Structural change and gender sectoral segregation in sub‐Saharan African countries

Zuazu, Izaskun Article — Published Version Structural change and gender sectoral segregation in sub‐ Saharan African countries Journal of International Development Provided in Cooperation with: John Wiley & Sons Suggested Citation: Zuazu, Izaskun (2024) : Structural change and gender sectoral segregation in sub‐Saharan African countries, Journal of International Development, ISSN 1099-1328, Wiley, Hoboken, NJ, Vol. 36, Iss. 6, pp. 2626-2654, https://doi.org/10.1002/jid.3925 This Version is available at: https://hdl.handle.net/10419/306121 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ RESEARCH ARTICLE Structural change and gender sectoral segregation in sub-Saharan African countries Izaskun Zuazu Institute for Socio-Economics, University of Duisburg-Essen, Duisburg, Germany Correspondence Izaskun Zuazu, Institute for Socio-Economics, University of Duisburg-Essen, Duisburg, Germany. Email: izaskun.zuazu-berme[email protected] Funding information Levy Economics Institute; INET-YSI Abstract Structural change has long been at the core of economic development debates. However, the gender implications of structural change are still largely unexplored. This paper helps to fill this gap by analysing the role of structural change in the gender distribution of sectoral employment in sub-Saharan African countries. I employ aggregate and disaggregate measures of gender sectoral segregation in employment, which measure the difference between the gender distribution across sectors with respect to the overall participation of women and men in the labour market. I build a panel database consisting of 10 sectors and 11 countries during 1960–2010. Fixed effects and instrumental variables' regression models show a significant, nonlinear link between labour productivity and gender segregation. Increasing labour productivity depresses gender segregation at initial phases of structural change. However, further productivity gains beyond a certain threshold of sectoral development increases gender segregation. Country-industry panel data models complement the analysis showing that relative labour productivity has a nonlinear impact in gender segregation: Initial increases in relative productivity increases feminization but further relative productivity gains foster the masculinization of sectors. The estimates suggest that manufacturing, utilities, construction, business, and government services are key to correct gender biases in employment along the process of structural change. Received: 13 September 2023 Revised: 12 March 2024 Accepted: 8 May 2024 DOI: 10.1002/jid.3925 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). Journal of International Development published by John Wiley & Sons Ltd. 2626 J. Int. Dev. 2024;36:2626–2654. wileyonlinelibrary.com/journal/jid KEYWORDS association index, dissimilarity index, gender sectoral segregation, instrumental variables, structural change JEL CLASSIFICATION E0, J1, Q5 1|INTRODUCTION Structural change is the process of shifting production from agriculture to manufacturing and service sectors, followed by a decline in manufacturing share and an increase in service sector share in the total economy. The prominent debates on economic development in sub-Saharan African (SSA) countries are concerned with structural change patterns that depart from the canonical model that depicts declining agriculture, hump-shaped manufacturing, and rising high-productive services (de Vries et al., 2015; Tregenna, 2015). Structural heterogeneity—the isolation of highly productive activities from the rest of the economy—and premature deindustrialization—a prompt shift into a service economy without proper development of the industrial sector—are among the pathological phenomena identified in the literature (Rodrik, 2016; Tregenna, 2016). While the canonical works on this topic (Kuznets, 1966; Lewis, 1965) have been complemented by research on the impacts of general structural change on economic development (de Vries et al., 2021; Herrendorf et al., 2014; McMillan et al., 2017; McMillan & Rodrik, 2011), the gender implications of such a transformation are less understood (Dinkelman & Ngai, 2022; Gottlieb et al., 2022; Seguino & Were, 2014). This paper adds to the literature in structural change by analysing whether and how labour productivity and gender segregation are linked in sub-Saharan African countries. Gender sectoral segregation refers to the sectoral distribution of female and male employment, that is, the proportion of women (men) in each sector relative to the total female (male) employment in the economy. Gender sectoral/horizontal segregation differs from occupational/vertical segregation: The former considers the sectoral economic structure, whereas the latter considers the occupational economic structure of a given country or region. 1 In this paper, we look at how key phenomena regarding structural change, such as labour productivity and sectoral productivity gaps, affect the gender distribution of sectors in both formal and informal employment. The key argument of this paper is that labour productivity—computed as the ratio between value added and employment—might have a nonlinear relationship to gender sectoral segregation. Initial productivity gains derived from the process of structural change can imply a lower demand for physical requirements. As lower physical requirements are found to increase the demand for women in the paid workforce (Rendall, 2013,2017), one might expect an increasing participation of women in all sectors of the economy. However, further productivity gains above certain levels can couple with gender stereotypes and discrimination to deter the entrance of women in specific sectors, thus fostering the crowding of female employment in other specified sectors (Bergmann, 1981; Seguino & Braunstein, 2019). This paper empirically tests the link between labour productivity and gender segregation using panel data models at both country level and country-industry levels. The term “country-industry”is employed here to differentiate between segregation measures at country level (which vary according to countries and years) and industry-level measures of gender-sectoral segregation (which vary according to sectors, countries, and years). These two measurements refer to the same phenomenon, namely, gender-sectoral segregation. I collect data on sectoral (formal and informal) employment, disaggregated by gender, and sectoral value added from the Africa Sector Database (ASD) by de Vries et al. (2015), and build a panel database consisting of 10 industries operating in 11 countries during 1960– 2010. Using this database allows a higher level of data disaggregation than previous related works (Borrowman & Klasen, 2020). Descriptively, I show that gender segregation has increased in certain countries (i.e., Senegal, Ethiopia, ZUAZU 2627 and Botswana), but it was reduced in others (i.e., Zambia, South Africa) during the period considered. At the same time, I identify that those countries with reduced gender segregation had, at the same time, higher levels of labour productivity. I merge the ASD database with information on female labour force participation and other country-level covariates that can play a role in gender segregation. As a preview of the econometric analysis, I find a nonlinear relationship between labour productivity and gender segregation: productivity gains depress segregation up to a certain threshold. Beyond that threshold, further productivity gains increase gender segregation by sectors. This result is robust to alternative estimation techniques, such as instrumental variables, that circumvent endogeneity issues regarding the inclusion of female labour force participation in the set of independent variables. Additionally, the main result holds when using alternative dependent variables, such as aggregate and disaggregate measures of gender sectoral segregation, namely, the Dissimilarity index of Duncan and Duncan (1955), the so-called IP index of Karmel and MacLachlan (1988), and the Association index of Charles and Grusky (1995). The remainder of the paper goes as follows. Section 2reviews the literature. Section 3provides the data and the measurements of aggregate gender sectoral segregation. Section 4specifies the econometric models while Section 5shows the results and provides different robustness checks. Section 6concludes and discusses policy implications. 2|LITERATURE REVIEW Structural change at large, and the differences in aggregate and sectoral labour productivity in particular, interact in complex ways with gendered labour markets. These structural changes come with profound demographic movements and urbanization and allow for technological diffusion at both market and home production levels (Boserup et al., 2013; Dinkelman & Ngai, 2022; McMillan et al., 2017; Uberti & Douarin, 2022). Fundamentally, these changes might transform the pre-existing gender distribution of paid and unpaid work. This theoretical section first analyses the general patterns of structural change at large and contextualizes them within developing regions, specifically sub-Saharan African countries. Second, this section reviews the stylized facts on the gender implications of structural change, together with the specific empirically informed factors of gender segregation. Finally, the section zooms in on specific implications of structural change in gender sectoral segregation in the region. Structural transformation affects productivity and growth: as resource reallocations shift labour out of lowproductive sectors towards high-productive, modern economic activities, there is an aggregate labour productivity rise and an expansion of income (McMillan & Rodrik, 2011; Naveed & Ahmad, 2016; Van Ark, 1995). As suggested by McMillan and Rodrik (2011), when labour and other resources move from less productive to more productive sectors, overall productivity grows even in the absence of within-sector productivity growth. The process of structural change shows great cross-country heterogeneity, as some countries transition faster from one phase of structural change to another, or fail to fully develop a modern manufacturing sector before moving into a service economy (Herrendorf et al., 2014; Rodrik, 2016). Thus, it is important to consider economy-wide labour productivity and sectoral gaps in labour productivity as key phenomena related to the process of structural change. Developing countries show large sectoral gaps in labour productivity and dual economies, with high shares of low-productivity, largely rural activities and low shares of high-productive (urban) activities. For the specific case of the sub-Saharan African region, cross-sectoral labour reallocation was productivity reducing during 1990s but it promoted productivity growth in the 2000s, and this improved performance in the 2000s (McMillan et al., 2014). While the existing literature has identified the relevance of structural change in the productivity levels, productivity growth, and sectoral gaps in productivity in SSA, its impact on labour market outcomes are far less well known (Mensah et al., 2023), let alone the gender labour market implications of structural change. In the sample of SSA countries here considered, manufacturing expanded greatly from 1960 to 1975, corresponding to the shifts from subsistence agricultural societies towards modern manufacturing (de Vries 2628 ZUAZU et al., 2015). However, after 1970, the region suffered a political and economic turmoil, which coincided with structural adjustment programmes. These programmes had crucial, gendered implications, which are still felt today (Elson, 1995). In the late phases of the period considered in this paper (1990–2010), SSA countries expanded employment shares in service sector whereas manufacturing shares remained low, which is coined as premature deindustrialization (Rodrik, 2016) or even, deindustrialization without industrialization (Tregenna, 2016). Mensah et al. (2023) provide an analysis of the effects of structural change in labour productivity growth in a set of 18 SSA countries during 1960–2018, expanding the country and time coverages of the ASD, originally developed by the Groningen Growth and Development Centre (GGDC) by de Vries et al. (2015). Their findings using this updated database suggest that structural change in SSA has been underestimated in previous studies. Agricultural employment declined during the period considered, leading to an increase of low-productive service sectors. Thus, these changes had limited enhancing effects in aggregate labour productivity. Mensah et al. (2023) point at typical labour market institutions setting of the SSA region, such as the significant variation in the degree of enforcement of laws and regulation, as barriers to successful experiences of structural change. The authors also highlight the link between structural change and labour mobility, with labour shifts defining the type of structural process. Structural change might increase aggregate labour productivity, but simultaneously, it might also foster temporary unemployment and worker reallocations rising low-paid jobs or informal employment and employee uncertainty. However, Mensah et al. (2023) do not consider gender disparities in these labour market implications of process of structural change. Hence, the current paper provides a direct contribution to their work in particular and to the literature on structural change at large. The process of structural change can lead to domestic disparities, such as increasing income inequality (Kuznets, 1966; Lewis, 1965) and gender redistribution of paid work and unpaid household production (Dinkelman & Ngai, 2022; Gaddis & Klasen, 2014; Uberti & Douarin, 2022). In this context, structural change is linked to the emergence of new types of paid work opportunities for women (Dinkelman & Ngai, 2022). Notwithstanding some broad similarities in female and male employment shifts over the structural change path, the literature identifies significant gender disparities. For instance, Dinkelman and Ngai (2022) use historical cross-country data for developed economies to find that women leave the agriculture sector and move into the service sector faster than men do and that manufacturing rises more steeply for men than for women. However, Dinkelman and Ngai's (2022) paper only focuses on a rather broad sectoral perspective. In the current paper, I complement their analysis by providing a more nuanced sectoral analysis that uses a greater level of data disaggregation by sector. This allows me to identify how structural change is linked to gender disparities as well as which sectors are driving these disparities. Economic development and sectoral composition have a profound impact on the women's distribution of paid and unpaid work, as predicted by the so-called feminization U-shape. In a nutshell, this theory was first uncovered using historical data for the United States in Goldin (1995), suggesting that initial increasing levels of economic development are associated with depressing women in the paid workforce due to an income effect. Further increases of economic development are governed by a substitution effect that pushes women back to the labour market. A growing body of research followed up the U-shaped feminization hypothesis of Goldin (1995) and casts doubts in the external validity of the hypothesis. Gaddis and Klasen (2014) suggest that structural change should be included in our understanding of the U-shaped correlation between economic development and female labour market participation. They also ensure that the nonlinear link is inconsistent depending on the data and quantitative method employed. Uberti and Douarin (2022) find that the use of the plough matters for the feminization U-shape, as physical requirements of the plough can mediate the bulk of women in paid and unpaid work. In related works, Rendall (2013,2017) considers the role of structural change in altering the composition of “brain”and “brawn”tasks by sectors: Lower physical requirements might lead to increasing opportunities for paid work for women, as women have a comparative advantage in brain jobs. Beyond female labour force participation, working conditions of female employment can be disparate and differ substantially from those of male employment. Extant literature has also focused on the role of structural change in the gender wage gap in SSA countries. Van den Broeck et al. (2023) use decomposition methods to analyse how structural change affected gender wage ZUAZU 2629 differentials in Malawi, Tanzania, and Nigeria to find that structural transformation does not consistently help bridge the gender pay gap. Additionally, their analysis suggests a rural–urban divide in the driving forces behind gender pay inequality; while in rural areas occupation is the most relevant factor, in urban areas both occupation and sector are similarly important. In this sense, the current paper complements the existing literature by placing special attention on the role of sectoral segregation in the process of structural change to affect differently the livelihoods of women and men, controlling at the same time for urbanization. Gender segregation in SSA countries is lower in comparison to other regions in the world. Borrowman and Klasen (2020) use a database of 69 countries, of which 24 are SSA countries, and find that gender segregation is generally lower in this region where women and men are disproportionately employed in agriculture. While they find a limited and mostly insignificant role of structural change in gender segregation, their estimates associate female labour force participation with lower gender-sectoral segregation. 2 Other recent works in the gender implications of structural change find that higher industrial productivity is associated with lower presence of women in good jobs, namely, industry jobs in 15 Latin American countries (Arora et al., 2023) using panel data econometric models during 1990–2018. Yeboah et al. (2022) analyse the role of female labour force participation in structural change, but they do not focus on the implications for gender segregation, but rather, the extent to which rising female labour force participation in SSA countries is linked to value-added shares in the agriculture, industry, and service sectors. Using dynamic panel data models on a balanced dataset of 33 SSA countries during 1990–2017, they find that rising female labour force participation leads to an increased share of services in total value added, but they do not find a significant role in industry or agriculture sectors. This is a mediating effect of infrastructure—measured in terms of either fixed telephone subscriptions or gross-fixed-capital formation as proportion of GDP—which magnifies this positive link between women in the paid workforce and the share of services. The current paper draws on the above-mentioned stylized facts of structural change and gender to speculate the extent to which productivity matters for gender segregation by sector. This paper comes close to the works of Borrowman and Klasen (2020) in identifying the drivers of gender-sectoral segregation and combines it with the sectoral-disaggregated perspective in de Vries et al. (2015). Further, the current paper complements the argument in Rendall (2013) in asserting that higher labour productivity can favour female employment by considering that productivity might have a nonlinear relationship with female employment in certain sectors. At sufficiently high levels of labour productivity, further gains can block the entrance of women mediated by gender discrimination and stereotypes in the competition between women and men for newly created “good”jobs (Seguino & Braunstein, 2019). 3|EMPIRICAL ANALYSIS 3.1 |Data I collect data from the ASD produced by de Vries et al. (2015). This database provides sectoral-level disaggregated data (ISIC Rev. 3.1) on value added, employment, and female share of employment for 10 sectors operating in 11 SSA countries, namely, Botswana, Ethiopia, Ghana, Kenya, Malawi, Mauritius, Nigeria, Senegal, South Africa, Tanzania, and Zambia, during 1960 to 2010. 3 Using this database, I look at two fundamental phenomena of structural change, as discussed in Section 2: aggregate labour productivity and relative labour productivity. The ASD provides information on formal and informal employment, as it defines employment as “all persons engaged,”thus including all paid employees and self-employed and family workers of 15 years and older. An important feature of the ASD is that it provides sectoral purchasing power parities (PPPs) for the year 2005 (Herrendorf et al., 2022). I convert labour productivity levels measured in domestic prices to comparable measures of labour productivity levels measured in international prices using the sectoral-level PPPs. 2630 ZUAZU Figure 1shows the sectoral distribution of female employment and male employment together with sectoralrelative labour productivity. 4 Agriculture concentrates the bulk of both female and male employment, with respectively 65% and 60% shares. To the contrary, the agricultural-relative labour productivity level is around 0.5 during the period, meaning that labour productivity in the sector is half that of the total economy. The trade services sector comprises a higher percentage of female employment (12%) than male employment (8%), and its relative productivity level is slightly above the total economy productivity level. The personal services sector also employs a greater proportion of females to males, although the labour productivity is lower than the average (0.8). The transport services, construction, and mining sectors concentrate low shares of female employment, while maintaining among the highest labour-productivity shares. The manufacturing sector concentrates a similar percentage of female and male employment, while its productivity levels are around two times that of the total economy. The sectoral perspective taken in Figure 1should be complemented with country-level measures of segregation that account for differences in structural change patterns. To do so, the next subsection proposes the use of standard country-level measures of segregation that are computed based on cross-country, time-series, and sectoral-level disaggregated data. 3.2 |Measuring gender segregation To measure gender segregation, I employ the Duncan index of dissimilarity (ID), which is a standard measure of either vertical or horizontal segregation (Charles & Grusky, 2005; Duncan & Duncan, 1955) at country level. For the purposes of this paper, I focus on sectoral segregation, and use a 10-sector level of industrial classification (ISIC Rev. 3.1) (see Equation 1). ID ¼1=2X n i¼1 Fi FMi M  100 ð1Þ FIGURE 1 Sectoral shares of employment by gender and relative labour productivity. Source: Own elaboration using the ASD (de Vries et al., 2015). ZUAZU 2631 i¼1,…,n½ F i is the number of women in sector i, where Fis the total of women employed in the economy, M i is the number of men in sector i,Mis the total of men employed in the economy, and nequals 10 (total number of sectors, as the database here employed there are 10 sectors i). One of the benefits of using the ID as a measure of segregation is its simple interpretation: It provides the percentage of women who need to change sectors in order to bring about a gender-equal distribution across sectors within an economy. The higher the value of the ID, the higher the segregation in a country. In the sample, ID has increased from 13% in 1960 to 20% in 2010. It should be noted that this measure of gender segregation compares the sectoral distribution of male employment with that of women. Nonetheless, the ID is not exempt from limitations. As noted in Borrowman and Klasen (2020), the foremost limitation of the ID is its mechanical sensitivity to cross-country and temporal changes in the employment share by sector. The index is thus influenced by large sectors, which can be worrisome in studying structural transformation and gender segregation in a panel-data setting. To alleviate this difficulty, I employ the so-called “IP index”(IP) (Karmel & MacLachlan, 1988) as an alternative measure of segregation, which serves also as a robustness check of the econometric models below. Figure A2 in Appendix Ashows the evolution of ID and IP. 5 A common limitation of the ID and IP is that both depend greatly on the breadth of the sectoral classification. Narrow classifications yield higher levels of ID than broad classifications; hence, they can be manipulated to offer higher or lower levels of segregation (Nelson, 2017). Table 1provides the evolution of ID in each country in the sample, together with that of the logarithm of total labour-productivity levels. In six out of the 11 countries (Botswana, Ethiopia, Malawi Nigeria, Senegal, and Tanzania), gender segregation has increased over the period at scrutiny. The most remarkable increase is shown by Senegal, where ID increased from 8% in 1970 to 23% in 2010. At the same time, the growth rate of total labour productivity between 1970 and 2010 is the lowest in the sample (1.6%). To the contrary, gender segregation decreased remarkably in Zambia (38 percentage points, p.p.) and, together with Ghana, is the country with the highest growth rate of total labour productivity in the period (10%). Figure 2documents correlations between either aggregate labour productivity (log) or female labour force participation (FLFP, %) and gender segregation (measured by ID). Figure 2a tends to show that at initial levels of labour productivity, gender segregation reduces. However, this curve is nonlinear, as it slightly flattens at higher levels of productivity. The correlation of aggregate labour productivity with gender sectoral segregation seems to vary by country, where Zambia shows the greatest change in both segregation and productivity. Ghana and Tanzania seem to follow a clearer nonlinear pattern between productivity and segregation, whereas South Africa and Nigeria show an inverted pattern. The stagnation of FLFP in SSA countries suggested in Backhaus and Loichinger (2022) can be seen in Figure 2b, as there is scarce variation of FLFP by country. The correlation between women in the paid workforce, proxied using FLFP (%) in x-axis, and gender sectoral segregation, proxied by ID (%) in y-axis, is a linear and negative. Thus, we can consider preliminary evidence of a negative, strong association between rising women in the labour market and the crowing of women in specific sectors of the economy, as previously found in Borrowman and Klasen (2020). There are limits to what can be discerned from aggregate cross-country analyses in the context of structural change and gendered impacts (Wamboye & Seguino, 2015). Both ID and IP indices are country-level measures of gender segregation. While they provide information on the share of workers who should change sectors to increase a gender-balanced distribution across sectors, these indices are not able to identify which precise sectors should be defeminized or demasculinized. To solve for this, in this paper, I combine the use of country-level measures of segregation (viz., ID and IP) with a measure of the concentration of gender employment, namely, the Association Index (A index) proposed by Charles and Grusky (1995). The A index takes a log-linear approach to circumvent the limitations of both ID and IP indices, solving therefore for the mechanical dependence of the latter on variations in sectoral shifts of employment and participation of women in the labour market. Additionally, the A identifies which 2632 ZUAZU TABLE 1 Gender segregation and labour productivity levels in sub-Saharan Africa. Dissimilarity index (%) Total labour productivity 1970 1980 1990 2000 2010 Change (p.p.) 1970 1980 1990 2000 2010 Change (%) Botswana 8.8 18.5 25.1 21.9 14.1 5.3 3.5 5.7 7.3 8.4 9.5 5.9 Ethiopia 6.3 5.7 5.5 7.3 16.0 9.7 4.4 4.7 4.7 5.4 6.9 2.4 Ghana 27.5 28.1 24.5 14.8 26.5 1.0 4.1 2.0 1.5 4.0 6.0 10.1 Kenya 22.0 23.1 20.1 20.3 19.9 2.1 6.1 7.2 8.1 8.9 9.6 3.5 Malawi 17.2 17.2 17.3 16.5 17.7 0.5 3.1 4.1 5.3 8.1 9.5 6.4 Mauritius 27.4 26.2 29.4 31.0 25.0 2.4 6.4 7.8 9.0 10.0 10.8 4.4 Nigeria 28.5 28.4 23.7 20.1 30.2 1.7 3.9 5.4 7.1 9.7 11.1 7.2 Senegal 7.7 14.0 20.2 19.9 22.5 14.9 10.2 10.6 10.9 11.4 11.8 1.6 South Africa 34.0 33.9 35.0 28.5 27.5 6.4 5.1 6.6 7.8 8.7 9.7 4.7 Tanzania 11.4 13.9 10.5 9.7 13.8 2.4 6.1 7.2 9.2 11.0 12.0 5.9 Zambia 49.2 35.3 23.2 14.6 11.6 37.7 4.6 5.6 8.6 12.7 14.6 10.0 Source: Own elaboration using the ASD (de Vries et al., 2015). ZUAZU 2633 FIGURE 4 Marginal effects of labour productivity in gender sectoral segregation. Source: Based on regression results of model in column 3 in Table 2. TABLE 3 Total labour productivity levels and gender segregation (instrumental variables). Dependent variable: (1) (2) (3) (4) ID IP ID 5 years IP 5 years Aggregate labour productivity (log) 0.349*** 0.356*** 0.736*** 0.756*** (0.025) (0.024) (0.190) (0.195) Aggregate labour productivity (log) sq. 0.024*** 0.023*** 0.050*** 0.050*** (0.002) (0.001) (0.009) (0.009) FLFP 0.154*** 0.154*** 0.297*** 0.303*** (0.008) (0.008) (0.056) (0.058) Full set of controls Yes Yes Yes Yes Time fixed-effects Yes Yes Yes Yes No. of observations 60 60 46 46 No. of groups 7 7 7 7 Within R 2 0.971 0.972 0.962 0.961 First stage Fstat 185.37 185.37 135.46 135.46 Underidentification test (Kleibergen–Paap rk LM statistic) 18.500 18.500 32.659 32.659 pvalue 0.000 0.000 0.000 0.000 Weak identification test (Kleibergen–Paap rk Wald F statistic) 26.589 26.589 18.666 18.666 Note: Driscoll and Kraay standard errors in parentheses. All independent variables one period lagged (columns 1 and 2) or 5-year period lagged (column 3 and 4). Dissimilarity index and IP index in logs. Kleibergen–Paap LM test null hypothesis is that the rank condition fails in second stage equation, that is, underidentified (Kleibergen & Paap, 2006). Kleibergen–Paap rk Wald Fcritical values varying between 5.53 and 16.38 (Stock & Yogo, 2005). Coefficient on the instrument in the first stage was negative (0.68) and statistically significant at the 0.01 level for columns 1 and 2 and 0.32 at 0.05 level for the 5-year overlapping for columns 3 and 4. Countries in sample: Botswana, Ghana, Kenya, Nigeria, Senegal, South Africa, and Zambia. *p< 0.1.**p< 0.05.***p< 0.01. 2640 ZUAZU similar results. It should be noted that, when using the instrumental variables approach, the sample of countries is reduced as there is no information on the instrument (size of the household) for Mauritius and Tanzania. Columns 3 and 4 replicate the models using augmented, overlapping five-period lags of the independent variables. The results remain similar: aggregate labour productivity is associated with a negative effect in segregation, but its quadratic term is associated with a segregation-enhancing effect. Rising women in the paid economy, measured by FLFP, is associated with a negative coefficient; thus, higher participation of women in the labour market is linked to reducing gender sectoral segregation. The magnitudes of these coefficients are larger when using 5-year lags models. I perform postestimation tests of the validity and weakness of the instrument. Concretely, I conduct the Kleibergen–Paap test for the underidentification of the first-stage regression because the models are computed using heteroscedasticity-robust standard errors. Further, the Kleibergen–Paap test is robust against violations of the i.i.d. assumption (Kleibergen & Paap, 2006). We reject the null hypothesis of underidentification at the 1% level of significance. As of the weakness of the instruments, the Kleibergen–Paap Fstatistic is higher than the standard rule-of-thumb of 10 and is beyond the critical values at any level of acceptable bias. To delve deeper on this nonlinear relationship between aggregate labour productivity and gender sectoral segregation, I replicate the model in column 1 (Table 3) using partitions of the database on the basis of low-productivity and high-productivity country-level values. The critical value of aggregate labour productivity that sets the threshold for a positive link between productivity gains and gender segregation is provided in Table A2 in Appendix A. This value is also shown in Figure 5, which sets this value at of 7 of the log-transformed aggregate labour productivity. Using the subsample of low-productive observations (at country-year level), the estimates suggest that initial gains of aggregate labour productivity increase the dissimilarity index (the proxy of gender sectoral segregation), and further increases are associated to lower segregation, hence, an inverted-U-shape link between productivity and segregation. To the contrary, using the partition of the high-productive observations, the estimates show again the U-shaped nonlinearity between aggregate labour productivity and gender segregation by industries. These partitions provide further leverage to the main findings of the paper (see the estimates of this partitions in Table A3 in Appendix A). 9 FIGURE 5 Marginal effects of aggregate labour productivity in gender sectoral segregation (IV estimates). Source: Based on regression results of model in column 1 in Table 3. ZUAZU 2641 5.3 |Country-industry-level panel data estimates Aict ¼β0þβ1RLPic,t1þβ2RLP2 ic,t1þβ3FLFPc,t1þX0 c,t1βþvict ð6Þ vict ¼ωiþδcþγtþεict i¼industry;c¼country;t¼year Equation (6) uses as dependent variable the country-industry measure of gender segregation; namely, the association index (A ic,t ), (RLP ic,t1 ) is the relative labour productivity level, a standard measure of sectoral labour productivity that is computed as the ratio between labour productivity of sector i, country c, and time tto aggregate labour productivity in country cand time t(de Vries et al., 2015). Relative labour productivity level refers to the share of each sector in total labour productivity. The expected sign of the relationship between relative labour productivity in gender segregation at industry-specific levels is increasing, and this will confirm the results above. If increasing labour productivity by sectors exerts a positive effect in the association index, it will imply that this sector employs a higher proportion of women. To correspond to the previous results; however, the link between relative productivity and the association index should reverse at higher levels of the former. Thus, the quadratic term in the model in Equation (4) should be negative, implying that a high level of sectoral productivity creates a tendency to employ men. FLFP and the full set of controls, as explained in the previous section, are included in the country-industry panel data model. Table 4shows fixed-effects and IV estimates of the country-industry panel data model. Structural change is linked with an increasing feminization of sectors. However, the estimates confirm a nonlinear relationship between relative labour productivity and gender segregation. At high levels of relative labour productivity, further increases of productivity reduce the female employment in highly productive sectors. It should be noted that the explanatory power of the country-industry panel data models is lower relative to that of country panel data models presented above, as there is limited availability of data at country-industry level. Nonetheless, some insights from the results in Table 4can be drawn related to fertility rates and income inequality. Fertility depresses the feminization of sectors, a result that can represent the higher unpaid care responsibilities that women shoulder with a rising number of children and a limitation to join the paid workforce (Bloom et al., 2009). Contrary to the country panel data models, income inequality exerts a significant role in the association index, reducing the presence of women in sectors using fixed-effects models. This result provides some empirical leverage to those in Borrowman and Klasen (2020), who find an increasing effect of income inequality in sectoral gender segregation using the dissimilarity index. When using an instrumental variables model, the coefficient associated with relative labour productivity is positive, and the coefficient of its quadratic term is negative. This suggests again that, for initial increases in relative productivity, sectoral feminization is increased, while further increases in relative productivity depress the presence of women. The effect of FLFP is not significant in the instrumental variables model. Figure 6shows IV estimates of the marginal effect of increasing relative labour productivity on the association index. The confidence intervals are reported in the graph, showing that, for certain levels of relative labour productivity, the effect is not significant. However, for low levels of relative labour productivity and high levels of relative labour productivity, the effect is significant. Further, I find again a reversal of the productivity link with segregation. As this last model uses the association index as the dependent variable, the interpretation of the estimates suggests that initial escalations in relative productivity increase the presence of women. However, further increases at already high levels of productivity are related to a decrease in women in the sector. The last step in the empirical analysis of the role of structural change in gender sectoral segregation is to consider each sector separately. Hence, I replicate the IV model in Equation (6) using one sector at a time. By doing 2642 ZUAZU this, I am able to identify the role of structural change in each particular sector and, at the same time, consider the gender domination of each. Since the association index is not an aggregate measure of segregation, but a sector-level one, in interpreting separate models by sector, one should consider the general gender label of each sector. TABLE 4 Structural change and industry-specific gender segregation (FE and IV estimates). Dependent variable: Association index (1) (2) (3) (4) (5) Regression model FE FE FE IV IV Relative labour productivity 0.009*** 0.029*** 0.024** 0.004*0.032* Relative labour productivity sq (0.002) (0.009) (0.009) (0.002) (0.016) 0.000** 0.000** 0.000* (0.000) (0.000) (0.000) Aggregate labour productivity 0.084** 0.100*** 0.133*** 0.001 0.000 (0.035) (0.036) (0.044) (0.088) (0.009) FLFP 0.009*0.002 0.003 (0.005) (0.046) (0.002) GDP pc (logs) 0.024 0.037 0.007 0.004 0.030 (0.020) (0.022) (0.037) (0.153) (0.019) Trade 0.000 0.001 0.000 0.000 0.000 (0.001) (0.001) (0.001) (0.002) (0.000) FDI 0.001* 0.001 0.001 0.000 0.000 (0.001) (0.001) (0.001) (0.002) (0.000) Urban pop. 0.003 0.000 0.001 0.004 0.001 (0.003) (0.003) (0.007) (0.028) (0.001) Fertility (logs) 0.315** 0.254* 0.561*** 0.005 0.176** (0.131) (0.141) (0.174) (0.832) (0.086) Gini net 0.012** 0.016** 0.032*** 0.002 0.000 (0.005) (0.006) (0.009) (0.073) (0.002) Education 0.000 0.001 0.000 0.001 0.002** (0.002) (0.002) (0.003) (0.005) (0.001) No. of observations 1,456 1,456 1,027 790 790 No. of groups 89 89 89 69 69 Within R 2 0.028 0.040 0.063 0.010 0.024 First stage Fstat 86.81 86.81 Underidentification test (Kleibergen–Paap rk LM statistic) 10.450 10.389 pvalue 0.0012 0.0013 Weak identification test (Kleibergen–Paap rk Wald F statistic) 8.345 8.319 Note: Driscoll and Kraay standard errors in parentheses (columns 1–3). Country-industry clustered standard errors in parentheses (columns 4 and 5). Kleibergen–Paap LM test null hypothesis is that the rank condition fails in second stage equation, that is, underidentified (Kleibergen & Paap, 2006). Kleibergen–Paap rk Wald Fcritical values varying between 5.53 and 16.38, and 6.66 at the 20% maximal IV size (Stock & Yogo, 2005). Coefficient on the instrument in the first stage was negative (1.27) and statistically significant at the 0.01 level. *p< 0.1.**p< 0.05.***p< 0.01. ZUAZU 2643 Table 5pools the 11 countries in the sample and focuses on regressions separately by each industry. The table provides information on the gender label of each sector, that is, F for female, M for male, and N for neutral (these categories were provided based on the average association index for each sector in the database). The only neutral sector is government services, where the rest are divided into F or M. To interpret the sign of the estimated coefficients, one should consider whether the sector is female dominated, male dominated, or gender neutral: a positive (negative) coefficient would imply an increase in gender segregation in a female-dominated (male-dominated) sector. A positive (negative) coefficient of a neutral sector will imply feminization (masculinization) of that sector. The results provided in the regression models above are driven by certain industries such as manufacturing, utilities, construction, financial and business services, and government services, as these are the industries where relative productivity gains are found to have a significant link to gender segregation. Increasing relative productivity in manufacturing (column 3, Table 5), which is on average a female-dominated sector, probably due to the textile subsector, reduces the gender segregation of the sector, making it a more gender-neutral sector. However, I do not find a nonlinear link in this association, as the quadratic term of relative productivity is not statistically significant. Rising relative labour productivity in utilities and construction (columns 4 and 5, Table 5) is associated with an increasing female representation in those sectors, but this reverses when this productivity gains occur in higher levels of relative productivity. Thus, for low levels of sectoral labour productivity, increasing productivity reverts the male domination of utilities and construction. This goes in line with the findings of Rendall (2013,2017), suggesting that technological adoption and subsequent rises in productivity reduces the entry barriers of women in such sectors. So long as relative labour productivity is considered a proxy of technological upgrading, as in Seguino and Braunstein (2019) and Tejani and Kucera (2021), we can distil two opposing mechanisms behind the findings in the industry-level models. A brawn versus brain trade-off, as suggested by Rendall (2017), might induce the entrance of women in previously dominated sectors where physical demands are particularly important, as it is the case for utilities and construction. Nonetheless, the estimates of the quadratic term of relative labour productivity might suggest that it depends on the level of sectoral technological adoption. At certain levels of technological adoption, gender stereotypes and discriminatory hiring practices might countervail the gender equality gains of a pro-brain effect of rising productivity and induce the crowding of women in gender traditional sectors and low-productive sectors. FIGURE 6 Marginal effects of relative labour productivity in association index. Source: Based on Regression results of model in column 5, Table 4. 2644 ZUAZU TABLE 5 Pooled industry-level regressions (instrumental variables). Dependent variable: Association index (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Agri. Mining Manuf. Utilities Construction Trade Transp. Fin. & Bus. Services Government services Personal services Gender association F M F M M F M F N F Relative labour productivity 0.470 0.044 1.283** 0.140*** 1.010*1.132 0.415 0.148*** 0.304** 0.621 (3.492) (0.029) (0.520) (0.038) (0.548) (2.425) (0.332) (0.027) (0.127) (1.940) Relative labour productivity sq 0.103 0.000 0.268 0.002*** 0.409** 0.117 0.055 0.003*** 0.096*** 0.914 (3.176) (0.000) (0.165) (0.001) (0.200) (1.010) (0.033) (0.001) (0.025) (1.332) FLFP 0.070** 0.192** 0.108** 0.104*0.027 0.134** 0.082** 0.068 0.064*0.001 (0.032) (0.081) (0.040) (0.061) (0.107) (0.059) (0.040) (0.042) (0.033) (0.035) Aggregate labour productivity 0.096 0.429 0.032 0.352*0.280 0.323 0.465** 0.040 0.071 0.046 (0.175) (0.463) (0.105) (0.203) (0.441) (0.303) (0.221) (0.148) (0.123) (0.104) Full set of controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No. of observations 80 80 80 80 80 80 80 80 79 80 No. of groups 7 7 7 7 7 7 7 7 6 7 Within R 2 0.792 0.895 0.962 0.989 0.874 0.927 0.915 0.942 0.980 0.981 Note: Country-industry clustered standard errors. Models are pooled by each of the 10 sectors in the ASD database. Gender association refers to the sample average of each sector: F refers to female-dominated, M refers to male-dominated, and N refers to gender-neutral industries. No data on government services for Zambia (column 9). *p< 0.1.**p< 0.05.***p< 0.01. ZUAZU 2645 Increasing relative labour productivity of financial and business services (column 8, Table 5) reduces gender segregation, but again this is reversed as relative productivity is large enough. The heterogeneity of the subindustries considered in financial and business services, which also account for real estate, should be considered when interpreting this model, as the limited access of African women in the financial sector (Morsy, 2020) can be countervailed by the presence of women in small firms in real estate services. Finally, government services (column 9, Table 5), which include care, health, and educational services, are the only industries on average that are gender-neutral sectors. Increasing relative productivity at low levels of that variable tilts the sector towards males, but further gains of productivity favour a more gender-balanced distribution in the sector. Gender equal access to education and the access of women to higher education in those countries where government productivity is high might be a potential mechanism behind these results. 6|CONCLUSION Development economics has long been directed at the role of structural change in understanding regional disparities in economic growth. Indeed, the role of structural change in sub-Saharan Africa is at the core of the economic development debates in recent literature. However, little is known regarding the gendered impacts of this transformation. Structural change produces composition shifts from low-productivity sectors to high-productivity sectors, both in terms of value-added and employment shares. These shifts are likely to affect women's and men's employment differently, mediated by complex interactions. This paper has documented a significant interplay between structural change and gender-sectoral segregation in a sample of sub-Saharan African countries. Using a database with information on 10 sectors operating in 11 SSA countries during 1960–2010, I identify that those countries where gender segregation has decreased show, simultaneously, an increase in labour productivity. To consider the causal role of structural change in the gender distribution of sectoral employment, the paper specifies data models at both aggregate (country-year) and disaggregate levels (country-industry-year). Together with instrumental variable approaches, the results here suggest a nonlinear correlation between labour productivity and gender segregation. This paper finds a nonlinear relationship between rising labour productivity and gender sectoral segregation. Initial gains in productivity increase female employment across sectoral levels. Nonetheless, further productivity gains imply increasing sectoral segregation by gender, possibly through higher barriers for women to enter highly productive sectors, and a crowding effect in the service sector. Another important result of this paper is in the role of female labour force participation in gender-sectoral segregation. Rising female labour force participation appears to reduce sectoral segregation, probably by changing cultural norms and eroding traditional gender roles in paid and unpaid work. The main results of the paper remain when circumventing endogeneity issues of female labour force by using an instrumental variables approach. The estimates strongly support the view that fertility decline can equalize the gender distribution of sectoral employment. Urbanization and income inequality are generally associated with increasing segregation. Finally, the results do not associate economic growth with a significant role in gender segregation. Country-industry-level panel data estimates further allow identification of which specific sectors are mediated by structural change in the feminization or masculinization of employment. Specifically, the effects of structural change in gender-sectoral segregation in SSA seem to be mediated through manufacturing, utilities, construction, business, and government services. These findings add to the general literature in structural change and gender-aware macroeconomics. There are important policy implications that can be derived from the empirical analysis here. First, the process of structural change comes along with complex transformations of the production of market and nonmarket activities, formal and informal sectors, as well as paid and unpaid work. A gender-sectoral perspective is needed to fully understand the implications of structural change for the whole economy and the workers. While female labour force participation is 2646 ZUAZU found to reduce gender segregation, other factors of structural change, such as employment shifts in highly productive sectors, can countervail these gender equality trends. The interplays between the participation of women in the paid force and sectoral segregation can be interpreted as evidence that, as some gender inequalities are eroded, other, new types of inequalities emerge. Finally, declining fertility appears to be of first-order importance in promoting a gender-balanced distribution of sectoral employment. ACKNOWLEDGEMENTS I appreciate the comments and suggestions of Ipek Ilkkaracan, David Kucera, Luiza Nassif Pires, Sheba Tejani, Fiona Tregenna, and Elissa Braunstein. I presented this paper at the Gender Inequalities and Economic Theory and Policies for a Post-Pandemic World at the Levy Economics Institute of Bard College (NY, USA) in September 2022, for which I am very thankful to Thomas Masterson and Ajit Zacharias for the invitation, and the other attendants for discussions. I am also grateful to the audiences of the Gender, Work and Organization (GWO) annual conference in June 2023 (Stellenbosch, South Africa), the Institute for New Economic Thinking (INET) –Young Scholar Initiative (YSI) 3rd Young Scholars Conference on Structural Change and Industrial Policy (University of Johannesburg) in June 2023 and the Alternative Approaches to Innovation in a Dynamic World workshop (Complutense University of Madrid) in March 2024. I acknowledge financial support in the form of travel stipends from the Levy Economics Institute and INET-YSI. This paper won the runner-up best paper prize at the INET-YSI 3rd Conference on Structural Change and Industrial Policy. DATA AVAILABILITY STATEMENT The data and replication materials of this paper are available from the author upon reasonable request. ORCID Izaskun Zuazu https://orcid.org/0000-0003-0638-2567 ENDNOTES 1 See Bettio et al. (2009) for a further insight in the distinction between vertical and horizontal segregation. For examples of structural analysis of gender vertical and horizontal segregation, see Borrowm and Klasen (2020) and Arora et al. (2023). 2 It should be noted that the measure of structural change in Borrowman and Klasen (2020) is exclusively based on sectoral employment, with is at odds with the suggestion in Tregenna (2015) on that both employment and value added should be considered in studying the consequences of structural change. 3 Some countries start the time series at different years (Botswana: 1964; Ethiopia: 1961; Kenya: 1969; Malawi: 1966; Mauritius:1970; Senegal: 1970; Tanzania: 1961; Zambia: 1965). Mensah and Szirmai (2018) updated the ASD although did not provide information on the female share of employment, and thus, the most recent year this paper utilizes is 2010. See de Vries et al. (2015) for more details on the ASD. 4 The statistical capacity of African countries suffered from limited funding and thus deteriorated the accuracy of estimates of informal economic activities. This should be considered when interpreting the descriptive and econometric analysis using ASD database (de Vries et al., 2013). 5 The IP index is given by the following formula: IP ¼1=TðÞ P n i¼1 FiaM iþFi ðÞ jj , where Tis total employment and ais the share of women in total employment. F i and M i correspond respectively to the number of women and number of men in sector i. See Watts (1998) for more discussion on segregation measures. 6 See Figure A4 in Appendix Afor the evolution of A and relative labour productivity by sector. 7 Models using overlapping and nonoverlapping 5 years periods, which are available upon request, yield similar results as exposed in the paper. 8 Augmenting lagged periods is also employed in a related work by Tejani and Kucera (2021), to ensuring the exogeneity of the regressors. 9 I am thankful to an anonymous reviewer for suggesting this partition, which serves as a robustness check of the non-linear link between labour productivity and gender sectoral segregation. ZUAZU 2647 REFERENCES Agarwal, B. (1997). ‘Bargaining’and gender relations: Within and beyond the household. Feminist Economics,3(1), 1–51. https://doi.org/10.1080/135457097338799 Arora, D., Braunstein, E., & Seguino, S. (2023). A macro analysis of gender segregation and job quality in Latin America. 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