Not all informal workers are poor and not all working poor are engaged in the informal economy. Some informal operators – especially those who hire others – are not poor, while some formal wage workers are poor. But there is a significant overlap between working in the informal economy and being poor. This section details what is known about the relationship between working in the informal economy and being poor. It summarizes findings of several recent studies that used different measurement approaches: average earnings of the various types of formal and informal employment; the earnings/expenditures of households; and the risk for informal workers of being in a poor household.
Making the link between informality and poverty means assessing the costs and benefits associated with different segments of informal employment against the location of the working poor, both women and men, within them. Statistical data on associated costs and benefits are limited: so testing these linkages statistically is very difficult. However, several recent sets of country-level data analyses have considered the average earnings and/or the poverty risk of different segments of the labour force, both formal and informal.
- The first set is the analysis of national data in five countries – Egypt, El Salvador, India, Russia and South Africa – commissioned by the Economic Policy Institute-Global Policy Network (EPI-GPN) for a comparative workforce development project funded by the Ford Foundation.
- A second set is the analysis of national data in five countries – Costa Rica, Egypt, El Salvador, Ghana, and South Africa – commissioned by the WIEGO network for the 2005 issue of UNIFEM’s flagship publication Progress of the World’s Women.
While there is an overlap in three countries – Egypt, El Salvador, and South Africa – between these two sets of country cases, there are also important differences in the objectives of the two studies. The EPI-GPN set of country cases was designed to look at broad trends in unemployment; formal and informal employment; and, where possible, earnings. The UNIFEM set of country cases was designed to look in depth at the links between employment status (formal and informal), earnings and household poverty at a single point in time. In the three countries that were common to the two sets of analysis – Egypt, El Salvador, and South Africa – the data sources and years were roughly comparable.
- A third relevant analysis is a compilation of data for 14 countries by Jacques Charmes.
- A fourth – and final – set is analyses of national data for urban India and for Tunisia.
For more information on these sources, see Links with Poverty: Data Sources.
The links between employment, gender, and poverty can be seen by comparing a) average earnings in formal and informal employment and b) average earnings of different categories of informal employment.
Average Earnings in Formal and Informal Employment
A first comparison is the contrast between average wages or earnings in formal and informal employment, taken as a whole. In three of the five countries in the EPI-GPN set of studies, the analysts were able to compare average wages or earnings data. The results confirm that, on average, wages or earnings are higher in formal than in informal employment, as summarized below:
- Egypt: Average real wages of the formal and informal workforce, both sexes, were measured at two points in time (1988 and 1998). The results suggest a large gap between formal and informal real wages in both years and for both sexes, but a narrowing of the gap by the second point of time as formal real wages declined more rapidly than informal real wages. However, between the two points in time, female informal wages declined faster than female formal wages (El Mahdi and Amer 2004).
- El Salvador: Earnings from formal and informal employment in relation to the minimum wage were compared for 2002. A relatively small share (14%) of the formal workforce earns below the minimum wage. Within the informal workforce, a higher share of rural workers (77%) than urban workers (49%) earn below the minimum wage (Lara 2004). It should be noted that the minimum wage is set at a level that would not cover the cost of “basic goods.”
- South Africa: The income of formal and informal sector workers for 2001 was compared. While the majority of formal workers earn above 1,000 rand per month, the majority of informal workers earn less than 1,000 rand. The estimated minimum level of income needed for a family of five is set at 1,777 rand per month (NALEDI 2003).
In all five UNIFEM-WIEGO study countries, average earnings in most forms of informal employment, particularly in agriculture, are well below earnings for formal employment. In Costa Rica and El Salvador, however, average earnings for informal employers are equal to or higher than earnings in formal employment; and in Ghana and South Africa, average earnings of informal public wage workers are higher than those of formal private-sector employment. In general, wage employment in the public sector, both formal and informal, has higher average earnings than wage employment in the private sector.
Comparative Earnings within Informal Employment
A second comparison is the difference in average earnings within informal employment. As noted earlier, the informal economy is diverse and segmented. The different segments are associated with different earning potentials that would be concealed by the average for the informal economy as a whole. For example, an analysis of 1997 data on employment in the informal sector (small unregistered enterprises) in Tunisia found that the employers who hired others – the micro-entrepreneurs – were not poor. Indeed, the average income of micro-entrepreneurs was found to be four times as high as the legal minimum salary and 2.2 times the average salary in the formal sector.
Although micro-entrepreneurs may have relatively high earnings in Tunisia – and elsewhere – most workers in informal employment do not fare so well. For example, the micro-entrepreneurs in Tunisia paid their employees on average roughly the legal minimum wage of 200 dinars per month. The Tunisian study also included information on earnings in jobs outside informal enterprises – notably for homeworkers. Homeworkers, who are paid by the piece, earned an average of 60 dinars per month, which is only 30 per cent of the minimum wage (Charmes and Lakehal 2003).
The national data from 14 countries compiled by Jacques Charmes show the disparities in earnings within informal employment (Charmes n.d., table, presented in Chen and Vanek 2004). In every case except Kenya, the average monthly income of micro-entrepreneurs is higher than the average monthly wages of the employees of micro-enterprises. Generally, the wages of employees tend to hover around the minimum wage – which in itself may be less than the minimum needed for survival (ibid).
Another important comparison is between the average earnings of micro-entrepreneurs and of own account operators. Among the 14 countries studied by Charmes, only two – Columbia and India – report separate earnings data for employers and own account operators. In India, the lowest multiple of average monthly income to legal minimum wage (1.34) was for own account operators in India. In marked contrast, the average monthly income of employers in India was 5.4 times the legal minimum wage. A similar contrast was found in urban Columbia, where employers earn 4.2 times the legal minimum wage and own account operators earn only 1.6 times. In fact, in urban Columbia the employees of micro-enterprises earn nearly as much as own account operators: 1.5 times the legal minimum wage. In sum, in both Colombia and India, micro-entrepreneurs/informal employers earn higher monthly average income than own account operators, and own account operators have only slightly higher average earnings than employees of informal enterprises (ibid).
The UNIFEM-WIEGO set of data analyses found a hierarchy of average earnings across the different segments of the informal economy. To begin with, average earnings in agricultural informal employment are lower than average earnings in non-agriculture informal employment. Among non-agricultural informal employment, in all five countries, informal employers have the highest average earnings followed by their employees1 and other “regular” (as opposed to casual) informal wage workers, then own account workers, and then casual wage workers and domestic workers (ibid.; Chen et al. 2005). This set of country studies did not separate out industrial outworkers who, as noted above, tend to have the lowest average earnings of all. These studies also did not provide systematic evidence on where employees of informal enterprises belong in the hierarchy.
In most of these country cases, separate data on employees of informal enterprises or informal employees more generally are not available. In Egypt, such data are available from an enterprise survey but these cannot be linked to the broader labour-force analysis. In South Africa, such data was available from the labour force survey. Wage employees in informal (unregistered) enterprises earn somewhat less than own account workers, while informal employees in registered enterprises earn more than own account workers. Therefore, in South Africa, informal employees in unregistered enterprises would be considered more closely linked with “casual wage workers” than with “regular wage workers” (Casale et al. 2005, cited in Chen et al. 2005).
Within informal employment, in all five countries in the UNIFEM-WIEGO set of analyses, women’s hourly earnings uniformly fall below those of men with identical employment status. The gender gap in earnings is particularly pronounced among own account workers – both agricultural and non-agricultural. This gender gap in earnings is compounded by the gendered segmentation of informal employment, as women are more likely to be own account workers than regular wage workers.
Two recent labour force surveys – the 2002 Labour Force Survey in South Africa and the 1999-2000 National Sample Survey of India on employment and unemployment – provide unique data that begin to answer questions regarding the relationship between employment and poverty. Both surveys collected household expenditure data as well as data on employment, including informal employment; and both studies tried to link these variables in a meaningful way by classifying households by sources of income and by expenditure categories. Analyses of these data sets all found an overlap between depending on informal employment and being poor at the household level.2
South Africa was one of the three countries studied in both the EPI-GPN and UNIFEM-WIEGO sets of country cases. The EPI-GPN study looked at the relationship between monthly household expenditure categories and the sources of employment income in households: that is, by whether a household had one or more persons in permanent employment, in informal employment, in domestic work or unemployed (NALEDI 2003). The higher the monthly expenditure category, the higher the percentage of households with persons in permanent employment. Moving down the expenditure categories, the percentage of households with persons in informal employment (including domestic services) increases. Not surprisingly, the lowest expenditure category had the highest percentage of households with an unemployed person or persons (using an expanded definition of unemployment). It should be noted that the unemployment rate is very high in South Africa (ibid).
The South Africa analysis in the UNIFEM-WIEGO set of country studies looked at the relationship of household income categories and whether the majority of household employment income is from formal or informal sources, as well the number of earners, the sex of the household head, and the sex of the primary earner (cited in Chen et al. 2005). Households that depend primarily on informal employment income have significantly higher poverty rates than households with a majority of income coming from formal employment. Female-headed households have significantly higher poverty rates than male-headed households. Similarly, households whose primary earner is female have significantly higher poverty rates than households in which the primary earner is male. However, these gender differentials are much less pronounced when households have access to formal employment (ibid).
A recent analyses of the 1999-2000 India data looked at poverty rates among urban Indian households that sustain themselves on informal employment income by broad industrial sector and employment type (Sastry 2004).3 In marked contrast to South Africa, unemployment in India is not high, and the vast majority of workers – 92 per cent – are in informal employment (using the expanded definition of informal employment). Households that depend on “regular” (as opposed to casual) informal wage employment have lower poverty rates relative to households that rely on self-employment, and households that depend on casual labour as their primary source of income are the most likely to be poor. This hierarchy of poverty risk – households depending on “regular” informal wage employment having the lowest, self-employment the next highest, and casual wage employment the highest risk – is robust across industrial sectors in urban India.
- Another study in India, also using data from the National Sample Survey but from two earlier surveys (1987/88 and 1993/94), found a similar relationship between poverty and the nature of employment. Dubey et al analyzed the probability of urban households being poor according to their main source of income – classified as regular salary, self-employment and casual wage labour – and by the size of the city or town in which they were located (Dubey et al. 2001). Their analysis shows that, for cities or towns of all sizes and both points in time, households with regular salaried employees (both formal and informal) have the lowest probability of being poor, while those that depend on casual day labour have the highest probability, and households that depend on self-employment falling roughly half-way in between.4 All employment groups fared better in larger cities. And, between the two rounds of the survey, the probability of being poor declined for all groups.
Poverty is usually measured at the household, not the individual, level. For the country studies for the 2005 UNIFEM publication, WIEGO used an innovative technique for measuring the risk of poverty among employed persons. According to this technique, the “poverty risk” associated with different employment statuses is defined as the share of all persons employed in a given status who live in households whose incomes place them below the national poverty line. This technique connects the type of employment, measured at the individual level, to the risk of poverty, measured at the household level. As such, it is only feasible in those countries where national data on employment and household income are linked. The hierarchy of poverty risk so defined is the reverse of the hierarchy of earnings detailed above: informal agricultural workers have the highest risk of poverty and, among the non-agricultural informally employed, informal employers have the lowest risk of poverty followed by their employees and other “regular” informal wage workers; own account workers have a higher risk of poverty; while casual wage workers and domestic workers have the highest risk (Chen et al. 2005). Since the UNIFEM-WIEGO country studies were not able to separate out industrial outworkers, it was not possible to measure the poverty risk of industrial outworkers.
In all five UNIFEM-WIEGO study countries, there is an overall gender gap in poverty risk within the informal economy as women are concentrated in forms of employment with high rates of poverty. However, no systematic pattern emerged in the country case studies in terms of differences between men’s and women’s poverty rates within a particular employment status. One possible explanation is that households in which women are engaged in remunerative work might have lower poverty rates relative to households in which women do not allocate time to income-generating activities. If this is the case, a household’s poverty status can be determined by women’s access to paid employment, no matter how low their earnings.
Hierarchies of Earnings and Poverty Risk
The statistical evidence from these various sets of national data analysis suggests a hierarchy of earnings and poverty risk across the various segments of the labour force. While average earnings are higher in formal employment than in informal, there is also a hierarchy of earnings within the informal economy. Employers have the highest average earnings followed by their employees and other “regular” informal employees, then own account workers, followed by casual wage workers and domestic workers, and finally industrial outworkers. Within this hierarchy, women are disproportionately represented in segments of the informal labour force with low earnings. The fact that women tend to be underrepresented among informal employers and “regular” informal wage workers and overrepresented among industrial outworkers leads to a gender gap in average earnings, as well as in poverty risk within the informal economy. Average earnings are lower and the risk of poverty is higher among all women workers in the informal economy compared to all men workers within the informal economy.
The hierarchy of poverty risk among households depends on whether households have some formal sources of employment income or only informal sources and also on what type of employment is the primary source of employment income. Households which rely primarily on informal sources of employment income face higher poverty risk than those that rely on formal sources. And households which depend on the most precarious forms of informal employment as their primary source of income are likely to have substantially higher poverty risk than those that have access to more stable and better quality employment.
For figures that graphically depict these hierarchies, see Hierarchies of Earnings & Poverty Risk.
In developed countries, there is also a move to more precarious temporary and subcontracted work. A 2012 study on temporary or subcontracted workers in California found they were twice as likely to live in poverty.
As this review of available evidence suggests, the labour force in most developing countries is highly segmented both between – and within – formal and informal employment and there is a remarkably similar hierarchy of average earnings and of poverty risk across these segments in all study countries. Within the informal workforce, informal employers typically have the highest average earnings followed by their employees and other “regular” informal wage workers, then own account workers, followed by casual wage workers and domestic workers, with industrial outworkers earning the least of all. A reverse hierarchy of poverty risk obtains within the informal economy: informal employers have the lowest risk of poverty followed by their employees and other “regular” informal wage workers, own account workers have a higher poverty risk, while casual wage workers and domestic workers have the highest poverty risk. The fact that women tend to be underrepresented among informal employers and “regular” informal wage workers and overrepresented among industrial outworkers leads to a gender gap in average earnings and in poverty risk within the informal economy: average earnings are lower and the risk of poverty is higher among all women workers in the informal economy, compared to all men workers within the informal economy.
It should also be noted that the poverty and other outcomes of work are a function not only of the level of earnings but also of the period over which earnings are sustained, and the arrangements through which they are achieved, including related costs and benefits. Three dimensions of work are instrumental in determining the social outcomes of work: place of work, production system, and employment status. Each place of work is associated with specific risks and, thus, different degrees of security or insecurity. Micro-entrepreneurs and wage workers tend to lose market knowledge and bargaining power as they move from traditional to industrial to global systems of production. And each employment status is associated with different degrees of autonomy and risk for those who work in them.
Read Chapter 4 of Chen et al., 2005, Progress of the World’s Women 2005 for more details.
While informal work may offer positive opportunities and benefits, such as flexibility of work hours and convenience of work location, the costs are often quite high. Some of these are “out-of-pocket” direct expenses needed to run an informal business or otherwise work informally; others are indirect, reflecting the more general conditions under which the working poor live and work. Some of these can be rather high over the long-term, such as when a worker has to sacrifice access to health, old-age pensions and education (or training) for herself or himself or for family members. Also, there are psychological and emotional costs – in terms of a worker’s self-esteem and dignity – associated with many forms of informal work. Read Chapter 4 of Chen et al. 2005, Progress of the World’s Women 2005 for a typology of the costs of working informally and examples illustrating the typology.
In conclusion, the quantity and quality of employment available to women, men, and households matter a great deal in determining who is poor and who is not – not only in terms of income poverty but also in terms of other dimensions of poverty. The benefits of informal employment are often not sufficient and the costs are often too high for those who work informally to achieve an adequate standard of living over their working lives.
1 In Egypt, the only country for which earnings by size of informal firm were available, the average wage of employees increases with firm size.
2 A related phenomenon, which deserves more study, is the concentration of certain racial or ethnic groups as well as immigrant populations in the informal economy. For example, in South Africa, 85 per cent of all workers in the informal sector are black (NALEDI 2003). In Guatemala, according to a 1989 household survey, indigenous workers are 4.3 times more likely to be working in the informal sector than in the formal sector (Funkhouser 1996).
3 In this study, households classified as sustaining themselves on informal employment income are households with at least one person employed as an informal workers and no other household member employed outside of the informal economy.
4 While the findings of both studies may not be surprising, there are very few empirical analyses linking household poverty and employment in this way.