Is there a threshold effect of global value chains on informal employment in Sub-Saharan Africa?

The International Labour Organization estimates that the informal economy accounts for 85% of employment in Africa and 68% in Asia, the Pacific and the Arab States (Bonnet et al., 2019). It also estimates that worldwide, more than 60% of workers and 80% of businesses operate in the informal economy (Dewick et al., 2022). This is equivalent to two billion workers or 61.2% of the world’s population employed in the informal sector. As a result, up to 88% of livelihoods in SSA and South Asia come from the informal sector (ILO and WIEGO, 2013). In Latin America and the Caribbean region, around 50% of the population is informally employed (Bonnet et al., 2019). Despite the resumption of growth in Africa, with average annual growth rates of 3–5% since 2004, poverty continues to affect 40% of the continent’s population, unemployment has reached alarming levels and booming informal economies employ 85.8% of the workforce. This is the result of concerns about the rising unemployment rate, which in SSA was 7.2% in 2017 according to the International Labour Organization. In Africa as a whole, 40% of the total population lives in poverty, which has led to the growth of informal economies employing 85.8% of the local workforce (African Development Bank and Development Centre, 2010; Bonnet et al., 2019).

According to Medina et al. (2017), the informal economy deserves special attention in SSA as it contributes between 25 and 65% to GDP and accounts for 30 to 90% of total non-agricultural employment. SSA has the highest share of informal work of any region in the world. Younger people are the most affected by informality (Kpognon, 2022). Espino and de los Santos (2021) report that 94.9% of people aged between 15 and 24 years on the African continent work in the informal economy. Yet this sector, described as the informal economy, was seen as a temporary phenomenon that would be transformed into a modern economy with the right combination of policies and technological progress (Chen, 2003). However, in the 1970s, the informal economy not only persisted but grew in size and volume (Chen, 2003). Today, it is even described by some authors as an important determinant of individual mental health (Álvarez et al., 2013; Saunders et al., 2017; Tran et al., 2022). However, in economic theory, the term informal employment, informal sector, underground economy or informal economy is commonly defined as jobs that are not subject to national regulation, taxation or social protection (Bonnet et al., 2019). Informal employment is most often also characterised by relatively poor employment conditions and low bargaining power for workers.

However, the first studies on informality associated it with the rural exodus. According to the Harris–Todaro model, only a fraction of workers in the urban labour force have access to jobs in the regulated formal sector. The reason for this segmentation of formal jobs was that the minimum wage was set above the market-clearing level (Slonimczyk, 2022). This is why Lewis’ (1954) theory of economic dualism suggests that there are two distinct sectors in an economy, the formal sector and the informal sector. According to this theory, informal employment emerges because of the formal sector’s inability to create enough jobs to absorb all the available labour (Debrah, 2007). Workers are thus forced to find work in the informal sector in order to survive.

Formality cost theory provides a better explanation for the growth of the informal sector. According to this theory, informal employment develops because of the high costs associated with formalising a business or a job, such as taxes, restrictive regulations and bureaucratic constraints. Amaral and Quintin (2006) explain the growth of the informal sector as the result of a reasoned choice by economic agents. Individuals therefore prefer to work in the informal sector, where these costs are often reduced or eliminated. Slonimczyk (2012) finds that the tax reforms carried out in Russia in 2001, including the reduction in average tax rates, personal income tax and payroll or social tax, have reduced a large proportion of informal employment. Many workers in developing economies find employment opportunities in the informal sector such as self-employment, family businesses and other small enterprises (Tanaka and Greaney, 2024). As the labour market becomes more formalised, much of the economic literature shows that some workers prefer the informal sector. Informality thus becomes a rational choice. Indeed, workers deliberately choose to work in the informal sector because of perceived advantages, such as flexibility of working hours, lack of supervision, the possibility of earning higher incomes through informal activities and circumvention of regulations (Tshuma and Jari, 2013). On the other hand, the informal sector, which is made up of independent informal activities carried out by “own account” workers and often relies on unpaid family labour (Kesar and Bhattacharya, 2020), consists mainly of work that is technologically primitive, not very productive and underpaid. This sector often aims to ensure the economic subsistence of self-employed workers and their families and generally does not generate sufficient gross value added to hire employees and/or produce a sufficient surplus to allow expansion (Kesar and Bhattacharya, 2020).

Informal employment offers short-term flexibility on the labour market but is correlated with lower wages and tax revenues, as well as increased poverty, inequality and job insecurity (Tanaka and Greaney, 2024). To counter this level of informality, global value chains (GVCs) are seen as a way of adding value to workers who would otherwise be considered surplus to requirements in the formal sector (Meagher, 2019a, b). Still referred to as the international production network, GVCs refer to the interconnected production process that involves goods and services from their conception, through their design to manufacturing, marketing, commercialisation and destruction (Gereffi and Fernandez-Stark, 2011). According to the neoclassical approach, GVCs are the result of the search for economic efficiency and the specialisation of countries in sectors where they have a comparative advantage. According to David Ricardo’s theory of comparative advantage, countries specialise in the production of goods and services in which they are competitive and source the rest of their products from abroad. GVCs allow countries to mutually benefit from these comparative advantages by creating trade relationships and maximising the global efficiency of production (Rodrik, 2018). In contrast to this analysis, Baldwin (2012) introduces the production fragmentation approach. According to this theory, GVCs are formed when the different stages in the production of a good are dispersed between different countries. In other words, it represents a new form of international production sharing in which each country specialises in particular stages of the production sequence of a product; this is known as vertical specialisation. These value chains bring together all the activities involved in bringing a product from its conception to its final purchaser. Companies seek to reduce their production costs by outsourcing certain stages of production to countries where labour costs or other production factors are lower. This fragmentation of production enables companies to benefit from international specialisation and to access cheaper or better-quality sources of supply.

Two theories can be considered to explain the relationship between participation in GVCs and informal employment, in particular, Lewis’s (1954) dualism of the labour market, which divides the traditional sector from the modern sector. The dualism theory supports the idea that developing economies are made up of two sectors: a formal, capitalist and productive sector, and an informal, traditional and poorly structured sector. When a country integrates into GVCs, according to Barrientos et al. (2011), GVCs can either absorb informal labour by formalising it (integration effect) or reinforce informality by outsourcing it to informal subcontractors in order to reduce costs. First, the informal sector serves as a reservoir of cheap labour, often mobilised by local subcontractors to respond to international competitive pressures. Indeed, participation in GVCs is expected to benefit the informal economy in terms of increased productivity (and profitability) through better access to technology and production markets, as well as improved wages and employment opportunities. This could lead to a reduction in the incidence of precarious and vulnerable self-employment (Kesar, 2023). In this way, the dual structure of the economy can be transformed into a more homogenous, “modern”, dynamic and formal structure, either through a dilution of the informal economy by facilitating its transition to greater dynamism and formality or through a disintegration of this economic segment as a result of market competition from large companies (Kesar and Bhattacharya, 2020; Kesar, 2024).

On the other hand, the fragmentation of global production encourages multinationals to subcontract part of their production to local suppliers. For Milberg and Winkler (2013), this fragmentation encourages the formation of atypical jobs in low-productivity countries. The theory of fragmentation and outsourcing argues that globalisation allows firms to divide production into relocatable tasks, which are entrusted to different countries according to their comparative advantage (Grossman and Rossi-Hansberg, 2008). In this context, companies are looking for lower production costs, favouring outsourcing to local suppliers. The latter, under pressure to be competitive, often use informal labour to reduce their costs. In this way, the fragmentation of value chains can indirectly fuel informal employment, especially in low value-added segments. However, Taglioni and Winkler (2016) find that the effect of participation in GVCs on informal employment depends on positioning in the chain. For them, countries or sectors with low value-added positions or less sophisticated production linkages tend to develop more precarious and informal forms of employment.

In summary, the theory of fragmentation and outsourcing suggests that integration into GVCs could improve the quality of employment. However, this effect depends on the position of each individual in the chain and the local institutional capacity to regulate the labour market. In the absence of active formalisation or technological upgrading policies, integration may perpetuate or exacerbate the dualism of the labour market, confining the majority of workers to precarious and informal sectors (Taglioni and Winkler, 2016; Gereffi, 1999).

The literature on the link between GVCs and informal employment is limited in terms of consensus. Informality involves complex economic activities, and there is no general consensus on the best modelling approach to study the informal sector. However, this recent literature provides the basis for our contribution by highlighting the fact that GVCs can potentially facilitate the transition of labour from informal to formal employment in developing countries (Meagher, 2019a, b; Tanaka and Greaney, 2024; Rohit, 2025). The limitation of these studies lies in the fact that most of them use linear models. As a result, they fail to capture the level at which the regime can change. Compared with previous studies, this article has the merit of investigating the threshold at which value chains reduce informal employment. In a context of increasing globalisation, participation in GVCs represents a major opportunity for African economies, but its effects on the quality of employment, particularly informal employment, remain little explored. Using threshold models, this study captures the non-linear and differentiated effects of GVC integration on informal employment, thereby providing a more detailed understanding of the conditions under which such participation can encourage the formalisation of the labour market.

The objective of this paper is to determine the threshold effect of GVCs on informal employment in SSA. We believe that our results contribute to the existing literature in two key areas. Most of the relevant studies cited above and in the literature review are based on panel data and use static models such as ordinary least squares and fixed-effect models or dynamic panel models, in particular the generalised methods of moments estimator. In contrast to these approaches, we use threshold models. Moreover, to our knowledge, very few studies have examined the effect of GVCs on informal employment. To this end we use two threshold estimation methods, the panel smooth transition regression (PSTR) model by González et al. (2005) and the dynamic panel threshold model (FD-GMM) introduced by Seo and Shin (2016) for the robustness of our analyses.

The rest of the paper is organised as follows: Section 2 presents the literature review, Section 3 presents the data as well as the evolution of GVCs and informal employment in SSA, Section 4 present the methodology, Section 5 comments on the results and Section 6 concludes the study.

The relationship between GVCs and informal employment is influenced by a number of factors, including the type of linkages within the value chain, the organisation of production, purchasing practices and the sector of activity (Ly, 2023). The organisation of production processes within GVCs are generally (1) offshoring without outsourcing, in which the leading companies organising GVCs assume direct responsibility for organising production and employment in these economies through their subsidiaries and branches, and (2) offshoring with outsourcing, in which the leading companies organising CVGs do not assume direct responsibility for organising production and employment but instead enter into contractual arrangements with some of the first-tier suppliers (generally located in the formal segments of the economies) for the production of goods and services to lower-tier suppliers, including those in the informal economy (Kumar, 2025; Guha-Khasnobis et al., 2023). As large companies often outsource some of their activities to subcontractors or suppliers located in low labour cost countries, this can lead to an increase in informal employment. This can lead to an increase in informal employment as informal workers are often hired to do this type of work. Thus, integrating informal workers into GVCs may bring only minimal improvements in their access to resources, and cooperative pricing arrangements may not prevent private firms from capturing most of the benefits (Le_Polain de Waroux and Lambin, 2013). Informal workers may be paid less and enjoy fewer rights and social protections than formal workers. This is largely due to the investment strategies of multinational enterprises to outsource resources from cheaper sources and remain competitive in this highly globalised world (Guha-Khasnobis et al., 2023). For Calvo-Calvo et al. (2025), GVCs have transformed employment landscapes, particularly in developing countries, leading to an increase in informal employment opportunities for women. While GVCs are creating new employment opportunities, many of these jobs are informal and unstable, often requiring workers to balance work responsibilities with family obligations (Timmer et al., 2019).

On the other hand, the study by Meagher (2019a, b) shows that drawing on examples of informal agricultural labour in South Africa and Morocco, it is clear that inclusion in GVCs does not necessarily benefit informal workers at the bottom of the chain as global connections can activate cheaper forms of labour control through intermediaries. Furthermore, many international companies and brands have developed codes and standards to eradicate the use of informal labour in GVCs, but scandals and occasional disasters highlight the current challenges (Kaplinsky and Morris, 2017). The use of informal labour in GVCs is often presented as legitimate as long as workers are free to move in and out, regardless of issues of remuneration and social protection (Meagher, 2019a, b). However, when it comes to value chains today, the issue of “unfree labour” is increasingly attracting the attention of researchers who focus on the phenomenon of “modern slavery” in capitalist export sectors. The Brass studies (2009) and the Walk Free Foundation report (2016) trace this modern slavery, which has emerged in a range of capitalist export sectors, from the Thai shrimp industry to large-scale cocoa production in West Africa. GVCs are also reshaping institutional systems and redistributing value, affecting the livelihoods of rural workers in Africa and other disadvantaged parts of the world (Meagher, 2019a, b). But to understand how inclusion in GVCs affects the vulnerable, it is necessary to focus not only on the broader livelihood context of marginalised populations in the global South but also on the processes by which GVCs reshape the local context. At the same time, formally registered companies offer better employment opportunities through formal employment contracts, legal minimum wages and other workplace regulations. Promoting formal employment in export industries can facilitate the reallocation of labour from informal to formal employment (Tanaka and Greaney, 2024).

Competition in the global market and the pressure to reduce prices and costs have had an impact on social protection. Employers have had to adapt to a more competitive environment, trade unions have been weakened and public services have been reduced (Barrientos and Barrientos, 2002). This is why some argue that GVCs are causing social tensions and economic and organisational realignments for informal workers as they reshape local work patterns, livelihoods and trading systems (Meagher, 2019a, b). The overpressure of production costs also appears to be a driver of GVCs’ promotion of the informal sector. Companies often seek to minimise their production costs in order to remain competitive on the global market (Barrientos and Barrientos, 2002). This can lead them to underpay their workers or to exploit informal workers who accept lower wages than formal workers. With regard to gender, gender disparities play an important role in the dynamics of informal employment within GVCs. Women are more exposed to informality than men in more than half of all countries (Ly, 2023). This disparity is particularly marked in sectors where women are over-represented in informal roles, such as the clothing and textile industries (Narula, 2019).

However, inclusion in GVCs may enable developing economies to specialise more in the production of low-skilled labour-intensive goods and services. This should lead to an increase in the demand for low-skilled workers in developing economies (largely employed in self-employed informal activities and/or in low-productivity informal enterprises employing salaried workers), which could lead to an improvement in their wages and working conditions or even to the creation of jobs in the formal sector. Thus, many studies have argued that GVCs could reduce the informal sector, but this depends on several factors, namely the tradability of products produced in the informal sector, the mobility of capital between the formal and informal segments, the nature and extent of links between the formal and informal sectors and the segmentation of the informal economy (Kumar, 2025). For example, several studies have shown that GVCs facilitate the transfer of technologies from developed to developing countries and subsequently the transfer of foreign technologies from leading firms integrated in formal sector GVCs to informal firms through various channels (Rodrik, 2018; Rigo, 2021; Park et al., 2023). This technology transfer can facilitate the transition of some of the most successful (dynamic) informal firms in developing economies to the formal sector and higher value-added activities (Narula, 2019). However, the increasing shift towards technology- and capital-intensive production in firms participating in GVCs is likely to lead to a decrease in the number of jobs per export unit while increasing the demand for skilled workers (Rodrik, 2018). It could also erode the comparative advantage of developing economies in low-skilled labour-intensive tasks (Rodrik, 2018). This means that the transition to formal production of the former informal units engaged in GVCs may not be accompanied by a transition of their entire workforce to formal employment (Rohit, 2023, 2025), particularly relatively low-skilled workers engaged in self-employment activities as self-employed workers and contributing family workers.

In SSA, the informal economy is deeply integrated into GVCs, particularly in sectors such as agriculture, where smallholder farmers and informal workers play a crucial role in the production and distribution of goods (Meagher, 2019a, b). In manufacturing, participation in GVCs has been associated with job creation, particularly in downstream industries (Obeng et al., 2022). However, the share of GVC-related jobs in formal manufacturing remains low, and the region’s dependence on upstream activities limits the potential for employment growth (Pahl et al., 2019). In this region, GVCs are often also characterised by low technological intensity, institutional enslavement and a predominance of informal work, including in segments integrated into global markets (Golub and Hayat, 2015). For Bosire (2023), several challenges hinder SSA’s participation in GVCs and the realisation of employment benefits. Infrastructure deficits, particularly in transport and logistics, are a major barrier to GVC integration. In addition, weak governance and institutional frameworks limit the region’s ability to attract investment and to upgrade into GVCs (Bosire, 2023).

In short, most studies on the influence of GVCs on informal employment conclude that they have a negative influence (Tanaka and Greaney, 2024). In contrast, studies such as Fukase (2013) and McCaig (2011) have examined the impact of the US-Vietnam bilateral trade agreement and found positive effects on wages and poverty reduction. The literature on the link between GVCs and informal employment is therefore limited in terms of consensus. Informality involves complex economic activities and there is no widely shared consensus on the best modelling approach to study the informal sector. However, this recent literature provides the basis for our contribution by highlighting the fact that GVCs can potentially facilitate the transition of labour from informal to formal employment in developing countries (Meagher, 2019a, b; Tanaka and Greaney, 2024; Rohit, 2025). The limitation of these studies lies in the fact that most of them use linear models. As a result, they fail to capture the level at which the regime can change. Compared with previous studies, this article has the merit of identifying the threshold at which value chains influence informal employment. Moreover, in the current context of increasing globalisation, participation in GVCs represents a major opportunity for African economies, but its effects on the quality of employment, particularly informal employment, remain little explored. Using threshold models, this study captures the non-linear and differentiated effects of GVC integration on informal employment, thereby providing a more detailed understanding of the conditions under which such participation can encourage the formalisation of the labour market.

To determine the threshold effect of GVCs on informal employment in SSA, we construct a panel of 32 Sub-Saharan African countries over a period from 1991 to 2017. Informal employment is our dependent variable, and its data come from the Shadow Economy Database developed by Medina and Schneider (2019). This global database estimates the size of the informal economy for 157 countries between 1991 and 2017 using the Multiple Indicator–Multiple Cause (MIMIC) approach. The database includes robustness tests using satellite data on the intensity of night lights as an indicator of the size of countries’ economies, which show stable and similar results.

Our main variables of interest and threshold variables are GVC indicators from the UNCTAD-Eora Global Value Chain Database developed by Casella et al. (2019). The UNCTAD-Eora GVC database offers comprehensive coverage and key indicators of GVCs for analytical purposes, providing a global perspective and time series from 1990 to 2018. GVCs indicators measure the extent to which countries participate in the fragmentation of production processes between different countries, reflecting the way in which value is added in the production of goods and services. This includes both domestic and foreign value added (FVA) in exports. The main indicators of GVCs are as follows: The indicator of participation in GVCs refers to the fragmentation of the production process between different countries, with each country specialising in a particular segment of the production chain and thus contributing to the overall value added of goods and services. In this context, the terms upstream and downstream tell us about the nature of the value added. Downstream participation measures the DVA content of third-country exports. In other words, if we consider two countries, country A (domestic) for which we estimate the downstream participation in GVCs, and country B (country of destination of A’s exports), the downstream contribution of country A is estimated by the DVA (created by country A) and exported (once processed) by the destination country (country B). It therefore corresponds to indirect DVA. Still in the context of measurement, upstream participation is measured on the basis of the content of FVA in domestic exports. It corresponds to the share of FVA in a country’s gross exports. The unit of measurement of GVCs is usually expressed in monetary terms, such as the percentage of FVA in exports or the value of exports in a specific sector or country (Casella et al., 2019). But the unit of measurement of a GVC indicator is usually expressed as a percentage of gross exports, as shown by the measures proposed by Hummels et al. (2001). Therefore, we divide each GVC indicator by gross exports, which we present as a percentage (Avom and Nguekeng, 2020).

We consider the various GVC indicators as threshold and transition variables. This choice is explained, on the one hand, by the fact that the effects of the GVCs on informal employment depend on the level of value added captured, and therefore on the level of integration in the GVCs. The more integrated you are, the greater the share of added value you capture, which in turn benefits several companies in the chain. Informality is thus reduced via the channels of technology transfer, productivity and well-being (Rodrik, 2018; Rigo, 2021; Park et al., 2023; Narula, 2019). Thus, in the case of low value added, the opportunity cost of informality for small, informal and low-productivity businesses remains high compared with formality. On the other hand, theoretically speaking, we see that it is the effects of the GVCs that appear insufficient at a certain threshold to reduce informal employment. This justifies the choice of GVCs as transition or regime variables.

In addition to our variables of interest, we identify in the literature certain control variables taken from the World Bank database. These variables are as follows: gross domestic product (GDP) per capita measures the country’s level of economic development, the unemployment rate as a percentage of the active population captures the equilibrium of the labour market, and inflation and life expectancy at birth reflect the country’s social development (Rohit, 2025; Erumban, 2024). The level of informality in a country depends negatively on the level of economic development because the more a country develops, the higher its institutional system, industry and standard of living, making it difficult to choose to work informally. Conversely, the higher the unemployment rate, the greater the incentive for individuals to enter the informal sector in order to survive. As for life expectancy at birth, a high value reflects the generally better living conditions in the country, generally offered by the formal sector.

In urban areas of SSA, informal employment is an important source of income for low-income households (Radchenko, 2017). It includes activities such as street vending, domestic work and petty trading. Nearly 83% of jobs in Africa and 85% in SSA are informal, absorbing large numbers of the continent’s young jobseekers (ILO, 2022). In the past, policy discourses in Africa have tended to neglect the informal economy or even view it as a potential threat to the formal economy, requiring its elimination and control rather than its support and investment for inclusive structural economic transformation (ILO, 2022). Globally, it is a fact that most of the working population earns its living in the vulnerable and insecure conditions of the informal economy, but it is in SSA that the share of informal employment is highest. It is estimated that informal employment accounts for around 65% of non-agricultural employment in developing countries in Asia, 51% in Latin America, 48% in North Africa and 72% in SSA (ILO, 2009). GVCs can reconfigure local institutional ecosystems, reshaping the livelihoods of informal workers. Figure 1 shows the evolution of informal employment and GVCs in SSA, while Figure 2 highlights the scatterplots of informal employment and GVCs in SSA.

Figure 1 shows the evolution of informal employment and the main indicators of GVCs in SSA over the period 1991 to 2017. From the graph we can see that informal employment has declined somewhat over the period from 1991 to 2017. The figure shows that the informal economy fell from 40% to 35% on average. However, over the same period, the FVA included in the exports of Sub-Saharan African countries remains minimal. But DVA or downstream participation is growing and appears to be double that of FVA. In Sub-Saharan African countries, it is common to find that DVA is higher than FVA in GVCs.

The fact is that SSA countries often have economies that are less integrated into GVCs than other regions. This is often due to poor infrastructure, trade barriers, burdensome regulations, skills gaps and other barriers that limit the participation of local firms in global production networks. This better explains Figure 2, which shows that the relationship between informal employment and GVCs tends to diverge over time in SSA.

The objective of this paper is to determine the threshold effect of GVCs on informal employment in SSA. To do this, we use the PSTR model of González et al. (2005), which is an extension of the Hansen (1999) fixed-effect panel threshold model (Wang, 2015). To do so, we used four threshold estimators of GVC indicators. The formulation of the threshold models (here we test for the existence of multiple thresholds) is as follows:

where $γ1$ and $γ2$ are thresholds that divide the equation into three regimes with coefficients and minimise the residual sum of squares, $β1$⁠; $β2$ and $β3$⁠. $InfEmpi,t$ is informal employment and is our dependent variable, and $GVCi,t$ $FVAi,t$ $DVAi,t$ $DVXi,t$ are the threshold variables and correspond to the index of participation in GVCs, FVA, DVA, indirect value added, respectively. $ui$ is the individual effect, while $ei,t$ is the disturbance. Like the Hansen (1999) model, the threshold effect test the PSTR of González et al. (2005) assumes that the covariates are strongly exogenous for the estimator to be consistent.

Thus, the model has been extended to the dynamic panel model with a potentially endogenous threshold variable as proposed by Seo and Shin (2016). It is very plausible that our threshold variables, GCVs, also depend on the level of informal employment. For example, countries with a very small informal sector have very high levels of GCVs since their economies are essentially made up of formal businesses that are recognised as being very productive, with facilities for international collaboration and partnership, and benefiting from certain public programs and subsidies. The same applies to certain control variables, such as GDP, which also depends on the level of informality. We use the lagged variables of GCVs and GDP to correct for endogeneity bias in line with Seo and Shin (2016) who use the same procedure. Indeed, various applications of Hansen’s fixed-effect estimation can benefit from dynamic modelling, which is why we use this second estimation to examine the robustness of our analyses. The dynamic panel threshold model is as follows:

where $xi,t´$ can include lagged dependent variables and $GVCi,t$ $FVAi,t$ $DVAi,t$ $DVXi,t$ are the threshold variables. In this model we assume that T is fixed, while size increases to infinity. $InfEmpi,t$ is informal employment, and $ei,t$ consists of the components of the error.

where $αi$ is an unobserved individual fixed effect and $vi,t$ is an idiosyncratic random disturbance with zero mean.

The purpose of this paper is to determine the threshold at which GVCs influence informal employment in SSA. To do this, we divide this section into two sub-sections. In the first part we present the descriptive statistics and the correlation table, and in the second part we present threshold estimates.

Table 1 presents the characteristics of individual and temporal inference for a sample of 18 Sub-Saharan African countries. For each variable in the model, we indicate the mean, standard deviation, minimum and maximum. The aim is to show the main characteristics of the variables used in the model.

According to Table 1, the average size of the informal economy is 38.39%. This value varies between 17.8%, which is the minimum value, and 67%, which is the maximum value. The Global Economic Prospects report (2019) shows that, on average, informality in SSA is higher in low-income countries, fragile states, West and East Africa and commodity exporters. With regard to GVC indicators, the index of participation in GVCs is 0.042% in SSA, broken down into downstream participation, which is 0.026% on average, and upstream participation, which is 0.016%. We can therefore see that, on average, DVA is higher than the FVA of exports in SSA. Some authors have argued that resource-rich countries, such as many Sub-Saharan African countries, may be subject to a “resource curse” (Henri, 2019). This means that natural resource wealth can discourage the development of other economic sectors, particularly manufacturing, as economies can become too dependent on raw material exports. This limits FVA in GVCs as the transformation of these resources is often not carried out locally. Table 2 shows the correlation matrix between the variables in the model.

Table 2 presents the correlation at the 5% significance level between the different variables in the model. According to the results, all GVC indicators are negatively associated with informal employment in SSA. The fact that informal employment is negatively associated with GVCs in SSA can be explained by low productivity. Informal jobs often have lower productivity than formal jobs as informal workers may lack access to vocational training, technology and modern infrastructure. This can lead to inefficiencies in production and reduced competitiveness of businesses in GVCs.

Table 3 presents the threshold effect test. We test the null hypothesis that there is no threshold against the alternative hypothesis that there is at least one threshold above which the index of participation in GVCs influences informal employment in SSA.

The results in Table 3 show that there is at least one threshold, so we reject the null hypothesis. We repeat the test in the context of the existence of a double threshold. According to the results of this second estimation, the Fischer statistic is significant at 5% for the first threshold and we find an absence of significance for the second threshold.

We performed the same tests for downstream participation. Our results point to the existence of a threshold above which it influences informal employment in SSA. On the other hand, for upstream participation and DVA, our results lead us to accept the null hypothesis that there is no threshold above which it could have any influence on informal employment in SSA. The fact is that many informal workers in SSA depend on their activity to meet the basic needs of their families. Even if formal employment opportunities are created, these workers may not be able to seize them immediately due to constraints such as location, skills or personal preferences. Thus, the additional DVA in the formal sector may not necessarily reduce informal employment in the short term. Table 4 presents the results of the threshold estimator and the confidence intervals for the index of participation in GVCs and indirect value added.

By taking the index of global GVCs as a threshold parameter, we realise that there is only one threshold above which it has an influence on informal employment in SSA. This threshold is 0.18% and varies between [0.16 and 0.19%]. With regard to downstream participation or indirect value added, we also find that there is a threshold above which national value added contained in exports from other countries influences informal employment in SSA. This threshold is 0.0117%, with a confidence interval of [0.0108 and 0.117%]. The low level of these threshold values reflects the proportion of GVCs participation relative to total exports, not relative to the economy as a whole. Given the low levels of GVCs integration in SSA, even minor increases above these thresholds may reflect structural changes in labour allocation or integration into higher value-added production segments.

However, the absence of threshold effects for upstream participation (FVA) and DVA is in line with the dualism and fragmentation theories present in the literature. Indeed, upstream participation often involves the export of raw materials or intermediate inputs, which are activities generally carried out by large formal firms with limited spillover effects for informal workers. Similarly, DVA may reflect localised processing or assembly, which does not necessarily affect informal labour markets unless these processes are integrated into competitive, export-oriented sectors with formal labour structures. Table 5 presents the regression results of the PSTR model for the index of participation in GVCs and indirect value added.

The results show that when the index of participation in GVCs reaches the threshold of 0.18%, life expectancy at birth, the unemployment rate and FVA have a negative influence on informal employment in SSA. The fact that, at this threshold, the unemployment rate has a negative influence on informal employment can be explained by the imbalance in the labour force. Integration into GVCs often leads to a redistribution of labour towards formal sectors, which are often more competitive and profitable (Rodrik, 2018). This leaves informal workers unemployed as companies prefer to hire a formal workforce to meet the demands of GVCs for quality and labour standards. On the other hand, we also find that when this index reaches the 0.18% threshold, DVA, indirect value added and inflation positively influence informal employment in SSA.

As for the second model, the results in Table 4 show that there is a single threshold above which indirect value-added influences informal employment. Thus, when downstream participation in GVCs reaches the threshold of 0.0117%, DVA, FVA, life expectancy at birth and the unemployment rate have a negative influence on informal employment in SSA. On the other hand, at the same threshold, the index of participation in GVCs and inflation have a positive influence on informal employment in SSA. However, the negative effect of life expectancy at birth on informal employment in SSA at a given threshold of downstream participation in GVCs can be explained by several socio-economic and demographic factors specific to the region. These include increased demographic pressure. In many Sub-Saharan African countries, the population is growing rapidly, putting pressure on available resources and the labour market (Sulemana et al., 2019). Higher life expectancy at birth exacerbates this pressure by increasing the size of the labour force, making it more difficult for the economy to create enough formal jobs to absorb this excess labour.

In the case of dynamic regression, Table 6 presents the analytical results of the dynamic panel threshold model proposed by Seo and Shin (2016) between informal employment and two GVC indicators (indirect value added and GVC participation index) in SSA.

With regard to the first model, although the Fisher statistic shows the existence of a single threshold above which indirect value-added influences informal employment in SSA, the results of the dynamic threshold regression show that this threshold is not significant. This test has a P value above the 1%, 5% and 10% thresholds (0.194). It is easy for us to validate the hypothesis of linearity between the two variables. However, the inconsistency between the static and dynamic models, in particular the loss of significance for downstream participation in the dynamic framework, can be explained by the fact that the dynamic model corrects for autocorrelation. Thus, the lag structure and data limitations may also dilute the apparent impact of the threshold. On the other hand, downstream participation may have only short-term effects, captured by the static PSTR model, but lose its effect when previous levels of informality and macroeconomic conditions are taken into account in the dynamic model. For example, participation in GVCs may initially increase informality through subcontracting and then reduce it as companies formalise to meet global standards. This process is gradual and trajectory-dependent, suggesting time lags that could explain the weaker results of the dynamic model.

The second model aims to determine the threshold at which the index of participation in GVCs influences informal employment in SSA. The results obtained after estimation allow us to verify the presence of non-linearity between informal employment and GVCs. Table 6 shows that the P-value is 0.001 and therefore the threshold is 0.04%. This test has a P-value below the 1%, 5% and 10% thresholds. It is easy for us to validate the hypothesis of non-linearity between the two variables.

In the first regime we find that FVA and life expectancy at birth negatively influence informal employment in SSA. On the other hand, the unemployment rate and per capita income have a negative influence on informal employment. This positive effect of the unemployment rate on informal employment explains the region’s economic survival. In many Sub-Saharan African economies, informal employment is often a matter of economic survival for many people. Individuals are in most cases forced to engage in informal activities such as street trading, small-scale craft production or domestic services to meet their basic needs, in the absence of other employment opportunities (World Bank, 2018). The fact that unemployment has a negative coefficient in several regressions (i.e. a lower unemployment rate correlates with greater informality) could reflect the fact that the informal sector absorbs underutilised labour, a feature consistent with Lewis’s two-sector model.

The second regime shows upstream participation in GVCs positively influences informal employment in SSA. Indeed, the fact that FVA has a positive effect on informal employment results from the stimulation of demand for local goods and services. In the same regime, the unemployment rate and indirect value added have a negative influence.

The objective of this paper is to determine the threshold effect of GVCs on informal employment in SSA. Based on the theory of structural dualism, which postulates a non-linear evolution of economic structure and informal employment as countries develop and integrate into GVCs, we use threshold models. On the one hand, GVCs can facilitate the transition of labour from informal employment to employment via transfers of technology, managerial practices and productivity. On the other hand, GCV are likely to increase informal employment through subcontracting, relocation and international competition. Using data from Casella et al. (2019), we collect three indicators of GVCs namely: FVA, DVA and the index of participation in GVCs. Based on the seminal study by Hummels et al. (2001), we divide these indicators by gross exports and multiply by 100. We then construct the index of participation in GVCs as the sum of FVA and indirect value added, divided by gross exports and multiplied by 100. The threshold effect of GVCs on informal employment is estimated first using the PSTR fixed threshold model by González et al. (2005).

The threshold effect test shows that only the index of participation in GVCs and indirect value added have a significant influence on informal employment. This test shows that for both indicators there is a single threshold above which these indicators influence informal employment in SSA. For the index of participation in GVCs, this threshold is 0.18% and varies between [0.16 and 0.19%], and for indirect value added (downstream participation), the threshold is 0.0117% and its confidence interval is [0.0108 and 0.117%]. In SSA, where many countries are heavily dependent on the primary and extractive sectors, upstream participation in a value chain may not be as closely linked to the formalisation of employment as downstream participation. This could explain why the downstream participation threshold has an influence on informal employment, while upstream participation does not. The robustness check analysis with dynamic threshold estimation shows that downstream participation does not influence informal employment in SSA. Furthermore, the dynamic model shows that there is a threshold, albeit very low, for the overall GVC index equal to 0.04%.

At least two economic policy implications emerge from this study. Firstly, Sub-Saharan African countries need to focus more on DVA by promoting processing sectors such as manufacturing and industry. Indeed, the share of FVA, which greatly benefits the industrialised countries, must be reduced as much as possible. The threshold of 0.0117% means that we should not set ourselves unrealistic industrialisation targets, but rather objective policies aimed at transforming production systems. Secondly, the fact that life expectancy at birth is increasing informality means that we need to promote a regulatory framework for the labour market that allows young people to enter and express themselves. Without an intergenerational rotation mechanism in the labour market, longer life expectancy at birth increases the size of the active population, making it more difficult to create enough formal jobs to absorb the young workforce.

However, while it is relevant to look for the threshold at which value chains influence informal employment, it must be recognised that it is difficult to address this issue as a whole without taking into account the impact of informal employment on poverty, informality in the agricultural sector or the share of informal non-agricultural employment. Future research could, for example, disaggregate by sector, examine the gender impacts of informality in GVCs, evaluate national case studies of formalisation induced by GVCs and explore the role of technology in reshaping informal work (e.g. platform economies).