Carlos Mendez

Carlos Mendez

Associate Professor of Development Economics

Nagoya University, JAPAN

About me

After studying Commercial Engineering in Bolivia and Chile, I worked as a consultant for Pro-Mujer International, The World Bank, DANIDA, and JICA. I have a M.A. and a Ph.D. in International Development from Nagoya University, Japan. My research interests focus on the integration of development economics, spatial data science, and applied econometrics to better understand and inform the process of sustainable development across regions. My current research deals with (1) geospatial big data analytics and socioeconomic development; (2) geospatial inequality, poverty, and growth interactions; (3) regional infrastructure and mobility flows; and (4) spatial structural change and productivity dynamics.

Download my CV.

Interests
  • Regional Development
  • Applied Econometrics
  • Spatial Big Data Analytics
  • Spatial Machine Learning
  • Development Macroeconomics
Education
  • PhD in International Development, 2015

    Nagoya University

  • MA in International Development, 2012

    Nagoya University

  • Lic in Commercial Engineering, 2008

    Bolivian Catholic University

QuaRCS-lab Japan

In the QuaRCS-lab and its global network, we conduct research on quantitative regional and computational science. We exploit the integration of development economics, spatial data science, and applied econometrics to understand and inform the process of sustainable development across subnational regions and countries.

When the sun goes down and the lights turn on, there’s still a lot to explore.
Let’s study regional development from outer space!


QUIZ: Based on the regression NTL = a + b(t), the map below shows the trends of nighttime lights. An RGB composite is used for visualization, where positive and negative slope values are represented by red and blue gradients respectively, and the intercept is represented by a green gradient.
Given these parameters, how would you interpret the yellow and cyan colors?
(Hint: Copy and paste this quiz into ChatGPT)

What about the distribution of population? What can we learn from the spatial dynamics of population?
(Click anywhere on the map below and discover it.)

Other Publications

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(2026). Minimum wage differentials and commuting across districts. Asia-Pacific Journal of Regional Science.

Cite DOI Video Published article

(2025). Predicting subnational GDP in Vietnam with remote sensing data: A machine learning approach. Letters in Spatial and Resource Sciences.

Cite DOI AI Podcast Open access article

(2023). Regional Okun’s law and endogeneity: evidence from the Indonesian districts. Applied Economics Letters.

PDF Cite Slides DOI

(2023). Convergence clubs and spatial structural change in the European Union. Structural Change and Economic Dynamics.

PDF Cite DOI

Recent & Upcoming Presentations

Posts & Tutorials

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The FWL Theorem: Making Multivariate Regressions Intuitive
The FWL Theorem: Making Multivariate Regressions Intuitive
Mar 14, 2026
Understanding the Frisch-Waugh-Lovell theorem to isolate causal relationships by partialling-out confounders in a simulated retail store dataset
Introduction to Partial Identification: Bounding Causal Effects Under Unmeasured Confounding
Introduction to Partial Identification: Bounding Causal Effects Under Unmeasured Confounding
Mar 13, 2026
Computing causal bounds under unmeasured confounding using Manski and Tian-Pearl bounds with the CausalBoundingEngine package in Python
Introduction to Causal Inference: The DoWhy Approach with the Lalonde Dataset
Introduction to Causal Inference: The DoWhy Approach with the Lalonde Dataset
Mar 12, 2026
Estimating the causal effect of a job training program on earnings using DoWhy’s four-step causal inference framework with the Lalonde dataset
Introduction to Causal Inference: Double Machine Learning
Introduction to Causal Inference: Double Machine Learning
Mar 10, 2026
Estimating the causal effect of a cash bonus on unemployment duration using Double Machine Learning with the Pennsylvania Bonus Experiment
Introduction to Machine Learning: Random Forest Regression
Introduction to Machine Learning: Random Forest Regression
Mar 10, 2026
Predicting municipal development in Bolivia using Random Forest regression on satellite image embeddings

Projects

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metricsAI
An introduction to econometrics with Python and AI in the cloud
GeoDevelopment Dashboards
Interactive geospatial dashboards for monitoring regional development
DS4Bolivia
A Data Science Repository to Study GeoSpatial Development in Bolivia
GeoDevelopment Observatory of Cambodia
A public access platform for the analysis, monitoring, and evaluation of sustainable regional development in Cambodia
Computational data science notebooks and apps for development studies
Computational data science notebooks and apps to foster development studies.

Students

Doctoral students

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Abdulah Rusli (Indonesia)

PhD student 2023-2026

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Bimo Arvianto (Indonesia)

PhD student 2025-2028

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Cesar Echevarria (Peru)

PhD student 2024-2027

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He Du (China)

PhD student 2025-2028

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Leiva Favio (Peru)

PhD student 2023-2024

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Li Jiaqi (China)

PhD student 2023-2026

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Li Xiaomeng (China)

PhD student 2025-2028

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Phon Sophat (Cambodia)

PhD student 2024-2027

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Restrepo Katerine (Colombia)

PhD student 2023-2026

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Sour Heng (Cambodia)

PhD student 2023-2026

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Theara Khoun (Cambodia)

PhD student 2022-2025

Master students

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Kanyama Yuna (Japan)

Master student 2024-2026

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Mujkanovic Adin (Bosnia and Herzegovina)

Master student 2025-2027

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Prieto Laura (Colombia)

Master student 2024-2026

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