How Does the Gig Economy Impact Gender Inequalities, and What Are Possible Ways to Mitigate These Gender Inequalities?

Abstract

This paper finds that the gig economy exacerbates gender inequalities by reinforcing gender occupational segregation and the gender wage gap, categorising the contributing factors into two main areas. First, unresolved issues from traditional workplaces continue into the gig economy: systemic gender discrimination persists, and the unequal distribution of household responsibilities continues to disproportionately fall on women’s shoulders. Second, new factors within the gig economy further entrench gender inequalities: task allocation and regulatory algorithms are gender-biased due to their reliance on historical data, undermining both women’s income, safety, and health. The lack of social, health, and safety protections in the gig economy also disproportionately harms women. The paper identifies two sets of solutions, each corresponding to an area of how the gig economy exacerbates gender inequalities. The first set of solutions focuses on tackling systemic gender discrimination at its roots in traditional workplaces and society. This includes strengthening anti-discrimination laws, extending employment protections to gig workers, adopting intersectional approaches to mitigate inequalities, and redistributing unpaid care responsibilities more equitably within households. The second set of solutions addresses the challenges specific to the gig economy. Strategies include ensuring diverse data inputs during platform algorithm development, implementing bias mitigation techniques, integrating women's experiences into algorithm design, expanding social and health protections for gig workers, and prioritising worker safety in algorithm design.

1. Introduction 

The gig economy has emerged as a labour-sharing market system characterised by short-term, flexible, task-based work, facilitated by platforms to connect service providers and customers.  The central research question guiding this report is: How does the gig economy impact gender inequalities, and what are possible ways to mitigate these disparities? The paper draws on a wide variety of scholarly literature, surveys, data, and case studies from both Global South and Global North countries, providing an international perspective focusing on the employment model instead of specific regions. The paper will analyse how existing social issues intersect with this new employment model and influence gender inequality, including workplace gender discrimination and traditional gender roles. Additionally, it will examine new factors caused by the gig economy, such as platform algorithms and the lack of social protection for gig workers, contributing to gender inequality. The investigation is significant given the gig economy’s projected growth from $556.7 billion  in 2024 to reach $1,847 billion by 2032. As more individuals enter the gig economy, it becomes crucial to address the systemic gender inequalities that may worsen. Worker welfare and equity are essential for the sustainability of this new employment model, making it crucial to identify both problems and solutions. Therefore, this paper explores these aspects in an interconnected manner.

2. Literature Review 

Women are systemically disadvantaged in labour markets, with the gender wage gap remaining a significant global issue. On average, women earn approximately 20% less than men across all regions. One key factor contributing to this disparity is gender stereotypes. Although some progress has been made towards gender equality in the workplace, entrenched biases continue to disadvantage women in the workplace to this day. Additionally, women bear a disproportionate share of unpaid domestic and caregiving responsibilities, which remain excluded from economic indicators. An Oxfam report reveals that 65% of women’s weekly working hours are unpaid, amounting to nearly 90 billion hours of care each week. This unequal burden limits women’s access to jobs that require long working hours and offer minimal benefits, reinforcing occupational segregation and the gender wage gap.^,  Due to low wages and instability in traditional workplaces, many women turn to part-time jobs in the gig economy in search of greater flexibility.

With the rise of the gig economy, scholars have debated its impact on gender inequalities. Some argue that the gig economy serves as an empowering workplace solution due to its low barrier to entry and flexible work arrangements, which enable women to balance paid work with caregiving responsibilities. Since the lack of workplace flexibility is a major contributor to the gender wage gap, some researchers suggest that gig work helps narrow this disparity.  Furthermore, gig platforms leverage big data and salary transparency, potentially reducing gender wage gaps caused by unequal access to salary-related information during negotiations. Additionally, some scholars highlight that gig economy algorithms expand employment opportunities for women by matching skills with a broad range of available tasks.

However, other research suggests that the gig economy exacerbates or perpetuates existing gender inequalities. Barzilay argues that the gig economy operates within the shadows of anti-discrimination laws and traditional labour markets, carrying forward systemic gender biases. Another concern is that gig work undermines workers' financial security, rights, and social protections, as most gig workers are classified as “independent contractors.” Additionally, biased algorithms reinforce gender inequalities by perpetuating the gender wage gap and occupational segregation. Customer bias against women is often amplified through platforms' ranking and scoring systems, reducing women's visibility and limiting their earnings. Moreover, algorithms detect women’s preference for flexibility—often shaped by their disproportionate caregiving responsibilities—and subsequently offer them lower-paying tasks or fewer job opportunities. The opaque nature of these algorithmic decisions makes such discrimination difficult to identify and rectify.

Despite ongoing debates about whether the gig economy mitigates or worsens gender inequalities, there is substantial evidence documenting the precarious conditions faced by female gig workers. James finds that women's participation in the UK gig economy is constrained by entrenched caregiving roles, which also contribute to health and safety risks. Gerber highlights that female crowd-workers in Germany and the US experience heightened precarity due to their unpaid domestic responsibilities. Similarly, Churchill and Craig argue that women in Australia’s gig economy endure low incomes and economic insecurity, leaving them in a vulnerable position within the labour market.

Several studies have also investigated the gender pay gap within the gig economy. Research on over a million Uber drivers in the US identified a 7% pay disparity between men and women, primarily driven by differences in non-paid time costs and gender-specific work preferences. Barzilay and Ben-David found that, even when controlling for factors such as feedback scores, experience, job category, hours worked, and education level, women still earned approximately two-thirds of men’s hourly wages. Other studies suggest that the gender pay gap in gig work is even more severe than in traditional employment. For example, research indicates that male freelancers charge 48% more than women for equivalent work—a gap nearly three times wider than in full-time jobs.

While the literature presents varied perspectives on the gig economy’s role in gender inequalities, it lacks a structured, categorised approach that systematically analyses these issues. This paper seeks to fill that gap by organising gender inequality factors into two broad categories: (1) traditional influences from society and conventional workplaces and (2) new factors emerging from the gig economy. This framework acknowledges the interconnections between these influences and aims to provide a clearer understanding of the mechanisms that perpetuate gender disparities, as well as solutions to address them.

3. Unresolved Problems from Society and Traditional Workplaces Continued into the Gig Economy

3.1 Gender Inequality is Learned in the Workplace

Firstly, gender inequality is learned and internalised in traditional workplaces, and this learned bias then carries over into the gig economy. Given that the job environment allows values to be learned, gender inequality is learned. Research has shown that women’s salary expectations decline as they gain more experience in the labour market, reinforcing the acceptance of being seen as "non-ideal" workers.  Indeed, employers frequently consider past salary history when determining a new candidate's pay, a practice that not only sustains but also exacerbates the gender pay gap. This is because past salary history is not a neutral factor—it often reflects historical and social dynamics that assign different values to the same work depending on gender, thus providing evidence of learned gender inequality. Barzilay then argues that the gig economy operates within the "shadows" of the labour market. This implies that individuals, including women themselves, bring values, experiences, habits, and norms from traditional workplaces where systemic gender disadvantage is prevalent. This workplace culture then continues in the gig economy. 

Moreover, the barriers women face in traditional employment drive them to seek work in the gig economy, which can result in greater financial dependence and lower wage expectations, thereby perpetuating the gender pay gap. Women often enter the gig economy due to challenges in traditional employment, such as maternity leave biases and inadequate support for illness, effectively being “chased out” of conventional jobs and turning to digital platforms for more empowering options. As a result, self-undervaluation worsens for women in the gig economy, where they are often under greater financial strain and precarity due to their already vulnerable position. In fact, a report found that women are more likely than men to rely on gig work as their only source of income. Furthermore, around half of female gig workers earn between 50-100% of their income from the gig economy, compared to just 37% of male gig workers. These findings indicate that women depend on the gig economy for financial security more than men, joining out of necessity rather than to supplement regular employment due to the low barriers to entry. This financial strain and dependence, compounded by learned gender inequality and women’s self-devaluation, lead them to lower their wage expectations in order to attract potential clients. This is a contributing factor to the persistence of the gender wage gap in the gig economy.

3.2. Traditional Gender Roles is Reinforced by Gig Economy Flexibility

The flexibility offered by the gig economy allows the disproportionate burden of unpaid caregiving responsibilities to fall on women, limiting their ability to compete equally with men. Some scholars argue that the gig economy offers a solution to gender inequality, pointing to its flexibility, which enables primary caregivers to schedule work around family responsibilities. Indeed, while men often join the gig economy to pursue autonomy and higher income, most women join for the flexibility required to manage household duties.^,  This distinction in motivation is supported by a survey of 2,000 female gig workers in the US, which found that 70% served as primary caregivers. Moreover, women often begin gig work during significant life events such as pregnancy, childbirth, children starting school, separation, divorce, or a partner’s increased working hours. Overall, women join the gig economy not only as individuals but as part of families and households.

On the surface, many argue that the gig economy resolves family-work conflicts by offering a better balance between paid work and childcare. It provides an alternative to traditional employment structures, enabling women, previously excluded due to caregiving responsibilities, to participate in the workforce. However, in the long run, this flexibility creates a dual burden for women as both primary caregivers and gig workers. Moreover, the accessibility of gig work often leads fathers to increase their paid work or fully commit to employment, while mothers adjust their schedules around caregiving. Research shows that women often join the gig economy to accommodate their partner’s full-time employment, frequently following a relocation for the male partner’s job. This shows that women continue to prioritize maximizing the "household utility function." Therefore, the gig economy, despite its flexibility, does not challenge but rather accommodates traditional gender roles.

This social norm creates a situation where women with caregiving responsibilities compete with men who can fully commit to gig work, resulting in gendered disparities. One manifestation of this is that, on average, women work fewer hours compared to their male counterparts, undermining their income and potential to gain experience. According to Figure 1, women globally participate less in the gig economy due to caregiving duties. In addition, the Gig Economy Data Hub report found that women tend to work fewer hours and are more likely to work part-time than men. This disparity in working hours further perpetuates the gender wage gap.

Figure 1 Share of women reported to work in the gig economy (Source: ODI Global)

Traditional gender roles also lead to gender occupational segregation. Women in the gig economy often take on low-paying and insecure jobs because their caregiving responsibilities limit their ability to pursue higher-paying opportunities. Another factor is that social norms can influence career aspirations by creating a gap between how individuals perceive their own abilities and their actual performance. As a result, in the gig economy, men continue to dominate sectors like ICT, transport, and delivery, while women are overrepresented in sectors like administration, care, and domestic help. For example, a report from Kenya showed that gender occupational segregation is particularly noticeable in traditional roles such as housekeepers and nannies, with all but one of the workers in these roles being women.

Another reason for gender occupational segregation is women’s dual role as primary caregivers and gig workers. Women’s primary caregiver role enhances their skills in food provisioning and caregiving, making it more practical for women to take on jobs that mirror their household duties rather than competing in male-dominated sectors. Women find it easier to juggle their dual role by taking on paid work that utilises the same skills as household responsibilities, such as domestic work, childcare, or service-oriented jobs. For example, some women working in platform-based food delivery “actively embrace” traditional gender roles, finding gendered gig work emotionally rewarding. These women “self-select” to enter the more feminine sectors of the gig economy, often unaware of the way this perpetuates gender inequality. In line with what Temma Kaplan calls the “female consciousness,” “Those with female consciousness accept the gender system of their society; indeed, such consciousness emerges from the division of labour by sex.” While this shift provides women with income-earning opportunities, it ultimately strengthens the existing divide between “feminine” and “masculine” work, limiting women’s access to higher-paying and more stable employment.

4. New Problems Arising due to the Gig Economy Employment Model

4.1 Gig Platform Algorithms Reinforce Gender Inequalities

Moreover, gig platform algorithms reinforce gender inequality. Gender bias has long been embedded in traditional labour markets, and when these biases are reflected in datasets used for algorithmic decision-making, they influence job opportunities for women in the gig economy. This issue is further exacerbated by the opaque nature of these algorithms, which operate in a "black box," making it difficult to detect and address biases—even for developers.^,  The lack of transparency creates systemic inequalities that gig workers may not even realise they are experiencing. This has led to "Discrimination 3.0," where bias operates in a more subtle yet deeply embedded way within digital platforms. Specifically, gig platform algorithms reinforce gender inequalities in three ways: perpetuating gender stereotypes, amplifying client gender discrimination, and being designed with male-centric models that disadvantage women.

One way biased algorithms exacerbate gender inequality is by reinforcing gender occupational segregation. Algorithms may associate certain jobs with male characteristics, directing job opportunities in fields like science and engineering primarily to male gig workers.  By "nudging" workers toward roles based on historical patterns rather than individual capabilities, these algorithms subtly but significantly restrict women’s access to higher-paying, male-dominated professions.

Moreover, algorithms amplify client gender biases, reducing women’s visibility on gig platforms and limiting their job opportunities. Algorithms on gig platforms utilise data on worker reputations to match employers with workers. The reputational metrics often include feedback from past tasks, meaning that discriminatory reviews, interactions, and ratings create a feedback loop that reduces women’s chances of being booked. This effect is also driven by implicit biases such as profile click in ways that are often unconscious. When these biases are factored into the algorithm’s decision-making, they become even more difficult to avoid, further limiting women’s opportunities. Consequently, research identified significant negative correlations between gender and search rankings. Moreover, Hannák et al. discovered that women on platforms like TaskRabbit received approximately 10% fewer reviews than men, showing the impact of gender bias on worker visibility. Algorithmic visibility is crucial in the gig economy, as it directly impacts job opportunities. A female gig worker highlighted this concern: “That is a big deal if people can't see my profile… This is my livelihood. There's a massive pressure to keep your ranking high.” Beyond reduced visibility, client discrimination in the driving occupation directly results in women receiving fewer ride requests than their male counterparts, reducing their earnings.

Moreover, the male-centric nature of gig platform algorithms fails to account for women's unique experiences, which diminishes their income. These algorithms employ mechanisms like “surge pricing” to control worker productivity, rewarding the “ideal worker” while penalising those deemed “non-ideal” based on performance. The concept of the “ideal worker” is deeply ingrained in workplace culture and refers to individuals who are free from caregiving responsibilities and fully available to meet employer demands. This expectation is even more pronounced in the gig economy, where unlimited working hours create a stark divide between men and women, as caregiving responsibilities often restrict women's availability. Women in the gig economy tend to complete fewer tasks, decline rides due to distance from home, respond less frequently, prioritise flexibility, and avoid busier shifts such as weekends and nights.    As a result, platforms classify them as “non-ideal workers” without considering the structural constraints they face. Consequently, these algorithms assign women lower scores, leading to reduced search rankings and lower pay. For instance, Figure 2 highlights pay disparities among Chinese food delivery workers, showing that payment per order increases with the number of completed deliveries. Since women typically complete fewer deliveries per month, they receive lower pay per order. These disparities accumulate to maintain the gender pay gap.

Figure 2 Food delivery riders’ pay per order in China. (Source: Sanlian Lifeweek)

4.2. Gig Platform Algorithms Positions Women at a Health and Safety Risk

Beyond economic disparities, the gig economy also exposes women to health and safety risks, undermining their actual safety in comparison to their male counterparts. This phenomenon is caused by the inherent lack of protections in working conditions in the gig economy. Gig workers, often classified as “self-employed independent contractors,” lack social protections such as health insurance, income protection, retirement benefits, paid leave, unemployment insurance, minimum wage guarantees, work-related accident insurance, disability insurance, and worker rights imposed by labour law.

Overall, while all gig workers face challenges regarding rights and protections, the lack of social protections disproportionately affects women due to their dual role in gig work and unpaid household responsibilities. One stark example is the lack of maternity protections. Research shows that 46% of self-employed women in Europe risk not qualifying for maternity benefits, compared to less than 0.1% of full-time workers. Without adequate support, many women, particularly single mothers, are forced to work through maternity leave, often resulting in serious health issues. A 2017 survey of 104 new mothers engaged in platform-based gig work found that 59% suffered from mental and physical health problems due to being compelled to resume work soon after childbirth. This not only affected their well-being but also hindered their ability to bond with their newborns.

Women in the gig economy are also at greater risk of physical and psychological harm, including sexual harassment, gender-based violence, and discrimination.^,  This heightened vulnerability stems from the loss of the “protective effect” found in traditional workplaces, as gig work often takes place in private homes or isolated environments.  Without the safeguards and oversight of public workspaces, women face increased exposure to unsafe conditions. The uncertain and unsafe characteristic of the gig economy then increases the risk of women developing certain health diseases such as musculoskeletal ailments, diabetes, and hypertension. 

Additionally, the male-centric design of gig economy algorithms puts women’s health and safety at risk by creating situations where they have to compromise their safety for financial earning. Firstly, the time pressure imposed by these algorithms is a key concern for gig workers' health and safety. Research reveals that about half of delivery workers admit to speeding, while a third have run red lights. Women are particularly disadvantaged by this pressure due to gender-specific experiences. For instance, time constraints can force women to dismiss or endure sexual harassment, which only perpetuates its prevalence in the gig economy. Apart from time pressure, algorithms pressure female gig workers to either pursue unsafe tasks or receive lower pay. For example, gig economy platforms often penalise women for rejecting potentially hazardous jobs. A study on the Chinese ride-hailing app Didi Chuxing revealed that drivers who are willing to work late at night or travel to remote locations tend to earn higher reputational ratings. This incentivises risky behaviour, which exposes women to greater dangers, such as working during unsafe hours or in isolated locations, all in pursuit of better pay and higher ratings. 

In addition, the gig economy not only places a double burden on women—leading to exhaustion and reinforcing gender norms—but also fails to effectively address family-work conflicts. Thelen describes a management practice known as “lean staffing,” or “just-in-time” scheduling, where workers are notified of their shifts only hours in advance. While this approach helps employers save on labour costs, it creates significant uncertainty for workers. This so-called flexibility benefits those using gig work as a secondary income, but offers little support for those who rely on it as their primary source of livelihood. In practice, the gig economy’s flexibility creates a new form of unregulated 9-to-5 work for gig workers with caregiving responsibilities, where patterns of overwork persist. To earn a living wage, many women are forced to overwork, constantly managing multiple tasks, dealing with vague gig requirements, and juggling irregular schedules—all while balancing childcare duties. Many women in the gig economy report working evenings, late nights, weekends, and public holidays, which often clash with family life and prevent them from setting their own schedules. This is especially difficult when working with clients in different time zones. The constant pressure from on-demand work and algorithmic management leads to significant physical and psychological stress. Interviews have shown that gig work negatively impacts women’s relationships with partners, family, and children, as irregular schedules and the illusion of flexibility make it harder to manage childcare. As a result, the uncertainty and false flexibility of gig platforms place an additional burden on women, exploiting both their time and health.

5. Recommended Solutions 

5.1. Solving the Problem of Internalised Gender Inequality Both Within and Outside of the Gig Economy

In response to the systemic gender discrimination that is learned in traditional workplaces and carried into the gig economy, it is crucial to update policies and legal frameworks to address gender inequality both within and outside of gig work.

The gig economy often operates in the “shadows” of traditional employment laws, even when those laws do not directly govern gig work. Policymakers must therefore strengthen employment and anti-discrimination laws beyond the boundaries of conventional employment. We are now in an era referred to as Discrimination 3.0, marked by subtler, systemic inequalities embedded in platforms, algorithms, customer behaviours, and even internalised biases among women themselves. Discrimination 3.0 is not adequately addressed by current laws, which tend to target individual acts of discrimination rather than broader, ingrained workplace cultures that disadvantage women. For example, Title VII of the Civil Rights Act in the United States prohibits sex-based discrimination in the workplace but requires proof of discriminatory intent, which is difficult to establish when the discrimination is widespread and systemic. Therefore, it is essential to strengthen existing anti-discrimination laws that apply to all forms of employment, including gig work. This remains an unresolved issue that contributes to the growing gender inequality, which is then further perpetuated within the gig economy.

Moreover, in addition to updating anti-discrimination laws for general employment, it is essential to extend these protections to gig workers. Currently, most gig workers are classified as independent contractors, which excludes them from coverage under anti-discrimination and other employment-related laws. While the classification of gig workers varies by country, the general principle should be to include them within existing legal frameworks or to create new frameworks specifically tailored to "independent workers" and "dependent contractors," offering them a degree of social protection. For instance, Kasliwal advocates for the establishment of a "Platform of Platforms," which would act as a centralised regulatory authority supported by legislation. This authority would create standardised guidelines that platforms must follow and ensure ongoing oversight, addressing gaps in protection for gig workers and reducing the potential for exploitation and inequality within the sector.

When implementing anti-discrimination laws, the concept of intersectionality should be a key consideration in developing solutions. A European Union seminar highlighted the importance of "intersectionality"—a concept that is essential for understanding prejudice and inequality. Discrimination should not be viewed through a single lens, such as gender or age, but as a combination of various "identity markers" like ethnicity, social class, and age. Despite the EU's robust anti-discrimination laws, their failure to adopt an intersectional approach has resulted in significant economic losses, estimated at €224–305 billion in GDP and €88–110 billion in lost revenue. These losses stem from the ineffective implementation of the Racial and Employment Equality Directives.^,  Therefore, while this paper has primarily focused on gender inequalities, it is clear that policies must adopt an intersectional approach to address the multifaceted nature of discrimination effectively.

5.2. Address the Problem of Family-work Conflicts and Disproportionate Unpaid Caregiving Responsibilities

Since the gig economy does not fundamentally address gender roles—where women bear most of the burden of unpaid caregiving responsibilities—and even exacerbates this issue by assigning women dual roles as gig workers and unpaid caregivers, it is essential to resolve this problem at its roots. Firstly, it is essential to support women gig workers who provide unpaid care via public services, infrastructure, and social protection policies. For example, a report by the OECD has given detailed recommendations on how infrastructure, social protection, public services, and financing options can be used to address women’s unpaid care work.

Moreover, it is crucial to encourage the shared responsibility of unpaid care within households by implementing targeted policies. The fifth Sustainable Development Goal of the United Nations focuses on gender equality, with goal 5.4 specifically aiming to "recognize and value unpaid care and domestic work through the provision of public services, infrastructure, and social protection policies, and the promotion of shared responsibility within the household and family, as nationally appropriate." One successful example is Iceland's paid parental leave policy, which seeks to challenge traditional childcare roles. This policy divides parental leave equally between both parents, with each parent allocated a unique quota. If the father does not take his designated portion, it is forfeited. Research has shown that this policy has positively impacted how men and women allocate their time between employment and childcare. Therefore, considering policies that subtly, gradually, and effectively alter behaviours and social norms, such as Iceland’s approach, is a promising strategy to achieve more equitable sharing of caregiving responsibilities within households.

5.3. Prioritise Equity and Diversity in Algorithm Design

To mitigate biases in gig platform algorithms, it is crucial to prioritise transparency and equity as guiding principles during both the development and implementation phases. In addition to the important task of raising public awareness about the gender biases inherent in gig economy algorithms, there are several areas for improvement during the algorithm development process.

Firstly, improving the quality of the input stage of algorithm development is essential for achieving better output. This can be done by enhancing the quality of datasets to more accurately reflect female experiences. Another approach is to increase the diversity of developers involved in creating algorithms, as research shows that biases introduced during development significantly influence the outcomes. A 2018 study found that 80% of AI professors were male, highlighting a significant power imbalance in the AI industry and suggesting that algorithmic outputs tend to be more male-centric due to these biases. Therefore, it is crucial to enhance diversity and representation in both input datasets and among the developers creating algorithms.

Furthermore, algorithms should undergo thorough bias checks using various mitigation strategies before deployment. For example, O’Connor and Liu have developed methods designed to identify and address biases during the testing phase. Another effective strategy is for algorithm operators to create a bias impact statement after ensuring the algorithm complies with non-discrimination laws. This statement acts as a flexible template with guiding questions to assist in navigating the design, implementation, and monitoring stages of algorithm development, promoting accountability and reducing the risk of bias.

Moreover, platform algorithm design must shift from being male- and white-centric to better accommodate the unique experiences and circumstances of female users. Current scoring systems often force women to choose between risking their safety or compromising their pay. Therefore, the algorithms used to assign scores based on participation hours, speed, and job rejections should be revised to consider the health and safety of female gig workers. A positive example of this approach is Uber's "Uber Ellas" feature in Argentina, which allows women drivers to have greater control over trip selection. This feature enables them to accept rides only from passengers identified as women. “Uber Ellas” has successfully reduced cancellation rates, increased the number of trips taken by women drivers, and allowed women to work more safely at night. As a result, the number of women drivers in Mendoza and Buenos Aires increased by 30% within the first year of the program's implementation. Similar modifications in algorithmic design could significantly empower female gig workers.

Fourth, governments and platform companies must ensure that gig workers have access to social protections and rights. Building on the idea of expanding anti-discrimination and employment laws to include gig workers, it is equally crucial to update legal frameworks to reflect the evolving nature of employment structures. Worker entitlements should be broadly defined to encompass various forms of work, ensuring that protections and benefits are not limited solely to those classified as "employees." Adapting these protections to the rise of the gig economy is essential for ensuring the sustainability of this employment model and safeguarding the well-being of gig workers.

Fifth, platform design must proactively prevent violence and abuse while offering support to gig workers during such incidents. This can include incorporating features like "panic buttons" and emergency hotlines within apps to assist workers facing safety threats. It is also essential for platforms to take reports of client abuse and harassment seriously, ensuring that these issues are addressed to deter such behaviour and prevent future occurrences. Furthermore, platforms should implement a two-way rating system, providing mutual transparency between workers and clients, which can help foster a safer, more respectful environment for all users.

6. Conclusion 

In conclusion, this paper finds that the gig economy continues to exacerbate gender inequalities. These inequalities arise in two key ways.

The first involves traditional factors and systemic issues that persist and worsen within the gig economy. Gender discrimination and inequality, already prevalent and internalised in traditional workplaces and society, carry over into the gig economy. Corresponding solutions include strengthening employment and anti-discrimination laws to address systemic and subtle biases, extending legal protections to gig workers, and incorporating intersectionality when formulating solutions. Additionally, while the gig economy offers flexibility, it reinforces traditional gender roles by enabling women to juggle unpaid household responsibilities with gig work. This dual burden creates disparities in work commitments between men and women, perpetuating the gender wage gap and occupational segregation. Solutions include supporting and redistributing unpaid household labour. Ultimately, the gig economy itself does not inherently create gender inequalities, it amplifies unresolved social issues. Therefore, effective solutions must address the root causes outside the gig economy. 

The second way the gig economy exacerbates gender inequalities is through new factors. The gig economy platform algorithms perpetuate gender inequality due to its reliance on biased data. This reinforces gender stereotypes and gender occupational segregation, amplifies client gender discrimination, and maintains the gender wage gap through male’centric mechanisms. Corresponding solutions include improving data representativeness, fostering diversity within algorithm development teams, implementing bias checks before algorithm deployment, and integrating female-specific experiences into platform design. Furthermore, the gig economy places women at heightened health and safety risks due to inadequate social protections, male-centric algorithmic incentives that encourage risky behaviour, and the irregular work schedules women must manage alongside household responsibilities. Addressing these issues requires expanding social protections for gig workers and designing algorithms that prioritise safety.

This research is not without limitations. One major constraint is the availability of sources, as some recent studies, emerging trends, and datasets were beyond the scope of this analysis. Additionally, while this study outlines broad challenges and recommendations, it does not tailor solutions to specific national or regional contexts. Moreover, the research primarily focuses on gender as a single protected characteristic, whereas intersectionality should be more comprehensively considered in discussions of discrimination. Future research should conduct detailed country- and region-specific analyses to develop more contextually relevant recommendations. Further studies should also explore the experiences of specific demographics and marginalised groups facing intersectional challenges, examining how various socioeconomic factors shape women's experiences in the gig economy. Additionally, research in computer science is essential to investigate how algorithms and AI can be designed more equitably. Despite these limitations, this study successfully achieved its main objectives, including developing a systematic framework to categorise how the gig economy impacts gender inequalities and proposing possible solutions to address these disparities.

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