Studies in Development Economics
Lightning Round SessionSaturday, Jan. 3, 2026 10:15 AM - 12:15 PM (EST)
- Chair: Manaswini Rao, University of Delaware
Road to Resilience: Floods, Highways and Indian Informal Manufacturing
Abstract
Road infrastructure is widely recognized as a driver of economic development, but can it also aid in disaster resilience? This study answers this question in the Indian context by examining the effect of dual and staggered treatments: national highway construction – the Golden Quadrilateral and the North-South and East-West corridors– and extreme flood exposure. Motivated by a structural model of disaster resilience at the plant level, the study focuses on India’s informal manufacturing, which not only is the largest in the world but also lacks formal safety nets. It combines geospatially matched, staggered flood exposure data from the Dartmouth Flood Observatory with phased highway rollout timelines from the National Highways Authority of India along with repeated cross-section plant-level survey data (1990–2016) on Indian informal manufacturing. Exploiting quasi-random variation in the timing of flood exposure and road construction, the study implements a stacked difference-in-differences design, matching treated districts to future-treated counterparts. Results show that floods significantly reduce both gross output and value added, while highway access counters those losses. Plants located directly on completed highway segments nearly offset the average 7 percent flood-related output loss, benefiting from higher output and reduced input (labor, materials, and energy) expenditures. These effects are magnified for plants that own transport equipment, highlighting a complementarity between internal logistics and external infrastructure. Off-highway plants exhibit similar resilience only when they possess transport assets, enabling access to distant road networks. These findings reveal that roads are not only engines of development but also critical to disaster resilience. Designing infrastructure with dual function—development and resilience—is essential for building a climate-smart and resilient economy.The Impact of Digital India Initiative on Economic Development
Abstract
The Digital India Initiative, launched in 2015, aims to transform India into a digitally empowered society and knowledge economy. This study examines the impact of the initiative on economic development, focusing on key sectors such as financial inclusion, e-governance, digital infrastructure, and entrepreneurship. The initiative has accelerated internet penetration, mobile connectivity, and digital payments, fostering growth in e-commerce and start-ups. Programs like Aadhaar-linked services, Unified Payments Interface (UPI), and e-Governance platforms have improved service delivery and financial transparency. However, challenges such as the digital divide, cybersecurity risks, and rural accessibility persist. This research evaluates the successes and limitations of Digital India and provides policy recommendations for maximizing its economic benefits while addressing structural gaps. The findings highlight how digital transformation is reshaping India’s economic landscape and contributing to inclusive and sustainable growth.Green Industrial Policy in a Globalized Economy
Abstract
How does green industrial policy work when renewable-energy equipment (solar panels and wind turbines) is traded internationally? I build a dynamic trade model with traded equipment and learning-by-doing and apply it to the U.S. Inflation Reduction Act (IRA), which combines renewable-energy production and equipment-manufacturing subsidies. The instruments differ in terms-of-trade effects and in the locus of learning: an energy subsidy increases equipment imports and raises global welfare more than U.S. welfare; an equipment subsidy concentrates learning at home and does the reverse. The IRA cuts global CO2 by 0.63% and raises U.S. and global welfare by 0.05% and 0.03%, respectively.Spillovers and the Direction of Innovation: An Application to the Clean Energy Transition
Abstract
This paper argues that cross-technology knowledge spillovers are critical for understanding policy's role in the clean energy transition. I develop an endogenous growth model with clean and dirty technologies and a network of cross-technology spillovers. The resulting formulas for the size and speed of technological transition, following a policy reform, show that greater spillovers induce faster transitions but reduce the long-run impact of policy. Such spillovers also prevent lock-in of dirty technology. I apply my model to US transportation and electricity generation and find that spillovers are mid-sized: they prevent lock-in but imply slow transitions with high long-run impact of policy. I then examine how cross-technology spillovers affect optimal policy, deriving innovation subsidy formulas under arbitrary carbon prices. Quantitatively, a "big push" of clean innovation subsidies is no longer optimal because the spillover network allows us to build toward clean technology in the future by innovating in other technologies today.Creative Destruction? Mass Layoffs and Innovation Spillovers
Abstract
Mass layoffs are typically viewed as detrimental to regional economies. Previous works focused on negative economic consequences such as increased unemployment, declining wages, and reduced consumer spending. However, mass layoffs may act as bridges between otherwise disconnected innovation hubs by facilitating the flow of information and ideas as displaced workers move to new firms. Despite these potential, positive spillover effects have received little attention which could offer different perspectives to understanding mass layoffs.This study fills this gap and examines the short- and long-term effects of mass layoffs on innovation of firms receiving displaced workers. I use novel micro-level data that merge LinkedIn employment histories with patent records. This enables detailed tracking of individual worker mobility, firm characteristics, and patenting activity before and after layoffs.
Descriptive statistics show that two-thirds of displaced workers move to small firms (< 50 employees), while the rest join larger firms. To estimate the impact of this labor reallocation on innovation, I employ a difference-in-differences design, comparing firms that absorbed displaced workers to similar firms that received no such workers in unaffected metropolitan statistical areas.
The results show that small firms absorbing displaced workers experience a short-term increase in innovation outcomes. Over the long term, these innovative gains remain positive up to seven years. In contrast, large firms show no sustained impact on innovation.
I further analyze channels and identify three mechanisms: (1) knowledge transfer from displaced workers, (2) formation of new teams through their networks, and (3) increased diversity in human capital in receiving firms. These channels appear to boost the innovative capacity of host organizations despite the overall disruptive nature of mass layoffs.
These findings highlight the direction for policy interventions such as targeted support or hiring incentives for small firms to transform labor market disruptions into opportunities for innovation growth.
Teams and Text: Collaborative Innovation in the Knowledge Space
Abstract
In this paper, I study the impact of an expanding scientific and technological frontier on team innovations. To do so, I present a novel framework that integrates inventor teams and their patent texts. I model collaboration directly through a Bayesian model of Natural Language Processing. Applied to patent text data, this model builds a map of inventors, teams, and research fields, referred to as the knowledge space. Applied to over 2.2 million U.S. patents from the USPTO PatentsView database, this framework allows me to tackle unanswered questions on how teams create new knowledge. Specifically, I investigate the effect of prior work on a team’s ability to produce a breakthrough—an innovation that sparks a new and successful research field. Leveraging high-dimensional patent text data, I back out two new measures: breakthrough patents and a team’s knowledge field, the set of research fields accessible to the team. I combine this with data on premature inventor deaths as a quasi-natural experiment. This identifies how team innovations change as they pivot to more or less advanced research fields. The framework unifies key elements of collaboration. Teams build on existing knowledge, and prior work both supports and obstructs innovation. I show that teams generate more breakthroughs when building on enough prior work to incorporate valuable knowledge, but not so much as to stifle novelty.Finance and Productivity Growth: The Role of Intangibles
Abstract
This paper investigates the impact of credit frictions on the contribution of intangible capital to productivity growth, using a structural model fitted to UK firm data and matching stylized facts on the relationship between intangibles, debt and TFP at the firm level. The model incorporates investment decisions in tangible and intangible assets, alongside debt issuance, with financial frictions modelled as a debt premium dependent on earnings-based leverage and intangible intensity. Employing a novel neural network estimation method for value function parameters, we find a significant decline in intangible capital's contribution to TFP growth post-2010, from approximately 8% to 3%. This decline is attributed to increased leverage constraining intangible investment. However, we also find evidence of a post-GFC increase in intangible-specific credit frictions, resulting in a 15% TFP growth reduction by 2018.Patent Statistics with Chinese Characteristics: Evidence from Chinese Listed Companies
Abstract
This paper quantifies the relationship between commonly used patent statistics and patent value in China, addressing the long-standing open question of how effectively these statistics predict the value of Chinese patents. We identify three notable patterns of Chinese patent value that challenge the validity of traditional patent statistics. First, standard indicators such as forward citations and claims exhibit relatively weak predictive power whereas the number of inventors emerges as a notably stronger predictor. Second, patent value distributions differ substantially between private firms and state-owned enterprises (SOEs). Within SOEs, an innovation ladder emerges where smaller SOEs typically produce a high proportion of low-value patents, while certain strategic SOEs contribute disproportionately to patents with high economic value, reflecting distinct roles assigned by the state. Third, Chinese patent values display strong seasonality: patents filed toward year-end have significantly lower economic values, despite a sharp increase in application volumes. We attribute these findings to distinctive institutional factors within China's innovation system, including administrative incentives related to year-end performance evaluations and the unique role played by SOEs. Our results suggest caution in interpreting standard patent statistics as reliable indicators of patent value in China, emphasizing the importance of accounting for these institutional contexts.Agglomeration, Productivity, and Informality
Abstract
Slow productivity growth and the persistence of informal, low-performing firms are central development challenges in low and middle-income countries. This paper studies how urban agglomeration shapes both the local distribution of firm-level productivity and the prevalence of informality. We measure agglomeration with block-level population data, calculating the number of people within one kilometer. To construct city-level measures, we aggregate these values at the local labor market level to obtain the average population density experienced by residents. To address endogeneity concerns, we follow the literature and instrument agglomeration using the 1921 population of the commuting zone, which is plausibly unrelated to contemporary shocks or policies affecting productivity and informality. Firm-level productivity and informality estimates at the firm level are constructed with data from the Mexican Economic Censuses and aggregated at the local labor market level for the main analysis.We document two novel and interrelated empirical patterns in Mexican cities. First, while agglomeration increases average productivity -- as commonly found in other contexts -- the effect is non-monotonic: productivity increases follow an inverse U-shape pattern along the firm size distribution, peaking at intermediate firm sizes. Second, informality -- measured as the absence of social security contributions -- declines with agglomeration in a U-shaped pattern, with the largest reductions among medium-sized firms.
Our findings contribute to two strands of literature. First, we deepen the understanding of the relationship between informality and productivity by showing how agglomeration reshapes the firm size and productivity distributions. Second, we add to the literature on agglomeration economies by providing causal estimates of how density affects firm-level informality. The results have important policy implications: by shaping the density of economic activity—through zoning, transport infrastructure, or urban design—governments can indirectly influence firm composition, reduce informality, and boost productivity in urban areas.
Trade Credit in a Developing Country: The Role of Large Suppliers in the Production Network
Abstract
Trade credit can be a substitute for bank credit when firms have limited access to financial institutions. This is particularly relevant in developing countries where bank interest rates are highly dispersed and smaller firms face a prohibitive cost of bank credit. This paper builds a production network model where each pair of sellers and buyers choose intermediate inputs' quantities, prices, and levels of trade credit in a decentralized fashion, given heterogeneous bank interest rates. The dispersion in interest rates is explained by both heterogeneous risk of firms' default and additional heterogeneous costs, labeled as `frictions.' In equilibrium, suppliers paying low bank interest rates are net providers of trade credit to clients paying high interest rates when this spread is due to frictions.We calibrate the model using balance sheet data, firm-to-firm transaction data, and bank-to-firm credit data for the Brazilian economy. We decompose the observed interest rates between the risk and the frictional components.
Trade credit attenuates shocks to financial frictions hitting downstream firms, while it amplifies these shocks when they hit upstream companies. We also use our model to evaluate the importance of trade credit for aggregate output, given the evolution of firm-level bank interest rates in the 2019-2023 period. Trade credit reduced the output loss due to frictions by 44% in 2019.
However, the importance of trade credit diminished between 2019 and 2023, together with the reduction in the dispersion of interest rates.
JEL Classifications
- O1 - Economic Development