 I'll be looking at the impact of conditional cash transfers programs on household work decisions in Ghana and I think the second speaker has really introduced my work for me in the sense of the methodology as well as some other things so it will make my work very easy. Well, by way of outline I look at some introduction, I look at objectives, evidence from other studies, I look at the LEAP program actually in Ghana, I look at some of the methodology, resource findings, some conclusions and then I'll go to some policy recommendations. So CCT programs or conditional cash transfer programs have been widely used in the past decade and these are used by mostly developing countries as a way of trying to use policy tool to increase human capital. Now there have been arguments that once you are giving people some cash then it will create some disintensives to work but then you try to see if that's the case for Ghana. Now there has been some successes that has been going on with these CCT programs and that can be recorded in short term improvements in consumption, education and in health as they're seen by work done by schools, Gettler and Rawlings and Rubio. Some studies have brought out some favours in terms of these conditional cash transfer schemes. One is school fias and maro and then they assess the impact of the medical progressor program and what they found out was that it had not discouraged people from working. Again I added in the others also in 2009 also so that some cash transfer programs had led to increased employment among prime-aged adults and Ferro and others also find that Borsa is a scholar for Brazil that increased probability of mothers and fathers participation in labour force in the country. Now Oliveira also finds that for the Brazil's Borsa family that labour market participation rate for households or people that were in the beneficiary households their participation rate was about 4.3% more than those who were not in the participating households. Now to those who have have something against CCT, malicious inflows also used in Nicaragua as red deportation, social, I hope I mentioned it right, they find no effect on labour supply. Again Bertrand and others also use some cross-sectional data and they find that pension receipts also substantially lower the labour market participation rate of working-age adults. Now to the main point of the leap in Ghana, the leap is a livelihood empowerment against poverty so the actual objective of this paper was trying to see whether this program that's this conditional cash transfer program has increased the number of hours that households are working. So again we try to not just look at the holistic approach but try to decompose it into agricultural work on non-farm as well as work that are coming from paid employment. Now the main objective of the leap in Ghana is first of all to elevate short-term poverty and then to encourage long-term human capital development. Now this was done with the aim of trying to increase school enrollment, also tendons and retention of children. So the reason the conditions for which you can receive this amount was the fact that you need to let those beneficiary children go to school and then you also have to be enrolled on the national health insurance scheme. So payments are being done based on the fact that you provide something that says you have been registered with the national health insurance scheme otherwise you don't get a fund. Now how was the targeting done? We have a DLSS-5 that's a Ghana Living Standard Survey 5 and that one was done around 2005 and identified 164,370 households being the bottom 20% of the extremely poor households in Ghana. So these are the ones that are being targeted. So it includes subsistence farmers and fisher folks and then the extremely poor citizens those above 65 years who have no subsistence support and again it looks at persons with severe disabilities so without any productive capacity and then they also looked at those caregivers who look after these orphans and vulnerable children so they are also beneficiaries and particularly children that are affected by HIV AIDS and then children with severe disabilities. Also pregnant women lactating mothers with HIV were also part of the target group. Now not all the fund is conditional. There are some group of people that are getting the money unconditionally and these are the ones that have severe disabilities as well as those who are very aged. So as of 2008 that was when the program started beneficiaries were receiving eight Ghana cities and with the exchange rate as at 2008 it was about $1 to one Ghana cities. This was immediately after Ghana's redenomination process. So in July 2012 that was last year this was revised to 12 Ghana cities to 36 Ghana cities so it depends on the number of beneficiaries in the household. So if it is just one beneficiary then you get 12 Ghana cities but if it's four or more then you get 36 Ghana cities and the exchange rate as at 2012 was $1 to $1.88 Ghana cities. Now in 2012 initially this whole program was the government of Ghana, the flagship of the government of Ghana. He was actually doing all that but now there have been some external supports that are coming in through DFID, the World Bank. These were the amounts that were received in last year to help with the program. Now as at 2010 the coverage for the program had been 35,000 households but as at this year there has been 71,456 households that have been covered and the targets for 25 minutes to get to 200,000 households. Now one thing that has to be clear here is that the amount that was supposed to be given to these guys were supposed to be adequate and acceptable and in the case a student encouraged unemployment so we are giving them money you shouldn't give you the opportunity to not work and then you shouldn't also create this dependency on the fund such that once you get the money that is it and then it shouldn't also benefit beneficiary households excessively to bring in the inequalities that will happen in these communities. Now to the methodology. I'll go straight to the table. We have two different times that we went to the field to collect data. So the first year that is the baseline was in 2010 and then the following period was in 2012. So the LEAP that is the program those are the treatment people those are the ones that are getting the benefits and then the control groups are those who are in the same communities but are not getting the benefits. So we named it Yale in the sense that it's Yale Yale University in collaboration with ESA that as in 2010 it's a national survey that they undertook so they had about 5,009 households. So out of that a propensity score matching was done and then because those in the LEAP communities were matched to those from the Yale communities and then we were able to get a match or a contractual for them maybe we had about 858 households in their control group. Now what actually we wanted to look at is to find any significant effect of the cash transfer program on the treatment group that they should have if the program has any impact then we should see that the treatment group are quite higher in terms of any the characteristics as opposed to those in the control group. So we also wanted to see if the number of hours that they were working had increased overall with the program or it has decreased and we also had the composition to that great paid employment and non-employment. So we adopted the double difference as the second speaker said. So it has been widely used in literature to look at these outcome variables in randomized experiments. So that is what we try to use. So this is the model we use and not going through that we go to some descriptive results. So for these households we find out that the average household size was 3.9 and then the sex of the household heads were 54% for women. Probably a question will come why women are having this high percentage in the sense that these caregivers are mostly women. Those who take care of their orphan and vulnerable children are mostly women. So once they target them that's why we are having the health of household heads for women being 54 as opposed to 45 for men. Now with educational level we see those who are having no education to be about 46.5% those with senior high school and below they are about 49.4%. In terms of distribution of age of the household heads we could see 44.5 for those who are about 65 plus years and in terms of marital status those who are married are about 42%. Those who are widowed are about 32.6%. Now in looking at the dependent variable the dependent variable is the total or average annual average labor hours worked in the whole year and so we try to look at the comparison between the those who are getting the benefits as opposed to those who are not getting the benefits to see what is happening. So we have hours worked for our great hours worked for paid employment and that of non-farm enterprise and we could see that for the baseline that is the treatment are having about 1041 hours whilst the controls are having about 1451. So in order not to get confused I brought another table below and those are in terms of days in a year. So you could see in total we have about 130 days of hours of work done by the treatment group those are the beneficiaries as opposed to 181 for the control group. Now these are the results so what actually is going to be measuring the impact of the program is the third variable that is we've named the three time variable. So it's an interaction between the treatment variable and then the time variable that's the trend variable and so we have equation 1 to 8. Equation 1 are mostly just the treatment variable the time variable and then interaction but then in question 2 we do more of controlling for other factors and so for equation 1 we have it for the total hours this total hours includes the work work for our great paid employment and then non-farm altogether and then the subsequent ones from equation 3 and 4 are just for our great work and then 5 and 6 is just for paid employment and then 7 and 8 is just for non-farm enterprise. So we wanted to see the variations in the impact of this program so the results are summarized these are some of the variables we use. So in conclusion and policy implications this is what we find we find that the program decrease total hours work relative to the control group specifically it decrease total labor hours work why probably we'll have financing for it later but then it's also increased hours work for paid employment so there seem to be some switch from hours work on a break to that that is work for paid employment now but we see no impacts on the program of the program on hours work for non-farm enterprises now we also see some huge influence of non-institutional transfer so this is not a transfer as I'm talking about these are other transfers aside that cash transfers that they are receiving so aside that other people to remit and then they get these transfers so we see that it actually reduces the total labor hours worked together for a break for paid employment and then for non-farm meaning that when money comes into the households it really does not give them the opportunity they don't really feel like working somehow okay so marginal impacts also to the non-institutional transfers it seems to be about hours worked for total labor hours work reduces about 10 percent for agriculture paid employment and in overall now some of the variables we considered or we controlled for we see that health status of the household members so if they are healthier they seem to increase the hours worked for a break all the categories if they have meals in the household okay so households that have meals and then those households that had larger household sizes and then those households that had cultivating cultivated land sizes large cultivated land sizes also increase the hours worked for agriculture but again we see that households with electricity they tend to increase their hours worked for non-farm enterprises non-farm enterprises because if they have lights they could stay on for quite a long time before going to bed and they could do marketing or trade during the evenings now to the recommendations the resource is suggesting that this cash transfer is leading to a reduction in labor supply well we try to get some possible explanations and so what we find out was that children are now attending school because in looking at the labor hours worked it was for men women and children so now that children are attending school definitely they are not in the house to go to the farms with the family and so that is one reason what that we looked out for and so this has been confirmed by work by handle these were consultants that were consulted to do an in-depth analysis on the LEAP program for Ghana and this is one of the findings that they had so they saw that the LEAP had increased school among secondary school age children by seven percentage points and also reduced grade repetition and among both primary and secondary age children also it had reduced absenteeism in class by about 10 percentage points so this could be used to explain why now the use of also high labor and heavy sites so we were thinking that probably once you are getting the money you are able to use the money to hire people to work on the farm and for that reason you stay back and not work also probably if you are using heavy sites instead of going to the farm and we didn't just go once pray the place and that is it okay so we tried to look at all that but then we realized that these were not contributing factors to why they were reducing the number of hours so probably the number of children who are going to school be the best way to explain the phenomenon so the recommendation is we highly recommend that in subsequent targeting because you are targeting the poor so if you don't target them well anyhow then we don't get the expected results but then we also recommend the continuation of the intervention because it has really chopped some successes so it should continue to give the broader positive outlook that it has now one thing that we realized was that these people beneficiaries were not receiving the income on time and so the actual thing supposed to be done is to be it should be coming in every two months so February April and then it continues that way to December but then when we went to the field what we saw was that if they could get it in say February and it could come in say April not coming in April but say May or June so it compounds every or no consumption once your income compounds and you get it it doesn't smoothing out here uh the pattern which we consume so measuring the impacts at these instances becomes quite tricky so what we recommend is that the payment should be done on time and on regular basis also we saw a drift from the aggregate work to paid employment but I think that is the next question we probably will have to look at why the drift and is the drift really growth enhancing for the bigger economy so that is the next step for researchers to continue so once you get opportunity again I think we'll be looking at some of these things thank you for your attention yeah