Gender in industry: how to close the gender data gap for better gender-responsive industrial strategies and policies (United Nations Industrial Development Organization)

Reliable data and statistics are crucial for policy makers to formulate and evaluate evidence-based gender-responsive strategies and policies essential for achieving inclusive and sustainable industrial development. Lack of data on the complex linkages between gender and industrial development holds back progress in addressing gender inequalities in industry and harnessing women’s full potential as leaders and economic agents of change, and thereby transforming economies and generating inclusive growth.  The limited availability of gender-disaggregated data on labour participation and employment quality by manufacturing sector, business ownership by sector, technology adoption and asset ownership, has made it difficult to monitor how the pandemic affected women and men. At the country level, surveys are the most frequently used source of industrial data, followed by administrative sources and censuses. For most countries, the data supplier is the National Statistical Office (NSO). In a few cases, the data supplier is a government ministry. Most of the countries that provide information rely on a single data source. However, European Union (EU) member countries and other countries with a more developed statistical system tend to use a combination of sources, most often administrative sources, surveys and registers.  Given these constraints, sex-disaggregated industrial data remains relatively scarce. Moreover, given the contents of industrial or establishment surveys, as recommended by international guidelines, only information about participation in employment and remuneration can be collected through these instruments. Little gendered data on employment characteristics, job quality, productivity or other important factors are available specifically for industrial sectors.  The paper will present obstacles in collecting gender-disaggregated industrial statistics including difficulties with linking traditional industrial data sources with other available gender-sensitive statistical information. It will also highlight future priorities on improving industrial gender statistics.