Broadband plays an important role in modern economic activity, yet remains unaffordable or uncompetitive for many U.S. households, prompting historic public investment. Two flagship initiatives, the Affordable Connectivity Program (ACP) and the Broadband Equity, Access, and Deployment (BEAD) program, form the core of the federal broadband policy. However, their effects on welfare and market structure remain poorly understood. Using rich data on markets, providers, and broadband products, I combine a difference-in-differences design with continuous treatment intensity and a structural model of demand, entry, and product choice to quantify their effects. The ACP, a $30 monthly household subsidy, increases social surplus and generates up to $1.90 in welfare per dollar spent. The BEAD program, which subsidizes providers’ fixed costs, expands entry and product variety, but also raises marginal costs and prices, with larger welfare gains per dollar spent that peak at intermediate subsidy intensities (25–50%) in the long run. These findings highlight a key policy trade-off: demand-side subsidies yield immediate, cost-effective affordability gains, while supply-side subsidies reshape competition to foster long-term efficiency, albeit at higher fiscal cost.
This paper introduces a new method that integrates machine learning techniques with traditional econometric tools to correct bias in two-step estimation for dynamic discrete choice models under incomplete information. Unlike most existing approaches that primarily address bias from conditional choice probabilities while taking the transition matrix as given, this method simultaneously addresses both sources of bias. I propose a two-step estimator that jointly includes the conditional choice probabilities and transition matrix as first-step estimates, providing a robust, non-iterative solution. The estimator achieves square root N consistency and asymptotic efficiency. In addition, I establish a simple connection between Neyman orthogonality and the Zero-Jacobian property, a key result that guarantees consistent and efficient estimators in single-agent models. Monte Carlo simulations using a common benchmark data-generating process demonstrate the good performance of the proposed method.
Mobile money is a core component of Senegal’s financial system, and provides essential transaction services to a largely unbanked population. As these services are delivered through retail agents, competition among operators at the store level directly influences prices and access. The recent entry of Wave, followed by sharp fee reductions from incumbents, highlights the sector’s sensitivity to competitive pressure. This paper analyzes how market structure shapes competitive behavior in Senegal’s mobile money industry. Using a novel dataset of 1,553 retail stores, I estimate a static discrete-choice entry game under complete information and obtain bounds on profitability and competitive effects through moment inequalities. I then use the estimated structure to assess how Wave’s entry affected competitive conditions and to simulate alternative market environments. Preliminary results show that larger stores are more likely to host multiple operators and that competitive interactions among firms are significant. The findings offer the first supply-side evidence of competition in Senegal’s mobile money sector.
We leverage a convex programming strategy and propose a new estimation strategy to estimate a discrete choice models which is robust to standard specifications.