metadata: authors: 0: author: Fernandez Romero,Jaime Estuardo
1: author: Olivieri,Sergio Daniel
2: author: Wambile,Ayago Esmubancha
count: Bangladesh
docna: 0: docna: Reconstructing 2010–2022 Poverty and
            Inequality Trends in Bangladesh : A Statistical Matching Approach
docty: Policy Research Working Paper
abstracts: cdata!: The 2022 Household Income and
            Expenditure Survey enhances fieldwork, data management, and
            information quality but poses comparability challenges with
            previous rounds. This study proposes a two-step process
            based on statistical matching to fill the information gap in
            previous survey rounds. This methodology uses the more
            comprehensive 2022 information to reconstruct comparable
            consumption measures over time. This allows for a consistent
            assessment of poverty and inequality measures, providing
            insights into the changes for policy makers, researchers,
            and stakeholders over the years. The results reveal that
            integrating this correction into previous survey rounds
            would have reduced poverty rates by around 10.6 percentage
            points between 2010 and 2016 and a further decrease of 7.8
            percentage points between 2016 and 2022. Likewise, extreme
            poverty rates would have witnessed a decline of
            approximately 3 percentage points in the earlier period and
            a more substantial drop of 3.6 percentage points in the more
            recent one. These poverty reduction trends mirror
            improvements in other dimensions of well-being, like
            reductions in infant mortality and stunting and increases in
            access to electricity, sanitary toilets, and literacy rates.
display_title: Reconstructing 2010–2022 Poverty and
            Inequality Trends in Bangladesh : A Statistical Matching Approach
listing_relative_url: /research/2024/04/34295891/reconstructing-2010–2022-poverty-inequality-trends-bangladesh-statistical-matching-approach
topicv3: Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707,Inclusive Growth m1327170 477
publishtoextweb_dt: 2024-04-04T00:00:00Z
dois: 10.1596/1813-9450-10749
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 32
author_keys: 0: author_key: 1783347
1: author_key: 926510
2: author_key: 1108212
model_extractions: Policy Research Working Paper 10749

# Reconstructing 2010–2022 Poverty and Inequality Trends in Bangladesh

A Statistical Matching Approach _Jaime Fernandez Sergio Olivieri Ayago Wambile_ Poverty and Equity Global Practice April 2024
Policy Research Working Paper 10749

# **Abstract**

The 2022 Household Income and Expenditure Survey enhances fieldwork, data management, and information quality but poses comparability challenges with previous rounds. This study proposes a two-step process based on statistical matching to fill the information gap in previous survey rounds. This methodology uses the more comprehensive 2022 information to reconstruct comparable consumption measures over time. This allows for a consistent assessment of poverty and inequality measures, providing insights into the changes for policy makers, researchers, and stakeholders over the years. The results reveal that integrating this correction into previous survey rounds would have reduced poverty rates by around 10.6 percentage points between 2010 and 2016 and a further decrease of 7.8 percentage points between 2016 and 2022. Likewise, extreme poverty rates would have witnessed a decline of approximately 3 percentage points in the earlier period and a more substantial drop of 3.6 percentage points in the more recent one. These poverty reduction trends mirror improvements in other dimensions of well-being, like reductions in infant mortality and stunting and increases in access to electricity, sanitary toilets, and literacy rates.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jfernandezromero@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Reconstructing 2010–2022 Poverty and Inequality Trends in Bangladesh: A Statistical Matching Approach**<sup>**1**</sup>

Jaime Fernandez, Sergio Olivieri and Ayago Wambile<sup>2</sup> JEL code: C53, D31, O15, I32 _Keywords: poverty trends, inequality, poverty measurement, LASSO; statistical matching_

> 1 This paper has benefited from discussions and technical support from Faizuddin Ahmed, Agustin Arakaki, Mohiuddin Ahmed, and the Bangladesh Bureau of Statistics 2021-22 HIES project team. The paper has also been enriched by feedback from reviewers Paul Corral, Nobuo Yoshida, and Maria Eugenia Genoni, additional comments from David Newhouse, seminar participants at the World Bank workshops, and the guidance of Ximena del Carpio.

> 2 All authors are with the World Bank.
# **I. Introduction**

Bangladesh has consistently implemented the Household Income and Expenditure Survey (HIES) every approximately five years since 2000. Before HIES, the Bangladesh Bureau of Statistics (BBS) monitored poverty using a smaller survey limited to collecting expenditure data (Household Expenditure Survey, HES). Four HIES rounds were collected until 2016/17 under the same survey design, data collection processes with no changes in critical questionnaires, like the food and non-food consumption modules, recall periods, and data entry and management (i.e., Pen-and-Paper Personal Interview (PAPI) and Computer Assisted Field-Based Data Entry (CAFE)).<sup>3</sup> Nevertheless, despite maintaining these aspects unchanged, comparability over time was not always fully guaranteed. For instance, the 2016/17 HIES introduced vital changes that could have affected its comparability with previous rounds. Notably, the sample size increased significantly from 12,240 households in 2010 to 46,080 in 2016, enhancing the survey's granularity for poverty estimates at various administrative levels. This expansion necessitated shifting the sampling frame from the Integrated Multiple-Purpose Sample to Enumeration Areas from the 2011 Population and Housing Census. This shift impacted the survey strata and increased the coverage of urban slum areas. Additionally, changes in geographic classifications and the replacement of Statistical Metropolitan Areas with a new Rural/Urban/City Corporation classification affected urban comparability. The larger sample size and revised fieldwork protocols led to more incomplete and inconsistent income data, particularly for wealthier rural households. Despite these issues, the changes are not deemed to significantly alter the overall findings of the poverty assessment (World Bank, 2019).

In the 2022 HIES round, Bangladesh implemented survey design and fieldwork operation improvements that impacted consumption comparability over time.<sup>4</sup> Introducing the Classification of Individual Consumption According to Purpose (i.e., COICOP) increased the number of food and non-food items from 149 to 263 and 261 to 441, respectively. The data collection method moved from PAPI to Computer Assisted Personal Interviewing (CAPI). Market prices were collected instead of deriving unit values from collected household total expenditure values and quantities, and weighing scales were implemented to ensure the accuracy of household consumed food items. A more rigorous fieldwork monitoring system

> 3 To mention a few examples, the 2010 HIES round redesigned the Remittances and Migration modules, and in the 2016/17 round, the Social Safety Net questionnaire was redesigned and expanded. Another difference between survey rounds is that the PSUs in all rounds but 2016/17 were selected from an Integrated Multipurpose Sample (IMPS) – a master sample updated for each Housing and Population Census. In the 2016/17 round, PSUs came from a list of Enumeration Areas used for the 2011 Population and Housing Census. For further details, see Ahmed et al. (2019).

> 4 As technology and survey design methods evolve, enhancements should be implemented. For instance, Argentina improved its survey instrument and periodicity of data collection in 2003, India made several changes in the late 1990s, Peru and Ecuador made significant changes in their household surveys in 2004 and 2007, and, more recently, Zambia and Bhutan in their 2022 survey round. In the ideal situation, a proper way to implement these changes is to simultaneously conduct old and new methods and then clearly identify their differences to maintain comparability over time. However, this process could be costly, challenging, and complex, leaving most countries with two options: break trends or find an analytical way to tackle this issue after implementing the survey.

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was implemented, and multiple enumeration training sessions were conducted throughout the year.<sup>5</sup> Finally, the sample size was reduced. The 2022 HIES is representative at the national, division, and rural and urban levels as before the 2016 rounds. The 2016 survey was an exception, with a sample representative at the Zila level.

The 2022 microdata provides a more comprehensive picture of Bangladeshi households' consumption levels and patterns but risks monitoring welfare indicators over time. In this setting, accurate and robust methodologies are crucial to address comparability over time, help rebuild trends, and evaluate policies' effectiveness. Survey-to-survey imputation methods have emerged as a promising avenue to address this issue, leveraging data from one survey to fill in gaps in another, thereby providing more frequent and robust estimates of poverty and inequality trends over time (Dang, 2021).

Traditional imputation methods, like regression-based and simple matching techniques, operate under the assumption that the missing data in a survey can be accurately imputed from another data source. However, such assumptions might not always hold, especially in cases where there are significant changes in the country's population structure or survey design (Dang, 2021) or a lack of consistency in collecting survey data over time (Mathiassen and Wold, 2021). Moreover, traditional imputation methods do not necessarily account for complex relationships between variables and could introduce bias when the model is misspecified.

There is a growing consensus in the literature towards adopting more advanced imputation methods, particularly in a data-scarce environment. One approach is implementing hot deck imputation methods bolstered with statistical learning techniques (Budhwani et al., 2022). These methods can handle missing data in large datasets with many variables and have the potential to capture the relationships between variables more accurately. Although not a perfect substitute, these methods can also address the challenges posed by survey costs or implementation and enable back-casting of consumption from a more recent survey for better comparison with older surveys (Dang, 2021; Edochie et al., 2022).

One of the main advantages of matching-based imputation methods is that they are relatively simple to implement and require minimal assumptions about the underlying data-generating process. Another advantage is that these preserve the structure of the original dataset (Andridge and Little, 2010). In other words, the imputed data in the receiver survey will have similar distributions and relationships to the original data in the donor survey.

This paper presents a two-step random hot deck imputation methodology, expanding upon the approach delineated by Fernandez, Olivieri, and Sanchez (2023). This strategy, initially grounded in the framework established by Lucchetti et al. (2020), aims to reconstruct the evolving poverty and inequality trends in Bangladesh. This method utilizes LASSO PMM (Predictive Mean Matching) and resampling techniques to predict a comparable consumption aggregate for 2010 and 2016 to the data collected in the 2022 HIES.

> 5 Two refresher training sessions were conducted during the 12-month survey period: the first refresher training took place on March 22 – 24, 2022, and the second on August 28 – 30, 2022. This is in addition to the initial training sessions conducted before the survey: the Enumerators Training session from December 4 to 24, 2021, and the Field Officials Training from December 27 to 29, 2021.

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The paper critically evaluates the precision of this approach, exploring its implications for Bangladesh's poverty dynamics and fluctuating poverty and inequality trends. Adjustments to the consumption and poverty lines were essential, as the original poverty lines could not serve as accurate indicators due to variations in the consumption item sets. Consequently, the 2022 thresholds were adopted for a more insightful comparison of the new consumption aggregates and for determining the poverty levels for each year. The analysis reveals that using the adjusted consumption aggregates would have indicated a rise in moderate poverty by roughly 2.2 percentage points in 2016 and 5.6 points in 2010 compared to initially reported levels. Conversely, the extreme poverty rates would have decreased by approximately 3.7 and 4.4 percentage points for 2016 and 2010, respectively.

The rest of the paper is organized as follows. Section II presents the methodological details for the twostep imputation method. Section III describes the limitations of the proposed method. Section IV presents the estimation results for the incidence, gap, and severity of poverty, inequality, and some sensitivity analysis and robustness checks. Section V discusses the final remarks.

# **II. Methodological approach: A two-step matching technique**

> 6 It is important to note that, although the approach is designed to impute missing data, data collection and fieldwork improvements will also be indirectly included in the imputation process.

4
For total consumption, food and non-food consumption, respectively. The imputation process requires not only these shares but also the consumption of comparable items ( ) of those in previous rounds. ii2022 Second, the two-step random hot deck imputation methodology is implemented following Fernandez, yy Olivieri, and Sanchez (2023), whose analysis is based on the approach proposed by Lucchetti et al. (2020). This method utilizes PMM (Predictive Mean Matching), LASSO regression, and resampling techniques to predict the 2010 and 2016 consumption aggregates comparable to the data collected in the 2022 HIES. This methodology aims to accurately rebuild the poverty and inequality trends that Bangladesh has experienced over the last 12 years while avoiding some assumptions behind parametrical alternatives. The implicit approach behind PMM-like methods (Little and Rubin 2002) does not require defining an explicit model for the distribution of the missing values. Because of this, predictive mean matching is less vulnerable to model misspecification than traditional parametric methods (Van Buuren, 2018).

Morris, White, and Royston (2014) conducted simulation studies to contrast the results of PMM versus a parametrical approach under different data missingness scenarios (entirely at random, at random, and not at random). They demonstrated that PMM-like methods are effective when the underlying imputation model is accurately specified. Furthermore, this approach offers an enhancement compared to parametric imputation in scenarios where the imputation model is not correctly specified. These findings are consistent with Schenker and Taylor (1996), who compared partially parametric (PMM-like) and fully parametric regression-based multiple-imputation methods. They found that a fully parametric approach yields marginally more efficient estimates of the outcome's marginal distribution function than the partially parametric methods. However, when there is a misspecification in the mean structure, all methods remain effective in estimating the marginal mean. However, the fully parametric approach exhibits a slight rise in bias and variance. In estimating the marginal distribution function, the fully parametric method encounters difficulties in various scenarios, while the PMM-like methods continue to perform well. In addition, when empirical data diverge from their assumed distributions, PMM typically excels at maintaining the original distribution of the data more effectively than fully parametric Multiple Imputation (MI) methods.

The random hot deck PMM imputation method, enhanced with statistical learning techniques and resampling, significantly improves the estimation of a comparable 2010 or 2016 consumption aggregate, thereby refining poverty and inequality measurements. This method incorporates regularization to prevent overfitting and employs cross-validation to reduce the mean square error (MSE) of predictions. Consequently, it offers more precise welfare predictions for data outside the estimation sample than traditional regression models. This increased accuracy is achieved through automatic variable and model selection, which involves penalizing coefficients and using cross-validation, as Tibshirani (1996) described. These advancements are particularly crucial in the context of the proposed approach and represent a significant improvement over traditional PMM methods.

However, despite these advantages over fully parametric approaches, the effectiveness of these methods still heavily relies on the correct specification of the imputation model. Morris, White, and Royston (2014) advised that researchers should focus on accurately specifying the imputation model 5
rather than relying solely on PMM or its variants to compensate for any model specification shortcomings.

Although the methodological approach described in this section applies to both the 2010 and 2016 surveys, for the sake of simplicity, this section will focus on the 2022 to 2016 imputation. However, it is essential to note that the same principles and procedures apply to the 2022 to 2010 imputations.

Data availability consists of at least two cross-sectional consumption surveys, the donor (i.e., HIES 2022) and the recipient (i.e., HIES 2016 or 2010). The official consumption aggregates are different from the two surveys. As mentioned, in 2022, many changes were introduced, like additional items, improvements in fieldwork and data collection, etc. First, to reconstruct trends in distributional indicators, a consumption aggregate is estimated for 2022 based on the commonly collected items in the two surveys. In both surveys, i is the comparable per capita consumption for household , (i.e., ), in survey ( ) with sample size , and i is a vector of characteristics observed yy ii ii= for household in survey . These household characteristics, observed in both survey years, include ii ii 1, ... , NN jj jj= 2016, 2022 NN XX household head characteristics (gender, ethnicity, civil status, education, age, etc.) as well as dwelling ii jj features (access to water, electricity, sanitary facilities, etc.) and other household variables (size, labor conditions, composition, etc.). Second, the linear model where the -th household's comparable log per capita consumption<sup>7</sup> i is explained by household characteristics i for each survey is given by: ii yy i i XX i (7) TT where i is an error term and yy = ααii + ββi is a vector of ii XX + εε _K_ regressors, and jj = 2016, 2022 is the intercept. In this context, + i would represent the portion of log consumption explained by the selected covariates. ii ii εε XX αα αα TT ii The objective is to estimate, for household ββ XX interviewed in the recipient survey (i.e., 2016 or 2010) the additional consumption that would have been obtained if the 2022 survey had been implemented in ii previous rounds. As an alternative to regression-based approaches and to avoid making assumptions about the behavior of the residuals, in this approach, the share of additional consumption is not obtained directly from an econometric prediction but through statistical matching (random hot deck) between households from the two surveys. Thus, the predicted log consumption from the model (7) is one of the variables on which the closeness between observations is measured. More specifically, and are predicted using LASSO regression as follows: ii2016 ii2022 yy yy i L L L L i (8) TT LLjj LLjj 2 where ( L L yy�L L = αα�) = argmin+ ββ̂ ) �∑XX i jj = 1, 2i � �1�, is the penalization TT NNjj TT parameter chosen through cross-validation, and αα� LLjj, ββ̂ LLjj (ααjj,ββjj ii=1�yy �−αα1 is the ii −ββlii1XX norm of the coefficient vector + λλ�ββii λλ . ii ii �ββ ββ

> 7 To simplify notation, in this paper i will be used to refer to the logarithm of household per capita consumption. yy

6
<!-- Start of picture text --> y y y<br>y<br>y<br>* (1 + shfood ) * cindex<br><!-- End of picture text --> <!-- Start of picture text --> y<br><!-- End of picture text -->
8. Calculate simulated total per capita consumption in 2016 that includes non-collected items and express it in 2022 prices, as in equation (9).

9. Calculate poverty and inequality using the new consumption aggregate.

10. Repeat 1,000 times all steps 1 to 9.

This process allows for estimating confidence intervals for poverty and inequality. A new LASSO model is fit for every bootstrap sample of the data, and the whole consumption vector is constructed via the improved LASSO PMM process described above. In this exercise, a thousand bootstrap repetitions were implemented. As a result, both point and interval estimates are calculated for every welfare indicator.

# **III. Limitations**

Despite the strengths of the matching approach implemented in this analysis, several limitations must be considered. First, the imputation process accounts for missing data, as well as for data collection and fieldwork improvements between the survey rounds. These last enhancements can introduce additional sources of variation in the imputed data, which may affect the accuracy of the imputation results.

Another limitation is that certain items collected in both surveys have changed. For example, rice, categorized as fine, medium, and coarse in previous rounds, was divided into six categories in 2022, primarily for capturing quality. This change in categorization introduces complexities in the imputation process and may impact the accuracy of the imputed consumption values for these items. The categorization in 2022 may align differently from the previous categorization, which could introduce discrepancies in the imputed data.

Furthermore, the 2022 poverty lines were defined at the domain level, whereas the 2016 (and 2010) lines were defined at the stratum level. This difference in the level of aggregation poses a challenge in determining the poverty status of each household during the imputation procedure. The imputation process involves identifying donor households in the 2022 survey and assigning their shares and domains to the corresponding 2016 (2010) households. This additional step introduces an element of uncertainty in the imputation and may affect the accuracy of poverty estimates.

Additionally, the imputed consumption data for 2016 (2010) are based on predictions and statistical matching techniques. While efforts have been made to improve the accuracy of the imputed values, there is inherent uncertainty associated with imputation methods. The imputed data may not perfectly capture the actual consumption patterns in 2016 (2010), and errors or biases in the imputed values may be introduced. This could be especially concerning if the assumption that the share of additional consumption relative to common items remained stable over time for comparable households does not hold. However, analysis of consumption patterns suggests they have not drastically changed, particularly for the last 6 years, as indicated in Table A.1 in Annex 1.

In summary, although the imputation approach provides a valuable tool for reconstructing poverty and inequality trends, it is essential to consider the limitations of the methodology. The inclusion of data 8
collection and fieldwork improvements, changes in item categorization, differences in poverty line definitions, and the inherent uncertainty in imputation methods all contribute to the potential limitations of the approach. These limitations should be carefully considered when interpreting results .

# **IV. Results**

Evaluating the quality of the imputation process hinges on a critical principle: maintaining the consistency of imputed parameter distributions between the donor and recipient surveys, mainly when these surveys are conducted within a relatively short time frame. Assuming that the populations have similar characteristics and patterns is reasonable in such occasions. Given the six-year interval between the surveys, this situation is expected to apply to the imputation exercise between 2016 and 2022.

However, the reliability of preserving the imputed parameter distributions becomes tenuous when there is a substantial time gap between the surveys. This is particularly true when the parameters are connected to variables that may have undergone significant changes over time. Therefore, when data from 2022 is utilized to project values back to 2010, more critical shifts in consumption patterns might be anticipated.

Being mindful of these limitations, the rest of the chapter is divided into four sections: reconstructing poverty and inequality levels in 2016 and 2010, rebuilding trends between 2010 and 2022, and robustness checks.

1. Reconstructing poverty and inequality levels in 2016

- a. Imputed shares

When examining the total imputed share (food and non-food) in 2016, the confidence intervals of the average share of additional consumption in 2022 and the imputed values in 2016 overlap for all deciles (Figure 1). This indicates no significant differences by decile between the original 2022 and the imputed average shares. In other words, the method successfully captures the distribution of additional food and non-food consumption in 2022 within the corresponding deciles of the 2016 data. However, the imputed shares generally appear slightly subdued compared to the original 2022 distribution. This trend might imply that the simulated consumption data for 2016 slightly underestimates the increase in consumption, or it might point to actual shifts in consumption patterns between 2016 and 2022.

9
Figure 1: Average shares by deciles of per capita consumption <!-- Start of picture text --> 27.0%<br>22.0%<br>17.0%<br>12.0%<br>7.0%<br>2.0%<br>1 2 3 4 5 6 7 8 9 10<br>Lower bound - imputed Upper bound - imputed<br>Lower bound 2022 Upper bound 2022<br><!-- End of picture text -->

# Source: Own estimations based on 2016 and 2022 HIES

The analysis reveals that the imputation method performs well in preserving the distribution of imputed parameters for both food and non-food components. Therefore, the 2016 simulated consumption aggregate, which incorporates the imputed components, is expected to exhibit an approximate increase of 15 percent compared to the official consumption aggregate for the bottom 40 of the distribution.

- b. How should the poverty incidence be estimated in 2016 to reconstruct trends?

The 2016 consumption distribution has shifted to the right due to the inclusion of new components in the welfare aggregate (Figure 2). Comparing it with previous 2016 official poverty thresholds will underestimate the incidence of poverty by construction. The 2016 poverty lines were drawn from the 2005 consumption aggregate based on 2005 HIES and updated by inflation. These reflect old consumption patterns and missing components of the welfare aggregate measured in 2022.<sup>8</sup> Thus, the re-estimated 2022 poverty lines should be contrasted to the imputed 2016 aggregate for estimating comparable poverty and extreme poverty headcount levels and trends.

> 8 The 2016 official poverty lines were approximately 17 years old, constructed using HIES 2005, and later updated mainly by inflation. The table below provides how the poverty lines for the last five HIES rounds were estimated over the years.

|Year|2000|2005|2010|2016/17|2022|
|---|---|---|---|---|---|
|Food PL|Updated from 1991/92|Re-estimated (CBN)*|Updated from 2005|Updated from 2010|Re-estimated (CBN)*|
|Non-food PL|Updated from 1991/92|Re-estimated (CBN)|Re-estimated (CBN)|Updated from 2010|Re-estimated (CBN)|

*Re-estimation involves pricing the same food basket (11 food categories) for 2005 and 2022, respectively.

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Table 2 shows the variations in the poverty and extreme poverty headcounts by area and division.<sup>10</sup> The main differences in poverty incidence come from rural and Sylhet when using the upper line for areas and divisions, respectively. While this also holds for rural areas with extreme poverty headcounts, Khulna presents the most significant gap in percentage points.

Table 2. Differences in 2016 poverty and extreme poverty incidence by area and division

||Po<br>|verty (upper li<br>|ne)<br>|Extrem<br>|epoverty (lo<br>|wer line)|
|---|---|---|---|---|---|---|
||Official<br>headcount<br>(a)|Simulated<br>headcount<br>(b)|Percentage<br>points<br>(b - a)|Official<br>headcount<br>(a)|Simulated<br>headcount<br>(b)|pp change<br>(b - a)|
|_Area_|||||||
|Rural|26.4|29.0|2.6|14.9|10.8|-4.1|
|Urban|18.9|19.4|0.5|7.6|5.3|-2.3|
|_Division_|||||||
|Barisal|26.5|30.7|4.2|14.5|12.0|-2.5|
|Chattogram|18.4|19.1|0.7|8.7|5.9|-2.8|
|Dhaka|16.0|19.2|3.2|7.2|4.8|-2.4|
|Khulna|27.5|28.4|0.9|12.4|6.7|-5.8|
|Rajshahi|28.9|27.8|-1.1|14.2|11.8|-2.5|
|Rangpur|47.2|45.8|-1.5|30.6|25.8|-4.7|
|Sylhet|16.2|22.8|6.6|11.5|6.8|-4.7|

Note: results for the Mymensingh division are available upon request. Source: Own estimations based on HIES 2016 and 2022 The imputation process has an impact on the poverty incidence levels within the divisions and their relative positions. However, it is expected that the relative positions of the divisions will not be significantly affected by this method. As expected, the results indicate only minor changes in the relative positions of divisions in both poverty and extreme poverty rankings. (Figure 4). However, these jumps are not significant enough to impact, for instance, the east-west regional divide. After imputation, Chattogram, Dhaka, and Sylhet are the divisions with the lowest poverty incidence, while Rangpur, Barisal, Khulna, and Rajshahi are in the opposite spectrum.

> 10 The simulated headcount was computed using the average of 1,000 simulations for per capita consumption with newly introduced items.

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# 4. Additional robustness checks

This section provides the results of extensive robustness checks to ensure the reliability and consistency of the analysis. Various parameter configurations for nearest neighbor searches and training data are explored. Additionally, established alternative approaches, including Survey of Well-being via Instant and Frequent Tracking (i.e., SWIFT) and GDP-poverty elasticity methods, are employed. Moreover, Annex 3 presents results from a straightforward trend reconstruction using common items to define alternative poverty lines and consumption aggregates. These comprehensive checks consistently align with the core findings of this study.

## a. Same methodological approach under different options

To conduct additional robustness checks to the proposed methodology, additional simulations have been implemented in three distinct scenarios that relax the default setup. The first scenario eliminates the constraint of restricting the nearest neighbor search to the same decile. The second scenario involves using only the 2022 dataset for model training and then making predictions for both 2022 and 2016 (or 2010), analogous to the more traditional Lasso PMM approach. The final scenario combines elements of both the first and second scenarios.

Poverty and extreme poverty rates for 2016 show no significant differences across the scenarios (Figure 15.a). For 2010, where the removal of the decile-based constraint results in slightly larger differences, these remain statitically insignificant (Figure 15. b). This outcome is anticipated given the longer backcasting period and the associated challenges in satisfying the assumptions underlying the approach.

Figure 15. Poverty and extreme poverty rates under different scenarios <!-- Start of picture text --> a. 2016 b. 2010<br>29.0 13.0 39.0 16.0<br>27.0 26.46 26.39 25.94 26.24 12.0 37.0 37.08 37.08 15.0<br>25.0 11.0 35.0 35.40 35.4314.0<br>9.22 9.22 9.19 8.95 10.0 13.0<br>23.0<br>9.0 33.0 12.18 12.23 12.0<br>21.0 8.0 31.0 11.54 11.47<br>11.0<br>19.0 7.0<br>29.0 10.0<br>Poverty Extreme poverty (right axis)<br>Poverty Extreme poverty (right axis)<br>Poverty<br>Poverty<br>Extreme poverty<br>Extreme poverty<br>Sc. 2 + Sc. 3<br>Sc. 2 + Sc. 3<br>constraint<br>constraint<br>Current approach training stage<br>Sc.2: Remove decile Current approach training stage<br>Sc.3: Use only 2022 in Sc.2: Remove decile<br>Sc.3: Use only 2022 in<br><!-- End of picture text --> Source: Own estimations based on 2016 and 2022, and 2010 and 2022 HIES.

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Figure 16 displays the Gini coefficients from the various checks for 2010 and 2016. These results indicate no significant differences across the scenarios, reinforcing one of the analysis's central messages that inequality has remained relatively stable over the past 12 years.

Figure 16. Gini coefficients in 2010 and 2016 under different scenarios <!-- Start of picture text --> 36<br>35<br>34<br>33.49 33.47<br>33.08<br>33 33.02<br>32.96 33.04 32.67<br>32.67<br>32<br>31<br>30<br>Current approach Sc.2: Remove decile Sc.3: Use only 2022 in Sc. 2 + Sc. 3<br>constraint training stage<br>2010 2016<br><!-- End of picture text --> Source: Own estimations based on 2010, 2016, and 2022 HIES.

These additional scenarios and their outcomes enhances the robustness of the analysis. None of the alternative scenarios significantly change the overall trends or conclusions drawn from the study of poverty and inequality evolution in Bangladesh over the past 12 years.

# b. The SWIFT method

A second round of robustness checks was conducted using the SWIFT methodology (Yoshida et al., 2022) as an alternative method. SWIFT typically is implemented for estimating total household consumption. However, it was adapted to predict only the additional consumption from previous rounds. This adjustment allowed for a comparison of the SWIFT results with those obtained through the methodology outlined.

The application of the SWIFT method necessitated a cross-validation process before prediction, resulting in a p-value of 0.04. This value indicated the most suitable fit for minimizing the mean square error and the mean absolute difference in poverty rate estimations within the test sample. Multiple imputations were conducted, each with 20 repetitions. Findings showed that the estimated poverty rates for both 2016 and 2010 were consistent across both methodologies. However, in terms of extreme poverty, the SWIFT method produced lower estimates, as presented in Figure 17. These lower estimates of extreme poverty do not fully align with the expected reduction trend, especially when considering the significant improvements observed in various other indicators between 2016 and 2022. The decline in extreme 23
poverty rate from 7 percent in 2016 to an official rate of 5.6 percent in 2022 appears to slightly underestimate the improvements in living conditions for the economically disadvantaged population segments.

Figure 17. Poverty and extreme poverty rates using SWIFT <!-- Start of picture text --> a. 2016 b. 2010<br>33.0 15.0 18.0<br>40.0<br>37.08<br>13.0 16.0<br>28.0 26.46<br>35.0 34.77<br>25.86<br>11.0 14.0<br>23.0<br>9.22 30.0<br>9.0 12.18 12.0<br>18.0<br>7.00 7.0 25.0 10.0<br>9.58<br>13.0 5.0 20.0 8.0<br>Current approach Swift Current Swift<br>approach<br>Poverty Extreme poverty (right axis) Poverty Extreme poverty (right axis)<br>Poverty Poverty<br>Extreme poverty Extreme poverty<br><!-- End of picture text --> Source: Own estimations using the presented approach and SWIFT, based on 2010, 2016 and 2022 HIES

- c. The GDP-poverty elasticity with a neutral distribution

Another approach involved scaling the 2022 observed consumption distribution by a fraction of the real GDP per capita growth, known as the pass-through rate (Mahler, Castañeda Aguilar & Newhouse, 2022). In this case, the exercise involves backcasting instead of nowcasting, yet the procedure remains identical. Data from HIES revealed that between 2010 and 2016 (comparable rounds), the mean per capita consumption increased by 13 percent in real terms. Concurrently, the World Development Indicators (WDI) Database from the World Bank shows that the real GDP per capita growth during this period was 35 percent, resulting in a pass-through rate of 0.37.<sup>16</sup> Utilizing the 2022 consumption distribution and real GDP per capita values for 2010, 2016, and 2022, the backcasting exercise estimated poverty rates at 25.3 percent for 2016 and 31.3 percent for 2010. The backcasted extreme poverty rates were calculated at 8.9 percent for 2016 and 12.2 percent for 2010.

Interestingly, except for the 2010 poverty rate, these backcasting results align well with the findings of this study. The 2010 poverty rate obtained through this alternative approach closely matches that derived from the 2010 HIES using the official welfare aggregate. However, if this consistency holds true,

> 16 Mahler, Castañeda Aguilar & Newhouse (2022) suggest, as a general guideline for international comparability, using a passthrough rate of 0.7. Applying this rate, estimated poverty and extreme poverty rates for 2016 would be 33.1% and 13.1%, respectively, and for 2010, 47.5% and 22.8%. These results show a decreasing trend, but the poverty and extreme poverty levels differ significantly from other robustness checks. For this reason, the chosen pass-through rate of 0.37, specific to Bangladesh, is used in this paper due to its sensitivity to the rate's value.

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it suggests that the additional items collected in 2022 might have a negligible impact on the 2010 consumption aggregate, which raises questions about the plausibility of this implication. Nevertheless, the comprehensive robustness checks performed, including the use of the SWIFT method and backcasting with a pass-through rate, collectively reinforce the robustness of the study's results. These checks confirm that the study's findings are not only resilient to variations in methodological specifications but also to different approaches.

# **V. Final remarks**

This study marks a significant advancement in the analysis of poverty and inequality trends in Bangladesh, showcasing the importance of innovative and robust methodologies in socioeconomic research. Central to its contribution is the reconstruction of these trends using a novel methodology, which incorporates previously unaccounted items into the consumption distributions of 2010 and 2016. This methodological innovation is crucial, as it not only aligns with but also validates the results against other benchmark approaches, thereby reinforcing the reliability of its findings.

The research is particularly significant for public policy, providing a nuanced and accurate depiction of poverty trends, which is vital for effective policy making. While the method introduces a refined understanding of these trends, it importantly does not alter the profile of the poor as identified using the official aggregate. This ensures the compatibility of the new approach with existing studies and recommendations, enabling a consistent poverty profile while simultaneously enhancing the understanding of temporal trends.

The study highlights the critical need for comparable consumption aggregates in analyzing poverty and extreme poverty headcounts, as well as inequality measures over time. For instance, the average poverty lines for 2022 were found to be 21% and 19% higher than the original 2010 and 2016 lines at 2022 prices. In contrast, extreme poverty lines showed a smaller increase of only 6% in both periods. These adjustments suggest higher poverty rates when considering the imputed consumption data. Specifically, the poverty rate in 2016 would have been between 1.7 and 2.3 percentage points higher, and the extreme poverty rate between 3.5 and 3.7 points lower, had a comparable consumption aggregate been available. Similarly, the incidence of poverty in 2010 would have been between 5.5 and 5.8 percentage points higher, and the extreme poverty incidence between 5.1 and 5.4 points lower.

In conclusion, this study not only enhances the understanding of poverty and inequality trends in Bangladesh but also ensures these insights are consistent with established poverty profiles. This balance between novel methodologies and consistency with previous findings makes the study invaluable for policy makers and researchers, aiding significantly in efforts to reduce poverty and improve living standards in Bangladesh. The approach offers a model that could be replicated in similar socioeconomic studies globally, emphasizing the importance of accurate and comparable measurements in understanding poverty dynamics.

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