Data Disaggregation

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FEATURED

USAID How-To Note on Gender Integration in Performance Plans and Reports

This note provides a comprehensive discussion of USAID requirements, including newly established gender indicators that all Missions are expected to integrated into their Performance Management Plans (PMPs).

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ALSO SEE

Data Disaggregation by Geographic Location
November 2013

This 2016 ADS Additional Help document highlights the use of maps and GPS positioning for data disaggregation with indicators that focus on economic differences by region, ethnicity and other variables that may be involved in site selection for the delivery of assistance or expectations about the types and magnitude of change USAID’s interventions will stimulate.

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Men and women have different access to development programs and are affected differently by USAID activities. USAID seeks to understand these differences to improve the efficiency and overall impact of its programs so that both women and men have equitable access to development activities and their benefits.

USAID places a high priority on the disaggregation of performance data by sex for all performance indicators that can be collected in this way, and it encourages Mission staff to utilize gender-sensitive indicators to improve the Agency’s understanding of how the roles and status of men and women in a community affect their participation and the benefits they realize from USAID supported programs. USAID ADS 201 defines the collection of sex disaggregated data for performance indicators and use of gender-sensitive indicators as mandatory where analyses demonstrate that:

  • The different roles and status of women and men within the community, political sphere, workplace, and household (for example, roles in decision-making and different access to and control over resources and services) affect the activities to be undertaken; and
  • The anticipated results of the work would affect women and men differently.

USAID also encourages Missions to identify other types of data disaggregation in their PMPs, including, for example, disaggregation on the basis of income levels, rural/urban residence, or ethnic or cultural group affiliation might be valuable in certain situations. Still other types of data disaggregation may be very specific to particular program elements. For a trade performance strategy that looked to reductions in the time and cost required to move goods across borders, for example, data on the national average time for clearing customs might show improvements that only if disaggregated by entry point would reveal that shorter clearance times at the international airport masked the fact that land border crossings were stagnant at an average of 14 days, negatively affecting both female and male entrepreneurs.

Combining gender disaggregation with relevant data disaggregation from other perspectives enhances the value of performance information for USAID managers. For trade and other economic growth measures, USAID’s E3 Bureau has developed lists of gender-sensitive indicators in an indicator list highlighted on this page; some of those indicator are shown in the table below.

Export Promotion, Customs Reforms, and Small- and Medium-Sized Enterprise (SME) Support

Export-Oriented Clusters and Value Chains

  • Number of exporters entering new clusters, disaggregated by sex.
  • Average sales of women-owned and men-owned export businesses by sector and size of business.
  • Number of workers employed in sectors, per year, disaggregated by sex (after workforce development activities).
  • Salaries of workers employed per year, disaggregated by sector, by sex, and by job category (after workforce development activities).
  • Number of “female-value chains” developed by sector.
  • Change in income of women engaged in “female-valued chains” measured annually.
  • Marketing practices adopted by enterprises as evidenced by business plans, reorganization, product design, and pricing and strategic linkages with other firms or sub-sectors, disaggregated by the size of enterprise and sex of owner.
  • Number of women entrepreneurs involved in creation of a web portal for women.
  • Number of links established with Fair Trade organization for women’s goods and annual sales from Fair Trade contracts.
  • Number of links/contracts established with other entrepreneurs to form a women’s goods cluster. Annual sales from this link.
  • Annual sales for women artisans via web (e-commerce), in person, etc.
  • Annual sales from contract with supermarkets, disaggregated by sex of exporter.

Reduction of Customs-Related Operational and Administration Constraints

  • Number of exporters in the country, disaggregated by sex.
  • Number of policy measures implemented to address costs of customs procedures and constraints of poor producers.
  • Number of women’s groups, associations, and women leaders engaged in advocacy for pro-poor customs policy.
  • Number of users of online customs forms, disaggregated by sex.
  • Number of customs forms processed online, disaggregated by sex.

Business Services and Training for SMEs

  • Number of new entrants entering SME sector assisted by project, disaggregated by sex.
  • Percentage of ownership of businesses/sex of owner/sector.
  • Average size of loans by sector and size of business, disaggregated by sex of business owner.
  • Number of women’s associations created or assisted.
  • Number of gender-sensitive policies implemented in areas that will assist entrepreneurs.
  • Number of loans dispersed through funding mechanism.
  • Number of clients that receive loans, disaggregated by sex.
  • Number of clients that receive pre- and post- investment counseling.
  • Number of clusters developed that present opportunities for women owners and workers.
  • Number of workers employed per year, disaggregated by sex.
  • Salaries of workers employed in cluster, disaggregated by sex and by job category.
  • Number of daycares provided on-site.

 

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