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The Effect of Corruption on Economic Development: An Empirical Analysis of Western Balkans Countries

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International Journal of Innovation and Economic Development
Volume 8, Issue 3, August 2022, Pages 27-38

The Effect of Corruption on Economic Development: An Empirical Analysis of Western Balkans Countries

DOI: 10.18775/ijied.1849-7551-7020.2015.83.2003
URL: https://doi.org/10.18775/ijied.1849-7551-7020.2015.83.2003

Teuta Xhindi1, Ira Gjika1

1 Mediterranean University of Albania, Tirana, Albania

Abstract: The progress of Western Balkan countries has been influenced by similar factors of a political, social, and geographical nature. Their recent transition into a functional market economy has brought economic growth, but it is accompanied by negative phenomena as well. Stimulating economic development, gender equality, urbanization, human development, and economic freedom challenged these countries. Since the beginning of the 1990-s, international institutions have strongly supported these countries. They have contributed to encouraging advancement, as well as overcoming the obstacles that have been similar in these countries. Corruption is one of them that can hinder development and has consequently attracted the attention of all stakeholders to fight against it. Many indicators are generated in recent decades to measure both economic development and encumbering factors. Analyzing the indicators and comparing them between countries intends to promote their improvement and to identify the best practices at the regional level. This paper aims to examine the relationship between corruption and economic development in Western Balkan countries in the last two decades. The panel time-series autoregressive distributed lag (ARDL) model is used for making the empirical analysis for a period from 2003 to 2019, based on official data from local and international sources. The ARDL model used and the time span of data involved make this study distinct from the other surveys done for the same region. The results from the model application have enabled a detailed analysis and discussion of the main findings. There are recommendations drawn for the future, applicable both at the country and region level, especially with recent advancements of two countries – Albania and North Macedonia – toward EU integration.

Keywords: Economic development, Corruption, Human Development, Urbanization, ARDL model

1. Introduction

The history of the economic and social development of countries goes through different stages, which distinguish by the pace of this development, the factors and events that encourage or hinder it, and by the role the political elites have played in each country. The countries of the Western Balkans (WB) – Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, and Serbia – in addition to proximity and geographical similarities, have been under similar political systems. The beginning of the 1990s marked a new stage for these countries in their efforts to ensure economic growth and a functional market economy, democratic institutions, political stability, and social development. At the same time, the fact that considerable geopolitical and geoeconomic interests intersect in this region was – gradually but clearly – reconfirmed (Dabrowski dhe Myachenkova, 2018). In these conditions, the achievement of sustainable economic growth and political and social stability are conditioned by both internal factors and external forces.

The economic development of the region during the last decades is encouraged by the Western Balkan countries themselves, and by international institutions, especially from the year 2000 onwards. According to the EU Institute for Security Studies, the perspective of the integration of these countries in the EU, although not very fast, has brought a significant commitment from all the forces interested in democratic transformation in the WB countries (EU-ISS, 2013). The Stabilization and Association process that initiated by the EU in May 1999 was followed by the Zagreb (November 2000) and Thessaloniki (June 2003) summits. This process confirmed the perspective for EU accession of the countries of the Western Balkans (EU-ISS, 2013), whose main objectives were:

– To guarantee the political stability of these countries.

– To encourage the creation of an open market economy as an urgent need for all the countries of this region.

– To promote regional cooperation between these countries as a prerequisite for progress on the path of integration.

– To complete the legal framework for meeting the obligations of EU membership (europa.eu).

The publication by EU-ISS points out that in 2010, the European Union launched the “Europe 2020” Strategy to achieve a “smart”, sustainable and comprehensive growth through five main objectives: (i) employment; (ii) innovation; (iii) climate change and energy; (iv) education and (v) poverty reduction and social inclusion. This strategy was also considered suitable for WB countries in the context of their aspiration and preparation to become future members of the EU (EU-ISS, 2013). Recently, in July 2022, the EU organized the Intergovernmental Conference on accession negotiations with two countries: Albania and North Macedonia. The Commission started the screening process for both (EU, 2022).

Despite the optimistic point of view, over the years, the stimulating factors of economic and social development have been considered inseparable from the ways and means to fight those “diseases” or phenomena that seem to accompany development by hindering it. Corruption, economic and social inequality, illegal immigration, and money laundering are some of the factors that negatively affect the prosperity of a country or region. In particular, corruption and its corrosive effects have been at the center of attention of international institutions and local governments to coordinate efforts to reduce and/or eliminate them. Referring to the latest report of Transparency International (2021), Albania has suffered a drop of one point in corruption compared to last year. North Macedonia and Kosovo are the only two countries in the Western Balkans that have made progress. Meanwhile, Montenegro is still above the world average (43) with 46 points, while Serbia is stuck with 38 points (Transparency Intl.).

The World Bank (2020) considers the collaboration of all parties to be an essential factor in overall governance effectiveness. Such collaboration includes civil society, the media, the private sector, and the country’s citizens while considering the fight against corruption as a collective responsibility (World Bank, 2020).

This paper is motivated by the civic and intellectual responsibility to participate in the processes of development and democratization of the country (Albania) and the whole Western Balkans region. The paper explores the connection between economic development, corruption, and urbanization observed for a period of about two decades in the countries of the region. The aim is to identify how the phenomena of corruption and urbanization have influenced the economic development of the region’s countries. Recognition of similarities and differences enables common strategies and coordinated efforts for the future.

2. Problem Framework

The complex and multifaceted nature of a country’s development has led to various methods and indicators to evaluate it. Given the importance of human capital for any country, since the 1990-s, human development has been conceptualized as a synthesis of health, nutrition, and basic education (Anand and Sen, 1994) as a way to achieve development. The Human Development Index (HDI) is the starting point for assessing how a country responds to various economic and social challenges. As defined by UNDP, the Human Development Index includes variables such as life expectancy in a country and health, knowledge, the standard of living (good), environmental sustainability, human rights and security, and gender equality (Human Development Report Office, 2015).

Researchers emphasize that the essence of the concept lies in efforts to improve people’s lives and not in the expectation that economic growth will automatically lead to higher levels of well-being for all (Becherair and Tahtane, 2017). The efforts of individuals, groups, and governments to improve these indicators/variables often encounter obstacles, among which corruption takes the primary place. According to World Bank (2019), corruption is often defined as the abuse of public power for private gain and appears in forms of behavior and at different levels. Corrupt behaviors are encountered in the basic services of education and health for the population, in the evasion of the implementation of laws and regulations for individuals and businesses, in the civil service, in the judicial system, and in many other areas.

The Corruption Perception Index (CPI) is one of the ways to measure the level of corruption and assess changes over time. According to Transparency International, it measures the perception of the extent to which public power is used for private gain, be it low-level or high-level corruption, as well as the “capture” of the state by elites and private interests (Transparency Intl.). This index is the most widely available and used measure for corruption, although any accepted shortcomings (Lopez, 2019; WB, 2019).

Corruption brings economic, political, social, and environmental costs, often combined between them that multiply their consequences. The analysis of World Bank experts (2019) confirms that the impact on the poor and most vulnerable strata comes from the increase in costs and the reduction/impossibility of access to health services, education, electricity, and other essential services, which deepen inequality (WB, 2019). In terms of economic development, corruption reduces private investment, which harms growth and employment by increasing the risks for investors. Public investments are affected in terms of their quantity, quality, and safety under the influence of corrupt actions. On the other hand, the credibility of the central or local government decreases, and peace and stability in the country are at risk.

Meanwhile, phenomena that positively affect human development, in certain situations, under the influence of corrupt tendencies, turn into risks for countries, especially those with a less consolidated economy and governance. Among them, urbanization – the process of population displacement from rural to urban areas, accompanied by the reduction of the population living in the countryside – stands out.

Urbanization itself has the potential to accelerate economic growth because:

-First, cities play an important role in the structure of the economy and society of developed and developing countries by providing opportunities for people to enjoy educational services, employment, and health (Aghion and Howitt, 2009). Human capital determines a nation’s ability to develop new technologies and adopt existing technologies.

-Secondly, as Kumar and Kober (2012) point out, urbanization involves the agglomeration of people and enterprises and reduces production costs. Urbanization allows economies to gain advantages, reduces transaction costs, and allows internal specialization in enterprises leading to lower production costs (Kumar, Kober, 2012).

-Thirdly, urbanization is an important factor in business activity. Glaeser et al., (2010) point out that the concentration of population and enterprises in urban areas enables easier access to finance, promotes business ideas, and provides a larger local market (an urban market has a higher density of consumers) to do business (Glaeser et al., 2010).

On the other hand, corruption in urban planning, according to the latest evidence from GIZ (2021), has strong and far-reaching negative effects on the development of urban areas in terms of the use of budgets, public services that are provided and the rights of many people. Consequently, sustainable urban development becomes a real challenge in the face of corruption. The United Nations and the World Economic Forum have identified rapid urbanization as a major global risk because cities can be risk convergence points in many aspects (WEF, 2015).

According to Joan Clos, head of UN-Habitat, urbanization is a political, collective process. It involves many conflicting elements, generates a lot of money, and has the risk of corruption. There can be no good urbanization without public accountability in a transparent, democratic system (OSCE, 2021). Zinnbauer (2020) considers the observation of connections between corruption, informality, and cities as an optimal opportunity to analyze the phenomenon. Meanwhile, it is expected that new technologies such as “smart cities” and “urban digital empowerment” will contribute to the reduction of corruption.

3. Literature Review

The relationships between human development, corruption, urbanization, economic freedom, and other similar variables have attracted the attention of researchers, especially in the last two or three decades. The geography coverage is worldwide, spread both in developing and developed countries. Becherair and Tahtane (2017) have studied the causal relationship between corruption and human development using a descriptive analysis approach for 17 North African countries over a multi-year period (1996-2012). The empirical results from their study have clearly demonstrated the negative impact of corruption on human development in each of the years and an increase in the impact of corruption on economic development in the Arab world (Becherair and Tahtane, 2017).

In Latin America, the study of corruption’s effects on the economic development of Chile, Mexico, and Brazil showed mixed results. It found a strong positive relationship between the perception of corruption and HDI yet found a strong negative relationship between the perception of corruption and GDP per capita (Lopez, 2019).

The analysis made by Hysa (2011) using regression analysis for the relationship between Human Development Index and Corruption Perception Index covered the period 2003-2010 for the countries of the Western Balkans. The study demonstrated a statistically significant negative relationship between the corruption index and human development, with slight differences between countries (Hysa, 2011). Observing the situation after more than a decade in different economic and political conditions is a challenge for our work.

Corruption, as a present and worrying phenomenon for all countries of the Western Balkans, is recently analyzed by Dabrowski and Myachenkova (2018) based on the Corruption Perception Index according to Transparency International and the Index of Economic Freedom according to the Heritage Foundation. This last index pays attention to key factors like economic liberalization, property rights, corruption, and governmental/public integrity.

The impact of corruption, economic freedom, and urbanization on economic development is addressed by Feruni et al. (2020) in a comparative study between five Western Balkan countries with 27 EU members. The multiple linear regression model applied by Feruni et al (2020) to the data for a 10-year period showed that higher levels of corruption are associated with lower levels of HDI. On the other hand, the impact of CPI on HDI is greater in the Western Balkans countries than in the EU members, with a more destructive impact on WB countries. For Sarabia et al. (2020), corruption is a variable that explains the radical changes in a democracy even when the human development index is presented with high values, as happened in the countries of the European Union (2008-2018). In such situations, the relationship between HDI and CPI is seen as a factor that influences the increase in social conflicts and the loss of confidence of citizens in their democracies.

3.1. New Challenges for Anti-Corruption

Despite the high level of awareness of individuals and institutions to minimize corruption and its consequences, there are several current factors to consider due to their multiple impacts. Some of them are mentioned here.

– According to World Bank (2019), globalization has positively influenced the growth and distribution of investments in different countries. On the other side, it is accompanied by new challenges in the fight against corruption. The development of international business and the great opportunities for the movement of capital and funds have made it easier to hide illegal income (WB, 2019).

– Technological developments, new tools, and opportunities that have emerged and are being used intensively in the last decade (FinTech, GovTech, CivicTech, Big Data Analytics, artificial intelligence) can all contribute more effectively to the prevention and fighting corruption, especially if the potential they have is recognized and well used. But at the same time, new technologies also enable the rapid transfer of large sums of money and the concealment of stolen assets (WB, 2019).

– The risk and devastating human and economic consequences of Covid-19 forced governments to allocate large amounts for spending in a short time, bypassing standard operating procedures. The speed of carrying out procedures, the lack of rigorous control, and other local factors are suspected to have paved the way for corrupt behavior during this period in the field of health services, social protection, trading of food, and other basic products for the population. The World Bank (2020) suggests using past experience for finding ways to avoid such situations in the future.

4. Methodology and Empirical Analysis

4.1. Variables Description and Hypothesis

Human Development – Human Development (HD) is defined as “expanding the choices people have to lead lives that they value” (Human Development Report 2001: 9). HD is measured by the Human Development Index (HDI), which captures three key dimensions of human development: health, education, and the standard of living. The indicator for health is Life Expectancy at Birth (years); the indicator for education is the arithmetic mean of mean years of schooling and expected years of schooling; while the indicator for standard of living is the gross national income per capita (2011 PPP$) (Human Development Report 2001). More precisely, HDI is the geometric mean of life expectancy at birth, education, and gross national income per capita. The values of HDI range from 0 to 1, where 0 indicates the lowest value of human development while 1 indicates the highest one.

The data about Human Development Index are taken from countryeconomy.com.

Urbanization – Urban population; the proportion of people living in cities and towns compared to the total population.

The data about the urban population are taken from the World Bank for the 2003-2019 period.

Corruption – According to Transparency International (1998), corruption is the abuse of public power for private benefit.

Corruption is measured by the Corruption Perception Index (CPI). Till 2011, the values of CPI ranged from 0, for countries with a high level of corruption, to 10, for not corrupted countries. After 2011, the minimum and maximum values became 0 and 100. Corruption Perception Index ranks countries “by their perceived levels of public sector corruption”. The higher the position in the list, the higher the perception of the level of corruption.

The values of CPI for the 2003-2011 period are multiplied by 10 to be comparable with values from the 2012-2019 period. The data about CPI for the 2003-2019 period are taken from Transparency International for Western Balkans countries: Albania, North Macedonia, Serbia, Bosnia and Herzegovina, and Montenegro.


  1. Corruption negatively affects the level of human development
  2. Urbanization positively affects the evel of human development

4.2. Methodology

To empirically analyze the effect of corruption and urbanization on human development, the autoregressive distributed lag model (p,q,q) (Pesaran, Shin, 1995):

y_it=∑_(j=1)^p▒δ_i  y_(i,t-j)+∑_(j=0)^q▒〖β_ij^’ X_(i,t-j)+φ_i+e_it 〗                    (1)

is taken into consideration at first.

In equation (1), y_it is the response variable, Human Development Index.

X_it – Independent variables, Corruption Perception Index and Urbanization.

φ_i – the variables that are specific for groups are time-invariant and affect the response variable.

e_it – the error term

i – represents the group

t – represents time

In our study, the other presentation of the model ARDL (shown below) is used:

Δ〖HDI〗_it=θ_i[〖HDI〗_(i,t-1)-λ_i^’ X_(i,t)] +∑_(j=1)^(p-1)▒〖α_ij Δ〖HDI〗_(i,t-j)+∑_(j=0)^(q-1)▒〖β_ij^’ ΔX_(i,t-j)+φ_i 〗〗 + ε_(i,t)     (2)


θ_i –  speed of adjustment of specific equilibrium according to groups

ECT=[〖HDI〗_(i,t-1)-λ_i^’ X_(i,t)] – error correction term

λ_i^’ – vector that indicates the long-run relationship

α_ij, β_ij^’ – parameters that indicate short run relationship

The ARDL model helps in determining both the short and long-run relationship between variables and is used when the variables are I(0) or I(1) but not I(2). So, initially it is important to determine the order of integration for the variables. The LLC and Breitung test are used to test for unit root in the group of panel series. The LLC test was proposed by Levin, Lin, Chu (2002). However, these tests do not consider the cross-section dependency problem that could emerge due to several factors. Therefore, to check for cross-section dependency, the so-called second-generation unit root tests are carried out: IPS test, proposed by Im, Pesaran, and Shin, CADF (cross-sectional Augmented Dickey-Fuller), also proposed by Pesaran (2007), and the Phillips-Perron PP test.

The null hypothesis for the presence of unit roots is tested in all these tests. To conclude about stationarity, the result got from the majority of the five tests is used. The selection of the appropriate number of lags is made automatically.

Before the estimation of the model, the tests for the selection of the appropriate number of lags are done for every variable.For the estimation of the model ARDL (p,q,q), the PMG method (pooled mean group) is used. This method was proposed by Pesaran, Shin, and Smith (2001) and allows constants, coefficients in the short run, and standard errors to differ between groups. Meanwhile, in the long run, estimators are equal for all groups. As a result, when the homogeneity condition between groups is true, the PMG estimators are consistent.

Thus, in the short term, the relationship depends on the specific characteristics of each country, which may be caused by different responses to economic policies, external shocks, or different crises.

Originality. The autoregressive distributed lag (ARDL) model is one of the most general dynamic models in econometric literature. The use of the ARDL model for analyzing the relationships CPI-HDI-Urbanization, with all the advantages it gives in the generated indicators, and the inclusion of a long period of time to support the analysis, constitute the novelties of this work, compared to previous studies carried out for the same region.

Limitations: the main one is the limited number of observations, conditioned by the lack of data. The Corruption Perception Index in Albania has been measured starting from 2002, consequently, the data in this study cover only the 2003-2019 period. Also, Kosovo is excluded from the study as the data for the variables taken into consideration are missing.  Moreover, measures of corruption are based on perceptions; consequently, they may have limited usefulness in tracking actual levels of corruption over time (WB 2019). Future surveys can contribute to deeper analysis at the country or whole region’s level regarding the effectiveness of anti-corruption measures.

5. Empirical Results and Analysis

5.1. Empirical Analysis

The results of all data processing and their presentation in tables and graphs done by the authors are submitted here below.

Table 1 presents the results of unit root tests for all the variables analysed.

Table 1: The results from unit root tests

Variable Result In first difference
HDI Stationary NA
CPI Not stationary Stationary
Urbanization Not stationary Stationary

The variable HDI is stationary at level while the variables CPI and Urbanization are first difference stationary.

Table 2 presents some statistics about the variables HDI, CPI, and Urbanization.

From Table 2, as the p(JB) values are greater than significance value α=0.05, we conclude that the variables HDI and Urbanization are normally distributed while the variable CPI is not (p(JB) =0.000<0.05).

The log transformation of the data was not done since HDI values range from 0 to 1 and thus the logarithm is a negative number.

Table 2: Statistics about variables

 Mean 0.760 34.765 55.191
 Median 0.764 35.000 55.942
 Maximum 0.829 46.000 67.150
 Minimum 0.691 3.000 43.330
 Std. Dev. 0.035 7.020 6.585
 Skewness -0.125 -1.137 -0.121
 Kurtosis 2.166 6.283 2.136
 Jarque-Bera 2.685 56.479 2.852
 Probability 0.261 0.000 0.240
 Sum 64.629 2955.000 4691.212
 Sum Sq. Dev. 0.105 4139.294 3642.545
 Observations 85.000 85.000 85.000

The figures below graphically present the relationship between variables.

Figure 1: The graph of the relationship between HDI and CPI

 Figure 2: The graph representing the relationship between HDI and Urbanization

Table 3: Matrix of correlation coefficients

Correlation coefficient HDI CPI URBANIZATION
HDI 1.000 0.667 0.689
CPI 0.667 1.000 0.409
URBANIZATION 0.689 0.409 1.000

From Table 3, we conclude that the multicollinearity phenomenon does not exist as the correlation coefficient between the independent variables is 0.409.

Before the estimation of model (2), it is necessary to determine the appropriate number of lags. Based on LR, FPE, AIC, SC, and HQ criteria, lag order 2 was chosen for HDI and CPI & lag order 3 for Urbanization.

So, lag order 2 is selected since this order was determined for two out of the three variables.

The table below presents the estimation of ARDL (2,2,2) model using the PMG method.

Table 4: The estimation of the ARDL (2,2,2) model

Variables Coefficients and p values
Long run CPI 0.000466
Urbanization 0.060034
Short run ECT -0.37976
HDI(-2) 0.005762
CPI(-1) -0.0006
CPI(-2) -0.00023
Urbanization(-1) -0.24915
Urbanization (-2) 0.132255
c -0.84358
Trend -0.00519

From Table 4, we conclude that in long run both independent variables are statistically significant at a 95% confidence level for all groups, while in the short run only CPI (-1) is statistically significant.

Table 5 presents the results in the short run for all groups.

Table 5: The short-run results for all groups



Albania Serbia Montenegro North Macedonia B-H
Variables Coefficients and p values
Short run ECT 0.00251








HDI(-2) 0.317646








CPI(-1) -0.00137








CPI(-2) -0.00086


















Urbanization (-2) -0.09433








5.2. Findings

The above-presented results indicate that:

  1. Urbanization has a significant positive effect on human development for all Western Balkan countries. More precisely, if urbanization increases by 1 percentage point, then HDI will increase by 0.06 on average, cp.
  2. Corruption has a significant negative effect on human development. When CPI increases by 1 (so, the country is less corrupted), HDI will increase by 0.000466 on average, cp.

As a result, both hypotheses are confirmed.

Based on Table 5, we conclude that all the variables are statistically significant for all groups, except the variable HDI (-2), which is not statistically significant for Bosnia and Herzegovina.

Also, we conclude that the variables CPI (-1) and CPI (-2) positively affect human development in the case of Albania, meanwhile both negatively affect human development in the case of Serbia. For the other three countries, the result is: CPI (-1) positively affects human development in North Macedonia and Bosnia and Herzegovina while negatively affects it in Montenegro. CPI (-2) positively affects human development in Montenegro but negatively affects it in North Macedonia.

Urbanization (-1) positively affects human development in Albania and North Macedonia, while it has a negative effect in Serbia, Montenegro, and Bosnia and Herzegovina. Urbanization (-2) positively affects human development only in Bosnia and Herzegovina, while it has a negative effect on human development in all the other countries.

The differences between countries may derive from a multitude of factors, which go beyond the scope of this article. The national and international anti-corruption initiatives and their successful implementation remain a key challenge for future sustainable development.

6. Conclusions

The economic development of the Western Balkan countries has been evident during the last decades. There is increased attention, both at the national and international level to encouraging economic growth, human development, gender equality, urbanization, economic freedom, etc. This has gone in line with the fight against several negative phenomena that accompany such advancement, and which have been similar in these countries. There are close relationships between indicators generated to measure both economic development and factors hindering this development, including corruption.

This article is focused on the relationship between corruption and economic development in the Western Balkan countries in the last two decades. It uses the autoregressive distributed lag model ARDL (2,2,2) to analyze the effect of corruption and urbanization on human development in Western Balkan countries (Albania, North Macedonia, Serbia, Bosnia, and Herzegovina, and Montenegro) for the 2003-2019 period. The use of ARDL model for making the analysis and its application to a more extended data series for the region constitute the original contribution of this study to a rich body of knowledge.

Based on the above findings and conclusions, several recommendations can be drawn to the attention of policymakers, national executive bodies, civil society, and other stakeholders. For Western Balkans countries to pursue the European integration path, the corruption problem must be resolved by strengthening their main institutions and guaranteeing their independence, denouncing corrupt officials, increasing public transparency, and promoting media independence.

Moreover, since Urbanization has a positive effect on the level of human development, the governments of the respective countries should take measures to assure that the process of population displacement is well managed and accompanied by the implementation of land use rules, infrastructure development, efficient tax system, environmental protection, etc. The central governments, the local governments, the private sector, and finally individuals must cooperate and interact effectively in order to take advantage of the opportunities offered by urbanization, considered an engine for sustainable and comprehensive economic growth.


  • Aghion F., Howitt P., (2009) The Economics of Growth, The MIT Press.
  • Becherair A., Tahtane M., (2017) The casuality between corruption and human development in MENA countries: a panel data analysis. East-West Journal of Economics and Business, Vol.XX, No.2.
  • Dabrowski M., Myachenkova Y., (2018) The Western Balkans on the road to the European Union, Policy Contribution, Issue no.4, Bruegel.org.
  • EU Institute for Security Studies (2013), The European Future of Western Balkans, Thessaloniki @10 (2003-2013), Paris ISBN 978-92-9198-226-4 https://european-union.europa.eu/principles-countries-history/history-eu/2000-09_enhttps://european-union.europa.eu/principles-countries-history/joining-eu_en (July 2022)
  • Glaeser E., Rosenthal S.S., Strange W.C., (2010). Urban Economics and Entrepreneurship, Journal of Urban Economics 67 (15536), DOI: 10.1016/j.jue.2009.10.005. CrossRef
  • Human Development Report (2001) UNDP: http//www.undp.org. (2002) UNDP: http//www.undp.org.
  • Im K.S., Pesaran M.H., Shin Y., (2003) Testing for unit roots in heterogeneous panels, Journal of Econometrics 115(1), 2003. CrossRef
  • Kumar A., Kober B., (2012). Urbanization, human capital, and cross-country productivity differences, Economics Letters, Elsevier, vol. 117(1), DOI: 10.1016/j.econlet.2012.04.072. CrossRef
  • Levin A., Lin C-F., Chu C-Sh.J. (2002) Unit roots in panel data: asymptotic and finite -sample properties, Journal of Econometrics, vol.108, issue 1, 2002. CrossRef
  • Lopez, J. (2019) Corruption and its Effect on Economic Development in Chile, Mexico and Brazil. Governance: The Political Science Journal at UNVL.OSBE, https://www.osce.org/magazine/318341
  • Pesaran M.H., Smith R.J., (1995) Estimating long-run relationships from dynamic heterogeneous panels, Journal of Econometrics 68(1), 1995. CrossRef
  • Pesaran M.H., Shin Y., Smith R.J., (2001) Bounds testing approaches to the analysis of level relationships, Journal of Applied Econometrics, https://doi.org/10.1002/jae.616. CrossRef
  • Prokiç L., (2021). ”Lufta kundër korrupsionit është përgjegjësi e vetë qeverive të Ballkanit Perëndimor”, interview for DW. https://www.dw.com/sq/lufta-kund%C3%ABr-korrupsionit-%C3%ABsht%C3%AB-p%C3%ABrgjegj%C3%ABsi-e-vet%C3%AB-qeverive-t%C3%AB-ballkanit-per%C3%ABndimor/a-60553484
  • Sarabia M., Crecente F., del Val M.T., Giménez M. (2020) The Human Development Index (HDI) and the Corruption Perception Index (CPI) 2013-2017: analysis of social conflict and populism in Europe, Economic Research-Ekonomska Istraživanja, 33:1, 2943-2955, DOI: 10.1080/1331677X.2019.1697721 CrossRef
  • UNDP http://hdr.undp.org/en/humandev/
  • TransparencyInternational,https://www.transparency.org/en/countries/afghanistan?redirected=1
  • WEF (2015) http://reports.weforum.org/global-risks-2015/part-2-risks-in-focus/2-3-city-limits-the-risks-of-rapid-and-unplanned-urbanization-in-developing-countries/
  • World Bank (2020) “Enhancing Government Effectiveness and Transparency: The Fight Against Corruption”, World Bank, Washington, DC.
  • World Bank (2019) Änticorruption initiatives. Reaffirming commitment to a development priority”, World Bank, Washington, DC.
  • Xhindi, N. (2010). Regional Development: an opportunity or a challenge for Albania joins the EU. Journal of Studies on Economics & Society, 213-223.
  • Zinnbauer D., (2020) Urbanisation, informality, and corruption. Designing policies for integrity in the city. Chr. Michelsen Institute, Norway, U4 Issue 2020:6.

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