[Paper Review] Measuring growth and convergence at the mesoscale

Isaak Mengesha, Debraj Roy|arXiv (Cornell University)|Jan 17, 2026

Economic and Technological Innovation0 citations

TL;DR

This paper analyzes growth and convergence at the scale of Functional Urban Areas (FUAs) using high-resolution GDP data and the Economic Complexity Index to reveal capability-driven, regime-based growth patterns that national aggregates obscure.

ABSTRACT

Global inequality has shifted inward, with rising dispersion increasingly occurring within countries rather than between them. Using 8,790 newly harmonised Functional Urban Areas (FUAs), micro-founded labour-market regions encompassing 3.9 billion people and representing approximately 80% of global GDP, we show that national aggregates systematically, and increasingly, misrepresent the dynamics of growth, convergence, and structural change. Drawing on high-resolution global GDP data and country-level capability measures, we find that the middle-income trampoline that previously drove global convergence is flattening. This divergence in the lower-income regime does not reflect poverty traps: low-income FUAs exhibit positive expected growth, and the transition curve displays no stable low-income equilibrium. Instead, productive capabilities, proxied by the Economic Complexity Index, define distinct growth regimes. FUAs converge within capability strata but diverge across them, and capability upgrading follows a predictable J-curve marked by short-run disruption and medium-run acceleration. These findings suggest that national convergence policies may be systematically misaligned with the geographic scale at which capability accumulation operates.

Motivation & Objective

• Motivate measuring growth and convergence at the mesoscale rather than at administrative or national scales.
• Use FUAs to capture integrated labour markets and endogenous capability accumulation.
• Link GDP dynamics to country-level Economic Complexity Index to study capability-driven growth regimes.
• Test whether poverty traps exist at the urban scale and how aggregation affects convergence signals.

Proposed method

• Construct a panel of 8,790 FUAs using 1 km GDP data harmonised from nighttime lights (1992–2019).
• Assign country-level Economic Complexity Index (ECI) to each FUA to proxy productive capabilities.
• Decompose inequality and convergence across spatial scales (ADM0, ADM1–ADM3, FUAs) to assess MAUP effects.
• Estimate beta-convergence and analyze nonlinear growth regimes and regime switching in growth laws.
• Examine capability-dependent growth by relating FUA growth to contemporaneous country ECI and test for nonlinear ECI effects.
• Use event-study designs to trace growth responses to discrete ECI upgrades and identify J-curve dynamics.

Figure 1 : We utilize FUAs as aggregating unit over various spatial fields. Those include, GDP, Population, HDI, RWI and several national level statistics such as TFP or ECI if no higher resolution was available. With this we construct the timeseries of observation for each FUA.

Experimental results

Research questions

• RQ1 Do FUAs reveal different growth and convergence dynamics compared to administrative or national aggregates?
• RQ2 How do productive capabilities, as measured by ECI, shape urban growth regimes and convergence patterns?
• RQ3 Is there evidence of poverty traps at the urban scale when using FUAs as units of analysis?
• RQ4 How does aggregation affect the estimated relationship between growth and initial income (Solow-based convergence)?
• RQ5 What are the short- and medium-run growth dynamics following capability upgrades in FUAs?

Key findings

• Within-FUA inequality dominates at finer scales, but FUAs preserve a stronger between-country variance share, indicating aggregation matters for inequality patterns.
• Convergence is stronger at the FUA scale than at administrative scales or country-level benchmarks, illustrating MAUP in growth analysis.
• There is no evidence of local poverty traps at the urban scale; low-income FUAs exhibit positive expected growth across the distribution.
• Capability-conditioned convergence is nonlinear; intermediate ECI levels show a sharp rise in convergence speed, while very high ECI levels show weaker convergence.
• Capability upgrades trigger a J-curve in FUA growth: short-run disruption followed by medium-run and longer-run gains; this pattern is not observed at ADM1 scales.
• Spatial aggregation reverses ECI dynamics: country-level ECI shows conditional convergence, while ADM1 regions show divergence in ECI.

Figure 2 : Spatial inequality structure: functional vs. administrative geography. Left column decomposes GDP per capita variance into between-country (purple) and within-country (red) components (1992–2019). Right column plots within-unit RWI variance share against GDP per capita for 20 countries wi