Articles | Carlos Mendez

Okun's law and spatial regimes in Indonesia: A machine learning approach

Thu, 28 May 2026 00:00:00 +0000

The puzzle

Okun’s law is one of macroeconomics’ most reliable regularities: when output grows, unemployment falls. Yet estimated for Indonesia as a single economy, the relationship does not hold. The reason is aggregation. A vast, heterogeneous archipelago is not one labor market, and a national average quietly cancels out regions that move in opposite directions. The question, then, is not whether Okun’s law holds in Indonesia, but where.

A two-step, data-driven approach

Rather than imposing geographic groups in advance (say, “West” versus “East”), the authors let the data sort districts into groups with similar growth–unemployment dynamics, then model how those dynamics spill across space. The framework is descriptive — it maps associations, not causal effects.

graph LR
A("<b>Step 1 — C-Lasso</b><br/>(machine learning)<br/><i>Sorts districts into latent regimes<br/>sharing a growth-unemployment pattern</i>")
B("<b>Step 2 — spatial Durbin model</b><br/><i>Splits each regime's response into a<br/>direct (local) and indirect (neighbor<br/>spillover) association</i>")
A --> B
classDef blue fill:#1f2b5e,stroke:#6a9bcc,stroke-width:3px,color:#e8ecf2
classDef orange fill:#1f2b5e,stroke:#d97757,stroke-width:3px,color:#e8ecf2
class A blue
class B orange

Four latent regimes

The classifier uncovers four distinct regimes that cut across administrative and physical geography — districts in the same group need not be neighbors. Each tells a different story about how growth meets the labor market.

Regime	Structural profile	Examples	Okun behavior
Group 1	Labor-absorbing centers — metropolises, industrial belts, smallholder plantations	Bekasi, North Jakarta, Makassar, Medan	Strong, textbook. Growth co-moves with falling unemployment, locally and in neighbors.
Group 2	Capital-intensive hubs — resource zones, mechanized corporate farming	Balikpapan, Central Jakarta, rural Java pockets	Reversed. Faster growth tracks higher measured open unemployment.
Group 3	Transitional centers — secondary cities shifting from agriculture to services	Cilacap, Indramayu, Malang	Weak. Little baseline link; adjustment runs through hours worked, not layoffs.
Group 4	Peripheral markets — thin, isolated rural island economies	Remote Papua, East Nusa Tenggara	Negligible. Pervasive informality severs the link to formal unemployment.

Why does growth co-move with rising unemployment in Group 2? Where capital-intensive industry and corporate agriculture dominate, growth often arrives through mechanization that displaces traditional farm labor. As displaced workers leave informal or family work to look for formal wage jobs, they enter the statistics as “openly unemployed.” Measured unemployment rises alongside output through search frictions and skills mismatch — not because growth itself destroys jobs.

Robustness at the province level

To check that the pattern is not an artifact of district-level noise, the authors re-run the framework on 34 provinces. It reproduces a similar structure, now in three regimes:

Diversified, demand-rich provinces — industrial and consumer hubs with a steep Okun coefficient of $-0.262$.
Agricultural commodity heartlands — large corporate plantations where growth is locally “jobless” but generates spillovers to neighbors.
Commodity-frontier enclaves — thin labor markets tied to mining and heavy industry, with a near-flat coefficient of $-0.033$.

Spatial spillovers matter

Labor-market adjustment does not stop at district borders. Changes in unemployment are correlated across neighboring districts ($\rho = 0.135$), so a growth shock in one place reaches the next.

Separating local responses from spillovers is what makes this visible. In Group 1, growth is associated with lower unemployment at home (direct association $-0.112$) and with lower unemployment next door (indirect spillover $-0.077$). A model that ignores spillovers would miss roughly the entire neighboring footprint of regional momentum.

Key takeaways

Aggregate estimates mislead. A single national Okun coefficient averages over regimes whose growth–unemployment associations point in opposite directions.
Diversified hubs are the engine. In metropolitan and smallholder-plantation districts (Group 1), output gains translate most reliably into job creation, at home and nearby.
Structural change reshapes absorption. Moving from family farming to mechanized corporate agriculture lowers how many workers the local economy absorbs per unit of output.
Informality hides slack. In peripheral regions (Group 4), open-unemployment figures understate distress because people fall back on underemployment and informal work.

Open questions

If capital-intensive growth (Group 2) keeps pushing displaced farm workers into open unemployment, how can local governments build training pipelines that move them into modern service jobs?
Given how much of the total association runs through spillovers in labor-absorbing zones, should planning shift from isolated district targets toward coordinated multi-district economic corridors?

AI Podcast: Okun's Law and Spatial Regimes in Indonesia

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Minimum wage differentials and commuting across districts

Mon, 16 Feb 2026 00:00:00 +0000

🌍 Motivation 1: Decentralization and Wage Gaps

District-level minimum wages after 2001
Large cross-border wage differentials
High daily mobility within Jabodetabek

Following Indonesia’s fiscal decentralization, districts in Jabodetabek set distinct minimum wages. The 2015 distribution shows substantial variation across adjacent districts. Because commuting costs are lower than relocation costs, workers may respond to wage gaps by crossing district borders rather than migrating permanently.

🚆 Motivation 2: Why Commuting Matters

3.5–4.4 million daily commuters
21% of formal workers commute
Employment data reflect residence, not workplace

During 2011–2015, commuting was widespread and predominantly in-person. Since labor surveys record residential location, ignoring cross-district mobility may bias estimates of minimum-wage effects. Policy evaluation must consider spillovers across administrative borders.

📊 Data and Methods

Sakernas pooled cross-sections (2011–2015)
Binomial logit model of commuting probability
Home vs. cross-border minimum wages
District and year fixed effects

The analysis exploits district-pair comparisons within a single metropolitan labor market. The key variables are real minimum wages at home and neighboring districts. The specification controls for demographics, macroeconomic factors, and spatial spillovers (SLX model).

🎓 Education Heterogeneity

+0.9 ppts commuting per 100k IDR increase (average)
College graduates: +1.7 ppts
< High school: +0.3 ppts

Cross-border minimum wages significantly raise commuting probabilities. Responses are strongest among higher-educated workers, suggesting they benefit more from wage differentials and face lower mobility frictions.

💰 Income Heterogeneity

High-income workers most responsive (+1.9 ppts)
Minimum-wage earners moderately responsive
Below-minimum earners: no significant effect

Commuting responses concentrate among workers earning above the minimum wage. Non-compliance weakens incentives for low-wage earners, highlighting institutional constraints in developing-country labor markets.

🌐 Spatial Spillovers

Neighboring wages affect commuting
Evidence robust to SLX model
Spillovers vary across worker groups

Spatial lag models confirm that district policies generate measurable cross-border effects. While magnitude varies with specification, the qualitative conclusion remains consistent: wage-setting fragmentation transmits local shocks across the metropolitan system.

⚖️ Policy Implications

Decentralized wage-setting creates internal borders
Spillovers may distort employment statistics
Coordination across districts is essential

Minimum wage differentials shape mobility patterns in integrated urban regions. Policymakers should consider harmonized frameworks or bounded dispersion rules to balance local autonomy with labor-market integration.

🔎 Key Takeaways

Cross-border wage gaps increase commuting
Effects heterogeneous by education and income
Ignoring mobility biases policy evaluation
Regional coordination improves welfare design

The evidence indicates that commuting is a key adjustment margin in decentralized labor markets. Incorporating worker mobility is essential for credible minimum-wage evaluation in metropolitan regions such as Jabodetabek.

Harmonized luminosity and economic activity across provinces in China: Cross-sectional differences, regional time series, and inequality dynamics

Sat, 20 Dec 2025 00:00:00 +0000

AI Podcast (made with NotebookLM)

cmg777 · Harmonized luminosity and economic activity across provinces in China: cross-sectional differences, regional time series, and inequality dynamics

Mapping the dimensions of poverty through big data, socioeconomic surveys and machine learning in Cambodia

Mon, 06 Oct 2025 00:00:00 +0000

🌏 Introduction

Rapid economic growth, yet persistent poverty (17.8% below national line in 2019)
Traditional poverty data: outdated, costly, and coarse
Poverty: not only income but health, education, and living standards (MPI framework)

Notes: Cambodia has seen strong growth but poverty remains. The study applies a multidimensional approach aligned with the Global MPI to capture deprivations beyond income, focusing on education, health, and living standards.

📊 Research Objectives

Use big earth data + CSES survey + machine learning
Map 10 poverty indicators across 3 MPI dimensions
Generate high-resolution poverty maps
Support targeted, cost-effective policy interventions

Notes: The aim is to integrate spatial and survey data using AI/ML to produce detailed poverty maps. This helps policymakers allocate resources efficiently and identify local vulnerabilities.

📚 Literature & Motivation

Household surveys = costly, infrequent, spatially coarse
Nighttime lights & satellite imagery → proxies for poverty
Machine Learning (RF, XGBoost, CNNs) improve predictions
Gap: Few studies integrate survey + EO data for multidimensional poverty

Notes: Prior research shows satellites and ML can help predict poverty, but integration with socioeconomic surveys for multidimensional poverty is limited. This study fills that gap.

🗂️ Data Sources

CSES survey (10k households) – health, education, housing, income
Satellite & EO data – nightlights, land cover, population density
Infrastructure data – roads, schools, hospitals, utilities
Building footprints – 3.8M residential/commercial buildings

Notes: A wide set of data was used: CSES for household info, EO data for environment and infrastructure, and building footprints to scale down predictions to household level.

⚙️ Methodology

Random Forest model for classification
Predicts deprivation probability for each indicator
Training & validation split (90/10)
Outputs: household & regional deprivation maps

Notes: The Random Forest algorithm was selected due to robustness and ability to process mixed data types. Models produce probability maps that can be aggregated at township, district, or province level.

📑 MPI Indicators

Health (2): Food consumption, access to healthcare

Education (2): Attainment, school attendance

Living Standards (6): Cooking fuel, sanitation, water, electricity, housing, assets

Notes: Ten indicators were chosen following the Global MPI. Equal weights applied across three main dimensions. These indicators reflect SDG priorities like education, health, clean water, and energy.

📈 Results – Variable Importance

Nighttime lights = key predictor across indicators
Population density & road networks also significant
Strongest predictions: cooking fuel, clean water, sanitation, electricity
Weak predictions: school attendance, healthcare, assets

Notes: Nightlights and population density best explain deprivation. Infrastructure access is also crucial. Indicators with spatial correlation (e.g., utilities) performed better than those tied to household-specific conditions.

🗺️ Results – Spatial Poverty Patterns

Urban centers: Phnom Penh, Siem Reap, Battambang → low deprivation
Remote provinces: Preah Vihear, Ratanakiri, Mondulkiri → high deprivation
Poverty lower near main roads & borders (trade effects)

Notes: Spatial maps show concentration of deprivation in remote, poorly connected regions. Urban and border areas with infrastructure show lower poverty.

💡 Discussion

Spatial ML useful but limited for indicators with weak spatial signals
Household survey data not designed for ML → location approximation issues
Need for richer survey integration (e.g., accessibility questions)
EO + ML offer granular, dynamic poverty mapping

Notes: While promising, ML struggles when data lack spatial correlation. Improved survey design can enhance integration. This hybrid approach shows potential for real-time, fine-grained poverty monitoring.

✅ Conclusion

10 MPI indicators mapped using EO + survey + ML
Best results for infrastructure-related deprivations
Enables household-level poverty estimates
Supports SDGs: No Poverty, Quality Education, Health, Clean Water, Energy
Future research: spatial autocorrelation, inequality decomposition, advanced AI

Notes: This work shows how AI and EO data complement traditional surveys to map multidimensional poverty. Future directions include advanced spatial analysis and deep learning models for better accuracy.

Bayesian average of classical estimates for panel data: Can the puzzle of the shape of the regional Kuznets curve be solved?

Thu, 05 Jun 2025 00:00:00 +0000

🗺️ Motivation

Regional inequality shapes social cohesion, migration & political stability
Kuznets (1955): inverted-U link between development & inequality
Recent evidence hints at more complex (N-shaped) patterns
Need robust econometric tools to settle the “shape” debate

📚 Literature Snapshot

Inverted-U support: List & Gallet (1999); Thornton (2001)
Mixed / non-U evidence: Tam (2008); Huang (2012)
N-shape claim: Lessmann (2014); Lessmann & Seidel (2017)
Gap: model uncertainty rarely addressed explicitly

🎯 Research Goals

Extend Bayesian Averaging of Classical Estimates (BACE) to panel fixed-effects
Test the robustness of Kuznets curve shape under model uncertainty
Identify determinants that consistently drive regional inequality

🛠️ Methodology Highlights

Search space: 14 candidate regressors → 2¹⁴ = 16 384 models, each estimated with two-way (country + period) fixed effects.
Robustness sweep: Allowing four fixed-effects options (none, time, country, two-way) expands the universe to 65 536 models; posterior model probabilities (PMPs) concentrate entirely on the two-way specification.
Bayesian Averaging of Classical Estimates (BACE):
- Retains simple FE-OLS for every model—no heavy MCMC.
- Translates each model’s BIC into an approximate marginal likelihood.
- Uses a uniform prior so PMPs sum to 1, then forms probability-weighted averages for all coefficients, predictions, and derivatives.
Variable screening: Posterior Inclusion Probability (PIP) highlights robust determinants—“substantial evidence” at PIP ≥ 0.75, “strong” at PIP ≥ 0.90.
Curve peaks: Inequality turning points come from the BACE-weighted derivative of the cubic GDP polynomial, with analytic standard errors for credible bands.
Validation: Monte-Carlo experiments with a known data-generating process show BACE pinpoints the correct fixed-effects structure and true drivers, underscoring the method’s reliability.

📈 Data Overview

180 countries, five 5-year windows (1990-2013)
Dependent variable: population-weighted Gini from satellite night-lights
Key covariates (14): GDP pc (linear–quintic), resource rents, arable land, ethnic Gini, trade, FDI, etc.

🧪 Simulation Check

Simulated panel with known DGP
BACE recovered:
- Correct two-way FE spec (PMP ≈ 100 %)
- True drivers (GDP pc, rents, land, ethnic Gini)

🔍 Determinant Robustness (Real Data)

High PIP (> 0.75)

Natural-resource rents ↑ inequality
Arable land share ↓ inequality
Ethnic Gini ↑ inequality Kuznets terms
GDP pc (linear & quadratic) robust
Cubic term not robust (PIP ≈ 0.48)

📐 Shape of the Curve

Inequality rises: USD 189 → 2 189
Stabilises: USD 2 189 → 3 935
Falls: USD 3 935 → 71 682
Stabilises again beyond USD 71 682

Evidence favours an inverted-U with plateau in rich economies, not a full N-shape.

🧭 Policy Takeaways

Redistribute natural-resource rents across regions
Invest in agricultural productivity & equitable land access
Target ethnic inclusion to curb spatial disparities
Growth alone won’t close gaps after the peak—active regional policy required

🏁 Conclusion

Panel-BACE offers transparent, probabilistic insight into inequality drivers
Robust inverted-U confirmed; inequality stabilises, not rebounds, at high incomes
Future work: interact technology diffusion & institutions in the Kuznets framework

On the political and socioeconomic geography of violence: Spatial heterogeneity and scale effects in Brazil

Sat, 22 Mar 2025 00:00:00 +0000

🤖 AI Podcast Summary

QuaRCS-lab · On the political and socioeconomic geography of violence: Spatial heterogeneity and scale effects in Brazil

💻 Replication Notebooks

All analyses are fully reproducible in cloud-based Jupyter notebooks via Google Colab:

📊 1. Descriptive Statistics

📈 2. Ordinary Least Squares (OLS)

🗺️ 3. Geographically Weighted Regression (GWR)

📐 4. Multiscale GWR (MGWR)

🔄 5. Comparing GWR vs MGWR Coefficients

🌍 Introduction

Territorial violence in Brazil exhibits strong spatial patterns.
Builds on Ingram & Marchesini da Costa (2019) using MGWR.
Goal: Explore spatial heterogeneity & scale effects on lethal violence.

🧪 Methodological Innovations

Used cloud-based computational notebooks for full replication and open science.
Adopted Multiscale Geographically Weighted Regression (MGWR).
Identified persistent geographical violence clusters.
Applied a multiple testing correction to ensure robustness.

📊 Data Overview

Unit of analysis: 5,562 municipalities (2007–2012)
Dependent variable: Change in homicide rate (Δ 2011–2012 vs. 2007–2008)

Political Variables

Margin of victory (%)
Party alignment with state governor
Vote abstention (%)
Mayor’s party identification:
- Brazilian Democratic Movement Party (PMDB)
- Brazilian Social Democracy Party (PSDB)
- Workers’ Party (PT)

Socioeconomic Variables

Population density
Young male population (%)
Gini index (income inequality)
Human Development Index (HDI)
Households headed by single mothers (%)
Adult employment rate (%)
Bolsa Família eligibility (%)

🧭 Modeling Frameworks

OLS: Global effect estimates.
GWR: Localized effect estimates with a single spatial scale.
MGWR: Localized effect estimates　with multiple spatial scales.

🔍 OLS Results

PMDB mayors → Increased violence.
PT & PSDB → No consistent effect.
Vote abstention → Strongly linked to higher homicide rates.
Unexpected: GINI index showed negative correlation.

🗺️ GWR Results: Political Variables

PMDB: Positive correlation in the northeast.
PT: Violence-reducing in many regions.
PSDB: Mixed effects—north (↑), south (↓).
Abstention: Violence-increasing in several regions.

🧮 GWR Results: Socioeconomic Variables

Population density & Bolsa Família → Heterogeneous effects.
Young male % & single mothers → Generally increased violence.
Effect direction & significance vary spatially.

🗺️ MGWR Results: Political Variables

PMDB still ↑ violence in northeast.
PSDB now only ↓ violence in southern Brazil.
PT effect mostly disappears after statistical corrections.
Abstention: Consistent with GWR in some regions.

🌆 MGWR Results: Socioeconomic Variables

Bolsa Família: No significant impact.
Young male %: Significant across more areas due to large spatial scale.
Population density & single mothers: Small-scale, heterogeneous influence.

📌 Mapping Residual Clusters

Intercept mapping (MGWR): Reveals unexplained clusters.
Central Brazil: Positive residuals → unobserved structural factors?
South & Central-East: Negative residuals.

🧩 Conclusion

MGWR provides nuanced spatial insights.
Confirms heterogeneity in violence determinants.
Suggests need for regional, tailored policy interventions.
Further investigation needed into residual violence clusters.

Predicting subnational GDP in Vietnam with remote sensing data: A machine learning approach

Thu, 20 Mar 2025 00:00:00 +0000

🤖 AI Podcast Summary

QuaRCS-lab · Vietnam Subnational GDP Prediction Using Remote Sensing and Machine Learning

🛰️ Introduction & Context

Challenge: Limited subnational GDP data in Vietnam before 2010
Need: Long-term data for economic development analysis
Solution: Predict GDP using remote sensing & machine learning

🌌 Data Sources Used

Official GDP data (2010-2020)
Nighttime Lights (NTL): Harmonized DMSP & VIIRS-like datasets
Agricultural land data (ESA)
Climate data: Temperature & precipitation (CRU)

🧠 Machine Learning Approach

Six algorithms compared:
- Artificial Neural Networks (ANN)
- Random Forest (RF)
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Ridge Regression
- eXtreme Gradient Boosting (XGBoost)

🔦 Key Findings

Predictions consistent across different nighttime datasets
Ridge Regression chosen for final model
Important features: Temperature & Agricultural Land more influential than NTL

🌍 Application & Significance

Created GDP data from 1992-2009
Enables detailed long-term analysis of regional economic trends
Assists policymakers and researchers in addressing regional inequality and growth

⚠️ Limitations

Remote sensing measurement/calibration discrepancies
Dependence on official GDP benchmarks
Interpretability challenges of machine learning methods

🚀 Future Research Directions

Explore additional remote sensing datasets
Estimate broader socioeconomic indicators
Improve models with larger datasets

🎯 Conclusion

Machine learning + Remote sensing effectively address subnational data gaps
New dataset supports informed economic policy decisions
Potentially replicable method for other developing countries

Exploring Economic Activity from Outer Space: A Python Notebook for Processing and Analyzing Satellite Nighttime Lights

Wed, 17 Apr 2024 00:00:00 +0000

Access the computational notebook HERE.

AI Video (made with Invideo AI)

Slides (made with Canva PRO)

Slides by Carlos Mendez

AI Podcast (made with NotebookLM)

cmg777 · Mendez 2024 Exploring economic activity from outer space

Can higher-quality nighttime lights predict sectoral GDP across subnational regions? Urban and rural luminosity across provinces in Türkiye

Sun, 07 Apr 2024 00:00:00 +0000

Regional unemployment dynamics in Indonesia: Serial persistence, spatial dependence, and common factors

Fri, 24 Nov 2023 00:00:00 +0000

Regional Okun’s law and endogeneity: evidence from the Indonesian districts

Thu, 12 Oct 2023 00:00:00 +0000

Convergence clubs and spatial structural change in the European Union

Wed, 02 Aug 2023 00:00:00 +0000

Interactive figures

Spatial distribution of the convergence clubs.

Measuring and understanding regional inequality through the lens of the Indonesian experience

Tue, 02 May 2023 00:00:00 +0000

Regional income convergence and conditioning factors in Turkey: Revisiting the role of spatial dependence and neighbor effects

Mon, 08 Aug 2022 00:00:00 +0000

Schooling ain’t learning in Europe: A club convergence perspective

Thu, 16 Jun 2022 00:00:00 +0000

Interactive figures

Dynamics of schooling in EU countries.

Dynamics of learning in EU countries.

Convergence clubs of learning outcomes.

Dynamics of per-capita income in EU countries.

Social and economic convergence across districts in Indonesia: A spatial econometric approach

Sat, 09 Apr 2022 00:00:00 +0000

Regional convergence and spatial dependence in Thailand: Global and local assessments

Thu, 03 Mar 2022 00:00:00 +0000

Economic and social disparities across subnational regions of South America: A spatial convergence approach

Tue, 04 Jan 2022 00:00:00 +0000

Highlights

Are initially poor regions catching up with initially rich regions?

Yes, but regional heterogeneity still matters!

How can we study the role of spatial dependence in the convergence process?

How spatially heterogenous is the convergence process?

Summary slides

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Investment constraints and productivity cycles in Bolivia

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The World Productivity Distribution: Convergence and Divergence Patterns in the Postwar Era

Sun, 01 Nov 2015 00:00:00 +0000

Divergent and Unequal Development in Latin America: Causes and Policy Challenges

Thu, 01 Oct 2015 00:00:00 +0000

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Tue, 03 Feb 2015 00:00:00 +0000