# Designing surveys and analysing results from a gender perspective in economic research

Jacques Charmes
International Development Research Centre

## Introduction

This document provides guidance on the integration of gender and diversity considerations into applied research in economics focusing on countries in which the informal sector is predominant. It draws inspiration from the support given to the West African research centres involved in researching solutions to the socioeconomic challenges posed by the Covid-19 pandemic, particularly the livelihoods of vulnerable groups and the informal sector. The document was written with the assistance of the International Development Research Centre (IDRC) and is intended to be a guide to applied research.

Section 1 sets out the principal orientations of gender analyses. Section 2 examines how, in practice, considerations of gender and diversity are integrated into the design and formulation of statistical and qualitative surveys, and into their descriptive and logistic analyses. Section 3 contains a brief compilation of the resources available on gender, the informal economy, and the Covid-19 pandemic.
# 1 Overview of the conceptual framework of gender analyses

In the analysis of living standards, poverty and inequality, wellbeing, and human development, gender occupies a central place since it represents a source of inequality that has its roots in the patriarchal nature of most societies, regardless of how developed they are. Women have a subordinate status and role, resulting in constraints on their access to economic, social and political life.

The consideration of the gender division of labour between men and women must account for so-called 'productive' work (paid work accounted for in gross domestic product (GDP)) and 'reproductive' work including domestic and care tasks (unpaid work, not accounted for in GDP except within the context of satellite accounting). The gender division of labour in paid tasks is, in many countries, largely determined by the gender division of labour in domestic and care tasks, which are unpaid. Where a more egalitarian distribution of paid tasks is achieved, this is often without a concomitant improvement in the sharing of unpaid tasks. As such, there is an imbalance that penalises women when organising their time spent on work overall (paid and unpaid). Furthermore, the constraints imposed on the work of women are also reflected by their relatively higher presence in lower paid sectors of activity, professions and statuses. This has an impact on their 'capabilities' (in terms of health and education), contributes to limiting their 'opportunities' (limited access and limited availability for paid work) and their 'agency' (the increased workload borne by women prevents them from accessing or accepting responsibilities not only in the economic sphere, but also in other spheres, notably political and social).

The theories of Amartya Sen (1993) and their transcription for gender analysis by Martha Nussbaum (2000) are key within the conceptual framework of gender, all the more so as they have been popularised by the *World Human Development Report* (UNDP 2022) and the corresponding *Human Development Index (HDI)* (UNDP n.d.a) since 1990. The various associated gender indices (the Gender Development Index (GDI) which is the HDI disaggregated by sex, the Gender Empowerment Measure (GEM), and the *Gender Inequality Index* (UNDP n.d.b)) have contributed to an awareness that has become largely recognised and adopted today. For Africa, the *African Gender and Development Index (AGDI)* created and implemented by the Economic Commission for Africa and applied to more than forty countries, has contributed to the compilation and collection of numerous statistical data disaggregated by gender at the continent level.

At a less macro level, Oxfam (March, Smyth and Mukhopadhyay 1999), the International Labour Organization (ILO 2014), and Jhpiego (2016) provide structures for research questions intended to guide the collection of data for the implementation of gender analyses at the development project level.

Oxfam's *A Guide to Gender-Analysis Frameworks* (March et al. 1999) sets out to synthesise the principal existing gender analysis frameworks used in development projects and initiatives and includes comments from their users on the ground. It recalls the background of accounting for gender issues in the development process using the approaches of women and development, gender and development, gender mainstreaming, and the role of the works of
Naila Kabeer (1994; 1995), Diane Elson (1995) and Caroline Moser (1993) among others. Lastly, it defines the key concepts. The guide defines gender analysis as an analysis that:

> ... explores and highlights the relationships of women and men in society, and the inequalities in those relationships, by asking: Who does what? Who has what? Who decides? How? Who gains? Who loses? When we pose these questions, we also ask: Which men? Which women? Gender analysis breaks down the divide between the private sphere (involving personal relationships) and the public sphere (which deals with relationships in wider society). It looks at how power relationships within the household interrelate with those at the community, market, and national and international levels.

> (March et al. 1999: 18)

The guide then presents six different conceptual frameworks, and the choice of framework might depend on the needs of the research. We will cite two.

The Harvard Analytical Framework is presented in the form of grids of indicators for collecting data at the local level and disaggregated by gender, concerning:

* Productive and reproductive activities;

* Access to and control of resources and other benefits;

* Influencing factors (norms, demographic factors, institutional structures, economic factors, political factors, etc.);

* Lists of questions about women's needs and the impact of the project on certain dimensions of the aforementioned grids.

The Moser Analytical Framework has six tools:

1. Identification of gender roles and notably the triple role (use of time) of women, consisting of reproductive, productive and community-managing activities (social events or ceremonies). In contrast, men primarily undertake productive activities and community politics responsibilities;

2. Gender needs assessment at the practical level (particularly access to water, healthcare, paid work, basic services and food, all of which benefit all members of the household but for which women assume responsibility in order to meet the needs of the household members) and at the strategic level (in particular easing the domestic and care work burden, eliminating institutional forms of discrimination, offering reproductive healthcare, protecting against domestic violence);

3. Disaggregation of the control of resources and decision-making within the household;

4. How to achieve a better sharing of tasks in the triple role of women;

5. Identification of the various objectives pursued by the action undertaken: wellbeing, equity, combatting poverty, efficiency, empowerment;

6. The involvement of women and gender-aware organisations in the objectives pursued and the corresponding process.

Generally, the neutrality of legislations, institutions and policies relating to gender is challenged, as illustrated in the analytical social relations framework developed by Naila Kabeer (1994) on gender policies. Policies blind to gender (so-called neutral policies, and often biased in favour or men) must be rethought in view of the implicit hypotheses on which they are based and the practices on which they rely, in order to define gender-aware policies which
can be neutral (without calling into question the distribution of resources and responsibilities), specific (intended to meet the specific needs of women and men within the existing distribution framework of resources and responsibilities) and redistributive (intended to transform the existing distribution of resources and responsibilities to achieve balanced gender relations).

The ILO guide (2014), which was established from a didactic perspective (it includes exercises for work in groups), aims to have gender issues integrated into the formulation, monitoring and evaluation of labour policies and the labour market. It sets out the principal obstacles to gender equality in the world of work and to the promotion of this equality, the ILO standards in this area and their inherent concepts and indicators, the entry points and the measures promoting gender equality in labour policies and finally, what accounting for the gender dimension looks like in monitoring and evaluating these policies.

Jhpiego's guide (2016) is orientated towards projects relating to health systems. It sets out the five key areas defined by the United States Agency for International Development (USAID):

1. Laws, regulations and institutional practices;

2. Cultural norms and beliefs;

3. Gender roles, responsibilities and time use;

4. Access to and control over assets and resources;

5. Patterns of power and decision-making.

It explains the difference between focusing on women's health and examining unequal social dynamics (the subordinate position of women in the household, in the community and in wider society, the unequal division of labour, the unequal allocation of resources, the inequalities in decision-making and mobility), which have consequences on health, health behaviour and health benefits. Focusing on gender equality means examining and trying to rectify the way in which differences in power relations translate to risks, exposure to risks, vulnerabilities and health benefits that differ between men and women. Hence the need for gender analyses that are multi-sectoral, intersectional, and interdependent. Gender analysis is therefore a systematic methodology allowing for the study of the roles and norms that apply to women and men, to boys and girls, the different levels of power they hold, their different needs, constraints and opportunities, the impact of these differences on their lives and, lastly, the entry points with a view to reducing these differences and bringing about women's empowerment.

The graphic used on page 23 of the Jhpiego guidance (2016) summarises the different dimensions of the analysis. Note the similarity with Sen's analysis framework (1993). The guide proposes a series of questions about each of the points listed for the four areas.
**Definition of terms related to gender and inclusion**

**Gender** is the socially constructed roles, behaviours, expressions, and identities of girls, women, boys, men, and gender-diverse persons. It is distinct from biological sex and outside of the gender binary. Gender is not a synonym for women and it is experienced differently across cultures. It is recognised that intersectionality impacts the experience of gender and inequality. Gender relations are constituted, like all other social relations, through the rules, norms, and practices by which resources are allocated, stakes and responsibilities are assigned, value is given, and power is mobilised.

**Gender analysis** refers to the variety of methods used to understand relations between persons of different gender, their access to resources, their activities and the constraints faced by some in relation to others. Gender analysis provides information for policy development and service delivery that recognises that gender, and its relationship with race, culture, class, ability and/or any other status, is important in order to understand the different models of engagement, behaviour and activity that women, gender-diverse persons and men have in economic, social and legal structures.

**Gender equality** represents the goal of equal access to resources, opportunities, benefits and rights for persons of all genders.

**Gender equity** is a term acknowledging that the playing field is not even, so systematically or historically marginalised groups may need intentional, preferential, or alternative treatment to redress systematic barriers and exclusion. Gender equity therefore goes beyond equality and relates to equity and justice for all genders.

**Gender-transformative research** is an approach to research that examines, analyses, and builds an evidence base to inform long-term practical changes in structural power relations and norms, roles, and inequalities that define the differentiated experiences of men and women and gender-diverse persons. Gender-transformative research should lead to sustained change through action such as partnerships, outreach and interventions.

**Intersectionality** is a concept that shows how gender intersects with other aspects of identity, such as age, ethnicity/nationality, ability, education, class, religion, sexual orientation, geographic location, and any other relevant factors, to impact experiences, agency, access to and control of resources, power, and knowledge.

**Marginalised groups** are those who have been systematically or historically excluded from participation or influence in society and/or who frequently experience exclusion from exercising rights and freedoms.

Source: Extracted from Clancy et al. (2019) and Government of Canada (2019).
## 2 Guidance for integrating gender and diversity considerations into research

Given that women represent half the population, the question of how to analyse survey results, whatever they may be, from a gender perspective shouldn't even arise. But if the question is asked, it's because there's a problem whose solution is too often unsatisfactory. Doesn't devoting a section of a questionnaire or a chapter of a report to gender mean that the rest of the questionnaire or the report is insensitive or blind to gender issues? The contradiction is clear: disregarding half the population from subjects that overlook the gender variable would mean you have made the hypothesis that these subjects are neutral in respect of this variable. Of course that's not the case and there's no reason for a specific chapter on gender to contain analyses that should have been made within the context of the subjects addressed in other chapters.

Since women represent half the population, gender shouldn't be treated like all the other variables (age, education level, activity status, marital status, etc.) that we cross-tabulate with others based on the more or less explanatory or pertinent nature of the cross-tabulation. In fact, external shocks, the policies to mitigate their effects and the macroeconomic or sectoral policies impact differently on women and men for demographic reasons (maternity, health), economic reasons (lower rate of activity, nature of the jobs, the importance of informal activities, the weighting of unpaid work), and social and cultural reasons (personal status, place within the public space). There is therefore reason to consider that the population studied should not be analysed simply by disaggregating the data by gender, but rather that the female population – just like the male population – should be filtered through all the collected variables. This observation is not only valid when it comes to establishing the tabulation programme (in principle, the programme shouldn't have table for which disaggregation by gender is judged secondary or unnecessary), but is valid also for the econometric analyses, which measure the impact of gender (in parallel with the age group, marital status, etc.) on the whole of the population surveyed: so the filtering of the other variables should also be tested on the male and female populations separately. In other words, we should apply the equations and models to all the population studied, and then to each of the two subsets (women and men), then to the two subsets: young women and young men, male unemployed and female unemployed, etc. With regard to the questionnaires, they should not, save for exceptions, have modules, sections or even questions reserved exclusively for women since the objective is to compare their situation with that of men (the same applies to young people who should be compared with other age groups).

As far as the dissemination of results and their use by policy decision makers and other stakeholders is concerned, while not overlooking the explanatory power of the econometric models and analyses of the determinants of the situations observed, it should be recognised that pertinent and well-presented descriptive statistics (and graphics to see them visually) can have the power to convince decision makers more effectively than sophistically and largely esoteric tables in the eyes of those same decision makers.

For all these reasons, studies or surveys focusing exclusively on women should not in principle be appropriate. In terms of reducing gender issues to a chapter or a section of a report, their
role can only be to synthesise the analyses already present in the other areas of analysis, due
to the transversality of the issue of gender.

Now we will look at what concrete implications and what recommendations can be produced
from the common-sense observations above: in terms of data collection methodology and in
terms of results analyses.

## 2.1 Methodologies of the surveys and questionnaires

By design, surveys conducted with households identify the gender of the individual members
of the household and so no recommendation is really necessary here. The information is
collected and present in databases: the variable can then be cross tabulated with all the
others. Even surveys on health and reproduction focusing on women of reproductive age have
included questions addressed to both sexes.

The observation above is less true for surveys conducted with businesses: we need to ensure
that not only the gender of the head of the business or the manager (in the case of
companies) is identified, but also that the gender of the workers in the business is identified,
and that a certain number of variables can be cross tabulated with the gender of the workers,
for example the wages paid by the business. In the case of small businesses in the informal
sector, this last point presents no difficulty other than trust of the person surveyed. But this
isn't always the case for larger businesses as this can involve calculations and recourse to
accounting records by the person surveyed (e.g. for businesses that don't have a social
report): this explains why the wage statistics arising from business surveys are sometimes,
even often, not disaggregated by gender.

While the sampling plans of surveys conducted with households don't have to be specially
designed to ensure the representativeness of both sexes, the same isn't true for business
statistics. If the sample base is a business directory, it might be necessary – to achieve a
sufficiently representative sample – to select a greater number of businesses run by women
since their numbers are generally lower and sometimes far lower than businesses run by men.
Of course the overall extrapolated results will then be weighted to account for this over-
representation of women in the sample.

In some countries, and depending on the subjects covered, it is also worth having mixed
interviewer teams, which has proved to be a good practice in terms of gender, but also a
necessity if you want access to the respondents and get reliable responses. The mixed notion
also holds true for the spoken languages in which the questionnaires will be delivered.

## 2.2 Focus groups

It is good practice to conduct focus groups in parallel to statistical surveys. If conducted
before the statistical surveys, these qualitative surveys can be used to refine the design of the
quantitative questionnaires and the formulation of the questions and, for example, to
formulate lists of proposed pre-responses to prevent unforeseen categories from overly
flooding the 'other, please specify' category, or even envisaged categories from referring
excessively to masculine situations.

When conducted after the statistical surveys (and their preliminary analyses), focus groups
can draw upon the interview guides and focus on the questions highlighted by the preliminary
results (or, conversely, on questions that remain obscure), as well as the general issues
concerning the statistical questionnaire.
In both cases (before or after the statistical surveys), the results of the focus groups should
allow for the consolidation and fleshing out of the statistical results by translating them into
the ordinary language of lived experience of the individuals, hence the need to record and
transcribe them literally.

From a gender perspective, focus groups are ideally and simultaneously exclusively female,
exclusively male, and mixed. The same principles can be applied to age groups: young people,
adults, elderly people.

## 2.3 Tabulation programmes

When designing the questionnaires, it is good practice to plan the tabulation programme, i.e. design the tables of results cross tabulating the different variables and information collected, all tables and all variables (in absolute figures and in horizontal, vertical, and even transversal proportions). All of this is subject to the limits imposed by the size of the sample, and the sequence of the questionnaire should be followed, question after question. Designing the questionnaire and the tabulation programme at the same time allows the appropriateness of asking certain questions to be evaluated and therefore the collection of excessive volumes of information, which might not all be ultimately analysed, to be avoided. Only the large statistical organisations can collect large volumes of information destined to be stored and not all processed immediately. Survey propositions should optimise collection based on the objectives pursued and by lightening the burden they represent for the respondents.

The first principle to apply when preparing the list of tables is that the gender variable is a fundamental variable. It's not a variable like the others, it's a variable that concerns half the population. All other variables (education level, activity status, job status, etc.) can only be contiguous to gender. In principle, gender can't be avoided in any cross-tabulation of variables and most importantly, you shouldn't limit the cross-tabulation to certain questions only. In other words, most tables will have three columns (or three lines): Women/Men/All of both sexes.

This doesn't mean that all these tables will ultimately be deemed worthy of being published.
The content can be the only judge of that, and it can't be decided a priori that the
disaggregation by gender of a variable or a set of variables is unnecessary.

Specifically, questions concerning the household as a whole will be disaggregated by the
gender of the head of household: female heads of household / male heads of household / all
heads of household.

One might say that these descriptive statistical tables are secondary to the analyses of the
determinants of the phenomena studied, based on which efforts should be focused. Nothing
could be further from the truth. Certainly descriptive statistics don't show the causality or
allow its impact to be quantified, but once causality is identified and quantified, studying it
with simple variable cross-tabulation tables increases its heuristic value to better convince
decision makers using well-chosen curves and diagrams, rather than using esoteric equations
and formulations that risk alienating them.

Descriptive statistics don't imply a reduction simply to a distribution of the variable, but rather
multiple cross-tabulations of chosen variables, classifying their values in increasing or
decreasing order, in order to identify potential relationships between them or trends.
## 2.4 Logistic analyses

Similar principles should be applied to the analyses of the determinants of the phenomena studied. Most of the models used in econometric analyses indeed make gender a quasi-systematic variable, but the gender perspective in their case must go further than that.

Generally, the models examine the part played by gender, age, education level, marital status, environment (urban, rural), etc. in the determination of a variable taken as the research objective (e.g. wage level), concluding from this that each of the determinant variables examined (where the others are controlled) explains X per cent of the variable studied.

An analysis of gender and, more generally, an intersectional analysis could and should go further still. Logistic modelling applies to all the population studied, but it should also be applied separately to the two (or more) constituent sub-populations: women and men, young women and young men, adult women and adult men, workers and unemployed, urban and rural. In most cases, this shouldn't pose any particular problem. In some cases, this might justify an over-sampling of women if the population studied is that of heads of businesses or that of working people (in countries with a low rate of female workers). We can therefore identify if one or other factor plays a more or less determinant role based on the gender (and/or the age) of the individual. This unquestionably represents useful information for the designers of gender or female empowerment policies, or policies aimed at young people.

## Recommendations

1. Always keep in mind that women represent half the population and that it's essential to measure the differences between the two sub-populations.

2. If women represent a smaller fraction of a sub-population (e.g. business owners), ensure they are over-sampled to enable comparisons between both sexes.

3. As far as possible, try to avoid dealing with gender issues in a specific chapter as this will mean that certain research questions or subjects aren't considered from this angle.

4. In a survey of households, the gender perspective must be applied: i) to the heads of household and therefore the characteristics of the household; ii) to the members of the household and their individual characteristics.

5. In a business survey, the gender perspective must be applied: i) to the heads of the business and the characteristics of the business; ii) to the workers in the business and their individual characteristics.

6. From a gender perspective, focus groups should preferably be made up of individuals of the same gender as well as, complementarily, mixed groups.

7. If held before the quantitative survey, focus groups should be used to: i) design and formulate the questionnaires of the quantitative survey; ii) interpret the quantitative results.

8. If held after the quantitative survey, focus groups should be used to: i) interpret the quantitative results; ii) drill down into the grey areas the quantitative analysis was unable to clarify.
9. An exhaustive tabulation programme of descriptive statistics should preferably be established from the design of the questionnaire in order to: i) identify the unnecessary or superfluous questions because it's immediately apparent they won't be exploited as a priority; ii) apply the gender perspective systematically without leaving out any variable.

10. Raw descriptive statistical tables cross tabulating several variables may not necessarily be intended to be published 'as is' in the analysis report: it might be necessary to simplify them (e.g. by grouping together classes or groups), in order to avoid null or low values). Detailed tables can be indicated in an annexe if necessary.

11. The tabulation programme should be established with the following in mind: i) the utility of descriptive tables and graphics that are easy to understand in order to convince decision makers and actors in public life; ii) their role as an essential complement to the causal analysis of the determinants of the phenomena studied.

12. A suitable table of descriptive statistics and a graphic should order (increasing or decreasing order) the variables in lines or columns. The colours used in the graphics should be contrasted, thereby highlighting (using a lighter colour for example) the results you want to stand out.

13. From a gender and intersectional perspective, econometric and logistic analyses should not be limited to examining the role of the gender variable among the other variables studied, they should also apply fully and separately to the sub-populations (women/men, young people/adults, young women/young men, female adults/male adults, workers/unemployed, urban/rural) to the extent permitted by the number of observations.

Source: Authors' own.
## 3 Resources on gender, the informal sector and the Covid-19 pandemic

The informal economy represents more than 60 per cent of employment in the world and more than 50 per cent of non-agricultural employment (ILO 2018). In Sub-Saharan Africa, these proportions are in the region of 90 per cent and 80 per cent, respectively. While women don't systematically represent more than half of informal employment, it is, however, often the case in Sub-Saharan Africa and, moreover, most of the time, that female informal employment rates are higher than the rates for men.

The informal economy is made up of four components:

1. Micro-businesses in the informal sector (ranging from the single, independent home worker to the small workshop or shop in purpose-built premises, and also including street vendors and other itinerant workers);

2. Informal workers in the formal sector;

3. Domestic workers;

4. Producers of goods for their own final use.

In Sub-Saharan Africa, the informal sector is largely predominant. Generally considered a factor of countercyclical resilience (in an economic crisis, the sector absorbs a mass of workers who find in it a means of subsistence), the informal sector was unable to harness this resilience during the last crisis and was heavily impacted by the Covid-19 pandemic, which saw it lose its proximity to customers (due to the lockdowns) while being unable to make use of the digital economy and ecommerce.

In a broader sense, the informal economy could include the unpaid work represented by domestic tasks and caring for household members. Falling essentially to women, these unpaid activities are measured by time use surveys. Overall, the domestic and care workload falling on women is 2.4 times higher as a global average (2.9 times higher for Sub-Saharan Africa) than that of men (and of a different nature). As such, in the final analysis, the total workload falling on women is higher than that of men (1.11 times higher globally, compared with 1.17 times higher in Sub-Saharan Africa). This explains what we call the feminisation of poverty and the limited access of women to paid work and to political and economic responsibilities. It's therefore important to measure the effects of the pandemic against this neglected aspect of gender relations, since the closures of businesses and schools, the lockdowns, and the restrictions on mobility impacted heavily on these aspects of daily life. Only 13 Sub-Saharan African countries conducted surveys that covered this aspect (among which Benin in 2015 and Cameroon in 2014), and three repeated them (Benin, South Africa and Tanzania). However, partial data was able to be collected in certain surveys on the living conditions of households (see Charmes 2019a).
## 3.1 Trackers and guides from international organisations and national statistics institutes

The Covid-19 pandemic gave rise to numerous statistical collections aimed at measuring its impact on different areas including employment in general and women's employment in particular, and income and poverty. The major international organisations supported the national statistics institutions in these collections, although some national organisations did so at their own initiative and used their own resources. It must be said that the pandemic really stretched the national statistics systems by preventing the collection of the usual data, which not only made it impossible to measure the impact of the pandemic, but also led to the interruption of historic series (notably the quarterly, half-yearly or yearly surveys of households on employment). According to the survey conducted by the United Nations (UN) Statistics Division and the World Bank with national statistics organisations, it is estimated than 96 per cent of national institutes had been disrupted and had partially or totally ceased (69 per cent) collecting information face-to-face (UN and World Bank 2020). We suspect that the rapid surveys using modern interview technologies (telephone, Internet) don't reach the same populations as the face-to-face surveys and this can have consequences on the representativity of the data collected, particularly with respect to informal activities and vulnerable populations.

The International Labour Organization (ILO) has regularly published an update on the impacts of the Covid-19 pandemic on the world of work (ILO 2021a). Since then, only documents targeting a subject or a country have been published and are available online. The ILO has also proposed a rapid methodology for assessing the impact of the pandemic on employment (ILO 2020). It keeps an up-to-date list of the measures taken by governments to stimulate the economy and jobs; to support enterprises, employment and incomes; to protect workers in the workplace; to support social dialogue to find solutions; other measures; ILO actions in the country, actions of workers' organisations and employers' organisations (ILO n.d.). Lastly, its flagship publication *World Employment and Social Outlook: Trends 2021* is devoted to the impacts of the pandemic (ILO 2021b). The 2022 version focuses on (re)building a resilient world after the pandemic and the actions undertaken by governments (ILO 2022).

The International Monetary Fund (IMF) tracks the measures adopted by various countries in its Covid-19 Policy Tracker (IMF n.d.a, last updated July 2021). It also publishes a *Special Series on Covid-19 (IMF n.d.b) including Gender Equality and Covid-19: Policies and Institutions for Mitigating the Crisis* (Tang et al. 2021).

The UN has established a framework for an immediate socioeconomic response to the pandemic (UNDP 2020a). UN Woman launched rapid gender assessment surveys on the impacts of Covid-19 in numerous countries in the world, notably Africa. The methodology can be found in UN Women (2020a). The organisation has also established a methodological note on the subject (UN Women and UNDP 2021) and a Toolkit to Review Covid-19 Related Socio-Economic Impact Studies and Related Activities (UN Women 2020b). You can also consult the fact sheets for Western Africa and other regions (UNDP 2020b), as well as national reports (notably for Benin, Côte d'Ivoire, Senegal and South Africa) (UN Women n.d.a) and examine the data from all surveys by region and sub-region and by indicator (UN Women n.d.b). These surveys have also been consolidated in a publication for Asia and the Pacific (Seck et al. 2021). The article abstract outlines perfectly the results one might anticipate from these surveys. Based on data collected using UN Women's rapid survey methodology, the study seeks to show the impact of the pandemic on the lives of women and men in eleven countries in the Asia-Pacific region: beyond the health consequences, fragile gains in gender equality in paid
and unpaid work are threatened. With the closure of schools and the reduction of economic activity, and the increased need to care for the elderly and sick adults, the demand for unpaid domestic and care work has increased dramatically, and women bear the brunt of the burden as they have lost their livelihoods to a greater extent than men. The impact on mental health is also a critical area. The study's findings inform both the emergency responses required by the situation and longer-term measures to address gender inequalities.

On the subject of violence against women during the Covid-19 pandemic, UN Women has established and keeps up to date a database (UN Women n.d.c) and has also initiated a five-year programme (2018-22) to strengthen the methodologies for measuring violence against women and strengthen national and regional capacities to collect data (UN Women 2020c). Lastly, UN Women published a programmatic report for the post-pandemic with a view to transitioning towards an economy more mindful of women's ways of life, placing care at the centre of a fairer economy and leading to a new feminist social contract (UN Women 2021).

The World Bank focuses its response to the pandemic on four areas:

1. Saving lives threatened by the pandemic;

2. Protecting the poor and vulnerable;

3. Helping save jobs and businesses;

4. Working to build a more resilient recovery.

(World Bank 2020a)

Its operational strategy document *World Bank Group Covid-19 Crisis Response Approach Paper: Saving Lives, Scaling-up Impact and Getting Back on Track* sets out the details (World Bank 2020b). It also has a hub on the response to the pandemic which lets you select 155 indicators for each country and each wave of surveys, prepare tables, graphics and trends, etc (World Bank n.d.a). Similarly, a business survey dashboard has been organised in 79 countries based on the same model (World Bank 2022). The World Bank also has a country-by-country list of its operational projects in response to the pandemic (World Bank n.d.b).

UNESCO has tracked global school closures and the duration of the closures, as well as the priority given to the vaccination of teachers (UNESCO n.d.). The World Health Organization publishes weekly reports on the epidemiological situation (WHO n.d.).

We also note that numerous national statistics offices and institutes, with the support of the World Bank or other institutions (such as the United Nations Development Programme (UNDP), have conducted surveys on the socioeconomic impact of the pandemic on households and businesses in both the formal sector and the informal sector. For example:

* The National Institute of Statistics and Economic Analysis of Benin: *Enquêtes réalisées par l'Institut National de la Statistique et de l'Analyse Economique dans le cadre de l'évaluation des impacts socio-économiques de la COVID-19 au Bénin* (Chognika 2021);

* The National Institute of Statistics and Demography of Burkina Faso released its 11th edition in July 2021: *Covid-19. Suivi des impacts au niveau des ménages* (INSD 2021);

* The National Statistics Institute of Côte d'Ivoire: Evaluation de l'impact de la Covid-19 sur l'activité des entreprises du secteur formel en Côte d'Ivoire (INS Côte d'Ivoire 2021a) and Evaluation des effets de la Covid-19 sur les unités de production informelles en Côte d'Ivoire (INS Côte d'Ivoire 2021b);
* The National Agency of Statistics and Demography of Senegal: Page Spéciale Covid-19: informations statistiques sur la conjoncture économique et sociale dans le contexte de la pandémie (ANSD n.d.a). Here, you'll find all the reports published in 2020 concerning the impact of the pandemic on formal and informal businesses, on households and also on prices, access to water and electricity, investment intentions and other subjects besides. The micro databases of the surveys tracking the impact Covid-19 are also publicly accessible (ANSD n.d.b).

* The National Institute of Statistics of Cameroon: Enquêtes panels d'évaluation de l'impact socio-économique du Coronavirus (Covid-19) au Cameroun (INS Cameroon 2022) provides data on the living conditions of households and the situation of businesses. Furthermore, a publication of detailed tables on the evaluation of the effects of Covid-19 on decentralised territorial communities (INS Cameroon and UNDP 2020) provides highly detailed statistics notably covering the feeling of mayors about the impact of the various activities at the local level, how satisfied they are with the recovery measures, their implementation, the budget allocated, concrete actions, the difficulties encountered, the effects on the social, economic and cultural spheres, and lastly suggestions. Note that the results are presented on the basis of the gender of the mayors, their age, level of education and seniority. Conversely, the number of cases and deaths are not disaggregated by gender and the only table that differentiates women is the one focusing on concrete actions.

* Statistics South Africa has published numerous reports on the impact of the pandemic on demography (Statistics South Africa 2020a), mobility (Statistics South Africa 2020b), migration and education (Statistics South Africa 2020c), businesses, jobs and incomes (Statistics South Africa 2020d), behaviours and health (Statistics South Africa 2020e).

Generally, the rapid assessments highlight the aggravated impact of the pandemic on women whose domestic and care tasks for members of the household increased on account of the lockdowns, the closure of schools, and restrictions on mobility. This weakened their position on the labour market, reduced their income and saw the level of domestic violence they suffered increase. Overall, past progress made in gender equality has been undermined and fallen back, even if in absolute terms, (and in certain countries), the particular circumstances linked to the pandemic were able to open a window of opportunity towards a better balance in the sharing of domestic and care tasks between sexes (although we can't measure the longevity of this at this stage).

## 3.2 Estimations and surveys on informal employment and the informal sector

Several publications provide an opportunity to look at informal employment estimations. However, at this stage they don't measure the impact of the pandemic due to the fact they pre-date it.

ILO's Women and Men in the Informal Economy: A Statistical Picture (ILO 2018) is the fruit of its cooperation with WIEGO (Women in Informal Employment, Globalizing and Organizing)<fnref n="1" />, a 25-year-old international network made up of researchers, statisticians and activists working to support these vulnerable populations. This third edition uses the micro data from the

<footnote n="1">¹ See WIEGO website.</footnote>
surveys, which presents benefits in terms of the tabulation of variables (particularly the
gender variable), but also drawbacks (certain national statistics institutes didn't share the
micro databases, meaning that the surveys that were used were sometimes less reliable or
less targeted). The third edition also covers the developed countries and extrapolates the
results to the countries missing, allowing for a global estimation of employment in the informal
economy to be obtained (61.2 per cent of the global working population, 50.5 per cent of the
global non-agricultural working population). You can also refer to the ILOSTAT database for
detailed and chronological data by country (n.d.). Charmes (2019b) provides statistics on a
larger number of developing countries than the ILO publication, as well as time series.

Lastly, the West African Economic and Monetary Union (UEMOA) programme enabled national
statistics institutes in the region of West Africa to conduct an integrated regional survey on
informal employment and the informal sector in the eight member countries: Benin, Burkina
Faso, Côte d'Ivoire, Guinea Bissau, Mali, Niger, Senegal and Togo). The results for 2017–18
period, as well as a regional synthesis, have been published (UEMOA n.d.). You can also find
these reports on the sites of the national statistics institutes of the countries in question. In
Central Africa, only Cameroon and the Democratic Republic of Congo have conducted similar
surveys (although now outdated), but the survey scheduled in Cameroon for 2018 has still not
been conducted.
Conclusion

In many ways, the economic and social crisis caused by the Covid-19 pandemic differs from crises caused by intrinsically economic shocks such as the financial crisis of 2008-09, both by its suddenness, its global reach, the magnitude of the immediate consequences on millions of individuals, and the reactions of governments. It also had extremely disruptive effects on the collection of data by official organisations. The scale and ambition of the response measures taken by governments have not always been successful in mitigating their neutrality – or rather their blindness to questions of gender and diversity. What is certain is that the specific characteristics of the crisis caused by the pandemic have produced large-scale structural effects and we still don't know how long they will last, which relate to how we exercise economic activities at the usual places of work, or how domestic and care tasks in households are shared. In Sub-Saharan Africa where the informal sector is predominant and in which women find their main livelihoods, all the while contributing more disproportionately than ever to domestic and care tasks, and where the measures taken by government have undoubtedly been less sensitive to issues of gender, these two aspects (informality and gender) are of particular relevance in any research on the impact of the pandemic on the living conditions of populations.

Research projects that aim to identify the impact of the pandemic on gender inequality must therefore design surveys and approaches that place these issues centre stage, in the design, analysis and presentation of the results to the various stakeholders.

The aim of this document was to present a number of simple ideas, which might even be considered trivial, but which are too often ignored, and to signpost the reader to the resources available online, in order to facilitate this unveiling of the realities by the research teams involved. It clearly won't have been able to identify all the aspects one would have liked to address, and it will fall to the comments that will undoubtedly follow in order to fill in the gaps and add to the series of recommendations.
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**Suggested citation**

Charmes, J. (2022) *Designing Surveys and Analysing Results from a Gender Perspective in Economic Research*, Brighton: Institute of Development Studies, DOI: 10.19088/CORE.2022.009

**About this report**

This output was published by the CORE Knowledge Translation Programme, led by the Institute of Development Studies (IDS), which supports the translation of knowledge emerging from the CORE initiative. Supported by the International Development Research Centre, CORE brings together 21 projects to understand the socioeconomic impacts of the pandemic, improve existing responses, and generate better policy options for recovery. The research is being led primarily by local researchers, universities, thinktanks and civil society organisations across 42 countries in Africa, Asia, Latin America and the Middle East.

The views expressed herein are those of the authors and do not necessarily represent those of IDRC or its Board of Governors, or IDS.

It is distributed under the terms of the [Creative Commons Attribution 4.0 International licence (CC BY)](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use or distribution in any medium, provided the original authors and sources are credits and any modifications or adaptations are indicated.

© Institute of Development Studies 2022

DOI: 10.19088/CORE.2022.009