Research Sponsors
National Science Foundation
NRF South Korea
NSFC China
Asian Development Bank
ASU Global Futures
Fulbright Program
Dept. of Education
Dept. of State
National Science Foundation
NRF South Korea
NSFC China
Asian Development Bank
ASU Global Futures
Fulbright Program
Dept. of Education
Dept. of State
Anthony Howell
Director, Center on Technology, Data & Society | Associate Professor of Public Policy & Management | Editor for Geographic Methods, Annals of the AAG | Arizona State University
Arizona State University
Dr. Howell is the Director of the Center on Technology, Data, and Society and an Associate Professor of Public Policy and Management in the School of Public Affairs at ASU. He is an affiliate faculty member in the School of Geographical Sciences and Urban Planning and a senior sustainability scientist in the School of Global Futures. Prior to ASU, Dr. Howell served as an Associate Professor of Applied Microeconomics in the School of Economics at Peking University, China's flagship university. He also previously held several visiting positions as a Fulbright scholar at the Lincoln Institute of Urban Development and Land Policy (Beijing), a Science & Technology policy fellow at the National Academies of Sciences (Washington D.C.), and a research fellow at the Asian Development Bank (Manila). Dr. Howell holds a PhD in Geography (UCLA), M.S. degrees in Statistics (UCLA) and GIScience (MSU), and B.A degrees in Political Science, International Development, and Chinese Language and Culture (MSU).
- Economic Development
- Place-Based Policy
- Industrial Policy & Firm Innovation
- Migration & Labor Market Analysis
- Cash Transfers
- Computational Social Science
- Large Language Models
- Geospatial Analysis
- Quasi-Experimental Design
- China Economy
Research Overview
Dr. Howell's research sits at the intersection of generative AI, policy, governance, and computational social science, with broader expertise in economic development, public policy evaluation, innovation, and technological change. His portfolio includes more than 35 peer-reviewed articles in leading journals (Nature Human Behaviour, Annals of the AAG, Journal of Development Economics, Journal of Urban Economics, Journal of Economic Geography, Energy Economics, Research Policy). He has secured nearly $1 million in external funding from the private sector and government agencies in the U.S. (National Science Foundation; Department of State), South Korea (National Research Foundation), and China (National Natural Science Foundation). His research is internationally recognized and is frequently cited in media and policy reports.
Dr. Howell serves as Editor for Geographic Methods at the Annals of the American Association of Geographers. Dr. Howell's work combines advanced quantitative methods, computational social science, and field-based research to examine how technology, policy, and institutions shape economic and social outcomes. He has carried out household and business surveys across multiple cities in China for over a decade, bringing extensive field experience to his comparative and policy-oriented scholarship.
Research Themes
Dr. Howell's most recent research applies large language models and multimodal LLMs both as policy-grade measurement instruments and as objects of study in their own right. His forthcoming paper in npj Urban Sustainability demonstrates that GPT-4o, deployed on Google Street View imagery, recovers neighborhood poverty with census-benchmark accuracy and reproduces the causal legacy of 1930s redlining, outperforming conventional pixel-based segmentation baselines (Howell et al., 2026). This work establishes a proof-of-concept for MLLM-powered urban policy intelligence in settings where administrative data are absent, delayed, or prohibitively expensive to collect. A companion paper under revision at Scientific Reports audits LLMs for institutional bias, finding that simulated peer reviewers assign systematically higher rejection risk to identical manuscripts from lower-ranked institutions — a prestige penalty that persists after controlling for manuscript content. Additional projects examine how political framing shapes LLM assessments of equity in municipal climate action plans (National Research Foundation of South Korea), and explore whether multi-agent LLM workflows can support public agency equity assessments at scale.
Supported by Anthropic's Economic Futures Program, a parallel strand investigates how AI capability shocks reshape labor demand, skill requirements, and the geography of technological change across U.S. local labor markets. The rapid diffusion of generative AI represents one of the most consequential economic disruptions of the current era, reorganizing the demand side of labor with distributional consequences for workers, firms, and regions that are only beginning to be understood. By combining LLM-assisted classification pipelines with quasi-experimental designs, this research quantifies how successive waves of AI capability have displaced routine cognitive tasks while generating new complementary skill requirements, with effects that vary systematically across metropolitan labor markets by industry composition, worker education, and prior technology exposure. Taken together, these two strands reflect a broader commitment to developing computational social science methods capable of generating policy-grade evidence at the frontier of measurement and societal impact.
Dr. Howell's research on place-based policy spans the full policy lifecycle. His work on China's Economic and Development Zones documents heterogeneous but real productivity gains of 18–30% for incumbent firms while separating agglomeration economies from selection effects (Howell, 2019; 2020; Howell et al., 2023). A paper under revision at Regional Science and Urban Economics provides the first natural experiment on agglomeration disruption, showing that premature zone termination reduces firm patenting, export competitiveness, and labor market resilience. Using China's 2009–10 stimulus as a natural experiment, he further shows that suspending matching mandates crowds in local investment and spurs migrant entrepreneurship, while penalizing fiscally constrained localities (Howell, 2024). His Nature Human Behaviour paper provides the first causal evidence on policy retrenchment, documenting that scaling back an ecosystem payments program blocks farm-to-nonfarm transitions, with adverse effects concentrated where land rights and job training are weakest (Howell, 2022). A paper under review examines how state-led industrial relocation reshapes export competitiveness and spatial inequality.
Dr. Howell's research on industrial policy and firm competitiveness draws on a decade of work examining how policy environments, market structure, and knowledge geography shape innovation outcomes. Foundational papers establish that cautious innovators survive longer and generate greater efficiency gains (Howell, 2015), that corporate tax reform spurs commercialization but fails to raise R&D investment (Howell, 2016), and that co-located related firms outperform on survival and productivity through all three Marshallian channels (Howell, 2017). Subsequent work shows that FDI liberalization amplifies relatedness spillovers into indigenous innovation (Howell, 2019), that absorptive capacity mediates agglomeration gains (Howell, 2019), that outward FDI generates positive knowledge feedback into domestic innovation (Howell et al., 2020), and that state R&D subsidies accelerate technological upgrading with heterogeneous effects across firm type (Boeing, Eberle and Howell, 2021). Market-oriented reforms amplify recombinant innovation while industrial support dampens agglomeration benefits for co-located competitors (Howell et al., 2022; 2023). A paper under revision at Strategic Management Journal with Prud'homme and collaborators examines how a supranational intellectual property institution reform reshapes firm innovation and commercialization globally.
Grounded in nearly two decades of fieldwork in Xinjiang, Dr. Howell's social policy program documents how labor markets and institutions distribute economic consequences unevenly across ethnic and socioeconomic groups. Early work established persistent Han-Uyghur wage and self-employment gaps unexplained by human capital (Howell and Fan, 2011; Howell, 2011), systematic financing barriers for ethnic minority entrepreneurs (Howell, 2018), and lower returns to migration among minority households (Howell, 2017). Minimum wages compress the Han-minority wage gap and reduce the urban Gini by 10–12% (Howell, 2020). Dibao cash transfers raise migration probability among poor minority households (Howell, 2022) and narrow the first-documented ethnic energy poverty gap through a fuel-stacking mechanism (Howell, 2025).
A machine learning analysis of household carbon footprints shows that uniform carbon taxation would be regressive in urban China, exacerbating ethnic emissions inequality (Howell, 2024). Road infrastructure investment spurs labor reallocation and migrant entrepreneurship in fiscally constrained localities (Howell, 2024), while a paper under review examines whether ethnic households adapt to climate shocks through local venture formation or migration, and what role guaranteed minimum income programs play in enabling those choices. Most recently, this program extends to the United States, examining how ethnic migration and local labor market dynamics shape income disparities in U.S. metropolitan workforces (Berg et al., 2025).