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Vulnerability analysis: Methodologies, Purpose, and Policy Application

Vulnerability analysis: Methodologies, Purpose, and Policy Application. Susanne Milcher Specialist, Poverty and Economic Development UNDP Regional Centre Bratislava (14 June 2006). Outline. The need of analysis and data for policy/project design and monitoring

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Vulnerability analysis: Methodologies, Purpose, and Policy Application

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  1. Vulnerability analysis:Methodologies, Purpose, and Policy Application Susanne Milcher Specialist, Poverty and Economic Development UNDP Regional Centre Bratislava (14 June 2006)

  2. Outline • The need of analysis and data for policy/project design and monitoring • Qualitative versus quantitative approach • Examples of both approaches: • Semi-structured interviews/focus groups on people living with HIV/Aids • Household survey on Roma and the displaced

  3. Need of vulnerability analysis • How can vulnerable groups, their causes of exclusion and particular needs be identified without disaggregated quantitative data (multidimensional aspects, double marginalisation)? • How can national-level policies that aim ensuring the sustainable inclusion of vulnerable groups be designed and resources be allocated without estimates of their size, problems and causes of problems? • How can priorities and sector capacity deficiencies be identified without complementary in-depth qualitative research? • How can policies be monitored and evaluated on their impact on vulnerable groups without data?

  4. The need of analysis • What kind of analysis? • Socioeconomic status • Human rights aspects • Legal aspects (frameworks) • Who elaborates it? • The issue of trust and credibility • Participation • Who is its target? • Public en large • National governments • International organizations/partners

  5. The role of data/indicators • Relevant profiles of vulnerability in the region are necessary for adequate programmatic and policy responses • Those most in need remain “hidden” behind the national averages • Without a clear picture of the status and determinants of exclusion and/or discrimination, actions are rather intuitive • Can any analyses, resource allocation and policy be serious if not backed by data? • Setting targets, baselines • Monitoring the progress • Measuring the outcomes • Assessing the impact

  6. Quantitative versus qualitative approach • Quantitative approach • Theory, hypotheses (i.e. women are more vulnerable to poverty because they face higher unemployment, lower education, discrimination, etc.) • Indicators needed (employment rate, unemployment rate, poverty rate, educational attainments) • Data collection (labour force survey) – questions on employment activity, income in the last month, job search, educational attainments and enrolment – individual level data to be able to account for sex, age, ethnicity, etc.) • Goal => Status registration, correlations and causal links

  7. Quantitative versus qualitative approach (cont.) • Qualitative approach • Social reality, social constructs • The meaning and reasons of human actions and decision-making result from interaction and therefore can only be observed through understanding the social structures determining these actions • Information/data collection (interviews, observation, focus groups) – e.g. information on the extent and types of discrimination, quality of social services, satisfaction of beneficiaries • Theory, hypotheses development • Goal => Perceptions and attitudes registration, priority identification

  8. Characteristics of quantitative research • Representative and comparable • Causal analysis, identification of inequalities • Researcher pre-determines the communication (close) • Limited participation • Hypotheses, questionnaires, (random) sampling, fieldwork • Objective and distanced analysis • Conclusions based on a statistical logical analysis (deductive)

  9. Characteristics of qualitative research • Interactive and communicative • Hypotheses developing • Interpretative, understanding linkages • Dynamic and flexible process • Subjective • Theoretical sampling • Explanatory data analysis • Conclusions based on repeated experiences (inductive)

  10. Criticism to both approaches • Qualitative: • - sample too small (1-5 people or single case study) • - analysis not representative, subjective • - cannot make generalizations • Quantitative: • - distance to reality • - reductive • - limited participation or dynamic interaction

  11. Which approach to choose? • Choose the approach that better fits to the type of information you want to get (status or perceptions) and the need for this information (resource allocation, priority setting, causal analysis) • Combining both approaches for proper vulnerability analysis possible but time and cost-extensive • Better use existing data and research, proxies • Both approaches have to be adapted to objective of research and social reality (i.e. MDG indicators, questions to address gender or issues,sampling and fieldwork; focus group design, types of questions asked, moderation)

  12. Example: People living with HIV/Aids • Type of information collected • Perceptions of people living with HIV/Aids and relevant stakeholders on the type of challenges for this group and the institutions in terms of access, quality and availability to health care, education, employment • Process • Identification of participants, close cooperation with NGOs working with target community • Develop focus groups/interviews sensitive and responsive to different sub-groups (IDUs, men having sex with men, sex workers and parental infected children) • Questions and moderation • Transcript processing and analysis

  13. Example: Roma and displaced household survey (cont.) • Type of information collected • Status of Roma, displaced (IDPs/refugees) and majority living in close proximity and determinants of vulnerability • Process • Two separate questionnaires (status of the household and of each individual member) • Sampling – households in areas with compact Roma population (municipalities or neighborhoods with share of Roma population at and above the national average), majorities living in close proximity to Roma and IDPs/refugees where relevant • Fieldwork (interviewer training, Roma assistant interviewers) • Data clean up, processing and analysis

  14. Example: Roma and displaced household survey (cont.) • MDG indicators: poverty rate, enrolment rate, maternal and infant mortality rate, access to water and sanitation • Social exclusion indicators: (long-term) unemployment rate, ethnic and gender ratio of unemployment, items in household, political participation, access to health and credit services, land • Vulnerability profiles of all members of the household (special needs of elderly, women, children, low educated, unemployed, poor) • Comparability across countries • Comparability to national HBS and LFS could give an idea of the distance from national averages • Data on the status of “non-Roma living in close proximity” could give an idea of the non-group related determinants of vulnerability

  15. The issue of “Schools for disabled” - SEE

  16. The issue of “Schools for disabled” – a broader picture

  17. The issue of “Schools for disabled”

  18. Thank you! • Bratislava Regional Center • 35 Grosslingova • 81109 Bratislava, Slovak Republic • +421 2 59337 111 • www.undp.org/europeandcis • http://roma.undp.sk • http://vulnerability.undp.sk

  19. Education gender gap, Macedonia

  20. Correlation between occupation and skill level

  21. Unemployment by skill level

  22. Unemployment by age

  23. Unemployment by sex

  24. Vicious circle of outstanding payments

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