2. Improving skills and employment opportunities in Tunisia

Robert Grundke
Steven Cassimon

For decades, high unemployment has been a characteristic of the Tunisian economy, with unemployment rates hovering above 12% since the 1990s (Figure 2.1). A broad range of structural factors complicates the adjustment of labour demand and labour supply and prevents the clearing of the labour market. These include institutional factors hampering business dynamics, investment and job creation, education and professional training systems that do not equip workers with the skills demanded by firms, and labour market policies and regulations that complicate the matching process in the labour market.

The rise in the working age population has not been matched with sufficient increases in labour demand leading to particularly high unemployment rates among youth (Figure 2.2) (Boughzala, 2019[1]). The youth unemployment rate rose from 25% in the 1990s to 35% in the early 2010s (ONEQ, 2013[2]). In 2018, more than 85% of the unemployed were younger than 35 years, and more than two thirds younger than 30 years. After entering the labour market, it takes on average around 26.5 months for the young labour market entrants to transition to their first job (Boughzala, 2019[1]). This transition period to the first job has lengthened since 2011, signalling that the insertion of labour market entrants is becoming increasingly difficult (OECD calculations based on ANETI data). The pandemic has led to a strong drop in labour demand, which has exacerbated existing structural fault lines and led to rising unemployment, particularly among youth (see chapter 1).

As access to secondary and tertiary education has significantly improved, the education level of labour market entrants has steadily increased (UNICEF and INS, 2019[3]). The share of the working age population with a tertiary degree has almost quadrupled since the 1990s and reached 28% in 2017. However, as the private sector has mainly created jobs in low-skill intensive and low-productivity activities, the rising supply of high-skilled labour has led to particularly high unemployment rates among tertiary graduates (Figure 2.3) (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Highly educated women are particularly affected, as they account for more than two thirds of tertiary graduates. Cultural norms, leading to relatively low interregional mobility of single women, as well as job offers with particularly low wages due to labour market discrimination, also contribute to high unemployment of tertiary educated women (Boughzala, 2019[1]).

Although unemployment rates for tertiary graduates are much higher than for other groups, around 60% of the unemployed have not obtained a tertiary education degree (Figure 2.4). This concerns particularly young men who have dropped out of secondary education or finished it with relatively weak results (Boughzala, 2019[1]). Due to their low level of technical and soft skills, including communication and language skills, they face strong difficulties in finding formal employment and typically end up in low-paid jobs in the informal sector, are long-term unemployed or participate in public work programmes (UNICEF, 2020[5]; Boughzala, 2019[1]). Informality is particularly high among young low-skilled men (see below). This group, despite being particularly vulnerable, has so far not been in the focus of active labour market and training policies and more attention is necessary to facilitate integration into formal employment (Angel-Urdinola, Nucifora and Robalino, 2015[4]).

In addition to the large group of unemployed youth, another group suffers from the structural weaknesses of the labour market: the large share of young women and men that have been discouraged in their job search and have left the labour market (Figure 2.5). Although cultural reasons related to family and household work play an important role in the case of women, difficulties to find a job are the predominant reason for leaving the labour force (Boughzala, 2019[1]). For young women without a tertiary education degree, the labour force participation rate is below 25%, whereas it is around 50% for women with tertiary degree and above 70% on average for men (Figure 2.5). More than half a million of young women are not in education, employment or training (NEET) and do not search for a job (Boughzala, 2019[1]). Together with more than 400 000 young men who are unemployed, this means that more than one third of the young working age population aged between 15 and 29 years are not in education, employment or training (OECD, 2015[6]). This has serious consequences for their human capital, social cohesion and the growth potential of the economy.

The regional dimension is key to understand the labour market mismatch in Tunisia. Unemployment rates range from below 10% in some coastal governorates to almost 30% in some southern ones (Figure 2.6). This is mainly due to a strong concentration of economic activity and job creation in coastal regions, which dates far back in history and is mainly related to access to maritime trade and favourable conditions for agriculture (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Past economic policies have reinforced regional disparities through the development of industrial clusters and special exporting (“offshore”) zones close to the coast (OECD, 2015[7]). Infrastructure, industrial and innovation policies have favoured coastal regions and few economic linkages between firms in coastal regions and firms in interior provinces exist (OECD, 2018[8]; Angel-Urdinola, Nucifora and Robalino, 2015[4]). The strong centralisation of the state and public administration and the weak adaptation of economic and social policies to regional contexts have contributed to these inequalities (OECD, 2018[8]).

Persisting regional disparities in unemployment rates indicate a relatively low internal mobility of labour (Figure 2.6). Although migration from interior to coastal regions and in particular to the Tunis metropolitan area took place during the last decades, significant obstacles to labour mobility remain. Due to weak income support to the unemployed, many of them rely on support from the family for housing and food, which reduces their geographic action space in the labour market (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Moreover, cultural norms amplify these barriers for single women, who may face difficulties to travel and live alone far away from the family (Bouchoucha, 2018[9]). This is not only a problem for lower skilled young women, but also for tertiary graduates, who have to return to their family after finishing their studies, if they do not find a job directly after graduation (OECD, 2015[6]). Rising house and rental prices complicate migration from interior regions to urban centres (see first chapter). Weak road and public transport infrastructure make commuting difficult, particularly for low-skilled and poorer workers, who cannot afford a car or motorcycle. High tariffs and excise taxes as well as restrictive import and distribution licenses raise prices for these products, contributing to low labour mobility (WTO, 2016[10]).

Although unemployment rates are high, many firms in low-skill intensive sectors, such as textile, construction, tourism, and agriculture, report that they do not find workers with the skills they need (Boughzala, 2019[1]; IACE, 2019[11]). This is related to many idiosyncratic factors, but some common factors contribute to these mismatches between labour demand and supply. First, the regional concentration of economic activities combined with low interregional labour mobility reduces the potential labour supply for these sectors. For example, the textile industry is highly concentrated in the province of Monastir and tourism activities are mainly located in coastal areas, particularly in the bay of Hammamet. A second important explanation are skill and qualification mismatches resulting from the low quality of basic education, initial VET and tertiary education, which fail to internalise the skill needs of the private sector (see third section of this chapter).

Third, low wages, difficult working conditions and weak human resource (HR) practices in low-skill intensive sectors make job offers unattractive (Angel-Urdinola, Nucifora and Robalino, 2015[4]). In particular, reservation wages of unemployed tertiary graduates are relatively high due to the negative cultural connotation of blue-collar work and the high attraction of public employment, which has strongly increased since 2011. Moreover, active labour market policies are mostly focused on tertiary graduates and coastal regions, failing to better prepare the lower skilled as well young tertiary graduates from interior regions and insert them into the formal labour market. Public employment services are weak and existing labour market institutions reduce labour mobility. These issues are discussed in more detail in the fourth section of this chapter. In what follows, the report sheds some light on the current and future structure of labour demand and policies to create better job opportunities.

Not only has net job creation been too weak in absolute values to absorb young labour market entrants and lower unemployment, but it has also been concentrated in low-productivity activities and skewed towards low-skilled employment (Figure 2.7) (Boughzala, 2019[1]). Compared to other Emerging Market Economies (EMEs), the share of workers in low-productivity sectors is high in Tunisia reflecting past economic policies that have focused on attracting low-value added activities (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Although textile and wearing apparel industries have slightly decreased employment, other low-skill intensive sectors such as retail and construction have been the main drivers of employment growth since 2007 (Figure 2.8, Figure 2.9) (Boughzala, 2019[1]).

However, employment has also grown in some high-skill intensive sectors, particularly in the public sector (including public administration, education and health as well as state-owned enterprises), which has absorbed the largest share of tertiary graduates since 2007 (Figure 2.8). Other activities that have generated jobs for a significant amount of tertiary graduates are Information and Communication Technology (ICT) and business services, and retail. Although some manufacturing sectors such as mechanical and electrical industries have increased employment of tertiary graduates, 52% of jobs in mechanical and electrical industries are low-skilled blue collar and 28% are high skilled-blue collar (Figure 2.9). This indicates that many tertiary graduates might be overqualified for these jobs and that the absorption capacity for tertiary graduates in these industries is limited (Cassimon and Grundke, forthcoming[12]). As the precarious fiscal situation will not allow to further increase public employment, increasing demand for high-skilled labour needs to come from a more dynamic private sector that expands into higher value-added activities (Boughzala, 2019[1]; Angel-Urdinola, Nucifora and Robalino, 2015[4]).

Formal job creation in the private sector has been dominated by offshore firms, which are predominantly export-oriented and enjoy preferential conditions in terms of taxes, tariffs, administrative procedures, and access to customs and trade infrastructure (Joumard, Dhaoui and Morgavi, 2018[13]; World Bank, 2020[14]). Offshore firms account for 47% of formal sector jobs created by the private sector from 2005 until 2019, although they represent only 4% of registered firms (OECD calculations based on RNE firm level data). Employment in offshore firms has grown by 60% from 2005 until 2019 and they increased their share in total formal private sector employment to 35%. In contrast, employment in onshore firms, which primarily serve the domestic market and are shielded from international and domestic competition by significant tariff and non-tariff barriers, administrative barriers to firm entry and weak competition enforcement, has only grown by 28% since 2005 (World Bank, 2020[14]). Formal job creation in onshore firms has been driven by wholesale and retail trade, food manufacturing, and private education and health services (Figure 2.10).

The offshore sector is shifting towards more skill-intensive and higher value-added activities, such as manufacturing of electrical and ICT equipment, as well as ICT and business services, which have strongly increased employment since 2005 (Figure 2.10). The 1998 association agreement with the European Union has improved access to better quality inputs and capital goods for manufacturing firms and opened up potential markets for higher value-added products (European Commission, 2021[15]). This has particularly benefitted the mechanical and electrical industries, which have increased their share in GDP from 3% in 2002 to 5.4% in 2019, and their share in total merchandise exports from 19% to 47%.

Textile and wearing apparel industries, which are intensive in low-skilled labour and are characterised by low labour productivity, have traditionally dominated the offshore sector, and still account for 44% of total offshore employment in 2019 (Figure 2.7, OECD calculations based on RNE firm level data). Due to the phasing-out of the Multi-fibre Arrangement and increasing competition from China, their share in GDP has declined from 5.4% in 2002 to 2.5% in 2019, and their share in total merchandise exports from 49% to 21%, respectively (OECD calculations based on data from INS). However, these industries are also undergoing structural change towards higher value added activities, as employment in wearing apparel and footwear has increased, whereas employment in the production of textile fibres has strongly decreased (Figure 2.10). The unit value of Tunisian textile exports to the EU is among the highest across EU importers (Plank and Staritz, 2014[16]).

Informality, which is defined as working without social security card or at firms not registered with the tax administration, has increased, particularly in the onshore sector. When comparing sectoral employment growth from firm level data, which include only formal private sector employment, with data from the labour force survey, which includes formal and informal employment, it becomes clear that a significant share of job creation in the onshore sector has been informal. In particular, construction, retail, and hotels and restaurants have increased informal employment (Figure 2.8, Figure 2.10). Since the mid-2000s, the share of informal employment in the economy has increased to 45% in 2019 (INS, 2020[17]; CRES, 2016[18]). The informality rate is highest in agriculture with 86%, followed by construction and public works with 69% and retail with 65% (Figure 2.11). Even in manufacturing industries dominated by offshore firms, such as textile and wearing apparel and mechanical and electrical industries, informality rates are higher than 15%. Jobs in the public sector are mostly formal, contributing to their high attractiveness for young tertiary graduates (Boughzala, 2019[1]).

Informal employment is particularly concentrated among men below 30 who have not completed secondary education (INS, 2020[17]). They mostly work in small informal firms, receive low wages, have no access to social security, and suffer from difficult working conditions due to non-compliance with health and safety standards (Boughzala, 2019[1]). Although in many EMEs, informal work might help young people to transition to the formal labour market, transition rates from informal to formal jobs have been relatively low in Tunisia (Angel-Urdinola, Nucifora and Robalino, 2015[4]). The share of informal employment is very high in border regions, where smuggling activities and illicit retail trade are widespread (Ayadi et al., 2013[19]; CRES, 2016[18]). Incentives for illicit cross-border trade are high as price differentials for many goods are large due to significant differences in subsidy and tax systems and high tariff and non-tariff barriers in Tunisia (Ayadi et al., 2013[19]). Moreover, weak enforcement capacities encourage smuggling activities and informal employment.

Even in the formal sector, employment conditions are often precarious. More than 40% of young women and men work in low-skilled blue collar jobs, with women predominantly working in assembly activities in textile and mechanical and electrical industries, and young men as non-qualified labourers (Boughzala, 2019[1]). More than 55% of the young are employed on the basis of oral work contracts, which are of short duration (Boughzala, 2019[1]). The share of open-ended contracts is low, as many firms in low-skill intensive sectors avoid high firing costs and tap a large pool of unemployed or inactive youth (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Employed tertiary graduates have generally better working conditions and earn higher wages than lower-skilled workers, but often have to accept jobs different from their field of study and for which they are overqualified. This concerns particularly graduates from technical tertiary education and humanities (Boughzala, 2019[1]).

To reap the potential that the increasing number of secondary and tertiary education graduates could have for economic growth, structural reforms are needed to allow for a more dynamic domestic business sector and the development of higher-value added activities. Real wages and living standards can only rise in the long-run, if productivity increases. This requires more and better investment in physical and human capital, but also a more efficient allocation of labour and capital to more productive firms and sectors (Haltiwanger et al., 2013[20]; Hsieh and Klenow, 2009[21]).

Barriers to firm entry and anti-competitive regulation reduce competition and weaken incentives for innovation and improvements in production processes among incumbent firms (Morsy, Bassem and Selim, 2018[22]). Authorisation regimes for entering a new market or offering a new product or service are numerous and involve opaque and lengthy procedures, discouraging entrepreneurship and investment (World Bank, 2020[14]). The tax system is over-complex due to many different subsidy and tax-incentive regimes, leading to high administrative burden and discouraging market entry and formalisation, particularly for smaller firms (OECD, 2018[8]). The competition framework and enforcement capacities of the Competition Council are weak, making it more difficult to fight anti-competitive practices of incumbent firms (Morsy, Bassem and Selim, 2018[22]). In addition, SOE prevalence extends to non-strategic sectors and price controls and producer subsidies distort the functioning of markets hindering market entry and competition (see chapter 1).

Analysis conducted for this survey finds that firm entry rates in Tunisia are low compared to benchmark countries and have decreased over the last decade, particularly in protected sectors dominated by onshore firms (Figure 2.12). This has coincided with a decreasing share of firms innovating new products or production processes and investing in physical and human capital or research and development (Figure 2.13). Labour productivity of firms has decreased across all sectors. High entry barriers and administrative burden also contribute to informality, as smaller and less productive firms cannot afford employing the necessary personnel to comply with costly administrative procedures and choose to operate in the informal sector.

To foster competition and innovation and to raise productivity and formal job creation, it is crucial to lower entry barriers and administrative burden related to authorisation requirements and complex tax incentive and subsidy regimes (World Bank, 2020[14]). The ongoing digitalisation of many administrative procedures is a step in the right direction, but needs to be accompanied by a reduction of prior-authorisation and licensing requirements and the introduction of “silent is consent” rules whenever possible (see chapter 1). Moreover, centralising administrative procedures for opening a business through one-stop shops or single windows has successfully reduced administrative burden for firms and facilitated market entry in many countries, for example in Portugal (OECD, 2019[23]; OECD, 2020[24]). This needs to be complemented by a simplification of the tax system, as numerous tax incentive and subsidy regimes create heavy administrative burden particularly for small firms (see chapter 1). In addition, strengthening competition enforcement, mainly through ensuring independence and sufficient staffing of the Competition Council, would help reducing anti-competitive behaviour of incumbents (see chapter 1) (OECD, forthcoming[25]).

Exporting and importing have become more complicated for firms in Tunisia (Figure 2.13). Weak transport and digital infrastructure hinder the domestic and international integration of the economy, which is complemented by high tariffs and non-tariff barriers protecting domestic firms (see chapter 1). In addition to raising production costs for all firms, these import barriers also reduce competitive pressures on incumbent firms, lower innovation incentives and hinder the reallocation of workers and capital to more productive firms and activities (Morsy, Bassem and Selim, 2018[22]; Bloom, Draca and Van Reenen, 2016[26]).

In particular, lengthy and complex administrative procedures for non-automatic import licences and custom clearance significantly raise import costs and create opportunities for political interferences and corruption (Figure 2.13) (World Bank, 2020[14]). Although the ongoing digitalisation and simplification of custom and licensing procedures is a significant step forward, automatic licensing combined with ex-post controls should be introduced for all products. Non-automatic import licensing procedures and related controls are meant to ensure the health and safety of Tunisian consumers, but the selection of products subject to these requirements is open to discretion and does not follow clear criteria based on risk-assessment procedures, which can open the door for protectionist motives (Grundke and Moser, 2019[27]). Moreover, foreign conformity assessment and product quality certificates are not recognised and many imported products are required to receive a domestic conformity assessment certificate or authorisation, entailing lengthy and complex administrative procedures (European Commission, 2019[28]). Introducing automatic import licenses for all products combined with transparent ex-post controls based on risk-assessment procedures can significantly reduce import costs and ensure the health and safety of consumers (Grundke and Moser, 2019[27]; OECD, 2019[29]).

Similarly, complex and discretionary export licensing procedures pose a major obstacle for many agricultural and food producers (World Bank, 2020[14]). Automatic export licensing procedures, which are the norm for offshore firms, have been introduced recently for onshore firms, which have a recognised product quality testing facility or quality label. However, many small firms cannot meet these requirements, which complicates their access to export markets. Improving the domestic product quality testing and certification system and its international recognition, as well as risk-control systems, and introducing automatic export licensing procedures combined with ex-post controls for all firms, could significantly contribute to raising agricultural and food exports and creating jobs in rural areas (Rudloff, 2020[30]). Improving mutual recognition of conformity assessment procedures and certificates with important trade partners, for example in the context of comprehensive trade agreements, could be an important step to facilitate exporting and importing for onshore firms (Rudloff, 2020[30]).

Average tariffs on intermediate inputs and capital goods are high, raising input prices and reducing access to high-quality inputs and capital goods (Figure 2.14). This is mainly driven by high tariffs on imports from China, as tariffs for manufacturing imports from the EU have been reduced since 1998 in the context of the EU association agreement (European Commission, 2021[15]). However, some imports from the EU, such as motor vehicles and parts, are still subject to high excise taxes, import quotas, and non-automatic import or distribution licenses (OECD, 2019[31]; European Commission, 2019[28]). Improving sourcing options for intermediate inputs and capital goods would lower production costs and allow domestic firms to upgrade their production processes through technology embedded in new machinery (Goldberg et al., 2009[32]; Amiti and Konings, 2007[33]). Firm-level analysis conducted for this report finds a significant positive relationship between the use of imported intermediate inputs and total factor productivity of Tunisian firms (Box 2.1) (Cassimon and Grundke, forthcoming[12]). Moreover, firms that use technology licensed by a foreign company have a 5.6% higher total factor productivity, an outcome that emphasises the importance of reducing import barriers for facilitating technology diffusion.

Further analysis using Tunisian sectoral panel data shows that a reduction in input tariffs by 50% would raise labour productivity (measured as value added per worker) by around 10% (Figure 2.15). Achieving these productivity gains would boost exports, particularly in onshore firms, where exports would increase by more than 25% (Figure 2.15). Offshore firms are exempted from tariffs which is why input tariff changes do not significantly affect their exports (Cassimon and Grundke, forthcoming[12]; Joumard, Dhaoui and Morgavi, 2018[13]).

For onshore firms, the access to cheaper and higher quality inputs and capital goods would lead to significant productivity gains and stronger competitiveness, which is the basis for improvements in real wages. Many domestic producers of intermediate goods would react to the stronger foreign competition by upgrading their production processes and improving their products, and only the least productive ones would lose market share (Amiti and Khandelwal, 2013[34]; Topalova and Khandelwal, 2011[35]; Pavcnik, 2002[36]). Stronger international competition in services sectors could also reduce prices and improve quality increasing the productivity of manufacturing sectors using these services as inputs (Hoekman and Mattoo, 2008[37]; Arnold et al., 2015[38]; Eppinger, 2019[39]). Moreover, there is evidence that increased importing activities of firms can help building foreign networks and acquiring knowledge about foreign markets, which is crucial for increasing export activities (Blalock and Veloso, 2007[40]; He and Dai, 2017[41]).

Opening up to trade will bring long-term productivity, employment and wage gains, but is likely to trigger structural changes in the economy. These are a crucial element of the productivity benefits, but can raise challenges in the transition. Firms need to increase product quality and reduce high prices that result from low domestic competition (Amiti and Khandelwal, 2013[34]; De Loecker et al., 2016[42]). While this leads to a revitalising effect on the more productive domestic firms, which seize newly arising export opportunities, expand, invest in new technologies and hire new workers, some low-productivity firms leave the market, freeing resources for the more productive firms and sectors to grow (Melitz, 2003[43]; Pavcnik, 2002[36]; Criscuolo, Gal and Menon, 2014[44]; Araújo and Paz, 2014[45]). It is precisely this reallocation process that will allow capital and labour to flow to more productive sectors or firms where new and better-paying jobs can be created (Brandt, Van Biesebroeck and Zhang, 2012[46]; Criscuolo, Gal and Menon, 2014[44]). A significant share of productivity growth in advanced economies can be attributed to these reallocation effects (Hsieh and Klenow, 2009[21]).

Analysis conducted for this survey using firm-level data for Tunisia is also consistent with the international evidence that shielding domestic producers from foreign competition tends to cement existing industry structures and hampers the reallocation of resources towards their most productive use (Figure 2.16, Box 2.1). In food manufacturing, where tariffs and non-tariff measures (NTMs) are relatively high, the allocation of resources across firms only explains 5% of average sectoral productivity, which is much lower than in metal manufacturing, which is characterised by lower import protection. This indicates that in food manufacturing resources are trapped in low-productivity firms, while they should move to more productive usage in higher productivity firms.

Exposing protected sectors to more domestic and international competition would not affect all firms in the same way. Stronger competition would likely drive some low-productivity firms out of the market, but at the same time, the high productivity dispersion in food manufacturing suggests that there are also firms in the sector that could probably withstand foreign competition (Cassimon and Grundke, forthcoming[12]). External competition would lead these to upgrade their production processes through more advanced technologies, increase product quality and create new job opportunities (Pavcnik, 2002[36]; Criscuolo, Gal and Menon, 2014[44]). The most productive food manufacturing firms could start exporting to niche markets in advanced economies or to regional peers. As high informality and low skills of agricultural workers in the upper segment of the supply chain have so far complicated the establishment of a modern supply chain management, increasing external pressure to introduce quality control and certification systems could be key to open new export markets (Maertens and Swinnen, 2009[49]). This could end up providing new employment opportunities within the sector, as recent pilot projects in the dairy supply chain have shown.

The reallocation processes triggered by more domestic and international competition would require some workers to move from less to more productive firms within the same sector. As producing the same type of products usually requires a similar set of skills, the need for training to help displaced workers move to other firms in the same sector is likely to be smaller than for moves to firms on other sectors (OECD, 2019[50]; Bechichi et al., 2018[51]). Nevertheless, firms within the same sector differ in their production processes and the type of technology they use (Andrews, Criscuolo and Gal, 2015[52]). Increasing technology adoption among highly productive frontier firms will change the task-content of occupations and require workers who move to more productive firms to update their skills (Bechichi et al., 2019[53]; Hummels et al., 2012[54]; Hummels, Munch and Xiang, 2018[55]). Workers that stay in their jobs also need to update their skills, as the rising digitalisation of production processes changes the task-content of jobs and the skills required to perform them (Spitz-Oener, 2006[56]; OECD, 2019[50]). The digitalisation and globalisation of production processes increasingly requires a good mix of cognitive and social-interactive skills (Grundke et al., 2018[57]; Hummels, Munch and Xiang, 2018[55]).

As the current structure of import protection varies considerably across different sectors of the economy, opening up to trade would have heterogeneous effects across sectors (Cassimon, Grundke and Kowalski, forthcoming[58]). Simulations for this survey using the OECD METRO general equilibrium model show that a unilateral cut in tariffs and NTMs by 50% would lead to an expansion of production and employment in electronic equipment, automobile industries, textile, agriculture and food manufacturing as well as ICT and business services and tourism (Figure 2.17). Better access to inputs and capital goods would raise productivity and export competitiveness (Cassimon, Grundke and Kowalski, forthcoming[58]). The production of grains would decrease, as this sector is currently strongly protected and Tunisia has no comparative advantage in grains production. However, other agricultural activities as well as food manufacturing would significantly expand and absorb displaced workers from grain production.

Trade opening would strongly raise labour demand and reduce unemployment (see chapter 1) (Figure 2.17). In particular, demand for high skilled labour will increase, as 160 000 additional jobs are created for managers and professionals, an increase in employment in these occupations by 16%. The number of jobs for clerical support workers and services and sales workers will increase by 30 000 each, and for technicians and associate professionals as well as blue-collar workers by 20 000 each. Many currently unemployed workers would have to move to newly created jobs in sectors and occupations, where they have not worked before (Cassimon, Grundke and Kowalski, forthcoming[58]). This necessary reallocation of labour to expanding sectors will require substantial investments into re-training of workers, as skill-requirements and task-content differ substantially between sectors and occupations (Bechichi et al., 2019[53]). Identifying sectors and occupations with large expected training needs can help to target training and education policies effectively. At the same time, identifying those sectors and occupations with particularly strong future employment potential may help to guide the choice of training content.

In particular, ICT and business services, which are high-value added activities, have the potential to create good quality jobs for a large number of tertiary graduates (Box 2.2). Lowering restrictions on services trade through bilateral or regional trade agreements, particularly with African partners, but also with the EU, could be one important policy lever to further raise services exports and demand for high-skilled labour. Moreover, formal job creation in offshore firms has almost exclusively taken place in coastal regions in the past, as industrial policies and lower trade costs have attracted foreign direct investment to these regions (OECD, 2015[7]; World Bank, 2020[14]). Investing in digital infrastructure in interior regions and attracting ICT and business services activities could be one solution to lower high unemployment of tertiary graduates in interior regions.

Agriculture and food manufacturing could also play a crucial role for creating more and better jobs in rural areas. Thereby, comprehensive trade agreements are key to open up new export markets for agricultural products, but need to be accompanied by domestic improvements in supply chain management and quality assurance through tracing, testing and certification procedures (Box 2.3, see also above) (European Commission, 2019[28]; European Commission, 2021[15]; Rudloff, 2020[30]). High informality and low skills of agricultural workers in the upper segment of supply chains have so far complicated quality assurance and many firms face difficulties to enter foreign markets due to a lack of recognised conformity assessment certificates (Rudloff, 2020[30]). Firm-level analysis conducted for this report shows that Tunisian firms with an internationally recognised quality certification for their products have on average a 2.2% higher total factor productivity compared to firms without such a certification (Box 2.1) (Cassimon and Grundke, forthcoming[12]). Improving supply chain management and domestic quality testing and certification procedures requires strong coordination and cooperation among different stakeholders (Box 2.3).

To realise the potential of agriculture in Tunisia, it is also crucial to reduce existing market distortions, such as price controls, subsidies and distribution and export licensing regimes, to set the right incentives for agricultural and food producers (OECD, 2019[31]; Rudloff, 2020[30]). Improving the functioning of land markets and reducing the share of unutilised arable land are also key to incentivise firm entry and create more and better jobs in rural areas.

Although unemployment rates are high, a large and increasing share of job vacancies posted by the public employment service cannot be filled (Figure 2.18). Many firms in low-skill intensive sectors such as textile and wearing apparel, construction, tourism, retail and agriculture complain that they do not find workers with the skills they need (Boughzala, 2019[1]; IACE, 2019[11]). This is even more surprising as the number of unemployed graduates in initial vocational education and training (VET) programmes related to these sectors is higher than for other VET programmes (ONEQ, 2017[61]). The same phenomenon exists for higher-skilled workers. Many firms, particularly in ICT and business services as well as manufacturing, complain that they do not find sufficiently qualified tertiary graduates in the field of science, technology, engineering and mathematics (STEM). However, in 2018, 65% of unemployed tertiary graduates held at least a three-year STEM degree and 30% even held a STEM master degree (Boughzala, 2019[1]; IACE, 2019[11]). It is also hard for firms to recruit candidates with the required set of skills to fill vacancies related to business administration and other white-collar jobs.

Difficulties to recruit workers with the right skill sets are related to the low quality and outcomes of the basic education and initial VET system. Dropout rates in secondary education are high and only the weakest students and those dropping out select into the initial VET system (UNICEF and INS, 2019[3]). As a result, many VET graduates do not possess basic technical and soft skills and are not able to properly read, write and communicate (UNICEF, 2020[5]). The skill and qualification mismatch in the labour market is also explained by the low ability of the vocational education and training (VET) as well as the tertiary education system to internalise the specific skill needs of the private sector. Many curricula are outdated and education institutions do not prepare students with the technical knowledge, tools and soft skills required by firms, which is particularly an issue for STEM graduates (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Moreover, the failure to properly inform lower secondary school students about labour market trends and guide their educational choices leads to misallocation of students to VET and tertiary education subjects facing relatively low labour demand (Boughzala, 2019[1]).

Besides technical and job-specific skills, many job applicants are missing fundamental soft skills, as confirmed by an online survey conducted for this report among larger domestic and foreign firms in Tunisia (Figure 2.19, Box 2.4). Many firms have difficulties to find candidates with a sufficient level of oral and written communication skills, team-working skills as well as problem solving and conflict resolution skills. Foreign language skills are also difficult to find among applicants, which is a particular an issue for firms that are more integrated into global value chains and need to communicate with suppliers and clients in foreign markets (Grundke et al., 2017[62]). These types of soft skills are also the ones that many current employees lack, which is why continuing VET activities of larger firms are focused on improving these type of skills (Cassimon and Grundke, forthcoming[12]). In addition, firms dedicate a significant amount of training hours to improve management and IT skills of their employees.

To prepare labour market entrants with a sufficient level of basic cognitive and soft skills, the broad access to basic education needs to be complemented with significant improvements in the quality of education. Tunisia has strongly increased access to education since the 1980s, supported by high education spending to hire new teachers and expand school infrastructure (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Gross enrolment rates in secondary education increased from 45% in the early 1990s to 92% in 2018, and for tertiary education from 8% to 32% (UNICEF, 2020[5]; World Bank, 2021[63]). Educational attainment of the younger generation has significantly improved, in particular for women (Figure 2.20). The share of 25-34 year olds that have completed upper secondary or tertiary education is not far below the respective OECD average, and the rise in educational attainment compared to the older generation has been the most pronounced among countries in the sample (Figure 2.20).

However, although public spending for education is relatively high in international comparison, educational outcomes have been weak and even deteriorated (Figure 2.21). Average performance of Tunisia’s students in the OECD Programme of international student assessment (PISA) has been one of the lowest among participating countries and has deteriorated since 2012. The difference in student performance between the OECD average and Tunisia is equivalent to around 3 years of schooling (UNICEF, 2020[5]). Performance has been particularly weak in literacy and communication skills, with more than 70% of students not being able to correctly understand short texts with simple syntax (UNICEF, 2020[5]). In addition, average results in numeracy and science-related knowledge and skills are also weak (OECD, 2016[64]).

Another indicator for the weak quality of basic education is the high number of students that fail the final exam of secondary education each year. From 2010 until 2019, the share of students failing the final exam has increased from 30% to 70%, whereby performance has deteriorated across all education tracks in secondary education (Figure 2.22). Taking into account students repeating the exam, around 51% of enrolled students did not complete upper secondary education, and 26% of enrolled students did not even complete lower secondary education (UNICEF and INS, 2019[3]). More than 10% of students drop out from secondary education each year and around 20% have to repeat a class, and this concerns young men more than women (data from the Ministry of Education).

The weak performance of students is strongly related to the misallocation of resources and the low quality of teaching. The significant increase of the young population since the 1990s in combination with rising enrolment rates have put enormous pressures on the education system, which have not been managed well (UNICEF, 2020[5]). Although many new teachers have been hired since 2011, recruitment has not been concentrated in subjects or regions with the highest shortage of teachers. Average student to teacher ratios are relatively low in international comparison, but they vary widely across regions and many schools in remote areas face difficulties in providing classes for all secondary education tracks due to teacher shortages (UNICEF, 2020[5]; OECD, 2016[64]).

Many newly employed teachers lack a formal pedagogical education, as recruitment policies have been relaxed since 2011 and many graduates from related subjects and with a one year master’s degree could access the teacher career in the public sector (UNICEF and INS, 2019[3]; UNICEF, 2020[5]). The lack of pedagogical skills among young teachers is also related to the deterioration in the quality of initial and continuing teacher training caused by a lack of teacher instructors and outdated curriculums. From the 2007 until 2016, the institutionalised education of teacher instructors was suspended (UNICEF, 2020[5]). As many older and better qualified teachers will retire during the next years, it should be a priority to properly train the younger already employed teachers, including on pedagogical methods, besides improving initial teacher training and selection of new recruits.

The system of teacher evaluation does not set the right performance incentives (UNICEF, 2020[5]). Linking teacher evaluation and existing bonus systems to improvements in yearly nation-wide exams for students of primary, lower and upper secondary education could raise incentives for teachers to participate in additional training and to improve pedagogical methods (OECD, 2020[24]). Wage and bonus systems could also be used to improve the allocation of high performing teachers to the more challenging schools in disadvantaged regions, where dropout rates are higher and student performance is lower. This should be combined with an improved recruitment, evaluation and allocation system that classifies candidates according to several performance criteria to improve the matching of teachers to open positions with differing skill requirements. Skill needs for teacher positions vary widely across school types, and according to the socio-economical background of children. Selecting more students from disadvantaged areas for teacher careers could also facilitate their allocation to challenging schools after their graduation.

A decaying school infrastructure has deteriorated the learning atmosphere, reduced the choice of education tracks in secondary schools and contributed to high dropout rates (UNICEF, 2020[5]). Public investment into school infrastructure has dwindled during the last decade. Strong wage increases and additional hiring have put pressure on the education budget, raising the share of wage payments to over 95% in 2019 (UNICEF, 2020[5]). Around 70% of schools have no proper access to the sanitation system (Benstead, 2021[66]). Many schools lack functioning laboratories and equipment for technical subjects, natural sciences and informatics, which forces many students to choose the language or business administration track and reduces their motivation and performance (UNICEF and INS, 2019[3]). This partly explains the particularly low performance of students of these tracks in the national secondary education exam (Figure 2.22). Moreover, many low-income households do not have sufficient access to internet, and E-learning opportunities in many schools are limited (Benstead, 2021[65]). As ICT technologies and the skills to use them will play an increasing role throughout life, it is crucial to improve the ICT infrastructure in schools and increase access for all children (OECD, 2019[50]).

As fiscal space is very limited and education spending already very high (Figure 2.21), improvements in education infrastructure are only possible by raising spending efficiency and reducing the high public wage bill. Low average student to teacher ratios indicate room for adjustments, which should be accompanied by a better allocation of teachers across schools and subjects. However, the national dialogue on education reform is currently blocked due to a standoff between teacher unions and the government. This conflict has caused significant collateral damage for children, as schools remained closed for several months in 2018 due to strikes (UNICEF and INS, 2019[3]). It is a priority to restart the national dialogue and find solutions to improve education infrastructure and teaching quality and to finalise the pending education reform. Parent associations should participate more actively in this process to represent the interests of their children (UNICEF, 2020[5]).

Changing the instruction language from lower to upper secondary education leads to a strong drop in performance, particularly for poor children. Until the 9th grade all subjects are taught in Arabic, which is the language spoken in families and communities. However, after the 9th grade, the teaching language switches to French for all subjects, which leads to confusion and a strong drop in performance for many students, not only in languages, but also in mathematics and sciences (Figure 2.23) (UNICEF, 2020[5]). In particular, students from low-income households suffer from this change, as their French language skills are worse due to fewer opportunities for speaking it within their families and lower financial resources to pay for private lessons (Figure 2.24). Whereas around 53% of students from the lowest income quantile of households completed lower secondary education, this share shrinks to 24% for upper secondary education (Figure 2.23). Providing more continuity through a single teaching language in primary and secondary education would particularly benefit children from lower-income households, but risks to lower French language skills of secondary graduates, with negative effects on future labour market outcomes (Angrist and Lavy, 1997[66]). A better policy option to lower barriers to advancement between primary and secondary education is to ensure the provision of high quality language teaching from an early age, in particular for children from low-income households.

Learning standards, curricula and related teaching methods for primary and secondary education are outdated and should be revised (UNICEF, 2020[5]). Existing learning standards and curricula are biased towards academic content and mainly prepare students for university studies. They lack a focus on teamwork, communication and presentation skills, as well as other soft-skills that become more important with globalisation and digitalisation (Grundke et al., 2018[57]; OECD, 2019[50]). Brazil has recently revised its learning standards and curricula for early-childhood, primary and secondary education and adapted them to include the 21st century skills, comprising a rich set of cognitive and socio-emotional skills according to international best practices (OECD, 2020[24]). Combining modern and less-academic learning standards and curricula with new teaching methods to foster group-work and self-initiative would not only favour children from lower income households, who could benefit from more engagement and cooperation with other classmates, but would also help developing entrepreneurial skills which many graduates are currently lacking (UNICEF, 2020[5]; IACE, 2019[11]). Fostering entrepreneurship among young Tunisians is key to improve business dynamics and create more and better jobs, but the development of the necessary mind-set needs to start early in basic education, as basic cognitive and soft skills are formed early in life (Heckman and Mosso, 2014[67]; Heckman, Pinto and Savelyev, 2013[68]).

Besides teaching quality, socio-economic background and good early-childhood education are key determinants for educational outcomes across countries (Figure 2.24) (OECD, 2019[69]; Heckman and Mosso, 2014[67]). Although in Tunisia access to early-childhood education increased from 16% in 2000 to 44% in 2018, it is still low in comparison to other countries, particularly for children from low-income households (Figure 2.25). Only 17% of poor families with children between 3 and 5 years have access to early-childhood education, compared to 71% of the more affluent families (UNICEF, 2020[5]). Enrolment rates also differ strongly across provinces. As spending for secondary and tertiary education is relatively high compared to other countries, improving spending efficiency and reallocating resources to early-childhood education to expand access for children from low-income households could have significant social returns (Figure 2.21). This would also help to raise female labour market participation, as low access to early-childhood education and childcare reinforces cultural factors that hinder women with small children to participate in the labour market.

Access to food, clean drinking water and good health services from an early age is crucial for the development of cognitive and social skills (Heckman, Pinto and Savelyev, 2013[68]; Heckman et al., 2010[70]). Thus, improving access to early childhood education for children from low-income households should be complemented with continued efforts to eradicate extreme poverty, raise access to universal health care and improve water and sanitation services in schools and communities (UNICEF, 2020[5]; Benstead, 2021[65]). Shifting more resources to the newly introduced electronic cash-transfer program would be one way to improve access to food and health care for many poor children. Food could also be directly provided in schools to ensure nutrition quality, but this would require improving school infrastructure. Eventually, the cash transfer programme could also be partly linked to enrolment in early childhood education or home visits of teachers consulting parents on education practices. When allocating scarce spaces in early childhood education, preference should be given to low-income households and single-parent families.

Although improving access to early-childhood education for the poor would have the largest effects on equalising opportunities for all children, other structural weaknesses of the basic education system benefiting children from richer families with higher educational background should be addressed (Benstead, 2021[65]). The dual structure of the public system for lower and upper secondary education with 46 elite schools, which are accessible through a general exam after primary education and offer around 3000 places per year, favours social polarisation and weakens the learning environment for ordinary students (UNICEF, 2020[5]). Separating the high performing students from ordinary classmates significantly reduces the performance of peers, particularly for students with average skills (Burke and Sass, 2013[71]). Children from richer households have access to costly private lessons to prepare for entry exams and, hence, have higher chances to access these elite schools, where they benefit from higher quality of teaching and school equipment, and positive peer effects (Benstead, 2021[65]). Also in ordinary schools, the ability to pay for private lessons increases chances for better exam results, sometimes directly linked to cash payments (UNICEF, 2020[5]; Benstead, 2021[65]).

Increasing teaching quality in all public schools, strengthening the work ethic of teachers, and providing targeted support for disadvantaged students would reduce dropouts, improve opportunities for all students and ensure that the education system can contribute to raising social mobility. This should include scaling up the project “école de la deuxième chance”, which allows young dropouts to repeat secondary school or VET until the age of 20 through a more targeted pedagogical and financial support. However, to prevent students from dropping out of school in the first place, it is crucial to provide targeted pedagogical and psychological support to students at risk already in school. A new government project has developed indicators to identify students at risk of dropping out, and this should be used to better target support to these students.

The low quality and attractiveness of initial VET contribute to high dropout rates in general secondary education. After completing lower-secondary education, many students do not choose the initial VET path, although a curriculum involving subjects that are more practical would suit them better and bring better job prospects than the more academic curriculum in general secondary education (Figure 2.26) (OECD, 2016[72]). This is related to infrastructure bottlenecks that prevent students from choosing technical education tracks, insufficient information about labour market trends, vacancies and skill needs of firms, and a culture that values white-collar jobs much more than blue-collar jobs. Obtaining a tertiary diploma has guaranteed a secure and well-paid job for many decades in Tunisia, particularly in the public sector, which is why many parents still advice their children not to choose the VET path or take up a blue collar job (Boughzala, 2019[1]; OECD, 2015[6]).

It is crucial to better inform students and their parents, but also workers and unemployed adults, about employment and wage prospects, the skill needs of firms as well as available VET programs and their content and quality (OECD, 2016[73]) (ONEQ, 2017[61]). Introducing an IT system that provides region-specific information on vacancies and skill needs of firms according to a detailed classification of occupations and maps this information to the number of unemployed in corresponding education levels and fields of study or occupations would be one-step into this direction (OECD, 2021[74]). This should also include information about the content and quality of existing VET and university programmes as well as labour market outcomes of former graduates (Fouarge et al., 2020[75]). This IT system should be complemented with personalised counselling services to students in lower-secondary education, but also to workers and the unemployed, to better support their educational choices, training and career development (OECD, 2021[74]; OECD, 2016[73]). The national employment agency is visiting schools from time to time to give short presentations on labour-market trends. However, these efforts are insufficient due to a significant under-staffing of the agency and the lack of a publicly available IT system on labour market trends and evaluations of existing VET and university programs (World Bank, 2021[76]).

Firms need to play a larger role in promoting and raising the attractiveness of blue-collar and low-skilled white-collar jobs. Difficult working conditions, low wages and weak human resource (HR) practices contribute to explain why firms in textile and wearing apparel, construction, tourism and agriculture have issues to fill vacancies for blue-collar and low-skilled white collar jobs (Box 2.5) (Angel-Urdinola, Nucifora and Robalino, 2015[4]; Boughzala, 2019[1]). Many HR departments in firms are exclusively dealing with administrative matters and lack comprehensive strategies for training and professional development of employees. For many blue-collar workers, the sole training opportunities are related to health and safety standards. Due to rigid wage schedules and career paths strongly linked to diplomas that mirror the public sector system, blue-collar workers have little perspectives for professional development in many firms. This strongly reduces the attractiveness of initial VET and blue-collar jobs (Angel-Urdinola, Nucifora and Robalino, 2015[4]).

Positive examples exist, where firms have introduced incentive systems and career development strategies. In the automotive industry, some firms open white-collar jobs and sometimes even management positions to experienced blue-collar workers, with no requirement to possess formal diploma. Such an approach can make initial VET and blue-collar jobs more attractive for lower-secondary students. Positive experiences need to be shared among firms and business associations. Moreover, firms should support their promising blue-collar workers to continue formal education in their field of interest, by financing part of the necessary investments. This should be facilitated by improving access to technical and other tertiary degrees for initial VET graduates and the recognition of prior learning to reduce study time and costs (Boughzala, 2019[1]; Arfa et al., 2018[79]). Creating a positive corporate culture can also contribute to raise the attractiveness of initial VET and blue-collar jobs (OECD, 2018[80]; OECD, 2010[81]).

The weak quality of many initial VET programmes contributes to reducing their attractiveness for lower-secondary students (Arfa et al., 2018[79]). Although comprehensive evaluations of existing initial VET programs do not exist, many firms complain that VET programs do not prepare graduates with the technical and soft skills as well as specific tools required for the job (IACE, 2019[11]). This is further corroborated by the fact that many graduates with a 2-year technical degree are unemployed whereas firms have difficulties to fill vacancies for the respective occupations (Figure 2.29) (Boughzala, 2019[1]). This is particularly the case in areas like electrical equipment and mechatronics, informatics, office administration, but also in textile and wearing apparel, construction, and tourism (ONEQ, 2017[61]; Boughzala, 2019[1]). In addition, initial VET programmes do not exist for some occupations such as blue-collar workers in the cable industry, which employs more than 80 000 workers and is present in Tunisia since decades (Arfa et al., 2018[79]).

Initial VET is mainly conducted by public training institutes, where it is free of charge. The large majority of public training institutes is managed by the public professional training agency (ATFP), which employs more than 10 000 instructors and administrative staff. It covers initial VET for almost all sectors, except agriculture, tourism and health, which are managed by specific bodies. The lack of cooperation between the public training system and firms is mainly due to the weak organisational structure of the ATFP, including a lack of cooperation and coordination with other agencies, insufficient IT systems observing labour market trends and skill needs of firms, and a missing culture of impact evaluation and stakeholder involvement (Arfa et al., 2018[79]; World Bank, 2021[76]). Better targeting of IT systems and communication to sector-specific business associations would help improving coordination to better adapt initial VET programs and curricula to the skill needs of firms.

Improving the matching of study places in initial VET by subject to local skill needs of firms can significantly raise employment chances of VET graduates, as regional heterogeneity in production structures is large and labour mobility is low (OECD, 2020[24]). Information on the regional supply of initial VET places by subject as well as an evaluation of these programmes and skill needs of local firms is missing (Arfa et al., 2018[79]). The offer of different fields of study in regional training institutes is mainly driven by the capacity of the system and does not sufficiently reflect the needs of the local economy (OECD, 2015[7]). Intensifying cooperation and coordination between the ATFP and local private sector representatives would be a first step into the right direction. Combining this with IT systems that link firm’s skill needs with the supply of initial VET places on a regional basis can significantly raise the effectiveness and attractiveness of the public initial VET system (OECD, 2020[24]).

More competition from private initial VET institutes could improve the adaptation of initial VET programmes and curricula to private sector skill needs. Only 19% of students in initial VET are registered in private institutes, which mostly offer initial VET for services occupations such as office and sales workers and jobs in the tourism industry, but very little careers in manufacturing or IT and business services (ONEQ, 2019[82]). The Ministry of professional training supervises private VET institutes and decides together with other public agencies about the official recognition of initial VET diploma. This administrative process is complicated and time-consuming: recognition of degrees and related curricula takes up to 3 years, which creates the risk that curricula related to technology-intensive subjects are out-dated when they receive approval. Only about 56% of students in private VET institutes receive a degree officially recognised by the Ministry (UNICEF, 2020[5]). Facilitating changes to existing curricula and the recognition of new programs in public and private VET institutes is crucial to better adapt initial VET to constantly evolving skill needs of firms and increase the employability of graduates.

A more active participation of firms and business associations is required to better link existing workplace training to the content of formal VET courses. Although more than 80% of initial VET students currently study in programs including workplace training in firms, communication and coordination between instructors in training institutes and supervisors in firms is insufficient (Arfa et al., 2018[79]). This is due to a weak legal framework regulating the employment of apprentices and coordination with VET institutes, but also due to insufficient pedagogical training and engagement of supervisors of apprentices in firms. Many apprentices do not follow formal VET courses, are informally employed and do not receive a formal degree at the end of their apprenticeship (Arfa et al., 2018[79]; Boughzala, 2019[1]). Many firms do not see initial VET as an opportunity to train their future employees, mainly because they fear that training investments will be lost when graduates leave to work in other firms. Thus, efforts to improve the attractiveness of initial VET also need to change the mind-set of business associations and firms to solve the public goods issue related to training investments into young workers (OECD, 2015[7]; OECD, 2010[81]).

Although only a small share of lower secondary students chooses initial VET, dropout rates in initial VET are high reaching around 30% in 2017 (Arfa et al., 2018[79]). This is related to negative selection of the weakest students into technical VET tracks of lower secondary education (“colleges techniques”), which is reinforced by the institutional design that automatically allocates dropouts from the general education track to the VET track of lower-secondary schools. Many students that arrive in initial VET at the upper-secondary level have very low levels of basic cognitive and soft skills, which makes it hard for them to follow VET courses (UNICEF, 2020[5]). Dropout rates are particularly high in the first year of upper secondary VET (Arfa et al., 2018[79]). Integrating technical tracks into the general track in lower-secondary education and avoiding a too early separation of school forms would reduce the negative selection and has the chance to improve the attractiveness of upper-secondary VET for average students.

The low quality of teaching and a decaying infrastructure reduce the quality of VET and contribute to high dropout rates (Arfa et al., 2018[79]). Many VET teachers do not have a background in technical education and have not worked in the private sector, as the allocation of teachers to initial VET was mainly driven by the large supply of unemployed tertiary graduates with a background in humanities, waiting for a public sector job (Arfa et al., 2018[79]; Angel-Urdinola, Nucifora and Robalino, 2015[4]). Thus, many of them have internalised the negative connotation of VET and blue-collar work, which does not help to motivate young students in initial VET. Moreover, as in basic education, many of these teachers have weak pedagogical skills due to weak criteria for teacher selection and the low quality of initial teacher training. A comprehensive system of teacher evaluation and targeted continuous teacher training are lacking, while existing bonus systems are not linked to student performance and do not incentivise teachers to improve teaching quality (Arfa et al., 2018[79]). Many curricula are outdated and lack sufficient soft-skill training, particularly related to communication and presentation skills as well as teamwork, which strongly reduces the employability of VET graduates (OECD, 2015[7]).

Due to relatively low spending for VET compared to other education levels and the rising wage bill (Figure 2.21), public investment has strongly decreased and infrastructure in many training centres is very poor (Arfa et al., 2018[79]). Outdated training machines and equipment add to the weak adaptation of curricula to skill needs of firms, as students cannot train and learn on machines and technology used by firms. Moreover, housing conditions for VET students are problematic, particularly in the second year as not all students can access subsidised housing (Arfa et al., 2018[79]). This is an important reason for dropping out, as many students from interior provinces cannot afford paying high rents in cities where many VET institutes are located (Arfa et al., 2018[79]). Increasing spending efficiency in tertiary education and reallocating resources to initial VET could improve the quality and attractiveness of VET, reduce dropout rates and provide firms with a larger pool of graduates with the right skills (Figure 2.21) (OECD, 2015[6]).

Age limits for apprenticeships prevent students that have dropped out from secondary education, as well as other low-skilled adults who want to up-skill or change occupations, to enter parts of the initial VET system (ILO, 2013[83]; Arfa et al., 2018[79]). The current system only allows entry of students that are younger than 20 years, whereas apprenticeship systems in many other countries have much higher age limits (OECD, 2019[84]; OECD, 2015[6]). As basic education for adults is non-existent, this leaves many young men and women that have dropped out from school without any alternative to upgrade their basic and technical skills. Moreover, high administrative burden and poor recognition of prior-learning hinder students dropping out from tertiary education, who have realised that a more practical education would suit them better, to enter initial VET and pursue a blue-collar career (Angel-Urdinola, Nucifora and Robalino, 2015[4]). This is particularly detrimental as these students generally have higher average cognitive and social skills, as they have successfully finished general secondary education, and could contribute to a better learning atmosphere in initial VET classes. In addition, these candidates would be ideal for firms, as they have more experience and higher motivation to benefit from VET courses to improve their employability.

The success in raising access to education since the 1990s has been most pronounced in tertiary education (Figure 2.20). Gross enrolment rates rose from 8% in the early 1990s to 32% 2018, and are around 50% higher for women than for men (Boughzala, 2019[1]). The number of students in public universities has more than tripled since the 1990s, from around 100 000 in the early 1990s to over 300 000 in the 2010s, which was enabled by large public investments into education infrastructure and hiring of teaching staff (Angel-Urdinola, Nucifora and Robalino, 2015[4]). However, the increase in the number of tertiary graduates has led to rising unemployment, as job creation has been concentrated in low-skill intensive activities with little demand for tertiary graduates (Figure 2.30, Figure 2.7, Figure 2.8).

At the same time, many firms in higher value added activities such as ICT and business services, pharmaceuticals or other technology intensive manufacturing activities complain that they do not find enough tertiary graduates with the skills they need (IACE, 2019[11]; Boughzala, 2019[1]). This is even more surprising as the majority of tertiary students graduates in STEM subjects and unemployment among them is high (Figure 2.31). Around 30% of unemployed tertiary graduates hold a master’s degree in STEM subjects and more than 20% hold a three-year technical tertiary degree (BTS). The unemployment rate among ICT technicians is with 17% one of the highest across all occupations (Figure 2.29). Although some of the graduates with STEM master’s degrees register as unemployed to obtain necessary documents for migration to Europe, the major part does not possess the skills required for high-skilled white-collar jobs in the private sector (Boughzala, 2019[1]).

Significant mismatches exist between the skills that graduates obtain at university and the skills required for high-skilled white-collar jobs in the private sector. Half of firms in ICT services indicate that they have difficulties filling vacancies for white-collar jobs requiring a tertiary degree, because candidates do not possess the skills needed for the job (Figure 2.32) (UNESCO, 2021[59]). In particular, candidates lack specific technical and ICT skills, which indicates that university curricula do not include frontier technologies and tools used in ICT services firms. Many firms need to provide costly additional technical training to new employees to integrate them into production processes (IFC and UTICA, 2017[60]). Moreover, many candidates also lack key soft skills such as communication and language skills, team-working skills and problem solving and conflict resolution skills (Figure 2.32). This is also the case in many manufacturing firms, which have difficulties to find the right candidates for vacancies of medium to high-skilled white-collar jobs (Box 2.6).

This signals that the public education system does not equip graduates with a sufficient level of key soft skills, which are crucial for increasing the globalisation and digitalisation of production processes (Figure 2.19, Figure 2.32) (Grundke et al., 2017[62]; OECD, 2019[50]). Compared to other countries, 15-year old students show particularly weak performance in literacy skills (Figure 2.24). At the root of the weak performance in soft-skills are structural issues of the basic education system (see above). As the digital revolution and a further integration into the world economy have large potential to foster the creation of good jobs and raise the living standards, it is a priority to reform the basic education system to provide all students with a sufficient level of cognitive and soft skills. In particular, for ICT services, which have the potential to create many high-skill intensive jobs in interior regions of Tunisia, a rising supply of graduates with the right skill-set could attract more foreign firms and stimulate the entry of domestic start-ups (Box 2.2).

The public tertiary education system operates in isolation from the private sector and labour market needs, similar to the initial VET system (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Cooperation between universities, the Ministry of Higher Education and the private sector are weak due to a missing culture of impact evaluation and stakeholder involvement (Arfa et al., 2018[79]; Angel-Urdinola, Nucifora and Robalino, 2015[4]). Many firms complain that in addition to weak adaptation of university curricula to skill needs of firms, certain profiles do not exist at all, such as a specific track for purchasing managers in business schools or specialised engineers in rubber manufacturing. Thus, intensifying information exchange, coordination and cooperation with the private sector is key to make the tertiary education system more responsive to labour market needs. Systematic evaluations of existing study-programmes and the resulting employability of graduates are missing. If alumni-surveys exist, they are not systematically used to inform universities and potential students about existing skill mismatches and the labour market success of past graduates.

A more active participation of firms is necessary to make skills development in tertiary education more demand driven (Angel-Urdinola, Nucifora and Robalino, 2015[4]). This concerns a more active cooperation and coordination in the design of new programs and curricula, but also in research and development and the combination of formal education programmes with workplace training. Firm-level analysis conducted for this report finds that firms which invest into research and development have a 3% higher total-factor productivity compared to other firms (Box 2.1) (Cassimon and Grundke, forthcoming[12]). Intensifying the cooperation between public research institutions and the private sector holds large potential to improve innovation capacities, better prepare graduates with the skills needed by firms and raise productivity of Tunisian firms.

Administrative burden for adapting course content to new technologies, adding additional courses into curricula or introducing new programs and degrees is high. These procedures can take up to 3 years, which is too long especially for technical subjects, as some technologies can already be outdated again after 3 years. To make existing programs and curricula more responsive to skill needs of firms, these procedures should be streamlined and curricula should be made more flexible by reducing the number of hours for obligatory core subjects. However, as inflation of new degrees and certificates can also distort signals in the labour market and increase skill mismatch, the introduction of new programs and degrees should be limited and complemented with a comprehensive quality evaluation and certification system for tertiary degrees in public and private universities. This would support secondary graduates in their career choices and set the right incentives for public universities to improve their programmes.

Introducing workplace-training into tertiary education can help reducing skill-mismatch, as it allows students to get familiar with frontier technologies and methods used in firms. However, the legal framework for integrating workplace training into tertiary education programs is weak. Firms are not allowed to employ students with longer-term work contracts, leaving them with short-term internship contracts as the sole option. This prevents them from undertaking training investments, as they fear that their investments will have no return when graduates leave to work for other firms. Allowing for more flexibility in work-contracts for tertiary students, however, needs to be complemented with stronger engagement from sectoral business associations to solve the coordination issues regarding training investments of single firms into young workers (OECD, 2015[7]; OECD, 2010[81]).

The quality of tertiary education can also make a difference in reducing skill mismatches related to soft-skills, and partly make-up for structural weaknesses in basic education. Many firms report that graduates from private universities have better communication and presentation as well as team-working skills, although the best students according to high-school exams select into public universities (IACE, 2019[11]). This is due to a stronger focus of curricula and performance assessment on soft-skills, including more curricula hours dedicated to specific soft-skill courses using modern pedagogical methods. Moreover, private universities are also more responsive to technical skill needs of firms. Recently, many private universities have started to cooperate with firms to develop three- to five-year study programs with workplace-training elements, allowing students to get familiar with frontier technologies and methods used in firms. This higher responsiveness of private universities to the skill needs of firms might partly explain the increasing attractiveness of private universities, as indicated by rising student and graduate numbers (Figure 2.34).

The skill mismatch in the labour market is also related to the misallocation of students to fields of study. Over 40% of students study and graduate in humanities, law or business administration and economics, and around 30% of unemployed tertiary graduates hold a master’s degree in these fields (Figure 2.31). Since 2011, many of them have been hired by the public sector, particularly in the public administration and the education system, and many unemployed and current students still hope for future employment in the public sector (Angel-Urdinola, Nucifora and Robalino, 2015[4]; Boughzala, 2019[1]). This is mainly due to high wages and social security benefits as well as long-term contracts. However, due to the difficult fiscal situation, the pace of public sector hiring and wage increases are unlikely to continue. Although private education services are expanding, they can only partly absorb these tertiary graduates, and unemployment among teachers and graduates in humanities will remain high (Figure 2.34, Figure 2.10, Figure 2.29).

Better informing secondary school students about the content and quality of tertiary education programmes and labour market outcomes of graduates is crucial to improve the allocation of students to fields of study. It would also set the right incentives for universities to improve the quality of their programmes and better adapt them to the skill needs of firms. Comprehensive information about labour market outcomes of former graduates does not exist. Some universities have introduced personal counselling services, but these need to be extended and complemented with a comprehensive and publicly available IT system providing information on the quality of study programmes (World Bank, 2021[76]). Moreover, reducing the large gap between public and private sector wages for labour market entrants, which including jobs in SOEs reached 35% in 2018, is also key to encourage entrepreneurship and promote fields of study that prepare for a career in the private sector (Angel-Urdinola, Nucifora and Robalino, 2015[4]; World Bank, 2021[76]).

Although spending for tertiary education is high and spending per student has increased since 2013, supply constraints in tertiary education exist that lead many students to choose humanities, law or business administration and economics (Figure 2.21). Many regional universities do not offer the full range of tertiary programs due to a lack of equipment and specialised university teachers. Secondary students that cannot afford to pay the high rents in bigger cities choose to study closer to the family, restricting their study choices. Moreover, the lack of entry requirements for humanities, law and business administration and economics contributes to the high number of students. Better allocating resources across different fields of studies to reflect existing and future labour market needs and adapting entry requirements accordingly could raise spending efficiency (World Bank, 2021[76]). Supporting poorer qualified students from interior regions to raise their mobility should also be considered.

Public spending for continuing VET policies, at 0.1% of GDP, is slightly below the OECD average, and is financed through a training levy designed as a payroll tax for firms (OECD, 2015[6]). To finance their continuous VET activities, firms can recover up to 60% of their training levy payments through an online platform. The administrative procedures to register and approve training activities and related tax refunds have considerably improved in recent years raising incentives for training investments into workers. Since firms have been granted freedom to choose training providers, competition and supply have increased, particularly from private providers. The remaining revenues from the training levy are used to finance initial VET and support training activities of smaller firms through specific subsidies.

These tax incentives are exclusively targeted at firms, which decide about the training content and the workers to train. This ensures that training content is aligned with skill needs of firms. However, firms choose the workers with the highest marginal return from the training, but not necessarily the low-skilled workers that would need training the most to prepare for the structural changes that digitalisation and globalisation of production processes will bring about (OECD, 2019[84]; OECD, 2019[50]). In addition, informal workers, the unemployed and the inactive population are excluded from these training subsidies. Although the public employment agency is providing training linked to wage subsidies for registered unemployed workers, in case they find a new employer, only about 17% of all the unemployed are registered and it takes on average around 26 months for them to find a new employer to benefit from training subsidies (Boughzala, 2019[1]).

The social and economic benefits of expanding continuous VET and adult learning policies to make more space for low-skilled and informal workers could be substantial. The share of adults that has not finished primary education is larger than in OECD countries (Figure 2.35). Although Tunisia is doing better than some of its regional peers, more than 20% of Tunisia’s adult population cannot read or write. This is not only a serious social issue, but also partly explains the low productivity in many firms. Adoption of new technologies and production processes or compliance with product quality standards, often necessary for accessing export markets, is very difficult, if a large part of the blue-collar workforce has an insufficient level of basic skills to follow continuous VET courses. Thus, establishing a system of basic education for adults, which provides the opportunity to finish lower-secondary education, is crucial to prepare the population for the increasing digitalisation and globalisation of production processes. Raising spending efficiency in other parts of the education system could free financial resources and teachers to support this endeavour, in addition to key fiscal reforms discussed in the first chapter (Figure 2.21).

Training opportunities for low-skilled, unemployed, informal or inactive adults with sufficient basic skills to follow training courses need to be expanded. The capacity of the existing public training infrastructure directly serving workers is small, with only 9 000 inscriptions in 2016 (ONEQ, 2019[82]). Direct allocation of training vouchers has been a successful strategy in many countries to raise access to training for disadvantaged individuals (Box 2.7). The allocation could be based on administrative data, such as the register of poor households used to administer the cash transfer programme (AMEN), which would reduce registration and information costs. If combined with a transparent quality certification system for training institutions, vouchers would improve competition between training providers (OECD, 2018[88]). The vouchers could be used to select courses from a region-specific training catalogue that is closely linked to local private sector skill demands and should be combined with high-quality career counselling (Grundke et al., 2021[89]). Another option could be to allocate a certain share of places in courses requested by firms in the current training levy system to disadvantaged workers, following a successful experience in Singapore (Box 2.7).

By linking the allocation of training vouchers to career counselling and job placement services, the cost effectiveness of training courses could be improved (Box 2.7). Access to courses could be made conditional on career counselling and an evaluation of the necessary prior-knowledge to better match trainees’ skills and experience with training content (Box 2.7). Counselling services could provide better information on training opportunities and help to direct those interested in training to the right course. Incentivising the supply of evening, part-time or distance learning courses and providing a worker-specific subsidy linked to income or living area would facilitate the participation of disadvantaged workers living in remote areas. During the pandemic, the supply of online training courses has increased, but this needs to be combined with better internet access, particularly for disadvantaged individuals.

Social or development impact bonds could be one option to finance training provision to disadvantaged individuals (OECD, 2020[24]; CGD, 2021[92]). In this type of public-private partnership arrangements, providers would get significant autonomy in designing training courses and content, but their remuneration would be made contingent on specific outcome targets, such as the future employment rate of training participants. The success of such arrangements depends on how contracts are designed (OECD, 2016[93]). In particular, the definition and measurement of social outcomes and the selection of target and control groups to evaluate whether objectives have been met are complex tasks that entail significant transaction costs and risks for the public sector. So far, the empirical evidence on the effectiveness of social impact bonds has been mixed and governments should be careful in providing specific social services completely through these types of arrangements (OECD, 2016[93]). However, as a complement to traditional forms of public social service provision, social impact bonds have the potential to nurture a culture of monitoring and evaluation in social service delivery, which is currently missing in Tunisia.

Similar to the initial VET and the tertiary education system, the public training institutes providing continuous VET courses operate in isolation from the private sector and labour market needs (World Bank, 2021[76]). The content of many courses is outdated and the supply is not well adapted to course demand, particularly in interior regions. Improving impact evaluation and quality control of courses through better coordination with firms and former trainees are crucial. Collecting training requests from local firms through an electronic platform could be combined with a skill anticipation assessment system focusing on skill needs in local labour markets to better guide course supply. This could build on experiences from firms in Tunisia, which have introduced such a platform to inform public and private training providers about their plant-specific training needs, monitor training needs of employees and evaluate the impact of training courses. In Brazil, the para-statal training provider SENAI has introduced a region-specific skill anticipation system in the state of São Paulo, which is combined with a comprehensive impact evaluation and content update of training courses that follow international best practices (OECD, 2018[88]; Grundke et al., 2021[89]).

As administrative procedures to approve subsidies for training courses have been improved and firms can freely choose providers, the supply of private training providers has strongly increased (World Bank, 2021[76]). So far, neither a comprehensive registry nor an evaluation of private providers for continuous VET exist, which increases information costs for firms and workers searching for training. A comprehensive evaluation of existing training providers and programmes combined with a credible certification system would reduce asymmetric information and set the right incentives for training providers to improve the quality of their programmes. This would also increase training incentives for workers (Angel-Urdinola, Nucifora and Robalino, 2015[4]).

Improving management and organisational skills in small and medium sized firms can be very effective to increase human capital investments and on the job training for workers (Angel-Urdinola, Nucifora and Robalino, 2015[4]; Bloom and Reenen, 2010[94]). It also has potential to significantly raise the productivity of firms in Tunisia and contribute to the reduction of informality. Many small and medium size firms in Tunisia suffer from weak management practices and underinvestment in human capital of their staff (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Since 2013, the share of firms providing formal training has significantly decreased (Figure 2.13). Firm-level analysis conducted for this report finds that in Tunisia the provision of formal training programmes for workers is associated with a 3% higher total factor productivity (Box 2.1) (Cassimon and Grundke, forthcoming[12]). Subsidising targeted training for managers of small firms can be a suitable policy tool to improve human resource management and prepare low-productivity firms for rising domestic and international competition (Dutz, 2018[95]).

Although spending for active labour market policies, at 0.9% of GDP in 2017, is significantly above the OECD average of 0.5% of GDP, the design and implementation of these policies is weak due to a lack of coordination and impact evaluation (World Bank, 2021[76]). The governance structure is highly fragmented with several ministries, agencies and a public bank responsible for different types of programs, and a comprehensive review of all programmes and their impacts does not exist.

The public employment agency (ANETI) manages wage and training subsidies for the unemployed, job placement services as well as some programs to promote entrepreneurship and self-employment, with total spending of about 0.5% of GDP (World Bank, 2021[76]). Public works programmes, including construction works, but also a broad range of community services such as cleaning of public spaces, are managed by the Ministry of Regional Development, local communities as well as state-owned enterprises (SOEs) with an estimated cost of about 0.2% of GDP. The promotion of entrepreneurship and distribution of micro-credits is mainly managed by a public bank with a budget of around 0.2% of GDP, but programmes targeting women in remote areas or youth are administered by other ministries. Moreover, the 2016 investment law has introduced additional wage and training subsidies that are independent from the ANETI programmes and administered by the investment agency and the Ministry of Industry. Consolidation of programmes and better coordination would allow for consistent monitoring and impact evaluation and improve resource allocation, effectiveness and spending efficiency of active labour market programmes.

Only around 17% of all unemployed individuals are registered at the public employment agency ANETI, and most of them are young tertiary graduates that have never worked before (Figure 2.36). Many unemployed are not registered, particularly young men without a tertiary degree and the unemployed with work experience, since the main incentive to register at ANETI is to receive wage and training subsidies (World Bank, 2021[76]). These are mainly focused on unemployed tertiary graduates to provide them with a first professional experience and facilitate their transition to the formal labour market (World Bank, 2021[76]; Angel-Urdinola, Nucifora and Robalino, 2015[4]). To be eligible for these subsidies, unemployed persons need to register and regularly visit the offices of the public employment agency ANETI.

The fact that so many unemployed workers do not register at ANETI indicates that public job placement services are not working well (Figure 2.36) (World Bank, 2021[76]). The large majority of the unemployed is searching for a job through their personal network and the family or by randomly sending out applications or passing by employers close to their community (IACE, 2019[11]). This leads to many inefficient job matches and contributes to low productivity of firms. Moreover, it generates high frustration among qualified job seekers, who do not find jobs due to a lack of the right personal contacts (IACE, 2019[11]; OECD, 2015[6]).

Improving job placement services is crucial to reduce structural unemployment and raise productivity through better job matches. Job placement services of ANETI suffer from significant understaffing and high administrative burden, with many job counsellors lacking the necessary skills and spending most of their time on administering wage and training subsidy contracts (World Bank, 2021[76]; OECD, 2015[6]). Moreover, the IT system fails to properly match skill requirements of vacancies with competences and abilities of job seekers, because detailed evaluations of occupational skill requirements and the quality and content of education certificates are missing (World Bank, 2021[76]). Improving coordination and data exchange across ministries and agencies is key to build the necessary statistical infrastructure to monitor labour market trends and better inform stakeholders, such as firms, students, and workers as well as education and training institutes, about skill supply and skill needs in the labour market. This requires the introduction of a unique individual identifier which would allow to connect different databases and monitor and evaluate the effects of active labour market policies and education and training programmes on individual labour market outcomes.

To improve public job placement services, resources should be increased and reallocated from costly and ineffective wage subsidy programmes to employment services and the provision of training and adult education, expanding services to unemployed with work-experience and low-skilled labour market entrants. Job counsellors should have full access to individual training records and employment histories to better match job seekers with skill demands of firms and tailor training supply to individual needs (Box 2.7). This also requires better training and incentives for job counsellors, whose performance should be measured by longer-term labour market outcomes of clients, and the reallocation of administrative tasks to office clerks (OECD, 2015[6]). Improving and expanding the certification system for work competences and prior-learning, in particular for informal workers, would help raising employability of low-skilled workers and encourage training investments (Dutz, 2018[95]). Moreover, sharing databases and engaging private providers in job placement and counselling services with performance-based remuneration could complement improvements at ANETI (OECD, 2020[24]). Allowing for more competition through the entry of private providers could also improve labour market matching, as currently private job placement services are forbidden and only selected firms are allowed to operate through discretionary authorisations (World Bank, 2021[76]; OECD, 2015[6]).

Wage and training subsidies have not been very effective in raising employability of the unemployed, as the selection of candidates is not based on their individual needs and conditionality of contracts has not been monitored and enforced well (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Although most contracts require firms to provide training or keep subsidised workers for a certain period, the information for monitoring and enforcing these conditions is not available. IT systems collecting this information do not exist and capacity constraints hinder job counsellors to follow up with labour market entrants and firms (OECD, 2015[6]). In addition, administrative burden to recover costs for firm specific training is high, which prevents many firms from providing training to young labour market entrants. Insertion rates of participants in most programmes are low, and a high share of contracts is terminated ahead of schedule (World Bank, 2021[76]). The number of wage and training subsidy programmes has been reduced since 2019, and more programmes now allow participation of low-skilled labour market entrants, which is an important step to improve their chances to enter the formal labour market. However, workers that got unemployed later in life are still not eligible for many of these activation policies, and selection of candidates remains independent of the length of the unemployment spell. Moreover, a comprehensive and continuous monitoring and impact evaluation of existing programs is still missing.

The low effectiveness of wage subsidies is also related to low training incentives for firms, who use wage subsidies to lower unit labour costs, do not provide training and often fire workers when subsidy payments end (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Most vacancies posted at ANETI are from firms in low-productivity activities which offer low-paid and low-skill intensive blue-collar jobs, which are not attractive enough for many tertiary graduates registered at ANETI (Figure 2.29) (Boughzala, 2019[1]). Most vacancies for high-skill white collar jobs are not posted at ANETI, but in social media, university networks or the few online job placement services that were allowed to enter the market (Boughzala, 2019[1]). To raise the effectiveness of wage subsidy programmes, the matching of worker profiles with skill requirements and task-content of subsidised vacancies needs to improve. This also requires more engagement with the private sector to convince firms to post more vacancies for skill-intensive white-collar jobs at ANETI, or at least inform ANETI about the nature of their vacancies and skill needs to allow for better counselling job seekers (OECD, 2015[6]).

High reservation wages contribute to explain why more than three out of four tertiary graduates registered at ANETI have been unemployed for longer than 24 months, although 45% of posted vacancies could not be filled (Figure 2.18). High reservation wages are due to negative cultural connotations of blue-collar work and the high attractiveness of public sector employment (World Bank, 2021[76]). Wages in the public sector and state-owned enterprises (SOEs) are more than 35% higher than in the private sector, complemented by generous social security benefits (OECD, 2018[8]). Recruitment in some parts of the public administration and SOE’s has given priority to unemployed tertiary graduates registered at ANETI as labour market entrants, mainly due to pressure from the union of unemployed tertiary graduates (OECD, 2015[7]; Marzouk, 2020[96]; Marzouk, 2021[97]). This has led to a phenomenon of queuing, whereby many tertiary graduates particularly from humanities, law and economics register as unemployed to wait for a public sector job, and receive support from their family or work in the informal sector (Angel-Urdinola, Nucifora and Robalino, 2015[4]; Boughzala, 2019[1]). This phenomenon is particularly pronounced in southern provinces home to large SOEs, where unemployment, ANETI registrations and informality are high, but at the same time daily wages for simple private sector jobs are more than double the wages in provinces with very low unemployment rates, such as Monastir (Figure 2.36) (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Moreover, being registered at ANETI is often an administrative requirement for unemployed tertiary graduates to receive a visa for labour migration (Boughzala, 2019[1]).

It is key to change the cultural mind-set that underlies the phenomenon of queuing for public sector employment towards a culture of entrepreneurship. This requires reducing the gap between public and private sector wages, clear signals that being an unemployed labour market entrant is not a valid criterion for public recruitment and that public hiring will have to significantly slow down over the next years due to low fiscal space (OECD, 2015[7]; Marzouk, 2020[96]; Marzouk, 2021[97]). Public recruitment processes should be open to all applicants including experienced workers from the private sector, which is currently hindered by strict maximum age limits of 35 and 40 years depending on the position, and recruitment should be exclusively based on performance in standardised tests and interviews.

Moreover, a culture of entrepreneurship needs to be developed, which requires a reform of basic education to improve teaching quality and emphasise entrepreneurial and soft skills in learning standards and curricula. However, above all, significant improvements in the business environment are necessary to facilitate the market entry of young start-ups and allow the development of innovative products and services (see above). Programs for the unemployed to stimulate entrepreneurship and provide access to credit can support this process, but should be better targeted to improve spending efficiency (World Bank, 2021[76]). Succeeding as an entrepreneur requires implicit knowledge and experience about markets and production processes, which many unemployed labour market entrants do not have (World Bank, 2020[14]; Morsy, Bassem and Selim, 2018[22]; Boughzala, 2019[1]).

The lack of comprehensive unemployment insurance and assistance programmes hinders regional mobility and partly explains the large heterogeneity of unemployment rates across provinces (Angel-Urdinola, Nucifora and Robalino, 2015[4]). For many unemployed youth, and especially women, the family is the only support for housing and food, which strongly reduces their geographic action space for searching a job. Moreover, many wage subsidy programs have effectively functioned as implicit passive labour market support in the past, as monitoring and enforcement of activation and training components were weak and many unemployed just subscribed to receive a monthly transfer (ILO, 2019[98]; World Bank, 2021[76]). The same is true for public work schemes, which comprise very low-skill intensive activities often missing any activation component. As these programs are place-based, they impede the registered unemployed to move to other regions for finding a job. Reallocating resources from these ineffective activation policies to create a general income support scheme for the unemployed, which is conditional on job-search efforts and acceptance of job offers, would help increasing regional labour mobility (OECD, 2011[99]; ILO, 2019[98]; OECD, 2015[7]). However, this needs to be accompanied by more effective job placement and counselling services that operate nation-wide and provide mobility support and housing subsidies to facilitate reallocation (OECD, 2005[100]). Improving transport infrastructure and access to affordable housing are also crucial to improve regional labour mobility (OECD, 2018[8]).

The labour tax wedge is slightly below the average of OECD countries, but relatively high when compared to low labour productivity in Tunisia (Figure 2.37). Particularly for low-skilled workers, a relatively steep income tax schedule reduces incentives for formalisation (Figure 2.37) (OECD, forthcoming[101]). Lowering the tax rate for the first income bracket and raising the allowable deduction would raise incentives for formal job creation and formalisation and could be financed by better tax enforcement (Rocha, Ulyssea and Rachter, 2018[102]; OECD, forthcoming[101]).

Social security contribution rates are similar to peers, but comprise additional payroll taxes that could be shifted to general tax revenues to extend the existing unemployment assistance scheme (OECD, forthcoming[101]). Social security contributions include contributions to the health and pension system, family allowances, housing allowances, a training tax as well as contributions to a rudimentary unemployment assistance programme. Unemployment assistance is financed by a 0.9% tax on wages, but only 6% of dismissed formal workers receive such benefits (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Extending the coverage of the existing unemployment assistance scheme would not only raise labour mobility, but would also make formal employment more attractive. This could be financed by marginal increases in the contribution rates, in combination with shifting the financing of family allowances and housing to general tax revenues, enhancing tax enforcement and reducing regressive deductions for personal income and capital gains taxes as well as VAT (IMF, 2021[103]; OECD, forthcoming[101]; ILO, 2019[98]). Raising spending efficiency in the health system and better linking pension benefits to contributions would help to prevent future rises in contribution rates (OECD, 2018[8]).

The high fragmentation of the social security system reduces labour mobility across sectors and firms as well as out of unemployment. Separate regimes exist by sector and employment status, e.g. for civil servants, agricultural workers, professional services, non-salaried workers, and low-wage workers, among others, with very different rules and contribution rates. Portability arrangements between the different regimes do not exist, which significantly reduces incentives to change jobs (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Many well-paid services professions such as architects, lawyers and other professional services are included in the regime for non-salaried workers and face very low contribution rates. Introducing a universal system with progressive contribution rates could improve labour mobility and raise social security revenues, while providing incentives for low-wage workers to join the formal sector.

Collective wage bargaining agreements, which establish wage floors by occupation, educational attainment and seniority of workers in around 70 sectors, hinder formal job creation and reduce incentives for productivity improvements. The complex wage floors are fixed at the national-level, although the cost of living can be much lower in remote regions. Larger firms dominate these wage negotiations, which leads to wage floors that many smaller and less productive competitors cannot afford, raising informality and reducing competition (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Entry wages for tertiary graduates are around 40% above the sector-specific minimum wage, and often above average wages, which reduces demand for tertiary graduates without work experience, as their average productivity is still low. Moreover, for many smaller firms the complex collective wage schedule becomes binding, as they cannot afford to top up wages above negotiated wage floors. This prevents them from rewarding employees who are more productive compared to peers with the same occupation, seniority and education level. It also reduces incentives for workers to take training courses that do not lead to a formal education degree recognised in the collective wage schedule. Allowing for more flexibility in wage setting for smaller firms and adapting collective wage agreements to economic conditions is crucial to raise formal job creation and incentives for productivity improvements.

Large differences in employment protection between permanent and temporary contracts raise worker turnover and create a dual labour market (Figure 2.38) (OECD, 2015[7]). Firms are reluctant to hire on permanent contracts as dismissals for economic reasons are forbidden and even dismissals due redundancy or misconduct are complicated and involve high litigation costs (Angel-Urdinola, Nucifora and Robalino, 2015[4]). In contrast, temporary contracts, which can be extended up to four years, entail almost no firing costs for employers, who can dismiss workers without any notice or compensation. This regulatory discrepancy strongly increases worker turnover, as many firms hire workers on temporary contracts and fire them after four years (Figure 2.39) (Stampini and Verdier-Chouchane, 2011[104]). Although legal provisions exist to incentivise permanent contracts by preventing firms to re-fill the position of a fired temporary worker after four years, enforcement is very weak due to missing labour inspections. More than 55% of young workers are employed on temporary contracts, which are in many cases even oral contracts (Boughzala, 2019[1]). Temporary contracts do not entail any social security benefits and wages are on average 25% lower than in permanent contracts, controlling for education, gender, sector and experience of workers (Angel-Urdinola, Nucifora and Robalino, 2015[4]). Transition rates from temporary to permanent contracts are low.

High turnover among workers with temporary contracts reduces training incentives and contributes to low productivity (Figure 2.39). The return on investments in firm-specific human capital is low for workers, and firms have no incentives to provide general training as they cannot appropriate the returns (OECD, 2015[7]). In addition, high firing costs for permanent workers can also reduce incentives for productivity improvements and the realisation of more productive worker-firm matches. Thus, the large gap in employment protection between permanent and temporary contracts should be reduced to lower worker turnover and increase incentives for training and productivity improvements. Permanent contracts should allow for dismissals due to economic or technological reasons, while reinforcing controls and penalties for wrongful dismissal. Providing clear legal definitions for wrongful dismissals is crucial to reduce the scope for ambiguity and related litigation costs, as for example recent reforms of the labour code in Italy and France have shown (Silva, Almeida and Strokova, 2015[105]; OECD, 2020[24]; Bellan, 2018[106]). Notice periods and compensations for dismissals on temporary contracts should be introduced, and social security benefits should be aligned to permanent contracts. Moreover, these changes in employment protection legislation need to be accompanied with a comprehensive income support for the unemployed irrespective of whether they are dismissed from a permanent or a temporary contract (ILO, 2019[98]).


[34] Amiti, M. and A. Khandelwal (2013), “Import Competition and Quality Upgrading”, The Review of Economics and Statistics, Vol. 95/2, pp. 476-490, https://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00271 (accessed on 22 September 2018).

[33] Amiti, M. and J. Konings (2007), “Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia”, American Economic Review, Vol. 97/5, pp. 1611-1638, https://doi.org/10.1257/aer.97.5.1611.

[52] Andrews, D., C. Criscuolo and P. Gal (2015), “Frontier firms, technology diffusion and public policy: Micro evidence from OECD countries”.

[4] Angel-Urdinola, D., A. Nucifora and D. Robalino (2015), Labor Policy to Promote Good Jobs in Tunisia: Revisiting Labor Regulation, Social Security, and Active Labor Market Programs, The World Bank, https://doi.org/10.1596/978-1-4648-0271-3.

[66] Angrist, J. and V. Lavy (1997), “The Effect of a Change in Language of Instruction on the Returns to Schooling in Morocco”, Journal of Labor Economics, Vol. 15/1, pp. 48-76.

[45] Araújo, B. and L. Paz (2014), “The effects of exporting on wages: An evaluation using the 1999 Brazilian exchange rate devaluation”, Journal of Development Economics, Vol. 111, pp. 1-16, https://doi.org/10.1016/j.jdeveco.2014.07.005.

[79] Arfa, A. et al. (2018), La réforme de la formation professionnelle en quête d’une concrétisation, Institut Tunisien de la Compétitivité et des Études Quantitatives (ITCEQ), Tunis, http://www.itceq.tn/files/emploi/reforme-de-la-formation-professionnelle.pdf (accessed on 27 June 2021).

[38] Arnold, J. et al. (2015), “Services Reform and Manufacturing Performance: Evidence from India”, The Economic Journal, Vol. 126/590, pp. 1-39, https://doi.org/10.1111/ecoj.12206.

[19] Ayadi, L. et al. (2013), Estimating Informal Trade across Tunisia’s Land Borderss, World Bank, Washington DC, http://econ.worldbank.org. (accessed on 18 June 2021).

[51] Bechichi, N. et al. (2018), “MOVING BETWEEN JOBS AN ANALYSIS OF OCCUPATION DISTANCES AND SKILL NEEDS”, OECD SCIENCE, TECHNOLOGY AND INNOVATION POLICY PAPERS, No. 52, OECD, Paris, http://www.oecd.org/going-digital (accessed on 27 September 2018).

[53] Bechichi, N. et al. (2019), “Occupational mobility, skills and training needs”, http://www.oecd.org/going-digital. (accessed on 26 January 2020).

[106] Bellan, M. (2018), “Pourquoi les recours aux prud’hommes chutent”, Les Echos, https://www.lesechos.fr/economie-france/social/pourquoi-les-recours-aux-prudhommes-chutent-138568.

[65] Benstead, L. (2021), “6. The Impact of Poverty and Corruption on Educational Quality in Tunisia”, in The Political Economy of Education in the Arab World, Lynne Rienner Publishers, https://doi.org/10.1515/9781626379442-008.

[114] Bernard, A. et al. (2007), “Firms in International Trade”, Journal of Economic Perspectives, Vol. 21/3, pp. 105-130, https://doi.org/10.1257/jep.21.3.105.

[40] Blalock, G. and F. Veloso (2007), “Imports, Productivity Growth, and Supply Chain Learning”, World Development, Vol. 35/7, pp. 1134–1151, https://doi.org/10.1016/j.worlddev.2006.10.009.

[26] Bloom, N., M. Draca and J. Van Reenen (2016), “Trade induced technical change? The impact of chinese imports on innovation, IT and productivity”, Review of Economic Studies, Vol. 83/1, pp. 87-117, https://doi.org/10.1093/restud/rdv039.

[94] Bloom, N. and J. Reenen (2010), “Why do management practices differ across firms and countries?”, Journal of Economic Perspectives, Vol. 24/1, pp. 203-224, https://doi.org/10.1257/jep.24.1.203.

[112] Bosch, M. and R. Campos-Vazquez (2014), “The Trade-Offs of Welfare Policies in Labor Markets with Informal Jobs: The Case of the “Seguro Popular” Program in Mexico”, American Economic Journal: Economic Policy, Vol. 6/4, pp. 71-99, https://doi.org/10.1257/pol.6.4.71.

[9] Bouchoucha, I. (2018), “The Migration of Women in Tunisia: Between Tradition and Modernity”, in Gender and Mobility in Africa, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-65783-7_3.

[1] Boughzala, M. (2019), MARCHÉ DU TRAVAIL, DYNAMIQUE DES COMPÉTENCES ET POLITIQUES D’EMPLOI EN TUNISIE, European Training Foundation, https://www.etf.europa.eu/sites/default/files/2019-08/labour_market_tunisia_fr.pdf (accessed on 14 June 2021).

[46] Brandt, L., J. Van Biesebroeck and Y. Zhang (2012), “Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing”, Journal of Development Economics, Vol. 97/2, pp. 339-351, https://doi.org/10.1016/J.JDEVECO.2011.02.002.

[71] Burke, M. and T. Sass (2013), “Classroom Peer Effects and Student Achievement”, Journal of Labor Economics, Vol. 31/1, pp. 51-82, https://doi.org/10.1086/666653.

[113] Bustos, P. (2011), “Trade Liberalization, Exports, and Technology Upgrading: Evidence on the Impact of MERCOSUR on Argentinian Firms”, American Economic Review, Vol. 101, pp. 304-340, https://doi.org/10.1257/aer.101.1.304.

[48] Cadot, O., J. Gourdon and F. van Tongeren (2018), “Estimating Ad Valorem Equivalents of Non-Tariff Measures: Combining Price-Based and Quantity-Based Approaches”, OECD Trade Policy Papers, No. 215, OECD Publishing, Paris, https://dx.doi.org/10.1787/f3cd5bdc-en.

[12] Cassimon, S. and R. Grundke (forthcoming), “Improving skills and employment opportunities in Tunisia”, OECD Economics Department Working Papers.

[58] Cassimon, S., R. Grundke and P. Kowalski (forthcoming), “The opportunities associated with Tunisia’s greater integration with international markets”, OECD Economics Department Working Papers.

[92] CGD (2021), Investing in Social Outcomes: Development Impact Bonds, Centre for Global Development, https://www.cgdev.org/page/investing-social-outcomes-development-impact-bonds-0.

[18] CRES (2016), Protection sociale et économie informelle en Tunisie - Défis de la transition vers l’économie formelle, Centre des Recherches et d’Etudes Sociales, http://www.cres.tn/uploads/tx_wdbiblio/Secteur_informel_Tunisie.pdf.

[44] Criscuolo, C., P. Gal and C. Menon (2014), “The Dynamics of Employment Growth: New Evidence from 18 Countries”, OECD Science, Technology and Industry Policy Papers, No. 14, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jz417hj6hg6-en.

[42] De Loecker, J. et al. (2016), “Prices, Markups, and Trade Reform”, Econometrica, Vol. 84/2, pp. 445-510, https://doi.org/10.3982/ECTA11042.

[95] Dutz, M. (2018), Jobs and Growth: Brazil’s Productivity Agenda, The World Bank, https://doi.org/10.1596/978-1-4648-1320-7.

[39] Eppinger, P. (2019), “Service offshoring and firm employment”, Journal of International Economics, Vol. 117, pp. 209-228, https://doi.org/10.1016/j.jinteco.2019.01.007.

[87] Eurofound (2021), European Reshoring Monitor, https://reshoring.eurofound.europa.eu/.

[15] European Commission (2021), Ex-post Evaluation of the impact of trade chapters of the Euro-Mediterranean Association Agreements with six partners: Algeria, Egypt, Jordan, Lebanon, Morocco and Tunisia, European Commission - Directorate General for Trade, https://op.europa.eu/en/publication-detail/-/publication/fab9bddd-9106-11eb-b85c-01aa75ed71a1.

[28] European Commission (2019), Rapport du sous-comité UE-TUNISIE “Commerce, industrie et service”et du sous-comité UE-TUNISIE “Marché interieur”, https://trade.ec.europa.eu/doclib/docs/2019/october/tradoc_158410.pdf.

[75] Fouarge, D. et al. (2020), “Do labour market opportunities affect VET students’ educational choice? Evidence from stated choice and field experiments”, https://www.oecd.org/els/emp/OECD-ELS-Seminars-Fouarge.pdf.

[115] Garcia-Marin, A. and N. Voigtländer (2019), “Exporting and Plant-Level Efficiency Gains: It’s in the Measure”, Journal of Political Economy, Vol. 127/4, pp. 1777-1825, https://doi.org/10.1086/701607.

[32] Goldberg, P. et al. (2009), “Trade Liberalization and New Imported Inputs”, American Economic Review, Vol. 99/2, pp. 494-500, https://doi.org/10.1257/aer.99.2.494.

[89] Grundke, R. et al. (2021), “Improving skills to harness the benefits of a more open economy in Brazil”, OECD Economics Department Working Papers, No. 1661, OECD Publishing, Paris, https://dx.doi.org/10.1787/222c1741-en.

[62] Grundke, R. et al. (2017), “Skills and global value chains: A characterisation”, OECD Science, Technology and Industry Working Papers, No. 2017/05, https://dx.doi.org/10.1787/cdb5de9b-en.

[57] Grundke, R. et al. (2018), “Which skills for the digital era?: Returns to skills analysis”, OECD Science, Technology and Industry Working Papers, No. 2018/09, OECD Publishing, Paris, https://dx.doi.org/10.1787/9a9479b5-en.

[27] Grundke, R. and C. Moser (2019), “Hidden protectionism? Evidence from non-tariff barriers to trade in the United States”, Journal of International Economics, Vol. 117, pp. 143-157, https://doi.org/10.1016/j.jinteco.2018.12.007.

[20] Haltiwanger, J. et al. (2013), “Cross-Country Differences in Productivity: The Role of Allocation and Selection”, American Economic Review, Vol. 103/1, pp. 305-334, https://doi.org/10.1257/aer.103.1.305.

[70] Heckman, J. et al. (2010), “The Rate of Return to the High/Scope Perry Preschool Program”, Journal of Political Economy, Vol. 94/1-2, pp. 114-128.

[67] Heckman, J. and S. Mosso (2014), “The Economics of Human Development and Social Mobility”, Annual Review of Economcs, Vol. 6, pp. 689-733, https://doi.org/10.1146/annurev-economics-080213-040753.

[68] Heckman, J., R. Pinto and P. Savelyev (2013), “Understanding the mechanisms through which an influential early childhood program boosted adult outcomes”, American Economic Review, Vol. 103/6, pp. 2052-2086, https://doi.org/10.1257/aer.103.6.2052.

[41] He, Z. and M. Dai (2017), “Learning by Importing”, Columbia University, http://www.columbia.edu/~zh2178/Learning%20by%20Importing.pdf (accessed on 22 September 2018).

[37] Hoekman, B. and A. Mattoo (2008), Services Trade And Growth, The World Bank, https://doi.org/10.1596/1813-9450-4461.

[21] Hsieh, C. and P. Klenow (2009), “Misallocation and Manufacturing TFP in China”, The Quarterly Journal of Economics, Vol. 124/4, pp. 1403-1448, https://www.jstor.org/stable/pdf/40506263.pdf (accessed on 17 January 2019).

[54] Hummels, D. et al. (2012), “Offshoring, Transition, and Training: Evidence from Danish Matched Worker-Firm Data”, American Economic Review, Vol. 103/2, pp. 424-428, https://doi.org/10.1257/aer.102.3.424.

[55] Hummels, D., J. Munch and C. Xiang (2018), Offshoring and labor markets, American Economic Association, https://doi.org/10.1257/jel.20161150.

[11] IACE (2019), Rapport National de l’Emploi, Institute Arabe des Chefs d’entreprise, https://iace.tn/docs/?limit=15&q=emploi&catid=0&theme=default.

[109] IBGE (2016), Pesquisa nacional de saúde do escolar 2015, Instituto Brasileiro de Geografia e Estadisticas.

[60] IFC and UTICA (2017), Evaluation des écarts de compétences dans le secteur des TIC en Tunisie, http://www.digitaltalent.tn/u_p_l_d/etudes/evaluation-des-ecarts-de-competences-dans-le-secteur-des-tic-en-tunisie_sommaire_1_025668900150488704659b2c1063eb78.pdf.

[98] ILO (2019), Tunisie Étude de faisabilitÉ d’un fonds perte d’emploi (options et coûts), International Labour Organsiation, https://www.social-protection.org/gimi/gess/RessourcePDF.action;jsessionid=67Hr7bj8uHfcK1NlhZ-K1t-atlfCuIFmZN30KBmqcr0h7cxceFP9!539423187?id=56782 (accessed on 6 July 2021).

[83] ILO (2013), L’amélioration de l’apprentissage informel en Tunisie, International Labour Organisation, https://www.ilo.org/wcmsp5/groups/public/---ed_emp/---ifp_skills/documents/presentation/wcms_218821.pdf.

[103] IMF (2021), Tunisia : 2021 Article IV Consultation, International Monetary Fund, https://www.imf.org/en/Publications/CR/Issues/2021/02/26/Tunisia-2020-Article-IV-Consultation-Press-Release-Staff-Report-and-Statement-by-the-50128.

[17] INS (2020), Indicateurs sur l’emploi informel 2019, Institute National de Statistiques Tunisie, http://ins.tn/publication/indicateurs-sur-lemploi-informel-2019.

[13] Joumard, I., S. Dhaoui and H. Morgavi (2018), “Insertion de la Tunisie dans les chaines de valeur mondiales et role des entreprises offshore”, Documents de travail du Département des Affaires économiques de l’OCDE, No. 1478, Éditions OCDE, Paris, https://dx.doi.org/10.1787/546dbd75-fr.

[49] Maertens, M. and J. Swinnen (2009), “Trade, Standards, and Poverty: Evidence from Senegal”, World Development, Vol. 37/1, pp. 161-178, https://doi.org/10.1016/j.worlddev.2008.04.006.

[97] Marzouk, H. (2021), “Enseignement supérieur : vers le recrutement de 1130 enseignants-chercheurs”, L’Economiste Maghrébin, https://www.leconomistemaghrebin.com/2021/09/23/enseignement-superieur-recrutement-1130-docteurs/.

[96] Marzouk, H. (2020), “ARP : Initiative législative pour embaucher les anciens chômeurs”, Economiste Maghrebin, https://www.leconomistemaghrebin.com/2020/06/18/arp-initiative-legislative-embaucher-anciens-chomeurs/.

[43] Melitz, M. (2003), The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity, https://www.jstor.org/stable/pdf/1555536.pdf?refreqid=excelsior%3Ae3382ca8aee44f583882d975e0cdb85e (accessed on 22 September 2018).

[22] Morsy, H., K. Bassem and R. Selim (2018), Tunisia Diagnostic paper: Assessing Progress and Challenges in Unlocking the Private Sector’s Potential and Developing a Sustainable Market Economy, European Bank for Reconstruction and Development, https://www.ebrd.com/documents/strategy-and-policy-coordination/tunisia.pdf?blobnocache=true.

[74] OECD (2021), Career Guidance for Adults in a Changing World of Work, Getting Skills Right, OECD Publishing, Paris, https://dx.doi.org/10.1787/9a94bfad-en.

[77] OECD (2021), OECD Tourism Trends and Policies 2020, OECD Publishing Paris, https://www.oecd.org/cfe/tourism/2020-Tourism-Brochure.pdf.

[107] OECD (2020), Education at a Glance 2020: OECD Indicators, OECD Publishing, Paris, https://dx.doi.org/10.1787/69096873-en.

[78] OECD (2020), “Mitigating the impact of COVID-19 on tourism and supporting recovery”, OECD Tourism Papers, No. 2020/03, OECD Publishing, Paris, https://dx.doi.org/10.1787/47045bae-en.

[24] OECD (2020), OECD Economic Surveys: Brazil 2020, OECD Publishing, Paris, https://dx.doi.org/10.1787/250240ad-en.

[90] OECD (2019), Financial Incentives to Promote Adult Learning in Australia, Getting Skills Right, OECD Publishing, Paris, https://dx.doi.org/10.1787/c79badcc-en.

[84] OECD (2019), Getting Skills Right: Future-Ready Adult Learning Systems, Getting Skills Right, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264311756-en.

[31] OECD (2019), OECD Competition Assessment Reviews: Tunisia, OECD Publishing Paris, https://www.oecd.org/daf/competition/ca-tunisia-review-2019-en.pdf.

[23] OECD (2019), OECD Economic Surveys: PORTUGAL, OECD.

[29] OECD (2019), OECD Economic Surveys: Argentina 2019, OECD Publishing, Paris, https://dx.doi.org/10.1787/0c7f002c-en.

[91] OECD (2019), OECD Economic Surveys: Malaysia 2019, OECD Publishing, Paris, https://dx.doi.org/10.1787/eaaa4190-en.

[50] OECD (2019), OECD Skills Outlook 2019 : Thriving in a Digital World, OECD Publishing, Paris, https://dx.doi.org/10.1787/df80bc12-en.

[69] OECD (2019), PISA 2018 Results (Vol II): Where all students can succeed., OECD Publishing Paris, https://doi.org/10.1787/b5fd1b8f-en.

[8] OECD (2018), Études économiques de l’OCDE : Tunisie 2018: Évaluation économique, Éditions OCDE, Paris, https://dx.doi.org/10.1787/eco_surveys-tun-2018-fr.

[88] OECD (2018), Getting Skills Right: Brazil, Getting Skills Right, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264309838-en.

[108] OECD (2018), OECD Economic Surveys BRAZIL.

[80] OECD (2018), Seven Questions about Apprenticeships: Answers from International Experience, OECD Reviews of Vocational Education and Training, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264306486-en.

[111] OECD (2017), OECD Skills Outlook 2017: Skills and Global Value Chains, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264273351-en.

[73] OECD (2016), Getting Skills Right: Assessing and Anticipating Changing Skill Needs, Getting Skills Right, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264252073-en.

[72] OECD (2016), Low-Performing Students: Why They Fall Behind and How To Help Them Succeed, PISA, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264250246-en.

[64] OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education, PISA, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264266490-en.

[93] OECD (2016), Understanding social impact bonds, OECD, Paris, http://www.oecd.org/cfe/leed/UnderstandingSIBsLux-WorkingPaper.pdf (accessed on 19 February 2020).

[6] OECD (2015), Investing in Youth: Tunisia: Strengthening the Employability of Youth during the Transition to a Green Economy, Investing in Youth, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264226470-en.

[7] OECD (2015), Tunisia: A reform agenda to support competitiveness and inclusive growth, OECD Publishing, Paris, https://www.oecd.org/countries/tunisia/Tunisia-a-reform-agenda-to-support-competitiveness-and-inclusive-growth.pdf (accessed on 28 June 2021).

[99] OECD (2011), OECD Employment Outlook 2011, OECD Publishing, Paris, https://dx.doi.org/10.1787/empl_outlook-2011-en.

[81] OECD (2010), Learning for Jobs, OECD Reviews of Vocational Education and Training, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264087460-en.

[100] OECD (2005), Trade and Structural Adjustment: Embracing Globalisation, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264010970-en.

[25] OECD (forthcoming), Competition Assessment Tunisia, OECD Publishing Paris.

[101] OECD (forthcoming), Soutenir la Tunisie dans la mise en œuvre de mesures fiscales, OECD Publishing Paris.

[47] Olley, G. and A. Pakes (1996), “The Dynamics of Productivity in the Telecommunications Equipment Industry”, Econometric, Vol. 64/6, pp. 1263-1297, https://www.jstor.org/stable/pdf/2171831.pdf?refreqid=excelsior%3A0e474c9384e818e114e409d9172a6487 (accessed on 17 January 2019).

[82] ONEQ (2019), La formation professionnelle en chiffres 2017, Observatoire National de l’Emploi et des Qualifications, http://www.emploi.tn/uploads/pdf/ONEQ/FP2017_VF.pdf (accessed on 27 June 2021).

[61] ONEQ (2017), Etude sur l’insertion professionnelle des diplômés du dispositif national de la formation professionnelle, Observatoire Nationale de l’Emploi et des Qualifications, http://www.emploi.nat.tn/ckeditor/ckfinder/userfiles/files/Rapport_dip_fp.pdf.

[2] ONEQ (2013), Le marché du Travail en Tunisie, Observatoire National de l’Emploi et des Qualifications, http://www.emploi.tn/uploads/pdf/ONEQ/2013_Rapport_sur_le_marche_du_travail.pdf (accessed on 11 June 2021).

[36] Pavcnik, N. (2002), “Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants”, The Review of Economic Studies, Vol. 69/1, pp. 245-276, https://doi.org/10.1111/1467-937X.00205.

[16] Plank, L. and C. Staritz (2014), “Global Competition, Institutional Context, and Regional Production Networks: Up-and Downgrading Experiences in Romania’s Apparel Industry”, ÖFSE Working Paper, Vol. 50, https://www.econstor.eu/bitstream/10419/104631/1/805462295.pdf (accessed on 9 July 2021).

[102] Rocha, R., G. Ulyssea and L. Rachter (2018), “Do lower taxes reduce informality? Evidence from Brazil”, Journal of Development Economics, Vol. 134, pp. 28-49, https://doi.org/10.1016/j.jdeveco.2018.04.003.

[30] Rudloff, B. (2020), “A Stable Countryside for a Stable Country? The Effects of a DCFTA with the EU on Tunisian Agriculture”, Stiftung Wissenschaft Politik Research Papers, Vol. 2, https://doi.org/10.18449/2020RP02.

[105] Silva, J., R. Almeida and V. Strokova (2015), Sustaining Employment and Wage Gains in Brazil: A Skills and Jobs Agenda, The World Bank, https://doi.org/10.1596/978-1-4648-0644-5.

[56] Spitz-Oener, A. (2006), “Technical Change, Job Tasks, and Rising Educational Demands: Looking outside the Wage Structure”, Journal of Labor Economics, Vol. 24/2, pp. 235-270, https://doi.org/10.1086/499972.

[104] Stampini, M. and A. Verdier-Chouchane (2011), “Labor Market Dynamics in Tunisia: The Issue of Youth Unemployment”, Review of Middle East Economics and Finance, Vol. 7/2, pp. 1-35, https://doi.org/10.2202/1475-3693.1394.

[85] TAA (2020), Présentation du Pacte pour l’Industrie Automobile en Tunisie et des résultats de l’enquête impact COVID-19, https://taa.tn/wp-content/uploads/2020/12/20200723-TAA-Pr%C3%A9sentation-AG-TAA-Jeudi-23-Juillet.pdf.

[86] TIA (2020), Automotive Sector Representation, Tunisian Investment Authority, https://tia.gov.tn/storage/app/media/TUNISIA%20VALUE%20PROPOSITION/TIA%20TUNISIA%20AUTOMOTIVE%20SECTOR.pdf.

[35] Topalova, P. and A. Khandelwal (2011), “Trade Liberalization and firm productivity: The case of India”, The Review of Economics and Statistics, Vol. 93/3, pp. 995-1009, https://doi.org/10.1162/REST_a_00095.

[59] UNESCO (2021), Talents TIC en Tunisie - l’adéquation entre l’offre et la demande, https://en.ichei.org/Uploads/Download/2021-10-13/61667cfbd9c78.pdf.

[5] UNICEF (2020), Analyse de la situation des enfants en Tunisie 2020, UNICEF, https://www.unicef.org/tunisia/media/2986/file/SITAN-11-2020.pdf (accessed on 15 June 2021).

[3] UNICEF and INS (2019), Tunisie: Enquête par grappes à indicateurs multiples (MICS), le Ministère du Développement de l’Investissement et de la Coopération Internationale (MDICI)., https://washdata.org/report/tunisia-2018-mics-report.

[63] World Bank (2021), World Development Indicators, https://databank.worldbank.org/source/world-development-indicators.

[76] World Bank (2021), Youth Employment In Tunisia : Concept Note for Activities Under the New Window, Better Employment Policies, World Bank, https://documents.worldbank.org/pt/publication/documents-reports/documentdetail/368251622210279606/youth-employment-in-tunisia-concept-note-for-activities-under-the-new-window-better-employment-policies.

[14] World Bank (2020), TUNISIA ECONOMIC MONITOR Rebuilding the Potential of Tunisian Firms, World Bank, Washington DC, https://documents1.worldbank.org/curated/en/194331608565600726/pdf/Tunisia-Economic-Monitor-Rebuilding-the-Potential-of-Tunisian-Firms-Fall-2020.pdf (accessed on 17 June 2021).

[110] World Bank (2016), Brazil Systematic Country Diagnostic: Retaking the path to Inclusion, Growth and Sustainability, The World Bank Group.

[10] WTO (2016), Trade policy review - Tunisia, World Trade Organsiation, https://www.wto.org/english/tratop_e/tpr_e/s341_e.pdf.

Metadata, Legal and Rights

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided.

© OECD 2022

The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at https://www.oecd.org/termsandconditions.