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Finance, Accounting and Business Analysis 
Volume 2 Issue 1, 2020 

http://faba.bg 

 

Influence Analysis of  The Active Part of Human Resources on The 

GDP of The Level 2 Regions in Bulgaria 

 
Tanakow Nikola*, Tsolov Georgi 
  
Department of Regional Development, University of National and Word Economy, Sofia, Bulgaria 

Info Articles  
 

Abstract 

History Article: 
Submitted 23 January 2020 
Revised 4 March 2020 
Accepted 17 May 2020 

 
This report focuses on the impact of human resources on the GDP of 

Level 2 regions in Bulgaria. The problems of unemployment and 

economically active population in Bulgaria are developing a centralized 
policy by the Ministry of Labor and Social Policy (MLSP). However, we 

believe the increase in GDP per capita is crucial for the development of 
regions and requires a comprehensive approach in its analysis. The report 

aims to analyze the problems of GDP per capita, some of them can be 
addressed regionally through forms of competitive advantage for 

economically active people. The report presents the spatial characteristics 

of the peculiarities of the regions. The author's team outlines the relevant 
positive aspects of the development of the regions in Bulgaria and their 

advantages and disadvantages. 

Keywords:  
Regional development, GDP per 
capita, unemployment, 
economically active persons, 
regional economy, region. 

 

  

   

Address Correspondence:   
E-mail : nikolatanakov@gmail.com 
 

 

  



Tanakow Nikola, Tsolov Georgi / Finance, Accounting and Business Analysis 2 (1) 2020 

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INTRODUCTION 
 

In the context of the second EU membership program for our country, the 6 NUTS 2 regions in 

Bulgaria face several new challenges. They build competitive industries and structures that will strengthen 

regional markets and will create opportunities for the sale of local products and services. In the process of 

achieving the Europe 2020 Strategy goals and objectives, countries with a well-developed regional 

economy demonstrate good practices and models based on state involvement in building viable local 

industries and innovative business associations (Nikolov, 2016). That is how we need to take the concept 

of the role of the economically active population as part of national human resources to an entirely new 

level. 

The management practice in Bulgaria has established the regional policy as one of the most 

dynamically developing areas in the last few years. Following the country's accession to the EU, a 

regulatory framework and an institutional structure were created, which gained experience in the process 

of planning and coordination across sectors.  

We have created real prerequisites for conducting a modern regional development policy by adopting 

and gradually supplementing the Regional Development Act and the related by-laws. The administrative 

approach, of Pan-European importance, applied in our country to this complex matter, has led to the 

development of many strategies and plans for expansion at different levels. They were originally 

summarized in the National Development Plan (2007-2013), and today they are being upgraded through 

the current National Development Program "Bulgaria 2020".  

However, by applying a Systematic and Functional approach, we will recognize that the need for 

regional development policy is conditioned by the fact that the principle of territorial solidarity and 

cohesion requires the creation of relatively sustainable and equitable living conditions in different parts of 

the country. The availability of a unified strategic documents does not automatically lead to overcoming 

existing disparities and differences within and between regions. Their specific problems create social and 

economic confusion that can quickly affect the national economy to which they are closely linked. 

On the other hand, a well-known fact is that market forces alone cannot ensure balanced regional 

development. Regional development is a new concept that aims to stimulate and diversify economic 

activities, incite investment in the private sector, contribute to reducing unemployment, and, last but not 

least, is a concept that should lead to an increase in the standard of living of the population. Scientific 

interest and practical results show that contemporary regional development can only be realized through 

conscious action and active participation of society. Efforts should be directed to coordinating and 

regulating the processes taking place in each territory in order to harmonize them and create sustainable 

conditions for work, life, and recovery of the population (Nikolov et al., 2019). The development of public 

well-being is mostly a result of the qualitative characteristics of the workers and is realized by increasing 

the efficiency of social production. Therefore, in a market economy the managers of an enterprise should 

strive to run efficiently the flow of material and financial resources; the deliveries and distribute properly 

the working capital (Hristozov, 2017). 

   

Theoretical statement of the study 
Official economic and social data1 clearly show that in the current socio-economic situation we can 

recognize an urgent need for state economic policy in Bulgaria to further focus on more active and directed 

support for the development of the regions. This gives us a reason to analyze the relationship between the 

centrally conducted socio-economic policy in the country and the economic performance of the individual 

regions represented by the GDP per capita indicator. The authors of the report aim to enrich the research 

toolkit and methodology of evaluations to support the introduction of effective measures and guidelines for 

raising GDP per capita in the six regions in Bulgaria.  

The subject of this analysis is the impact of two main categories of human resources - the "active 

population" and the "unemployment rate" on GDP per capita. The impact was verified in the six NUTS 2 

regions of the country - Northwest, North-Central, Northeast, Southeast, Southwest and South-Central. 

The relationship between the variables is examined by analyzing data from official national statistics for 12 

years (2007 to 2018), that we initially processed on a multiple regression model: 

                       
Where: 

   – GDP per capita for the i-th region; 

    – active population in the i-th region; 

    – unemployment rate in the i-th region; 

   – random component. 

                                                      
1 Only official data from the National Statistical Institute (NSI) was used in the Report. 



Tanakow Nikola, Tsolov Georgi / Finance, Accounting and Business Analysis 2 (1) 2020 

27 

 

This model allows us to evaluate what is the impact of each factor (X) on the outcome (Y). 

After correct examination of the models in the different regions, it turned out that in none of them the 

factor “unemployment rate” shows statistical significance. The authors have removed this factor from the 

analysis, and we based it on the following single-factor model: 

 

               
Where: 

   – GDP per capita for the i-th region; 

    – active population in the i-th region; 

   – random component; 

The research task of the authors of the report under these objective conditions is to identify the main 

problems marked by the negative or positive processes in the dynamics of the active population in the 

regions and their effects on the GDP per capita indicator..  

 

An empirical statement of the study 
Our original intention was to clarify the impact of the factors "unemployment rate" and "active 

population" on a key regional policy indicator - the GDP of the region. It was dictated by the ability to 

make several policy evaluations in the respective territorial community. The correct statistical processing 

of the data led to the need for the authors of the report to analyze in particular the data on the regional 

economic activity of the population and its impact on GDP per capita. The empirical study covers the 

dynamics of activity in different periods of economic booms and crises of regional development and the 

national economy. The results for the different regions are examined separately: 

 

North-Central Region 
The best model here is the quadratic model (Rsquare = 0.702). However, to ensure the adequacy of 

the estimated relationships, the authors choose to apply the linear model, according to the rule that the 

best model exceeds the linear model by more than 10% explanatory ability (Bozev et al., 2019) should be 

selected (Rsquare = 0.664). The linear model is adequate (Sig. = 0.001) and has the following graphic 

image and estimated form: 

 

Table 1. Baseline data for the North-Central region of NUTS 2 of Bulgaria 

years Unemployment rate (%) Active population (%) GDP per capita (in BGN) 

2007 10,7 50,3 5 848 

2008 8,5 50,2 6 577 

2009 8,4 49,3 6 523 

2010 11,6 49 6 435 

2011 12,8 49 7 416 

2012 14,3 50,1 7 779 

2013 15,3 50,5 7 925 

2014 13,2 50,6 8 336 

2015 10,6 51,2 8 635 

2016 9,3 51 9 129 

2017 6,9 51,5 9 865 

2018 6,7 51,9 10 654 

Source: NSI, 2020 

 
 

 



Tanakow Nikola, Tsolov Georgi / Finance, Accounting and Business Analysis 2 (1) 2020 

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Figure 1. Dynamics of the active population in the region 

 

 
Estimated form: 

                                                         

The constant (Sig. = 0.003) and the parameter (Sig. = 0.001) are statistically significant and can be 

interpreted. 

The estimated model shows that with one percent increase in the active population, GDP per capita in this 

region would increase by BGN 1 275. 

 

North-East Region 
The best models here are the cubic, quadratic, logarithmic, and linear models (Rsquare = 0.406). 

However, for the reasons outlined above, the linear model (Rsquare = 0.406) will again be applied to the 

selected model. The linear model is adequate (Sig. = 0.026) and has the following graphic image and 

estimated form: 

 

Table 2. Baseline data for the North-East region of NUTS 2 of Bulgaria 

years Unemployment rate (%) Active population (%) GDP per capita (in BGN) 

2007 10,8 54,5 7 110 

2008 8,6 55,3 8 259 

2009 10,4 53,9 7 943 

2010 14,6 54,4 7 971 

2011 15,4 53,5 8 951 

2012 18,2 54,3 9 323 

2013 16,8 54,6 9 316 

2014 12,6 54,8 9 778 

2015 10,3 55,9 10 246 

2016 9,7 55,1 10 717 

2017 9,4 57 11 525 

2018 7,4 55,4 12 506 

Source: NSI, 2020 

 

 
 

0

2000

4000

6000

8000

10000

12000

48.5 49 49.5 50 50.5 51 51.5 52 52.5

North-Central Region 



Tanakow Nikola, Tsolov Georgi / Finance, Accounting and Business Analysis 2 (1) 2020 

29 

 

 
Figure 2. Dynamics of the active population in the region 

 

 
Estimated form: 

                                                 

The constant (Sig. = 0.053) is not significant, but more importantly, the parameter before the factor 

variable is significant (Sig. = 0.026) and can be interpreted. 

The estimated model shows that with a one percent increase in the active population, GDP per capita in this 

region would increase by 1,076 BGN. 

 

South-East Region 
The best models here are the cubic and quadratic models (Rsquare = 0.691). However, for a selected 

model to evaluate the relationship, the linear model will again be applied according to the rule that the best 

model exceeds the linear model with more than 10% explanatory power to be selected (R square = 0.690). 

The linear model is adequate (Sig. = 0.001) and has the following graphic image and estimated form:

 

Table 3. Baseline data for the South-East region of NUTS 2 of Bulgaria 

years Unemployment rate (%) Active population (%) GDP per capita (in BGN) 

2007 6,5 50,5 6 735 

2008 5,8 51,9 7 864 

2009 6,6 51 8 019 

2010 10,5 53 8 115 

2011 11,5 52,3 8 931 

2012 11,9 52,9 9 400 

2013 13 52,3 9 509 

2014 11,9 51,9 10 012 

2015 10,4 52,7 10 312 

2016 7,9 52,5 11 755 

2017 7 54,8 12 655 

2018 5,4 54,7 12 787 

Source: NSI, 2020 

 

0

2000

4000

6000

8000

10000

12000

14000

53 53.5 54 54.5 55 55.5 56 56.5 57 57.5

Northeast Region 



Tanakow Nikola, Tsolov Georgi / Finance, Accounting and Business Analysis 2 (1) 2020 

30 

 

 
Figure 3. Dynamics of the active population in the region 

 
Estimated form: 

                                                   

The constant (Sig. = 0.002) and the parameter (Sig. = 0.001) are significant and can be interpreted. 
The estimated model shows that with a one percent increase in the active population, GDP per capita in this 

region would increase by BGN 1,270. 

 

South-Central Region 
The best models here are the power, exponential, s-shaped, compound, and growth models (Rsquare 

= 0.390). Again, to evaluate the relationship, the authors choose the linear model according to the rule that 

the best model exceeds the linear model by more than 10% explanatory power to be selected (Rsquare = 

0.387). The linear model is adequate (Sig. = 0.031) and has the following graphic image and estimated 

form:

 

Table 4. Baseline data for South-Central region of NUTS 2 of Bulgaria 

years Unemployment rate (%) Active population (%) GDP per capita (in BGN) 

2007 5,6 51 5 943 

2008 5,1 52,7 6 646 

2009 7,3 51,8 6 754 

2010 11,5 51,8 6 892 

2011 12,9 50,7 7 665 

2012 13,8 52,1 7 979 

2013 13,5 54 7 940 

2014 12 54,9 7 874 

2015 9,2 53,1 8 756 

2016 7,1 52 9 290 

2017 5,2 55,3 10 076 

2018 4,2 54,3 10 988 

Source: NSI, 2020 

 

0

2000

4000

6000

8000

10000

12000

14000

50 50.5 51 51.5 52 52.5 53 53.5 54 54.5 55 55.5

South-East Region 



Tanakow Nikola, Tsolov Georgi / Finance, Accounting and Business Analysis 2 (1) 2020 

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Figure 4. Dynamics of the active population in the region 
 

Estimated form: 

                                                        

The constant (Sig. = 0.089) is not significant, but more importantly, the parameter before the factor 

variable is significant (Sig. = 0.031) and can be interpreted. 
The estimated model shows that with a one percent increase in the active population, GDP per capita in this 

region would increase by BGN 612. 

 

North-West and South-West 
In these two regions, the authors found no statistically direct correlation between the "active 

population" factor and the GDP per capita indicator. None of the 11 models tested proved adequate to 

describe their relationship. Graphic expressions also do not show a clear link between them:

 

Table 5. Baseline data for the Northwest region of NUTS 2 of Bulgaria 

years Unemployment rate (%) Active population (%) GDP per capita (in BGN) 

2007 9 46,4 5 551 

2008 7,1 47,9 6 224 

2009 8 46,6 6 087 

2010 11,2 46,4 6 060 

2011 12,8 45,7 6 941 

2012 12,3 45,1 7 019 

2013 14 46,7 7 034 

2014 14,2 46,9 7 415 

2015 12,1 46,6 7 599 

2016 10,6 44,5 8 078 

2017 11,3 47 9 048 

2018 11,2 47,8 10 244 

Source: NSI, 2020 

 

 

0

2000

4000

6000

8000

10000

12000

50 51 52 53 54 55 56

South-Central Region 



Tanakow Nikola, Tsolov Georgi / Finance, Accounting and Business Analysis 2 (1) 2020 

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Figure 5. Dynamics of the active population in the region 

 

Table 6. Baseline data for the Southwest region of NUTS 2 of Bulgaria 

years Unemployment rate (%) Active population (%) GDP per capita (in BGN) 

2007 3,9 57,7 13 665 

2008 2,9 59,1 15 919 

2009 4,1 59 16 284 

2010 6,9 58,9 16 933 

2011 7,5 57,5 18 253 

2012 8,2 57,6 18 341 

2013 9,8 58,4 18 277 

2014 8,9 58,5 18 634 

2015 6,7 58,4 20 268 

2016 5,4 58 21 572 

2017 3,3 59,5 23 295 

2018 2,6 60,3 25 261 

Source: NSI, 2020 

 

 
Figure 6. Dynamics of the active population in the region 

0

2000

4000

6000

8000

10000

12000

44 44.5 45 45.5 46 46.5 47 47.5 48 48.5

North-West  

0

5000

10000

15000

20000

25000

30000

57 57.5 58 58.5 59 59.5 60 60.5

South-West 



Tanakow Nikola, Tsolov Georgi / Finance, Accounting and Business Analysis 2 (1) 2020 

33 

 

Comparing the empirical results obtained, it can be reasonably concluded that, at a regional level, the 

active population exerts the greatest effect on GDP per capita in the North-Central region, and the least 

effective in the South-Central region. However, it is not these data that cause the greatest interest in the 

present analysis. The authors are puzzled by the fact that the Southwestern region, which is the richest 

region in the country (including the capital Sofia), cannot express the much sought after a statistical link 

between the active population and GDP. The result is the same in the Northwestern region, although it is 

the poorest and lagging region in economic and social terms. The mere fact that the statistical models used 

in the survey did not work in the two regions located at the extremes of the socio-economic pendulum (the 

richest and the poorest)2 make us accept that the chosen approach is adequate.  

 

Cross-regional comparisons  
To build sustainable regional production systems to enhance the competitiveness of our national 

economy in the European and global markets, we need a detailed analysis of regional GDP and a clear 

idea of how different factors affect it. Logically, after trying to evaluate the correlation between the factors 

"unemployment rate" (for which we have not found statistically significant relationships) and "active 

population" (statistical estimates are presented above) by individual regions, we also need to make some 

cross-regional comparisons: 

Within the EU, Bulgaria has long-term GDP, which is the lowest indicator of GDP in the other 

Member States. The pace of development shows that it will take more than 15 years for us to catch up with 

the average EU GDP standard. Therefore, the authors of the report compare GDP per capita by region 

from before Bulgaria joined the EU and after 12 years of EU membership. 

 

 
Figure 7. GDP per capita by region in the country - comparison between 2007 and 2018 (in BGN) 

 

The results are obtained after processing the data with the SPSS statistical program and it is noticed in 

Fig. 7 that the 6 regions of NUTS 2 in Bulgaria have increased their GDP almost twice during the period 

of EU membership. This is undoubtedly due to market opportunities offered by the Union, the impact of 

the European Structural Funds on socio-economic development, attracting foreign direct investment, the 

gradual formation of the national capital, and the increase in entrepreneurial activity among Bulgarians. 

In the same way, we should look at and compare the status of the economically active population by 

region in the country, again setting the 2007 figures as a baseline. 

 

 
Figure 8. State of the economically active population by region in the country - comparison between 2007 

and 2018 (in%) 

                                                      
2 www.nsi.bg – NSI official website. See the Regional Statistics section. 

5551 
10244 

5848 
10654 

7110 
12506 

6735 

12787 13665 

25261 

5943 
10988 

0

10000

20000

30000

2007 2018

North-West North-Central North-East

South-East South-West South-Central

46.4 47.8 50.3 51.9 54.5 55.4 
50.5 54.7 57.7 60.3 

51 54.3 

0

20

40

60

80

2007 2018

North-West North-Central North-East

South-East South-West South-Central

http://www.nsi.bg/


Tanakow Nikola, Tsolov Georgi / Finance, Accounting and Business Analysis 2 (1) 2020 

34 

 

The picture presented in Figure 8 also shows the percentage increase of the active population in the 6 

regions of NUTS 2 in Bulgaria. In both cases, the Northwest region has the lowest index (1.4% growth for 

12 years) and the Southwestern region has the best indicator (2.6% growth for the period). The highest 

change was observed in the values of the South-East region (4.25%), and the lowest change was observed 

in the values of the North-East region (0.9%). 

 

 CONCLUSION 

 

Without claiming absolute exhaustiveness and comprehensiveness, the above estimates of the effect of 

the dynamics of the active population on GDP per capita in the individual regions and the inter-regional 

comparisons allow us to conclude the necessary state measures supporting regional development in 

Bulgaria. Since the report was written in the context of the crisis caused by COVID-19, that will 

undoubtedly lead to a restructuring of the economy on a regional, national, European and global scale, the 

authors of the report recommend that the following conclusions be taken into account: 

Shortly, regions will develop their economic potential without much link to their unemployment rate. 

This is due to the widespread introduction of new technologies in the production of goods and services for 

final consumption and the replacement of human labor by machine. This condition can be offset by the 

targeted involvement of people in developing local resources by seeking new approaches to them. 

Social capital in the regions will be judged the most by its quality indicators - education, skills, 

competences, creativity and innovation. The use of these personality features in the economic system gives 

businesses a chance to survive in the fast-changing environment and they will build their localization 

strategies according to the availability of such capital. Knowledge acquisition and development systems 

need to be deployed extensively regionally. 

The management of spatial and territorial processes can only be effective if it also engages the public's 

attention. People determine the needs and priorities of the region, and the state creates the conditions to 

accumulate the necessary resources to meet them. A regional policy cannot be budget based but should 

stimulate the creation of effective self-developing instruments - regional funds, free industrial zones, 

guaranteed municipal and district loans, bond issuance, and more. 

Achieving sustainable employment in the regions should be a top priority. The approach used by the 

authors shows that, despite the contingencies in the analysis, it can be argued that there is a relationship 

between the percentage of the active population in the regions and the amount of GDP in them. In the 

future, many other factors affecting GDP per capita in the territorial units of the country will be subjected 

to statistical analysis, but cross-regional comparisons suggest that long-term growth in the active 

population is achieved by expanding entrepreneurship and creating new forms and business organization. 

 
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Nikolov, G. (2016). Durjavni politiki I strategii za regionalno razvitie. Publishing House – UNWE. Sofia. 

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Nikolov, G. et. al, (2019). Regionalno I prostranstveno razvitie na gradovete ot Severozapaden rayon, 

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