A Case for Geographic Targeting of Basic Social Services to Mitigate Inequalities in Bangladesh

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3 A Case for Geographic Targeting of Basic Social Services to Mitigate Inequalities in Bangladesh UNICEF Bangladesh 3

4 C o n t e n t s Preface... 3 I. Background on the MICS II. Making a case for blanket geographic targeting of basic social services... 6 III. Pursuing an optimal composite deprivation index... 7 IV. Conclusions and potential policy implications...17 Annexes

5 Preface In January 2010, UNICEF Bangladesh published a paper arguing for geographic targeting of basic social services to the most deprived geographic areas of Bangladesh as one of the means of accelerating progress towards achieving MDGs with equity. The present paper continues with the same argument but uses a different methodology to reach the same conclusion. In the earlier paper, the methodology used involved correlating the 2009 Multiple Indicator Cluster Survey (MICS) data with poverty data. In this paper a composite deprivation index (CDI) has been created, which is strongly related to deprivation poverty and usable for multi-sector social targeting of the most deprived upazilas as it represents the education, health and water and sanitation sectors. The index captures and the results of the index can easily be used and understood: the lower the number the worse the situation and, therefore, the more has to be done to achieve Millennium Development Goals (MDGs). The Bangladesh Bureau of Statistics (BBS) in collaboration with UNICEF has recently published the technical report on the 10th round of the Multiple Indicator Cluster Survey (MICS) with the objective of providing up-to-date disaggregated information on children and women at the national and subnational levels. Similar surveys are planned for 2012 and 2015 to continuously monitor progress towards the achievement of the MDGs with equity in Bangladesh. The 2009 round of MICS differed from earlier ones in that a shorter questionnaire was used to collect time a large amount of relevant data on social sector indicators has been simultaneously collected and made available at upazila (sub-district) level. Besides household characteristics, the MICS 2009 included information on education, water and sanitation, knowledge of HIV and AIDS, child mortality, attendant at delivery, birth registration, and early learning. Most of the 23 indicators used are aligned with MDG indicators. I hope this report will be useful to policy-makers, elected leaders, researchers and administrators as well as to development partners in better targeting investments towards the most needy geographic areas. I also hope that MICS 2009 data will be used as a baseline for monitoring progress towards achievement of MDG targets, most of which are directly or indirectly related to women and children. Given that the same survey will be repeated in 2012 and 2015, overall progress will be tracked but, more importantly, individual upazilas and/or districts will be able to see how much relative progress they have made in comparison to other upazilas and districts as their ranking changes over time. The frequent availability of detailed data will enable assessment of the effects of geographic targeting. Multiple opportunities will hence derive from this, including the ability to acknowledge those upazilas and districts that advance most in their ranking, be it on the basis of individual indicators or of a composite index. The important thing about the 2009 MICS is that it shows that Bangladesh is less homogenous than thought. The robust MICS data at the upazila level shows the disparities, especially within urban districts or districts with isolated areas. The composite deprivation index helps to focus on the most deprived areas. 5

6 I. Background on the MICS 2009 Introduction Properly designed and conducted household surveys are the most reliable way to assess progress toward achievement of MDGs in countries where the routine development outcome or impact information is not readily available. Analysis of the 2000, 2003 and 2006 MICS data indicated that considerable disparities exist between districts in terms of achieving MDGs. A further preliminary assessment undertaken by UNICEF 1 to better understand the determinants of such disparities suggested that poverty and geographic isolation are among the likely predisposing factors. The 2009 MICS was conducted by the BBS. It was designed to collect data related to all MDGs except for MDG 1: Eradicate extreme poverty and hunger, as other household surveys had already history of Bangladesh, designed to collect representative data at upazila level. A total of 23 indicators were assessed, along with demographic and household composition information. The results of the 2009 MICS provide valuable and detailed information for the Government of Bangladesh (GoB) as well as its development partners to better prioritize, target, plan and budget for social sector interventions and investments, in the context of the 6th NDP currently being prepared. The MICS 2009 Final Report ranked 64 districts and 481 upazilas from the best performing (smallest number) to the worst performing (highest number) according to a number of separate indicators. The resulting data-base and 20 maps allow for optimal geographic targeting of sectoral interventions using individual indicators or alternatively targeting districts and/or upazilas where needs are greatest. Survey Objectives The 2009 Bangladesh MICS has four objectives: To provide up-to-date and disaggregated information for assessing the situation of children and women; To provide data needed for monitoring progress towards achievement of the MDGs as a basis for future action; To assist the GoB at national, district and upazila level to establish a baseline and assess future progress made in achieving MDGs with equity by 2015; To provide detailed thematic and geographic social sector information that will facilitate prioritization and better targeting of future investments made in the context of the 6th NDP currently being prepared by the GoB. 6 1 UNICEF 2009: Monitoring the Millennium Development Goals, Trend Analysis of Child Risk Measure

7 Survey Methodology and Design The sample for the Bangladesh MICS was designed to provide estimates on a number of indicators relating to the situation of children and women in urban and rural areas, at domains and the sample was selected in two stages. Within each upazila, at least 26 census enumeration areas (EA) were selected with probability proportional to size. A segment with and is not self-weighting. For reporting national and district level results, sample weights were used. Questionnaires Three questionnaires were used in the survey. In addition to a household questionnaire which was used to collect information on all household members, individual questionnaires were provided to MICS3 model questionnaire, included the following modules: Household questionnaire Questionnaire for individual women Fieldwork and Processing A total of 7,683 interviewers and 1,154 supervisors were trained in April Fieldwork was undertaken from 28th April to 31st May was entered on 64 microcomputers using the CSPro software. In order to ensure quality control, all questionnaires were double-entered and internal consistency checks were performed. Procedures and standard programmes developed under the global MIC3 project and adapted for the Bangladesh questionnaire were used throughout. processing was conducted in October was analyzed using the SPSS software programme and the model syntax and tabulation plans developed for this purpose. Sample Coverage Of the 300,000 households selected, 299,842 were successfully interviewed for a household response rate of 99.9 per cent. 7

8 II. Making a case for blanket geographic targeting of basic social services This paper will demonstrate that large geographic disparities exist between the best and worst that effective geographic targeting of interventions in favour of the least performing districts and upazilas has the potential to accelerate progress towards achievement of MDGs with equity. It s important to distinguish the two poverty concepts, income poverty and deprivation poverty: Income poverty: The poverty headcount index simply describes the percentage of population that consumes below the food/total poverty line; Convention on the Rights of the Child, focuses on the resources children need to survive and grow. Bangladesh participated in the worldwide poverty study 2 as part of recent research that has shed more light on child deprivation, family income and usefulness of composite indicators. The poverty study views poverty through the lens of seven severe deprivations of human needs (Bristol poverty headcount). The term absolute poverty is used for cases where children have been exposed to two or more severe deprivations. The seven dimensions assessed were shelter, sanitation, safe drinking water, information, food, education and health. The report found that people in the lowest poverty quintile are typically deprived of four of these seven basic services, and that for each rise to the next quintile in the poverty ladder, people are deprived of access to one less basic service. The question thus arises should one wait for income/expenditure poverty to be reduced, or should one target the most deprived population with basic social services to accelerate the reduction of poverty? A recent study of great importance that bridges the differences between the two poverty concepts, and in Brazil, China and India by Martin Ravallion of the World Bank. This study makes a strong case for reducing income poverty by eliminating deprivation poverty stressing the importance of geographical targeting. In China, achievements in basic health and education predated poverty reduction and economic reform but, nowadays, lack of secondary schooling is a very important constraint. Despite enormous sustained economic growth over the past two decades, and an impressive absolute number of people pulled out of poverty, socio-economic disparities are increasing. In India, inequalities in human development have undoubtedly retarded poverty reduction. Disparities in regard to rates of female literacy have greater explanatory power for rates of poverty reduction. As in China, rapid economic growth has pulled millions of people out of poverty, especially in urban areas, but socio-economic disparities are on the rise. Brazil shows that while economic growth generally helps reduce poverty, there can also be an important role for redistributive policies. Brazil, which has traditionally had enormous socio-economic disparities, has used both unconditional transfers and CCT (Conditional Cash transfers) targeted at poor families to keep children in school ( Bolsa familia ). The targeting of poor families involved a proxy means test based upon readily observed co-variations of poverty, including location. The result has been, despite only one third to half the economic growth of China and India, a greater reduction of poverty in relative terms and a reduction of inequalities as well. This report argues in favour of geographic targeting of basic social services to the most deprived geographic areas of Bangladesh as a means of achieving MDGs with equity, as well as accelerating 8 2 UNICEF 2010: Child Poverty and Disparities in Bangladesh

9 the overall reduction of poverty given the strong association and synergy between the deprivation of access to basic social services and income/expenditure related poverty. Several programmes and government initiatives have already attempted to target the poorest portion of the country s population, some of which, at least, have had sub-optimal effectiveness. One example is the primary education stipend (PES), one of the interventions of the national social safety net system. A recent assessment 3 of the stipend revealed it reached only 40 per cent of the poorest children entitlements were not entitled to these and; that over 30 per cent of eligible households in the study did not receive the cash transfer. Overall, the effects of the PES programme are remarkably small given its size and expense, said Dr. Bob Baulch, coordinator of the Poverty Dynamics and Economic Mobility Theme at the Chronic Poverty Research Centre (CPRC). Attempting to directly target the poorest children was clearly ineffective in the above-mentioned example. Had the most in-need districts, or even better, the most deprived upazilas been targeted, with all children within these geographic entities receiving the cash transfers, the effectiveness of the stipend could have been much better. This is because, if the national average of children below the poverty threshold is 45 per cent, obviously the average of children below the poverty threshold is much larger in the districts, and especially the upazilas or unions, where poverty is more acute and/or the lack of access to basic services is more prevalent. The most deprived upazilas could be considered as objects of social targeting. In this case the majority of the target population is likely to be reached, as has been done in Brazil. This concept is not new and was referred to in the context of the above mentioned study on the primary education stipend (PES): In the future, providing larger cash transfers to fewer households, but the poorest ones, could improve the education, health, and well-being of millions of children and ultimately end the cycle of poverty that plagues generation after generation of rural Bangladeshis, Dr. Baulch said. The recommendations put forward by Dr. Baulch for reforming the PES include placing more focus on poverty pockets, enlarging the stipend and tightening the household selection process to ensure transparency. The above evidence and argument suggests that the acceleration of poverty reduction in Bangladesh will along with several other interventions such as the supply of energy and improved infrastructure not have the resources available to provide an adequate level of subsidy to all children, and that other targeting strategies seem to have been ineffective so far. Therefore the case is made here for effective blanket geographic targeting of basic social services. III. Pursuing an optimal composite deprivation index The methodology One of the immediate objectives of this analysis was to develop a summary measure of various indicators that could be used to compare geographical areas. The 2009 MICS captured information on all MDGs except for poverty. It is possible to create an index related to deprivation poverty that could be used for multi-sector social targeting of the most deprived upazilas. This composite deprivation index represents the education, health and water and sanitation sectors. 3 Conducted by CPRC and the International Food Policy Research Institute (IFPRI) as part of the larger project, What Development Interventions Work? The Long-Term Impact and Cost-Effectiveness of Anti-Poverty Interventions in Bangladesh, funded by the UK Department for International Development and the Economic and Social Research Council 9

10 The index indicators The composite deprivation index includes all those MICS indicators with a strong relation to poverty/ deprivation. Based upon the theory above, departing from the premise of having only one indicator per cluster and after correlation analysis, the decision was taken to work with the following indicators: 1. mortality (U5MR) and maternal mortality, the key health deprivation indicator (MDG 5). 2. Net Attendance Ratio (NAR) in secondary education. Primary NAR was dropped; the reason being that we need one indicator per area and secondary NAR takes care of the effectiveness of primary education (without completion of primary education one could not attend secondary education) and the goal in Bangladesh is to universalize basic education. Secondary education is also strongly related to poverty according to various studies (MDG 2). 3. Proportion of the population using an improved sanitation facility (as per the Joint UNICEF- Poverty study (MDG 7). 4. The female adult literacy rate, the most important indicator associated with poverty. The index s the index. For every indicator, the range (spread of indicator s) was divided into ten intervals (deciles) and all the cases within one interval got the same integer. Depending on the location of the national average (that is the 0 interval) the 10 points go, for example, from minus 7 to plus 2 (or minus 3 to plus 6) and in the case of a normal distribution, from minus 4 to plus 5. The use of intervals avoids placing too much focus on small differences and recognizes the fact that (5-10 points in many cases). The next step was to calculate the for the composite deprivation index for all upazilas based upon the sum of the four indicators. The index gives equal weight to all four indicators. The corresponding total s have a meaning, the lower the, the worse the situation (more The advantage of this index is that smaller differences disappear and extreme s get less attention. By adding up all the points for the four indicators one derives the s for the composite deprivation index (interval method). As we will see, the range goes from minus 18 to plus 12. The rank In Annex 2, all 64 districts and 481 upazilas have been ranked from the worst performing (lowest number of the index) to the best performing (highest number of the index). How to identify priority areas? Once that ranking has been made the question arises as to how to identify the areas requiring extra attention? This is ultimately, of course, a government decision but we present here two proposals: First, we propose an analysis based upon maps using the quintile method, but without manipulating 10

11 the intervals. Thus all intervals per map will have the same range; consequently some intervals will capture many more areas than the others. Maps are often based upon quintiles of areas, thus resulting determine the numbers. Thus, the most deprived areas (or worst performing areas) are those areas that fall into the two categories: quintile 4 (high deprivation) and quintile 5 (highest deprivation) of the range of s for the index. Theoretically these quintiles can contain any number of areas, a very high number as with skilled birth attendant (over 350) or a low number as with sanitation (73) (see maps in Annex 1). the upper limit of the standard deviation from the national average of the index, in short, in the above one standard deviation group. We will also include this method as it was applied in the 2009 UNICEF assessment of district performance in making progress towards MDGs in Bangladesh that However, as we will see, both methods produce exactly the same number of most deprived upazilas better with the maps. The composite deprivation index analysis and results This composite index allows for multi-sector social targeting of the most deprived upazilas. It also serves as a tool for identifying those upazilas where multi-sector service deprivation occurs. The composite index could, therefore, be effectively used for simultaneous reduction of several basic social service deprivations in deprived upazilas thereby holistically accelerating poverty reduction (MDG 1) as well as the achievement of MDGs 2, 5 and 7 (on education, maternal mortality and water and sanitation/environment respectively). Division level The composite deprivation index (CDI) by division is most negative in Sylhet Division (minus 4) and highest in Khulna and Barisal Division (3). Despite the further very small differences, three divisions are in quintile 2 (Khulna, Barisal and Dhaka) and the other three are in quintile 3. Range of CDI s by geographical unit: Division: minus 4 to 3 7 points District: minus 12 to points Upazila minus 18 to points Bangladesh is quite a uniform country at the division level and even at the districts level, as is demonstrated by the results for several indicators and the deprivation index. The real disparities appear at upazila level, particularly between upazilas within the same district (especially urban districts or districts with isolated areas). The ranges are enormous for every indicator at the upazila level. The following table presents the distribution of the deprivation index by quintile for divisions, districts and upazilas. Relatively few areas are outside quintiles 2 or 3. 11

12 Deprivation index distribution by quintiles for divisions, districts and upazilas Interval CDI s Quintile 1 (best performance) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (worst performance) minus 6 minus 7- minus 12 minus13 - minus18 Divisions 3 3 Districts Upazilas Districts are tilted more towards quintile 2 (47%) while at the upazila level, the majority are in quintile 3 (37%). No division falls in the most deprived category (quintiles 4 and 5), but eight districts (12.5% of all districts) and 80 upazilas (16.6% of all upazilas) fall in this category. Map 1: Composite deprivation index by district, 2009 District level Map 1 shows an interesting pattern: with a few exceptions, the whole west and south of the country falls within the same category (above average performance) while the north and east are the more 12

13 deprived regions. Thus the map reveals continuous spots of the same color rather than a patchwork. The center of Bangladesh is where the best performing districts are located. Map 2: Composite deprivation index by upazila, 2009 Eight districts are revealed as most deprived areas: Division District CDI Chittagong Bandarban -12 Sylhet Habiganj -9 Sylhet Sunamganj -9 Chittagong Cox's Bazar -7 Chittagong Khagrachhari -7 Dhaka Netrokona -7 Dhaka Sherpur -7 Barisal Bhola -7 These eight districts contain 64 upazilas, of which 45 are most deprived. It is worth noting that in Khulna and Rajshahi divisions, no districts fall in the most deprived category. 13

14 Upazila level The upazila map (Map 2) with its much greater geographic resolution, features many other worst performing areas indicating upazila disparities, which were obscured by district averages on Map 1. This combined with the 25 highest deprivation upazilas (in purple colour, which are non-existent at the district level), adds up to 80 upazilas (16.6% of all upazila) being recognized as most deprived. From west (best performing) to east (high deprivation), the line of the old Brahmaputra River appears as the distinctive line of division. The upazila map reveals large variations and disparities. Average district performance is often a combination of above average and below average performance at the upazila level within that district. Mapping the data at lower administrative levels allows us to recognize important patterns. relation with roads/access is distinctive), the strongest yellow (best performance) concentration is not around Dhaka but along the road from Barisal to Khulna and Jessore. From right to left, the colors generally move from red (highest deprivation) to yellow (lowest deprivation). The high deprivation areas are mainly concentrated in the divisions of Chittagong and Sylhet and in the tribal areas of Chittagong. The most important concentration of highest deprivation areas (quintile 5) occurs in the region between Dhaka division and Sylhet division and in the far south point of Chittagong. The map clearly shows the importance of access (and the disadvantages that result from geographic isolation). surprises at the upazila level. For example, one division (Rajshahi) does not have a single district with high deprivation (or highest) but has several upazilas with the highest deprivations. On the other hand, Khulna and Barisal are the divisions with the best CDI. Five out of the 11 best upazilas are located in Khulna and four in Barisal. Khulna does not have a single most deprived upazila. The following graph 1 shows the distribution of the most deprived upazilas by division. 14

15 Sylhet leads proportionally (especially by upazilas in the districts of Sunamganj and Habiganj) but Chittagong has the highest number of the most deprived upazilas (especially in Bandarban, Khagrachari and Rangamati). All of the most deprived upazilas in Barisal are in the Bhola district. Dhaka is more dispersed (with concentrations in Netrokona and Kishoreganj) while Rajshahi is the most dispersed. Map 3 shows the 80 most deprived upazilas. Map 3: The 80 most deprived upazilas Deprivation performance assessed by the composite deprivation index Note: The four upazilas Pekua (Cox sbazar), Kamalnagar (Laksmipur), Subarnachar (Noakhali) and Dakshin Sunamganj (Sunamganj) are among the 80 deprived upazilas but the above map does not show them as they are newer upazilas and maps were not readily available at the time of this publication 15

16 Map 4. The burden of deprived upazilas per district Not just a picture: incorporate progress, good governance as well An innovative idea is to test the most deprived areas on progress/good governance. A selection of the most deprived areas made upon only negative criteria is dangerous; we might select those areas that are hard to change, for example, because of lack of good governance, because of high corruption, or because of extreme geographical conditions. For that reason we will also embrace a kind of progress indicator; a proxy for good governance. The progress indicator has been used in other situations with very good results. The selected indicator is literacy rate and we will compare the youth literacy rate with adult literacy rate. The difference between the two rates represents the progress made within a generation. The average un-weighted difference is 20.8%, a very impressive number, showing the tremendous progress that Bangladesh is currently making within a generation. But, as we will see, progress is normally the lowest where it is most needed. 16

17 The graph below shows upazilas by level of progress. The upazilas are divided in three groups: above average progress (18 upazilas), below average progress up to minus 1 standard deviation (29 upazilas) and far below below one standard deviation average progress (33 upazilas). Only 18 of the most deprived upazilas have above average progress and 62 upazilas show below average progress of which 33 achieved very low progress (graph 2). Several interesting observations are thus made possible: The graph shows the desperate situation in Chittagong: upazilas in Chittagong make the least progress and have often had little or no progress. Only a few upazilas in Chittagong achieved good progress. Are all the upazilas victims of an absence of good governance? Rajshahi is making good progress as is Sylhet but, even there, the positive examples are outnumbered by the negative examples. But a graph does not tell the whole picture: 17

18 Map 5: Progress in literacy in high deprivation upazilas Map 3 is a good example of the added of analyzing geographical patterns, something that cannot be detected by other means. The map reveals that in a cluster of high deprivation areas, it is the outer-layer areas and not the central areas that make the most progress. It is the stand-alone upazilas that make more progress than the border areas. Thus, rather than good governance, it is geographical isolation that is the most important determining variable for progress and poverty (and not being located in Chittagong). This makes sense, as it is hard to believe that only the central areas of a cluster of high deprivation areas (or all border areas in Chittagong) are without good governance. The color green is hard to detect in Chittagong - among the most isolated, less densely populated areas - but it is the outer layer that improves most (and is represented by yellow in this case). The map paints a clear picture about the location of the most deprived areas the islands, remote areas in divisions, near rivers (along the Jamuna river) and frontiers. Thus an important factor is geographic isolation. Besides pattern, we have to add the dimension of the deprivation. It is also the most deprived that make least progress. Among the 25 highest deprivation upazilas only 2 achieved good literacy progress. Thus in Rajshahi it is all stand-alone areas, no clusters, but in general the most deprived make least progress. And even the central areas of the cluster of deprivation areas in Dhaka-Sylhet have the highest deprivation. 18

19 We have to add one other dimension: population. The average upazila population size is Only 33 upazilas have a population below 100,000 and divisions like Sylhet do not have any upazila with a population below Those upazilas with a low population are concentrated in just a few districts like Bandarban and Rangamati, familiar names in our deprivation rankings. We have 33 out of 481 sub-districts with a population less than 100,000 and 12 out of the 25 highest deprivation upazilas (hard core) have a population less than 100,000; thus the low population upazilas a small population means most deprived (see several areas in Barisal) but there is some relation, as sparsely populated areas pose additional challenges in achieving good coverage. In Bangladesh, the areas with the highest deprivation tend to be both the least and most (urban slums 4 ) densely populated areas. Final results Combining the map results and statistical results we can conclude that among the 25 highest deprivation upazilas, only two have above average progress (six have below average and 17 have very limited progress) and that the stagnant areas are the hard core, the most remote areas, and those with the lowest population density. Thus the worse the CDI, the less chance for progress. The analysis reveals that more than good governance, remoteness seems to be the driver behind stagnation. Thus, efforts to reach and open up these areas should be increased. IV. Conclusions and potential policy implications The complementary and converging evidence comprises: Earlier assessment undertaken by UNICEF pointing towards income/expenditure poverty and geographic isolation as likely reasons to explain disparity between districts in their progress toward achieving MDGs; The added of upazila level social indicators indicating the existence of disparities at upazila level regarding progress toward achieving MDGs, and substantiating that such disparities are linked to geographic isolation; The limited effectiveness of the PES cash transfer programme where only 40% of the target population was reached. The available evidence calls for geographic targeting of sector programmes (water, sanitation, health, nutrition, education) as well as social safety net interventions (primary and secondary school stipends, etc.). Targeting can be either sector-based or more holistic considering multiple-sector social deprivations using a composite index as demonstrated in the map composite deprivation index by upazila. The latter is likely to have a greater impact upon poverty reduction because of the synergy created by simultaneously addressing several social deprivations. 4 Multiple Indicator Cluster Survey 2009 Volume I: Technical Report. 19

20 Potential policy implications The line ministries (MoHFW, MoPME and MoLG/DPHE) could use, among other criteria, individual sector-related indicators to respectively target their national or sub-national programmes and/or projects more equitably. resources could be considered by the Ministry of Finance using, among other criteria, the abovepresented composite index as it comprises data on three different sectors. This would enable targeting the 80 least performing upazilas with additional resources compared to the average. Targeting the urban poor is equally valid, feasible and relevant, given that 27 per cent of the country s population is urban and given the fact that the slums are the most deprived areas (see MICS 2009 Volume I: Technical Report). One third of the urban population of the six largest cities are slum dwellers. Given that the population density in urban slums is 7.5 times greater than the average city Such an approach, either sectorial, but even more-so if undertaken multi-sectorally using the composite index, could be quite effective in accelerating the reduction of poverty and achieving MDGs with equity. The combination of the 80 most deprived but least populated upazilas and the most deprived slums means targeting the bottom billion of Bangladesh. The combination of statistical analysis and maps brings to light that the highest deprivation areas are the ones that make the least progress, largely because of remoteness. 20

21 ANNEX 1: The Maps making up the composite deprivation index, by district and upazila Map I: Births attended by skilled health personnel: District peformance ANNEXES Map II: Births attended by skilled health personnel: Upazila peformance 21

22 Map III: Proportion of population using an improved sanitation facility: District performance Map IV: Proportion of population using an improved sanitation facility: Upazila performance 22

23 Map V: Net attendence ratio in secondary education: District performance Map VI: Net attendence ratio in secondary education: Upazila performance 23

24 Map VII: Female adult literacy rate: District performance Map VIII: Female adult literacy rate: Upazila performance 24

25 Annex 2: Composite deprivation index ranking by district and upazila Sixty four districts and 481 upazilas are ranked by the composite deprivation index. The ranking of the geographic areas from the worst performing (smallest number ) to the best performing (highest number ) provides a solid baseline for the government at all levels to account for achieving MDG s with equity in Bangladesh by Composite deprivation index - district District Composite Deprivation Index Quintile CDI rank Value SBA Sanitation JMP NAR Secondary Female Literacy Jhalokati Bagerhat Gazipur Dhaka Pirojpur Barisal Khulna Jhenaidah Barguna Narail Jessore Feni Chittagong Rajshahi Patuakhali Meherpur Munshiganj Gopalganj Faridpur

26 District Composite Deprivation Index Quintile CDI rank Value SBA Sanitation JMP NAR Secondary Female Literacy Rangpur Joypurhat Magura Rajbari Manikganj Thakurgaon Panchagarh Pabna Naogaon Dinajpur Satkhira Kushtia Chuadanga Narayanganj Chandpur Lalmonirhat Madaripur Noakhali Comilla Bogra Tangail Narsingdi Sylhet Lakshmipur Natore

27 District Composite Deprivation Index Quintile CDI rank Value SBA Sanitation JMP NAR Secondary Female Literacy Kurigram Shariatpur Maulvibazar Sirajganj Nilphamari Chapai Nawabganj Mymensingh Jamalpur Gaibandha Brahmanbaria Kishoreganj Rangamati Bhola Sherpur Netrokona Khagrachhari Cox's Bazar Sunamganj Habiganj Bandarban

28 Composite deprivation index- upazila Division District Upazila Composite deprivation index Quintile CDI rank SBA Sanitation JMP NAR Secondary Female Literacy Barisal Jhalokati Jhalokati Barisal Barisal Banaripara Khulna Khulna Phultala Barisal Barisal Gaurnadi Khulna Bagerhat Bagerhat Chittagong Lakshmipur Ramganj Chittagong Chittagong Raozan Rajshahi Rajshahi Mohanpur Barisal Pirojpur Pirojpur Barisal Jhalokati Rajapur Khulna Khulna Dumuria Dhaka Dhaka Nawabganj Chittagong Chittagong Boalkhali Rajshahi Naogaon Manda Rajshahi Dinajpur Dinajpur Barisal Jhalokati Kanthalia Barisal Barisal Wazirpur Khulna Jhenaidah Kotchandpur Khulna Bagerhat Chitalmari Dhaka Gazipur Gazipur Chittagong Chittagong Hathazari Rajshahi Natore Baraigram Rajshahi Natore Bagatipara Barisal Patuakhali Mirzaganj Barisal Barisal Babuganj Barisal Barguna Patharghata Khulna Narail Narail Khulna Bagerhat Fakirhat Dhaka Gopalganj Gopalganj Dhaka Gazipur Kaliakair Dhaka Faridpur Alfadanga Chittagong Noakhali Sonaimuri Chittagong Noakhali Begumganj Chittagong Feni Parshuram Rajshahi Panchagarh Atwari Rajshahi Bogra Dhupchanchia Barisal Pirojpur Nesarabad Barisal Pirojpur Nazirpur Barisal Patuakhali Patuakhali Barisal Patuakhali Bauphal Barisal Jhalokati Nalchity Barisal Barisal Agailjhara Barisal Barguna Betagi Khulna Khulna Dighalia Khulna Jhenaidah Kaliganj Khulna Jessore Manirampur Khulna Jessore Bagherpara Khulna Bagerhat Morrelganj Dhaka Munshiganj Sreenagar Dhaka Manikganj Manikganj Dhaka Gazipur Kaliganj Chittagong Comilla Laksam Chittagong Chandpur Hajiganj Rajshahi Rangpur Rangpur Rajshahi Rangpur Mithapukur Rajshahi Rajshahi Baghmara Rajshahi Pabna Pabna Rajshahi Natore Lalpur Rajshahi Joypurhat Khetlal Rajshahi Dinajpur Khansama

29 Division District Upazila Composite deprivation index Quintile CDI rank SBA Sanitation JMP NAR Secondary Female Literacy Rajshahi Dinajpur Hakimpur Rajshahi Dinajpur Bochaganj Rajshahi Bogra Bogra Barisal Barguna Bamna Khulna Satkhira Debhata Khulna Jhenaidah Shailkupa Khulna Jhenaidah Jhenaidah Khulna Jhenaidah Harinakunda Khulna Jessore Jhikargachha Khulna Jessore Kotwali Khulna Jessore Abhaynagar Khulna Bagerhat Rampal Khulna Bagerhat Mollahat Khulna Bagerhat Kachua Dhaka Rajbari Rajbari Dhaka Rajbari Baliakandi Dhaka Munshiganj Gazaria Dhaka Manikganj Ghior Dhaka Gazipur Kapasia Dhaka Faridpur Madhukhali Dhaka Dhaka Dohar Sylhet Sylhet Golapganj Chittagong Feni Feni Chittagong Comilla Manoharganj Chittagong Chandpur Matlab Uttar Rajshahi Thakurgaon Thakurgaon Rajshahi Sirajganj Tarash Rajshahi Rajshahi Charghat Rajshahi Pabna Ishwardi Rajshahi Joypurhat Joypurhat Rajshahi Dinajpur Chirirbandar Rajshahi Dinajpur Birganj Rajshahi Bogra Kahaloo Barisal Pirojpur Mathbaria Barisal Barisal Barisal Barisal Barisal Bakerganj Khulna Satkhira Tala Khulna Narail Lohagara Khulna Meherpur Meherpur Khulna Meherpur Gangni Khulna Magura Sreepur Khulna Kushtia Mirpur Khulna Chuadanga Alamdanga Dhaka Gopalganj Tungipara Dhaka Gazipur Sreepur Dhaka Faridpur Faridpur Dhaka Dhaka Keraniganj Dhaka Dhaka Dhamrai Chittagong Rangamati Rangamati Chittagong Noakhali Senbagh Chittagong Lakshmipur Roypur Chittagong Feni Daganbhuiyan Chittagong Feni Chhagalnaiya Chittagong Comilla Comilla Adarsha Chittagong Comilla Burichang Chittagong Chittagong Satkania Chittagong Chittagong Patiya Chittagong Chittagong Fatikchhari Chittagong Chittagong Chandanaish Rajshahi Rajshahi Paba

30 Division District Upazila Composite deprivation index Quintile CDI rank SBA Sanitation JMP NAR Secondary Female Literacy Rajshahi Rajshahi Durgapur Rajshahi Rajshahi Bagha Rajshahi Naogaon Naogaon Rajshahi Dinajpur Ghoraghat Rajshahi Dinajpur Birampur Barisal Pirojpur Zianagar Barisal Pirojpur Bhandaria Barisal Patuakhali Dumki Barisal Patuakhali Dashmina Barisal Barguna Barguna Khulna Kushtia Kushtia Khulna Kushtia Kumarkhali Khulna Kushtia Bheramara Khulna Khulna Koyra Khulna Khulna Dacope Khulna Khulna Batiaghata Khulna Jessore Chaugachha Khulna Bagerhat Sarankhola Khulna Bagerhat Mongla Dhaka Narsingdi Shibpur Dhaka Narsingdi Manohardi Dhaka Narayanganj Sonargaon Dhaka Manikganj Saturia Dhaka Madaripur Madaripur Dhaka Kishorganj Pakundia Dhaka Gopalganj Kashiani Chittagong Feni Fulgazi Chittagong Chittagong Rangunia Chittagong Chandpur Shahrasti Rajshahi Sirajganj Sirajganj Rajshahi Sirajganj Kamarkhanda Rajshahi Rajshahi Puthia Rajshahi Panchagarh Panchagarh Rajshahi Panchagarh Boda Rajshahi Pabna Bhangura Rajshahi Pabna Atgharia Rajshahi Nilphamari Saidpur Rajshahi Naogaon Raninagar Rajshahi Dinajpur Fulbari Rajshahi Bogra Shahjhanpur Rajshahi Bogra Nandigram Barisal Pirojpur Kawkhali Khulna Satkhira Assasuni Khulna Khulna Paikgachha Khulna Jhenaidah Maheshpur Khulna Jessore Sharsha Dhaka Tangail Tangail Dhaka Tangail Mirzapur Dhaka Tangail Ghatail Dhaka Rajbari Pangsha Dhaka Narayanganj Narayanganj Dhaka Mymensingh Bhaluka Dhaka Munshiganj Serajdikhan Dhaka Munshiganj Munshiganj Dhaka Munshiganj Lohajang Dhaka Manikganj Singair Dhaka Madaripur Rajoir Dhaka Jamalpur Sarishabari Dhaka Faridpur pur Dhaka Faridpur Bhanga Sylhet Sylhet Beani Bazar

31 Division District Upazila Composite deprivation index Quintile CDI rank SBA Sanitation JMP NAR Secondary Female Literacy Chittagong Comilla Brahmanpara Rajshahi Rangpur Pirgachha Rajshahi Pabna Santhia Rajshahi Naogaon Niamatpur Rajshahi Naogaon Atrai Rajshahi Lalmonirhat Patgram Rajshahi Lalmonirhat Aditmari Rajshahi Kurigram Ulipur Rajshahi Joypurhat Panchbibi Rajshahi Joypurhat Akkelpur Barisal Patuakhali Kalapara Barisal Barisal Muladi Barisal Barguna Amtali Khulna Satkhira Satkhira Khulna Satkhira Kaliganj Khulna Satkhira Kalaroa Khulna Meherpur Mujibnagar Khulna Magura Shalikha Khulna Magura Magura Khulna Khulna Terokhada Khulna Chuadanga Chuadanga Dhaka Tangail Dhanbari Dhaka Tangail Gopalpur Dhaka Tangail Bhuapur Dhaka Narsingdi Palash Dhaka Narsingdi Belabo Dhaka Jamalpur Jamalpur Dhaka Gopalganj Muksudpur Dhaka Faridpur Nagarkanda Dhaka Faridpur Boalmari Sylhet Sylhet Fenchuganj Sylhet Sylhet Bishwanath Sylhet Maulvibazar Maulvibazar Chittagong Rangamati Kaptai Chittagong Lakshmipur Lakshmipur Chittagong Feni Sonagazi Chittagong Comilla Chauddagram Chittagong Chandpur Kachua Chittagong Brahmanbaria Kasba Rajshahi Thakurgaon Pirganj Rajshahi Sirajganj Ullahpara Rajshahi Sirajganj Kazipur Rajshahi Naogaon Mahadebpur Rajshahi Lalmonirhat Kaliganj Rajshahi Bogra Adamdighi Barisal Bhola Tazumuddin Khulna Magura Mohammadpur Khulna Kushtia Khoksa Khulna Chuadanga Jibannagar Khulna Chuadanga Damurhuda Dhaka Tangail Delduar Dhaka Tangail Basail Dhaka Shariatpur Shariatpur Dhaka Shariatpur Gosairhat Dhaka Narayanganj Rupganj Dhaka Munshiganj Tongibari Dhaka Manikganj Shibalaya Dhaka Madaripur Kalkini Dhaka Kishorganj Hossainpur Dhaka Gopalganj Kotalipara Sylhet Sylhet Dakshin Surma

32 Division District Upazila Composite deprivation index Quintile CDI rank SBA Sanitation JMP NAR Secondary Female Literacy Sylhet Maulvibazar Barlekha Chittagong Khagrachhari Khagrachhari Chittagong Comilla Debidwar Chittagong Comilla Chandina Chittagong Chittagong Mirsharai Chittagong Chandpur Matlab Dakshin Chittagong Chandpur Faridganj Rajshahi Thakurgaon Baliadangi Rajshahi Rangpur Pirganj Rajshahi Rangpur Badarganj Rajshahi Pabna Faridpur Rajshahi Naogaon Patnitala Rajshahi Naogaon Dhamoirhat Rajshahi Naogaon Badalgachhi Rajshahi Lalmonirhat Hatibandha Rajshahi Kurigram Bhurungamari Rajshahi Joypurhat Kalai Rajshahi Gaibandha Sadullapur Rajshahi Bogra Sherpur Rajshahi Bogra Gabtali Khulna Jessore Keshabpur Dhaka Sherpur Nakla Dhaka Shariatpur Naria Dhaka Shariatpur Damudya Dhaka Shariatpur Bhedarganj Dhaka Mymensingh Gaffargaon Dhaka Manikganj Harirampur Dhaka Madaripur Shibchar Dhaka Dhaka Savar Sylhet Maulvibazar Kulaura Sylhet Habiganj Habiganj Sylhet Habiganj Bahubal Chittagong Noakhali Noakhali Chittagong Comilla Nangalkot Chittagong Comilla Barura Chittagong Chittagong Lohagara Rajshahi Thakurgaon Haripur Rajshahi Panchagarh Debiganj Rajshahi Pabna Sujanagar Rajshahi Nilphamari Domar Rajshahi Naogaon Sapahar Rajshahi Kurigram Kurigram Rajshahi Dinajpur Kaharole Rajshahi Bogra Sariakandi Barisal Patuakhali Galachipa Khulna Narail Kalia Dhaka Tangail Nagarpur Dhaka Mymensingh Mymensingh Dhaka Kishorganj Kishoreganj Dhaka Faridpur Saltha Sylhet Sylhet Zakiganj Chittagong Noakhali Companiganj Chittagong Noakhali Chatkhil Chittagong Comilla Meghna Chittagong Comilla Daudkandi Chittagong Comilla Comilla Chittagong Chittagong Sandwip Chittagong Chittagong Anowara Chittagong Brahmanbaria Akhaura Rajshahi Thakurgaon Ranisankail Rajshahi Sirajganj Royganj Rajshahi Rangpur Taraganj

33 Division District Upazila Composite deprivation index Quintile CDI rank SBA Sanitation JMP NAR Secondary Female Literacy Rajshahi Rangpur Kaunia Rajshahi Rangpur Gangachara Rajshahi Rajshahi Tanore Rajshahi Pabna Chatmohar Rajshahi Nilphamari Nilphamari Rajshahi Nilphamari Jaldhaka Rajshahi Nilphamari Dimla Rajshahi Nawabganj Chapai Nawabganj Rajshahi Natore Singra Rajshahi Natore Gurudaspur Rajshahi Lalmonirhat Lalmonirhat Rajshahi Kurigram Rajarhat Rajshahi Gaibandha Saghatta Rajshahi Gaibandha Gaibandha Rajshahi Dinajpur Nawabganj Rajshahi Bogra Sonatola Barisal Barisal Mehendiganj Khulna Khulna Rupsa Dhaka Tangail Madhupur Dhaka Netrakona Purbadhala Dhaka Netrakona Netrokona Dhaka Narayanganj Bandar Dhaka Mymensingh Ishwarganj Dhaka Mymensingh Haluaghat Sylhet Sylhet Sylhet Sylhet Sunamganj Derai Sylhet Maulvibazar Juri Chittagong Cox's Bazar Chakaria Chittagong Chittagong Sitakunda Chittagong Chandpur Chandpur Chittagong Brahmanbaria Nabinagar Rajshahi Nawabganj Nachole Rajshahi Nawabganj Gomastapur Rajshahi Naogaon Porsha Rajshahi Kurigram Nageshwari Rajshahi Gaibandha Gobindaganj Rajshahi Bogra Shibganj Barisal Bhola Daulatkhan Khulna Satkhira Shyamnagar Khulna Kushtia Daulatpur Dhaka Tangail Sakhipur Dhaka Tangail Kalihati Dhaka Shariatpur Zanjira Dhaka Narayanganj Araihazar Dhaka Mymensingh Muktagachha Dhaka Mymensingh Gauripur Dhaka Kishorganj Tarail Dhaka Kishorganj Kuliarchar Sylhet Sylhet Balaganj Sylhet Sunamganj Jagannathpur Chittagong Rangamati Kawkhali Chittagong Noakhali Kabirhat Chittagong Comilla Homna Chittagong Chandpur Haimchar Chittagong Brahmanbaria Ashuganj Chittagong Brahmanbaria Brahmanbaria Rajshahi Sirajganj Shahjadpur Rajshahi Sirajganj Chauhali Rajshahi Panchagarh Tentulia

34 Division District Upazila Composite deprivation index Quintile CDI rank SBA Sanitation JMP NAR Secondary Female Literacy Rajshahi Kurigram Raumari Rajshahi Dinajpur Parbatipur Dhaka Rajbari Goalanda Dhaka Netrakona Kendua Dhaka Netrakona Barhatta Dhaka Narsingdi Roypura Dhaka Mymensingh Fulbaria Dhaka Mymensingh Dhobaura Dhaka Kishorganj Karimganj Dhaka Kishorganj Bhairab Dhaka Jamalpur Melandaha Dhaka Jamalpur Madarganj Dhaka Faridpur Char Bhadrasan Sylhet Maulvibazar Rajnagar Chittagong Rangamati Rajasthali Chittagong Cox's Bazar Ramu Chittagong Cox's Bazar Cox's Bazar Chittagong Comilla Titas Chittagong Comilla Muradnagar Chittagong Chittagong Banshkhali Rajshahi Nawabganj Bholahat Rajshahi Kurigram Chilmari Rajshahi Dinajpur Biral Dhaka Sherpur Sreebardi Dhaka Sherpur Sherpur Dhaka Narsingdi Narsingdi Dhaka Mymensingh Phulpur Dhaka Manikganj Daulatpur Sylhet Sylhet Kanaighat Sylhet Sunamganj Sunamganj Sylhet Maulvibazar Sreemangal Sylhet Maulvibazar Kamalganj Sylhet Habiganj Chunarughat Chittagong Rangamati Naniarchar Chittagong Khagrachhari Ramgarh Chittagong Khagrachhari Panchhari Chittagong Brahmanbaria Banchharampur Chittagong Bandarban Bandarban Rajshahi Rajshahi Godagari Barisal Bhola Bhola Barisal Barisal Hizla Dhaka Netrakona Mohanganj Dhaka Kishorganj Katiadi Dhaka Jamalpur Islampur Dhaka Jamalpur Bakshiganj Sylhet Sylhet Jaintiapur Sylhet Habiganj Nabiganj Chittagong Noakhali Subarnachar Chittagong Noakhali Hatiya Chittagong Lakshmipur Ramgati Chittagong Khagrachhari Matiranga Chittagong Cox's Bazar Kutubdia Rajshahi Nawabganj Shibganj Rajshahi Gaibandha Sundarganj Rajshahi Gaibandha Palashbari Barisal Bhola Char Fasson Barisal Bhola Burhanuddin Dhaka Netrakona Durgapur Dhaka Mymensingh Trishal Dhaka Mymensingh Nandail

35 Division District Upazila Sylhet Sunamganj Dakshin Sunamganj Composite deprivation index Quintile CDI rank SBA Sanitation JMP NAR Secondary Female Literacy Chittagong Cox's Bazar Ukhia Rajshahi Pabna Bera Rajshahi Nilphamari Kishoreganj Rajshahi Gaibandha Fulchhari Dhaka Sherpur Nalitabari Dhaka Netrakona Madan Dhaka Netrakona Atpara Sylhet Sunamganj Chhatak Sylhet Habiganj Lakhai Chittagong Rangamati Juraichhari Chittagong Rangamati Baghaichhari Chittagong Khagrachhari Mahalchhari Chittagong Khagrachhari Manikchhari Chittagong Cox's Bazar Pekua Chittagong Cox's Bazar Maheshkhali Dhaka Kishorganj Bajitpur Chittagong Rangamati Barkal Chittagong Lakshmipur Kamalnagar Dhaka Sherpur Jhenaigati Dhaka Netrakona Kalmakanda Dhaka Netrakona Khaliajuri Dhaka Jamalpur Dewanganj Sylhet Habiganj Baniachong Chittagong Rangamati Belaichhari Chittagong Brahmanbaria Sarail Chittagong Brahmanbaria Nasirnagar Barisal Bhola Manpura Dhaka Kishorganj Nikli Sylhet Sylhet Gowainghat Sylhet Sunamganj Bishwambarpur Sylhet Habiganj Ajmiriganj Chittagong Khagrachhari Dighinala Rajshahi Natore Natore Rajshahi Kurigram Char Rajibpur Barisal Bhola Lalmohan Dhaka Kishorganj Mithamain Sylhet Sylhet Companiganj Sylhet Sunamganj Dharmapasha Sylhet Habiganj Madhabpur Chittagong Rangamati Langadu Chittagong Khagrachhari Lakshmichhari Chittagong Cox's Bazar Teknaf Chittagong Bandarban Lama Rajshahi Kurigram Phulbari Sylhet Sunamganj Tahirpur Sylhet Sunamganj Jamalganj Sylhet Sunamganj Dowarabazar Chittagong Bandarban Rowangchhari Chittagong Bandarban Alikadam Dhaka Kishorganj Itna Dhaka Kishorganj Ashtagram Chittagong Bandarban Ruma Sylhet Sunamganj Sulla Chittagong Bandarban Naikhongchhari Rajshahi Bogra Dhunat Rajshahi Sirajganj Belkuchi Chittagong Bandarban Thanchi

36 Annex 3: Correlations Despite the limitations of correlations, it is still a quick, simple and useful tool. Remember that the correlation coefficient measures only straight-line relationships and not all relations between variables trace a straight line. Correlation does not imply causation. The fact that Variable X and Variable Y are correlated does not necessarily mean that X caused Y or Y caused X. The true relation may be another one; the negative correlation between prolonged breastfeeding and malnutrition is a good example, the all important background variable is poverty. The relation may also be fake, the perfect example of that is the relation between the number of storks and the crude birth rate in the Netherlands. That correlation is almost one, but the storks still do not deliver babies. Positively correlated means that as the of one indicator increases so does the of the other. Negatively, or inversely, correlated means that as of one indicator increases (for example vaccination) the of the other decreases (for example U5MR). In this example the outcome is positive but the correlation negative. There are weak and strong relations, and some relations appear uncorrelated, in social sciences a correlation of 0.5 may be more meaningful than a correlation of 0.75 in science. The correlation results All 23 indicators of the 2009 MICS were analyzed, correlations were taken into account and the decision was taken to develop the strongest deprivation index possible based upon the available information but with one indicator per dimension. The four indicators that emerged are included in the following table. Correlation between 2005 income poverty and 2009 U5MR with key MICS indicators at district level: Skilled Birth Attendant NAR Secondary School Female Adult Literacy Sanitation U5MR Composite Deprivation Index Poverty U5MR The composite deprivation index, a multi-sector index, has a relation with income poverty and a stronger relation with U5MR (correlation with income poverty is just -0.2 and U5MR is -0.5 at the district level). The deprivation index has an excellent relation with U5MR at the division level (correlation is -0.85). But because the 2005 poverty data is out-dated and the U5MR at upazila level is not statistically representative, the composite deprivation index will be treated essentially as an index with independent merits, a deprivation index built around education, health, sanitation and adult literacy indicators.

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