Introduction
Central America has one of the world’s largest gender gaps in labor force participation, which highlights the importance of this untapped resource to boost economic growth. The relationship between women’s participation in the labor market and economic growth is well documented (IMF 2013). Low female labor force participation in traditional societies has been found an important restriction on growth potential. Education gaps between men and women, besides explaining the lower participation of women in the labor force that are described in this chapter, give rise to women in work having a lower human capital capacity than men, which directly dampens growth potential.
Global gender gaps in labor participation vary strongly by region, with the largest gaps in Middle Eastern and North African countries, followed by South Asia and by Central America, where gaps are well above those seen in advanced economies. Within Central America, Panama, and the Dominican Republic (CAPDR), gaps in Guatemala and Honduras are the largest, with labor force participation (LFP) rates for men being close to 40 percentage points higher than for women (Figure 3.1).1 With male LFP broadly in line—though in some cases higher—than in more advanced Latin American economies, low female LFP is the main driver of these large gender gaps in the CAPDR labor force.
Another channel for the impact of female LFP on growth is through its effect on income inequality (IMF 2015). Adding to that, the unequal status of female workers in CAPDR countries puts them in a more vulnerable economic and social position than male workers. Higher female unemployment rates and a higher share of informal employment among working women exacerbate income inequality and raise the risk that women who do not have family support may fall into poverty. Raising female LFP should therefore be an important policy objective in CAPDR.
Increased female participation in the labor force will be critical for improving growth potential and generating the inclusive growth needed to reduce entrenched poverty in many countries in the region. Higher female LFP will also be vital in helping to mitigate the impact of a shrinking workforce expected to result from demographic pressures in many countries (Chapter 8). A country’s potential output is determined by the availability of physical and human capital, and by the state of technology supporting productive use of these resources. Availability of human capital in turn depends on demographic trends, the size of the labor market, and educational attainment.
To better understand the drivers of female participation in the labor forces of CAPDR and actions that could be taken to raise it, this chapter extracts the main determinants of low female LFP in CAPDR from analysis of household surveys and cross-country data. It also draws on lessons from other countries to identify policies needed to raise female LFP and potential growth in the region.
Main Drivers of Female LFP
Differences in income levels appear to be a primary factor in the female participation in the labor force in many countries. However, income does not explain everything. Large variations in female LFP rates exist even among upper-middle-income countries. Several countries in the world with similar GDP per capita as CAPDR countries have female LFP rates ranging from 20 percent to 80 percent. This suggests other drivers of female LFP are important too, such as fertility rates and education attainment. The economy’s production structure is also relevant—for example, service-dominated structures are associated with higher female LFP (Bloom and others 2007, Klasen and Pieters 2015, Gaddis and Klasen 2014).
Income
Female LFP varies with per capita income. Low female LFP ratios in CAPDR are consistent with the region’s middle-income status, according to the U-shaped relationship between the level of economic development (for example, GDP per capita) and female LFP rates found in the literature (Figure 3.2).2 Women tend to work out of necessity in poor countries, mainly in subsistence agriculture or home-based production, and without social protection programs. With income growth, activity tends to shift from agriculture to industry, with jobs away from the home making it more difficult to juggle childcare and employment— especially with public childcare services typically limited at intermediate levels of economic development. Within households, as the husband’s wage rises, there is a negative income effect on the supply of women’s labor. With increasing social protection, women also find it easier to leave employment in favor of household work and childcare. Once wages for women start to rise, however, the substitution effect increases incentives for women to increase their labor supply, until this effect tends to cancel out the negative effect of an increase in the husband’s wages. At advanced economy income levels, LFP rebounds because of changes in other correlated factors, such as better education, lower fertility rates, access to labor-saving household technology, and the availability of market-based household services (IMF 2013).
Female Labor Force Participation and Main Underlying Factors
Note: CRI = Costa Rica; DR = Dominican Republic; GTM = Guatemala; HND = Honduras; NIC = Nicaragua; PAN = Panama; SLV = El Salvador. LA5 = Latin America 5; PPP = purchasing power parity.Female Labor Force Participation and Main Underlying Factors
Note: CRI = Costa Rica; DR = Dominican Republic; GTM = Guatemala; HND = Honduras; NIC = Nicaragua; PAN = Panama; SLV = El Salvador. LA5 = Latin America 5; PPP = purchasing power parity.Female Labor Force Participation and Main Underlying Factors
Note: CRI = Costa Rica; DR = Dominican Republic; GTM = Guatemala; HND = Honduras; NIC = Nicaragua; PAN = Panama; SLV = El Salvador. LA5 = Latin America 5; PPP = purchasing power parity.Fertility
Significant differences between female LFP rates of women with and without children point to the importance of fertility rates in explaining female LFP. The difference tends to be particularly large for women of ages 20 to 40, which is also a prime age for the accumulation of employment experience. There is a similar difference between participation rates of married and unmarried women (Figure 3.2). Both of these trends have been extensively documented in the United States (for example, Attanasio and others 2008).
Education and Other Accessibility Factors
Educational gaps and other factors affecting accessibility to jobs are also key determinants of female LFP. Gender gaps in education can drive inequality of opportunity to access labor markets in emerging markets and low-income countries (Klasen and Lamanna 2009). Enrollment in secondary and tertiary education is much lower in CAPDR than in Latin America, with the sole exception of Costa Rica (Figure 3.2). Investments in infrastructure are also important as they reduce the costs and increase accessibility for working outside the home. For example, poor transport infrastructure dampens female LFP as women living in areas with limited access to roads are less likely to be in the labor force. Access to electricity and water sources closer to home also frees up time for work outside the house and allows women to integrate into the formal economy (IMF 2015). Other factors identified in the literature as encouraging market access include the ease of internet access and efficiency and flexibility of labor market rules to allow matching of workers with jobs most suited to their skill sets. All of these factors are accounted for in the following analysis of female LFP based on cross-country data and microdata.
Cross-Country Evidence from Regression Analysis
Cross-country data help explain differences between the main drivers of female LFP in CAPDR and other countries. A panel is constructed for 184 countries from 1990–2016, mostly using World Development Indicators complemented by labor market efficiency data from the Global Competitiveness Report. The following regression is estimated following Bloom and others (2007):
where log(GDPCapitat) and log(GDPCapitat)2 control for the countries’ GDP per capita levels, FLFPirt is the female LFP rate for country i in region r at year t, fertilityit, is the fertility rate, and internetit, is the number of internet users per 100 people. share female secondary eduit, share female tertiary eduit., and share male tertiary eduit are the ratios of total female (male) enrollment for secondary and tertiary education to the total female population (male population). urbanit is the percentage of urban residents in the total population, labor market qualityit is an indicator for labor market efficiency, and investmentit is the log of investment in telecommunications and transportation with private participation.3 Dummies include the regional dummy δr, the year dummy δt, and the year-region dummy μrt. Error terms εirt are clustered at the country level.
Results are consistent with existing literature. Regression results are reported in Table 3.1. The polynomial of the log of GDP per capita is statistically significant and generates the well documented U-shaped relationship between female LFP and the level of economic development (Goldin 1994; Gaddis and Klasen 2014). Investments in transportation and telecommunications have positive and significant coefficients, as do coefficients on internet access, consistent with their expected positive impact on access to the job market. In the latter case, the effect is stronger when LFP of women under the age of 25 is considered, perhaps suggesting the importance of technology for younger cohorts. The share of female tertiary enrollment also has positive and significant coefficients, while that of male tertiary educational attainment is negative and statistically significant, in line with the expected impact of a husband’s earning capacity on female LFP. Fertility rates, which serve as a proxy for the effect of children on women’s decision to work, also have negative and marginally significant coefficients. Measures of labor market efficiency are positively and significantly related to LFP rates. Lastly, the share of urban residents has negative and marginally significant coefficients. Given the two contradictory intuitive effects of urbanization on female LFP, it appears that the need to commute, which may impair women’s availability to work—when compared to rural areas where women work much closer to home—would offset increased access to services jobs markets, which tend to be larger employers of women and be located in urban areas.
Cross-Country Regression Results
Cross-Country Regression Results
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
---|---|---|---|---|---|---|---|---|
Dependent variables | Independent variable: FLFP rate (FLFP rate for under age 25 In (6)) | |||||||
Log GDP per capita | -46.918*** (11.909) | -52.519*** (13.460) | -54.902*** (11.701) | -55.646*** (11.391) | -69.790*** (12.611) | -66.596*** (14.006) | -92.947*** (19.612) | -113.19*** (32.321) |
[Log GDP per capita)’ | 2.511*** (0.641) | 2.757*** (0.719) | 2.845*** (0.619) | 2.944*** (0.605) | 3.638*** (0.672) | 3.525*** (0.758) | 4.623*** (0.987) | 5.963*** (1.857) |
Fertility rate per 100 | -1.881 (1.282) | -1.949 (1.352) | -2.267* (1.368) | -2.171 (1.506) | -0.831 (1.728) | -0.695 (1.840) | -2.051 (1.955) | |
Internet users | 0.077 (0.052) | 0.096* (0.049) | 0.110** (0.053) | 0.317*** (0.077) | 0.049 (0.066) | -0.071 (0.107) | ||
Share of urban residents | -0.102* (0.060) | -0.116 (0.079) | -0.105 (0.090) | -0.135 (0.094) | -0.203 (0.132) | |||
Share of female secondary education | 0.023 (0.050) | 0.003 (0.064) | 0.086 (0.078) | 0.039 (0.093) | ||||
Share of female tertiary education | 0.182*** (0.061) | 0.066 (0.101) | 0.147** (0.066) | 0.342** (0.146) | ||||
Share of male tertiary education | -0.228*** (0.071) | -0.127 (0.135) | -0.091 (0.06) | -0.416** (0.204) | ||||
Labor market efficiency | 9.012*** (2.053) | 9.424*** (2.499) | ||||||
Invest in transportation and telecoms | 1.461* (0.776) | |||||||
Observation | 4073 | 4069 | 3514 | 3514 | 1789 | 1789 | 592 | 303 |
R square | 0.495 | 0.502 | 0.507 | 0.513 | 0.556 | 0.458 | 0.692 | 0.710 |
Cross-Country Regression Results
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
---|---|---|---|---|---|---|---|---|
Dependent variables | Independent variable: FLFP rate (FLFP rate for under age 25 In (6)) | |||||||
Log GDP per capita | -46.918*** (11.909) | -52.519*** (13.460) | -54.902*** (11.701) | -55.646*** (11.391) | -69.790*** (12.611) | -66.596*** (14.006) | -92.947*** (19.612) | -113.19*** (32.321) |
[Log GDP per capita)’ | 2.511*** (0.641) | 2.757*** (0.719) | 2.845*** (0.619) | 2.944*** (0.605) | 3.638*** (0.672) | 3.525*** (0.758) | 4.623*** (0.987) | 5.963*** (1.857) |
Fertility rate per 100 | -1.881 (1.282) | -1.949 (1.352) | -2.267* (1.368) | -2.171 (1.506) | -0.831 (1.728) | -0.695 (1.840) | -2.051 (1.955) | |
Internet users | 0.077 (0.052) | 0.096* (0.049) | 0.110** (0.053) | 0.317*** (0.077) | 0.049 (0.066) | -0.071 (0.107) | ||
Share of urban residents | -0.102* (0.060) | -0.116 (0.079) | -0.105 (0.090) | -0.135 (0.094) | -0.203 (0.132) | |||
Share of female secondary education | 0.023 (0.050) | 0.003 (0.064) | 0.086 (0.078) | 0.039 (0.093) | ||||
Share of female tertiary education | 0.182*** (0.061) | 0.066 (0.101) | 0.147** (0.066) | 0.342** (0.146) | ||||
Share of male tertiary education | -0.228*** (0.071) | -0.127 (0.135) | -0.091 (0.06) | -0.416** (0.204) | ||||
Labor market efficiency | 9.012*** (2.053) | 9.424*** (2.499) | ||||||
Invest in transportation and telecoms | 1.461* (0.776) | |||||||
Observation | 4073 | 4069 | 3514 | 3514 | 1789 | 1789 | 592 | 303 |
R square | 0.495 | 0.502 | 0.507 | 0.513 | 0.556 | 0.458 | 0.692 | 0.710 |
Low female education attainment and limited investments in infrastructure appear the main factors behind low female LFP rates in CAPDR. Results from the cross-country panel show that inequality of opportunity driven by limited access to education for women and low investments in infrastructure needed to help access to job markets are the main drivers of the female LFP gap with the LA5 shown in Figure 3.34—LA5 comprises Brazil, Chile, Colombia, Mexico, and Peru —the five biggest economies in Latin America. While higher fertility rates and less efficient labor markets are other factors, their contributions to the female LFP gap with the LA5 are less significant, according to the regression results. All these factors offset the impact of men’s lower educational attainment relative to LA5 countries. This would contribute to higher female LFP rates in CAPDR than in LA5, given that families of less-educated husbands would struggle unless wives worked too.
Contributions to Female Labor Force Participation between CAPDR and LA5
(Average of CAPDR country estimates, percent, latest year available)
Sources: World Development Indicators and IMF staff calculations.Contributions to Female Labor Force Participation between CAPDR and LA5
(Average of CAPDR country estimates, percent, latest year available)
Sources: World Development Indicators and IMF staff calculations.Contributions to Female Labor Force Participation between CAPDR and LA5
(Average of CAPDR country estimates, percent, latest year available)
Sources: World Development Indicators and IMF staff calculations.Evidence from Microdata
We use microdata from household surveys for selected CAPDR countries to estimate a model containing many of the drivers of female LFP identified in the literature. The following regression uses household surveys for Costa Rica, Guatemala, and Honduras:5
Here prim_second_edui, second_tertiary_edui, and more_than_tertiary_edui are dummy variables for the woman i's final educational attainment, and urbani, marriedi, cell phonei, and computeri, are dummy variables for the location of the household in an urban area, the household being a married couple, and having a cellphone. kid_0 to_6, kid_6 to_12., and old more than_70. are equal to one if a household has a member in these categories, respectively. log (head income)i is the log of income of a household head. Regional fixed effects are also included.
Microdata confirm that education, marital status, fertility, and informational and physical accessibility to job markets are important factors. The regression results for the three countries covered are reported in Table 3.2. The results show the usual “hump-shaped” relationship between female LFP rates across the life cycle with the age terms being significant and with the expected signs. Educational attainment is positively related to participation rates. Ownership of cellphones and computers and living in an urban area are positively and significantly associated with higher female LFP—these results signal the importance of having information about job opportunities and an ability to reach workplaces, showing the more positive effect of urbanization relative to results from the cross-country analysis. Being married is shown as having a negative and significant association with female LFP—the differences reach nearly 15 percentage points from ages 20 to 40. The presence of young children and elderly people in the household are also related to lower participation, though insignificantly for the latter. Lastly, attesting to the wealth effect in household labor supply, the higher the income of the household head the lower the female LFP rate—this is consistent with the negative impact of male educational attainment on female LFP found in the cross-country analysis.
Costa Rica: Regression Results using Microdata
Costa Rica: Regression Results using Microdata
Dependent variable | All women | All married women | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Independent variables: | Dummy on labor force participation | |||||
Less than secondary | 0.202*** (0.019) | 0.059*** (0.018) | 0.090*** (0.021) | 20.019 (0.030) | 20.027 (0.033) | 20.030 (0.038) |
Less than university | 0.283*** (0.019) | 0.104*** (0.019) | 0.140*** (0.022) | 0.038 (0.031) | 0.025 (0.034) | 0.051 (0.040) |
University and more | 0.509*** (0.020) | 0.278*** (0.021) | 0.289*** (0.024) | 0.271*** (0.034) | 0.263*** (0.037) | 0.302*** (0.043) |
Age | 0.036*** (0.001) | 0.065*** (0.002) | 0.016*** (0.002) | 0.025*** (0.003) | 0.024*** (0.004) | |
(Age)2 | 20.0004*** 0.00001 | 20.001*** (0.00002) | 2.0002*** (0.00002) | 2.0004*** (0.00004) | 2.0004*** 0.00005 | |
Cellphone | 0.035** (0.016) | 0.050** (0.019) | 0.003 (0.027) | 0.017 (0.031) | 0.036 (0.038) | |
Computer | 0.014 (0.009) | 0.006 (0.009) | 0.061*** (0.014) | 0.049*** (0.014) | 0.074*** (0.016) | |
Urban | 0.054*** (0.008) | 0.058*** (0.008) | 0.057*** (0.013) | 0.060*** (0.013) | 0.063*** (0.014) | |
Married | 20.120*** (0.008) | 20.157*** (0.009) | ||||
With children 0–6 | 20.005 (0.009) | 20.097*** (0.014) | 20.104*** (0.015) | |||
With children 6–12 | 20.055*** (0.009) | 20.058*** (0.013) | 20.061*** (0.014) | |||
With seniors 701 | 20.027* (0.014) | 20.018 (0.026) | 20.051 (0.035) | |||
Log head income | 20.044*** (0.008) | |||||
# obs. | 15,256 | 15,251 | 14,164 | 6,454 | 6,162 | 5,344 |
Region FEs | Ye s | Ye s | Ye s | Ye s | Ye s | Ye s |
Costa Rica: Regression Results using Microdata
Dependent variable | All women | All married women | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Independent variables: | Dummy on labor force participation | |||||
Less than secondary | 0.202*** (0.019) | 0.059*** (0.018) | 0.090*** (0.021) | 20.019 (0.030) | 20.027 (0.033) | 20.030 (0.038) |
Less than university | 0.283*** (0.019) | 0.104*** (0.019) | 0.140*** (0.022) | 0.038 (0.031) | 0.025 (0.034) | 0.051 (0.040) |
University and more | 0.509*** (0.020) | 0.278*** (0.021) | 0.289*** (0.024) | 0.271*** (0.034) | 0.263*** (0.037) | 0.302*** (0.043) |
Age | 0.036*** (0.001) | 0.065*** (0.002) | 0.016*** (0.002) | 0.025*** (0.003) | 0.024*** (0.004) | |
(Age)2 | 20.0004*** 0.00001 | 20.001*** (0.00002) | 2.0002*** (0.00002) | 2.0004*** (0.00004) | 2.0004*** 0.00005 | |
Cellphone | 0.035** (0.016) | 0.050** (0.019) | 0.003 (0.027) | 0.017 (0.031) | 0.036 (0.038) | |
Computer | 0.014 (0.009) | 0.006 (0.009) | 0.061*** (0.014) | 0.049*** (0.014) | 0.074*** (0.016) | |
Urban | 0.054*** (0.008) | 0.058*** (0.008) | 0.057*** (0.013) | 0.060*** (0.013) | 0.063*** (0.014) | |
Married | 20.120*** (0.008) | 20.157*** (0.009) | ||||
With children 0–6 | 20.005 (0.009) | 20.097*** (0.014) | 20.104*** (0.015) | |||
With children 6–12 | 20.055*** (0.009) | 20.058*** (0.013) | 20.061*** (0.014) | |||
With seniors 701 | 20.027* (0.014) | 20.018 (0.026) | 20.051 (0.035) | |||
Log head income | 20.044*** (0.008) | |||||
# obs. | 15,256 | 15,251 | 14,164 | 6,454 | 6,162 | 5,344 |
Region FEs | Ye s | Ye s | Ye s | Ye s | Ye s | Ye s |
Policy Recommendations
Public policies can support the long-term process of increasing female LFP. Increasing female LFP is likely to take place naturally over the long term as countries develop further and fertility rates fall. At the same time, based on experience in other countries, a range of policies could help accelerate the increase. This will require creating fiscal space to accommodate expenditure in priority areas.
Based on evidence on the drivers of female LFP obtained from the analysis, a range of policy measures would help boost female LFP in Central America, Panama, and the Dominican Republic. Among them, policies are needed to improve access and raise educational attainment. In addition to increases in overall education spending, policymakers should consider making greater use of cash transfers that are conditional on families sending all children to school.
Increasing investments in infrastructure and information technology would help reduce the costs of working outside the home and help job-seekers. Boosting the quality of infrastructure in rural areas in particular—for example by making clean water more accessible and improving transportation systems—can reduce the time women spend on domestic tasks and facilitate their access to labor markets (IMF 2013).
Policies to increase women’s access to finance can also be important. The availability of microfinance has been found to help reduce the gender productivity gap in many low-income countries. A credit line targeting women in rural areas helped increase women’s credit share in rural development financing programs in Brazil in the early 2000s (IMF 2015).
Guatemala: Regression Results using Microdata
Guatemala: Regression Results using Microdata
All women | All married women | |||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Variables | Dummy on labor force participation | |||||
Less than Secondary | 0.055*** (0.007) | 0.033*** (0.008) | 0.031*** (0.008) | 0.057*** (0.010) | 0.038*** (0.011) | 0.035*** (0.011) |
Less than University | 0.164*** (0.011) | 0.103*** (0.013) | 0.100*** (0.013) | 0.170*** (0.019) | 0.114*** (0.020) | 0.117*** (0.021) |
University and more | 0.281*** (0.020) | 0.174*** (0.021) | 0.169*** (0.021) | 0.311*** (0.034) | 0.237*** (0.034) | 0.243*** (0.035) |
Age | 0.019*** (0.001) | 0.019*** (0.001) | 0.018*** (0.002) | 0.016*** (0.002) | ||
Age2 | -0.000*** (0.000) | -0.000*** (0.000) | -0.000*** (0.000) | -0.000*** (0.000) | ||
Cellphone | 0.083*** (0.007) | 0.083*** (0.007) | 0.058*** (0.010) | 0.056*** (0.010) | ||
Urban | 0.093*** (0.007) | 0.092*** (0.007) | 0.095*** (0.011) | 0.096*** (0.011) | ||
Married | -0.076*** (0.008) | -0.070*** (0.008) | ||||
With children 0–6 | -0.036*** (0.007) | -0.043*** (0.011) | ||||
With children 6–12 | 0.009 (0.007) | 0.003 (0.010) | ||||
With seniors 701 | 0.022** (0.011) | -0.005 (0.018) | ||||
Log head income | -0.006* (0.003) | |||||
Observations | 17,718 | 17,718 | 17,718 | 8,396 | 8,396 | 8,128 |
R-squared | 0.041 | 0.092 | 0.094 | 0.035 | 0.072 | 0.074 |
Guatemala: Regression Results using Microdata
All women | All married women | |||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Variables | Dummy on labor force participation | |||||
Less than Secondary | 0.055*** (0.007) | 0.033*** (0.008) | 0.031*** (0.008) | 0.057*** (0.010) | 0.038*** (0.011) | 0.035*** (0.011) |
Less than University | 0.164*** (0.011) | 0.103*** (0.013) | 0.100*** (0.013) | 0.170*** (0.019) | 0.114*** (0.020) | 0.117*** (0.021) |
University and more | 0.281*** (0.020) | 0.174*** (0.021) | 0.169*** (0.021) | 0.311*** (0.034) | 0.237*** (0.034) | 0.243*** (0.035) |
Age | 0.019*** (0.001) | 0.019*** (0.001) | 0.018*** (0.002) | 0.016*** (0.002) | ||
Age2 | -0.000*** (0.000) | -0.000*** (0.000) | -0.000*** (0.000) | -0.000*** (0.000) | ||
Cellphone | 0.083*** (0.007) | 0.083*** (0.007) | 0.058*** (0.010) | 0.056*** (0.010) | ||
Urban | 0.093*** (0.007) | 0.092*** (0.007) | 0.095*** (0.011) | 0.096*** (0.011) | ||
Married | -0.076*** (0.008) | -0.070*** (0.008) | ||||
With children 0–6 | -0.036*** (0.007) | -0.043*** (0.011) | ||||
With children 6–12 | 0.009 (0.007) | 0.003 (0.010) | ||||
With seniors 701 | 0.022** (0.011) | -0.005 (0.018) | ||||
Log head income | -0.006* (0.003) | |||||
Observations | 17,718 | 17,718 | 17,718 | 8,396 | 8,396 | 8,128 |
R-squared | 0.041 | 0.092 | 0.094 | 0.035 | 0.072 | 0.074 |
Honduras: Regression Results using Microdata
Honduras: Regression Results using Microdata
All women | All married women | |||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Variables | Dummy on labor force participation | |||||
Less than Secondary | 0.159*** (0.017) | 0.049*** (0.017) | 0.049*** (0.017) | 0.142*** (0.036) | 0.057 (0.037) | 0.103** (0.044) |
Less than University | 0.315*** (0.019) | 0.171*** (0.021) | 0.167*** (0.021) | 0.259*** (0.042) | 0.136*** (0.045) | 0.185*** (0.054) |
University and more | 0.179*** (0.019) | 0.063*** (0.021) | 0.049** (0.022) | 0.346*** (0.044) | 0.220*** (0.047) | 0.288*** (0.056) |
Age | 0.026*** (0.001) | 0.026*** (0.001) | 0.018*** (0.004) | 0.026*** (0.005) | ||
Age2 | -0.000*** (0.000) | -0.000*** (0.000) | -0.000*** (0.000) | -0.000*** (0.000) | ||
Cellphone | 0.139*** (0.012) | 0.137*** (0.012) | 0.143*** (0.025) | 0.148*** (0.029) | ||
Urban | 0.066*** (0.011) | 0.064*** (0.011) | 0.000 (0.025) | 0.015 (0.028) | ||
Married | -0.079*** (0.013) | -0.077*** (0.013) | ||||
With children 0–6 | -0.052*** (0.010) | -0.040 (0.025) | ||||
With children 6–12 | 0.002 (0.010) | -0.041* (0.024) | ||||
With seniors 701 | 0.014 (0.014) | 0.012 (0.044) | ||||
Log head income | -0.030*** (0.011) | |||||
Observations | 9,615 | 9,615 | 9,615 | 2,184 | 2,184 | 1,737 |
R-squared | 0.033 | 0.104 | 0.106 | 0.049 | 0.088 | 0.090 |
Honduras: Regression Results using Microdata
All women | All married women | |||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Variables | Dummy on labor force participation | |||||
Less than Secondary | 0.159*** (0.017) | 0.049*** (0.017) | 0.049*** (0.017) | 0.142*** (0.036) | 0.057 (0.037) | 0.103** (0.044) |
Less than University | 0.315*** (0.019) | 0.171*** (0.021) | 0.167*** (0.021) | 0.259*** (0.042) | 0.136*** (0.045) | 0.185*** (0.054) |
University and more | 0.179*** (0.019) | 0.063*** (0.021) | 0.049** (0.022) | 0.346*** (0.044) | 0.220*** (0.047) | 0.288*** (0.056) |
Age | 0.026*** (0.001) | 0.026*** (0.001) | 0.018*** (0.004) | 0.026*** (0.005) | ||
Age2 | -0.000*** (0.000) | -0.000*** (0.000) | -0.000*** (0.000) | -0.000*** (0.000) | ||
Cellphone | 0.139*** (0.012) | 0.137*** (0.012) | 0.143*** (0.025) | 0.148*** (0.029) | ||
Urban | 0.066*** (0.011) | 0.064*** (0.011) | 0.000 (0.025) | 0.015 (0.028) | ||
Married | -0.079*** (0.013) | -0.077*** (0.013) | ||||
With children 0–6 | -0.052*** (0.010) | -0.040 (0.025) | ||||
With children 6–12 | 0.002 (0.010) | -0.041* (0.024) | ||||
With seniors 701 | 0.014 (0.014) | 0.012 (0.044) | ||||
Log head income | -0.030*** (0.011) | |||||
Observations | 9,615 | 9,615 | 9,615 | 2,184 | 2,184 | 1,737 |
R-squared | 0.033 | 0.104 | 0.106 | 0.049 | 0.088 | 0.090 |
References
Attanasio, Orazio, Hamish Low, and Virginia Sanchez-Marcos. 2008. “Explaining Changes in Female Labor Supply in a Life-Cycle Model.” American Economic Review 98(4): 1517–52.
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.
Gaddis, Isis and Stephan Klasen. (2014) “Economic Development, Structural Change, and Women’s Labor Force Participation: A Reexamination of the Feminization U hypothesis.” Journal of Population Economics 27: 639–681.
Goldin, Claudia. (1994) “The U-shaped Female Labor Force Function in Economic Development and Economic History.” NBER Working Paper #2707.
International Monetary Fund. (2013) “Women, Work and the Economy, Macroeconomic Gains from Gender Equity.” IMF Staff Discussion Note, SDN/13/10.
International Monetary Fund. (2015) “Catalyst for Change: Empowering Women and Tackling Income Inequality” IMF Staff Discussion Note, SDN/15/20.
International Monetary Fund. (2016a) “Costa Rica Selected Issues and Analytical Notes,” Analytical Note IV, IMF Country Report No. 16/132.
International Monetary Fund. (2016b) “Guatemala Selected Issues and Analytical Notes,” Analytical Note I,C. IMF Country Report No. 16/282.
Klasen, Stephan, and Francesca Lamanna, 2009, “The Impact of Gender Inequality in Education nd Employment on Economic Growth: New Evidence for a Panel of Countries.” Feminist Economics, Vol. 15(3) pp. 91–132.
Klasen, Stephan and Janneke Pieters. (2015) “What Explains the Stagnation of Female Labor Force Participation in Urban India?” World Bank Policy Research Working Paper 7222.
The female labor force participation rate is the proportion of the female population ages 15 and older that is economically active (i.e. that supplies labor for the production of goods and services) during a specified period.
Investment in telecoms with private participation is the value of telecom projects that have reached financial closure and directly or indirectly serve the public, including operation and management contracts with major capital expenditure, greenfield projects, and divestitures. Investment in transport with private participation is the value of transportation projects that have reached financial closure and directly or indirectly serve the public, including operation and management contracts with major capital expenditure, greenfield projects, and divestitures.
The model’s estimated coefficients are used to explain the differences between female LFP between each CAPDR country and LA5, according to the model.
Based on Costa Rica’s 2012 Encuesta Nacional de Hogares (ENAHO), Guatemala’s 2014 Encuesta Nacional de Condiciones de Vida (ENCOVI), and Honduras’ 2016 Encuesta Permanente de Hogares de Propósitos Múltiples (EPHPM).