Current population projection models face significant challenges, particularly in accounting for detailed heterogeneity and quantifying uncertainty. To address these limitations, the project proposes transitioning from traditional macro-type models to dynamic spatial microsimulations, which can better capture the complexity and diversity of population dynamics. The ultimate goal is more accurate and reliable projections, particularly for local areas, which will enhance the ability of policymakers to plan for the future. 

A key focus of the project is to deepen the understanding of the processes driving centralization and rural depopulation. By analyzing factors such as immigration, local labor markets, and the dynamics between urban and rural areas, the project seeks to better understand these important demographic trends and identify their potential future trajectories. 

In addition to deepening our knowledge of demographic shifts, the project will evaluate the effectiveness of public policy interventions aimed at influencing population distribution. This includes assessing the impact of investments in infrastructure, such as airports and road networks, and their role in promoting regional development and addressing population imbalances. 

Researchers and academics from Statistics Norway, Norwegian University of Science and Technology, University of Leeds, and Queen’s University Belfast will carry out the project in collaboration with partners representing all levels of government. These include the Ministry of Local Government and Regional Development (KDD), Centre of Competence on Rural Development, Norwegian Association of Local and Regional Authorities (KS), County Governor of Troms and Finnmark, Panda Analyse, and a scientific advisory board. 

Project leader: Stefan Leknes (SSB)

Project participants:

Michael James Thomas (SSB)

Sturla Andreas Løkken (SSB)

Zhiyang Jia (SSB)

Nico Keilman (SSB)

Ane Tømmerås (SSB)

Jørn Rattsø (NTNU)

Hildegunn Ekroll Stokke (NTNU)

Eric Wee (NTNU)

Nik Lomax (University of Leeds)

Ian Shuttleworth (Queen's University Belfast)

Statistisk sentralbyrå (SSB)

Norges teknisk-naturvitenskapelige universitet (NTNU)

University of Leeds

Queen’s University Belfast

Kommunal- og distriktsdepartementet (KDD)

Distriktssenteret – Kompetansesenter for distriktsutvikling

KS – Kommunesektorens organisasjon

Statsforvalteren i Troms og Finnmark

Panda Analyse

Funder: Norwegian Research Council (project number 352911

Periode: 2025 – 2028