About

Jose Lobo is a Clinical Associate Professor at the School of Sustainability at Arizona State University, where he has been a faculty member since 2005. His research interests include metropolitan economic performance, location-specific economic growth, innovation, socioeconomics, spatial analysis, data mining, machine learning, urban systems, social networks, regions of innovation, and sustainable cities and communities. He is involved in studying the determinants of urban economic performance, the causes and consequences of urban size and scale, and how characteristics of individuals, organizations, institutions, and social networks interact to create regions of innovation. Professor Lobo has acted as a visiting researcher at the Santa Fe Institute and Italy's Universita di Modena e Reggio Emilia. He is currently on the faculty steering committee for Arizona State University's Center for Social Dynamics and Complexity and serves as an associate research professor in the School of Sustainability. His educational background includes a Ph.D. in Regional Science from Cornell University, earned in 1996, along with a master's degree in Regional Science and a bachelor's degree in Physics from Cornell. His work encompasses forecasting technological progress in solar energy, studying the dynamics of slums in global urban areas, and developing real-time metrics on the effects of investments on technological invention, among other projects.

Research topics

• Sociology
• Engineering
• Economics
• Economic geography
• Geography
• Demography
• Civil engineering
• Archaeology
• Regional science
• Political Science
• Social Science
• Computer Science
• Statistics
• Statistical physics
• Economic growth
• Epistemology
• Ecology
• Mathematics
• Econometrics
• Environmental planning
• Physics

Selected publications

• Decoding the city: multiscale spatial information of urban income

  Research Square · 2025-10-09

  preprintOpen accessSenior author
  PublisherDOI

• Toward understanding the impact of artificial intelligence on labor

  UNC Libraries · 2025-07-18

  articleOpen access

  Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human-machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

  PublisherDOI

• Technological complexity and combinatorial invention in small-scale societies

  Science Advances · 2025-09-24 · 1 citations

  articleOpen access

  Technology plays a central role in all human societies, from foraging to industrial economies. However, technological solutions come with associated costs, and in small-scale societies, technological complexity reflects this trade-off between efficiency and resource constraints. Here, we analyze this trade-off and show a sublinear scaling relationship between toolkit richness and tool part richness in ethnographic societies. This result indicates diminishing returns where each additional part contributes less to overall toolkit diversity. This scaling holds across diverse ecological and cultural contexts, suggesting a general principle of optimization in tool design. Ethnographic toolkits achieve their adaptability by reusing a core set of versatile parts and selectively incorporating more specialized parts. However, increasing richness also increases complexity, and complexity is costly. We formalize these dynamics within a combinatorial optimization framework and discuss the implications.

  PublisherDOI

• Adolescence socioeconomic segregation and high-skill jobs in adulthood

  Papers of the Regional Science Association · 2025-07-18 · 1 citations

  articleOpen accessSenior author

  The influence of neighborhood environments on children’s future outcomes has attracted significant scholarly attention in recent years. This study contributes to the existing literature by examining how neighborhood segregation during adolescence affects the likelihood of securing a high-skill occupation in adulthood. Utilizing comprehensive Swedish microdata, we control for intergenerational persistence in labor market outcomes and cognitive abilities through military enlistment data. Our findings show that growing up in neighborhoods characterized by high poverty and low educational attainment reduces the probability of pursuing jobs that require advanced higher education. In contrast, obtaining managerial positions seems less influenced by socioeconomic background. Furthermore, our findings reveal that obtaining a higher education and relocating to metropolitan areas can help mitigate neighborhood disadvantages, underscoring the importance of spatial mobility and access to university studies for disadvantaged youth. However, socioeconomic disadvantages at the family level remain persistent and challenging to overcome.

  PublisherDOI

• A macroecological theory of social complexity

  2025-12-17

  preprintOpen access

  How are scale and complexity related in human societies? To answer this long-standing question, we develop a theoretical model of polities as territorial social networks that are functionally integrated by institutions. Our model hypothesizes how agricultural intensity, territorial area, and social complexity are related and how they scale relative to population size. We test the model's predictions using data from "Seshat: Global Databank" which describe hundreds of polities worldwide from across the Holocene. We find that intensity, territory, and complexity scale with population size as predicted. Our results provide evidence in support of theories of social complexity that emphasize its role in fostering large-scale cooperation.

  PublisherDOI

• Noise Reduction and Sustainability in Physical Education: Impacts on Health and the School Environment

  NOISE-CON proceedings · 2025-10-22

  article1st authorCorresponding

  Health promotion at school is fundamental, as this environment facilitates educational actions in multiple dimensions, including physical, social and community (Hao et al., 2022). However, there is a gap in the literature on the integration between health and sustainability in physical education, especially in relation to continuous exposure to noise in multifunctional rooms (AMOATEY et al., 2020; GUSKI, SCHRECKENBERG and SCHUEMER, 2017). This study systematically investigated the effects of using recyclable and sustainable materials on reducing noise in physical education learning environments, evaluating its impact on the health of students and teachers in public schools. The bibliographic search was carried out in the PubMed and Web of Science databases, covering articles published in English between 2018 and 2023. Several studies analyzed intelligibility and noise-induced stress using questionnaires and biological indicators (DEPRÁ et al. 2022). Noise must be treated as a public health problem, and the adoption of sustainable strategies, such as installing ecological acoustic panels in multifunctional rooms, can significantly reduce noise levels, providing a more appropriate teaching environment and promoting teacher health.

  PublisherDOI

• Do Neighborhoods Matter for Individual Decision‐Making? The Case of COVID‐19 Vaccination in Sweden

  Journal of Regional Science · 2025-03-19

  articleOpen access

  ABSTRACT Much research has highlighted the significance of neighborhood effects on individual‐level choices and outcomes. But it has proven difficult to disentangle the influence of those that an individual shares a residential space with from that of other peers, such as work colleagues and family members. Neighbors, work colleagues, and family members constitute different sources of information. The decision to accept or refuse a vaccine is intensely personal and involves the processing of information about phenomena likely to be unfamiliar to most individuals. To examine the information effect of different peer groups we use microlevel data on COVID‐19 vaccination in Sweden. We investigate the extent to which an individual's decision not to get vaccinated is influenced by the presence of other unvaccinated individuals in their household, workplace, or residential neighborhood. Our findings reveal that workplace peers tend to be most strongly connected to the decision not to get vaccinated. We also find that the role of neighborhood peers tends to be overestimated when we do not control for peers at home and at work.

  PublisherOA PDFDOI

• Transitioning to a green economy: Radical labor transformation or building upon existing skills?

  Sustainability Analytics and Modeling · 2025-01-01

  articleOpen accessSenior author

  Transitioning to a “green” economy will require many industries to change their activities, raising concerns about the elimination of occupations and the need for significant retraining of the workforce. These concerns have increased resistance to a green transition from some sectors of society. Yet if skills embodied in current economic tasks can be reapplied to activities that facilitate a green transition, the retraining challenge might be lessened. Using a new taxonomy of sustainable economic activities – those that can contribute to climate change mitigation or adaptation – we estimate the number of US, German, and Canadian workers already employed in industries that are equipped to undertake sustainable economic activities. While the fraction of potential green workers varies considerably across metropolitan areas, in each country over one third of workers could conceivably contribute to a green economic transition by applying their existing skills to new activities. This represents more than 47 million workers in the US. Thus, a transition to a green economy may require more that firms reconfigure their workforces than individual workers reconfigure their skill sets.

  PublisherDOI

• Decoding the city: multiscale spatial information of urban income

  ArXiv.org · 2025-09-26

  preprintOpen accessSenior author

  Cities are characterized by the coexistence of general aggregate patterns, along with many local variations. This poses challenges for analyses of urban phenomena, which tend to be either too aggregated or too local, depending on the disciplinary approach. Here, we use methods from statistical learning theory to develop a general methodology for quantifying how much information is encoded in the spatial structure of cities at different scales. We illustrate the approach via the multiscale analysis of income distributions in over 900 US metropolitan areas. By treating the formation of diverse neighborhood structures as a process of spatial selection, we quantify the complexity of explanation needed to account for personal income heterogeneity observed across all US urban areas and each of their neighborhoods. We find that spatial selection is strongly dependent on income levels with richer and poorer households appearing spatially more segregated than middle-income groups. We also find that different neighborhoods present different degrees of income specificity and inequality, motivating analysis and theory beyond averages. Our findings emphasize the importance of multiscalar statistical methods that both coarse-grain and fine-grain data to bridge local to global theories of cities and other complex systems.

  PublisherOA PDFDOI

• The Changing Character of Chinese Urbanization: 2000 - 2021

  Research Square · 2025-02-11

  preprintOpen access
  PublisherOA PDFDOI

Recent grants

• Collaborative Research: RAPID: Developing Real Time Metrics on the Effects of ARRA Investments on Technological Invention

  NSF · $68k · 2009–2010

Frequent coauthors

• Luís M. A. Bettencourt

  University of Chicago

  51 shared

• Deborah Strumsky

  Jönköping University

  38 shared

• Scott G. Ortman

  University of Colorado Boulder

  34 shared

• Charlotta Mellander

  University of Chicago

  20 shared

• Michael E. Smith

  20 shared

• Hyejin Youn

  15 shared

• Shade T. Shutters

  Global Climate Forum

  14 shared

• Geoffrey B. West

  14 shared

Education

• Ph.D., Regional Science

  Cornell University

  1996

• M.S., Regional Science

  Cornell University

  1992

• B.S., Physics

  Cornell University

  1984

Awards & honors

• NSF-SES Center for Nanotechnology at ASU (2010 - 2016)
• Collaborative Research: RAPID: Developing Real Time Metrics…
• University as Entrepreneur. KAUFFMAN FDN (2007 - 2012)
• Forecasting and influencing technological progress in solar…
• Studying the Dynamics of Slums in Global Urban Areas at Sant…