Does women's higher education reduce wage inequality? Evidence from Palestine using repeated cross-sectional data

The human capital theory suggests that education affects individual earnings, countries’ economic growth, and level of development, highlighting education as an investment in personal and societal growth. Empirical evidence shows education as a key driver of social mobility and a reducer of gender inequality and inequality of opportunities. Education expansion is viewed as a policy instrument for combating income inequality by promoting growth and reducing intergenerational poverty and inequality of opportunity. Higher education may compress wage gaps by enhancing productivity but can also increase inequality through skill-biased technological change, with the net effect depending on context and time. Women worldwide face barriers to labor force participation; in Palestine, despite a high share of educated women, labor force participation is low and disparities in working conditions and wages persist due to structural, cultural, and institutional factors. To the best of the researchers' knowledge, no previous attempt has assessed the impact of women's higher education on earnings and income inequality in Palestine using a combined approach. This study investigates the impact of women's higher education on wage inequality in Palestine using a mixed methodology of the Mincer earnings function and the Gini index. The study asks: What is the impact of women's higher education on wages? What is the contribution of education to wage inequality? The main hypotheses are: (i) women's higher education in Palestine reduces gender wage inequality; and (ii) the contribution of higher education to total wage inequality is decreasing over time. The paper estimates wage determinants and decomposes wage inequality using Palestinian Labor Force Survey data for 2010–2020, and outlines methods, descriptive statistics, main results, and conclusions with policy implications.

Prior research underscores education’s dual role in income distribution. Studies find that higher education enhances productivity and earnings, potentially compressing wage differences (Knight and Sabot, 1983; Autor et al., 2005; Menezes-Filho et al., 2006). Conversely, skill-biased technological change can raise the wage premium for skilled workers, increasing inequality (Menezes-Filho et al., 2006). Meta-regression evidence suggests education can initially raise inequality but beyond a threshold reduces it by lowering the wage premium, affecting both tails of the distribution (Abdullah et al., 2015). Cross-country evidence attributes a strong role to educational attainment in shaping income distribution (Gregorio and Lee, 2002). Field of study matters: numeracy/scientific fields yield higher wages than literacy fields (Bol and Heisig, 2021; Altonji et al., 2012; Kirkeboen et al., 2016). Government policies and the distribution of education subsidies influence the equity impact of educational expansion, often disproportionately benefiting middle- and upper-class families unless targeted (Abdullah et al., 2015). Broader socioeconomic, demographic, political, and technological factors also affect income inequality, with education’s impact varying by time and context (Coady and Dizioli, 2018; Abdullah et al., 2011; d’Hombres et al., 2012; Ramadan et al., 2015). Gender-focused studies show that increased women’s education and shifts in occupational structure can reduce gender wage gaps, yet gaps persist even among equally educated new graduates (Harkness, 2010; Fan and Sturman, 2019). Sectoral analyses find larger gender wage gaps in industry than services (Sri-dadia and Prihantonob, 2020). In Palestine, women face barriers to participation, poorer working conditions, and wage discrimination, affected by occupation-related structural imbalances, cultural norms, and sectoral segregation (Albotmeh and Irsheid, 2013; Khattab, 2002; Hilal et al., 2008; Harkness, 2010; Daoud and Shanti, 2016; Alkafri, 2011).

Data: Annual Palestinian Labor Force Surveys (LFS) from the Palestinian Central Bureau of Statistics (PCBS), 2010–2020, covering West Bank and Gaza Strip. The LFS includes individuals aged 10+ (employed, unemployed, or out of labor force) and demographic variables (gender, age, education, marital status, locality type, region) and employment type. This study focuses on wage employees only (about 65–70% of employed). Model 1 (Mincer earnings function): log(w_i) = b0 + Σ β_k d_ki + δ1 age + δ2 age^2 + Σ γ_j y_ji + ε_i, estimated by OLS. Key regressors: education dummies (School = secondary school; High education = diploma or bachelor; Higher education = master’s or PhD). Other controls: gender, locality type (urban, rural, refugee camp), marital status. Age and age^2 proxy experience. Inequality measurement and decomposition: Total wage inequality measured by the Gini index. Decomposition based on Wagstaff et al. (2003): G(w) = Σ α_k G(d_k) + Σ α_m G(age_m) + Σ α_j G(y_j) + G(e), where contributions α are functions of estimated coefficients and means (e.g., α_k = β_k μ_w / μ_dk). Inequality due to education is considered legitimate inequality; inequality due to other factors beyond individual control is considered illegitimate. The decomposition quantifies each factor’s contribution to overall wage inequality over time.

- Descriptive wages (ILS/day): Across 2010–2020, men’s average daily wages exceed women’s at all education levels; wages increase with education for both genders. In 2010 (school level), women: 48.79, men: 60.72 (gap 19.64%). Among high education, the gender wage gap declined from 21.96% (2010) to 16.27% (2020). For higher education, the gap generally decreased during 2011–2019 (from 20.21% to 19.88%), with exceptions in 2010 and 2020 where women’s wages slightly exceeded men’s (−5.97% and −4.67%, respectively). - Labor force status: Women’s share outside the labor force remained very high but slightly decreased (87.4% in 2010 to 86.4% in 2020). Female unemployment (as share of labor force) rose from 24.85% (2010) to 38.90% (2020). Male unemployment remained near 22–26% over the period. - Wage determinants (Mincer OLS, selected years): Male wage premium declined from 29.8% in 2010 to 23.3% in 2020 (holding other factors constant). Age shows an inverted-U effect on wages, weaker by 2020. Being married raises wages, but the premium decreased (8.3% in 2010; 5.5% in 2015; 3.5% in 2020). Locality: Compared with refugee camps, rural and urban residents earn more; in 2020, rural and urban premiums are about 50.8% and 14.2%, respectively. - Education premiums: Relative to school education, high education and higher education are associated with substantially higher wages. Using coefficients in 2010, individuals with higher education earned about 112.5% more than those with high education and 176.7% more than those with school education; by 2020 these differences declined to 92.1% and 116.3%. The wage difference between high education and school education decreased from 64.2% (2010) to 24.2% (2020), indicating a narrowing of education-based wage gaps over time. - Inequality and decomposition (Gini): Overall wage inequality rose from 0.322 (2010) to 0.393 (2020). Education’s contribution to overall wage inequality declined from 37.3% (2010) to 29.0% (2020). Within education, the contribution of higher education fell from 9.4% to 4.6%; high education from 18.9% (2010) to 11.4% (2020); school education from 18.9% (2010) to 13.0% (2020). Gender’s contribution to wage inequality decreased from about 12% (2010–2015) to 7.8% (2020). Locality’s contribution increased markedly, reaching 30.0% in 2020 (about triple its 2010 share of 11.7%).

The findings indicate a persistent yet declining gender pay gap in Palestine over 2010–2020, alongside strong positive returns to education that diminish somewhat over time, especially for higher degrees. The rising share of educated women (high and higher education increased from 13.55% in 2010 to 23.25% in 2020), policy efforts toward gender equality, and improved opportunities for skilled women help explain the narrowing gender gap and reduced education-related inequality shares. However, occupational segregation and concentration of women in lower-paying sectors (e.g., teaching and education) sustain pay disparities. Locality plays an increasingly important role: rural residents often access higher-paying jobs, including employment in Israel, contributing to rising inequality shares attributed to locality. Despite overall wage inequality increasing, the declining contributions of gender and education support the hypothesis that women’s higher education helps reduce gender-related wage inequality and that education’s share in overall inequality is falling. Robustness checks (sensitivity analyses) yield similar shares for gender and education, reinforcing these conclusions.

The study applied the Mincer earnings function and the decomposed Gini index to Palestinian LFS data (2010–2020) to assess women’s higher education and wage inequality. Results show a persisting but decreasing gender pay gap over time; male wages remain higher at all education levels. The contribution of higher education to overall wage inequality is smaller than that of other education levels and has declined over time, as has the overall contribution of education. Policy implications include investing in women’s higher education, promoting and enforcing workplace gender equality, enhancing labor market social protections, and increasing gender diversity in leadership. Educational policies (programs, mentorship, career development) should bridge skills gaps and reduce gender disparities in male-dominated sectors to help women access higher-paying positions. Supporting the National Employment Strategy, expanding quality education for women, financing higher education, and partnering with the private sector to create jobs can reduce wage inequality and foster growth.

Limitations include omission of potentially relevant variables not captured in the LFS (e.g., religion, traditions, ethnic background, age at marriage, occupational segregation, access to professions, ability, institutional discrimination). The limited sample size of women with higher education may bias some estimates; in two periods the wage gap appeared negative, a misleading result given underlying wage patterns. Sensitivity analyses adding/removing variables (e.g., region, occupation) suggest robustness of main conclusions (e.g., higher education’s contribution to inequality around 4.6% in 2020). Future research should incorporate qualitative indicators on societal and economic factors influencing wage inequality and address women’s opportunities (access to higher education, college experiences, family responsibilities, and job matching).