of the sample design – producing accurate quarterly poverty estimates at the national level and producing accurate annual estimates at the division level for urban and rural areas. First, we round up numbers calculated using the modified version of the Neyman equation. Second, although the modified Neyman allocation is supposed to minimize standard errors of poverty rates at the district level given the original allocation of the sample across districts, this would fail to assign sub-samples of sufficient size to some CCs. Since producing reliable poverty statistics at the stratum level (urban, rural and CC areas of each division) is also a goal of the HIES 2016/17, we deviate from the original modified Neyman allocation and move some of the PSUs from urban and rural areas to CCs. This deviation will slightly increase the standard errors of poverty statistics for the district level including the CCs, but it will overall reduce poverty statistics at the stratum level.<sup>19</sup> Third, we allocate a minimum of four PSUs to each stratum of a district so that at least one PSU is allocated to each quarter. Fourth, the HIES 2010 sample did not assign any PSU to the urban Khulna district, but the minimum of four PSUs are assigned to this stratum in the HIES 2016/17.<sup>20</sup> Lastly, to produce nationally representative poverty rates for each quarter, the sample needs to be allocated to the four quarters of the data collection uniformly. Munoz (2004) suggested that to avoid seasonality, the entire sample should be randomly split into four quarters. However, randomly allocating the sample across quarters does not guarantee a full removal of seasonality. In particular, if the number of PSUs allocated to a stratum in a specific district is a multiple of four, then we can allocate the same number of PSUs to all quarters. Yet, if the number of PSUs allocated to a stratum in a specific district is not a multiple of four, there is

> 19 Note that we are not changing the sample size of districts. This is certainly suboptimal. Ideally, we can shift samples from districts with very low projected standard errors to districts with the problematic strata; however, how to design such an allocation requires more careful analysis and is beyond the scope of this paper. 20 The HIES 2010 sample frame was based on the IMPS, which in urban areas was constructed using the concept of Statistical Metropolitan Areas (SMA). The concept of SMA was replaced by the concept of Rural/Urban/CC (RUC) in the 2011 Census of Population and Housing. Of the 64 districts, only in three does the old SMA concept not match perfectly with the new RUC: in the districts of Gazipur and Narayanganj in the Dhaka division (districts 33 and 67, respectively), and the district of Khulna in the Khulna division. For Gazipur and Narayanganj, a perfect match can be achieved by replacing all SMA areas to Other Urban areas. For Khulna district, however, a match is not straightforward as the SMA area was divided into CC and Other urban areas. In addition, all of the PSUs from the Khulna district available from the HIES 2010 come from SMA areas, and there is therefore no baseline for Other Urban Khulna district. To conduct all the analysis for the design of the sample for the HIES 2016/17, we use the same sub-stratum values for both CC and Other Urban areas.

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