Manila Bay

 

In recent years, rapid population growth and poverty have driven thousands of people to the once uninhabited coastline, where they informally settled along the shore of Manila Bay.


 

A central promise of the Sustainable Development Goals (SDGs) is to leave no one behind. Going beyond aggregates and averages is the first step to understanding that every human life is worth being counted. But how can we ensure that everyone, including the poorest, women, children, the elderly, indigenous people, migrants and people with disabilities are included into the big picture? A project in the Philippines tells the story of how more and better subnational gender statistics can help protect vulnerable groups from falling through the cracks.

The province of Bataan, located on a peninsula close to the country’s capital Manila, is home to around 760,000 inhabitants. This number, however, only represents those who were officially counted in the last census in 2015. In recent years, rapid population growth and poverty have driven thousands of people to the once uninhabited coastline, where they informally settled along the shore of Manila Bay. Closely-spaced dwellings – some of them floating on the sea – provide shelter, but lack access to basic infrastructure like sanitation. The precarious living conditions of residents are further aggravated by natural hazards like floods and landslides.

To improve service provision and support to residents, the government must have an accurate picture of the population and its needs. However, without proper registration or street names, many residents are unable to be included in official health, demographic and socioeconomic surveys.

Comprehensive, disaggregated data collection often poses significant challenges to governments. In Balanga, the capital of Bataan, for example, sex-disaggregated data is not up to date. Moreover, many officials lack training in using statistical tools, while the government cannot afford to employ experienced statisticians.

 

Manila Bay pollution

 

Overpopulation and pollution along the coast take their toll on the natural ecosystem of Manila Bay.


 

To bring change in the city of Balanga and the municipalities of Samal and Dinalupihan, the Philippine Statistical Research and Training Institute (PSRTI), the Philippine Statistics Authority (PSA) and the Partnership in Statistics for Development in the 21st Century (PARIS21) joined forces.

The intervention began with a survey among local staff in order to reveal the most pressing training needs. Based on these insights, the partnership tailored and organised a series of workshops. During the workshops, 35 local participants learned about the importance of gender statistics for policymaking, among other topics. How does unemployment for women differ from men? Why is this the case? Do we need different social protection schemes? These questions posed by policymakers can all be answered.

During the training, participants became familiar with the interpretation and visualisation of existing data using Excel and the design tool Canva. The workshop was complemented by a technical training module on how to efficiently manage data, create statistical tables and use the database of the Community-Based Monitoring System (CBMS) . "The CBMS is a technology-based system for collecting, processing and validating disaggregated data used for planning, programme implementation, and impact monitoring at the local level while empowering communities, including women, to participate in the process. The trainings have proven beneficial in the implementation of the system", says Dr. Dennis Mapa, National Statistician at PSA.

 

Mayor Aida Macalinao

 

“Learning how to create infographics and visual data presentations made data analysis in the municipality faster, more accurate and more granular", explains Aida Macalinao, mayor of Samal.


 

The pilot project has proven to be a valuable contribution to SDG 5 (Gender Equality), SDG 10 (Reduced Inequalities) and SDG 17 (Partnerships for the Goals) and triggered concrete impact on the ground, as Aida Macalinao, mayor of Samal, explains: “The training provided easy-to-understand ways to present and interpret data. Learning how to create infographics and visual data presentations made data analysis in the municipality faster, more accurate and more granular.”

In its effort to tackle inequalities and improve livelihoods, the local government in Samal is building affordable housing. “The sustainable development and housing programmes we have are worth nothing if we can’t identify those who need our services the most.” With national official statistics often being demographically limited, local governments play a renewed critical role for mapping citizens and their needs as well as monitoring progress towards the SDGs. “Knowing how to collect, analyse and present subnational gender data helps us identify citizens in poor, hard-to-access environments who are often overlooked. From available data we can also tell that the lack of adequate basic services in informal settlements disproportionately affects women and girls. We are using this information to decide which groups and individuals to prioritise in our activities,” adds Aida Macalinao.

More and better local data are needed to address local issues. When putting them in perspective, they also have the power to influence discourses and decisions at higher scales. Following positive feedback and results, “we decided to update our gender statistics course programme for officials by offering one comprehensive training instead of several separate courses. It will be based on the materials of the virtual workshop,” summarises Dr. Josefina Almeda, Executive Director of PSRTI. “Using the holistic Capacity Development 4.0 approach by PARIS21 we will be able to better detect and close knowledge gaps and enhance the collection and analysis of gender statistics.” To conclude, when planning new development programmes for citizens, local government officials will be able to use the new skills to make better gender-responsive decisions around resource allocation, policy design and urban development activities.

 


 

Gender equality and statistics in the Philippines

The Philippines has a rich past of gender equality dating back far into Filipino history. In recent years, it has been considered as the most gender equal nation in Asia. This achievement is partly due to the country’s progress in statistics. Examples include legislation and policies providing subnational statistical systems with roadmaps for gender statistics or criteria set by local government units for the inclusion of gender statistics in their budgets. Inspired by the workshop, PSRTI will develop a new free course for local government staff as part of their SDG seminar series.

About Capacity Development 4.0

The pilot trainings build on the Capacity Development 4.0 approach developed by PARIS21 and launched in January 2020 at a high-level meeting with the United Nations Statistics Division. It helps to assess and strategically develop the capacity of statistical systems, including their legal framework, funding infrastructure, coordination, communications and advocacy. It aims to support a country’s national statistical system, its organisations and individuals in obtaining, strengthening and maintaining their abilities to collect, produce, analyse and disseminate high quality and reliable data to meet users’ needs. "The emergence of new data needs, such as the need for subnational level data, entails the conduct of additional activities, and this may put pressure on the already limited resources of institutions such as PSA. The comprehensive framework of Capacity Development 4.0 eases this pressure as it allows various actors operating at different levels to work hand-in-hand in the production of high-quality statistics", explains Dr. Dennis Mapa, National Statistician at PSA.

 

Credits:

  • Image 1: https://commons.wikimedia.org/wiki/File:Manila_bay_from_cavite_City_(Cavite_City)(2017-04-03)_8.jpg
  • Image 2:
    https://commons.wikimedia.org/wiki/File:Manila_Bay_Pasig_and_Pampanga_River_Basins_pollution_2008.jpg
  • Screenshot training: PARIS21, PSRTI, PSA
  • Text: Caroline Bernreiter

 

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