Assessing the social and macroeconomic impacts of labour market integration : a holistic approach.
Date: 2016-01-21
Institutions: Institute for Prospective Technological Studies
Subjects: economic growth, education policy, employment policy, holistic approach, labour market, labour mobility, migration, professional qualifications, research report

Assessing the Social and Macroeconomic 
Impacts of Labour Market Integration: 
A Holistic Approach 

Pavel Ciaian 

d’Artis Kancs 

2 0 1 6  

Report EUR 27689 EN 

 
 
 
 
 
 
 
 
European Commission 
Joint Research Centre 
Institute for Prospective Technological Studies  

Contact information 
d’Artis Kancs 
Address: Edificio Expo. c/ Inca Garcilaso, 3. E-41092 Seville (Spain) 
E-mail: d’artis.kancs@ec.europa.eu 
Tel.: +34 954488318 
Fax: +34 954488300 

JRC Science Hub  
https://ec.europa.eu/jrc 

Legal Notice 
This publication is a Technical Report by the Joint Research Centre, the European Commission’s in-house science service.  
It aims to provide evidence-based scientific support to the European policy-making process. The scientific output expressed does not 
imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the 
Commission is responsible for the use which might be made of this publication. 

All images © European Union 2016 

JRC99645 

EUR 27689 EN 

ISBN 978-92-79-54542-9 (PDF) 

ISSN 1831-9424 (online) 

doi:10.2791/91191 

Luxembourg: Publications Office of the European Union, 2016 

© European Union, 2016 

Reproduction is authorised provided the source is acknowledged. 

Abstract 

In the age of globalisation and the knowledge economy, skill mobility is perceived as one of the key factors for fully unlocking the 
labour market potential. Assessing the social and macroeconomic impacts of increased skill mobility is an important though also 
challenging task, which requires a holistic approach. This study presents the dynamic spatial general equilibrium approach taken in 
the Regional Holistic Model (RHOMOLO) to better understand the relationship between education, skills, migration and economic 
growth. Two key channels of labour market adjustment -- upward skill mobility and spatial skill mobility -- are presented and 
explained in particular detail. By performing numerical simulations and conceptual analysis of labour market integration, we aim to 
facilitate understanding of the advantages and limitations of the approach taken in RHOMOLO, and its potential for education, skills 
and employment policy impact assessment. The results from our analysis suggest that a holistic approach is indeed crucial for 
capturing all the direct and indirect, short- and long-run effects, and it has a wide potential for assessing region-, sector- and skill-
specific macroeconomic and social effects of policies aiming at integration e.g. of marginalised communities, such as Roma or 
refugees, into the EU labour markets.  
JEL codes: C68, D58, F22, J20, J61, J64, O15. 
Keywords: Dynamic spatial general equilibrium model, skills, education, employment, labour, migration, wage, human capital. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Contents

1  Education, skills and migration in the RHOMOLO model 

2  The RHOMOLO model

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2.1 Overview . . . . . . . . . . . .
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2.2 Labour market . . . . . . . .
2.3 Spatial dimension . . . . . .
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2.4 Data and empirical implementation .

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3 Simulations of labour market integration with RHOMOLO
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3.1 Simulation setup . . . . . . .
3.2 Step 1: Econometric estimations of skill mobility .
3.3 Step 2: Simulations using the RHOMOLO model
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Social and labour market effects .
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3.3.1
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4 Conceptual analysis of labour market integration
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4.1 Setup: upward and spatial skill mobility .
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Impact of the high-skill labour market integration .
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Social and labour market effects .
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5 Conclusions

1

Assessing the Social and Macroeconomic Impacts of Labour
Market Integration: A Holistic Approach(cid:73)

Pavel Ciaiana, d’Artis Kancsa,∗

aEuropean Commission DG Joint Research Centre, E-41092 Seville, Spain

Abstract

In the age of globalisation and the knowledge economy, skill mobility is perceived as
one of the key factors for fully unlocking the labour market potential. Assessing the
social and macroeconomic impacts of increased skill mobility is an important though also
challenging task, which requires a holistic approach. This study presents the dynamic
spatial general equilibrium approach taken in the Regional Holistic Model (RHOMOLO)
to better understand the relationship between education, skills, migration and economic
growth. Two key channels of labour market adjustment – upward skill mobility and spatial
skill mobility – are presented and explained in particular detail. By performing numerical
simulations and conceptual analysis of labour market integration, we aim to facilitate
understanding of the advantages and limitations of the approach taken in RHOMOLO,
and its potential for education, skills and employment policy impact assessment. The
results from our analysis suggest that a holistic approach is indeed crucial for capturing
all the direct and indirect, short- and long-run effects, and it has a wide potential for
assessing region-, sector- and skill-speciﬁc macroeconomic and social effects of policies
aiming at integration e.g. of marginalised communities, such as Roma or refugees, into
the EU labour markets.

Keywords: Dynamic spatial general equilibrium model, skills, education, employment,
labour, migration, wage, human capital.
JEL code: C68, D58, F22, J20, J61, J64, O15.

JRC99645

(cid:73)

The authors are solely responsible for the content of the paper. The views expressed are purely those
of the authors and may not in any circumstances be regarded as stating an ofﬁcial position of the European
Commission.

∗Corresponding author
Email address: d’artis.kancs@ec.europa.eu (d’Artis Kancs)

1. Education, skills and migration in the RHOMOLO model

In the age of globalisation and the knowledge economy, skill mobility is perceived as
one of the key factors for fully unlocking the labour market potential. Two types of skill
mobility are of particular importance: upward skill mobility and spatial skill mobility. In ad-
dition to skill mobility, there are also other channels through which labour markets adjust
to macroeconomic or policy shocks, such as regional economic integration or economic
crisis (Blanchﬂower and Oswald, 1995). Generally, the economic theory (see for example
Agenor, 1996) suggests that labour markets can adjust in response to macroeconomic and
policy shocks through several channels: adjustments in employment/unemployment,
investment in education, adjustments in labour force participation/nonparticipation,
labour migration, adjustments in worker remuneration, and others.

According to Blanchard and Katz (1992); Decressin and Fatás (1995), regional labour
markets are fundamentally different from national labour markets, and region-speciﬁc
shocks trigger different adjustment mechanisms compared to national shocks. For
example, in the EU there is more inter-regional migration in response to region-speciﬁc
shocks than international migration in response to country-level shocks (European
Commission, 2015). A second reason to consider labour markets at the regional level is
the pattern and degree of specialisation in the production of goods and services, which is
higher at the regional level when compared to the national level, such that analysing the
national labour market response to macroeconomic or policy shocks would give only a
partial picture. Third, regions differ in their response to policy or macroeconomic shocks
also due to regional differences in labour supply characteristics. Finally, in regionally
integrated economies, such as EU regions, the channels of labour market adjustments
may mutually accelerate each other or neutralise themselves over time.

From the policy perspective, it is important that the particular mechanisms, through
which the labour market adjustments occur are identiﬁed at the regional level, as they
may have important policy implications (Boeters and Savard, 2012). For example, in the
regional development policy, there is an ongoing debate surrounding the appropriateness
of people-based policies versus place-based strategies (Barca et al., 2012). One of the key
issues in this debate is that attempts to improve the prospects of people in particular
regions via place-based strategies may be confounded, if the immigration response is
large. In other words, regional policy could end up beneﬁting new entrants to the region
rather than the initial target population or community. Therefore, looking at inter-regional
dynamics from a holistic perspective is likely to provide more policy relevant results, as

2

many of them may average out at the national level. In order to capture the inter-regional
dynamics and assess the relative strength of each labour market channel of adjustment,
a dynamic spatial general equilibrium approach is a particularly helpful tool for policy
impact assessment.

This study presents the dynamic spatial general equilibrium approach taken in the
Regional Holistic Model (RHOMOLO) to better understand the relationship between
education, skills, migration and growth. RHOMOLO is developed by the Directorate-
General Joint Research Centre of the European Commission for policy impact assessment
and provides sector-, region- and time-speciﬁc support to EU policy makers on structural
reforms, growth, infrastructure and human capital policies.1 In the past, the RHOMOLO
model has been used together with the Directorate-General for Regional and Urban Policy
for impact assessment of Cohesion Policy, and with the European Investment Bank for
impact assessment of EU investment support policies. The current version of RHOMOLO
covers all NUTS2 regions of the EU, and each regional economy is disaggregated into
six economic sectors. Spatial interactions between regions are captured through trade
of goods and services (which is subject to trade costs), factor mobility and knowledge
spillovers, making RHOMOLO particularly well suited for simulating human capital,
R&D and innovation policies. RHOMOLO simulation results allow for providing a
scientiﬁc model-based support to EU policy makers, and deriving skill-speciﬁc policy
recommendations related to the accumulation, employment and remuneration of human
capital on the one hand, and macroeconomic impacts on regional economies, such as
growth, income, consumption, investment, savings, etc., on the other hand.

An important advantage of the RHOMOLO model is that it is able to capture all
induced direct and indirect, short- and long-run general equilibrium effects of labour
market policy interventions, such as changes in the relative wage for low-, medium- and
high-skill labour, according to which both labour supply and labour demand would
adjust in RHOMOLO. As a thought experiment, think about a policy measure leading to
deeper a high-skill labour market integration between EU Member States. As regards
labour supply, in RHOMOLO reduced migration costs and the triggered emigration of
high-skill workers from low-wage regions would exert an upward pressure on high-skill
wages, which in turn would create incentives for low- and medium-skill workers to
invest into education and acquire medium and high skills, respectively, in the low-wage
sending regions. Hence both, migration-induced wage-adjustments in sending and

1See Brandsma et al. (2015) for a formal description of the key mechanisms in the RHOMOLO model.

3

receiving regions, and wage-driven adjustments in skill accumulation and composition
of workforce in each region are captured in RHOMOLO. As regards labour demand, in
RHOMOLO there would be two types of induced effects. On the input side, changes
in the relative low-, medium- and high-skill wages would trigger adjustments in factor
demand for production of goods, because low-, medium- and high-skill labour and
capital are mutually substitutable. On the output side, companies would adjust their
specialisation patterns according to changes in the relative skill-abundance in the region
where the company is located. In addition, ﬁrms may ﬁnd it proﬁtable to relocate to
another region, which also would change labour demand on regional labour markets.
Hence, both skill demand and skill supply channels are modelled endogenously and
simultaneously in RHOMOLO.

In order to facilitate the understanding of social and macroeconomic impacts related to
education and migration decisions of people in RHOMOLO, the present study explains
two key mechanisms through which policy shocks are absorbed in regional labour
markets – upward skill mobility and spatial skill mobility – in two different ways: performing
numerical simulations with the RHOMOLO model, and undertaking conceptual analysis
in a diagrammatic framework. The two approaches are complementary and are presented
in a non-formal way to intuitively explain the potential impacts of a hypothetical policy
scenario related to labour market integration, which is simulated as migration cost
reduction, e.g. through a full EU-wide recognition of qualiﬁcations for high-skill workers.
In this hypothetical example, high-skill workers become fully mobile across EU regions,
and have perfect information about skill demand and supply in every EU region. The
social and macroeconomic impacts of the high-skill labour market integration are assessed
against the segmented labour market baseline.

The results from our analysis suggest that a holistic approach is indeed crucial for
capturing all the direct and indirect, short- and long-run effects. The presented approach
taken in RHOMOLO has a wide potential to assess holistically the macroeconomic and
social effects of policies aiming at integration e.g. of marginalised communities, such as
Roma or refugees, into the EU labour markets, as it allows to assess region- and sector
speciﬁc social and macroeconomic effects in the short-, medium- and long-run.

4

2. The RHOMOLO model2

2.1. Overview

In the tradition of Computable General Equilibrium (CGE) models, RHOMOLO relies
on an equilibrium framework à la Arrow-Debreu where supply and demand depend
on the system of prices. Policies are introduced as shocks to the existing equilibrium of
prices, which drive the system towards a new equilibrium by clearing all the markets
after policy shocks. Therefore, CGE models have the advantage of providing a rigorous
view of interactions between all the markets in an economy.

Given the territorial focus of RHOMOLO, a particular attention is devoted to the
explicit modelling of spatial linkages, interactions and spillovers between regional units
of analysis. For this reason, models such as RHOMOLO are referred to as Spatial
Computable General Equilibrium (SCGE) models. A richer market structure has been
adopted to describe pricing behaviour, as RHOMOLO deviates from the standard large-
group monopolistic competition à la Chamberlin (1890). Given the potential presence
of large ﬁrms in small regional markets, the assumption of atomistic ﬁrms of negligible
size has been relaxed in favour of a more general small-group monopolistic competition
framework (Baldwin et al., 2003).

Each region is inhabited by households, whose preferences are captured by a rep-
resentative consumer, who consumes with a love for variety (Dixit and Stiglitz, 1977).
Households derive income from labour (in the form of wages), capital (proﬁts and rents)
and transfers (from national and regional governments). The income of households is
split between savings, consumption and taxes. Households own factors of production
(labour and capital), which they supply them to ﬁrms and receive remuneration in return.
Firms in each region produce goods that are consumed by households, government or
ﬁrms (in the same sector or in others) as an input in their production process. Transport
costs for trade between and within regions are assumed to be of the iceberg type and
are sector- and region-pair-speciﬁc. This implies a 5 x 267 x 267 asymmetric trade cost
matrix derived from the European Commission’s transport model TRANSTOOLS.3

The economic sectors in each region differ with respect to the scope for product
differentiation between varieties. The public sector operates under the constant-returns-to-
scale sectors produce undifferentiated commodities and price at marginal costs. Firms in

2See Brandsma et al. (2015) for a formal description of the key mechanisms in the RHOMOLO model

and Brandsma and Kancs (2015) for policy applications.
3http://energy.jrc.ec.europa.eu/transtools/

5

the differentiated good sector produce one particular variety of a good, under increasing
returns to scale. These ﬁrms can price-discriminate their export markets and, given the
small-group monopolistic competition structure, can set different levels of mark-ups in
the different destination markets. The number of ﬁrms in each sector-region is empirically
estimated through the national Herﬁndahl indices, assuming that all the ﬁrms within
one region share the same technology. Given their higher weight in the price index, ﬁrms
with higher market shares are able to extract higher mark-ups from consumers than their
competitors, and, since market shares vary by destination market, also mark-ups vary by
destination market.

2.2. Labour market

The labour market in RHOMOLO captures all key interactions between labour de-
mand and labour supply. On the labour supply side, each region is populated by workers,
who are differently skilled. The RHOMOLO model distinguishes between three skill
levels of workers: low-skill, medium-skill and high-skill, which correspond to three levels
of education: primary and lower-secondary education (ISCED 0-2), upper-secondary and
post-secondary education (ISCED 3-4), and tertiary education (ISCED 5-6).4 As it will be
explained in Section 2.4, using the Labour Force Survey (LFS) qualiﬁcation data allows
us to calculate the educational attainment rates for each EU region and education level in
the base year, which are required for calibration of the RHOMOLO model. On the labour
demand side, the relative price of labour together with the demand for ﬁnal goods and
services determine labour demand. Companies (and public sector) rent labour services
from households, for what they receive wage income. In line with the empirical evidence,
in RHOMOLO wages differ between different skill levels: high-skill workers earn higher
wage than medium-skill workers, which in turn is higher than wage of low-skill workers.
There are two ways how workers from a particular region can increase the remuner-
ation per unit of their labour supply in RHOMOLO. First, workers can upgrade their
skills through investment into education (upward skill mobility), which however is costly
for workers. Moreover, while in education, workers do not receive any wage income.
Upgrading skills from low to medium or from medium to high would increase worker
wage, if employed. Second, workers can migrate to another EU region with a higher wage
rate and offer their skills there (spatial skill mobility). In RHOMOLO, labour migration
between EU regions is costly for workers. Migration costs capture not only the physical

4International Standard Classiﬁcation of Education (ISCED), see Annex 1 for details.

6

relocation costs, but also social, cultural, linguistic costs, information imperfections,
people’s preferences for a particular region, climate, etc. Moving to a high-wage region
would increase worker’s wage for the same skill level, if employed. These two channels
of labour adjustment – upward skill mobility and spatial skill mobility – are the main focus
of the present note.

In addition, in RHOMOLO labour supply decisions are also modelled through labour
market participation choices of people and employment decisions of companies (see
Brandsma et al. (2014); Persyn et al. (2014)). In the same time, also worker remuneration
may change, which in turn may affect labour supply decisions of workers and labour
demand decisions of ﬁrms. As noted by Boeters and Savard (2012), it is crucial to account
for all four adjustment channels of regional labour markets in policy impact assessment,
as there exist important interactions between them. Failing to model them simultaneously
may result in suboptimal policy recommendations.

In addition to those labour market mechanisms, which are modelled explicitly in
RHOMOLO, many more peculiarities of regional labour markets are captured implicitly.
For example, differences in skill and hence productivity levels among people with the
same level of education are captured by calibrating RHOMOLO to the base year regional
labour market data. Productivity differences between workers living in different regions
together with migration costs between regions account for sizeable inter-regional wage
differences for people with the same education level.

2.3. Spatial dimension

The structure of the RHOMOLO model engenders different endogenous agglomer-
ation and dispersion patterns of ﬁrms by making the number of ﬁrms in each region
endogenous (see Di Comite and Kancs (2014)). Three effects drive the mechanics of
endogenous agglomeration and dispersion of economic agents: the market access effect,
the price index effect and the market crowding effect. The market access effect captures
the fact that ﬁrms in central regions are closer to a large number of consumers (in the
sense of lower transportation costs) than ﬁrms in peripheral regions. The price index
effect captures the impact of having the possibility of sourcing cheaper intermediate
inputs because of the proximity of suppliers and the resulting price moderation because
of competition. Finally, the market crowding effect captures the idea that, because of
higher competition on input and output markets, ﬁrms can extract smaller mark-ups
from their customers in central regions. Whereas the ﬁrst two forces drive the system
of regional economies towards agglomeration by increasing the number of ﬁrms in core

7

regions and decreasing in the periphery, the third force causes dispersion by reducing
the margins of proﬁtability in the core regions.

RHOMOLO contains three endogenous location mechanisms that bring the agglomer-
ation and dispersion of ﬁrms and workers about: the mobility of capital, the mobility
of labour, and vertical linkages. Following the mobile capital framework of Martin and
Rogers (1995), we assume that capital is mobile between regions in the form of new
investments, and that the mobile capital repatriates all of its earnings to the households
in its region of origin. Following the mobile labour framework of Krugman (1991), we
assume that workers are spatially mobile; workers not only produce in the region where
they settle (as the mobile capital does), but they also spend their income there (which is
not the case with capital owners); workers’ migration is governed by differences in the
expected income, and differences in the cost of living between regions (the mobility of
capital is driven solely by the equalisation of the nominal rates of return).5 Following
the vertical linkage framework of Venables (1996), we assume that, in addition to the
primary factors, ﬁrms use intermediate inputs in the production process; similarly to
ﬁnal goods consumers, ﬁrms value the variety of intermediate inputs. Furthermore, the
trade of intermediate inputs is costly.

In addition to these effects, which are common to theoretical New Economic Geogra-
phy models with symmetric varieties, the speciﬁc characteristics of spatial CGE models,
such as RHOMOLO, implicitly add important stability mechanisms in location patterns
by calibrating consumer preferences over the different varieties in the base year. Through
calibration, the regional patterns of intermediate and ﬁnal consumption observed in the
base year data are translated into variety-speciﬁc preference parameters, which ensure a
given level of demand for varieties produced in each region, including peripheral ones.
Therefore, it would be impossible to obtain extreme spatial conﬁgurations in terms of
agglomeration or dispersion because ﬁrms in the regions with very low number of ﬁrms
would enjoy very high operating proﬁts due to the high level of consumer marginal
provided by their relative scarce variety and thus would attract more ﬁrms in the region.

2.4. Data and empirical implementation

The RHOMOLO model covers 267 NUTS2 regions in the EU27, which are disaggre-
gated into six NACE Rev. 1.1 sectors plus an R&D sector (see Table 1 and Figure 1,

5As noted above, in RHOMOLO also the regional unemployment rates enter the migration problem of

workers.

8

respectively).6 The regional and sectoral disaggregation implies considerable data needs.
In particular, for the empirical implementation of the RHOMOLO model, data for all
exogenous and endogenous variables at regional (and sectoral) level for the base year
(2010), as well as numerical values for all behavioural parameters are required.

Table 1: Sectoral disaggregation of the RHOMOLO model

NACE code Sector description
AB
CDEF
GHI

Agriculture, hunting and forestry
Mining and quarrying, manufacturing, electricity and gas and construction
Wholesale and retail trade, repair of motor vehicles, motorcycles, personal
and household goods, hotels and restaurants, transport and communications
Financial intermediation, real estate and business services, excluding R&D
Research and development activities (R&D)
Non-market services

JK
R&D
LMNOP

Source: Authors’ aggregation based on the EUROSTAT (2003) NACE Rev. 1.1 classiﬁcation. R&D sector is separated out from the

standard NACE group JK.

The base year (2010) data are compiled in form of regional Social Accounting Matrices
(SAMs) (see Thissen et al., 2014, for details). For the construction of national SAMs, data
are taken from the World Input Output Database (WIOD) project and the Global Trade
Analysis Project (GTAP). The WIOD database consists of International Input-Output
tables, International and National Supply and Use tables, National Input-Output tables,
and Socio-Economic and Environmental Accounts covering all EU27 countries and the
rest of the world for the period from 1995 to 2009. In the context of the RHOMOLO
model, an attractive feature of the WIOD data is that an attempt is made to identify and
correct for the re-exports before calculating the total value of exports by country.

Inter-regional labour migration patterns are captured in RHOMOLO by data on
net changes in the regional labour force (see Brandsma et al., 2014, for details). Using
these data, the relocation of workers between any pair of regions is modelled as a
function of expected income and migration costs. For the estimation of migration
elasticities data are required on labour migration, consumer price index (CPI), regional

6The simulations presented in this paper were performed with the RHOMOLO model, which was
calibrated to 2010 base year data. In the next updates of the base year RHOMOLO will be updated to
2013/2014 and extended to include mode regions. See https://ec.europa.eu/jrc/rhomolo for the latest
version of the RHOMOLO model and base year data.

9

Figure 1: Spatial disaggregation of the RHOMOLO model. Notes: The number of NUTS2 regions in each
country are in parentheses (in total these numbers sum up to 267).

GDP and unemployment. The EUROSTAT’s Regional Migration Statistics provides data
on migration within Member States. In order to complete the regional migration matrix,
national totals are brought in line with the OECD data on migration ﬂows between
countries. The Household Income and Active Population data, which are extracted from
EUROSTAT, together with data on unemployment and wages, which are computed from
the Labour Force Survey (LFS), provide the necessary input to the estimation, calibration
and modelling of regional labour markets and interactions between them in RHOMOLO.
Inter-regional trade ﬂows are estimated using detailed inter-regional transport and
freight data from Thissen et al. (2013, 2014). These data are aligned with the available
regional accounts: the distribution of production and consumption over EU regions and
national SAMs to ensure consistency with the rest of RHOMOLO data. Asymmetric
region-pair-speciﬁc trade costs are extracted from the TRANSTOOLS model, which add
up to country level trade and transportation margins calculated from WIOD.

Knowledge capital enters RHOMOLO through the R&D sector, whose data are also
retrieved from the World Input Output Database. In RHOMOLO the stock of capital

10

AT (9)BE (11)BG (6)CY (1)CZ (8)DE (39)DK (5)EE (1)ES (18)FI (5)FR (22)GR (13)HU (7)IE (2)IT (21)LT (1)LU (1)LV (1)MT (1)NL (12)PL (16)PT (5)RO (8)SE (8)SI (2)SK (4)UK (37)Countries and regions in RHOMOLObuilds up over time by accounting for the annual national R&D services supplied to the
market (which is a ﬂow variable).

The regional stock of human capital is proxied in RHOMOLO by 3 different levels
of education: primary and lower-secondary education (ISCED 0-2), upper-secondary
and post-secondary education (ISCED 3-4), and tertiary education (ISCED 5-6). Wages
are differentiated on the basis of the corresponding categories of education levels to
account for the decision of households to spend their time on education. Data for this
are available in the LFS and EU KLEMS databases.

Data on the regional stock of physical capital are constructed using the Perpetual
Inventory Method (PIM). This approach starts with an estimate of the initial stock by
country and industry, regionalised by the share in gross value added (GVA) in 1995 and
calculates the ﬁnal capital stock by region and by industry in 2010 by adding the yearly
capital investments and making assumptions on depreciation. The following data can be
estimated: gross ﬁxed capital formation by sector at the NUTS2 level in current prices for
the years 1995-2010; price deﬂators for conversion into constant prices; initial stocks for
calculating the net capital stocks for each year applying the PIM from the EU KLEMS
database. These data are available at the national level, which are regionalised by the
GVA share. Depreciation rates are calculated by weighing the average service life of each
of the six types of assets for each country.

The structural parameters of RHOMOLO are estimated econometrically or, where
econometric estimations are not possible due to data limitations, borrowed from the
literature (Okagawa and Ban, 2008). The parameters related to inter-regional labour
migration are estimated in a panel data setting for each country separately. Similarly, the
parameters related to the elasticities of substitution both on the consumer and on the
producer side are estimated econometrically.

Finally, as usual in spatial computable general equilibrium models, all shift and
share parameters are calibrated to reproduce the base year (2010) data in the SAMs. In
order to determine the sensitivity of simulation results with respect to the implemented
parameters in RHOMOLO, we perform extensive sensitivity analysis and robustness
checks. Among others, the sensitivity analysis allows us to establish conﬁdence intervals
(in addition to the simulated point estimates) for RHOMOLO’s simulation results.

11

3. Simulations of labour market integration with RHOMOLO

3.1. Simulation setup

Generally, there are many different ways how to model policy interventions in
RHOMOLO. For example, in RHOMOLO labour market policy interventions can be
modelled as a reduction of geographical relocation/migration costs, which facilitates the
spatial skill mobility. The European Qualiﬁcations Framework (EQF), as one of such policy
initiatives, aims to improve the recognition of skills and qualiﬁcations across different
countries and systems in the EU. In RHOMOLO, an EU-wide recognition of skills and
qualiﬁcations increases the mobility of workers across EU regions and improves the
match between skill demand and skill supply in every EU region.7

Assessing the impacts of labour market policy interventions in RHOMOLO consists
of two main steps. The ﬁrst step consists of econometric estimations of behavioural
parameters. In the above example, the link between EU policies deepening the labour
market integration and regional human capital stock needs to be estimated econometri-
cally using historical data. The econometric analysis has to provide answers to questions
such as: how many people would migrate from low-income to high-income regions
given certain net income differences between regions? How many additional people
would migrate when migration costs would decrease? Estimating the link between the
EU labour market policy interventions and human capital growth in each region is one
of the main empirical challenges, which have to be addressed when assessing the labour
market policy interventions and deriving skill-speciﬁc and policy-relevant conclusions.
In the second step, the estimated skill-speciﬁc policy impact on the regional human
capital growth is used as policy scenario input into the RHOMOLO model to simulate
the labour market policy impact on regional macroeconomic outcomes, such as growth,
income, consumption, employment, savings, investments, trade, inequality, etc. An
important advantage of the RHOMOLO model is that it is able to capture all the induced
direct and indirect general equilibrium effects of labour market policy interventions, such
as changes in the relative wage for low-, medium- and high-skill labour, according to
which both labour supply and labour demand would adjust in RHOMOLO.

In addition, also the government budgetary effects of policy interventions are fully
captured in RHOMOLO. On the government expenditure side, any type of policy inter-
vention needs to be ﬁnanced through taxes, which is captured explicitly in RHOMOLO.

7http://ec.europa.eu/education/policy/strategic-framework/skills-qualifications_en.htm

12

On the government income side, potential policy beneﬁts include higher contributions
to income taxes, welfare (e.g. pension) system, as well as resolving the labour market
shortages in speciﬁc sectors and regions, leading to higher value added tax revenue. Also
they are accounted for in RHOMOLO.

3.2. Step 1: Econometric estimations of skill mobility

As noted above, ﬁrst the elasticity of skill mobility needs to be estimated econometri-
cally. The main challenge of econometric estimations is to derive an estimable migration
equation which is able to relate the aggregate inter-regional migration ﬂows to labour
mobility parameters of RHOMOLO. We follow Brandsma et al. (2014), who have derived
such an estimable migration equation starting from the individual migration decision
of Sorensen et al. (2007); Grogger and Hanson (2011). The dependent variable in the
estimable equation is inter-regional labour migration, whereas the explanatory variables
are related to expected work income.

Dependent variable: inter-regional labour migration. The estimation of migration equation
requires a complete matrix of gross bilateral migration ﬂows between all NUTS2 regions
in the EU. However, such data are currently not available. In order to address this issue
of data paucity, we merge two available data sets: EUROSTAT’s data on within-country
interregional migration ﬂows and OECD’s data on international migration. This allows
us to construct an imputed matrix of bilateral migration ﬂows, which can be used
in empirical estimations. Data on migration between NUTS2 regions within countries
are available from EUROSTAT for most of the EU Member States.8 The ﬁrst step in
constructing an approximate dataset of gross bilateral migration ﬂows between NUTS2
regions consists of calculating migration probabilities between every pair of regions within
each country, for each country separately. Secondly, international migration ﬂows without
any regional dimension were extracted from the OECD data base. These international
migration ﬂows were subsequently ‘regionalised’, assuming that international migrants
distribute themselves between the regions of the country of destination following the
same pattern as within-country migrants do. Analogously, international migrants are
assumed to originate from speciﬁc regions of origin in the source country in the same
proportions as the within-country migrants originate from different regions in the source

8The countries for which internal regional migration data are available are Austria, Belgium, Bulgaria,
the Czech Republic, Spain, Finland, Hungary, Italy, the Netherlands, Poland, Romania, Sweden, Slovenia
and Slovakia.

13

country.9

Explanatory variables. In line with the underlying conceptual framework of RHOMOLO,
we measure the indirect utility for living in region d for an individual from origin
region o, by the expected real income in destination region d, net of migration costs
for migrating between o and d. We approximate the real expected income in region
r by incomer = W(r) · (1 − u(r))/P(r), with W(r) the average regional wage, u(r) the
unemployment rate, and P(r) the regional consumer price index. Migration costs are
approximated by a log-linear function of the great circle distance between the geographic
centre of the origin and destination NUTS-2 region. In the estimable equation we include
a log-linear distance term that captures the elasticity of migration with respect to distance.
In order to obtain results, which are as realistic as possible, a ﬁfth-order polynomial in
distance is used. A dummy variable, I(intlod), for international migration equals 1 in
case region o and d are located in different countries. The empirical speciﬁcation of the
estimation equation then becomes

ln

(cid:19)

(cid:18) sod
soo

= β1 ln(incomeo) + β2 ln(incomed) + β3 ln(distanceod)

(1)

+ β4 I(intlod) + β5 ln(sod|cd

) + (ξod − ξoo) .

Although, according to the underlying conceptual framework, coefﬁcients β1 and β2
should be of opposite sign and of equal size, we follow Kancs (2011) and do not impose
this restriction in the econometric estimations.

The estimation of equation (1) requires an instrumental variable approach, because of
the endogeneity of the conditional probability (share). Following the common approach
in the literature on discrete choice in the context of product demand estimation, we chose
the number of regions in country as an instrument for the probability of choosing a
speciﬁc region as the destination of choice, conditional on the destination country choice.
The share of people choosing a particular region in a country will on average be inversely
related to the number of regions in the country. The number of regions in a country is
exogenous to the migration decision in itself, as the size of countries and the number

9This approach is an approximation, and will introduce errors, if the true distribution of international
migrants differs signiﬁcantly from the observed distribution of within-country migrants. One can imagine,
for example, that Austrian migrants would have a stronger tendency to migrate towards northern Italy
when compared to the average Italian inter-regional migrant, given the geographic proximity of Austria to
northern Italy. Whereas we believe that this effect exists and will affect the estimated migration ﬂows, its
impact on the estimated parameters of the migration equation, which are our key interest, will likely be
limited.

14

of NUTS-2 regions contained in them are clearly unrelated to contemporary migration
patterns.

Dependent variable: log(sod/soo)

Table 2: Estimation results

OLS
−0.520∗∗∗
(0.0185)
0.824∗∗∗
(0.0242)
−0.453∗∗∗
(0.0219)
−4.478∗∗∗
(0.041)

ln(incomeo)

ln(incomed)

ln(distanceod)

I(intlod)

ln(condshared)

_cons

N

−4.168∗∗∗
(0.284)

14485

IV

−0.509∗∗∗
(0.0221)
0.787∗∗∗
(0.0273)
−0.656∗∗∗
(0.0326)
−4.119∗∗∗
(0.062)
−1.227∗∗∗
(0.0461)
−5.566∗∗∗
(0.433)

14485

Source: Authors’ estimations. Notes: estimates of equation (1). Standard errors are in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 2 reports the estimation results. Column OLS of Table 2 shows the results of
estimating equation (1) using the ordinary least squares (OLS) estimator. Column IV
reports the instrumental variables (IV) estimation described above. The estimated effect
of income in the destination region decreases and the elasticity of distance increases,
when taking into account the endogeneity of the conditional probability, though overall
the results of OLS and IV estimators are rather similar. The size of the effect of the
international migration dummy, I(intlod), is remarkable, emphasising the importance of
international borders (often corresponding to important cultural and language barriers)
on labour mobility. One important point is that the coefﬁcient on the conditional share
implies an estimate of σ, the measure of within-country correlation in taste has the
opposite sign and is outside of the theoretically consistence range between 0 and 1. In
this light, and because the difference between the OLS and IV estimates are quite close,
we opt to use the OLS estimates in the simulation analysis.

15

Being rooted in the discrete choice theory, the estimated migration model allows
us to infer the structural parameters of RHOMOLO governing skill mobility from the
observable aggregate labour migration ﬂows. An important advantage of this approach
is that when assessing the effects of policy simulations – to which we turn in the next
section – the predicted migration ﬂows will obey all the key macro-accounting rules.
In particular, the simulated increase in migration inﬂow resulting from an increasing
attractiveness of regions must imply an equal increase in outgoing migration from other
regions, such that the total EU population is unaffected by migration internal to the EU.
Such properties do not hold, when modelling migration ﬂows in an ad-hoc way, or as a
Poisson process (for a discussion, see Brandsma et al., 2014).

3.3. Step 2: Simulations using the RHOMOLO model

This section illustrates the impact of a policy intervention that targets labour market
integration through an improved spatial skill mobility. Note that, even though in this
example we simulate labour market policy intervention through improved spatial skill
mobility, both adjustments in the upward skill mobility and adjustments in the spatial
skill mobility take place simultaneously in RHOMOLO. That is, workers respond to an
increase (decrease) in the remuneration of skills in regional labour markets both through
investment into education (upward skill mobility), and through immigration (emigration)
from (to) other regions, where the high-skill wage rate is lower (higher) (spatial skill
mobility). In order to better identify the skill mobility effects, in these simulations two
other important labour market adjustment channels – participation/nonparticipation and
employment/unemployment – are shut down.

3.3.1. Social and labour market effects

In order to assess the social impacts of labour market integration, ﬁrst a segmented
labour market baseline scenario, against which we will assess the impact of the high-skill
labour market integration, needs to be simulated. In the base year data, we observe
signiﬁcant high-skill wage differences across EU regions, because the spatial skill mobility
channel is partially inactive (due to high migration costs, non-recognition of skills
acquired in other Member States, etc.), only the upward skill mobility channel is fully
active. However, the education channel alone is not strong enough to evade completely
or to reduce signiﬁcantly the inter-regional wage differences. Depending on the sectoral
structure, in each region there is a lower or higher demand for low- and medium-skill
workers. If all workers in a region would attain higher education and become highly

16

skilled then, because of lower supply, wages for low- and medium-skill workers would
go up, reducing in such a way the skill premium. The skill premium must by large
enough to cover the education costs, as no worker would invest into education, if the
skill premium would not cover her education costs.

The labour market integration scenario reinforces the second channel of adjustment –
spatial skill mobility. Figure 2 reports the impact of the high-skill labour market integration
on the real (adjusted for price changes) high-skill wages in the EU regions as percentage
differences between the baseline and the counterfactual equilibrium. The simulation
results reported in Figure 2 suggest that in most regions the real wage of workers would
increase (green colour), only in few regions the real wage would decrease (red and
orange colours). High-skill wages would increase particularly strong in those regions,
where they are below the EU-average, and where the largest emigration of high-skill
workers would take place. High-skill wage would increase more than +2% in a number
of less developed regions of Eastern Europe, Greece, and Portugal. In contrast, high-skill
wage would decrease by more than -1% in several more developed regions in Benelux,
Denmark, France and Ireland regions.

In RHOMOLO, high-skill workers from regions, where the demand and hence the
relative wage for high-skill labour is low, would migrate to regions with higher wages for
high-skill work. Arriving in high-wage regions and supplying their skills there would
increase the supply of high-skill labour, which in turn would exert a downward pressure
on high-skill wages. The opposite would happen in low-wage regions, where the wage
rate for high-skill labour would increase because of a decrease in skill supply. In such a
way the spatial skill mobility reduces high-skill wage differences across EU regions. Note,
however, that wages are not fully equalised across EU regions, e.g. due to differences in
labour productivity.

Wage adjustments trigger further general equilibrium effects in regional labour mar-
kets. As regards labour supply, emigration of high-skill workers from low-wage regions
exerts an upward pressure on high-skill wages, which in turn creates incentives for low-
and medium-skill workers to invest into education and become medium and highly
skilled, respectively. As regards labour demand, there are two types of induced effects.
On the input side, changes in the relative low-, medium- and high-skill wages trigger
adjustments in factor demand for production of goods, because low-, medium- and
high-skill labour and capital are mutually substitutable. On the output side, companies
adjust their specialisation patterns according to changes in the relative skill-abundance

17

Figure 2: Simulation results: The impact of high-skill labour market integration on real high-skill wage in
EU regions. Source: Authors’ simulations with the RHOMOLO model.

in the region where the company is located. Firms located in the high-skill abundant
regions would specialise in the production of skill-intensive goods, whereas ﬁrms located
in the low-skill abundant regions would specialise in the production of skill-extensive
goods. In addition, also companies may ﬁnd it proﬁtable to relocate between regions,
if ﬁerce competition on input and/or output markets drives proﬁts to zero, whereas
in other regions they are positive. These and other adjustments within and between
regional economies take place until a new global equilibrium is established, in which
no worker and no company can be better off by changing their education, location, em-
ployment, consumption, investment, production, trade, migration, etc. patterns. Hence,

18

(2,3.5](1.5,2](1,1.5](.5,1](0,.5](−.5,0](−1,−.5](−1.5,−1](−2,−1.5]Impact on high−skill wagethe new (counterfactual) equilibrium describes the optimal response of utility maximis-
ing households and proﬁt maximising ﬁrms to the labour market integration shock in
terms of macroeconomic outcomes, such as growth, income, employment, consumption,
production, savings, investment behaviour, etc. In order to identify the pure policy
impact, in Figure 2 each of the counterfactual equilibrium variables is compared to the
corresponding baseline variable. The difference between the two can be attributed to the
net policy impact of labour market integration.

3.3.2. Macroeconomic effects

As for the macroeconomic impacts, Table 3 reports selected aggregated results of the
high-skill labour market integration for the whole EU. An important result from the
simulated hypothetical high-skill labour market integration scenario appears that at the
aggregate level the total EU economy would gain in terms of GDP growth (+0.94%). As
reported in Table 1, the impact would be differentiated between different skill levels
at the aggregated EU level: high-skill workers would gain the most in terms of wage
increase. A further interesting result is that also low- and medium-skill workers would
gain from the high-skill labour market integration. Among others, a more efﬁcient use of
labour in the production process is an important driver for these gains.

One of the key mechanisms, which generates the aggregate gains for the EU economy,
is a more efﬁcient resource allocation, particularly of high-skill labour. Productivity
gains are one of many induced general equilibrium effects in the RHOMOLO model,
although they themselves are only indirectly linked to spatial skill mobility. On the input
side, because of the high-skill labour market integration, high-skill workers are employed
relatively more in those regions and sectors where the marginal product and hence the
wage rate of high-skill labour is the highest. More efﬁcient allocation of high-skill labour
yields productivity gains at the aggregate level. On the output side, regional economies
can better exploit their comparative advantages by deeper specialising in the production
of skill intensive (skill extensive) goods, yielding additional productivity gains.

Note that in the simulated hypothetical labour market integration scenario we as-
sumed that high-skill workers become fully mobile across EU regions and receive full
information about skill demand and supply in every EU region. As a result, labour
migration considerably reduces spatial wage disparities across EU regions. In reality,
however, workers do not have perfect information, national labour markets are rather
segmented in the EU, there are important quality differences between different education
systems across the EU Member States, etc. Hence, in reality, the impact of a deeper

19

Table 3: Simulated policy impact on selected macroeconomic variables for the EU

Macroeconomic variable Percentage change from the baseline
GDP
Wage: low-skill
Wage: medium-skill
Wage: high-skill
Output
Productivity (TFP)
Investment
Exports
Imports

+0.94%
+0.27%
+0.31%
+0.88%
+1.15%
+0.60%
+0.79%
+2.63%
+1.42%

high-skill labour market integration on the inter-regional wage differences may not be as
pronounced as simulated in our hypothetical labour market integration scenario.

For the sake of expository simplicity, only few effects and results of the RHOMOLO
model have been presented and discussed in this section.
In RHOMOLO, however,
there are many more channels of adjustment present and many more effects take place
simultaneously. The simple simulation example presented above aims solely at providing
intuition for a better understanding of the key mechanics of the RHOMOLO model and
its potential for policy impact assessment. Analogously, in Table 1 only few selected
output variables of RHOMOLO are reported and even they have been aggregated together
for the whole EU. The RHOMOLO model, however, produces more than 50 different
macroeconomic indicators, for each regional economy, each industrial sector, each skill
level and each year.

4. Conceptual analysis of labour market integration

4.1. Setup: upward and spatial skill mobility

In this section we decompose and explain graphically two key channels – upward skill
mobility and spatial skill mobility – through which regional labour markets adjust to the
high-skill labour market integration. We also show how the interaction between the two
channels affects labour market outcomes. We employ a diagrammatic framework, as it
greatly facilitates the understanding of the numerical simulation results presented in
the previous section, and helps highlighting the most important channels of adjustment
without involving a formal mathematical analysis.

20

For the sake of graphical tractability of the diagrammatic analysis, we make several
simplifying assumptions. First, we assume that there are only two regions (one Less
Developed Region (LDR) and one More Developed Region (MDR)), and only two types
of skills (low-skill and high-skill). Second, as in the simulations presented in Section 3.3,
we consider only two channels of labour market adjustment (education and migration),
whereas we abstract from two other important channels (participation and employment),
which are present in RHOMOLO (see Brandsma et al. (2014); Persyn et al. (2014)). Third,
we assume that the demand for low-skill labour is inﬁnitely elastic. If the elasticity of
low-skill labour demand would be lower, then an additional low-skill wage effect would
arise, which would affect the ratio of high-skill/low-skill workers in LDR.10 Finally, in
the presented diagrammatic analysis we abstract from labour market demand effects,
although in reality (and in RHOMOLO), an increase in population in a region would
increase local consumption for goods, which in the presence of non-zero transportation
costs would favour local producers, resulting in higher production and higher demand.
These simpliﬁcations are required to keep the analytical exercise as simple as possible,
while showing the main mechanics of skill adjustments.

Suppose that the less developed region, LDR, is endowed with LLDR units of labour,
which is shown on the horizontal axis in the left panel of Figure 3. High-skill labour,
H , is measured from the left to right, whereas low-skill labour, LLDR
LLDR
, from the right
to the left, with LLDR = λH LLDR
and DLDR
H + λLLLDR
represent ﬁrm
L
demand for high-skill and low-skill labour, respectively, and SLDR
is worker supply of
high-skill labour in LDR. λH and λL are efﬁciency parameters measuring the relative
labour productivity of high-skill and low-skill workers, respectively.

. Curves DLDR

H

H

L

L

In line with the simulation analysis presented in Section 3.3, we abstract from par-
ticipation and employment decisions and focus on two decisions of workers: education
and migration. First, we consider the education decision, where workers choose between
offering low-skill labour versus investing into education and acquiring high skills in LDR.
Given that education is costly, workers invest into education only when education would
increase their net wage.11 Net of schooling costs, workers must earn at least the low-skill
wage, which is equal to wLDR∗
in Figure 3. The vertical difference between the high-skill
labour supply, SLDR
, represents the cost of acquiring
education (Figure 3, left panel). The last low-skill worker, who enters education at LLDR∗
,

H , and the low-skill wage rate, wLDR∗

L

L

H

10Moreover, it can be easily veriﬁed that, as long as the condition LLDR

H/Lm
results with low-skill labour migration would be qualitatively equal to those presented here.

H /LLDR

≤ Lm

L

L holds, the

11We implicitly assume that all workers, for whom it pays off to become skilled, invest in education.

21

is just able to compensate his education costs, though his skill premium is equal to zero.
In equilibrium, the education cost of the marginal worker who enters education is equal
to EC∗. Abstracting from participation and employment channels of adjustment, the rest
of workers, LLDR − LLDR∗
, remain with low skills. The equilibrium wage of low-skill
workers without migration, wLDR∗
, is at the point where the demand for low-skill labour,
. The equilibrium stock of high-skill labour is LLDR∗
, intersects vertical line at LLDR∗
DLDR
L
and the high-skill wage rate is wLDR∗
.

H

H

H

L

H

The labour market equilibrium for receiving region, MDR, is analogous. In absence
of migration, the equilibrium high-skill wage, low-skill wage and the stock of high-skill
workers are wMDR∗

, respectively (Figure 3, right panel).

, and LMDR∗

H

, wMDR∗
L

H

Next, consider the high-skill worker decisions where to offer their skills, locally or
abroad. The trade-off, which high-skill workers face here, is given by the expected
wage increase after migration versus migration costs, MC.12 Differences in development
and hence wage levels between the less developed and more developed regions trigger
migration from LDR to MDR. Workers migrate if the expected wage income arising
from relocation is higher than migration costs.13 In presence of positive migration costs,
MC > 0, the net wage which high-skill migrant workers earn in MDR is lower than the
high-skill wage of incumbent worker, because the net wage of migrants is high-skill wage
in destination region, wMDR

, minus migration costs, MC.

H

According to migration network theory and the estimated migration elasticities in
Section 3.2, migration costs are not constant, they are decreasing in the number of
migrants from LDR residing in MDR. In Figure 3 (middle panel) these network effects
are captured by a decreasing distance between curves Sm
MC and Sm. Curve Sm is migrant
work supply on the migrant labour market, which is derived by subtracting the high-skill
labour supply, SLDR
MC is
migrant supply adjusted for migration costs, MC.

H , from the high-skill labour demand, DLDR

H , in LDR. Curve Sm

In equilibrium, Lm

MC∗, and receive net wage, wm
H
of high-skill labour, wMDR∗

H high-skill workers emigrate from LDR incurring migration cost,
Ho − MC∗(cid:1) in MDR. The equilibrium wage rate
, is determined by the intersection of migration supply, Sm
MC,

(cid:0)= wMDR∗

Ho

12These costs include not only the direct transportation costs to the destination country, but also
employment uncertainty (which is higher abroad than at home), social costs of leaving family and/or
friends behind, cultural adjustment costs, language barriers etc. (Kancs, 2011).

13We recognise that in reality the migration decision of workers is driven not only by wage differences
but also by non-economic considerations. However, in the present study we abstract from all other
determinants of migration and consider cross-country wages differences as the only force driving labour
migration.

22

and migration demand, Dm (Figure 3, middle panel). Migration demand, Dm, is derived
by subtracting the high-skill labour demand, DMDR
, from the high-skill labour supply,
SMDR
in MDR (right panel in Figure 3). In presence of migration, the high-skill wage in
H
(high-skill equilibrium wage without migration) to wMDR∗
MDR is reduced from wMDR∗
(high-skill equilibrium wage with migration).

Ho

H

H

4.2. Impact of the high-skill labour market integration

The high-skill labour market integration affects the spatial labour market equilibrium
by reducing migration costs and improving access to high-skill jobs in the more developed
region, MDR, which in the short-run leads to a positive high-skill wage gap between
MDR and LDR. Positive net of migration cost wage differences trigger migration of
high-skill workers from LDR to MDR. In the long-run, in addition to these direct labour
market effects, high-skill labour migration triggers further induced general equilibrium
effects, the most important of which are explained in the following sections.

4.2.1. Social and labour market effects

As shown in Section 3.3.1, through reduced migration costs and improved access to
high-skill jobs in MDR, in the short-run, the high-skill labour market integration increases
the net of migration cost wage gap between MDR and LDR, and hence disturbs the
spatial labour market equilibrium. The increased wage differences trigger skill migration
– driven by higher expected earnings in MDR – high-skill workers migrate from LDR
to MDR. In the long-run, in addition to this direct short-run effect on high-skill labour
force distribution between LDR and MDR, migration itself affects factors which drive
migration, e.g. inter-regional wage differences and migration costs. Both the high-skill
worker outﬂow (direct effect) in LDR, and the induced second-round migration effects –
triggered by changes in the relative wages in LDR and MDR and changes in migration
costs – are examined in this section.

The most direct and visible effect induced by the high-skill labour market integration
is transfer of human capital embodied in migrant skills from LDR to MDR.14 Given
that high-skill migrants embody private productive skills, the exclusiveness of human
capital implies that a migration-driven increase in the stock of human capital in MDR is
proportional to a decrease in the stock of human capital in LDR. In Figure 3, high-skill
labour migration increases from Lm
HB (middle panel). Under integrated high-skill

H to Lm

14Given that the human capital embodied in skilled workers is draining out of country, in the migration

literature this effect is often referred to as a ’brain drain’.

23

HB < LLDRn
labour market equilibrium, LDR has less high-skill workers, LLDRn
(Figure 3, left panel). Thus, because of brain drain, the high-skill labour market integration
has a strictly negative impact on human capital in LDR.

, with LLDRn

HB

H

In the medium- to long-run, high-skill worker migration affects wages in LDR. By
reducing the high-skill labour supply in LDR, high-skill worker emigration will exert
an upward pressure on high-skill wages in LDR (assuming that labour demand does
not change). This will narrow the migration-driving wage gap between LDR and MDR,
implying less migration in the long-run. Thus, because of increasing skill premium
in LDR, the long-run losses of human capital induced by the high-skill labour market
integration may be lower compared to the short-run equilibrium.

Notice that the inter-regional labour migration affects wage rate not only in the
sending regions but also in the receiving regions. Through increased labour supply of
high-skill workers in MDR, emigration will exert a downward pressure on high-skill
wages. Lower high-skill/low-skill wage ratio in MDR will narrow the migration-driving
wage gap between LDR and MDR, which in turn will attract fewer migrants from LDR.
Thus, the long-run losses of human capital induced by the high-skill labour market
integration are lower compared to the short-run also because of decreasing skill premium
in MDR.

Ho

to wm

Both wage effects on inter-regional distribution of labour force are shown in Figure
3, where in the long-run high-skill labour migration equalises the high-skill wage rate
between LDR and MDR. High-skill labour migration reduces high-skill wage in MDR
from wMDR∗
H. In contrast, in LDR high-skill wage increases from wm
HB (Figure
3). Again, these ﬁndings are in line with RHOMOLO simulation results presented in
Section 3.2. In the long-run, the equilibrium wage rate for high-skill labour, wm
HB, is
equalised across regions and migration equals to Lm
HB. Hence, because of LDR and MDR
wage effects, the long-run losses of human capital induced by the high-skill labour market
integration are lower compared to the short-run.

H to wm

The high-skill labour market integration affects also the long-run education equi-
librium, which determines the share of educated workforce in each region. Through
high-skill worker emigration, the high-skill labour market integration induces changes in
the relative wages in LDR, which in turn affects education incentives. Given that in the
high-skill labour market integration scenario only high-skill workers can migrate or, alter-
natively, more high-skill workers migrate than low-skill workers, LLDR
H/Lm
L ,
the ratio of high-skill/low-skill workers will decrease in LDR. A declining supply of

H /LLDR

(cid:48) Lm

L

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                 𝑀𝐶 𝑤𝐻0𝑀𝐷𝑅∗ Less developed region (LDR) More developed region (MDR) Migration 𝑆𝐻1𝐿𝐷𝑅 𝐷𝐿𝐿𝐷𝑅 𝐿𝐿𝐷𝑅  𝐿𝐻𝐿𝐷𝑅∗ 𝑤𝐻𝐿𝐷𝑅∗  𝑤𝐿𝐿𝐷𝑅∗ 𝐿𝐻𝐵𝐿𝐷𝑅𝑚 𝑆𝐻𝐿𝐷𝑅 𝑆𝐻𝑀𝐷𝑅 𝑤𝐿𝑀𝐷𝑅∗ 𝐿𝐻𝐵𝑀𝐷𝑅𝑛 𝐷𝐻𝑀𝐷𝑅 𝐿𝐻𝑀𝐷𝑅∗ 𝑆𝑚  𝐷𝐻𝐿𝐷𝑅  𝑆1𝑚  𝐷𝐿𝑀𝐷𝑅 𝐷𝑚  𝐿𝐻𝐵𝑀𝐷𝑅𝑚 𝑤𝐻𝐵𝑚  𝐿𝐻𝐿𝐷𝑅𝑛 𝑤𝐻𝑀𝐷𝑅∗ 𝐿𝐻1𝑚  𝐿𝐻𝐵𝑚  a b 𝑤𝐻1𝑚  𝐿𝐻1𝐿𝐷𝑅𝑚 𝐿𝐻1𝐿𝐷𝑅𝑛 𝑆𝐻2𝐿𝐷𝑅 𝐿𝐻2𝐿𝐷𝑅𝑛 𝑆2𝑚  𝑤𝐻2𝑚  𝐿𝑀𝐷𝑅  𝑤 𝑆𝑀𝐶𝑚  𝑤𝐻𝑚  𝐿𝐻𝑚  𝐿𝐻𝐵𝐿𝐷𝑅𝑛 𝐿𝐻𝐿𝐷𝑅𝑚 𝐿𝑚  𝐿𝐻2𝑚  𝐷𝐻1𝑀𝐷𝑅 𝐿𝐻0𝑚  high-skill labour will exert an upward pressure on high-skill wage rate in LDR. Increased
high-skill/low-skill wage gap will provide incentives for additional low-skill workers to
enter the education system, to acquire skills. Thus, through wage adjustments in LDR,
the high-skill labour market integration will increase the long-run education equilibrium
in LDR.

The education effect of high-skill/low-skill wage ratio is shown in Figure 3. In the
segmented high-skill labour market baseline, the equilibrium wage of high-skill labour
H and the equilibrium wage of low-skill labour is wLDR∗
in LDR is wm
(Figure 3, left
panel). The high-skill labour market integration reduces migration costs, MC. To simplify
the graphical exposition, we assume that migration costs, MC, are reduced to zero.
As a result, the excess supply of high-skill labour increases from Sm
MC to Sm and the
equilibrium high-skill wage in LDR increases to wm
HB, implying that the wedge between
high-skill and low-skill wage in LDR increases (Figure 3, middle panel).15 Because of
higher skill premium, more workers acquire education under the high-skill labour market
integration. The amount of LDR workers that acquire education increases from LLDRm
to
LLDRm
HB

H

L

.

4.2.2. Macroeconomic effects

In this Section, as an example, we show and explain one speciﬁc macroeconomic
effect triggered by skill mobility – the impact on government budget revenue through
household taxes. Through the high-skill worker emigration, the high-skill labour market
integration reduces the number of taxpayers and hence the tax revenue in LDR. Given
that, on average, high-skill workers are higher net contributors to government budget
than low-skill workers, government revenue decreases both due to fewer tax contributors
and less taxpayers of high taxes.16 On the other hand, higher high-skill/low-skill wage
ratio (due to an upward pressure on high-skill wages) increases government tax revenue
per high-skill worker. Thus, because of smaller government budget (larger per capita
budget), the impact of the high-skill labour market integration may be either decreasing
or increasing on the accumulation of human capital.

The ﬁscal impact of the high-skill labour market integration is shown in Figure 4. The

15The exact magnitude of this wage ratio effect depends on the elasticity of low-skill labour demand. In
Figure 3 the elasticity of low-skill labour demand is assumed to be inﬁnitely elastic implying no unskilled
wage effect.

16Because on average the wage rate for high-skill work is higher than for low-skill and the unemployment
rate among high-skill workers is lower, per capita, high-skill workers contribute more to tax revenue than
low-skill workers.

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        e 𝑀𝐶∗  𝑤𝐻0𝑀𝐷𝑅∗ Less developed region (LDR) More developed region (MDR) Migration 𝐷𝐿𝐿𝐷𝑅 𝐿𝐿𝐷𝑅  𝐿𝐻𝐿𝐷𝑅∗ 𝑤𝐿𝐿𝐷𝑅∗ 𝐿𝐻𝐵𝐿𝐷𝑅𝑚 𝑆𝐻𝐿𝐷𝑅 𝑆𝐻𝑀𝐷𝑅 𝑤𝐿𝑀𝐷𝑅∗ 𝐿𝐻𝐵𝑀𝐷𝑅𝑛 𝐷𝐻𝑀𝐷𝑅 𝐿𝐻𝑀𝐷𝑅∗ 𝑆𝑚  𝐷𝐻𝐿𝐷𝑅 𝐷𝐿𝑀𝐷𝑅 𝐷𝑚  𝐿𝐻𝐵𝑀𝐷𝑅𝑚 𝑤𝐻𝐵𝑚  𝑤𝐻𝑀𝐷𝑅∗ 𝐿𝐻𝑚  𝐿𝐻𝐵𝑚  𝐿𝐻𝐿𝐷𝑅𝑛 𝐿𝑀𝐷𝑅  𝑤 𝑆𝑀𝐶𝑚 𝑤𝐻𝐵𝑚  𝐿𝐻𝐵𝐿𝐷𝑅𝑛 𝐿𝐻𝐿𝐷𝑅𝑚 𝐿𝑚  a b d c tL tH tH1 g l k h p income taxes paid by high-skill and low-skill workers under segmented high-skill labour
markets are tH and tL, respectively, where tH > tL. If taxes are paid as a ﬁxed rate of
gross wages, then the absolute value of taxes paid by high-skill labour increases under
market integration, tH1, because of higher wage, tH < tH1. The total tax revenue under
segmented high-skill labour markets is equal to area abcd, and to area ade under the
integrated high-skill labour market. The high-skill labour market integration reduces the
number of individuals who pay taxes in LDR by LLDRn
for high-skill labour and
by LLDRm
for low-skill labour, which reduces tax revenues by area bc. Because
the high-skill wage increases with free migration, the tax revenue increases by area e. If
area bc is larger than area e, then tax revenue declines, otherwise, it increases due to the
high-skill labour market integration. Thus, the total impact of high-skill labour market
integration on government budget depends on the relative size of these areas, which in
turn depends on the brain drain effect in LDR, low-skill labour productivity effect and
the above discussed remittance effect.

HB − LLDRm

− LLDRn
HB

H

H

Analogous budgetary effects occur in MDR. The total tax revenue under segmented
high-skill labour markets is equal to area gkhp, and to area gkhl under integrated high-
skill labour markets. The high-skill labour market integration decreases the number
of low-skill labour in MDR, which reduces tax revenues by area k. Low-skill labour is
replaced by high-skill labour, which increases tax revenues in MDR by area kl. Further,
high-skill wage decreases, which reduces tax revenue by area p. If area l is larger than
area p, then tax revenue in MDR increases, otherwise it decreases due to the high-skill
labour market integration (Figure 4).

Note that the budgetary effects shown in Figure 4 take into account only changes
in income tax revenues induced by the high-skill labour market integration. They do
not account for other macroeconomic effects or budgetary costs & revenues, such as
changes in welfare transfers, unemployment payments, or education costs. The reason for
neglecting them is to simplify the exposition of the diagrammatic analysis, while focusing
on the direct budgetary effects of high-skill labour integration. As explained above,
RHOMOLO considers all the induced budgetary costs and beneﬁts in a holistic general
equilibrium framework, which however cannot be captured in a simple diagrammatic
framework.

28

5. Conclusions

In the age of globalisation and the knowledge economy, skill mobility is perceived
as one of the key factors for fully unlocking the labour market potential. Assessing the
social and macroeconomic impacts of increased skill mobility is an important though also
challenging task, which requires a holistic approach. This study presents the dynamic
spatial general equilibrium approach taken in the Regional Holistic Model (RHOMOLO)
to better understand the relationship between education, skills, migration and growth.
Two key channels of labour market adjustment – upward skill mobility and spatial skill
mobility – are presented and explained in two different ways: performing numerical
simulations with the RHOMOLO model, and undertaking conceptual analysis in a
diagrammatic framework. The two approaches are complementary and are presented
in a non-formal way to intuitively explain the potential impacts of a hypothetical policy
scenario related to labour market integration, which is simulated as migration cost
reduction, e.g. through a full EU-wide recognition of qualiﬁcations for high-skill workers.
In this hypothetical example, high-skill workers become fully mobile across EU regions,
and have a perfect information about skill demand and supply in every EU region.
The social and macroeconomic impacts of the high-skill labour market integration are
assessed against the segmented labour market baseline.

RHOMOLO is a dynamic spatial general equilibrium model developed by Directorate-
General Joint Research Centre of the European Commission for policy impact assessment
and provides sector-, region- and time-speciﬁc support to EU policy makers on structural
reforms, growth and human capital policies. In RHOMOLO, labour market captures all
the key interactions between labour demand and labour supply. On the labour supply
side, each region is populated by workers, who are differently skilled. The RHOMOLO
model distinguishes between three skill levels of workers: low-skill, medium-skill and
high-skill, which correspond to three levels of education: primary and lower-secondary
education, upper-secondary and post-secondary education, and tertiary education. On
the labour demand side, the relative price of labour together with the demand for the
‘labour demand. Note
the ﬁnal demand goods and services determine the total
that, in addition to policies related to education, skills and migration, RHOMOLO can
also answer policy questions related to the labour market participation and employment.
These questions, however, are beyond the scope of the study, they are detailed in
Brandsma et al. (2014); Persyn et al. (2014).

The results from the numerical simulations and the conceptual analysis suggest that

29

a holistic approach is indeed required for capturing all the direct and indirect, short-
and long-run effects of labour market adjustments. The holistic approach taken in
RHOMOLO has a wider potential for assessing the macroeconomic and social effects of
policies aiming at integration e.g. of marginalised communities, such as Roma or refugees,
into the EU labour markets. Indeed, the policy interventions such as Country Speciﬁc
Recommendations (CSRs), which the European Commission has issued to promote the
participation of Roma children in quality inclusive early childhood and school education
for several Member States, aim to facilitate upward skill mobility.17 In RHOMOLO they
could be simulated by reducing monetary participation costs in education, e.g. by
subsidising fees for early childhood care and education, physical participation costs, e.g.
by providing transport to and from the schools, or social participation costs, e.g. by
providing integrated education, increases primary, secondary and tertiary educational
attainment rates. The RHOMOLO model allows to holistically assess region-, skill- and
sector speciﬁc social and macroeconomic effects in the short-, medium- and long-run.

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30

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31

Annex 1: Skill and education correspondence in RHOMOLO

Low-skill: Primary and lower secondary education (ISCED 0-2)
Medium-skill: Upper-secondary and post-secondary education (ISCED 3-4)
High-skill: Tertiary education (ISCED 5-6)
ISCED 0: pre-primary education
ISCED 1: primary education or ﬁrst stage of basic education
ISCED 2: lower secondary education or second stage of basic education
ISCED 3: upper secondary education
ISCED 4: post-secondary non tertiary education
ISCED 5: ﬁrst stage of tertiary education (not leading directly to an advanced research

qualiﬁcation)

ISCED 6: second stage of tertiary education (leading to an advanced research qualiﬁ-

cation)

32

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EUR 27689 EN – Joint Research Centre – Institute for Prospective Technological Studies 

Title: Assessing the Social and Macroeconomic Impacts of Labour Market Integration: A Holistic Approach 

Author(s): Pavel Ciaian and d'Artis Kancs, 

Luxembourg: Publications Office of the European Union 

2016 – 32 pp. – 21.0 x 29.7 cm 

EUR – Scientific and Technical Research series – ISSN 1831-9424 (online) 

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doi:10.2791/91191 

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