Part 1:Measuring the Global Gender Gap Table 2:Calculation of weights within each subindex ECONOMIC PARTICIPATION AND OPPORTUNITY SUBINDEX Ratio Standard deviation Standard deviation per 1%point change Weight Ratio:female labour force participation over male value 0.160 0.063 0.199 Wage equality between women and men for simiar work (converted to female-over-male ratio) 0.103 0.097 0.310 Ratio:female estimated eamned income over male value 0.144 0.069 0.221 Ratio:female legislators,senior officials and managers over male value 0.214 0.047 0.149 Ratio:female professional and technical workers over male value 0.262 0.038 0.121 T0 TA EDUCATIONAL ATTAINMENT SUBINDEX Ratio Standard deviation Standard deviation per 1%point change Weight Ratio:female literacy rate over male value 0.145 0.069 0.191 Ratio:female net primary enrolment rate over male value 0.060 0.167 0.459 Ratio:female net secondary enrolment rate over male value 0.120 0.083 0.230 Ratio:female gross tertiary enrolement ratio over male value 0.228 0.044 0.121 TOTAL. 1 HEALTH AND SURVIVAL SUBINDEX Ratio Standard deviation Standard deviation per1%point change Weight Sex ratio at birth (converted to female-over-male ratio) 0.010 0.998 0.693 Ratio:female healthy life expectancy over male value 0.023 0.441 0.307 TOTAL.. 1 POLITICAL EMPOWERMENT SUBINDEX Ratio Standard deviation Standard deviation per 1%point change Weight Ratio:females with seats in parliament over male value 0.166 0.060 0.310 Ratio:females at ministerial level over male value 0.208 0.048 0.247 Ratio:number of years with a female head of state (ast 50 years)over male value 0.116 0.086 0.443 TOTAL. .1 Note:Calculations are based on the Global Gender Gap Report 2006. Calculate subindex scores subindex than an indicator with a larger variability,such as The third step in the process involves calculating the tertiary enrolment rate.Therefore,a country with a large weighted average of the indicators within each subindex gender gap in primary education(an indicator where most to create the subindex scores.Averaging the different countries have achieved near-parity between women and indicators would implicitly give more weight to the measure men)will be more heavily penalized.Similarly,in the case that exhibits the largest variability or standard deviation.We of the sex ratio indicator(within the Health and Survival therefore first normalize the indicators by equalizing their subindex),where most countries have a very high sex ratio standard deviations.For example,within the Educational and the spread of the data is small,the larger weight will Attainment subindex,standard deviations for each of the penalize more heavily those countries that deviate from this four indicators are calculated.Then we determine what a value.Table 2 displays the values of the weights used.4 1%point change would translate to in terms of standard deviations by dividing 0.01 by the standard deviation for Calculate final scores each indicator.These four values are then used as weights In the case of all subindexes,the highest possible score is to calculate the weighted average of the four indicators. 1 (equality)and the lowest possible score is 0(inequality), This way of weighting indicators allows us to make thus binding the scores between inequality and equality sure that each indicator has the same relative impact benchmarks.5 An un-weighted average of each subindex on the subindex.For example,an indicator with a small score is used to calculate the overall Global Gender Gap variability or standard deviation,such as primary enrolment Index score.As in the case of the subindexes,this final rate,gets a larger weight within the Educational Attainment value ranges between 1 (equality)and 0(inequality),thus 6 The Global Gender Gap Report 2015Part 1: Measuring the Global Gender Gap 6 | The Global Gender Gap Report 2015 Calculate subindex scores The third step in the process involves calculating the weighted average of the indicators within each subindex to create the subindex scores. Averaging the different indicators would implicitly give more weight to the measure that exhibits the largest variability or standard deviation. We therefore first normalize the indicators by equalizing their standard deviations. For example, within the Educational Attainment subindex, standard deviations for each of the four indicators are calculated. Then we determine what a 1% point change would translate to in terms of standard deviations by dividing 0.01 by the standard deviation for each indicator. These four values are then used as weights to calculate the weighted average of the four indicators. This way of weighting indicators allows us to make sure that each indicator has the same relative impact on the subindex. For example, an indicator with a small variability or standard deviation, such as primary enrolment rate, gets a larger weight within the Educational Attainment subindex than an indicator with a larger variability, such as tertiary enrolment rate. Therefore, a country with a large gender gap in primary education (an indicator where most countries have achieved near-parity between women and men) will be more heavily penalized. Similarly, in the case of the sex ratio indicator (within the Health and Survival subindex), where most countries have a very high sex ratio and the spread of the data is small, the larger weight will penalize more heavily those countries that deviate from this value. Table 2 displays the values of the weights used.4 Calculate final scores In the case of all subindexes, the highest possible score is 1 (equality) and the lowest possible score is 0 (inequality), thus binding the scores between inequality and equality benchmarks.5 An un-weighted average of each subindex score is used to calculate the overall Global Gender Gap Index score. As in the case of the subindexes, this final value ranges between 1 (equality) and 0 (inequality), thus Table 2: Calculation of weights within each subindex ECONOMIC PARTICIPATION AND OPPORTUNITY SUBINDEX Ratio Standard deviation Standard deviation per 1% point change Weight Ratio: female labour force participation over male value 0.160 0.063 0.199 Wage equality between women and men for similar work (converted to female-over-male ratio) 0.103 0.097 0.310 Ratio: female estimated earned income over male value 0.144 0.069 0.221 Ratio: female legislators, senior officials and managers over male value 0.214 0.047 0.149 Ratio: female professional and technical workers over male value 0.262 0.038 0.121 TOTAL.........................................................................................................................................................................................................................................1 EDUCATIONAL ATTAINMENT SUBINDEX Ratio Standard deviation Standard deviation per 1% point change Weight Ratio: female literacy rate over male value 0.145 0.069 0.191 Ratio: female net primary enrolment rate over male value 0.060 0.167 0.459 Ratio: female net secondary enrolment rate over male value 0.120 0.083 0.230 Ratio: female gross tertiary enrolement ratio over male value 0.228 0.044 0.121 TOTAL.........................................................................................................................................................................................................................................1 HEALTH AND SURVIVAL SUBINDEX Ratio Standard deviation Standard deviation per 1% point change Weight Sex ratio at birth (converted to female-over-male ratio) 0.010 0.998 0.693 Ratio: female healthy life expectancy over male value 0.023 0.441 0.307 TOTAL.........................................................................................................................................................................................................................................1 POLITICAL EMPOWERMENT SUBINDEX Ratio Standard deviation Standard deviation per 1% point change Weight Ratio: females with seats in parliament over male value 0.166 0.060 0.310 Ratio: females at ministerial level over male value 0.208 0.048 0.247 Ratio: number of years with a female head of state (last 50 years) over male value 0.116 0.086 0.443 TOTAL.........................................................................................................................................................................................................................................1 Note: Calculations are based on the Global Gender Gap Report 2006