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.
We study a randomized controlled trial of an entrepreneurial training program in Zimbabwe that successfully converted many individuals from itinerant self-employment and casual labor into full-time business owners, generating significant business growth and higher incomes for treated individuals. We find that the treatment additionally increased participants' rates of civic engagement, with treated individuals being substantially more likely to contact political leaders across all levels of society with grievances (including traditional leaders, government agencies, local councilors, and MPs). However, none of our hypothesized mechanisms, such as networks, income, or empowerment, adequately explain this result. Instead, exploratory analyses reveal that the effect is broadly mediated by entrepreneurship itself -- specifically, treated individuals' attempts to grow their business, including hiring employees, taking out loans, and opening business accounts. These steps toward business growth appear to have exposed entrepreneurs to a broader array of institutional failures, leading them to voice more complaints about challenges such as corruption, cash shortages, and unfair loan terms. We conclude that policies designed to promote entrepreneurship may generate the unintended consequence of fostering greater civic engagement, as entrepreneurs encounter and respond to institutional constraints that impede their business activities. Our work suggests that even the smallest businesses engage in forms of grassroots lobbying.
Support organizations in under-resourced settings often prescribe “best practices,” yet firms facing severe resource constraints frequently struggle to adopt them -- or to benefit when they do. We argue that practices differ in the reliability of their returns under constraint: some are reliable, generating consistent value even in low-resource settings, whereas others are contingent, yielding gains only when firms can meet underlying resource or coordination requirements. We examine how external support shapes both practice adoption and productivity in such contexts. Using data from 1,480 smallholder coconut farmers in the Philippines, we show that several "Good Agricultural Practices'' (GAPs) deliver negligible or even negative returns for the most constrained farms. Access to Philippine Coconut Authority extension services helps farmers navigate this challenge by steering adoption toward reliable GAPs and away from contingent ones. Benefits may therefore arise from judicious subtraction as well as addition. The study reframes capability development in poverty settings as a portfolio-optimization problem, and positions support organizations as critical brokers of that optimization.
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.