Developing a Master Sample Design for Household Surveys in Developing Countries: A Case Study in Bangladesh
Maligalig, D. S., & Martinez, A. Jr (2013). Developing a Master Sample Design for Households Surveys in Developing Countries: A Case Study In Bangladesh. Survey Methods: Insights from the Field. Retrieved from https://surveyinsights.org/?p=2151
© the authors 2013. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0)
Abstract
For evidencebased policy making, socioeconomic planners need reliable data to evaluate existing economic policies. While household surveys can serve as a rich source of socioeconomic data, conducting them often entails a great deal of administrative, technical and financial resources. With limited resources for data collection, this often puts pressure on national statistical systems to meet the continuously growing data demand of its stakeholders, especially in developing countries. Using a master sample design that can be used to select samples for multiple household surveys provides an opportunity to minimize the resources needed to collect household survey data regularly. In particular, using the same sampling design and frame to select samples either for multiple surveys of different content or for different rounds of the same survey could induce significant costsavings instead of developing an independent design each time a household survey is to be carried out. This paper provides a stepbystep guide for developing a master sample design for household surveys in developing countries. Using Bangladesh as a case study, issues like effective sample allocation to ensure the reliability of domain estimates, stratification measures to reduce design effects and introducing household sample size adjustment when to maintain uniform selection probability within domain are discussed.
Keywords
design effects, master sample design, sampling frame
Acknowledgement
This study was funded by Asian Development Bank’s Regional Technical Assistance (RETA) 6430: Measuring the Informal Sector. In general, ADB RETAs aim to build, strengthen and improve statistical systems and services of the Bank’s developing member countries. The study also acknowledges the valuable inputs and coordination efforts of the management and staff of Bangladesh Bureau of Statistics particularly Ms. Mir Suraiya Arzoo, Mr. Nowsher Ahmed Chowdry, Mr. Ghose Subobrata, Mr. Mohammad Abdul Kadir Miah, Mr. Md. Rafique Islam, Mr. Kabir Uddin Ahmen and Ms. Sabila Kahtun.
Copyright
© the authors 2013. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0)
Copyright
© the authors 2013. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0)
1. Background
Household surveys have been important source of various socioeconomic information that are indispensable in development planning and policy analysis. In some countries, especially developing ones, household surveys have become more dominant form of data collection than other administrative data collection programs such as civil registration systems (UNSD 2005). Thus, there is a need to ensure that household surveys follow scientificallysound design to assure the quality of information that can be derived from it. While many countries have put in place national statistical systems for collecting household surveys, they have varying levels of experience and infrastructure in data collection. Many developing countries usually confront budgetary constraints and thus, heavily rely on technical assistance from international development agencies. To promote sustainability of statistical data collection activities, different strategies have been proposed to economize the technical and financial resources needed for collecting household surveys. One of these strategies is the development of a master sample design. For multistage household surveys, a master sample design allows one or more stages to be combined or shared among different household surveys. In turn, a master sample refers to the sample resulting from the shared stages (UNSD 2005). The UNSD (2005) identified several advantages of adopting a master sample design. First, it reduces costs of developing and maintaining sampling frames as more household surveys share the same master sample design. It also simplifies the technical process of drawing individual samples by facilitating operational linkages between different surveys. In this study, we document our experiences in helping Bangladesh develop a master sample design for collecting household surveys.
The Bangladesh Bureau of Statistics (BBS) is the government agency mandated to undertake data collection for the compilation of official statistics for Bangladesh. Household surveys of national coverage are the primary data collection tool of BBS. Prior to the 20092010 Labour Force Survey, the 2005 LFS and 2005 Household Income and Expenditure Survey (HIES) were the last household surveys conducted by BBS, both of which employed the Integrated MultiPurpose Sampling Design (IMPS). However, previous studies such as that of Maligalig and Barcenas (2008) identified technical deficiencies in IMPS. In particular, large design effects were derived for important characteristics of interest such as unemployment rate in the statistical metropolitan areas (SMA) and for large divisions such as Dhaka, Chittagong and Rajshahi because of ineffective stratification measures. In addition, the survey weights used in IMPS did not reflect the selection probabilities that were applied at the time when the sampled households were drawn. Moreover, Maligalig and Barcenas (2008) also noted that the number of households sampled per primary sampling unit (PSU) can still be reduced and the number of PSUs increased to mitigate the very large design effects. Due to these issues, BBS requested technical assistance from Asian Development Bank in 2008 to develop a new sampling design and sampling frame that could be used for the then forthcoming 20092010 Labour Force Survey. Moreover, the main objective of the activity is to reimplement the proposed sampling design once the data from the 2011 Census of Population becomes available. In turn, the updated version will serve as master sample design for the succeeding household surveys that BBS will conduct. This paper documents the processes that were undertaken to develop the sample design for 20092010 LFS which will also be the basis for a new master sample design. The study aims to provide survey statisticians from developing countries with empirical guidelines in developing a master survey design for multiple household surveys.
After this introduction, the succeeding discussions are outlined as follows. Section 2 provides a guide for constructing primary sampling units for household surveys. Section 3 identifies the statistical and practical issues that must be considered in designating the survey domain. Section 4 discusses survey stratification as a tool for improving precision of survey estimators. Section 5 discusses sample selection schemes that control for design effects of complex surveys. The last section provides a brief summary of the discussions.
2. Sampling Frame of the Primary Sampling Units
Multistage sampling is usually the most appropriate, cost effective and commonly used design for household surveys of national coverage in developing countries. Households (or housing dwellings) are the ultimate sampling units while the primary sampling units (PSUs) are usually clusters of contiguous households. Although stratified simple random sampling is perhaps the most efficient among the conventional sampling designs, it is not practical and workable for most household surveys in developing countries because an updated list of all households in a country is not commonly available. In general, a good sampling frame is needed to ensure that each ultimate sampling unit has a chance of being selected and hence, conclusions on the target population can be drawn from the sample.
Constructing a frame of the primary sampling units is the first step in developing a multistage sampling design. At this point, it is important to decide carefully on what should be designated as the PSU. There are several considerations. Ideally, all units in the target population should belong to one and only one of the PSUs. To this end, PSUs must have clear boundaries which can be easily located in the field. In addition, auxiliary information about the “size” of the PSU to be used for selecting which unit will be in the sample should be available. If the total number of households is used as measure of PSU’s size, a PSU has to be as manageably small as possible but large enough to have adequate number of ultimate sampling units. This would permit sampling rotations for different surveys which will be implementing the master sample design. Moreover, availability of information to be used for stratification and sample allocation should also be among the practical considerations in constructing the PSUs.
In Bangladesh, unions, mauza, villages and enumeration areas as defined in the 2001 Census of Population are possible candidates for designating PSU. However, preliminary analysis shows that unions vary widely in size and in general, they are too large to permit manageable field operations. Villages, on the other hand, are almost the same as enumeration areas for rural areas and their boundaries are not clear in the case of urban areas. Given this information, only mauza and enumeration areas were considered for PSUs in the succeeding discussions. Using data from the 2001 Census of Population, Tables 1 and 2 summarize the distribution of the number of households by mauza, and by enumeration area, respectively. Noticeably, the total number of households vary widely at the mauza level, from 1 to 22,366 (Other Urban Areas, Dhaka). If mauza is designated as the PSU, then some mauzas will still have to be divided further to ensure that each PSU will not be selected more than once. At the same time, some mauzas may need to be combined to ensure that there is sufficient number of households that can be drawn from each PSU. In contrast, although there are still many enumeration areas that need to be combined if they are designated as PSUs, enumeration areas need not be broken down further since the maximum total number of households per enumeration areas is 497 (Table 2). Thus, forming PSUs using the enumeration areas presents a better option than designating mauzas as PSUs.
Table 1. Summary statistics of total household of Bangladesh by Mauza
Division  No. of Mauzas  Urban block  No. of Mauzas by block  Distribution of the Number of Households by Mauza  
Total  Min  Median  Mean  Max  Std dev  
Barisal  3414  Rural  2,896  1,411,766  1  321  487.49  5,126  481.19 
Urban block  419  144,911  11  249  345.85  3,196  333.86  
SMA  99  91,408  26  718  923.31  4,342  794.92  
Other urban areas  —  —  —  —  —  —  —  
Chittagong  8879  Rural  7,367  3,317,141  1  258  450.27  11,943  600.50 
Urban block  1,175  743,076  1  284  632.41  9,831  1,130.94  
SMA  207  257,432  9  794  1,243.63  8,045  1,398.13  
Other urban areas  130  154,899  10  823  1,191.53  5,328  1,208.63  
Dhaka  18295  Rural  14,660  5,399,312  1  219  368.30  8,820  473.40 
Urban block  2,616  1,824,745  1  303  697.53  9,218  1,175.43  
SMA  289  254,248  1  576  879.75  5,325  952.55  
Other urban areas  730  758,382  1  316  1,038.88  22,366  2,283.58  
Khulna  7483  Rural  6,300  2,472,098  1  264  392.04  5,119  422.51 
Urban block  913  433,156  1  307  474.43  5,823  544.95  
SMA  166  131,468  51  583  791.98  4,101  705.05  
Other urban areas  105  82,880  1  408  789.33  4,938  1,015.84  
Rajshahi  18887  Rural  16,423  5,643,537  1  221  343.64  5,758  382.17 
Urban block  1,951  645,620  1  232  330.92  2,597  304.51  
SMA  340  280,392  5  588  824.68  4,042  703.76  
Other urban areas  173  58,248  1  278  336.69  2,026  301.33  
Sylhet  5708  Rural  4,989  1,213,085  1  167  243.15  3,052  256.24 
Urban block  608  110,982  1  136  182.54  1,328  166.86  
SMA  111  64,155  20  427  577.97  2,865  559.31  
Other urban areas  —  —  —  —  —  —  — 
Source: Authors’ computations using data from 2001 Census of Population conducted by BBS
Table 2. Summary statistics of total household of Bangladesh by Mauza
Division  Urban block  No. of enumeration areas  Households  
Total  Min  Median  Mean  Max  Std dev  
Barisal  Rural  14,473  1,411,766  1  96  97.54  354  25.58 
Urban block  1,573  144,911  1  88  92.12  233  29.94  
SMA  898  91,408  2  98  101.79  267  28.13  
Other urban areas  —  —  —  —  —  —  —  
Chittagong  Rural  36,172  3,317,141  1  94  91.70  321  34.25 
Urban block  7,943  743,076  1  92  93.55  339  31.73  
SMA  2,997  257,432  1  87  85.90  237  39.46  
Other urban areas  1,428  154,899  2  107  108.47  310  35.52  
Dhaka  Rural  54,822  5,399,312  1  99  98.49  483  31.38 
Urban block  18,819  1,824,745  1  93  96.96  471  37.97  
SMA  2,418  254,248  1  102  105.15  404  36.49  
Other urban areas  7,030  758,382  1  100  107.88  478  43.49  
Khulna  Rural  23,530  2,472,098  1  104  105.06  320  30.28 
Urban block  3,998  433,156  1  103  108.34  344  35.53  
SMA  1,187  131,468  8  108  110.76  239  31.07  
Other urban areas  744  82,880  1  106  111.40  305  34.66  
Rajshahi  Rural  55,004  5,643,537  1  101  102.60  463  29.94 
Urban block  6,707  645,620  1  93  96.26  497  35.07  
SMA  2,639  280,392  1  103  106.25  286  33.12  
Other urban areas  546  58,248  1  104  106.68  295  34.59  
Sylhet  Rural  14,875  1,213,085  1  84  81.55  258  36.29 
Urban block  1,302  110,982  1  84.5  85.24  276  39.11  
SMA  723  64,155  1  90  88.73  258  37.99  
Other urban areas  —  —  —  —  —  —  — 
As mentioned earlier, it is ideal for every PSU to be large enough to have adequate number of ultimate sampling units to ensure the feasibility of adopting a rotating sample design for different surveys which will be implementing the master sample. In the case of Bangladesh, we set the threshold to be 40 households per PSU. Out of the 259,828 EAs, 12,273 EAs have less than 40 households. These small EAs should be considered as candidates for merging. When combining small EAs to form PSUs, the main consideration is that the enumeration areas to be combined are contiguous. However, due to the lack of reliable (geographic) maps for these EAs, we decided to combine the small enumeration areas based on the criteria provided below. In addition, due to the conceptual and logistical problems in the classification of statistical metropolitan areas (SMA) and other urban areas, it was decided that these two areas will be classified under urban area instead.
Criteria for combining enumeration areas to form a primary sampling unit
 An EA with more than 40 households is directly considered as a PSU.
 A small EA is attached to an adjacent EA that belongs to the same urban/rural classification and mauza.
 A small single EA in a mauza can be combined with an EA of another mauza provided that both mauzas belong to the same union and the EAs to be combined belong to the same urban/rural category.
Following this criteria, a total of 248,904 PSUs were constructed out of the 259,828 original EAs. Table 3 provides the distribution of the number of households by PSU. As shown in this table, there are still PSUs that have less than 40 households. These correspond to cases where the unions were very small in terms of number of households. Since these very small units constitute only of 11 PSUs, we decided to exclude them from the sampling frame.
Table 3. Summary statistics of total household of Bangladesh by PSU
Division  Urban block  No. of PSUs  Households  
Total  Min  Median  Mean  Max  Std dev  
Barisal  Rural  14,280  1,411,766  41  97  98.86  354  24.30 
Barisal  Urban  2,414  236,319  42  94  97.90  267  27.36 
Chittagong  Rural  33,721  3,317,141  41  97  98.37  321  28.17 
Chittagong*  Urban  11,810  1,155,407  23  95  97.84  339  30.98 
Dhaka  Rural  52,667  5,399,312  19  100  102.52  483  27.88 
Dhaka*  Urban  27,317  2,837,375  21  98  103.88  478  36.93 
Khulna  Rural  22,886  2,472,098  31  105  108.02  320  27.01 
Khulna  Urban  5,823  647,504  41  105  111.20  344  32.69 
Rajshahi  Rural  53,554  5,643,537  41  102  105.38  463  27.28 
Rajshahi  Urban  9,614  984,260  13  98  102.38  497  32.20 
Sylhet  Rural  12,992  1,213,085  41  92  93.37  266  29.79 
Sylhet  Urban  1,826  175,137  21  93  95.91  296  33.27 
Source: Authors’ computations using data from 2001 Census of Population conducted by BBS.
Notes: * – There are 3 PSUs that have very few number of identified households on the basis of the latest census data. In particular there are one PSU from Chittagong (urban) and two from Dhaka (urban) that have less than 10 households. These were not included in the computation of summary statistics provided above.
3. Survey Strata, Determination of Sample size and Sample Allocation
Design domains or explicit strata are subpopulations for which separate samples are planned, designed and selected (Kish, 1987). The choice of explicit strata depends on several factors such as reporting requirements, sampling design and more importantly, available budget and workload that will be used (Kish, 1965;1987). Both statistical and practical issues must be considered in designating the strata. In general, there is now greater demand for statistics at finer levels of disaggregation (Elbers, Lanjouw and Lanjouw 2003). In turn, this would require increasing the number of strata. Since the total sample size is usually determined at the stratum level, increasing the number of strata would necessarily entail increasing the total sample size. Because the workable sampling designs would all involve cluster sampling, the expected design effects should also be considered and used to determine the final sample size. Average design effects for cluster samples is expected to be three or more and hence, the final sample size would have to be increased by this value. However, these things should be contextualized within the available budget allocated for survey data collection.
Once the strata have been clearly specified, the sample size for each stratum is then determined so that reliable estimates at the stratum level can be derived. Information on the variability of the sampling units within each stratum, the acceptable error level, and the associated costs are the factors needed to determine the sample size. For example, suppose the primary characteristic of interest to be measured can be expressed as a proportion. Under simple random sampling (SRS), the tentative sample size for a particular stratum is computed such that
(1)
where is the abscissa of the distribution given risk , and the population size ; is the true proportion of the characteristic of interest and error level (Cochran, 1977). Since is unknown, we can either set or used any prior information about the value of from previous studies. Note that setting would produce the most conservative or largest sample size. The resulting sample sizes on the application of (1) are then inflated by the corresponding design effects (Deff), assuming that prior information about the magnitude of the design effect is available.
(2)
In the case of Bangladesh, the geographic divisions were designated as the design domains or explicit strata. If the 64 zilas (provinces) were specified as the strata, the sample size required will be inflated by approximately ten times and that would be beyond the budget of BBS. Moreover, we used the estimated unemployment rate using the 2005 LFS to provide a value for . Table 4 shows the tentative sample sizes that were computed at risk and varying error level . The corresponding design effects of unemployment rates from the 2005 LFS are also shown in Table 4. Note that at , the total sample size is 115,277. This sample size is 100,000 more households than what the budget of BBS has allocated for the 20092010 LFS can afford. At , which may not be very appropriate considering that unemployment rates are quite small, the total sample size is about 12,814 households or within budget. The total sample size became very large because of large design effects especially for Dhaka and Khulna. The perceived large variability among these divisions may not really reflect the large variation across households in these divisions but the wide variation in the artificial weights that were attributed to the households. Given this backdrop, the sample sizes that were computed in Table 4 were used only as guides for determining the final total sample size. In particular, we proposed to sample 10 households per PSU following the recommendation of Maligalig and Barcenas (2008) instead of the 40 households per PSU followed in IMPS. This allows us to increase the number of sampled PSU from 1000 in IMPS to 1500 in the new sample design. Considering that there is positive intracorrelation among households in the same PSU, then increasing the number of sampled PSU while reducing the number of sampled household per PSU is deemed reasonable.
If the survey weights used to compute the sample size in Table 4 were correct, the estimates at the division level will have margin of error of about .03. This is not acceptable since this error level is quite large considering that division level unemployment rates only varies from .01 (Sylhet) to .06 (Barisal). On the other hand, since the survey weights in the 2005 LFS have technical flaws and stratification measures used were not effective in controlling the design effects, the resulting estimates from the 20092010 LFS using the proposed master sample design can still render acceptable design effects even with only 15,000 households total sample size. This favorable outcome depends on the quality of implementation of a better design for the master sample, specification of the correct survey weights and better stratification.
Table 4. Tentative Sample Sizes
Division  Unemployment Rate  # of households  DEFF  SRS sample size  Sample size: Complex survey  
d=0.05  d=0.03  d=0.01  d=0.05  d=0.03  d=0.01  
Barisal  0.0622  1,648,085  5.12  89.57  248.77  2236.24  460.31  1278.51  11492.75 
Chittagong  0.0461  4,472,548  8.38  67.51  187.53  1687.17  567.31  1575.81  14177.52 
Dhaka  0.0474  8,236,687  27.00  69.37  192.70  1733.99  1878.49  5217.95  46952.74 
Khuha  0.0545  3,119,602  18.58  79.18  219.92  1978.19  1475.64  4098.83  36868.64 
Rajshahi  0.0311  6,627,797  3.41  46.26  128.51  1156.41  158.07  439.08  3951.07 
Sylhet  0.0182  1,388,222  2.66  27.53  76.47  687.90  73.40  203.88  1834.07 
Total  4613.22  12814.05  115276.79 
Source: Authors’ computations using data from 2005 LFS conducted by BBS.
Several allocation strategies were examined to allocate the 15,000 sample households across domains: equal allocation, proportional allocation, square root allocation and Kish allocation.
Equal Allocation:
Proportional Allocation:
Square Root Allocation:
Kish Allocation:
where is the sample size in the domain, is the sample size, is the number of domains, is the total number of households in domain , is the total number of households in Bangladesh, per the 2001 Census of Population, is the proportion of households in domain , and is the Kish allocation index denoting the relative importance assigned to estimates at the national or subgroups that cut across domains (type (i)) as compared to those estimates at the domain levels (type (ii)). To illustrate, we can relate (i) to characteristics of interest such as numbers of crop farmers and female unpaid workers, proportions of persons in poverty in Bangladesh, number of persons in the labor force who are unemployed, proportion of households with electricity, and estimates of the differences between subgroups. When computed at the domain level, these become type (ii) parameters. If the primary interest is to derive estimates for characteristics of interest of type (ii), one of the best approaches in allocating the total sample size is to allocate it proportionally with respect to the population size of each domain. However, the ideal approach for type (ii) is to divide the total sample size equally among the domains (Kish, 1987). Moreover, it should be emphasized that these two approaches may yield very different sample allocations particularly when the domains differ in measure of size. Further, it is possible that a particular approach may perform satisfactorily when estimating a certain type of characteristic of interest but not necessarily for the other types. A possible way around this problem is to use Kish allocation which is basically a compromise between equal and proportional allocation. With , it reduces to the equal allocation while it tends to proportional allocation approach with . Table 5 provides estimates of sample size per domain using different allocation procedures. Kish allocation at was chosen to ensure that precision of both type (i) and type (ii) characteristics of interest will be approximately the same.
Table 5. Sample Allocation of Number of Sample Households per Domain
Division

Total 
Equal 
Proportional 
Square root 
Kish 

Barisal 
1,648,085 
0.064649 
2,500 
969.73 
1,633.65 
1,817.68 
Chittagong 
4,472,548 
0.175443 
2,500 
2,631.64 
2,691.21 
2,460.51 
Dhaka 
8,236,687 
0.323097 
2,500 
4,846.45 
3,652.13 
3,696.56 
Khulna 
3,119,602 
0.122371 
2,500 
1,835.57 
2,247.60 
2,102.39 
Rajshahi 
6,627,797 
0.259986 
2,500 
3,899.78 
3,276.08 
3,140.06 
Sylhet 
1,388,222 
0.054455 
2,500 
816.83 
1,499.34 
1,782.81 
Bangladesh 
25,492,941 
1.000000 
15,000 
15,000.00 
15,000.00 
15,000.00 
Source: Authors’ computations using different sample allocation procedure.
4. Implicit stratification of Primary Sampling Units
(Implicit) Stratification of PSUs is critical to ensuring that the (limited) sample size afforded by BBS will still render reliable estimates at the domain level and those that cut across domains. Ideally, a implicit stratification measure should be available and measured consistently for all PSUs in the domain. Examples of such stratification measures are geographical information such as zila (provinces) and urban/rural areas since each PSU carry the provincial code as well as the urban/area classification. Further stratification may be applied to ensure that the final groups of PSUs are more homogeneous. The candidates for stratification measures that are available for all PSUs are those variables that are in the 2001 Census of Population. In addition, an effective stratification measure is one that is highly correlated with major characteristics of interest in the survey. Those perceived to be correlated to income and employment which are the major characteristics of interests in LFS includes the proportion of households with strong housing materials (PStrong), proportion of households with agriculture as major source of income (PAgri); and proportion of households that own agricultural land (POal). Table 6 present the summary statistics for these three variables by division and rural/urban classification.
Table 6. Summary Statistics of Stratification Measures by Division and Urban/Rural
Division  Stratification Measures 
Urban/Rural  Minimum  Median  Mean  Max  Standard Deviation 
Barisal  PStrong  Rural  0  0.99  2.93  100  7.11 
Urban  0  14.93  25.37  100  26.45  
PAgri  Rural  0  61.68  59.75  100  23.59  
Urban  0  7.75  20.33  100  24.91  
POal  Rural  0  69.46  66.26  100  22.92  
Urban  0  50.57  50.46  100  23.62  
Chittagong  PStrong  Rural  0  4.05  7.46  100  10.70 
Urban  0  30.48  38.11  100  31.68  
PAgri  Rural  0  46.94  48.99  100  25.85  
Urban  0  4.55  15.21  100  22.29  
POal  Rural  0  58.33  57.26  100  22.87  
Urban  0  38.63  40.41  100  25.21  
Dhaka  PStrong  Rural  0  1.85  5.37  100  9.49 
Urban  0  57.56  53.90  100  35.59  
PAgri  Rural  0  67.42  62.93  100  24.50  
Urban  0  1.25  10.24  100  19.36  
POal  Rural  0  61.54  61.37  100  20.77  
Urban  0  48.54  48.32  100  26.22  
Khulna  PStrong  Rural  0  15.27  17.87  100  14.37 
Urban  0  44.17  46.28  100  27.19  
PAgri  Rural  0  71.07  65.90  100  22.95  
Urban  0  6.49  19.61  100  25.59  
POal  Rural  0  61.17  60.87  100  20.36  
Urban  0  43.33  44.54  100  22.60  
Rajshahi  PStrong  Rural  0  3.80  7.68  100  11.05 
Urban  0  33.33  39.39  100  30.84  
PAgri  Rural  0  76.09  70.46  100  22.39  
Urban  0  12.00  24.86  100  27.29  
POal  Rural  0  57.03  57.14  100  19.51  
Urban  0  39.39  40.72  100  20.87  
Sylhet  PStrong  Rural  0  11.83  17.92  100  18.68 
Urban  0  49.07  47.42  100  29.50  
PAgri  Rural  0  58.76  56.09  100  28.57  
Urban  0  7.25  18.55  100  23.43  
POal  Rural  0  49.38  49.36  100  22.63  
Urban  0  38.65  40.68  100  22.71  
Bangladesh  PStrong  All  0  6.06  17.47  100  25.36 
PAgri  All  0  56.82  51.12  100  31.86  
POal  All  0  56.43  55.64  100  23.06 
Source: Authors’ computations using data from 2001 Census of Population conducted by BBS.
There are several findings that may be indicative that the urban/rural classification should be reviewed carefully. In particular, there are PSUs for urban areas in which all households have agriculture as main source of income while there are PSUs in rural areas with not even one household that has agriculture as main source of income. Table 6 also shows that ownership of agricultural land is not a very good distinguishing factor for urban/rural areas. This probably shows that there are many owners in urban areas who rent or lease their agricultural land and hence, decreasing the value of POal as a stratification measure.
As indicated by the standard deviation, minimum, median and maximum values, PStrong does not vary widely in rural areas. On the average, there is considerably much lower proportion of households that have strong housing materials in the rural areas. On the other hand, although the variation of PAgri is about the same for urban and rural areas in some divisions, the number of households with agriculture as main source of income is significantly much lower in the urban areas, on the average. These results prompted us to stratify urban areas using PStrong and rural areas using PAgri. In particular, since the numbers of households and PSUs in rural areas are more than twice those of the urban areas, four and two strata were planned for rural and urban areas, respectively. Strata boundaries were first set as the quartiles of PAgri for rural areas and the median of PStrong for urban areas. However, small strata or those that have total households that is less than the division’s sampling interval are combined with the adjacent strata. The number of PSUs for each of the 336 strata that were formed are summarized in Appendix 3.
In general, the key advantage of the (implicit) stratification procedure adopted here is that it is straightforward to implement and provide satisfactory results. Nevertheless, future studies may consider implementing more optimal stratification procedures such as those proposed by Sethi (1963) and Kozak (2004).
5. Sample Selection
Another measure for controlling design effect is to ensure that the survey weights within the domains do not vary widely. A wide variation of weights within a domain will unnecessarily contribute to the increase of variances of estimates. Hence, survey statisticians usually opt to maintain almost similar base weights within a domain. Since base weight is the inverse of the selection probability of an ultimate sampling unit, then maintaining similar or almost uniform base weights is tantamount to maintaining the same or almost the same selection probabilities within a domain. This section discusses the procedures on how this can be achieved. Here, we propose a simple twostage sampling design such that in a domain : (i) PSU will be selected with probability proportional to size and (ii) household from PSU will be selected by simple random or systematic sampling, in a domain in which all PSUs are also grouped into implicit strata. Thus, in domain and (implicit) stratum , the uniform selection probability that a household is selected from PSU will be:
(3)
,
where is the total sample size for domain as defined in the last column of Table 5 (Kish Allocation, Index=1), is the measure of size for domain (i.e., total number of households per division based on the 2001 Census of Population data) and is the measure of size for PSU at stratum (i.e., total number of households for PSU from stratum ),
(4)
In a twostage cluster sampling design,
(5)
where is the selection probability of PSU and is the probability of selecting household given PSU in stratum is selected. Hence,
(6)
where is the number of PSUs to be sampled from stratum , and is the number of households to be selected from stratum .
The term represents the sampling fraction to be used in the systematic sampling of households at the final sampling stage. Its inverse is the sampling interval to be applied in the selection of households from the sampled PSUs.
Considering (6), will be uniform in a domain when and do not depend on stratum and hence, are both constant across all strata in domain . Since the recommendation that for all sampled PSUs will be implemented, and if can be maintained to remain constant, will be uniform in domain . To do latter, the number of PSUs to be selected for stratum , must be proportional to the stratum measure of size , which is actually the 2001 Census of Population total number of households for stratum . However, since must be a whole number and the strata measure of sizes also vary, the resulting selection probabilities across strata in domain will not be totally the same but will not vary widely.
To maintain a uniform in the whole domain, the same sampling interval can be applied on the list of all PSUs that are already sorted by strata. This implies that the selection of PSUs will not be done separately for each stratum in a domain but rather, will be performed collectively for all of the strata. The stepbystep procedure for maintaining a uniform selection probability within the domain is outlined below. Table 7 below shows the resulting uniform selection probabilities for each domain.
Sample Selection of Primary Sampling Units
(1) For a domain , determine the number of PSUs to be sampled , such that , where is the recommended number of households per PSU (in this case, b=10), is the number of households allocated to domain (Table 5, last column).
(2) Compute the sampling interval:
(7)
(3) Sort all the PSUs in domain by zila, urban/rural classification, by strata and lastly, by PStrong values.
(4) Compute the cumulative value of the measure of size (total number of households based from 2001 Census of Population), using the sorted list in step (3).
(5) Select a random start () by drawing a random number between 0 and 1 and multiplying it by the interval in step 2. The first sampled PSU will be the first PSU with cumulative value of containing the value of the random start (). The next sample PSU will be the PSU for which the cumulative value of contains , the next will be the PSU for which the cumulative value contains , etc.
Table 7. Summary of Sample Statistics by Domain
Division  Total No. of Households  Computed Sample PSUs  Sampling Interval  Actual Number of sample PSUs  Tentative Sample Households  Selection Probability 
Barisal  1,648,085  181.77  9066.992  182  1820  0.001104 
Chittagong  4,472,548  246.05  18177.35  246  2460  0.000550 
Dhaka  8,236,687  369.66  22282.06  370  3700  0.000449 
Khulna  3,119,602  210.24  14838.39  210  2100  0.000673 
Rajshahi  6,627,797  314.01  21107.21  314  3140  0.000474 
Sylhet  1,388,222  178.28  7786.691  178  1780  0.001282 
Source: Authors’ computations using data from 2001 Census of Population conducted by BBS.
Sample Selection of Households
Since the measure of size (i.e., total number of households) that was used for selecting the PSUs is based on 2001 Census of Population which is quite far from the 20092010 reference period of the LFS, the number of households to be sampled must be adjusted accordingly to maintain the uniform selection probabilities within domain. In particular, since the households will be selected from a sampled PSU with and if the 20092010 value of the measure of size is denoted as , then maintaining the same household level selection probability means that
(8)
and hence,
(9)
where is the actual total number of households to be selected in PSU in stratum . This implies that the there should be a listing operation of all households in the selected PSUs before the conduct of the 20092010 LFS.
6. Survey Weights and Estimation
The complex design of the master sample has to be considered in analyzing the 20092010 LFS and other surveys that will use the master sample in the future. Survey weights must be used to produce estimates of population parameters and design features such as the stratification measures, PSUs and domains must be taken into account in variance estimation and inference.
6.1 Survey Weights
The final survey weights are the product of at most three successive stages of computations. First, base weights are computed to counteract the unequal selection probabilities in the sample design. Then the base weights are adjusted to balance uneven response rates and if data are available, the nonresponse adjusted weights are further adjusted to ensure that the weighted sample distributions conform with known distributions from valid auxiliary data sources.
The base weight for sampled household is the inverse of its selection probability. In the master sample design, the selection probability is uniform within a domain and hence, base weights will not also vary within domains. In general,
(10)
Table 8 presents the base weights of sampled households by division.
Table 8. Base Weights by Domain
Division  Selection Probability  Base Weight 
Barisal  0.001104  905.7971 
Chittagong  0.000550  1818.1820 
Dhaka  0.000449  2227.1710 
Khulna  0.000673  1485.8840 
Rajshahi  0.000474  2109.7050 
Sylhet  0.001282  780.0312 
Source: Authors’ computations using data from 2001 Census of Population conducted by BBS
Nonresponse adjustments will have to be incorporated in the final survey weights if the degree of unit nonresponse cannot be ignored. Unit nonresponse occurs when an eligible household fails to participate in the survey. For example, households may refuse to participate or an eligible respondent may not be available at the times that the survey interviewer visits. In general, the nonresponse adjustment inflates the base weights of “similar” responding units to compensate for the nonrespondents. The most common form of nonresponse weighting adjustment is a weighting class type. The full sample of respondents and nonrespondents is divided into a number of weighting classes or cells and nonresponse adjustment factors are computed for each cell (Kalton, 1990) as
(11)
where the denominator of is the sum of the weights of respondents (indexed ) in weighting cell while the numerator adds together the sum of the weights for respondents and the sum of the weights for eligible nonrespondents (indexed for missing) in cell which is equal to the sum of the weights for the total eligible sample (indexed ) in cell . Thus, the nonresponse weight adjustment is the inverse of the weighted response rate in cell . Note that the adjustment is applied with eligible units. Ineligible sampled units (e.g., vacant or demolished housing units and units out of scope for a given survey) are excluded.
Weighting cells need not conform with the strata boundaries. They may cut across strata but it is important that the weighting cells will capture “similar” households. Similarity is viewed here in the perspective of the households propensity to response. In general, the response rates across weighting cells will vary widely. Moreover, there may be instances that the weighted sample distributions will not conform with projected population counts. When this happens, further weighting adjustments or what is known as population weighting adjustments can be incorporated in the final survey weight to ensure that the sample distribution conforms with the population distribution. Population weighting adjustment is performed similar to the nonresponse weighting adjustments described earlier. Calibration methods such as raking are used in this process. Using an iterative proportional fitting algorithm, raking is performed on the nonresponse adjusted weights such that the weighted survey estimates of some characteristics of interest (e.g. age group and sex) conform with the corresponding population distributions.
6.2 Estimation
Assuming that the final survey weight for household is or what can be viewed as the number of population units that the responding household represent. Then the estimator of a population total for characteristic of interest will be , where is the value of the variable for household .
The simple estimator has many applications. For example, it can be applied to estimate the count of population with specific characteristic of interest, by setting if household has the specific characteristic, otherwise.
To estimate the population mean, , the following ratio estimator can be used:
(12)
with the total of the survey weights of all responding households, , as an estimator for the total number of households. A more general form of the ratio estimator (Kalton, 1983) would be:
(13)
Note that with complex sample design such as the master sample, the means depicted in (12) and (13) are ratio estimators that involve the ratio of two random variables and hence, must be carefully considered in the computation of sampling errors.
6.3 Variance Estimation
The variances of survey estimates are needed to evaluate the precision of the survey. The sampling design in addition to the sample size is critical to the precision of survey estimates. The statistical software packages have modules that can approximate the variance of estimates from complex surveys. Most of these software packages make use of the Taylor series approach in computing the variance, although some software also offers alternate approach in the form of replication, resampling or bootstrap procedures. In general, each variance estimation approach has its own advantages and limitations. For instance, while Taylor series expansion approach is more straightforward to implement, incorporating nonresponse adjustments may render this technique less appropriate. In such context, resampling procedures may give more accurate approximations of the true variance. Nevertheless, in all these variance estimation techniques, specifying the features of the survey design is required. Also, these approaches involve approximations, most are anchored on the assumption that the first stage sampling fractions are small.
Note that survey estimates at the (geographic) division level are expected to have sampling error at acceptable level. This is also expected for estimates at the national level that cut across domains. For example, unemployment rates at urban/rural area levels are expected to have tolerable sampling errors. It is important that sampling errors of major estimates should be derived to validate these expectations. Moreover, sampling errors are also needed to evaluate the reliability of estimates at the subdivision level (e.g., zila level in the case of Bangladesh). Estimates for subdivision with sufficient sample size may render acceptable sampling errors. In the case of Bangladesh, some zilas still have relatively large sample size. Thus, although the divisions are set as the design domains or explicit strata, some estimates at the zila level may still have tolerable sampling error. However, disaggregating zilalevel estimates by urban/rural may not at all be possible because of insufficient sample size.
7. Summary
The paper documents the technical processes that were undertaken in the development of the new sample design that was used for the 20092010 Labor Force Survey conducted in Bangladesh. The new sample design addresses the weaknesses identified in the previous design adopted in 2005 LFS. Some of the (proposed) changes are as follows: first, considering the positive intra class correlations of major characteristics of interest, the total number of households to be enumerated per was reduce from 40 to 10 while the number of PSUs to be selected was increased from 1000 to 1500. Second, effective sample allocation procedure was implemented to ensure the reliability of estimates at the divisionlevel as well as those that cut across divisions. Third, implicit stratification measures were introduced to reduce design effects. Fourth, a sample selection procedure that maintains uniform selection probability for each division was also adopted to counter the large design effects noted from 2005 LFS.
Appendix 1
The Integrated MultiPurpose Sample Design
The Integrated MultiPurpose Sample Design (IMPS) was used by the Bangladesh Bureau of Statistics (BBS) to sample households for surveys of national coverage. Two such surveys are the 200506 Labour Force Survey (LFS) and the 2005 Household Income and Expenditure Survey. In general, IMPS has a stratified cluster design. Clusters of about 200 households each were formed as enumeration blocks for each zila (municipality) on the basis of the 2001 Census of Population. These enumeration blocks served as the primary sampling units (PSUs) in IMPS and were classified as urban, rural and statistical metropolitan areas (SMA). Further geographical stratification were also introduced by classifying the zilas according to six divisions – Barisabal, Chittagong, Dhaka, Khulna, Rajshahi and Sylhet. In all, there were 129 strata formed – 64 strata corresponding to 64 rural zilas, 61 strata classified under urban with the other three, Gazipur, Narayanganj and Khulna taken together to form one strata under SMA in addition to the other three SMA strata formed from urban areas with very large population – Dhaka, Chittagong, Rajshahi.
Of the 109,000 (?) PSUs, 1000 were selected. The procedure for allocating the PSUs to the 129 strata was not clarified in the documentation. Appendix 1 presents the distribution of the PSUs to the strata. Moreover, the procedure for selecting the PSUs was not also included in the documentation. For each selected PSUs, 40 households were selected at random making the total sample households equal to 40,000.
The survey weight usually derived as the product of the base weight (equal to the inverse of the selection probability) and the adjustments for nonresponse and noncoverage, was not determined as such. Instead, the survey weight was derived as the ratio of total households in the strata (updated as of April 2006) to the sample households. Appendix 2 presents the survey weights that were derived.
Summary of PSU Allocation Across Strata
Strata  National  Rural  Urban  SMA 
Barisal Division  80  55  25  – 
06 Barisal zila  17  12  5  – 
09 Bhola zila  14  10  4  – 
42 Jhalokati zila  12  8  4  – 
79 Perojpur zila  12  8  4  – 
04 Barguna zila  12  8  4  – 
78 Patuakhali zila  13  9  4  – 
Chittagong Division  179  116  49  14 
03 Bandarban zila  12  8  4  – 
15 Chittagong zila  34  16  4  14 
22 Cox’s Bazar zila  12  8  4  – 
12 Brahmanbaria zila  15  10  5  – 
13 Chandpur zila  15  10  5  – 
19 Comilla zila  26  20  6  – 
46 Khagrachhari zila  12  8  4  – 
30 Feni zila  12  8  4  – 
51 Lakshmipur zila  12  8  4  – 
75 Noakhali zila  17  12  5  – 
84 Rangamati zila  12  8  4  – 
Dhaka division  289  172  73  44 
26 Dhaka zila  34  8  4  22 
33 Gazipur zila  18  8  –  10 
56 Manikganj zila  12  8  4  – 
59 Munshiganj zila  12  8  4  – 
67 Narayanganj zila  20  8  –  12 
68 Narshingdi zila  15  9  6  – 
29 Faridpur zila  14  10  4  – 
35 Gopalganj zila  12  8  4  – 
54 Madaripur zila  12  8  4  – 
82 Rajbari zila  12  8  4  – 
86 Shariatpur zila  12  8  4  – 
39 Jamalpur zila  15  10  5  – 
89 Sherpur zila  13  9  4  – 
48 Kishoreganj zila  17  12  5  – 
61 Mymensingh zila  33  23  10  – 
72 Netrokona zila  14  10  4  – 
93 Tangail zila  24  17  7  – 
Khulna division  146  89  45  12 
41 Jessore zila  20  12  8  – 
44 Jhenaidah zila  15  9  6  – 
55 Magura zila  12  8  4  – 
65 Narail zila  12  8  4  – 
01 Bagerhat zila  13  8  5  – 
47 Khulna zila  20  8  –  12 
87 Satkhira zila  14  10  4  – 
18 Chuadanga zila  13  8  5  – 
50 Kushtia zila  15  10  5  – 
57 Meherpur zila  12  8  4  – 
Rajshahi division  251  170  71  10 
10 Bogra zila  21  16  5  – 
38 Joypurhat zila  12  8  4  – 
27 Dinajpur zila  18  13  5  – 
77 Panchagar zila  12  8  4  – 
94 Thakurgaon zila  12  8  4  – 
76 Pabna zila  16  10  6  – 
88 Sirajganj zila  18  13  5  – 
64 Naogaon zila  17  13  4  – 
69 Natore zila  14  10  4  – 
70 Nowabganj zila  12  8  4  – 
81 Rajshahi zila  24  10  4  10 
32 Gaibandha zila  16  12  4  – 
49 Kurigram zila  15  10  5  – 
52 Lalmonirhat zila  12  8  4  – 
73 Nilphamari zila  13  9  4  – 
85 Rangpur zila  19  14  5  – 
Sylhet division  55  38  17  – 
36 Hobiganj zila  13  9  4  – 
58 Maulvibazar zila  13  9  4  – 
90 Sunamganj zila  14  10  4  – 
91 Sylhet zila  15  10  5  – 
Total  1000  640  280  80 
Appendix 2
Integrated Multi Purpose Sampling Design
Survey Weights by Stratum
Stratum  Total updated households  Sample households  Sampling weights  
Rural  Urban  SMA  Rural  Urban  SMA  Rural  Urban  SMA  
06  Barisal  442170  94384  0  480  200  921.19  471.92  0.00  
09  Bhola  327262  61052  0  400  160  818.15  381.58  0.00  
42  Jhalokati  130186  27988  0  320  160  406.83  174.92  0.00  
79  Perojpur  208895  42063  0  320  160  652.80  262.89  0.00  
04  Barguna  169695  22626  0  320  160  530.30  141.42  0.00  
78  Patuakhali  303056  29761  0  360  160  841.82  186.00  0.00  
03  Bandarban  47290  18186  0  320  160  147.78  113.66  0.00  
15  Chittagong  749021  19916  840746  640  120  600  1170.34  165.97  1401.24  
22  Cox’s Bazar  347072  45420  0  320  160  1084.60  283.87  0.00  
12  Brahmanbaria  469347  61421  0  400  200  1173.37  307.11  0.00  
13  Chandpur  439039  61017  0  400  200  1097.60  305.08  0.00  
19  Comilla  933277  95049  0  800  240  1166.59  396.04  0.00  
46  Khagrachhari  80753  31643  0  320  160  252.35  197.77  0.00  
30  Feni  235576  33292  0  320  160  736.17  208.08  0.00  
51  Lakshmipur  286643  44258  0  320  160  895.76  276.61  0.00  
75  Noakhali  520274  55253  0  480  200  1083.91  276.27  0.00  
84  Rangamati  79856  34145  0  320  160  249.55  213.41  0.00  
26  Dhaka  167198  3491  2191848  320  160  880  522.50  21.82  2490.74  
33  Gazipur  257960  0  247896  320  0  400  806.13  0.00  619.74  
56  Manikganj  274091  21605  0  320  160  856.53  135.03  0.00  
59  Munshiganj  255236  37078  0  320  160  797.61  231.73  0.00  
67  Narayanganj  222924  0  331883  320  0  480  696.64  0.00  691.42  
68  Narshingdi  350319  80406  0  360  240  973.11  335.03  0.00  
29  Faridpur  346816  48608  0  400  160  867.04  303.80  0.00  
35  Gopalganj  237833  23708  0  320  160  743.23  148.18  0.00  
54  Madaripur  225717  30776  0  320  160  705.36  192.36  0.00  
82  Rajbari  190272  25943  0  320  160  594.59  162.15  0.00  
86  Shariatpur  223253  22255  0  320  160  697.66  139.09  0.00  
39  Jamalpur  397902  79792  0  400  200  994.76  398.96  0.00  
89  Sherpur  255789  31996  0  360  160  710.53  199.98  0.00  
48  Kishoreganj  505921  74145  0  480  200  1054.00  370.73  0.00  
61  Mymensingh  875150  136206  0  920  400  951.25  340.52  0.00  
72  Netrokona  406897  40560  0  400  160  1017.25  253.50  0.00  
93  Tangail  646284  93884  0  680  280  950.42  335.30  0.00  
41  Jessore  470209  99110  0  480  320  979.60  309.72  0.00  
44  Jhenaidah  314635  46953  0  360  240  873.98  195.63  0.00  
55  Magura  167265  22115  0  320  160  522.70  138.22  0.00  
65  Narail  144385  15628  0  320  160  451.20  97.67  0.00  
01  Bagerhat  293772  55545  0  320  200  918.04  277.72  0.00  
47  khulna  255885  0  327109  320  0  480  799.64  0.00  681.47  
87  Satkhira  394005  30802  0  400  160  985.02  192.51  0.00  
18  Chuadanga  170231  61525  0  320  200  531.97  307.63  0.00  
50  Kushtia  360554  39471  0  400  200  901.39  197.35  0.00  
57  Meherpur  120478  14850  0  320  160  376.49  92.82  0.00  
10  Bogra  603687  93314  0  640  200  943.26  466.57  0.00  
38  Joypurhat  179002  18762  0  320  160  559.38  117.26  0.00  
27  Dinajpur  526401  84258  0  520  200  1012.31  421.30  0.00  
77  Panchagar  172454  20929  0  320  160  538.92  130.81  0.00  
94  Thakurgaon  257353  22835  0  320  160  804.22  142.72  0.00  
76  Pabna  389278  112917  0  400  240  973.19  470.49  0.00  
88  Sirajganj  549959  67454  0  520  200  1057.61  337.27  0.00  
64  Nogaon  503878  46560  0  520  160  969.00  290.99  0.00  
69  Natore  300966  49842  0  400  160  752.41  311.51  0.00  
70  Nawabganj  252512  77584  0  320  160  789.10  484.90  0.00  
81  Rajshahi  347804  26871  166673  400  160  400  869.51  167.94  416.68  
32  Gaibandha  448228  43073  0  480  160  933.81  269.21  0.00  
49  Kurigram  347381  59961  0  400  200  868.46  299.81  0.00  
52  Lalmonirhat  222298  32851  0  320  160  694.68  205.32  0.00  
73  Nilphamari  314999  46125  0  360  160  874.99  288.29  0.00  
85  Rangpur  489418  95717  0  560  200  873.96  478.59  0.00  
36  Habiganj  359402  47883  0  360  160  998.34  299.27  0.00  
58  Maulvibazar  339274  34324  0  360  160  942.43  214.53  0.00  
90  Sunamganj  413830  48966  0  400  160  1034.58  306.03  0.00  
91  Sylhet  482356  114533  0  400  200  1205.90  572.67  0.00 
2009 Master Sample
PSU Count by Division, Zila and Urban/Rural Classification
Division  Zila  Rural  Urban  Total  
1  2  3  4  1  2  
Barisal  Barguna  231  386  497  541  214  1869  
Barisal  1328  1046  841  780  314  511  4820  
Bhola  434  604  824  1034  313  170  3379  
Jhaloka  425  392  227  116  105  96  1361  
Patuakh  553  613  662  679  173  175  2855  
Pirojpu  599  525  521  422  184  159  2410  
Chittagong  Bandarb  236  421  253  910  
Brahman  498  763  1074  1171  340  231  4077  
Chandpu  738  1160  1084  704  605  4291  
Chittagong  2344  1358  1126  702  2321  4306  12157  
Comilla  1203  2032  2428  1712  518  466  8359  
Cox’s B  460  481  698  828  444  2911  
Feni  870  579  445  285  2179  
Khagrac  364  669  452  1485  
Lakshmi  577  693  547  654  424  2895  
Noakhal  1476  1133  734  828  706  4877  
Rangama  311  620  459  1390  
Dhaka  Dhaka  756  274  366  5530  10919  17845  
Faridpu  693  762  789  779  450  3473  
Gazipur  698  629  505  384  1099  577  3892  
Gopalga  429  539  474  500  204  2146  
Jamalpu  447  989  1229  1347  761  4773  
Kishorg  988  1252  1170  1281  759  5450  
Madarip  412  515  497  556  270  2250  
Manikga  793  753  620  471  213  2850  
Munshig  1234  520  335  265  2354  
Mymensi  993  1920  2371  2402  938  358  8982  
Narayan  1443  302  1367  899  4011  
Narsing  1372  867  524  313  658  3734  
Netrako  268  600  1105  1712  378  4063  
Rajbari  284  424  499  443  218  1868  
Shariat  450  500  519  606  238  2313  
Sherpur  297  713  880  750  285  2925  
Tangail  1608  1702  1514  1300  931  7055  
Khulna  Bagerha  932  751  487  389  417  2976  
Chuadan  176  328  504  528  317  267  2120  
Jessore  926  978  1065  995  279  506  4749  
Jhenaid  350  602  721  926  227  223  3049  
Khulna  592  500  476  466  1174  1167  4375  
Kushtia  1278  738  663  524  186  253  3642  
Magura  205  330  367  490  216  1608  
Meherpu  448  348  312  150  1258  
Narail  310  334  276  273  147  1340  
Satkhir  824  840  810  824  138  156  3592  
Rajshahi  Bogra  1967  1376  1114  999  233  531  6220 
Dina  1050  1216  1273  1202  307  460  5508  
Gaiba  1109  1490  1154  873  487  5113  
Joypu  234  424  486  438  255  1837  
Kurig  504  986  967  847  606  3910  
Lalmo  272  482  596  711  353  2414  
Naoga  578  911  1336  1912  403  5140  
Nator  546  615  725  842  413  3141  
Nawab  755  533  484  352  520  2644  
Nilph  412  688  731  788  225  249  3093  
Pabna  1261  702  654  659  449  407  4132  
Panch  274  373  486  449  153  1735  
Rajshahi  551  802  880  813  717  1009  4772  
Rangp  1090  1400  1202  950  556  382  5580  
Siraj  2490  997  692  782  685  5646  
Thaku  288  376  622  783  214  2283  
Sylhet  Habigan  400  636  909  1037  264  101  3347 
Maulvib  1109  792  607  324  146  153  3131  
Sunamga  292  629  1002  1455  290  91  3759  
Sylhet  1445  1193  729  433  213  568  4581  
Bangladesh  48032  47496  47471  47101  32078  26726  248904 
Appendix 4
2009 Master Sample
Sample PSU Count by Division, Zila and Urban/Rural Classification
Division  Zila  Rural  Urban  Total  
1  2  3  4  1  2  
Barisal  Barguna  3  4  5  6  2  20  
Barisal  15  11  9  8  4  5  52  
Bhola  5  6  9  11  4  1  36  
Jhaloka  5  5  3  1  1  1  16  
Patuakh  7  7  7  7  2  2  32  
Pirojpu  7  5  6  4  2  2  26  
Chittagong  Bandarb  1  1  1  3  
Brahman  3  5  6  7  2  1  24  
Chandpu  4  7  6  3  4  24  
Chittagong  13  8  7  4  13  23  68  
Comilla  7  11  13  9  3  3  46  
Cox’s B  2  3  4  4  3  16  
Feni  5  3  3  1  12  
Khagrac  2  2  2  6  
Lakshmi  3  4  3  4  2  16  
Noakhal  8  6  4  4  3  25  
Rangama  2  2  2  6  
Dhaka  Dhaka  4  1  2  24  50  81  
Faridpu  3  3  4  3  2  15  
Gazipur  4  3  2  2  6  3  20  
Gopalga  3  2  2  2  1  10  
Jamalpu  2  5  6  6  3  22  
Kishorg  5  5  5  6  3  24  
Madarip  2  2  3  2  1  10  
Manikga  4  3  3  2  1  13  
Munshig  6  2  2  1  11  
Mymensi  5  9  12  11  5  1  43  
Narayan  8  1  7  5  21  
Narsing  6  4  2  2  3  17  
Netrako  1  3  5  8  2  19  
Rajbari  1  2  2  2  1  8  
Shariat  2  2  2  3  1  10  
Sherpur  1  3  4  4  1  13  
Tangail  8  8  7  5  5  33  
Khulna  Bagerha  7  6  3  3  3  22  
Chuadan  1  3  3  4  2  2  15  
Jessore  7  8  8  7  2  3  35  
Jhenaid  3  4  6  7  1  2  23  
Khulna  4  4  4  3  9  10  34  
Kushtia  8  6  4  4  1  2  25  
Magura  2  2  2  4  1  11  
Meherpu  3  3  2  1  9  
Narail  3  2  2  2  1  10  
Satkhir  6  6  6  6  1  1  26  
Rajshahi  Bogra  11 
References
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