This paper is not the first paper that evaluates the statistical precision and cost-effectiveness of the hybrid approach. A Fujii and van der Weide (2013) study examined the hybrid approach in a general environment. Their paper focused on the statistical properties of indicators derived from the hybrid approach and, after considering the implementation cost of the hybrid approach, derived the optimal sample sizes of both a large survey and its subsample given a total implementation cost. There are several differences between this paper and Fujii and van der Weide (2013). First, this paper not only estimates the statistical property of the hybrid approach, but also compares it with a simpler survey-to-survey imputation method, while Fujii and van der Weide (2013) focus on the statistical property of the hybrid approach. Second, this paper evaluates the accuracy of price adjustments—as mentioned, this can be a big obstacle in obtaining precise and reliable poverty statistics using the hybrid approach. Third, survey cost estimation requires detailed information on survey logistics, which can differ from country to country. By focusing on one country, this paper produces more precise estimates of survey implementation costs.

Finally, this paper focuses on The following section presents the three proposed approaches to increase the frequency of poverty data in Bangladesh and the methods used to evaluate these approaches. Section III presents the key results of the performance of each approach. Section IV discusses the effects of updating inflation rates and poverty lines using a subsample or an independent price survey. Cost is an important factor in survey implementation, and section V assesses cost implications each approach. Section VI summarizes the study findings and their validation of the usefulness of the hybrid survey approach.

# **II. Three Approaches for High-Frequency Poverty Measurement in Bangladesh**

This paper examines three low-cost approaches to increase the frequency of poverty data using Bangladesh 2000 and 2005 Household Income Expenditure Surveys (HIESs) data: Subsample Imputation, Subsample Direct Estimation, and Full Sample Imputation.

The Subsample Imputation approach estimates consumption models from a subsample of a hybrid survey, where both consumption and nonconsumption data are collected. It then uses the models to impute household expenditures for all households in the sample, even though there are consumption data for only the households in the subsample. Poverty rates are estimated from the imputed expenditures.

The advantage of this approach is that the consumption models are estimated from current year data. So, if a crisis hits a country and changes consumption patterns, the consumption models are also updated. The SE of this estimation includes both imputation errors and sampling errors, but the latter can be reduced significantly by ensuring a sufficiently large sample size of the overall hybrid survey. Because collection of the nonconsumption data is significantly cheaper, the cost of implementing such a survey is far lower than implementing a HIES. The imputation errors are expected to be non-negligible, although the literature on poverty mapping suggests that with proper care, imputation errors can be small, even when consumption models are estimated with a very small sample size.

The Subsample Direct Estimation approach simply estimates poverty statistics from the consumption data in the subsample of a hybrid survey. This approach is easier than the Subsample Imputation approach because it does not estimate imputation models and simulate poverty rates. However, there is a challenge, because the poverty estimates will likely come with higher SEs than the official poverty estimates due to the small sample size.

The Full Sample Imputation estimates consumption models using the previous round of household survey, like the HIES, where both consumption and nonconsumption data are collected, and imputes household expenditure in the future round where only nonconsumption data are collected. Like the Subsample Imputation, this approach will also face two types of errors—imputation and sampling errors.

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