The Demography-Economy-Land use interaction (DELi) model : a modular and integrated approach to regionalise socioeconomic projections.
Date: 2024-01-10
Institutions: Joint Research Centre
Subjects: EU statistics, demographic analysis, economic model, land use, population statistics, regional development, research report

ISSN 1831-9424 

JRC TECHNICAL REPORT 

The Demography-Economy-Land use 
interaction (DELi) model 

A modular and integrated approach to regionalise  
socioeconomic projections 

Curtale, R., Schiavone, M., Deuster, C., 
Alessandrini, A., Ueffing, P., Natale, F., Dijkstra, L., 
Batista E Silva, F. 

2024 

 EUR 31796 EN 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and 
knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The contents 
of this publication do not necessarily reflect the position or opinion of the European Commission. Neither the European 
Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this 
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and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of the 
European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the 
delimitation of its frontiers or boundaries. 
 Contact information 
Name: Filipe Batista e Silva 
Address: Via E. Fermi 2749, I - 21027 Ispra - TP 263 
Email: filipe.batista@ec.europa.eu  
Tel.: +39 0332 78 6470 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC136498 

EUR 31796 EN 

PDF  

ISBN 978-92-68-11115-4  

ISSN 1831-9424   doi:10.2760/025883  

KJ-NA-31-796-EN-N 

Luxembourg: Publications Office of the European Union, 2024 

© European Union, 2024 

The reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 
December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse of 
this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence 
(https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any 
changes are indicated. 

For any use or reproduction of photos or other material that is not owned by the European Union permission must be sought 
directly from the copyright holders. The European Union does not own the copyright in relation to the following elements: 
- Cover page illustration, © Daniel Bastos 

Cover page illustration 
The illustration in the cover page by © Daniel Bastos shows three people working to extract olive oil with a traditional European 
method. The arms represent the three main components of the DELi model: demography, economy and land use. This photo is 
also used here as an allegory and tribute to the virtues of proof of work, a necessary condition to create value. 

How to cite this report: Curtale, R., Schiavone, M., Deuster, C., Alessandrini, A., Ueffing, P., Natale, F., Dijkstra, L. and Batista E 
Silva, F., The Demography-Economy-Land use interaction (DELi) model, Publications Office of the European Union, 
Luxembourg, 2024, doi:10.2760/025883, JRC136498.  

 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents   

Executive summary ................................................................................................................................................................................................................................................... 1 

Acknowledgements.................................................................................................................................................................................................................................................... 2 

1 

Introduction ..............................................................................................................................................................................................................................................................3 

2  Methodology............................................................................................................................................................................................................................................................ 5 

2.1  The input layer ........................................................................................................................................................................................................................................6 

2.2  The core layer...........................................................................................................................................................................................................................................6 

2.2.1 

Demographic module....................................................................................................................................................................................................6 

2.2.1.1 

Exogenous population ....................................................................................................................................................................................6 

2.2.1.2 

Endogenous population ................................................................................................................................................................................6 

2.2.2 

2.2.3 

Employment module......................................................................................................................................................................................................8 

Gross Domestic Product module.......................................................................................................................................................................8 

2.2.4 

Land Use module............................................................................................................................................................................................................10 

2.2.4.1 

Residential built-up ........................................................................................................................................................................................10 

2.2.4.2 

Industry, commerce and services built-up............................................................................................................................. 11 

2.2.4.3 

Available land for development........................................................................................................................................................ 12 

2.3  The output layer ................................................................................................................................................................................................................................. 12 

3  Data sources ........................................................................................................................................................................................................................................................13 

4  Discussion and future avenues ........................................................................................................................................................................................................ 15 

References ....................................................................................................................................................................................................................................................................... 17 

List of abbreviations and definitions ...................................................................................................................................................................................................18 

List of boxes...................................................................................................................................................................................................................................................................19 

List of figures................................................................................................................................................................................................................................................................20 

List of tables.................................................................................................................................................................................................................................................................. 21 

i 

 
 
 
Executive summary 

Demographic and economic projections are valuable analytical tools for policy-making, but these are typically 
produced at  country-level aggregations.  In  this  report,  we  propose a  new  model  that  allows  to  regionalise 
socioeconomic  and  demographic  projections  from  the  country  level  to  the  NUTS3  regional  level:  the 
Demography-Economy-Land use interaction (DELi) model.  

DELi relies on a set of linked equations that integrate empirically-derived assumptions regarding future regional 
growth,  and  estimate  regional  levels  of  GDP,  employment, population and  land  use  recursively  and  in  an 
integrated manner, thus capturing demography-economy feedbacks as well as ongoing, expected or assumed 
processes of divergence and convergence across regions. The model can be configured to generate different 
regionalisation scenarios based on the same  or different input country-level projections. DELi  was built  as a 
modular,  flexible  structure  that  allows  to  expand  or  substitute  any  of  its  components  by  alternative 
methodological approaches or assumptions. 

To the best of our knowledge, a comprehensive, integrated, transparent and flexible framework to regionalise 
socioeconomic projections like DELi was still lacking for EU policy support. DELi can be used to quantify scenarios 
of regional socioeconomic trends for anticipatory policy support on domains with a strong regional or territorial 
dimension.  Concretely,  DELi  can  be  used  to  explore  scenarios  of  regional  convergence  or  divergence, 
demographic change, land use policies, and their  territorial  implications at  NUTS3  level. Examples of country 
level  projections  that  could  be  regionalised  using  DELi  are  those  regularly  produced  by  the  European 
Commission Ageing Reports or the Shared Socioeconomic Pathways (SSPs). 

DELi is still an exploratory tool, with substantial room for future improvement. As it stands, the model presents 
some limitations  such as the  lack  of sectoral  detail,  and  limited degrees of freedom to  deviate from  given 
country-level projections. However, DELi  offers already  a  valuable template to  regionalise or  even generate 
socio-economic projections and can be subject to  further  methodological improvement and expanded in its 
scope, functionality and applications. 

Keywords 

Regional projections, demography, economy, land use , NUTS3, downscaling, convergence 

1 

 
Acknowledgements  

The authors would like to thank Luc Feyen, Ignazio Mongelli, Juan Carlos Ciscar Martinez and David Garcia Leon 
from the JRC, whose comments stimulated a critical thinking of the model that finally led to the current version. 

Authors 

Riccardo Curtale,  Matteo Schiavone, Christoph  Deuster, Alfredo  Alessandrini, Philipp Ueffing, Fabrizio Natale, 
Lewis Dijkstra, Filipe Batista e Silva 

2 

 
1 

Introduction   

Demographic  and  economic projections  and  scenarios  are  valuable  analytical  tools  for  anticipatory  policy 
support.  For  example, European Services  such  as  Eurostat  and  the  Directorate -General  for  Economic and 
Financial  Affairs  produce demographic and  economic projections with  a   2-4  year  frequency, to  assess the 
economic and budgetary impacts of  an  ageing population (DG ECFIN,  2021).  However, such  projections are 
typically only developed at the country level: a too coarse geographical aggregation for policy domains with a 
strong territorial  dimension. 

Assessing future pressures on the environment, socioeconomic risks to natural or other hazards, impacts and 
challenges associated with demographic and urbanisation trends , for  example on the access to services and 
infrastructure, assessing territorial  cohesion, are all  areas requiring projections and/or  scenarios at  the  sub-
national  level.  Researchers  and  analysts  often  resolve  this  problem  by  disaggregating,  or  downscaling, 
socioeconomic projections from national to regional level, a procedure often also referred to as regionalisation. 
The most simple calculation rule for regionalising country-level socioeconomic projections is to keep observed 
regional shares constant throughout the projection period. Such an assumption, however, does not  provide a 
realistic growth trajectory  of the regions, as it simply replicates the status quo of a given observed moment in 
time  by  keeping  the  regional  variability  constant,  and  ignoring  ongoing  or  expected  processes  of 
divergence/convergence  of  regions  and  their  factors.  Another  issue  arises  when  the  regionalisation  of 
demographic and economic projections are performed separately, without considering the interaction between 
economic development, demography and migration. 

To fill the gap of sub-national socioeconomic projections and overcome the limitations of simplistic downscaling 
approaches, we propose a novel model that  regionalises long-term, country-level demographic and economic 
projections in an integrated and recursive manner, while taking also into account a parsimonious set of well-
established regional socioeconomic growth factors. The new model, coined DELi (Demography-Economy-Land 
use interaction), relies on a set of linked equations that integrate assumptions regarding future regional growth, 
and estimate regional levels of GDP, employment and population dynamically and recursively, while ensuring 
consistency with  any  given  set  of  socioeconomic projections at  the  country  level.  The  approach integrates 
functional causality between economic factors and human mobility  based on the  observed strong  empirical 
association between regional net migration rates and GDP per capita levels. In addition, DELi estimates future 
land use demand for the residential and industrial,  commercial and services sectors, also based on empirically 
derived equations.  

DELi  can be configured to  generate different regionalisation scenarios based on the  same or  different input 
country-level  projections.  The  herein  described  implementation  of  the  model  downscales  country-level 
socioeconomic projections to the NUTS3 regions in the EU27 Member States. The model was built as a modular, 
flexible structure,  which allows to  expand or substitute  any of its  components by alternative methodological 
approaches or assumptions. 

Previous work and what’s new in DELi 

DELi results from the consolidation of previous experience and attempts to obtain demographic and economic 
projections at the regional level. A first approach was proposed by Batista e Silva et al.  (2016), who introduced 
the idea of downscaling demographic and economic p rojections from country  level to  the regional level (i.e., 
NUTS3)  in an  integrated and recursive manner. In  this first  implementation, GDP  per capita and productivity 
growth  were estimated simply  via a  linear convergence function. The two  growth  differenti als would lead to 
employment growth  or  decline, leading to  population change ( adopting a “people follow  jobs” assumption). 
Finally, GDP  would be the result of the multiplication between previously estimated GDP  per capita and total 
population. When calculating employment, population and GDP, all values at NTUS3  level would be rescaled to 
match the totals at country level from the source demographic and economic projections. 

In  subsequent work, Pontarollo et al.  (2017)  improved the empirical basis for the  estimation of the  GDP  per 
capita and  productivity growth  rates, introducing a  number of relevant  factors of  regional growth, including 
spatial  spillovers,  with  empirical  estimations  based  on  data  from  the  period  2000 -2011.  However,  the 
remainder structure of the approach remained the same. 

In 2023, a completely new implementation of the regionalisation approach was written in R, as self -contained 
model, and coined DELi. Similar to the earlier regionalisation approaches, DELi is NUTS3-based, runs on yearly 
iterations, with  integrated population and economic dynamics, constrained by national level demographic and 
economic projections. The main novelties of DELi in relation to the previous implementations include: 

-  New empirical estimation for a longer time -frame (2000-2019); 

3 

-  New modelling sequence for the yearly iterations, with  population (projected first based on previous 
year GDP per capita), followed by estimation of employment, followed by estimation of GDP per capita 
and total GDP; 

- 

Addition of a  land use module, projecting future land  use demand for  the residential and industrial, 
commercial and services sectors, based on empirically derived equations . 

On-going work is being carried out to assess whether national level constraints can be relaxed and how. 

Applications 

The  DELi  model  can  be  used  for  a  variety  of  applications , and  generally  as  input  to  any  forward-looking 
assessments requiring population and economic projections at sub-national level.  

A  first  application of  DELi  was  done in  the  context  of  the  European Commission project on  Territorial  Risk 
Assessment on Climate Change in Europe (TRACE), whereby GDP from the European Commission Ageing Report 
(DG  ECFIN,  2021)  was  regionalised from country level to  NUTS3.  In this  case , though, population per  NUTS3 
was taken exogenously from the EUROPOP 2019 population projection, as per the specifications of the project.  

Future applications of DELi will  tend to use fully  endogenous population projections at NUTS3 level, to assess 
how  different regional convergence scenarios impact on  territorial  cohesion and urban -rural  population and 
economic growth.  Also, DELi  may be used to regionalise alternative GDP  and population projections, such as 
those tied to the Shared Socioeconomic Pathways (SSPs) (Riahi et al., 2017). 

Aim of the report 

The aim of this report is to provide the conceptual framework of the DELi  model, with a technical focus on its 
components. The most important aspects covered in the report are the structure of the model, the sequence of 
its components and their interlinkages. Where components of the model are based on econometric estimations, 
we provide the results of the best estimates available at the time of writing. However, empirical estimates can 
be updated as new official data are released.  The structure of the model is not affected by the methodological 
choices of  its  components, so that  the  estimations could be updated in  the  future  w ith  revised approaches 
without  invalidating  the  underlying  broad  structure.  In  parallel  with  the  development  of  the  DELi  model, 
dedicated  scientific  research  is  conducted  to  validate  and  consolidate  the  methodology  behind  it.  Where 
necessary, references to the specific articles are provided. 

The core section of this  report is  chapter 2,  with  a detailed description of the structure of the  model and its 
components. Chapter 3 lists the data sources used in the current implementation of DELi, and chapter 4 wraps-
up the report with the main takeaways, limitations and future work. 

4 

 
 
 
 
2  Methodology 

The DELi  model is designed to generate sub-national estimates of population, employment, GDP, and land use 
for  the  EU  Member  States  by  downscaling or  disaggregating a  set  of  existing  demographic and economic 
projections  from  national  to  NUTS3  level,  a  process  also  referred  to  as  regionalisation .  The  downscaling 
approach implies that the sum of the NUTS3  outputs is identical to the source of projections used as input at 
the national level. DELi’s outputs at NUTS3  level can be aggregated to higher level analysis (e.g., NUTS2, metro 
regions). Technically, DELi can be configured to relax the national constraints in different degrees, but this type 
of functionality requires further testing.  

The regionalisation of projections can be generated based on a variety  of assumptions and scenarios, which 
can  be  switched  on  or  off,  so  that  for  the  same  exogenous country  level  projections,  different  regional 
distribution of population, GDP  and land  use can be obtained. Examples of these assumptions and scenarios 
relate  to  demography (e.g.,  migration  might  depend or  not  on economic growth)  or  economy (e.g.,  regional 
economic growth might increase depending on changes in education level or quality of institutions). To generate 
scenarios, different projection periods and assumptions can be explicitly specified when launching the model.    

The DELi model is organised in 3 layers: 

 

Input layer. In the input layer, historical data at the NUTS3 level are harmonised and prepared for the 
core layer. This layer includes also the set of projections at higher levels (e.g., NUTS0) from exogenous 
source(s). 

  Core layer.  The  core layer  generates projections of  population, employment, GDP  and land  use at 
NUTS3  level. The model is dynamic in the sense that variables are interlinked, so that  what happens 
to  population affects employment, GDP  and  land  use, and vice -versa.  The  model  is  also recursive, 
because variables estimated in the previous yearly  iterations (t-1) affect the estimation of variables 
in the current yearly iteration (t).  

  Output layer. The output layer generates tables, charts and maps from the output of the core layer.   

Figure 1  represents graphically the structure of the DELi  model. The subsequent sub-sections of this chapter 
describe in more details the different layers and their components. 

Figure 1. Structure of the DELi model 

5 

 
 
 
2.1  The input layer 

The input layer is the starting point of the DELi  model. It  allows to import data from multiple sources, process 
and format them to create a unique dataset which will be passed to the core layer, where  regional projections 
will  be generated. Data  with  different granularity  are imported, including historical data at  NUTS3  level, and 
projected data at  NUTS0  and NUTS3  level.  In some cases (e.g., education level or quality  of institution), data 
are  available  at  NUTS2  level, in  these  cases, for every  NUTS3  region we  assume the  v alue  of  the  specific 
variable in the respective NUTS2 region.  

Different data sources can be used for historical values and projections. The default options are ARDECO  and 
Eurostat datasets for the historical data at NUTS3  level, and demographic and economic projections at NUTS0 
level from Eurostat and DG ECFIN,  respectively. These projections are published as part of The Ageing Report, 
with new editions usually every 3 years. More details about the data sources are provided in section 3. 

2.2  The core layer 

The core layer generates regional values of population, GDP, employment and land use , for every yearly time-
step of the projection period. Within each time-step, the sequence of estimation is: population, employment, 
GDP, and land use. The new values are estimated recursively based on the most recent (historical or projected) 
available data. 

2.2.1  Demographic module 

Population is the first variable generated at every time-step. Age-specific data about population can be taken 
as  an  exogenous component from  external  data  sources  (e.g.,  Eurostat).  Alternatively,  population  can  be 
assumed to be endogenous, accounting for the effect of economic activity on sub-regional migration patterns.  

2.2.1.1  Exogenous population 

In  DELi,  population  can  be  taken  exogenously from  a  population projection  available  at  NUTS3  level.  For 
example, Eurostat has  produced in  the  past demographic projections at  the  NUTS3  level and  these can  be 
plugged directly into DELi. For each year, the projected number of people by one-year age group for each NUTS3 
region  is  aggregated to  obtain  the  working  age  population (individuals aged  20 -64 years).  This  exogenous 
population is used in the subsequent parts of the analysis as the demographic baseline scenario. 

When using an exogenous source for the NUTS3 population, the demography-economy interaction is limited to 
unidirectional impact of population on employment and GDP.  Because GDP and employment do not influence 
population in subsequent yearly iterations, DELi’s output variables are therefore only parti ally  endogenous. A 
scenario without full interaction between population and the economy, although less plausible, may still be used 
in applications requiring the regionalisation of the economic variables based on a mandated regional population 
projection. 

2.2.1.2  Endogenous population 

Population growth  is  the sum of  natural growth  (births  minus deaths) and  net migration (in-migrants minus 
out-migrants). In  the fully-fledged DELi  setup, population growth at  the NUTS3  level is partially-endogenous. 
Natural  growth  is  calculated by  applying age  and sex  specific fertility  and  mortality  trends  at  NUTS3  level 
obtained from Eurostat (e.g., population projection, or trend extrapolation). Instead, net migration is endogenised 
by accounting for the effect of economic factors as per an econometrically estimated function (equation 1).  

𝑛𝑚𝑟𝑟

𝑎 = 𝛽0 + 𝛽1 · 𝐺𝐷𝑃𝑝𝑐𝑟 + 𝐹𝐸 + 𝛽2 ·  𝛿𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑀𝑒𝑡𝑟𝑜 + 𝛽3 · 𝛿𝑀𝑒𝑡𝑟𝑜 + 𝜖𝑟 

(1) 

Where:  

̶ 

̶ 

̶ 

̶ 

̶ 

𝑎 = five-year age group 

𝑟 = region at the NUTS3  level 

𝑛𝑚𝑟 = net migration rate 

𝐺𝐷𝑃𝑝𝑐  = GDP per capita (in logarithmic terms) 

𝐹𝐸 = country fixed effect  

6 

̶ 

̶ 

̶ 

̶ 

𝛿𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑀𝑒𝑡𝑟𝑜  =  dummy variable for the NUTS3  belonging to the metro region of the capital city of a 
country 

𝛿𝑀𝑒𝑡𝑟𝑜 = dummy variable for the NUTS3  belonging to a metro region 

𝛽 = coefficient of the parameters 

𝜖 = error term 

Box 1. Econometric estimation of net migration  

For  the econometric estimation of net migration, the number of net migrants are pooled for the years 2011-
2019 for each five-year age group and NUTS3 region. The dependent variables for the estimation are obtained 
by dividing these pooled numbers of net migrants by the mid -period population in each corresponding NUTS3 
region. The cross-sectional regression analysis provides the coefficients for the key independent variable s of 
GDPpc (in logarithmic terms) and the set of country, capital and metro area fixed effects for all EU27 countries. 
The sample does not include the years that were most notably affected by the COVID-19  pandemic, to reduce 
the risk of bias in the estimation results for net migration patterns. Results of the estimation for each five-year 
age group are depicted in Table 1. 

Table 1. Net migration parameter estimates by five-year age group (2011-2019) 

Log(GDPpc) 

S.E. 

Capital 

S.E.  Metro 

S.E. 

Country FE  Obs. 

R^2 

Variable 
Net migration rate:   
Age  0-4 
Age  5-9 
Age  10-14 
Age  15-19 
Age  20-24 
Age  25-29 
Age  30-34 
Age  35-39 
Age  40-44 
Age  45-49 
Age  50-54 
Age  55-59 
Age  60-64 
Age  65-69 
Age  70-74 
Age  75-79 
Age  >79 

0.037** 
0.016 
0.047** 
0.091** 
0.346** 
0.188** 
0.043** 
0.012 
0.053** 
0.055** 
0.030** 
0.009 
-0.016° 
-0.008 
0.044** 
0.032** 
0.038** 

0.021** 
0.008  0.040**  0.004 
0.007 
0.043** 
0.009 
0.012  0.002 
0.011 
0.035** 
0.011  0.002 
0.008 
0.009 
0.009 
0.012  0.019* 
0.014 
0.011 
-0.113**  0.027  0.054**  0.014 
0.024 
0.011 
0.018  0.004 
-0.003 
0.014 
0.010 
0.015  0.005 
0.054** 
0.013 
0.009 
0.013  0.005 
0.063** 
0.011 
0.009 
0.010  0.000 
0.037** 
0.010 
0.008 
0.009  0.000 
0.005 
0.009 
-0.006 
0.008 
0.013 
0.008 
0.008 
0.008 
-0.018* 
0.008 
0.005 
0.008 
-0.025**  0.008 
0.010 
0.008 
0.008 
-0.028**  0.008 
0.010 
0.002 
0.009 
0.008 
0.009  0.000 
0.006 
0.009 
0.009 
-0.003 
0.009 
0.011 
0.009 
0.012 
0.010 
-0.008 
0.013 
0.016 
Signif. codes:  **:  p < 0.01, *: p < 0.05, °: p < 0.1 

Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 

1,169  0.631 
1,169  0.274 
1,169  0.252 
1,169  0.352 
1,169  0.390 
1,169  0.409 
1,169  0.468 
1,169  0.387 
1,169  0.329 
1,169  0.263 
1,169  0.315 
1,169  0.274 
1,169  0.239 
1,169  0.355 
1,169  0.432 
1,169  0.446 
1,169  0.389 

The  results indicate that  economic factors  have a  stronger effect on net  migration patterns  for  certain age 
groups. For instance, the coefficient associated with the effect of GDPpc is larger for younger age g roups, with 
a peak effect for the individuals aged 20 -24.  

The coefficients of the econometric estimation of net migration described in box.1 and the GDPpc projected by 
the model (obtained as described in section 2.2.3)  are used to project the net migration rates and volumes for 
each five-year age group and NUTS3  region for the projection period, and effectively linking the demographic 
and economic modules of DELi.  This may  result in  projected net migration  figures that  deviate substantially 
from other population projections, such as Eurostat’s.  

The NUTS3  projected net migration numbers as well as the Eurostat data on fertility and mortality are plugged 
into a demographic cohort component sub-module which breaks down the population into age  and sex specific 
cohorts. The sub-module then projects the changes in each cohort over time by considering three demographic 
components: fertility, mortality, and migration. 

7 

 
 
 
 
 
 
 
 
 
 
In  the  final step, the  projected endogenous NUTS3  population is  rescaled to  match the  population size and 
structure of the input demographic projection at country level. The final, rescaled values of population for the 
projection period are obtained by applying equation 2: 

𝑃𝑜𝑝𝑟 ,𝑡

𝑅 =

𝑃𝑜𝑝𝑟 ,𝑡

∑

𝑟∊𝐶

𝑃𝑜𝑝𝑟 ,𝑡

· 𝑃𝑜𝑝𝐶 ,𝑡 

(2) 

Where 𝑃𝑜𝑝𝐶 ,𝑡  is the exogenous total Population at  the country  level of all  the NUTS3  belonging to country  C. 
The source for 𝑃𝑜𝑝𝐶 ,𝑡 can be Eurostat’s demographic projections or projections from other data sources. ‘R’ and 
‘r’ denote “rescaled” and “NUTS3 regions”, respectively. 

2.2.2  Employment module 

Employment is  the second variable generated at  every time -step. It  is calculated based on the population in 
working age and projected employment rate, as shown in equation 3: 

𝐸𝑚𝑝𝑟 ,𝑡 = 𝑃𝑜𝑝𝑤𝑎 (20−64),𝑟,𝑡 · 𝐸𝑅𝑟,𝑡 

(3) 

Where: 

̶ 

̶ 

𝑃𝑜𝑝𝑤𝑎  = working age population (20-64) 

𝐸𝑅 = employment rate  

𝑃𝑜𝑝𝑤𝑎  is obtained from the demographic module (section 2.2.1). Historical levels of ER are available at NUTS2 
level. The ER is the same for all the NUTS3  sub-regions belonging to the same NUTS2 region. Projections of ER 
are based on the last observed historical value and projected based on the estimated growth of ER at the NUTS0 
level from the input demographic projection. Alternative regional projections of ER can be defined according to 
assumptions on convergence/divergence trends. 

The final rescaled values of employment are obtained by applying equation 4: 

𝐸𝑚𝑝𝑟 ,𝑡

𝑅 =

𝐸𝑚𝑝𝑟 ,𝑡

∑

𝑟∊𝐶

𝐸𝑚𝑝𝑟 ,𝑡

· 𝐸𝑚𝑝𝐶 ,𝑡 

(4) 

Where 𝐸𝑚𝑝𝐶 ,𝑡 is the exogenous total Employment at the country level of all  the regions belonging to country 
C, obtained from the input economic projection (e.g., DG ECFIN’s Ageing Report). 

2.2.3  Gross Domestic Product module 

Gross Domestic Product (GDP) is the third variable generated at every time-step. The regional GDP is calculated 
based on expected population and GDP per capita (GDPpc), as shown in equation 5. 

𝐺𝐷𝑃𝑟 ,𝑡 = 𝑃𝑜𝑝𝑟 ,𝑡 ·   𝐺𝐷𝑃𝑝𝑐𝑟 ,𝑡 

(5) 

Population for every region r is taken from the output of the demographic module (section 2.2.1). NUTS3  GDPpc 
is estimated based on an econometric model, where GDPpc growth depends on well-established factors such 
human capital, quality  of institutions, and several  geographical factors, including agglomeration effects. The 
econometric approach ca be described as an augmented growth  equation (equation 6) following Barro  et  al. 
(1991), which estimates the impact of factors affecting average GDPpc growth, capturing  the so-called beta 
convergence (i.e., regions with initially lower GDP per capita values are expected to record a faster growth) and 
additional idiosyncratic factors explaining the heterogeneity of GDPpc growth across regions.  

1

𝑥

· 𝑙𝑜𝑔 (

𝐺𝐷𝑃𝑝𝑐𝑟 ,𝑡+𝑥
𝐺𝐷𝑃𝑝𝑐𝑟 ,𝑡

) = 𝛽1 · log (𝐺𝐷𝑃𝑝𝑐𝑟 ,𝑡) + 𝛽2 ·   𝑙𝑜𝑔(𝐺𝐷𝑃𝑝 𝑐𝑟,𝑡)

2

+ 𝛽3 · 𝛿𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑀𝑒𝑡𝑟𝑜 +   𝛽4 · 𝐸𝑑𝑢𝑟,𝑡 + 

 𝛽5 · ℎ𝑖𝑔ℎ𝐸𝑄𝐼𝑟 ,𝑡 +  𝛽6 · 𝑙𝑜𝑤𝐸𝑄𝐼𝑟,𝑡 + 𝛽7 · 𝐴𝑔𝑔𝑟 ,𝑡 +  𝛽8 · 𝑂𝐴𝐷𝑅𝑟 ,𝑡 + 𝛽9 · 𝑊𝐴𝑃𝑟 ,𝑡 + 𝐹𝐸 + 𝜖𝑟 ,𝑡+𝑥  

        (6) 

Where:  

̶ 

𝑟 = region at the NUTS3  level 

8 

̶ 

̶ 

̶ 

̶ 

̶ 

̶ 

̶ 

̶ 

̶ 

𝑡 = starting year 

𝑡 + 𝑥 = ending year 

𝐺𝐷𝑃𝑝𝑐  = GDP per capita 

𝛿𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑀𝑒𝑡𝑟𝑜  =  dummy variable for the NUTS3  belonging to the metro region of the capital city of a 
country 

𝐸𝑑𝑢 =  education level, measured as the share of population with a tertiary  education (levels 5–8 of 
the international standard classification of education – ISCED). 

𝐻𝑖𝑔ℎ𝐸𝑄𝐼 = regions with high values of European Quality of government Index (Charron et al. 2019) 

𝐿𝑜𝑤𝐸𝑄𝐼 = regions with low values of European Quality of government Index  

𝐴𝑔𝑔 = agglomeration. Calculated as  𝑃𝑂𝑃𝑟 ,𝑡 ·

𝑃𝑜𝑝𝑟,𝑡
𝑅𝐵𝑈𝑟,𝑡

 , where RBU is residential built-up (see 2.2.4.1) 

𝑂𝐴𝐷𝑅  = Old age dependency ratio. Calculated as 

𝑃𝑜𝑝(>65 𝑦𝑒𝑎𝑟𝑠 𝑜𝑙𝑑 )𝑟,𝑡
𝑃𝑜𝑝(20−64 𝑦𝑒𝑎𝑟𝑠 𝑜𝑙𝑑 )𝑟,𝑡

̶  𝑊𝐴𝑃  = Share of working age population. Calculated as 

𝑃𝑜𝑝(20−64 𝑦𝑒𝑎𝑟𝑠 𝑜𝑙𝑑 )𝑟,𝑡
𝑃𝑜𝑝𝑟,𝑡

̶ 

̶ 

̶ 

𝐹𝐸 = country fixed effect 

𝛽 = coefficient of the parameters 

𝜖 = error term 

Box 2. Econometric estimation of GDP per capita (GDPpc) growth  

The starting and ending year of the estimation period for the factors affecting the average GDPpc growth can 
be chosen arbitrarily. The default is the period 2000 -2019. 2000 is selected as starting year as the oldest with 
data available for all the EU27  countries. 2019 is selected as ending year as the most recent data which is not 
affected by the COVID-19  pandemic, to reduce the risk of bias. Results of the estimation are shown in Table 2. 

Table 2. GDPpc parameters Estimates (2000 -2019) 

Variable  
Intercept  
Log(GDPpc)  
Log(GDPpc)^2   
Capital metro 
Share of tertiary education  
HighEQI  
LowEQI  
Old age dependency  ratio (OADR)  
Working age population (WAP)  
Log(Agglomeration)  
Country fixed effects  
Number  of NUTS3-level sub-regions  
Adj. R^2  

S.E.   
0.055  
0.011  
0.001  
0.001  
<0.001  
0.001  
0.001  
<0.001  
0.016  
<0.001  

Est.  
0.253***  
-0.048***  
0.002***  
0.003**  
<0.001***   
0.002***  
-0.002°  
-0.001**  
0.044**  
0.001**  
Yes  
1,161  
0.703  

Signif. codes:  ***:  p < 0.001, **:  p < 0.01, *: p < 0.05, °: p < 0.1 

The results are coherent with the expectations, indicating both convergence and divergence effects, or J-shaped 
GDPpc growth curve (faster growth  for regions with  lower starting  values of GDPpc as well  as for very  high-
GDPpc regions). Additionally, regions belonging to metropolitan areas of the capital regions, higher education, 
higher quality of institutions, higher agglomeration and higher share of population in working age  are associated 
with higher GDPpc growth rates.  A negative effect on GDPpc growth  is given by a higher old age dependency 
ratio.  A  dedicated scientific article  (Curtale  et  al.  2024)  shows  that  the  use  of  the  presented methodology 
provides projections of GDP closer to real values compared to a fixed shares approach. The performance of the 
methodology improves by increasing the length of the estimation and projection periods. 

9 

 
 
 
  
   
  
The specific growth rate of GDPpc per region and year is applied to region specific GDPpc of the previous year. 
Future values of the independent variables are updated depending on changes in population and land use.  

The final rescaled values of the GDP are obtained by applying equation 7: 

𝐺𝐷𝑃𝑟 ,𝑡

𝑅 =

𝐺𝐷𝑃𝑟 ,𝑡

∑

𝑟∊ 𝐶

𝐺𝐷𝑃𝑟 ,𝑡

· 𝐺𝐷𝑃𝐶 ,𝑡 

(7) 

Where 𝐺𝐷𝑃𝐶 ,𝑡 is the exogenous total GDP at the country level of all the regions belonging to country C, obtained 
from the input economic projection (e.g., DG ECFIN’s Ageing Report). 

2.2.4  Land Use module 

Projections of demand for land use are estimated in the last module of DELi  at every time-step. Land use can 
be  classified  in  different  categories  according  to  the  CORINE  Land  Cover 
(CLC)  nomenclature 
(Feranec et al. 2016), including artificial  surfaces, agricultural  areas, forest and semi-natural areas, wetlands 
and water  bodies. We model changes of two subcategories of the artificial surfaces: the  residential built-up, 
which corresponds to categories 111 and 112 and built-up for Industry, Commerce and Services, corresponding 
to category 121  of the CLC  nomenclature. The land use can be completely saturated in certain NUTS3  regions, 
meaning that  there is no available land for development. For  such regions, we maintain a constant land use 
over time. For  the other NUTS3  regions, we estimate econometrically the future demand for residential built-
up, and industry, commerce and services built up. 

2.2.4.1  Residential  built-up 

Residential  built-up  is  calculated  as  the  product  between  the  population,  and  the  expected  demand  for 
residential built-up per capita, as shown in equation 8. 

𝑅𝐵𝑈𝑟 ,𝑡 = 𝑅𝐵𝑈𝑝𝑐𝑟 ,𝑡 ∗ 𝑃𝑜𝑝𝑟 ,𝑡 

(8) 

Where: 

̶ 

̶ 

𝑅𝐵𝑈 = Residential built-up 

𝑅𝐵𝑈𝑝𝑐 = Residential built-up per capita 

Future  demand of  residential built-up  per  capita  (RBUpc)  is  estimated econometrically, based on  a  refined 
specification of the model presented in Schiavina et al. (2022), as shown in equation 9: 

𝑅𝐵𝑈𝑝𝑐𝑟 ,𝑡 +1 = 𝛽0 + 𝛽1 ·  𝑅𝐵𝑈𝑝𝑐𝑟 ,𝑡 +  𝛽2 · 𝛿𝑈𝑟𝑏𝑎𝑛 + 𝛽3 · 𝛿𝑅𝑢𝑟𝑎𝑙 +   𝛽4 · log(𝐴𝐿𝐷𝑝𝑐) +  𝛽4

· log(𝐴𝐿𝐷𝑝𝑐)2 +  𝜖𝑟,𝑡 +1 

(9) 

Where: 

̶ 

̶ 

̶ 

̶ 

̶ 

𝛿𝑈𝑟𝑏𝑎𝑛  =  dummy  variable  for  NUTS3  regions classified as  ‘predominantly urban’  according to  the 
urban-rural typology (Eurostat, 2018).  Intermediate regions are used as the reference  

𝛿𝑅𝑢𝑟𝑎𝑙 = dummy variable for NUTS3 classified as rural, reference is intermediate NUTS3 regions 

𝐴𝐿𝐷𝑝𝑐  = Available land for development per capita 

𝜖 = error term 

𝛽 = coefficient of the parameters 

10 

 
 
 
 
 
 
Box 3. Econometric estimation of residential built-up per capita (RBUpc) 

We estimated the residential built-up per capita of 2018 based on the values of 2012, which are the only years 
with data availability.    

Table 3. Residential built-up per capita (RBUpc) estimates (2012-2018) 

Variable  
Intercept  
RBUpc  
Urban regions  
Rural regions 
Log(available land per capita)  
Log(available land per capita)^2 
Number  of sub-regions  
Adj R^2  

S.E.   
<0.001  
0.005  
<0.001 
<0.001 
<0.001  
<0.001  

Est.  
0.001***  
0.982***  
-0.001***  
0.001*** 
0.007***  
-0.001***  
1,161  
0.988  

Signif. codes:  ***:  p < 0.001, **:  p < 0.01, *: p < 0.05, °: p < 0.1 

The  results  show  a  strong  correlation  of  the  residential built-up  per  capita  in  the  two  years,  with  a  higher 
increase  of  residential  built-up  per  capita  correlated  positively  with  rural  regions  and  (non -linearly)  with 
available land per capita, while negatively with urban regions. 

2.2.4.2 

Industry,  commerce and services built-up 

For  the estimation of industry commerce and services (ICS) built-up, we follow an approach similar to that  of 
residential built-up, but using employment instead of population as normalization factor. Therefore, projections 
of  industry  commerce and services built-up  is  obtained multiplying  the  total  number of  employees by the 
estimated ICS per employee, as shown in equation 10. 

𝐼𝐶𝑆𝑟,𝑡 = 𝐼𝐶𝑆𝑝𝑒𝑟 ,𝑡 ∗ 𝐸𝑚𝑝𝑟 ,𝑡 

(10) 

Where 

̶ 

̶ 

𝐼𝐶𝑆 = built-up for Industry, Commerce and Services 

𝐼𝐶𝑆𝑝𝑒 = built-up for Industry, Commerce and Services per employee  

The expected 𝐼𝐶𝑆𝑝𝑒 is obtained econometrically by estimating equation 11. 

𝐼𝐶𝑆𝑝𝑒𝑟 ,𝑡+1 = 𝛽0 +  𝛽1 ·  𝐼𝐶𝑆𝑝𝑒𝑟 ,𝑡   +  𝜖𝑟,𝑡+1 

(11) 

Box 4. Econometric estimation of industry commerce and services per employee (ICSpe)  

We estimated the built-up for industry, commerce and services per employee of 2018 based on the values of 
2012, which are the only years with data availability.    

Table 4.  ICSpe estimates (2012-2018) 

Variable  
Intercept  
ICSpe 
Number  of sub-regions  
Adj R^2  

S.E.   
<0.114  
0.004  

Est.  
0.475***  
0.989***  
1,161  
0.982  

Signif. codes:  ***:  p < 0.001, **:  p < 0.01, *: p < 0.05, °: p < 0.1 

ICSpe is function of its previous value and a constant term, the additional factors used to estimate RBUpc (Box 
3) were not significant to estimate ICSpe, and were therefore not included. 

11 

 
   
  
 
 
   
  
2.2.4.3  Available  land for development 

The available land for development (ALD) in each region at  the start  of the simulation (t)  corresponds to the 
total  area of  the  region subtracted  of  the already  developed land (all  artificial  surfaces present at  year  t), 
protected areas (Natura 2000), wetlands, water bodies and terrain with high slopes. ALD is then updated every 
time  step  by  subtracting  the  difference between new  and  old  built  up,  for  both  residential  and  industry, 
commerce and services, as shown in equation 12. 

𝐴𝐿𝐷𝑟 ,𝑡+1 = 𝐴𝐿𝐷𝑟 ,𝑡 − (𝑅𝐵𝑈𝑟 ,𝑡+1 −  𝑅𝐵𝑈𝑟 ,𝑡) − (𝐼𝐶𝑆𝑟,𝑡+1 −  𝐼𝐶𝑆𝑟,𝑡) 

(12) 

Where 

-  𝐴𝐿𝐷 = Available land for development 

-  𝑅𝐵𝑈 = Residential built-up 

- 

𝐼𝐶𝑆 = built-up for Industry, Commerce and Services 

At  the end of the estimation of land use, which is the last for each time period t, the core layer continues the 
loop by estimating population, employment, GDP and land use for year  t+1.  The loop ends when reaching the 
last year of projections. 

2.3  The output layer 

The output layer stores the projections in a reusable format for analysis. The main output of the DELi model is 
a comprehensive table with NUTS3  projections of population, GDP, employment and land use for all the years 
defined in the projection period. The table is organised in a long-format, sorted by NUTS3  code and year. The 
files  are  exported as  xlsx,  csv  and  shapefile.  Additional  automatic  generation  of  charts  and  maps  can  be 
integrated in future developments of the DELi model. 

12 

 
 
 
3  Data  sources 

The  model can  be applied to  regionalise different sources of  projections at  the  national  level.  DELi’s  2023 
release used socioeconomic projections at the  national level  provided by  ECFIN  in  the  2021  Ageing  Report 
(Directorate-General for Economic and Financial Affairs, 2021).  

For the calibration, or estimation of the various equations, we used historical data at NUTS3 level based mostly 
on  datasets  from  Eurostat  and  ARDECO1.  ARDECO  is  the  “Annual  Regional  Database  of  the  European 
Commission” and  its  main  data  source   is  Eurostat  complemented,  where necessary, by  other  appropriate 
national  and  international  sources.  DELi’s  input  database contains  a  set  of  long  time -series variables  and 
indicators for EU27 regions at various geographical scales (NUTS0, NUTS2, NUTS3, metro regions). For land use, 
data are based on LUISA2 and GHSL3.  Specific details and references are provided in Table 5. 

Table 5. Data sources for historical data 

Module 

Variable 

Granularity 

Source 

Demography 

Population 

Employment 

Employment 

Employment rate 

GDP 

GDP 

Capital metro region 

Share of population with tertiary 
education 

NUTS3 

NUTS3 

NUTS2 

NUTS3 

NUTS3 

NUTS2 

JRC-ARDECO;  Eurostat 

JRC-ARDECO 

Eurostat 

JRC-ARDECO 

Eurostat 

JRC-ARDECO 

Agglomeration 

NUTS3 

JRC 

European Quality of Institution 

NUTS0/NUTS2 

Old Age Dependency Ratio 

NUTS3 

Working Age Population 

NUTS3 

Land use 

Residential built-up 

NUTS3 

University of Gothenburg4 
(Charron et al. 2019)  

Calculated based on 
Eurostat data 

Calculated based on 
Eurostat data 

JRC (Pigaiani and Batista e 
Silva 2021) 

Industry, Commerce and Services built-
up 

NUTS3 

JRC (Pigaiani and Batista e 
Silva 2021) 

Available land for development 

NUTS3 

JRC 

Urban region 

Rural region 

NUTS3 

NUTS3 

Eurostat 

Eurostat 

1 https://urban.jrc.ec.europa.eu/ardeco?lng=en 
2 https://data.jrc.ec.europa.eu/collection/luisa#datasets 
3 https://ghsl.jrc.ec.europa.eu/ 
4 https://www.gu.se/en/quality-government 

13 

 
 
 
 
                                                 
 
Table 6. Data sources for projections (2023 release of DELi) 

Module 

Variable 

Granularity 

Source 

Demography 

Population 

NUTS0 / NUTS3 

Eurostat-EUROPOP19 

Age specific fertility rates 

NUTS3 

Eurostat-EUROPOP19 

Age specific probabilities of dying  

NUTS3 

Eurostat-EUROPOP19 

Employment 

Number of employees 

GDP 

GDP 

NUTS0 

NUTS0 

ECFIN  (2021 Ageing report) 

ECFIN  (2021 Ageing report) 

14 

 
 
 
 
 
4  Discussion  and  future  avenues 

In this report we present a new, modular framework to regionalise demographic and economic projections from 
national  to  the  NUTS3  regional  level  –  the  Demography-Economy-Land use interaction  (DELi)  model.  DELi 
downscales  population, employment  and  GDP  in  an  integrated  and  recursive  manner,  so  that  population 
dynamics  influence  economic  growth  and  vice -versa,  in  yearly  iterations.  GDP  growth  is  determined  by 
empirically derived regional factors including demographics, human capital, and geographical factors, including 
agglomeration. Population growth is a function of natural growth and net migration. Net migration is influenced 
by regional levels of GDP per capita in the previous yearly iteration. Residential and industrial, commercial and 
services land uses are projected for each year to meet the socioeconomic demand. In turn, agglomeration is in 
itself a function of population and land use dynamics, impacting on future GDP per capita growth.  

The relationships currently  encoded in DELi are according to  expectations and coherent with the literature  on 
regional economic growth and migration, but using parsimonious specifications. It has been demonstrated that 
DELi is a stronger predictor of future regional GDP growth than null or quasi-null models such as assuming non-
changing regional GDP or GDP per capita shares (Curtale  et al. 2024). 

DELi is structured in four different modules: demography, employment, GDP and land use. Although interlinked, 
each module is sufficiently standalone, requiring only a set of inputs and producing a set outputs. As such, the 
modelling assumptions within  each module can be  updated o r  improved without  affecting the  overall  DELi 
workflow. 

To the best of our knowledge, a comprehensive, integrated, transparent and flexible framework to regionalise 
socioeconomic projections like DELi was still lacking for EU policy support. DELi can be used to generate regional 
scenarios for  the  chief socioeconomic variables, consistently with  exogenous country-level projections, thus 
providing  socioeconomic baselines for anticipatory  policy  support.  Concretely,  DELi  can  be  used  to  explore 
scenarios of regional convergence or divergence, demograph ic change, land use policies, and  their  territorial 
implications at NUTS3  level. 

DELi is still an exploratory tool, with substantial room for future improvement. As it stands, the model presents 
some limitations: 1) the lack of detailed sectoral data constrains the flexibility of the model to react to changes 
in the regional economic structure, 2) the absence of variables including technological progress does not allow 
a detailed projection of the labour productivity and its impact on employment, 3) it was calibrated on observed 
data   between 2000 and 2019, thus it  is best used for  projections for an equivalent period of 20 years, 4) it 
cannot predict shocks and has limited capacity to  assess how the society and the economy reacts to  shocks. 
Other model types, such as general-equilibrium model, are arguably more suited for policy impact assessment 
and/or assess impacts of shocks, like the Rhomolo model (Brandsma et al.  2015).  Finally,  5) DELi  is currently 
constrained by national level demographic and economic projections. This means that the growth of one region 
is made at the expense of another region, making DELi an implausible zero -sum game. 

With regard to the latter limitation, future work will test whether national level constraints can be relaxed and 
how.  Currently,  GDP  is  obtained by  multiplying  population with  econometrically estimated regional GDP  per 
capita, and then rescaling the regional GDP  to  match the exogenously projected GDP  totals  per  country.   For 
population and employment, rescaling is also applied at the end of each yearly iteration. For GDP, an alternative 
is  to use national growth  rates  from exogenous economic projections and sum  regional growth  differentials 
estimated by  DELi.  This  would allow  for  some deviation from  the exogenous GDP  projections, and  could be 
useful to assess whether regional growth heterogeneity (e.g., scenarios of higher and lower convergence) impact 
on aggregate, national outcomes. The relaxation could also be attained by using EU-level totals or growth rates 
instead of national ones, allowing for cross-border population and economic flows. And finally, the relaxation 
of national level constraints on GDP projections from exogenous sources opens up opportunities for exploring 
alternative  approaches  to  economic  forecasting,  such  as  considering  the  role  of  savings,  capital 
accumulation/formation, monetary and economic freedoms (Ammous 2023)  on aggregate economic growth. 

Projecting future economic growth at national level is beyond DELi’s  primary  scope, and so it  is unlikely that 
DELi will develop in this direction to avoid duplication with other Commission forecasting tools and models. But, 
because economic outcomes are more likely the result of the interaction between both top-down and bottom-
up processes, rather  than just  one or  the other, exploring ways to  relax  na tional constraints may remain  an 
interesting endeavour to explore. 

Although an exploratory  tool, DELi  offers already  a valuable template to  regionalise or  even generate socio -
economic projections and can be subject to  further methodological improvement and  expanded in its  scope, 

15 

functionality and applications. While DELi will continue evolving, the layout presented in this report will remain 
a key reference for applications in the coming years. 

16 

 
References  

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Barro,  R.  J.,  Sala-I-Martin,  X.,  Blanchard, O.  J.,  &  Hall,  R.  E.  (1991).  Convergence Across States  and  Regions. 

Brookings Papers on Economic Activity, 1991(1),  107.  https://doi.org/10.2307/2534639 

Batista e Silva, F., Dijkstra, L., Martinez, P. V., & Lavalle, C. (2016). Regionalisation of demographic and economic 
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Charron, N., Lapuente, V., & Annoni, P. (2019). Measuring quality of government in EU regions across space and 

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Technical Report. Publications Office of the European Union. https://doi.org/10.2760/503006. 

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17 

 
List of abbreviations and definitions  

ALD 

DELi 

ER 

GDP 

ICS 

Available land for development 

Demography, Economy, Land use interaction 

Employment rate 

Gross Domestic Product 

Industry, Commerce and Services 

NUTS 

Nomenclature of territorial units for statistics 

pc 

pe 

POP 

RBU 

per capita 

per employee 

Population 

Residential Built-up 

18 

 
 
 
List of boxes  

Box 1. Econometric estimation of net migration....................................................................................................................................................................... 7 

Box 2. Econometric estimation of GDP per capita (GDPpc) growth.......................................................................................................................9 

Box 3. Econometric estimation of residential built-up per capita (RBUpc).................................................................................................11 

Box 4. Econometric estimation of industry commerce and services per employee (ICSpe) ....................................................11 

19 

 
 
List of figures  

Figure 1. Structure of the DELi model ................................................................................................................................................................................................. 5 

20 

 
List of tables  

Table 1. Net migration parameter estimates by five-year age group (2011-2019) ......................................................................... 7 

Table 2. GDPpc parameters Estimates (2000-2019) ..........................................................................................................................................................9 

Table 3. Residential built-up per capita (RBUpc) estimates (2012-2018) .................................................................................................11 

Table 4. ICSpe estimates (2012-2018) ...........................................................................................................................................................................................11 

Table 5. Data sources for historical data.......................................................................................................................................................................................13 

Table 6. Data sources for projections (2023 release of DELi)............................................................................................................................... 14 

21 

 
 
 
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