write a research report outline about the social issue of poverty on covid 19 period

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Period Poverty and the Pandemic: A Forgotten Crisis

Have you ever had to make a decision about whether to buy food or tampons? Use a sock as a pad? Or to wear tight-fitting pants to bed so your makeshift pad doesn’t move around too much and stain your  sheets ? 

These are some of the experiences for those facing period poverty, the inability to afford menstrual products and manage menstruation effectively and comfortably. If you don’t have a period, these situations might come as a shock to you. If you do have a period, they may resonate with your own experiences. While  period poverty in the United States is increasingly being seen as a problem , the day-to-day experiences of those facing period poverty are only just beginning to be researched and understood. Additionally, new research has been conducted to understand the impacts of the COVID-19 pandemic on period poverty. 

It is evident that the  impacts of the pandemic are gendered , with women bearing burdens like high unemployment, increased childcare and home responsibilities, and worsened mental health. For those experiencing period poverty, the lack of resources, like access to free products, is a problem that has been exacerbated by the pandemic but is by no means a new public health  issue . While supports like SNAP (Supplemental Nutrition Assistance Program) and its counterpart, WIC (Women, Infants, and Children) help to support low-income women, many public assistance programs do not cover purchasing period products (more on this later).

For my summer 2021 practicum as a Columbia Public Health student, I worked with the  Gender, Adolescent Transitions, and Environment (GATE)  program as a Menstrual Health Research Fellow where I worked to understand how the pandemic impacted period poverty. This mixed methods study involved GATE partnering with the City University of New York, which administered  a nationwide survey  to assess how the pandemic was impacting many facets of people’s lives, including a few questions on menstruation. From answers to these menstruation questions, a sample of people participated in a series of qualitative interviews led by the GATE team to gain a deeper understanding of their menstruation management experiences during the pandemic.

The research team published the quantitative findings  from the study, which showed the gendered implications of COVID-19, namely the effect on managing menstruation and securing menstrual products. The study highlighted:

1.     the negative economic impacts that COVID-19 had on women;

2.     that income loss had a high association to product insecurity; 

3.     how often someone changed their product;  

4.     the use of makeshift materials to soak up blood.

Overall, the study found that income loss was a strong predictor of product menstrual insecurity. Product insecurity is the inability to consistently afford or have access to necessary products to manage menstruation effectively. It can result in a number of physical, mental, emotional, and financial challenges that can make monthly menstruation a crisis.

During my first year at Mailman I completed a qualitative data analysis class about the process of collecting data, transcribing, coding, and finding themes and findings to report on; all skill sets which served me well for the work I did on the qualitative portion of the study. This included supporting the qualitative analysis of interviews collected with US women (ages 18-45), examining the relationship between income loss and menstrual product insecurity, and how women coped with these challenges.

Translating the skills I gained in class to a real-world example was rewarding and further emphasized the importance of understanding day-to-day experiences when problem-solving. This research has real implications for policy and program implementation that address period poverty and increase the support and resources available to people. 

I feel incredibly grateful to have been able to hear people’s firsthand stories about managing their periods during a crisis. Given the stigma that menstruation still faces in our society, the openness and honesty of study participants is even more impactful, and it often reflects the experiences of many. The women interviewed were relieved to hear that others were going through the same things and they hoped that being part of this research could help shed light on the important yet overlooked issue of period poverty.

Making Progress in Ending Period Poverty

Despite the period poverty challenges  found across our country , some recent progress has been made to lessen the burden. These efforts are focused in policy reform and the non-profit sectors, as illustrated by the   passage of new period-friendly legislation  like mandates that schools and government buildings provide free period products. More specifically, this includes Illinois’ inclusion of period products under SNAP and WIC and the historic win in Ann Arbor, Michigan  that enacted the provision of free products in all public bathrooms . More cities and states are starting to pass similar legislation, with  27 states passing laws  removing the tampon tax, and many others ensuring that free products be made available in federal buildings and publicly funded schools and universities.

Some initiatives are also donation-based, like the nationally operating  Alliance for Period Supplies , run through the National Diaper Bank Network. In New York City, the  Woman to Woman campaign  provides free products and other necessities through the New York Food Bank. In addition to the legislation mentioned above, it is also important that free products are available on larger scales. Further research could help identify if product provision through donation is sustainable in reducing product insecurity. There are lessons to be learned from how the non-profit sector has mobilized to ensure their communities are provided with the necessities they need.

While I’ve had my own “oh no” period moments, I have never had to worry about affording products and for that I am grateful. Through my experience at GATE I learned that by better understanding what people go through to manage their periods with limited resources, we can aid in creating sustainable and far-reaching solutions and policies that help people manage menstruation confidently and comfortably.

I am excited that this research will contribute to the relatively new conversation about period poverty and the pandemic and that it will shed light on potential pathways to reduce the burden of period poverty for so many menstruators. No person should have to decide between food or tampons or be dependent on makeshift supplies, or feel that their period holds them back in any way. 

Katie Dimond is a 2022 MPH candidate in the Heilbrunn Department of Population and Family Health and a certificate in Sexuality, Sexual, and Reproductive Health. She received her BA in Anthropology from Trinity College, where she conducted research on women's birth experiences. Prior to Columbia Public Health, Katie worked at the Cancer Support Community where she developed psychosocial support programs for patients and families impacted by cancer.

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• Published: 25 May 2022

Who and which regions are at high risk of returning to poverty during the COVID-19 pandemic?

• Yong Ge   ORCID: orcid.org/0000-0002-5175-5812 1 , 2   na1 ,
• Mengxiao Liu 1 , 2   na1 ,
• Shan Hu 1 , 2   na1 ,
• Daoping Wang   ORCID: orcid.org/0000-0001-5221-4965 3   na1 ,
• Jinfeng Wang 1 , 2 ,
• Xiaolin Wang 4 ,
• Sarchil Qader 5 , 6 ,
• Eimear Cleary 5 ,
• Andrew J. Tatem   ORCID: orcid.org/0000-0002-7270-941X 5 &
• Shengjie Lai   ORCID: orcid.org/0000-0001-9781-8148 5 , 7 , 8  

Humanities and Social Sciences Communications volume  9 , Article number:  183 ( 2022 ) Cite this article

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Pandemics such as COVID-19 and their induced lockdowns/travel restrictions have a significant impact on people’s lives, especially for lower-income groups who lack savings and rely heavily on mobility to fulfill their daily needs. Taking the COVID-19 pandemic as an example, this study analysed the risk of returning to poverty for low-income households in Hubei Province in China as a result of the COVID-19 lockdown. Employing a dataset including information on 78,931 government-identified poor households, three scenarios were analysed in an attempt to identify who is at high risk of returning to poverty, where they are located, and how the various risk factors influence their potential return to poverty. The results showed that the percentage of households at high risk of returning to poverty (falling below the poverty line) increased from 5.6% to 22% due to a 3-month lockdown. This vulnerable group tended to have a single source of income, shorter working hours, and more family members. Towns at high risk (more than 2% of households returning to poverty) doubled (from 27.3% to 46.9%) and were mainly located near railway stations; an average decrease of 10–50 km in the distance to the nearest railway station increased the risk from 1.8% to 9%. These findings, which were supported by the representativeness of the sample and a variety of robustness tests, provide new information for policymakers tasked with protecting vulnerable groups at high risk of returning to poverty and alleviating the significant socio-economic consequences of future pandemics.

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Introduction.

Crises and shocks, such as climate-related disasters, violent conflicts and pandemics can result in severe socio-economic losses, ecological impacts, and human suffering (Helbing, 2013 ). The impacts usually differ across population groups, with low-income groups often suffering the most (Mcphillips et al., 2018 ). These crises are regularly accompanied by shrinking economies and damage to people’s livelihoods, which in turn have a strong impact on poverty. The poor suffer not only directly in terms of jeopardized personal safety, but also indirectly through a shortage of work and financial uncertainty (ESCAP, 2021 ). Therefore, understanding who and which regions are at high risk of falling into poverty and how crises shape the impact of poverty is critical to developing efficient responses.

The COVID-19 pandemic has disrupted the functioning of our natural, economic, and social systems, and has directly or indirectly impacted people depending on those systems. Poor people are particularly susceptible, and it was forecast that 71 million people would fall back into extreme poverty in 2020 following the outbreak of the pandemic (World Bank, 2020 ). Based on a review of the literature on the effects of COVID-19 (Acuto et al., 2020 ; Asare and Barfi, 2021 ; Bouman et al., 2021 ; Decerf et al., 2021 ; Douglas et al., 2020 ; Fajardo-Gonzalez et al., 2021 ; Hwang et al., 2020 ; Laborde et al., 2020a , 2020b ; Leach et al., 2021 ; Padhan and Prabheesh, 2021 ; Rume and Islam, 2020 ; Rutz et al., 2020 ), the potential mechanism underlying a return to poverty as a result of the COVID-19 pandemic was identified (Fig. 1 ). Measures adopted to contain the pandemic, including social distancing, travel restrictions, and local transportation lockdowns, have affected the economy, society, and the environment. Assuming this, we focused on vulnerable groups who have recently emerged from poverty. There are two paths by which those who have been lifted out of poverty could fall back into poverty. The first is the direct impact on health as a result of contracting COVID-19. Health impacts might lead to loss of employment and increased expenditure on healthcare, driving them back into poverty. The second path is the indirect impact on people’s income. Vulnerable people usually work as casual labourers with no assurance of regular employment, they have a single source of income and few savings, and thus are more likely to be pushed back into poverty as a result of lost wage income. The COVID-19 pandemic has destroyed tens of millions of jobs and livelihoods, resulting in migrant workers, daily wage labourers and some informal sector workers losing their source of income (ESCAP, 2021 ; UNDP, 2020 ; ILO, 2020 ). Identifying vulnerable groups at risk of falling back into poverty is critical to supporting the design of policies attempting to mitigate their losses.

There are two paths by which those who have been lifted out of poverty could fall back into poverty, which are direct impacts and indirect impacts.

Hubei Province, in which the city of Wuhan is located, was significantly affected in the early stages of the pandemic, and is also one of the main areas for poverty reduction in China. Understanding the impacts of COVID-19 on poor households in Hubei Province could provide policy implications on how to deal with the intertwined situation between poverty alleviation and future crises response. Therefore, in this study, we focused on the population that was lifted out of poverty at the end of 2019 in Hubei Province in an effort to identify those at high risk of returning to poverty as a result of the loss of employment caused by the COVID-19 pandemic. A total of 78,931 government-identified poor households in 100 towns in 10 counties were included in the sample used in this study. The 10 counties were located in the eastern, central, and western regions of Hubei Province to reflect the differences in geographical conditions and levels of socio-economic development in the various regions (Fig. 2 ). The per capita gross domestic product in central Hubei is much higher than that in the eastern and western regions, while the incidence of poverty in the western region is particularly high. The number of confirmed cases in central Hubei was much higher than that in the eastern and western regions in the initial stage of COVID-19. As can be seen from Fig. 2 , over all the sample households, the average proportion of wage income to total income was 72%, indicating that wage income is critical for the poor households.

The incidence of poverty, per capita gross domestic product, the number of confirmed COVID-19 cases in each county in Hubei Province; and the household income structure in sample counties.

To assess the impacts of different periods of the lockdown, we set three scenarios: lockdowns of 1, 2, and 3 months since the lockdown in Wuhan lasted for 76 days, from 23 January to 8 April 2020. We assumed that migrant workers in Hubei Province would not return to work for 3 months, and that the Wuhan lockdown primarily impacted those migrant workers who had to travel for work and relied on their wage income to support their families. Migrant workers account for a large proportion of poor households, and the lockdown would have had a significant impact on them. Under our three scenarios, migrant workers could not return to their workplace for either 1, 2, or 3 months, and thus lost their wage income for the period of the lockdown. The case study aimed to determine who was at high risk of returning to poverty, where they were located, and how the various risk factors shaped the return to poverty under the three scenarios. To evaluate the associations between the likelihood of a household returning to poverty and the household’s characteristics, a logistic regression model was employed. To identify the geographic distribution of the risk of returning to poverty and how it was affected by regional characteristics, we first assessed the relative importance of risk factors using the Lindeman, Merenda and Gold (LMG) method (Grömping, 2015b ), and then constructed a semi-parametric generalized additive model (GAM) to predict the risk of returning to poverty (Hastie and Tibshirani, 1987 ). Regional characteristics were extracted from multiple data sources, including remote sensing images, the Internet, and statistical records (Ge et al., 2021 ; Liu et al., 2020 ). Detailed information on data collection, pre-processing, and analysis can be found in the “Methods” section and the Supplementary Information .

Measuring the risk of returning to poverty

We conducted two field surveys in January and September of 2020 covering 10 counties in various regions of Hubei Province, as shown in Fig. 2 , and obtained data on the total income and income structure of 91,125 impoverished households as at the end of 2019 in 100 towns in the 10 counties. After eliminating incomplete observations (those that were outliers or were missing data), we obtained a final sample of 78,931 households for our analysis. To investigate the factors affecting the risk of returning to poverty at the household level, we also collected data on the characteristics of each household, which are shown in Fig. 3 . All the household-level data were obtained from the Office of Poverty Alleviation and Development in each county.

Values are odds ratios with 95% confidence intervals. a Correlation between income characteristics and the likelihood of returning to poverty (calculated using data from all the counties included in the sample ( n  = 78,931)). b Correlations between the other 11 household characteristics and the likelihood of returning to poverty (calculated using data from Yingshan County ( n  = 17,972). All estimates are presented in Supplementary Table S1 .

Here, we present a brief introduction to poverty identification and progress in poverty alleviation in China to help better understanding of the term “returning to poverty”. Under China’s national poverty identification standard, poor households were primarily identified based on their income, with consideration given to their housing, education level, and health conditions (SCIO, 2021 ). A household that was confirmed as poor was registered, and a file was created in the national poverty alleviation information system, which was the source of the data on total income, income structure, and household characteristics used in this study. The impoverished population was then adjusted every year based on national poverty standards. Early in 2021, China’s government announced that after years of effort, it had achieved the goal of eradicating extreme poverty. Thus, in this study, we focused on poor households that had been lifted out of poverty at the end of 2019 in Hubei Province with the aim of identifying the risk of them returning to poverty as a result of the loss of employment and income caused by the COVID-19 pandemic.

In this study, the total income of each household was considered to include four components: operating, wage, property, and transfer income. We also obtained the number of working months for migrant workers in 2019. The threshold for returning to poverty, as defined by the Office of Poverty Alleviation and Development in Hubei Province, was an annual household income of <5000 CNY.

We used three scenarios in which migrant workers who could not return to their workplace for i  = ( i  = 1, 2, 3) months lost their wage income for i months. Here, n denotes the number of working months of migrant workers and W denotes the wage income of each household. Assuming that the wage income is the same each month, the wage income of each household under the i -month scenario, denoted by w i , was calculated as follows:

Then, under the i -month scenario, when a household’s total income was less than 5000 CNY, we considered that the household had been pushed back into poverty. Here, the number of households pushed back into poverty for a town is denoted by P , the total number of poor households in a town is denoted by M , and the risk of returning to poverty in a town is denoted by R , which is calculated as follows:

Identifying the risk factors

Household-level characteristics were measured using 13 indicators: the Shannon–Weiner diversity index (see the Supplementary Information for details), the proportion of wage income, working hours of migrant workers, number of family members, gender of family head, fruit crop planting area owned by the household, type of path to the house, area of grain to green (cropland returning to forest) owned by the household, area of woodland owned by the household, irrigation area, shortest distance to the village’s main road, whether the household has access to power (binary value), and whether the household was helped by leading enterprises (binary value). The income structure data for each household were collected from all counties. However, detailed information on the other household characteristics was only available for Yingshan County. Therefore, we used two logistic regression models to identify the household characteristics that distinguished households at high risk of returning to poverty from those at lower risk. In our study, the response variable pertained to whether a household returned to poverty. In the first model, we analysed the impact of income characteristics on poor households in all counties and found that the proportion of wage income was consistently significantly associated with the risk of returning to poverty under all lockdown scenarios (Fig. 3a ). In the second model, the other 11 indicators of household characteristics were analysed using data from Yingshan County. The results are shown in Fig. 3b . Supplementary Table S1 lists the estimated odds ratios of returning to poverty and not returning to poverty.

The potential risk factors and selected factors at the town level are listed in Supplementary Tables S2 and S3 , respectively. The variable selection and data sources can also be found in the Supplementary Information . We identified the relative importance of risk factors and their detailed relationship with the risk of returning to poverty through variable importance metrics using the regression model, which is the LMG approach (Fig. 4a ). The LMG approach is able to identify a variable’s direct contribution and its contribution in combination with all other predictors (Grömping, 2015b ). The explanatory variables (risk factors) in the initial model were denoted as S as x 1 ,…, x p , and the response variable (risk of returning to poverty) was denoted as Y. The R 2 of S was defined as the ratio of the residual sum of squares, denoted as RSS, to the total sum of squares, denoted as SST, as follows:

When the variable x m was added to the model to form the new model M, the additional R 2 was defined as seq R 2 ( M / S ) as follows:

a The importance ranking of various factors in relation to the risk of returning to poverty using the LMG approach in the multiple linear regression model and the bivariate relationship between risk and the distance to the nearest railway station under 1-, 2- and 3-month lockdown scenarios. b The estimated risk of returning to poverty for each town in Hubei Province under the three lockdown scenarios.

The order of the explanatory variables is a permutation of the available variables, denoted by the tuple of indices r  = ( r 1 … r p ). Let S k ( r ) denote the set of variables entered into the model before variable x k in order r . Then, the portion of R 2 allocated to variable x k in order r can be defined as follows:

Then, for explanatory variable x k , the LMG metric is calculated as follows:

The LMG metrics were calculated using the R package relaimpo_2.2-3 (Grömping, 2015a ). Supplementary Tables S4 – S6 show the importance ranking of the nine selected variables for the three scenarios based on the multiple linear regression model. The proportional marginal variance decomposition (PMVD) metrics were also calculated to verify the results of the LMG method in Supplementary Tables S4 – S6 . The results indicated that both the LMG method and PMVD identified the most important factor (distance to the nearest railway station). However, PMVD allocated more importance to this factor and less importance to other factors.

Mapping the risk of returning to poverty

We used the GAM to predict the risk of returning to poverty in Hubei Province at the town level based on the selected risk factors (Fig. 4b ). The GAM is a semi-parametric extension of the generalized linear model, which assumes that the function is additive and the composite of the functions is a smoothing function (Hastie and Tibshirani, 1987 ). The GAM allows the regression coefficients of explanatory variables to be smooth curves, which can better capture the non-linear relationship between the response variable and the explanatory variable. The basic form of the GAM can be written as follows:

where E ( Y ) represents the expectation of the response variable Y , g () represents the link function, β 0 represents the intercept and f 1 … f m represents the smooth function of the explanatory variables, which can be in the form of specified parameters, non-specified parameters, or semi-parameters.

The implementation of the GAM was divided into four steps. (A) Variable selection : We removed multicollinearity among the explanatory variables by computing the variance inflation factor of the explanatory variables. The selected variables are shown in Supplementary Table S3 . (B) Link function : Since our response variable is a proportion within the interval (0,1), we selected the beta family function as our link function. (C) Model fitting : All possible models were compared and analysed, and the optimal model was selected. In this step, all variables selected after the collinearity diagnosis were imported into the full GAM, and then insignificant variables with a p -value > 0.01 were deleted. The estimated degrees of freedom and p -value for each variable in the initial full GAM and the final optimal variable under different lockdown scenarios are presented in Supplementary Tables S7 and S8 , respectively. (D) Model evaluation : The residual distribution was evaluated for the optimal model. The optimal principles of the model were as follows: (i) the influence of all explanatory variables in the model reached a significant level and (ii) the adjusted R 2 was greatest, while the Akaike information criterion was the smallest. The results of the model validation process are presented in Supplementary Table S9 . The optimal GAM was then applied to the rest of the towns in Hubei Province to estimate the risk of returning to poverty at the town level. The selected variables and their influence on the risk of returning to poverty in the optimal GAM model under different lockdown scenarios are plotted in Fig. 4 .

Households that depend on a single source of income are at high risk of returning to poverty

The results based on the full household sample revealed that the percentage of households at risk of returning to poverty increased from 5.6% to 22% under the 3-month lockdown scenario. It was also found that the proportion of wage income was significantly consistently correlated with the risk of returning to poverty under all scenarios (Fig. 3a and Table S1 ). The changes in income structure and per capita income under the different scenarios are presented in the form of histograms in Supplementary Figs. S1 and S2 . Working hours, number of family members, whether the household was supported by leading enterprises, and whether the household had access to power were factors that were consistently associated with the risk of returning to poverty under all scenarios (Fig. 3b and Table S1 ).

In general, high-risk households tended to have a single source of income, shorter working hours, and more family members, were not supported by leading enterprises and had no access to power. For every one-unit increase in income diversity and working hours, the likelihood of returning to poverty decreased by 15% and 32%, respectively (Supplementary Table S1 ). When households were supported by leading enterprises and supplied with power, the likelihood of returning to poverty decreased by 41% and 22%, respectively (Supplementary Table S1 ). This is primarily because households with diverse income sources are less affected by shocks, and more able to sustain their income and consumption (Porter, 2012 ). Poor households supported by leading enterprises and supplied with power are less likely to return to poverty. These two characteristics indicate that industrial development in villages could not only help poor households to overcome poverty, but also protect them from returning to poverty by cushioning them from external shocks. Therefore, efforts should be made to help households engaged in casual jobs with more family burdens, and those that are not supported by leading local enterprises.

Regions closer to train stations are at high risk of returning to poverty

Previous studies have found that people living in remote rural areas are more vulnerable and easily returned to poverty during an economic recession, such as that caused by the COVID-19 pandemic (Bennett et al., 2018 ; Dang et al., 2017 ; Hallegatte et al., 2020 ; Hone et al., 2019 ; Mueller et al., 2021 ; Schwarz et al., 2011 ). Conversely, this study found that in the early days of the COVID-19 outbreak, poor people who lived closer to train stations had a higher risk of returning to poverty as a result of traffic lockdowns and the travel ban in China. Our analysis revealed that the factors indicating transportation conditions were the most important factors related to the risk of returning to poverty in different regions under different scenarios (Fig. 4a1 ). The distance to the nearest railway station had a negative linear relationship with the risk of returning to poverty under all scenarios, as can be seen in Fig. 4a2–a4 . On average, a decrease of 10 km in the distance to the nearest railway station increased the number of households being returned to poverty by approximately 2%. The impact of the variable distance to the nearest railway station remained almost unchanged over time, confirming its importance.

Figure 4b shows the estimated risk of returning to poverty under the three scenarios for each town in Hubei Province. We found that high-risk regions were always distributed in railway station buffer zones, while low-risk regions were distant from railway stations, indicating that the spatial pattern was relatively stable under different scenarios and that the results were stable under the chosen GAM. Furthermore, the spatial cluster map of the risk of returning to poverty also confirmed a stable spatial pattern (Supplementary Fig. S3 ).

We also found that some areas had a good balance in terms of development, and thus a lower risk of returning to poverty as a result of the COVID-19 pandemic. As can be seen from Fig. 5 , the four-quadrant vulnerability diagram representing the relationship between the risk of returning to poverty and the distance to the nearest railway station, regions in the third quadrant were closer to a railway station but less vulnerable. These regions benefited from the prosperity brought about by the presence of the station, and might have had fewer migrant workers, making them less vulnerable to the impact of the COVID-19 pandemic.

LF represents a lower risk of returning to poverty while being far from a railway station; HF represents a higher risk of returning to poverty despite being far from a railway station; LN represents a lower risk of returning to poverty despite being near a railway station; and HN represents a higher risk of returning to poverty while being near a railway station. The Pearson correlation test for vulnerability and the distance to the nearest railway station are presented in Supplementary Table S10 .

This case study in Hubei Province provided new information in terms of who was at high risk of returning to poverty, where they were located, and why they were at high risk in the initial stages of the COVID-19 pandemic. The responses to COVID-19 gradually improved in the later stages, most notably with the development of vaccines (Wouters et al., 2021 ). However, at the beginning of the pandemic, the entire society was overwhelmed (Wang et al., 2020 ), with low-income groups suffering the most. Lessons from the early stages of the COVID-19 pandemic on the links between the pandemic and poverty could provide early warnings in relation to future shocks. This case study identified the households and regions at high risk of returning to poverty as a result of loss of income. This enables the targeting of high-risk populations and regions, and the development of effective policy responses in the initial stages of a shock. The analysis of risk factors provides guidance for policymakers aiming to initiate rapid responses in an effort to mitigate the effects of lost income.

The COVID-19 pandemic spread swiftly, and continues to disrupt the world today. Many countries have had to implement stringent lockdown measures, including travel restrictions and local transportation lockdowns, in an effort to control the pandemic (Liu et al., 2021 ). To minimize the risk of returning to poverty, China quickly adopted a host of policies when the COVID-19 pandemic emerged, and succeeded in eliminating absolute poverty by the end of 2020. For instance, local governments chartered point-to-point buses, trains, and planes to transport migrant workers back to their workplaces, provided personal protective gear, and facilitated the flow of labour and materials to enable enterprises to resume operations (McDonald, 2020b ; Zhang, 2020 ). However, the people and regions that were lifted out of poverty remain vulnerable, and many other developing countries are still trapped in poverty as a result of the COVID-19 pandemic. Thus, identifying the households that are most vulnerable and where they are located is a key task in numerous countries.

While the construction of additional railway stations has accelerated the pace of urban development, the outbreak of the COVID-19 pandemic reversed this process and brought about deurbanization (Givoni, 2006 ; Puaschunder, 2021 ). Proximity to railway stations makes it more convenient for poor people to seek migrant work, and thus become more dependent on the wage income from that work. Consequently, when they were locked down, they lost part or all of the income they were relying on to support their families, and thus were more vulnerable to the impact of COVID-19. Therefore, policies aimed at preventing a return to poverty should target rural households living in remote areas and those living near railway stations.

Once the lockdown was lifted, poor households were able to return to work, however, it is difficult to determine whether their lives returned to normal. Some poor households might be infected with the virus and lost their entire employment and income, while others might have only lost their income temporarily, but still fallen back into poverty because, for example, they were unable to make the required repayments on their loans. The development of vaccines was an important factor affecting the risk of returning to poverty in later stages. Given that this was one of the most crucial innovations in the fight against the virus (Vuong et al., 2022 ), as vaccination levels increased, many countries gradually abandoned their various non-pharmaceutical interventions (NPIs), including lockdowns (Sonabend et al., 2021 ). However, studies from China (Yang et al., 2021 ), the United States (Borchering et al., 2021 ) and the European Union (Bauer et al., 2021 ) suggested that NPIs would still be required even when the population was fully vaccinated. The synergistic effect of NPIs and vaccination was 46.9% in 27 countries, whereas the effects of NPIs and vaccination alone were 20.7% and 28.8%, respectively (Ge et al., 2021 ). Therefore, despite rising vaccination levels, the relaxation of NPIs might have prevented enterprises and transportation systems from resuming operations, thereby increasing the risk of people falling into poverty.

There are also some limitations to our analysis. First, the study period was limited to the 3-month lockdown, even though many countries are still in the grips of the COVID-19 pandemic, and the impact could continue for a long time, even after the resumption of normal activity. Second, this study only focused on poor households that have been lifted out of poverty, but there would also be a large number of households that were not previously considered to be poor based on the poverty line that have fallen into poverty as a result of the COVID-19 pandemic. Third, since the COVID-19 pandemic and the measures imposed in an effort to control its spread have had a significant impact on the environment, economy, and society, and have affected the livelihoods of poor households. However, this study mainly focused on loss of household income induced by the COVID-19 pandemic control measures.

The COVID-19 pandemic continues to affect people’s livelihoods, and households returning to poverty caused by COVID-19 will still appear. In future research, first, attentions on the later stages of the pandemic also deserved. Following the implementation of various measures aimed at mitigating the effects of COVID-19, the patterns of who and which regions are at high risk of falling into poverty is going to change. Therefore, identifying high-risk populations in the later stages of the pandemic is crucial when making policy decisions. In addition, the study sample should be extended to cover a wider range of people. Attention is usually focused on households that are considered poor, while marginalized populations are likely to be ignored. Finally, the impact of COVID-19 on other dimensions of poverty requires further exploration. In addition to causing loss of employment and income, COVID-19 impacts the poor in other ways, for example, through reduced availability of housing, education, and healthcare. Furthermore, the compound risk of the pandemic combined with other shocks, such as natural disasters and violent conflicts, also needs to be addressed.

Furthermore, global crises caused by COVID-19 could slow or even reverse many Sustainable Development Goals (SDG) implementation processes (Gulseven et al., 2020 ; Shulla et al., 2021 ). It is important to understand the consequences of the COVID-19 pandemic related to other SDGs, such as the SDG3 (Health & Well-Being), SDG4 (Quality Education), SDG8 (Decent Work & Economic Growth), SDG12 (Consumption & Production), and SDG13 (Climate Action) (Guerriero et al., 2020 ). As a representative of SDG (SDG1: No poverty), our work provides a quantitative basis for exploring the risk of returning to poverty across different groups and regions, as well as policy-relevant evidence to lower the risk, which could provide guidance to increase the efficiency of the post-pandemic recovery process.

Data availability

The data and materials needed to support the paper’s conclusions are included in supplementary information files.

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Acknowledgements

This work was supported by the National Natural Science Foundation for Distinguished Young Scholars of China (No. 41725006 and 81773498) and the Bill & Melinda Gates Foundation (INV-024911).

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These authors contributed equally: Yong Ge, Mengxiao Liu, Shan Hu, Daoping Wang.

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Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China

Yong Ge, Mengxiao Liu, Shan Hu & Jinfeng Wang

University of Chinese Academy of Sciences, 100049, Beijing, China

School of Urban and Regional Science, Shanghai University of Finance and Economics, 200433, Shanghai, China

Daoping Wang

The Institute for Six-sector Economy, Fudan University, 200433, Shanghai, China

Xiaolin Wang

WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK

Sarchil Qader, Eimear Cleary, Andrew J. Tatem & Shengjie Lai

Natural Resources Department, College of Agricultural Engineering Sciences, University of Sulaimani, Sulaimani, 334, Kurdistan Region, Iraq

Sarchil Qader

Institute for Life Sciences, University of Southampton, Southampton, SO17 1BJ, UK

Shengjie Lai

Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, 200031, Shanghai, China

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Ge, Y., Liu, M., Hu, S. et al. Who and which regions are at high risk of returning to poverty during the COVID-19 pandemic?. Humanit Soc Sci Commun 9 , 183 (2022). https://doi.org/10.1057/s41599-022-01205-5

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How many people is the COVID-19 pandemic pushing into poverty? A long-term forecast to 2050 with alternative scenarios

Roles Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Writing – review & editing

* E-mail: [email protected]

Affiliation Frederick S. Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, Colorado, United States of America

Roles Formal analysis, Writing – original draft, Writing – review & editing

Roles Data curation, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – review & editing

Roles Data curation, Writing – review & editing

Roles Data curation, Formal analysis, Writing – review & editing

Roles Writing – original draft, Writing – review & editing

Roles Data curation, Software

Roles Conceptualization, Methodology, Software, Writing – review & editing

Roles Conceptualization, Methodology, Software, Writing – original draft

• Jonathan D. Moyer, 
• Willem Verhagen, 
• Brendan Mapes, 
• David K. Bohl, 
• Yutang Xiong, 
• Vivian Yang, 
• Kaylin McNeil, 
• José Solórzano, 
• Mohammod Irfan, 

• Published: July 8, 2022
• https://doi.org/10.1371/journal.pone.0270846
• Reader Comments

The COVID-19 pandemic has changed the course of human development. In this manuscript we analyze the long-term effect of COVID-19 on poverty at the country-level across various income thresholds to 2050. We do this by introducing eight quantitative scenarios that model the future of Sustainable Development Goal 1 (SDG1) achievement using alternative assumptions about COVID-19 effects on both economic growth and inequality in the International Futures model. Relative to a scenario without the pandemic (the No COVID scenario), the COVID Base scenario increases global extreme poverty by 73.9 million in 2020 (the range across all scenarios: 43.5 to 155.0 million), 63.6 million in 2030 (range: 9.8 to 167.2 million) and 57.1 million in 2050 (range: 3.1 to 163.0 million). The COVID Base results in seven more countries not meeting the SDG1 target by 2030 that would have achieved the target in a No COVID scenario. The most pessimistic scenario results in 17 more countries not achieving SDG1 compared with a No COVID scenario. The greatest pandemic driven increases in poverty occur in South Asia and sub-Saharan Africa.

Citation: Moyer JD, Verhagen W, Mapes B, Bohl DK, Xiong Y, Yang V, et al. (2022) How many people is the COVID-19 pandemic pushing into poverty? A long-term forecast to 2050 with alternative scenarios. PLoS ONE 17(7): e0270846. https://doi.org/10.1371/journal.pone.0270846

Editor: Alison Parker, Cranfield University, UNITED KINGDOM

Received: July 13, 2021; Accepted: June 20, 2022; Published: July 8, 2022

Copyright: © 2022 Moyer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: Gift from Frederick S Pardee. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Additionally, no one receives a salary from Mr. Pardee—he contributed financial support through gift funding of the endowment of the Center.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The sustainable development goals (SDGs) include a broad set of human development outcomes, with the first target indicator aiming to, “end poverty in all its forms,” by 2030 [ 1 ]. Over the previous 20 years, much progress has been made to improve the livelihoods of millions around the world by reducing extreme poverty, alleviating malnutrition, and improving human development capabilities [ 2 ]. However, the spread of COVID-19 is expected to disrupt these development gains, making it more difficult for economies to function, for individuals to earn income, and for governments to raise revenues.

Previous research has analyzed long-term global poverty trajectories in the absence of COVID-19 [ 3 – 6 ] and explored the possibility of achieving SDG 1 across various socio-economic scenarios [ 7 , 8 ], with estimates ranging from 1% to 18% of the global population living in extreme poverty by 2030. Recent analyses suggest that COVID-19 will increase poverty in the short-run but we have less insights about the potential longer-term effects of COVID-19 on extreme poverty projections in the SDG horizon or beyond [ 9 – 12 ]. An exception is a study by Lakner et al. (2022) that does incorporate COVID-19 into poverty projections to 2030, but does not quantify the effect relative to a world without COVID-19. This paper addresses this gap.

We introduce eight scenarios modeling different assumptions related to economic growth and income distribution that reflect two key dimensions of uncertainty associated with the COVID-19 global pandemic, operationalized within the International Futures (IFs) integrated assessment model. We begin by comparing a No COVID Base against a COVID Base scenario modeling the future of global poverty following the current country-level development trajectory with and without COVID-19 effects on GDP growth. We then create six additional uncertainty scenarios with alternative assumptions related to uncertainty associated with the effects of the global pandemic on GDP growth and income inequality for 186 countries from 2021–2050.

COVID-19 and development: Dimensions of uncertainty

Household income and its distribution are two key determinants of poverty levels [ 4 , 13 , 14 ], as well as two major sources of uncertainty associated with the impact of COVID-19 on development. Here we review our current understanding of how COVID-19 is changing patterns of economic growth as well as the distribution of resources. Across the studies surveyed in this section, we identify greater consensus in the understanding of the effect of COVID-19 on economic growth, and greater uncertainty when analyzing its effect on resource distribution.

The macro-economic impacts of the virus have been broad and complex, effecting development patterns across changing patterns of labor participation [ 15 ], economic growth [ 16 ], changes in trade, remittances, foreign direct investment [ 17 ], education [ 18 ] and more [ 19 ]. COVID-19 has proven distinct from other economic crises by its constraining effects on both supply-side production and demand-side consumption [ 20 ]. These dual shocks have impacts with considerable variation across countries as governments implement a range of policy responses for both virus containment and economic stimulation.

Labor markets were affected in unprecedented ways in 2020 with working-hour losses approximately four times greater than in the 2009 global financial crisis [ 15 ]. The ILO estimates that 8.8 percent of global working hours were lost in 2020 relative to the fourth quarter of 2019–equivalent to 255 million jobs [ 15 ]. Although there was a larger rebound than anticipated in working hours in the second half of 2020, global working hours still declined by 4.6 percent in the fourth quarter of 2020 [ 15 ]. The labor market rebound seen in the second half of 2020 did not continue into 2021, as estimations for lost labor hours in quarters one, two, and three of 2021 represent a respective 4.5, 4.8, and 4.7 percent decrease from the fourth quarter of 2019 [ 21 ]. In the United States alone, the economic consequence of lost working hours amounts to an estimated $138 billion [ 22 ].

The World Bank’s Global Economic Prospects (GEP) presents anticipated economic recovery levels in 2022 and 2023. Following an economic rebound in global GDP growth to 5.5 percent in 2021, the GEP predicts a pullback in both 2022 and 2023, to 4.1 and 3.2 percent respectively [ 23 ]. However, variation in factors such as the speed and efficacy of vaccination program and economic policy implementation as well as structural characteristics such as reliance on tourism, trade, or remittances will lead to significant variation in how quickly economies recover. As such, emerging markets and developing economies may face more significant lasting effects while advanced economies are likely to recover more quickly [ 24 ].

While COVID-19 has direct and measurable effects upon the growth of overall economic output, its effect on the distribution of income is less clear and historical evidence from other pandemics is mixed. In prior pandemics, the working-population mortality rate is a key determinant in driving reductions in income inequality, with higher rates associated with labor shortage driving an increase in real wages [ 25 – 27 ]. The Black Death, for example, decreased income inequality in both England and Italy due to a large mortality-driven decrease in the labor supply [ 25 , 26 , 28 , 29 ]. Pandemics may also create a production crisis as well as prompt a reduction in consumption due to an increase in savings, thus reducing the rate of return on capital and disproportionately affecting the wealthy [ 27 , 30 ]. Some previous pandemics have increased inequality, for example in 1629–1630 [ 28 ]. COVID-19 is unlikely to increase the wage-rental ratio, as the death rate is not high enough nor are its consequences evenly distributed enough across low and high paying occupations to have lowered inequality on its own [ 31 ].

Another dimension of inequality relates to the distribution of job losses and reduced output across sectors. Job losses from COVID-19 have been disproportionately located within certain sectors including hotels and restaurants, agriculture, construction and commerce [ 32 , 33 ] making certain workers, industries, countries and regions more vulnerable to pandemic driven downturns [ 24 ]. Generally, remote work has been more common in better educated and higher paid industries [ 34 ] while more labor-intensive industries and those with limited use of information and communications technology have proven less amenable to remote work [ 32 ]. Persistent job losses and the impact on consumer spending, savings and productivity will continue to prolong recovery, especially in countries with a large portion of labor-intensive industries and weak social safety nets. However, the effects of COVID-19 on inequality are not limited to the characteristics of the virus itself but include the reactions of the societies that it affects; herein lies the uncertainty as to the overall effect of the pandemic upon inequality.

The emerging evidence concerning the effects of COVID-19 on between-country inequality is mixed but seems to point towards an increase. On a between-country basis, international income inequality was found to have both decreased [ 35 ], increased [ 36 ], and increasing when countries are weighted by population [ 35 ]. This is due in part, to the influence of both China and India over population weighted studies, as rising incomes in China were not able to fully offset declining incomes in India [ 35 ]. Other studies have estimated an increase in the global Gini-index of 1.2–2% based on previous pandemics [ 37 ], whereas preliminary data suggest the between country Gini-index increased by 0.4 points throughout 2020 [ 23 ]. While this increase is lower than some predicted, it is still significant as it returned between-country inequality to levels last seen during the early 2010s [ 23 ].

Evidence for effects of COVID-19 on within-country inequality is highly heterogeneous and can be both positive and negative. Within-country inequality experienced an estimated increase, of 0.3 points in emerging market and developing economies and 0.4 points in low-income countries [ 23 ]. A working paper by Hill & Narayan [ 38 ] provided evidence for the high uncertainty and heterogeneity of Covid-19’s effects upon short-to-medium term within-country inequality; but also concluded that Covid-19 will likely generate negative consequences for within-country inequality in the long-term. Additional country-level research sheds light onto the differential impacts of COVID-19 on within-country inequality, including research examining Mexico, Brazil, Argentina, and Colombia. In Brazil, within-country inequality fell by an estimated 2.9 points below its pre-pandemic levels [ 39 ]. In Mexico, the Gini-index had an estimated increase of 1.3–3.7% for the 2020 fiscal year [ 40 ] while Columbia and Argentina both experienced an estimated increase of only 0.9 and 0.1 points during 2020 [ 39 ].

Overall, data and analysis with respect to the growth and inequality impacts of COVID-19 during the pandemic period continue to emerge, but the implications of those impacts through the 2030 horizon of the current SDGs and beyond remain highly uncertain. This reality affects the scenarios used in this study that will be elaborated below.

Poverty projections and COVID-19

Projections of poverty often include quantitative simulations that model structural factors in isolation or interaction. These models frequently include assumptions or dynamics related to three sets of variables: a) growth in economic production/income/consumption [ 41 – 44 ]; b) the distribution of resources/income/consumption [ 13 , 45 ]; and c) demographic change. Extensions of earlier approaches have innovated in their representation of income distribution [ 7 ], integrated poverty research with Shared Socio-economic Pathways (SSPs) long-term economic growth projections [ 7 , 46 ], developed income distributions along the SSP projections [ 47 ], studied the relative importance the Gini-index for extreme poverty [ 10 ], or integrated these three systems dynamically in one framework [ 4 , 5 , 8 , 48 ].

COVID-19 caused many researchers to revise their estimates of global poverty often focused on pandemic-period impacts ( Fig 1 ). Sumner et al. [ 12 , 49 ] provide early and updated poverty estimates using an “augmented poverty line approach” to model the impact of income per capita contraction across various scenarios due to COVID-19. The updated study estimated that between 77 and 390 million people could fall into extreme poverty due to the pandemic, revised downward from 89 to 419 million people in the earlier version of the study.

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If no impact year was specified in the original study, it was assumed that the year assessed for the impact of COVID-19 was the year of study publication.

https://doi.org/10.1371/journal.pone.0270846.g001

Research using MIRAGRODEP (a dynamic recursive CGE model built on a microsimulation framework) projects that COVID-19 will have increased extreme poverty by almost 150 million in 2020, with the majority of increased poverty burden (~80 million) occurring in sub-Saharan Africa [ 50 ].

Other estimates of the short term impact of COVID-19 on poverty include: a) analysis from Kharas & Hamel [ 51 ] and Kharas & Dooley [ 9 ] showing that COVID-19 could increase global extreme poverty by 50 million people and 97.1 million people respectively in the short term; b) figures from Mahler et al. [ 11 ] showing that COVID could increase poverty between 40–60 million in 2020; and c) figures from the Bill & Melinda Gates Foundation and IHME [ 52 ] report an initial poverty increase of 37 million driven by the global pandemic, with a more recent report citing a 31 million increase. Lakner et al. [ 10 ] and provides a number of alternative scenarios that vary GDP and Gini-index assumptions by 1%. The analysis from Kharas & Hamel [ 51 ] and Kharas & Dooley [ 9 ] build on the methodology from Crespo-Cuaresma et al. [ 7 ] used for the World Poverty Clock to account for decreased global economic output. A summary of these alternative estimates is shown in Fig 1 .

Many of the studies surveyed in Fig 1 do not include an assessment of the impact of COVID-19 beyond the short-term (2020–2021). Exceptions include work associated with the World Poverty Clock project, estimating that COVID-19 will increase poverty by 100 million in the short-run and 50 million by 2030 [ 9 ]. Reedy [ 53 ] also explores the effect of different GDP growth rates on 2030 poverty outcomes, with a range of 6%-11% under high and low growth scenarios, respectively, but shifts in within-country inequality are not explored in the study. There remain very few estimates of the impact of COVID-19 through 2030 on global poverty in academic literature. Lakner et al. [ 10 ] is an exception, which builds upon earlier work from the same authors to test the impact of a range of growth rate and inequality assumptions on the effects of the pandemic across the SDG horizon, estimating poverty ratio outcomes ranging from 5.3%-11.7% by 2030. This study however, does not seek to explicitly estimate the progress lost on poverty reduction due to the COVID-19 pandemic over the SDG horizon.

Modeling methodology

IFs models economic growth and household consumption, income distribution at various thresholds, and demographics to forecast poverty headcounts under alternative scenarios for 186 countries through the year 2050. The poverty model is embedded within a larger system of agricultural, demography, education, economic, energy, environmental, health, infrastructure, governance, security, and technological models [ 54 ]. IFs has been used in a variety of research on poverty and other measures of human development [ 4 , 5 , 8 , 48 , 55 – 57 ], with a recent focus on the effects of COVID-19 on extreme poverty, and more generally human and economic development [ 17 , 19 , 58 – 60 ].

The full model, and individual sub-modules, have also been documented in several publications [ 4 , 54 , 61 – 64 ] and documentation and model are freely available for use ( https://pardeewiki.du.edu/index.php?title=Understand_the_Model ). The following sections outline how IFs forecasts changes in average income as a result of economic growth, how changes in average income are transformed to changes in average consumption, and the calculation of income distribution using the Gini-index. These components are the input into a log-normal distribution to calculate extreme poverty per country.

Using economic growth, disposable income and a social accounting matrix (SAM) to forecast consumption

Existing poverty models base their projections on changes in average income or average consumption [ 3 , 6 , 46 ]. The IFs model also uses consumption to drive its poverty forecasts using three steps. First, IFs projects economic growth using a Cobb-Douglas production function that determines value added by economic sector from capital, labor and productivity. That economic activity drives gross household wages, a key source of income. Second, these earnings are considered along with other income and expenditure flows, for example taxes and transfers, in a social accounting matrix (SAM) that computes a measure of disposable income. Finally, disposable income is used in a multi-step process to calculate final consumption in balance with final savings.

Economic growth drives changes in gross income, the next step in the calculation of overall consumption. This process begins with an estimate of country-level household earnings measured as the share of value added that goes to labor. Next, to translate earned income into disposable income (the income that a household can choose to either consume or save), the model computes the other financial flows relying on a SAM structure. Data to initialize the production function and the broader SAM come from GTAP, the World Bank, and IMF [ 24 , 68 , 69 ] with priority attention to sources based on the extensiveness and recency of data, as well as on the differential focus of the sources on the variable. When data do not exist for specific country values, holes are generally filled using cross-sectionally functions of GDP per capita at purchasing power parity.

The SAM represents monetary flows within and across economies and includes the following elements: a) actors (households, governments, firms) and b) domestic and global financial flows among these actors (including input-output matrices for each country with related sectoral flows, governmental revenue and expenditure streams, trade, investment, remittances, aid, loans, etc.) [ 70 ].

At the end of this process, the model calculates country-level disposable household income which is then used in the final stage to calculate overall consumption. To calculate consumption, IFs relies on a multiple-step process that factors in a) global patterns of changing consumption/savings based on levels of development; b) the age structure of a society; and c) signals from changing patterns of price and interest rates. To connect changing patterns of consumption propensity with levels of development, IFs calculates a long-range consumption target as a share of price adjusted GDP potential, using the historical relationship of consumption with GDP per capita at PPP. A preliminary consumption value is then computed from a consumption ratio of income driven by level of development and responsive to the gap in household saving needs and converges to the target value over 150 years. Next, following insights from Modigliani [ 71 ], we track the life-cycle savings and consumption patterns reflecting an understanding that both young and old have higher levels of consumption relative to income compared with working age individuals.

Finally, IFs adjusts the relationship between consumption and savings based on signals sent from two price mechanisms: interest rates and sectoral prices. Both interest rates and prices are calculated across time in IFs using long-term equilibrating mechanisms that are connected to underlying inventory stocks. For instance, if inventory stocks fall as a portion of annual production, prices and interest rates rise and over time direct more of income to savings. The final product of this multi-step process is country-level consumption, which is an input to the next stage of modeling poverty.

Calculating the distribution of consumption

There are multiple approaches and functional forms used to estimate the distribution of consumption [ 10 , 45 , 46 ]. IFs estimates poverty using the assumption of a log-normal distribution of per capita consumption [ 13 ]. The log-normal functional form can be specified using two parameters: mean consumption and its standard deviation. In contrast to income and consumption, we treat Gini-index as exogeneous, holding it constant in the Base scenario and adjusting it for alternative scenarios. Poverty rates are initialized using data from PovcalNet [ 72 ].

Modeling demographic change

The IFs demographic model uses an age-sex cohort structure and forecasts demographic changes based on changing patterns of fertility, mortality and migration [ 54 ]. Population data are initialized using the UN population division [ 76 ]. Data and projections on migration come from WIC/IIASA projections [ 77 ] in work for the shared socioeconomic pathways project [ 78 ]. The drivers of change in fertility rates include contraception use, infant mortality, GDP per capita, and average levels of education. The drivers of mortality are broad and documented in other publications [ 62 ].

Comparison with other approaches

Several models aim to project extreme poverty over long time horizons. There are some important differences between the IFs methodology and other existing poverty projections. A major difference between IFs and other approaches is that most approaches use exogeneous growth rates to project income and/or consumption, whereas our approach aims to endogenously represent many processes with details on economic activities, agent decisions and accounting flows, making poverty reduction pathways more transparent. The choice to represent the world as an interconnected system and aiming to represent many of these interactions in itself represent a different modelling philosophy with advantages to more broadly representing patterns of economic and human development but arguably makes it more challenging to succinctly describe the full model.

In addition to these broader philosophical distinctions in modeling approaches, more concrete differences are also important to mention. First, previous research has used changes in average income (GDP per capita) to estimate poverty [ 3 , 46 ]. The use of average income provides clear computational advantage, increases data availability, and allows for integration of the work with existing scenario frameworks such as the SSPs, but the downside of this approach is that extreme poverty is generally defined in terms of consumption rather than income. Some poverty models, for example [ 3 , 10 ] address this by computing an adjustment factor to convert income to consumption. Here we also calculate poverty based on consumption, with the main differences being the use of the SAM to convert income to disposable income, and then using a ratio of income to consumption couple with population dynamics to arrive at final consumption.

A second difference relates to the choice of the distributional function. IFs uses a log-normal distribution to estimate extreme poverty given its computational advantages and applicability across different countries [ 13 , 45 , 79 ]. Other distributional forms include Beta-Lorentz Curves using the Gini-index [ 7 ]. An alternative to these Gini-based approaches, is to rather use the entire distribution, either by using microdata or binned data, from household surveys. These distributions can then be fitted using multiple distribution types such as Beta-Lorentz Curve or a generalized quadratic Lorentz Curve depending on fit to the data, allowing for greater flexibility in the functional form [ 10 ]. Growth projections in these models are applied to each of the micro-data and poverty is recomputed for the same cut-off. Some of these bottom-up models simulate distributional change through non-parametric techniques like the growth-incidence curves used by [ 10 ].

Fig 2 presents the IFs model projections and contextualizes them relative to other model results in the year 2030. As the model results presented here do not include COVID-19 impacts, we present the No COVID Base (International Futures version 7.82), which is described in a subsequent section. This scenario projects 7.1% of the global population to live in extreme poverty by the year 2030. This value is towards the middle of other published 2030 poverty projections (ranging from 1% to 19% poverty ratio across various methodologies and scenario assumptions) and towards the pessimistic side of more recently published studies [ 7 , 53 , 54 , 80 – 83 ].

Each point represents a scenario outcome value–point color reflects year published. Studies published in or before 2015 expressed using extreme poverty line of $1.25/day (USD2005). Hughes et al. [ 4 ] figures expressed using poverty line of $1.00/day (USD2005). Studies after 2015 use extreme poverty line of $1.90/day (USD2011). Different income thresholds are comparable because they are expressed using different real dollar thresholds that attempt to capture similar levels of PPP across time.

https://doi.org/10.1371/journal.pone.0270846.g002

Scenario assumptions

The analysis presented here begins with a No COVID Base scenario representing expected development patterns in a world without the pandemic. To simulate this we rely on economic growth rates produced just prior to the pandemic in the World Economic Outlook (WEO) [ 84 ]. We apply growth rates from this report for 2019–2025 and then use IFs endogenous growth projections through 2050. For this scenario we maintain 2017 country Gini-index values through 2050. As noted previously, this scenario produces similar results to other medium-variant forecasts prior to the outbreak ( Fig 1 ). We compare this No COVID Base scenario with the COVID Base scenario. This scenario simulates the effect of COVID-19 by including WEO growth projections published for the years 2021–2023 [ 16 ]. From 2023–2050 we also rely on IFs endogenous growth projections for this scenario and keep country-level income inequality values flat across time.

We compare these two scenarios with six alternative scenarios that frame uncertainty by varying GDP growth and income inequality. We vary GDP growth by 1.5 percentage points for 2022 around the COVID-Base values and then converge these to the COVID Base growth trajectory by 2025. The 1.5 percentage points variation is a high-end assumption that falls within the standard variation across world GDP growth rates from the WEO during the COVID-19 period (~1.6%), and the mean difference across countries in GDP growth rates comparing the World Bank Global Economic Prospects [ 85 ] and the IMF WEO April 2021 release (~1.6%) [ 24 ].

After 2025 the alternative scenario growth calculations are a product of the IFs model described above. These scenarios also differ in their assumptions regarding inequality, which we vary by -2%, +2% and +5% relative to each country’s COVID Base value. This reflects literature described earlier that suggests that the pandemic will increase inequality but recognizes that this is highly uncertain. Thus, these additional six scenarios allow a sensitivity analysis of longer-term poverty futures around the COVID Base scenario. Their variation from that scenario therefore represent not just unknowns about the direct and indirect longer-term impacts of COVID, but also other uncertainties with respect to longer-term patterns of economic growth and change in distribution. Table 1 describes the scenario assumptions.

https://doi.org/10.1371/journal.pone.0270846.t001

COVID-19 has reduced global economic activity. Our simulation suggests that global GDP in the COVID Base is $5.7 trillion less than the No COVID Base in 2020 and $3.5 trillion less in 2021. Annually, the gap between these scenarios grows to $6.1 trillion in 2050, resulting in a 3.2% reduction in the COVID Base relative to the No COVID Base . Cumulatively through 2050 the pandemic is projected to have reduced economic output by $122.6 trillion dollars. Table 2 highlights the economic growth results across different scenarios.

https://doi.org/10.1371/journal.pone.0270846.t002

The scenarios that include high economic growth assumptions see global annual GDP levels return to the No COVID Base by 2023. While the volume of economic activity returns quickly, the reductions in economic output in 2020–2022 remain large, totaling $10.2 trillion. Scenarios with low growth assumptions result in reduced economic output relative to the No COVID Base by a cumulative $27.5 trillion by 2025, $57 trillion by 2030, and around $250 trillion by 2050. Across the low-growth scenarios GDP in 2050 is between 6.5% and 6.7% lower than in a world where the pandemic did not occur. By 2050, differences in population size are relatively minor across scenarios, with the largest difference (between the No COVID Base and the Low Growth Very High Inequality scenarios) totaling 15.0 million people, or 0.15% of the projected population in that year.

In 2019, IFs estimates that the global population living in extreme poverty to be 693.1 million with 1.79 billion people living on less than $3.20 per day. In the No COVID Base , the number of people living on less than $1.90 is projected to fall to 607.8 million by 2030 and 383.0 million by 2050 while those living on less than $3.20 is projected to fall to 1.5 billion by 2030 and 1.1 billion by 2050. In percentage terms, the share of the global population living on less than $1.90 per day was 9.0% in 2019 and projected to reach 7.1% in 2030 and 3.9% by 2050 while the share of the population living on less than $3.20 was 23.2% in 2019 and projected to decline to 18.0% by 2030 and 11.1% by 2050. Of the 186 countries analyzed here, 102 had already achieved the target value (of less than 3%) for SDG 1 by 2019 and an additional 14 countries were projected to achieve the goal by 2030 in a No COVID scenario.

The COVID-19 Base increases both the number of people and the share of the population living on less than $1.90 and $3.20 compared with the No COVID Base , from 2020–2050. In 2020 and 2021, an additional 73.9 and 86.8 million people are projected to live on less than $1.90 per day (0.95 and 1.1 percentage point increase in the share of the population) and 149.5 and 180.5 million people are projected to live on less than $3.20 per day (1.9 and 2.3 percentage point increase in the share of the population). Over the long-run the COVID Base increases the number of people living on less than $1.90 per day relative to the No COVID Base by 63.6 million in 2030 and 57.1 million in 2050 (0.7 and 0.6 percentage point increase in the share of the population), and the number of people living on less than $3.20 per day by 152.7 million in 2030 and 136.8 million in 2050 (1.8 and 1.4 percentage point increase in the share of the population).

The Low Growth , High Inequality scenario is the most pessimistic and projects an increase in extreme poverty of 163.0 million by 2050 relative to the No COVID Base . More optimistically, the High Growth , Low Inequality scenario estimates an increase of 56.5 million people in extreme poverty by 2021, 9.8 million by 2030, and 3.1 million by 2050, representing increases of 0.7 percentage points in 2021, 0.1 percentage points in 2030 and 0.03 percentage points by 2050 relative to the No COVID Base . See Table 3 for a summary of findings across scenarios for the global population living on less than $1.90 and $3.20 per day.

https://doi.org/10.1371/journal.pone.0270846.t003

In the short-run, the largest increase in extreme poverty headcount due to COVID-19 occurs in South Asia and sub-Saharan Africa. In the long-run the region most likely to see the greatest COVID-related increases in extreme poverty in absolute terms is sub-Saharan Africa. Headcount increases in extreme poverty driven by COVID-19 in South Asia range from 29.8 to 73.3 million in 2021, 7.0 to 49.9 million in 2030, and 0.6 to 19.9 million by 2050. In Sub-Saharan Africa, increases in extreme poverty range from 17.4 to 49.3 million in 2021, -3.6 to 70.5 million in 2030, and -2.0 to 108.6 million in 2050 compared with a No COVID Base .

In 2021, the countries that experience the largest increase in the number of people living in extreme poverty are geographically diverse, with the greatest projected increase in India (an additional 36.6 million people), followed by Yemen (4.7 million), Ethiopia (3.3 million), Nigeria (3.1 million), and the Philippines (2.3 million). The countries with the largest share of the population projected to be pushed into $1.90 poverty in 2021 are Yemen (15.4%), Rwanda (7.9%), Lebanon (7.2%), Cambodia (6.5%) and Venezuela (6.1%). Fig 3 tracks country level changes in extreme poverty headcount in 2030 and 2050 comparing COVID Base and No COVID Base scenarios and Fig 4 shows the absolute increase in extreme poverty driven by the global pandemic.

The top panel shows the results for 2030, the bottom panel shows 2050. Source: Author’s computation. Shapefiles for map sourced from the NaturalEarth project ( naturalearthdata.com ).

https://doi.org/10.1371/journal.pone.0270846.g003

The top panel shows the results for a comparison of the No COVID Base and the COVID-Base for 2030, the bottom panel shows the same comparison for 2050. Source: Author’s computation. Shapefiles for map sourced from the NaturalEarth project ( naturalearthdata.com ).

https://doi.org/10.1371/journal.pone.0270846.g004

We analyzed the impact of COVID-19 on the achievement of SDG1, which targets an elimination of extreme poverty (often operationalized as bringing rates below 3% of each country’s population by 2030). In the No COVID Base 70 out of 186 countries do not achieve the 3% threshold by 2030. In the COVID Base this number increases to 77 countries. However, in a worst-case scenario, the number of countries that do not achieve SDG1 increases to 87.

This paper has highlighted the high costs for core aspects of human material well-being that can be associated with changing patterns of economic production and its distribution. Any modelling exercise has methodological limitations, and this is even more true for an analysis of the impacts on human development of an evolving and continuously changing pandemic.

First, there are challenges in assessing the immediate impacts of the pandemic. Similar to other COVID impact assessments [ 9 , 10 , 50 ], this analysis estimated the COVID-19 impact on GDP as the difference between growth rates anticipated in 2020 and later years anticipated in 2019 prior to the pandemic and those that have subsequently been achieved. The resulting differences in GDP growth between these two series can be primarily attributed to COVID-19 but obviously also include other impacts on GDP growth.

A second limitation is the lack of sectoral differentiation of COVID’s economic growth impact. Here, largely for reasons of current data and analysis availability, we looked at overall GDP growth without accounting for how these differences play out across different economic sectors (but see [ 39 ]).

A third limitation is the large uncertainty associated with the future of the pandemic, the rise of new variants and consequential vastly different growth and inequality trajectories. The analysis presented here frames part of this uncertainty by using a total of seven COVID-scenarios. With the emergence of more data and better understanding of the economic and human development costs of COVID-19, future analysis could explore its poverty impacts in more detail, and even support scenarios that simulate various patterns of pandemic resurgence over time.

Looking forward, research should allow a more structurally sophisticated analysis of the implications of shocks like the COVID-19 pandemic on human development. For instance, inevitable future shocks will come from a variety of sources, including unexpected intra- and inter-state conflict [ 86 ], economic recessions, natural disasters, future health emergencies and other unanticipated change [ 87 ]. To better prepare for analysis of disruptions, models should be structured to better represent both short-term shocks as well as longer-term structural transformations. In addition to and supporting examination of differential impact across economic sectors, modeling should represent the broader socio-political and demographic dynamics of the unfolding shocks. Toward this end, progress is being made in model-based approaches that link impacts of conflict and of climate change to long-term human development indicators [ 88 – 91 ].

Furthering our ability to model such structural dynamics will allow for identification of resilient policies that improve humans’ ability to adapt and thrive. In addition to integrating shock analysis into our modeling, policy-analysis research must focus on trade-offs and synergies in exploring developmental policy, including pathways in the pursuit of sustainable development broadly [ 56 , 92 ]. The impact of COVID-19 on human development isn’t limited to direct, proximate drivers of poverty, on GDP growth or inequality. It includes effects on education, government debt and finance, international financial flows and trade, undernourishment, child stunting, and broad SDG achievement [ 17 – 19 , 60 , 93 , 94 ]. Combinations of impacts will jointly shape the future impact of COVID-19 on extreme poverty, and more broadly human development. Prior to the pandemic, the world was not on track to meeting SDG goals [ 7 , 8 ], and COVID-19 further complicates reaching these [ 19 ]. Additional efforts to accelerate progress on human and environmental sustainability (including climate change) are partly synergistic, but will also need to navigate trade-offs, stemming from limited resources and budgets.

Quantitative tools can be useful resources to enhance how we think about these integrated, complex issues. Integrated assessment tools can be a useful resource to frame how sustainable development systems can unfold, and they should continue to be embedded into conversations about policy strategies. While these tools can be helpful, they are also not panaceas, and mixed research methods that combine qualitative and quantitative approaches cutting across levels of analysis as well as disciplinary boundaries should be the focus of future work.

The pandemic has hurt our ability to achieve the SDGs, leading to increased suffering among the poor and most vulnerable around the world. While the COVID-19 pandemic will not prevent the world from making progress toward eliminating extreme poverty over the coming decades, it will both disrupt progress during the pandemic period and shift the trajectory of ongoing advancements. We show that the rise in poverty from COVID-19 is potentially long-lasting with, in the absence of policy changes, higher poverty levels out to 2030 and 2050 than we would otherwise have anticipated.

Prior to the pandemic, many countries were not on track to meet the SDGs within the 2030 horizon period [ 8 ]. Here we show that the ambition to eradicate poverty globally by 2030 is projected to have moved further out of reach for many countries, and that the negative effects of COVID-19 will primarily be felt in countries and world regions already struggling with high levels of poverty. Even more troublesome is that poverty is only one of the SDG indicators projected to be negatively affected by COVID-19 over the long-term, while losses in education, growth in food insecurity and increasing levels of child mortality in today’s most vulnerable regions and populations are also expected to occur [ 17 – 19 , 50 , 60 , 95 ]. The real challenge for academics and policy makers will be to come up with integrated, innovative, and inclusive policy-based solutions to realize the potential of the SDGs and minimize the long-term setback of COVID-19 on poverty and other development indicators.

In the most optimistic scenario of this study, poverty projections do not differ significantly from a No COVID Base in the long-term, but this requires a two percent improvement in the distribution of income and strong economic growth in the recovery period. This will also likely require aggressive policy around environmental and other commons-related challenges (such as accelerated climatic change) that might be associated with increased economic growth; however, these issues fall outside the scope of this study. There is still room for optimism, but it requires prioritizing policies that both encourage economic growth as well as improve resource distribution. The aim of these policies should be to not only realize a rapid recovery coupled with equality in the near term, but to build resilient populations and resilient countries that can deal with future shocks, be they economic, health or environment related.

Supporting information

S1 appendix..

https://doi.org/10.1371/journal.pone.0270846.s001

https://doi.org/10.1371/journal.pone.0270846.s002

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• Understanding Poverty

Projected poverty impacts of COVID-19 (coronavirus)

Poverty projections suggest that the social and economic impacts of the crisis are likely to be quite significant. Estimates based on growth projections from the June 2020 Global Economic Prospects report show that, when compared with pre-crisis forecasts, COVID-19 could push 71 million people into extreme poverty in 2020 under the baseline scenario and 100 million under the downside scenario. As a result, the global extreme poverty rate would increase from 8.23% in 2019 to 8.82% under the baseline scenario or 9.18% under the downside scenario, representing the first increase in global extreme poverty since 1998, effectively wiping out progress made since 2017. While a small decline in poverty is expected in 2021 under the baseline scenario, projected impacts are likely to be long-lasting.

The number of people living under the international poverty lines for lower and upper middle-income countries – $3.20/day and $5.50/day in 2011 PPP, respectively – is also projected to increase significantly, signaling that social and economic impacts will be widely felt. Specifically, under the baseline scenario, COVID-19 could generate 176 million additional poor at $3.20 and 177 million additional poor at $5.50. This is equivalent to an increase in the poverty rate of 2.3 percentage points compared to a no-COVID-19 scenario.

A large share of the new extreme poor will be concentrated in countries that are already struggling with high poverty rates and numbers of poor. Almost half of the projected new poor will be in South Asia, and more than a third in Sub-Saharan Africa. Under the baseline scenario, the number of extreme poor in IDA, Blend and FCV countries is projected to increase by 21, 10 and 18 million, respectively.

The increase in the extreme poverty rate and number of extreme poor are projected to be significantly higher under both the baseline and downside scenario if inequality were to increase as a consequence of the crisis. For instance, a 1% increase in the Gini coefficient (on the upper side of the experience of previous pandemics2) would translate into an additional 19 million in extreme poverty, bringing the global total to 90 million in the baseline scenario. In the downside scenario, there would be an additional 16 million extreme poor, bringing the global total to 116 million. Further increases in inequality would only worsen this picture.

The same is true were GDP growth to be lower than projected under the downside scenario. Specifically, a 2 percentage point decline in GDP growth, vis-a-vis the downside scenario, would raise the number of additional extreme poor to 124 million.

Read the full note[PDF]: Projected poverty impacts of COVID-19 (coronavirus)

• Smart containment: How low-income countries can tailor their COVID-19 response
• How the World Bank Group is helping countries with COVID-19 (coronavirus)
• Poverty and Distributional Impacts of COVID-19: Potential Channels of Impact and Mitigating Policies
• COVID-19 will hit the poor hardest. Here’s what we can do about it
• The coronavirus is not gender-blind, nor should we be
• Poverty and Equity Data
• Inequality and Shared Prosperity
• Poverty and Shared Prosperity Series
• Fragility and Conflict: On the Front Lines of the Fight against Poverty

New ways of looking at poverty

Luis Felipe López-Calva

Global Director, Poverty and Equity Global Practice, World Bank Group

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COVID-19 made it harder to access period products: The effects of a pandemic on period poverty

Associated data.

The data analyzed in this study is subject to the following licenses/restrictions: the authors do not own the data. Alliance for Period Supplies owns the data. Requests to access these datasets should be directed to the corresponding author: Anne Sebert Kuhlmann, [email protected]

Prior to the COVID-19 pandemic, a few studies started to highlight the extent of period poverty in the U.S., especially among low-income women and girls. Preliminary data documenting the effects of the pandemic, subsequent economic downturn, and closure of schools and businesses on menstrual hygiene management are now emerging.

This study explores the relationship between the effects of the COVID-19 pandemic and period poverty among a nationally representative sample of U.S. adults.

Cross-sectional, secondary analyses of a 2021 nationwide, self-administered, online panel survey used weighted logistic regressions to assess the relationship between the COVID-19 pandemic making it more difficult to access products and missing work due to a lack of products. Responses from 1,037 menstruating individuals age 18–49 were included.

Overall, 30% of the sample indicated the COVID-19 pandemic made it more difficult to access period products, 29% struggled to purchase period products in the past year, and 18% missed work due to a lack of period products. Those who identified as Hispanic (aOR 2.06 95% CI 1.29–3.29) and had children under 18 (aOR 15.3 95% CI 1.03–2.26) were more likely to indicate that the pandemic made it harder to access period products. Subsequently, those who indicated that the pandemic made it more difficult to access period supplies were more likely to report missing work due to a lack of period products in the past 12 months (aOR 4.32 95% CI 4.69–6.94).

The COVID-19 pandemic exacerbated period poverty, especially among those in the U.S. who struggle with accessibility and affordability of products. Future pandemic response planning should consider period products as a basic need for vulnerable households. In addition, policies that increase the affordability and accessibility of period products for all should help reduce menstruation-related absenteeism from work.

Introduction

For several years, public health literature has focused on menstrual hygiene needs and the impact of unmet needs on women and girls in lower-income countries ( 1 ). Now, literature is emerging from high-income countries as well, such as New Zealand ( 2 ), Spain ( 3 ), and France ( 4 ). In the U.S., women and girls also face challenges accessing needed menstrual hygiene products and the privacy to change them regularly ( 5 , 6 ).

The American Medical Women's Association defines period poverty as, “the inadequate access to menstrual hygiene tools and education, including but not limited to sanitary products, washing facilities, and waste management” ( 7 ). Prior to the COVID-19 pandemic, some of the first data from the U.S. documenting the extent of unmet menstrual hygiene needs among low-income adult women were published ( 8 ). In addition, data were collected prior to and then published during the pandemic that showed the magnitude of period poverty among high school students and the extent to which they relied on resources available at school to access period products ( 9 ). At a Missouri high school, where 99% of students are eligible for free or reduced lunch, around 60% of girls indicated they obtained period products through their school and 48% reported that at least once during the past school year they needed products but did not have money to buy them ( 9 ). A 2019 survey documented period poverty and the subsequent effects on mental health among college students ( 10 ) while another 2019 study found an association between lack of access to period products at school and students' ability to learn ( 11 ), but data about the general population are still limited.

Efforts to alleviate period product insecurity have gained momentum over the past few years. An increasing number of public schools, universities, prisons, and workplaces provide free menstrual hygiene products as a result of recent policy changes ( 12 ). Progress is ongoing, however, as only 17 states and Washington D.C. currently require menstrual products be provided in public schools, and 24 states still tax them as luxury items ( 13 ). Diaper banks, food banks, prisons, and homeless shelters are increasingly distributing free menstrual hygiene products, but they often rely on donations which may limit the consistency and variation in their supply ( 14 ). Furthermore, these relief efforts are not available to all women, as most require in-person interaction to obtain assistance and access to these locations can be hindered by numerous barriers, such as inconsistent supplies at facilities or “gatekeepers” who provide small numbers of products upon request only ( 5 ).

As the COVID-19 pandemic hit the U.S., schools shut down for months, an economic downturn hit the most vulnerable households especially hard, and demand at food banks and for other essential services such as assistance with rent and utilities skyrocketed ( 15 ). A diaper bank in North Carolina saw an 800% increase in the demand for period products during the COVID-19 pandemic ( 16 ); food banks around the country faced shortages as unemployment rates rose ( 17 ). Policy changes to combat period poverty were enacted when the Coronavirus Aid, Relief, and Economic Security (CARES) Act, passed in March 2020, deemed period products as “medical necessities”, authorizing the use of funds from health saving accounts and flexible spending accounts to purchase period products ( 18 ). While this classification allows use of non-income dollars for period products, individuals first must have benefits with health spending accounts, established funds in those account, and the knowledge about this new policy in order to benefit from it. Low-income, uninsured women and families are still unable to use other government benefits to purchase period products ( 19 ).

Others have called for research into how the COVID-19 pandemic has affected period poverty in the U.S. given the economic impact of the pandemic on financially vulnerable households ( 20 ). Now, data are emerging to be able to address these questions. A recent study conducted in 2020 in the U.S. found associations between income loss from the pandemic and a greater inability to afford products among menstruators who were already enrolled in a large, longitudinal cohort study about the spread of COVID-19 nationwide ( 21 ), but other data are limited. Therefore, the purpose of this study is to explore the relationship between the effects of the COVID-19 pandemic and period poverty among a nationally representative sample of U.S. adults.

Cross-sectional, nationally representative data were collected in April and May 2021 through a self-administered, online survey that took approximately 15 min to complete. The survey was a collaboration between YouGov®, U by Kotex®, and the Alliance for Period Supplies (APS). YouGov maintains a panel of thousands of U.S. adults. Enrolled panelists receive periodic survey invites via email. Once they initiate the screening process from an invite, they are directed to active surveys in the queue for which they meet inclusion criteria. Surveys remain open until quotas for a representative sample for that survey have been reached. Panelists accrue points for each survey they complete through YouGov which can then be redeemed for incentives such as gift cards. The survey was administered in compliance with industry standards for market research. Respondents were asked a variety of questions about their use of and access to period products, but the specific term “period poverty” was not defined or used in the survey. Data were made available to the authors by APS for secondary analysis. Here, we present results from secondary analyses of these de-identified survey data to determine the associations between the effects of the COVID-19 pandemic and access to period products, such as tampons, pads, and liners, in the U.S., controlling for socio-demographic characteristics.

In order to restrict the final sample for analyses to respondents who indicated they were currently menstruating, inclusion criteria for these analyses were participants who had experienced at least one period in the past 12 months and were considered to be adults of reproductive age, i.e., 18–49 years old. Out of 5,178 individuals who initiated the survey, 1,037 (20%) records met these criteria. Gender was not used as an inclusion criterion.

Dependent variables

Three main outcome variables were identified: (1) the COVID-19 pandemic making it more difficult to access period products, (2) struggling to purchase period products in the past year, and (3) missing work in the past year due to a lack of access to period products. If the pandemic made it more difficult to access products was a categorical yes/no variable. Struggling to purchase period products in the past year was recoded from a five option Likert scale into a yes/no response with strongly agree and somewhat agree recoded as “yes” and other responses as “no.” Since data collection occurred in April and May 2021, “the past year” refers to a time-period fully encapsulated by the COVID-19 pandemic. Missing work included missing either in-person or virtual/remote work in the past year due to a lack of period products. The first two outcomes, the COVID-19 pandemic making it more difficult to access products and struggling to purchase products in the past year, were also included as predictors in analyses of the missing work outcome since missing work may be a consequence of more difficulty accessing or struggling to purchase products.

Independent variables

Demographic variables for inclusion in regression models were selected based on existing literature and bivariate analyses. Significant predictors at p  < 0.05 in bivariate analyses for two outcomes - the COVID-19 pandemic making it more difficult to access products and struggling to purchase products - included age, race/ethnicity, education level, family household income, partnership status, and if respondents had children under 18. Household size was not a significant predictor in bivariate analyses for the pandemic difficulty and struggling to purchase outcomes, so it was not included in regression models for these outcomes. Missing work due to a lack of products had similar significant demographic predictor variables in bivariate analyses except family income was significant instead of household size so family income was included in the regression model with the other demographic predictors.

Additional covariates for all three outcomes included ever experienced period poverty personally, ever struggled to purchase products, and found period products to be unaffordable. Respondents were considered to have experienced period poverty if they responded yes to any of the following situations: (1) worn a period product longer than recommended in order to “stretch” its use because they did not have access to more period products; (2) struggled with the decision on whether to buy other basic necessities (e.g., food, soap, etc.) or period products; (3) used a substitute to a period product (e.g., toilet paper, paper towels, socks, etc.); (4) went without using period products because they could not afford to purchase any; or (5) asked someone for a period product (tampon, pad, liner, etc.) because they could not afford to purchase any. In our analyses, ever experienced period poverty refers to issues of period product access over the course of one's lifetime due to financial barriers; it does not include a lack of privacy or knowledge about menstrual hygiene. Respondents reporting ever experiencing period poverty could have experienced this before the COVID-19 pandemic, since the onset of the pandemic, or both.

Statistical analyses

Data cleaning and analyses were completed using IBM SPSS Statistics Version 27 ( 22 ). Measures of association between demographic variables and categorical survey questions were completed via Chi Square Tests of Independence. A complete case analysis was performed to account for any missing data; only complete cases were then included in the regression analyses. A check for multicollinearity between variables in the regression models confirmed that the variance influence factor was less than 2, and no variables needed to be removed. Sampling weights were utilized to approximate a nationally representative sample. Multivariate weighted logistic regressions were performed using a P value of <0.05 as the level of significance to determine the adjusted odds ratio (aOR). As these were secondary analyses of de-identified data, we received a non-human subjects research determination from the IRB at Saint Louis University.

The majority of respondents who met inclusion criteria (56.4%) identified as white, 35.4% had some college level of education, and 39.5% were between the ages of 25 and 34. Among the final sample, 61.5% of respondents had ever personally experienced a period poverty situation, 38.1% found period products to be unaffordable based on their income, and 43.1% had ever struggled to purchase products ( Table 1 ). Specific frequency breakdowns of each period poverty situation are included in Table 2 .

Characteristics of survey participants. Unweighted ( n  = 1037).

Period poverty situations. Unweighted ( n  = 1037).

COVID-19 pandemic effect on period poverty

Of the 1,037 included respondents, 291 indicated that the COVID-19 pandemic made it more difficult for them to access period products. Of those 291, struggling to afford products was the most frequently cited reason as to why the pandemic made it more difficult to access products (18.5%), more than struggling to find them (13.3%) or transportation barriers to go buy products (4%). Weighted regression analyses included 870 complete cases. A greater proportion of those who indicated that the COVID-19 pandemic made it more difficult for them to access period products are Hispanic (aOR 2.06 95% CI 1.29–3.29) or have children under the age of 18 (aOR 1.53 95% CI 1.03–2.26). Participants who said they have struggled to afford products at some point in their life were 2 times more likely to indicate the COVID-19 pandemic created additional challenges for them accessing period products (aOR 2.09 95% CI 1.37–3.19). Moreover, ever personally experiencing one of the five period poverty situations nearly quadrupled the odds of the COVID-19 pandemic making it more difficult to access products (aOR 3.96 95% CI 2.38–6.59) ( Table 3 ).

Weighted logistic regression for predictors of the COVID-19 pandemic having made it more difficult to access period products ( n  = 870).

C-Statistic: 0.843.

Struggling to access supplies over the past year

While 43% of respondents indicated that they had struggled to purchase period products at some point in their life, 29% said they had struggled in the past year. Finding period products to be unaffordable based on their income (aOR 6.27 95% CI 4.07–9.64) and struggling to purchase ever in their life (aOR 8.63 95% CI 5.39–13.83) were two statistically significant predictors for struggling to purchase in the past year. Having children under 18 (aOR 1.85 95% CI 1.20–2.85), making less than $30,000 a year (aOR 1.67 95% CI 1.03–2.71), and having a high school education or less (aOR 2.39 95% CI 1.34–4.26) were also statistically significantly associated with struggling to purchase products in the past year. This weighted regression model analyzed 922 complete cases ( Table 4 ).

Weighted logistic regression for predictors of struggling to purchase period products in the past year due to the COVID-19 pandemic ( n  = 922).

C-Statistic: 0.907.

Missing work due to lack of supplies

The COVID-19 pandemic and the inability to afford and access period products were significantly associated with missing work. Overall, 18.4% of participants reported having to miss work, either virtual or in-person, during the prior 12 months due to a lack of access to period products ( Table 1 ). Of the 18.4% who indicated yes, 58% missed in-person work, 29% missed virtual work, and 13% reported missing both virtual and in-person work. In a weighted regression model consisting of 943 complete cases, people aged 25–34 (aOR 6.34; 95% CI 1.813–22.14) and those with children under 18 (aOR 1.72; 95% CI 1.11–2.67) were significantly more likely to report missing work due to a lack of access to period products. Furthermore, those who stated that the pandemic made it more difficult to access period supplies were over four times more likely (aOR 4.32 95% CI 2.69–6.94) to report missing work. However, participants living in smaller households were less likely to miss work compared to those in households of 4 or more people (aOR 0.53 95% CI 0.35–0.81) ( Table 5 ).

Weighted logistic regression for predictors of missing work due to a lack of access to period products ( n  = 943).

C-Statistic: 0.837.

This study aimed to investigate the effects of the COVID-19 pandemic on period poverty among adults in the U.S. through a nationally representative survey. Nearly one-third of the sample reported that the pandemic made it more difficult for them to access period products, and a similar percentage said they struggled to afford period products over the past year during the pandemic. These large percentages demonstrate the substantial impact the pandemic had on access to basic needs such as period products. Furthermore, having children under the age of 18 was significantly associated with all three major outcomes, which may reflect how the pandemic was particularly hard on these households that are especially vulnerable in times of economic downturn. Additionally, other predictor variables that reflect pre-pandemic struggles to access period products, such as ever experiencing period poverty and struggling to afford products ever in your life, were also significant results, which suggests the pandemic had an exacerbating effect on those already struggling to afford and access products.

Over half the sample indicated they have experienced a situation of period poverty. The framing of the question as “ever in your life” likely contributed to this high percentage, as individuals only needed to experience period poverty one time in order to respond “yes”. Occasionally having to use a substitute period product such as toilet paper or paper towels may have arisen from a lack of preparedness rather than a true inability to afford products. Therefore, finding period products to be unaffordable based on their income (38%) may provide a more accurate representation of current challenges those in the U.S. face.

Our results show a statistically significant association between struggling to access period products during the COVID-19 pandemic and Hispanic ethnicity, households with children, and lower incomes, which concurs with documentation of monthly poverty rates in the U.S. between February and September 2020 ( 23 ). As may be expected, those who struggled economically prior to the pandemic indicated additional challenges during COVID-19. Despite the economic benefits of the CARES Act, these households were hit particularly hard by the impacts of the pandemic, and this shows up in data around basic needs ( 14 ), including our data around accessing period products. With the closure of stores, childcare centers, and schools combined with the health risks of leaving home or using public transportation, many individuals may also have faced non-financial challenges to obtaining period products even though in this study only 4% of those who reported the COVID-19 pandemic made it more difficult for them to access period products indicated transportation barriers as a reason.

The disproportionate impact of the COVID-19 pandemic on lower-income menstruating individuals has been documented in countries like India ( 24 ). Recent data from the U.S. suggest that students' access to period products during the pandemic was negatively affected by school closures, especially for Latinx, rural, low-income, and college students ( 25 ). The percent of students who reported they have struggled to afford products (23%) ( 25 ) is similar to our results indicating that 29% struggled to purchase period products due to a lack of income/funds at some point in the past year. Our results among adults 18–49 show the impacts of the pandemic in the U.S. beyond just students. Like India, the U.S. experienced similar effects on lower-income populations, despite the country's higher overall GDP and broader economic resources at a national level. Those living in the U.S. with only a high school degree or lower level of education or a yearly income of less than $30,000 face similar challenges to affording products as those in lower-income countries.

In countries like Nigeria, the pandemic curtailed the ability of many non-governmental organizations to distribute menstrual hygiene products and education ( 26 ). Mass media stories from the U.K. and the U.S. during the early months of the pandemic reported a dramatic uptick in demand for period products from non-profit organizations that continued serving those in need ( 27 ). In France, a study conducted during the country's first and second pandemic lockdowns reported a significant relationship between period poverty and depression and anxiety ( 4 ). Here in the U.S., the shuttering of schools and transmission risks associated with public transportation early in the pandemic likely contributed to the increased rates of period poverty observed, especially during the first year of the pandemic. Given the substantial percentage of students who report relying on schools for access to period products ( 9 , 11 , 25 ), planning for future pandemic response must consider how to counteract the negative consequences of these necessary public health measures in order to minimize the ripple effects of a pandemic.

A smaller subset of our overall sample (18%) reported missing work due to lack of sufficient period products, with the majority of those who reported missing work aged 25–34. The association between missing work during the pandemic and struggling to access products because of the COVID-19 pandemic is consistent with findings from Sommer and colleagues who found that women who were out of work or unemployed reported more issues accessing period products ( 20 ). It is also consistent with data from prior to the COVID-19 pandemic in lower- and middle-income countries that highlight some of the challenges of managing menstruation in the workplace, including absenteeism ( 28 , 29 ). Interestingly, several respondents in our study reported missing virtual or remote work due to a lack of period products. This effect on virtual/remote work should be explored further in future research as an increasing number of employers are codifying remote or hybrid work environments for their employees as standard. Given that higher paid jobs are more likely to be remote than low wage jobs, the relationship between income, period poverty, and missing work must be disentangled across the wage spectrum.

Reporting pre-existing challenges with access and affordability of period products prior to the pandemic was a significant predictor across all three outcome variables, contributing to higher proportions of respondents indicating that the pandemic made it harder for them to access products, that they struggled to afford products in the past year, and that they have missed work due to a lack of products. This finding shows how those already living in economically fragile situations were hit particularly hard by the COVID-19 pandemic which concurs with the findings from Sommer and colleagues ( 21 ) among a cohort of menstruating individuals already enrolled in a study about the pandemic.

Limitations

This study has several limitations to note. First, as any secondary analyses are, we were limited to variables as provided in the dataset. For example, racial and ethnic identity were collected via a single variable instead of two as standard per the U.S. Census Bureau ( 30 ). Annual income and household size were categorical variables instead of ratio, limiting our ability to calculate federal poverty level thresholds. There were no variables about heads of household who might not be menstruating themselves but are buying products for others in their house. Similarly, there were no variables about education, stigma, facilities, or waste management so our analyses focus on the financial aspects of period poverty for those who are menstruating themselves. Next, respondents self-reported their experiences; there were no objective measures of their level of product insecurity. Furthermore, the cross-sectional nature of the study design limits us to testing associations. We cannot establish temporality or test causation with these data. Finally, given the self-reported, online nature of the survey administration, there may have been response bias in that only those with internet access are enrolled in the panel and those more interested in the topic may have been more likely to complete the survey. The sample weighting should have minimized this bias, however. Despite these limitations, this study represents one of the first nationally-representative looks at the impact of the COVID-19 pandemic on access to period products and period poverty in the U.S.

Our findings illustrate a high-level of need for access to affordable period products during the COVID-19 pandemic, especially among Hispanics, those with children under 18, household incomes less than $30,000/year, and a high school education or less. These findings, highlighting the disproportionate impact of the COVID-19 pandemic on vulnerable households, point to the need for governments, organizations, and schools such as colleges and universities to include menstrual hygiene and period products in their pandemic preparedness and emergency response and relief planning. For example, they should consider how vital access to these products may need to shift to essential services and businesses when existing access points such as schools are forced to close, how to make basic needs such as period products available to those who must isolate or quarantine, and what communication will be made to ensure that the most vulnerable households are aware of how to access these basic needs during emergencies. From a policy perspective, our findings suggest that increasing affordability of products, such as through reducing or eliminating taxation on period products, and implementing supportive workplace policies, such as providing period products in restrooms just as toilet paper is provided or bathroom breaks as needed to manage menstruation, may help reduce menstruation-related work absenteeism. Finally, future research should focus on tracking trends in period poverty over time, utilizing qualitative methods to better understand how vulnerable households manage their basic needs during acute economic downturns and emergency situations such as lockdowns and isolation or quarantine, and developing validated measures of “period poverty” that capture a holistic perspective beyond just affordability and accessibility. These measures need to include access to facilities and waste disposal as well as education, awareness, and stigma around menstruation and be appropriate for the context of higher-income countries. We must learn from the COVID-19 pandemic and be better prepared to mitigate the effects of emergency situations on the most vulnerable individuals and households in our communities.

Acknowledgments

The authors would like to thank the Alliance for Period Supplies for their data sharing partnership and Sandra Dimitri for her help reviewing literature.

Data availability statement

Ethics statement.

This study of de-identified secondary data received a non-human subjects research determination from the IRB at Saint Louis University. Written informed consent was not required for this study in accordance with the local legislation and institutional requirements.

Author contributions

All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Extreme poverty in the time of COVID-19

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Homi kharas and homi kharas senior fellow - global economy and development , center for sustainable development meagan dooley meagan dooley former senior research analyst - global economy and development , center for sustainable development.

June 2, 2021

Introduction

The short-term economic and well-being costs of COVID-19 have been severe. Though we hope the pandemic will be a temporary shock, in the interim it has pushed many vulnerable households living at the margins back into poverty. Due to lockdowns and social distancing measures, people have lost jobs and livelihoods, leaving them unable to pay for housing and food. Schools have been closed and some children may not return, shutting off one of the main pathways out of poverty for low-income children. Women and girls have been especially impacted by these school closures. Mothers at all socio-economic levels have dropped out of the labor force to supervise online learning and care for children and older relatives, and many will not reenter. Even before the pandemic, women and girls of reproductive age were overrepresented among the poor, making these setbacks all the more concerning. 1

We likely will not know the full impacts of COVID-19 on poverty for a few years, as most poverty data comes from household surveys, which have been difficult to carry out during the last year. However, we do know that economic growth is the largest driver of poverty reduction. Conversely, economic recessions drive a rise in poverty, other things being equal. In 2020, however, other things were not equal; national and local governments were able to mitigate the impact of COVID-19 on their poorest people to varying degrees and assessing the economy-wide impact of these measures cannot yet be done systematically. What can be done at this juncture is to use new estimates for economic growth through 2030 to capture the potential impact of COVID-19 on poverty in the long run.

For example, some countries, like India, saw a substantial fall in economic activity in 2020, but are expected to see a strong economic recovery in 2021, despite the recent fresh wave of infections. India, in our view, will soon return to its pre-COVID-19 poverty trends. Other countries, like Nigeria and the Democratic Republic of the Congo, will likely be slow to recover, and could experience low growth for the next decade. As a result, they may see higher poverty headcounts in 2030 than in 2019.

While these facts are sobering, this long-term poverty stagnation is not inevitable. Countries have responded to the pandemic with a number of social protection measures to try and protect the most vulnerable. There has been a proliferation of mobile cash transfer programs, taking advantage of big data and machine learning to better target those in need. The needs are great, but not insurmountable; the amount needed to lift people out of extreme poverty is less than the current annual official development assistance (ODA) budget. Eliminating extreme poverty will increasingly depend on better targeting as well as greater resource mobilization. We believe that geographic targeting of specific people in specific places offers considerable potential.

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Nishant yonzan, christoph lakner, daniel gerszon mahler, r. andres castaneda aguilar.

The Poverty and Shared Prosperity Report 2020 (PSPR2020) reported that COVID-19 was likely to push between 88 and 115 million people into extreme poverty—those living under $1.90-a-day—around the globe in 2020 (also see our related blog ). These estimates were based on growth forecasts from the Global Economic Prospects (GEP) from June. We will update these numbers following the next GEP release in January 2021. In this blog, we analyze the robustness of these headline estimates to using growth forecasts from other sources—in particular, the World Bank’s October-2020 Macro Poverty Outlook (MPO) and the IMF’s October-2020 World Economic Outlook (WEO)—and to varying assumptions about the impact of COVID on inequality.

The fast-evolving nature of the pandemic has meant significant revisions of growth forecasts. These revisions have consequently changed our estimate of the COVID-19-induced new poor. [1] Whereas our first estimate from April this year suggested between 40 and 60 million new poor, in June we updated this to between 71 and 100 million, and last month we further updated this to between 88 and 115 million. Some of these differences, especially between the estimates in June and October, were due to the change in the underlying poverty data , but most of the changes come from updates to the macro growth forecasts. For example, comparing the two WEO rounds from this year, the added number of poor more than doubles from 62 million using the April vintage to 131 million with the October vintage.  Figure 1 compares the estimates of the COVID-19 induced poor that we have reported over the last half a year, together with the regional decomposition. The regional growth forecasts, particularly for South Asia, have become more depressed due to the spike in COVID-19 cases in the latter part of the summer. Whereas using the April WEO forecast suggested a total of 62 million new poor globally in 2020, the October forecasts suggests that there will be between 78 million (using the MPO forecast) and 82 million (using the WEO forecast) new poor in South Asia alone.

Figure 1: COVID-19-induced new poor in 2020, using various growth vintages

The increase in the new poor is not limited to the $1.90 line. We also find that the new poor under the $3.20 line more than doubles between April and October this year, from 125 million in April to between 242 and 257 million in October.   South Asia contributes 63% of the new poor at the $1.90 line and 71% at the $3.20 line.

The various estimates we reported above reveal the truly uncertain environment we currently live in. Additional variation in the estimated new poor can arise if we relax an assumption implicit in all estimates above; that everyone within a country is losing income or consumption at the same rate from COVID-19, implying no change in inequality. In Figure 2, we present estimates of the new poor with different growth rates and with different assumptions about how COVID impacts inequality. The growth forecasts we use are derived from the various growth vintages that have been available through the course of this year from the GEP, MPO, and WEO. For inequality changes, we increase/decrease the Gini index by either 1% or 2% in 2020 for each country. Each cell of the figure presents an estimate that combines a growth vintage with a scenario that changes inequality. We expect the number of poor to increase as we increase inequality holding growth fixed (i.e. moving from left-to-right in the same row). Likewise, as the growth outlook has worsened, moving up from the bottom of the chart progressively increases the number of new poor in most cases.

Figure 2: Estimates of the COVID-19-induced new poor in 2020, different scenarios for growth and inequality (in millions)

Using the October growth forecasts with no change in inequality results in between 125 and 131 million new poor (these estimates are reported in rows 1 and 2 of the 3rd column). But the distribution-neutral assumption might be too strong (more on that below).  A 1% increase in the Gini index in each country in 2020 would increase the additional poor by around 15% in 2020   (144 million using MPOs and 152 million for WEO). A 2% increase in the Gini would result in an almost 30% increase (161 million for MPO and 170 million for WEO). Figure 2 also reports regional projections of new poor for 2020.  A 2% increase in the Gini index and using the October MPO forecast would increase the additional poor in Sub-Saharan Africa by a third  , from 32 million with constant inequality to 42 million using the October MPO in 2020. This unprecedented global shock could very well have a larger negative impact on inequality. If that were the case, we should expect an even larger increase in the number of new poor. At the same time, it is important to stress that appropriate government policy can dampen this impact. However, even our most optimistic scenario with a 2% reduction in the Gini index in each country would still imply an increase in extreme poverty by between 90 and 99 million using the most recent growth forecasts.

The changes in inequality that we simulate are  not atypical of year-to-year changes observed over the medium and long-term . The worst historic increases in the Gini index would be larger for most regions, ranging from just under 2% per year in Latin America and the Caribbean to around 4% in South Asia. [2]  At the same time, it is obviously a strong assumption that all countries’ inequality changes at the same rate, and it is not clear what these historic changes can tell us about such an unprecedented shock that is COVID-19. The emerging evidence on the impact of COVID-19 on inequality suggests that our inequality increasing scenario might be conservative, though little is known about the impact on the poorest countries. Exploiting variation in telework ability with income, the recent  World Economic Outlook  estimates that COVID-19 increases the average Gini index for emerging market and developing economies by more than 6%, with a an even larger impact for low-income countries. The average increase in the Gini index for EU countries  has been estimated  at 3.5% with a 2-month lockdown, rising to 13.5% with an additional 6-month partial lockdown. For four Latin American countries, a  recent paper  suggests that COVID-19 would increase the Gini index between 3.5% and 7.4%, which could be considerably lower with social assistance (increases between 0.6% and 4%). These estimates are all considerably larger than the increase in inequality during past pandemics ( estimated  to be around 1.25% five years after the pandemic), which underscores the fact that a pandemic of such a global scale as COVID-19 is truly unprecedented in modern times.

We gratefully acknowledge financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme.

[1]  The number of COVID-19-induced new poor is the difference in poverty between a world affected by the pandemic and a world free of it. To predict poverty in the former we use growth forecasts incorporating the impact of COVID, and for the latter, we use growth forecasts released before the pandemic spread. In both cases, we use these growth forecasts to extrapolate the last observed distribution of income or consumption we have for each country. We use a 0.85 global pass-through to adjust the growth rates for the projected years, i.e. 2019-2020 (for details on the pass-through calculation see  Lakner et al. 2020 ).

[2]  Similar to  Lakner et al. (2020)  (Figure 4), we look at annualized changes over comparable spells with a duration of five years or less. To abstract from outliers, we then look at the 90th percentile of the distribution of changes for each region (i.e. the value that separates the largest 10% of changes from the rest). With the exception of Latin America and the Caribbean, the estimates for the other regions all exceed 2% per year.

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• COVID-19 (coronavirus)
• Poverty and Shared Prosperity 2020

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COVID-19 : how the lockdown has affected the health of the poor in South Africa

Senior Lecturer, University of the Free State

Senior Lecturer, School of Economics and Finance, University of the Witwatersrand

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The COVID-19 pandemic has wreaked substantial damage on human lives and the economy in South Africa. But the impact of the measures used to combat the pandemic, such as lockdowns, have not been even. The pandemic has likely worsened the income inequalities that characterise the country’s economy.

Vulnerable populations such as low income earners in informal and precarious employment have been most affected by job losses and the resulting income loss. Moreover, while COVID-19 has affected every facet of people’s lives, it is essentially a health problem. The loss of jobs and income is likely to result in reduced ability to access healthcare and a nutritious diet. This, in turn, will negatively impact on people’s health.

Read more: Close look at some South African households gives insights into COVID-19 vulnerability

We recently conducted a study to estimate how closely health was related to income, in the context of COVID-19 in South Africa. We used data from the National Income Dynamics Study-Coronavirus Rapid Mobile Survey, a nationally representative survey collected in May/June 2020.

The survey collected information on health, income and other relevant factors during the higher levels of the lockdown. We compared these findings to data collected from the same individuals in 2017.

We found that poor populations bore a disproportionately higher burden of poor health. This was the case in both 2017 and the COVID-19 period. A remarkable finding was that income-related health inequality in the COVID-19 period was about six times that obtained in 2017. This shows that income had a much stronger relationship with health during the COVID-19 crisis than before.

Explaining the inequalities

To measure health inequalities related to income, we used a statistical measure known as the concentration index. The key factors that predicted the observed income-related health inequalities in the COVID-19 era were race, hunger, and income. Each of these factors worsened income-related health inequalities.

Race affected the inequalities in two ways: Africans were more likely to be poor and report being in poor health compared to their white counterparts. The same was true of hunger. On the other hand, income worsened health inequalities through the richer being less likely to be in poor health.

The impact of race on health outcomes, especially in this period, corroborates prior evidence in South Africa and elsewhere. Black people are among the worst affected by the COVID-19 epidemic in South Africa. One of the avenues through which this occurs is higher exposure to hazardous jobs such as working as cleaners or in fumigation of contaminated areas.

The relative disadvantage of historically disadvantaged racial groups to pandemics is well known – especially in the present situation. For instance, African Americans have disproportionately high infection and mortality rates due to COVID-19 in the United States.

Similarly, limited access to quality healthcare can contribute to race-based health inequalities. South Africa’s health system is deeply segmented. It consists of a well-resourced private sector – mostly funded by expensive medical aid scheme membership – and an overburdened public sector which caters for the majority poor masses (mostly Africans). It is estimated that only 10% of Africans belonged to medical aid schemes compared to 73% of whites in 2018.

This two-tier system is in dire need of reform if the country is to tackle health inequalities. Hopefully the country’s move to universal health coverage as envisaged in the proposed National Health Insurance Scheme will mitigate these inequalities and inequities.

Read more: Why South Africa needs to ensure income security beyond the pandemic

The second factor was hunger. Its strong contribution to health inequalities is disturbing, especially given the rights-based approach to food security enshrined in the South African Constitution. The state hasn’t been able to fulfil its constitutional role of ensuring that all South African residents have enough food to enjoy a dignified life. This was especially true during the period of the COVID-19 epidemic.

As we found in this study, hunger not only adversely affects people’s dignity; it also widens the health disparity between the rich and the poor. This is particularly worrying given the high prevalence of hunger during this epidemic. It has become absolutely necessary to protect the health of the poor in South Africa. That is why anti-hunger policies such as the National School Nutrition Programme are even more relevant now.

Read more: South Africa faces mass hunger if efforts to offset impact of COVID-19 are eased

The final factor contributing to widening health inequalities was income inequality. As earlier indicated, COVID-19 disproportionately affected the poor through a higher likelihood of them losing their jobs, among other things. A higher probability of job loss among already economically compromised individuals and households would not only exacerbate income inequality, but is likely to contribute to worsening health outcomes among the poor given their further limited ability to meet basic needs like food and medication.

Therefore, measures to save the livelihoods of the poor must be sustained during the crisis and beyond.

Way forward

Our paper underscores the fact that the poor bear a disproportionate burden of poor health and that income-related health inequalities seem to have gotten worse in the COVID-19 era.

We believe that this pandemic and the associated lockdown reinforced existing inequalities in South Africa. These were exacerbated by massive job cuts and a depressed labour market.

Policies that address race-based disadvantage – such as universal health coverage as well as anti-hunger measures are urgently needed to mitigate health disparities in the COVID-19 era and beyond.

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American Journal of Economics

p-ISSN: 2166-4951    e-ISSN: 2166-496X

2020;  10(4): 213-224

doi:10.5923/j.economics.20201004.02

The Extent of COVID-19 Pandemic Socio-Economic Impact on Global Poverty. A Global Integrative Multidisciplinary Review

Mohamed Buheji 1 , Katiane da Costa Cunha 2 , Godfred Beka 3 , Bartola Mavrić 4 , Yuri Leandro do Carmo de Souza 5 , Simone Souza da Costa Silva 6 , Mohammed Hanafi 7 , Tulika Chetia Yein 8

1 Founder, International Inspiration Economy Project, Bahrain

2 Professor PhD of the Medical Course, Pará State University, Marabá, Pará, Brazil

3 University for Development Studies, Faculty of Integrated Development Studies, Department of Social Science, Ghana

4 Istanbul University, Faculty of Economics, Department of Tourism Management, Turkey

5 Psychologist in Behavior Theory & Research, Federal University of Pará, Belém, Brazil

6 Professor of Behavior Theory & Research, Federal University of Pará, Belém, Brazil

7 College of Education Studies, University of Cape Coast, Cape Coast, Ghana

8 Poverty Community Expert, Assam, India

Copyright © 2020 The Author(s). Published by Scientific & Academic Publishing.

The outbreak of COVID 19 made many poor communities in different places of the world face very challenging socio-economic and livelihood consequences. This paper targets to analyse this socio-economic impact to determine how the pandemic is causing various problems to the impoverished. An integrative literature review was carried out to sample the consequences of the global pandemic economic crisis on the poor communities in four different continents. The research points out how it is hard on the poor to adhere to the restrictive measures of social isolation or the lockdown. Immediate strategies that minimize the pandemic impact on the livelihood and the socio-economic activities of the poor are suggested. The research opens future research about more specialised programs for the poor during any future lockdowns.

Keywords: Poverty, Poor Community, Socio-economic, COVID-19, Global Crisis, Pandemic Impact, Integrative Review, Asia, Africa, Europe, South America

Cite this paper: Mohamed Buheji, Katiane da Costa Cunha, Godfred Beka, Bartola Mavrić, Yuri Leandro do Carmo de Souza, Simone Souza da Costa Silva, Mohammed Hanafi, Tulika Chetia Yein, The Extent of COVID-19 Pandemic Socio-Economic Impact on Global Poverty. A Global Integrative Multidisciplinary Review, American Journal of Economics , Vol. 10 No. 4, 2020, pp. 213-224. doi: 10.5923/j.economics.20201004.02.

Article Outline

1. introduction, 2. literature review, 2.1. understanding the phenomenon of poverty, 2.2. multidimensional poverty and the challenges of covid-19 pandemic, 2.3. status of poverty in the world before covid-19, 2.3.1. case of asia (focus on india as an example), 2.3.2. case of africa (focus on ghana, nigeria and kenya as examples), 2.3.3. case of south america (focus on brazil as an example), 2.3.4. case of europe (focus on the eu performance towards the vulnerable as an example), 2.4. estimating the global poverty status due to covid-19 pandemic.

2.5. Socio-Economic Impacts of COVID-19 on the Poverty Communities

2.5.1. asian continent, 2.5.2. african continent, 2.5.3. south american continent, 2.5.4. european continent, 2.6. capacity of the poor in applying social distancing, 3. methodology, 3.1. introduction to integrative review (ir) method, 3.2. integrative review guiding question, 3.3. study design strategy, 3.4. study eligibility criteria, 3.5. data synthesis and quality assessment, 4. results and discussion, 4.1. the new normal wave of poverty due to covid-19, 4.2. covid-19 and the disparity between social classes, 4.3. political response to covid-19 and the extent of vulnerability, 5. conclusions and recommendations, 5.1. covid-19 as a new source of poverty creation, 5.2. covid-19 pandemic as an opportunity for more robust health system for the poor, 5.3. final recommendations.