Linking Internal Mobility, Regional Development and Economic Structural Changes in Romania

Abstract

The political context of the 1990s generated significant changes in the territorial structure and migratory behaviour of Romanian regions, as well as a severe economic decline and an increase in development disparities between the East and West. The main objective of this research is to analyse the empirical relationship between economic growth and the internal migration of the workforce, on both a national and regional level, by applying the standard production function and using panel data based on the annual series of eight regions for 19 years (2000–2018). To outline the effects on the structures of the economy, an empirical analysis of the relationship between economic growth and the structural development of the economy, considering the migration of the workforce among the five main sectors of the economy, was carried out. On a regional level, in the North West, North East and Bucharest Ilfov regions, internal migration positively influences economic growth, with migration in rural regions having a high impact. However, for the Central, South East, South West Oltenia, and West regions, migration has a negative influence on economic growth. The results regarding the effects of these structures on the economy indicate a high, but negative, influence on regional economic growth due to the active agricultural sectors in all eight regions of Romania. On the other hand, the construction sector determines positive effects in all regions of Romania.

1. Introduction

The phenomenon of internal migration in Romania is a process which has been constantly maintained, but has changed in intensity over the last three decades. After a short wave of rural–urban exodus, mobility was limited towards large urban centres. Both political and economic changes from the beginning of the 1990s generated considerable changes in the territorial structure and migration behaviour of Romanian regions. This was accompanied by severe economic decline and an increase in development disparities between the East and West.

Restructuring state enterprises and privatising economic structures were not easily accomplished, and were influenced by difficulties in the external payment balance of the country and the deficit of the central budget. Inflation grew significantly while, at the same time, closing industrial factories stimulated unemployment. All of these factors had a negative impact on the entire economy of the country. One of the most important effects of the economic processes which took place in 1990 was the change in the direction of internal migration as a reaction to deindustrialisation. In 1997, for the first time in the four decades, urban–rural migration started to prevail [1].

After integration into the EU, Romania experienced fast development. However, this was turbulent, and manifested through massive economic growth at the same time as experiencing permanent decline in the population due to political and economic crisis. For a period, Romania registered the fastest growth in EU, with a growth of GDP of almost 4.4% (2007–2009) [2]. Despite this, our country remained one of the less-developed countries in the European Union, a fact proven by the GDP per capita, which was the second smallest of all EU countries. As for the average life expectancy of 74.2 years, this is below the European average by more than 6 years [3]. The living standard is also below the European average, and the possibility to obtain a high income is limited. These are the main reasons for the emigration phenomenon in Romania in recent decades.

The issue of demographic change and mobility in Romania is polarized: negative demographic effects are considered to be the predominant result of declining birth rates, increased mortality, and changing family demographic practices in the context of the extended economic transition period. The effects of migration are assessed more in terms of the economic effects of remittances from external migrants than in terms of the role of internal mobility in regional economic and structural dynamics.

In papers published at a national level, scientific interest is oriented towards the analysis of the dynamics and effects of the flow of Romanian migrants to and from the European Union. This paper aims to analyse, through econometric methods, the effects of internal migration on regional development, and also the effects of labour migration on the structure of the economy.

In order to meet the proposed objective and to identify the effects of population migration within Romania on the development and structure of the economy, several hypotheses were identified, as follows:

Hypothesis 1 (H1).

The migration of the population between the urban and the rural environment has an impact on economic growth on a national and regional level in Romania.

Hypothesis 2 (H2).

The degree of employment exerts a positive impact on economic growth.

Hypothesis 3 (H3).

The level of remuneration of the employees positively influences economic growth both on a national and a regional level, irrespective of the development degree of the region.

Hypothesis 4 (H4).

Workforce migration in primary activity sectors (industry and agriculture) negatively influences the structural development of the economy on a national and regional level.

Hypothesis 5 (H5).

The migration of the workforce in the construction sector, which has intensively developed in the last few decades, positively influences the structural development of the economy on a national and regional level.

Hypothesis 6 (H6).

Workforce migration in the services sector negatively influences the structural development of the economy on a national level.

The scope of this paper was from the point of view of Kirchberger [4] who considers the measurement of internal migration to be important both for political purposes and for a better academic understanding of economic development and structural transformation.

The document is structured as follows: after a brief introduction, a literature overview of the recent studies conducted on the topic is presented in Section 2, the theoretical framework of the econometric approach and the applied data and sources are presented in Section 3, in Section 4 the results of the econometric analysis are discussed, and Section 5 ends the paper with the conclusions of the study.

2. A Brief Literature Overview

According to the neoclassical vision of Solow [5] for economic growth, the changes in the population of a country and the technological progress are the elements of long-term economic growth and of the tendency to grow. Therefore, considering the exogenous economic growth, the rhythm of growth of a region decreases if the region becomes more developed.

In contrast with the theory of exogenous growth, the endogenous considers the human capital concept as an endogenous growth factors determining the economic growth. Therefore, a growth of the population determines the growth of the workforce and of the consumers, but it also increases the diversity of the population which might lead to the growth of the population and of the technological progress. The followers of the endogenous theory [6,7,8] states that the knowledge and the abilities of the work are the real engines of economic growth.

Similarly to the effects generated by international immigration on the destination country [9], it is considered that internal migration might create a direct effect by the quantitative growth of the labour force in a region, influencing the growth of economic production, but may also create an indirect effect that is determined by the abilities and the knowledge of the migrants, complimenting those of the migration region.

Although more attention has been paid to international migration, migration among regions within countries remains less understood, although it has been proved [10] that, on a global level, levels of internal migration are four times higher than international migration.

Most migrants move within their own country as internal migrants. Thus, a key question arises as to how to measure internal migration and how many internal migrants exist in the world? The response from the United Nations Development Program (UNDP) to the overall estimate is 740 million internal migrants, which they consider to be “conservative” [11].

A real problem in measuring internal migration is the questions asked to obtain the length of stay at the destination. Few countries around the world have registration systems that record changes in their usual place of residence, and migration data are generated through retrospective tools that collect relevant information through one or more of the three questions: place of birth; place of residence in the past, usually one or five years ago; and the last place of previous habitual residence before coming to the present place, generally associated with another question as to the length of time since moving from that last place of former habitual residence [12]. The volume of internal migration collected varies significantly depending on the question asked.

Another study [13] analyses the effects of internal migration on the structures of the local population in Latin America. The conclusion of the study was that the effect of internal migration was the reduction in the local gender ratio, the decrease in the average years of schooling, and the increase in the share of the active population, triggered by the growth in the young adult population.

The studies carried out at European level to explore the intensity, the composition, and the spatial impact of the population movement [14,15,16] were confronted with obstacles connected to data and various definitions of migration [17].

In a recent study, Rowe and Patias [18] drafted a map of the internal migration flows in 38 European countries highlighting the major role of the national capitals in the internal migration system and a series of distinctive models of internal migration favouring the concentration of the population in Northern, Central, and Eastern Europe and the de-concentration of the population in Western and Southern Europe.

Rees et al. [19] identified the existence of some correlations between the measures of population redistribution and national development and proposed a general theoretical model which should outline the way in which internal migration redistributes population in places and regions during the development process.

In a study examining the determining factors of the internal migration of the active population between the provinces in Spain after the economic crisis, Maza [20] noticed that the economic facts and the facilities are more important for the adult population and the long-distance travels for the young population and the travels between the neighbouring provinces.

In the analysis of internal mobility in Romania we should consider the internal economic and social transformations that took place during the analysed period (industrial decline, economic restructuring of urban centres, rising unemployment, etc.), and legislative changes, especially regarding free movement. and liberalizing access to labour markets in other states [21,22,23,24].

Since the beginning of the 1990s, as a result of legislative changes that provided for the free movement of persons and the freedom of establishment of residence, in Romania there has been a spectacular increase in internal migration from 8.6% in 1985 to 33.9% in 1990 [25].

The evolution of internal mobility in Romania in the period 1990–2020 can be characterized by an increase in the mobility of the population from rural to urban areas until 1995, and following this year there is a change in the direction of internal migration represented by the intensification of the urban–rural flows. If, in 1991, more than half (70%) of the volume of internal migration returned to the flow from rural to urban, after 2001 the migration of the population from rural to urban areas and vice versa, recorded an approximately constant evolution, representing around 20% (in case of rural–urban migration), respectively, 30% (in case of urban–rural migration) of the total migratory flows [26].

Internally, in Romania, along with the economic prosperity and the continuous emigration, a growth of the spatial disparities was experienced [27,28], this is because the regions did not benefit equally from European integration. Romania’s domestic development has not been harmonized at the level of the eight development regions, and the differences between rural and urban areas continue to remain significant in terms of gross domestic product per capita [29].

In rural areas, there were continuous population losses caused by internal migration. They also come from the internal migration registered in the urbanised areas as Iaşi, Timișoara, Bucharest, and Cluj. Therefore, because of the natural decline of the population and because of suburbanisation, the speed of urbanisation in Romania is relatively low compared to other countries in EU [30,31]. It should be considered that internal migration is a manifestation of the local preferences and of the perception of the living standards of the Romanian population. The demand for and offer of infrastructure, the stability of the labour market, and the numerous cultural and social disparities are associated with the migration patterns and the resulting population [32].

This study analyses the empirical relation between the economic growth and the internal migration of the workforce, both on the level of the entire country, and on a regional level, by applying the standard production, because the economic production might register growth or decreases according to the quality of the production inputs and especially according to the quality of work. This analysis uses as influence factors on the national and regional economic growth the following independent variables: the amount of domicile changes on residence environments—urban and rural (SSD); civil employed population (PO); and the remuneration of the employees (RA).

Moreover, this study also encompasses the analysis of the empirical relation between economic growth and the structural development of the economy considering the migration of the self-employed workforce in the five main sectors of the economy (agriculture, industry, constructions, commerce, and services), on the level of the entire country and on a regional level.

3. Methodology

To obtain an econometric assessment of the impact of the workforce migration on national and regional economic growth in Romania, and of the structural development of the economy, the production function might be applied [9] and might be written as follows:

Y = f (SSD_U, SSD_R, RA, PO)

(1)

where Y represents the gross internal product of a certain regional economy (measured in million of lei); SSD_U refers to the amount of domicile changes on resident environments—urban (measured in number of people); SSD_R refers to the amount of domicile changes on resident environments—rural (measured in number of people); RA represents the remuneration of the employed population (expressed in current prices in million lei); and PO is the civil employed population (measured in thousands of people).

At the same time, to assess econometrically the impact of the workforce on the structural development, we used the production function under the following form:

Y = f (AGRI, IND, CONS, COM, SERV)

(2)

where Y represents the gross domestic product of a certain regional economy (measured in millions of lei); AGRI refers to the civil employed population (measured in thousands of people) from the agricultural sector; IND refers to the civil employed population (measured in thousands of people) from the industrial agricultural sector; CONS refers to the civil employed population (measured in thousands of people) from the construction field; COM refers to the civil employed population (measured in thousands of people) from commerce; and SERV refers to the civil employed population (measured in thousands of people) in the sector of services.

The above-mentioned functions which are assessed under a linear form, are expressed as follows:

Y = α0 + α1SSD_U + α2SSD_R + α3RA + α4PO

(3)

where α1, α2, α3, and α4 represent the coefficients associated with the independent variables and which denote the modification rate of the economic growth due to the migration of the workforce between the residence environments, the evolution of the employed population and its level of remuneration

Y = β0 + β1AGRI + β2IND + β3CONS + β4COM + β5SERV

(4)

where β1, β2, β3, β4, and β5 represent the associated coefficients of the independent variables and which denote the change rate of the economic growth due to the workforce employment in the main sectors of activities from Romania.

Based on Equations (3) and (4) there were elaborated three models of analysis. The first model is the combined assessment of the transversal section of the time series and the effect of the time series within these units. On this model the Ordinary Least Square (OLS) was applied and the regression model has the following form:

Yit = α0 + α1SSD_Uit + α2SSD_Rit + α3RAit + α4POit + εit

(5)

Yit = β0 + β1AGRIit + β2INDit + β3CONSit + β4COMit + β5SERVit + εit

(6)

where i = 1, 2, …, n refers to transversal section units (the 8 regions within this study); t = 1, 2, …, n is the number of the temporal series index (year in this case), while εit is the error term with a zero average value.

In order to avoid possible problems related to temporal autoregression, multicollinearity, and spatial heterogeneity, it is necessary to elaborate the model with random effects (RE) for the assessment of the workforce migration impact, the remuneration of the employed population and the sectorial development of the economy on the economic growth:

Yit = α0 + α1SSD_Uit + α2SSD_Rit + α3RAit + α4POit + ui + εit

(7)

Yit = β0 + β1AGRIit + β2INDit + β3CONSit + β4COMit + β5SERVit + ui + εit

(8)

where ui are εit supposed to be the two components of the term error of the model with random effects (RE). The term ui is specific for the 2nd error component in transversal section and it does not change in time, whereas the term εit is the idiosyncratic error term.

The choice between the simple linear regression model (pooled OLS) and the random effects model was taken with the help of the Lagrange Multiplier (LM) test proposed by Breusch and Pagan [33], which was created to test the random effects model based on OLS residuals.

In spite of these, the adequate estimate method for the random effects models (RE) relied on the properties of the two components of the error term. In the individual specific component (ui) is non-correlated or independent with the regressors, the OLS estimator for α and β would be consequent and it is known that the general error terms are variable, and that the usage of the random effects model (RE) would be adequate. Despite these, if the individual specific component (ui) is correlated with the regressors, the assessor OLS would be inconsequent, and the specific component of the individual is the variation of the dependent variable and must be treated as an additional set of parameters which need to be assessed. In consequence, this leads to the third assessment model which is a fixed effects model (FE):

Yit = α0 + α1SSD_Uit + α2SSD_Rit + α3RAit + α4POit + ϒidit + εit

(9)

Yit = β0 + β1AGRIit + β2INDit + β3CONSit + β4COMit + β5SERVit + ϒidit + εit

(10)

where dit is a dummy variable having the value one for the region i and zero in a contrary case and ϒi is the coefficient for the region i.

In the above-mentioned equations, the index i means that the intercepts might vary or might be different among the regions, because all regions have their own characteristics. In consequence, the fixed effects models (FE) allow each region to have its own interception value which does not vary along the period. The fixed effects mode offers a reasonable approach for each assessment when there is an unnoticed heterogeneity between the units, and its individual specific effects are correlated with the regressors. In other situations, the random effects model might be more adequate if the individual specific effects are correlated with the regressors. In other situations, the random effects model might be more adequate if the individual specific effects are independently distributed by regressors [34]. The choice between the models RE and FE was made with the help of the Hausman test, which is created to test if individual errors (ui) are correlated with the regressors. Data processing and testing were performed through Eviews software.

To analyse the regional economic impact of internal migration of the workforce in Romania, the data used for the assessment of the standard production function are arranged in a panel data form, by putting together the annual data in a time series and all the transversal units from the eight regions for 19 years, from 2000 up to 2018. The descriptive statistics for all the used variables are presented in

Table 1. Descriptive statistics of the variables in Equation (9).

	GDP	SSD_U	SSD_R	RA_T	PO_T
Mean	60,360.13	−3699.211	3699.211	21,312.68	1055.981
Median	56,299.90	−4102.000	3857.000	18,991.10	1054.150
Maximum	256,595.7	12,007.00	8298.000	105,613.7	1409.100
Minimum	7036.100	−11,195.00	−1159.000	2835.500	761.3000
Std. Dev.	43,125.18	3730.408	2067.671	16145.59	162.6694
Skewness	1.806648	1.361784	−0.214557	2.312283	−0.090895
Kurtosis	7.614577	6.003393	2.575190	10.29237	1.988374
Jarque-Bera	217.5515	104.1085	2.309151	472.2472	6.690750
Probability	0.000000	0.000000	0.315191	0.000000	0.035247
Observations	152	152	152	152	152

and

Table 2. Descriptive statistics of the variables in Equation (10).

	AGRI	IND	CONS	COM	SERV
Mean	309.4908	239.9020	69.52697	136.4638	300.5974
Median	326.5500	237.6000	66.15000	133.9000	275.6500
Maximum	710.3000	348.2000	163.5000	260.2000	744.9000
Minimum	27.20000	151.4000	32.90000	67.50000	175.4000
Std. Dev.	150.3962	47.05989	29.50317	40.03814	117.9568
Skewness	−0.058208	0.039036	1.637211	1.340697	2.218442
Kurtosis	2.786416	1.975799	5.520549	5.028011	7.524557
Jarque-Bera	0.374749	6.682190	108.1418	71.58379	254.3311
Probability	0.829133	0.035398	0.000000	0.000000	0.000000
Observations	152	152	152	152	152

. In this study, we used the data provided by the National Institute of Statistics of Romania which measures migration using the balance of changes of residence and provides aggregate data for flows entering or leaving each county or region of Romania.

4. Results and Discussions

The results in Table 1 and Table 2 show there is a difference and a significant variability among the observed variables which might be caused by the high disparities of the labour market characteristics for each region and the dimension differences of the regional economies. At the same time, the prevalence of the economic cycles appears along time, and are rendered to several economic situations being perceived as varied and different from one region to another. In consequence, the standard production function in this study is assessed using the random effects model and the fixed effects model. The variable and fixed effects models correct both the temporal and the spatial heterogeneity, which might appear in using the OLS pool procedures.

4.1. Economic Growth and Migration of the Workforce at National Level

For the assessment of the impact of the workforce migration, the remuneration of the employees and the population employment on the economic growth on a national level in Romania the results of the aggregate production functions as they are presented in Equations (5), (7), and (9) were assessed, and they were presented in

Table 3. Economic growth and migration of the workforce (simple regression).

Dependent Variable: GDP
Method: Panel Least Squares (Pooled OLS)
Sample: 2000–2018
Cross-sections included: 8
Total panel (balanced) observations: 152
Variable	Coefficient	Std. Error	t-Statistic	Prob.
SSD_U	5.844952	0.582370	10.03649	0.0000
SSD_R	4.432444	1.084869	4.085694	0.0001
RA_T	1.509758	0.139847	10.79575	0.0000
PO_T	31.96012	5.441839	5.873037	0.0000

,

Table 4. Hausman Test.

	Pooled OLS	Random Effects	Fixed Effects
Breusch–Pagan LM test a	-	X2(1) = 134.07 ***	-
Hausman test b	-	-	X2(4) = 42.67 ***

^a Random effects versus OLS pooled test. ^b Fixed effects model test versus random effects model. *** significant at 10%.

and

Table 5. Economic growth and the migration of the workforce (fixed effects FE and random effects RE).

Dependent Variable: GDP Sample: 2000–2018
	Panel Least Squares (Fixed Effects)		Panel EGLS (Random Effects)
Variable	Coefficient	p-Value	Coefficient	p-Value
C	−159,938.0	0.0000	−97,216.73	0.0000
SSD_U	1.328143	0.0718	2.904543	0.0000
SSD_R	3.036681	0.0016	4.485426	0.0000
RA_T	2.256311	0.0000	2.129020	0.0000
PO_T	157.0955	0.0000	100.7155	0.0000
Cross-sections included: 8 Total panel (balanced) observations: 152

. These tables offer combined production functions (pooled OLS), with random effects (RE) and fixed effects (FE), which are assessed through the division of the workforce variable between the residence environments (rural and urban).

The assessment of the pooled OLS (Table 3) indicates the fact that all variables included in this model have significant positive effects, statistically on the regional gross domestic product. At the same time, the results suggest that, despite this, the amount of domicile changes in the rural environment had a positive impact on the regional economic growth, in comparison with the amount of domicile changes in the urban area. This implies that a general growth in the amount of domicile changes is good, but not as much as a growth in the amount of domicile changes in the urban environment.

As for the influence of the remuneration of the employees on the regional GDP growth, the positive and significant effects from a statistical point of view are noticed, but with a relatively low influence as compared to the positive and significant influence generated by the employed civil population. In this context, a growth of one unit of workforce employment determined a growth of 31.96 units of the economic growth, while a growth with of unit of the remuneration of the employees determined a growth of only 1.5 of the regional GDP.

The model is assessed using RE and FE assessors and the assessed results are compared using the Hausman test (Table 4). The statistics of the Hausman test are extremely significant on the level of 1%. The test rejects decisively the null hypothesis while it uses the RE assessor, according to which the individual specific effects are not distributed independently of the regressors and supports the specification of the assessor with fixed effects. In consequence, in the interpretation of the econometric results, the main accent goes on the assessments of the fixed effects model, whereas the random effects model is also assessed in this study for reference purposes.

The fixed effects model (FE) in Table 5 shows that the variable of the amount of domicile changes in the urban environment has a positive effect and is significant from a statistical point of view, but with a lower impact on the economic growth as compared to the variable of the amount of domicile changes in the rural environment. The assessed results are according to the hypothesis that the amount of the domicile changes between the resident environments stimulates the regional economic growth, although on a small scale. As for the variable of the remuneration of the employees, the assessed result suggests that a growth with one unit of this variable generates a growth of only 2.25 of GDP as compared to the growth of 157.09 units of GDP determined by the growth with one unit of the civil employed population.

In the case of the assessment model with random effects, it should be noted that the hypothesis and the influences mentioned in the case of the fixed effects assessor, all four variables included in the model are significant from a statistical point of view and generate positive effects on the regional economic development, therefore as a growth with one unit of the civil employed population determined a growth with 100.71 units of the regional GDP variable.

4.2. Economic Growth and Sectorial Development at National Level

As for the influence generated by the evolution of the civil employed population in the main sectors of activity of the Romanian economy of the economic growth, there were also applied three analysis methods.

By applying the simple OLS method (

Table 6. Relation economic growth—sectorial development (simple regression).

Dependent Variable: GDP
Method: Panel Least Squares
Sample: 2000–2018
Cross-sections included: 8
Total panel (balanced) observations: 152
Variable	Coefficient	Std. Error	t-Statistic	Prob.
AGRI	−51.31007	11.81204	−4.343879	0.0000
IND	−80.18535	39.57337	−2.026245	0.0445
CONS	756.8052	195.3002	3.875086	0.0002
COM	558.7554	173.6035	3.218573	0.0016
SERV	−109.4314	45.53271	−2.403358	0.0175

) we notice that all five variables included in the model are significant from a statistical point of view and determine both positive and negative influences on the economic growth.

Results suggest that the evolution of the employed population in the services sector generates the strongest negative influence on GDP, in other words, a growth with one unit of the employed population in the field of services will generate a reduction with 109.43 units of the gross domestic product. At the same time, a growth with one unit of the employed population in the agricultural field will generate a reduction with 51.31 units of the GDP.

The results of the Hausman test (

Table 7. Hausman Test.

	Pooled OLS	Random Effects	Fixed Effects
Breusch–Pagan LM test a	-	X2(1) = 9.75 ***	-
Hausman test b	-	-	X2(5) = 288.37 ***

^a Random effects versus the combined OLS estimation test. ^b The fixed effects vs. random effects model test. *** significant at 10%.

) for this model are significant from a statistical point of view on the level of 1%. In this situation, the test does not reject the null hypothesis while it uses the RE assessor according to which the individual specific effects are not distributed independently from regressors and supports the specification of the random effects assessor. In consequence, in the interpretation of the econometric results, the focus is on the assessments of the random effects model, whereas the fixed effects model is estimated only for reference purposes.

The random effects model (RE) in

Table 8. Relation economic growth—sectorial development (fixed effects FE and random effects RE).

Dependent Variable: GDP Sample: 2000–2018
	Panel Least Squares (Fixed Effects)		Panel EGLS (Random Effects)
Variable	Coefficient	p-Value	Coefficient	p-Value
C	7040.001	0.7494	−29,523.81	0.0001
AGRI	−176.6974	0.0000	−36.13502	0.0000
IND	−185.8649	0.0017	−11.65458	0.6784
CONS	278.2935	0.0971	895.9835	0.0000
COM	−192.2727	0.2384	557.7435	0.0000
SERV	530.5604	0.0000	−114.9161	0.0000
Cross-sections included: 8 Total panel (balanced) observations: 152

supports the relationship between the variables and was also identified in the simple model of regression analysis with the least squares method. Despite these, the assessed results indicate the fact that the variable employed population from industry is not significant from a statistical point of view, whereas the rest of the variables are significant, having a probability of errors below 1%. From the results obtained it should be noted the significant positive influence of the employed population from the sector of constructions and commerce, these being two sectors of developing activities in recent years in our country, whereas the employed population from the sectors of services and agriculture generate a negative influence on the regional economic growth.

As for the assessment of the fixed effects model, the hypothesis and the influences identified in the case of the assessor with variable effects are not the same. Therefore, the variable employed population from the field of trade is not significant from a statistical point of view, and, more than that, it seems to generate negative effects on the economic growth. For the variables employed population in the agricultural and industrial field, the negative influence is maintained from a statistical point of view, whereas the employed population from services also generates positive and negative influence on the regional economic growth. As it was previously mentioned, the analysis of the results of this model is not significant for the present study, but it is carried out in reference purposes.

4.3. The Intra-Regional Relationship between Economic Growth and Migration of the Workforce

The analysis is focused on the two regression equations on a regional level, using the OLS method, based on the time series supplied by the National Institute of Statistics in our country for the eight administrative regions: North West (NV), Centre (C), North East (NE), South East (SE), South Muntenia (SM), Bucharest-Ilfov (BI), South West Oltenia (SVO), and West (V).

In the North West region, the second region from the point of view of the economic growth in 2018 after the region Bucharest-Ilfov, we should notice the positive and the significant impact from a statistical point of view of the variables included in the first linear regression equation, the employed civil population having the largest influence on the regional GDP. This thing can be explained by the growth of the number of employed people in this region in the last 5 years, as it results from the statistical data supplied by INSSE. At the same time, the amount of domicile changes in the rural environment generate a significant, positive influence higher than the amount of domicile changes in the urban environment, considering the orientation of the population towards the rural environment within the analysed interval.

The outlined results indicate a positive relation which is highly significant from a statistical point of view between the economic growth and the remuneration of the employed population in the Central region, whereas the amount of domicile changes both from the urban environment, but also from the rural one exerted a negative influence on the regional GDP. As for the influence of the civil employed population on this region, this variable is not significant from a statistical point of view.

For the region North East, it should be noted the positive and significant impact of the amount of domicile changes in the urban and rural environment, as it was the case of the North Western region, a more significant influence being exerted by the changes from the rural environment on the regional GDP growth. At the same time, a positive and strongly significant influence is given by the remuneration of the employees on the economic growth.

In the South East region, it should be noted, from

Table 9. The intra-regional economic growth—migration of the workforce.

Dependent variable: GDP Time interval: 2000–2018
	NV	C	NE	SE	SM	BI	SVO	V
SSD_U	3.88 (0.0064)	−6.02 (0.0017)	0.99 (0.0133)	−1.58 (0.0025)	1.51 (0.5181)	1.43 (0.0129)	−2.37 (0.0261)	−0.70 (0.0638)
SSD_R	4.46 (0.0003)	−7.70 (0.0024)	2.11 (0.0004)	−0.70 (0.0682)	1.13 (0.3196)	1.23 (0.0905)	−1.42 (0.1518)	−0.21 (0.0732)
RA	2.03 (0.0000)	2.91 (0.0000)	3.07 (0.0000)	2.84 (0.0000)	3.54 (0.0000)	2.55 (0.0000)	2.41 (0.0000)	2.52 (0.0000)
PO	8.35 (0.0100)	6.64 (0.2405)	−1.93 (0.5086)	−5.33 (0.3555)	−2.44 (0.0008)	3.49 (0.0022)	−3.28 (0.0972)	4.12 (0.0885)

, there is a significant negative influence on the amount of domicile changes in the urban environment and is less intense than in the rural environment on economic growth, as well as a positive and significant impact of the remuneration of the employees from the region on the regional GDP the rural environment.

At the same time, for the region South Muntenia it should be noted the significant and positive influence of the remuneration of the employed population on the economic growth, but at the same time the negative influence of the variable civil employed population, which is justified by the significant reduction in the number of employed people in this region. Regarding the amount of domicile changes in the urban or rural field, it should be noted that although these variables have a positive impact on economic growth, they are not significant from a statistical point of view.

Migration of workforce according to the residence environment exerted a positive impact on the GDP growth within the region Bucharest-Ilfov, considering the fact that it is the region with the highest economic growth in Romania, this attracts more and more inhabitants and future employees in the capital and in its periphery. The years after the economic crisis in 2008 changes the amount of domicile changes in the urban environment of this region from −3047 in 2013 to more than 12,000 people in 2018. The attraction of the population towards this region is also due to the level of the salary, justifying the positive influence triggered by the remuneration of the employed population on the regional GDP growth according to the results obtained by Zaharia et al. [35].

The South West Oltenia region is one of the less developed regions in Romania with the lowest level of GDP. The employed population in this region was continuously decreasing in the analysed time interval, generating significant negative influences on the regional economic growth. At the same time the amount of domicile changes in the urban field is significant and it has a negative impact on the evolution of the regional GDP. Significant positive influences are given by the remuneration of the employed population in this region.

From the econometric analysis it should be noted that the regions with the highest attractivity of the workforce, have most of the working places available [36], and those registered the highest migration of the workforce and had the highest contribution to GDP.

The results outlined in Table 9 show a positive and highly significant impact from a statistical point of view between the economic growth and the remuneration of the employed population within the Western region, while the amount of domicile changes both from the rural and from the urban field exerted a negative influence on the regional GDP. This thing can be explained by the migration of the population towards the developed countries from Western Europe in search for a more stable workplace and better remunerated. As for the influence of the civil employed population from this region it should be noted that this variable is significant from a statistical point of view, and it exerts a positive influence on the regional economic growth.

The validation of the hypothesis formulated after the results obtained on a national or regional level is represented in Figure 1. In this figure it is represented only the significant results from a statistical point of view (with a p-value < 0.1).

Figure 1 shows H1 is validated by the results obtained on a national level, as well as in other six regions from Romania. Regarding the H2 hypothesis, in the developed regions of RO it is valid, whereas in the less developed regions (such as the region SVO) the employed population negatively influences the economic growth, results justified by the degree of occupation of the growing population. The third hypothesis H3 is validated by the results obtained in all the regions, irrespective of their degree of development, as well as on a national level.

4.4. The Intra-Regional Relationship between Economic Growth and Sectorial Development

As for the impact of the employed population from the main sectors of the Romanian activity, it should be noted in

Table 10. Intraregional relation economic growth—sectorial development.

Dependent variable: GDP Period of time: 2000–2018
	NV	C	NE	SE	SM	BI	SVO	V
AGRI	−126.45 (0.0000)	−173.28 (0.0019)	−76.78 (0.0229)	−62.60 (0.0549)	−108.72 (0.0069)	−1765.63 (0.0165)	−87.04 (0.0036)	−238.68 (0.0019)
IND	−77.08 (0.4899)	−45.90 (0.5889)	−10.36 (0.9367)	−486.25 (0.0929)	35.55 (0.8268)	−330.74 (0.0608)	−308.41 (0.1858)	−335.37 (0.0228)
CONS	781.75 (0.0128)	1247.56 (0.0011)	1193.59 (0.0045)	936.45 (0.0392)	1477.10 (0.0067)	961.09 (0.0470)	831.39 (0.0080)	434.01 (0.0912)
COM	−382.68 (0.0213)	−589.80 (0.0373)	18.94 (0.9617)	−33.86 (0.9355)	319.24 (0.5731)	3.23 (0.0956)	−331.35 (0.0554)	494.47 (0.0298)
SERV	434.12 (0.0187)	421.81 (0.0369)	29.30 (0.8959)	404.28 (0.0476)	−140.14 (0.0987)	672.97 (0.0002)	550.77 (0.1797)	447.72 (0.0279)

that within the region North West there was a negative influence of the sectors agricultural and commercial on the regional GDP growth, whereas in the industrial sector, although it has a negative impact, it is not significant from a statistical point of view. In consequence, the regional GDP growth for the analysed region was strongly supported by the constructions and services sectors.

Analysing the results of the relation economic growth—sectorial development in the Centre region, a positive and significant impact of the employed population within construction on the regional GDP growth is observed. Therefore, for a growth with one unit of this independent variable it should be noted a growth with 1247 units of the dependent variable. It must also be noted that there was a positive impact generated by the sector of services in the economic development, unlike the agricultural and commercial sectors which also led to the reduction in economic growth. As in the case of the region North West, the variable of the employed population in the sector of the industry is not significant from a statistical point of view, and its impact on the growth of the regional GDP might be negative.

The impact of the employed population from the sector constructions in the region North East is positive and significant from a statistical point of view, therefore a growth of one unit of this variable will generate a growth of 1193 units of the regional GDP. Three of the variables included in the model are not significant from a statistical point of view in the case of the North East region, the probabilities associated with these variables being above 0.1. At the same time, the agricultural sector shows a significant negative impact on the regional GDP.

Analysing the influence of the sectorial development on the economic growth, it should be noted in the South East region that four of the five variables included in the regression equation are significant from a statistical point of view, two of them having positive influence on the economic growth (the employed population from constructions and that from the services), and two others with a negative impact on the regional GDP growth (the employed population from the agricultural and industrial field).

The sectorial development in the region South Muntenia influences positively the economic growth especially due to the sector of constructions, sector strongly exploited in this region and where the employed population would be in a continuous growth. Negative influence and significant from a statistical point of view were triggered from the perspective of the employed population in the field of agriculture and services, whereas the variables regarding the employed population in the field of industry and commerce are not significant from a statistical point of view.

The sectorial development in the region Bucharest-Ilfov was based on the evolution of the employed population in the field of services mainly followed by the field of constructions and the commercial one which were intensively exploited and developed in recent years, the civil employed population being continuously growing. Unlike the rest of the analysed regions, where agriculture influenced negatively but to a smaller extent the evolution of the regional GDP, this time it is observed a strong negative influence of the employed population from the agricultural sector, explained on one hand by the fields, which were used for agricultural purposes, construction purposes or even real estate purposes. At the same time, this influence is exerted by the low surface of the agricultural fields as compared to the agricultural fields granted for the other development regions.

As for the evolution of the activity sectors, the region South West Oltenia is acknowledged as having the best agricultural development, but the population interested in this sector of activity is decreasing, human activity being gradually replaced by technological systems more efficient both from the point of view of the costs and productivity. In this way it can be explained the negative influence, although it is reduced as compared to other development regions, on the employed population from the agricultural field on the regional GDP evolution. Additionally, within this region, the sector of constructions is that supporting the regional economic growth, the number of employed people within this sector is increasing.

If this analysis was carried out on the level of the West region, the negative influence of the sectors agricultural and industrial on the economic regional growth should be noted, but also the positive influence of the sectors of constructions, commerce, and services, from this administrative region.

The validation of the hypothesis formulated after the synthesis of the results obtained on this national and regional level is represented in Figure 2. Within this figure it is only represented the significant results from a statistical point of view (with a p-value < 0.1).

As for the impact of the workforce migration in the relation between the economic growth and the sectorial development, the H4 hypothesis is validated by the results obtained both on a national and on a regional level, the primary sectors of activity as agriculture and industry being less and less profitable, their contribution for the economic growth in Romania being negative.

Unlike the agricultural and the industrial sector, the sector of constructions registered the most significant contribution to the economic growth, the hypothesis H5 being validated in all regions irrespective of their degree of development, as well as on a national level. The H6 hypothesis according to which the migration of the workforce in the field of services has a negative influence on the economic growth was confirmed on a national level, but on a regional this is validated only by the results obtained in the SM region.

As for the structures of the economy determining the highest influence on the regional economic growth, the employed population from agriculture has a negative impact on GDP from all the eight regions of Romania, whereas the constructions sector determines positive effects, in all regions from Romania, the same as they generate them as it results from the analysis carried out on the level of the entire country.

The negative effects generated by the employment and the migration of the workforce from agriculture on economic growth are explained through the existing structural deficiencies from the agricultural field in Romania, as compared to the European agriculture.

Additionally, the low incomes of agricultural workers compared to other sectors have led the population to abandon agricultural activities in favour of other types of activities in urban areas [37].

The EU rural development policy is less adapted to the realities of rural Romania, because the small agricultural parcels characterizing this region are not eligible for the EU funding measures. It is considered that on the level of the decision factors in Romania it might give a significant to the agricultural sector in the sense of the growth of the competitiveness of the Romanian products for the export, the improvement of the professional training of the farmers, the facilitation of the access for funding, the stimulation of the association as other authors also concluded [29].

The employed population in the commerce field has negative effects on the economic growth in the regions North West, Centre, and South West Oltenia, and positive effects in the Bucharest-Ilfov region. The econometric results correspond to the conclusions of other analysis carried out on the level of Romania [38] according to which the discrepancies between the counties in the country are considerable regarding commerce. It should be noted a growth in the wealthy counties, with significant urban agglomerations (Bucharest, Cluj, Timiș, and Brașov), whereas the poor counties from the North West (Vaslui, Botoșani), and South West (Olt, Caraș-Severin, Mehedinți) registered stagnations or reductions. This situation can be explained as in the case of the manufacturing industry by the fact that commerce investments were not equally distributed from a geographical point of view, but also by the level of the purchasing power of the population [39].

The employed population in the field of services has a positive impact in North West, Centre, South East, West, and Bucharest-Ilfov and a negative impact for the region South Muntenia. The results obtained are circumscribed to the current trend on the level of the Romanian economy to reach the development model of EU where the sector services (without constructions) represent more than 66% of GDP whereas on the level of Romania the share is 57%. The result obtained from the region South Muntenia can be explained by the migration trend of the highly qualified staff [40], especially towards the development pole Bucharest-Ilfov which is close enough to encourage internal migration.

The employed population in the industry sector has a negative influence in the regions South East and West, whereas for the other regions the econometric assessments are not significant. The identified negative effects within this econometric analysis carried out, generated by the occupation and the migration of the workforce from the industrial sector corresponds to the situation on the level of the Romanian industry: the low productivity, the offer of industrial products for export [29]. Therefore, the stimulation and the development of the industries oriented towards export is considered essential and also the attraction of foreign direct investment in the industry as being favourable measures for economic development. Similar results regarding the reduction in the importance of the industrial sector in Romania and a negative net migration were highlighted in the counties with predominantly agricultural profile in southern Romania or other counties that lost their economic power through the decline of mining or heavy industry [37].

5. Conclusions

The purpose of this research was to analyse the regional economic impact of the internal migration of the workforce in Romania, using the standard production function and panel data, based on the annual series from all eight regions for 19 years from 2000 up to 2018. The dependent variable used to assess the economic growth was represented by regional and national GDP and the independent variables used are the amount of domicile changes, the remuneration of the employed population, and the employed population.

Following the results obtained, at national level, it can be stated that both the changes of domicile in the rural or urban environment, as well as the remuneration of the employees and the employed civil population determine significant positive influences on the economic growth. Regarding the sectorial development, it can be seen that the employed population in sectors such as agriculture, industry, and trade have had negative effects on economic growth, while the employed population in the construction and services sectors has contributed positively to economic growth.

At the regional level it can be concluded that the migration of the population between the urban and the rural environment generates positive influences in the North West, North East and Bucharest-Ilfov regions, while in the West, Centre and South East regions they can be observed negative influences. The employment rate of the civil population determined significant positive effects in the West, North West and Bucharest-Ilfov regions and negative effects in the South West Oltenia, and South- Muntenia regions. Instead, the positive effects determined by the level of remuneration of the population in all the eight regions of Romania can be observed.

Regarding the intra-regional evolution, it can be concluded that workforce migration in primary activity sectors (industry and agriculture) negatively influences the structural development of the economy. Instead, the migration of the workforce in the sector of constructions, sector intensively developed in the last period, influences positively the structural development of the economy.

The results outline the implications of the internal migration on the regional development and on the structural development of the Romanian economy, under the circumstances where there are highly skilled human resources, but it does not have the framework to involve it. It is necessary that governments should assign and encourage the workforce employment in productive sectors, because this will contribute to the development of the competitiveness in Romania, and to the reduction in the disparities related to the European Union.

Some limits of the research regarding the low availability of the data for long time series were identified, which could give us more precise results in the assessment of the effects of the internal migration phenomenon on the economic development. At the same time, a significant factor which should be considered in future research would be the undeclared work, a phenomenon which is frequently found in Romania. This might change the results considering that in certain sectors of activity (as the agricultural) most of the workers carry out their activity without an employment contract. Another element which should be considered would be the flow of seasonal workers, but the statistical data regarding this indicator are low and on short periods, being practically impossible to analyse such an indicator.