Authors: Erika Martínez Fernández, Lina Tafur Marín, Laura Silva Aguilar, Pablo Cortés, Susana Martinez-Restrepo
The COVID-19 pandemic has heavily impacted national Statistical Offices (NSOs) worldwide and, as a result, the data available to make timely and evidence-based policy decisions. Nine out of ten NSOs in low- and lower-middle-income countries faced a decrease in funding due to the COVID-19 crisis. [1] As a result, funding constraints are affecting their ability to report on the impact of the COVID-19 crisis on variables such as labor force participation and other labor market indicators, as well as time-use and poverty. Considering that the COVID-19 crisis is affecting women’s time, poverty, employment, and inactivity, insufficient data to respond to the crisis will significantly affect women’s already precarious involvement in the labor market and their livelihoods.
Limited data entails limited action: how incomplete data holds women back is the sixth and final brief of the Gender and COVID series by CoreWoman and Cepei. The analysis conducted in this brief, which explores the state of data availability in the Global South to quantify the COVID-19 effects on women in labor markets, time use, and poverty, takes into consideration all reported data between 2000 and 2021 from the ILO, the World Bank, UN Women, and the UNDP. Over the series, the authors have collected data to analyze the COVID-19 toll on women. Previous briefs from the series address the following topics: The Latin American female staircase fall; the many faces of vulnerabilities for women in labor markets; women’s inactivity and their participation in agriculture jobs; time use and the care economy; and how COVID-19 has set a poverty trap detrimental for women.
Although all datasets to conduct the analysis are official, solid, and reliable, they have some limitations that even multilateral organizations have acknowledged. Notably, the ILO has cited comparability issues due to disparate data sources (censuses vs. surveys) and different concepts and methodologies across countries, which the institution has adjusted in modeling and projections. Additionally, there are striking differences in the reaction capacity of NSOs to produce data, which reflects in their availability and independence to conduct statistical analysis under crises such as the COVID-19.
THE LIMITATIONS OF LABOR MARKET DATA DURING THE COVID-19 SHOCK
Labor market data is globally available, but not all regions of the Global South have kept their records updated during COVID-19. Labor Force Participation (LFP) rate, which refers to the share of a country’s working-age population (usually >15 years old) that engages actively in the labor market, either by working or looking for work, [2] is the most common variable to measure labor market dynamics. As a result, it is updated consistently among most regions, including the Global South.
The availability of the LFP indicator, which is the primary source of information for employment policy analysis and decision making in the field, has been critical to reflect the shock of COVID-19 on Global South’s women. Since the onset of the pandemic, the LFP rate has consistently revealed that females have been the most affected group by social distancing mandates and quarantines during COVID-19, reversing decades of the progress achieved.
Figure 1 shows that Latin America behaves similarly to Global North countries in updating LFP rates regularly. Additionally, Africa is falling behind in tracking and publishing up-to-date labor market statistics, although South Africa and Botswana are exceptions. Reasons for data backwardness suggest budget allocation issues to NSOs, making it more challenging to adhere to international standards for international comparisons and account for measurement needs of specific populations of interest, such as women’s employment in rural areas.
As depicted in Figure 1, the LFP rate has been permanently updated in several Global South countries compared to other variables. Thus, it has been a helpful variable to illustrate labor market trends during COVID-19. However, using this indicator as the benchmark for policy action has not been enough for designing employment policies with a gender lens that economic recovery in the Global South urgently demands amid the pandemic. Some of the LFP rate limitations are that not all regions have updated data to break it down by sex, neither use complementary variables to fill this gap, such as the share of unpaid workers, the status of employees, and compensation levels, among others. Moreover, differences in data sources and methodologies across countries that, for example, exclude workers engaged in small establishments or the informal economy and leave them out of the measurement scope do not fulfill ILO’s standards and make some available data unfitted for global comparisons. [3]
A 360 VIEW BEYOND LFP IS NEEDED TO UNDERSTAND THE LABOR MARKET’S COMPLEX EFFECTS ON WOMEN
Other variables that provide a better understanding of labor market dynamics beyond LFP are inactivity, unemployment, and informality. However, some Global South countries fail to report updated statistics regularly, limiting the possibility of accounting for the magnitude of the COVID-19 effects on women. Regarding informality rates, few countries manage to have 2020 informality rates available, even when it is a pressing issue that affects a large share of women across Global South economies. Before COVID-19, women outnumbered men in the informal economy representing 70% of all employment in emerging and developing countries. Estimations suggest that it could have increased during the pandemic, but data available is not enough to confirm it as a fact.
Table 1 showcases two types of information that encapsulate the complexity of the labor market’s indicators in a glimpse. On the one hand, it provides the share of countries by region with available data in nine critical variables readily for disaggregation by sex. On the other hand, it enables a practical color-based reading to identify the status of data availability across Global South regions. Although Economic Activity is not an indicator per se, it is on the table to reflect the percentage of countries that factor in employment disaggregation by sector, allowing vertical and horizontal segregation identification. In other words, it tracks female-led sectors’ trends and how they are affected by changes in consumer behaviors, automatization, technological innovation, among others. When countries do not acknowledge Economic Activity sector breakdowns by sex, it might suggest they cannot keep up with disruptive changes that could increase women’s vulnerability to unemployment, among other issues.
As seen in Table 1, vulnerable employment is the most up-to-date variable in Global South regions; however, the largest share of countries lack 2020 data that can reveal how COVID-19 has affected women. Identifying the status of employment in rural areas also poses some limitations since it uses the “contributing family workers” variable as a proxy to make inferences about women in rural jobs. Additionally, rural women are usually engaged in activities that overlap between productive and caregiving activities. As a result, national accounts statistics offices might disregard their contribution as “economically active employment” and unintentionally impede identifying accurate effects of COVID-19 over women in agriculture. [4]
THE MISSING LINK: TIME POVERTY DUE TO DOMESTIC WORK IS A FUNDAMENTAL CAUSE OF THE FEMALE STAIRCASE FALL DURING COVID-19
Available labor market data must be complemented with other sources of information that are central for gender equality, such as time use concerning paid and unpaid work. Time-use data has become increasingly relevant for policy analysis, as it enables understanding how people make decisions about time and how gender norms and roles mold it. For example, during COVID-19, time-use data confirmed how measures to prevent the spread of COVID-19, such as the closure of schools and daycares, reinforced women’s role as caregivers.
Time use can determine women’s access to decent work opportunities and high-quality paid jobs. Yet, the toll that social expectations put on women to fulfill unpaid care responsibilities, housework, and even farm labor, have been usually neglected by employment policymakers. However, COVID-19 has paradoxically and conspicuously shown the missing link in the analysis of gender gaps in labor outcomes. [5] It is possible to observe how many hours individuals devote to paid, unpaid work, and self-care activities through time-use data. Therefore, countries that regularly conduct time use data surveys have been able to identify gender patterns over the years and confirm how the COVID-19 crisis exacerbated women’s time use in unpaid domestic work.
Figure 2 shows the most up-to-date surveys conducted across the world. This is not to say that there is no data concerning time-use in many countries, but that only a handful of countries regularly conduct specialized surveys on this kind. As seen in Figure 2, the Global South is falling behind the Global North when conducting time use surveys, limiting countries’ ability to understand some of the constraints women face to access the labor market. However, stark differences stand out across Global South regions. Overall, Latin America has collected time use data in the last ten years, while many countries in Africa have never done so.
MORE WOMEN IN THE BRINK: THE FEMINIZATION OF POVERTY DURING COVID-19
During COVID-19 times, for every 100 men aged 25 to 34 living under extreme poverty (meaning living on 1.90 USD a day or less), 118 are women. The feminization of poverty is a consequence of unequal historical access to economic opportunities. Estimations indicate that the pandemic will add 47 million more women and girls into impoverished lives. [6] Some experts suggest that the number could be higher, but available poverty data barely report the COVID-19 effects. At the same time, some official statistic institutions still fail to disaggregate poverty rates by sex, limiting the sample size of countries.
As seen in Table 2, only three out of the eight Global South regions have a large share of countries with updated information on women’s poverty after the COVID-19 shock. Therefore, since the start of the COVID-19 pandemic, most Global South governments have failed to collect any information on poverty disaggregated by sex. The African continent is the exception since almost 80% of countries have gathered data on women’s poverty after COVID-19.
RECOMMENDATIONS FOR POLICY ACTION
National Statistics Offices (NSOs) need more funding for tracking data of population groups brutally hit by the COVID-19 economic crisis
It is necessary to collect data with relevant gender indicators to tackle the differential effects of the COVID-19 crisis on women. Still, it could imply scaling up institutional operations and increasing technology use among National Statistics Offices whose capacity has also been affected by the COVID-19 economic crisis. For example, 81% of NSOs in low- and lower-middle-income countries reported that the impact of COVID-19 has delayed or negatively affected surveys and censuses operations. [7] Indeed, as of May 2021, one-third of NSOs globally remained closed to all staff or non-essential staff. In Latin America, for instance, less than 40% of NSOs were partially open as of May 2021. [8]
An increase in budget allocation to NSOs is imperative to continue data collection promptly to support informed policy decisions for governments and other institutions. Similarly, NSOs’ operations should be rigorous, have predictable continuity, and follow ILO statistical standards for international comparisons. [9] These include breaking down data by sex and other observable characteristics that enable an intersectional outlook.
Surveys and censuses need a gender lens
Different populations need different instruments to understand how their life dimensions are affected by inequality in terms of personal characteristics such as gender, age, ethnicity, race, education levels. Yet, surveys and censuses face the challenge of measuring women’s perspectivesand end up anonymizing their situation and experiences in different dimensions that are crucial for women’s economic empowerment. Therefore, it is necessary to adapt instruments of data collection and questions not only to local contexts but also from a gender lens.
[1] Department of Economic and Social Affairs. 2020. “Monitoring The State Of Statistical Operations Under The COVID -19 Pandemic”. United Nations. https://unstats.un.org/unsd/covid19-response/covid19-nso-survey-report.pdf.
[2] “Indicator Description: Labour Force Participation Rate – ILOSTAT”. 2021. ILOSTAT. https://ilostat.ilo.org/resources/concepts-and-definitions/description-labour-force-participation-rate/.
[3] Ibid.
[4] Food and Agriculture Organization of the United Nations. 2011. “THE STATE OF FOOD AND AGRICULTURE”. Rome: United Nations. http://www.fao.org/3/i2050e/i2050e.pdf.
[5] OECD Development Centre. 2014. “Unpaid Care Work: The Missing Link in the Analysis of Gender Gaps in Labour Outcomes”. OECD. https://www.oecd.org/dev/development-gender/Unpaid_care_work.pdf.
[6] Azcona, Ginette, Antra Bhatt, Jessamyn Encarnación, Juncal Plazaola-Castaño, Papa Seck, Sike Staab, and Laura Turquet. 2020. “From Insight To Action Gender Equality In The Wake Of COVID-19.”. UNWOMEN. https://www.unwomen.org/-/media/headquarters/attachments/sections/library/publications/2020/gender-equality-in-the-wake-of-covid-19-en.pdf?la=en&vs=5142.
[7] World Bank Group. 2021. “One Year into the Pandemic: Monitoring the State of Statistical Operations Under COVID-19”. Washington, D.C.: World Bank Group. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/911901627637432764/one-year-into-the-pandemic-monitoring-the-state-of-statistical-operations-under-covid-19.
[8] Ibid.
[9] Discenza, Antonio, and Kieran Walsh. 2021. “Lessons from the COVID-19 Pandemic: Closing Gender Data Gaps in The World Of Work – Role Of The 19Th ICLS Standards”. International Labour Organization. https://ilo.org/wcmsp5/groups/public/—dgreports/—stat/documents/publication/wcms_757964.pdf.