance demonstrate advantages of our approach. Two applications, namely the coal-mining disaster data and the real United States Gross Domestic Product growth, are provided. We detect a single change-point for both the disaster data and US GDP growth. All the change-point locations and posterior inferences of the two applications are in line with existing methods.

math.ST↗