From 1996 to the Challenges of implementing the Agenda

2030

Adriana Oropeza Lliteras

UNECE Work Session on Gender Statistics

1995-1996. INEGI’s early partnerships and developments:

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• INMUJERES First set of Gender Statistics covering 13 domains including violence and political empowerment (municipal level).

•MX Foreign Affairs Ministry. Regional Seminar on Gender perspective and Statistics

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Where are we in 2019? (1/4)

Gender statistics

• Strong Statistics Office and Strong statistical system = A wide set of statistical programs produced regularly.

* 3 Examples of statistics available produced by 3 different instituions: INEGI (Labor), Rural land Registry (land property), Social PolicyCouncil (poverty)

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Where are we in 2019? (1/2)

Gender statistics

• Strong INMUJERES (MAM) = Policy priorities, data needs and indicators defined

1. Porcentaje de productoras o responsables de unidades de producción agropecuaria

2. Porcentaje de mujeres que trabajan en las unidades de producción agropecuaria sin recibir pago

3. Porcentaje de mujeres poseedoras de tierras

4. Distribución de la población poseedora de tierras con certificado por sexo

5. Razón de feminidad de las personas poseedoras de tierras que recibieron certificado o título

6. Porcentaje de mujeres ejidatarias

Estrategia 3.4 Promover el acceso de las mujeres a la propiedad de tierra, agua, tecnología e información de mercados, para fines productivos

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Where are we in 2019? (1/4)

Gender statistics 3

• Innovative tools: Gender statistics from a GISGender Atlas: http://gaia.inegi.org.mx/atlas_genero/ Violence information system: https://sc.inegi.org.mx/SIESVIM1/

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Where are we in 2019? (2/4)

Gender statistics

• Coordination mechanism: Gender Statistics Committee (since 2010)

• Annually developed: set of Gender Indicators, International meeting on Gender Statisticas and Regional Expert Meeting on Time Use

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Where are we in 2019? (3/4)

Gender statistics

• Pilot projects: EDGE methodology, Gender and Environment and Gender Story Map

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First Global Conference on Gender Equality & the Measurement of Unpaid Care and Domestic Work

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“Alice: Would you tell me, please, which way I ought to go from here?The Cheshire Cat: That depends a good deal on where you want to get to.

Alice: I don't much care where.The Cheshire Cat: Then it doesn't much matter which way you go.

Alice: ...So long as I get somewhere.The Cheshire Cat: Oh, you're sure to do that, if only you walk long enough.”

― Lewis Carroll, Alice in Wonderland

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Adapting the National Statistical and Geographical System to the Agenda 2030

Defining a coordination mechanism • 2010. Technical Committee (TC) to integrate the MDG’s• 2015. It was adapted into the TC on the SDG’s• 2016.

• First phase: National contribution to the Global agreement.• Assessment of national statistical capacities• Tier validation

• Second stage• Technical discussions and consultations on concepts, statistical

programs available, methodologies

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Adapting the National Statistical and Geographical System to the Agenda 2030

• 2016. INEGI put the SDG’s in the center of the Strategic Program 2016-2040• 2017. TC SDG’s

• Defining the indicators and national capacities • Building a new platform to publish the indicators• Integrating the indicators

• 2018. SNIEG• Identifying data needs and trying to define priorities• Building the National Framework through broad consultation • Updating indicators

• 2018. National SDG Council established and National Planning reformed

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México’s SDG hub. 121 indicators

Linking the National Statistical and Geographical System to the National Development Plan

Implementation challenges through the lens of the Gender Story Map

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Some challenges ahead

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1.Reporting. • How to align different agendas?• How to choose the right platform?• How to strengthen interoperability between statistical/geographic information and several

initiatives?2.Setting priorities and data needs • How to define priorities?• How to use administrative records for curated / integrated /disaggregated (thematically,

geographically and targeted populations) datasets?• How to integrate the use of harmonized catalogues? Occupation classification into births

registered or poverty subsidies?• How to build high quality metadata?

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Some challenges ahead

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3.Face budget constraints

4.Dissemination• How to move from gender “advocacy” indicators to relevant stakeholders. Ex. Public

electric utility need very specific data of the dispersed population without electricity. Do we need age or sex disaggregation?

• How to present indicators to show targeted populations characteristics. Ex. Older widowed women in rural areas face more poverty.

• How to facilitate access to our data and indicators. Ie “difficult to export and difficult to sift through” or pdf vs open data.

• How to review sources? Ex. Electric utility provides aggregated data vs. Household surveys.

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