European Institute for Gender Equality (EIGE)
ICF
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European Institute for Gender Equality, Gedimino pr. 16, LT-01103 Vilnius, Lithuania
+370 5 215 7444
28/11/2019
In 2017 EIGE conducted a study on the Economic Benefits of Gender Equality in the European Union. The study is the first of its kind to use a robust econometric model to estimate a broad range of macroeconomic benefits of gender equality in several broad policy areas such as education, labour market activity and wages. It also considers the demographic consequences of such improvements.
The study used the E3ME macroeconomic model to estimate the economic impacts of improvements in gender equality. E3ME is an empirical macroeconomic model tailored specifically to model outcomes at EU and Member-State levels. The model includes a detailed representation of the labour market and captures interactions at sectoral and national levels. It is a model widely acknowledged as suitable for modelling economic trends at EU level — for example, Cedefop uses this model for its skills forecasts (https://www.cedefop.europa.eu/en/events-and-projects/projects/skills-forecast/data-visualisations).
The Study modelled the social and economic impacts of the following five main pathways/outcomes that were identified to have significant macroeconomic impacts at EU level. The term ‘pathway’ refers to a certain gender inequality, for which at least a theoretical link to macroeconomic performance has been established in literature. The term ‘outcome’ refers to potential consequences of gender equality (i.e. change in fertility) that can affect the performance of the economy.
In addition, the Study developed a baseline scenario, which forecasted economic development assuming that no additional improvements in gender equality will be achieved beyond what could be expected based on recent historical trends. All results in the Study were presented as (absolute, percentage or percentage points) difference from the baseline.
Two versions of the forecasts are available for each pathway: a slow progress and a rapid progress scenario. The ‘slow-progress’ scenarios assumed some additional, gradual improvement in gender equality compared to the baseline. Five “slow progress” scenarios were developed, one for each pathway. The ‘rapid-progress’ scenarios assumed considerable, swift additional improvement in gender equality compared to the baseline. Five “rapid progress” scenarios were developed, one for each pathway.
The time frame of the forecasts is from 2030 to 2050.
Forecasts are presented at the EU28 level.
For further methodological details, users are advised to consult the following resources available on the EIGE website:
“How the evidence was produced: briefing paper on the theoretical framework and model”: https://eige.europa.eu/publications/economic-benefits-gender-equality-how-evidence-was-produced-briefing-paper-theoretical-framework-and-model
“Economic benefit of gender equality in the EU: report on the empirical implementation of the model”: https://eige.europa.eu/publications/economic-benefits-gender-equality-european-union-report-empirical-application-model
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The Study was conducted at the economy-wide level for EU28 countries.
The gender education gap is defined as the proportion of women graduates in the total number of graduates divided by the corresponding proportion of men deducted from 1.
If the gender education gap equals to one the educational field is completely dominated by men; if it is zero, there is equal share of men and women; and if it is negative there is more women than men among graduates.
The gender gap in labour market activity rates is defined as a ratio between activity rate of women aged 20 to 64 (i.e. the percentage of employed and unemployed women aged 20 to 64 in relation to the total population of women aged 20 to 64) and activity rate of men aged 20 to 64 (i.e. the percentage of employed and unemployed men aged 20 to 64 in relation to the total population of men aged 20 to 64)[1], deducted from 1.
If the gender gap in labour market activity rates equals one, only men are active in the labour market; if it is zero, there is equal share of men and women; and if it is negative, there is more women than men in the labour force.
Gender pay gap
The Study uses the unadjusted gender pay gap, which is defined according to the Eurostat methodology (http://ec.europa. eu/eurostat/statistics-explained/index.php/Gender_pay_gap_ statistics) as ‘the difference between the average gross hourly earnings of men and women expressed as a percentage of the average gross hourly earnings of men’ and is computed as the average gross hourly earnings of women divided by the average gross hourly earnings of men, deducted from 1.
If the gender pay gap is positive, men earn more than women; if it is zero, there is an equal share of men and women; and if it is negative women earn more than men. The maximum positive value of the gender pay gap is 1, but there is no negative limit to its value.
Fertility rates
The definition of fertility rate adopted in the Study is: the mean number of children that would be born alive to a woman during her lifetime if: (1) she were to experience the exact current age-specific fertility rates; and (2) she were to survive from birth through the end of her reproductive life. The total fertility rate is obtained by summing the single-year age-specific rates at a given time.
GDP per capita at constant (2014) prices
Gross Domestic Product (GDP) per capita at constant prices refers to the level of gross domestic product expressed in terms of the price terms of a base period (normally a year) (See Eurostat glossary). The base year for the GDP per-capita measure used in the Study was 2014.
Consumer spending at constant (2014) prices
According to the OECD definition, consumer spending is the of final consumption expenditure made by resident households to meet their everyday needs, such as food, clothing, housing (rent), energy, transport, durable goods (notably cars), health costs, leisure, and miscellaneous services. Consumer spending is computed at constant prices, i.e. it is expressed in terms of the level of the price level of the year 2014.
Consumer prices
Consumer prices are the prices of final goods and services that a reference population acquires, uses or pays for consumption (see the OECD definition).
Investments at constant (2014) prices
Industrial investment is defined as gross fixed capital formation. According to Eurostat’s definition, this consists of resident producers’ investments, deducting disposals, in fixed assets during a given period. It also includes certain additions to the value of non-produced assets realized by producers or institutional units. Fixed assets are tangible or intangible assets produced as outputs from production processes that are used repeatedly, or continuously, for more than one year.
Investments values reported in the Study are at constant 2014 prices, i.e. they are expressed in terms of the price level of the year 2014.
Exports at constant (2014) prices
Exports of goods and services are defined as the total value of goods leaving the statistical territory of a country (typically its economic territory). See the Eurostat’s definition for further details.
Exports values reported in the Study are at constant 2014 prices, i.e. they are expressed in terms of the price level of the year 2014.
Imports at constant (2014) prices
Imports of goods and services are goods which add to the stock of material resources of a country by entering its statistical territory (typically its economic territory). See the Eurostat’s definition for further details.
Imports values reported in the Study are at constant 2014 prices, i.e. they are expressed in terms of the price level of the year 2014.
Labour force
Total number of employed and unemployed persons (see https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Labour_force)
Employment level
Eurostat defines the employment level as the number people engaged in productive activities in an economy. The concept includes both employees and the self-employed. The two main measures used for employment are the number of persons employed or the number of employees.
Employment rate
Total number of employed persons as a share of comparable total population (https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Employment_rate )
Unemployment rate
Total number of unemployed persons as a share of the labour force. See https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Unemployment for a formal definition
The analysis was conducted at the country-level (EU28 countries). However, results, both in the Study and in the EIGE’s Gender Statistics Database are aggregated the EU28 level. GDP per-capita results are aggregated at the country-cluster level (see section 3.1 for the definition of the clusters).
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EU28
Forecasts are available from 2030 to 2050
The following variables are computed at constant prices, with 2014 as base year:
For the other results, a base period is not applicable.
Results in the Gender Statistics Database are presented as difference from the baseline scenario. This can be absolute difference, percentage and percentage points difference from baseline
The reference period for the forecasts is 2030-2050. However, GDP per-capita at the country-cluster level, consumer spending, investments, exports and imports forecasts, are available only for 2030 and 2050.
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All results and publications from the Study are available on the EIGE’s website and can be consulted at this link:
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No regular new releases.
All the publications related to the study are available at the following link: https://eige.europa.eu/gender-mainstreaming/policy-areas/economic-and-financial-affairs/economic-benefits-gender-equality
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Available details on the macroeconomic model used in the Study, the parameters used for the simulation of pathways and outcomes are available in the report on the empirical implementation of the model, at this link: https://eige.europa.eu/publications/economic-benefits-gender-equality-european-union-report-empirical-application-model In particular, users should refer to Annex 4, devoted to the description of the model equations.
Moreover, specific details on the theoretical model are available in the briefing paper “How the evidence was produced: briefing paper on the theoretical framework and model”, available at this link: https://eige.europa.eu/publications/economic-benefits-gender-equality-how-evidence-was-produced-briefing-paper-theoretical-framework-and-model
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The E3ME model consists of various econometric equations, each using a data set of annual time series that date back to 1970.
The results from the estimation have been tested using standard measures of fit and tests for significance. The Akaike Information Criterion (AIC) was used to select the equation specification that best fits and explains the historical data.
The robustness of each individual econometric equation in the model was formally tested by constructing confidence intervals. Moreover, the R-squared value was uses to assess to what extent the estimated equations explain variation in the data.
Based on the R-squared, most of the econometric equations that are used for EU Member States have high explanatory power. For example, the average adjusted R-squared value for the consumption equations (by EU Member States) is over 92 % in the long term. The equations in E3ME use time-series data, where R-squared values are typically high (90 % or higher) as they pick up trends in the historical data. Adjusted R2 is used to adjust for the number of parameters in the model, as the R-squared value increases with the number of parameters included.
Notice there is no equivalent method to for estimating robustness or explanatory power of the modelling system as a whole — i.e. how well the equations fit together.
The following limitations of the E3ME modelling approach are highlighted in the Study, and should be taken into account when interpreting and re-using the results:
The publication of the results of the EIGE “Study on the Economic benefits of gender equality in the European Union” provides researchers and policymakers with robust and consistent statistical evidence (see sections 11.1 and 11.2), which comes from an accredited source. These results can inform further research on gender equality and foster the policy debate on gender mainstreaming.
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Forecasts are available only at the EU28 level and at the country-cluster level (for GDP per-capita forecasts only).
Where relevant, results are also disaggregated by sex.
Results are available for the time period 2030-2050, but for the following variables they are available only for 2030 and 2050:
A potential source of inaccuracy mentioned in the Study may result from the underlying data sources used in the model.
As highlighted in section 11.2, the model only considers data harmonised across EU Member States that are available over long historical periods. Previous revisions to published data have shown that there is uncertainty in the information held in the model’s historical databases
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Results computed at the country-cluster level are comparable across country-clusters as they were estimated using the same modelling approach and data sources.
Full comparability over time as the forecasts for each year are generated using the same modelling approach and data sources.
At the time of publication of the results in the Gender Statistic Database, the Study is the only one providing robust econometric evidence on the economic benefits of gender equality policies. Hence, there is no other publication or data to which the Study’s estimates can be compared.
The results published in the Gender Statistics Database correspond to those published in the Study. Forecasts for the labour market variables (employment, employment rates, unemployment rates and labour force) are only available for 2030 and 2050.
Moreover, while for most indicators results are available at the EU28 level only, GDP-per capita results are also available at the country-cluster level (see section 3.1 for the definition of the country-clusters).
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Results from the Study have been thoroughly quality-assured by the study team and a panel of experts before publication.
There is no fixed revision schedule.
The data sources of the E3ME model are:
Eurostat Labour Force Survey for the following variables:
National Account branch of the Eurostat database for the following variables:
DG Ecfin AMECO database
Furthermore, population data were taken from the Eurostat Database and the technology index was derived from Eurostat National Account investment data combined with Research and Development data (see E3ME manual for formal definition).
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No specific data collection was conducted for this Study. The Study used publicly available data sources (see section 18.1)
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The E3ME model consists of different equations to model the entire economy and the labour market. For the purpose of the analysis carried out in the EIGE’s Study on the Economic Benefits of Gender Equality in the European Union, the model was modified to include gender-specific labour market equation.
The full specification of the equation of the model can be found in the E3ME manual. The gender-specific labour market equations are described in Section 2, Annex 4 of the report on the empirical implementation of the model, available at: https://eige.europa.eu/publications/economic-benefits-gender-equality-european-union-report-empirical-application-model
The Study modelled the social and economic impacts of the following five main pathways/outcomes that were identified to have significant macroeconomic impacts at EU level. The term ‘pathway’ refers to a certain gender inequality, for which at least a theoretical link to macroeconomic performance has been established in literature. The term ‘outcome’ refers to potential consequences of gender equality (i.e. change in fertility) that can affect the performance of the economy. The main assumptions related to each pathways and the parameters for the slow and rapid progress scenario under each pathway are listed below:
The key modelling assumptions for this pathway are:
The slow progress scenario assumes that the gender gap in computing closes by 2-14 percentage points, and that the gender gap in engineering closes by 4-12pp
The key modelling assumptions for this pathway are:
The slow progress scenario assumes a 0-13 percentage points reduction in the activity rate gap by 2030, while the rapid progress scenario assumes a 0-20 percentage points reduction in the activity rate gap by 2030.
Key modelling assumptions for this pathway are:
The slow progress scenario assumes a 0-5pp reduction in the gender pay gap by 2030, while the rapid progress scenario assumes a 0-14pp reduction in the gender pay gap by 2030.
Key modelling assumptions for this outcome are:
The slow progress scenario assumes a 0-5% increase in fertility rate by 2030, while the rapid progress scenario assumes a 0-8% increase in fertility rate by 2030.
Key modelling assumptions for this pathway are:
The slow and rapid progress scenario for this pathway combine the slow and rapid progress scenario assumptions for all the previous four pathways.
The effects computed under each pathway/outcome were compared to a baseline scenario. The baseline scenario forecasted economic development assuming that no additional improvements in gender equality would be achieved beyond what could be expected based on recent historical trends. Specifically, the baseline was made consistent with:
Results presented in the Study and in the EIGE Gender Statistics Database are aggregated at the EU28 level.
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