3 stories matched (16-21 or 22-30) and Male and business and loan and woman
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1 distinct clusters within this collection: 1 - City: nairobi - 3 stories
q2:Due to the harsh economic times There comes a time where both the parents are up in arms looking for money to feed the family due to this being so and how the woman and men in the slums do odd jobs so as to earn a living.This leads to the shortage of resources to feed the families.And as the woman decided to improve the living standards by uplifting themself.
They came up with chamas.One evident was in kienda where the woman came up together by colllecting funds and helping other members of their chama uplift themself economic wise by loaning them cash so as to start business so as to uplift the well being of their family.This inturn has raised the economic states of the families living their since now they dont lack food and their children are for school with no problem of being sent home for school fees. Q3:Women affair Q4:Chama cha wanawake Q6:KENYA Q7:NAIROBI Q8:kianda Q9:1-2 years ago Q10:Male Q12:Saw it happen Q13:The right people Q15:No Q17:<<2012-04-18-DAGORETI-S#012-p#01>>_ Q18:Important Q19:Security,Knowledge,Creativity Q37:Good idea that succeeded Q38:Mixed Q39:Mixed Q40:Mixed Q122:22-30
q2:Due to the harsh economic times There comes a time where both the parents are up in arms looking for money to feed the family due to this being so and how the woman and men in the slums do odd jobs so as to earn a living.This leads to the shortage of resources to feed the families.And as the woman decided to improve the living standards by uplifting themself.
They came up with chamas.One evident was in kienda where the woman came up together by colllecting funds and helping other members of their chama uplift themself economic wise by loaning them cash so as to start business so as to uplift the well being of their family.This inturn has raised the economic states of the families living their since now they dont lack food and their children are for school with no problem of being sent home for school fees. Q3:Women affair Q4:Chama cha wanawake Q6:KENYA Q7:NAIROBI Q8:kianda Q9:1-2 years ago Q10:Male Q12:Saw it happen Q13:The right people Q15:No Q17:<<2012-04-18-DAGORETI-S#012-p#01>>_ Q18:Important Q19:Security,Knowledge,Creativity Q37:Good idea that succeeded Q38:Mixed Q39:Mixed Q40:Mixed Q122:22-30 Q132:-1.31201900000000 Q133:36.77454400000000 Q134:Unnamed Organization
q2:The kenya woman foundation is helping many in kenya both how income earners and potential business woman in the country to acquire loans from the organisations.
the big organization is even coming to the grass roots levels at the kibera slums in nairobi. A group of twenty fire woman come together to form a chama. They decided that they would concide with the rules and regulations of the organisation they offer loans for,
-paying school fees for children(Tausi account)
-business expension loan.
-starting of a project e.g water tank project.
This organisation helps the woman in making their lives better. with the loans, the woman are able to pray school fees for their children once while they pay slowly. the other member can pay for the woman are given a year on completing the loans that they have acquired from the organisation. Q3:importance belongs to everyone. Q4:kenya woman foundation Q5:big, country, year, organisations, organization Q6:Kenya Q7:Nairobi Q8:kibera Q9:2-6 months ago Q10:Male Q12:Was affected by what happened Q13:The right people Q15:Yes Q17:<<2011-08-30-kawangware kibera-s#010-p#01>>_ Q18:Inspired Q37:Good idea that succeeded Q38:Mixed Q39:Physical well-being Q40:Mixed Q122:16-21 Q132:-1.32614600000000 Q133:36.77880700000000 Q134:UNITED NATIONS
 
Found 3 records. mysql icon_filters: group_id between 0 and 5000 and q10 = 'Male' and q122 in ('16-21','22-30') and (( q2 like '%business%' and q2 like '%loan%' and q2 like '%woman%' ) or ( q3 like '%business%' and q3 like '%loan%' and q3 like '%woman%' ) or ( q4 like '%business%' and q4 like '%loan%' and q4 like '%woman%' ) or ( q5 like '%business%' and q5 like '%loan%' and q5 like '%woman%' ) or ( q6 like '%business%' and q6 like '%loan%' and q6 like '%woman%' ) or ( q7 like '%business%' and q7 like '%loan%' and q7 like '%woman%' ) or ( q8 like '%business%' and q8 like '%loan%' and q8 like '%woman%' ) or ( q11 like '%business%' and q11 like '%loan%' and q11 like '%woman%' ) or ( q17 like '%business%' and q17 like '%loan%' and q17 like '%woman%' ) or ( q26 like '%business%' and q26 like '%loan%' and q26 like '%woman%' ) or ( q27 like '%business%' and q27 like '%loan%' and q27 like '%woman%' ) or ( q28 like '%business%' and q28 like '%loan%' and q28 like '%woman%' ) or ( q29 like '%business%' and q29 like '%loan%' and q29 like '%woman%' ) or ( q35 like '%business%' and q35 like '%loan%' and q35 like '%woman%' ) or ( q41 like '%business%' and q41 like '%loan%' and q41 like '%woman%' ) or ( q42 like '%business%' and q42 like '%loan%' and q42 like '%woman%' ) or ( q43 like '%business%' and q43 like '%loan%' and q43 like '%woman%' ) or ( q46 like '%business%' and q46 like '%loan%' and q46 like '%woman%' ) or ( q47 like '%business%' and q47 like '%loan%' and q47 like '%woman%' ) or ( q60 like '%business%' and q60 like '%loan%' and q60 like '%woman%' ) or ( q65 like '%business%' and q65 like '%loan%' and q65 like '%woman%' ) or ( q70 like '%business%' and q70 like '%loan%' and q70 like '%woman%' ) or ( q71 like '%business%' and q71 like '%loan%' and q71 like '%woman%' ) or ( q72 like '%business%' and q72 like '%loan%' and q72 like '%woman%' ) or ( q73 like '%business%' and q73 like '%loan%' and q73 like '%woman%' ) or ( q74 like '%business%' and q74 like '%loan%' and q74 like '%woman%' ) or ( q75 like '%business%' and q75 like '%loan%' and q75 like '%woman%' ) or ( q76 like '%business%' and q76 like '%loan%' and q76 like '%woman%' ) or ( q77 like '%business%' and q77 like '%loan%' and q77 like '%woman%' ) or ( q80 like '%business%' and q80 like '%loan%' and q80 like '%woman%' ) or ( q81 like '%business%' and q81 like '%loan%' and q81 like '%woman%' ) or ( q86 like '%business%' and q86 like '%loan%' and q86 like '%woman%' ) or ( q87 like '%business%' and q87 like '%loan%' and q87 like '%woman%' ) or ( q88 like '%business%' and q88 like '%loan%' and q88 like '%woman%' ) or ( q89 like '%business%' and q89 like '%loan%' and q89 like '%woman%' ) or ( q98 like '%business%' and q98 like '%loan%' and q98 like '%woman%' ) or ( q99 like '%business%' and q99 like '%loan%' and q99 like '%woman%' ) or ( q110 like '%business%' and q110 like '%loan%' and q110 like '%woman%' ) or ( q111 like '%business%' and q111 like '%loan%' and q111 like '%woman%' ) or ( q116 like '%business%' and q116 like '%loan%' and q116 like '%woman%' ) or ( q117 like '%business%' and q117 like '%loan%' and q117 like '%woman%' ) or ( q123 like '%business%' and q123 like '%loan%' and q123 like '%woman%' ) or ( q125 like '%business%' and q125 like '%loan%' and q125 like '%woman%' ) or ( q132 like '%business%' and q132 like '%loan%' and q132 like '%woman%' ) or ( q133 like '%business%' and q133 like '%loan%' and q133 like '%woman%' ) or ( q134 like '%business%' and q134 like '%loan%' and q134 like '%woman%' ) or ( q135 like '%business%' and q135 like '%loan%' and q135 like '%woman%' ) or ( q136 like '%business%' and q136 like '%loan%' and q136 like '%woman%' ) or ( q138 like '%business%' and q138 like '%loan%' and q138 like '%woman%' ) or ( q141 like '%business%' and q141 like '%loan%' and q141 like '%woman%' ) or ( q142 like '%business%' and q142 like '%loan%' and q142 like '%woman%' ) or ( q151 like '%business%' and q151 like '%loan%' and q151 like '%woman%' )) LIMIT 4000; filter_questions ['q10', 'q122'] merge:ignore A[2HdRzOGMZwt8]:SUCCESS: 1 rows inserted. not enough values to unpack (expected 3, got 2)