Guatemala: Closing Gender Gaps in Labor Markets: Untapped Opportunities to Boost Economic Growth

International Monetary Fund. Western Hemisphere Dept.

doi:10.5089/9798400287459.002.A005

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International Monetary Fund. Western Hemisphere Dept.

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Publication Date:: 05 Aug 2024

eISBN:: 9798400287459

Language:: English

Keywords:: participation gap; labor market participation; labor market efficiency; labor market outcome; labor market gender gaps; Gender diversity; Labor markets; Global

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Gender gaps in labor markets have remained significant overtime in Guatemala and hinder economic growth. The estimated potential large gains of closing labor force participation gaps are about 30 percent of GDP. This note describes the gender gaps in labor market in Guatemala, estimates the macroeconomic gains of closing the identified gaps, and describes the link between remittances and female labor force participation in Guatemala. The analysis finds evidence that migration and remittances are linked to lower female labor force participation in Guatemala. Other factors such as social norms, gender gaps in education, and low female empowerment play also a role.

Closing Gender Gaps in Labor Markets: Untapped Opportunities to Boost Economic Growth ^¹

A. Gender Gaps in Guatemala’s Labor Market

1. At 40 percent, Guatemala’s female employment continues to trail male employment levels (Figure 1). Despite some co-movement in the aftermath of the Global Financial and the Covid-19 crisis in the female and male employment ratios, gaps widened pre-COVID, with the female employment-to-population ratio further dropping, while male employment increasing until year 2019 (Figure 1, left panel). The male-female employment gap ^², was estimated at 48 percent in 2010. The latest data shows the 2022 female employment ratio is about half of the males (Figure 1, right panel).

^{Figure 1.}

Employment Gap in Guatemala

Citation: IMF Staff Country Reports 2024, 267; 10.5089/9798400287459.002.A005

Source: Word Bank, ILO, and IMF Staff’s calculations.¹ Prepared by Paula Beltran and Maria Oliva, with inputs from SPR Gender Unit.

2. Significant labor force participation gaps have contributed to a sizable male-female employment gap if compared against regional and income-group peers (Figure 2). In 1980, Guatemala’s female labor force participation levels were close to 14 percent, with 2010–2019 levels surging to a 40 percent average rate and stalled in recent years. Despite this significant progress, Guatemala’s gender gap in labor force participation, estimated at 54 percent in 2021–22, remains the highest among CAPDR region economies and well above Guatemala’s similar income-group peers’ levels (Figure 2, left panel). Guatemala’s large labor force participation gap (Figure 2, right panel) is due to low female labor market participation, which exceeds 15 percentage points of the regional median levels.

^{Figure 2.}

Participation and Employment Gap, 2022

Citation: IMF Staff Country Reports 2024, 267; 10.5089/9798400287459.002.A005

Source: Word Bank, ILO, and IMF Staff’s calculations.Note: The gap is defined as the ratio difference with respect to equality. For example, a gap of 51 percent implies female participation is below male participation by 51 percent.

3. Reducing gender gaps in labor markets in Guatemala will unlock untapped resources and foster inclusive economic growth. Low female labor participation is a constraint on growth potential (IMF, 2013). The correlation between female labor force participation and GDP per capita among middle- and high-income countries is positive (Figure 3). For example, countries with GDP per capita levels exceeding Guatemala’s level by about 10 percent are associated with female labor force participation rates that are roughly 9 percent higher.

^{Figure 3.}

Female Labor Participation and Development Labor Force Participation Across Countries

(In Percent of Female Population)

Citation: IMF Staff Country Reports 2024, 267; 10.5089/9798400287459.002.A005

Source: World Development Indicators 2019 and IMF Staff’s calculations.

B. Macroeconomic Gains of Closing Gender Gaps in Guatemala

4. Staff measured the potential economic gains of closing gender gaps in Guatemala’s labor market using the back of the envelop SPR’s Equity Gains method. This estimation calculates the GDP gains of closing unemployment, participation, wage, and occupational gender gaps by conducting counterfactual scenarios under the assumption that wages reflect the marginal product of labor (Box 1). ^³

5. Estimated GDP gains of closing gender gaps for Guatemala are at 33.7 of GDP (Figure 4). Major gains would be achieved by reducing female participation gaps in the market, followed by a reduction of the wage gap (Figure 4). By factor, the 33.7 percent estimated gains can be decomposed as follows:

Unemployment Gap (potential gains = 0.3 percent of GDP): Female formal unemployment In Guatemala was 3.4 percent in 2022, but 2.2 percent for males. Equity gains associated with closing the unemployment gap are small (albeit positive), in line with small unemployment gaps.
Participation Gap (potential gains = 25.4 percent of GDP): Female participation in the labor market shows a large gap, and thus, there are large potential output gains by closing it over the medium-term. For instance, closing the participation gap by 1/5 of the existing gap would bring significant gains—i.e., about 0.5 percent in potential output growth per year. Furthermore, GDP potential gains are twice the median of income peers (Figure 4, right panel), consistent with an above the median participation gap that continues to widen for Guatemalan females.
Wage Gap (potential gains = 7.8 percent of GDP): Gains associated with closing the wage gap would be driven by the 14.4 percent wage differential between Guatemala’s female and male working population.
Occupational Gaps (potential gains = 0.2 percent of GDP): Occupational gaps in Guatemala in high skill jobs remain and potential gains are estimated at 0.2 percent of GDP.

^{Figure 4.}

GDP Gains of Closing Gender Gaps in Labor Markets in Guatemala

Citation: IMF Staff Country Reports 2024, 267; 10.5089/9798400287459.002.A005

Source: Word Bank, ILO, and IMF Staff’s calculations.1/ Based on Ostry, Alvarez and Papgeorgiou (2018) using δ = 0.6, σ = 100. π = -1

^{Box 1.}

Computing GDP Gains of Closing Gender Gaps

The quantitative analysis of Guatemala’s economic gains of closing gender gaps in the labor market draws from SPR’s tool (2023). The tool uses a shift-share technique developed in Buckman et. al. (2021) to construct a counterfactual world with no gender gaps if different labor market measures (employment rates, earnings, and occupational shares) are used. The analysis computes the total gains to GDP to be obtained by closing existing gender gaps. The computation assumes the floor of each labor market measure equals that of men.

The group-specific labor contribution to GDP is calculated as:

G D P^{C o u n t e r f a c t u a l} = \underset{g = {f e m a l e, m a l e} o c c u p a t i o n}{Σ} P o p_{g} * P a r t i c i p a t i o n_{g} * (1 - u_{g}) * S_{g, o c c u p a t i o n} * W_{g, o c c u p a t i o n},

where g is gender and occupation is the type of worker. We classify workers in 3 skill-levels: manager and professional; technicians; and clerical support, services, agricultural, machine operators and craft workers. The key explanatory variables used to explain the counterfactual GDP (GDP^{Counterfactual}) are:

Pop_g is the working age population over the age of 15 from the World Bank.
Participation_g is the participation rate from the World Bank.
u_g is the gender-specific unemployment rate from ILO.
S_g,occupation is the gender-specific occupation share from ILO.
W_g,occupation is the gender-specific average wages from ILO.

The counterfactual exercises adjust the GDP by closing the gaps sequentially as follows:

Closing the participation gap: This counterfactual assumes an increase in the female participation rate, while holding the remaining variables fixed. The implied GDP is:
$G D P_{P a r t i c i p a t i o n}^{C o u n t e r f a c t u a l} = \underset{g = {f e m a l e, m a l e}}{Σ} \underset{o c c u p a t i o n}{Σ} P o p_{g} * P a r t i c i p a t i o n_{m a l e} * (1 - u_{g}) * S_{g, o c c u p a t i o n} * W_{g, o c c u p a t i o n},$

and the gains with respect with the baseline are computed as:
$Δ G D P_{P a r t i c i p a t i o n}^{C o u n t e r f a c t u a l} = \underset{g = {f e m a l e, m a l e}}{Σ} \underset{o c c u p a t i o n}{Σ} P o p_{g} * P a r t i c i p a t i o n_{m a l e} * (1 - u_{g}) * S_{g, o c c u p a t i o n} * W_{g, o c c u p a t i o n} .$
Closing unemployment gaps: This counterfactual assumes a reduction in the female unemployment rate, while holding the remaining variables fixed. The implied GDP is:
$G D P_{P a r t i c i p a t i o n}^{C o u n t e r f a c t u a l} = \underset{g = {f e m a l e, m a l e}}{Σ} \underset{o c c u p a t i o n}{Σ} P o p_{g} * P a r t i c i p a t i o n_{m a l e} * (1 - u_{m a l e}) * S_{g, o c c u p a t i o n} * W_{g, o c c u p a t i o n},$

and the gains with respect with the baseline are computed as:
$Δ G D P_{u n e m p l o y m e n t}^{C o u n t e r f a c t u a l} = G D P_{u n e m p l o y m e n t}^{C o u n t e r f a c t u a l} - G D P_{p a r t i c i p a t i o n}^{C o u n t e r f a c t u a l} .$
Closing earning gaps: This counterfactual assumes an increase in wages by occupation, while holding the occupational shares fixed. The implied GDP is:
$G D P_{e a r n i n g s}^{C o u n t e r f a c t u a l} = \underset{g = {f e m a l e, m a l e}}{Σ} \underset{o c c u p a t i o n}{Σ} P o p_{g} * P a r t i c i p a t i o n_{m a l e} * (1 - u_{m a l e}) * S_{g, o c c u p a t i o n} * W_{o c c u p a t i o n}^{M a x},$

where $W_{o c c u p a t i o n}^{M a x}$ is the male average wage when it is higher than that of female average wage and vice versa.
Closing occupation gaps: This counterfactual assumes placing workers in more skilled occupations. The exercise assumes the share of workers by occupation of the counterparts. The implied GDP is:
$G D P_{o c c u p a t i o n s}^{C o u n t e r f a c t u a l} = \underset{g = {f e m a l e, m a l e}}{Σ} \underset{o c c u p a t i o n}{Σ} P o p_{g} * P a r t i c i p a t i o n_{m a l e} * (1 - u_{m a l e}) * S_{o c c u p a t i o n}^{b e s t} * W_{o c c u p a t i o n}^{M a x},$

Where $S_{o c c u p a t i o n}^{b e s t}$ is the counterfactual occupational share.

Source: IMF Staff based on SPR tool adapted for Guatemala

C. Social Norms and Women Empowerment in Guatemala

6. Social norms in Guatemala and low women empowerment can impact labor market gender gaps. The literature shows that social norms can constrain female labor market participation. ^⁴ Previous studies show women empowerment, visibility, and social norms about childcare and domestic tasks weigh on female labor market participation.

7. Women in Guatemala have lower political and economic empowerment than men (Figure 6 and 7). Political representation remains below that of men in Guatemala and below that of regional and income peers. The legal framework is also not fully aligned with women’s empowerment, and women’s economic empowerment remains below that of men. In addition to lower labor force participation rates, women’s earnings in the private sector are lower than men. At the same time, women’s self-employment rate is 40 percent, reflecting high levels of informality and low incentives to participate in the formal labor market.

8. Social norms related to teen pregnancy and early marriage can also affect women’s labor market outcomes. Teenage pregnancy is six percent, higher than the income peer average, and women suffer high maternal mortality rates. In addition, seven percent of women in Guatemala are married before the age of 15 and 30 percent before the age of 18, in line with the regional average but above the levels of income peers. Staff estimates significant economic benefits of reducing child marriage in Guatemala (Box 2). Female childcare and domestic work are also important for women, as women spend 7.5 times more hours on unpaid work than men.

^{Figure 5.}

Gender Indicators: Health

Citation: IMF Staff Country Reports 2024, 267; 10.5089/9798400287459.002.A005

Source: IMF staff using UNDP’s Gender Development Index Database, UNDP’s Gender Inequality Index Database, WEF’s Global Gender Gap Index, ILO’s ILOSTAT Database, WB’s World Development Indicators.

^{Figure 6.}

Gender Indicators: Leadership, Social, and Demographic

Citation: IMF Staff Country Reports 2024, 267; 10.5089/9798400287459.002.A005

^{Figure 7.}

Gender Indicators: Labor and Income

Citation: IMF Staff Country Reports 2024, 267; 10.5089/9798400287459.002.A005

D. What is the Impact of Ample Remittances on Gender Gaps?

9. The broad-based surge in remittance inflows to Guatemala in the last decade disproportionally impacted women. In 2022, remittance inflows accounted for 19 percent of GDP, well above export receipts. According to the 2022 OIM survey conducted for Guatemala, ^⁵ about 40 percent of Guatemalans are directly benefiting from remittance inflows. Women are more likely to receive remittances and receive larger receipts per remittance inflow than men. The survey reports that, on average, women receive U.S.$168 per capita per month, while men receive U.S. $125on average.

10. Significant remittance inflows could impact labor market outcomes. The effect of remittances on labor market outcomes could arise from many channels. On the one hand, remittances could positively impact the labor market when proceeds foster labor productivity via physical and human capital investment. On the other hand, remittances could reduce labor force participation by reducing the dependence on labor-related income and increasing the incentives to focus on non-market activities, such as caretaking. Previous research found households that receive remittances exhibit lower female labor force participation because of the incentives to focus on non-market activities (Cabegin, E. 2006; Ayalew, H. et al., 2022).

Guatemala: Remittances

(Percent of GDP)

Citation: IMF Staff Country Reports 2024, 267; 10.5089/9798400287459.002.A005

Sources: World Bank World Development Indicators, and IMF Staff’s calculations

11. Remittances are associated with reduced female labor force participation in Guatemala (Figure 8). The 2022 OIM remittances survey revealed that, among households receiving remittances in 2022, the labor force participation rates were approximately 79 percent for men and 30 percent for women. These findings, along with national gender-based participation rates ^⁶, suggest that individuals receiving remittances usually exhibit lower labor force participation rates, with a more pronounced effect observed among females. Women receiving remittances are approximately 20 percent less likely to participate in the labor market than those not. Nevertheless, the lower participation rates of women with respect to men also likely reflect caretaking responsibilities, social norms and development drivers (e.g., educational outcomes, poverty, among others).

^{Figure 8.}

Labor Force Participation and Remittances

(Percent, 15+)

Citation: IMF Staff Country Reports 2024, 267; 10.5089/9798400287459.002.A005

Sources: World Bank World Development Indicators, IOM remittances survey, and IMF Staff’s calculations. Note: LFP of population that does not receive remittances is based on aggregate LFP ratios for 2022. Back of the envelope calculation assumes a 63 percent working age population.

12. Cross-country empirical analysis shows that remittances are associated with lower female market participation (Table 1). The analysis covered a panel of 133 countries between 1990–2022. After controlling for development levels ^⁷, remittance inflows are one of the factors that help explain differences in female labor force participation across countries. Estimates indicate that one percent of GDP increase in remittance receipts is linked to a decline in female labor force participation of about ½ percentage points. Nevertheless, the analysis is not causal, and remittances are also capturing other confounding factors, including hours spent on unpaid work, child and elderly care. Given the large inflow of remittances in Guatemala, these results suggest that the level of remittances, along with caretaking responsibilities, social norms and women’s empowerment, may affect women’s labor market participation relative to the world average.

^{Table 1.}

Guatemala: Cross-Country Female Labor Market Participation Rate and Drivers

Sources: World Bank World Development Indicators, World Economic Forum, and IMF Staff’s calculations. Notes: Box 2 describes the method and data.

^{Table 2.}

Guatemala: Female Labor Market Participation Rate and Remittances: Microdata Evidence

Sources: 2022 IOM Remittances Survey, Fundesa, and IMF Staff’s calculations. Note: Municipality GDP is the GDP by territory in U.S.$ terms for square kilometer.

13. Cross-sectional evidence for Guatemala shows that social and demographic characteristics and educational attainment affect female labor market participation (Table 2). There is evidence that family composition in Guatemala affects female participation. Larger families tend to have lower participation rates, suggesting the importance of child and elder care. Married women also have lower participation rates, likely reflecting social norms. Access to public services is associated with higher participation rates, suggesting that poverty levels may also be associated with lower participation rates. Education improves women’s participation. Women with primary and secondary education have lower participation rates than women with tertiary education.

14. Microdata evidence also suggests that, while other factors are likely more important in explaining female labor market participations, increases in remittances are associated with lower female labor market participation. ^⁸ (Table 2). The estimates show one standard deviation increase in remittances per household member decreases female labor force participation by 4.2 percent. The impact is more pronounced on women who directly receive and administer these inflows. Interestingly, no significant effect of remittances on male labor force participation is observed (Table 2, column 4), suggesting a female-specific remittances effect on labor force participation.

15. Staff’s empirical results are likely a lower bound (Table 3). The results in Table 2 are subject to endogeneity concerns, given other confounding factors that explain migration and participation, and should therefore not be interpreted as causal. An instrumental variable approach is used to address the endogeneity concerns. The instrument exploits the variation in the city of origin of remittances. The exogeneity assumption is that the city of origin of remittances is not correlated with other factors that explain the labor participation of recipients in Guatemala. The identification assumption is plausible because, conditional on migration, the city of destination is mostly related to network effects in the literature and is unlikely to be correlated with unobserved characteristics of remittance recipients. At the same time, in the cross-section, the city of destination can explain differences in remittance levels, as it captures local strength in local labor markets. The estimated impact of higher remittance proceeds perceived by female household members almost doubled when using instrumental variables. The impact is even more pronounced among households without a tight budget constraint, ^⁹ reflecting economic trade-offs weigh in more for households with tighter constraints. These results appear to be robust to alternative specifications and estimation methods addressing potential biases in estimates (see Table 1).

^{Table 3.}

Guatemala: Female Labor Market Participation Rate and Remittances: Robustness

Sources: 2022 IOM Remittances Survey, Fundesa, and IMF Staff’s calculations. Note: Specifications control for individual and households’ characteristics including a polynomial of age, marriage status, number of children, family size, education levels, illiteracy, public services, access to public services (including internet, water swage, electricity, phone service), and business owner indicator. The regression also controls for department fixed effects and municipality controls, including log of GDP, log of population, size, urbanization, Fundesa Development Index, and the percentage of indigenous population.

16. Development factors can also influence the effect of remittances on female labor market participation (Table 4). The empirical analysis also shows local GDP per capita helps increase the incentives to work by increasing the local marginal productivity of labor. A one standard deviation increase in local GDP per capita decreases the elasticity of female labor market participation by 0.3 percentage points on average. Local development levels also affect the incentives to work, with a negative mark driven by remittances received in municipalities with different development levels.

^{Table 4.}

Guatemala: Female Labor Market Participation Rate, Remittances and Development Levels

Sources: 2022 IOM Remittances Survey, Fundesa, and IMF Staff’s calculations. Note: Specifications control for individual and households’ characteristics including a polynomial of age, marriage status, number of children, family size, education levels, illiteracy, public services, access to public services (including internet, water sewage, electricity, phone service), and business owner indicator. The regression also controls for department fixed effects and municipality controls, including log of GDP, log of population, size, urbanization, Fundesa Development Index, and the percentage of indigenous population. Development level categories are based on the sample distribution of Fundesa Development Ranking.

E. Modeling the Economic Forces Behind Female Participation in the Labor Market

17. Women receiving remittances face an economic trade-off according to the literature: higher consumption in the future if participating in the labor market against instant gratification without paying the cost of participating in the labor market. This section describes a theoretical model that captures the empirical findings described in previous sections. First, remittances reduce female labor market participation when households are beyond subsistence levels. Second, market returns and labor market efficiency alleviate the effect of remittances on gender gaps in the labor market. Third, traditional caretaking roles diminish the incentives to work and magnify the impact of remittances on female labor market participation.

^{Box 2.}

Unveiling the Economic Incentives of Remittances

The stylized model below captures the economic choices involved in female labor market participation, that is, the economic incentives and costs of working as identified in the data.

Model Setting

We consider a stylized woman i who lives two periods and maximizes the lifetime utility V by choosing to participate in the labor market or staying home in period t=1 against the future return to labor market experience in period t =2. The lifetime utility is the weighted sum of the instant utility that follows a Stone-Gary function, an expansion of a Cobb-Douglas function, with subsistence consumption $\bar{c}$ a cost for working weighted by parameter ε_i, and impatience parameter β:

V_{i} = \log (c_{1} - \bar{c}) - ε_{i} I (L_{i, 1} = 1) + β \log (c_{2} - \bar{c}),

where c₁ and c₂ denote consumption in periods 1 and 2, respectively; L_i,1 equal to 1 if the individual i participates in the labor market in period 1. Parameter ε_i captures the individual’s exposure to shocks to the value of non-market activities and follows a logistic distribution with mean ε and variance σ. Shocks are revealed ex-post.

Woman i’s budget constraint is:

\begin{matrix} c_{1} = w I (L_{i, 1} = 1) + R, & t = 1 \\ c_{2} = w (1 + θ I (L_{i, 1} = 1)) + R, & t = 2, θ > 0 \end{matrix}

where R denotes remittance proceeds and w is the market labor return.

Model Implications

Assuming inelastic labor demand, the female labor market participation rate is:

p = {(1 + μ {(1 + \frac{w}{R - \bar{c}})}^{(β - 1) / σ} {(1 + \frac{w θ}{R - \bar{c}})}^{- β / σ})}^{- 1} .

- Furthermore, the labor market participation rate:Is decreasing in μ, the average value of staying at home, and more so when remittances are large (large R to $\bar{c}$ )
- Is increasing in wθ for β > 0 and σ < ∞.
- Is decreasing in remittances, R.

18. The analysis is well-aligned with the extensive literature on labor market participation and incentives. Some studies (IMF, 2016; López, et al., 2021; Ernst et al., 2022; Sosa et al, 2022; Urquidi et al., 2023) discuss the link between boosting labor productivity to reduce labor market participation gaps. Some reforms to boost productivity include closing infrastructure gaps, improving quality and access to education and health, and improving agricultural market functioning. Other reforms identified in the literature include aligning wages to reflect labor market productivity by improving labor market efficiency and flexibility, reaping the gains of digitalization, and reducing informality (IMF, 2016; López et al., 2021). Bolstering financial inclusion, enhancing non-discrimination practices in financial markets, and facilitating credit to SMEs could also be effective measures to increase female labor market participation (Sosa et al., 2022; Urquidi et al, 2023).

F. A Roadmap to Closing Gender Economic Gaps: The Global Parity Initiative

19. Closing gender economic gaps requires the involvement of both the public and private sectors. Public policy, namely government programs and various tools used to encourage economic agents to close these gaps, is not sufficient. Involvement of the private sector is necessary, making them co-leaders and champions of gender policies within their associations and their own companies.

Global Parity Initiatives

20. The Gender Parity Initiatives (GPIs) serve as accelerators for closing these economic gaps in various areas. The Inter-American Development Bank (IADB), along with the World Economic Forum (WEF) and the French Development Agency (FDA), has been promoting actions aimed at closing gender economic gaps for over nine years through public-private partnerships (GPIs) that convene leaders at the highest level within government and the private sector. ^¹⁰ GPIs start with a multidimensional diagnosis of gender economic gaps. This diagnosis can cover different dimensions such as education, labor force participation, access to financing, wages, leadership in the public or private sector, digitization, as well as care and household chores, among others. Based on this diagnosis, GPI leaders prioritize objectives to be achieved through an Action Plan, usually over a period of three or four years and framed within national government cycles. From 2016 to date, nine countries have implemented GPIs in the Latin American region. ^¹¹

21. These GPIs have managed to create a regional, innovative, and collaborative methodology that enables the closing of gender economic gaps, while engaging the main economic actors of the countries and articulating the work of different stakeholders, facilitating participatory decision-making, generating, and disseminating knowledge. ^¹² In aggregate, GPIs achieved the following results to date: nine countries in Latin America have adopted IPGs; eight national action plans have been approved; there have been nine diagnoses elaborated characterizing the gender economic gaps of 9 countries; 60 ministers or public officials and 70 CEOs or general managers of companies committed to gender equality; 558 companies adhered to the GPIs; and 16 reports published with data, evidence, and best practices to accelerate the closure of gender economic gaps.

Guatemala and Gender Disparities: An Option

22. Guatemala could implement a gender gap closure accelerator (e.g., GPIs.) Addressing gender inequalities is a top priority of the administration’s governance policy. And the current cabinet is gender-balanced cabinet. Within the various ministries, addressing gender inequalities in the traditional labor market, household chores, education, among other areas, is among the main objectives ^¹³. Furthermore, the Guatemalan private sector is a key stakeholder to play an essential role in closing existing gender economic gaps. Firstly, the private sector represents a great opportunity for creating new formal employment positions. Secondly, it is a potential funding source for small and medium enterprises lead by women, and private-sector led initiatives, such as “Guatemala No se Detiene” (Guatemala Doesn’t Stop), identify sectors—agribusiness, BPO services, pharmaceuticals and medical devices, manufacturing of electronic devices, among others—where Guatemala could become a regional hub, making them a priority for investments ^¹⁴ and consequently a source of new job positions.

References

Ayalew, Hailey Ademe and Mohanty, Pratap. C. (2022). Do remittances affect labour participation decisions and hours worked? Evidence from Ethiopia. Applied Economics Letters, 1–6. https://doi.org/10.1080/23322039.2022.2093821
- Search Google Scholar
- Export Citation
Bloom, David E, David Canning, Gunther Fink, and Jocelyn E. Finlay. 2007. “Fertility, Female Labor Force Participation, and the Demographic Dividend.” NBER Working Paper #13583.
- Search Google Scholar
- Export Citation
Buckman, Shelby R., Laura Y. Choi, Mary C. Daly¸ and Lily M. Seitelman. 2021. “The Economic Gains from Equity.” BPEA Conference Draft, Fall.
- Search Google Scholar
- Export Citation
Cabegin, Emily, The Effect of Filipino Overseas Migration on the Non-Migrant Spouse’s Market Participation and Labor Supply Behavior (August 2006). IZA Discussion Paper No. 2240, Available at SSRN: https://ssrn.com/abstract=924567 or http://dx.doi.org/10.2139/ssrn.924567
- Search Google Scholar
- Export Citation
Chami, R., Ernst, E., Fullenkamp, C., & Oeking, A. (2018). Are remittances good for labor markets in LICs, MICs and fragile states? (IMF working papers No. 18/102). International Labour Office. https://www.imf.org/en/Publications/WP/Issues/2018/05/09/pdf
- Search Google Scholar
- Export Citation
Ernst, E., Merola, R., Reljic, J. 2022. Labor market policies for inclusiveness: A literature Review with a gap analysis, ILO Working Paper 78 (Geneva, ILO).
- Search Google Scholar
- Export Citation
International Monetary Fund. (2013) “Women, Work and the Economy, Macroeconomic Gains from Gender Equity.” IMF Staff Discussion Note, SDN/13/10.
- Search Google Scholar
- Export Citation
International Monetary Fund. (2019). Paving the Way to Sustained Growth and Prosperity in Central America, Panama, and the Dominican Republic (Chapter 3: Female Labor Force Participation). ISBN: 9781484353844. http://dx.doi.org/10.5089/9781484353844.071
- Search Google Scholar
- Export Citation
López, A., Ruiz-Arranz, M., & Ochoa, E. (2021). Closing Gender Gaps in the World of Work: Central America, Mexico, Panama, and the Dominican Republic. Inter-American Development Bank. http://dx.doi.org/10.18235/0003504
- Search Google Scholar
- Export Citation
Sosa, L., Ochoa, E., & Coj-Sam, J. (2023). Economics Guatemala: Panorama de Oportunidades. En A. Gómez Osorio (Ed.), Inter-American Development Bank. http://dx.doi.org/10.18235/0005143
- Search Google Scholar
- Export Citation
Urquidi, M., & Chalup, M. (2023). The Gender Earnings Gap in Latin America and the Caribbean: An Analysis of Its Components. Inter-American Development Bank. http://dx.doi.org/10.18235/0004785
- Search Google Scholar
- Export Citation

Prepared by Paula Beltran and Maria Oliva, with inputs from SPR Gender Unit.

The male-female employment gap is defined as $\frac{M a l e E m p l o y m e n t R - F e m a l e E m p l o y m e n t R}{M a l e E m p l o y m e n t R}$ . This ratio ranges between 0 and 1 and uses employment ratios to population by gender (i.e., 0 male-female employment gap means full equality).

This exercise does not consider equity gains from closing gaps in hours worked due to data limitations.

See, for example, Alesina et. al., 2013; Bertrand et. al., 2015; Chamlou et. al., 2016; Fernandez, 2013; Gauri et. al., 2019; and the World Bank, 2012.

The survey includes 3,500 households that were remittance recipients in 2022. It provides information on the size of remittances received, the number of household members, education, age, occupation, and gender, among other characteristics.

The gender-specific (g) participation rate of the population not receiving remittances is defined as follows: $(L F P_{g} - L F P_{g}^{r e c e i v e s} * s h a r e_{-} r e c e i v e r s_{g}) / (1 - s h a r e_{-} r e c e i v e r s_{g})$ , where LFP_g is the national estimate, $L F P_{g}^{r e c e i v e s}$ is the participation rate estimate from the OIM survey, and share_receivers_g is the estimated population share of individuals who receive remittances. This share is calculated assuming the survey’s sample weights and a working-age population of 63 percent.

The estimation controls for other observed and unobserved confounders following the literature (IMF, 2018). Regressions include a second-order polynomial of the logarithm of GDP per capita, fertility rates, internet users, female secondary and tertiary education, male tertiary education, and urban population rate from the World Bank World Development Indicators. Regressions also include year fixed effects. Robustness checks include the labor market efficiency index of the World Economic Forum and the logistics performance index from the World Bank. The results on other covariates are broadly in line with the literature (IMF, 2013; IMF, 2016; Bloom et al, 2007; Chami et al, 2018)

Staff’s empirical specification is discussed in Box 2. The empirical analysis uses the 2022 IOM survey.

Defined as one if the household reports income is not enough to cover expenses.

IADB (2023). Iniciativas de Paridad de Género en América Latina 2016–2022: una alianza público-privada para acelerar la igualdad. IDB-Iniciativas de Paridad de Género.

These include Chile (2016), Panama (2018), Argentina (2018), Costa Rica (2019), Colombia (2019), Dominican Republic (2020), Ecuador (2021), Mexico (2022), and Honduras (2022).

Key contributions of GPIs include, engaging leaders from both the public and private sectors involved in the economic and productive development of countries; providing companies with a roadmap and tools to close their gender gaps; accelerate political and legislative reforms., committing companies to gender equality; promoting opportunities for women in non-traditional and high-growth sectors; deliver tailored solutions to foster female leadership, fostering dialogue and networks to advance economic recovery from a gender perspective; and generating and disseminating knowledge and exchanges of best practices.

SEGEPLAN (2024). Política General de Gobierno 2024–2028. SEGEPLAN-PGN

FUNDESA (2024). Guatemala no se detiene. FUNDESA-GNSD.

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