The Lab conducts data-driven research on entrepreneurship, venture capital, and private equity, creating original datasets, combining diverse sources, and applying a wide range of empirical methods — from large-scale surveys and field experiments to machine learning, text analysis, and LLM-based tools. Here are some examples of the projects we are currently conducting:

How Do Startups Grow
This project explores how startups scale around the world by analyzing comprehensive workforce data and career histories of thousands of individuals. By constructing novel datasets from CVs and employment records, we investigate the role of talent, human capital bottlenecks, and organizational structure in shaping firm growth and leadership dynamics in entrepreneurial ecosystems.

Barriers to Capital: Perception vs. Reality
We study why global VC and PE capital often bypasses the poorest regions of the world. Using a combination of large language models (LLMs), text analysis of global investment narratives, in-depth interviews, and original surveys, this project investigates how stereotypes and risk perceptions may distort capital allocation to emerging and frontier markets — and what can be done to correct them.

Investor Behavior Across Developed and Emerging Markets
Through the largest-ever survey of venture capital and private equity investors operating in over 100 countries — combined with experimental methods — this project identifies the preferences, heuristics, and structural constraints that shape investment decision-making in early-stage entrepreneurial finance globally.

Equity Compensation Around the World
How startups share ownership remains an open question in entrepreneurship. This project maps global practices in startup equity: who offers it, how it's structured, how it's valued, and how much ultimately accrues to founders, early employees, and investors. By gathering and analyzing cross-country data, we aim to illuminate one of the least transparent aspects of high-growth entrepreneurship.

The Role of Finance and Accounting in Startup Performance
Partnering with key players in Africa’s financial ecosystem, we investigate how managerial, financial, and accounting practices shape outcomes for early-stage firms. A large-scale survey and randomized evaluation with over 400 growing startups examines how capability gaps affect performance, and how targeted interventions can build stronger entrepreneurial foundations.

Entrepreneurial Education in the Age of AI
This project surveys startup founders and early-stage teams across emerging markets to better understand their preferences for training and education — including whether AI-powered tools should play a greater role. By aligning educational offerings with actual demand from entrepreneurs, we seek to inform the design of more relevant and scalable founder support programs.

What Governments Can Do to Catalyze Entrepreneurship
Focusing on China and the MENA region, this project evaluates the effectiveness of government-led investment initiatives — such as sovereign wealth funds and state-sponsored venture programs — in fostering innovation and entrepreneurship. Using detailed capital flow data and causal analysis, we assess whether public capital crowds in or crowds out private investment.

Mapping Gaps in Early-Stage Funding
Why do so many promising ventures in emerging markets fail to raise capital? In partnership with DFIs and accelerators, we combine localized data, field experiments, and surveys to identify the key barriers to early-stage finance — from investor-side frictions to founder-side constraints — and test practical solutions in real-world startup ecosystems.

Private Market Returns in Developed and Emerging Economies
This project evaluates confidential fund-level data across a wide range of VC and PE funds operating in both developed and emerging markets. We benchmark private capital performance against public markets and other asset classes, offering rare insight into the financial dynamics of entrepreneurial finance in lower-income and high-growth regions.