 Okay good afternoon everybody. Welcome to my presentation and it's my pleasure to be here today and giving this presentation. Basically this is the outline of the presentation. I'll be giving some introductory comments and then I'll talk about the objective of the paper, then econometric model, data, results and then the final I'll talk about the conclusions. You see Bangladesh is a natural disaster point country and it's affected by climate change related disasters and natural disaster has got you know impact on poverty it increases poverty as well as deprivation of affected areas and you know if you look at the map of Bangladesh southern part is mostly affected by natural disasters and the thing is that when initial disaster happens the negative impacts are not equal to all households and you see and these negative impacts are disproportionately distributed among the households and in terms of if we talk about the negative losses of assets and really it we can see that the losses of assets are relatively longer term and is more acute for people belonging to the lower state of the society and compared to the rich people are negative the poor people are severely affected and the thing is that due to natural disasters we have been actually experimenting some development innovations or projects in the areas of natural disasters the thing is that when a household goes above the poverty line and whenever a natural disaster happens immediately due to that natural disaster those households which have already crossed the poverty line they come back below go back below to below the poverty line again due to natural disaster and some of them due to this national disaster they fall into poverty traps for a longer period of time and it becomes very difficult for them to come out of that trap in a short in a shorter term and the thing is that you know if we talk about the recovering the losses of assets for these households you know the extent of recovery depends on on the access to local markets by these households okay especially we are talking about the financial markets and the thing is that in a country or in an economy where you have got markets are full and complete and all households are have the same level of access to the markets but the thing is that in a country like Bangladesh where markets are not complete or perfect so all households do not have got the same access to the markets especially to the financial markets and we know that in a country like Bangladesh where 40 percent households do not have any land asset so these households do not have the capacity to provide with some collateral to the financial institutions to get loans from the former sector financial institutions so ultimately due to decisions they have to rely on the informal sources of credit and considering this limitation of the former sector financial institutions you see 30 years back Prof. Inus started microcredit programs in Bangladesh you know through establishing come in bank and now it has become a popular model all over the world so ultimately the point is that access to credit is very important to households and from the literature on access to credit we know that access to credit has got positive impact on well-being it has got negative impact on poverty and it has got positive impact on entrepreneurship as well and we know that access to credit has got a gender dimension women are more discriminated discriminated compared to male members in the society and the thing is that whenever look at the literature on the impact of access to credit on the recovery of disaster losses we don't see that much things in the literature only one paper is available so far I know that is Carter at all 2007 he looked at the impact of access to credit on household losses which are related to only assets that means this paper doesn't take into consideration the known asset losses okay so considering this limitation or this gap in the literature this paper intends to examine how financial markets market institutions formal as well as informal help households in rural areas of Bangladesh in recovering total law total asset and non asset losses that income from national disaster national disasters okay these are the three econometric models that we have in the first model if you look at that you know dependent variable is mitigation the extent of mitigation or extent of disaster loss recovery by the households and on the right-hand side of the equation you see that the first one is access and which is actually a dummy variable which denotes that takes one if household has got an access to a financial mark to a to credit or zero otherwise in the second model and on the right-hand side we have got two variables important and apart from the access we have got some other control variables like you know household household level control variables and village level control variables okay in the second equation if you look at that the most two important independent variables are loans and then square loan that means loans that's in this in this variable we are taking the putting total amount of current loan of the household as an independent variable and we have got like a square loan to see the non-linearity in the relationship between loan and extent of disaster loss recovery and then in third equation we have put loans from different sources commercial bank microfinance community-based organizations and then other informal organizations fenced and families family members and then supply us get it as well okay so data that I have got for this paper from a survey of 2,860 80 households from 140 villages in different parts of the country and we have actually in during the survey we divided the whole country into three disaster areas one particularly flood pond flood torn areas then second one cyclone third one is desertification so these are the three climate change related areas we have identified we identified during the data collection and besides information on natural disaster loss as well as access to KD we also collected detailed information at the household level as well as at the village level during the survey so these are the summary statistics of important independent as well as dependent variables if you look at the first second row is that's the mitigation that is dependent variable extent of household disaster loss mitigation and we have found that on an average households were able to recover 26% of their losses during the time at the time of data collection and then excess that 39% households had access to credit to any sources of credit and then loan total amount of loan average loan size was 8,936 taka is in Bangladesh Taka if you convert that into dollars it's going to be like hundred and ten dollars roughly then loan amount average loan amount from commercial banks 2,494 taka and then from my microfinance institutions only 785 is around ten dollars from community-based organization towards 282 taka from other NGOs you know 2,713 from money lenders informal money builders 1,618 from friends and family 6761 and from suppliers 282 taka now we on table one you can see that extent of disaster loss compared to non-land household assets we divided all the cloud all the households into three categories hardcore poor poor non-poor for hardcore poor you see that total non-land average total non-land assets where 41485 roughly six hundred six hundred dollars around and then loss was 23,548 and loss was in dollars 400 3,341 and you can see that the loss was like 57% of the total assets they lost due to a natural disaster and for poor household this percentage of loss reduces to 48% and for non-poor it is 22% so you can see that the natural disaster affects households disproportionately poor households are more vulnerable and their their extent of loss is higher than than richer households then if we look at the loss recovery compared to total loss then total loss of average loss recovery was 4175 taka which was 18% of the total loss for hardcore poor for poor households the loss recovery was 17% and for non-poor it was 15% so you see from the even loss recovery perspective hardcore poor households were not in a better position okay although they are relatively they had a little bit higher recovery but if you look at their total assets and the loss and the recovery so they have actually lost a significant amount of their non-land assets due to natural disaster and they couldn't be able to recover those losses through any means now if we look at this disaster loss recovery in terms of geographical distribution in terms of disaster so here we have divided all the areas into T3 cyclone area flood area and we have also considered some areas as non-disaster areas where the the extent of flood or cyclone was not that severe but still they had got they had some affected households if you you can see from the table that the recovery in the non-disaster he was higher 22% and in flood areas recovery was 19% and cyclone areas it was 15% now we come to the results I have been I have reported only results of the important variables I've been reported the results of other variables this are these results are given in the paper if you want to look at you can see it from there if you look at the access variable it's you can see that from the first model access is significant that means access to credit significantly help households in recovering the losses then if we look at the second model loan and loan square that means what did come of the loan and from the results we can see that there is a non-linearity in the relationship it it with the increase in the loan the the reduction in the loss reduce is is is goes off and then it reduces so there is a non-linearity in the relationship then the interest in result coming from the loans from different sources if we look at the loan from the commercial bank is negatively related with the extent of loss recovery from microfinance source this is positive from community based organizations this is significant from other NGOs this is significant from money lender this is also significant but family friends it was positive not significant from suppliers it was positive not significant the what is the main policy actually thing that is coming from these results that is that you know the formal sector financial institution are not helping households in recovering the losses and the results is actually logical in the sense that in terms of getting a loan from a commercial bank from a formal sector institution is very lengthy it's cumbersome time consuming so immediately after a disaster households do not have access to formal sector institution due to collateral due to this procedural thing and that's why the what is the main that conclusion that is coming that access to KD is important in terms of recovering losses and there is a non-linearity in in in the relationship between access to KD and the extent of loss recovery and then the former sector institutions are not helping and the government needs to do something to help households in recovering losses through giving them credit from the former sector institution as it is important for them that interest rate in the former sector is lower than the interest rate in the informal sector and government needs to do something to they need to actually reduce the cumbersome procedures in getting loans for the households as well as it should be available to everybody so these are the findings from my paper thank you