Assessing progress in achieving WEE-related goals/targets

Transcription

1 National Workshop on Gender Policy-Data Integration in the context of Women s Economic Empowerment in the Philippines 1 st 3 rd August 2018, Manila Assessing progress in achieving WEE-related goals/targets Arman Bidarbakht Nia UNESCAP bakhtnia@un.org

2 Content Progress assessment a) Setting regional Target values b) Measuring progress Gender analysis

3 If you are interested to know As of now, where does the country/region stand for each of goals (e. g. SDGs)?

4 Asia-Pacific

5 If you are interested to know By 2030, how likely will the country/region be able to achieve the individual targets under each of the goals, judging by the pace of progress thus far?

6 Asia-Pacific

7 And need to know How much acceleration needed? How much we have regressed?

8

9 Need to specify. How far have we progressed? How much unfinished work is left? How far will be from here? How much acceleration is needed? Since 2000 Until Now Compared to Target value By 2030 By 2030 To reach Target value

10 Challenges.. Lack of data 2000, 2015, 2018, 2030 Lack of SDGs target values Comparability Visualization Methodology - For predicting 2018 and For indexing and assessment

11 Ingredients Data for Target values Predictions for 2018 & 2030 Measurement methods

12 Predictions for 2018 & 2030 Applied a time related weighting system for extrapolation More recent data, greater weights When data is available only during years t 1 and t n, Weights work as a multiplier to inflate the rate of change in each period in proportion to its temporal distance to the target year (t n +a )

13 Example

14 Target values 51 Specific target value 96 Clear direction 22 Not specified Distribution of SDG targets by specificity of target values?

15 Setting regional targets: Champion area Guiding Principles Data efficiency optimum use of scarce data Objectivity facilitate objective policy evaluation over time Feasibility aspirational, but realistic

16 Setting regional targets: Champion area Step1 Select one major indicator under target Step2 Step3 Step4 Create champion area (top five countries in terms of rate of change) Estimate average rate of change among top performers Apply average rate to the baseline value (2015) to reach target value for 2030

17 Setting regional targets: Champion area Example: Intentional homicide/per 100,000 population Macao, china Singapore French Polynesia Samoa Tonga Average annual rate of change (r) Regional target value= (1+r) 15 I base Champion area

18 Setting regional targets: Champion area Exceptions: Sufficient data are not available to estimate the rate of change Many countries started from a very low level and have made significant progress (e g. internet users) Alternative approach: latest available indicator value instead of rate of change

19 Measures for tracking progress Current status (takes into account previous progress) Progress made since 2000 in relation to the progress needed by 2030 Anticipated progress Assuming the same pace of progress, how close we can get to the target by the end of 2030?

20 Progress assessment: current status Step1- Normalize at the scale of 0 to 10 I N cr = current value 2000 value 2030 value 2000 value 10 Step 2- Average over indicators under each goal 0 I N cr 10 Or I N cr 0

21 current status: How it is done?

22 This is what you get

23 Progress assessment: anticipated progress Progress gap P = Target value 2030 value Target value 2015 value P 10 ( Will meet the target with current rate or minor extra effort) 10 < P < 100 ( Need to enhance the current rate of progress to achieve the target) P < 0 (Regression or no progress expected)

24 How is it actually done?

25 This is what you get

26 References: Asia and the Pacific SDG Progress Report 2017 Bidarbakhtnia. A. (2017): A weighted extrapolation method for measuring the SDGs progress, ESCAP working paper series SD/WP/04/March Bidarbakhtnia. A. (2017): Tracking progress towards the SDGs: measuring the otherwise ambiguous progress, ESCAP working paper series SD/WP/05/May 2017.

27 Statistical methods for WEE analysis

28 Beyond Gender Statistics Gender statistics are defined as statistics that adequately reflect differences and inequalities in the situation of women and men in all areas of life (United Nations, 2006) Why? By whom? to whom? Where? How? Gender Analysis

29 Gender Analysis: Use of gender statistics to understand patterns, sources and impacts of the gender issues o Conceptual framework dimensions factors issues groups o Hypothesis o Assumptions o Research questions o Data sources o Measurement issues o Methodologies Descriptive Inferential

30 Understanding patterns: descriptive Example: gender and health What is Conceptual framework? What are data sources and indicators? What are measurement issues? What descriptive statistics?

31 Conceptual framework Health & nutrition of children causes of death Maternal health HIV and AIDS life style

32 Health and nutrition of children Gender issues: Data needs: Measurement: -Survival chance: Non-biological factors -Health care discrimination -Nutritional status -Death (under 1&5) -Ever born & surviving (by age of mother) -Vaccination -Symptoms -Health exp -Nutritional status -Reporting error/bias -Rare events (wide CIs) -Age disaggregation (Nutritional status; age Statistics 2) Division 33

33 Maternal Health Gender issues: -Outcome: mortality -Access to prenatal care -Access to skilled personnel -Who are at more risk? Data needs : -Maternal death -Live birth -Visits to health facilities -Contraceptive use -Attendance by skilled personnel Measurement: -Misclassification -Rare events (wide CIs) -Concepts (types of contraceptive) -Underreporting (abortions) -Disaggregation (age, m.s., edu, geo, eco, hh,..) 34

34 HIV and AIDS Gender issues: -Risk: F vs M -Knowledge: M vs F -Youth: condom use (M vs F) Data needs (by age and sex): -Prevalence -Death -HIV testing -Access to medicine -High risk sexual behavior -Knowledge Measurement: -Sensitive topic - Sex bias in HIV testing in surveys - Bias towards socially desirable answers 35

35 Under 5 mortality rate per 1,000 live birth by sex, , and (Source: UN 2010) F M World Africa Asia Europe latin America & Caribbean Northen American Oceania 36

36 Maternal Mortality Ratio (MMR), World Latin America and the Caribean Asia Oceania Africa Source: WHO, Maternal Mortality in 2005 (2007)

37 Percentage of women among HIV-positive adults by region, 2001 and Source: UNAIDS, Report on the Global AIDS Epidemic (2008)

38 Understanding sources and impacts: inferential analysis Descriptive vs Inferential There is a difference Is it significant? More difference in Than Why? How? is it difference or discrimination? How does it impact?

39 Example: case of gender wage gap 4.0 Average wage per hour F M

40 Where the issue may be? 4.0 Average wage per hour 2.0 Average wage per hour F M Public (F) Private (F) Public (M) Private (M)

41 Inequality vs Discrimination

42 To what extent gender wage gap is due to discrimination? and to what extent difference in characteristics? What if there was no difference between the two? How much women actually gain from opportunities?

43 Example method: Oaxaca-Blinder model Assumption: The wage gap that cannot be explained by differences in quality and characteristics of men and women employees are due to discrimination Men wage= Men Qualification & characteristics + Unknown Factors Women wage= Women Qualification & characteristics + Unknown Factors

44 Example method: Oaxaca-Blinder model (Men wage - Women wage)= Men Qualification & characteristics Women Qualification & characteristics ( - ) + Women Qualification & characteristics + Men Qualification & characteristics + Unknown Factors Discrimination / advantage Discrimination / advantage Differences

45 Factor Difference Men gain Women loss Age No education Diploma College Postgraduate Widowed Single Urban Private sec

46 Thank You