Women in transition: The dynamic effects of inward FDI on female employment in the economy and across sectors

Ghazalian, Pascal L. Article Women in transition: The dynamic effects of inward FDI on female employment in the economy and across sectors Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Ghazalian, Pascal L. (2024) : Women in transition: The dynamic effects of inward FDI on female employment in the economy and across sectors, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 12, pp. 1-22, https://doi.org/10.3390/economies12120318 This Version is available at: https://hdl.handle.net/10419/329245 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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Economies 12: 318. https://doi.org/10.3390/ economies12120318 Academic Editor: Bruce Morley Received: 5 October 2024 Revised: 13 November 2024 Accepted: 19 November 2024 Published: 25 November 2024 Copyright: © 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Women in Transition: The Dynamic Effects of Inward FDI on Female Employment in the Economy and Across Sectors Pascal L. Ghazalian Department of Economics, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; [email protected] Abstract: This paper examines the effects of inward Foreign Direct Investment (FDI) on the female employment rate in the economy and the share of female employment across sectors. The empirical analysis is implemented through the Generalized Method of Moments (GMM) System estimator for dynamic panel models using different empirical specifications and FDI openness indicators. The main results show that the overall effects of inward FDI on the national female employment rate are not statistically significant. However, they reveal that inward FDI has promoted the share of female employment in the service sector and has led to decreases in the share of female employment in agriculture. The FDI effects on the share of female employment in the industrial sector are found to be statistically insignificant. These results are generally supported when running the empirical analysis through alternative FDI openness indicators. Also, supplementary analysis reveals some variations in the magnitude of these effects over different national income categories. The findings in this paper emphasize FDI’s gendered influences in the labour market. They are consistent with the prevalence of macroeconomic channels through which inward FDI impacts female employment across sectors, and they encompass the underlying implications of various counteracting microeconomic factors. Keywords: female employment; foreign direct investment; gender inequality; multinational enterprises; economic development; dynamic panel model 1. Introduction The relationship between economic globalization and gender inequality in the labour market is a complex theme, and it has been regularly debated in policy forums and analyzed in the academic literature. The favourable standpoints on global economic integration for reducing gender inequality in the labour market are mainly associated with the positive effects of economic growth, improved social institutions, and resulting structural changes in women’s economic and educational opportunities and social status (Gray et al. 2006;The World Bank 2011;Potrafke and Ursprung 2012). In contrast, some critical perspectives on the implications of economic globalization for women cast concerns over the deterioration in the working conditions and the pervasiveness of the gender wage gap, particularly in exporting labourintensive industries (Wood 1997;Blecker and Seguino 2002;Baliamoune-Lutz 2007), and over the diffusion of advanced technologies that disproportionally displace female workers who constitute larger shares of employment in several labour-intensive industries (Tejani and Milberg 2016). Despite these diverging reviews, there is a general emphasis that economic globalization has led to a reconfiguration of international economic activities (McMillan and Rodrik 2011;Wood 2019) and has fostered gradual shifts in national economies from agriculture toward manufacturing and service (Mehra and Gammage 1999;Barrientos et al. 2004). Accordingly, there have been significant dynamic implications for the labour market in terms of employment characteristics and composition across countries and sectors. One of the main characteristics of economic globalization is the spectacular surge in Foreign Direct Investment (FDI) and the activities of Multinational Enterprises (MNEs). Inward FDI often generates positive macroeconomic effects on capital formation and economic growth (Borensztein et al. 1998;Alfaro et al. 2004), and fosters various microeconomic Economies 2024,12, 318. https://doi.org/10.3390/economies12120318 https://www.mdpi.com/journal/economies Economies 2024,12, 318 2 of 22 conduits in benefiting from under-utilized female labour (Siegmann 2006;Oostendorp 2009;Standing 2010), transferring new business culture (Lawler and Bae 1998;Tang and Zhang 2021), assigning capital-intensive industrial processes and advanced technology (Harrison and Rodríguez-Clare 2010;Tejani and Milberg 2016;Pham et al. 2021), and increasing market competition (Chen et al. 2013;Heyman et al. 2013;Vahter and Masso 2019). 1 Accordingly, inward FDI generates significant effects on women’s economic status and employment in the economy and across sectors. However, increases in female employment may not necessarily reflect improvement in women’s conditions in the workplace and labour market. For instance, Standing (1989) notes that economic globalization has led to significant growth in production accompanied by increases in female employment. However, Standing (1989) also underlines that the adoption of flexible labour configurations was followed by the deterioration in the working conditions of female workers. This point was further emphasized by Moghadam (1999), who highlights the drawbacks that economic globalization inflicts on female working conditions. Foreign affiliates of MNEs are commonly portrayed to have higher propensities to employ female workers compared to domestic firms in host countries. This characterization is supported by firm-level statistics and empirical evidence that underscore higher female employment rates in foreign affiliates of MNEs in developed and developing countries (e.g., Chen et al. 2013;Fakih and Ghazalian 2015;Kodama et al. 2018;Siegel et al. 2019). 2 Firm-level empirical studies significantly contribute to comprehending the microeconomic and business mechanisms through which foreign affiliates respond to female employment. However, they do not encompass the macroeconomic effects of inward FDI on female employment in the economy and the dynamics of female employment transition across sectors. Furthermore, firm-level empirical analyses are typically conducted for specific countries, regions, and industries. Hence, they do not stretch to cover information on international variations in inward FDI, national economic and socio-economic characteristics, and female employment rates. Despite the growing attention on the influence of FDI on labour markets, limited research has specifically explored its dynamic gendered influences and sectoral variations in employment outcomes. To address this limitation, this paper examines the effects of inward FDI on female employment in the economy and across sectors using a panel dataset that spans the time period 2001–2019 and covers 148 countries. The empirical analysis accounts for the overall implications of various macroeconomic and microeconomic channels through which inward FDI affects female employment, and it examines the distinct effects across sectors. The empirical analysis is implemented through the Generalized Method of Moments (GMM) System estimator for dynamic panel models using different empirical specifications, and it estimates the short-run and the long-run effects of inward FDI on the female employment rate in the economy and on the share of female employment across sectors. Also, the supplementary contribution of this paper is carried through the empirical methodology that controls for the confounding factors and through the use of alternative FDI openness indicators that depict the extent of foreign investment openness, capital operation freedom, and freedom of foreigners to visit. The remainder of this paper is structured as follows. Section 2overviews the macroeconomic and microeconomic channels through which inward FDI affects female employment in the economy and across sectors. Section 3presents the empirical model and econometric methodology, and it overviews the data and variables. Section 4presents and discusses the benchmark empirical results, and Section 5implements robustness checks by executing the empirical analysis using alternative FDI openness variables and empirical specifications. Section 6provides concluding remarks. 2. How Does Inward FDI Affect Female Employment? FDI is often characterized as a macroeconomic vector that accelerates economic growth and fosters economic development (Borensztein et al. 1998;Alfaro et al. 2004;Iamsiraroj 2016). 3 Economic growth eventually leads to increases in national income, and it generally Economies 2024,12, 318 3 of 22 induces structural economic changes that broaden women’s employment opportunities and induce the transition of female workers from the agricultural sector toward the (expanding) industrial and service sectors (Boserup 1970;Goldin 1995;Tam 2011;Ghazalian 2022). Hence, the catalyst role of FDI in stimulating economic growth constitutes an indirect channel through which FDI inflows decrease the shares of female employment in the agricultural sector and raise the share of female employment in the industrial and service sectors. Moreover, higher levels of national income are normally accompanied by increases in school enrolment of women, improvements in social infrastructure and women’s welfare and well-being, and reductions in social barriers and prejudice facing women’s participation in the labour market (Mammen and Paxson 2000;Cuberes and Teignier 2014;Tam 2011; Verme 2015). In this context, FDI would further exercise indirect positive influences on the female employment rate in the economy and promote the transitional effects on the share of female employment across sectors through its role as a catalyst for economic growth and development. The transition of the female labour force across sectors could be partially observed in employment statistics, accompanying higher returns to education and higher school enrolment rates of women in an open economy (Wacker et al. 2017). Inward FDI could induce reductions in the female employment rate over some stages of economic development. These impacts could be outlined through the relationship between economic growth and female labour force participation (Goldin 1995;Luci 2009; Tam 2011). The income effect generates decreases in female labour force participation rates, stemming from rising demand for children in the early stages of economic development, where agriculture plays a significant role in the economy. Also, the introduction of machinery would give a comparative advantage to male workers, leading to declines in the demand (and employment) of female workers through the substitution effect. However, with further economic growth and expansion of the service sector, cognitive abilities become more prevalent in economic activities, and the demand for female workers increases through more-cognitive/less-physical employment positions. Also, in labour-abundant countries, economic growth is generally marked by the expansion of the labour-intensive manufacturing sector, accompanied by increasing demand for unskilled female workers. Foreign affiliates of MNEs often have higher propensities to employ female workers compared to domestic firms in host countries as they tend to benefit from under-utilized female labour resources, a compliant female labour force, gender wage gap, and absence of (or inefficient) gender equality regulations (Ozler 2000;Oostendorp 2009;Standing 2010). 4 This channel is particularly prevalent in the case of export-oriented foreign affiliates that seek to employ low-wage, unskilled female workers in developing countries where gender wage gaps prevail. Many empirical studies (e.g., Fernández-Kelly 1983;Chen et al. 2013; Coniglio et al. 2017;Fernandes and Kee 2020) show that foreign affiliates of MNEs tend to disproportionally employ unskilled female workers in developing countries to benefit from the gender wage gap and compliant female labour force.5 The role of FDI in spreading new cultural norms and business practices in the host country, including those associated with women in the workplace, is well established in the literature (Watson 2006;Lawler and Bae 1998;Monge-González et al. 2021). In this context, foreign affiliates of MNEs often adopt corporate social responsibility schemes and gender-inclusive policies that tend to reduce gender inequality in the workplace (Kucera 2002;Kodama et al. 2018). Lawler and Bae (1998) underline the role of MNEs in reducing gender-based employment discrimination in developing countries. They attribute this role to the cultural factors that are expressed through the existing wedge in gender equality levels between the source—developed countries—and the host—developing countries. Several studies (e.g., United Nations Conference on Trade and Development (UNCTAD) (2014); Tang and Zhang 2021;Choi and Greaney 2022) present empirical evidence that emphasizes the cultural and socio-economic characteristics of FDI’s source country in determining the effects on female employment in the host country. These studies find that foreign affiliates of MNEs originating from countries with higher levels of gender equality Economies 2024,12, 318 4 of 22 feature higher female employment rates and narrower gender wage gaps, and they tend to generate cultural spillovers to domestic firms.6 MNEs are generally more resistant to the implications of gender-biased social norms when undertaking FDI in host countries. This relative immunity is transmitted into the business culture of their foreign affiliates, eventually spilling over into the hiring practices of domestic firms in host countries (Lawler and Bae 1998;Monge-González et al. 2021). Also, managers of foreign affiliates of MNEs are often expatriated from MNEs’ home countries or selected among nationals that tend to have an affinity to the culture of MNEs’ home countries (e.g., nationals that completed education in developed countries). As such, they tend to be more resistant to the existing discriminatory cultural norms and practices that prevail in host countries (Lawler and Bae 1998). Monge-González et al. (2021) describe the cultural transmission mechanisms from foreign affiliates of MNEs to domestic firms by (1) the demonstration effect through which social norms in foreign affiliates are emulated and infused into the work environment of domestic firms, and (2) the learning effect that mainly occurs through the mobility of workers from foreign affiliates to domestic firms or through other business networks. Foreign affiliates of MNEs do not normally bend to the social norms of the host country since such inclinations would be profit-decreasing and inefficient. This point is emphasized by Siegel et al. (2019), who indicate that the neoclassical theories where foreign affiliates abide by social norms that exist in the host country do not account for the advantages of non-conformism to those norms, which allow foreign affiliates to benefit from the talents and work contributions of the female labour force. Becker (1957) hypothesizes that increases in market competition tend to lessen the extent of employment discrimination since such practices would be particularly costly to the employing firms. Hence, these firms would be compelled to overcome discriminatory cultural norms and organizational practices to survive market competition (Chen et al. 2013;Heyman et al. 2013;Vahter and Masso 2019). Foreign affiliates of MNEs are usually tightly connected to the competitive global market, rendering gender-biased discriminatory practices intrinsically detrimental to their performance. Furthermore, increases in FDI inflows typically lead to rises in market competition in the host country, inducing domestic firms to lessen their gender-biased practices and promoting better allocation of labour resources (Black and Brainerd 2004;Chen et al. 2013). Hence, this supplementary channel would generate positive effects of inward FDI on gender equality in the workplace and on female employment. Some studies (e.g., Aguayo-Tellez 2012;Juhn et al. 2014;Vahter and Masso 2019) underline that FDI constitutes a significant vector that transmits skill-intensive technology through the foreign affiliates of MNEs. This channel tends to be marked by higher complementarity levels with female workers since women feature, in general, comparative advantage in cognitive skills vis-à-vis physical skills. The demand for the cognitive abilities of workers would rise with technology transfer, whereas the demand for the physical abilities of workers, which are often expressed through tasks performed by men, tends to decrease. Also, upgraded technologies that are introduced by foreign affiliates into the host country could horizontally and vertically spill over into other domestic firms (Blomström et al. 2003;United Nations Conference on Trade and Development (UNCTAD) (2014); Fernandes and Kee 2020). In such cases, the complementarity between new technologies and female employment would be further emphasized in the economy. It is worth noting that FDI in one sector could have indirect effects on female employment in domestic firms with upward or downward linkages to foreign affiliates of MNEs through the supply chain (Saadi 2010;Fernandes and Kee 2020). In many cases, inward FDI leads to significant growth of the industrial sector in host countries. This growth is normally accompanied by a rise in demand for sub-contracted services offered by some specific service sectors (e.g., information technology, financial services) or administrative positions within domestic firms. The more-cognitive and less-physical nature of such Economies 2024,12, 318 5 of 22 services aligns with women’s comparative advantage, eventually leading to increases in female employment rates in these sectors. There are, however, some counteracting microeconomic factors through which inward FDI decreases female employment rates. In some cases, FDI brings capital-intensive production processes and advanced technologies that tend to replace unskilled labour in host countries. Female workers, who often make up larger proportions of the unskilled labour force in some labour-intensive sectors (e.g., export-oriented manufacturing industries in developing countries), will be significantly affected and, as such, female employment will be disproportionally displaced (Tejani and Milberg 2016;Pham et al. 2021). Also, foreign affiliates of MNEs typically require more skilled labour compared to domestic firms to match their technological endowment. Then, their production conditions could imply lower demand for unskilled female workers, particularly in the industrial sector (Tejani and Milberg 2016). 3. Empirical Model The empirical analysis aims to estimate the effects of inward FDI on the national female employment rate and to determine its role in the transition of female labour across sectors (i.e., the agricultural, industrial, and service sectors). The primary hypothesis posits that inward FDI promotes the transition of women from agriculture through the industrial sector toward the service sector. As such, a negative relationship is expected between inward FDI and the share of female employment in the agriculture sector, whereas a positive relationship is expected between inward FDI and the share of female employment in the service sector. Meanwhile, the FDI effect on the share of female employment in the industrial sector is indeterminate, contingent upon various market and economic conditions, along with the relative inflow from the agricultural sector and outflow toward the service sector. The dynamic panel specifications are estimated through the GMM System estimator that accounts for country-specific unobserved heterogeneity and that involves the estimation of a system of first-difference and level equations (Arellano and Bover 1995;Blundell and Bond 1998). 7 Then, lags in the level form are used as instruments in the first-difference equations, whereas first differences are used as instruments in the level equations. Letting the superscript “ e ” denote the overall national economy, FEmpe it represents the national female employment rate in country i at time t . The corresponding dynamic panel equation is given by the following: FEmpe it =θe+βeFEmpe it−1+γeFDIOpit +δe′Xit +ωe t+ηe i+ϑe it (1) where FDIOpit depicts FDI openness, and Xit is a vector of regressors comprising standard control variables (e.g., fertility rate, population size, economic growth rate, school enrolment rate, inflation rate). The empirical model includes the time-specific effect ωe t that controls for time-specific shocks. Also, ηe i is the unobserved country-specific effect, and ϑe it is the remaining stochastic term. The GMM System estimation involves the following set of moment conditions for the first-difference and level equations:    EFEmpe it−z·∆ϑe it=0 for z≥2 EFDIOpit−z·∆ϑe it=0 for z≥1 E[xit−z·∆ϑe it]=0 for xit ∈Xit and z≥1 (2)    E∆FEmpe it−z·ηe i+ϑe it =0 for z≥2 E∆FDIOpit−z·ηe i+ϑe it =0 for z≥1 E∆xit−z·ηe i+ϑe it =0 for xit ∈Xit and z≥1 (3) Next, let k denote a given sector with k=a for the agricultural sector, k=m for the industrial sector, and k=s for the service sector. Let ShFEmpk it represent the share of female employment in sector k in country i at time t , with ∑kShFEmpk it = 1 ∀i , t . The basic dynamic empirical model is determined as follows: Economies 2024,12, 318 6 of 22    ShFEmpa it =θa+βaShFEmpa it−1+γaFDIOpit +δa′Xit +ωa t+ηa i+ϑa it ShFEmpm it =θm+βmShFEmpm it−1+γmFDIOpit +δm′Xit +ωm t+ηm i+ϑm it ShFEmps it =θs+βsShFEmps it−1+γsFDIOpit +δs′Xit +ωs t+ηs i+ϑs it (4) where ωk t is the time-specific effect, and ηk i and ϑk it represent the unobserved countryspecific effect and the remaining stochastic term, respectively. The corresponding sets of moment conditions are as follows:      E[ShFEmpk it−z·∆ϑk it] = 0 for z≥2 E[FDIOpit−z·∆ϑk it] = 0 for z≥1 E[xit−z·∆ϑk it] = 0 for xit ∈Xit and z≥1 (5)          E[∆ShFEmpk it−z·ηk i+ϑk it] = 0 for z≥2 E[∆FDIOpit−z·ηk i+ϑk it] = 0 for z≥1 E[∆xit−z·ηk i+ϑk it] = 0 for xit ∈Xit and z≥1 (6) The estimated coefficients on the FDI openness variable depict the short-run effects, whereas the corresponding long-run (enduring) effects are determined by γe/(1−βe) in the case of the national female employment rate ( FEmpe it ) and by γk/(1−βk) in the case of the share of female employment in sector k ( ShFEmpk it ). The long-run effects account for the cumulative effects of FDI openness on the female employment variables. Notably, there is a high persistence over time when γe and γk are closer to one, over the process of moving toward the long-run equilibrium. The GMM System estimations are assessed by the following two standard tests (Arellano and Bond 1991;Arellano and Bover 1995;Blundell and Bond 1998): the Sargan–Hansen (SH) test of over-identifying restrictions for the joint validity of instruments, and the Arellano–Bond test of second-order autocorrelation AR(2) for the consistency of the estimates. The empirical analysis is implemented using a panel dataset that spans the time period 2001–2019 and covers 148 countries (for a total of 2812 observations). 8 The datasets on the female employment rate in the economy and the share of female employment across sectors are derived from the International Labour Organization’s database (ILOSTAT), where employment is defined as “persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e., who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements”. The working-age population in this database is defined as those aged 15 and older. The variable FEmpe it represents the women’s employment-to-population ratio (expressed in percentage terms), designating the proportion of a country’s female population that is employed. The variables ShFEmpa it , ShFEmpm it , and ShFEmps it represent the percentage share of female employment in the agricultural, industrial, and service sectors, respectively, in total female employment.9Table 1 presents descriptive statistics for the overall female employment rate in the economy and the share of female employment across sectors. The basic FDI openness measure ( FDIOpit ) is represented through the ratio of net inflows of FDI to GDP, where FDI refers to direct investment equity flows in the reporting economy and covers the sum of equity capital, reinvestment of earnings, and other capital. 10 This FDI dataset is derived from the Balance of Payment (BoP) database of the International Monetary Fund (IMF). The benchmark empirical analysis also uses alternative indicators measuring the extent of foreign capital openness since sector-specific inward FDI datasets are scarce and only available for a subset of (mostly developed) countries. These indicators are sourced from the Fraser Institute’s database, and they comprise the foreign ownership/investment openness index ( ForOwnOpit ), capital freedom index ( CapitalFrit ), and freedom of foreigners to visit index ( ForVisitFrit ). These indicators range between Economies 2024,12, 318 7 of 22 zero and 10, where higher values indicate lower restrictions (or higher freedom levels) (Gwartney et al. 2021). 11 Table 1displays descriptive statistics for the benchmark FDI openness measure, total FDI inflows (in constant USD 2010), and FDI freedom indicators. The empirical specification includes conventional control variables in the female employment equations, covering macroeconomic factors [economic growth rate ( EconGrowthit ), unemployment rate ( Unempit ), and inflation rate ( In f lationit )] and socio-economic/sociodemographic factors [fertility rate ( Fertilityit ), log of population size ( lnPopulationit ), and primary school enrolment rate ( PrSEnrolit )]. The corresponding datasets are derived from the World Bank database. Table 1. Descriptive statistics. (i) (ii) (iii) (iv) Mean St. Dev. Min. Max. FEmpe it 48.15 15.77 4.49 86.01 ShFEmpa it 27.24 27.47 0.01 96.50 ShFEmpm it 12.08 6.88 0.33 37.52 ShFEmps it 60.68 25.59 2.79 98.67 FDIOpit 5.42 17.97 −40.29 449.08 FDIit 11.10 39.75 −296.27 758.61 ForOwnOpit 5.55 3.77 0 10 CapitalFrit 3.66 2.92 0 10 ForVisitFrit 5.11 3.50 0 10 Notes: The descriptive statistics in this table cover a total of 2812 observations and include the following variables: FEmpe it (female employment-to-population ratio, %); ShFEmpa it (share of female employment in the agricultural sector, % of female employment); ShFEmpm it (share of female employment in the industrial sector, % of female employment); ShFEmps it (share of female employment in the service sector, % of female employment); FDIOpit (FDI inflows to GDP ratio, %); FDIit (FDI inflows in constant USD 2010, billion); ForOwnOpit (foreign ownership/investment openness, indicator); CapitalFrit (capital freedom, indicator); and ForVisitFrit (freedom of foreigners to visit, indicator). 4. Benchmark Empirical Results The empirical results from the benchmark dynamic panel regressions are presented in Table 2for the women’s employment-to-population ratio ( FEmpe it ), and in Tables 3–5for the share of female employment in the agricultural, industrial, and service sectors (ShFEmpa it, ShFEmpm it , and ShFEmps it ), respectively. The SH tests for over-identifying restrictions and the AR(2) tests for the prevalence of second-order serial correlation do not reject the corresponding null hypotheses, and they confirm the validity of the estimates across the empirical models in these tables. Columns (i) in these tables show the results from the one-step GMM System estimations. The effect of FDI openness ( FDIOpit ) on the female employment rate ( FEmpe it ) is not statistically significant. This outcome may not necessarily imply that inward FDI does not have any impact on female employment, but it could rather depict the net effects of counteracting macroeconomic and microeconomic implications within and across sectors. A closer look into columns (i) of the following tables reveals that the effect of FDI openness on the share of female employment in the agricultural sector ( ShFEmpa it ) is negative and statistically significant at the 1% level. The estimates in Table 3indicate that a two-fold increase in FDI openness would lead to a decrease in the share of female employment in the agricultural sector by 0.35 percentage points (pps) in the short run and by [0.349/(1 − 0.838)] = 2.15 pps in the long run, ceteris paribus. In contrast, the effects of FDI openness on the share of female employment in the service sector ( ShFEmps it ) are positive and statistically significant at the 1% level. The estimates in Table 5indicate that a two-fold rise in FDI openness leads to an increase in the share of female employment in the service sector by 0.44 pps in the short run and by [0.437/(1 − 0.853)] = 2.97 pps in the long run, ceteris paribus. Table 4shows that the effect of FDI openness on the share of female employment in the industrial sector (ShFEmpm it ) is statistically insignificant. These Economies 2024,12, 318 8 of 22 outcomes could stem from the net effects of counteracting factors as female employment shifts from agriculture through the industrial sector toward the service sector. Table 2. Effect of FDI on female employment rate. (i) (ii) (iii) (iv) (v) GMM System GMM System [FOD] GMM System [Two-Step] GMM System GMM System [Two-Step] FEmpe it−10.855 a 0.849 a 0.860 a 0.847 a 0.842 a (0.023) (0.026) (0.024) (0.024) (0.025) FDIOpit 0.092 0.105 0.087 0.064 0.068 (0.070) (0.076) (0.069) (0.073) (0.076) d_FDIOpit (Trc1)0.070 b 0.066 b (0.028) (0.029) d_FDIOpit (Trc2)0.045 0.042 (0.030) (0.032) Fertilityit −0.096 −0.089 −0.093 −0.086 −0.083 (0.075) (0.077) (0.076) (0.076) (0.078) lnPopulationit −0.081 −0.078 −0.065 −0.092 −0.084 (0.065) (0.070) (0.068) (0.069) (0.074) EconGrowthit 1.710 b 1.654 b 1.904 b 2.049 b 2.103 b (0.817) (0.852) (0.875) (0.895) (0.870) PrSEnrolit 0.017 0.018 0.017 0.020 0.022 (0.012) (0.013) (0.012) (0.013) (0.014) In f lationit −0.005 −0.007 −0.004 −0.003 −0.004 (0.006) (0.007) (0.006) (0.006) (0.007) Unempit −0.068 b −0.079 b −0.070 b −0.056 c −0.070 c (0.031) (0.035) (0.033) (0.033) (0.036) Long-Run Effects FDIOpit 0.634 0.695 0.621 0.418 0.430 (0.479) (0.498) (0.483) (0.474) (0.477) d_FDIOpit (Trc1)0.458 b 0.418 b (0.182) (0.181) d_FDIOpit (Trc2)0.294 0.266 (0.196) (0.202) N×T2812 2812 2812 2812 2812 SH Test (Pr >χ2)0.451 0.455 0.449 0.487 0.481 AR(2)Test (Pr >z)0.628 0.639 0.625 0.596 0.589 Notes: The dependent variable is FEmpe it (female employment-to-population ratio, %). Robust (Windmeijercorrected) standard errors are reported in parentheses, with “a”, “b”, and “c” denoting statistical significance at the 1% level, 5% level, and 10% level, respectively. The SH test is the Sargan–Hansen test of instrument overidentification restrictions. The AR(2) test is the Arellano–Bond test for second-order serial correlation. Columns (iv) and (v) present the results when specifying the effect of FDIOpit by the RGDPC tercile categories. The third tercile ( Trc3 ) is set as the reference, whereas the first and second terciles ( Trc1 and Trc2 ) are presented as deviations from the reference. Columns (ii) of these tables present the results when carrying out the GMM System estimation with Forward Orthogonal Deviations (FODs) instead of first-differencing. The FOD approach consists of subtracting the average of all available future observations rather than subtracting the previous observations (Arellano and Bover 1995). Also, columns (iii) of these tables present the results from the two-step GMM System estimations, where the covariance matrix is subjected to finite-sample correction to tackle downward bias Economies 2024,12, 318 15 of 22 Table 7. Cont. (i) (ii) (iii) (iv) GMM System GMM System [Two-Step] Short-Run Effect Long-Run Effect Short-Run Effect Long-Run Effect Share of Female Employment in the Agricultural Sector (ShFEmpa it ) xFDIOpit −0.193 a −1.149 a −0.181 a −1.097 a (0.065) (0.423) (0.061) (0.404) d_xFDIOpit (Trc1)−0.109 a −0.649 a −0.101 a −0.612 a (0.033) (0.212) (0.032) (0.208) d_xFDIOpit (Trc2)−0.174 a −1.036 a −0.166 a −1.006 a (0.036) (0.237) (0.038) (0.261) Share of Female Employment in the Industrial Sector (ShFEmpm it ) xFDIOpit −0.047 c −0.376 c −0.049 c −0.402 c (0.026) (0.206) (0.027) (0.218) d_xFDIOpit (Trc1)−0.005 −0.040 −0.005 −0.041 (0.022) (0.179) (0.022) (0.183) d_xFDIOpit (Trc2)−0.063 b −0.504 b −0.068 b −0.557 b (0.030) (0.237) (0.032) (0.236) Share of Female Employment in the Service Sector (ShFEmps it ) xFDIOpit 0.227 a 1.455 a 0.235 a 1.546 a (0.049) (0.353) (0.052) (0.379) d_xFDIOpit (Trc1)0.108 a 0.692 a 0.113 a 0.743 a (0.028) (0.198) (0.029) (0.212) d_xFDIOpit (Trc2)0.190 a 1.218 a 0.196 a 1.289 a (0.047) (0.329) (0.050) (0.356) Notes: The corresponding dependent variables are as follows: FEmpe it (female employment-to-population ratio, %); ShFEmpa it (share of female employment in the agricultural sector, % of female employment); ShFEmpm it (share of female employment in the industrial sector, % of female employment); and ShFEmps it (share of female employment in the service sector, % of female employment). Robust (Windmeijer-corrected) standard errors are reported in parentheses, with “a”, “b”, and “c” denoting statistical significance at the 1% level, 5% level, and 10% level, respectively. This table presents the results when specifying the effect of xFDIOpit by the RGDPC tercile categories. The third tercile ( Trc3 ) is set as the reference, whereas the first and second terciles ( Trc1 and Trc2 ) are presented as deviations from the reference. 6. Conclusions FDI is one of the main aspects of economic globalization, and it is often characterized as a promoting vector of economic growth and structural changes that impact women’s economic and social conditions. As such, it is deemed to have important effects on gender inequality and female economic participation. This paper empirically examines the shortrun and the long-run effects of inward FDI on the female employment rate and the share of female employment across sectors. The corresponding hypothesis posits that inward FDI stimulates the transition of female labour from agriculture to manufacturing and, ultimately, to the service sector. Consequently, FDI openness is expected to decrease female employment in agriculture while increasing it in services. The effect on female employment in the industrial sector is less predictable, depending on economic factors and the relative flow of female labour from agriculture toward services through the industrial sector. The empirical analysis is executed through the GMM System estimator for dynamic panel models using different empirical specifications and FDI openness indicators. The main results show that the overall effects of inward FDI on the national female employment rate are not statistically significant. However, they reveal that higher levels of FDI openness lead to increases in the share of female employment in the service sector and reductions in the share of female employment in the agricultural sector. The effects of FDI openness Economies 2024,12, 318 16 of 22 on the share of female employment in the industrial sector are found to be statistically insignificant. These results are generally supported when running the empirical analysis using different FDI openness indicators that cover foreign ownership/investment openness, capital freedom, and ease of foreign nationals to visit. Supplementary analysis reveals some variations in the magnitude of these effects over national income categories, suggesting that the FDI effects on the share of female employment across sectors moderately decrease at higher national income levels as the transition of female labour across sectors relatively decelerates. It is worth noting that, due to data limitations, this paper does not account for the heterogeneous nature of FDI (e.g., greenfield investments versus mergers and acquisitions) and female labour in the informal sector. Also, external shocks, societal dynamics, and policy changes tend to alter FDI’s effects over time, rendering long-term patterns difficult to capture. These findings are basically consistent with the prevalence of macroeconomic channels through which inward FDI impacts female employment across sectors. As such, they signify the promoting effects of inward FDI on economic development and restructuring and on the transition of women from the agricultural sector—through low-skill industrial employment—toward the service sector, which is often characterized by more cognitive, administrative, and managerial occupations. The estimates also embody the implications of various counteracting microeconomic channels with varying significance across sectors. For instance, foreign affiliates of MNEs tend to benefit from the prevalence of the gender wage gap in host countries, leading to higher female employment rates, particularly in the industrial sector in low-income countries. In contrast, they could bring in capital-intensive production processes and advanced technologies that disproportionally replace unskilled female workers. Also, foreign affiliates of MNEs normally feature cultural and business norms that encourage gender equality in the workplace. As such, they stimulate increases in female employment, particularly in the service sector in emerging economies and catalyze spillovers of these norms into domestic firms. FDI openness policies are significant in raising female employment in the service sector, which often comprises positions and occupations that align with women’s comparative advantage. These policies, which typically aim at reducing restrictions on foreign ownership of capital, operation of foreign affiliates of MNEs, and cross-border movements of foreign nationals, would foster women’s economic transition across sectors—from agriculture toward the service sector through the industrial sector—over the process of economic development. These policies could be combined with economic and social development schemes that raise women’s well-being, social status, and economic opportunities in rural areas to sustain their economic and social roles and mitigate their migration out of the agricultural sector. The favourable effects of inward FDI on female employment could be generally enhanced through female educational and training programs that confer employment competitiveness to women, public and judicial reforms that support women’s social status, gender-inclusive business strategies that uphold corporate social responsibility, and economic policies that facilitate women’s accessibility to employment and resources. Funding: This research received no external funding. Informed Consent Statement: Not applicable. Data Availability Statement: The datasets on the female employment rate in the economy and the share of female employment across sectors are derived from the International Labour Organization’s database (ILOSTAT) at: https://ilostat.ilo.org/ (accessed on 25 January 2024). The basic FDI dataset is derived from the International Monetary Fund (IMF) database at: https://www.imf.org/en/Data (accessed on 12 February 2024), and the FDI openness indicators are sourced from the Fraser Institute’s database at: https://www.fraserinstitute.org/ (accessed on 19 February 2024). The macroeconomic and socio-economic/socio-demographic variables are sourced from the World Bank database at: https: //databank.worldbank.org/source/world-development-indicators (accessed on 11 March 2024). Conflicts of Interest: The author declares no conflict of interest. Economies 2024,12, 318 17 of 22 Appendix A Table A1. Supplementary empirical results—GMM System, total effects. (i) (ii) (iii) (iv) Female Employment Rate (FEmpe it) Share of Female Employment in the Agricultural Sector (ShFEmpa it) Share of Female Employment in the Industrial Sector (ShFEmpm it ) Share of Female Employment in the Service Sector (ShFEmps it) Short-Run Effects FDIOpit (Trc1)0.134 b −0.331 a 0.018 0.385 a (0.063) (0.106) (0.045) (0.113) FDIOpit (Trc2)0.109 −0.373 a 0.045 b 0.427 a (0.080) (0.074) (0.022) (0.101) Long-Run Effects FDIOpit (Trc1)0.876 b −1.982 a 0.133 2.533 a (0.408) (0.685) (0.322) (0.800) FDIOpit (Trc2)0.712 −2.234 a 0.333 b 2.809 a (0.516) (0.495) (0.161) (0.722) Notes: The corresponding dependent variables are as follows: FEmpe it (female employment-to-population ratio, %); ShFEmpa it (share of female employment in the agricultural sector, % of female employment); ShFEmpm it (share of female employment in the industrial sector, % of female employment); ShFEmps it (share of female employment in the service sector, % of female employment). Robust (Windmeijer-corrected) standard errors are reported in parentheses, with “a” and “b” denoting statistical significance at the 1% level and 5% level, respectively. This table shows the estimated coefficients on FDIOpit by the RGDPC tercile categories Trc1 (the lowest tercile) and Trc2 (the middle tercile). The corresponding estimates for the Trc3 (the highest tercile) category are given in Tables 2–5. Table A2. Supplementary empirical results—GMM System [Two-Step], total effects. (i) (ii) (iii) (iv) Female Employment Rate (FEmpe it) Share of Female Employment in the Agricultural Sector ShFEmpa it) Share of Female Employment in the Industrial Sector (ShFEmpm it ) Share of Female Employment in the Service Sector (ShFEmps it) Short-Run Effects FDIOpit (Trc1)0.134 b −0.316 a 0.024 0.353 a (0.066) (0.106) (0.045) (0.116) FDIOpit (Trc2)0.110 −0.357 a 0.044 c 0.398 a (0.087) (0.081) (0.025) (0.102) Long-Run Effects FDIOpit (Trc1)0.848 b −1.927 a 0.185 2.369 a (0.413 (0.690) (0.339) (0.861) FDIOpit (Trc2)0.696 −2.177 a 0.338 c 2.671 a (0.551) (0.546) (0.193) (0.728) Notes: The corresponding dependent variables are as follows: FEmpe it (female employment-to-population ratio, %); ShFEmpa it (share of female employment in the agricultural sector, % of female employment); ShFEmpm it (share of female employment in the industrial sector, % of female employment); ShFEmps it (share of female employment in the service sector, % of female employment). Robust (Windmeijer-corrected) standard errors are reported in parentheses, with “a”, “b”, and “c” denoting statistical significance at the 1% level, 5% level, and 10% level, respectively. This table shows the estimated coefficients on FDIOpit by the RGDPC tercile categories Trc1 (the lowest tercile) and Trc2 (the middle tercile). The corresponding estimates for the highest tercile category ( Trc3 ) are given in Tables 2–5. Economies 2024,12, 318 18 of 22 Table A3. Supplementary empirical results—overall FDI openness index, total effects. (i) (ii) (iii) (iv) GMM System GMM System [Two-Step] Short-Run Effect Long-Run Effect Short-Run Effect Long-Run Effect Female Employment Rate FEmpe it ) xFDIOpit (Trc1)0.120 c 0.896 c 0.121 c 0.877 c (0.064) (0.473) (0.065) (0.471) xFDIOpit (Trc2)0.093 0.694 0.096 0.696 (0.072) (0.522) (0.076) (0.518) Share of Female Employment in the Agricultural Sector (ShFEmpa it ) xFDIOpit (Trc1)−0.302 a −1.798 a −0.282 a −1.709 a (0.100) (0.639) (0.091) (0.603) xFDIOpit (Trc2)−0.367 a −2.185 a −0.347 a −2.103 a (0.085) (0.542) (0.086) (0.554) Share of Female Employment in the Industrial Sector (ShFEmpm it ) xFDIOpit (Trc1)−0.052 c −0.416 c −0.054 c −0.443 c (0.030) (0.236) (0.031) (0.250) xFDIOpit (Trc2)−0.110 b −0.880 b −0.117 b −0.959 b (0.055) (0.432) (0.056) (0.453) Share of Female Employment in the Service Sector (ShFEmps it ) xFDIOpit (Trc1)0.335 a 2.147 a 0.348 a 2.289 a (0.085) (0.596) (0.087) (0.624) xFDIOpit (Trc2)0.417 a 2.673 a 0.431 a 2.836 a (0.101) (0.702) (0.105) (0.770) Notes: The corresponding dependent variables are as follows: FEmpe it (female employment-to-population ratio, %); ShFEmpa it (share of female employment in the agricultural sector, % of female employment); ShFEmpm it (share of female employment in the industrial sector, % of female employment); ShFEmps it (share of female employment in the service sector, % of female employment). Robust (Windmeijer-corrected) standard errors are reported in parentheses, with “a”, “b”, and “c” denoting statistical significance at the 1% level, 5% level, and 10% level, respectively. This table shows the estimated coefficients on xFDIOpit by the RGDPC tercile categories Trc1 (the lowest tercile) and Trc2 (the middle tercile). The corresponding estimates for the highest tercile category ( Trc3 ) are given in Table 7. Notes 1 The effects of inward FDI on employment and wages are highlighted in many empirical studies (e.g., Aitken et al. 1996;Brown et al. 2004;Hijzen et al. 2013). 2 Among the wide range of empirical studies, Chen et al. (2013) show that MNEs’ foreign affiliates located in China have higher propensities to employ women compared to domestic firms. Kodama et al. (2018) and Siegel et al. (2019) find that MNEs’ foreign affiliates located in Japan and South Korea, respectively, feature higher proportions of female employment compared to domestic firms and that they adopt better accommodating policies for female workers. Yu et al. (2019) show that inward FDI has led to improvements in the economic status of women and reductions in the gender wage gap in China’s urban regions. Also, Fakih and Ghazalian (2015) find that private foreign ownership is associated with higher female employment rates in manufacturing firms in the Middle East and North Africa (MENA) region. 3 FDI inflows and MNEs’ operations are determined by various national characteristics, international trade and investment conditions, and exogenous events (e.g., Biswas 2002;Bénassy-Quéréet al. 2007;Naudéand Krugell 2007;Ghazalian and Furtan 2008;Blonigen and Piger 2014;Saini and Singhania 2018;Ghazalian and Amponsem 2019;Ghazalian 2022,2023). 4 The competition in attracting FDI among host countries may compel governments to maintain low standards of gender equality in the workplace, ensuring a compliant female labour force and a sufficiently wide gender wage gap. In such cases, increases in FDI inflows would be arguably accompanied by higher levels of female employment with deficient working conditions. However, there exists some empirical evidence that does not support this proposition (e.g., Kucera 2002;Brown 2007). 5 Fernández-Kelly (1983) highlights the case of the maquiladoras in Mexico, where foreign affiliates of MNEs discretionally employ female workers to take advantage of the existing gender wage gap. Chen et al. (2013) find that exporting foreign affiliates Economies 2024,12, 318 19 of 22 of MNEs in China have higher female employment rates compared to non-exporting domestic firms. In the case of Vietnam, Coniglio et al. (2017) show that foreign affiliates of MNEs have higher propensities to employ (primarily unskilled) female workers compared to domestic firms, but they pay lower wages. Also, Fernandes and Kee (2020) examine the case of the apparel and textiles industry in Bangladesh, and they find that foreign affiliates of MNEs are characterized by higher levels of female employment compared to domestic firms. 6 For instance, Tang and Zhang (2021) find that, in China, foreign affiliates of MNEs headquartered in countries featuring higher gender equality levels employ more female workers and appoint more female managers. 7 The GMM estimations were initially implemented for the first-difference equations to remove bias generated from unobserved individual effects (Holtz-Eakin et al. 1990;Arellano and Bond 1991). Blundell and Bond (1998) show that using lagged levels as instruments is ineffective in first-difference equations when individual series show long-term persistence and when there is a limited number of time series observations, and that the first-difference GMM estimator has a large finite sample bias and limited precision. The GMM System approach overcomes these shortcomings by combining the estimation of a system of first-difference and level equations. 8 The empirical analysis is executed for the pre-COVID-19 period dataset to circumvent the confounding and disrupting effects of this pandemic on the estimates. 9 In this database, the agricultural sector consists of activities in agriculture, hunting, forestry, and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4). The industrial sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2–5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4). The service sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; as well as community, social, and personal services, in accordance with divisions 6–9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4). 10 Direct investment is a type of cross-border investment in which a resident (or entity) in one economy exerts control or substantial influence over the operation of an enterprise in another economy. Holding 10% or more of the ordinary shares of voting stock is basically the primary condition for establishing a direct investment relationship. 11 The ForOwnOpit indicator is basically derived from the World Economic Forum (WEF)—Global Competitiveness Reports, and it is based on surveys examining foreign ownership and regulatory restrictions related to international capital flows. The CapitalFrit indicator is based on the IMF—Annual Reports on Exchange Arrangements and Exchange Restrictions, and it covers up to 13 types of international capital controls. Also, the ForVisitFr indicator is based on visa requirements from foreign visitors, and it reflects the freedom of foreigners to travel to the corresponding country for tourism and short-term business purposes. See Gwartney et al. (2021) and corresponding reports for technical details. 12 These columns show the total effects for Trc3 (the reference group), and the deviations in these effects from the reference for Trc1 and Trc2 . The corresponding total effects for these latter categories from the one-step and two-step GMM System estimations are presented in Tables A1 and A2 of the Appendix A. 13 Table 7presents the total effects for Trc3 (the reference group), and the deviations in these effects from the reference for Trc1 and Trc2 . 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