📈 economics

Breaking the Center-Periphery Trap? Return Migration Entrepreneurship and Intra-County Spatial Restructuring in China

This paper utilizes a quasi-natural experiment in China to demonstrate that the National Return Migration Entrepreneurship Pilot Program breaks the core-periphery trap by simultaneously boosting aggregate economic growth and reducing intra-county spatial inequality through mechanisms like urban-rural income convergence and digital infrastructure amplification.

Original authors: BO XU, Yingde Hu, Kunpeng Xu, Gang Zou

Published 2026-07-22

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CC BY 4.0

Original authors: BO XU, Yingde Hu, Kunpeng Xu, Gang Zou

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the economy of a country as a giant, bustling city. For a long time, scientists studying how cities grow have believed in a rule called the "Core-Periphery Model." Think of it like a magnet. The "Core" is the city center, where the lights are brightest, the jobs are best, and the money flows fastest. The "Periphery" is the quiet suburbs or the countryside, where things are dimmer and slower. The theory says that once the city center gets a little bit of an advantage, it becomes a self-reinforcing loop: more people move there for jobs, which creates more jobs, which attracts even more people. It's a "trap" where the rich center gets richer, and the poor outskirts get left behind, no matter how hard they try. The big question for scientists and policymakers has always been: Is this trap unbreakable? Can we ever have a growing economy that also shares the wealth fairly across the whole map, or do we have to choose between being rich overall or being fair?

This paper dives into that exact puzzle, but instead of looking at whole countries, it zooms in on the "counties"—the smaller building blocks that make up a nation. The researchers wanted to see if a specific type of magic trick could stop the magnet from pulling everything into the center. They looked at a program in China called the "Return Migration Entrepreneurship Pilot Program." Imagine thousands of people who had left their hometowns to work in big cities, suddenly packing up their savings, skills, and new ideas to go back home and start businesses. The big question was: Would these returnees just open shops in the busy county towns (the mini-cores), making the gap between town and country even wider? Or would they spread out, lighting up the rural villages and breaking the trap?

The researchers, using a clever new tool they invented called the "Inclusive Light Index," tracked the glow of economic activity across 2,367 counties over nearly two decades. They found that the program did something surprising: it broke the rules of the old magnet theory. The policy didn't just make the whole county richer; it made the wealth spread out more evenly. It was a "dual dividend." The average economic glow went up by 0.336 units, but at the same time, the inequality (how unevenly the light was spread) went down. The study suggests that when people with strong ties to their hometowns return to start businesses, they don't just follow the crowd to the center; they create new pockets of prosperity in the villages.

How did this happen? The paper traces a chain reaction. First, the returnees brought money and jobs to the rural areas, which narrowed the income gap between the city and the country. Second, this extra cash woke up local markets in small towns, creating demand for shops, restaurants, and services that had been sleeping. Third, it changed the types of jobs available, moving people from simple farming to higher-skilled work. But there was a secret weapon: digital infrastructure. The study found that in counties with better internet and broadband, the equalizing effect was 7.5% stronger for every step up in connectivity. The internet acted like a "spatial equalizer," letting rural businesses compete with city ones. However, this magic wasn't the same everywhere. It worked best in the middle regions of the country, moderately in the west, and barely at all in the east or northeast, suggesting that the "trap" can only be broken if the local conditions are just right. Ultimately, the paper suggests that the core-periphery trap isn't an inevitable law of nature, but a condition that can be reversed with the right mix of human talent and digital tools.

Technical Summary: Breaking the Center-Periphery Trap? Return Migration Entrepreneurship and Intra-County Spatial Restructuring in China

Problem Statement
The paper addresses a central debate in regional science: whether policy interventions can break the "core-periphery trap" predicted by Krugman's (1991) New Economic Geography (NEG) model. The NEG model posits that agglomeration economies create a self-reinforcing cycle where economic activity concentrates in core regions, locking peripheral areas into relative backwardness. While this tradeoff between aggregate growth and spatial equity is well-documented at national and inter-regional scales, the paper identifies a theoretical gap at the intra-county level. In developing countries like China, counties often exhibit sharp hierarchical structures where county seats act as dominant cores and surrounding townships/villages remain peripheral, despite low internal transport costs. The authors argue that traditional NEG drivers (transport costs) cannot fully explain this micro-scale inequality, suggesting that the spatial distribution of human capital and social capital plays a decisive role. The study investigates whether Return Migration Entrepreneurship (RME)—specifically the return of human capital with strong "place attachment"—can disrupt this agglomeration cycle and trigger a countervailing dispersion cycle that fosters inclusive spatial development.

Methodology
The study employs a quasi-natural experiment design leveraging China's National Return Migration Entrepreneurship Pilot Program (RMEPP), launched in 2016 and expanded in 2017, which supported over 13 million returnee entrepreneurs.

Data: The analysis utilizes a balanced panel dataset of 2,367 Chinese counties spanning 2005–2023.
Dependent Variable (Inclusive Light Index - ILI): To overcome the limitations of aggregate GDP statistics which mask internal disparities, the authors construct a novel metric, the Inclusive Light Index (ILI). This index integrates two dimensions derived from high-resolution (1km) DMSP-OLS nighttime light data:
1. Average Nighttime Light Intensity: Measuring aggregate economic density.
2. Spatial Equity: Measured as $(1 - \text{Gini coefficient})$ of light distribution across grid cells within a county.
  The ILI is defined as: $ILI_{it} = \overline{light}_{it} \times (1 - Gini_{it})$ .
Empirical Strategy: The authors use a staggered Difference-in-Differences (DID) design. The treatment group consists of 262 pilot counties (154 in 2016, 108 in 2017), while non-pilot counties serve as controls. The model includes county and year fixed effects, with robust standard errors clustered at the county level.
Robustness and Validation: The study employs extensive robustness checks, including:
- Alternative dependent variables (real GDP).
- Exclusion of special administrative regions.
- Control for competing policies (e.g., Digital Village Pilot).
- Double Machine Learning (DML) using Random Forest and Lasso algorithms to address model misspecification.
- Placebo tests involving random treatment assignment and random policy timing (500 repetitions).
- Propensity Score Matching (PSM-DID).
Mechanism and Moderation Analysis: The study tests three sequential mechanisms (Urban-Rural Income Gap, Township Consumption Level, Rural Employment Structure) and a moderating variable (Digital Infrastructure, proxied by broadband penetration).

Key Results

Dual Dividend (Hypothesis H1): The RMEPP significantly increases the Inclusive Light Index by 0.336 units on average. Decomposition reveals a "dual dividend": the policy simultaneously boosts aggregate economic output (average light intensity increases by 0.218) and reduces intra-county spatial inequality (light Gini coefficient decreases by 0.029). This challenges the conventional growth-inequality tradeoff.
Mechanisms (Hypotheses H2a–H2c): The spatial equalization effect operates through a sequential dispersion cycle:
- Income Convergence: The policy significantly narrows the urban-rural income gap.
- Local Demand Activation: It activates previously underserved township consumer markets, increasing the final consumption rate.
- Employment Upgrading: It transforms rural employment structures, shifting labor from low-value agriculture to higher-value non-agricultural sectors (specialty agriculture, tourism, e-commerce).
Digital Infrastructure as an Equalizer (Hypothesis H3): Digital infrastructure acts as a critical spatial enabler. The interaction term between RMEPP and digitalization is positive and significant. For every standard deviation increase in broadband penetration, the policy's inequality-reducing effect is amplified by 7.5%. Digitalization alone has no significant direct effect on spatial structure but is essential for amplifying the policy's impact.
Regional Heterogeneity (Hypothesis H4): The policy effects exhibit a clear regional gradient:
- Central China: Strongest effect (coefficient 0.555), where counties have moderate disparities and latent potential.
- Western China: Moderate but significant effect (coefficient 0.229), limited by infrastructure and market constraints.
- Eastern and Northeastern China: Negligible or insignificant effects. In the East, spatial structures are already integrated; in the Northeast, population hollowing-out prevents the dispersion cycle from taking hold.

Significance and Claims
The paper claims three primary contributions to regional science and the core-periphery literature:

Theoretical Challenge to Determinism: It provides causal evidence that the core-periphery trap is not an inevitable outcome of economic growth but a conditional one. When growth is driven by agents with strong place attachment (returnee entrepreneurs) rather than purely footloose capital, the balance between agglomeration and dispersion forces shifts, allowing for inclusive spatial development.
Identification of Micro-Mechanisms: The study elucidates the specific transmission channels (income convergence, demand activation, employment upgrading) through which place-based human capital policies reshape intra-county spatial structures, offering a causal explanation for the diffusion of economic activity from cores to peripheries.
Reframing Digital Infrastructure: The paper clarifies the conditional nature of digitalization. Contrary to narratives of a "digital divide" (where tech widens gaps) or "digital utopia" (where tech automatically solves them), the authors find that digital infrastructure acts as a spatial equalizer only when combined with targeted human capital policies. It is a complement, not a substitute, for entrepreneurship support.

The study concludes that targeted return migration policies can break the core-periphery trap at the county level, offering generalizable insights for designing inclusive regional development strategies in developing countries, provided that initial spatial structures and digital capacities are taken into account.

Technical Summary: Breaking the Center-Periphery Trap? Return Migration Entrepreneurship and Intra-County Spatial Restructuring in China

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