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Studies

The role of public services quality in shaping migration intentions in Central, Eastern and Southeastern Europe

Anna Katharina Raggl ²

What role does the quality of public services play in shaping migration intentions? Using OeNB Euro Survey data collected in 2018, we study the impact of individual perceptions of public services quality on individuals’ migration intentions in ten Central, Eastern and Southeastern European (CESEE) countries. We apply ordinary least squares (OLS) as well as instrumental variable (IV) estimations, using externally merged infrastructure-related variables and individuals’ opinions on the adequacy of public spending on services as instrumental variables. Our findings suggest that dissatisfaction with the quality of public services in the home countries increases the likelihood of individuals having migration intentions. Broken down by the type of public service, we find that dissatisfaction with social security, health, public infrastructure and with services that target businesses and regional development is associated with higher migration aspirations. Furthermore, for people with young children, we see a higher effect on migration intentions resulting from dissatisfaction with education, health and public safety. For self-employed individuals, the effect of dissatisfaction with public services that address companies and regional development is particularly important. The results further confirm that sociodemographic characteristics, economic factors and network effects are closely associated with the aspiration to move abroad.

JEL classification: J61, F22, O52

Keywords: migration intentions, quality of public services, Central, Eastern and Southeastern Europe

In a recent IMF report, Batog et al. (2019) comprehensively assess the implications of demographic developments in CESEE on labor supply, age-related fiscal spending and on productivity and the prospects for economic growth in the region. They conclude that without mitigating policies, growth and convergence toward Western European living standards would slow down considerably. Implementing policies that increase labor supply is one way to counteract demographic developments. But as it is unlikely that this is sufficient, a more comprehensive approach that also addresses emigration, immigration and return migration is needed.

This study attempts to contribute to a better understanding of why individuals intend to emigrate from their home country and to provide insights for designing policies that increase people’s willingness to stay in their home countries (and/or to return or move to CESEE from abroad). Such policies can be important tools to counteract labor force declines, human capital deterioration and challenged public finances, and, as a consequence, to remove obstacles to continued and sustainable convergence to Western Europe. Following up on previous research dedicated to understanding why people in CESEE want to emigrate, we address the link ­between the perceived quality of public services – social security, public infrastructure, education, health, defense and public safety, regional development ­measures – and individuals’ migration intentions.

We use individual-level data from the 2018 wave of the OeNB Euro Survey, which covers ten CESEE countries. Making use of a series of questions on public debt, we investigate the relationship between the perceived quality of public services and individual migration intentions. Our data allow us to control for a rich set of covariates that have previously been identified as relevant for the emergence of ­migration intentions and that range from sociodemographic and economic factors to networks and the trust in local and EU institutions. In addition, we take advantage of the fact that the data are geo-referenced and use matched data on nighttime light combined with regional averages of unemployment and income to control for the level of development in the surroundings of respondents’ places of residence. We use instrumental variable estimations in order to address identification issues that can stem from the simultaneity of migration intentions and contentment with public services and from the omission of factors that influence both these variables. As instrumental variables we use exogeneous data on road density as well as land coverage in individuals’ neighborhoods as measures of infrastructure, and we further employ individuals’ assessment of the adequacy of public spending on public services, a variable that is covered in the survey (an instrumental variable).

The remainder of this paper is structured as follows. Section 1 provides an overview of related literature. The empirical setting is explained in section 2. ­Section 3 summarizes the data and provides descriptive statistics of the key variables. In section 4, the estimation results are presented, and section 5 concludes. Additional material is included in the annexes.

1 Literature

More recently, nonpecuniary factors feature more prominently in studies on the determinants of migration intentions. Otrachshenko and Popova (2014), for example, relate life satisfaction measures to individual migration intentions. Using data from Western and Central Europe, they find that individuals that are dissatisfied with life are more likely to have the intention to emigrate. Similarly, van Dahlen and Henkens (2013) show for the Netherlands that discontent with the quality of the public domain (with regard to mentality, space and overcrowdedness, nature, pollution, crime, etc.) is an important set of factors for explaining migration intentions. In a recent study, Williams et al. (2018) use data from nine European countries – among them Romania as the only country also covered in our analysis – and find that although socioeconomic factors have strong explanatory power, nonpecuniary factors also play a certain role. Several related studies work with different waves and country sets of Gallup World Poll (GWP) data to understand what drives individuals’ migration intentions. Dustmann and Okatenko (2014) use GWP data for sub-Saharan Africa, Asia and Latin America (2005, 2006) to study migration intentions. They find that contentment with local amenities, such as public services and public security, are key determinants of migration intentions and explain a considerable share of variation in migration intentions. Manchin and Orazbayev (2018) focus predominantly on the impact of networks but confirm that satisfaction with local amenities and local security decreases the probability of people moving away from the current region of residence (150 countries, GWP data, 2010-2013). In its flagship Transition Report (2018), the EBRD highlights the role of satisfaction with local amenities, using GWPs data for the EBRD region (2010–15). Further related studies addressing this link often use only one country and study internal migration (see for example Chen and Rosenthal, 2008). Studies by Tran et al. (2019, 2021) also address factors that are related to political outcomes and institutions, focusing on the impact of institutional quality and institutional quality differentials between host and origin countries on return migration to Vietnam. Similarly, Etling et al. (2018) look into the relationship between political discontent and migration intentions in the Arab Mediterranean region. ³

This paper contributes to the literature in several ways. It concentrates on a region that has been faced with particular challenges related to demographic change and emigration. Furthermore, the data we use allow us to put a clear focus on public services, that is, factors that can be changed by policymakers, as opposed to studying the impact of fairly vague concepts, such as amenities. In addition, we take into account the possible endogeneity of the variable measuring dissatisfaction with public services in an attempt to limit biased coefficient estimates.

2 Empirical setting

2.1 OLS estimations

In particular, we estimate the following basic relationship:

mi is a binary variable that takes a value of 1 if an individual has the intention to move abroad within the next 12 months, di is a measure of dissatisfaction with public services and the key variable of interest. xij Socio are J variables that belong to the group of sociodemographic characteristics, xik Econ represents the group of K economic factors, xl Region the L regional characteristics, xim Network the M variables capturing network effects and xip Trust are P variables capturing trust in institutions. In addition, a constant and a full set of country dummies, denoted in the equation by a country-­specific constant αr, is included in all specifications. The country dummies control for all factors that are the same for all individuals in a country, such as institutional characteristics, the political environment, historical ties to other countries, geographic location and the like. ϵi is the remaining error term. Standard errors are clustered at the regional level in countries where the regions are defined according to NUTS 2 or a finer classification (HR, BG, MK).

We would expect individuals who are dissatisfied with the provision of public services to have stronger migration intentions ceteris paribus. These findings would be in line with other empirical studies that highlight the relevance of nonpecuniary factors for the emergence of migration intentions.

The data allow us to study coefficient heterogeneities along different dimensions. First, the magnitude of the effects might differ by country. The ten countries in the sample differ in size, average income levels, EU membership, etc., and the importance of public services quality for migration intentions could vary across countries. We assess these possible heterogeneities by interacting the dissatisfaction variable with country dummies. Second, distinguishing by the type of service – social security, public infrastructure, education, health, defense and public safety as well as economic development – allows us to study whether the perceived quality of certain services has a higher impact on migration intentions than that of other services. We would expect ex ante that the magnitude of the effects could be related to the exposure of an individual to a specific service, however, and therefore ­heterogeneous effects with respect to the individual and household situation can be assumed. This exposure can be empirically approximated with interaction terms that allow, for example, the effect of dissatisfaction with education, health or social security services to differ between individuals with (school-aged) children and those without. Similar interactions are to be tested between being unemployed or retired and ­reacting to the quality of social security services or between being self-employed and public economic development (and business support) services.

Regarding the sign of the effects of the control variables, no large deviations from Raggl (2019) are expected: Migration intentions are expected to be stronger among the young and among men, and weaker for respondents who are married and have children (represented by a principal component based on marital status and the number of children, see below). Furthermore, being unemployed is expected to raise migration intentions, and, ex ante, a similar effect would be expected for ­income (the latter has not been found in Raggl, 2019). In addition, we anticipate that having networks abroad increases migration intentions, and trust in domestic (foreign) institutions is associated with weaker (stronger) migration intentions. Apart from being interesting in its own right, the trust variables can also help ­capture people’s general satisfaction with the institutional situation in the home country and can act as an important proxy for this otherwise unobservable factor.

As there is a large number of covariates and some of them are highly correlated with each other, we use (Polychoric) Principal Component Analyses, (P)PCA, to reduce the dimensionality of the data (see box below). For a full list and description of the variables included in the estimations, please refer to table B1 in annex B.

2.2 Endogeneity issues and instrumentation

We use instrumental variables (IV) as sources of exogeneous variation and two-stage least squares (TSLS) estimations in addition to the basic OLS estimations to take into account potential endogeneity. In particular, we pursue two avenues for instrumentation. First, we use external measures of infrastructure in the close proximity of respondents’ residences: road density (Meijer et al., 2018) and land use (artificial continuous and discontinuous urban fabric surfaces, based on CORINE land cover nomenclature). Broadly speaking, these variables can serve as proxies for general infrastructure in close proximity of individuals’ residences and contribute to explaining contentment with the quality of public services based on the actual infrastructure in the neighborhood. These measures of infrastructure might also represent the employment opportunities in the region where the respondents live and which could compromise the exogeneity (and therefore validity) of the instruments if these employment opportunities are not controlled for in the main equation. But the main equation does control for regional economic development. It does so by including (PCA) measures that are based on nighttime light intensity in a respondent’s neighborhood (measured for different radii around the respondents’ residences) as well as on the average unemployment and income of respondents living nearby. Second, we make use of a question in the survey that asks about ­respondents’ opinion on the adequacy of state spending on specific public services ⁵ . The idea is that the view of whether state spending on a particular service should be increased, maintained at the same level or decreased should not influence ­migration intentions per se, except through its influence on the perceived quality of public services. We use Kleibergen-Paap statistics and Hansen-J statistics to ­assess the relevance and exogeneity of the instruments and present first-stage ­results of the estimations in table D1 in the annex.

3 Data and descriptive evidence

3.1 Migration intentions

Pooling the data for all ten CESEE countries, the data indicate that approximately 9.1% of the respondents aged 25 to 64 indicate that they intend to move abroad within the next 12 months. This average refers to an “average CESEE ­country” and does not correct for the differences in the size of countries. We can further see in the data that the share of individuals with migration intentions varies considerably with age: among those aged 29 and younger, over 20% have migration intentions, among the 30- to 39-year-olds, 12% to 13% intend to emigrate, and in the working-age population above 40 years of age, just above 5% intend to leave their home country. When we look at gender differences, the data indicate that migration intentions are more common among men than among women. This holds for both the pooled and the population-weighted CESEE averages as well as for most individual countries. With respect to educational attainment, the differences are less well defined. While, driven by Poland, the population-weighted average indicates higher emigration intentions among the highly skilled, this does not hold for individual countries (except for Serbia and Poland) or for the pooled average of respondents (see table A1 in annex A). ⁸

Chart 1 shows the gender-age-education-migration nexus using a population pyramid for an average CESEE country (not population-weighted). The colors represent the level of education of the gender-age group and the hatched areas the part of the subgroup that has the intention to move abroad. The chart further indicates the shape of the pyramid that we would expect if the individuals with migration intentions actually left the country. The stylized pyramid drawn by the black line is more constrictive, and the tapering at the bottom indicates the shrinking and aging population structure prevalent in CESEE.

3.2 Dissatisfaction with the quality of public services

The level of dissatisfaction with public services is high in CESEE. If we look at the average across all public service categories, more than 55% of respondents say that they are either strongly dissatisfied or dissatisfied with the public services provided in their country. Dissatisfaction is high for all service types, but particularly so for health services (see chart 2). As regards the differences across countries (chart 3), the data reveal that in Poland and Czechia, but also in Albania and Serbia, dissatisfaction is below the sample average. In Bosnia and Herzegovina, dissatisfaction is highest; here, an average of close to 70% of respondents are not satisfied with the provision of public services.

Chart 4 provides first descriptive evidence on the relationship between people’s contentment with public services and individuals’ migration intentions after pooling these data for all ten countries. It shows that migration intentions are stronger among individuals that are dissatisfied with public services. This finding holds for all service categories and is particularly striking for education and economic development services. Among those not dissatisfied with education services, for example, 7.7% intend to emigrate, while among those dissatisfied with the public provision of education, over 10% intend to leave their home countries.

As such a correlation can be driven by other (observable or unobservable) ­factors, we estimate OLS regressions that control for sociodemographic, economic and regional characteristics, network effects, etc. as well as TSLS regressions to mitigate a possible bias in the estimates due to reverse causality and/or omitted variables.

4 Estimation results

4.1 OLS estimation

With regard to other covariates, the results closely mirror Raggl (2019), a study that relies on OeNB Euro Survey data collected a year earlier, i.e. in 2017. Women are less likely to have migration intentions, just like respondents who have small children or large families and who are married. Also, the likelihood of migration intentions declines with respondents’ age ¹⁰ . Unemployment is a strong predictor of migration intentions, while (equivalent) income is not statistically significant. Respondents’ wealth, approximated by a PPCA of the ownership of the main residence, a second or other residence, land and/or a car, is negatively associated with migration intentions. By contrast, migration intentions increase significantly if people have networks abroad, either direct and indirect ones, with the latter being measured by the networks of respondents living in close proximity (primary sampling unit and region).

4.2 Heterogeneous effects

This high effect for Albania might give rise to the presumption that the overall effect in the pooled sample might be driven by a single country (e.g. Albania). For robustness, we run ten pooled regressions, omitting one country in each regression. The resulting regression coefficients of the dissatisfaction with public services variable are plotted in chart C1 in annex C. It shows that while the overall coefficient is smaller when we omit Albania, the estimates remain statistically significant and positive in all specifications.

Table 1: OLS estimation (standardized coefficients)  

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Socio-dem	Economic	Wealth	Regional	Networks	Trust	PSU-FE
PPCA: dissatisfaction	0.049**	0.062***	0.064***	0.061***	0.047***	0.047***	0.048***
with public services	(2.18)	(2.82)	(2.83)	(2.92)	(3.14)	(3.03)	(3.32)
Female	–0.053***	–0.056***	–0.056***	–0.054***	–0.055***	–0.049***	–0.046***
	(–4.67)	(–4.74)	(–4.65)	(–4.53)	(–4.48)	(–4.17)	(–3.80)
Age	–0.498***	–0.457***	–0.438***	–0.436***	–0.439***	–0.424***	–0.378***
	(–5.69)	(–5.04)	(–4.88)	(–4.85)	(–4.99)	(–4.86)	(–4.75)
Age sq.	0.251***	0.219***	0.203**	0.202**	0.212***	0.200**	0.181**
	(3.25)	(2.78)	(2.60)	(2.58)	(2.71)	(2.53)	(2.44)
Medium education	0.004	0.011	0.011	0.017	0.041**	0.045**	0.058***
	(0.22)	(0.73)	(0.72)	(1.08)	(2.43)	(2.60)	(2.70)
High education	–0.019	–0.011	–0.010	–0.005	0.029*	0.031*	0.035*
	(–0.83)	(–0.50)	(–0.49)	(–0.27)	(1.78)	(1.88)	(1.74)
PPCA: Large family	–0.066***	–0.085***	–0.084***	–0.084***	–0.079***	–0.080***	–0.055***
	(–6.02)	(–6.44)	(–5.91)	(–5.91)	(–5.80)	(–5.43)	(–3.22)
Log(size of town)	0.007	0.031	0.029	0.033	0.010	0.011
	(0.41)	(1.62)	(1.55)	(1.37)	(0.44)	(0.47)
Log(equiv. income)		–0.164	–0.163	–0.110	–0.189	–0.231	–0.326
		(–0.59)	(–0.58)	(–0.39)	(–0.73)	(–0.91)	(–1.17)
Log(equiv. income) sq.		0.092	0.090	0.093	0.129	0.158	0.287
		(0.35)	(0.34)	(0.35)	(0.52)	(0.65)	(0.99)
Unemployed		0.101***	0.099***	0.090***	0.093***	0.089***	0.081***
		(5.77)	(5.72)	(5.05)	(5.56)	(5.23)	(4.78)
PPCA: wealth			–0.002	–0.005	–0.039**	–0.029*	–0.029*
			(–0.08)	(–0.25)	(–2.14)	(–1.67)	(–1.73)
PPCA: direct networks					0.151***	0.148***	0.141***
					(6.97)	(6.51)	(5.71)
PCA: indirect networks					0.137***	0.137***
					(4.09)	(4.00)
PCA: trust in local						0.002
inst.						(0.13)
PCA: trust in EU						0.037***	0.017
						(2.95)	(1.05)
R^2	0.092	0.108	0.106	0.108	0.151	0.153	0.080
N	9,407.000	7,123.000	7,055.000	7,029.000	6,969.000	6,568.000	6,593.000
Source: Author’s calculations.
Note: The table contains standardized beta coefficients. t statistics in parentheses. The dependent variable is binary and takes a value of 1 if the respondent intends to move abroad within the next 12 months. The addition “PCA”/”PPCA” in a variable name ­indicates that the variable is taken from a (polychoric) principal component analysis. All specifications contain a constant. Specifications (1) to (6) include country fixed effects, specification (7) includes PSU fixed effects. Specifications (4) to (6) include three principal components representing regional economic development. * p < 0.1, ** p < 0.05, *** p < 0.01

The country-specific estimates point toward differences in the average effects between EU and non-EU CESEE countries, with Romania and Bulgaria being ­notable exceptions. When we interact the dissatisfaction variable with an EU dummy (in the pooled sample), we see this confirmed: The relationship between dissatisfaction with public services and migration intentions is statistically significantly lower in EU CESEE countries.

Distinguishing by the six different types of public services, the estimates plotted in chart 6 show that dissatisfaction with the public social security system, health services, services related to economic development and the public infrastructure are associated with statistically significant increases in migration intentions. Here, the variables capturing dissatisfaction with each service are dummy variables, hence we refrain from portraying standardized coefficients and show the unstandardized ­coefficient estimates instead. Dissatisfaction with a public service type is associated with a 1.2 to 1.6 percentage point increase in migration intentions. Dissatisfaction with education and ­defense and safety does not show up as significant.

We further investigate heterogeneous effects with respect to individuals’ exposure to public services. ¹² More specifically, we interact respondent-­specific characteristics that indicate that a respondent may be particularly exposed to an individual service with a dummy that indicates dissatisfaction with this service. We find that having children under the age of 6 leads to a significant effect of dissatisfaction with education services and increases the ­effect of dissatisfaction with health and defense and public safety. Furthermore, among the self-employed, the impact of dissatisfaction with public services related to economic development is higher than among non-self-employed. We also interact dissatisfaction with social security with having small children, being unemployed or retired, but do not find statistically significant interactions.

4.3 IV estimation

Table 2: IV estimations  

	(1)	(2)	(3)	(4)	(5)	(6)
	OLS	IV	IV	IV	IV	IV
PPCA: dissatisfaction	0.00755***	0.124**	0.0925**	0.0145***	0.0161***	0.0160***
with public services	(3.80)	(2.48)	(2.11)	(2.71)	(3.00)	(2.98)
Observations	6,615	6,604	6,604	6,615	6,604	6,604
Kleibergen-Paap F		4.400	3.173	27.33	24.11	22.50
Hanson-J		1.633	7.073	24.25	31.58	36.34
Hanson-J p		0.652	0.215	0.390	0.248	0.164
Instrumental variables
PCA road density (4 components)		Yes	Yes	No	Yes	Yes
PCA urban fabric (2 components)		No	Yes	No	No	Yes
State spending inadequate		No	No	Yes	Yes	Yes
Source: Author’s calculation.
Note: All specifications include a list of covariates and a full set of country dummies. t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

The results remain positive and statistically significant after instrumentation, and the coefficients increase in magnitude. When only externally merged geo-referenced data are used as IVs (road density and urban fabric), the IVs are weak, as indicated by low Kleibergen-Paap statistics. The high coefficient estimates in columns (2) and (3) should thus be disregarded due to weak instruments. The first-stage results, displayed in table D1 in the annex, confirm that urban fabric has no significant impact on the perceived quality of public services, and also the PCAs based on different road types are only weakly associated with the rating of public services quality. The first-stage F statistic improves considerably, however, when the variable measuring the respondents’ view of the adequacy of public spending on public services is added as instrument. The first-stage results show that respondents who think that state spending on public services should be maintained at the current level (as opposed to increased) exhibit a lower degree of dissatisfaction with public services. This confirms that the opinion on the adequacy of state ­spending can be related to the perceived quality of public services. Columns (4) to (5) display the results for different combinations of IVs, all including the adequacy of state spending as instrumental variables, and indicate that coefficients are considerably larger than when estimated with OLS. When we standardize the coefficients in (4) to (6), they average at approximately 0.1 and are thus twice as high as the OLS standardized coefficients. A 1 standard deviation increase in dissatisfaction with public services thus increases migration intentions by approximately 3 percentage points.

While contentment with public services is not the most important factor for explaining migration intentions, with sociodemographic factors, unemployment and networks explaining a large share of the variation in the data, it is certainly an important nonpecuniary factor that robustly relates to individuals’ aspirations to emigrate.

5 Conclusions

Ongoing emigration from CESEE, especially of young people, adds to ­population aging, declining labor forces and increasing dependency ratios, and poses further demographic challenges to the countries in the region. Many factors that have been identified as relevant drivers of migration intentions are difficult to directly ­address by policymakers, such as sociodemographic factors or networks abroad. For this reason, we think it is particularly important to study contributing factors that can be tackled by policymakers. Therefore, we focus on individuals’ contentment with public services in their home countries, studying its relationship to migration ­intentions. Descriptively, we see that migration intentions are stronger among ­respondents who are dissatisfied and weaker among those who are not ­dissatisfied with the quality of public services. This finding is confirmed in an OLS framework, where we control for sociodemographic and economic factors, proxies for wealth holdings, economic development in the home region, network effects and trust in local and EU institutions. Respondents who are more dissatisfied with public services are more likely to have the intention to move abroad. This effect holds for our index of dissatisfaction that combines the responses of the six public services categories distinguished in the survey: social security, public ­infrastructure, education, health, defense and public safety, and economic development. Apart from education and public safety, the relationship is also significant when we ­distinguish between public services categories. We allow the effects of ­dissatisfaction with public services to differ across countries and find that in many countries, the effect is similar to the average effect in the sample. A notable exception is Albania, where the coefficient estimate is more than twice as high as in Romania and Serbia, the two countries that rank second and third in terms of effect size. Robustness checks confirm, however, that the overall effect is not driven by Albania, or another single country in the sample. Instrumental variable estimations, which we carry out to address possible endogeneity issues, reassure the OLS findings: The relationship between dissatisfaction with public services and migration intentions is positive and statistically significant. The IV estimates indicate a higher coefficient estimate than the OLS framework.

The link between people’s contentment with public services quality and migration bears the risk of a vicious cycle: With more individuals emigrating, public finances can get under increasing pressure, which leaves less room for improving the quality of public services or may even lead to a further deterioration. This, in turn, may further add to emigration pressures. At the same time, there is also the chance of creating a virtuous cycle: Appropriate policies would lead to a strengthening of public services in CESEE countries. Sound social security systems, high quality education and health care, good infrastructure, safe living conditions and publicly supported regional economic development initiatives would provide for an environment that may reduce individuals’ aspirations to move abroad, thereby strengthening public finances and creating space for public service improvements that further incentivizes people to stay. At the same time, these developments can have a positive impact on return migration and/or immigration to CESEE. Public services quality is certainly not the only factor that influences migration intentions, but it is a relevant one and, importantly, one that policymakers have the power to change.

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Annex A: Migration intentions by gender, education and by country

Table A1: Share of individuals aged 25 to 64 with migration intentions  

	Gender			Difference	Education			Difference
	All	Men	Women	Men vs. women	Low	Medium	High	High vs. low + medium
	%
Czechia	2.7	3.3	2.0		10.9	2.0	4.8
Poland	3.7	5.8	1.9	**	1.9	2.9	7.6	*
Hungary	4.3	3.6	5.0		3.3	3.9	6.0
CESEE average (population-weighted)1	6.0	7.5	4.7	***	4.8	5.8	7.8	**
Croatia	6.8	8.3	5.5		2.1	6.3	9.5
Romania	6.9	7.6	6.3		0.0	7.7	4.4
Bulgaria	7.5	8.1	7.0		14.9	6.7	7.7
Average over 10 countries2	9.1	11.0	7.3	***	9.4	8.8	9.8
Serbia	11.1	13.9	8.4	**	7.8	9.4	18.7	***
Bosnia and Herzegovina	12.9	16.7	8.8	***	5.2	16.4	10.8
Albania	14.2	17.1	11.7	**	17.7	15.1	12.4
North Macedonia	20.0	24.2	16.0	**	20.6	22.0	13.3	**
Source: Author’s calculations based on OeNB Euro Survey data (2018).
Notes: Survey weights applied. The columns labeled “Difference” indicate whether the mean is statistically different between two subgroups. Statistical significance is based on t-tests from robust OLS estimations of migration intentions on gender and education dummies, respectively. *, ** and *** indicate a 10%, 5% and 1% level of significance, respectively.
1 Survey-weighted country averages are weighted by the population aged 25 to 64 of each country.
2 Simple average over survey-weighted averages of the ten countries.

Annex B: Variables used in the OLS and IV estimations

Table B1: List of variables used in OLS and IV estimations  

Variable	Description
Dependent variable
Migration intentions	Dummy variable that takes a value of 1 if respondent intends to move abroad within the next 12 months; respondents stating “don’t know” or “no answer” are excluded from the analysis.
Quality of public services
PPCA: dissatisfaction with public services	Principal component that represents low satisfaction with the six categories of public services; the PCA is based on the six original variables asking about satisfaction with public services (Likert-type scale of answers).
Dissatisfaction with…	Dummy variable that takes a value of 1 if respondent is (very) dissatisfied with…
… social security	… social security (unemployment compensation, public pension, benefits for families and children)
… public infrastructure	… public infrastructure (e.g. public road and town construction, railway network, public transport)
… education services	… education (e.g. public kindergartens, schools, universities)
… health services	… health (e.g. public hospitals)
… defense and public safety	… defense and public safety (e.g. police, justice system)
… economic development	… economic development (e.g. support for small and medium-sized companies, investment allowances, ­financial support for disadvantaged regions)
Sociodemographic factors
Age, age squared	Age of respondent and its square
Medium education	Dummy variable that takes a value of 1 if respondent has medium education (i.e. lower and upper ­secondary, post-secondary but non-tertiary)
High education	Dummy variable that takes a value of 1 if respondent has high education (i.e. first and second stage of tertiary)
Female	Dummy variable that takes a value of 1 if respondent is female
PPCA: large family	Principal component that represents members of large families, i.e. married individuals from large households, with small children
Log(size of town)	Logarithm of the size of the residence town
Individual economic factors
Log(equiv. household income) [sq.]	Logarithm of the equivalized household income [and its square]; equivalized household income is ­computed using a weight of 1 for the first adult in the household, 0.5 for each additional person aged 13 and over and 0.3 for each child under the age of 13. (It is more common to use the age of 14 as a cutoff ­between a weight of 0.5 and 0.3, but this is not possible in our data, and we use 13 instead.) ­Non-­response in the income variable enters as missing values.
Unemployment	Dummy variable that takes a value of 1 if respondent is not working but seeking a job
PPCA: wealth	Principal component that represents real estate ownership (ownership of residence, secondary residence, other real estate and other land, including car ownership)
Regional development
PCA: prosperous region	Principal component that represents individuals living in regions with low unemployment, moderate ­income, high activity and low growth in activity (prosperous, stable region)
	Regional income/unemployment is calculated as the (survey-weighted) average of equivalized household income/individual unemployment using survey weights. Economic activity is measured as the logarithm of night light intensity in 2015, 2016, 2018 (data source: Earth Observation Group, Visible and Infrared Imaging Suite, VIIRS). Growth in activity is measured as the log-difference in night light intensity between 2005 and 2013 (data source: Earth Observation Group, Defense Meteorological Satellite Program – Operational Linescan System, DMPS-OLS). All variables are calculated at different levels of regional aggregation: For night light data, we use the 10km and 20km radius around respondents’ residences and the NUTS 2 level; average income and unemployment are aggregated to the PSU and the regional level. PSU is the ­primary sampling unit and represents households in close proximity of the respondent’s residence, the ­regions are defined based on NUTS 2 classifications – or finer in some countries (HR, BG, MK).
PCA: depressed region	Principal component that represents individuals living in regions with high unemployment, low income, moderate activity and moderate growth in activity
PCA: developing region	Principal component that represents individuals living in regions with moderate unemployment, high ­income, low activity but high growth in activity
Source: Author’s compilation.
Note: Unless otherwise noted, the source of all variables is the OeNB Euro Survey carried out in fall 2018.

Table B1 continued: List of variables used in OLS and IV estimations  

Variable	Description
Network effects
PPCA: direct networks	Principal component that represents individuals with direct networks abroad. PPCA contains a dummy variable that takes a value of 1 if respondent and/or their partner receives remittances from abroad and a dummy variable that indicates close family living abroad.
PCA: indirect networks	Principal component that represents individuals with indirect networks abroad. PCA contains the share of remittance receivers in the PSU and in the region and the share of respondents in the PSU and in the region that has family living abroad.
Trust in institutions
PCA: trust in local institutions	Principal component that represents trust in national institutions (trust is measured on a Likert-type scale; trust variables are demeaned before they enter the PCA)
PCA: trust in the EU	Principal component that represents trust in the EU
	(The PCA is performed based on trust in the government/cabinet of ministers, the police, domestically owned banks, the national central bank, foreign owned banks, and the EU.)
INSTRUMENTAL VARIABLES
PCA road density (4 comp.)	The first four components of a PCA on the following indicators of road density: The density (m/km2) of five types of roads (highways, primary, secondary, tertiary and local roads) based on 8km/8km raster data as well as based on vector data calculated for 5km/10km/20km radii around the place of residence (data source: Global Road Inventory Project (GRIP) dataset, Meijer et al., 2018).
PCA urban fabric (2 comp.)	The first two components of a PCA on the following indicators of urban fabric: CORINE continuous and discontinuous urban fabric in 500m/1km/2km/5km/10km/20km radii around the place of residence (data source: CORINE landcover data).
PPCA: inadequate state spending on public services	Principal component that represents individuals that consider state spending on the six types of public services as inadequate. The six original variables enter the PPCA.
Source: Author’s compilation.
Note: Unless otherwise noted, the source of all variables is the OeNB Euro Survey carried out in fall 2018.

Annex C: Robustness

Annex D: First-stage results of IV estimations

Table D1: IV estimations – first-stage results corresponding to columns (2) to (6) in table 2  

	(2)	(3)	(4)	(5)	(6)
	1st stage IV	1st stage IV	1st stage IV	1st stage IV	1st stage IV
PCA: high road density	–0.0164	–0.0200		0.00433	–0.00350
	(–0.87)	(–0.89)		(0.24)	(–0.16)
PCA: highways	–0.0344**	–0.0357**		–0.0192	–0.0201
	(–2.41)	(–2.49)		(–1.45)	(–1.51)
PCA: primary roads	0.0419**	0.0425**		0.0393**	0.0399**
	(2.46)	(2.49)		(2.43)	(2.46)
PCA: few medium roads	–0.0171	–0.0206		–0.0312	–0.0314
	(–0.85)	(–1.00)		(–1.64)	(–1.60)
PCA: high urban fabric		–0.0167			0.00225
		(–0.57)			(0.08)
PCA: high urban fabric close to home		–0.0209			–0.0237
		(–0.69)			(–0.83)
State spending on social security
Maintained			–0.242***	–0.243***	–0.243***
			(–4.83)	(–4.82)	(–4.83)
Lowered			0.218***	0.216***	0.214**
			(2.62)	(2.58)	(2.55)
Do not know			0.234*	0.226*	0.224*
			(1.87)	(1.81)	(1.79)
No answer			0.0810	0.0856	0.0796
			(0.12)	(0.13)	(0.12)
State spending on public infrastructure
Maintained			–0.309***	–0.310***	–0.309***
			(–6.41)	(–6.41)	(–6.39)
Lowered			0.0559	0.0601	0.0616
			(0.78)	(0.84)	(0.86)
Do not know			0.236	0.242	0.244
			(1.56)	(1.60)	(1.61)
No answer			1.766**	1.770**	1.762**
			(2.46)	(2.45)	(2.42)
State spending on education
Maintained			–0.306***	–0.300***	–0.299***
			(–5.99)	(–5.82)	(–5.80)
Lowered			0.0280	0.0270	0.0266
			(0.27)	(0.26)	(0.26)
Do not know			0.268*	0.277*	0.277*
			(1.85)	(1.90)	(1.90)
No answer			1.110***	1.121***	1.125***
			(2.85)	(2.85)	(2.88)
State spending on health
Source: Author’s calculations.
Note: t statistics in parentheses . * p < 0.1, ** p < 0.05, *** p < 0.01 Adequacy of the state spending on the different public services: All answer categories are included in the estimations, with the response “state spending should be increased” being the reference category. First-stage estimations also include all control variables of the second stage, but they are omitted from this table.

Table D1 continued: IV estimations – first-stage results corresponding to columns (2) to (6) in table 2  

	(2)	(3)	(4)	(5)	(6)
	1st stage IV	1st stage IV	1st stage IV	1st stage IV	1st stage IV
Maintained			–0.303***	–0.304***	–0.304***
			(–5.17)	(–5.16)	(–5.15)
Lowered			0.130	0.122	0.124
			(1.32)	(1.24)	(1.25)
Do not know			0.416**	0.414**	0.415**
			(2.26)	(2.25)	(2.25)
No answer			0.404	0.389	0.399
			(0.56)	(0.53)	(0.55)
State spending on defense
Maintained			–0.0630	–0.0645	–0.0645
			(–1.30)	(–1.33)	(–1.33)
Lowered			0.135**	0.134**	0.135**
			(2.10)	(2.07)	(2.08)
Do not know			0.555***	0.543***	0.543***
			(4.83)	(4.74)	(4.73)
No answer			–0.248	–0.278	–0.272
			(–0.75)	(–0.86)	(–0.85)
State spending on economic devel.
Maintained			–0.0269	–0.0293	–0.0285
			(–0.54)	(–0.59)	(–0.57)
Lowered			0.156*	0.150*	0.152*
			(1.80)	(1.72)	(1.74)
Do not know			0.591***	0.590***	0.590***
			(6.07)	(6.08)	(6.08)
No answer			0.403	0.435*	0.432
			(1.53)	(1.65)	(1.64)
Observations	6,604	6,604	6,615	6,604	6,604
Source: Author’s calculations.
Note: t statistics in parentheses . * p < 0.1, ** p < 0.05, *** p < 0.01 Adequacy of the state spending on the different public services: All answer categories are included in the estimations, with the response “state spending should be increased” being the reference category. First-stage estimations also include all control variables of the second stage, but they are omitted from this table.

The e-motion of car manufacturing in CESEE: the road ahead

Tomáš Slačík ¹³

Central, Eastern and South-Eastern European (CESEE) countries have benefited considerably from the grand-scale relocation of car production sites to emerging markets over the last two decades. On the back of strong foreign direct investment, the automotive industry has thus become a major economic pillar in several countries and firmly integrated into global, predominantly European, value chains. More recently, the automotive industry has seen some challenging times, though. Global and particularly European car production has been losing steam, and the industry has been hit by major black swan events, most painfully the coronavirus pandemic and Russia’s invasion of Ukraine. What is more, the global automotive industry has been undergoing unprecedented structural shifts on the demand and supply side, such as autonomous driving, shared mobility, connectivity and, most notably, the transition to electric vehicles.

Against the background of these great changes and challenges, the present paper explores the emergence of the largely foreign-owned automotive industry in CESEE and its level of preparedness for managing the risks and uncertainties and seizing the opportunities implied by the ongoing development of the automotive industry. After collecting and analyzing relevant qualitative information we find that the CESEE car industry will be walking a thin line between adopting new technologies and sticking to the internal combustion engine for longer than Western countries. For CESEE countries to maximize the benefits and minimize the risks of the technological transformation in the car industry the key priority is to preserve close links with Germany, stay tuned for battery production and focus on activities and promising industries with higher value added.

JEL classification: F15, F60, L62

Keywords: automotive, electric, battery, transformation, CESEE

However, the automotive industry has been hit hard by the COVID pandemic globally and in Europe in particular. In Europe, motor vehicle production plummeted by nearly a quarter year on year in 2020 (–13% worldwide) and dropped by another 4% from a record low base in 2021 (+3% worldwide). ¹⁴ Aside from subdued demand and the impact of numerous lockdowns and (cross-border) mobility constraints, this collapse was the result of major supply chain disruptions. In particular, semiconductor shortages have been slowing down car production significantly since late 2020. Meanwhile, Russia’s invasion of Ukraine and the ensuing economic sanctions have added to the strain on already battered automotive supply chains. Disruptions stem from the suspension of car production by several manufacturers, including Czech Skoda, and from manufacturers being cut off from key supplies of automotive inputs by Russian and Ukrainian firms such as wire harnesses and raw materials. In addition to the real ramifications, the war in Ukraine has put further upward pressure on already elevated prices of crucial raw materials and energy.

The pandemic- and war-triggered shocks to the automotive industry arrived amid a cyclical slowdown as car production had stagnated or even contracted for several years even before the pandemic across the globe, including Europe (especially Germany). Nonetheless, CESEE countries with a strong automotive focus largely defied these developments and navigated the challenges comparatively well. On top of these cyclical trends, the global automotive industry has been undergoing a fundamental transformation driven by unprecedented structural shifts on the ­demand and supply side. These include, in particular, autonomous driving, shared mobility, connectivity, new players entering the automotive arena and, last but not least, electrification as the most widespread means to address the ever-stricter CO2 emission targets. The COVID-19 crisis as well as the war in Ukraine are likely to accelerate many of these trends reflected in, inter alia, changes in supply chains, more rapid digitalization as well as acceleration of the electrification process to fast-track independence from fossil fuels.

Indeed, the automotive sector’s future is very much oriented toward electric mobility, at least in Europe. Apart from mounting peer and market pressures, this trend is largely driven by strengthening global efforts to address climate change and regulate carbon emission. With respect to the latter, Europe has been the world’s trend setter and frontrunner. Since the ever-stricter CO2 emission regulation standards refer to tailpipe emissions only, electric vehicles are carbon-neutral by definition. As a result, all major traditional automakers keep announcing ambitious electrification targets and time schedules. With car producers expanding and accelerating the provision of new electric vehicle models, their adoption has gained pace and the European market has moved into the driving seat regarding electrification. While pure electrics, plug-in hybrids and hybrids ¹⁵ accounted for some 4.5% of all new passenger car registrations across the EU in 2017, this share climbed to about 40% in 2021. ¹⁶ However, despite the recent boom of electric cars sales, internal combustion engine vehicles will not vanish overnight and will continue to play a role. Especially in CESEE and other emerging countries, the transition to electric vehicles is expected to be much slower than in advanced markets.

Given the advancing powertrain transformation, the development of battery technology and battery production will play a crucial role in the future. Battery packs and their main features such as size, weight and driving range are not only set to become the most important performance component of electric vehicles, creating differentiation among competitors, but also the key cost determinant. With China accounting for about three quarters of the Li-ion battery ¹⁷ production capacity, the European automotive industry needs to develop a competitive and innovative ­battery industry with all up- and downstream stages. To cover the rising battery demand, it will likely take some 20 battery pack production sites (so-called gigafactories) in Europe in 2030 and about 35 in 2040 (Deloitte, 2021).

Against this background, the present paper seeks to sketch out where the largely foreign-owned automotive industry in CESEE is coming from and where it is heading in light of the current dynamics and high uncertainties in the sector. By collecting and analyzing relevant qualitative economic arguments we aim to shed some light on what these historical structural and cyclical developments in the ­automotive industry imply for the CESEE economies and how they will walk the thin line between seizing the opportunities and managing the risks associated with electrification on the one hand and continuing to meet demand for (not) outgoing internal combustion engines on the other. ¹⁸ The paper is structured as follows. While the next section recapitulates the development of the automotive industry in CESEE over the last 30 years, section 2 collects evidence to assess how the car industry in the region is braced for the big trends of the near future. Section 3 complements the macroeconomic view with a firm-level perspective based on rather unique firm survey data collected by the European Investment Bank. Section 4 discusses the potential future impact of the big trends in the automotive sector, particularly of car electrification, on major macroeconomic variables in CESEE before last section concludes.

1 Dawn and heyday of the automotive industry in CESEE during transition

1.1 The presence of CESEE start-ups in the automotive sector

The automotive sector in particular and manufacturing in general has been the main target of the FDI flows that started pouring to CESEE countries in the 1990s, with Hungary having attracted the highest manufacturing share (43% of FDI stocks). Over the last two decades, the investment rate in the automotive sector measured as the share of gross fixed capital formation relative to gross value added averaged more than 36% in CESEE, nearly twice as much as in the rest of the EU.

However, as chart 3 shows, FDI stocks have remained rather flat in most CESEE countries since the global financial crisis in 2009. This echoes the fact that the strong relocation of automotive production to the CESEE region slowed down substantially after the global financial crisis. As a result, in contrast to the EU as a whole, the automotive industry in most CESEE countries experienced a markedly higher growth in value added between 2000 and 2008 than in the period after the financial crisis (chart 4). In the wake of strong FDI inflows, the automotive sector in CESEE countries has become one of the region’s key export drivers. Automotive exports thus accounted for a record high of 34% of total exports in Slovakia in 2019 and for more than 20% in Hungary, Czechia and Romania.

1.2 Growth of FDI stocks and value added by the automotive sector in selected CESEE countries

The deep integration of the CESEE automotive industry in global and particularly regional value chains and the high interlinkages with Germany notwithstanding, it has to be borne in mind that the automotive sector has a very different functional profile in Germany than in CESEE countries. The latter tend to specialize in value chain functions with lower value added and serve as factory economies in the European production networks (Stöllinger, 2021). Hence, the sector in CESEE typically shows a revealed comparative advantage in production while other more skill- and knowledge-intensive pre- and post-production functions – including headquarter services and R&D, but also sales and business services – are underrepresented. Conversely, Germany’s automotive industry has a comparative disadvantage in production, but substantial comparative advantages in headquarter services, R&D and business services, i.e. functions which tend to generate higher value added and pay higher wages.

2 Automotive industry in CESEE in e-motion?

Going hand in hand with the strengthening production of electric vehicles in CESEE has been the rising significance of electric powertrain technologies in the region’s exports in recent years. The year 2020 saw a particularly strong jump, with the share of electric and hybrid vehicle exports in total car exports climbing as high as 30% in Slovakia and Romania (see chart 6).

In addition to the production of electric vehicles, CESEE countries are making significant efforts to secure a role in battery production. On the back of the strongly rising demand for batteries, Deloitte (2021) estimates that 16 to 22 gigafactories will have to be built in total in Germany, Czechia, Hungary, Poland and Slovakia to keep electric vehicle assembly in this region. This is because the heavy weight of batteries renders long-distance logistics and shipping costly so that it is reasonable to place the production of batteries close to the assembly of electric vehicles. Against this background, Poland, Hungary, Slovakia and Czechia have already taken action to attract investment in gigafactories. While there has been a gigafactory in operation in Poland since 2017 and there are two in Hungary, one more is currently planned in each of these two countries. In addition, one gigafactory is planned in Slovakia and (up to) two in Czechia. Poland, whose exports of Li-ion batteries already amount to 2% of total exports, has attracted investments in gigafactories by means of direct financial support, free land transfer and tax incentives. Similarly, the Hungarian government has incentivized investments in its two existing gigafactories and planned future ones via direct financial support as well as free provision of the enabling infrastructure or some utilities. In addition to some financial support in favor of a planned gigafactory, Slovakia has set up the Slovak battery alliance, an independent advocacy group, to kick-start its battery industry. In ­Czechia, the government has so far only signed a memorandum of understanding with the electricity company CEZ concerning support for a gigafactory project.

Yet despite the strong involvement of the CESEE region in electrification, the implications are far more complex. The shift in production toward electric vehicles entails risks and opportunities for CESEE and the respective firms. Car manufacturers will need to find a right balance between electric and internal combustion engine vehicles, depending on the market they serve. This entails not only large investments in new and possibly also still in outgoing technologies but in light of the high uncertainty also optimization challenges when it comes to organizing R&D and production. Hence, some car producers in the CESEE region such as Skoda and Dacia are planning a much slower transition to electric vehicles, as they have been and will be serving markets where the onset of e-mobility will be slower. Skoda for instance has been mandated to manage activities and to strengthen the position of the entire Volkswagen Group in India and South-East Asia. This involves the launch of a slew of competitively priced mass-market cars with an internal combustion engine. Moreover, Skoda has also been charged with developing cars on a common platform (MQB-A0) for the entire Volkswagen Group. Based on this platform, individual car brands in the Volkswagen Group will produce vehicles destined in particular for India, Russia, South America and Africa. Overall, the reasons why internal combustion engine production will stay longer in CESEE are mainly the following: (i) the tendency of older technologies to persist longer in peripheral locations; (ii) the CESEE automotive industry’s relatively weak innovation capacities and (iii) its continued (labor and energy) cost advantage in the more labor-intensive internal combustion engine production (see Pavlínek, 2021; and CLEPA/PwC, 2021).

3 Transition in the automotive sector through firms’ lens