










































 

 

AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes 

ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 18, No. 2 (2024), pp. 344-355 

 

344 

 

DOCTOR MIGRATION: UNPACKING ECONOMIC AND SOCIAL 

IMPACTS 

 

M. TOSUNOĞLU, T. ASLAN, A. CİEŚLİK, T. VALIYEV 

 

Mahir Tosunoğlu¹, Tunahan Aslan², Andrzej Cieślik³, Turgud Valiyev⁴ 
1 School of Social Sciences, Ege University, İzmir, Turkey  

https://orcid.org/0000-0002-9941-0151, E-mail: mtosoglu00@gmail.com  
2 Graduate School of Social Sciences, Yuzuncu Yıl University, Van, Turkey  

https://orcid.org/0000-0001-8958-699X, E-mail: tunahanasln@gmail.com  
3 Faculty of Economic Sciences, University of Warsaw, Warsaw, Poland  

https://orcid.org/0000-0002-7834-7384, E-mail: cieslik@wne.uw.edu.pl  
4  Faculty of Economic Sciences, University of Warsaw, Warsaw, Poland  

https://orcid.org/0000-0002-7834-7384, E-mail: t.valiyev@student.uw.edu.pl  

 

Abstract: Health expenditures are vital indicators of a nation’s social and economic 

progress, playing a key role in welfare and sustainable development. A well-functioning 

healthcare system relies on an adequate and skilled workforce, yet many countries face 

shortages, prompting reliance on international labor migration. This study investigates the 

economic and social drivers of health labor migration, focusing on doctors in 15 high-income 

countries from 2011 to 2019. Using panel data analysis, we found that lower wages 

significantly drive migration, with a 1% increase in wages reducing health worker migration 

by 0.08%. The results underscore the importance of fair remuneration in retaining healthcare 

professionals within their home countries. Moreover, destination countries with better training 

infrastructure and career advancement opportunities create additional pull factors, 

compounding the challenges faced by source countries. Globalization further facilitates 

migration by lowering barriers and harmonizing professional standards. These findings 

highlight the urgent need for policy interventions to address wage disparities and mitigate 

brain drain while ensuring global health workforce equity. 

Keywords: Health, Reasons for Migration, Panel Data Analysis 

 

1. Introduction 

Health expenditures are one of the important indicators of social and economic 

development. Additionally, expenditures on the health sector are among the most basic 

elements of welfare and sustainable development. For a health system to achieve its primary 

purpose, which is to provide health services, it must have a sufficient number and quality of 

healthcare workforce capacity. Building the necessary capacity is possible by employing 

appropriate health workers at the right place and at the right time. This capacity can be created 

from domestic resources as well as through the employment of foreign resources (Diallo, 2004, 

p. 603). International migration of healthcare professionals has become an increasingly 

important issue on the international health policy agenda in recent years. Brain drains of highly 

qualified human capital are an important social phenomenon. When we look at the causes of 

migration in general, external migration is made from low-income countries to higher-income 

https://orcid.org/0000-0002-9941-0151
mailto:mtosoglu00@gmail.com
https://orcid.org/0000-0001-8958-699X
mailto:tunahanasln@gmail.com
https://orcid.org/0000-0002-7834-7384
mailto:cieslik@wne.uw.edu.pl
https://orcid.org/0000-0002-7834-7384
mailto:t.valiyev@student.uw.edu.pl


 

 

Mahir TOSUNOĞLU, Tunahan ASLAN, Andrzej CİEŚLİK, Turgud VALIYEV 

 

345 

 

countries to improve working conditions and economic conditions. The top-level factors that 

determine the general dynamics of the free movement of healthcare workers are globalization, 

technological developments, aging of the population, economic and political factors, increasing 

societal expectations, changing disease patterns, EU dynamics, and international organizations. 

According to this, the factors in the upper plan are mainly composed of factors other than 

families and individuals (WHO, 2023). 

This study aims to contribute to the literature by examining the top-level factors, 

econometrically, for developed economies and supporting them with empirical practice. In this 

way, this migration movement of health workers will be examined and tried to be better 

understood. To better understand the dynamics of the econometric model, the 7 top factors that 

explain the reasons for migration, as identified by WHO (2000), will be detailed. First, when 

we consider the concept of globalization, it is a dynamic process that covers the mobility of the 

workforce through its structure and nature. Thus, globalization is highly intertwined with the 

international movement of the health workforce. Free trade agreements are an important 

dynamic of globalization, and this affects the health sector significantly by accelerating 

international migration and reducing the barriers to the movement of goods and services, 

people, and even health workers (Bundred, Matineau, and Kitchiner, 2004, p. 77-78; 

Martineau, Decker et al., Kitchiner, 2002, p. 3-4). In addition, with the increasing global trade, 

specialized institutions dealing with the employment of international health workers are also 

increasing, thus maintaining international migration mobility (WHO, 2006). 

Another important development related to globalization in health workforce migration 

is that various occupational groups, including health professions, have achieved common 

standards in certain areas. Another factor is that the decreasing barriers between borders with 

international agreements have led to the emergence of new legal frameworks that control the 

circulation and production of the health workforce. When it comes to the technological 

innovation factor, it is a factor that increases and offers alternative opportunities in the supply 

and structuring of health services. It is the dynamics in the light of new technologies by keeping 

the scientific knowledge skills and usability in the health workforce. Development in 

technology determines the type of work to be performed or the services to be provided, the 

environment, and the structure of the practices, and its impact on the health workforce is very 

important. With the development of technology, new service areas have emerged, leading to a 

change in the form of supply and professional talent components in the service structure, 

bringing the demand for healthcare professionals who will work in these areas (OECD, 2008). 

However, access to information on opportunities and gaps in relevant positions on 

global migration has been facilitated by the widespread use of the Internet, that is, job search 

processes are simplified and accelerated with the Internet. Developments in technology cause 

the migration of the health workforce in two different ways. First, new business areas and 

increased specialization increase the need and demand for healthcare professionals. Secondly, 

the fact that the countries have underdeveloped technology is a factor that increases the health 

workforce migration to countries with better technology, to countries with higher technology 

to get education and even to work (OECD, 2007). 

Demographic trends are another crucial component of health workforce optimization, 

influencing both the makeup and delivery of the health workforce directly and indirectly thro



 

 

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346 

 

ugh thedemand for goods and services (Dubois, Mckee et al., Rachel, 2006). Considering the 

population dynamics, while life expectancy at birth increases, the fertility rate decreases and 

the population is getting older, especially in developed countries (Dubois, Mckee, & Rechel, 

2006; RAND, 2005; Lanzieri, 2008). With the increasing elderly population in population 

dynamics, health needs and disease structures are also changing (WHO, 2008). This change 

increases the demand for a larger healthcare workforce by increasing the need for healthcare 

services. In addition, with the increasing number of elderly people in the population dynamics, 

the health workers in that country are both aging and decreasing in number. Therefore, the need 

for a health workforce increases bilaterally, with both the decreasing health workforce in that 

country and the increase in the elderly population in the changing population dynamics. 

In developed countries such as Norway, Sweden, France, Denmark and Iceland, the 

effects of changes in population dynamics on the health workforce are quite clear. Since the 

average age of nurses is in the 41-45 age band, aging nurses are experiencing labor force 

problems (WHO, 2007). Similarly, these problems apply to doctors. In New Zealand, the 

average age of physicians is 44, while nurses are 43, and allied health workers are over 40. In 

France, 55% of physicians were under the age of 40 in 1985, but this rate decreased to 23% by 

2000 (Dubois, Mckee and Rechel, 2006; Bagat and Sekelj, 2006, p. 378). Between this demand 

and supply in healthcare, the workforce is met by the imported labor force, rather than policies 

aimed at encouraging participation in the workforce, especially by developed countries. 

Developed countries especially do not produce the unhealthiest workforce and consciously 

prefer imported workforce (WHO, 2006). Because by policy makers, eliminating the health 

workforce gap is seen as a situation that needs to be solved urgently. This is because it takes 3 

to 5 years to train a nurse, while it takes 15 to 20 years to train a senior doctor, similarly. 

Therefore, when the imported health workforce is preferred, the health workforce shortage 

problem can be solved urgently, without training costs (Buchan, 2007, p. 10). 

Economic conditions also play a crucial role in driving health worker migration. 

Research indicates that migration often flows from less affluent nations to more prosperous 

ones. However, movement also occurs among developed countries, driven by opportunities for 

improved quality of life and professional benefits. Furthermore, rising societal expectations, 

fueled by advancements in education, globalization, and technology, have made populations 

more aware and demanding. With these heightened expectations, healthcare professionals 

strive to enhance their expertise and seek environments offering advanced education, cutting-

edge technology, and efficient administrative systems. This pursuit of better prospects 

significantly influences health workforce migration patterns. 

This paper addresses a critical gap in the literature by focusing on the economic and 

social drivers of doctors’ migration within developed economies, an area that remains under-

explored. It provides empirical insights using panel data analysis to uncover how factors like 

wages, globalization, and human development influence the migration of health workers. The 

structure of the paper is as follows: the next section reviews the relevant literature, followed by 

an analysis of the key factors driving health worker migration and their implications. 

 

2. Literature Review 

When the international migration literature of healthcare professionals is examined, it 

is possible to encounter many different studies. The international migration of health workers, 



 

 

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347 

 

which is frequently on the agenda today, is not the first time that it has come to the fore. It is 

seen that researchers studied the reasons for the migration of health workers such as physicians 

and nurses to another country in 1962 (Seale 1962). In previous years, it is seen that the most 

important reasons for migration are from countries with lower economies to countries with 

higher welfare. Here, the occupational group that has the largest share of health migration is 

doctors compared to nurses. However, the number of nurses emigrating after doctors is quite 

high (Mej'ia et al., 1979). Physicians are a more attractive profession group compared to nurses 

in terms of immigration country. Because physicians receive more training compared to nurses 

and this causes a higher cost (Bezuidenhout et al., 2009; Saluja et al., 2020). 

Medicine is one of the highly skilled professions in Europe. It is also quite inadequate 

in terms of workforce (Becker and Teney, 2020, Botezat and Ramos, 2020). In this respect, it 

has been subjected to serious criticism in the public due to the immigration of doctors who 

have completed their specialization to a better European country due to free movement in 

Europe (Żuk et al., 2019). The reasons for migration vary from country to country. A better 

career and quality of life has been on the main causes of migration in countries in the southern 

part of Europe (Becker and Teney, 2020). Some of the doctors working in the United Kingdom 

stated that the working hours are too much and this situation disrupts the balance between work 

and life, and they want to migrate because they cannot get enough help from their colleagues 

(Brugha et al., 2020). 34% of doctors who immigrated from Poland stated that high earnings 

and better working conditions were among the reasons for their migration (Dubas-Jakonczyk 

et al., 2020). Health workers in Ireland, on the other hand, stated that they had to migrate 

because they were not satisfied with their working conditions and thought that their career 

prospects would be bad (Brugha et al., 2020). 

Martineau et al. (2002), examined the international migration of healthcare 

professionals from historical, contemporary, and future perspectives. While some of the world's 

wealthiest countries have benefited from international migration, some of the world's poorest 

countries have said it has had an overall negative impact on healthcare. However, they added 

that the responsibilities of both source and recipient countries need to be clarified, saying that 

the effects on international migration are often more complex than portrayed. They underlined 

that despite the development of codes of practice regarding ethical international recruitment, 

the increase in the demand for healthcare professionals is inevitable, and they stated that more 

radical strategies are needed to protect the health systems of the world's poorest countries. 

Võrk et al. (2004), analyzed the size and determinants of potential migration flows of 

Estonian health professionals using a 2003 opinion survey. According to the results of the 

analysis, it has been reached that about half of the Estonian health workers, about 5% of them 

want to work abroad, either permanently or temporarily. The results of the logistic regression 

models show that the intention to migrate depends on the usual socio-demographic and 

economic variables such as age, gender, marital status, living area, risk of losing a job, and 

dissatisfaction with current wages. 

Mcelmurry et al. (2006), argue that international nurse migration is a natural and 

expected situation. Migration flow models have stated that it occurs largely from developing 

countries to developed countries, and in their study, they examine nurse migration using 

primary health care (PHC) as an ethical framework. While PHC principles state to bring 



 

 

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healthcare as close as possible to where people live and work, nurse migration often moves 

nurses away from where they are most needed, and this conflicts with the principles of health 

for all. They stated that nurse migration policies and procedures should address nurse 

workforce migration, and that fair financial arrangements and PHC ethical criteria could be 

met and improved. 

Henderson and Tulloch (2008), show that there is no global model for improving the 

retention and performance of health workers. While economic factors play an important role 

in workers' decisions to stay in the health sector, the evidence shows that they are not the only 

ones, saying that this is a critical issue that needs to be addressed through the policy, planning 

and implementation of innovative strategies such as incentives to retain and motivate 

healthcare workers in Pacific and Asian countries. Based on research findings from the Asia-

Pacific region, they concluded that salaries and benefits, along with working conditions, 

supervision and management, and education and training opportunities, are important. They 

underlined the offering of financial and non-financial incentives in the form of packages. 

Bradby (2014), pointed out that the widely estimated reason for the shortage of skilled 

health personnel in Africa is the widespread belief that rich countries are stealing trained health 

professionals from poor countries. They have criticized this widespread notion that it promotes 

medical professional interests and ignores historical patterns of underinvestment in health 

systems and structures. In response to the global debt crisis of the late 1970s, African countries 

had to prioritize investment in their social sectors, including health and education, in favor of 

promoting an export currency, and poor working conditions in areas where HIV spread led to 

a migration of medical personnel. pointed out that it exacerbated the famine. They also said 

that together with globalization, the means and most importantly the motivation to migrate 

between nations and continents causes the unavoidable desire for migration and it is very 

difficult to regulate. 

Gruber et al. (2020), investigated the immigration of Croatian doctors after Croatia 

acceded to the EU. According to their study there are both economic and non-economic factors 

that affect the choice of individuals to migrate. For Croatian physicians, the benefits of 

immigration are expressed in higher satisfaction with standard of the living, income, 

professional development and better working conditions. However, they also point out that 

there are some clear psychological costs, such as being away from family members, friends 

and familiar surroundings, mastery of another language, which hinder immigrants and their 

families and make it difficult to establish a social network and integrate into society. They say 

that high-income countries should strive for self-sufficiency by training, retaining and 

maintaining sufficient numbers of doctors to staff their health systems. 

Hagopian et al. (2005), investigated the immigration of West African-trained doctors to 

rich countries, especially the USA and England. In their study, qualitative data were collected 

from six medical schools in Africa to investigate the magnitude, causes and consequences of 

migration. According to the results of the research, they concluded that there is a developed 

medical migration culture. They also concluded that this culture is firmly rooted and even 

encourages immigration, and that they are proud of their students who migrated as role models 

in medical school. 

When the literature is evaluated in general, international migration movements of health 

workers have been examined from different perspectives. When the literature is considered 



 

 

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349 

 

from a broad perspective, it is generally deduced that countries that migrate their health 

workforce should protect their health workforce by regulating their migration policies. 

 

3. Methodology 

In this study, we analyzed a group of 15 high-income countries, estimating coefficients 

using both random effects and fixed effects models through panel data analysis. These 

countries were chosen based on their high Human Development Index (HDI) rankings. 

Additionally, they are classified as middle- or high-income economies under the European 

Union’s economic classification system. The primary reason for selecting developed countries 

is the availability of comprehensive data and evidence from the literature indicating significant 

health worker migration occurring between developed nations. The selected countries are as 

follows: 

 

Table 1. Developed Countries Group 

Belgium Estonia Germany Ireland Slovenia 

Czech Republic Finland Holland Italy United Kingdom 

Denmark France Hungary Norway  

 

This study utilized the Stata-17 software package for data analysis, following a 

methodology similar to that employed by Rutten (2009). Panel data analysis was chosen as it 

enables multiple observations to be gathered from the same sample over time. The functional 

model applied in this analysis is structured as follows: 

        𝑌𝑖𝑡 = 0𝑖𝑡 + 
1𝑖𝑡

X1𝑖𝑡0 + 
𝑘𝑖𝑡

𝑋𝑘𝑖𝑡 + 𝑖𝑡                                                                                     (1) 

                     𝑖 = 1, 2, 3, . . . , 𝑁  𝑡 = 1, 2, 3, . . .  , 𝑇                                        

In equation (1) shown above 𝑖  cross sections,  𝑡  represents the unit of time. In this 

equation, there are individual effects that cannot be observed in terms of independent variables, 

do not change over time, but include cross-section-specific features. It is also included in the 

error term of the different effects of the units (Baltagi 2005, p. 11-12). 

There are two basic approaches used in regressions with panel data. The first of these 

is the "Fixed Effects Model" while the other is the "Random Effects Model". In the fixed effects 

model, a different fixed value occurs for each cross-section. It is assumed that the slope 

coefficients in the model (β)  do not change, however, the constant coefficients can vary only 

between cross-section or time data, or even within both. If differentiation is only time-

dependent, it is called a one-way fixed effects model. However, if the differentiation between 

data depends on both cross-section and time, it is called a two-way fixed effects model.  

However, in panel data analysis, the cross-section effect is considered rather than the 

time effect, so panel data models appear as one-way models (Hsiao 2002, p. 30). The fixed 

effects model can be shown as equation (1) and equation (2) as one and two-sided, respectively, 

as follows; 

𝑌𝑖𝑡 = (
𝑖𝑡

+ 
𝑖𝑡

) + 
1𝑖𝑡

X1𝑖𝑡 + 
𝑘𝑖𝑡

𝑋𝑘𝑖𝑡 + 𝑖𝑡                                                                                                     (2) 

𝑌𝑖𝑡 = (
𝑖𝑡

+ 
𝑖𝑡

+   𝑖𝑡) + 
1𝑖𝑡

X1𝑖𝑡+. . . +
𝑘𝑖𝑡

𝑋𝑘𝑖𝑡 + 𝑖𝑡                                              (3)    



 

 

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350 

 

There it ≈  iid (0, 2)  is an assumption here. In other words, 𝑖𝑡   It is assumed to 

have a white noise feature. In addition, the independent variables are independent of the error 

term (Baltagi 2005, p. 12). In the fixed effects model, different constants are estimated for each 

cross-section, ensuring that the constant coefficient is different for the cross-sections. 

In the random effects model, changes in sections or time-dependent changes in sections 

are included in the model as a component of the error term. Compared to the fixed effects 

model, its prominent feature is that there is no loss of degrees of freedom. It also allows the 

inclusion of out-of-sample effects in the model. The random effects model can be shown as 

follows; 

𝑌𝑖𝑡 = 𝑖𝑡 + 
1𝑖𝑡

X1𝑖𝑡+. . . +
𝑘𝑖𝑡

𝑋𝑘𝑖𝑡 + (
𝑖𝑡 

+ 𝑖𝑡 + 𝑖𝑡 )                                                                       (4) 

𝑌𝑖𝑡 = 𝑖𝑡 + 
1𝑖𝑡

X1𝑖𝑡+. . . +
𝑘𝑖𝑡

𝑋𝑘𝑖𝑡 + (
𝑖𝑡 

+ 𝑖𝑡 + 𝑖𝑡 )                                                                       (5)                                                                       

Equations (4) and (5) represent the one-way and two-way random effects models, 

respectively. The error term in these models consists of two distinct components. 𝑖𝑡 ≈

 iid (0, 2) and 
𝑖 

≈  iid (0, 2)there is an assumption. 
𝑖 
error term,𝑖 = 1, 2, 3, … , 𝑁 The 

first component represents the value of a cross-section that remains constant across the time 

dimension. Conversely, the second component accounts for the remaining parts that vary over 

time but are interconnected within the time dimension. This component is independent of the 

section effect within the model. Additionally, both components are uncorrelated with any 

independent variable. Consequently, these components, along with the overall model, are 

consistent and unbiased when estimated using the least squares method. 

 

3.1.Data Description and Variables 

The model includes the following variables: GERD [a high GERD/GDP ratio can be 

used to measure a country's success in technological advancement and knowledge creation 

(R&D intensity), migration of specialist doctors, wages of health workers (in US dollars), and 

the population aged 65 and over (percentage of the total population).Data on R&D expenditures 

as a percentage of GDP, the Human Development Index (HDI), and the Globalization Index 

(KOF) were gathered annually for 15 nations between 2011 and 2019. Globalization (KOF) 

from the Swiss Institute of Economics, HDI from the UNDP, migration of specialist doctors 

(MIG), wages of health workers (WAGE), population aged 65 and over (POP), and GERD 

were all sourced from the OECD. The following is a summary table that provides the sources 

and explanations for the variables. 

 

Table 2. Table of Variables 

Variables Explanation Resources 

mig Migration of specialist doctors OECD 

wage Wages of health workers (us dollars), OECD 

hdi Human Development Index UNDP 

pop The population aged 65 and over (percentage of the 

total population) 

OECD 



 

 

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gerd The high GERD/GDP ratio can be used to measure 

a country's success in a technological advancement 

and knowledge creation (R&D intensity). 

OECD 

kof Globalization index OECD 

 

In the study, 15 groups of developed countries were chosen between 2011 and 2019, 

and with the aid of panel data analysis, the coefficients were attempted to be estimated using 

the fixed effects model and the random effects model. The model uses logarithmic form for the 

wage and mig variables. The following is the econometric model that was developed. 

 

(𝐼𝑛𝑚𝑖𝑔)𝑖𝑡 = 
0

+
1

(𝐼𝑛wage)𝑖𝑡 + 
2

(hdi)𝑖𝑡 + 
3

(pop)𝑖𝑡 + 
4

(gerd)𝑖𝑡 +  
5

(kof)𝑖𝑡 +  𝑖𝑡      

(6)            

In equation (6), lnmig serves as the dependent variable, while lnwage, hdi, pop, gerd, 

and kof are the independent variables. The constant term coefficient of the model is denoted 

by β₀, while β₁, β₂, and subsequent terms represent the coefficients of the explanatory variables. 

The model's error term, denoted by ε, captures unexplained variability not accounted for by the 

independent variables.                       

              

3.2.Empirical Strategy 

The descriptive summary statistics of the variables used in the model are as follows. 

 

Table 3. Summary Statistics 

Stat. lnmig lnwage hdi pop gerd kof 

Mean 10.71 11.52 0.91 17.7 2.23 85.3 

Median 10.40 11.77 0.92 18.1 2.14 85.8 

Maximum 12.92 12.41 0.96 22.9 5.14 91.1 

Minimum 8.54 9.99 0.83 10.1 1.17 75.7 

Std. Dev. 1.26 0.64 0.03 2.82 0.84 3.85 

 

The variables utilized in the Table 3 are summarized statistically. There are no outliers 

in the data set when the statistics are analyzed generally. Furthermore, the variables employed 

in the summary are also used to extract a priori information from their correlation links. The 

variables utilized in the model have the following correlation table. 

 

Table 4. Correlation Statistics 

Corr. lnmig lnwage hdi pop gerd kof 

lnmig 1.00      

lnwage 0.29 1.00     

hdi 0.06 0.79 1.00    

pop 0.41 -0.23 -0.03 1.00   

gerd -0.05 0.39 0.36 -0.27 1.00  

kof 0.41 0.40 0.41 0.41 -0.21 1.00 



 

 

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It is evident from examining the correlation relations via demonstrating in Table 4 that 

there are strong relationships between the variables. Wages and health migration have a 

positive correlation of 0.29, the population over 65 has a positive correlation of 0.41, and the 

globalization index has a positive correlation of 0.41. Gerd, which represents high technology, 

is negatively correlated with health migration. 

 

Table 5. Panel Data Regression Estimates 

Model LnWAGE HDI KOF POP GERD 

Fixed 

Effects 

-0.078(0.007) 

*** 

5,997(0.000) 

*** 

0.011(0.097) 

* 

-0.005(0.460) 0.018(0.148) 

Random 

Effects 

-0.076(0.009) 

*** 

5,919(0.000) 

*** 

0.011(0.080) 

* 

-0.005(0.500) 0.018(0.149) 

Hausman 10,934 (0.052)* 

1- *, ** and *** indicate critical values at 10%, 5% and 1% significance levels, 

respectively. 

2- Values in parentheses indicate probability values. 

3- According to the Hausman test, the Fixed Effects Model was found to be preferable. 

 

In Table 5, we may observe Fixed and Random effects throughout the independent 

predictors. When the coefficients in the fixed effects model are interpreted; As expected, a 

negative and significant relationship was found between the migration of health workers and 

the wages of health workers. A 1% increase in wages reduces the migration of health workers 

by 0.08%.  

As anticipated, a positive and significant relationship was identified between health 

worker migration and globalization. Specifically, a one-unit increase in globalization leads to 

a 0.01-unit rise in healthcare worker migration. Similarly, a significant connection was 

observed between health worker migration and human development, with a one-unit increase 

in the human development index resulting in a 5.9-unit rise in migration. However, in the 

current model sample, no significant relationship was found between the proportion of the 

population aged 65 and over, GDP expenditures on R&D, and the migration of health workers. 

 

4. Conclusions 

This study aims to contribute to the literature by supporting the top-level migration 

reasons of health workers, for developed economies, by modeling them econometrically and 

supporting them with empirical practice. In our study, 15 high-income country groups were 

selected, and the coefficients were tried to be estimated using the random effects model and 

fixed effects model with the help of panel data analysis. This country group has been selected 

according to countries with a high human development index. In addition, this group of 

countries is classified as middle or high-income economies according to the economic 

classification of the European Union. 

According to panel data regression estimations, lnwage and HDI variables were 

significant in 1% and of variable in 10% in both fixed and random effects models. Pop and 

GERD variables are not statistically significant. According to the Hausman test, the Fixed 



 

 

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Effects Model was found to be preferable. When the coefficients in the fixed effects model are 

interpreted; As expected, a negative and significant relationship was found between the 

migration of health workers and the wages of health workers. A decrease in wages due to the 

desire to increase welfare from one country to another is a factor that increases health 

workforce migration. In this analysis for developed economies, a 1% increase in wages reduces 

the migration of health workers by 0.08%. 

A clear and significant positive relationship was observed between globalization and 

the migration of health workers. Globalization, as a dynamic process, inherently facilitates 

workforce mobility by breaking down barriers and establishing common professional standards 

across borders. Consequently, health professionals are less inclined to work in countries with 

lower welfare levels if the nature of their work remains unchanged. Furthermore, international 

agreements have introduced legal frameworks to regulate the movement and production of the 

health workforce. In the context of developed economies, a one-unit rise in globalization 

correlates with a 0.01-unit increase in healthcare worker migration. 

Similarly, a strong positive relationship was identified between migration and human 

development. This is unsurprising given that the countries examined in this study are developed 

nations. Literature suggests that migration often occurs between developed nations, driven by 

factors such as wage differentials. The Human Development Index (HDI), comprising health, 

education, and income components, highlights that income changes play a significant role in 

influencing labor migration. In this analysis, a one-unit increase in HDI corresponds to a 5.9-

unit rise in healthcare worker migration. However, no significant relationship was found 

between migration and variables such as the proportion of the population over 65 years or GDP 

expenditure on R&D in this sample, rendering them uninterpretable. 

Health workforce migration poses a significant loss of human capital for emigrant 

countries and presents challenges to national economies. Policymakers must adopt diverse 

strategies to protect the welfare of health workers at international standards and retain this 

skilled workforce domestically, ensuring sustainable human capital through harmonized 

standards. 

 

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