Marize Kalliny
email: marize.kalliny@feps.edu.eg

Chahir Zaki
email: zaki@feps.edu

Are Global Value Chains Women Friendly in Developing Countries? Evidence from Firm-Level Data

Keywords: JEL Classification : F12, F16, F23, J16 Global Value Chains, Gender, Empowerment, Firm-level, MENA

Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is

Introduction

Women have always played a significant and pivotal role in the economy through their remarkable presence in business, agriculture, industry and even through their unpaid care work. Despite the efforts made to increase women's inclusion in the economy, around 2.4 billion women do not have access to equal economic opportunities as men according to the World Bank's Women, Business and the Law report (2022). Furthermore, based on a report published on women by the United Nations Development Programme (UNDP), women perform 66% of the world's work, produce 50% of the food, but earn only 10% of the income. In addition, different stages of the production process are increasingly being fragmented across different countries through global value chains (GVCs), especially in emerging economies. For this reason, this study attempts to assess the impact of GVCs on women's empowerment in international trade. Indeed, in recent years, the world has witnessed a growing interest in the impact of globalization on economic patterns in general and gender equality in particular. A significant focus has been on the gender impact of GVCs as they have proven their power to generate employment, drive development, and increase income. These value chains can help women by providing more income that can support their economic empowerment but can also downgrade them to poorly paid and undervalued jobs. Hence, considering gender issues and addressing them is critical in order to take advantage of the potential of GVCs, which, in turn, guarantees a better achievement of sustainable economic and social goals by 2030.

In the previous literature, the relation between women's empowerment and international trade was ambiguous. The first strand of studies was in line with the neoclassical theory based on [START_REF] Becker | The economics of discrimination[END_REF] and according to which discrimination is costly. Hence, the increase of industry competitiveness due to trade participation reduces the incentives to discriminate against women especially in concentrated industries than in competitive ones. For instance, [START_REF] Boler | Globalization: a woman's best friend? Exporters and the gender wage gap[END_REF] use matched employer-employee data from the Norwegian manufacturing sector and prove that trade participation has narrowed the gender wage gap in exporting firms relative to non-exporters. Similarly, [START_REF] Juhn | Men, women, and machines: How trade impacts gender inequality[END_REF] and [START_REF] Black | Importing equality? The impact of globalization on gender discrimination[END_REF] reach the same conclusion using various datasets. Nevertheless, contradictory results have been proven in other studies. For instance, in a study based in India, Taiwan, and China, [START_REF] Berik | International trade and gender wage discrimination: Evidence from East Asia[END_REF] show that competition resulting from international trade increases wage discrimination against female workers, which does not go in line with the neoclassical theory. Moreover, using difference-in-difference estimation and data from the Demographic Census for 1991 and 2000 fielded by the Brazilian Census Bureau, [START_REF] Gaddis | The gendered labor market impacts of trade liberalization evidence from Brazil[END_REF] show that trade liberalization decreased the male and female labor force participation rate. The effect is significantly larger on men, which means that liberalization reduced the gender gap in employment and participation rates. Nevertheless, the results show no evidence that women got any benefit from the competitive effects of liberalization as their employment and participation rate did not increase relative to those of men. The literature also shows that the limited or negative employment effect was due to other concomitant factors such as the effect of anti-sweatshop activism [START_REF] Harrison | Multinationals and anti-sweatshop activism[END_REF] and the constitution of minimum wage for export tariff privileges [START_REF] Del Carpio | Do minimum wages affect employment? Evidence from the manufacturing sector in Indonesia[END_REF]. By studying the opposite dimension of the subject, [START_REF] Karam | On women participation and empowerment in international trade: Impact on trade margins in the MENA region[END_REF] argue that female labor participation in the MENA region has a positive significant impact on both trade margins. Furthermore, their results show that female ownership positively affects the probability of exports of large firms.

Second, we analyze how this effect is moderated through external (gender provisions in trade agreements) and internal (investment climate variables) factors. To do so, we use firm-level data for 154 developing economies and emerging markets with a special focus on the Middle East and North Africa region. Our main findings show that GVC integration increases the likelihood of being a female owner and the share of female employees, especially production ones. A less robust negative effect is found regarding the impact on being a female top manager. These effects are moderated by the inclusion of gender provisions in trade agreements and by the characteristics of the investment climate (especially tax policy, courts, access to finance and corruption). These results remain robust after we control for the endogeneity of GVC using an instrumental variable approach and a propensity score estimation method where the treatment is being part of a GVC. Thus, GVCs can be perceived as a tool that boosts women's empowerment in emerging economies, especially in the MENA region.

The remainder of the paper is organized as follows. Section 2 presents the data we use and some stylized facts on GVCs and women's participation. Section 3 is dedicated to the methodology and the econometric specification. Section 4 presents the empirical results. Section 5 provides robustness checks. Section 6 concludes and provides some policy implications to increase women's employment through trade and GVC channels.

Data and Stylized Facts

To explore the nexus between firms' integration into GVCs and women's trade participation as entrepreneurs and employees, firm-level pooled data from the WBES is used. These surveys cover a broad range of business environment topics such as access to finance, trade, corruption, competition, and infrastructure for 143,598 firms in 154 developing economies and emerging markets. The manufacturing and services sectors are the primary business of interest in these surveys conducted in a range of time that varies from 2006 to 2021 (see Appendix 1). The objective of this section is to provide some descriptive statistics related to the nexus between GVCs and women's participation.

To define GVCs, we follow the definition of [START_REF] Dovis | Global Value Chains and Local Business Environments: Which Factors Really Matter in Developing Countries?[END_REF] where the least strict definition includes firms that export and import simultaneously (GVC1). Second, two stricter definitions are related to firms who are simultaneously exporters and importers and have either an international certification (GVC2) or a share of its capital owned by a foreign firm (GVC3). The strictest definition combines the four criteria altogether (GVC4). This variable is a dummy variable that takes a value of one if the firm is part of a GVC and zero otherwise. Based on these definitions, around one-third of firms are part of GVCs with most of them being two-way traders and only 2.3% that are two-way trader, have a foreign capital and an international certification (see Table A1 in Appendix 3). It is important to note that these two definitions help us measure GVC participation at the extensive margin level, not the intensive margin one. At the regional level (see Figure A1 in Appendix 3), Europe and Central Asia (ECA) is the most integrated region in GVCs (for all definitions) and South Asia the least integrated one. The MENA region, while being slightly better than South Asia, is still far from other top performers such as ECA and Latin American and the Caribbean. This confirms previous findings of the literature on GVCs in the MENA region that is characterized by an unfriendly business climate [START_REF] Dovis | Global Value Chains and Local Business Environments: Which Factors Really Matter in Developing Countries?[END_REF], political connections [START_REF] Kruse | Standards and political connections: Evidence from Tunisia[END_REF]Aboushady and[START_REF] Aboushady | Assessing EU-Middle East trade relations: Patterns, policies and imbalances[END_REF] and the presence of different trade barriers (whether tariffs or non-tariff measures, see [START_REF] Karam | On women participation and empowerment in international trade: Impact on trade margins in the MENA region[END_REF]).

As it was mentioned before, this paper attempts to examine the nexus between women's labor participation and GVCs integration. LAC has the highest share of full-time female employees followed by EAP and ECA and on the other extreme South Asia and the MENA region. In addition, for all the regions production workers are much higher than non-production ones (see Table A2 in Appendix 3). While the former are mainly working in the manufacturing sector, the latter are in the services one. This is confirmed by Table 1 that shows that being integrated in a GVC is positively associated to a larger number of females, whether production or non-production workers but more production ones. This result applies to the different GVCs definitions bearing in mind that for the most restrictive definition, the results are also driven by the firm size (as larger firms are more likely to be part of GVCs). Such a positive association is rather good news given that GVCs should mainly take place in the manufacturing sector, which is likely to create more jobs for female production workers (blue collars) that are abundant in emerging economies. Constructed by the authors using the WBES. Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

Yet, it is important to look at women's participation from a broader lens by taking into consideration, not only female workers, but whether the owner or the manager of the firm are females. These two measures can give a clearer picture of women's empowerment as they are associated to more female power within the firm. Thus, Figure 1 compares firms that are part of GVCs whose owner or manager are women. Three remarks are worth mentioning. First, generally, the share of firms that are owned or managed by females is limited, compared to those owned or managed by males. Second, females that own a firm that is part of a GVC are higher than those who just manage it. This is closely related to the concept of empowerment as it captures the effect on women's empowerment more than management given that the manager is, at the end of the day, an employee in the firm that takes orders from the owner, while the owner is an entrepreneur that takes financial and business risks on his/her own [START_REF] Karam | On women participation and empowerment in international trade: Impact on trade margins in the MENA region[END_REF]. Third, these shares decrease with more restrictive definition of GVCs (for ownership it decreased from 36% to 26% of firms and for management from 13% to 12%).

Figure 1. Share of Female Ownership and Management in Firms integrating into GVCs

Source: Constructed by the authors using the WBES. Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

When we look at the relationship between female participation, firm size, and GVC integration, two remarks are worthwhile. First, for large firms and the strict definition of GVC, females (whether they are owners or managers) tend to be under-represented. Second, generally, for less restrictive definitions of GVC, we do not observe significant differences between small, medium, and large firms when it comes to female ownership and management. As for regional differences, Figure 3 shows that females are doing better in terms of ownership compared to management when it comes to GVC integration. Indeed, for all GVC definitions in different regions, the share of firms that are part of GVCs and that are owned by females is greater than the one of firms managed by females with a slightly lower figures for the most restrictive definition (GVC4) as it is shown in Figure 2. In addition, for the most restrictive definition, EAP followed by SAR are the best performers whereas MENA and LAC are the worst in terms of female ownership and management (see Figure 3). This is due to the low labor cost that helps develop value chains, especially in the textile and garments sector [START_REF] Kumar | Global Value Chains: a way to create more, better and inclusive jobs[END_REF]. Indeed, South Asia has the second-highest level of GVC exports out of total exports among developing regions, chiefly thanks to final and intermediate apparel products [START_REF] Lopez-Acevedo | South Asia's Turn: Policies to Boost Competitiveness and Create the Next Export Powerhouse[END_REF]. This is why, as it was mentioned before, the MENA region's problem of women's participation is also reflected in their integration into GVCs as owners or managers. Source: Constructed by the authors using the WBES. Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether. We argue that the effect of GVC on women's participation is moderated through internal and external factors. Generally, both factors might be gender blind as rules and regulations (to start a business or in trade agreements) do not have a gender lens (see Figure 4). However, their impact differs across individuals, and must be either gender mainstreamed or gender focused to improve women economic empowerment in a more explicit way. This is the case of gender provisions in trade agreements or rules that might be more gender-friendly. At the internal level, Table 2 presents the share of firms reporting each variable as the biggest obstacle. A large heterogeneity is observed across different regions. For instance, while firms report that access to finance is a major obstacle in Sub-Saharan Africa, those in the MENA region complain mainly about business licensing, permits, corruption, and political instability. In Latin America, business and licensing, crimes and disorder, customs and trade regulations, and practices of the informal sector seem to be major obstacles. Finally, in Europe and Central Asia, problems related to inadequately educated workforce, labor regulations, tax rate, and administration are onerous. Obviously, such problems that affect the business environment might also hinder women's participation. This is why we take different obstacles into consideration to see how they can amplify the impact of GVC on women's participation. When the gender dimension is considered, some obstacles turn to be more impeding than others. As such barriers are primarily faced by owners and managers, we do not include in this part female employees. Thus, Figure 5 shows that access to finance, tax rates, practices from informal competitors, political instability, inadequately educated labor force and corruption are the highest obstacles for firms that either owned or managed by women. In addition, electricity turns to be impeding for firms that are owned by women. This is why, we are going to focus only on these barriers in the empirical part, as it will be shown later. 4 Source: Constructed by the authors using the WBES. 4 The results of other barriers are available upon request. In addition, at the external level, having a gender provision in an RTA that takes into account gender issues and include an explicit mention of gender, sex, women, girls, the international instruments promoting women's rights (such as the Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW), the Beijing Declaration and Platform for Action for the Rights of Women and Girls, the Buenos Aires Declaration on Trade and Women's Economic Empowerment or the UN Sustainable Development Goal 5 on gender equality (SDG 5)) can increase women's participation, and thus might amplify the positive effect of GVC. Figure 6 shows that the number of trade agreements including such provisions has been increasing significantly since 2000. Yet, it is important to note that, while the inclusion of such provisions is necessary, it might not be sufficient with a weak enforcement.

After presenting these different stylized facts, the next section provides empirical evidence on the association between women's participation and GVCs integration.

Methodology

Using the WBES, we examine the impact of firms' integration into GVCs on women's empowerment as follows:

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝐺𝐺𝐺𝐺𝐺𝐺 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝜆𝜆 + 𝑋𝑋 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝜌𝜌 + 𝜁𝜁 𝑖𝑖 + 𝛾𝛾 𝑖𝑖 + 𝜃𝜃 𝑖𝑖 +𝛿𝛿 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ( 1
)
Where Female is measured by three variables: first, whether the top manager of the firm is female (dummy variable equals to one and zero otherwise); second, whether the owner of the firm is female (dummy variable equals to one and zero otherwise); third, the number of full-time female employees (we also distinguish between the proportion of permanent full-time female production and non-production workers). GVC is measured using several dimensions: export status, import status, international certification, and type of ownership, as it has been explained before. The first definition (GVC1) is the most lenient as it encloses firms that are exporters and importers at the same time. The second definition (GVC4) is stricter as it combines the four dimensions together: firms that are simultaneously exporters and importers, that also have an international certification and a foreign ownership of its capital [START_REF] Dovis | Global Value Chains and Local Business Environments: Which Factors Really Matter in Developing Countries?[END_REF]. The subscripts i, j, c, g and t denote firms, sector, country, region and year respectively.

𝑋𝑋 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is a vector that includes firms' characteristics that are expected to affect female entrepreneurs and women's participation in the workforce of the firm such as firms' age, size, share of government ownership and city of operation. 𝐹𝐹𝑎𝑎𝐹𝐹 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the difference between the survey year and the year in which the establishment began operation. 𝑎𝑎𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the share of government ownership that is likely to attract women given that public employment is more women friendly than private one. 𝐹𝐹𝐹𝐹𝑚𝑚𝑔𝑔𝑚𝑚𝑚𝑚𝑚𝑚𝑦𝑦 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is a dummy variable that takes the value 1 if the firm is operating in the main business city and is expected to positively affect women's participation due to agglomeration economies. 𝑠𝑠𝑚𝑚𝑠𝑠𝐹𝐹 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is a categorical variable that takes the value 1 for small firms, 2 for medium firms and 3 for large ones. Large firms get advantage of their size and engage in economies of scale which, in turn, allows them to enjoy lower costs of production and might hire more women. Appendix 2 summarizes the definition of different variables.

Given that we pool data for different countries and years, we include year, country, and sector fixed effects (𝜁𝜁 𝑖𝑖 , 𝛾𝛾 𝑖𝑖 and 𝜃𝜃 𝑖𝑖 ) to control for unobservables. 𝛿𝛿 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the disturbance term. Our estimations are run using a pooled Ordinary Least Squares (OLS) estimation method (when the dependent variable is a continuous variable, namely share of females' workers) and a Linear Probability Model (when the dependent variable is binary, which is the case of a female manager or a female owner).

We extend the analysis in three ways. First, we investigate whether the impact of firms' integration into GVCs on female participation is conditional on some external factors such as gender provisions in RTAs. The literature on the efficiency of gender provisions in regional trade agreements (RTAs) is relatively scarce. Hence, there is little empirical evidence that supports the idea that gender-related provisions or labor provisions with clauses related to gender equality significantly promote gender equality and women's empowerment in the workplace. In that vein, López Mourelo and Samaan (2018) run a difference-in-differences model using the Cambodian Socioeconomic Survey (CSES) conducted by the National Institute of Statistics of the Ministry of Planning over the period 1993-2012 in order to assess the average effect of the 1999 Cambodia-US Bilateral Textile Agreement (CUSBTA) on the gender wage gap. The CUSBTA encloses labor provisions, which mainly aim to improve working conditions through two main pillars. First, the elimination of discrimination between employees and especially those based on gender basis. Second, the reduction of gender-wage gap. The results of the study show that labor provisions decrease gender wage gap and gender discrimination in workplace only in the textile sector. However, the gap is still increasing in the other manufacturing sectors that are not concerned by these provisions. Furthermore, the impact of these provisions is proven to be significant and important only during the period of the agreement and while the International Labor Organization (ILO) is monitoring the proper implementation of these provisions. However, the impact of the provisions starts to decrease during the post-agreement period. In our paper, the impact of gender related provisions in RTAs is studied using the World Trade Organization (WTO) database that compiles provisions related to women's empowerment and gender equality. In this dataset, provisions are filtered by parties, date of signature, date of entry into force and the type of gender issues they address.

The second extension pertains to internal factors measured by the investment climate variables. Indeed, females might face more barriers because of discrimination in access to finance, access to land, etc. This is why we make use of firms' own perception regarding the main obstacles affecting their operation by identifying each problem as the main obstacle. As it was mentioned before, we focus on the most impeding barriers, namely, access to finance, tax rates, practices from informal competitors, political instability, inadequately educated labor force, corruption, and electricity. These obstacles are expected to have a negative impact on women's engagement in international trade as well as their integration into GVCs ( ). Since the time dimension in the WBES is very weak, the inclusion of firm-level fixed effects will not be possible. Therefore, following Dovis and Zaki (2020), firms' perception about obstacles to doing business is calculated using industry, country, and year averages minus firms' own responses to control for endogeneity.

The third extension checks the robustness of our results in two ways. First, a Propensity Score Matching (PSM) model that solves for endogeneity is estimated. This method assumes the conditional exogeneity of the treatment (GVCs participation in our case) or the selection on the observables only as it consists of finding a proper counterfactual group by matching a firm participating into GVCs with a non-participant firm with similar pre-intervention characteristics.

The average treatment effect (ATE) will be estimated as follows:

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝐸𝐸(𝑌𝑌 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 / 𝑇𝑇 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 1, 𝑋𝑋 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ) -𝐸𝐸(𝑌𝑌 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 / 𝑇𝑇 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 0, 𝑋𝑋 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ) (2)
where 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the outcome measured using four dimensions: if the firm's owner is female, if the top manager is female, the number of full-time female employees and the proportion of permanent full-time female production and non-production workers. T=1 if the firm is receiving the treatment (participating in GVCs in our case) and 0 if not. The vector 𝑋𝑋 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 represents the observables that are similar between the treatment and control group such as firm's age, size, share of government ownership and city of operation in addition to the dummies mentioned before.

Yet, as PSM is based on observables, the endogeneity that is due to unobservables is not controlled for. This is we rely on an Instrumental Variable approach (IV) to control for the endogenous characteristics of firms' integration into GVC. The IV must satisfy two main criteria. First, it must be highly correlated with the endogenous variable (GVC in our case). Second, it should not be correlated with the error term and does not affect women's participation directly. Following Dovis and Zaki (2020), a shift-share of GVC aggregated by country-year-sector-geographical zone (where the firm is located) minus the firm's own performance is used as an instrument. GVC corrected from individual firm idiosyncrasies is expected to affect firms' trade performance without having any direct impact on women's participation.

The endogeneity problem is tackled following a Two-Stage Least Squares (2SLS) technique. The first stage predicts GVCs as follows:

𝐺𝐺𝐺𝐺𝐺𝐺 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = Ln(𝐹𝐹𝑎𝑎𝐹𝐹) 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝛼𝛼 + Ln(𝑎𝑎𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔) 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝛽𝛽 + 𝐹𝐹𝐹𝐹𝑚𝑚𝑔𝑔𝑚𝑚𝑚𝑚𝑚𝑚𝑦𝑦 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝜏𝜏 + 𝑠𝑠𝑚𝑚𝑠𝑠𝐹𝐹 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝜉𝜉 + 𝑠𝑠ℎ𝑚𝑚𝑖𝑖𝑚𝑚_𝑠𝑠ℎ𝐹𝐹𝑎𝑎𝐹𝐹_𝐺𝐺𝐺𝐺𝐺𝐺 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝜐𝜐 + 𝜁𝜁 𝑖𝑖 + 𝛾𝛾 𝑖𝑖 + 𝜃𝜃 𝑖𝑖 +∈ 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 (3)
Where 𝑠𝑠ℎ𝑚𝑚𝑖𝑖𝑚𝑚_𝑠𝑠ℎ𝐹𝐹𝑎𝑎𝐹𝐹_𝐺𝐺𝐺𝐺𝐺𝐺 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is a shift share of firms' integration into GVCs calculated using industry, country, year, and geographical zone averages minus firms' own performance. ∈ 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the error term. Different tests are performed to assess the validity and the strength of the instruments.5

Empirical Results

Our empirical analysis focuses on women's participation measured by three variables: whether the owner is a female, whether the manager is a female and the share of females in the total number of workers. For each variable, we will run two different sets of regressions for two definitions of GVCs (GVC1 and GVC4): first, we run the regressions for all the regions (including the MENA region) as it is shown in Table 3a). Second, we run the same regressions but for the MENA region only (see Table 3b).

The extensions mentioned above are presented as follows: first, we show how GVC impact on women's participation is moderated by gender provisions in trade agreements (in Table 4). Second, we examine how GVC impact is moderated by some internal factors that affect the investment climate (for all regions in Table 5); and for the MENA region in Table 6. Finally, we control for the endogeneity of GVCs using a PSM approach (Table 7) and an IV estimation method (Table 8).

Gender and GVCs 6

Table 3 presents our baseline regression. Regarding our control variables, larger, older firms, located in the main city and having a higher share of government ownership are more likely to be owned by females for all the regions (Table 3a) and the MENA region (Table 3b). Larger firms are generally better performing, listed and might have a diverse board. Li and Chen (2018), using a panel data from listed non-financial firms in China, find that gender diversity in the board has a positive impact on firm performance. Similar results are confirmed by Said et al. (2021) for Egyptian firms. As per the sector of operation, a higher share of government ownership increases the likelihood of female ownership. Generally, the public sector remains a larger employer of women than the private one. In addition, females also enjoy a higher wage premium in the public sector compared to those employed in the private sector.

As per our variable of interest, Tables 3a (overall) and 3b (MENA region) shows that the least restrictive definition of GVC is positively associated with a higher probability of having a women as the owner of the firm or with the share of female employees (especially production ones) whereas a deeper integration into GVC (GVC4) reduces the probability of female ownership. In addition, GVC integration exerts a negative impact on the likelihood of a women being the top manager of the firm (for GVC1) and an insignificant impact with the stricter definition (GVC4). Three remarks are worth mentioning. First, there are fundamental differences between female owner and manager given that an owner physically owns the business, while the manager is an employee of the business and works for the owner. In addition, the owner is more affected by 14 profits and losses, while the manager earns a salary and is not affected by external conditions or fluctuating sales. Management is also operational in the sense that it is concerned with the ongoing activities of the business [START_REF] Woods | Owner-managers and the practice of strategic management[END_REF]. Thus, from an empowerment perspective, being an owner gives more power to a woman, compared to the management position. This is why our results show that GVC might be associated to more empowerment given the positive effect on female ownership, whereas the one for top management is negative.

Second, GVC can also improve women's participation as it increases the share of female employees. While this is rather good news, this result must be cautiously analyzed as we do not measure the quality of jobs associated to these GVC. For instance, on 24 April 2013, the collapse of the Rana Plaza building in Dhaka, Bangladesh, raised several issues regarding the supply chain of garments and the working conditions associated to them (Koenig and Poncet, 2022). Indeed, the collapse of this building that housed five garment factories killed more than a thousand people and injured more than 2,500. This is why, while GVC can improve job creation (Kumar, 2017), job quality is still questionable.

Third, most of the emerging economies are abundant in blue collar or production employees. In addition, most of the sectors where they have a comparative advantage (processed food, textile, ready-made garments) are intensive in blue collar workers. Thus, the positive effect of GVC on female production workers can help address the Sustainable Development Goal number five (promoting gender equality) by increasing female labor force participation, especially for production ones. This result corroborates the results of Guha-Khasnobis et al. ( 2022) who find that, in India, stronger forward linkage has created employment opportunities for the unskilled workers. In the same vein, [START_REF] Kumar | Global Value Chains: a way to create more, better and inclusive jobs[END_REF] show that lower skilled, young, female workers account for the largest share of jobs that are created in labor-intensive value chains (especially, apparel, footwear, and electronics).

When the regressions are run separately for the MENA region (Table 3b), the positive effects on female owners and female employees are confirmed but with higher magnitudes, especially for production workers. This is in line with the findings of Aboushady and Zaki (2021a) who argue that exports and innovation in core production techniques increase the demand for skilled production (blue-collar) workers in the manufacturing sector rather than non-production workers (white collars). However, a major problem in the MENA region is limited employability and skill shortages in blue-collar workers. Additionally, female workers are concentrated in low valueadded sectors and in the informal sector. Therefore, inclusive trade policy that promotes GVCs needs to be coupled by public private cooperation to enhance vocational training and improve the skills of blue-collars, especially women (Aboushady and Zaki, 2021b). This will help address the low female labor force participation that has been well-documented in the literature on the MENA region (Assaad and Artz, 2005 and Assaad and Krafft, 2015, Assaad and Boughazala, 2018). Considering the sectoral activity of firms, the regressions for all the regions (Table A4a in the appendix) and for the MENA region (Table A4b) show that firms operating in the textile and garments sector are more likely to have a female owner and manager as well as a higher number of female production workers. Moreover, female employees are hired intensively in firms operating in leather sector. In the MENA, textile and leather seem to be the most female intensive, while the other sectors have a negative bias against women. These results are in line with the findings of Frederick et al. (2022) who argue that female employment is highest in apparel manufacturing sector. They find that textiles and leather sectors are the most important employer of women across developing countries as the percent of all female employment working in textiles and leather ranges from 2% in the Arab Republic of Egypt to 16% in Cambodia. As for the interaction with GVCs, textile and leather sectors integrating into GVCs hire more women and are likely to have a female owner or manager. This can be due to the fact that the increase of industry competitiveness (especially that several emerging countries have a comparative advantage in these sectors) due to trade participation reduces the incentives of these firms to discriminate against women [START_REF] Becker | The economics of discrimination[END_REF].

Moderating Factors 7

The previous analysis is extended by examining how the effect of GVC on women's participation can be moderated through internal and external factors.

Gender Provisions in Trade Agreements

As it was mentioned before, while the global economy witnessed a proliferation of gender provisions in regional trade agreements, only 20% of them include explicit chapters or clauses that endeavor to achieve gender equality and to empower women [START_REF] Monteiro | The evolution of gender-related provisions in regional trade agreements[END_REF]. This might make GVC integration more women friendly and thus might increase women's participation in international trade through several channels. First, gender inequalities and discrimination against women are the most addressed issues in RTAs as they guarantee equitable access for men and women to opportunities generated by the RTA. Second, some provisions address the participation of women in economic activities, while fewer provisions promote women's access to productive resources, such as credit and financial services, land, and technology as they might affect their participation in international trade. Finally, a handful of provisions address issues related to women's leadership and decision-making roles (WTO, 2022).

Table 4a shows the results of GVC, gender provisions, and their interaction on women's participation for all countries. The positive effect of GVC on female owners and female employees is still confirmed in most of the regressions, with an insignificant impact on GVC4 (mainly due to a limited number of firms who are deeply integrated into GVCs). Moreover, gender provision per se exert a positive impact on female ownership and employees, and a negative impact on managers and production workers. As per the MENA region, Table 4b shows that gender provisions' impact remains positive for female employees. In contrast, it becomes non-significant for the other variables measuring female empowerment. When GVCs are interacted with gender provisions, we also find an insignificant effect in the MENA region. This might be due to the de jure inclusion of gender provisions in trade agreements without real enforcement. Indeed, the number of provisions or length of chapters including gender considerations matter much less than their enforcement. In addition, the vast majority of gender provisions are non-binding in nature. This is why it is important to distinguish whether such provisions are enforced or not or are subject to a dispute settlement mechanism or not.

In a nutshell, the positive effect of gender provision on female owners and employees is thus promising given that trade policy-related factors could be mobilized to make GVC more women friendly. Yet, more efforts are needed to make such provisions better enforceable and monitored.

Internal Factors

In addition to external factors, there is growing evidence that an adverse business environment impedes firms' performance and hence negatively affect women's participation. Thus, the impact of GVC on women can also be moderated through the characteristics of the investment climate. This can be explained by two main reasons.

Investment climate affects trade performance and GVC integration. Dovis and Zaki (2020) show that the number of days that are required to pay taxes, the number of procedures that are necessary to register property, and the time to export and to import have a significantly negative relationship with the likelihood of a firm's integration into a GVC. In the same vein, Aboushady and Zaki (2019), using the WBES for Egypt, show that access to finance, tax payments and competition from the informal sector affect the firms' decision to become exporters, which is a part of GVCs. Second, several constraints hinder women's participation. Indeed, ILO (2021) argues that women's access to finance and markets, their land and property rights, and business registration and informality are key issues to be addressed to increase women's participation in the labor market. In addition, the World Bank's Women, Business and the Law index providing the link between legal gender equality and women's economic inclusion shows that the MENA and South Asia regions have the lowest index score (World Bank 2020b). The workplace indicator shows that, in many countries, the law does not prohibit gender-based employment discrimination that covers mainly four areas: the existence of limited laws that stipulate equal remuneration, laws that hinder women from working similar night hours as men, laws that limit female participation in at least one industry or at certain jobs deemed as dangerous specially. Moreover, it shows that many countries such as Egypt, Turkey, Pakistan, and Bangladesh do not allow woman to get a job in the same place as a man. Thus, it is worth investigating how these barriers might reduce the positive impact of GVC on women's participation.

Table 5 shows the impact of GVC, different obstacles and their interaction at the world level. Globally, institutional barriers limit female participation in the workforce. First, female owners are negatively affected by access to finance and by the inadequate educate labor force. Access to finance is of particular importance as World Bank (2021) shows that, although 115 out of 190 mapped economies do not prohibit discrimination in access to credit based on gender (needing husband's approval or signature for financial transactions), female entrepreneurs can still face several discriminatory practices from banks and credit facilities (because of lack of collaterals due to the lack of resources for instance). As per the inadequate labor force, it also exerts a negative impact on female owners, pointing out the skills mismatch that characterizes several developing countries. Finally, it is worthy to note that in most of the cases the interaction of GVC with the obstacles is positive and statistically significant, which shows that GVC firms might face less obstacles (as, on average, they are more productive). Thus, the net effect of GVC on female ownership remains positive and statistically significant.

As per female managers, and in addition to inadequate labor force, political instability and corruption turn to be the most impending barriers, while the rest of the barriers are statistically insignificant. Such a finding has been documented in the literature, as women are perceived as more vulnerable and less likely to know and claim their rights. Thus, this will make them less confident in seeking legal redress and thus subject to abusive corruption. This is why, to empower women at the leadership level, more transparent and enforced rules are needed to have good governance. Access to finance and inadequate labor force are also exerting a negative impact on female employees. Finally, it is important to note that the GVC positive impact on female employees (and for production employees) is attenuated by most of the barriers as the interaction coefficient is negative with access to finance, electricity, and inadequate labor force (for GVC4 at the world level). Such a finding is of particular importance given that, as it was mentioned before, female production workers are abundant and hired intensively in several sectors where the emerging economies and have a comparative advantage. This is why, to increase the impact of GVC on this vulnerable category, it is crucial to improve the business environment. Thus, globally, improving skills to let them better match the labor market demand is a key issue to improve female labor force participation, management, and ownership.

Moving to the MENA region (Table 6), the picture looks slightly different as tax rates have a negative impact on female owners and female employees. There is strong evidence that tax policy has a gender bias [START_REF] Stotsky | Gender bias in tax systems[END_REF]AWID, 2013). Indeed, even if tax systems do not include explicit gender biases, there are several implicit biases in dealing with tax collectors, tax procedures, and fair implementation of rules. One of the important barriers that affects female managers and production employees in the competition coming from the informal sector. The latter is still a major problem in the region. Indeed, cheaper products offered by the informal sector may harm the performance of formally registered firms, and thus affect women's employment, namely managers and production employees. Most of the interaction between GVC and such barriers are not statistically significant. iii) Country-year-sector averages are used to reduce the risk of endogeneity between the business environment and firm-level. iv) Each column for each GVC definition represents a separate regression. v) All the variables are in log.

vi) The intercept is included.

Robustness Checks 8

As it was mentioned before, we use a PSM 9 (see Table 7) where the treatment is being part of a GVC. Clearly, this method assumes that the selection to be treated (being part of GVC) is based on observables only. The average treatment effect shows that the positive effect on female ownership (for GVC1), female employees and female production workers and the negative one on female top manager (for GVC1) are similar to those of the baseline regression (in Table 3). Matching statistics and the results are presented in Appendix 4. Tables A9-A15 shows that there is a high level of common support for the two definitions of GVC and for the total sample and the MENA region. This is also confirmed by Figure A2 and A3. Thus, our PSM results converge to those of the baseline. Moving to the second robustness check, an Instrumental Variable approach (IV) is used in order to control for the endogenous characteristics of firms' integration into GVC. A shift-share of GVC aggregated by country-year-sector-geographical zone minus the firm's own integration into GVC is used as an instrument. GVC corrected from individual firm idiosyncrasies is expected to affect firms' trade performance without having any direct impact on women's participation. Table 8 shows that our results regarding the positive effect on female ownership and female production workers are robust and become stronger. Hence, our previous estimates of GVC must be interpreted as lower bounds due to the downward bias resulting from the endogeneity problem. Moreover, the effect of GVC on top management is still insignificant. When the same IV approach is applied to the MENA region, we find a positive effect of GVC on female owners and female workers (especially production ones), with an insignificant impact on female managers. This provides evidence for the causal link between GVC and female empowerment (measured by ownership and employment). 10 -The intercept is included.

Conclusion and Policy Implications

This paper aims at examining the impact of GVCs on women's trade participation as entrepreneurs and employees. We also analyze how this effect is moderated through external (gender provisions in trade agreements) and internal (investment climate variables) factors. To do so, we use firmlevel data for 154 developing economies and emerging markets with a special focus on the Middle East and North Africa region. Our main findings show that GVC integration increases the likelihood of being a female owner and the share of female employees, especially production ones. A less robust negative effect is found regarding the impact on being a female top manager. These effects are moderated by the inclusion of gender provisions in trade agreements and by some characteristics of the investment climate, namely corruption, access to finance and tax policy. These results remain robust after we control for the endogeneity of GVC using an instrumental 10 Results of the first stage are in Table A16 in Appendix 4. variable approach and a propensity score estimation method where the treatment is being part of a GVC.

From a policy perspective, this topic is of particular importance as it addresses two important, and correlated, challenges in the MENA region that are low female labor force participation and a weak integration into GVCs. Indeed, if MENA countries are to improve firms' insertion into GVCs, female labor participation can increase given that there are several sectors that are female intensive and that have a comparative advantage in the MENA region such as the textile, ready-made garments, processed food, and electronics sectors. Hence, from the Sustainable Development Goals (SDG) perspective, our paper is related to two goals, namely promoting inclusive and sustainable industrialization, and fostering innovation (SDG9) and promoting gender equality (SDG5).

Yet, to move forward, three recommendations are worth taking into consideration. First, at the conception and the implementation of trade and industrial policies, it is important to mainstream gender as both trade agreements and GVCs can be used as tools to improve women's participation. Yet, given that the majority of gender provisions are non-binding, it is important to have enforcement mechanisms that guarantee the implementation of gender provisions in trade agreements. Second, as the positive impact of GVCs on female owners or female production workers is attenuated by some obstacles (namely corruption, access to finance, and tax policies), it is important to address such barriers to maximize the impact of GVCs. This will require deeper and more structural reforms to improve the business environment. Finally, one of the important obstacles that hinder women's empowerment is inadequate labor force participation. This is why investing in education (especially technical and vocational education for production workers) would provide women with the required skills that are relevant to firms that operate in GVCs. Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether. Source: Constructed by the authors using the WBES. Notes: (i) SAR stands for South Asia, MENA Middle East and North Africa, EAP East Asia and Pacific, SSA Sub-Saharan Africa, LAC Latin America and the Caribbean and ECA Europe and Central Asia. (ii) GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether. 11 Given that some firms might have one of the variables missing, the sum of production and non-production does not necessarily add to the total number of employees. ii) All the regressions include country, year, sector and size fixed effects. iii) Country-year-sector averages are used to reduce the risk of endogeneity between the business environment and firm-level. iv) Each column for each GVC definition represents a separate regression. v) All the variables are in log.

Appendix 3: Descriptive Statistics

vi) The intercept is included.

Figure 3 .

 3 Figure 3. The Percentage of Firms Owned/ Managed by Female integrating into GVCs, by region

Figure 5 .

 5 Figure 5. Percent of Firms Choosing Their Biggest Obstacle and Gender (%) (a) Female owners (b) Female managers

Figure 6 .

 6 Figure 6. Evolution of RTAs with Gender Provisions over Time

Figure A1 .

 A1 Figure A1. The Distribution of Firms' integration into GVC, by region

[ 1 ]

 1 Figure A2. Overlap (common support) of propensity scores between the treated and untreated group (a) Treatment: GVC1, All regions (b) Treatment: GVC4, All regions

Table 1 .

 1 The Average Number of Female Employees and GVCs

		GVC1	GVC2	GVC3	GVC4
		No	Yes	No	Yes	No	Yes	No	Yes
	Female Employees	19.81	43.19 20.60 79.27 21.12 87.86 21.30 108.81
	Female Production Workers	14.35	72.83 21.45 85.13 23.95 129.54 26.50 126.43
	Female Non-Production Workers	5.30	18.48 6.20 25.86 7.66	28.18	7.90	35.07
	Source:

  Figure 2. The Percentage of Firms Owned/ Managed by Female integrating into GVCs, by size

	100%
	90%	64%	64%	73%	74%	87%	89%	87%	88%
	80%
	70%
	60%
	50%
	40%
	0% 10% 20% 30%	36%	36%	27%	26%	13%	11%	13%	12%
		GVC1	GVC2	GVC3	GVC4	GVC1	GVC2	GVC3	GVC4
			(a) Female Ownership			(b) Female Top Manager
					Yes No

Table 2 .

 2 Percent of Firms Choosing Their Biggest Obstacle (%) Constructed by the authors using the WBES. Note: EAP stands for East Asia and Pacific, ECA Europe and Central Asia, LAC Latin America and the Caribbean, MENA Middle East and North Africa, SAR South Asia and SSA Sub-Saharan Africa.

		All Countries	EAP	ECA	LAC	MENA	SAR	SSA
	Access to finance	14.2	13.6	9.4	9	12.1	12.1	23.9
	Land	2.9	5.6	1.7	1.4	1.5	4.6	4.3
	Business licensing and permits	2.5	2.8	2.7	4.4	3.5	1	1.4
	Corruption	6.5	6.6	3.8	9.7	9.7	10.5	7.3
	Courts	0.9	0.8	1.2	1	1	0.4	0.6
	Crime, theft, and disorder	2.9	3	1.7	6.7	1.6	2.7	3.3
	Customs and trade regulations	3.3	2.9	2.6	4.2	3.4	1.8	4.5
	Electricity	8.4	6.5	3.5	5.4	10.9	21.8	13.9
	Inadequately educated workforce	10.3	7.6	19.7	7.8	8.8	2.9	1.8
	Labor regulations	3.5	2.9	5.3	4.5	2.4	4.8	1
	Political instability	11.9	15.3	9	11.1	19.5	20.7	10.8
	Practices of the informal sector	11.5	14.4	11.6	17.4	6.8	4.6	10.6
	Tax administration	3.9	2.4	4.5	4.4	2.7	2.2	4.3
	Tax rates	13.2	10.6	18.6	10.9	11.7	6	9.7
	Transportation	3.8	5	4.8	2.2	4.3	3.7	2.6
	Source:

  Christian et al., 2013; Staritz and Reis, 2013; Barrientos, 2014; Doss, 2014; Bamber and Staritz, 2016

Table 3 .

 3 GVCs and Female Labor Force Participation -Baseline Results

					(a) All regions included
		Female Ownership	Female Top Manager	Female Employees	Female Production
								Workers
	Variables	GVC1	GVC4	GVC1	GVC4	GVC1	GVC4	GVC1	GVC4
	Ln(Age)	0.047***	0.047***	-0.005**	-0.005**	0.075***	0.077***	0.014	0.019*
		(0.003)	(0.003)	(0.002)	(0.002)	(0.008)	(0.008)	(0.010)	(0.010)
	Ln(Gov own.)	0.015***	0.015***	0.0005	0.0003	0.015	0.017	0.081***	0.083***
		(0.004)	(0.004)	(0.003)	(0.003)	(0.014)	(0.014)	(0.019)	(0.019)
	Main city	0.015***	0.015***	0.011***	0.011***	0.114***	0.115***	-0.033**	-0.024
		(0.004)	(0.004)	(0.003)	(0.003)	(0.010)	(0.010)	(0.015)	(0.015)
	Medium	0.002	0.004	-0.033***	-0.034***	0.997***	0.998***	0.717***	0.755***
		(0.003)	(0.003)	(0.003)	(0.003)	(0.009)	(0.009)	(0.011)	(0.011)
	Large	0.003	0.013***	-0.053***	-0.055***	2.462***	2.460***	2.189***	2.271***
		(0.005)	(0.005)	(0.004)	(0.003)	(0.020)	(0.020)	(0.021)	(0.020)
	GVCs	0.013***	-0.098***	-0.011***	-0.006	0.093***	0.424***	0.323***	0.402***
		(0.004)	(0.011)	(0.003)	(0.008)	(0.014)	(0.059)	(0.017)	(0.044)
	Constant	0.163***	0.165***	0.194***	0.193***	0.839***	0.841***	0.552***	0.579***
		(0.009)	(0.009)	(0.007)	(0.007)	(0.024)	(0.024)	(0.032)	(0.032)
	Country dum.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Year dum.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Sector dum.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Observations	83,949	83,949	84,341	84,341	41,224	41,224	41,234	41,234
	R-squared	0.115	0.116	0.099	0.099	0.548	0.548	0.498	0.495
					(b) MENA region
		Female Ownership	Female Top Manager	Female Employees	Female Production
								Workers
	Variables	GVC1	GVC4	GVC1	GVC4	GVC1	GVC4	GVC1	GVC4
	Ln(Age)	0.037***	0.036***	3.85e-05	-3.26e-07	0.067***	0.072***	-0.069***	-0.064***
		(0.006)	(0.006)	(0.004)	(0.004)	(0.024)	(0.024)	(0.023)	(0.023)
	Ln(Gov own.)	-0.017	-0.016	-0.005	-0.005	0.074	0.082	-0.010	-0.013
		(0.011)	(0.011)	(0.005)	(0.005)	(0.056)	(0.055)	(0.065)	(0.065)
	Main city	0.023***	0.024***	0.019***	0.018***	0.200***	0.209***	-0.035	-0.030
		(0.008)	(0.008)	(0.005)	(0.005)	(0.033)	(0.033)	(0.033)	(0.034)
	Medium	0.023***	0.027***	0.0005	-0.0001	0.848***	0.857***	0.448***	0.497***
		(0.080)	(0.008)	(0.005)	(0.005)	(0.030)	(0.030)	(0.025)	(0.025)
	Large	0.047***	0.061***	-0.009	-0.011*	2.311***	2.320***	1.449***	1.578***
		(0.011)	(0.011)	(0.006)	(0.006)	(0.067)	(0.066)	(0.051)	(0.051)
	GVCs	0.031***	-0.070**	-0.007	-0.003	0.228***	0.753***	0.417***	0.645***
		(0.011)	(0.029)	(0.006)	(0.017)	(0.043)	(0.236)	(0.045)	(0.153)
	Constant	0.047**	0.051***	0.050***	0.049***	0.445***	0.447***	0.602***	0.619***
		(0.019)	(0.019)	(0.012)	(0.012)	(0.073)	(0.073)	(0.073)	(0.073)
	Country dum.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Year dum.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Sector dum.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Observations	11,714	11,714	11,730	11,730	4,857	4,857	6,863	6,863
	R-squared	0.109	0.109	0.019	0.019	0.503	0.503	0.471	0.465
	Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

Table 4 .

 4 GVC, Female Labor Force Participation, and Gender Provisions in RTAs

	(a) All Regions

Robust standard errors clustered by country and year in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: -We control for firms' age, size, city of operation and the share of government ownership.

-Country dummies have been removed from panel (b) given the high collinearity between countries and provisions. Moreover, our sample drops when we focus only on the MENA region. -The intercept is included.

Table 5 .

 5 Female Labor Force Participation, GVCs and Barriers -All regions

		Finance	Tax.Rates	Pol. Inst.	Corrup.	Comp.	Elec.	Inad.Edu
			(a) Female Ownership
	(1) GVC	-0.0154	0.0174	-0.0126	-0.0144	0.000437	0.00378	0.0281**
		(0.0121)	(0.0143)	(0.0123)	(0.0110)	(0.0128)	(0.0145)	(0.0124)
	(2) Obstacle	-0.0788*	0.0114	-0.0160	-0.0617	-0.0276	0.0274	-0.0724*
	GVC1	(0.0457)	(0.0423)	(0.0433)	(0.0523)	(0.0406)	(0.0385)	(0.0395)
	(1)*(2)	0.160***	-0.0178	0.104***	0.125***	0.0681	0.0368	-0.0817
		(0.0543)	(0.0466)	(0.0394)	(0.0408)	(0.0543)	(0.0494)	(0.0514)
	Observations	83,896	83,896	83,896	83,896	83,896	83,896	83,896
	R-squared	0.115	0.115	0.115	0.115	0.115	0.115	0.115
	(1) GVC	-0.145***	-0.0469	-0.111***	-0.117***	-0.136***	-0.0665	-0.0303
		(0.0311)	(0.0410)	(0.0330)	(0.0330)	(0.0302)	(0.0416)	(0.0426)
	(2) Obstacle	-0.0529	0.00979	0.00591	-0.0399	-0.0202	0.0342	-0.081**
	GVC4	(0.0449)	(0.0408)	(0.0420)	(0.0519)	(0.0393)	(0.0377)	(0.0382)
	(1)*(2)	0.317**	-0.228*	0.0666	0.112	0.240	-0.130	-0.364**
		(0.139)	(0.124)	(0.0963)	(0.108)	(0.147)	(0.132)	(0.157)
	Observations	83,896	83,896	83,896	83,896	83,896	83,896	83,896
	R-squared	0.116	0.116	0.116	0.116	0.116	0.116	0.116
			(b) Female Manager
	(1) GVC	-0.0192**	-0.00447	-0.0175*	-0.023***	-0.0123	-0.0170	-0.0122
		(0.00861)	(0.00944)	(0.00899)	(0.00843)	(0.00833)	(0.0111)	(0.0102)
	(2) Obstacle	-0.0219	-0.0235	-0.0773**	-0.0896**	0.00312	-0.0228	-0.0704*
	GVC1	(0.0393)	(0.0348)	(0.0373)	(0.0386)	(0.0427)	(0.0311)	(0.0394)
	(1)*(2)	0.0490	-0.0249	0.0282	0.0559**	0.00969	0.0253	0.00989
		(0.0339)	(0.0285)	(0.0223)	(0.0275)	(0.0346)	(0.0337)	(0.0431)
	Observations	84,287	84,287	84,287	84,287	84,287	84,287	84,287
	R-squared	0.099	0.099	0.099	0.100	0.099	0.099	0.099
	(1) GVC	0.00141	0.0161	0.00753	0.0106	0.00932	0.0186	-0.00947
		(0.0152)	(0.0182)	(0.0154)	(0.0158)	(0.0154)	(0.0223)	(0.0199)
	(2) Obstacle	-0.00961	-0.0248	-0.0705*	-0.0767**	0.00867	-0.0138	-0.0699*
	GVC4	(0.0395)	(0.0346)	(0.0374)	(0.0389)	(0.0411)	(0.0306)	(0.0401)
	(1)*(2)	-0.0454	-0.0971*	-0.0629	-0.0924*	-0.0918	-0.102	0.0219
		(0.0679)	(0.0554)	(0.0403)	(0.0509)	(0.0702)	(0.0677)	(0.0776)
	Observations	84,287	84,287	84,287	84,287	84,287	84,287	84,287
	R-squared	0.099	0.099	0.099	0.099	0.099	0.099	0.099
			(c) Female Employees
	(1) GVC	0.0913**	0.131***	0.0467	0.0448	0.0831**	0.0694*	0.0570
	GVC1	(0.0376)	(0.0370)	(0.0352)	(0.0348)	(0.0378)	(0.0373)	(0.0386)
	(2) Obstacle	-0.568**	-0.240	-0.0555	-0.241	-0.405	0.558***	-0.826***
		(0.243)	(0.228)	(0.205)	(0.233)	(0.245)	(0.213)	(0.271)

Table 6 .

 6 Female Labor Force Participation, GVCs and Barriers -MENA region Each regression controls for firms' age, city of operation and share of government ownership ii) All the regressions include country, year, sector and size fixed effects.

		Finance	Tax.Rates	Pol. Inst.	Corrup.	Comp.	Elec.	Inad.Edu.
			(a) Female Ownership
	(1) GVC	-0.0312	-0.073***	-0.00214	-0.00956	0.0297	-0.0194	0.0285
		(0.0264)	(0.0230)	(0.0388)	(0.0373)	(0.0357)	(0.0261)	(0.0185)
	(2) Obstacle	-0.126	-0.234**	0.0158	-0.0172	-0.0365	-0.151	-0.00455
	GVC1	(0.0819)	(0.0903)	(0.103)	(0.0788)	(0.0718)	(0.0892)	(0.0990)
	(1)*(2)	0.251***	0.416***	0.0777	0.110	0.00616	0.185**	0.0164
		(0.0845)	(0.0842)	(0.0706)	(0.0836)	(0.140)	(0.0693)	(0.109)
	Observations	11,712	11,712	11,712	11,712	11,712	11,712	11,712
	R-squared	0.110	0.111	0.109	0.109	0.109	0.110	0.109
	(1) GVC	-0.172***	-0.142**	-0.0885	-0.0992	-0.120	-0.118**	-0.0993*
		(0.0541)	(0.0648)	(0.0963)	(0.0815)	(0.0764)	(0.0573)	(0.0516)
	(2) Obstacle	-0.0763	-0.160*	0.0337	0.00850	-0.0363	-0.118	-0.00169
	GVC4	(0.0867)	(0.0912)	(0.102)	(0.0829)	(0.0741)	(0.0895)	(0.0874)
	(1)*(2)	0.451**	0.281	0.0449	0.0793	0.214	0.184	0.148
		(0.176)	(0.253)	(0.193)	(0.196)	(0.321)	(0.195)	(0.218)
	Observations	11,712	11,712	11,712	11,712	11,712	11,712	11,712
	R-squared	0.109	0.109	0.109	0.109	0.109	0.109	0.109
			(b) Female Top Manager
	(1) GVC	-0.0328	-0.0324*	-0.0119	-0.0218	-0.0324*	-0.0223	-0.034***
		(0.0199)	(0.0186)	(0.0210)	(0.0195)	(0.0175)	(0.0144)	(0.0105)
	(2) Obstacle	-0.0487	-0.0802	-0.0421	0.0470	-0.142***	-0.0407	-0.0202
	GVC1	(0.0491)	(0.0625)	(0.0704)	(0.0497)	(0.0324)	(0.0422)	(0.0781)
	(1)*(2)	0.105	0.103*	0.0125	0.0409	0.112	0.0576	0.164***
		(0.0660)	(0.0582)	(0.0403)	(0.0426)	(0.0660)	(0.0437)	(0.0576)
	Observations	11,728	11,728	11,728	11,728	11,728	11,728	11,728
	R-squared	0.019	0.019	0.019	0.019	0.020	0.019	0.020
	(1) GVC	-0.0681	-0.0835*	-0.0223	-0.0689	-0.0931	-0.0142	0.00623
		(0.0400)	(0.0481)	(0.0566)	(0.0537)	(0.0631)	(0.0231)	(0.0350)
	(2) Obstacle	-0.0309	-0.0639	-0.0402	0.0522	-0.127***	-0.0298	0.0222
	GVC4	(0.0493)	(0.0594)	(0.0695)	(0.0482)	(0.0319)	(0.0396)	(0.0706)
	(1)*(2)	0.285*	0.309	0.0451	0.176	0.379	0.0411	-0.0483
		(0.165)	(0.199)	(0.112)	(0.139)	(0.271)	(0.106)	(0.179)
	Observations	11,728	11,728	11,728	11,728	11,728	11,728	11,728
	R-squared	0.019	0.019	0.019	0.019	0.020	0.019	0.019
			(c) Female Employees
	(1) GVC	0.217*	0.0746	0.151	0.182**	0.174**	0.197**	0.177*
		(0.110)	(0.111)	(0.112)	(0.0866)	(0.0816)	(0.0789)	(0.0876)
	(2) Obstacle	1.055***	-0.766**	0.701*	0.187	0.385	0.0464	-0.612
	GVC1	(0.313)	(0.362)	(0.356)	(0.430)	(0.357)	(0.463)	(0.514)
	(1)*(2)	0.0457	0.654	0.184	0.127	0.250	0.129	0.335
		(0.360)	(0.387)	(0.219)	(0.193)	(0.281)	(0.230)	(0.383)
	Observations	4,857	4,857	4,857	4,857	4,857	4,857	4,857

Table 7 .

 7 GVCs and Female Labor Force Participation -PSM

	(a) All regions

Table 8 .

 8 GVCs and Female Labor Force Participation -IV Approach

	(a) All regions

Note: -All the regressions include country, year, and sector fixed effects.

Table A1 .

 A1 Firms integrating into GVCs

		Number of firms	Percentage of total firms
	GVC1	28,681	19.97%
	GVC2	12,485	8.69%
	GVC3	6,083	4.24%
	GVC4	3,288	2.29%
	Total	50,537	35.18%
	Source: Constructed by the authors using the WBES.

Table A2 .

 A2 The Average Number of Female Employees, by region11 Constructed by the authors using the WBES. Note: SAR stands for South Asia, MENA Middle East and North Africa, EAP East Asia and Pacific, SSA Sub-Saharan Africa, LAC Latin America and the Caribbean and ECA Europe and Central Asia.

	Region	Female Employees	Female Production Workers	Female Non-Production Workers
	SSA	14.17	14.99	5.10
	EAP	28.49	72.15	13.85
	ECA	24.09	27.84	8.28
	LAC	39.77	25.59	12.98
	MENA	11.03	17.55	5.10
	SAR	9.33	42	4.25
	Source:

Table A4 .

 A4 GVC & Female Labor Force Participation -by Sector

	5.3% 8.1% 8.7% 12.3% 22.1% 43.4% GVC1 Appendix 4: Empirical Results 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Table A3. GVCs and Female Non-Production Workers -Baseline Results 5.9% 2.0% 2.5% 8.1% 6.3% 6.1% 8.9% 15.8% 16.5% 8.2% 18.8% 13.1% 19.4% 22.7% 23.2% 49.5% 34.4% 38.6% GVC2 GVC3 GVC4 SAR MENA EAP SSA LAC ECA (b) MENA region (a) All regions (b) MENA region Variables GVC1 GVC4 GVC1 GVC4 Ln(Age) 0.102*** 0.105*** 0.0647*** 0.0675*** (0.00721) (0.00719) (0.0167) (0.0168) Ln(Gov own.) 0.0697*** 0.0706*** 0.151*** 0.150*** (0.0153) (0.0154) (0.0541) (0.0537) Main city 0.104*** 0.110*** 0.122*** 0.125*** (0.0100) (0.0101) (0.0243) (0.0244) Medium 0.619*** 0.643*** 0.467*** 0.494*** (0.00763) (0.00747) (0.0181) (0.0177) Large 1.775*** 1.817*** 1.601*** 1.673*** (0.0142) (0.0140) (0.0392) (0.0379) GVC 0.229*** 0.446*** 0.227*** 0.336*** (0.0117) (0.0284) (0.0337) (0.114) Constant 0.125*** 0.142*** 0.00424 0.0139 (0.0222) (0.0221) (0.0524) (0.0524) Country dummies Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Sector dummies Yes Yes Yes Yes Observations 41,353 41,353 6,863 6,863 R-squared 0.538 0.537 0.469 0.466 Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Female Ownership Female Top Manager Female Employees Female Production Workers (a) All regions included Fem. Ownership Fem Top Manager Fem. Employees Variables GVC1 GVC4 GVC1 GVC4 GVC1 GVC4 GVC1 GVC4 Fem. Production Workers Variables GVC1 GVC4 GVC1 GVC4 GVC1 GVC4 GVC1 (1) GVC 0.0388 -0.0352 -0.00345 0.0286 0.120 1.013*** 0.668*** 0.426 GVC4 (1) GVC -0.020 -0.098*** -0.021** 0.0141 0.228 1.679** 0.668*** (0.0301) (0.0787) (0.0147) (0.0477) (0.448) (0.297) (0.113) (0.376) 0.426 (0.015) (0.035) (0.010) (0.0262) (0.252) (0.793) (0.113) (2) Textile 0.00911 0.0302* 0.0180 0.0234** 0.435 0.726* 0.764*** 1.094*** (0.376) (2) Textile 0.074*** 0.077*** 0.098*** 0.093*** -0.133 -0.0126 0.764*** (0.0187) (0.0172) (0.0123) (0.0106) (0.438) (0.402) (0.0623) (0.0606) 1.094*** (0.010) (0.009) (0.008) (0.007) (0.142) (0.132) (0.0623) (3) Leather -0.00764 0.0153 -0.026** -0.0243** 0.936** 1.112*** -0.156** -0.178** (0.0606) (3) Leather -0.046** 0.0017 -0.058*** -0.038*** 0.754*** 0.980*** -0.156** (0.0238) (0.0241) (0.0126) (0.0118) (0.395) (0.210) (0.0706) (0.0693) -0.178** (0.021) (0.021) (0.010) (0.011) (0.245) (0.0848) (0.0706) (4) Wood 0.00446 -0.0007 -0.0150 -0.0138 -0.467*** -0.587*** (0.0693) (4) Wood -0.052*** -0.042** -0.053*** -0.049*** -0.809*** -0.807*** -0.467*** (0.0205) (0.0197) (0.0116) (0.0107) (0.0523) (0.0552) -0.587*** (0.018) (0.017) (0.013) (0.012) (0.185) (0.185) (0.0523) (5) Publishing 0.0405 0.0423 0.0167 0.0143 -0.400* -0.325 -0.148 -0.213 (0.0552) (5) Publishing -0.055 -0.048 -0.075*** -0.075*** -0.292* -0.338** -0.148 (0.0635) (0.0622) (0.0403) (0.0377) (0.230) (0.220) (0.150) (0.155) -0.213 (0.037) (0.035) (0.022) (0.020) (0.149) (0.133) (0.150) (6) Chemicals 0.0404 0.0245 0.00497 0.0103 -0.385 -0.316 -0.156** -0.287*** (0.155) (6) Chemicals -0.016 -0.006 -0.025** -0.018* -0.462* -0.380 -0.156** (0.0247) (0.0216) (0.0148) (0.0132) (0.234) (0.222) (0.0762) (0.0751) -0.287*** (0.015) (0.013) (0.011) (0.010) (0.278) (0.262) (0.0762) (7) Rubb&Pla 0.0401 0.0721 0.0405 0.0275 0.349 0.416** -0.224* -0.247** (0.0751) (7) Rubb&Pla -0.053*** -0.050*** -0.050*** -0.052*** -0.421 -0.421 -0.224* (0.0465) (0.0456) (0.0325) (0.0278) (0.221) (0.207) (0.121) (0.121) -0.247** (0.017) (0.015) (0.013) (0.012) (0.438) (0.436) (0.121) (8) Machinery -0.0215 -0.0200 -0.0175 -0.0209* -0.620*** -0.701*** (0.121) (8) Machinery -0.117*** -0.103*** -0.088*** -0.086*** -0.65*** -0.779*** -0.620*** (0.0198) (0.0194) (0.0127) (0.0109) (0.0730) (0.0720) -0.701*** (0.012) (0.010) (0.009) (0.007) (0.153) (0.131) (0.0730) (9) Furniture 0.000521 0.00501 -0.00651 -0.00624 -0.539*** -0.618*** (0.0720) (9) Fab. Metals -0.089*** -0.078*** -0.083*** -0.076*** -0.972*** -0.820*** -0.539*** (0.0208) (0.0203) (0.0127) (0.0117) (0.0532) (0.0567) -0.618*** (0.011) (0.010) (0.008) (0.007) (0.144) (0.130) (0.0532) (10) Elect. -0.0173 -0.0159 0.0132 0.0107 -0.480** -0.412* -0.320*** -0.358*** (0.0567) (10) Furniture -0.144*** -0.119*** -0.090*** -0.086*** -0.801*** -0.789*** -0.320*** (0.0359) (0.0351) (0.0244) (0.0227) (0.243) (0.233) (0.0913) (0.0947) -0.358*** (0.018) (0.017) (0.013) (0.012) (0.146) (0.111) (0.0913) (11)OtherMan. 0.0163 0.0165 -0.00255 0.00197 -0.260 -0.115 -0.520*** -0.551*** (0.0947) (11) Electro. (0.0138) (0.0129) (0.00809) (0.00723) (0.245) (0.224) (0.0402) (0.0411) -0.096*** -0.088*** -0.083*** -0.073*** -0.789*** -0.787*** (0.022) (0.019) (0.018) (0.016) (0.279) (12) Services 0.0142 0.00990 0.0160** 0.0147** -0.0605 0.0216 -0.556*** -0.680*** (0.278) (12) Oth. Man. -0.060*** -0.047*** -0.053*** -0.045*** -0.350*** -0.325*** -0.520*** (0.0118) (0.0114) (0.00767) (0.00710) (0.220) (0.204) (0.0797) (0.0777) -0.551*** (0.007) (0.006) (0.005) (0.005) (0.0841) (0.0790) (0.0402) (13)Wholesale 0.0337 0.0298 -0.00478 -0.00635 -0.0620 0.0110 -0.242 -0.357** (0.0411) (13) Services -0.0162** -0.0146** -0.00258 -0.003 -0.192** -0.189*** -0.556*** (0.0251) (0.0229) (0.0138) (0.0119) (0.224) (0.209) (0.164) (0.161) -0.680*** (0.007) (0.006) (0.005) (0.005) (0.0768) (0.0725) (0.0797) (1)*(2) 0.0571 0.142 0.0224 0.0410 0.957 0 0.661*** 0.798* (0.0777) (14) Wholesale -0.049*** -0.042*** -0.047*** -0.047*** -0.254*** -0.246*** -0.242 (0.0422) (0.109) (0.0234) (0.0736) (0.801) (0) (0.150) (0.460) -0.357** (0.0127) (0.0120) (0.010) (0.009) (0.0801) (0.0758) (0.164) (1)*(3) 0.134 -0.154* 0.00498 -0.0292 0 0 -0.355 2.411*** (0.161) (1)*(2) 0.0254 0.113* -0.011 -0.0733* 0.402 0 0.661*** (0.0845) (0.0835) (0.0307) (0.0494) (0) (0) (0.269) (0.633) 0.798* (0.0210) (0.0597) (0.0160) (0.0433) (0.358) (0) (0.150) (1)*(4) -0.0525 -0.23*** 0.00326 -0.0686 -1.339*** -1.315** (0.460) (1)*(3) 0.227*** -0.0735 0.104*** -0.0116 0 0 -0.355 (0.0616) (0.0819) (0.0267) (0.0501) (0.201) (0.556) 2.411*** (0.0597) (0.0452) (0.0343) (0.0287) (0) (0) (0.269) (1)*(5) -0.0332 -0.201** -0.0570 -0.0865 0 0 -0.535 1.782*** (0.633) (1)*(4) 0.0485 -0.220*** 0.0191 -0.137*** 0 0 -1.339*** (0.231) (0.0998) (0.0423) (0.0605) (0) (0) (0.885) (0.404) -1.315** (0.0490) (0.051) (0.0323) (0.0484) (0) (0) (0.201) (1)*(6) -0.0950* -0.133 0.0106 -0.0772 1.648*** 0.745** -0.830*** 0.214 (0.556) (1)*(5) 0.0290 -0.0762 -0.0199 0.0121 -0.481 0 -0.535 (0.0491) (0.0985) (0.0287) (0.0494) (0.455) (0.319) (0.211) (0.669) 1.782*** (0.116) (0.0497) (0.0272) (0.0331) (0.312) (0) (0.885) (1)*(7) 0.253* 0.719*** -0.0286 0.405 0 0 -0.396 1.145* (0.404) (1)*(6) 0.0495* 0.117* 0.0396* 0.0177 0.974** 0 -0.830*** (0.133) (0.0911) (0.0670) (0.352) (0) (0) (0.386) (0.660) 0.214 (0.0290) (0.0638) (0.0209) (0.0475) (0.452) (0) (0.211) (1)*(8) -0.00438 -0.0275 -0.0186 -0.0392 -0.685*** 0.0955 (0.669) (1)*(7) -0.000906 -0.0454 -0.00354 -0.0465 0 -0.185 -0.396 (0.0570) (0.140) (0.0182) (0.0487) (0.216) (0.723) 1.145* (0.0371) (0.0742) (0.0247) (0.0531) (0) (0.832) (0.386) (1)*(9) 0.0406 0.118 -0.00412 -0.0577 -0.962*** -0.392 (0.660) (1)*(8) 0.0394* -0.0367 0.0188 -0.0131 -0.626** 0 -0.685*** (0.0693) (0.298) (0.0271) (0.0499) (0.228) (0.707) 0.0955 (0.0225) (0.0498) (0.0148) (0.0350) (0.318) (0) (0.216) (1)*(10) -0.00377 -0.183** -0.0526* -0.0876* 1.469*** 0.560* -0.879* 0 (0.723) (1)*(9) 0.0446* -0.0378 0.0320** -0.0231 0.344 0 -0.962*** (0.137) (0.0851) (0.0274) (0.0525) (0.458) (0.325) (0.513) (0) -0.392 (0.0233) (0.0546) (0.0151) (0.0361) (0.329) (0) (0.228) (1)*(11) -0.0191 -0.115 0.0102 -0.0878* 0.231 -1.36*** -0.336*** -0.153 (0.707) (1)*(10) 0.137*** 0.230 0.0150 -0.0513 -0.123 0 -0.879* (0.0352) (0.0953) (0.0173) (0.0481) (0.496) (0.380) (0.127) (0.452) 0 (0.0456) (0.178) (0.0282) (0.0403) (0.311) (0) (0.513) (1)*(12) -0.0254 -0.0976 -0.0196 -0.0306 0.127 -0.209 -0.443** 0.00548 (0) (1)*(11) 0.00799 -0.0318 0.0390 -0.0304 0 (0.0357) (0.0984) (0.0179) (0.0688) (0.450) (0.415) (0.216) (0.389) -1.394 (0.0400) (0.0675) (0.0331) (0.0584) (0) (1)*(13) -0.0272 -0.30*** -0.0114 -0.0840 -0.00772 -1.056** -0.177 3.109*** (1.018) (1)*(12) 0.0460*** -0.0308 0.0342*** -0.0119 -0.130 0 -0.336*** (0.0551) (0.0943) (0.0234) (0.0525) (0.457) (0.412) (0.686) (0.440) -0.153 (0.0162) (0.0383) (0.0114) (0.0287) (0.263) (0) (0.127) Observations 11,714 11,714 11,730 11,730 4,861 4,861 6,864 6,864 (0.452) (1)*(13) 0.0142 0.0453 -0.0132 -0.0412 -0.173 -1.828** -0.443** 0.00548 R-squared 0.108 0.107 0.016 0.017 0.496 0.495 0.478 0.456
	(0.0163) Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 (0.0440) (0.0116) (0.0323) (0.252)	(0.806)	(0.216)	(0.389)
	(1)*(14) Note: -All the regressions control for firm's size, age, share of government ownership and city of operation. 0.0319 -0.212*** -0.0102 -0.0148 -0.127 -1.286 -0.177 3.109***
		(0.0328)	(0.0383)	(0.0197)	(0.107)	(0.259)	(0.795)	(0.686)	(0.440)
	Observations	83,949	83,949	84,341	84,341	41,227	-0.727	6,864	6,864
	R-squared	0.111	0.111	0.090	0.090	0.528	(0.872)	0.478	0.456

-Country and year fixed effects are included. -The intercept is included.

Table A8 .

 A8 Common Support, Outcome: Female Ownership

		(a) GVC1, All regions			(b) GVC4, All regions
	Treatment	Common	Total	Treatment	Common	Total
	assignment	support		assignment	support
		On support			On support
	Untreated	66,790	66,790	Untreated	75,726	75,726
	Treated	17,097	17,097	Treated	1,842	1,842
	Total	83,887	83,887	Total	77,568	77,568
		(c) GVC1, MENA region		(d) GVC4, MENA region
	Treatment	Common	Total	Treatment	Common	Total
	assignment	support		assignment	support
		On support			On support
	Untreated	9,235	9,235	Untreated	10,017	10,017
	Treated	2,195	2,195	Treated	187	187
	Total	11,430	11,430	Total	10,204	10,204

Table A9 .

 A9 Common Support, Outcome: Female Top Manager

		(a) GVC1, All regions		(b) GVC4, All regions
	Treatment	Common	Total
	assignment	support
		On support
	Untreated	67,059	67,059
	Treated	17,220	17,220
	Total	84,279	84,279
		(c) GVC1, MENA region	(d) GVC4, MENA region
	Treatment	Common	Total
	assignment	support
		On support
	Untreated	9,246	9,246
	Treated	2,200	2,200
	Total	11,446	11,446

Table A10 .

 A10 Common Support, Outcome: Number of Full-Time Female Employees

	(a) GVC1, All regions	(b) GVC4, All regions
	Treatment	Common	Total
	assignment	support
		On support
	Untreated	35,900	35,900
	Treated	5,207	5,207
	Total	41,107	41,107
	(c)

GVC1, MENA region (d) GVC4, MENA region

				Treatment	Common	Total
				assignment	support
					On support
				Untreated	76,103	76,103
				Treated	1,857	1,857
				Total	77,960	77,960
				Treatment	Common	Total
				assignment	support
					On support
				Untreated	10,033	10,033
				Treated	187	187
				Total	10,220	10,220
				Treatment	Common	Total
				assignment	support
					On support
				Untreated	28,901	28,901
				Treated	334	334
				Total	29,235	29,235
	Treatment	Common	Total	Treatment	Common	Total
	assignment	support		assignment	support
		On support			On support
	Untreated	3,954	3,954	Untreated	3,101	3,101
	Treated	622	622	Treated	35	35
	Total	4,576	4,576	Total	3,136	3,136

Table A11 .

 A11 Common Support, Outcome: Number of Full-Time Female Production Workers

		(a) GVC1, All regions		(b) GVC4, All regions
	Treatment	Common	Total
	assignment	support
		On support
	Untreated	29,503	29,503
	Treated	11,600	11,600
	Total	41,103	41,103
		(c) GVC1, MENA region	(d) GVC4, MENA region
	Treatment	Common	Total
	assignment	support
		On support
	Untreated	5,270	5,270
	Treated	1,575	1,575
	Total	6,845	6,845

Table A12 .

 A12 GVCs and Female Labor Force Participation -PSM .040*** -0.060*** -0.04*** -0.034*** 0.338*** 1.157*** 0.952*** 1.466*** 0.870*** 1.415*** Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: All the regressions include country, year and sector dummies.

							Treatment		Common		Total
							assignment		support
								On support
							Untreated		37,563	37,563
							Treated		1,417	1,417
							Total		38,980	38,980
							Treatment		Common		Total
							assignment		support
								On support
							Untreated		6,147	6,147
							Treated		152		152
							Total		6,299	6,299
						(a) All regions
		Fem.		Fem.	Fem.	Fem. Production	Fem. Production
		Ownership	Top Manager	Employees	Workers	Workers
		GVC1	GVC4	GVC1	GVC4	GVC1	GVC4	GVC1	GVC4	GVC1	GVC4
	Treatment 0(0.004)	(0.011)	(0.003)	(0.009)	(0.018)	(0.067)	(0.017)	(0.043)	(0.012)	(0.031)
	Constant 0.309*** 0.329*** 0.169*** 0.160*** 1.645*** 1.698*** 1.151*** 1.381*** 0.919*** 1.136***
		(0.002)	(0.002)	(0.001)	(0.001)	(0.006)	(0.007)	(0.009)	(0.008)	(0.006)	(0.006)
	Obs.	83,887	77,568	84,279	77,960	41,107	29,235	41,103	38,980	41,215	39,175
						(b) MENA region
		Fem.		Fem.	Fem.	Fem. Production	Fem. Production
		Ownership	Top Manager	Employees	Workers	Workers
		GVC1	GVC4	GVC1	GVC4	GVC1	GVC4	GVC1	GVC4	GVC1	GVC4
	Treatment 0.111***	0.008	-0.005	-0.0009	0.634*** 1.664*** 1.250*** 1.801*** 0.953*** 1.290***
		(0.009)	(0.029)	(0.005)	(0.017)	(0.054)	(0.213)	(0.041)	(0.126)	(0.030)	(0.093)
	Constant 0.170*** 0.195*** 0.055*** 0.054*** 1.206*** 1.399*** 0.661*** 0.962*** 0.601*** 0.831***
		(0.004)	(0.004)	(0.002)	(0.002)	(0.020)	(0.023)	(0.020)	(0.020)	(0.015)	(0.014)
	Obs.	11,430	10,204	11,446	10,220	4,576	3,136	6,845	6,299	6,845	6,299

Table A16 .

 A16 GVCs and Full-Time Female Non-Production Workers -IV Approach Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Note: The intercept is included.

		(a) All regions	(b) MENA region
	Variables	GVC1	GVC4	GVC1	GVC4
	Ln(Age)	0.0986***	0.111***	0.0659***	0.107***
		(0.00726)	(0.00851)	(0.0179)	(0.0266)
	Ln(Gov own.)	0.0708***	0.0713***	0.155***	0.0923**
		(0.0106)	(0.0123)	(0.0315)	(0.0461)
	Main city	0.0986***	0.105***	0.117***	0.110***
		(0.0102)	(0.0117)	(0.0243)	(0.0317)
	Medium	0.589***	0.590***	0.431***	0.457***
		(0.0134)	(0.0160)	(0.0319)	(0.0330)
	Large	1.689***	1.542***	1.475***	1.334***
		(0.0284)	(0.0627)	(0.0754)	(0.118)
	GVCs	0.456***	3.151***	0.524***	5.147***
		(0.0694)	(0.607)	(0.166)	(1.599)
	Country dummies	Yes	Yes	Yes	Yes
	Year dummies	Yes	Yes	Yes	Yes
	Sector dummies	Yes	Yes	Yes	Yes
	Observations	40,750	40,750	6,797	6,797

Table A17 .

 A17 Female Non-Production Workers, GVCs and Barriers Robust standard errors clustered by country and year in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: i) Each regression controls for firms' age, city of operation and share of government ownership.

		Finance	Tax rates	Pol. Inst.	Corrup.	Comp.	Elec.	Inad. Edu.
				(a) All Regions
	(1) GVC	0.277***	0.311***	0.229***	0.264***	0.299***	0.259***	0.285***
		(0.0325)	(0.0470)	(0.0336)	(0.0296)	(0.0415)	(0.0378)	(0.0358)
	(2) Obstacle	-0.0342	0.0453	0.00585	0.0534	-0.182	-0.198*	0.281**
	GVC1	(0.114)	(0.131)	(0.120)	(0.151)	(0.136)	(0.112)	(0.141)
	(1)*(2)	-0.257**	-0.313**	0.00194	-0.148	-0.377**	-0.106	-0.292**
		(0.125)	(0.134)	(0.140)	(0.130)	(0.166)	(0.137)	(0.133)
	Observations	41,324	41,324	41,324	41,324	41,324	41,324	41,324
	R-squared	0.538	0.538	0.538	0.538	0.538	0.538	0.538
	(1) GVC	0.507***	0.523***	0.524***	0.499***	0.491***	0.511***	0.603***
		(0.0449)	(0.0647)	(0.0500)	(0.0456)	(0.0468)	(0.0610)	(0.0680)
	(2) Obstacle	-0.110	-0.0615	0.0250	0.0251	-0.297*	-0.221**	0.245*
	GVC4	(0.112)	(0.150)	(0.134)	(0.163)	(0.162)	(0.104)	(0.145)
	(1)*(2)	-0.411	-0.349	-0.372	-0.315	-0.296	-0.271	-0.834***
		(0.302)	(0.283)	(0.238)	(0.246)	(0.266)	(0.203)	(0.264)
	Observations	41,324	41,324	41,324	41,324	41,324	41,324	41,324
	R-squared	0.537	0.537	0.537	0.537	0.537	0.537	0.537
				(b) MENA region
	(1) GVC	0.339**	0.206	0.255**	0.245*	0.464***	0.307**	0.392***
		(0.140)	(0.134)	(0.116)	(0.138)	(0.130)	(0.139)	(0.0908)
	(2) Obstacle	-0.467**	-0.133	-0.816**	-0.684*	-0.227	-0.283*	-0.0198
	GVC1	(0.190)	(0.298)	(0.319)	(0.336)	(0.183)	(0.163)	(0.210)
	(1)*(2)	-0.435	0.0825	-0.0635	-0.0464	-0.985**	-0.281	-0.972**
		(0.397)	(0.456)	(0.207)	(0.320)	(0.456)	(0.332)	(0.363)
	Observations	6,861	6,861	6,861	6,861	6,861	6,861	6,861
	R-squared	0.470	0.469	0.469	0.469	0.470	0.469	0.470
	(1) GVC	0.643**	0.762*	0.509	0.632*	0.436	0.632*	1.046***
		(0.292)	(0.428)	(0.332)	(0.358)	(0.387)	(0.331)	(0.164)
	(2) Obstacle	-0.519***	-0.148	-0.833**	-0.683**	-0.471***	-0.347**	-0.190
	GVC4	(0.186)	(0.258)	(0.306)	(0.326)	(0.158)	(0.160)	(0.209)
	(1)*(2)	-1.299	-1.610	-0.390	-0.768	-0.414	-1.109	-3.474***
		(1.006)	(1.389)	(0.894)	(0.971)	(1.319)	(0.884)	(0.658)
	Observations	6,861	6,861	6,861	6,861	6,861	6,861	6,861
	R-squared	0.466	0.466	0.466	0.466	0.466	0.466	0.468

The minimum Eigenvalue is higher than all the critical values at 10% and the p-value is significant at 1%. Therefore, we reject the null-hypothesis according to which the instruments are weak.

Regressions for Non-Production Workers are presented in Table A3 (Appendix 4).

Regressions for Non-Productions Workers are presented in Table A5 (Appendix 4) and Table A17 (Appendix 5).

Results of IV-First Stage are presented in Table A6 in Appendix 4. Results of IV regressions for Non-Production Workers are presented in Table A7.

To check the robustness of the results, propensity scores are estimated using alternative matching methods with different choice of bandwidths (Kernel pair matching with replacement and cross validation with respect to the means of x). Matching statistics and the results are presented in Appendix 5.

The authors gratefully acknowledge the financial and analytical support from the Office of the Chief Economist for the Middle East and North Africa (MNACE) under the regional Labor and Gender Research Programs (TTLs: Nelly Elmallakh and Nazmul Chaudhury). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. Bank for Reconstruction and Development/

The number of gender-related provisions in regional trade agreements.

Source: Constructed by the authors using the WBES.


Haydeé Ceballos
email: m.h.ceballos@uu.nl

Theo Van Den Bogaart

Jeroen Spandaw

Stan Van Ginkel

Paul Drijvers

Three complementary frameworks to capture student peer discussion in problem solving

Keywords: Peer discussion, problem solving, Schoenfeld, Florida taxonomy of cognitive behaviour, scheme for educational dialogue analysis

Peer discussions play a major role in students' collaborative problem-solving activity. These discussions provide researchers and teachers with a wealth of information about the students' reasoning. To analyse such discussions, different theoretical lenses are available, such as Schoenfeld's problem solving model, the Florida Taxonomy of Cognitive Behaviour, and the Scheme for Educational Dialogue Analysis. The question is, however, how these three perspectives can complement each other. To investigate this, the discussion between four students was analysed through the three lenses. Results indicate that these frameworks are both complementary and connected. This connection allows an in-depth analysis of the discussion and reveals possibilities and limitations for an integration of the three models, which will guide future discussions' analyses in our study.

Introduction

In mathematics classrooms, students work together to solve mathematics problems. During these collaborations, students talk over their peers' arguments or ask for clarification, and so on. Many studies have shown the effectiveness of peer discussion for students' conceptual development [START_REF] Barth-Cohen | What are middle school students talking about during clicker questions? Characterizing small-group conversations mediated by classroom response systems[END_REF][START_REF] Crouch | Peer Instruction: Ten years of experience and results[END_REF][START_REF] Wang | Possibilities and limitations of integrating peer instruction into technical creativity education[END_REF]. Mathematical discussions have been analysed from different perspectives. Some authors focus on the cognitive and the sociocultural perspective [START_REF] Sfard | Cognition as communication: Rethinking learning-by-talking through multi-faceted analysis of students' mathematical interactions[END_REF]. Others look at these discussions from a dialogic perspective [START_REF] Kazak | The importance of dialogic processes to conceptual development in mathematics[END_REF] and mainly study the form of the student contributions. However, the issue is that most analyses in the literature are fragmented because each framework has a different perspective. There is no integrative framework that can tell us what factors affect the success or failure of a peer discussion. Additionally, guidance is needed to improve discussions in mathematical problem solving. Our short-term goal is to develop a framework to analyse peer discussions integrally; the long-term goal is to design learning activities in which mathematically productive discussions are promoted, including higher-order thinking skills. The pilot study described here is the first step towards a more comprehensive model. An extensive review of the literature revealed that three frameworks capture crucial aspects of peer discussion in problem solving best: Schoenfeld's problemsolving model, the Florida Taxonomy of Cognitive Behaviour (FTCB) and the Scheme for Educational Dialogue Analysis (SEDA). These frameworks are used to analyse a discussion between students. The research question addressed is as follows: Which aspects from the three different frameworks co-emerge in the analysis of a peer discussion?

Theoretical framework

The study's theoretical framework consists of the three frameworks mentioned above. First, [START_REF] Schoenfeld | Mathematical problem solving[END_REF] framework for analysing problem-solving skills presents four categories of knowledge and behaviour for an adequate characterization of mathematical problem solving performance. These categories are necessary and sufficient for the analysis of the success or failure of someone's problem-solving attempt: It is important to highlight that with good control, problem solvers can make the most of their resources and solve rather difficult problems with some efficiency. Without it, they can waste their resources and fail to solve problems within their grasp [START_REF] Schoenfeld | Mathematical problem solving[END_REF]. Therefore, in this article we focus on the category control. We have split this category into two sub-aspects called resources control (RC) and heuristics control (HC) because control cannot exist by itself, but is always related to resources or heuristics.

The second framework (FTCB) can be used to measure the cognitive level of students' input on all aspects of problem solving, regardless of content type. In this pilot study, we focus on the level of analysis students demonstrate when justifying their choice of mathematical content or strategy. FTCB is based on Bloom's Taxonomy and is used as a tool to assign Bloom's Taxonomy levels to utterances from the target audience. It was designed by [START_REF] Brown | Florida taxonomy of cognitive behavior[END_REF] and has been used frequently in different contexts, for example to investigate the level of cognitive behaviour exhibited by secondary agriculture teachers [START_REF] Ulmer | An assessment of the cognitive behavior exhibited by secondary agriculture teachers[END_REF]. While Bloom's Taxonomy distinguishes six cognitive levels, FTCB uses seven. It is a hierarchical model, with each level of intellectual skills building on the previous. FTCB includes 55 behaviour descriptions, 21 of which are related to the three higher-order thinking codes: analysis, synthesis and evaluation. These descriptions indicate the observable behaviours that characterize each thinking level and help to classify the input in the dialogue.

As a third framework, we use SEDA to determine the type of input from individual students in the discussion. Many articles have been written about dialogue in education, and there is emerging consensus about the types of educational dialogue that seem to be productive for learning. These focus on atonement to others' perspectives and the continuous co-construction of knowledge through sharing, critiquing, and gradually reconciling contrasting ideas [START_REF] Littleton | Interthinking: Putting talk to work[END_REF]. In SEDA the essence of dialogic interactions is operationalized as systematic indicators for these productive forms of educational dialogue [START_REF] Hennessy | Developing a coding scheme for analysing classroom dialogue across educational contexts[END_REF]. SEDA contains 33 codes, and the authors later developed a shorter version, named T-SEDA framework (Teacher Scheme for Educational Dialogue Analysis). T-SEDA focuses on key dialogue features, highlighting those that are known to be productive for learning [START_REF] Kershner | Teachers as "natural experimenters": Using T-SEDA to develop classroom dialogue[END_REF]. We opted for the short version (T-SEDA) because it simplifies coding and allows us to see if further refinement is needed when coding the student discussion. Each category can be identified by words or short sentences that are uttered during the dialogue.

Methods

The set-up Four first-year in-service student teachers (P, Q, R, S) from the Bachelor Mathematics Teacher Training Program at Utrecht University of Applied Sciences worked together to solve a mathematics problem from the Mathematics Olympiad training. Their curriculum included a course called problem-solving, in which the students were faced with mathematics problems in which various heuristics had to be applied. They are used to explaining things and it was expected that they would not give up easily since they had practiced enough with heuristics in the aforementioned course. We aimed to elicit a discussion with much interaction at different levels that could be analysed using the previously mentioned frameworks. The students were asked to solve the problem together and to interact with each other as much as possible. In the present paper, a fragment of the discussion that happened while the students worked on the problem is analysed.

The students worked on the following problem (Stichting Nederlandse Wiskunde Olympiade, 2022): The students were alone in a room with no teacher or researcher nearby. They were videotaped and they handed in their written work afterwards. The recording was transferred to Atlas TI for data analysis. Three researchers individually coded a fragment of this video using the three coding schemas, and then discussed their findings. The developers of T-SEDA were consulted to clarify the background and the choices made regarding their codes. Additionally, the three frameworks were presented to a focus group within the research group 'Mathematical and analytical skills of professionals' of the first author's institute to search for potential theoretical connections between the codes and to identify typical examples of these codes in practice. These activities contributed to the validity of our application of the frameworks and the reliability of the coding of this fragment, which was subsequently coded by one of the authors.

The coding Table 1: The three frameworks with their associated codes

In the solution process, the students chose particular mathematical approaches or heuristics that determined the path to a possible solution. We describe these episodes as critical moments. Subsequently, three researchers separately determined the critical moments in the recording of the problem. In this paper we analyse one of these moments, which was identified by all three coders as particularly critical, since it sent the students on the wrong track for 80 percent of the time spent on this problem. The chosen episode of 16 minutes and 20 seconds was divided into clips. Each clip consists of one contribution in the form of a sentence or sentences per person.

Results

The mathematical approach and some examples of coding

The problem was not solved, but several attempts were made. In the chosen episode a mathematically wrong approach was proposed by student P. The entire group engaged in this approach during this episode. Figure 2 shows that P draws the centre of the circumscribed circle and then makes a triangle by connecting this point to point B and the intersection of the vertical centreline of the circle and the circle itself. P assumes that angle B is 90°. P also chooses an unknown 𝑥𝑥 which she then tries to calculate using the Pythagorean theorem. This results in three equations with four unknowns. P then looks for a fourth equation to solve the problem. It does not work. The episode ends when P arrives at an, considering Figure 2, unlikely conclusion that 𝑦𝑦 = 𝑥𝑥/4 . Student P is thinking aloud during the solution process. P says that angle B is 90° because of a mathematical rule that she cannot remember the name of. Q asks: Is that 90°?( RC; CH). Student P replies: Yes, that is 90° (A; R). Q does not continue questioning, and then this approach is worked out by P while Q, R and S watch. No one takes action to verify P's claim. Then P said: "Maybe my approach is too complicated, I don't know if I can solve this" (HC; RD) The rest of the group did not react to this. When P seemed to get stuck, Q and R suggested a different mathematical approach. P rejected these ideas through correct substantive arguments (RC; A; CH; R). Ten minutes later S mumbles "we are making things too complicated"; here again no response from the group (HC; RD).

The overlap between the three frameworks

The tables 2 and 3 and the diagrams 1 and 2 give the co-occurrence between the codes of the three frameworks. This sheds light on the question how the three frameworks complement each other and how they overlap. For this study, we chose to focus on two codes: Schoenfeld's Resources control (RC) and Heuristics control (HC) and their relations with the codes of T-SEDA and FTCB. The number after each code in parentheses in tables 2 and 3 shows how often the code is applied in the entire project. The number in the cell indicates the number of hits, how often the two codes co-occur. For example, the evaluation code was assigned 9 times throughout the project, and 3 of them coincided with the RC code. The row and column entities of the tables are represented in the corresponding charts (Fig 3 and4) as nodes and edges, showing the strength of co-occurrence between the pairs of nodes.

Conclusion

Three frameworks were used to analyse a discussion between four students. Schoenfeld describes the categories that play a role in mathematical problem-solving. The thinking level of the students was studied through the lens of FTCB and the character of the personal input was classified with T-SEDA. We focused on one of the most important factors that determine the success or failure of mathematical problem-solving [START_REF] Schoenfeld | Mathematical problem solving[END_REF], namely: resources control and heuristics control. We observed that resources control in our data was more prevalent than heuristics control. That means that more mathematical knowledge was discussed and thereafter applied or rejected, during the search for the solution, than strategies or heuristics.

It should also be noted that the level of thinking displayed during the moments when students rejected different mathematical approaches was generally high. The expressions that were observed linked to resources control were: points out unstated assumption, shows interaction or relation of elements, points out particulars to justify conclusions, checks hypotheses with given information, distinguishes relevant from irrelevant statements and detects error in thinking. Several moments were observed in which the mathematical results were evaluated from evidence and from criteria. The code translation is connected to heuristics control only once. That is a lower order thinking skill according to FTCB. In this case, the students translate verbalization into graphic form.

The moments in the discussion when doubts about the approach came to light were, unfortunately, not further analysed by the students. The opportunity to take a different, more promising path, was lost as a result of the approach not being evaluated. In this case, these doubts were caused by the fact that the students did not make any progress with the problem because the mathematical fallacy at the start had not been discovered sooner. It appears that if this particular mathematical approach had been evaluated earlier, it might have led to a change in strategy. Whereas when the students focussed their evaluation on resources, they failed to evaluate heuristics, leading to ineffective, time-consuming problem-solving behaviour. This study therefore shows the need to investigate further how we can teach students to assess their problem-solving approach earlier in order to improve their problemsolving skills. The analysis of the connections of resources control and heuristics control to the T-SEDA codes may provide insights to address this issue.

Figure 4 illustrates a striking phenomenon about the relation between T-SEDA and the control codes.

The codes: invite to build on ideas, build on ideas, challenge, and invite reasoning were only linked to resources control. The moments of analysis and evaluation described earlier were often started when a student challenged others, asked them to build on ideas, or explained their reasoning more clearly. These invitations to encourage others to actively participate in the discussion were followed by explicit reasoning. Consequently, thinking errors were discovered and hypotheses were tested, only referring, however, to the manipulation of mathematical knowledge but not about the approach.

It is remarkable that heuristics control is linked to the T-SEDA code reflection. Indeed, those moments of reflection were individual expressions that provoked no response from the rest of the group. These seem to be missed opportunities to change the approach. Therefore, it seems plausible that moments of reflection (T-SEDA) should naturally cause moments of evaluation (FTCB), but in this case, they did not.

According to the literature [START_REF] Kershner | Teachers as "natural experimenters": Using T-SEDA to develop classroom dialogue[END_REF], T-SEDA offers an opportunity to teach students how to discuss constructively, for example, by challenging others or inviting others to express ideas. This pilot research indicates that the kind of contributions classified by T-SEDA are strongly connected to the level of thinking (FTCB) involved when students respond to the expressions of others. For example, inviting someone to reason implies a certain level of analysis. However, there are also striking missing connections between T-SEDA and FTCB, for example, reflecting on the dialogue is not necessarily accompanied by evaluation. By understanding these connections, it may be possible to improve mathematical problem-solving, in particular Schoenfeld's crucial category heuristics control. Then we conclude that the three frameworks are complementary. We also suspect that they are connected at a deeper level where different aspects of these frameworks may influence each other. More research is needed to explore these connections further.

  (a) Resources: Mathematical knowledge possessed by the individual; (b) Heuristics: Strategies and techniques for making progress on unfamiliar or nonstandard problems; (c) Control: The individual's monitoring and self-regulation; global decisions regarding the selection and implementation of resources and strategies; (d) The individual's belief systems and their origins in the student's mathematical experiences.

  Figure 1: Problem situation

  on ideas (IB); build on ideas (B); challenge (CH); invite reasoning (IRE); make reasoning explicit (R); coordination of ideas and agreement (CA); connect (C); reflect on dialogue or activity (RD); guide direction of dialogue or activity (G), express or invite ideas (EI)

Figure 2 :

 2 Figure 2: Student P's scratch paper

FigureFigure 4 :

 4 Figure 3: Co-occurrence between the two control codes and five FTCB codes

Table 2 : Co-occurrence between the two control codes and five FTCB codes

 2

	Resources control (19)	Heuristics control (3)

Acknowledgments

We would like to acknowledge the students who provided their collaboration in their spare time. We also thank Sara Hennessy, Ruth Kershner and Lydia Cao (University of Cambridge) for their collaboration and background information about T-SEDA.