Page 141 - IJPS-11-6

International Journal of
Population Studies Social inclusion for refugees

Table 3. The description of the main indicators of integration and social support for refugees
Factor Explanation Indicator Symbol
Level of social support Access to social services (%) Percentage of refugees with access to social services (housing, medical care, and Х 1
social benefits)
Amount of social assistance (€) Average amount of benefits or social assistance received by refugees Х
2
Access to integration Percentage of refugees participating in language courses and adaptation Х 3
programs (%) programs
Demographic Distribution by age groups (%) Youth: 15 – 35 years old Х 4
characteristics Middle-aged and elderly: 36 years and older Х
5
Gender (%) Women or men (choose one) Х
6
Education (%) Primary and secondary education Х
7
Higher education Х
8
Length of stay Average duration of residence Average length of stay of refugees in the country Х 9
(years)
Host country policy Composite index assessing Migration policy index Х 10
migration policy (units)
Legislation and regulations Number of laws to support migrant integration (e.g., access to citizenship and Х 11
employment)
Employment rate of Employment rate (%) Percentage of refugees officially employed in the host country Х 12
refugees Employment stability (%) Percentage of employed refugees working on permanent contracts Х
13
Level of language Language test (%) Percentage of refugees who have successfully passed language proficiency tests Х 14
proficiency in the host Participation in language Percentage of refugees who have completed language courses Х
country courses (%) 15
Level of involvement in Participation in social Percentage of refugees participating in community and volunteer organizations Х 16
social activities organizations (%)
Social ties (%) Percentage of refugees with local social ties (e.g., participation in local events) Х
17
Table 4. Data sources of information

Data source Information used
EUR-Lex: Council Implementing Decision (European Union) 2022/382 Scope and nature of assistance provided to Ukrainian
refugees
United Nations International Organization for Migration: Displacement Tracking Matrix Internal displacement data and demographic
characteristics of refugees
Organization for Economic Co-operation and Development: The potential contribution of Employment data and employment potential of refugees
Ukrainian refugees to the labor force in European host countries
Rights and support for Ukrainian refugees in receiving countries Refugee reception policies in different countries
Ukrainian Crisis Situational Analysis Humanitarian aid data and demographic characteristics

to classify the host countries into distinct groups (Fusco Where K is the number of clusters, x is the data point
i
and Perez, 2019). This algorithm is widely used for its for each country (vector of indicators), C is the set of
k
simplicity, computational efficiency, and ability to handle countries in cluster k, and μ is the centroid of cluster C .
k
k
large datasets. K-means clustering works by minimizing The advantages of the algorithm include its speed and
within-cluster variance while maximizing between-cluster ease of implementation, particularly for partitioning large
variance, making it an effective tool for grouping similar datasets into distinct groups. In addition, its iterative
data points. approach ensures the optimization of cluster centroids,
K-means objective function: facilitating meaningful classifications.
The elbow method was employed to determine the
K
2
Minimize ∑ k= 1∑ i C∈ k  x − µ  (III) optimal number of clusters, balancing explained variance
k
i
Volume 11 Issue 6 (2025) 135 https://doi.org/10.36922/ijps.4502