The Nexus Between Urbanisation and Human Empowerment: Income Group Based Findings of Bidirectional Causality

Christos KolliasORCID iD: https://orcid.org/0000-0003-2876-4304 and Panayiotis TzeremesORCID iD: https://orcid.org/0000-0002-0746-3839

Abstract

The economic and social drivers of democratisation and the emergence and establishment of democratic institutions are longstanding themes of academic discourse. Within this broad body of literature, it has been argued that the process of urbanisation is also conducive to the emergence and consolidation of democracy through a number of different channels. Cities offer better access to education and facilitate organised public action and the demand for more democratic rule and respect of human rights. The nexus between urbanisation and human rights is the theme that is taken up in the present paper. Using a sample of 123 countries for the period 1981–2011, the paper examines empirically the association between urbanisation and human empowerment using the Cingranelli-Richards Index. In broad terms, the findings reported herein do not point to a strong nexus across all income groups. Nevertheless, there is evidence suggesting the presence of such a statistically significant positive association in specific cases.

Introduction

The ongoing process of urbanisation and its multidimensional and interlinked economic, social and political effects have attracted considerable attention by a steadily growing body of multidisciplinary literature (Wheaton & Shishido 1981; Krugman 1991; Henderson 2003; Portes & Roberts 2005; Castells-Quintana & Royuela 2014; Cohen 2014). Both rural push and/or urban pull factors have been used to explain the urbanisation process (Jedwab, Christiaensen & Gindelsky 2017). A central theme of the literature on its effects, is the nexus between increasing urban agglomeration and growth performance through the beneficial impact on productivity (Bertinelli & Strobl 2007; Brülhart & Sbergami 2009; Frick & Rodríguez-Pose 2018). Other studies have focused on the social and political aspects of the effects and determinants of urbanisation such as inequality, democratisation, poverty, and conflict (Christiaensen, Weerdt & Todo 2013; Anthony 2014; Castells-Quintana & Royuela 2014). For instance, findings reported by K. Sekkat (2017) indicate that the poverty reduction effects of urbanisation are conditional upon the size of cities, with big or very big urban concentrations having no effect on levels of poverty. Results by C. Oyvat (2016) indicate that over-urbanisation increases income inequality.

Within this broader strand of the literature, recent papers have focused on the nexus between urbanisation and democracy (Barnett 2014; Glaeser & Steinberg 2017). As C. Barnett (2014) observes, the urbanisation and democratisation nexus, and in particular the question of whether the process of urbanisation promotes democracy, is a comparatively under-researched area. Building on these contributions, this paper addresses a related question. It examines the presence, or lack of such, of a nexus between urbanisation and human rights. To this effect, it uses the Cingranelli-Richards Index (henceforth CIRI) of human empowerment (Cingranelli & Richards 1999, 2010). The CIRI index has been widely used in studies that examine human rights and empowerment (Clark & Sikkink 2013; Clark 2014).

Within the broader literature that postulates a strong positive association between urbanisation and the emergence of democratic institutions, the objective of this paper is to address empirically the nexus between urban-isation and human rights. The relationship between the two variables is examined on a global level using a sample of 123 countries over the period 1981–2011. The time period chosen was dictated exclusively by the availability of the CIRI data series. Following standard preliminary checks for cross-sectional dependence, and tests that determine the order of integration between the variables, time-varying Granger causality tests are used in order to probe the issue at hand.

The structure of the paper is as follows. A brief discussion of the issues on the drivers of democratisation can be found in the section that follows. The data used and its descriptive statistics are presented in section three while the methodology employed and the findings are presented and discussed in section four. Section five concludes the paper.

Urbanisation and human empowerment

A rather hefty and steadily growing body of theoretical and empirical literature, the roots of which can be traced back to Aristotle, addresses and debates the economic and social drivers of the democratisation process, and the emergence and consolidation of democracy and democratic institutions that augment and guarantee human rights (Barro 1999; López-Córdova & Meissner 2005; Glaeser, Ponzetto & Shleifer 2007; Papaioannou & Siourounis 2008; Sommer & Asal 2014). As N. Thede (2009) observes, democratisation, development and human rights are complementary processes with complex interactions amongst themselves. For instance, the modernisation hypothesis postulates that educated and prosperous societies form a fertile environment conducive to the emergence of democracy (Lipset 1959, 1994). A central proposition within the modernisation theory is the causal nexus between per capita income and democracy (Huntington 1991; Rueschemeyer, Stephens & Stephens 1992; Huntington 1993; Barro 1999). This is a thesis strongly challenged by the findings of D. Acemoglu et al. (2008) indicating that despite the strong positive correlation between the two, there is no strong evidence pointing to a causal nexus between income per capita and democracy. On the other hand, results reported by E. Papaioannou & G. Siourounis (2008) suggest that democratisation is more likely to take place in educated and affluent societies with economic development and education emerging as important drivers and determinants of democratic transitions. Others have examined the possible effects that the deepening process of globalisation has on national democratic governance (Li & Reuveny 2003; López-Córdova & Meissner 2005; Eichengreen & Leblang 2008; Ardalan 2011). For example, the findings of B. Eichengreen & D. Leblang (2008) suggest positive bidirectional links between globalisation and democracy. A positive but not universally applicable impact of globalisation on democracy is also the finding reported by C. Kollias & S. M. Paleologou (2016).

Within this broader discourse on the drivers of democratisation, it has been argued that urbanisation is also conducive to the emergence and consolidation of democracy and democratic institutions. E. Glaeser & B. Steinberg (2017) point to three channels through which urbanisation can potentially facilitate political change and democratic transition in countries. They argue that urban centres, due to the agglomeration of citizens, have the potential to act as facilitators to organised public action and render popular uprisings and protests more effective. Cities also enable the organised demand for more democratic rule and human rights or, as suggested by C. Barnett (2014), cities allow for coordinated action by citizens and communities around common interests including demands for increased human rights such as workers and women’s rights, respect of ethnic and religious minorities. Cities also form a fertile ground where social capital can flourish and assist in the establishment and enhanced operation of democratic institutions in the broad context described by Alexis de Tocqueville in his work Democracy in America (Ferragina 2010, 2013). In a similar vein, R. Barro (1999) argues that cities offer an environment that facilitates the communication and coordination between citizens. Collective forms of action such as trade unions are obvious mechanisms through which democratic improvements and human empowerment can be augmented and strengthened. Nevertheless, R. Barro (1999) also stresses that a reverse effect can be the case. In comparison to the countryside, an autocratic or authoritarian government can better monitor and control, if needed, citizens and their activities in cities1.

As pointed out in the preceding section, the present paper builds on this literature on the association between urbanisation and democracy. In particular, it examines the possible nexus between urbanisation and human empowerment. The CIRI index of human rights allows for a slightly different angle of approach into the issue at hand. CIRI offers a measurement of government respect of human rights across a spectrum of different indicators that encapsulate the various dimensions and spheres of respect (or the absence of it) for human rights. For instance, they include indicators for freedom of speech and religion, workers’ rights, women’s economic, social and political rights, independence of the judiciary, electoral self-determination (Cingranelli & Richards 1999, 2010). For the purposes of our investigation, we use two main indicators included in the CIRI dataset. The Physical Integrity Rights Index and the Empowerment Rights Index (henceforth PHYS and EMP respectively). The former – i.e. PHYS - is an additive index constructed from the Torture, Extrajudicial Killing, Political Imprisonment, and Disappearance indicators that are contained in the CIRI dataset. Its values range from zero (0) in the case of total absence of any respect by the incumbent government for these four different rights to eight (8) that indicates full respect. The second index, the Empowerment Rights Index (EMP), is also an additive index. It is constructed using seven different sub-indexes: Freedom of Speech, Freedom of Assembly & Association, Freedom of Religion, Workers’ Rights, Electoral Self-Determination, Foreign Movement and Domestic Movement. Its values range from zero (0) indicating the absence of any government respect for these seven rights, to fourteen (14) in cases of full respect by the government for these seven individual rights. The association between urbanisation and human empowerment as encapsulated by these two indices is examined on a global level using 123 countries over the period 1981–2011. In the section that follows, the descriptive statistics of the data are shown before we proceed with the empirical investigation.

The data

For the purposes of the empirical examination that follows, two urbanisation metrics are used drawn from the World Bank’s World Development Indicators database. The first is the urban population of each country in the sample used here expressed as a percentage of the total population (henceforth URB). On a world level, in 1981, the population living in urban areas was estimated to be 39.7% of the total population. By 2011, the end year of our sample period, it increased to 51.9%, a testament to the ongoing global trend to urbanisation. The second urbanisation metric used herein is the percentage of the total population living in cities with more than one million inhabitants (henceforth URBM). Just as one would expect, this percentage has also increased on a global scale, from a world average of 16.7% in 1981 to 21.9% by 2011. As already mentioned, the sample consists of 91 countries2. However, the postulated relationship between urbanisation and human empowerment as encapsulated by the two CIRI series may not be uniform across all countries given their wide variety of attributes and characteristics. For instance, the nexus may differ depending on the level of development. Hence, in the empirical tests that follow in the next section, it was decided to allow for this. The sample, containing 123 countries, was split into sub-samples where countries were grouped on the basis of the World Bank’s developmental categories3: High Income (HIC) with thirty-seven countries, thirty-three Upper Middle Income Countries (UMC), thirty-two Lower Middle Income Countries (LMC), and twenty-one Low Income Countries (LIC). In this way, we get more homogenous groups in terms of level of development and hence allow for more robust inferences to be drawn from the empirical findings on the nexus between urbanisation and human rights.

A cursory look at the descriptive statistics presented in Table 1 reveals significant differences between the income groups in both the urbanisation metrics as well as the two human empowerment indices used. For example, for the entire sample of 91 countries, the average percentage of their population living in cities (URB) was 55.4% during the period in question4. If one concentrates on the different groups of countries, noteworthy variations are present between the four income categories. The share of population living in urban areas ranges from 77.5% for the high-income group to 22.6% for the low-income sample of countries. The same observation applies for the urban concentration in cities with over one million inhabitants (URBM). As expected, the smallest percentage is observed in the case of the low-income sub-sample of countries. On average, during the period examined here, 8.8% of the population lived in urban agglomerations of more than one million inhabitants in the low-income group compared to 32.4% in the case of the high-income group, 24.6% for the upper middle-income and 16.8% for the lower middle-income countries. Significant in-group heterogeneity is also present in terms of the size of the urban population. For example, in the high-income group, the minimum value of the URBM metric is found in the case of Poland for 1988 (4.3%) while the maximum (100%) is found in the case of Singapore. Similarly, for the same urbanisation index (i.e. URBM) Nepal has the lowest value in 1981 (1.5%). For the same year but the other urbanisation metric (URB), Rwanda has the lowest share of people living in cities (4.8%).

TABLE 1

Descriptive statistics

Source: own study

Entire sample

(123 countries)
High Income

(37 countries)
Upper Middle Income

(33 countries)
Lower Middle Income

(32 countries)
Low Income

(21 countries)
URBMean52.9%76.4%58.1%37.4%27.1%
Max100%100%91.1%68.5%60.3%
Min4.8%8.5%17.6%10.6%4.8%
St. Dev.24.215.816.914.713.1
Median53%78.3%58.2%37.2%26%
Skewness0.81−0.45−0.36−0.47−0.51
Kurtosis3.425.294.942.533.16
URBMMean23.1%33.4%24.6%17.4%9.0%
Max100%100%43.7%45.1%26.8%
Min1.5%4.3%6.7%6.7%1.5%
St. Dev.16.221.09.78.45.3
Median19.2%26.6%25.2%14.4%7.1%
Skewness0.49−0.53−0.22−0.61−0.41
Kurtosis3.044.414.543.142.85
PHYSMean4.86.64.23.64.2
Max8.08.08.08.08.0
Min0.00.00.00.00.0
St. Dev.2.31.52.32.12.0
Median57445
Skewness−0.95−0.34−0.28−0.15−1.74
Kurtosis6.894.653.083.855.37
EMPMean8.310.88.26.66.9
Max14.014.014.014.014.0
Min0.00.00.00.00.0
St. Dev.4.34.04.33.93.3
Median912967
Skewness−1.03−0.54−0.74−0.48−0.70
Kurtosis5.454.453.243.874.97

Important and noteworthy differences and heterogeneity are also observable in the case of the two human empowerment indices. The entire sample average for the period in question is 4.5 and 8.2 for the Physical Integrity Rights Index (PHYS) and Empowerment Rights Index (EMP) respectively. The values of the former range from 0 to 8 while the corresponding range for the latter is 0–14. Not surprisingly, the high-income group scores the best average in both indices 6.5 and 11.1 respectively - followed by the low-income and upper middle-income groups. For these groups the average in PHYS is 3.9 and 3.5 while the respective average scores in the EMP index are 7 for the low income group and 7.3 for the upper middle income. Lastly, the low middle-income scores are PHYS: 3.2 and EMP: 6.3 (Tab. 1.). Looking at the Max and Min values in the relevant table, it is interesting to highlight the fact that both the highest and lowest scores in terms of the two CIRI human empowerment indices (PHYS: 0-8 and EMP: 0-14) are present across all income groups. This indicates major in-group differences in terms of observing human rights. In the high-income group West European countries, the USA and Canada, Japan, Australia, New Zealand, are countries that consistently achieve very high or the highest possible scores in those two CIRI human rights indices. On the other hand, in the same group there are countries that perform poorly in terms of the two human empowerment metrics. For instance, Saudi Arabia achieves a period average of 0.7 in terms of EMP and a score of 4 in terms of PHYS. Similarly, low scores in both or one of the two indices can be found in the case of the United Arab Emirates (PHYS: 6.7 & EMP: 1.4), Oman (EMP: 2.2), Kuwait (PHYS: 5.4 & EMP: 4.1). The same significant heterogeneity is found across all income groups where over-performers and under-performers vis-à-vis the group’s average can be found in both or one of the two human rights indices. Random examples include Costa Rica (PHYS: 7.2 & EMP: 12.4), Iran (PHYS: 1 & EMP: 1.6), Libya (PHYS: 2.2 & EMP: 2.4) in the upper middle-income group. Myanmar (PHYS: 1.9 & EMP: 0.9), Bolivia (PHYS: 5.4 & EMP: 10.5), Philippines (PHYS: 1.5 & EMP: 11.3) in the lower middle-income group. Benin (PHYS: 6.2 & EMP: 9.2), Mali (PHYS: 5.9 & EMP: 9.4), Ethiopia (PHYS: 1.6 & EMP: 4.4), Rwanda (PHYS: 3.5 & EMP: 5.1) in the low-income sample where the corresponding averages for the group are PHYS: 4.2 & EMP: 6.9 for the period under examination here.

Methodology and findings

In order to probe into the nexus between urbanisation and human empowerment the econometric methodology adopted is as follows5. Beginning with the pioneer work of C. Granger (1969, 1988), and considering an s-dimensional vector autoregressive (VAR) model with order the model can be expressed as follows6:

wt=q+A1wt-1+A2wt-2++Akwt-k+zt,
in expression (1)wt = [w1t, w2t, …, wst], moreover, q and Ai (i = 1,2, …, k) are coefficient matrices specified as:
q=[q1q2qs],andAi=[a11ias1ia1siassi]
and zt is an error vector of random variables with zero mean and a covariance matrix Σ specified as:
=[σ112σs1σ1sσss2]

Following the notation of R. Dahlhaus, M.H. Neumann & R. von Sachs (1999), J.R. Sato et al. (2007) extended the Granger causality by focusing on the theoretical pattern of locally stationary procedure and they demonstrated a different VAR model. Briefly, J.R. Sato et al. (2007), presenting a time-smooth variation in the pattern, proposed a time-varying VAR model, known as dynamic VAR. This dynamic VAR framework, encompasses a multivariate time series (qt,T) with dimension (s) and observations (T), qt,T = (q1t,T, q2t,T, q3t,T, …, qst,T)′. In a more compact form, we can represent the dynamic VAR as:

qt,T=z(t/T)+l=1rBl(t/T)qt-l,T+lt,T
In (2)z(t/T) displays the vector of constants, Bl(t/T) shows the VAR coefficients and lt,T shows the error vector, which can be given by matrices:
z(t/T)=[z1(t/T)z2(t/T)zs(t/T)]and,
Bl(t/T)=[b11(l)(t/T)b1s(l)(t/T)bs1(l)(t/T)bss(l)(t/T)]

A. N. Ajmi et al. (2015), re-estimated expression by applying the M- and B-splines functions (Eilers & Marx 1996). Giving priority to M- and B-splines functions, A. N. Ajmi et al. (2015) managed to determine the dynamic VAR via a multiple linear regression model. This time-varying vector autoregressive model can be expressed as:

Qt=n=0Mznwn(t)+l=1rBnlwn(t)qt-l+lt
where zn displays the vectors and Bnl shows the B-splines coefficients. It should be noted that, using the classical Wald test on the coefficients, we can examine the time-varying causality between the variables. More precisely, not only can we observe the causality among the covariates (by checking if the coefficients are equivalent to zero or not), but we can also study if the causality is time-varying or constant by checking the significance of coefficients for each B-spline. Moreover, according to A.N. Ajmi et al. (2015), we apply a dynamic VAR of order l = 1, M = 3. Additionally, for comparison purposes, the traditional Granger causality test is estimated on a lag = 1 for a bivariate VAR framework (for extensive description see: Sato et al. 2007; Ajmi et al. 2015). However, before we proceed with the estimation of the Granger causality tests, we have to conduct some preliminary tests in order to test the validity of the sample. In particular, we must identify if the specified time series are cross-sectionally dependent. M.H. Pesaran’s (2004) cross-sectional dependence test (CD) is used to examine this. Following this, the next step is to determine the order of integration of the covariates by employing M.H. Pesaran’s (2007) CIPS unit root test.

Table 2 presents the results of the cross-sectional dependence test (Pesaran CD test, column 2). Obviously, the null hypothesis of cross-sectional independence is acceptable at the 1% significance level. Hence, the estimated variables are cross-sectionally dependent. Consequently, we can investigate the degree of integration of the variables involved, i.e. the two metrics of urbanisation (URB and URBM) and the two human empowerment indices (PHYS and EMP). The stationarity test helps ascertain whether the samples contain a unit root in their time-series representations. It should be noted here that we cannot apply the (first-generation) classical unit root tests owing to the fact that they postulate no dependency. This in turns implies possible forecasting errors. Consequently, we employ M.H. Pesaran’s (2007) CIPS unit root. The lag length of the test was determined by the AIC statistic. The CIPS unit root is also performed and tabulated in Table 2 (CIPS test, column 3) and rejects the null hypothesis of a unit root and integrated of order one (in differences). This, in turn, signifies that all covariates are integrated of order one I(1).

TABLE 2

Tests for cross-sectional dependence and unit root

Source: own study

SamplePesaran CD testCIPS test
Level1st difference
Full sample
URB290.5a0.56−5.99a
URBMIL112.59a−0.79−2.95b
PHYS10.36a−2−3.71a
EMP27.89a−1.67−3.40a
High-income
URB91.7a−1.04−2.75b
URBMIL8.31a−1.29−2.79b
PHYS7.07a−2.04−3.51a
EMP17.00a−1.57−3.77a
Upper middle-income
URB85.37a−0.82−11.63a
URBMIL22.86a−0.10−3.06b
PHYS4.44a−1.82−3.70a
EMP8.40a−1.49−3.05b
Lower middle-income
URB57.98a−0.56−3.45a
URBMIL65.43a−0.68−4.21a
PHYS3.42a−1.91−3.88a
EMP3.65a−1.99−3.48a
Low-income
URB50.82a−2.01−4.13a
URBMIL23.81a−0.40−3.82a
PHYS3.37a−2.28−3.56a
EMP8.72a−2.12−3.58a

Notes: a, b and c denote values significant at the 1%, 5% and 10% level. Critical values for the CIPS test at 10%, 5% and 1% are −2.76, −2.94, and −3.3, respectively.

Following these preliminary tests, we proceed with the estimation of the various causality tests that include dynamic and time-varying tests. The results of the conventional Granger tests are presented in Table 3. Table 4 presents the findings of the dynamic tests while the results of the time-varying Granger causality tests are shown in Table 5. We start with a general and overarching observation concerning the findings. In broad terms, they do not unveil a strong, systematic and robust association between urbanisation and human empowerment as encapsulated by the two CIRI variables used in the estimations. On the other hand, however, they do offer evidence in favour of such a positive association, albeit not universally present across all income groups and human rights indicators.

TABLE 3

Results of the conventional Granger causality test

Source: own study

H: URB to PHYSH: PHYS to URBH: URB to EMPH: EMP to URB
Entire sample0.1210.021**0.8850.01***
High-income0.6930.3710.8750.363
Upper middle-income0.5560.2530.6140.88
Lower middle-income0.1910.067*0.80.048**
Low-income0.6720.6390.3360.115
H: URBM to PHYSH: PHYS to URBMH: URBM to EMPH: EMP to URBM
Entire sample0.5710.1120.5460.205
High-income0.100*0.290.940.168
Upper middle-income0.7020.5660.9380.122
Lower middle-income0.100*0.018**0.9420.186
Low-income0.6040.3730.8320.002***

Notes: The values in the table are p-values.

The symbols ***, ** and * denote significance at the 1%, 5% and 10% level. The number of lags used to implement the test is equal to one.

TABLE 4

Results of the dynamic Granger causality tests

Source: own study

H: URB to PHYSH: PHYS to URBH: URB to EMPH: EMP to URB
Entire sample0.5350.8110.9390.322
High-income0.9640.3820.8880.753
Upper middle-income0.2780.016**0.8440.229
Lower middle-income0.9310.1850.002***0.025*
Low-income0.5810.4410.8950.25
H: URBM to PHYSH: PHYS to URBMH: URBM to EMPH: EMP to URBM
Entire sample0.7360.7940.8880.003***
High-income0.1520.007***0.6560.151
Upper middle-income0.2660.7330.9610.596
Lower middle-income0.90.7780.3720.227
Low-income0.9660.3790.4030.199

Notes: The values in the table are p-values.

The symbols ***, ** and * denote significance at the 1%, 5% and 10% level. The number of lags used to implement the test is equal to one.

TABLE 5

Results of the time-varying Granger causality test

Source: own study

H: URB to PHYSH: PHYS to URBH: URB to EMPH: EMP to URB
Entire sample0.3320.2630.980.033**
High-income0.9770.4430.9560.747
Upper middle-income0.3810.02**0.8930.364
Lower middle-income0.6820.100*0.004***0.006***
Low-income0.7080.5360.8250.123
H: URBM to PHYSH: PHYS to URBMH: URBM to EMPH: EMP to URBM
Entire sample0.8120.4910.9140.004***
High-income0.096*0.01***0.8030.164
Upper middle-income0.3930.8260.990.391
Lower middle-income0.530.1620.5350.198
Low-income0.9680.4450.5580.005***

Notes: The values in the table are p-values.

The symbols ***, ** and * denote significance at the 1%, 5% and 10% level. The number of lags used to implement the test is equal to one.

Focusing on the results from the estimations where the first of the two urbanisation metrics is used (URB), we can observe that most of the statistically significant coefficients are found in the case of the lower middle-income group of countries. In particular, a fairly consistent and bidirectional nexus is established between this urbanisation metric (URB) and the Empowerment Rights Index (EMP). With the exception of the conventional causality tests (Tab. 3.), all other results indicate a statistically significant nexus between urbanisation and the empowerment index (EMP), a composite index that includes freedom of speech, assembly and association, religion and workers’ rights, electoral self-determination and freedom of foreign and domestic movement. As pointed out in a preceding section, cities facilitate organised actions by citizens demanding improvements in human rights such as workers’ and women’s rights or greater respect for religious and ethnic minorities (Barnett 2014; Glaeser & Steinberg 2017). A reverse causal ordering is also established between the two variables (URB and EMP) in the case of the lower middle-income group of countries. The results of all three causality tests (conventional, dynamic and time-varying) yield a statistically significant causal ordering from the human empowerment index (EMP) to urbanisation (URB). In many countries, especially states with weak institutions, human rights are not stringently observed in rural areas compared to the cities7. The presence of civil society associations, such as trade unions, in urban centres creates a comparatively more protective environment vis-à-vis the countryside. Hence, they act as one of the many urban-pull factors that contribute to the urbanisation trend (Jedwab, Christiaensen & Gindelsky 2017). Furthermore, in countries other than developed ones, rural areas are usually more vulnerable to the operation of organised crime networks8 and other illegal activities such as trafficking, narcotics networks, plundering and smuggling. In many developing and low-income countries, law enforcement in rural areas is not as effective as that in urban centres. In addition, the countryside is more often the venue of conflict, insurgencies and armed fighting than cities9. As has been shown, conflict, armed insurgencies or the operation of armed crime gangs and terrorism cause displacement and internal and external migration (Ibáñez & Vélez 2008; Balcells & Steele 2016; Borstel, Gobien & Roth 2017; Suzuki 2018). In comparative terms, in many countries cities offer or are seen to offer a safer and more secure environment where law is better observed and enforced vis-à-vis the countryside. Hence, we tentatively claim that this causal ordering from the human empowerment index (EMP) to urbanisation (URB) in the case of the lower middle-income countries may be picking up this effect. For the rest of the income groups, the results are quite weak. No statistically significant association is established between the two human rights variables (PHYS and EMP) in the case of the high-income and low-income groups. A nexus from the Physical Integrity Rights Index (PHYS) to urbanisation (URB) is once again found in the case of the lower middle-income group (Tab. 3. & Tab. 5.) and the upper middle-income group (Tab. 4. & Tab. 5.). Just as argued above, a similar tentative inference can be proposed to explain this result. Noteworthy is the fact that the only statistically significant results for the entire sample of 123 countries point to a causal ordering from the PHYS and EMP indices to urbanisation (URB) but not a reverse causal nexus (Tab. 3. & Tab. 5.).

Turning the focus onto the findings when the second urbanisation metric is used (URBM), the results yielded from the estimation are similarly not systematic and do not offer the premises for strong inferences. Just as in the case of the URB metric of urbanisation, they vary between income groups and, as a general observation, they appear to be weaker. A unidirectional causal ordering from EMP to URBM is the only statistically significant finding for the entire sample yielded by the dynamic Granger causality tests as well as by the time-varying ones (Tables and 5 respectively). Once again, the tentative explanation is that human rights are better observed in cities vis-à-vis rural areas and this may positively affect the influx into urban agglomerations. A bidirectional causal nexus is established in the case of the high-income group between URBM and PHYS by the time-varying Granger causality tests reported in Table 5. A causal ordering from URBM to PHYS is also established by the conventional causality tests (Tab. 3.) and a reverse nexus from PHYS to URBM by the dynamic Granger causality tests (Tab. 4.). In the case of the upper middle-income group of countries, in all three causality test procedures no statistically significant association is found between the two human rights indices (PHYS and EMP) and the share of population living in urban agglomerations with more than one million inhabitants (URBM). Bidirectional causality by the conventional Granger tests is established between URBM and PHYS in the case of the lower middle-income group of countries (Tab. 3.). Finally, a causal association from EMP to URBM is suggested by the conventional (Tab. 3.) and the time-varying Granger causality tests (Tab. 5.) for the low-income countries.

Concluding remarks

The social and economic drivers of democratisation, the emergence and consolidation of democratic institutions have been the theme of a large and steadily growing body of literature and academic debate. As suggested by a number of papers, urbanisation may be one of the contributing factors to the process of democratisation. The polis is an environment that can potentially facilitate political change and democratic transition in countries through a number of channels (Barro 1999; Barnett 2014; Glaeser & Steinberg 2017). Cities allow for coordinated action by citizens demanding respect and improvement of human rights or the protection and respect of the rights of ethnic and religious minorities, facilitate the emergence of organisations such as trade unions, and enable the emergence of social capital. As noted by C. Barnett (2014), the association between urbanisation and democracy is a comparatively under researched theme and, as suggested by R. Barro (1999), a reverse effect can also be postulated.

Within this strand of the literature, the paper examined the nexus between urbanisation and human rights using the Cingranelli-Richards Index of human empowerment (Cingranelli & Richards 1999, 2010). In particular, two indices encapsulating human rights were used in the estimation processes: the Physical Integrity Rights Index (PHYS) and the Empowerment Rights Index (EMP). The objective of the preceding analysis was to examine empirically the association between urbanisation and the two human rights indices. To this effect a sample of 123 countries was used for the period 1981–2011. The sample of countries was also split into income groups in order to allow for possible differences in the relationship investigated that stem from the level of income. The findings were not uniform across the two human rights indices, the two metrics of urbanisation and the income groups. Hence, no strong and unequivocal inferences can be drawn. Nevertheless, they do reveal a significant positive bidirectional association between the Empowerment Rights Index and urbanisation in the case of the lower middle-income group of countries. A similar but not as pronounced nexus between urbanisation and the Physical Integrity Rights Index is found in the case of the high-income group when the over one million inhabitants urbanisation metric was used in the estimation processes.

Clearly, the theme examined here warrants further investigation that could allow for further insights into the complex nexus between human rights and urbanisation. The methodological decision to split the sample of countries into income groups is but one criterion for categorising them. Other criteria, such as for instance religion or geographic location can be used in order to bring to the forth further insights that enhance our understanding of this association.

References

  • Acemoglu, D., Johnson, S., Robinson, J. A. & Yared, P. (2008) Income and democracy, American Economic Review, 98(3), 808–842.

  • Adams, S. & Klobodu, E. K. M. (2019) Urbanization, economic structure, political regime, and income inequality, Social Indicators Research, 142(3), 971–995.

  • Ajmi, A. N., Hammoudeh, S., Nguyen, D. K. & Sato, J. R. (2015) On the relationships between CO2 emissions, energy consumption and income: the importance of time variation, Energy Economics, 49, 629–638.

  • Anthony, R. (2014) Bringing up the past: political experience and the distribution of urban populations, Cities, 37, 33–46.

  • Ardalan, K. (2011) Globalization and democracy: four paradigmatic views, Transcience Journal, 2(1), 27–53.

  • Balcells, L. & Steele, A. (2016) Warfare, political identities, and displacement in Spain and Colombia, Political Geography, 51, 15–29.

  • Barnett, C. (2014) What do cities have to do with democracy?, International Journal of Urban and Regional Research, 38(5), 1625–1643.

  • Barro, R. (1999) Determinants of democracy, Journal of Political Economy, 107(S6), 158–183.

  • Bertinelli, L. & Strobl, E. (2007) Urbanisation, urban concentration and economic development, Urban Studies, 44(13), 2499–2510.

  • Borstel, J., T. Gobien & Rot h, D. (2017) Terror and internal migration in Israel, Defence and Peace Economics. DOI: .

    • Crossref
    • Export Citation
  • Brülhart, M. & Sbergami, F. (2009) Agglomeration and growth: cross-country evidence, Journal of Urban Economics, 65, 48–63.

  • Castells-Quintana, D. & Royuela, V. (2014) Agglomeration, inequality and economic growth, Annals of Regional Science, 52(2), 343–366.

  • Christiaensen, L., Weerdt, J. & Todo, Y. (2013) Urbanization and poverty reduction: the role of rural diversification and secondary towns, Agricultural Economics, 44(4–5), 435–447.

  • Cingranelli, D. & Richards, D. (1999) Measuring the level, pattern, and sequence of government respect for physical integrity rights, International Studies Quarterly, 43(2), 407–418.

  • Cingranelli, D. & Richards, D. (2010) The Cingranelli and Richards (CIRI). Human Rights Data Project, Human Rights Quarterly, 32(2), 401–424.

  • Clark, A. M. & Sikkink, K. (2013) Information effects and human rights data: is the good news about increased human rights information bad news for human rights measures?, Human Rights Quarterly, 35(3), 539–568.

  • Clark, R. (2014) A tale of two trends: democracy and human rights 1981–2010, Journal of Human Rights, 13(4), 395–413.

  • Cohen, M. (2014) The city is missing in the Millennium Development Goals, Journal of Human Development and Capabilities, 15(2–3), 261–274.

  • Dahlhaus, R., Neumann, M.H. & von Sachs, R. (1999) Nonlinear wavelet estimation of the time-varying autoregressive processes, Bernoulli, 5, 873–906.

  • Eichengreen, B. & Leblang, D. (2008) Democracy and globalization, Economics and Politics, 20(3), 289–334.

  • Eilers, P.H.C. & Marx, B.D. (1996) Flexible smoothing with B-splines and penalties, Statistics Science, 11, 89–121.

  • Ferragina, E. (2010) Social Capital and Equality, Tocqueville Review, 31(1), 73–98.

  • Ferragina, E. (2013) The socio-economic determinants of social capital, Making Democracy Work revisited, International Journal of Comparative Sociology, 54(1) 48–73.

  • Frick, S. & Rodríguez-Pose, A. (2018) Change in urban concentration and economic growth, World Development, 105, 156–170.

  • Glaeser, E. L., Ponzetto, G. A. & Shleifer, A. (2007) Why does democracy need education?, Journal of Economic Growth, 12, 77–99.

  • Glaeser, E. & Steinberg, B. (2017) Transforming cities: does urbanization promote democratic change?, Regional Studies, 51(1), 58–68.

  • Granger, C. (1969) Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 37(3), 424–438.

  • Granger, C. (1988) Causality, cointegration, and control, Journal of Economic Dynamics and Control, 12(2–3), 551–559.

  • Henderson, J. V. (2003) The urbanization process and economic growth: The so-what question, Journal of Economic Growth, 8(1), 47–71.

  • Huntington, S. (1991) How countries democratize, Political Science Quarterly, 106(4), 579–616.

  • Huntington, S. (1993) The Third Wave: Democratization in the Late Twentieth Century, University of Oklahoma Press, Oklahoma.

  • Ibáñez, A. M. & Vélez, C. E. (2008) Civil conflict and forced migration: the micro determinants and welfare losses of displacement in Colombia, World Development, 36(4), 659–676.

  • Jedwab, R., Christiaensen, L. & Gindelsky, M. (2017) Demography, urbanization and development: Rural push, urban pull and ... urban push?, Journal of Urban Economics, 98, 6–16.

  • Kollias, C. & Paleologou, S. M. (2016) Globalization and democracy: a disaggregated analysis by income group, Global Economy Journal, 16(2), 213–228.

  • Krugman, P. (1991) Increasing returns and economic-geography, Journal of Political Economy, 99(3), 483–499.

  • Lewis, B. D. (2014) Urbanization and economic growth in Indonesia: good news, bad news and (possible) local government mitigation, Regional Studies, 48(1), 192–207.

  • Li, Q. & Reuveny, R. (2003) Economic globalization and democracy: An empirical investigation, British Journal of Political Science, 33, 29–54.

  • Liddle, B. & Messinis, G. (2015) Which comes first–urbanization or economic growth? Evidence from heterogeneous panel causality tests, Applied Economics Letters, 22(5), 349–355.

  • Lipset, S.M. (1959) Some social requisites of democracy: economic development and political legitimacy, American Political Science Review, 53 (March), 69105.

  • Lipset, S.M. (1994) The social requisites of democracy revisited: 1993 Presidential Address, American Sociological Review, 59, 122.

  • López-Córdova, E. & Meissner, C. (2005) The globalization of trade and democracy, 1870–2000, working paper No. 11117, National Bureau of Economic Research, Cambridge, MA.

  • Megeri, M. N., & Kengnal, P. (2016) Econometric Study of Urbanization and Economic Development, Journal of Statistics and Management Systems, 19(5), 633–650.

  • Oyvat, C. (2016) Agrarian structures, urbanization, and inequality, World Development, 83, 207–230.

  • Papaioannou, E. & Siourounis, G. (2008) Economic and social factors driving the third wave of democratization, Journal of Comparative Economics, 36, 365–387.

  • Pesaran, M.H. (2004) General Diagnostic Tests for Cross-Section Dependence in Panels, working Paper, University of Cambridge.

  • Pesaran, M. H. (2007) A simple panel unit root test in the presence of cross-section dependence, Journal of applied econometrics, 22(2), 265–312.

  • Portes, A. & Roberts, B. (2005) The free-market city: Latin American urbanization in the years of the neoliberal experiment, Studies in Comparative International Development, 40(1), 43–82.

  • R Core Team (2019) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. Available from: http://www.R-project.org/ [accessed: 25.10.2019]

  • Rueschemeyer, D., Stephens, E. & Stephens, J. (1992) Capitalist Development and Democracy, University of Chicago Press, Chicago.

  • Sato, J. R., Morettin, P. A., Arantes, P. R. & Amaro Jr, E. (2007) Wavelet based time-varying vector autoregressive modelling, Computational Statistics & Data Analysis, 51(12), 5847–5866.

  • Sekkat, K. (2017) Urban concentration and poverty in developing countries, Growth and Change, 48(3), 435–458.

  • Sommer, U. & Asal, V. (2014) A cross-national analysis of the guarantees of rights, International Political Science Review, 35(4), 463–481.

  • Su, C. W., Song, Y., Ma, Y. T., & Tao, R. (2019) Is financial development narrowing the urban–rural income gap? A cross-regional study of China, Papers in Regional Science, 98(4), 1779–1800.

  • Suzuki, T. (2018) Civil war, migration and the effect on business cycles: the case of Sri Lanka, Defence and Peace Economics. DOI: .

    • Crossref
    • Export Citation
  • Thede, N. (2009) Decentralization, democracy and human rights: a human rights-based analysis of the impact of local democratic reforms on development, Journal of Human Development and Capabilities, 10(1), 103–123.

  • Wheaton, W. C. & Shishido, H. (1981) Urban concentration, agglomeration economies, and the level of economic development, Economic Development and Cultural Change, 30(1), 17–30.

Appendix

List of countries in the sample per income group

Source: own study

High Income

(37 countries)
Upper Middle Income

(33 countries)
Lower Middle Income

(32 countries)
Low Income

(21 countries)
AustraliaAlgeriaBangladeshBenin
AustriaAngolaBhutanBurkina Faso
BahrainArgentinaBoliviaCentral African Republic
BelgiumBelizeCameroonEthiopia
CanadaBotswanaCongo BrazzavilleGambia
ChileBrazilEgyptGuinea
CyprusBulgariaEl SalvadorGuinea-Bissau
DenmarkChinaGhanaHaiti
FinlandColombiaGuatemalaKorea (North)
FranceCosta RicaHondurasMadagascar
GermanyCubaIndiaMalawi
GreeceDominican RepublicIndonesiaMali
HungaryEcuadorIvory CoastMozambique
IcelandFijiKenyaNepal
IrelandGabonLaosNiger
IsraelGuyanaLesothoRwanda
ItalyIranMauritaniaSenegal
JapanIraqMongoliaTanzania
Korea (Republic)JamaicaMoroccoTogo
KuwaitJordanMyanmar (Burma)Uganda
LuxembourgLibyaNicaraguaZimbabwe
NetherlandsMalaysiaNigeria
New ZealandMauritiusPakistan
NorwayMexicoPapua New Guinea
OmanNamibiaPhilippines
PolandPanamaSri Lanka
PortugalParaguaySudan
Saudi ArabiaPeruSwaziland
SingaporeRomaniaSyria
SpainSouth AfricaTunisia
SwedenThailandVietnam
SwitzerlandTurkeyZambia
Trinidad & TobagoVenezuela
UAE
UK
Uruguay
USA

Footnotes

1

The former Soviet Union can be cited as such an example. On the other hand, there have been regimes such as the Khmer Rouge in Cambodia that imposed more brutal rule in rural rather than urban areas.

2

When the URBM metric of urbanisation is used, the sample is slightly smaller consisting of 93 countries.

3

The list of countries per income group can be found in the appendix. The groupings are solely based on per capita income (GDP per capita) rather than the broader development level of each country. Given the period of the empirical estimations that follow (1981–2011), the countries were included in each of the four categories on the basis of the World Bank’s current groupings.

4

A clarification is in order here. In the World Bank’s database, urban population (the variable URB used here) refers ‘to people living in urban areas as defined by national statistical offices’. Given the heterogeneous number of countries included in the sample, it is inevitable that there exist significant differences in the standards used by different national authorities to define a place as an urban area.

5

The software R was used in order to conduct all the statistical analyses (R Core Team 2019).

6

Recent urban studies papers such as B.D. Lewis (2014), B. Liddle & G. Messinis (2015), M.N. Megeri & P. Kengnal (2016), C. Su et al. (2019), S. Adams & E.K.M. Klobodu (2019) have used VAR models for their empirical investigation.

7

Clearly, this is not a universally applicable case. It is a characteristic of institutionally weak states rather than developed democratic countries with strong institutions such as, for example, countries in Europe and North America. The absence of data on human rights in rural vs urban areas does not allow a comparative empirical investigation.

8

On the other hand, it should be pointed out that large urban agglomerations do offer anonymity that is conducive to criminal activity.

9

Examples include past/current insurgencies in a number of Latin American countries (for example FARC in Colombia, Sendero Luminoso in Peru) as well as the presence and operation of drug producing cartels in rural areas. Similarly, in countries such as Nigeria, Afghanistan, and Pakistan rural areas offer safe havens for the operation of terrorist organisations such as Boko Haram and the Taliban.

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  • Acemoglu, D., Johnson, S., Robinson, J. A. & Yared, P. (2008) Income and democracy, American Economic Review, 98(3), 808–842.

  • Adams, S. & Klobodu, E. K. M. (2019) Urbanization, economic structure, political regime, and income inequality, Social Indicators Research, 142(3), 971–995.

  • Ajmi, A. N., Hammoudeh, S., Nguyen, D. K. & Sato, J. R. (2015) On the relationships between CO2 emissions, energy consumption and income: the importance of time variation, Energy Economics, 49, 629–638.

  • Anthony, R. (2014) Bringing up the past: political experience and the distribution of urban populations, Cities, 37, 33–46.

  • Ardalan, K. (2011) Globalization and democracy: four paradigmatic views, Transcience Journal, 2(1), 27–53.

  • Balcells, L. & Steele, A. (2016) Warfare, political identities, and displacement in Spain and Colombia, Political Geography, 51, 15–29.

  • Barnett, C. (2014) What do cities have to do with democracy?, International Journal of Urban and Regional Research, 38(5), 1625–1643.

  • Barro, R. (1999) Determinants of democracy, Journal of Political Economy, 107(S6), 158–183.

  • Bertinelli, L. & Strobl, E. (2007) Urbanisation, urban concentration and economic development, Urban Studies, 44(13), 2499–2510.

  • Borstel, J., T. Gobien & Rot h, D. (2017) Terror and internal migration in Israel, Defence and Peace Economics. DOI: .

    • Crossref
    • Export Citation
  • Brülhart, M. & Sbergami, F. (2009) Agglomeration and growth: cross-country evidence, Journal of Urban Economics, 65, 48–63.

  • Castells-Quintana, D. & Royuela, V. (2014) Agglomeration, inequality and economic growth, Annals of Regional Science, 52(2), 343–366.

  • Christiaensen, L., Weerdt, J. & Todo, Y. (2013) Urbanization and poverty reduction: the role of rural diversification and secondary towns, Agricultural Economics, 44(4–5), 435–447.

  • Cingranelli, D. & Richards, D. (1999) Measuring the level, pattern, and sequence of government respect for physical integrity rights, International Studies Quarterly, 43(2), 407–418.

  • Cingranelli, D. & Richards, D. (2010) The Cingranelli and Richards (CIRI). Human Rights Data Project, Human Rights Quarterly, 32(2), 401–424.

  • Clark, A. M. & Sikkink, K. (2013) Information effects and human rights data: is the good news about increased human rights information bad news for human rights measures?, Human Rights Quarterly, 35(3), 539–568.

  • Clark, R. (2014) A tale of two trends: democracy and human rights 1981–2010, Journal of Human Rights, 13(4), 395–413.

  • Cohen, M. (2014) The city is missing in the Millennium Development Goals, Journal of Human Development and Capabilities, 15(2–3), 261–274.

  • Dahlhaus, R., Neumann, M.H. & von Sachs, R. (1999) Nonlinear wavelet estimation of the time-varying autoregressive processes, Bernoulli, 5, 873–906.

  • Eichengreen, B. & Leblang, D. (2008) Democracy and globalization, Economics and Politics, 20(3), 289–334.

  • Eilers, P.H.C. & Marx, B.D. (1996) Flexible smoothing with B-splines and penalties, Statistics Science, 11, 89–121.

  • Ferragina, E. (2010) Social Capital and Equality, Tocqueville Review, 31(1), 73–98.

  • Ferragina, E. (2013) The socio-economic determinants of social capital, Making Democracy Work revisited, International Journal of Comparative Sociology, 54(1) 48–73.

  • Frick, S. & Rodríguez-Pose, A. (2018) Change in urban concentration and economic growth, World Development, 105, 156–170.

  • Glaeser, E. L., Ponzetto, G. A. & Shleifer, A. (2007) Why does democracy need education?, Journal of Economic Growth, 12, 77–99.

  • Glaeser, E. & Steinberg, B. (2017) Transforming cities: does urbanization promote democratic change?, Regional Studies, 51(1), 58–68.

  • Granger, C. (1969) Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 37(3), 424–438.

  • Granger, C. (1988) Causality, cointegration, and control, Journal of Economic Dynamics and Control, 12(2–3), 551–559.

  • Henderson, J. V. (2003) The urbanization process and economic growth: The so-what question, Journal of Economic Growth, 8(1), 47–71.

  • Huntington, S. (1991) How countries democratize, Political Science Quarterly, 106(4), 579–616.

  • Huntington, S. (1993) The Third Wave: Democratization in the Late Twentieth Century, University of Oklahoma Press, Oklahoma.

  • Ibáñez, A. M. & Vélez, C. E. (2008) Civil conflict and forced migration: the micro determinants and welfare losses of displacement in Colombia, World Development, 36(4), 659–676.

  • Jedwab, R., Christiaensen, L. & Gindelsky, M. (2017) Demography, urbanization and development: Rural push, urban pull and ... urban push?, Journal of Urban Economics, 98, 6–16.

  • Kollias, C. & Paleologou, S. M. (2016) Globalization and democracy: a disaggregated analysis by income group, Global Economy Journal, 16(2), 213–228.

  • Krugman, P. (1991) Increasing returns and economic-geography, Journal of Political Economy, 99(3), 483–499.

  • Lewis, B. D. (2014) Urbanization and economic growth in Indonesia: good news, bad news and (possible) local government mitigation, Regional Studies, 48(1), 192–207.

  • Li, Q. & Reuveny, R. (2003) Economic globalization and democracy: An empirical investigation, British Journal of Political Science, 33, 29–54.

  • Liddle, B. & Messinis, G. (2015) Which comes first–urbanization or economic growth? Evidence from heterogeneous panel causality tests, Applied Economics Letters, 22(5), 349–355.

  • Lipset, S.M. (1959) Some social requisites of democracy: economic development and political legitimacy, American Political Science Review, 53 (March), 69105.

  • Lipset, S.M. (1994) The social requisites of democracy revisited: 1993 Presidential Address, American Sociological Review, 59, 122.

  • López-Córdova, E. & Meissner, C. (2005) The globalization of trade and democracy, 1870–2000, working paper No. 11117, National Bureau of Economic Research, Cambridge, MA.

  • Megeri, M. N., & Kengnal, P. (2016) Econometric Study of Urbanization and Economic Development, Journal of Statistics and Management Systems, 19(5), 633–650.

  • Oyvat, C. (2016) Agrarian structures, urbanization, and inequality, World Development, 83, 207–230.

  • Papaioannou, E. & Siourounis, G. (2008) Economic and social factors driving the third wave of democratization, Journal of Comparative Economics, 36, 365–387.

  • Pesaran, M.H. (2004) General Diagnostic Tests for Cross-Section Dependence in Panels, working Paper, University of Cambridge.

  • Pesaran, M. H. (2007) A simple panel unit root test in the presence of cross-section dependence, Journal of applied econometrics, 22(2), 265–312.

  • Portes, A. & Roberts, B. (2005) The free-market city: Latin American urbanization in the years of the neoliberal experiment, Studies in Comparative International Development, 40(1), 43–82.

  • R Core Team (2019) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. Available from: http://www.R-project.org/ [accessed: 25.10.2019]

  • Rueschemeyer, D., Stephens, E. & Stephens, J. (1992) Capitalist Development and Democracy, University of Chicago Press, Chicago.

  • Sato, J. R., Morettin, P. A., Arantes, P. R. & Amaro Jr, E. (2007) Wavelet based time-varying vector autoregressive modelling, Computational Statistics & Data Analysis, 51(12), 5847–5866.

  • Sekkat, K. (2017) Urban concentration and poverty in developing countries, Growth and Change, 48(3), 435–458.

  • Sommer, U. & Asal, V. (2014) A cross-national analysis of the guarantees of rights, International Political Science Review, 35(4), 463–481.

  • Su, C. W., Song, Y., Ma, Y. T., & Tao, R. (2019) Is financial development narrowing the urban–rural income gap? A cross-regional study of China, Papers in Regional Science, 98(4), 1779–1800.

  • Suzuki, T. (2018) Civil war, migration and the effect on business cycles: the case of Sri Lanka, Defence and Peace Economics. DOI: .

    • Crossref
    • Export Citation
  • Thede, N. (2009) Decentralization, democracy and human rights: a human rights-based analysis of the impact of local democratic reforms on development, Journal of Human Development and Capabilities, 10(1), 103–123.

  • Wheaton, W. C. & Shishido, H. (1981) Urban concentration, agglomeration economies, and the level of economic development, Economic Development and Cultural Change, 30(1), 17–30.

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