Chapter 4 The Changing Nature of Work: Improving the Functioning of Labor Markets

Sidra Rehman; Agustin Velasquez

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Sidra Rehman

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Agustin Velasquez

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Language:: English

Keywords:: MENA region; MENA economy; sector employment share; industry-employment share; inclusion Index; Employment; Automation; Middle East; North Africa; East Africa

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Abstract

While annual GDP growth has accelerated in the Middle East and North Africa (MENA) region by about 5 percentage points since 2000, job creation has been limited and unemployment remains high (see Chapter 1). In the MENA region, the elasticity of employment to growth is estimated to be lower than in high-income countries and below levels that would be required to employ the region’s growing young population (IMF 2021; Crivelli, Furceri, and Toujas-Bernate 2012).

Introduction

A rich and growing literature (Crivelli, Furceri, and Toujas-Bernate 2012; IMF 2018; IMF 2021; ElGanainy and others 2022) has attributed limited job creation in the MENA region to a host of structural and macroeconomic factors, including (1) skills mismatches; (2) widespread rigidities in labor markets, mainly from restrictive labor legislation for the hiring and dismissal of workers; and (3) a large government sector. A confluence of these factors has resulted in stagnant employment and unemployment rates in the region, with segmented labor markets, characterized by low female and youth labor force participation rates and a large informal sector (IMF 2018 and Chapter 1 of this book).

Emerging global trends could change the MENA region’s labor markets over the next few decades, in directions that are difficult to fully gauge. On the one hand, the working-age population in the MENA region will increase by 15 percent over the next two decades (equivalent to 30.8 million people),¹ further raising pressures on economies in the region to generate employment. On the other hand, the MENA region will not be immune to the transformation drivers that started in advanced economies a few years ago and were accelerated by the pandemic. In particular:

Advances in automation technologies and artifcial intelligence (AI) have led to growing fears about job losses, especially for unskilled labor. While economic theory suggests that technological progress is likely to create both winners and losers, a disproportionate impact on low-skilled workers could, at least temporarily, increase labor market segmentation and income inequality.
The pandemic is likely to have permanently changed the nature of work, with a widespread adoption of remote work arrangements and persistent reallocation of jobs across sectors (including from hospitality and high-contact sectors to IT and e-commerce). The nature of these changes will likely vary across countries depending on the distributions of jobs and occupations, the possibility of working from home, and the quality of internet access.

Automation, AI, and remote work are likely to present challenges to MENA economies but also provide opportunities, especially to some excluded groups, like youth and women. AI will increase the demand for high-skill workers to support automation, while remote work will reduce location and mobility impediments, and allow for more flexible work schemes for jobs that do not require physical proximity. While jobs affected by AI, automation, and remote work do not necessarily overlap one-to-one, one factor is clear: up-skilling the labor force is key to adapting to this new labor market landscape and mitigating long-lasting job deficits in the MENA region.

Against this background, this chapter addresses a few key questions: How will these trends impact the nature of work and labor market outcomes in MENA countries? Will they lead to further polarization in regional labor markets, and further entrench inequality, or will they provide an opportunity for more inclusion? To shed some light on these questions, in this chapter we first review recent academic literature on the future of jobs globally and in the MENA region. Ten, leveraging this literature, we build indices that measure the degree of exposure of MENA economies to both automation and remote work trends, and discuss the implications for different demographic groups and countries in the region. We conclude by discussing policy measures to help navigate the changing landscape so that the MENA region is poised to take advantage of these trends that will shape the future of work.

Automation

We first look at the potential impact of advances in AI and related forms of automation technologies on the MENA region’s labor markets.

According to many—though by no means all— economists, advances in AI and related technologies will allow machines to substitute for human labor across a much wider range of tasks than earlier waves of automation. Autor and Price (2003) and Autor (2013) suggest that automation tends to hurt workers by substituting humans in performing noncognitive and routine tasks. Technological progress has been skill-biased, disproportionately favoring high-skilled workers over low-skilled ones, leading to a polarization of the labor market and income gains at the global level (Berg, Bufe, and Zanna 2018; Jaumotte, Lall, and Papageorgiou 2013; Korinek, Schindler, and Stiglitz 2020). Sachs (2019) and Yusuf (2017) suggest profound implications of automation on development pathways and strategies as well as the prospect of premature deindustrialization, along with reductions in demand for unskilled labor. Nedelkoska and Quin-tini (2018) find that developing countries are more vulnerable to automation, based on differences in industrial structure and, more importantly, in the way work is organized in these countries (notably a greater dependence on unskilled labor).

The overall impact of automation trends on employment, however, is likely to be both complex and ambiguous—with creative and destructive forces that impact the demand for labor, and the distribution and reallocation of tasks across factors of production. Acemoglu and Restrepo (2019) identify two effects: (1) a displacement effect, where automation results in job losses, and (2) a reinstatement effect, which has a positive effect on job creation whereby new technologies create new employment opportunities. It remains to be seen if the nature of automation will be employment reducing or enhancing on balance.

In general, the impact of automation on jobs is likely to be greater in (1) manufacturing, a sector characterized by many occupations involving manual and routine tasks, and (2) administration and middle management, which tend to involve many cognitive, but routine, tasks. In contrast, employment in sectors with many occupations involving cognitive and not-routine work, like education and health care sectors, is likely to be less affected by technological change. Moreover, there could be at least a few forms of manual employment that could be difficult to computerize, such as those in agriculture, and are thus less likely to be affected by automation. Based on the skill composition of its labor force, employment in the MENA region could be relatively more susceptible to automation, given its large concentration of employment in low-skill occupations. The share of the workforce in high-and medium-skill occupations continues to be lower in the MENA region than in the rest of the world. This has been worsened by limited employment creation in these types of occupations (Figure 4.1).² As of 2020, only 19 percent of the region’s workers were in high-skill occupations, compared to 21 percent in non-MENA economies, 44 percent were in medium-skill occupations (41 percent in non-MENA economies), and 37 percent were in low-skill occupations (38 percent in non-MENA economies). Over the past two decades, high-income countries in the MENA region (for example, oil-reliant economies in the Gulf Cooperation Council) have lagged in creating high-skill jobs, continuing to rely on medium-skill occupations in a dominant public sector (Figure 4.2) (World Bank 2022).

Next, we examine if countries in the MENA region have employment concentrated in occupations that are more vulnerable to automation. Viollaz and Winkler (2020) show that a few MENA countries—Egypt, Jordan, and Tunisia (for which detailed survey data is available)—are characterized by an occupational structure with significantly fewer tasks requiring nonroutine (both interpersonal and analytical) skills (the so-called “jobs of the future,” that are less vulnerable to automation) compared to Germany, Turkey, and Chile (Figure 4.3). Interestingly, the difference is even greater in these countries’ private sectors, which seem to require fewer nonroutine cognitive tasks than the public sector—a finding that underscores the relative underdevelopment of the private sector in the MENA economies.

We gauge the vulnerability of employment to automation across MENA countries by building an employment vulnerability index for all countries in the region. The index is based on measures of the automatability of “occupations” that have been suggested in the literature by Frey and Osborne (2017) and used in recent research (Abdychev and others 2018).

Using data from the O*NET database, a comprehensive system developed by the US Department of Labor that provides information for over 1,100 occupations within the US economy, Frey and Osborne estimate indexes that measure the degree by which an occupation can be automated.³ Combining these indexes by the share of occupations in sectors yields an index of vulnerability to automation by sector:

\begin{matrix} A V_{i} = Σ_{o} s h_{o, i} A V_{o} & (4.1) \end{matrix}

where AV_o is the automation vulnerability index of occupation o and sh_o,j is the share of workers with occupation o employed in industry i. By doing this, Frey and Osborne show that the sectors with the greatest vulnerability to automation in the United States are a few service sectors that may be affected by disruptive trends, including driverless cars and self-checkout counters (accommodation and food, retail trade, real estate, transportation), followed by manufacturing, mining, and construction, given advances in robots and machinery used in these sectors (Figure 4.4).

To assess the vulnerability to automation in different countries in the MENA region, we combine these industry-level indexes (AV_i) with MENA country–specific industry-employment shares shl_c,j, (with subscript c referring to country)

\begin{matrix} A V_{c}^{E} = Σ_{i} s h l_{c, i} A V_{i} & (4.2) \end{matrix}

While in principle the same industry in different countries could employ a different combination of occupations, in the absence of detailed country-specific data, we assume that all countries in the MENA region use the same mix of occupations employed in US industries (thus implicitly that the technology is the same within industries).

We also create an index that measures the overall vulnerability of employment to automation by gender. The index is constructed for male and female employment, where the weights of total employment for each industry are replaced by the share of male and female employment, captured by the subscript g as follows:

\begin{matrix} A V_{c}^{E g} = Σ_{i} s h l_{c, i}^{g} A V_{i} & (4.3) \end{matrix}

Based on our index, employment tends to be more exposed to automation in advanced MENA economies (Figure 4.5), where employment is relatively more concentrated in industries with a high density of largely routine and noncognitive tasks (such as construction, manufacturing, accommodation, and food services).

The employment vulnerability index by gender shows that employment for male workers is relatively more susceptible than female employment across the MENA region (Figure 4.6). This is driven by the fact that male employment tends to be higher in more automatable sectors across the MENA region such as construction, transportation, and warehousing, whereas females tend to be employed in less automatable sectors, such as agriculture and education.

Remote Work

The pandemic has impacted labor markets in an unprecedented way. In response to the lockdowns and other social distancing measures to curb the spread of COVID-19, many firms turned to remote working arrangements, relying on information and communication technology (ICT) to carry out tasks that previously required physical presence. This unparalleled and synchronized work reorganization has helped mitigate the impact of COVID-19 on output, job losses, and productivity, especially for occupations that do not require close physical proximity. Even after the pandemic subdues, remote work setups are likely to remain in place and shape the future of work in many occupations.

Remote work setups depend largely on two key factors: (1) the nature and the task content of jobs, and (2) the availability of proper ICT infrastructure:

High-skilled jobs that require high degrees of cognitive nonroutine and/or interpersonal tasks are more likely to be performed remotely through ICT (World Bank 2019). By contrast, jobs intensive in routine and/or manual tasks, often performed by low-skilled workers, are less likely to be performed remotely (WEF 2020).
Even if the content of certain jobs can be performed remotely, the actual possibility to work remotely is crucially dependent on the availability of adequate infrastructure, such as computing power and high-quality internet, that allows adequate telecommunication.

These two factors explain the bifold labor market outcomes through the pandemic across countries. In advanced economies, during the first year of the pandemic, close to half of the workers were able to work from their homes, given the analytical and nonroutine nature of their jobs (WEF 2020). By contrast, it is estimated that only 10 to 20 percent of urban jobs in developing countries could be performed remotely (Gottlieb, Grobovšek, and Poschke 2020; Gottlieb and others 2021), owing to the relatively larger share of workers engaged in noncognitive and routine jobs and the relatively less developed ICT infrastructure.

Remote work could bring new opportunities for (high-skilled) women and the young, who have generally found it more difficult to find (formal) jobs in the MENA region. The reduced need to commute from home to work could mitigate issues associated with the lack of efficient transportation infrastructure, which has been shown to hinder women’s labor market participation in many MENA countries (Fogli and Veldkamp 2011). In addition, working from home could allow workers to rely on more flexible work schedules, something that can be particularly useful for women (who may need to balance paid work with household duties and family care) and young people (who may need to balance work with studying) (ILO 2021). On the downside, however, remote work could forestall the building of formal and informal professional and social networks that are useful to find (formal) jobs (IMF 2021).

Recent literature has measured the extent to which jobs can be done remotely and identified the sectors and countries that are more likely to be impacted by the widening use of remote work. Using the O*NET database and detailed labor surveys, Dingel and Neiman (2020) review the characteristics of almost 1,000 occupations in the US economy to assess whether they can be performed remotely. For example, they look at whether the job requires physical activity, an outdoor location, specific equipment, or frequent contact with the public. Using this information, they build an index of tele-workability for each occupation. They find that occupations that rely on cognitive skills and carry a wage premium are more adaptable to remote settings (Figure 4.7). Using the share of occupations within economic sectors, Dingel and Neiman build indexes of tele-workability for US sectors and use them to determine the extent to which a country has jobs that can be performed remotely.⁴ Their sample covers 85 countries, including 4 MENA countries (Afghanistan, Egypt, Pakistan, and United Arab Emirates). They find that there is a strong positive correlation between the share of jobs that can be performed remotely and a country’s GDP per capita (Figure 4.8).⁵ MENA countries ft within this general correlation pattern, with the exception of the United Arab Emirates, which shows a lower value of the index relative to what is predicted by the level of income, possibly reflecting the importance of the oil sector in value added and the fact that few jobs in that sector can be performed remotely.

One limitation of Dingel and Neiman’s study is that they utilize US sector-specific indexes of tele-workability, while technological differences across countries may lead to very different job characteristics compared to the United States, especially for low-income countries. To mitigate this problem, many studies have relied on country-specific labor surveys (see Garrote Sanchez and others 2021). In particular, Hatayama, Viollaz, and Winkler (2020) build an index of remote work amenability for Egypt, Jordan, and Tunisia that is based on the specific characteristics of occupations in these three countries, controlling for internet access both at work and at home.⁶ They find that, on average, for these MENA countries:

Tasks performed by professionals and clerical workers are more likely to be done remotely than those performed by crafters and elementary workers (for which in-person, routine tasks requiring some degree of physical activity are more prevalent) (Figure 4.9).
IT, finance, professional services, and education are more amenable to remote work than sectors such as construction, agriculture, and manufacturing (Figure 4.10).
Women are more likely to have jobs amenable for remote work, as they are less likely to work in physical/ manual jobs than men (Figure 4.11).⁷
Workers with higher (college) education are more likely to work remotely, given the more cognitive tasks performed at their jobs, especially compared to noncollege workers, who are more likely to perform more manual and routine tasks.
Older workers are less likely to have jobs amenable to remote work. This finding is the result of counteracting forces. On the one hand, physical/manual tasks tend to decline with age while supervision roles tend to increase with age, making jobs of older workers more feasible to be performed remotely. On the other hand, ICT adoption declines with age, which tends to reduce older workers’ remote work amenability. For the MENA countries in the sample, the second effect seems to dominate.⁸
Workers in formal jobs are more likely to be amenable to remote work than those in informal jobs, as they tend to have fewer manual intensive tasks and higher ICT usage (also thanks to appropriate training from their employers). To measure the effective remote work amenability in the MENA region, we follow a similar strategy as with the automation indexes, that is, we adapted the indexes of remote work amenability by sectors built by Hatayama, Viollaz, and Winkler (as the average for Egypt, Jordan, and Tunisia) with MENA country–specific sector employment shares.⁹

As in Dingel and Neiman (2020), we find that high-income MENA countries are more amenable to setting up remote work, as a relatively larger share of their workforce is employed in professional services sectors (Figure 4.12).¹⁰ Among middle-income MENA countries, Jordan and Lebanon also stand out for displaying high remote work amenability. On the other hand, Morocco, Pakistan, and Afghanistan present the least opportunities for remote work, consistent with their relatively larger concentration of employment in the agricultural sector.

As stated previously, remote work may offer greater opportunities for women in the MENA region to join the labor market. In which countries in the region is remote work expected to create the most labor market opportunities for women? To answer this question, we build a national remote work female inclusion index (described in Box 4.1) based on three indicators: (1) female labor force participation, (2) internet access, and (3) female human capital index. Intuitively, the setup of remote work would have the most impact on female labor market opportunities in the MENA countries with lower female labor participation, greater internet access, and higher estimated female human capital.

This seems to be the case mainly for Saudi Arabia, Iran, and Jordan (Figure 4.13). By contrast, countries with low connectivity, such as Yemen, and/or low educational scores, such as Iraq or Egypt, are less likely to benefit from remote job opportunities for women. Several high-income MENA countries, such as the United Arab Emirates, Kuwait, and Qatar, have relatively good access to the internet and high human capital indicators but score low in the overall index as they already have relatively high female labor participation rates, although mainly because of the significant number of female expatriate workers.

^{Box 4.1.}

Remote Work: Female Inclusion Index

The composite index is built as the geometric average of three indexes, each scaled between 0 and 1 (with a higher value pointing to greater potential benefit from remote work):

The female labor participation index, from the International Labour Organization, defined as FLFP_index = 1-(I_actual – I_min )/(I_max – I_min ), where I refers to female labor force participation in the country in 2019. I_max is defined as 60 percent, a high percentage of participation often found in advanced economies and emerging markets in other regions, and I_min is defined at 6 percent, the lowest female participation rate in our country sample. The index provides higher scores to countries that have lower female labor force participation rates (as they stand to benefit the most from remote work, holding everything else constant).
Internet access (from the World Bank and the International Telecommunication Union) is the percentage of the population that had access to the internet in the past three months (through computers, mobile phone, digital television, and so on).
The female human capital index combines indicators of a country’s education and health into a metric of the human capital that a female newborn can expect to accumulate by her 18th birthday in 2020 (for more details, see World Bank 2020).

Conclusions

The acceleration in automation and remote work presents both challenges and opportunities for the MENA region in the years to come. We find that its labor force is relatively more vulnerable to automation compared to other regions, but with differences varying across skill, gender, and sector. The overall vulnerability is attributable to the dominance of sectors such as construction and manufacturing—all of which are performed largely by occupations that tend to be routine and noncognitive in nature. The MENA region has not pivoted away from high levels of employment in low-and medium-skill occupations, which tend to be routine and nonanalytical in nature. This reliance is even more prominent in the private sector—a feature that may limit the region’s dynamism. On a positive note, our analysis suggests that female employment is relatively less vulnerable to automation.

Remote work has undergone a rapid expansion following the COVID-19 pandemic and may be here to stay for years to come. Establishing remote work environments can be logistically challenging, but may provide opportunities to some excluded demographics, such as women. In general, countries that tend to have more employment in professional areas (like countries from the Gulf Cooperation Council) or that have invested more in ICT infrastructure (like Jordan) are more likely to leverage remote work opportunities.

These trends, which are already shaping the job landscape in the region, make it even more imperative to remove existing barriers and prepare the labor market for the future, thus ensuring that the MENA region can tap into its demographic dividend and growing labor force. This requires a well-sequenced policy agenda with a focus on the following pillars:

Strengthen and improve the education system to remove skills gaps and mismatches. This can be achieved by upskilling/reskilling the current workforce and better aligning educational programs with employer needs. Additionally, improving the quantity and quality of education at all levels, including vocational training for middle-age workers, will create a more productive workforce.
Boost public investments in digitalization and internet connectivity, both in urban and rural areas, to ensure broad access and adequate connectivity to the whole population.
Reform labor market codes and regulation to cater to the shifting attitudes and changing makeup of the workforce. This would not only require removing obstacles to more flexible work arrangements at firm level, but also introducing measures that better regulate and protect the “jobs of the future.”

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Based on UN population prospects projections based on the medium-fertility variant for MENA countries.

Following the International Labour Organization’s definition, high-skill occupations include managers, professionals, technicians, and associate professionals. Medium-skill occupations include clerical support workers, service and sales workers, skilled agriculture, forestry and fishery workers, craft and related trades workers, plant and machine operators, and assemblers. Low-skill workers are those in elementary occupations such as selling goods in streets and public places, or from door to door; providing various street services; cleaning, washing, pressing; taking care of apartment houses, hotels, offices and other buildings; and washing windows.

The database contains an accurate description of the mix of knowledge, skills, and abilities required as well as the activities and tasks performed by each occupation in its taxonomy.

The country indexes are estimated by first building an index of tele-workability for US sectors, using the share of occupations o in sector i in the US economy. Once the sector indexes are obtained, country indexes are obtained by using the shares of employment by sector in the 85 countries considered in the study.

Gottlieb, Grobovsek, and Poschke (2020); Mongey, Pilossoph, and Weinberg (2020); Garrote Sanchez and others (2021); and Brussevich, Dabla-Norris, and Khalid (2020) all extend Dingel and Neiman’s influential study and find that high-income countries have a higher share of jobs that can be performed remotely compared to low-income countries.

Based on data from Labor Market Panel Surveys.

This finding has also been documented in other regions of the world (see Garrote Sanchez and others 2021).

Garrote Sanchez and others (2021) find that the first effect seems to dominate in other regions of the world, as remote work amenability increases with age in Turkey, Brazil, Mexico, India, and the European Union.

The remote work amenability index for the MENA region is built based on the relative values in Figure 4.10.

One exception is Qatar, which, despite having a high GDP per capita, displays low levels of remote work amenability due to a large share of its workforce concentrated in the construction sector.

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