Territorial inequality & rural policy

In-depth analysis

TE1 — Sub-regional development index

• Mechanism: The development index has 6 dimensions (employment, income, education, health, housing, digital access), each with 3–5 indicators, at district level. The data are fed by KSH T-STAR, NAV (tax authority) income data, NEAK (health insurance fund) healthcare data, and geospatial data from the Lechner Knowledge Centre. Weights are not determined by political decision but by factor analysis (based on correlations between dimensions). The dashboard is updated quarterly, publicly accessible, and sends an automatic alert if a district’s indicator deteriorates.
• Quantified target: By 2027, the full index is available for all 174 districts; the gap between the worst 33 districts (LHH — most disadvantaged) and the national average narrows by at least 10% by 2032 (the current ~3.5-fold GDP/capita difference drops to ~3.0).
• International precedent: United Kingdom — the “Index of Multiple Deprivation” (IMD) is the main tool for allocating local government funding and central development resources. Since the IMD was introduced (2004), targeting of funds has improved, though overall territorial inequality has not decreased — the lesson is that the index is a necessary but not sufficient condition.
• Trade-off / risk: District-level aggregation masks settlement-level differences — within an “average” district there can be both extremely poor and relatively developed settlements. However, settlement-level data collection is statistically unreliable in small-population settlements (below 500 inhabitants). The solution: alongside the district index, “hot-spot” maps of extreme-poverty micro-regions.

TE2 — Data-driven allocation of EU cohesion funds

• Mechanism: Public algorithmic oversight of the allocation of EU cohesion funds (ERDF, ESF+, Cohesion Fund). All project and tender data are loaded into a public database (beneficiary, location, amount, target indicator, actual outcome). AI analysis automatically flags when: (1) funds do not flow to the districts identified as most in need by the development index; (2) beneficiaries are concentrated (e.g. a few companies receive most of the funds); (3) target indicators are not met. The analysis is “comply or explain”: the decision-maker may deviate, but must justify it.
• Quantified target: The share of cohesion funds flowing to LHH districts reaches 25% by 2030 (current estimate: ~12–15%); public database coverage is 100%; the proportion of “comply or explain” deviations is <10%.
• International precedent: Poland — considered the EU country with the best absorption rate of cohesion funds (~98%), with territorial allocation aligned with the development level of its 16 voivodeships. The key to the Polish model: decision-making power for regional operational programmes is delegated to the voivodeships, not centralised.
• Trade-off / risk: Algorithmic oversight reduces political discretion, which triggers resistance. In addition, “focusing on the most disadvantaged districts” can cause an absorption problem: it is precisely in these areas that project-writing and project-management capacity is weakest — directing funds there does not automatically mean they will be used.

TE3 — Segregated-area map and catching-up programme

• Mechanism: The segregated-area cadastre covers three dimensions: (1) spatial segregation (calculated from KSH census data using a segregation index — dissimilarity index — within a settlement); (2) educational segregation (deviation of school composition from residential composition); (3) employment segregation (labour-market exclusion). The catching-up programme runs in 3-year cycles with control-group impact evaluation (RCT or quasi-experimental diff-in-diff): it measures the effect of the intervention, not only the output.
• Quantified target: A full segregated-area cadastre by 2028 (current: partial, ~1,600 known sites); a 15% reduction in the number of segregated sites by 2032 (via integrated housing + education); a 20% drop in the educational segregation index in target areas.
• International precedent: USA — the HOPE VI programme (1993–2010) demolished the worst public-housing estates and built mixed-income neighbourhoods in their place. Results are mixed: the physical environment improved, but some former residents simply moved into other segregated areas (relocation effect). The lesson: desegregation requires simultaneous intervention not only in housing, but in education and employment too.
• Trade-off / risk: Publishing the segregated-area map can have a stigmatising effect: property values in the affected settlements may drop, and capital may flee. The solution: the map is not a “shame list” but a tool for targeted intervention — public access to the data should only be granted in the context of development-purpose use.

TE4 — Rural public-service minimum

• Mechanism: Legally established minimum standards by settlement category: (1) healthcare: GP reachable within 20 minutes, basic emergency care within 45 minutes; (2) education: primary school reachable within 30 minutes; (3) transport: at least 6 public-transport connections per day to the nearest district seat; (4) digital: at least 30 Mbps internet in every settlement. Where the indicator falls below the minimum, an automatic compensation mechanism is triggered: supplementary central funding + service-provider incentives (e.g. rural allowance for GPs).
• Quantified target: By 2030, 95% of settlements meet the public-service minimum across all 4 dimensions (current estimate: ~60–70%); the number of settlements without a GP drops to 0 (currently: ~250).
• International precedent: Finland — the “kuntien peruspalvelut” (municipal basic services) guarantee system establishes statutory minimums for healthcare, education and social services. Non-compliant municipalities are called to order by the regional administrative office, and if necessary the state takes over the service. The system is expensive, but Finland has one of the lowest levels of territorial inequality in the EU.
• Trade-off / risk: Maintaining the public-service minimum in small-village regions is extraordinarily expensive per unit — a settlement of 200 people cannot sustain an independent clinic and school. The solution is “networked provision” (shared-notary model, travelling specialists, school buses), but this can reduce service quality. The trade-off between equity and efficiency requires an explicit political decision.

TE5 — Local economic development and mobility support

• Mechanism: A district-level decision matrix: if the development index (TE1) shows that local economic potential exists in a district (agriculture, tourism, manufacturing — potential is measured by business demography, natural endowments and accessibility), then local economic development: a micro-enterprise loan programme (max. HUF 10M, no collateral, 0% interest for the first 2 years), cooperative-founding support, a local-product trademark. If economic potential in the district is low (no marketable local resources, persistent population decline), then mobility support: housing support in the nearby county seat, commuting-cost contributions, targeted retraining.
• Quantified target: In LHH districts, business density rises 20% by 2032; the number of recipients of inter-district commuting support reaches 10,000 persons/year; the survival rate of micro-enterprises participating in the local economic development programme is >70% after 3 years.
• International precedent: Ireland — the “Rural Regeneration and Development Fund” (2018–) focuses specifically on developing rural towns into economic hub settlements, rather than keeping scattered villages individually alive. The “hub-and-spoke” model accepts that not every settlement is independently viable, but guarantees access to services from the hubs.
• Trade-off / risk: The “not every settlement is viable” message is politically extremely sensitive — mobility support can easily be interpreted as “forced relocation”. The key to communication: the goal is not to eliminate the settlement, but to expand the opportunities of the people living there. Moreover, the success of local economic development programmes depends heavily on local capacity (project writing, management), which is weakest precisely in the most disadvantaged districts.