Between Returning from Abroad and Remigration: A Contextual Approach to the Case of Romania

Introduction

What is the relationship between returning to the country of origin and migrating abroad again (remigrating) or the intention to remigrate abroad? This is our research question, which we are trying to answer for an Eastern European society – Romania. The approach we use is, first and foremost, comparative and pursues, in particular, the specific (net) effects of individual, community, regional and national factors. The essential relationship we follow is that between returning to the country, short-term working, living abroad (temporary emigration) and the motivation for migration/migration aspirations, within transnational relations (Aslany, Carling, Mjelva and Sommerfelt 2021; Carling and Erdal 2014; Massey, Arango, Hugo, Kouaouci and Pellegrino 1998).

The theme and the questions associated with it are all essential, both practically and theoretically. Migrants returning from abroad could reduce the country’s demographic decline, population decline processes, ageing, labour shortages, etc. It could signal a reduction in the gaps in living standards between Romania and other European countries. Depending on where the returns take place, it could be a question of changing the development gaps between different regions or localities of the country. If we keep under control the characteristics of local communities and regions of residence, we can speak of a specific effect of returning to the home country on emigration or emigrating again (re-emigration). To answer such a question, we resort to multiple, multilevel data because, otherwise, they have limited coverage.

In the first section of the article, we present data from Eurostat and the National Institute of Statistics (NIS)¹ which introduce the surprise, for Romania, in 2022 of registering a flow of returns to Romania stronger than that of migrating abroad (emigration). Is it a structural change, a lasting transition or an emigration to an immigration society? We will elaborate. The second section follows, with data at the level of the local administrative unit (LAU) about the relationship between return migration from abroad and emigration, either for new emigrants or for re-migrants. What role does the development of localities play in these processes? What about the region to which the localities belong? What about the countries of destination of the migration flows? Multiple regression models are put to work to elucidate the relationship between development and migration abroad. Validation and sensitivity analyses are correlated procedures, both contributing to the higher credibility of the analysis to the reader. External or criterion validity (Babbie 2020) is accomplished by putting intentions to emigrate in relation to the status of former migrants among the predictors of these intentions. Sensitivity analysis (Treiman 2014) is done by comparing general rates of emigration from local communities (Table 1) with rates of emigration to specific countries (Table 2). Even if different, validation and sensitivity approaches contribute to a higher credibility of the analysis for the reader. It is assumed – based on this approach – that, if the results are similar for different method specifications, then the analysis can be considered valid.

The third section presents survey results with data at the individual level, for the prediction of the intention to emigrate from Romania, compared to those of European societies (Black, Pantîru, Okólski and Engbersen 2010). A special subsection is dedicated to comparing the motivations or aspirations (de Haas 2021) of the potential emigration of Romanians with the motivations of persons from the other 5 countries in the Eastern part of the European Union – Poland, Czech Republic, Slovakia, Hungary and Bulgaria (Sandu 2024a).

Methodological premises

The article uses either specific models for a single level or models with the integration of variables measured at different levels (Hox, Moerbeek and van de Schoot 2017) in the same multiple-regression model. The first approach is at the national level and is strictly descriptive. The second is at the community level, where we contextualise by historical regions. The third model is at the individual level, with survey data collected from Eurobarometer interviewees.

We use data collected at different levels – national, community and individual – because the essential relationship of the analysis, that between return and remigration/emigration, is a transnational one. The type of transnationalism (Carling and Erdal 2014) that interests us here is a post-return type. Those who have returned to their localities of origin are either influenced by their own experience of living abroad when re-migrating or they are influenced by their culture or by the relationships that other people in the community have with going abroad. The data at the place of origin for the first emigration and, subsequently, for the first return, are relatively poor. There are very few exceptions proving that international migration is a multi-level phenomenon, with specific drivers at these levels (Williams, Jephcote, Janta and Li 2018). A comparative survey of young people in 9 European countries proves that the drivers of potential emigration are different from country to country and that the social category of interviewees counts a lot. Additionally, international migration systems proved to be multilevel ones, especially involving heterogeneous societies (Sandu 2025). Data at individual and community levels are compared to better understand the effects of socio-economic status on mortality (Moss, Johnson, Yu, Altekruse and Cronin 2021). These are the main reasons why, in the same analysis, we use multiple methods and data collected at different levels. In addition, post-return transnationalism has strong interdependencies with a person’s remigration or with the emigration of other people from the same living space.

The analysis at the national level, in the first section, is descriptive, with a focus on presenting the data on return from abroad to Romania and emigration from the country, over time (Reștea 2024; Sandu 2023). The approach at the level of the local community (administrative-territorial unit ATU), in the second section, is of a predictive-explanatory type and uses data from the NIS, produced at censuses. The regression models at the community level operate with a minimum of 2,728 ATU, out of 3,151 that existed at the 2021 census. The variation is given by the availability of computing data. The local human development index (LHDI 2018) was computed only for localities that had more than 1,000 inhabitants in order to avoid the instability of the figures for the very small communes or towns, which was the case for about 100 small ATU. Similar problems were recorded for other predictors or by administrative redefinition of localities in time, after 2002.  However, data from administrative territorial units are not a sample; they represent approximately 85 per cent of the country’s ATU, thus we had adequate measures to build regression equations.

In the third section, we operate with individual data from the Special Eurobarometer 528 survey, collected in June 2022.² The basic theme of the survey was intra-European mobility. We operate, also at this level, with multiple regression models in which the dependent variable is, however, the intention to emigrate abroad, as a dichotomous variable (logit, not ologit or regress commands in STATA).

The sections on community and individual levels are structured around the hypothesis of former migration abroad as a factor to stimulate remigration based on learned behaviours and/or life dissatisfaction at personal and community levels. It starts from previous studies proving that remigration intentions or behaviours are, caeteris paribus, learned behaviours by those formerly living or working abroad (Bernard and Perales 2021). We underline, through this hypothesis, that remigration is not only an individual-level phenomenon but is also favoured by community interactions among former migrants and non-migrants. Living in a local community with a great number of returnees stimulates the remigration of former returnees but also the emigration of former non-migrants. Interpersonal knowledge and social contagion allow for this process.

The main predictors that are of interest in this case are the return migration from abroad and the individual evaluation of social justice in the reference society. The meaning of social justice could be different in the minds of interviewees. Very probably, it could be associated with distributive justice, institutional fairness, marginality, critical thinking etc. (Scott and Marshall 2009; Veeramoothoo 2022). Gender, age, personal education, parents’ education and the residential environment are control variables. Due to the fact that we are interested in the role of perceived social justice for emigration intentions, we also present a descriptive table with the perception of social justice in the residence country (Table A3). We can see that returnees on the move, in Romania, are the most dissatisfied with the social justice in their country. Multiple regression models predicting migration intentions also include the perception of social justice. We expect to find Romanians with emigration intentions, as significantly predicted by their dissatisfaction with social justice.

Multiple regressions in Tables 1 and 2 used predictors that measure factors or conditions of emigration at the community level for previous censuses (2002, 2011), moments before the measurement time for the dependent variable (measured at the 2021 census). Predictors that are also measured in 2021 (rates of returnees, for example) are proxy measures for a culture/values favouring migration abroad at the community level.

All the time that we are using regression analysis, we intend to reveal causal relations among migration abroad variables and the factors influencing them, at the community, regional or national level. Descriptive analysis is more for stimulating the emergence of research questions. Where the data allowed it, regardless of the level of analysis, we also promoted sensitivity analysis (Treiman 2014), in the sense of varying the analysis method to see to what extent the results are relatively stable.

A first example of the validation of results by sensitivity analysis is related to the national level. With Eurostat data, we found that the number of returns from abroad increased considerably in Romania in 2022, to about 190,000 people. At this level, however, the data are poor. They allow more questions, such as: Why did the number of returns suddenly increase in 2022? Was it only a compensation effect of the Covid-19 pandemic of 2020–2021 or were other factors also acting? With what verifiable proxies can the unreported data for the 2023 recovery be estimated?

The second example of sensitivity analysis is related to the individual level. We are also using individual data to better explain the intention to migrate abroad for the largest countries in the Eastern European Union (EEU). Indeed, the mechanism allowing for consistency between community and individual data analysis could be quite different. We can see in Table 1, for example, that local communities with more emigrants abroad are, at the same time, communities with more returnees from abroad. This could be an effect of learning about migration or the contagion of migration culture. In Table 3, the data are at the individual level and one finds that, for the majority of the countries, there is a trend of returnees remigrating. This, again, could be an effect of learning about foreign societies through migration experience. Contagion is no longer an obvious latent and intermediate variable, as in the case of community analysis. So, parts of intermediate latent variables could be different but the consistency of relations is obvious.

Combining community- and individual-level analysis is required by the fact that the intention to work and live abroad is a multidimensional phenomenon, including new migrants and return migrants. Both of them are conditioned by latent variables of learning abroad about migration and contagion effects between former migrants and intending ones. It is rare, if not impossible, to get representative samples of individuals from the same community. Combining community and survey data, as we did, is a reaction to the nature of migration abroad and to the constraints on research in the area. All the regression models (Table 1 to Table 4) are tested by the variation inflation factors (VIF) for multicollinearity, in STATA. As indicated in the literature, no VIF is larger than 4.

A growing trend of return to the country?

The trend (see Figure 1) was for the number of Romanian citizens who emigrated for more than a year to other countries to be higher than for those who returned to the country. This is what the long-term process looks like with data from Eurostat. Unfortunately, the Eurostat data to which we refer (because they do not amalgamate foreign immigrants with Romanian citizens returning to the country) are not available for the years 2019 and 2020. In addition, Eurostat’s reporting on that indicator stops in 2022.

Figure 1. Short-term emigration of Romanian citizens and Romanian returnees in the country 2013–2022

Source: Eurostat. Own design, DS. Data for 2019 and 2020 are unreported by Eurostat. Reștea (2024) indicates the National Institute of Statistics (NIS) as a source for returnees in 2019 and 2020. Immigrants who are not Romanian citizens and emigrants from Romania who are not Romanian citizens are not included here. These are stock data.

With the series from 2013 to 2022, however, the data indicate that 2022 is an exceptional year, one in which the returns exceed by about 15,000 the number of temporary emigrations by Romanian citizens. Is this the beginning of a new trend in which returns to the country outnumber emigrations? It could be so. The data published by Eurostat indicate that the immigration of Romanian citizens who were former migrants abroad increased in 2023 to 218,000 and, in the same year, the number of Romanian emigrants decreased to 151,000. Difficult or, more precisely, impossible to say – with the poor and contradictory data which we have – what the trend will be in the future. This is the reason why we consider it more prudent to adopt the procedure of multiple comparisons, to integrate return into all international migration processes (actual emigration, intention to emigrate, intention to return), to mention the conditioning of those processes and the European trends. This is what we are still trying to do. When the 2021 census microdata become available, the analyses will be able to be detailed. For now, we can talk about the increase in returns in 2022 and 2023 as a possible recovery from the decline of 2020 and 2021. In support of this interpretation, the associated NIS data regarding the permanent immigration of Romanians (Table A1 in the Annex) are also provided. With the Eurostat data, it is very clear that the permanent, long-term immigration of Romanian citizens who have been abroad increased from 190,000 in 2022 to 218,000 in 2023.

As there is probably a significant and positive relationship between permanent and temporary immigration, it can be considered that, for the time being, we have empirical evidence for the fact that the increase in immigration in 2022, whether permanent or temporary, was predominantly a recovery of the reductions in returns following the pandemic. It will be possible to speak of a lasting trend of returns to the country if Eurostat data for 2024 indicate this. For now, this is not the case.

We are still trying to better understand the relationship between the returns to the locality from abroad – before the 2021 census – and the departures abroad, in the short term, as recorded in the respective census, from the same locality. We move from a discussion related to the volumes of returns at the national level to one regarding conditioning, the causes of return to the locality and departures. In this section, the focus is on the relationship between returns from abroad and departures locally. Departures are short-lived – less than a year – and returns can be at any time before the 2021 census. We do not know, with the aggregated data at the level of the ATU (administrative-territorial unit), whether some of the people temporarily absent from the country have not also returned from abroad, at any time before the census.

Why does the return bring emigration from many localities of Romania?

If you ‘take a trip’ in the more than 2,700 localities for which you have the necessary data for analysis, you find the answer to one of the questions in the introduction. We made just such an imaginary journey with the help of multiple regression models in which the dependent variables are the rates of return to the locality and those of short-term departure (emigration) from the locality. Predictors and independent variables can be classified into 5 categories:

1. Conditions of local development (urban or rural residential environment, a synthetic index of proximity to large IURCON cities, shares of active population in industry and, separately, in services, the average age of the population in the locality);
2. Regional conditions of local development (the historical region to which it belongs, with Muntenia as a reference, plus the gross domestic product per capita at the county level);
3. The level of human development of the locality, estimated based on an index (LHDI) for 2018 (Sandu 2024b);
4. Return-to-locality rates from the main destination countries (Italy, Spain, France, Germany, Austria, the United States of America and Canada); and
5. The experience of migration in the locality (IMIGEXPER integrating local data on departures abroad in 2002 and 2011, the rate of returns from abroad recorded in the 2021 census and the net migration rate of the population residing in the locality in 2021).
6. We used these 25 predictors from the 5 classes to estimate the rate of emigration from the locality at the 2021 census. In a separate regression equation, we used the rate of returns from abroad, before the 2021 census, as the dependent variable, together with 24 of the previously mentioned predictors. The results of the estimates are presented in Table 1.

This is how we found out that Romanians leave the host country abroad, among other things – but in a significant proportion – because they have learned this from previous migration experiences. In other words, the probability of leaving the country in the short term (or perhaps in the long term but we do not have the necessary data in this regard) is higher because this is what some locals have learned from previous migration experiences – their own or those of their cohabitants. Specifically, those who have returned home from abroad tend to be among the first to opt for re-migration – to go back to another country. Of course, this is not the only reason for emigration. It is, however, very likely one of the more significant reasons.

Otherwise, the data we present will say what we expected but with some nuances. High values of urban accessibility favour emigration only for the case of Italy as a destination (Table 2). If the place of residence is rural and with reduced development (lower LHDI) in Romania, the emigration trend will be stronger. As poverty in Romania is consistently higher in the countryside than in the city, the emigration trend will be stronger for those in rural areas, especially in relatively isolated rural areas, where more young people live. Here, the desires of the type ‘we want a country, like, outside’ (affirmed as such in the strong social movement in Palace Square, in August 2017) are systematically stronger and better crystallised. Correspondingly, emigration is higher from the relatively isolated countryside, far from the big cities.

Local migration experiences can be internal – within the country, estimated by the net migration rate – or external. The data in Table 1 support the idea that both types of experiences matter for EU emigration. Fewer people go abroad (relatively speaking, about the local population), where net migration rates are higher (more arrivals than departures) and returns from abroad are lower.

Nevertheless, the migration experience of the local community matters considerably in the equation of overseas placements. The statement is based on the results of the hierarchical regression (with variable blocks) in Table A2, in the Annex.

Surprisingly, also with the data from there, we find that local development in synthetic form (LHDI) does not matter significantly in the equation. The premises of local development (residential environment, urban accessibility, average age and employment areas of the working population) are more important in determining emigration abroad. This finding is important because it suggests the increased initiation of actions to positively modify the conditions of local development within development policies.

How are the communities with a high share of returnees already highlighted as places of intense departure abroad? The answer to the question results from the second regression equation in Table 1. In many respects, they resemble the localities from which many short-term departures abroad took place. They are also predominantly rural. However, there are also major differences in the sense that the localities of return are no longer mostly poor.

Urban connectivity stimulates short-term emigration from rural communities (IURCON) only for Italy as a destination (Table 2), not for all short-term emigration abroad (Table 1). It is not clear why the selectivity of the emigration function only for this destination country. The historical region of Moldova is the preferred origin of emigration to Italy. One could infer, consequently, that the rule of conditioning emigration to Italy is conditioned by urban accessibility, especially in this region.

Table 1. Predicting short-term emigration rates from Romanian communities, irrespective of the destination country, 2021

Source: National Institute of Statistics (NIS). Own computations. Two OLS models. Y – dependent variable in the regression model. Emigration and returnees rates are computed as rates to 1,000 usual residents in the locality, 2021. See Sandu (2024b) for the computations on LHDI 2018 and IURCON. GDP data are from Eurostat. Cells are shaded where the regression coefficients are significant for p<0.05. Example: a one-unit increase on LHDI 2018 reduces the short-term emigration rate by -.302 units, on average. The B coefficient is statistically significant for p=0.001.

Table 2. Predicting the short-term emigration rates from Romanian communities to the main destination countries, 2021

Source: National Institute of Statistics (NIS). Six computations. Two OLS models. Y – dependent variable in the regression model. Emigration and returnee rates are computed as rates to 1,000 usual residents in the locality, 2021. The cells where the regression coefficients are significant for p<0.05 are shaded. Example: a one-unit increase on LHDI 2018 reduces the short-term emigration rate to Italy by -.118 units, on average. The B coefficient is statistically significant for p=0.001. Example: the higher the rate of returnees from Italy in a Romanian community, the higher the rate of emigrants in one of the 6 countries that are the main destinations for Romanians in Europe (Italy, Spain, France, Germany, Austria, the UK).

So what are the localities like, in terms of their regional and development profile, according to the emigration rates by specific destination countries? We ran 6 multiple regression equations, 1 for each type of emigration to the main destination countries (Table 2). The regional specificity of short-term emigration is very high. Departures to Italy, for example, are mostly made from the historical region of Moldova, those to France from Crișana-Maramureș, to Austria from Transylvania and to Great Britain from the Bucharest-Ilfov area. The picture of emigration destinations in 2021 is largely consistent with that recorded at the penultimate census in 2011 (Sandu 2018).

Next, we move on to a more detailed understanding, at the individual level, of the relationship between potential emigration and return from abroad, using survey data. Such an analysis, we hope, will help us, to a greater extent, to understand the motivations of migration abroad/non-migration. If regressions with individual-level data reconfirm the significant relationship between return and remigration, then the findings from community-level analyses are largely maintained and validated.

Approach at the individual level

The chain of the process of emigration–return to the country of origin and, eventually, remigration abroad can be observed, at the origin of the process and the individual level, especially concerning the relationship between the experience abroad and the intention to emigrate. In the case of Romania, we continue to focus on this relationship, using data from a special Eurobarometer survey in the summer of 2022. Is there a specific effect of previous residence abroad on the intention to emigrate, even if the influence of other factors is controlled? To answer the question, we analysed 24 countries in the European Union (except for 3 of them for which the samples were very small). The results, presented in Table 3, clearly support the affirmative answer to the previous question. In 18 of the 24 countries for which we ran the same logistic regression model, the intention to emigrate was stronger in the case of people returning from abroad. The mentioned significant relationship is also recorded in the case of Romania, the country of interest in this framework.

For the analysis of emigration intention, we used 10 predictors, most of which were status variables (gender, age, own education, parents’ education, residential environment), some of them measured at the nominal level. Status conditions differ from country to country but the dominant meaning is clear, with young people tending to emigrate in most cases.

In the equation, we also included as a predictor a subjective variable related to social justice satisfaction (SJS) in the country of residence at the time of the survey. In this regard, the participant’s agreement was requested about 3 statements regarding the perception of social justice in his case, both for others and in terms of social non-discrimination. The Social Justice Perception Index was constructed as a factor score, multiplied by 100. Only 4 of the 24 countries analysed – Romania, Poland, France and the West German regions – had a negative relationship in the sense that the intention to go abroad is stronger among those who perceive that social justice in their own country is lower. It is difficult to say why, in only these 4 countries, statistically significant and negative regression coefficients are recorded, in the case of the respective predictor. Further analysis is needed to clarify the meaning of the prediction patterns for all the countries.

Table 3. Predictors of intentions to emigrate

Source: Eurobarometer 528, May–June 2022. Logistic regressions (command logit for dummy dependent variables), in STATA, for each of the 23 EU countries (not including sub-samples for small countries like Luxembourg, Malta, the Republic of Cyprus, Estonia). We used weighted data, with a variable constructed by the Eurobarometer surveyors (w93 in the original data file) so as to get a sample population that is representative of the country’s adult population. The total EU population who were interviewed, unweighted, is 26,390. The weighted interviewed population, including the small countries mentioned above, is 25,389. For coefficients that are significant for p<0.05, the cells are shaded. Example: The probability of declaring the intention to emigrate is, caeteris paribus, higher in Finland for young returnees.

DEW – West Germany. DEE – East Germany. See, for details, the source of the table in Sandu (2024c). Figures in the table indicate estimated coefficients, not odd ratios. The sensitivity analysis (Treiman 2014), by doing regression analysis with weighted versus non-weighted data, indicates small variations in the results and the function of weighting versus non-weighting the data.

A hypothesis to be tested with other data can, however, be advanced. It is expected that, in the survey, countries where returnees who plan to go abroad again will be more dissatisfied with social justice in their own country than those who have not been and do not plan to go abroad. Table A3 in the Annex supports the hypothesis. Romania is one of the countries where the satisfaction of returnees who want to emigrate abroad is lower than satisfaction with social justice in their home country, compared to those who have not been abroad and are not going to emigrate. The same is true with Poland (White 2014) and Bulgaria in Eastern Europe, as well as with France and West Germany. In other words, in 4 of the 5 countries where there is a negative relationship between the intention to emigrate and the SJS, there are also very large gaps between the low SJS for returnees who want to go abroad again and the high SJS for non-migrants who do not intend to leave.

To better understand the situation, we focused the analysis on surveyed people in 6 countries of the Eastern European Union (EEU), each of them with a population greater than 5 million people – Poland, Romania, the Czech Republic, Bulgaria, Hungary and Slovakia. Which of these countries has a similar determination of intentions to go abroad? For lovers of technical arguments, the answer starts from the data presented in Table 4. For those less interested in the technical aspects of the analysis, I present below the interpretation of that table.

We grouped, by factor analysis, the reasons for potential emigration, as declared by interviewees, into 5 main categories, namely salaries + employment, professional development + improvement in knowledge of a foreign language, lifestyle + culture, perception of social justice + proximity to another destination country and relationships abroad + previous residence in another country. We have retained in the analysis here only the first 4 categories of reasons. To predict them, we have added 8 more socio-demographic variables as predictors (see Table 4).

In all 6 countries, we recorded 2 equally important variables in generating emigration intentions to the EU. In all 6 EEU countries, those who want well-paid jobs and are young tend to go abroad. The migration experience also matters a lot. Those who have been abroad (for work, accompaniment or study) and have returned to their country of origin tend to leave again to go abroad.

In the series of reasons for departure invoked, after employment and salaries, we have the aspiration to improve either professionally or to learn a foreign language spoken in the country of destination. These 2 families of motive–aspiration (de Haas 2021) significantly determine the intention to emigrate to 5 out of the 6 countries analysed (the exception occurs in the case of the Poles interviewed). The third category of reasons that matter is related to the quality of the institutions of the rules applied to ensure social justice. Of the 6 countries analysed, only in Romania and Bulgaria is the lack of social justice associated with the intention to go abroad. The resulting hypothesis is very important for those who would like to design well-founded social policies in the field: if you want to reduce the propensity of young people to emigrate, then, apart from economic measures to increase the number of well-paid jobs, it is fundamental to reduce corruption in the country.

In none of the 6 countries analysed here do we see a significant impact of lifestyle-related motivations for potential emigration. We could translate this finding by considering that providing jobs, especially for young people, increased salaries, better conditions for professional development and reducing corruption can contribute substantially to reducing emigration

For those interested in the multitude of comparisons used to contextualise the case of potential emigration from Romania, we mention that we have replicated the analysis in Table 1 for the subsamples related to the ‘old’ countries compared to the ‘new’ EU member states. The finding of interest here is that lifestyle and culture count as predictors of potential emigration, especially for the ‘old’ EU (results not presented here to avoid too many technical details).

Table 4. Predictors of intentions to work abroad: Romanians in a comparative perspective with interviewees of other people in the Eastern European Union

Source: Eurobarometer 528, data collected May–June 2022. Topic: Intra-European labour mobility. Logistic regressions by the main countries of the Central and Eastern European Union by their population size. Shadow for regression coefficients that are significant p<0.05.

From the same Table 4, we find out that, apart from gender, age and migration experience, the other predictors in that category, at least for the 6 countries analysed, matter less, especially in the case of Romania.

Conclusions

Each type of data that we used (national-, community- or individual-level) are quite poor, leaving us unable to offer the answer to the basic question of the relation between return migration and re-migration. This is why we had to use all 3 types of data. Sensitivity analysis encouraged us to use multiple methods and data for analysis.

In the long term, return migration from abroad to Romania was of lower intensity compared to temporary emigration flows. In 2022, the ratio between the 2 flows was exceptional, in the sense that return migration was stronger than emigration. Is this the beginning of a new trend? Was the reduction in returns in 2020 an effect of the Covid-19 pandemic? Was the increase in 2022 recoveries a catch-up effect of the 2020 decline? Were there other factors that led to the change in the relationship between the 2 external migration flows? With the data available for now, we do not know. It may also be that many of the emigrants have reached the retirement stage and returned to the country to live at lower costs (Dustmann and Weiss 2007).

In essence, it is likely to be a cumulative effect of the pandemic and the retirement of a significant number of Romanians abroad (the hypothesis of cumulative factors of remigration). Let us elaborate on the interpretative hypothesis of cumulative factors. This is, in fact, a modification of the initial hypothesis from the methodological section, supporting the view that re-migration is an effect of return migration. The Covid-19 pandemic has also been a major obstacle to circular migration between Romania and the host countries of Romanian immigrants. Hence, the trend is a strong reduction in returns to the country in 2020–2021. However, it is also the effect of a lasting trend. More and more Romanians abroad spent a considerable number of years there and then considered it time to return to the home country, to their families, friends and acquaintances, to occupy the homes which, in the meantime, they have built in the home country with money from migration (economic remittances). In the period 2015–2019, there was already a trend for an increase in the number of returns to the country. After the pandemic-induced decrease in this growth, in 2020–2021, a compensation process followed to recover the deferred returns, expressed through the increase in returns in 2022.

The analysis in this material also posits the hypothesis that, for a good many of the migrants returning from abroad to Romania, there are significant reasons for dissatisfaction that subsequently contribute to their re-migration or to them obtaining citizenship of another country (the hypothesis of re-migration as dissatisfaction). The feeling of social injustice in one’s own country, in particular, can contribute to remigration. With the Eurobarometer survey data analysed, the situation seems to be the same in Poland, Romania, France and West Germany. Public policies in the field can be effective due to the fact that they are documented, to a greater extent, through surveys on Romanian migrants abroad, returnees and non-migrants.

Simply registering those who return to the country, without such research on institutional satisfaction, is not enough to substantiate sustainable migration and development policies.

To the 2 explanatory hypotheses already mentioned, we also add a research question regarding the possible effect of the 2021 census. At least 4 million Romanian citizens were out of the country on the date of the census to which we refer. Some of them probably wondered why they cannot report, via the internet, their data as citizens of the country, even if they have been away for more than a year. Argumentation related to the fact that temporary emigrants, for over a year, are no longer part of the country’s resident population could be an official justification. In 2011, at the penultimate census, it was possible to report your situation through the census form, even if you had been out of the country for more than a year. Did blocking the possibility of not registering personal data for Romanians who left the country for more than a year, in the electronic form of the 2021 census, contribute to accentuating their dissatisfaction with the country’s institutions? With such questions related to the census, the decision to obtain another citizenship or to not return to the country of origin was most likely also considered.

Factors explaining return migration abroad seem to be selective by destination country. Urban accessibility, for example, stimulates re-migration not generally – but mainly in the case of Italy as a destination.

The motivational profile of potential emigration from Romania, from the perspective of the Eurobarometer survey used here, is very similar to that recorded in Bulgaria. Especially in these 2 countries, potential emigration is more due to the impact of the shortage/insufficiency of well-paid jobs and social living conditions based on social justice rules.

The extension of analysis to several countries (Table 3, for example) is an attempt to better contextualise Romanian re-migration abroad in the European context. One could see that emigration countries like Romania, Bulgaria and Portugal have similar clear determinants of emigration, especially related to return migration and the younger age of potential emigrants.

Across the European Union, however, we are seeing a significant, strong impact of return migration on potential re-migration (technical details not presented here). It follows that a good percentage of the returnees are not satisfied with the professional and living conditions in their countries of origin or have a higher level of aspirations that they can satisfy, especially through re-migration or circulatory migration. The related details would be worth studying.

It seems that the neighbourhood culture and situations (Romania and Bulgaria, Czech Republic, Poland, Hungary, Italy and Austria, etc.) explain some similarities of the causal profile of intentions to emigrate at the individual level (Table 3). Economic policies that consider that only work and salary matter in both potential and de facto emigration do not have a sufficient scientific basis. The institutional determination of potential emigration is extremely important and should be researched periodically, especially in countries such as Romania.

Notes

1. National Institute of Statistics (NIS), Census data 2021 (on request).
2. Eurobarometer 97.4 May–June 2022 ZA No. 7901, available at https://www.gesis.org/en/eurobarometer-data-service/data-and-documentati....

Conflict of interest statement

No conflict of interest was reported by the author.

ORCID ID

Dumitru Sandu  https://orcid.org/0000-0001-8618-2252

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Annex

Table A1. Emigrants from and immigrants to Romania 2013–2023

Source: National Institute of Statistics (NIS), TEMPO data basis. These are flow data.

Table A2. R-square change if one uses the 5 blocks of variables from Table 1 as predictors of short-term emigration in linear regression

SPSS results of running hierarchical regression to predict short-term emigration at the community level (2021 census) by 5 categories of predictors. The component predictors for each of the 5 categories are specified in Table 1. Example: if one adds the second bloc of predictors referring to regional conditions of development, R2 increases by 0.066 and this is a significant increase of R2 to predict short-term emigration from local communities.

Table A3. Perceived social justice by type of migration and country of residence

Source: Eurobarometer 528, 2022. Own computations: DS. The first 5 columns in the table are averages of the social justice satisfaction factor. Example: the average index of social justice of returnees in Romania intending to remigrate is -59, much lower than the average index for the same variable for the case of those that are non-migrants without intention to leave (11.5).

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