School enrolment and attendance are education performance outcomes that are important for human capital development. In Kenya and other developing countries, majority of children have low school enrolment and attendance ratios due to low access to social services by poor families and gender differences. This paper investigates the impact of cash transfers on human capital development through school enrolment and attendance in Kenya. We applied nonlinear and propensity score matching regression models on a nationally representative household survey to investigate the impact of non-conditional government cash transfers on children’s school enrolment and attendance. The empirical evidence shows that children in cash transfer-receiving households differ from those in non-recipient households. We note that the gender gap in school enrolment and attendance is narrowing but girls are still in a disadvantaged position. We find that cash transfers have an impact on human capital development through children’s school enrolment and attendance in Kenya and they are capable of addressing gender disparities with significant effects in both girls and boys, though the girls are still in a disadvantaged position. To effectively disrupt the intergenerational cycle of poverty, the building of sufficient human capital through cash transfers requires enhancement of the fiscal space and establishment of governance administrative structures that are accountable and transparent in their delivery mechanism of cash transfers. To bridge the gender gap, gender mainstreaming should take centre stage in the allocation of cash transfers.
Published in | International Journal of Economics, Finance and Management Sciences (Volume 13, Issue 4) |
DOI | 10.11648/j.ijefm.20251304.11 |
Page(s) | 156-176 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Gender, Enrolment, Attendance, Cash Transfers, Human Capital Development
Variables | Definitions | Justification of Variables |
---|---|---|
Dependent Variables | ||
School attendance | =1 if attending school and 0 otherwise. | Variable capable of explaining HCD in children schooling. CTs have an effect of improving school attendance. |
School enrolment | =1 if enrolled in school and 0 otherwise. | Variable capable of explaining HCD in children schooling. CTs are likely to increase school enrolment. |
Explanatory Variables | ||
Household received CTs | =1 if treated, 0 otherwise | The household that receives CTs can smoothen its consumption, produce human capital and undertake interventions that reduce poverty and improve gender differences at the same time have children in school. |
Age of child | Number of years the child has lived | Older children are likely to enrol and attend school. |
Gender of the child | =1 if a girl child, 0 otherwise | Gender differences likely to worsen enrolment and attendance. Girls likely to experience low enrolment and attendance rates and likely to be discriminated against in the receipt of CTs |
Gender of household head | =1 for Female Headed Household and 0 otherwise | Gender of household head is important in determining CT expenditures. Female Headed Households likely to experience worse enrolment and attendance rates. It is expected that CTs are distributed in a gender sensitive manner. |
Parental level of Education | =1 for mother with low level of education and 0 otherwise =1 for father with low level of education and 0 otherwise | Low education of the mother or father is detrimental to child school enrolment and attendance. Expected that parents with low level of education likely to receive CTs as they have limited opportunities |
Teenage pregnancies | =1 for teen pregnancies and 0 otherwise | Teenage pregnancies are likely to affect school enrolment and attendance while CTs are important in keeping young girls in school after early pregnancies. |
Early marriage | =1 for early marriage and 0 otherwise | Early marriages are likely to affect school enrolment and attendance while CTs are important in keeping young girls and boys in school and protecting them from early marriages. |
Child labour | =1 if in paid employment and domestic work and 0 otherwise | Child labour is likely to affect school enrolment and attendance while CTs might reduce the demand for employment activities that hinder children schooling. Twin impact of CTs on increased school enrolment and reduced domestic work for both boys and girls. |
Area of residence | = 1 if Rural and 0 otherwise | Rural residence likely to slow school enrolment and attendance while CTs are expected to impact the lives of children in rural areas as poverty is rampant in these areas. It’s expected that the rural children are likely to receive CTs. |
Poverty status of the household | = 1 if resides in a poor household and 0 otherwise | Poverty is associated with the number of children not enrolled or attending school as it displaces them from school. It’s expected that poor children are more likely to receive CTs. |
Variables | TREATMENT | CONTROL | ||||
---|---|---|---|---|---|---|
Boys | Girls | Diff | Boys | Girls | Diff | |
Mean | Mean | diff | Mean | Mean | diff | |
Poverty status of the household | 0.559 | 0.529 | 0.030 | 0.470 | 0.443 | 0.026 |
(0.014) | (0.014) | (0.019) | (0.004) | (0.004) | (0.006) *** | |
Area of residence | 0.622 | 0.645 | -0.023 | 0.687 | 0.688 | -0.001 |
(0.013) | (0.013) | (0.019) | (0.004) | (0.004) | (0.005) | |
Age of Child | 13.072 | 12.770 | 0.303 | 11.672 | 11.542 | 0.130 |
(0.101) | (0.104) | (0.145) ** | (0.030) | (0.031) | (0.043) *** | |
Early marriage | 0.000 | 0.000 | 0.000 | 0.000 | 0.002 | -0.002 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) *** | |
Child labour | 0.131 | 0.102 | 0.028 | 0.160 | 0.119 | 0.041 |
(0.009) | (0.008) | (0.012) ** | (0.003) | (0.003) | (0.004) *** | |
Gender of household head | 0.390 | 0.429 | -0.039 | 0.339 | 0.348 | -0.010 |
(0.013) | (0.014) | (0.019) ** | (0.004) | (0.004) | (0.006) * | |
Mother’s level of education | 0.328 | 0.407 | -0.080 | 0.529 | 0.545 | -0.016 |
(0.013) | (0.014) | (0.019) *** | (0.004) | (0.004) | (0.006) *** | |
Father’s level of education | 0.178 | 0.210 | -0.032 | 0.345 | 0.343 | 0.002 |
(0.010) | (0.011) | (0.015) ** | (0.004) | (0.004) | (0.006) |
Variables | TREATMENT | CONTROL | ||||
---|---|---|---|---|---|---|
Boys | Girls | Diff | Boys | Girls | Diff | |
mean | mean | diff | mean | mean | diff | |
Poverty status of the household | 0.555 | 0.523 | 0.032 | 0.466 | 0.444 | 0.022 |
(0.014) | (0.014) | (0.020) * | (0.004) | (0.004) | (0.006) *** | |
Area of residence | 0.627 | 0.646 | -0.019 | 0.688 | 0.694 | -0.006 |
(0.014) | (0.014) | (0.019) | (0.004) | (0.004) | (0.006) | |
Age of Child | 12.913 | 12.633 | 0.280 | 11.429 | 11.251 | 0.178 |
(0.103) | (0.106) | (0.148) * | (0.030) | (0.030) | (0.043) *** | |
Early marriage | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Child labour | 0.112 | 0.091 | 0.021 | 0.136 | 0.099 | 0.037 |
(0.009) | (0.008) | (0.012) * | (0.003) | (0.003) | (0.004) *** | |
Gender of household head | 0.387 | 0.427 | -0.040 | 0.336 | 0.346 | -0.010 |
(0.014) | (0.014) | (0.020) ** | (0.004) | (0.004) | (0.006) * | |
Mother’s level of education | 0.331 | 0.406 | -0.075 | 0.531 | 0.546 | -0.015 |
(0.013) | (0.014) | (0.019) *** | (0.004) | (0.004) | (0.006) ** | |
Father’s level of education | 0.178 | 0.212 | -0.033 | 0.346 | 0.346 | 0.000 |
(0.011) | (0.012) | (0.016) ** | (0.004) | (0.004) | (0.006) |
Variables | Pooled Sample | Girls Sample | Boys Sample | |||
---|---|---|---|---|---|---|
Coef. | dy/dx | Coef. | dy/dx | Coef. | dy/dx | |
Household received CTs | -0.366 | -0.020 | -0.330 | -0.019 | -0.420 | -0.021 |
(0.068) *** | (0.004) *** | (0.096) *** | (0.006) *** | (0.098) *** | (0.005) *** | |
Gender of the child | -0.328 | -0.018 | ||||
(0.047) *** | (0.003) *** | |||||
Gender of household head | 0.426 | 0.023 | 0.521 | 0.030 | 0.322 | 0.016 |
(0.051) *** | (0.003) *** | (0.071) *** | (0.004) *** | (0.074) *** | (0.004) *** | |
Poverty status of the household | -1.358 | -0.073 | -1.393 | -0.080 | -1.329 | -0.066 |
(0.053) *** | (0.003) *** | (0.073) *** | (0.004) *** | (0.079) *** | (0.004) *** | |
Area of residence | -0.905 | -0.049 | -0.952 | -0.055 | -0.859 | -0.043 |
(0.063) *** | (0.003) *** | (0.086) *** | (0.005) *** | (0.093) *** | (0.005) *** | |
Age of child | 0.595 | 0.032 | 0.580 | 0.033 | 0.622 | 0.031 |
(0.044) *** | (0.002) *** | (0.061) *** | (0.003) *** | (0.065) *** | (0.003) *** | |
Age of child squared | -0.020 | -0.001 | -0.020 | -0.001 | -0.020 | -0.001 |
(0.002) *** | (0.000) *** | (0.003) *** | (0.000) *** | (0.003) *** | (0.000) *** | |
Child labour | -1.343 | -0.072 | -1.192 | -0.069 | -1.495 | -0.075 |
(0.058) *** | (0.003) *** | (0.084) *** | (0.005) *** | (0.082) *** | (0.004) *** | |
Mother’s level of education | 2.387 | 0.128 | 2.506 | 0.144 | 2.268 | 0.113 |
(0.082) *** | (0.005) *** | (0.116) *** | (0.007) *** | (0.116) *** | (0.006) *** | |
Father’s level of education | 1.257 | 0.068 | 1.279 | 0.074 | 1.235 | 0.062 |
(0.080) *** | (0.004) *** | (0.110) *** | (0.006) *** | (0.117) *** | (0.006) *** | |
_cons | -0.252 | -0.380 | -0.505 | |||
(0.243) | (0.333) | (0.353) | ||||
Pseudo R-squared | 0.2596 | 0.2647 | 0.2558 | |||
Log likelihood | -6319.9557 | -3278.0613 | -3028.0007 | |||
LR chi2 statistics | 0.0000 | 0.0000 | 0.0000 | |||
Number of obs | 33,474 | 33,474 | 16,453 | 16,453 | 17,021 | 17,021 |
Household received CTs | Pooled Sample | Girls Sample | Boys Sample |
---|---|---|---|
Coef. | Coef. | Coef. | |
Gender of the child | 0.021 (0.039) | - | - |
Gender of household head | 0.021 (0.044) | 0.120 (0.063) | -0.071 (0.061) |
Poverty status of the household | 0.359 (0.039) *** | 0.351 (0.056) *** | 0.366 (0.055) *** |
Area of residence | -0.133 (0.041) *** | -0.105 (0.059) * | -0.164 (0.058) *** |
Age of child | -0.108 (0.038) *** | -0.109 (0.054) ** | -0.106 (0.054) ** |
Age of child squared | 0.008 (0.002) *** | 0.008 (0.002) *** | 0.008 (0.002) *** |
Child labour | -0.192 (0.057) *** | -0.169 (0.085) ** | -0.203 (0.077) *** |
Mother’s level of education | -0.587 (0.043) *** | -0.489 (0.060) *** | -0.689 (0.061) *** |
Father’s level of education | -0.574 (0.057) *** | -0.457 (0.080) *** | -0.686 (0.080) *** |
_cons | -1.968 (0.222) *** | -2.031 (0.311) *** | -1.891 (0.315) *** |
Log likelihood | -9644.8568 | -4777.9224 | -4858.4079 |
Number of obs | 33491 | 16462 | 17029 |
Prob > chi2 | 0.0000 | 0.0000 | 0.0000 |
Pseudo R2 | 0.0428 | 0.0345 | 0.0524 |
Estimator | Sample | Treated | Controls | Diff |
---|---|---|---|---|
pooled sample | ||||
NN (1) | ATT | 0.881 | 0.955 | -0.075 (0.012) *** |
NN (2) | ATT | 0.881 | 0.951 | -0.071 (0.010) *** |
NN (3) | ATT | 0.881 | 0.934 | -0.054 (0.009) *** |
NN (4) | ATT | 0.881 | 0.918 | -0.038 (0.009) *** |
Radius (0.01) | ATT | 0.881 | 0.909 | -0.028 (0.006) *** |
Radius (0.005) | ATT | 0.881 | 0.908 | -0.028 (0.006) *** |
Radius (0.0025) | ATT | 0.881 | 0.907 | -0.027 (0.006) *** |
Kernel (0.01) | ATT | 0.881 | 0.909 | -0.029 (0.006) *** |
Kernel (0.005) | ATT | 0.881 | 0.908 | -0.027 (0.006) *** |
Kernel (0.0025) | ATT | 0.881 | 0.908 | -0.027 (0.006) *** |
Girls Sample | ||||
NN (1) | ATT | 0.875 | 0.945 | -0.069 (0.017) *** |
NN (2) | ATT | 0.875 | 0.943 | -0.067 (0.014) *** |
NN (3) | ATT | 0.875 | 0.923 | -0.048 (0.013) *** |
NN (4) | ATT | 0.875 | 0.905 | -0.030 (0.013) *** |
Radius (0.01) | ATT | 0.875 | 0.903 | -0.027 (0.009) *** |
Radius (0.005) | ATT | 0.875 | 0.904 | -0.029 (0.009) *** |
Radius (0.0025) | ATT | 0.875 | 0.903 | -0.027 (0.009) *** |
Kernel (0.01) | ATT | 0.875 | 0.903 | -0.028 (0.009) *** |
Kernel (0.005) | ATT | 0.875 | 0.903 | -0.027 (0.009) *** |
Kernel (0.0025) | ATT | 0.875 | 0.902 | -0.027 (0.009) *** |
Boys Sample | ||||
NN (1) | ATT | 0.886 | 0.965 | -0.079 (0.015) *** |
NN (2) | ATT | 0.886 | 0.959 | -0.073 (0.013) *** |
NN (3) | ATT | 0.886 | 0.945 | -0.060 (0.012) *** |
NN (4) | ATT | 0.886 | 0.931 | -0.045 (0.012) *** |
Radius (0.01) | ATT | 0.886 | 0.916 | -0.030 (0.008) *** |
Radius (0.005) | ATT | 0.886 | 0.919 | -0.033 (0.008) *** |
Radius (0.0025) | ATT | 0.886 | 0.917 | -0.031 (0.008) *** |
Kernel (0.01) | ATT | 0.886 | 0.917 | -0.031 (0.008) *** |
Kernel (0.005) | ATT | 0.886 | 0.916 | -0.031 (0.008) *** |
Kernel (0.0025) | ATT | 0.886 | 0.917 | -0.031 (0.008) *** |
Variables | Pooled Sample | Girls Sample | Boys Sample | |||
---|---|---|---|---|---|---|
Coef. | dy/dx | Coef. | dy/dx | Coef. | dy/dx | |
Household received CTs | -0.025 | -0.002 | 0.060 | 0.005 | -0.125 | -0.010 |
(0.059) | (0.005) | (0.084) | (0.008) | (0.083) | (0.007) | |
Gender of the child | -0.331 | -0.029 | - | - | - | - |
(0.038) *** | (0.003) *** | - | - | - | - | |
Gender of household head | 0.270 | 0.023 | 0.354 | 0.032 | 0.180 | 0.015 |
(0.042) *** | (0.004) *** | (0.059) *** | (0.005) *** | (0.061) *** | (0.005) *** | |
Poverty status of the household | -0.924 | -0.080 | -0.930 | -0.084 | -0.933 | -0.077 |
(0.039) *** | (0.003) *** | (0.054) *** | (0.005) *** | (0.056) *** | (0.005) *** | |
Area of residence | -0.313 | -0.027 | -0.300 | -0.027 | -0.333 | -0.027 |
(0.043) *** | (0.004) *** | (0.060) *** | (0.005) *** | (0.063) *** | (0.005) *** | |
Age of child | 1.058 | 0.091 | 1.096 | 0.099 | 1.027 | 0.085 |
(0.035) *** | (0.003) *** | (0.049) *** | (0.004) *** | (0.051) *** | (0.004) *** | |
Age of child squared | -0.047 | -0.004 | -0.049 | -0.004 | -0.045 | -0.004 |
(0.001) *** | (0.000) *** | (0.002) *** | (0.000) *** | (0.002) *** | (0.000) *** | |
Child labour | -1.708 | -0.147 | -1.623 | -0.146 | -1.792 | -0.148 |
(0.043) *** | (0.004) *** | (0.063) *** | (0.005) *** | (0.060) *** | (0.005) *** | |
Mother’s level of education | 1.189 | 0.103 | 1.226 | 0.110 | 1.159 | 0.096 |
(0.043) *** | (0.004) *** | (0.061) *** | (0.005) *** | (0.063) *** | (0.005) *** | |
Father’s level of education | 0.736 | 0.063 | 0.821 | 0.074 | 0.642 | 0.053 |
(0.053) *** | (0.005) *** | (0.075) *** | (0.007) *** | (0.076) *** | (0.006) *** | |
_cons | -2.610 | -3.077 | -2.501 | |||
(0.197) *** | (0.273) *** | (0.283) *** | ||||
Pseudo R-squared | 0.2028 | 0.2061 | 0.2017 | |||
Log likelihood | -9826.3696 | -4997.3219 | -4809.0702 | |||
LR chi2 statistics | 0.0000 | 0.0000 | 0.0000 | |||
Number of obs | 33,474 | 33,474 | 16,453 | 16,453 | 17,021 | 17,021 |
Household received CTs | Pooled Sample | Girls Sample | Boys Sample |
---|---|---|---|
Coef. | Coef. | Coef. | |
Gender of the child | 0.021 (0.039) | ||
Gender of household head | 0.021 (0.044) | 0.120 (0.063) * | -0.071 (0.061) |
Poverty status of the household | 0.359 (0.039) *** | 0.351 (0.056) *** | 0.366 (0.055) *** |
Area of residence | -0.133 (0.041) *** | -0.105 (0.059) * | -0.164 (0.058) *** |
Age of child | -0.108 (0.038) *** | -0.109 (0.054) ** | -0.106 (0.054) ** |
Age of child squared | 0.008 (0.002) *** | 0.008 (0.002) *** | 0.008 (0.002) *** |
Child labour | -0.192 (0.057) *** | -0.169 (0.085) ** | -0.203 (0.077) *** |
Mother’s level of education | -0.587 (0.043) *** | -0.489 (0.060) *** | -0.689 (0.061) *** |
Father’s level of education | -0.574 (0.056) *** | -0.457 (0.080) *** | -0.686 (0.080) *** |
_cons | -1.968 (0.222) *** | -2.031 (0.311) *** | -1.891 (0.315) *** |
Log likelihood | -9644.8568 | -4777.9224 | -4858.4079 |
Number of obs | 33491 | 16462 | 17029 |
Prob > chi2 | 0.0000 | 0.0000 | 0.0000 |
Pseudo R2 | 0.0428 | 0.0345 | 0.0524 |
Estimator | Sample | Treated | Controls | Diff. |
---|---|---|---|---|
Pooled Sample | ||||
NN (1) | ATT | 0.838 | 0.855 | -0.016 (0.019) |
NN (2) | ATT | 0.838 | 0.853 | -0.014 (0.014) |
NN (3) | ATT | 0.838 | 0.838 | 0.000 (0.012) |
NN (4) | ATT | 0.838 | 0.826 | 0.013 (0.011) |
Radius (0.01) | ATT | 0.838 | 0.836 | 0.002 (0.007) |
Radius (0.005) | ATT | 0.838 | 0.837 | 0.002 (0.007) |
Radius (0.0025) | ATT | 0.838 | 0.834 | 0.004 (0.007) |
Kernel (0.01) | ATT | 0.838 | 0.838 | 0.001 (0.007) |
Kernel (0.005) | ATT | 0.838 | 0.836 | 0.002 (0.007) |
Kernel (0.0025) | ATT | 0.838 | 0.836 | 0.002 (0.007) |
Girls Sample | ||||
NN (1) | ATT | 0.839 | 0.831 | 0.008 (0.028) |
NN (2) | ATT | 0.839 | 0.84 | -0.001 (0.021) |
NN (3) | ATT | 0.839 | 0.826 | 0.013 (0.018) |
NN (4) | ATT | 0.839 | 0.818 | 0.022 (0.016) * |
Radius (0.01) | ATT | 0.839 | 0.826 | 0.013 (0.010) * |
Radius (0.005) | ATT | 0.839 | 0.828 | 0.011 (0.010) |
Radius (0.0025) | ATT | 0.839 | 0.827 | 0.012 (0.010) |
Kernel (0.01) | ATT | 0.839 | 0.83 | 0.010 (0.010) |
Kernel (0.005) | ATT | 0.839 | 0.828 | 0.012 (0.010) |
Kernel (0.0025) | ATT | 0.839 | 0.828 | 0.011 (0.010) |
Boys Sample | ||||
NN (1) | ATT | 0.838 | 0.877 | -0.039 (0.026) * |
NN (2) | ATT | 0.838 | 0.863 | -0.026 (0.020) * |
NN (3) | ATT | 0.838 | 0.848 | -0.011 (0.017) |
NN (4) | ATT | 0.838 | 0.832 | 0.006 (0.016) |
Radius (0.01) | ATT | 0.838 | 0.844 | -0.006 (0.010) |
Radius (0.005) | ATT | 0.838 | 0.852 | -0.014 (0.010) * |
Radius (0.0025) | ATT | 0.838 | 0.851 | -0.013 (0.010) * |
Kernel (0.01) | ATT | 0.838 | 0.848 | -0.010 (0.010) |
Kernel (0.005) | ATT | 0.838 | 0.847 | -0.010 (0.010) |
Kernel (0.0025) | ATT | 0.838 | 0.851 | -0.013 (0.010) * |
ATT | Average Treatment Effect on the Treated |
CT-OVC | Cash Transfer to Orphans and Vulnerable Children |
CTs | Cash Transfers |
FHHs | Female Headed Households |
HCD | Human Capital Development |
HCT | Human Capital Theory |
HSNP | Hunger Safety Net Programme |
NN | Nearest-neighbour |
PSM | Propensity Score Matching |
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APA Style
Ichwara, J. M. (2025). Impact of Cash Transfers on School Enrolment and Attendance by Gender in Kenya. International Journal of Economics, Finance and Management Sciences, 13(4), 156-176. https://doi.org/10.11648/j.ijefm.20251304.11
ACS Style
Ichwara, J. M. Impact of Cash Transfers on School Enrolment and Attendance by Gender in Kenya. Int. J. Econ. Finance Manag. Sci. 2025, 13(4), 156-176. doi: 10.11648/j.ijefm.20251304.11
@article{10.11648/j.ijefm.20251304.11, author = {Jared Masini Ichwara}, title = {Impact of Cash Transfers on School Enrolment and Attendance by Gender in Kenya }, journal = {International Journal of Economics, Finance and Management Sciences}, volume = {13}, number = {4}, pages = {156-176}, doi = {10.11648/j.ijefm.20251304.11}, url = {https://doi.org/10.11648/j.ijefm.20251304.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20251304.11}, abstract = {School enrolment and attendance are education performance outcomes that are important for human capital development. In Kenya and other developing countries, majority of children have low school enrolment and attendance ratios due to low access to social services by poor families and gender differences. This paper investigates the impact of cash transfers on human capital development through school enrolment and attendance in Kenya. We applied nonlinear and propensity score matching regression models on a nationally representative household survey to investigate the impact of non-conditional government cash transfers on children’s school enrolment and attendance. The empirical evidence shows that children in cash transfer-receiving households differ from those in non-recipient households. We note that the gender gap in school enrolment and attendance is narrowing but girls are still in a disadvantaged position. We find that cash transfers have an impact on human capital development through children’s school enrolment and attendance in Kenya and they are capable of addressing gender disparities with significant effects in both girls and boys, though the girls are still in a disadvantaged position. To effectively disrupt the intergenerational cycle of poverty, the building of sufficient human capital through cash transfers requires enhancement of the fiscal space and establishment of governance administrative structures that are accountable and transparent in their delivery mechanism of cash transfers. To bridge the gender gap, gender mainstreaming should take centre stage in the allocation of cash transfers.}, year = {2025} }
TY - JOUR T1 - Impact of Cash Transfers on School Enrolment and Attendance by Gender in Kenya AU - Jared Masini Ichwara Y1 - 2025/07/10 PY - 2025 N1 - https://doi.org/10.11648/j.ijefm.20251304.11 DO - 10.11648/j.ijefm.20251304.11 T2 - International Journal of Economics, Finance and Management Sciences JF - International Journal of Economics, Finance and Management Sciences JO - International Journal of Economics, Finance and Management Sciences SP - 156 EP - 176 PB - Science Publishing Group SN - 2326-9561 UR - https://doi.org/10.11648/j.ijefm.20251304.11 AB - School enrolment and attendance are education performance outcomes that are important for human capital development. In Kenya and other developing countries, majority of children have low school enrolment and attendance ratios due to low access to social services by poor families and gender differences. This paper investigates the impact of cash transfers on human capital development through school enrolment and attendance in Kenya. We applied nonlinear and propensity score matching regression models on a nationally representative household survey to investigate the impact of non-conditional government cash transfers on children’s school enrolment and attendance. The empirical evidence shows that children in cash transfer-receiving households differ from those in non-recipient households. We note that the gender gap in school enrolment and attendance is narrowing but girls are still in a disadvantaged position. We find that cash transfers have an impact on human capital development through children’s school enrolment and attendance in Kenya and they are capable of addressing gender disparities with significant effects in both girls and boys, though the girls are still in a disadvantaged position. To effectively disrupt the intergenerational cycle of poverty, the building of sufficient human capital through cash transfers requires enhancement of the fiscal space and establishment of governance administrative structures that are accountable and transparent in their delivery mechanism of cash transfers. To bridge the gender gap, gender mainstreaming should take centre stage in the allocation of cash transfers. VL - 13 IS - 4 ER -