I'm an Assistant Professor of Management at the Wharton School at the University of Pennsylvania. My research focuses on entrepreneurship and human capital, with a particular focus on emerging economies. My work draws on a variety of methodologies, with a focus on new computational methods and text as data. I received my Ph.D. at Columbia Business School.
Small unregistered firms contribute to a substantial proportion of global economic activity, particularly in developing regions. In explaining variation in productivity in these types of informal firms, research has focused primarily on the adoption of effective business practices and access to capital, with little focus on fundamental positioning. This article explores the nature of differentiation in microenterprises, introducing a text‐based measure of differentiation using state‐of‐the‐art sentence embeddings. Using a combined sample of nearly 10,000 microenterprises across eight developing countries, I examine whether (and which) microenterprises differentiate, whether differentiation is related to performance (and for whom), and whether any existing policy interventions affect differentiation.
We demonstrate how a novel synthesis of three methods—(a) unsupervised topic modeling of text data to generate new measures of textual variance, (b) sentiment analysis of text data, and (c) supervised ML coding of facial images with a cutting-edge convolutional neural network algorithm—can shed light on questions related to CEO oral communication. With videos and corresponding transcripts of interviews with emerging market CEOs, we use this synthesis of methods to discover five distinct communication styles that incorporate both verbal and nonverbal aspects of communication. Our data comprises interviews that represent unedited expressions and content, making them especially suitable as data sources for the measurement of an individual's communication style. We then perform a proof-of-concept analysis, correlating CEO communication styles to M&A outcomes, highlighting the value of combining text and videographic data to define styles. We also discuss the benefits of using our methods versus current research methods.
The emergence of large language models (LLMs) has opened new avenues for integrating artificial intelligence into research, particularly for data annotation and text classification. However, the benefits and risks of using LLMs in research remain poorly understood, such that researchers lack guidance on how best to implement this tool. We address this gap by developing a methodological framework for implementing LLMs in management research, providing structured guidance on key implementation decisions and best practices. We illustrate the implementation of this framework through an empirical application classifying sustainability claims in crowdfunding projects to assess the performance effects of these claims. We demonstrate that while LLMs can match or exceed traditional methods' performance at lower cost, variations in prompt design can significantly affect results and downstream analyses. We thus develop procedures for sensitivity analysis and provide documentation to help researchers implement these robustness checks while maintaining methodological integrity.
What is the best way to support capability development in severely resource-constrained firms? While improving managerial practices is widely believed to enhance productivity, this assumption often rests on trials conducted in ideal conditions that differ sharply from the reality faced by firms in challenging settings. We argue that practices vary in the reliability of their returns under constraint: some are reliable, generating consistent value even in low-resource settings, while others are contingent, yielding gains only when firms can meet underlying resource or coordination requirements. We then examine how institutional support shapes practice adoption and productivity in these constrained environments. Using data from 1,480 smallholder coconut farmers in the Philippines, we show that certain "Good Agricultural Practices" are associated with negligible or even negative returns for the most constrained farms. We find that institutional support -- access to extension services from the Philippine Coconut Authority -- helps farmers to navigate this challenge, with extension support leading to differentiated practice adoption based on constraints. Our findings reveal how standardized "best practices" may produce low returns under severe constraints, and how intermediaries help firms optimize practice adoption through careful assessment of returns given firm-specific limitations.
This paper examines how skilled immigrants at headquarters (HQ) influence firms’ global product strategies by serving as informal coordination mechanisms. Using remarkably detailed data on product launches in the consumer packaged goods sector and confidential H-1B visa records, we employ an instrumental variable approach to isolate the causal impact of hiring immigrants. We find that hiring workers from a specific country leads firms to launch more new products in that country. This effect is significant only in countries where the firm has a foreign subsidiary, highlighting the complementary roles of informal (immigrant workers) and formal (subsidiaries) coordination mechanisms. Immigrant employees at HQ also tilt firms’ product strategy in their home countries toward standardization when a subsidiary is present, resulting in more products resembling those previously launched in the HQ market. This study makes a theoretical contribution by exploring how human capital at HQ functions as an informal coordination mechanism that complements formal structures and influences the balance between standardization and adaptation. It also makes an empirical contribution by offering causal evidence linking immigrant talent to the international product strategies of firms.