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<p>This empirical model of income child poverty use variable of “income child poverty” as dependent variables. We model our income child poverty on set of explanatory such as child’s age (0-2 years old), child’s age (3-4 years old), child’s age (5-9 years old), child’s age (10-14 years old), child’s age (15-17 years old), female child (1 if female child; 0 otherwise), education of the child (1 if individual child completed at least secondary or higher education; 0 otherwise), married child (1 if child is married; 0 otherwise), hours worked of children in both primary and secondary occupation, education of household head, household size, health expenditure of household per capita per month, health subsidies per capita per year, education expenditure per person per years, severe sanitation (children with no access to a toilet facility of any kind, severe water (children using surface water such as rivers, pond, streams and dams), severe shelter (children living in a dwelling with 5 or more people per room or with no floor material), ethnicity such as Khmer (1 if Khmer; 0 otherwise), local group (1 if local group; 0 otherwise), Cham (1 if Cham; 0 otherwise), other local group (1 if Other local group; 0 otherwise), and regional characteristics such as urban (1 if urban; 0 otherwise), mountain zone (1 if mountain zone; 0 otherwise) and plain zone (1 if plain zone; 0 otherwise). </p><p>Because our dependent variable of income child poverty is dichotomy (1 if child poverty; 0 otherwise), we use Probit model to estimates in this analysis. Now, let <em>ChPov*<sub>i</sub></em> is the dichotomous variable of child poverty, take on the value {1} for child poverty, and zero {0} for otherwise. Therefore the specification model is expressed through the latent variable as follow:</p><p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAI8AAAA6CAYAAACJZApVAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAApVSURBVHhe7Zx7cFXFHccXh78swXYETQhagpUYHQtOBGlFiCDlUavoAAGEgEJQkYoQCJCCEO2AQlKIUOSpGCggpaggAcpDRFppaRSllKJV2lEgFZypDXXsDCPNZ7l7u3dz7mvvE7OfmTP33HPO3ed3f/vbc87vNrvw9dELwuGw4DLfp8MRNU48DmuceBzWOPE4rGky4jl37ksx9uFZ4vTpM74jjlhpEuJ5991jYvSYmXK/4M5R4vXX35T7jthoMpZnz56DYvPm3b5vjngQF/EwJZRMnidatb5dNLvsJtHltkJRU7NfjnA2porrvtdHnqusXO37VSBcx3lzKxxSIj788B++q+y45ZY88fcTu8T9998l9r2xWtx9dw/fmfShru5sQBvyuXLlJt9Ze0aOKgtoz7t6j5b9pdDPB+ubYMQsng0bakS7nN5y/89HXhMXvj4q9u55Uby8cYcY9WCZyM6+SmRltRbvHX5FDB7cV/TseZu81oQO3bplibjyym/LT9Kp//ch8fnn/xKPjnsqoMI2tGhxuVi+rFyWJd1gcN3e7QHRsmULKXLqPn1asZg2fYGccmPhpdVzZHvC+nXzxe5dq2RbKDg/adJI8U7tJlFSMsp3NDJiEg/WYuiwKWLxop+JyopSkZnZSh6ncM/MnShH+vXXf1ceU9ZDfQ/GFVe0EPn5N8p90unX9w5x4sSnor7+P/LYNxFE0q1bvpj15Dh/x+bm5si2UG1qEs0C4PjxE6JXr64NA7TAdySQG/OuC9svXliLhxGBZSkqulcMGdLfdzSQ7Oyr/Y2xd+8fRJfONweo3uTXm3bKRtStw/tHPhA5OW1FRsa3fEe+WdD5Bw7UikED+/iOXBTGmrVbRNXCspgtJWlt3/GWHIRebU/+tG2ofgmGtXgQAzwxYYT8NKHSjCQFImA0qRHDHKuvesxG5Lryp5aI6urXGvIo8lcOC6Z+r/sFymfimG7qmdPZEo1X/kzpfDf9DJ3a2r/IwVFQ0FmWk+v79C0WI4bfExffjPY6fPivsu294LyN1QEr8Sg1d+p0Q0QZI4xTpz5rqEA7sWBBtZgy+SFRMX+KNKcKGvGLL86Juc8sl52Q0bKzOHjwPfHB8Rp/I9IpP/jhMGnR8Id2/XalmF/xojzONadO7pOmXgkbmD7bt2/r+xYe08E0t2BCJH/lqyysWiNFTYedPfO7Rn6GDtZWWQX8D65fvGiGdJ71QWDLyZOfBbgCJkkXD/4Hfog+LYUCYbRpc5XYv/9PYuLEImmV/njoSIDzTCMixp07VshOYNtes8xfMQQ7pbRCdo7yDfAHzp8/LxsISJdpDyunIO87C7r4voWHDlT5e22cDwV1QvT5+TfJVV4oGFTHjn3UaBFh1kuhC5vBtWLFJtEmuyCksL1cAUUsUxbE5DBHihRGxxtE//7dZUFN51lNWcHmZdi375AU7LBhP/Ydubi8bd68uVzRKb5/c4eGRv+nFBsjt67ujOje/Vbf2cRDna65JrNRx3uxbt02ucIyR75XvUAXNpa3uHigtLbBhG26AiYMLFurA1biUSOcglFAEzpu1arfyH01upjTlfpN51k1VrBlPDDFmY4z0wMdpTeAmtsR6Jatb4gxYwbK75Tp6aeXys9Q2E5bCunntPqOHDA6Zv58BnNkaZ+uXTuGtVyRwK0PU4RAv2zZureReFQ56RN8Ne7PefUxWFseHGV8lHsHjPdbEmC04+gWFvaT3ykEna4KSUFe3rhd5OW1l9dSWC8RmCAKLA+/5zf4BNu2vSnmz5vcqPFxEJ9fukGUTPr/fQuumTnzkaCWTRHLtIVwMjNbi6mlo8VXX/1XlnPjxh2yzGb+tBnl3L3nbX/7qXohql8uniGPxQKDNa9hGf7wI7MD+oj9h0bPEPf8pGej9lDlZOp89ZVFcokfDGvxMCre/v066ad0yO0vRyUrhbW/2hpwv8LLynz88afSgQZuMLKi4vHBgPt+KhvQCxzSAQN6ybyY7/G7yN9rdFKmX1RODWgYLEaoVU8scGeWupM25WQQcHOzZ68H5T6daOZPu1DOsuljxQPDS2X75d86SLTNzpSdZnaqLYh9bPEgudBQ1vPxCXPEc1Vl/oWITjTt1GReQ1W3BbwaLBno+dMxDBSmrGjv6iYas5yTSp4V5bPHezrcSXGY04Had44GXa4mAz1/po1PPqkL6eOlimjaqUmIRzl8XqMnGZj54+PFyyGOJ9G2U5MQD0tSlu/cZ0oFZv74IaEc71RhlvOx8T+X95JGFE3z9IFc6I3Dmibj8zjijxOPwxonHoc1TjwOa5x4HNY48TisceJxWJNW4uGhnePSwVkehzVOPA5rrMVjvnFnvgOin482EjHV8IBQRbia9WKfY6pu1FPB73r/aIz/ASNQd3Wtet3BC5Wuys8rrXTDWjw82EtUJGKq4anyR3/bKVYsL5dv++lv4VFHXtYi+pV6qwecCKPbHcNF9Utz/U+lObbk+fXyPWO2CU/M8RQQAunY6T75QpyCNOY9WyL/mCEeURSJIKZpK1GRiOkE7wDzCoUObzFee22Wv950ftVza8SBt9b6hYP1WFhV7Q/cY2OfY7olA84hVsKRdHhlY8P6CvkaaTpaIGvx0ACJikRMB6hfff2Xonz2Y41e9OdV2twO7fx1I1y4b59ufuGAeuFLf/mcfY7pliwcCKhwcD+ZR7phLR4aIFGRiJh2fIRooy/jCeXPyLhc9OjRWX4nTEZRW8vbdhdvKyAqr9grQm+IvdJjzYPFY4WDtMkj3ayPtXhogERFIvL+LJEK0UZfxhMlEKwJYUZYWUTLdvr0WX/deIGKl911kcQb0iYP8konrMWTyEhEBSMu0uhLEywWlsprVYQAQsVwmQIhzEhNN2xZWa38ddNDpsPBdEe4ki3R5JUMrMSDOBIZiaggjUijL00I+Dvy/qv+eCtQQiLcB+sWTNw4xLpAVDmwgvqUFQr8GwIZEYxOsCC8SxFry5PISERFsOhLCGc9iB3TrSJLaiUkpr9Qlgzx6wJBRPxTB0GGxNjrdQvm83kJn1itcMGNoQiWV6qwEg+dkshIRAgVfQkqPTOfWCEfpjzTh8G3w8fL7ZATkCfHGUimheEaBKeW5pSbez7638WY6H/QoEPa5BFpSEyysLY8jORERCJGEn0JkaYXDazyiEZ9ctYiecNPt4zkO+7RoY1WVWogYVVMKD+3MkiTf7PgPg/HlD+mrC8b+ypylmlVvzFI2uShW9J0IGnRE+rOKo1Hh3tFIiJA5Z+EQ08v1dD5xOwvWzo7KseeEBcWFqF+Q9pFI6cH3LlOF6wtT7TEO2Iz1RGgOnQqwhkydHKAxQiGsjSrXtgcVjhYwAmPj0g74UBSxEMjQLwaIN7pxQNEwN/0lk6t9JcvGJSbxxHquZgXpIHF4ZFHOlhXL5IinmgjEcNhppcuIAr+6i4eoo5nWokirSJGo/F5HKknaT5PJDjhXFqklXgclxZOPA5rnHgc1jS70IBv3+GICvf/PKmgWXo9o7LFTVsOa5x4HJYI8T9K4DnkYuI0NwAAAABJRU5ErkJggg=="/> equation (1)</p><p>where <img src="data:image/x-wmf;base64,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"/> is the aggregated form of the explanatory variables. Therefore, the dependent variable (income child poverty) can be observed as follow:</p><p><img src="data:image/png;base64,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"/>equation (2)</p><p>Then the standard normal cumulative distribution function and the standard normal density are:</p><p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASwAAAA1CAYAAAD709aSAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAzaSURBVHhe7Z15kBXFHcd7yR+xEohUiYpiJeIBrpYERdCqIGcIl6JGQDRyKMhhJNwClgkYI6CCYGIMGyGBxRjkUkSuEJEFrMIgaKQEOQQsBYloVURMlRXihk/v9Nt5Q8+becfse2/e71M19ebe6Z7ub//617/pLas+jTJU73ZWBEEQCo96zq8gCELBI4IlCELRIIIlCELREFvB+uST42rosCnq5Mn/OHtqYR/HOEcQhOIhloKFIE2Z+ox6ZOoDqn797zh7a2HfiOH91OgxM6yCJghCYRJLwdq0abtq3qypuuCCc/X2rFkLVFm9q/TCOlxzTblq0/pqfa4gCNFAL+bSy7om1b1siKVgHTt2XHXqdL2zpVTDhg3Ulye2q81VlWrYsL7OXqXP4VxBEKJhy5YdauuW59XRI5vUuvVbs3bDxFKw9rx/0FmrYfDg23XXD6vK20X0nisIQu7o27eb7umwtGjR7LSB8JlzJDNiKVjlV1zirNWwf/+HWqj43bz5LWdvDd5zBcENDd2PuwzWS6n5O7GGuvxkSKBVFPa8K8svVZdf/gNnKzNiKViNG5+rNm58U68jUt26D1UNvtda9e4zOinDOIdzBcEGFfCHLW9Tr722zdlTOrz6apVqe+PdqnLh9IQv2A+OP/H4ONWh4yD19tt7nL3JcL/zz29kHQRLh1gKVocOrdU/tu/SmYdAfXBgvar+5j39azKfwrh33yF9riDYoKxQZmY+OcHZUxpQN57+7SLtewoSKwPulsV/namGDZ96hqWFWC1dtl7ddFN7Z0/mxFKwUPE5syepP8xdbDXj2Zcq7EEQSplJk2erbl3bhhYrA6J1R9/u+noDI4M397pfVVau1COFiFc2xFKwgMz+Y8UjVkFiH8fSfSGCEHewjvbs+SBplN1g/HkmRIhl4KCHnKM1cB3XGytr3LhBundjlmytrNgKlg3UvdG5P9KLX19bEEqZHTt2q88//7dq3LiRs6cW/MFjxwxMiM/+fWvV75952DlaA9dxPfeJgpIRLBR/1OhpOjPhyJFP9a8gCLXs3XvIWTsTunw9erTT68RXIU5+LpVU98mGJMEq+1YfZy1+oPj3j7hTtwyfHX8jJw5AQShFsLQuu+z7efH/loyFFZXiC0KcaN68qbNmh8BPfL9B/t+g+2RKyQjWu7v2OWuCkB6lVHZatbpSnXNOQ2tEOpbVl19+lbCs5s9ffkYIA9dxPfeJglCCxUiAe2QgKOqXRARFvoY5J9ekU/AYjrUNwUaVNv5WmLwtdGz5FjY/Msm3KOE5+HCXIXmCRy9u2qVgB2u8I3iZliMsp/LySxOB1wbyggDsZs17JP7G5i07zrC0uI7rw47AU17SeeZQgrVwwTS16pVntXLy+/cN8337rxTWMBGyHAuKjs01R478K9RL5ByCSr2tRJRpw6dGgGL3bjfmxTeQC2z5FjbPIB9lIhU8jwk6Nr5PHM+FzIABt+hnTVVHg5gxfYx6ccnapHfgzQsWdMENosYHzlwfFhP2EDY4N6VgrVmzWXXvMUyHARD8xQgbv2yzn+NueOB0ImRTRcfmE0zfBg2+m5SGqNNGZSc63xb/Uix48y3dPINCLROlBO+qYu5U1e/O8aEbDt4VDdOoX/QP/a4zwVewxo1/Qg0YOFnd/tMu6vChDWrnjmWqWbOL9S/bA08rOcfdgWOZRMhSQL3RsbkGMcC6CgIBpqLworZt+2fSjKVRpw3fwNln18/649B84JdvmeQZ1EWZEFLDO9j0+gL14MRZgQ0Hx9ECGqaoR9+tgoWqLly4Uk2edJ8aMqT3GaYl2/369VAL/jxNrV5dpc/noW0RshRcd9+aJSg6NioOHfpYC4MN+tKvbdymP9dp3/46tWzpHPX11/9VFRVLrGkzPidvECppc6fPL23kC40C15M/VVXb9d9jQsEw900X8x4y9W344Zdvv3lsrrU8QCGViXzgTT956N7Hu6cRwH9mygEL64sXr0k6z11Gcg0NzYa/zQtscMKelwusgmWCKm2FzU2TJufpX873i5DNNjqWgmxerG3JphIbEJ+167boSkcFMd2aFlc30456W9poSZiUDKvI7aCk/37JJRc5W/a08Tc6db5Hr2OtLqqcoebNX6769O4a+r7pQiODXyMb34aXVPm2fv0b1vIAxVAmogJhGjzkl+rJJ8brtNNjmT7jOd1Q8W7w5bRseYVq1+467Y/a9e7Lun5d26q3zo8HRj6WuLZnz/Z6BpI4irofVsEyQuQdKfBCRpKJ4BfnhGmZTXQsjj1TsG2L1/GXLhSgOU9XJpzdpLljhxqhRqyofH5po3K2bdsqafSRCtaxQxtnqxZzD/5e/wGT9EjKrJkP6r9JRUeMjLM6nfvmi6B8SyWKhV4mogRhWrJknRYgxNUIkUmnmRGX6ZBaXXuVLgs0Yggbg170asg/GD2qv/rii5NWUXdTzALvxSpYZAjqjfLPm7dMF043bGOaDrrnId0KhOm30qrmKzo2FTzXRx8d09Yk6TKjXLRafl0aNwiaGX3EPGfKZVpHPyiwdE3dIynsoxtF4TSke9+6JijfysuDJ0asqzJhq6R1vbjp3PkGPWW3W2QZLQPyAgsKf7ExHPxA6LHE/RpUQ1iBtz13lEsm+DrdSQRD0ctXbNDxJ7QE+/Yd1r9sL6xcqY+bxKaKbM0mOjbq1gEr8dSpU/rlU4FMt+aFF1ZrKwjxTpU2c4xrX1n1uvb52TDnMS8Q1pPJC8To148+q7uDbsLeN18E5Vuvmzs5Z9qpyzJhq6R1vbhJ5UtF8JkW6a67eobu7qUqn+lge+4ol0zwFSzAbF+7pkLHnxB/BfyyzX5j1gOtqy1ClsKcTXRs2NYhU2jF+NtgujWICCEGxq/ilzbDO++8rwvZuLE1raQbW9pOnvxKWyU4Vnfu3K19FrSU5JWbVPfNN0H5lirPCr1MRAkTRjZtepF2C1AGAF+gcbybedqm/Op+3bB5R0pp8Awc416lNAllSsEy0GIRfwX82kaaaCm9EbIUwiiiY3MJFtTQ+/qoewc/rFa8tEE9/5dVuos2f96jiQplS5sbBOepWROtXRtv2vA7VFW9pa3UEydOatFHmDDrvSENqe6bLogJo0qMPIVptYMIyje/PCv0MmGeKVeLF/KGQRasLPxUnIMI4bu69baR6rnnlmmfFO+LEXii7N317cCBDxP3xmXw8ku/y0n5KBbKqk/jrOtMqP7fUmcrfSiMt9z6gA46M47BILiGGI4wkdCZwsumMIDfC6aVe33Tm9oRbqNQ05Yu/BMOum9h0xBEqnzLJM8gn/mm68BpK63QQMD4bAmnu81nbMp4kybnF7SF6QcWJiPOQQIcysIKC4UrnQhZCmZdRMeSAbzIVFDpzCiXjUJNW1h4Hqyr+X9akTOxglT5lm6eQT7zrVDFSqglp4IFVIYwEbIcq6vo2CB4FpzINn+Jm2JMm4HKz7dguWx9w+Rb2DyDQsy3QsBYV4Q/4JLBqvXDzJ1uc9vkGt6X+a/OLKmeyw8sK64dP+FJZ09qctolLGTCmpxC6aLLv2NhURmx9A4e/FhvM9gkIloLYjh23ON6gIDGkPqF6ESdT0kWVlzFykC3UMRKCILKyGgdlh4CRvR5kGVTajC6PWJ4v0S3nUED4svco5hRkPMuYaFCrArBmIJgw21d5asyFhMEMbt9oWH8xLkg1oKFWU9fnrCMevXKAqPWBQHSqYxYY8xOgeDZlmwDm4sF8oEwC28AdK6JtWAxfA96xsiN25IKoSCExa8ysp+g1wnj71VTp/5cf7TOJzd9+3bT3/5hsRVjiEEmEAxMoG/UQayxFixaRr6Ap+D4xVcJQhB+lZHyNWpU/4RflC6k+VqhGOc1ywYChydNHJLIi6goGR+WIIDpqrlh2/ivbARVRiLSe93cUa/PeXqR6n93r8grbiHBCCGxeHXRgxHBEkqGIGGyEVQZ8ZPu3XdYW1SsHz0aHM8XJ8zIqQllIGaMrymiQgRLKBmMWPHrtbJshKmMzE7Rvl1rbVHxDeCFF56XGF2MO+QP4R7EXxnLlRkmouwOi2AJgoUwlZFzCEY2vi3CHgidwY/FJH1xBsuT/PHinjopCkpGsChczFZAAWxz/R163bSgQulhrCwWbzcxTGXE2mJOfibbM/4qxIryNbfixaSpl+KI+fdc3iXqUdGkT3NUdeqpVosBhpr5ap1/fGkg6K9zpxvUyJE/0/9UAggGnD27Uo0ZM6CkHKRCLYgVUNGE4iB2FhbiY0IZzML2WWd9Wx8/+smnKmjqWaE0EKEqPkqmS4jvgUnzYO26rXqdkR+xrkobEa3iInZdQkEQ4ouMEgqCUDSIYAmCUDQkdwkFQRAKmLLqb94TwRKSKSudT0uE4kK6hIIgFAlK/R+Q/nqvcvas1QAAAABJRU5ErkJggg=="/>equation (3)</p><p>And the likelihood function is: </p><p><img src="data:image/png;base64,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"/>equation (4)</p><p>We also did not ignore endogenous problem. First, we assume that our model may endogenous with hours worked of the child; however, we have tested that and we rejected the 2-stages least squared Probit model. Thus, the results from the Instrumental Variables (IV-Probit) is not used for the interpretation in this study.</p>
<p> គំរូ ដ៏ បំផុស គំនិត នៃ ភាព ក្រីក្រ របស់ កុមារ ដែល មាន ចំណូល នេះ ប្រើ អថេរ នៃ " ភាព ក្រីក្រ របស់ កុមារ ដែល មាន ចំណូល " ជា អថេរ ដែល ពឹង ផ្អែក លើ ។ យើង ធ្វើ គំរូ ភាព ក្រីក្រ របស់ កូន ដែល មាន ចំណូល របស់ យើង លើ សំណុំ នៃ ការ រុករក ដូច ជា អាយុ កុមារ ( ០-២ ឆ្នាំ ) អាយុកុមារ (៣-៤ឆ្នាំ) អាយុកុមារ (៥-៩ឆ្នាំ) អាយុកុមារ (អាយុ ១០-១៤ឆ្នាំ) អាយុកុមារ (១៥-១៧ឆ្នាំ) កុមារា (១៥-១៧ឆ្នាំ) កុមារី (១. ១. ០) ការអប់រំរបស់កូន (១) បើកូនម្នាក់ៗបានបញ្ចប់ការ អប់រំ យ៉ាងហោចណាស់ ២ ឬ ការអប់រំខ្ពស់។ ០. , រៀបការជាមួយកូន (1 បើកូនរៀបការ) 0 ម៉ោងធ្វើការរបស់កុមារទាំងក្នុងការងារបឋមសិក្សា និងទី២, ការអប់រំលើក្បាលផ្ទះ, ទំហំផ្ទះ, ការទូទាត់សុខភាពរបស់គ្រួសារក្នុងមួយខែ, ឧបត្ថម្ភសុខភាពក្នុងមួយមនុស្សម្នាក់ក្នុងមួយនាក់, ចំណាយការអប់រំក្នុងមនុស្សម្នាក់ក្នុងមួយឆ្នាំ, ការទូទាត់ការអប់រំក្នុងមនុស្សម្នាក់ក្នុងមួយឆ្នាំ, អនាម័យធ្ងន់ធ្ងរ (កុមារគ្មានការចូលបន្ទប់ទឹកប្រភេទណាមួយ, ទឹកធ្ងន់ធ្ងរ (កុមារប្រើប្រាស់ទឹកក្នុងផ្ទៃដូចជាទន្លេដូចជាទន្លេសាប) , ត្រពាំង ស្ទឹង និង ទំនប់ ទឹក ), ជម្រក ដ៏ ធ្ងន់ធ្ងរ (កុមារ រស់ នៅ ក្នុង បន្ទប់ មួយ ដែល មាន មនុស្ស ៥ នាក់ ឬ ច្រើន ជាង នេះ ក្នុង មួយ បន្ទប់ ឬ គ្មាន សម្ភារៈ កម្រាល ឥដ្ឋ) ជនជាតិ ភាគ តិច ដូចជា ខ្មែរ (១ បើ សិន ជា ខ្មែរ ១ បើ មិន អញ្ចឹង ទេ) ក្រុម ក្នុង ស្រុក (១ បើ សិន ជា ក្រុម ក្នុង ស្រុក។ ០ បើ មិន អញ្ចឹង ទេ) Cham (1 បើ សិន ជា ក្រុង) ។ , តំបន់ភ្នំ (១ ប្រសិនបើតំបន់ភ្នំ; ០) និងតំបន់ធម្មតា (១ ប្រសិនបើតំបន់ធម្មតា; ០) ។ </p> <p> ដោយសារ តែ ភាព ក្រីក្រ របស់ យើង ដែល ពឹង ផ្អែក លើ ភាព ក្រីក្រ របស់ កុមារ គឺ ខុស គ្នា ( 1 ប្រសិន បើ ភាព ក្រីក្រ របស់ កុមារ ; 0 បើ ពុំ នោះ សោត ទេ ) យើង ប្រើ គំរូ Probit ដើម្បី ប៉ាន់ ស្មាន នៅ ក្នុង ការ វិភាគ នេះ ។ ឥឡូវ នេះ សូម ឲ្យ [១៥៦៦] ChPov*[១៥៧៦]i[១៥៨២][១៥៨៨] ជា ថេរ នៃ ភាព ក្រីក្រ របស់ កុមារ មាន តម្លៃ {1} សម្រាប់ ភាព ក្រីក្រ របស់ កុមារ និង សូន្យ {0} បើ ពុំ នោះ សោត ទេ។ ដូច្នេះ គំរូជាក់លាក់ ត្រូវ បាន បង្ហាញ តាម រយៈ អថេរ ចុង ក្រោយ ដូច ខាង ក្រោម:</p><p><img alt="Mathematical formula" src="data:image/png;base64,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"/> សមីការ (1)</p><p>ដែល <img src="data:image/x-wmf;base64,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"/> ជា ទម្រង់ ផ្តាច់ មុខ នៃ អថេរ រុករក ។ ហេតុដូច្នេះហើយបានជាការពឹងផ្អែកលើភាពអថេរ (ភាពក្រីក្ររបស់កូនចំណូល) អាចសង្កេតឃើញថា ធ្វើតាម៖[៦៤២៤][៦៤២៨][៦៤៣១]សមីការ (២)[១១០១៧][១១០២១] បន្ទាប់មកអនុគមន៍ចែកចាយតាមធម្មតា និង ស្តង់ដានៃ ដង់ស៊ីតេធម្មតាគឺ:</p><p>] <img alt="Mathematical formula" src="data:image/png;base64,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"/>សមីការ (3)</p><p>ហើយអនុគមន៍ដូចមាន: </p><p>[1577]សមីការ (4)</p><p>យើងក៏មិនអើពើនឹងបញ្ហា endogenous។ ទី មួយ យើង សន្មត ថា គំរូ របស់ យើង អាច មាន ការ សប្បុរស ជា ច្រើន ម៉ោង ដែល ធ្វើ ការ លើ កុមារ ទោះ ជា យ៉ាង ណា ក៏ ដោយ យើង បាន សាក ល្បង វា ហើយ យើង បាន ច្រាន ចោល គំរូ Probit ដែល មាន ដំណាក់ កាល 2 ដំណាក់ កាល យ៉ាង ហោច ណាស់ ។ ដូច្នេះលទ្ធផលពី Instrumental Variables (IV-Probit) មិនត្រូវបានប្រើសម្រាប់បកស្រាយនៅក្នុងការសិក្សានេះទេ។ </p>
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brockfanning
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<p>This empirical model of income child poverty use variable of “income child poverty” as dependent variables. We model our income child poverty on set of explanatory such as child’s age (0-2 years old), child’s age (3-4 years old), child’s age (5-9 years old), child’s age (10-14 years old), child’s age (15-17 years old), female child (1 if female child; 0 otherwise), education of the child (1 if individual child completed at least secondary or higher education; 0 otherwise), married child (1 if child is married; 0 otherwise), hours worked of children in both primary and secondary occupation, education of household head, household size, health expenditure of household per capita per month, health subsidies per capita per year, education expenditure per person per years, severe sanitation (children with no access to a toilet facility of any kind, severe water (children using surface water such as rivers, pond, streams and dams), severe shelter (children living in a dwelling with 5 or more people per room or with no floor material), ethnicity such as Khmer (1 if Khmer; 0 otherwise), local group (1 if local group; 0 otherwise), Cham (1 if Cham; 0 otherwise), other local group (1 if Other local group; 0 otherwise), and regional characteristics such as urban (1 if urban; 0 otherwise), mountain zone (1 if mountain zone; 0 otherwise) and plain zone (1 if plain zone; 0 otherwise). </p><p>Because our dependent variable of income child poverty is dichotomy (1 if child poverty; 0 otherwise), we use Probit model to estimates in this analysis. Now, let <em>ChPov*<sub>i</sub></em> is the dichotomous variable of child poverty, take on the value {1} for child poverty, and zero {0} for otherwise. Therefore the specification model is expressed through the latent variable as follow:</p><p><img src="data:image/png;base64,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"/> equation (1)</p><p>where <img src="data:image/x-wmf;base64,183GmgAAAAAAAKACQAIBCQAAAADwXgEACQAAA/UAAAACABwAAAAAAAUAAAAJAgAAAAAFAAAAAgEBAAAABQAAAAEC////AAUAAAAuARgAAAAFAAAACwIAAAAABQAAAAwCQAKgAhIAAAAmBg8AGgD/////AAAQAAAAwP///6b///9gAgAA5gEAAAsAAAAmBg8ADABNYXRoVHlwZQAAYAAcAAAA+wKA/gAAAAAAAJABAQAAAgQCABBTeW1ib2wAAJUMCkqg8RIAuKTzd8Gk83cgMPV3lQtmJAQAAAAtAQAACAAAADIKgAFdAQEAAABieRwAAAD7AiD/AAAAAAAAkAEBAAAABAIAEFRpbWVzIE5ldyBSb21hbgC4pPN3waTzdyAw9XeVC2YkBAAAAC0BAQAEAAAA8AEAAAgAAAAyCuAB+AABAAAAaXkcAAAA+wKA/gAAAAAAAJABAQAAAAQCABBUaW1lcyBOZXcgUm9tYW4AuKTzd8Gk83cgMPV3lQtmJAQAAAAtAQAABAAAAPABAQAIAAAAMgqAAUwAAQAAAHh5CgAAACYGDwAKAP////8BAAAAAAAcAAAA+wIQAAcAAAAAALwCAAAAAAECAiJTeXN0ZW0AJJULZiQAAAoAIQCKAQAAAAABAAAAvPMSAAQAAAAtAQEABAAAAPABAAADAAAAAAA="/> is the aggregated form of the explanatory variables. Therefore, the dependent variable (income child poverty) can be observed as follow:</p><p><img src="data:image/png;base64,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"/>equation (2)</p><p>Then the standard normal cumulative distribution function and the standard normal density are:</p><p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASwAAAA1CAYAAAD709aSAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAzaSURBVHhe7Z15kBXFHcd7yR+xEohUiYpiJeIBrpYERdCqIGcIl6JGQDRyKMhhJNwClgkYI6CCYGIMGyGBxRjkUkSuEJEFrMIgaKQEOQQsBYloVURMlRXihk/v9Nt5Q8+becfse2/e71M19ebe6Z7ub//617/pLas+jTJU73ZWBEEQCo96zq8gCELBI4IlCELRIIIlCELREFvB+uST42rosCnq5Mn/OHtqYR/HOEcQhOIhloKFIE2Z+ox6ZOoDqn797zh7a2HfiOH91OgxM6yCJghCYRJLwdq0abtq3qypuuCCc/X2rFkLVFm9q/TCOlxzTblq0/pqfa4gCNFAL+bSy7om1b1siKVgHTt2XHXqdL2zpVTDhg3Ulye2q81VlWrYsL7OXqXP4VxBEKJhy5YdauuW59XRI5vUuvVbs3bDxFKw9rx/0FmrYfDg23XXD6vK20X0nisIQu7o27eb7umwtGjR7LSB8JlzJDNiKVjlV1zirNWwf/+HWqj43bz5LWdvDd5zBcENDd2PuwzWS6n5O7GGuvxkSKBVFPa8K8svVZdf/gNnKzNiKViNG5+rNm58U68jUt26D1UNvtda9e4zOinDOIdzBcEGFfCHLW9Tr722zdlTOrz6apVqe+PdqnLh9IQv2A+OP/H4ONWh4yD19tt7nL3JcL/zz29kHQRLh1gKVocOrdU/tu/SmYdAfXBgvar+5j39azKfwrh33yF9riDYoKxQZmY+OcHZUxpQN57+7SLtewoSKwPulsV/namGDZ96hqWFWC1dtl7ddFN7Z0/mxFKwUPE5syepP8xdbDXj2Zcq7EEQSplJk2erbl3bhhYrA6J1R9/u+noDI4M397pfVVau1COFiFc2xFKwgMz+Y8UjVkFiH8fSfSGCEHewjvbs+SBplN1g/HkmRIhl4KCHnKM1cB3XGytr3LhBundjlmytrNgKlg3UvdG5P9KLX19bEEqZHTt2q88//7dq3LiRs6cW/MFjxwxMiM/+fWvV75952DlaA9dxPfeJgpIRLBR/1OhpOjPhyJFP9a8gCLXs3XvIWTsTunw9erTT68RXIU5+LpVU98mGJMEq+1YfZy1+oPj3j7hTtwyfHX8jJw5AQShFsLQuu+z7efH/loyFFZXiC0KcaN68qbNmh8BPfL9B/t+g+2RKyQjWu7v2OWuCkB6lVHZatbpSnXNOQ2tEOpbVl19+lbCs5s9ffkYIA9dxPfeJglCCxUiAe2QgKOqXRARFvoY5J9ekU/AYjrUNwUaVNv5WmLwtdGz5FjY/Msm3KOE5+HCXIXmCRy9u2qVgB2u8I3iZliMsp/LySxOB1wbyggDsZs17JP7G5i07zrC0uI7rw47AU17SeeZQgrVwwTS16pVntXLy+/cN8337rxTWMBGyHAuKjs01R478K9RL5ByCSr2tRJRpw6dGgGL3bjfmxTeQC2z5FjbPIB9lIhU8jwk6Nr5PHM+FzIABt+hnTVVHg5gxfYx6ccnapHfgzQsWdMENosYHzlwfFhP2EDY4N6VgrVmzWXXvMUyHARD8xQgbv2yzn+NueOB0ImRTRcfmE0zfBg2+m5SGqNNGZSc63xb/Uix48y3dPINCLROlBO+qYu5U1e/O8aEbDt4VDdOoX/QP/a4zwVewxo1/Qg0YOFnd/tMu6vChDWrnjmWqWbOL9S/bA08rOcfdgWOZRMhSQL3RsbkGMcC6CgIBpqLworZt+2fSjKVRpw3fwNln18/649B84JdvmeQZ1EWZEFLDO9j0+gL14MRZgQ0Hx9ECGqaoR9+tgoWqLly4Uk2edJ8aMqT3GaYl2/369VAL/jxNrV5dpc/noW0RshRcd9+aJSg6NioOHfpYC4MN+tKvbdymP9dp3/46tWzpHPX11/9VFRVLrGkzPidvECppc6fPL23kC40C15M/VVXb9d9jQsEw900X8x4y9W344Zdvv3lsrrU8QCGViXzgTT956N7Hu6cRwH9mygEL64sXr0k6z11Gcg0NzYa/zQtscMKelwusgmWCKm2FzU2TJufpX873i5DNNjqWgmxerG3JphIbEJ+167boSkcFMd2aFlc30456W9poSZiUDKvI7aCk/37JJRc5W/a08Tc6db5Hr2OtLqqcoebNX6769O4a+r7pQiODXyMb34aXVPm2fv0b1vIAxVAmogJhGjzkl+rJJ8brtNNjmT7jOd1Q8W7w5bRseYVq1+467Y/a9e7Lun5d26q3zo8HRj6WuLZnz/Z6BpI4irofVsEyQuQdKfBCRpKJ4BfnhGmZTXQsjj1TsG2L1/GXLhSgOU9XJpzdpLljhxqhRqyofH5po3K2bdsqafSRCtaxQxtnqxZzD/5e/wGT9EjKrJkP6r9JRUeMjLM6nfvmi6B8SyWKhV4mogRhWrJknRYgxNUIkUmnmRGX6ZBaXXuVLgs0Yggbg170asg/GD2qv/rii5NWUXdTzALvxSpYZAjqjfLPm7dMF043bGOaDrrnId0KhOm30qrmKzo2FTzXRx8d09Yk6TKjXLRafl0aNwiaGX3EPGfKZVpHPyiwdE3dIynsoxtF4TSke9+6JijfysuDJ0asqzJhq6R1vbjp3PkGPWW3W2QZLQPyAgsKf7ExHPxA6LHE/RpUQ1iBtz13lEsm+DrdSQRD0ctXbNDxJ7QE+/Yd1r9sL6xcqY+bxKaKbM0mOjbq1gEr8dSpU/rlU4FMt+aFF1ZrKwjxTpU2c4xrX1n1uvb52TDnMS8Q1pPJC8To148+q7uDbsLeN18E5Vuvmzs5Z9qpyzJhq6R1vbhJ5UtF8JkW6a67eobu7qUqn+lge+4ol0zwFSzAbF+7pkLHnxB/BfyyzX5j1gOtqy1ClsKcTXRs2NYhU2jF+NtgujWICCEGxq/ilzbDO++8rwvZuLE1raQbW9pOnvxKWyU4Vnfu3K19FrSU5JWbVPfNN0H5lirPCr1MRAkTRjZtepF2C1AGAF+gcbybedqm/Op+3bB5R0pp8Awc416lNAllSsEy0GIRfwX82kaaaCm9EbIUwiiiY3MJFtTQ+/qoewc/rFa8tEE9/5dVuos2f96jiQplS5sbBOepWROtXRtv2vA7VFW9pa3UEydOatFHmDDrvSENqe6bLogJo0qMPIVptYMIyje/PCv0MmGeKVeLF/KGQRasLPxUnIMI4bu69baR6rnnlmmfFO+LEXii7N317cCBDxP3xmXw8ku/y0n5KBbKqk/jrOtMqP7fUmcrfSiMt9z6gA46M47BILiGGI4wkdCZwsumMIDfC6aVe33Tm9oRbqNQ05Yu/BMOum9h0xBEqnzLJM8gn/mm68BpK63QQMD4bAmnu81nbMp4kybnF7SF6QcWJiPOQQIcysIKC4UrnQhZCmZdRMeSAbzIVFDpzCiXjUJNW1h4Hqyr+X9akTOxglT5lm6eQT7zrVDFSqglp4IFVIYwEbIcq6vo2CB4FpzINn+Jm2JMm4HKz7dguWx9w+Rb2DyDQsy3QsBYV4Q/4JLBqvXDzJ1uc9vkGt6X+a/OLKmeyw8sK64dP+FJZ09qctolLGTCmpxC6aLLv2NhURmx9A4e/FhvM9gkIloLYjh23ON6gIDGkPqF6ESdT0kWVlzFykC3UMRKCILKyGgdlh4CRvR5kGVTajC6PWJ4v0S3nUED4svco5hRkPMuYaFCrArBmIJgw21d5asyFhMEMbt9oWH8xLkg1oKFWU9fnrCMevXKAqPWBQHSqYxYY8xOgeDZlmwDm4sF8oEwC28AdK6JtWAxfA96xsiN25IKoSCExa8ysp+g1wnj71VTp/5cf7TOJzd9+3bT3/5hsRVjiEEmEAxMoG/UQayxFixaRr6Ap+D4xVcJQhB+lZHyNWpU/4RflC6k+VqhGOc1ywYChydNHJLIi6goGR+WIIDpqrlh2/ivbARVRiLSe93cUa/PeXqR6n93r8grbiHBCCGxeHXRgxHBEkqGIGGyEVQZ8ZPu3XdYW1SsHz0aHM8XJ8zIqQllIGaMrymiQgRLKBmMWPHrtbJshKmMzE7Rvl1rbVHxDeCFF56XGF2MO+QP4R7EXxnLlRkmouwOi2AJgoUwlZFzCEY2vi3CHgidwY/FJH1xBsuT/PHinjopCkpGsChczFZAAWxz/R163bSgQulhrCwWbzcxTGXE2mJOfibbM/4qxIryNbfixaSpl+KI+fdc3iXqUdGkT3NUdeqpVosBhpr5ap1/fGkg6K9zpxvUyJE/0/9UAggGnD27Uo0ZM6CkHKRCLYgVUNGE4iB2FhbiY0IZzML2WWd9Wx8/+smnKmjqWaE0EKEqPkqmS4jvgUnzYO26rXqdkR+xrkobEa3iInZdQkEQ4ouMEgqCUDSIYAmCUDQkdwkFQRAKmLLqb94TwRKSKSudT0uE4kK6hIIgFAlK/R+Q/nqvcvas1QAAAABJRU5ErkJggg=="/>equation (3)</p><p>And the likelihood function is: </p><p><img src="data:image/png;base64,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"/>equation (4)</p><p>We also did not ignore endogenous problem. First, we assume that our model may endogenous with hours worked of the child; however, we have tested that and we rejected the 2-stages least squared Probit model. Thus, the results from the Instrumental Variables (IV-Probit) is not used for the interpretation in this study.</p>
<p> គំរូ ដ៏ បំផុស គំនិត នៃ ភាព ក្រីក្រ របស់ កុមារ ដែល មាន ចំណូល នេះ ប្រើ អថេរ នៃ " ភាព ក្រីក្រ របស់ កុមារ ដែល មាន ចំណូល " ជា អថេរ ដែល ពឹង ផ្អែក លើ ។ យើង ធ្វើ គំរូ ភាព ក្រីក្រ របស់ កូន ដែល មាន ចំណូល របស់ យើង លើ សំណុំ នៃ ការ រុករក ដូច ជា អាយុ កុមារ ( ០-២ ឆ្នាំ ) អាយុកុមារ (៣-៤ឆ្នាំ) អាយុកុមារ (៥-៩ឆ្នាំ) អាយុកុមារ (អាយុ ១០-១៤ឆ្នាំ) អាយុកុមារ (១៥-១៧ឆ្នាំ) កុមារា (១៥-១៧ឆ្នាំ) កុមារី (១. ១. ០) ការអប់រំរបស់កូន (១) បើកូនម្នាក់ៗបានបញ្ចប់ការ អប់រំ យ៉ាងហោចណាស់ ២ ឬ ការអប់រំខ្ពស់។ ០. , រៀបការជាមួយកូន (1 បើកូនរៀបការ) 0 ម៉ោងធ្វើការរបស់កុមារទាំងក្នុងការងារបឋមសិក្សា និងទី២, ការអប់រំលើក្បាលផ្ទះ, ទំហំផ្ទះ, ការទូទាត់សុខភាពរបស់គ្រួសារក្នុងមួយខែ, ឧបត្ថម្ភសុខភាពក្នុងមួយមនុស្សម្នាក់ក្នុងមួយនាក់, ចំណាយការអប់រំក្នុងមនុស្សម្នាក់ក្នុងមួយឆ្នាំ, ការទូទាត់ការអប់រំក្នុងមនុស្សម្នាក់ក្នុងមួយឆ្នាំ, អនាម័យធ្ងន់ធ្ងរ (កុមារគ្មានការចូលបន្ទប់ទឹកប្រភេទណាមួយ, ទឹកធ្ងន់ធ្ងរ (កុមារប្រើប្រាស់ទឹកក្នុងផ្ទៃដូចជាទន្លេដូចជាទន្លេសាប) , ត្រពាំង ស្ទឹង និង ទំនប់ ទឹក ), ជម្រក ដ៏ ធ្ងន់ធ្ងរ (កុមារ រស់ នៅ ក្នុង បន្ទប់ មួយ ដែល មាន មនុស្ស ៥ នាក់ ឬ ច្រើន ជាង នេះ ក្នុង មួយ បន្ទប់ ឬ គ្មាន សម្ភារៈ កម្រាល ឥដ្ឋ) ជនជាតិ ភាគ តិច ដូចជា ខ្មែរ (១ បើ សិន ជា ខ្មែរ ១ បើ មិន អញ្ចឹង ទេ) ក្រុម ក្នុង ស្រុក (១ បើ សិន ជា ក្រុម ក្នុង ស្រុក។ ០ បើ មិន អញ្ចឹង ទេ) Cham (1 បើ សិន ជា ក្រុង) ។ , តំបន់ភ្នំ (១ ប្រសិនបើតំបន់ភ្នំ; ០) និងតំបន់ធម្មតា (១ ប្រសិនបើតំបន់ធម្មតា; ០) ។ </p> <p> ដោយសារ តែ ភាព ក្រីក្រ របស់ យើង ដែល ពឹង ផ្អែក លើ ភាព ក្រីក្រ របស់ កុមារ គឺ ខុស គ្នា ( 1 ប្រសិន បើ ភាព ក្រីក្រ របស់ កុមារ ; 0 បើ ពុំ នោះ សោត ទេ ) យើង ប្រើ គំរូ Probit ដើម្បី ប៉ាន់ ស្មាន នៅ ក្នុង ការ វិភាគ នេះ ។ ឥឡូវ នេះ សូម ឲ្យ [១៥៦៦] ChPov*[១៥៧៦]i[១៥៨២][១៥៨៨] ជា ថេរ នៃ ភាព ក្រីក្រ របស់ កុមារ មាន តម្លៃ {1} សម្រាប់ ភាព ក្រីក្រ របស់ កុមារ និង សូន្យ {0} បើ ពុំ នោះ សោត ទេ។ ដូច្នេះ គំរូជាក់លាក់ ត្រូវ បាន បង្ហាញ តាម រយៈ អថេរ ចុង ក្រោយ ដូច ខាង ក្រោម:</p><p><img alt="Mathematical formula" src="data:image/png;base64,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"/> សមីការ (1)</p><p>ដែល <img src="data:image/x-wmf;base64,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"/> ជា ទម្រង់ ផ្តាច់ មុខ នៃ អថេរ រុករក ។ ហេតុដូច្នេះហើយបានជាការពឹងផ្អែកលើភាពអថេរ (ភាពក្រីក្ររបស់កូនចំណូល) អាចសង្កេតឃើញថា ធ្វើតាម៖[៦៤២៤][៦៤២៨][៦៤៣១]សមីការ (២)[១១០១៧][១១០២១] បន្ទាប់មកអនុគមន៍ចែកចាយតាមធម្មតា និង ស្តង់ដានៃ ដង់ស៊ីតេធម្មតាគឺ:</p><p>] <img alt="Mathematical formula" src="data:image/png;base64,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"/>សមីការ (3)</p><p>ហើយអនុគមន៍ដូចមាន: </p><p>[1577]សមីការ (4)</p><p>យើងក៏មិនអើពើនឹងបញ្ហា endogenous។ ទី មួយ យើង សន្មត ថា គំរូ របស់ យើង អាច មាន ការ សប្បុរស ជា ច្រើន ម៉ោង ដែល ធ្វើ ការ លើ កុមារ ទោះ ជា យ៉ាង ណា ក៏ ដោយ យើង បាន សាក ល្បង វា ហើយ យើង បាន ច្រាន ចោល គំរូ Probit ដែល មាន ដំណាក់ កាល 2 ដំណាក់ កាល យ៉ាង ហោច ណាស់ ។ ដូច្នេះលទ្ធផលពី Instrumental Variables (IV-Probit) មិនត្រូវបានប្រើសម្រាប់បកស្រាយនៅក្នុងការសិក្សានេះទេ។ </p>
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<p>This empirical model of income child poverty use variable of “income child poverty” as dependent variables. We model our income child poverty on set of explanatory such as child’s age (0-2 years old), child’s age (3-4 years old), child’s age (5-9 years old), child’s age (10-14 years old), child’s age (15-17 years old), female child (1 if female child; 0 otherwise), education of the child (1 if individual child completed at least secondary or higher education; 0 otherwise), married child (1 if child is married; 0 otherwise), hours worked of children in both primary and secondary occupation, education of household head, household size, health expenditure of household per capita per month, health subsidies per capita per year, education expenditure per person per years, severe sanitation (children with no access to a toilet facility of any kind, severe water (children using surface water such as rivers, pond, streams and dams), severe shelter (children living in a dwelling with 5 or more people per room or with no floor material), ethnicity such as Khmer (1 if Khmer; 0 otherwise), local group (1 if local group; 0 otherwise), Cham (1 if Cham; 0 otherwise), other local group (1 if Other local group; 0 otherwise), and regional characteristics such as urban (1 if urban; 0 otherwise), mountain zone (1 if mountain zone; 0 otherwise) and plain zone (1 if plain zone; 0 otherwise). </p><p>Because our dependent variable of income child poverty is dichotomy (1 if child poverty; 0 otherwise), we use Probit model to estimates in this analysis. Now, let <em>ChPov*<sub>i</sub></em> is the dichotomous variable of child poverty, take on the value {1} for child poverty, and zero {0} for otherwise. Therefore the specification model is expressed through the latent variable as follow:</p><p><img src="data:image/png;base64,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"/> equation (1)</p><p>where <img src="data:image/x-wmf;base64,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"/> is the aggregated form of the explanatory variables. Therefore, the dependent variable (income child poverty) can be observed as follow:</p><p><img src="data:image/png;base64,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"/>equation (2)</p><p>Then the standard normal cumulative distribution function and the standard normal density are:</p><p><img src="data:image/png;base64,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"/>equation (3)</p><p>And the likelihood function is: </p><p><img src="data:image/png;base64,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"/>equation (4)</p><p>We also did not ignore endogenous problem. First, we assume that our model may endogenous with hours worked of the child; however, we have tested that and we rejected the 2-stages least squared Probit model. Thus, the results from the Instrumental Variables (IV-Probit) is not used for the interpretation in this study.</p>
<p> គំរូជាក់ស្តែងនៃភាពក្រីក្ររបស់កុមារប្រើអថេរនៃភាពក្រីក្រកុមារដែលមានប្រាក់ចំណូលជាអថេរដែលពឹងផ្អែក។ យើងយកគំរូតាមភាពក្រីក្ររបស់កុមារលើប្រាក់ចំណូលដែលបានកំណត់ដូចជាអាយុកុមារ (០-២ ឆ្នាំ) អាយុរបស់កុមារ (អាយុ ៣-៤ ឆ្នាំ) អាយុរបស់កុមារ (អាយុ ៥-៩ ឆ្នាំ) អាយុរបស់កុមារ (អាយុ ១០-១៤ ឆ្នាំ) ) អាយុរបស់កុមារ (អាយុ ១៥-១៧ ឆ្នាំ) កូនស្រី (១ បើកូនស្រី; ០ បើមិនដូច្នេះទេ) ការអប់រំរបស់កុមារ (១ បើកុមារម្នាក់ៗបញ្ចប់ការសិក្សានៅកម្រិតមធ្យមសិក្សាឬឧត្តមសិក្សា ០ បើមិនដូច្នេះទេ) កូនរៀបការ (១ បើ កូនរៀបការ; ០ បើមិនដូច្នេះទេ) ម៉ោងធ្វើការរបស់កុមារទាំងការងារបឋមនិងមធ្យមការអប់រំក្បាលគ្រួសារទំហំគ្រួសារចំណាយសុខភាពគ្រួសារក្នុងម្នាក់ ៗ ក្នុងមួយខែការឧបត្ថម្ភសុខភាពសម្រាប់មនុស្សម្នាក់ក្នុងមួយឆ្នាំចំណាយការអប់រំសម្រាប់មនុស្សម្នាក់ក្នុងមួយឆ្នាំ។ អនាម័យធ្ងន់ធ្ងរ (កុមារដែលមិនមានលទ្ធភាពប្រើប្រាស់បង្គន់គ្រប់ប្រភេទទឹកធ្ងន់ធ្ងរ (កុមារប្រើប្រាស់ទឹកលើដីដូចជាទន្លេស្រះទឹកនិងទំនប់) ទីជម្រកធ្ងន់ធ្ងរ (កុមាររស់នៅក្នុងផ្ទះមានមនុស្ស ៥ នាក់ឬច្រើនជាងនេះក្នុងមួយបន្ទប់ឬជាមួយ មិនមានកម្រាលឥដ្ឋទេ) ជនជាតិដូចជាខ្មែរ (១ ប្រសិនបើខ្មែរ; ០ បើមិនដូច្នេះទេ) ក្រុមក្នុងស្រុក ( 1 ប្រសិនបើក្រុមក្នុងស្រុក; ០ បើមិនដូច្នេះទេ) ចាម (១ ប្រសិនបើចាម; ០ បើមិនដូច្នេះទេ) ក្រុមមូលដ្ឋានផ្សេងទៀត (១ ប្រសិនបើក្រុមក្នុងតំបន់ផ្សេងទៀត; ០ បើមិនដូច្នេះទេ) និងលក្ខណៈក្នុងតំបន់ដូចជាទីក្រុង (១ ប្រសិនបើទីក្រុង; ០ បើមិនដូច្នោះ), តំបន់ភ្នំ (១ ប្រសិនបើតំបន់ភ្នំ ; ០ បើពុំនោះសោតទេ) និងតំបន់ធម្មតា (១ ប្រសិនបើតំបន់ដីសើម ០ បើមិនដូច្នេះទេ) [១៣៩៦] [១៤០០] ដោយសារអថេរដែលពឹងផ្អែកលើភាពក្រីក្ររបស់កុមារដែលមានប្រាក់ចំណូលគឺឌីកូទីម៉ា (១ ប្រសិនបើភាពក្រីក្ររបស់កុមារ ០ បើមិនដូច្នេះទេ) យើងប្រើគំរូ Probit ដើម្បីប៉ាន់ស្មានក្នុងការវិភាគនេះ។ ឥឡូវសូមឱ្យ [១៥៦៦] ឈីប៉ូ * [១៥៧៦] ខ្ញុំ [១៥៨២] [១៥៨៨] គឺជាអថេរឌីកូតូម៉ុកនៃភាពក្រីក្ររបស់កុមារយកតម្លៃ {១} សម្រាប់ភាពក្រីក្ររបស់កុមារហើយសូន្យ {០} បើមិនដូច្នេះទេ។ ដូច្នេះគំរូនៃការបញ្ជាក់ត្រូវបានបង្ហាញតាមរយៈអថេរមិនទាន់ឃើញច្បាស់ដូចខាងក្រោមៈ [១៧៩៤] [១៧៩៨] [១៨០១] សមីការ (១) [៥៥២៦] [៥៥៣០] ដែលជាកន្លែង [៥៥៣៩] គឺជាទំរង់សរុបនៃអថេរពន្យល់។ ដូច្នេះអថេរដែលពឹងផ្អែក (ភាពក្រីក្ររបស់កុមារដែលមានប្រាក់ចំណូល) អាចត្រូវបានគេសង្កេតឃើញដូចខាងក្រោមៈ [៦៣៩៧] [៦៤០១] សមីការ [៦៤០៤] សមីការ (២) [១០៩៦៣] [១០៩៦៧] បន្ទាប់មកមុខងារចែកចាយធម្មតាតគ្នានិងដង់ស៊ីតេធម្មតាគឺ៖ [ ១១០៦៤] [១១០៦៨] [១១០៧១] សមីការ (៣) [១៥៦៥០] [១៥៦៥៤] ហើយមុខងារលទ្ធភាពគឺ៖ [១៥៦៨៩] [១៥៦៩៣] [១៥៦៩៦] សមីការ (៤) [២០៩១១] [២០៩១៥] យើងក៏មិនបានព្រងើយកន្តើយនឹងបញ្ហាដែលគ្មានទីបញ្ចប់ដែរ។ ។ ដំបូងយើងសន្មតថាគំរូរបស់យើងអាចបង្កគ្រោះថ្នាក់ជាមួយនឹងម៉ោងធ្វើការរបស់កុមារ។ ទោះយ៉ាងណាយើងបានសាកល្បងវាហើយយើងបានបដិសេធគំរូ Probit ដែលមានការ៉េតិចបំផុត ២ ដំណាក់កាល។ ដូច្នេះលទ្ធផលពីឧបករណ៍បំលែងអថេរ (IV-Probit) មិនត្រូវបានប្រើសម្រាប់ការបកស្រាយនៅក្នុងការសិក្សានេះទេ។ [២១២៣៥]
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x-wmfpng;base64,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"/> is the aggregated form of the explanatory variables. Therefore, the dependent variable (income child poverty) can be observed as follow:</p><p><img alt="Mathematical formula" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMoAAAA2CAYAAACGNQbKAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAzLSURBVHhe7Z17kBTFGcAbiz9SBDApwZwcGl7yUAjIcUjiASdIOEAFCe/XYUAwlAbweAiRKJriEDiBgLwp4EBEgujxJnCIQCIJHmJQnkFMKUhEq2LAlKmiQu7Xt73VO8zszOzt7s0t/aua2nnsTvf3dX/dX/dMf1vleinCyvUToR2DwQC3hD4NBkMUjKEYDB4whmIweMAYikFcvfofMWr08+KLLy6HzhisGEO5yfngg5NixMhpcj/7weFi27Z35b4hEmMoBlFcfFhs3rw3dGSww5eh0EXnTZglatV+QFS55V7R9v7+YseOA7IVYqPrbtioq7xWULA69KtI+B7XrVv/AXni7Nl/hL4VPJD9oS4j5Ma+DnJ3+flIW9cl6Dq7775m4tPze0Tv3g+J/e+sFg8/3DF0Jb4gm8o38kA0vQUNz4ayYcMOUa9+F7n/0fEicf1/H4t9xavEGxt3ieGPTxXp6beLO+6oLT489pbo1y9HdOp0v/yuFQpi65ZF4rbbfiA/uc+Vfx8RX3/9L/GrMS/eUAmDAAXZstVjsuW1QqFntR8iCtfkS/l1KovOqlevJpYtnX5D/uMFOlq0+HVx8cJ+uY0dN0OeI71ZL+dJlw8XMMh4MhSEGjhooli44DeiYM4kkZZWS55HwTPzx8vW6O67fyzPqRZOHTtx663VRUbGPXKf+3TLaS/On/9cXLnyrTwXJCjQc3/fLebMnhg6UwYGNP/3a8Whg+tuqGQ3u84UGPG8+YVi/rypUkds7HOOa/RoG16fI0Y/+UKgexZXQ8HSaf2GDespBgzoHjobSXr6j2TBwb59fxFtM1uEj+34w6bdIisrI6Jy/e34GVG/fl1Ro8b3Q2eCz7NT5oqcrlk3GEmq6uzAgfelG+mnB6MR+OyzS7L3VLDPOdVAYCz9+3WT+gwqroZCIcK4sUPlpxUK7vnfjgkdlRVekyb1pTKZctR9UqDVOHSoRPTt01Ue873pLy4ShYVFpWkMC1cWlKh+j3+/YsUmeZ57qXN6d507fKrckgVynDx5ztZdSlWdtW59j8hs01y6kxjMpUtfha44c+HCl+LatWvhHhXY5xzXFOgRfQa1V4lqKBTIzl0HRatWTV3dAkDIixe/LC30emLu3EIxccIvpbty+vT50DeEKCk5Ib755qrIn7lMFl6Nmpni8OEPxZnTO8IDSQrzpz8bJFtdfPE9f1whZs9ZJc/zHfxc3BBVIQF3pkGDuqEjZ6gYpOu0ea04yMEYQa8AkIo6U2CQ9JBfXf6TNJiO2bnSMFXPUB7QI/pE1iAS1VDwffGBdTchGghZp87tsoseP36YbDn/euR4RKuLC0El2r1ruRyUsu3csTRcqahoEyfNEVOefUK2uqRrbYG4L24ILbGCtB/Mbhs6cmbN6hnhdO02rntBr8g6qagzOzCY06e2i149O4vBQyaJbt1HezYYeiIM3w4nvVY0ngbzXpEF2rKp6N69gywspThVoMqFYBDqVIn27z8iK9qgQT1CZ8oUW7VqVenbKn7SonFpJfinrCS0mpcuXRYdOrQJXXUmXj1KvKgMOvPCnXemhfYiIX3yYXXTmMHT8xZ0ohqKaoUoKDvfEYWvXPmm3Oc6PmZ2dqb8HVgHqarwnKZBgRbFOkCdN3+tLAjdlcGnByrWlq3viJEj+8hj8vTSS0vkpx3x6lFU+lZSUWd28CyIZ0JvFxWL19bNktPLeloKzpEPfTyCjNa8KZz0WtG49igMSOkme/Z6KqJrpUViQNm/fzd5TIFSWEp4KsEbG3eKZs0ayO9SCHaFZwVF0Trye37DoHH79nfF7FkTbmhRjx07JRYv2SDynhkeOlPmR0+b9qRj61ueHkV3W5impVW0tpSQajpTcG+eDTEpULzvsNhS9KqjgSi4JxMOajqYPPJMRZ+EAHSBPtX0d9BwNRSm7t7783rpIzdu0l1WJhS17rWtYX8Y7FrCTz75XA5UgZkSZml4aNfrsael0uxg4NmrV2eZFoNWfH7SJx9WyNMrBZMjFE5Ft3t6roilR6FweXqu8o8syNesWcOIwbEi1XSmOHr0hDjy/kfySb7+bMgN8ofrSN7qpGfL5yhqEkKBLtCn6lmDRsqtcFTTqtaCSAQYEL3G0iUv2FbKykIydWYHehyWO8X27YagENfBfBAoOfpx0rpvChUjGTBwgnSVKivJ1JkVjIRXgMb+emhgjQRSylBQOiRT4fQkvEw4aXJBOP3KREXoTKF6El4BqqjezCspZSg8F2D6k2cSyYRKxgO+ILeITlSUzqAy6S3lxigGQyJIuTGKwZAIjKEYDB4whmIweMAYisHgAWMoBoMHjKEYDB4whmIweOCmNhReVjQYvGB6FIPBA8ZQDAYPOBoKaxNYo6AWNOmbn+WyvPimRwPUIway8Yq3fs7Pvf2g5PGy7sINq0yJhDUqBJ1o0rRHWEcqAordqsR45410SI/1NKTNZ37+8qTJT31QcrO5lZ9X+f3qydFQWNizd8/KcITCoyWb5MImjllM5KXCYQTWKIp5ecPlvTIzm8vIILw1yrnly6aL7dsWe16K6weU4RTpMRaQJRkRDllN2LxFT3Hy1DmxbWtZhEg2IqBwvlq170UswLLTd3lAbw9kDRY1a1aXi7VImwAWBa+sFoMG9khKhEfqgx4lkzqpy6zjR36/Zejb9aJiE9iNJaX6MlcrKNkpiiJLR1E+b64Cb65mZNwrAywkAtK3i/RYHni9PpERDil0FWnSutyWCChUWH19eTR9xwoB6Vj/r6/KJE3CHuXkZCVUftbkE9mFHuyRR8fIUEZ8csx5ruvEIr+fMoxpjEI0DzKuBwyw4hRFEVA64XyIQEKvRPdeGVcIkudERDikhXOLNEll1aOYRNN3LFBx9KB7QFmtXbclHB41UfLj6rFO5Re9u8ieDA+kceN68pPj3FK9cF1302OV36sM5R7MU6hYOVEJWVfOxjmnKIoKCoCCICJJ+/YZobMVBy248sFVV6wCKURzM5Ex3hEOCSiBIRCgzgkiqKjGhbTt9B2rTEBvT+ALIsRQIflN15wnxNAhj0Yssoq3/ORzzZoi2WMio9XN4pjGY/WqGTKABt93kh+QEVn1cY51HOxFBt+GQsbyZy4XnTu3K83Et6J1Rh/Zu3CObg8Xh57GLoqiDq0hkUoaNrzLVytgHdxZN6sSvELhKx+ciorh42YQFTGaX+wW4dBvfilYFlJZww/ZFTiNEoVL2nb6jlUmoLdXscQYJ/CbhQuek629MjqIt/zKS7Gr9DqqN+X7TvIDw4NnxueGx3dnz+wUry58LnS1DDcZwJOhcBMMAsH4JJLH228tkJZNd8hAS3XH4CXaH1E3aDX9rtWOJYqKHyggwpUyZvLjDjrJ7De/TpEm2adyM+kBjLdolNC5m779yuTUQlOh9OiTOvGSXxmAXXQbHdUYQzT5kVeNfQ8eLJEyODUQ0e7jyVD0WS82t9bIC8TIYqDo16dMNAyarQHbggS9MPGC9aiQbviVaf367XKyxRqvi7GkNfpkvKFi9+jRUXoo9IBW95Bj3Ec1htPdwGjQszRqdFfM9bbcYxQ79NkYO2ixrANFBYqIFrXQb1fuF+nD1/qhdD103PLlJLPf/NJw0IAo/1uHtAkAbm1g3PTtJBNY5VJp2IVwpZVv166lba8UL/mBXoYp3jc375GxzfBizpz5VH5yvKawSF5XvZGb/Bg4+nJrlKPdJyGGEi2KItBi0TLatUwUTrSohX67ch090qMdVKi0tNpi8qQR4rvv/isrzcaNu6RhO+ULGZHVyYWMJb8M4nFLmbbUp+DZZ1re2sBE03c0mcAql0pjb/F74bT5DWMTDMjq3ydCfsBdIhA5YyOenwCfHHNef5QQTX5kwJ1V8jF5pGRXuMkAjoaCchg8qjlsrNlq/STYp+84eZ3vMcsCWK5dFEW+zwB0wsTZ4Xuq3yhIw21Gxi8qXT3So95a82YAszqkSVeOy0H+OnV+XO4jj1O+EhHhkHvxd3XMBvJXDqr1ferp34nc3J5yJkrHTt9eZAKrXNyDMejUKaNklHrSzWjTV9RNT5PjUmtDkQj5dcgfdQv4tCsDO/mBcs/pNiocrZPtQOk4xZpXLzJUSVQUFjIZSxRFZThefc/ygOJo1bxgly9kDEqEw3jomwpI6FbcLt6WcCMV5PcqQ0JcLyDRWKIoVmTUwmhY84WCgxThMB76xk3hL+PcpmYhFeT3I0PCDAWwbD9RFNV3gqB4HWu+OKYVClqEw/Lqm2ctToN1nVSQ368MCXO9YgE3YMvWfWLI4Efi9gc30fDqeiU7X8kiVeVKBIEylGTjZ4xiuLlJqOsVdIyRGLxyUxuKweAVYygGgweMoRgMHrAfzBsMBg0h/g/54V/6kipRDwAAAABJRU5ErkJggg=="/>equation (2)</p><p>Then the standard normal cumulative distribution function and the standard normal density are:</p><p><img alt="Mathematical formula" src="data:image/png;base64,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"/>equation (3)</p><p>And the likelihood function is: </p><p><img alt="Mathematical formula" src="data:image/png;base64,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"/>equation (4)</p><p>We also did not ignore endogenous problem. First we assume that our model may endogenous with hours worked of the child; however, we have tested that and we rejected the 2-stages least squared Probit model. Thus the results from the Instrumental Variables (IV-Probit) is not used for the interpretation in this study.</p>