About SCORE Moldova

The SCORE Moldova is an innovative analytical tool designed to improve the understanding of societal dynamics in the country. The SCORE Moldova is based on the Social Cohesion and Reconciliation Index methodology, which was originally developed in Cyprus by the United Nations Development Programme (UNDP) and the Centre for Sustainable Peace and Democratic Development. Since then, the SCORE has been implemented in several countries in Europe and elsewhere to assist international and national stakeholders in the design of evidence-based solutions that can contribute to strengthening social cohesion efforts. In Moldova, the SCORE process was launched in November 2016.

Moldova is a diverse and multi-ethnic country, with approximately a quarter of its population mostly Russian-speaking minorities. Overall social cohesion in the country is considered weak, and the society remains divided, primarily along geopolitical and ethno-linguistic fault lines. The ongoing settlement process with the Transnistrian region continues to impact the internal stability of Moldova, and the progress of the work of Moldovan-Gagauz parliamentary working group has yet to yield sustainable results.

Moldova is also experiencing some of the most challenging demographic issues in the region: low fertility rates, rather low life expectancy, an ageing population, and an estimated one-third of the working-age population currently working abroad. The urban-rural divide has deepened, with 86% of the country’s poor residing in rural areas. While Moldova has been implementing reforms under the European Union Association Agreement, the progress of these reforms has been uneven, hampered by political instability, external trade shocks, and issues linked with corruption.

Although, given this challenging context, it is widely accepted that social cohesion is weak in Moldova, a common understanding of the underlying causes of the dividing and unifying forces in society has yet to be reached. The UN in Moldova launched the SCORE process to build this understanding, measure social cohesion, provide evidence to the government and other key actors, and inform well-targeted programming that can strengthen social cohesion. The process is led by the United Nations Office of the Resident Coordinator (UNRCO), together with its local partner, the Association for Participatory Democracy (ADEPT), and implemented by four UN agencies, the United Nations Development Programme (UNDP), the United Nations Children’s Fund (UNICEF), the United Nations Population Fund (UNFPA), and the United Nations Entity for Gender Equality and the Empowerment of Women (UN Women).

The SCORE Moldova process is of essential importance as the country undertakes its 2030 development agenda and strives to meet the Sustainable Development Goals. Furthermore, it is an opportune moment for the SCORE, as the Government and the UN system in Moldova implements the new Republic of Moldova – UN Partnership Framework for Sustainable Development (UNDAF) 2018–2022.

SCORE Process in Moldova

The SCORE is based on interdisciplinary scientific research, combining sociology, psychology and political theories, and is flexible enough to incorporate new research findings, global policy guidelines, and the realities of each local and regional context. The SCORE can also flexibly integrate different modalities of data collection as required, including surveys, discourse analysis, expert assessments, and draws its strength from advanced analytical and statistical toolkits.

The SCORE process is founded upon participatory research and begins with inclusive consultations with a broad cross-section of national stakeholders, such as civil society, academia, government, business leadership, and local communities (Figure 1). These consultations contribute to an initial in-depth understanding of societal dynamics, informing the conceptual model for social cohesion in a particular country and the calibration of the SCORE questionnaire, which is constant across countries but adjusted to meet the idiosyncratic differences of each context.

Figure 1 below illustrates the SCORE process cycle.

Large SCORE sample frames are designed in a way to ensure that results can be reported with a high level of confidence for different sub-regions within the country, but also for distinct societal groups of interest. The actual fieldwork is usually conducted in collaboration with established national researchers or research agencies. Results are processed using advanced data analysis techniques, including factor analysis, ANOVA analysis, regression, and structured equation modelling, from which robust metrics are designed for multiple indicators. These are then translated into network analysis and predictive models that can reveal the intricate relationship between different indicators and groups. This modelling process is used to suggest effective entry points to design evidenced-based projects and policies.

The SCORE Moldova process, with a focus on both youth and adults, was initiated in 2016. The first and pilot phase of the SCORE Moldova was calibrated in late 2016 and early 2017, with stakeholder consultations and numerous regional focus groups conducted across the country, including in Gagauzia. Following the completion of the calibration process and conceptual modelling, the SCORE Moldova questionnaire for adults was designed, translated, and piloted. The quantitative fieldwork for the general population data stream was conducted between October 2017 – January 2018 through face-to-face interviews with 1558 respondents aged 18 and above. The questionnaire for youth was finalized at the beginning of 2018, and the data collection was conducted between March – April 2018 through face-to-face interviews with 1233 respondents aged 14-18. The general population survey field work was conducted by CIVIS, and the youth survey field work was conducted by CBS-AXA, both local survey companies.

Key Elements of the SCORE Moldova
The SCORE Moldova builds evidence to address issues within five key outcome areas (Figure 2):

  • Fostering constructive civic engagement – assessing the degree to which communities and authorities encourage participation as well as assessing to what extent individuals possess the skills to participate and are motivated to participate;
  • Reversal of brain drain and emigration tendency – assessing perceptions in regard to availability of economic opportunities, access to services, and national identity;
  • Sharing a human rights ethos – assessing levels of internalization and respect towards universal human rights as well as the level of engagement in actively promoting universal human rights;
  • Prevention of estrangement tendencies in Gagauzia – assessing the level of social cohesion within Gagauzia as well as with the rest of Moldova;
  • Support for gender equity and inclusion – assessing the perceptions of the state of gender equity and gender inclusion in Moldova.

Figure 2. Simplified Conceptual Model for Social Cohesion in Moldova

The SCORE Vocabulary
  • Simplified conceptual model: Theory of change and system map of different indicators, assumptions, and societal dynamics designed based on multi-level stakeholder consultations simplified into a concise illustration. Initial conceptual models use fuzzy cognitive mapping and system mapping tools (e.g. mental modeller) to map assumptions and hypothesis about multiple theories of change that guide the design of the SCORE indicators and questionnaire.
  • Outcomes of interest: Identified desirable and high priority normative objectives that relate to assessing and fostering social cohesion in a given context (i.e. intergroup harmony, mitigating violent tendencies, fostering a human rights ethos). These outcomes are listed under the dimensions section of the SCORE Platform but should not be confused with categorical/thematic groupings. The outcomes of interest that make up the key components of social cohesion for Moldova are illustrated in the figure above. The other dimensions listed on the SCORE platform are categories that group different indicators thematically.
  • Indicators: The components of the conceptual model are translated into metrics and indicators that are quantifiable and measurable via public opinion polls. Each indicator that is measuring a particular phenomenon (e.g. economic security, discrimination towards out groups, belief in human rights, support for certain policy options, post-traumatic stress disorder, etc.) is usually assessed with minimum 3 questionnaire items, scaled following reliability tests, to ensure that the SCORE can robustly capture different dynamics underlying the given indicator.
  • Drivers/Predictors: Indicators that have a strong positive or negative impact on the outcome of interest are called drivers or predictors. They provide strategic entry points that hold the most likelihood of impact on the desired outcome of interest. These can be seen under the path analysis section.
  • Heatmaps: A score is calculated for each indicator. The scores range from 0 to 10, where 0 means that the phenomenon the indicator is measuring is not observed in the context at all, and 10 means that it is observed strongly and prevalently. Heatmaps demonstrate the regional differences of these scores in order to identify areas of concern and tailor interventions more precisely. For example, the Personal Security indicator is measured through the following questions in Moldova:
    • To what extent do you feel safe from violence in your daily life?
    • To what extent do you feel confident that the police or other institutions can protect you from violence?
    • To what extent would you feel safe walking alone in the street at night?
    A score of 0 for personal security would mean that no one in a given region feels secure at a personal level, while 10 would signify that every person feels absolutely secure. As such, a heatmap of personal security for Moldova illustrates the scores for each of the 5 development regions covered by the SCORE (Chisinau, North, Centre, South, and Gagauzia), allowing for an understanding of the regional variance in terms of personal security across the country.
  • Path analysis (predictive models): Based on advanced statistical analysis including regression, network analysis and structural equation modelling, predictive models investigate the relationship between different indicators and the outcomes of interest. Predictive models reveal those indicators that may have a reinforcing or mitigating influence on other indicators as well as the outcome. While the first wave of the SCORE can be used for identifying directional correlations and benchmarking, second and third waves of the SCORE, where temporal comparisons are possible, can help identify trends and causal relationships. The colour of the connecting lines represents the nature of the relationship - blue symbolizes a positive correlation, and red symbolizes a negative correlation. The thickness of the lines represents the strength of the correlation – the thicker the line, the stronger the relationship. This section provides a brief preview of the SCORE methodology. To explore the SCORE methodology in more detail, you can download the SCORE book or explore other SCORE publications.