Data coherence challenges in using gender responsive data for planning Case of Uganda

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Data coherence challenges in using gender
responsive data for planningCase of Uganda

• Presentation by
• Uganda Bureau of statistics
• 24 February 2011, North Lawn Building, Conf. Rm
  D.

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Introduction

• Increased demand for gender statistics confirms
  that gender is not only a social issue but also
  a development issue.
• Women and men experience lifes circumstances
  differently i.e. culturally, socially,
  economically and politically.
• Without measuring the concerns regarding social
  roles and relationships, the magnitude of gender
  discrimination and inequality are a challenge to
  policy makers and society at large.

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Gender Responsive Statistics

• Producing gender statistics consists of
• (i) Sex-disaggregation of individual data to
  reveal the differences and inequalities between
  Women and Men, Girls and Boys
• (ii) collecting data on issues that show
  attributes, responsibilities, culturally assigned
  roles, opportunities and privileges for women and
  men, girls and boys. These reveal the gender
  relations

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Gender Responsive Statistics (Contd)

• collecting gender responsive data goes beyond
  merely disaggregating data by sex, it takes
  account of the power relationships between women
  and men boys and girls.

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Key Dimensions of Gender Responsive Statistics

• Attributes sex, age, children, elderly,etc.
• Roles Who does what?
• Responsibilities societal expectations
• Opportunities control over resources, time use,
  etc
• Privileges inheritance, mobility, entitlement,
  participation,etc

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Scope of Gender responsive Statistics

• Population composition and change
• Household headship, Marital status and Fertility
  rates
• Education and Literacy
• Health services and nutrition
• Agriculture
• Economic activity and Labour force participation
• Access (and ownership) to land, equipment and
  credit
• Legal rights and access to justice
• Violence (Including Gender Based Violence)
• Participation in decision making and politics etc

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Data gaps

• Lack of gender statistics on
• Some dimensions of disability, literacy, time
  use, rights, etc.
• Key sectors of the economy like construction,
  energy, roads, etc
• Lack of prioritised needs for gender responsive
  data by Users

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Challenges

• Use of proxies to measure different dimensions of
  gender relations e.g. awareness, equity,
  empowerment etc.
• Conflicting gender concepts and definitions
  adopted by different stakeholders
• Limited involvement of women in the definition of
  key frameworks

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Challenges

• Inadequate gender responsive data management
• Inadequate gender statistical infrastructure in
  the NSS eg. classifications, concepts,
  definitions and methods
• Limited understanding and awareness about gender
  related issues
• Ignoring non-Sex-disaggregated data that reveals
  gender issues like the time taken to fetch water

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Way forward

• Develop positions, arguments, messages and
  alternatives with empirical gender inequality
  evidence for
• Decision making, planning and resource allocation
• Empowerment of women through capacity development
• Resource commitment towards reducing the gender
  gap

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• THANK YOU