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7PUBLIC AWARENESS AND PARTICIPATION IN LOCAL SELF-GOVERNANCE IN ARMENIA: COMPARATIVE ANALYSIS FOR
2015 AND 2019
SONYA MSRYAN
Introduction
For over two decades raising public awareness on Local Self-Government and promotion of participation in LSGs has been and continues to be one of the key components of development initiatives, governmental toolkits and civic organizations in Armenia as a strategy for achieving good governance and deepening democratization. Public awareness and participation are essential pre-conditions for "good governance" and a process aimed at enhancing democracy1. Despite efforts to enhance public awareness and engagement in LSGs, Armenian society still faces lack of interest towards Local Self-Governance2. After two decades of experience in promoting public awareness and participation in LSGs in Armenia through development projects, governmental process, NGOs and other social initiatives - it is important to ask the question, "Ultimately what is the current situation of public awareness and participation in LSGs in Armenia?"
To answer this question the researchers most commonly apply descriptive statistics introducing the frequencies of answers to the different questions relating the same concept to be measured3. Descriptive statistics can be easily understandable to policy makers and analysts, yet it may be not an efficient way to introduce broad range of individual variables tapping the same general concept. Encompassing the broad range of individual variables into low dimensional measures will help to get further insight into the patterns underlying the data set. To fill this gap this paper tries to answer the question:
How we can use multidimensional methods of data analysis and interpretation to get insight into the question of what is the composition of society based on dimensions of awareness towards LSGs and participation in LSGs?
1 e.g. The World Bank, The World Bank and Participation, Washington, DC: 1994, Kuehnast, K., Protection, Participation, and Public Awareness: Indonesia Coral Reef Rehabilitation and Management Project. Social Development Notes; No.57, World Bank, Washington, 2001
2 e.g. Caucasus Research Resource Center-Armenia, On the Long Way of Reforms: The System of RA Local Government in the Eyes of Citizens, Yerevan, 2015, Communities Finance Officers Association, Capacities of Local Self-government Bodies in Participatory Decision Making and Planning and Engagement of Civil Society and Private Sectors at Local level Lori and Tavush Marzes, Yerevan, 2011
3 See the previous reference
Literature review
Local government is an essential component of administrative systems of all modern societies, looking for the improvement of public services and good governance at local level4. For the recent three decades the concepts of rising public awareness and participation in local self-governance have become key directions in global discourses in terms of their power to represent democratic relationships between the governmental bodies and its citizens', who are now empowered through their informed engagement in decision making activities5. Participation along with voluntarism, tolerance and trust are announced as "civic values" under which only democracy appears legitimate6.
Participation is defined as a process through which stakeholders' influence and share control over development initiatives, decisions and resources, which affect them (stakeholders)7. Engagement or participation can manifest themselves in a variety of ways: they can take private or public, institutionalized or non-institutionalized forms8. Many researches outline positive outcomes of civic participation. The empiric results of studies clearly indicate civic participation to be related to lower corruption9, civic participation enforces civic orientations and enables collective action10 and contributes to the construction of citizenship, strengthens practices of participation, the building of responsive and accountable states, and more inclusive and cohesive societies11.
Raising public awareness towards LSGs and promotion of participation in LSGs appear in the same context as objectives set by development projects and governmental toolkits12. Researchers and policymakers outline the importance of civic participation based on awareness and voluntarism as one of the key principles
4 Kosecik M. and Sagbas L, Public Attitudes to Local Government in Turkey: Research on Knowledge, Satisfaction and Complaints, Local Government Studies, 30: 3, 2004, pp. 360-383
5 e.g. Bartoletti, R. & Faccioli, F., Public Engagement, Local Policies, and Citizens' Participation: An Italian Case Study of Civic Collaboration, SI: Media, Participation and Social Change July-September, 2016, pp. 1-11; Dalton, R. J., Citizenship norms and the expansion of political participation, Political Studies, 56, 2008, pp. 76-98, Rondinelli, A. D. (Ed.), Public administration and democratic governance: Governments serving citizens, United Nation Publications, United Neighborhood Centers of America, 2006
6 Inglehart, R., Mapping Global Values, Comparative Sociology 5 (2-3): 2006, pp. 115-136,
7 The World Bank, The World Bank and Participation, Washington, DC: 1994
8 WeBels, B., The development of civil society in Central and Eastern Europe: intermediary actors, trust and participation. In D. Gosewinkel, D. Rucht, W. van den Daele & J. Kocka (Ed-scivil) society - national und transnational, Berlin, 2004, pp. 173-199
9 e.g. Griesshaber, N., Forms of Civic Engagement and Corruption: Disentangling the Roles of Voluntary Associations, Elite challenging Mass Movements and the Type of Trust within Social Networks, World Values Research 7(1), 2014, pp. 1-23; O'Connell, M., Anti 'Social Capital. Civic Values versus Economic Equality in the EU, European Sociological Review 19(3): Princeton: Princeton University Press, 2003, pp. 241-248
10 Welzel, C., Inglehart, R. & Deutsch, F., Social Capital, Voluntary Associations and Collective Action: Which Aspects of Social Capital Have the Greatest 'Civic' Payoff?, Journal of Civil Society, 1(2), 2005, pp. 121-146
11 Gaventa J., Barrett G., Mapping the Outcomes of Citizen Engagement, World Development Vol. 40, No. 12, 2012, pp. 2399-2410,
12 See e.g. Kuehnast, K., Protection, Participation, and Public Awareness: Indonesia Coral Reef Rehabilitation and Management Project. Social Development Notes; No.57., World Bank, Washington, 2001, The World Bank, The World Bank and Participation, Washington, DC: 1994
of "good governance" to be applied at the all levels of governance13. Many authors outline development of greater civic and political knowledge, a greater sense of awareness of rights and procedures as an important first-level impact of citizen engagement, which serve as a prerequisite to deepen action and participation14.
Awareness towards LSGs and participation in LSGs are related to each other in many aspects: raising public awareness promotes meaningful and informed participation of citizens while participation promotes awareness on LSGs and their procedures among citizens. These two concepts are also employed together in empiric researches in the same context - to describe the relationship between citizens and LSGs15. To sum up the general findings of these analyses is that these studies did not reveal certain pattern of interrelation between awareness and participation in LSGs: some studies revealed high level of awareness and high level of participation in LSGs while others revealed high level of awareness and low participation among citizens.
As for national level, we lack empirical researches on LSGs in Armenia. Up to 2015 there had been no representative household survey database reflecting citizen's attitudes and perception towards LSGs in a comprehensive manner. Some representative surveys included questions on some aspects of LSGs16, however these researches did not capture the entire picture of interrelations between citizen's and LSGs. Due to lack of empiric data the analytic papers and reports on LSGs are quite limited. In 2015 CRRC Armenia conducted representative survey throughout Armenia and produced database including broad range of variables on various aspects of LSGs as a baseline survey. In 2019 CRRC Armenia conduced the endline survey. The report developed on the database included application of descriptive statistics lacking any multidimensional analytic underpinning17. To fill this gap in existing knowledge we employ multidimensional analysis methods trying to reveal the composition of society based on two dimensions: awareness on LSGs and participation in LSGs.
13 See e.g. European Governance, A White Paper, Commission of the European Communities, Brussels, 2001 http://europa.eu/rapid/press-release_D0C-01-10_en.htm
14 See e.g. Maykova E., Simonova E., The Participation of Russian Citizens in Local Self-government: Potential and Real-life Social Practices, International Journal of Economics and Financial Issues, 5(Special Issue), 2015, pp.142-150; Maykova E., Simonova E., The Participation of Russian Citizens in Local Self-government: Potential and Real-life Social Practices, International Journal of Economics and Financial Issues, 5(Special Issue), 2015, pp.142-150; Merrifield, J., Learning Citizenship, Discussion Paper prepared for Institute of Development Studies Participation Group and Society for Participatory Research in Asia, London: Learning from Experiences Trust, Goldsmiths College, 2001
5 See e.g. Esenaliev D., Kisunko G., Local Budget Transparency and Participation Evidence from the Kyrgyz Republic, Policy Research Working Paper, World Bank Group, 2015; Salamadze, V., Citizen Participation in Self-Governance, Civil Society Institute, 2009; Sukiasyan M., Determining the Factors Influencing Residents' Awareness and Knowledge about Local Self-Government Body's Activities in Armenia, CRRC Armenia, Yerevan, 2016, Maykova E., Simonova E., The Participation of Russian Citizens in Local Self-government: Potential and Real-life Social Practices, International Journal of Economics and Financial Issues, 5(Special Issue), 2015, pp.142-150
16 e. g. Caucasus Barometer 2004-2015, World Value Survey Wave 6, Quality of Life Survey 2014, etc.
17 Caucasus Research Resource Center-Armenia, On the Long Way of Reforms: The System of RA Local Government in the Eyes of Citizens, Yerevan, 2015
Research methodology
Objectives and hypothesis
The object of research paper is to derive composition of society based on two dimensions: the level of public cognition towards LGSs and participation practices in the latter.
The following research questions were under consideration:
■ How can public awareness on LSG bodies and participation in LSGs be measured based on available observed variables?
■ What is the composition of society based on dimensions of awareness on LSGs and participation in LSGs?
■ To what extent has the composition of society changed in the context of awareness on LSGs and participation in LSGs comparing data for 2015 and 2019?
Data source and sample
The databases were produced by Caucasus Research Resource Center-Armenia (CRRC Armenia) in 2015 and 2019 within the framework of Civic Engagement in Local Government (CELoG) Program. The research method included household survey based on face-to-face interviews using structured questionnaire. Sampling method included multilevel cluster sampling. Stratification was done by region and area of residence, combined with purposed sampling of target pilot communities. Household adult member was selected according to Kish selection grid. Sample size for 2015 is 1443 HHs out of which 386 in Yerevan, 444 in other urban areas and 613 in rural areas, across 133 communities in the country. Sample size for 2019 is 1408 out of which 352 are in Yerevan, 462 in other urban areas and 594 in rural areas, across 81 communities covering 86 settlement in Armenia.
Identification of variables
For identification of the variables database for 2015 was used. The variables were identified through a reliability test; particularly Tau-equivalent reliability test (Cronbach's alpha test or coefficient alpha) was applied. This test is used to measure internal consistency of hypothetic constructs, i.e. it describes the extent to which all the items in a test measure the same concept or con-struct18. Variables decreasing alpha's coefficient were not considered in further processing.
Public Awareness of Local Self-Governance: The CELOG questionnaire includes 14 items relating to the measurement of different aspect of cognition towards LSGs. 4 items were eliminated from the conceptual scheme based on reliability scale results. Cronbach's Alpha final value was 0.671 which is a
18 Tavokol M., Dennick R., Making sense of Cronbach's alpha, International Journal of Medical Education , 2, 2011, pp.53-55
"questionable" result (N of items=10), however, that was the best result combining available variables. Table 1 includes variables included in the further processing and test results for final identified variables.
Table 1
Reliability Statistics of Cognition towards Local Self-Governance
Scale Variance if Item Deleted Corrected Cronbach's
Identified variables Item-Total Correlation Alpha if Item Deleted
„I Do you know the head of your community? Scale (0-No, 1-Yes) 2.803 .339 .647
Do you know at least one Local Council (Avagani) X2 member of your community? Scale Recoded (0-No, 2.563 .437 .624
1-Yes)
Do you know any assessment tool that the govern-X9 ment uses to rate the performance of LSGs? Scale 3.241 .362 .654
(0-No, 1-Yes)
Do you know how local taxes, property rates, fees, X10 fines and licenses are determined by the LSG? Scale 3.034 .422 .638
(0-No, 1-Yes)
xo Are you familiar with the decisions passed at your LSG bodies? Scale (0-No, 1-Yes) 2.826 .487 .619
Do you know any assessment tool that the govern-X7 ment uses to rate the performance of LSGs? Scale 3.122 .370 .647
(0-No, 1-Yes)
X6 Are you aware of the community consolidation proc-ess?(0-No^1.10., 1-Yes) 2.650 .380 .639
Do you know where the LSG bodies of your com-
X5 munity direct the revenue generated from local taxes, property rates, fees, fines and licenses to? Scale 2.966 .223 .671
Recoded (0-No, 1-Yes)
X4 Do you know the budget of your community for 2015? Scale Recoded (0-No, 1-Yes) 3.321 .294 .663
Do you know if the head of your community has X3 adjacent advisory bodies? Scale Recoded (0-No, 1- 2.870 .275 .663
Yes)
Participation in Local Self-Governance: The questionnaire involves 5 general questions relating to different aspects of participation in Local Self-Governance, which are as follows: 1) Have you ever participated in the monitoring of the community-level service provision? 2) Have you ever inspected the regulations passed by your LSG? 3) In the past 6 months how often did you participate in meetings of local council (Avagani)? 4) In the past 6 months how often did you participate in Local Community budget hearings/discussions? 5) In the past 6 months how often did you participate in discussions of local community policies and plans? The reliability analysis revealed poor internal consistency (Cronbach's Alpha = 0.084, N of items = 5), thus we did not proceed with the construction of measurement model of participation in Local Self-Governance.
However, we searched for one individual variable which will reflect the concept of participation in LSGs. Among the questions associated to participa-
tion in LSGs was "How often in the past 6 months did you participate in the following activities: a) Meetings of local council (Variable 1), b) Local Community budget hearings/discussions (Variable 2), c) Discussions of local community policies and plans (Variable 3), which was selected as an indicator of participation in Local Self-Governance. As the correlations between Variables 1, 2 and 3 were critically strong (r>0.95, sign= 0.000), we selected only Variable 1 - "participation during the meetings of local council (Avagani)" as an indicator of participation in LSGs.
Thus, the variables associated to the awareness of LSGs performed good ability to tap the same construct, while variables on civic participation demonstrated poor results. Thus, we processed construction of a composite indicator for only awareness of LSGs.
Constructing composite index for public awareness of local self-governance
We identified 10 variables which are to be combined into one composite index of public awareness. Building a composite index is a delicate task and full of pitfalls: from the obstacles regarding the availability of data and the choice of individual indicators, to their treatment in order to compare (normalization) and aggregate them (weighting and aggregation)19. There is no one general approach to build a composite index universally valid for all areas of application. One of the approaches is application of principal component analysis to group together individual items under general indicators. The idea of this approach is to account for the highest possible variation in the indicator set using the smallest possible number of factors20.
To calculate the composite index of cognition towards LSGs we applied approach of using Principal Components Analysis (PCA) method. The individual indicators were ensured to have the same unit of measurement, particularly the variable "How many Local council (Avagani) members of your community do you know?" was recoded to "Do you know Local council (Avagani) members of your community" (X2) to have dichotomous answers. The weights were constructed from the matrix of factor loadings after rotation (Varimax rotation with Kaiser Normalization), given that the square of factor loadings represents the proportion of the total unit variance of the indicator which is explained by the factor21 [e.g. used by Nicoletty et al. 2000, OECD 2008]. In particular, individual indicators with the highest factor loadings were grouped into intermediate composite indicator.
19 Mazziotta M., Pareto A., Methods for Constructing Composite Indices: one for all or all for one? Rivista Italiana di Economia Demografia e Statistica, Volume LXVII n. 2 Aprile-Giugno 2013
2 OECD, Handbook on Constructing Composite Indicators, methodology and user guide, Paris, 2008, p. 162,
21 e.g. used by Nicoletti G., Scarpetta S. and Boylaud O., Summary indicators of product market regulation with an extension to employment protection legislation, OECD, Economic Departments Working Papers, No. 226, 2000, p. 86, OECD, Handbook on Constructing Composite Indicators, methodology and user guide, Paris, 2008, p. 162,
Table 4
Rotated Component Matrix_
Items Component
Factor 1 Factor 2
X1 Do you know the head of your community? X3 Do you know if the head of your community has adjacent advisory bodies? -.083 .175 .808 .405
X2 Do you know Local council (Avagani) members of your community? .082 .788
X5 Do you know where the LSG bodies of your community direct the revenue generated from local taxes, property rates, fees, fines and li- .682 .186
censes to?
X9 Do you know any assessment tool that the government uses to rate the performance of LSGs? X8 Are you familiar with the decisions passed at your LSG bodies? .692 .610 .066 .351
X7 Are you aware of the law on accountability of LSG bodies? X4 Do you know the budget of your community for 2015? X10 Do you know how local taxes, property rates, fees, fines and licenses are determined by the LSG? .611 .574 .466 .166 .058 .006
X6 Are you aware of the community consolidation process? .245 .529
Accordingly, the following aggregation formula was applied to aggregate individual items onto intermediate composite indicator (ICI):
k
ICI= ^wj XjL
j=i
where, is factor loading of each j individual items, individual items to be
aggregated into the given factor and k is number of individual items.
Two intermediate composites were aggregated by assigning a weight to each one of them equal to the proportion of the explained variance in the data set22.
Table 5
_Extraction Method: Principal Component Analysis_
Component Initial Eigenvalues Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 2.879 28.794 28.794 2.340 23.398 23.398
2 1.372 13.719 42.513 1.911 19.114 42.513
3 .953 9.528 52.040
4 .909 9.087 61.128
5 .795 7.954 69.081
6 .711 7.112 76.193
7 .656 6.563 82.756
8 .630 6.299 89.054
9 .575 5.753 94.808
10 .519 5.192 100.000
22 Ibid
Accordingly, the following aggregation formula was applied to calculate the final Composite Indicator for two Intermediate Composite Indicators: VariFl) . . Var1
CI =
The formula of calculation of final indicator is as follows: CI = 0.55*ICI(1) + 0.449*ICI(2),
where weights are calculated based on the proportion of the explained variance in the data set (Table 5)._
Figure 1. Distribution function of Cognition towards LSGs in 2015
Figure 2. Distribution function of Cognition towards LSGs in 2015
a
.00 1 00
Cognition Index (2015)
M
X
.4» i« 1 .CO 1.50 200 2.50 IK
Cognition Index (2019)
Final indicator of public awareness of LSGs is a number between [0 -3.02] with the following parameters and distribution: mean (2015)= 0.8988, Std. Deviation (2015) = 0.601, mean (2015)= 1.0418, Std. Deviation (2015) = 0.492,(Figure 1 and 2). Results and discussion
Figure 3
Ideal Types of Clusters
L / e v el of P ar ti ci P ^ Cluster II _ Fictional Participation Low cognition towards LSGs and high participation in LSGs Cluster IV _ Realized Participation High cognition towards LSGs and high participation in LSGs
Cluster I _ Insulated Citizens Low cognition towards LSGs and low participation in LSGs Cluster III _ Unrealized knowledge High cognition towards LSGs and low participation in LSGs
Level of Cognition towards LSG
Cluster Analysis: The general aim of identification of variable as an indica-
tor for participation in LSGs and construction of composite index for public awareness of LSGs is to combine these two dimensions into one analytic scheme to reveal underling structure of society. The simple matrix of combination of level of public awareness of LSGs and participation enables us to distinguish extreme types of clusters (Figure 3). The following hypothetic clusters were identified as ideal types of segregation: the first extreme type of cluster is "insulated citizens" which occurs when citizens have low level of cognition and participation in LSGs. Second type is characterized with low level of cognition and high level of participation, as type of "fictional participation", where citizens participate in LSGs actuality not having enough knowledge on them. This is especially typical for societies where participation being required by governmental procedures is forced by LSGs and voluntarism is lacking. The third group is cluster of "unrealized knowledge", when population has high level of knowledge, however, participation patterns are lacking. In the end, the forth cluster are those who have high level of knowledge on LSGs and high level of participation and is labeled as cluster of "realized participation".
We used constructed indicator of public awareness towards LSGs and selected variable of "participation during the meetings of local council (Avagani)" for cluster analysis. The selected variables include categorical (participation in Community Council Meetings with recoded scale [0, 1, 2, 3]) and continuous variable (awareness of LSGs). We assume that these variables are independent (as Kendall's tau=0.189, Sig. (2-tailed)=0.000, Spearman's coefficient =0.226, Sig. (2-tailed) =0.000) (based on 2015 database). TwoStep Cluster Analysis procedure using Schwarz's Bayesian Criteria (BIC) and log-likelihood distance measure technique was selected as cluster analysis method based on types of variables. In addition, the TwoStep Cluster Analysis procedure is an exploratory tool designed to reveal natural groupings (or clusters) within a dataset that would otherwise not be apparent. By comparing the values of a model-choice criterion across different clustering solutions, the procedure automatically determines the optimal number of clusters. The results were quite promising for both databases. Cluster Quality was estimated "good", average Silhouette = 0.6 for both 2015 and 2019.
Clustering Results for 2015 The importance of all predictor indicators was equal to "1.00". The cluster analysis identified three clusters in the society. The largest group (57%) includes citizens classified as "insulated" as they have low level of cognition towards LSGs and low level of participation. As for this group, the most frequent category of participation for 100% cases was "0" in line with the low level of cognition (mean = 0.48 of maximum 3.03). The second largest group comprising 40.5% of population is classified as group of "unrealized knowledge", are those having high level of cognition towards LSGs (mean=1.42 of maximum 3.03), however low level of participation in LSGs ("0" the most frequent category for 100% cases). The
smallest cluster members, 2.4% of population, are those having high level of cognition and high level of participation. The most frequent category of participation for this group was "almost always", mean for cognition towards LSGs - "2.09" out of maximum 3.03. Cluster having high level of participation and low level of cognition (fictional participation) as described in analytic plan was not identified (Figure 4).
Figure 4
Clusters Identified Within the Society in 2015: Twostep Cluster Analysis
Cluster analysis for 2019 The cluster analysis conducted for 2019 applying the same method as for 2015 revealed four groups. Two almost equal groups were identified: "Isolated Citizens" comprising 35.8% (mean of level of cognition towards LSGs equaling 0.53 out of maximum 3.03) and "Improved Knowledge" comprising 36.8% of total population (mean of level of cognition towards LSGs equaling 1.08 out of maximum 3.03). Representatives of both these groups do not participate in LSGs, however the representatives of the second group have a bit higher awareness than the first one which is a step forward compared to 2015. The next largest group is the one labeled "Unrealized knowledge" which comprises 25.6% of total population (mean of level of cognition towards LSGs equaling 1.64 out of maximum 3.03). The representatives of this group have higher awareness on LSGs, however do not participate in local governance. The smallest group is the "Realized Participation" group with 1.8% share in total population. The representatives of this group participate in local governance and have high level knowledge on LSGs (mean of level of cognition towards LSGs equaling 1.83 out of maximum 3.03). Compared to 2015 the share of this group decreased due to decreased participation of citizens in local governance (Figure 5).
Figure 5
Clusters Identified Within the Society in 2015: Twostep Cluster Analysis
Comparative analysis for 2015 and 2019
Mann-Whitney non parametric test revealed that there is a statistically significant difference for LSG cognition index comparing 2015 and 2019 (Asymp. Sig. (2-tailed)=.000)*. The awareness towards LSGs improved for the last five years (mean for 2015 equals "0.8988" and for 2019 equals 1.0418). However, as cluster analysis shows the improvement in awareness is not supplemented with the improvement in participation (2.45% "Realized Participation" in 2015 compared to "1.8%" in 2019). The share of the group labeled "Realized Participation" decreased for the last five years.
Conclusion
Thus, Application of Cluster analysis using two dimensions - cognition of LSGs and participation in Community Council meetings has revealed good ability to cluster the society. Three clusters have been identified comprising the society in 2015 and four clusters in 2019. Generally we revealed that the Armenian society consists of a population having low level of cognition towards LSGs and even lower level of participation in local self-government processes. Only 2.4% of the population were actually involved in the LSG process in 2015 and 1.8% in 2019, while 35.8% of the population had a low level of participation and low level of awareness of LSGs in 2019. 25.6% of the population has a high level of knowledge towards LSGs, however, practically they are not involved in LSG processes. In general, over the last five years the awareness towards LSGs improved, however participation decreased.
Key words: Community participation, public awareness, cluster analysis, measurement model, index construction
* One-Sample Kolmogorov-Smirnov Test indicated that the variable does not have normal distribution (Asymp. Sig. (2-tailed) = .000)
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СОНЯ МСРЯН - Информирование и участие общественности в местное самоуправление в Армении: сравнительный анализ на 2015г. и 2019 г. - Повышение информированности общественности и активного её участия в местном самоуправлении является ключевым компонентом инструментов управления, общественных организаций и социальных инициатив в Армении, направленных на достижение «благого управления» и укрепление демократии. Однако в армянском обществе отсутствует интерес к системе МСУ. Это исследование анализирует степень информированности общественности о МСУ и ее участия в нем в сравнительной перспективе на 2015 и 2019 годы. В частности, статья направленна на выявление структуры армянского общества с использованием двух основных измерений: осведомленность общественности о МСУ и участие в МСУ. По существующим переменным участие общественности в МСУ было невозможно измерить, в то время как результат измерения информированности общественности о МСУ был достаточным. Одна отдельная переменная была выбрана в качестве показателя участия в МСУ. Был создан индекс общественной информированности о МСУ. Кластерный анализ выявил три кластера, составляющих общество в 2015 году, и четыре кластера на 2019 год. Анализ показал, что подавляющее большинство населения имеет низкий уровень знаний о МСУ и участия общественности как в 2015 году, так и в 2019 году. В 2015 г. только 2,4 % населения имело высокий уровень участия и информированности о МСУ, который снизился до 1,8% в 2019 г.
Ключевые слова: Участие общественности, информирование общественности, кластерный анализ, индикатор измерения, создание индекса
V 10.03.2020, Орш^пи^ V 09.06.2020, ОЦт-ЦЬ^ t тщшцрт.р]ши' 24.07.2020
