metadata: authors: 0: author: Dang,Hai-Anh H.
1: author: Kilic,Talip
2: author: Abanokova,Ksenia
3: author: Carletto,Calogero
count: Bangladesh,Ethiopia,Malawi,Nigeria,Tanzania,Viet Nam,World
docna: 0: docna: Imputing Poverty Indicators without
            Consumption Data : An Exploratory Analysis
docty: Policy Research Working Paper
abstracts: cdata!: Accurate poverty measurement relies on
            household consumption data, but such data are often
            inadequate, outdated, or display inconsistencies over time
            in poorer countries. To address these data challenges, this
            paper employs survey-to-survey imputation to produce
            estimates for several poverty indicators, including
            headcount poverty, extreme poverty, poverty gap,
            near-poverty rates, as well as mean consumption levels and
            the entire consumption distribution. Analysis of 22
            multi-topic household surveys conducted over the past decade
            in Bangladesh, Ethiopia, Malawi, Nigeria, Tanzania, and Viet
            Nam yields encouraging results. Adding household utility
            expenditures or food expenditures to basic imputation models
            with household-level demographic, employment, and asset
            variables could improve the probability of imputation
            accuracy by 0.1 to 0.4. Adding predictors from geospatial
            data could further increase imputation accuracy. The
            analysis also shows that a larger time interval between
            surveys is associated with a lower probability of predicting
            some poverty indicators, and that a better imputation model
            goodness-of-fit (R2) does not necessarily help. The results
            offer cost-saving inputs for future survey design.
display_title: Imputing Poverty Indicators without
            Consumption Data : An Exploratory Analysis
listing_relative_url: /research/2024/08/34377257/imputing-poverty-indicators-without-consumption-data-exploratory-analysis
topicv3: Poverty m1326687 695,Inequality and Shared Prosperity m1327655 700,Household Surveys m1504071 2761
publishtoextweb_dt: 2024-08-14T00:00:00Z
dois: 10.1596/1813-9450-10867
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 79
author_keys: 0: author_key: 747967
1: author_key: 886702
2: author_key: 1288641
3: author_key: 550790
model_extractions: Policy Research Working Paper 10867

# Imputing Poverty Indicators without Consumption Data

## An Exploratory Analysis

_Hai-Anh H. Dang Talip Kilic Kseniya Abanokova Calogero Carletto_ Development Economics Development Data Group August 2024
Policy Research Working Paper 10867

# **Abstract**

Accurate poverty measurement relies on household consumption data, but such data are often inadequate, outdated, or display inconsistencies over time in poorer countries. To address these data challenges, this paper employs survey-to-survey imputation to produce estimates for several poverty indicators, including headcount poverty, extreme poverty, poverty gap, near-poverty rates, as well as mean consumption levels and the entire consumption distribution. Analysis of 22 multi-topic household surveys conducted over the past decade in Bangladesh, Ethiopia, Malawi, Nigeria, Tanzania, and Viet Nam yields encouraging results.

Adding household utility expenditures or food expenditures to basic imputation models with household-level demographic, employment, and asset variables could improve the probability of imputation accuracy by 0.1 to 0.4. Adding predictors from geospatial data could further increase imputation accuracy. The analysis also shows that a larger time - interval between surveys is associated with a lower probabil ity of predicting some poverty indicators, and that a better imputation model goodness-of-fit (R<sup>2</sup> ) does not necessarily help. The results offer cost-saving inputs for future survey design.

This paper is a product of the Development Data Group, Development Economics. 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 hdang@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
# **Imputing Poverty Indicators without Consumption Data: An Exploratory Analysis**

Hai-Anh H. Dang, Talip Kilic, Kseniya Abanokova, and Calogero Carletto<sup>*</sup>

**Keywords:** consumption, poverty, survey-to-survey imputation, household surveys, Viet Nam, Ethiopia, Malawi, Nigeria, Tanzania, Sub-Saharan Africa.

**JEL Codes** : C15, I32, O15.

> * Dang (hdang@worldbank.org; corresponding author) is a senior economist in the Living Standards and Measurement Unit, Development Data Group, World Bank and is also affiliated with GLO, IZA, Indiana University, and London School of Economics and Political Science; Kilic (tkilic@worldbank.org) is the senior program manager of the Living Standards Measurement Unit, Development Data Group, World Bank; Abanokova (kabanokova@worldbank.org) is an economist in the Living Standards Measurement Unit, Development Data Group, World Bank and is a junior research fellow at the Higher School of Economics, National Research University, Russia; Carletto (gcarletto@worldbank.org) is the senior manager of the Strategy and Collaboratives Unit, Development Data Group, World Bank. We would like to thank Madeleine Gauthier, Nathan Ives, Hoa Nguyen, Lars Osberg, Anne Swindale, Petra Todd, and seminar participants at Australian National University, the Center for the Study of African Economies conference (Oxford), IARIW-TNBS conference (Arusha), Econometric Society’s Summer Meeting (Vanderbilt), and Statistics Canada’s International Methodology Symposium for their helpful discussion and feedback on the earlier drafts. We are grateful for the funding from the United States Agency for International Development (USAID).
# **1. Introduction**

Accurate poverty measurement is the prerequisite for policies aiming at reducing poverty. Yet, development practitioners face the typical challenges that the available household survey data underlying poverty estimates are either inadequate (e.g., do not offer nationally representative estimates) or outdated (e.g., do not offer timely estimates of poverty trends). Worse still, in the few countries where survey capacity is well established, data were known to turn out to exhibit varying degrees of incompatibilities over time due to changes in survey design (Deaton and Kozel, 2005). These data challenges could likely hinder effective policy implementation, especially for poorer countries with low statistical capacity (Devarajan, 2013; Jerven, 2019).<sup>1</sup> To address these challenges, alternative methods to obtain poverty estimates that rely on data imputation (instead of direct data collection through surveys) have become increasingly common (World Bank, 2021; Dang and Lanjouw, 2023).<sup>2</sup> Building on the seminal technique that imputes from a household consumption survey into a census to generate poverty maps (Elbers _et al.,_ 2003), recent studies have imputed from a household consumption survey into another survey to provide poverty estimates.<sup>3</sup> The central idea is to build an imputation model using appropriate predictor variables from an existing older consumption survey, which can be subsequently applied to the same variables in a more recent survey (that does not collect consumption data) to provide poverty estimates for the latter survey.

> 1 Serajuddin _et al._ (2015) show that over the period 2002-2011, of the 155 countries for which the World Bank monitors poverty data using the World Development Indicators (WDI) database, almost one-fifth (i.e., 28) have only one poverty data point and as many as 29 countries do not have any poverty data point in the same period. Furthermore, poorer countries have fewer surveys: a 10-percent increase in a country’s household consumption level is associated with almost one-third (i.e., 0.3) more surveys (Dang, Jolliffe, and Carletto, 2019). The ongoing Covid-19 pandemic could increase poverty and further exacerbate these data deprivations and digital divides for poor countries (Naude and Vinuesa, 2020).

> 2 Imputation techniques are regularly used by international organizations and national statistical agencies to fill in missing data gaps such as education statistics (UOE, 2020) and income data (US Census Bureau, 2017).

> 3 The poverty-mapping technique combines a household consumption survey and a non-consumption census, which allows us to provide poverty estimates at a more disaggregated level than available in the household survey.

2
> ~ 3 +6 Oo

5
~~+4~~ ~~—t—-I~~
We analyze multi-topic household survey data from a total of 22 survey rounds from six countries: Bangladesh (3), Ethiopia (1), Malawi (5), Nigeria (3), Tanzania (6), and Viet Nam (4), with the number of survey rounds for each country noted in parentheses. In the four Sub-Saharan African countries (Ethiopia, Malawi, Nigeria, and Tanzania), the data originate from the nationally-representative, multi-topic household surveys that have been implemented by the respective national statistical offices with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative. Being similar to the LSMS-type surveys supported by the World Bank, the surveys from Viet Nam are implemented biennially by the country’s General Statistical Office (GSO) with technical support from the World Bank. These surveys are generally regarded as being of high quality and are regularly employed by the national governments, international organizations, and academic researchers to provide estimates on household welfare.<sup>9</sup> The data sets include

- i. the Bangladesh Integrated Household Survey (BIHS) 2011/12, 2015, and 2018/19

- ii. the Ethiopia Socioeconomic Survey (ESS), 2018/19 round

- iii. the Malawi Integrated Household Survey (IHS), 2010/11, 2016/17, 2019/20 rounds

- iv. the Malawi Integrated Household Panel Survey (IHPS), 2010 and 2013 rounds

- v. the Nigeria General Household Survey (GHS)–Panel, 2010/11, 2012/13 and 2018/19

- rounds<sup>10</sup>

> 9 For example, Baulch (2011) considers the VHLSSs as having high quality data and heavily uses these surveys for poverty analysis. Other researchers analyze the LSMS-ISA surveys for various topics such as agricultural input uses (Sheahan and Barrett, 2017) or temperature shocks and household consumption (Letta, Montalbano, and Tol, 2018).

> 10 We did not include the 2015/16 round for Nigeria for comparability issues (e.g., the total consumption aggregate for this round does not include health care expenditures and it is not adjusted using temporal and spatial price deflators).

13
- vi. the Tanzania National Panel Survey (TZNPS) 2008/09, 2010/11, 2012/13, 2014/15, 2019/20, and 2020/21 rounds.

- vii. the Viet Nam Household Living Standards Survey (VHLSS) 2010, 2012, 2014, and 2016 rounds.

The sample sizes hover around 3,000 to 5,000 households in each survey round for the LSMSISA surveys (including Nigeria and Tanzania), 5,500 to 7,000 households for the BIHSs and the ESS, 9,300 households for the VHLSSs, and over 12,000 households for the Malawi IHS. The consumption data are deflated in the same survey year’s prices and are comparable across survey rounds for each country.<sup>11</sup> The objective is to produce the imputation-based welfare estimates of interest as if we did not have consumption data and then evaluate these imputation-based estimates against those based on the actual survey data (i.e., the “true” welfare rates).

For the poverty line, we use the national poverty lines for Ethiopia, Malawi, Tanzania, and Viet Nam and the international poverty lines of $1.90 (in 2011 Purchasing Power Parity (PPP) prices) for Bangladesh and Nigeria.<sup>12</sup> The extreme poverty line is defined as US$1.25 (2011 PPP) per day per capita for Bangladesh and Nigeria and half of the national poverty line for Viet Nam and Ethiopia. For Malawi and Tanzania, we use the national food poverty lines as the extreme poverty lines.

> 11 In particular, for Bangladesh, Tanzania, and Viet Nam, consumption data are deflated to <mark>2018/19 prices, 2020/21 prices, 2010 prices</mark> respectively <mark>. For the Malawi</mark> IHPSs and IHSs, c <mark>onsumption data are deflated to 2013 prices and 2010/11 prices r</mark> espectively.

> 12 For Bangladesh and Nigeria, we employ the international poverty line for analysis since official poverty data for these countries are based on different data sources that are not available to us, such as the Bangladesh <mark>Household Income Expenditure Survey (HIES) and the Nigeria Living Standards Surveys (NLSSs). However, note that Nigeria’s national poverty line calculated using NLSS 2018/19 is close to the international poverty line of $1.90 per person per day in 2011 PPP (Lain and Vishwanath, 2022). Furthermore, data comparability issues also exist with various rounds of the Bangladesh HIESs (Fernandez</mark> _<mark>et al.</mark>_ <mark>, 2024).</mark>

14
# _Main results_

To examine the sensitivity of imputation accuracy to various predictor variables, we build the estimation models on a cumulative basis, with the later models sequentially adding more variables to the basic models (Model 1 or Model 2). On the whole, we employ nine core imputation models across five countries.<sup>14</sup> Model 1 is the most parsimonious (or basic) model and consists of household size, household heads’ age and gender, household heads’ highest completed levels of schooling, a dummy variable indicating whether the head belongs to the ethnic majority group, the shares of household members in the age ranges 0-14, 15-24, 25-59 and 60 and older, a dummy variable indicating whether the head worked in the past 12 months, and a dummy variable indicating urban residence. Model 2 adds household asset variables and house (dwelling) characteristics to Model 1. Household assets include variables indicating whether the household has a car, motorbike, bicycle, desk phone, mobile phone, DVD player, television set, computer, refrigerator, air conditioner, washing machine, or electric fan. House characteristics include the construction materials for the house’s roof and wall and the type of water and toilet the household has access.<sup>15</sup> Models 1 and 2 include standard variables available in most LSMS-type surveys and other types of micro surveys.

Model 3 adds total food expenditures to Model 2, and Model 4 adds total non-food expenditures to Model 2. Models 5 to 8 add to Model 2, respectively, durables expenditures, health expenditures, education expenditures, and utilities expenditures (such as on electricity, water, and

> 14 For misspecified regressions, adding more variables may result in larger inconsistency (Snijders and Bosker, 1994; De Luca, Magnus, and Peracchi, 2018). As such, it is useful to examine imputation accuracy for different models.

> 15 For Viet Nam, house wall material is assigned numerical values using the following categories: 6 "cement", 5 "brick", 4 "iron/wood", 3 "earth/straw", 2 "bamboo/board", and 1 "others". Toilet type is assigned numerical values using the following categories: 6 "septic", 5 "suilabh", 4 "double septic", 3 "fish bridge", 2 "others", and 1 "none".

16
Unlike the indicators discussed above, multiple models perform better than average for the USAD poverty gap. Models 8 and 3 have the highest imputation accuracy of 65 percent, followed by Models 1, 2, 7 and 9 (62 percent) and Model 5 (58 percent).

We further plot in Figure 2 the imputation accuracy for mean consumption. Model 3, again, is the best performer that achieves an imputation accuracy rate of 65 percent, followed by Model 9 with an imputation accuracy rate of 46 percent. Adding soil quality and distance to facilities to Model 9 increase imputation accuracy for this model to 50 percent and adding nightlight to Model 9 increases imputation accuracy to 100 percent for Viet Nam.

# _Machine learning as alternative_

We consider machine learning (ML) as an alternative imputation method.<sup>17</sup> The standard ML procedures split a data sample into a training sample (to estimate the imputation model) and an estimation sample (to obtain out-of-sample predictions). In our context, the base survey and the target survey respectively correspond to the training sample and the estimation sample. Employing three common ML techniques, LASSO, Elastic Net, and Random Forest, we show the estimation results in Appendix B, Tables B.1 and B.2 for Tanzania and Viet Nam, respectively. The ML poverty estimates do not work for both countries, except for the estimates of consumption mean that are within one standard error of the true mean for LASSO and Elastic Net in Malawi.<sup>18</sup> These

> 17 See <mark>Mullainathan and Spiess (2017) and Athey and Imbens (2019) for recent reviews of ML in economics.</mark> 18 Lasso linear model and Elastic net linear model are trained in the first round and tested against the second round. Lambda in LASSO is selected by 10-fold cross-validation for out-of-sample prediction. Alpha and lambda in Elastic Net are selected by 10-fold cross-validation for out-of-sample prediction. The final selected variables and prediction models with statistics for Lasso and Elastic Net using postselection coefficient estimates are shown in Table B.3 for Tanzania and Table B.4 for Viet Nam (Appendix B). Random forest model is trained in the first round and tested against the second round. The number of sub-trees is set at 1,000. Both out-of-bag error and validation error are used to determine the best possible model. Importance matrix of the variables is shown in Table B.5 for Tanzania and Table B.6 for Viet Nam.

21
estimates presents a cost-effective option.<sup>23</sup> Furthermore, in contexts where relatively less intensive survey efforts can be spent on collecting such data (e.g., especially where receipts for such expenditures are strongly digitalized), these advantages appear even stronger. Given the increasingly popular digitalization of payment transactions around the world, these contexts may become much more available in the near future.

> 23 Dang _et al._ (2024) offer experimental evidence from Tanzania that suggests collecting data on either reduced or more aggregated food consumption categories could help significantly improve the imputation accuracy of poverty estimates.

31
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- <mark>Deaton, Angus, and John Muellbauer. (1980).</mark> _<mark>Economics and Consumer Behavior</mark>_ <mark>. C</mark> ambridge, UK: <mark>Cambridge University Press.</mark>

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- <mark>Letta, Marco, Pierluigi Montalbano, and Richard SJ Tol. (2018). "Temperature shocks, short-term growth and poverty thresholds: Evidence from rural Tanzania."</mark> _<mark>World Development</mark>_ <mark>, 112: 1332.</mark>

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- Mathiassen, Astrid, and Bjørn K. Getz Wold. (2021). "Predicting poverty trends by survey-tosurvey imputation: the challenge of comparability." _Oxford Economic Papers_ 73, no. 3: 1153-1174.

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- <mark>Snijders, Tom AB, and Roel J. Bosker. (1994). "Modeled variance in two-level models."</mark> _<mark>Sociological Methods & Research</mark>_ <mark>, 22(3): 342-363.</mark>

- Stifel, D. and Christiaensen, L. (2007) “Tracking Poverty over Time in the Absence of Comparable Consumption Data”. _World Bank Economic Review_ , 21, 317-341.

- Tanzania’s National Bureau of Statistics. (2011). _Basic Information Document—National Panel Survey 2010-11_ .

- Tarozzi, Alessandro. (2007). “Calculating Comparable Statistics from Incomparable Surveys, With an Application to Poverty in India”. _Journal of Business and Economic Statistics_ 25, no. 3:314-336.

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- Items.  Accessed on the Internet on May 24, 2021 at <u>https://www.census.gov/programs surveys/cps/technical-documentation/methodology/imputation-of-unreported-data-items.html</u>

34
# **Table 1. Predicted Poverty Rates Based on Imputation (percentage)**

|**Idi**|||||**Second round**|||||**True rates**|
|---|---|---|---|---|---|---|---|---|---|---|
|**ncators**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|**Model 7**|**Model 8**|**Model 9**||
|**_Panel A: Bangladesh 2015-2018/19_**|||||||||||
|Headcount poverty rate|13.0|**7.7***|8.5|**7.0***|**8.0**|**7.0***|**7.8***|N/A|N/A|7.3|
||(0.7)|(0.6)|(0.8)|(0.7)|(0.7)|(0.6)|(0.7)|||(0.6)|
|Near-poverty rate|**12.2***|9.5|**11.3**|10.2|9.9|9.2|9.5|N/A|N/A|12.2|
||(0.5)|(0.6)|(0.7)|(0.6)|(0.6)|(0.6)|(0.6)|||(0.6)|
|Extreme poverty rate|2.3|1.0|**0.9**|**0.6***|0.9|**0.8**|1.0|N/A|N/A|0.6|
||(0.3)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|||(0.2)|
|Poverty gap|2.6|1.4|1.4|**1.1***|1.4|**1.2***|1.4|N/A|N/A|1.1|
||(0.2)|(0.2)|(0.2)|(0.1)|(0.2)|(0.1)|(0.2)|||(0.1)|
|USAID Poverty gap|19.9|17.5|**16.5**|**15.9***|17.2|17.0|17.6|N/A|N/A|15.2|
||(0.7)|(1.0)|(0.9)|(0.9)|(0.9)|(1.0)|(1.0)|||(0.8)|
|N|5,604|5,604|5,604|5,604|5,604|5,604|5,604|||5,604|
|**_Panel B: Malawi 2016/17-2019/20_**|||||||||||
||53.2|**52.7**|60.5|**52.7**|53.6|**52.6**|**52.6**|**52.2**|**52.3**|51.1|
|Headcount poverty rate|(0.9)|(1.0)|(1.2)|(1.2)|(1.0)|(1.0)|(1.0)|(1.0)|(0.9)|(0.9)|
|Nt t|**14.0***|**14.3***|12.4|**14.2***|**14.4***|**14.4***|**14.3***|**14.5**|**14.4***|14.1|
|ear-povery rae|(0.4)|(0.4)|(0.5)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|
||<br>22.8|<br>**21.7**|<br>29.5|<br>**21.7**|<br>**22.0**|<br>**21.5**|<br>**21.8**|<br>**21.2***|<br>**21.8**|<br>20.6|
|Extreme poverty rate|(0.7)|(0.7)|(1.1)|(0.9)|(0.8)|(0.7)|(0.8)|(0.8)|(0.7)|(0.8)|
||<br>18.2|<br>**17.6**|<br>22.5|<br>**17.6**|<br>**17.9**|<br>**17.5***|<br>**17.7**|<br>**17.3***|<br>**17.7**|<br>17.1|
|Poverty gap|(0.4)|(0.5)|(0.7)|(0.6)|(0.5)|(0.5)|(0.5)|(0.5)|(0.4)|(0.4)|
||<br>**34.3**|<br>**33.5***|<br>37.2|<br>**33.4***|<br>**33.4***|<br>**33.3***|<br>**33.6***|<br>**33.3***|<br>**33.8***|<br>33.5|
|USAID Poverty gap|(0.4)|(0.4)|(0.6)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.5)|
|N|11,432|11,432|11,432|11,432|11,432|11,432|11,432|11,432|11,432|11,432|
|**_Panel C: Nigeria 2012/13-2018/19_**|||||||||||
|Headcount poverty rate|33.4|33.3|50.8|27.2|24.7|29.4|33.2|34.1|34.5|46.4|
||(1.9)|(2.0)|(2.6)|(2.1)|(1.9)|(1.9)|(2.1)|(2.1)|(2.0)|(1.9)|
|Near-poverty rate|<br>**12.2**|<br>**12.7**|<br>**13.6***|<br>**13.4***|<br>11.3|<br>**12.6**|<br>**12.5**|<br>**12.6**|<br>**12.2**|<br>13.7|
||(0.9)|(0.9)|(1.0)|(1.0)|(0.9)|(0.9)|(0.9)|(0.9)|(0.9)|(1.0)|
|Extreme poverty rate|15.1|14.8|26.4|9.2|9.9|12.3|14.9|15.5|15.8|22.2|
||(1.4)|(1.5)|(2.2)|(1.3)|(1.2)|(1.3)|(1.5)|(1.5)|(1.4)|(1.4)|
|Poverty gap|10.9|10.9|17.9|7.4|7.5|9.2|10.9|11.3|11.4|15.3|
||(0.8)|(0.9)|(1.3)|(0.8)|(0.8)|(0.8)|(0.9)|(0.9)|(0.9)|(0.8)|
|USAID poverty gap|**32.7***|**32.7***|35.3|27.2|30.4|31.3|**32.9***|**33.0***|**33.1***|33.0|
||(1.2)|(1.3)|(1.1)|(1.2)|(1.5)|(1.4)|(1.3)|(1.3)|(1.1)|(0.8)|
|N|4,976|4,976|4,976|4,976|4,976|4,976|4,976|4,976|4,976|4,976|
|**_Panel D: Tanzania 2019/20-2020/21_**|||||||||||
|Headcount poverty rate|**17.4***|**17.5***|**19.4**|**16.9***|**17.3***|**16.7**|**17.5***|**17.8***|**18.2***|17.8|
||(1.0)|(1.3)|(1.4)|(1.3)|(1.2)|(1.3)|(1.3)|(1.3)|(1.1)|(1.1)|
|Near-poverty rate|<br>**10.5***|<br>**10.9**|<br>**10.6***|<br>**10.9**|<br>**10.9***|<br>**10.3***|<br>**10.8***|<br>**11.0**|<br>**10.4***|<br>10.2|
||(0.7)|(0.7)|(0.8)|(0.8)|(0.8)|(0.7)|(0.7)|(0.8)|(0.7)|(0.7)|
|Extreme poverty rate|**9.7***|**9.4***|**11.1**|**9.1***|**9.3***|**9.0***|**9.5***|**9.7***|**10.3***|9.8|
||(0.8)|(0.9)|(1.1)|(1.0)|(0.9)|(1.0)|(1.0)|(1.0)|(0.9)|(0.8)|

36
|Poverty gap|**4.6***|**4.4***|5.3|**4.2**|**4.3***|**4.2**|**4.4***|**4.5***|**4.9***|4.6|
|---|---|---|---|---|---|---|---|---|---|---|
||(0.4)|(0.4)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.3)|
|USAID Poverty gap|**26.3***|**25.3***|**27.5**|**24.9***|**25.1***|**25.2***|**25.4***|**25.5***|**26.9***|25.9|
||(1.1)|(1.1)|(1.3)|(1.1)|(1.1)|(1.2)|(1.1)|(1.1)|(1.1)|(1.1)|
|N|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|**_Panel E: Viet Nam 2014-2016_**|||||||||||
|Headcount poverty rate|15.0|13.3|6.1|8.4|10.6|12.5|13.3|11.4|**9.6***|9.6|
||(0.5)|(0.6)|(0.5)|(0.5)|(0.5)|(0.6)|(0.6)|(0.6)|(0.5)|(0.4)|
|Near-poverty rate|9.3|8.5|4.9|6.0|**7.1***|8.0|8.5|7.7|**6.8***|6.9|
||(0.4)|(0.4)|(0.3)|(0.4)|(0.3)|(0.4)|(0.4)|(0.4)|(0.3)|(0.3)|
|Extreme poverty rate|2.0|2.0|0.7|**1.2***|**1.5**|2.1|2.1|2.0|2.0|1.2|
||(0.2)|(0.3)|(0.1)|(0.2)|(0.2)|(0.3)|(0.3)|(0.3)|(0.3)|(0.2)|
|Poverty gap|4.0|3.7|1.5|**2.3**|2.9|3.5|3.7|3.2|2.9|2.5|
||(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.3)|(0.2)|
|USAID Poverty gap|**26.7**|**27.6**|**24.9**|**27.4**|**27.1**|28.1|**27.8**|28.3|30.1|26.1|
||(0.7)|(0.9)|(1.3)|(1.2)|(1.0)|(1.0)|(0.9)|(1.2)|(1.7)|(0.9)|
|N|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347|
|**_Control variables_**|||||||||||
|Food expenditures|||Y||||||||
|Non-food expenditures||||Y|||||||
|Furnishings and durable household expenses|||||Y||||||
|Health expenditures||||||Y|||||
|Education expenditures|||||||Y||||
|Utilities expenditures||||||||Y|Y||
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Normal linear regression with bootstrapped standard errors is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. ’Near poor’ status is defined as living on an income between 100 and 125% of the poverty line. All indicators are expressed in percentage. The true rate is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

37
# **Table 2. Predicted Mean (Log) Consumption Based on Imputation**

|||||**S**|**econd roun**|**d**||||**True rates**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Model**<br>**1**|**Model**<br>**2**|**Model**<br>**3**|**Model**<br>**4**|**Model**<br>**5**|**Model**<br>**6**|**Model**<br>**7**|**Model**<br>**8**|**Model**<br>**9**||
|Bldh f 2015 2018/19|10.7|10.8|**10.8**|10.8|10.8|10.9|10.8|N/A|N/A|10.8|
|angaes, rom to|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|||(0.0)|
||12.1|12.1|**12.0**|12.1|12.1|12.1|12.1|12.1|12.1|12.1|
|Malawi, from 2016/17 to 2019/20|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
||11.3|11.3|11.0|11.4|11.4|11.3|11.3|11.3|11.3|11.0|
|Nigeria, from 2012/13 to 2018/19|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
||<br>**13.7**|<br>13.7|<br>13.7|<br>**13.7**|<br>**13.7**|<br>**13.7***|<br>**13.7**|<br>13.7|<br>13.7|<br>13.7|
|Tanzania, from 2019/20 to 2020/21|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
||<br>9.6|<br>9.7|<br>10.0|<br>9.8|<br>**9.8***|<br>9.7|<br>9.7|<br>9.7|<br>**9.8***|<br>9.8|
|Viet Nam, from 2014 to 2016|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
|Food expenditures|||<br>Y||||||||
|Non-food expenditures||||Y|||||||
|Furnishings and household expenses|||||Y||||||
|<br>Health expenditures||||||Y|||||
|<br>Education expenditures|||||||Y||||
|<br>Utilities expenditures||||||||Y|Y||
|<br>Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Normal linear regression with bootstrapped standard errors is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed consumption per capita for the second round uses the estimated parameters based on the data from the first round. 100 simulations are implemented. True consumption per capita is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

38
||Headcountp<br>fined.|overty rate<br>fipec.|Near pove<br>SpecoI|rty rate<br>spect|Extreme p<br>Spee.I|overty rate<br>jipead|Pover<br>fipec.I|ty zap<br>Spend|USAID P<br>jipec.I|overty gap<br>Spect|Consump<br>spec.I|tionmean<br>jipee.2|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Model 2: Demographics, employment,|oo7e|-0.017|<br>-0.036|-0.545|<br>-0037|0.185|<br>-0.000|0.205|<br>-0.000|<br>0.530%|<br>-0.000|0.263|
|assets, house characteristics<br>|(0.28)<br>|(0.29)<br>|(0.03)<br>|(0.27)<br>|(0.19)<br>|(050)<br>|(022)<br>|(035)<br>|(0.13)<br>|(0.27)<br>|(0.12)<br>|(0.26)<br>|
|Model 3|O53)"|ons|0.196|-0.735|O.314e|o.890*|oslo"|0.855|0.040|1.529|0.272|1.2274"|
|(adds food exp. toModel2)|(0.06)|(0.443|(0.18)|(0.83)|(0.12)|(49)|(0.15)|(053)|(0.04)|(0.54)|(0.05)|(0.58)|
|Model4|oo7e|-0.128|oo74|-0.626|0.104|0.591|-0.000|O49|-O.077|1.0g2**|-0.000|0.583|
|(adds nonfood exp. to Model 2)|(0.08)|(0.34)|(O.14)|(0.48)|(0.13)|(044)|(ol?)|(043)|m.07)|may|(0.00)|(0.43)|
|Model4|oov7e|-0.032|0.036|-0.527|-0077|O.178|-0.000|0.234|-0.089|O58|0.039|0.335|
|<br>(adds durables exp. toModel2)|(0.23)|(0.30)|tos)|(0.45)|(O18)|(032)|(0.26)|(41)|(0.104|(0.304|0.113|(0.30)|
|Model 6|oo7s|-0.028|0.036|-0.578|-0037|0.209|0.038|0.264|-0.115*|0.467"|-0.000|0.289|
|(addshealthexp.toModel2)|(0.17)|(0.26)|(0.03)|(0.30)|(0.19)|(031)|(0.19)|(034)|(0.06)|(0.24)|(0.14)|(0.25)|
|<br>Model7|0.130|0053|-0.036|-0.348|-0.037|0.186|-0000|0.205|-0.000|0.538"|0.039|0.304|
|<br>(adds educationexp. toModel2)|(0.28)|(0.313|(0.03)|(0.27)|(0.19)|(030)|(022)|(0335)|(0.13)|(0.28)|(0.113|(0.27)|
|Model 8|O186|o083|o074|-0.273|0.036|0.281|O.144|O.375|0.040|O.617"*|0.039|0.329|
|(adds utilities exp.taModel2)|(0.17)|(0.27)|(0.08)|(0.29)|(0.15)|(028)|(0.16)|(030)|(0.11)|(0.24)|(0.113|(0.29)|
|<br>Model 9|o571**|o357**|o.112*|-0.000|O17le|O.2see*|O.178*|O.25he*|-0.000|0.176|o.0rs|0.150%|
|(adds utilities exp. to demographic &|||||||||||||
|employirert)|(0.17)|(0.15)|(0.06)|CO.1L7)|(0.08)|(0.08)|(O10)|(O.13)|(0.13)|(0.18)|(0.03)|0.01)|
|<br>Model 10|0056|-0.046|-0.114|-0.597|-O.109|a.098|-0010|0.160|-0.048|0.483|-0.063|0.206|
|(adds distance to facilities to Model 2)<br>|(0.31)<br>|(0.33)<br>|O11)<br>|(0.29)<br>|(028)<br>|(036)<br>|(O31)<br>|m42)<br>|(0.18)<br>|(0.34)<br>|(0.21)<br>|(0.32)<br>|
|Model 11<br>|0.105<br>|on008<br>|-0.114<br>|-0.596<br>|-0.109<br>|0.129<br>|-O071<br>|0.134<br>|-0.048<br>|0.481<br>|o.004<br>|0.255<br>|
|(adds agricultural soilqualitytoModel2)|(0.30)|(0.32)|(0.113|(0.29)|(028)|(036)|(052)|(043)|(0.13)|(0.33)|(0.14)|(0.28)|
|<br>Model 12|o592|0351|o.o90|-0.009|0.004|0.064|0.093|0.146|-0.096|0.101|0.056|0.157|
|(adds distance to facilities to Model 9)|(0.25)|(0.22)|(0.0%)|(0.18)|(0.12)|(017)|(023)|(028)|(0.15)|(0.22)|17|(0.14)|
|Model15|o4soe*|O424e#|o.os0|0001|o.004|0.081|o.044|o.117|-0.048|0.125|0.056|0.136|
|<br>(adds agricultural soilqualitytoModel9)|(0.14)|(0.12)|(0.0%)|(0.17)|(0.12)|(017)|(020)|(024)|(0.18)|(0.23)|(0.05)|(0.02)|
|Trug estimates|||||||||||||
|Headcountpovertyrate||0.006|||||||||||
|||(0.013|||||||||||
|Nearpovertyrate||||o0go#|||||||||
|<br>||||(0.04)|||||||||
|Extremepov<br>rby rate||||||O02<br>|||||||
|||||||(0.00)|||||||
|Povertygap||||||||O03ge|||||
|||||||||(00007|||||
|USAIDpov<br>ry gap||||||||||0.010|||
|||||||||||(0.02)|||
|Corsumptionmean||||||||||||O.208+*<br>|
|||||||||||||(0.10)|
|Other modelparameters|||||||||||||
|Log of sample size ofbase sunny||-0.1 57+||-0.S90||O.071||0.005||-0.011||-0.076|
|||(0.04)||(0.13)||(O.10)||(O.10)||(0.14)||(0.05)|
|Interval lenzthbetweenbase & tamzet survey:||-0.160||-0.015||0.1 74g||-0.132**||-0.052||-0.041|
|||(0.05)<br>||(0.08)<br>||(O04)<br>||(O08)<br>||(0.05)<br>||(0.05)<br>|
|Normallivearregressionmodel||o.100%*||goog||o.022||o.004||-0.008||o.019|
|||(0.053||(0.01)||(02)||(0.02)||(0.013||(0.04)|
|Number of rounds used||-0.015||-0.143*||o.064*||o.103||0.029||-0.230°*|
|||(0.053||(0.08)||(003)||(O04)||(0.053||(0.10)|
|RB squared||0426||2079||-1295||-1.189||3.26]#4||-1.962|
|||(0.86)||(1.90)||(080)||(0.85)||(1.213||(1.33)|
|Counmy FE|||||||||||||
|Tana|oo0s||O.Dgat||0.36266||0.366"||O.413¢*||2.410||
||(0.027<br>||(0.01)<br>||fool)<br>||(oa)<br>||(0.02)<br>||tos)<br>||
|Malasri|-0.090"**||O24aeer||o.18o**||O.19])#*||O.022*||2.455"||
||(0.007||(0.00)||(0.004||(o.oo)||(0.007||tos)||
|Nigena|-0.105+**<br>||D695<br>||o.205**<br>||0.166"<br>||o.393*<br>||2.4626<br>||
|N|0.00)<br>314|314|O01<br>314|314|00<br>314|314|00<br>314|314|0.00<br>314|314|O19<br>314|314|
**Figure 1. Imputation Accuracy for Different Imputation Models, Poverty Indicators**

<!-- Start of picture text --> Headcount poverty rate Near poverty rate<br>m1 m1<br>m2 m2<br>m3 m3<br>m4 m4<br>m5 m5<br>m6 m6<br>m7 m7<br>m8 m8<br>m9 m9<br>m2+dist_facilities m2+dist_facilities<br>m2+soil_quality m2+soil_quality<br>m9+dist_facilities m9+dist_facilities<br>m9+soil_quality m9+soil_quality<br>m3+dist_facilities m3+dist_facilities<br>m3+soil_quality m3+soil_quality<br>m3+nightlight m3+nightlight<br>m2+nightlight m2+nightlight<br>m9+nightlight m9+nightlight<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Imputation accuracy (%) Imputation accuracy (%)<br>Extreme poverty rate Poverty gap<br>m1 m1<br>m2 m2<br>m3 m3<br>m4 m4<br>m5 m5<br>m6 m6<br>m7 m7<br>m8 m8<br>m9 m9<br>m2+dist_facilities m2+dist_facilities<br>m2+soil_quality m2+soil_quality<br>m9+dist_facilities m9+dist_facilities<br>m9+soil_quality m9+soil_quality<br>m3+dist_facilities m3+dist_facilities<br>m3+soil_quality m3+soil_quality<br>m3+nightlight m3+nightlight<br>m2+nightlight m2+nightlight<br>m9+nightlight m9+nightlight<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Imputation accuracy (%) Imputation accuracy (%)<br>USAID poverty gap<br>m1<br>m2<br>m3<br>m4<br>m5<br>m6<br>m7<br>m8<br>m9<br>m2+dist_facilities<br>m2+soil_quality<br>m9+dist_facilities<br>m9+soil_quality<br>m3+dist_facilities<br>m3+soil_quality<br>m3+nightlight<br>m2+nightlight<br>m9+nightlight<br>0 10 20 30 40 50 60 70 80 90 100<br>Imputation accuracy (%)<br><!-- End of picture text -->

**Note:** Imputation accuracy is the share of the estimates that are statistically insignificantly different from the true poverty rates for all countries and years. Red dashed line indicates mean accuracy.

40
# **Figure 2. Imputation Accuracy for Different Imputation Models**

<!-- Start of picture text --> Consumption mean<br>m1<br>m2<br>m3<br>m4<br>m5<br>m6<br>m7<br>m8<br>m9<br>m2+dist_facilities<br>m2+soil_quality<br>m9+dist_facilities<br>m9+soil_quality<br>m3+dist_facilities<br>m3+soil_quality<br>m3+nightlight<br>m2+nightlight<br>m9+nightlight<br>0 10 20 30 40 50 60 70 80 90 100<br>Imputation accuracy (%)<br><!-- End of picture text --> 41
**Figure 3. Distribution of imputed log of consumption**

<!-- Start of picture text --> Bangladesh 2015-2018/19 Malawi 2016/17-2019/20<br>m9<br>m9<br>m8<br>m8<br>m7<br>m7<br>m6<br>m6<br>m5<br>m5<br>m4<br>m4<br>m3<br>m3<br>m2<br>m2<br>m1<br>m1<br>5th10th 25th 50th 75th 90th95th 5th10th 25th 50th 75th 90th 95th<br>Percentile Percentile<br>Nigeria 2012/13 - 2018/19 Tanzania 2019/20-2020/21<br>m9 m9<br>m8 m8<br>m7 m7<br>m6 m6<br>m5 m5<br>m4 m4<br>m3 m3<br>m2 m2<br>m1 m1<br>5th10th 25th 50th 75th 90th95th 5th10th 25th 50th 75th 90th 95th<br>Percentile Percentile<br>Vietnam 2014-2016<br>m9<br>m8<br>m7<br>m6<br>m5<br>m4<br>m3<br>m2<br>m1<br>5th 10th 25th 50th 75th 90th95th<br>Percentile<br><!-- End of picture text --> 42
**Figure 4. Predicted consumption distribution, Model 3**

<!-- Start of picture text --> Bangladesh 2015-2018/19 Malawi 2016/17-2019/20<br>13.5<br>11.5<br>13<br>11<br>12.5<br>12<br>10.5<br>11.5<br>10<br>11<br>5 10 25 50 75 90 95 5 10 25 50 75 90 95<br>Percentile Percentile<br>Nigeria 2012/13-2018/19 Tanzania 2019/20-2020/21<br>15<br>12<br>14.5<br>11.5<br>14<br>11<br>13.5<br>10.5<br>13<br>10<br>12.5<br>5 10 25 50 75 90 95 5 10 25 50 75 90 95<br>Percentile Percentile<br>Vietnam 2014-2016<br>11<br>95% CIs of true value<br>10.5<br>True percentile value<br>10<br>95% CIs of imputed value<br>9.5<br>Imputed percentile value<br>9<br>8.5<br>5 10 25 50 75 90 95<br>Percentile<br>Log of consumption Log of consumption<br>Log of consumption Log of consumption<br>Log of consumption<br><!-- End of picture text --> 43
# **Figure 5. Predicted FGT indexes, Tanzania 2019/20- 2020/21**

<!-- Start of picture text --> Panel 1: FGT 1 Panel 2: FGT 2<br>7 3<br>6 2.5<br>5 2<br>4 1.5<br>3 1<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Panel 3: FGT 3 Panel 4: FGT 4<br>1.5 1<br>.8<br>1 .6<br>.4<br>.5 .2<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Panel 5: FGT 5<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model<br>Estimated poverty gap (%) Estimated poverty gap (%)<br>Estimated poverty gap (%) Estimated poverty gap (%)<br>Estimated poverty gap (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true poverty gap. Dashed lines represent the true poverty gap. Dotted lines represent confidence intervals of the true poverty gap. Estimates are obtained using the normal linear regression models.

44
# **Figure 6. Number of Models with Predicted Estimates That Are Statistically Insignificantly Different from the True Poverty Estimates, Within-Year Imputation**

<!-- Start of picture text --> Panel A: Headcount poverty Panel B: Near poverty<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>Bangladesh Malawi Vietnam Bangladesh Malawi Vietnam<br>Ethiopia Nigeria Tanzania Ethiopia Nigeria Tanzania<br>Panel C: Extreme poverty Panel D: Poverty gap<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>Bangladesh Malawi Vietnam Bangladesh Malawi Vietnam<br>Ethiopia Nigeria Tanzania Ethiopia Nigeria Tanzania<br>Panel E: USAID poverty gap Panel F: Consumption mean<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>Bangladesh Malawi Vietnam Bangladesh Malawi Vietnam<br>Ethiopia Nigeria Tanzania Ethiopia Nigeria Tanzania<br>Number of models Number of models<br>Number of models Number of models<br>Number of models Number of models<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. Estimates are obtained using the normal linear regression models.

45
# **Appendix A: Additional Tables and Figures**

# **Table A.1. Household consumption model, Bangladesh 2015**

||**Model 1**<br>***|**Model 2**<br> ***|**Model 3**<br> ***|**Model 4**<br> ***|**Model 5**<br> ***|**Model 6**<br> ***|**Model 7**<br> ***|
|---|---|---|---|---|---|---|---|
|Household size|-0.054<br> (0.00)|-0.067<br> (0.00)|-0.024<br> (0.00)|-0.031<br> (0.00)|-0.055 <br> (0.00)|-0.062<br> (0.00)|-0.072<br> (0.00)|
|Age of HH Head|0.002***<br>|0.001**<br>|0.000<br>|0.000<br> <br>|0.002***<br>|0.001**<br>|0.000<br>|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
||0.058***|-0.005|0.033***|-0.023**|-0.014|0.003|-0.020|
|HH Head is Female|(002)|(001)|(001)|(001)|(001)|(001)|(001)|
|Head has less than 5 years of schooling|.<br>0.091***<br> (0.02)|.<br>0.039***<br> (0.01)|.<br>0.022***<br> (0.01)|.<br>0.005<br> (0.01)|.<br>0.029**<br> (0.01)|.<br>0.031**<br> (0.01)|.<br>0.035**<br> (0.01)|
|Head has 5-9 years of schooling|0.204***<br> (0.01)|0.078***<br> (0.01)|0.040***<br> (0.01)|0.015*<br> <br> (0.01)|0.051***<br> (0.01)|0.072***<br> (0.01)|0.074***<br> (0.01)|
|Hd h 10 f hli|0.451***|0.151***|0.079***|0.033**|0.115***|0.145***|0.134***|
|ea as or more years o scoong|(0.02)<br>|(0.02)<br>|(0.01)<br>|(0.01)<br>|(0.02)<br>|(0.02)<br>|(0.02)<br>|
|Share of HH members in 0-14|-0.742***<br> (0.04)|-0.451***<br> (0.03)|-0.184***<br> (0.02)|-0.202***<br> (0.03)|-0.348***<br> (0.03)|-0.386***<br> (0.03)|-0.533***<br> (0.03)|
||-0.187***|-0.116***|-0.027<br>|-0.092***|-0.139***|-0.068**|-0.135***|
|Share of HH members in 15-24|<br> (0.04)|<br> (0.03)|<br> <br> (0.02)|<br> (0.02)|<br> (0.03)|<br> (0.03)|<br> (0.03)|
||-0.322***|-0.187***|-0.086***|-0.047**|-0.128***|-0.210***|-0.129***|
|Share of HH members in 60 and older|(0.03)<br>|(0.03)<br>|(0.02)<br>|(0.02)<br>|(0.03)<br>|(0.03)<br>|(0.03)<br>|
|HH Head did wage/salary work during the last 7 days|-0.181***|-0.094***|-0.046***|-0.029***|-0.060***|-0.076***|-0.095***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|HH Head was self-employed during the last 7 days|0.027**<br> (0.01)|0.022*<br> (0.01)|-0.014**<br> (0.01)|0.025***<br> (0.01)|0.015<br> <br> (0.01)|0.037***<br> (0.01)|0.013<br> (0.01)|
|Log of food expenditures|||0.698***<br> (0.01)|||||
|Log of nonfood expenditures||||0.504***||||
|||||(0.01)||||
|Log of durable expenditures|||||0.182***<br> (0.01)|||
|||||||0.094***||
|Log of health expenditures||||||(000)||
|Log of education expenditures||||||.|0.017***<br> (0.00)|
|||0021|0000|0025|0013|0016|0028|
|Household owns a radio||.<br> (0.04)|.<br> (0.02)|.<br> (0.03)|.<br> (0.04)|.<br> (0.04)|.<br> (0.04)|
|Household owns a television||0.134***<br>(001)|0.055***<br>(001)|0.072***<br>(001)|0.037***<br>(001)|0.120***<br>(001)|0.131***<br>(001)|
|Household owns a audio cassette/cd player||.<br>0.028<br> (0.02)|.<br>0.024*<br> (0.01)|.<br>0.006<br> (0.02)|.<br>0.001<br> (0.02)|.<br>0.028<br> (0.02)|.<br>0.030<br> (0.02)|
|||-0007|0019*|-0013<br>|-0039**|-0005|-0008|
|Household owns a sewing machine||.<br> (0.02)|.<br> (0.01)|.<br> <br> (0.01)|.<br> (0.02)|.<br> (0.02)|.<br> (0.02)|
|||0.164***|0.091***|0.062***|0.104***|0.145***|0.166***|
|Household owns a stove / gas burner||(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|
|Household owns a biccle||0.040***|0.029***|-0.001|0.001<br>|0.034***|0.026**|
|y||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Household owns a motor vehicles||0.242***<br> (0.02)|0.154***<br> (0.01)|0.088***<br> (0.02)|0.087***<br> (0.02)|0.227***<br> (0.02)|0.250***<br> (0.02)|
|||0.163***|0.084***|0.043***|0.029**|0.143***|0.154***|
|Household owns a mobile phone||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Hhld i||0.114***|0.076***|0.019<br>|0.088***|0.091***|0.110***|
|ouseo owns an ron||(0.02)|(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|
|Household owns an electric fan||0.127***<br> (0.01)|0.064***<br> (0.01)|0.048***<br> (0.01)|0.051***<br> (0.01)|0.119***<br> (0.01)|0.122***<br> (0.01)|
|||0.143***|0.075***|0.058***|0.093***|0.130***|0.141***|
|Log of total floor area of the dwelling||(0.01)|(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|
|Hhld dlli ll il||0.015<br>|0.023***|0.010|-0.015|0.017|0.012|
|ouseo weng wa materas||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Household dwelling roof materials||0.014<br>|0.032*<br>|-0.015<br>|-0.018<br>|0.003<br>|0.014<br>|
|<br>Household dwelling floor materials||(0.03)<br> 0.138***<br>|(0.02)<br> 0.084***<br>|(0.02)<br> 0.062***<br>|(0.03)<br> 0.102***<br>|(0.03)<br> 0.132***<br>|(0.03)<br> 0.135***<br>|
|Hhld dlli||(0.01)<br> 0.078***|(0.01)<br> 0.037***|(0.01)<br> 0.022*<br>|(0.01)<br> 0.064***|(0.01)<br> 0.066***|(0.01)<br> 0.076***|
|ouseo weng water access||(0.02)|(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|
|Household toilet is water sealed||<br>0.036***<br> (0.01)|<br>0.025***<br> (0.01)|<br>0.008<br> (0.01)|<br>0.015<br> <br> (0.01)|<br>0.031***<br> (0.01)|<br>0.034***<br> (0.01)|
|||-0.021|-0.002|-0.015|-0.016|-0.016|-0.019|
|Household toilet is other types||<br>|<br>|<br>|<br>|<br>|<br>|
|_cons|11.110***|(0.02)<br>10.085***|(0.01)<br>3.188***|(0.01)<br>5.777***|(0.01)<br>9.212***|(0.01)<br>9.495***|(0.02)<br>10.131***|
||(0.04)|(0.06)|(0.07)|(0.08)|(0.06)|(0.06)|(0.06)|
|sigma_e<br>sigma_u|<br>0.38<br>0.14|<br>0.32<br>0.11|<br>0.17<br>0.05|<br>0.24<br>0.07|<br>0.29<br>0.10|<br>0.30<br>0.09|<br>0.32<br>0.10|
|rho<br>|0.13<br>|0.10<br>|0.06<br>|0.09<br>|0.10<br>|0.09<br>|0.10<br>|
|r2_o<br>N|0.33<br>5447|0.54<br>5447|0.87<br>5445|0.75<br>5447|0.61<br>5441|0.60<br>5447|0.55<br>5447|

46
|Household size|Modell<br>-0.103*)<br>|Model?<br>-0.002<br>|_Model3___<br>0.083"<br>|—Modeld<br>0.107"<br>|Model<br>5<br>0.105"<br>|Model6<br>.100""*<br>|Model?<br>—-0.093***<br>|
|---|---|---|---|---|---|---|---|
||(0.01)<br>|(0.00)<br>|(0.01)<br>|(0.01)<br>|(0.01)<br>|(0.01)<br>|(0.01)<br>|
|Head'sage|-0.003*|—.001**||0.003"|-0.003**|-0.003**|—-0.004*|—-0.004***|
|<br>Head is female|<br>(0.00)<br>-0.037*<br>|<br>(0.00)<br>gois*|<br>|(0.00)<br>0.032<br>|(0.00)<br>-0.044**<br>|<br>(0.00)<br>0.040"<br>|<br>(0.00)<br>-0.036*<br>|(0.00)<br>-0.044**<br>|
|Headhasprimaryeducation|(0.02)<br>O.120**|(0.01)<br>0.034|(0.02)<br>O0eie|(0.02)<br>0|(0.02)<br>86|(0.02)<br>050**|(0.02)<br>0.045*|
|<br>|(0.02)<br>|(0.01)<br>|(0.02)<br>|(0.02)<br>|(0.02)<br>|<br>(0.02)<br>|(0.02)<br>|
|Headhassecondary education|O.259e*<br>|O.09se*)<br>|S74<br>|.249e"<br>|0.205***<br>|O.124ee)<br>|O17<br>|
|Headhashighereducation|(0.03)<br>O.44ae*|(0.01)<br>O14|(0.03)<br>0.270|(0.03)<br>0.436|(0.03)<br>0.986|(0.03)<br>ase)|(0.03)<br>0.249%"|
|<br>|(0.04)<br>|(0.01)<br>|(0.03)<br>|(0.04)<br>|(0.04)<br>|(0.04)<br>|(0.04)<br>|
|‘Share ofhousehomembers age 0-14<br>|-0.261*<br>|0.010<br>|0.236"<br>|-0.286**<br>|-0.205**<br>|0.093<br>|-0.090<br>|
|5<br>Share ofhousehoHmembers age 15-24|(0.06)<br>-0.122*<br>(0.04)|(0.02)<br>O43)<br>(0.01)|(0.05)<br>0.1<br>(0.04)|(0.06)<br>-0149**<br>(0.04)|(0.06)<br>0118"<br>(0.04)|(0.06)<br>0.053<br>(0.04)|(0.06)<br>-0.053<br>(0.04)|
|Share ofhousehoHmembers age 60 and alder|0.098<br>(0.07)|0069"<br>(0.02)|0.13<br>(0.04)|4".5*<br>(0.07)|0.162**<br>(0.04)|gy)<br>(0.06)|0.199"<br>(0.06)|
|Head isworking|0.037*<br>|a.o04<br>|-0.005<br>|0.034<br>|0.032<br>|0.036*<br>|0.033<br>|
|Rural|(0.02)<br>0.344"|(0.01)<br>0149")|(0.02)<br>0.140|(0.02)<br>0.3939"|(0.02)<br>OT|(0.02)<br>0130"|(0.02)<br>0.063|
||(0.04)|(0.01)|(0.03)|<br>(0.04)|<br>(0.04)|(0.04)|(0.04)|
|Log of foodconsumption peraeg||0.333**:<br>(0.01)||||||
|Logofnon-food consummtion meraea|||0.27304<br>(0.01)|||||
|Log ofeducationexpenditures peraeq||||g.og9**<br>(0.00)||||
|Lag of utilities peraeq|||||ggsa***<br>||0.042"<br>|
|Household owns a television|||||(0.00)|g.0a3***|(0.00)<br>|O.074**|
|||||||(0.03)|(0.03)|
|Householdowns an CD /DVD player||||||0.053*<br>|0.051<br>|
|Householdowns a refrigerator||||||(0.03)<br>O1s3**)<br>|(0.03)<br>O14<br>|
|||||||(0.03)<br>|(0.03)<br>|
|Householdowns a bicywek||||||0.069<br>(0.07)|0.060<br>(a.07)|
|Householdowns a matoreyele||||||0.027<br>|0.013<br>|
|Household owns a car||||||(0.09)<br>O24)<br>(0.06)|(0.09)<br>0.230"<br>(0.06)|
|Householdowns adeskphone||||||0.020<br>|0.002<br>|
|Househald owns a mobile phone||||||(0.03)<br>0.129***<br>(0.02)|(0.03)<br>0.041<br>(0.02)|
|Household owns a radio||||||gos)<br>|d.ogi*<br>|
|Household owns ekcrtic stave||||||(0.02)<br>0.001<br>(0.03)|(0.02)<br>-0.010<br>(0.03)|
|Log ofresidentialarea per capita||||||O423*)<br>|0.391"*<br>|
|Roofismade of concrete/metal sheets||||||(0.05)<br>a.s4*<br>(0.03)|(0.05)<br>0.037<br>(0.03)|
|Roofis made of other materials||||||g.aag**<br>|O.036**<br>|
|Wall is made of stanes/stanes and other||||||(0.04)<br>0.063*<br>(0.04)|(0.04)<br>0.055<br>(0.04)|
|Wallismadeofblacks/bricks||||||a.oao*<br>|0.054<br>|
|Wall<br>is made of<sup>ather</sup>||||||(0.04)<br>o.110*<br>(0.06)|(0.04)<br>0.039<br>(0.06)|
|Inyroved water||||||0.014|0.012|
|||||||(0.03)|(0.03)|
|Improved toikt||||||gov)<br>|O.072**<br>|
|Floor is made ofcement||||||(0.03)<br>gigs)<br>(0.03)|(0.02)<br>d.096"*<br>(0.03)|
|Floor is made ofother material||||||Oise)<br>(0.04)|0.175<br>(0.04)|
|_cans|10.485**<br>|1.438<br>|9.229"<br>|10 S00**<br>|10.122**<br>|9.789***<br>|age<br>|
|spmaé|0.08)<br>a4}|(0.06)<br>0.16|(0.10)<br>0.43|(0.03)<br>047|(0.03)<br>0.44|(0.09)<br>0.45|(0.09)<br>0.44|
|<br>sema u<br>|0.35<br>|0.08<br>|0.29<br>|0.35<br>|0.33<br>|0.33<br>|0.32<br>|
|rho<br>|0.36<br>|0.18<br>|0.32<br>|0.36<br>|0.34<br>|0.35<br>|0.34<br>|
|12_0<br>N|0.42<br>3377|0.95<br>3379|0.57<br>3377|0.43<br>3377|0.47<br>3377|0.49<br>3379|0.51<br>3379|
<!-- Start of picture text --> Model 1 Model 7 Model 3 Model 4 Model 5. Model 6. Model 7 Model § Model 9<br>ousehod size -0.034"* -O.006* 0.051*"* -O.02** 0.054" O.095""* -0.1 058" 40.06" -O.07 0<br>(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)<br>Head's aze -0.002** 0.002" 0.002 0.000 0,002 -0.00a** -O,00G* 40.00 -O.00ae*<br>Head « female (0.0010.016* (0.001-0.009 (0.0010.007 (0.00)-O.011* fo.00%0.006 0.00;0.006 (0.001-O.018"* (0.00100 (0.00%-O.016*<br>(0.01) (0.014 (0.00) fol (0.01) (0.01) (0.01) fo.01) fO.014<br>Head has primary educaton 0, 14 gre O07 O.0@0ReR 0.05 O05 3h O, Deere OO rRR O07 Lee 0.1 4orack<br>fO.011 fo.014 fO.01i foul fo.01i fO.014 fO.01i 0.014 fO.014<br>Head has jumor secondary education 0.21 (ee O.0g4 ee O.0tge O.0Gaee* O.0gee OOS O.Oegeee Oe OL 1 Seer<br>(0.01) (0.014 (0.01) fol (0.01) (0.01) (0.01) fo.01) fO.014<br>Head has secondary education O.35ghr 0,1) Sgesee O.0gaeRE 0058 O, Laser 0, | here 0.1 0.135 O34grack<br>(0.02) (0.01) (0.013 (O01 (0.01) (0.01) (0.01) (0.01) (0.02)<br>Head has dploma/degree O.eagek O.285"" O.12ee 0.034 OL adapeee O.2geee O.2raee Oaggee* Oa] ee<br>(0.02) (0.02) (0.01) (O02) (0.02) (0.02) (0.02) (0.02) (0.02)<br>Shae of househoW menbers aze 0-14 0.72) O45 0.216" - D0 RE O41 0.4 Tee Ofer 40.4346 -OB GA<br>Shane of household members aze 15-24 (0.08)40. 2a * (0.03)-O.11 6 (0.0130.051" (0.02)-O.03 7" (0.02)0.108 (0.02)40.126** 0.03)-0.13]*** (0.02)40.1116" (0.03)-O.21 ge<br>(0.02) (0.02) (0.01) (oly (0.02) (0.02) (0.02) (0.02) (0.02)<br>Shane of household menbers aze 60 and ober 2156 0.0 O.04g 46 -QWO 52h 0.07346 0.1] 0.0 sk 0.0 e -0.205<br>(0.08) (0.02) (0.013 (0.02) (0.02) (0.02) £0.02) (0.02) (0.03)<br>Head & employed fir a wage/salaryfcomuntssion m the<br>last 12 months Quek O.04ge* O.0LS** o.013* 0.04 1 O04)" O.0Ggeee O40 QuOSeee<br>fo.011 ro.014 fO.017 rool 0.01 0.014 fo.ali fO.011 ro.014<br>Head ergaged mm casualizarrar bor m the last 12 months Oat 0.0 0.0176 -QW0 55 0.091 #6 O.097ee O01 0. L004 -O.a0eet<br>Usban (2.01)0.44] ""* (0-O . 226"01) (00 . 00)100" (O-0. 0 111 (0.01)0.225""" (0.01)0.20 5"** (0.01)-O.225* (0.01)4.20" (- 0 .013.59]*#<br>0.021 roc fO.017 rool 0.02 0.021 fO.021 fo. roc<br>Log of food coms umepton per capita, O75ep<br>(0.00)<br>Log of non-food commmption per capita O.gagee<br>rool<br>Log of fixe: hmigs expenses per capita OL 105<br>(0.00)<br>Log of health expenditures per capita O01<br>0.001<br>Log of educationexpend tues per capita O.010#*<br>(0.00)<br>Lof of utes per capita OL0gg* QOS<br>(0.001 ro.oni<br>Househol owns a car 0.4276 O.S55re 0.054 Org 0.435) Oday O412ee*<br>(0.08) (0.00) (0.02) (0.03) (0.08) (0.03) (0.08)<br>HousehoH owns a motorcyce 0.207 O.0gp ee 0.02 OL Lege 020g adil O19<br>fO.031 fo.011 fod fO.02' fO.081 0.031 (0.061<br>HousehoH owns a bicyck O.08e** O.OL ge 0.03" O.06apee O.0ggee O.O8ae O.Og<br>(0.01) (0.00) (O01) (o.01) (0.01) (0.01) (o.01)<br>HousehoH owns a mobile phone 0.20466 O. 10s O.01L* 0, 15peee 0, L peek 0, ogee Ogee"<br>fo.014 0.001 foul fo.01i fO.014 fO.01i 0.014<br>HousehoH owns an CD / DVD player O.088** OOS 0.016 O.0g see Ose Gea O08]<br>(0.02) (0.01) fol (0.02) (0.02) (0.02) (0.0)<br>Househol own a teevision 0, 14g she O0546k 0.0G4 ee O.L3aee 0, La Ree 0. Lage 0.132<br>(O02 fO.01i foul fO.02 0.021 (0.021 (O01<br>HousehoH owns a commuter 0.18] ** O11 1 0.061* OL Lager O.1ash* 0.174 O.17aee*<br>HousehoH own a wfizerator (0.08%)0,1) 0-66 (0.01;O.05.36k (0.02)0.016 fo.)0.052 (0.08)0, Lape rk (0.03)0. Taq eke (0.08)O11 gee<br>HousehoH owns a am conditioner (0.02)-0.002 (0.01)4.012 (0.02)-0.008 (o.02)0.011 0.02)0.001 (0.02)-0.007 (0.02O05<br>(0.113 (0.08) (0.08) (0.115 (0.11) (0.11) (0.115<br>HousehoH cowry a fin OO Fee OD.geehk 0.068 OOS gRRRE O.0F Le OOF ark OO gAR RE<br>(0.02) (0.013 (0.02) (0.02) (0.02) £0.02) (0.02)<br>HousehoH owns a vras hing machme 0.28566" Cader 0.061 O.200Fe* 0.336 0.3016 Oagae*<br>(O.09) (0.05) (0.08) (os) (0.08) (0.09) (0.064<br>Log of resdential area per capita 0.30546 O.21 (Fe -O.021 O25 seh O314 O30 O27<br>(0.02) (0.013 (O01 (0.02) (0.02) £0.02) (0.02)<br>HouwsehoH dwelling has improved walk O.02geeee 0.007 0.004 O.020Fe* 0.025 0.0 OU<br>(0.013 (0.00) (O01 (0.013 (0.01) (0.01) (0.015<br>Househo dwelime has mmoroved 2o0f O06 Ae O02 0.001 OOS See OO eer aRE OO O.0e ere<br>(0.01) (0.00) (O01) (o.01) (0.01) (0.01) (o.01)<br>Household dwelling has mmproved floor O.08 OLD gree 0.003 O06 Ree OO 0.0 gee Oona<br>HousehoH vrater source is oroved (0O . 013055** ( 0. 01)005 (O01o.o14* (0.013O.0e* (0.01)OOS ARE (0O .0 1)Ga ee (0O . 0150Gae*<br>(0.014 (0.01) fol (0.01) (0.01) (0.01) fo.01)<br>HousehoH toilet facility nupnoved 0.10546 O.0g 3k O.0G3 ie OO RRR OOS RARE 0. 10g RR OO<br>(0.02) (0.01) (O01 (0.02) (0.02) (0.02) (0.02)<br>Househo has mosauito nets O.052*** 0.004 0.04 OO OOS O.OR4 ee O.0Gae*<br>cone 13.2446* (0.01413. 4se* (0.00)3g sek (O00)5.414 eRe (0.01)12.131" (012 soa . 01) (0.01)13.0 foLa . 01)290s 12. e726"<br>O04) (0.04) (0.05) (0.07) (0.05) (0.04) (0.04) (0.05) (0.05)<br>sigma e O41 036 ols 0.25 ost 036 0.35 0.35 a4<br>sige 1 0.16 O14 0.05 O.08 O12 Ola 0.14 0.13 O15<br>tho O.14 O13 0.08 0.08 O11 O11 0.15 o.12 0.12<br>ao 0.53 064 O92 0.84 0.69 0.66 0.65 0.68 0.37<br><!-- End of picture text -->

|ousehodsize|Model 1<br>-0.034"*|Model7<br>-O.006*|Model3<br>0.051*"*|Model4<br>-O.02**|Model 5.<br>0.054"|Model 6.<br>O.095""*|Model 7<br>-0.1058"|Model§<br>40.06"|Model9<br>-O.070|
|---|---|---|---|---|---|---|---|---|---|
|<br>Head's aze<br>|(0.00)<br>-0.002**<br>|(0.00)<br>0.002"<br>|(0.00)<br>0.002<br>|(0.00)<br>0.000<br>|(0.00)<br>0,002<br>|(0.00)<br>-0.00a**<br>|<br>(0.00)<br>-O,00G*<br>|(0.00)<br>40.00<br>|(0.00)<br>-O.00ae*<br>|
|Head «female|(0.001<br>0.016*<br>|(0.001<br>-0.009<br>|(0.001<br>0.007<br>|(0.00)<br>-O.011*<br>|fo.00%<br>0.006<br>|0.00;<br>0.006<br>|(0.001<br>-O.018"*<br>|(0.001<br>00<br>|(0.00%<br>-O.016*<br>|
||(0.01)|(0.014|(0.00)|fol|(0.01)|(0.01)|(0.01)|fo.01)|fO.014|
|Headhas primary educaton|0, 14gre<br>|O07<br>|O.0@0ReR<br>|0.05<br>|O053h<br>|O,Deere<br>|OO rRR<br>|O07Lee<br>|0.1 4orack<br>|
|Headhas jumorsecondaryeducation|fO.011<br>0.21 (ee<br>|fo.014<br>O.0g4ee<br>|fO.01i<br>O.0tge<br>|foul<br>O.0Gaee*<br>|fo.01i<br>O.0gee<br>|fO.014<br>OOS<br>|fO.01i<br>O.Oegeee<br>|0.014<br>Oe<br>|fO.014<br>OL1Seer<br>|
||(0.01)<br>|(0.014<br>|(0.01)<br>|fol<br>|(0.01)<br>|(0.01)<br>|(0.01)<br>|fo.01)<br>|fO.014<br>|
|Headhas secondary education|O.35ghr<br>(0.02)|0,1) Sgesee<br>(0.01)|O.0gaeRE<br>(0.013|0058<br>(O01|O,Laser<br>(0.01)|0, | here<br>(0.01)|0.1<br>(0.01)|0.135<br>(0.01)|O34grack<br>(0.02)|
|Headhas dploma/degree|O.eagek<br>|O.285""<br>|O.12ee<br>|0.034<br>|OLadapeee<br>|O.2geee<br>|O.2raee<br>|Oaggee*<br>|Oa] ee<br>|
||(0.02)<br>|(0.02)<br>|(0.01)<br>|(O02)<br><br>|(0.02)<br>|(0.02)<br>|(0.02)<br>|(0.02)<br>|(0.02)<br>|
|Shae ofhousehoWmenbers aze0-14<br>|0.72)<br>|O45<br>|0.216"<br>|-D0<br>RE<br>|O41<br>|0.4Tee<br>|Ofer<br>|40.4346<br>|-OBGA<br>|
|Shaneofhouseholdmembersaze15-24|(0.08)<br>40.2a*|(0.03)<br>-O.116|(0.013<br>0.051"|(0.02)<br>-O.037"|(0.02)<br>0.108|(0.02)<br>40.126**|0.03)<br>-0.13]***|(0.02)<br>40.1116"|(0.03)<br>-O.21ge|
||<br>(0.02)|(0.02)|(0.01)|<br>(oly|(0.02)|(0.02)|(0.02)|(0.02)|<br>(0.02)|
|Shane ofhouseholdmenbers aze60and ober<br> <br>|2156<br>(0.08)|0.0<br>(0.02)|O.04g46<br>(0.013|-QWO52h<br>(0.02)|0.07346<br>(0.02)|0.1]<br>(0.02)|0.0 sk<br>£0.02)|0.0 e<br>(0.02)|-0.205<br>(0.03)|
|Head & employed fir awage/salaryfcomunts<br>sion mthe<br>last12months|Quek|O.04ge*|O.0LS**|o.013*|0.041|O04)"|O.0Ggeee|O40|QuOSeee|
|<br>Headergaged mmcasualizarrarborm the last 12months<br>|fo.011<br>Oat<br>|ro.014<br>0.0<br>|fO.017<br>0.0176<br>|rool<br>-QW055<br>|0.01<br>0.091 #6<br>|0.014<br>O.097ee<br>|fo.ali<br>O01<br>|fO.011<br>0.L004<br>|ro.014<br>-O.a0eet<br>|
|Usban|(2.01)<br>0.44]""*<br>|(0**.**01)<br>-O226"<br>|(0**.**00)<br>0100"<br>|(O**0**1<br>-0.11<br>|(0.01)<br>0.225"""<br>|(0.01)<br>0.205"**<br>|(0.01)<br>-O.225*<br>|(0.01)<br>4.20"<br>|(**0**.013<br>-.59]*#<br>|
|Logoffood comsumepton per capita,|<br>0.021|<br>roc|<br>fO.017<br>O75ep<br>(0.00)|<br>rool|0.02|<br>0.021|fO.021|fo.|<br>roc|
|Logofnon-food commmptionper capita||||O.gagee<br>||||||
|Logoffixe:hmigs expenses percapita||||rool|OL105<br>(0.00)|||||
|Logofhealthexpenditurespercapita||||||O01||||
|||||||0.001||||
|Log ofeducation<br>expendtuespercapita|||||||O.010#*<br>|||
|Lofofutes percapita|||||||(0.00)|OL0gg*<br>|QOS<br>|
|Householowns acar||0.4276<br>|O.S55re<br>|0.054<br>|Org<br>|0.435)<br>|Oday<br>|(0.001<br>O412ee*<br>|ro.oni|
|||(0.08)<br>|(0.00)<br>|(0.02)<br>|(0.03)<br>|(0.08)<br>|(0.03)<br>|(0.08)<br>||
|HousehoHownsamotorcyce||0.207|O.0gpee|0.02|OLLege|020g|adil|O19||
|<br>HousehoHowns abicyck||fO.031<br>O.08e**<br>|<br>fo.011<br>O.OLge<br>|fod<br>0.03"<br>|<br>fO.02'<br>O.06apee<br>|fO.081<br>O.0ggee<br>|0.031<br>O.O8ae<br>|(0.061<br>O.Og<br>||
|HousehoHowns amobile phone||(0.01)<br>0.20466<br>|(0.00)<br>O.10s<br>|(O01)<br>O.01L*<br>|(o.01)<br>0,15peee<br>|(0.01)<br>0, L peek<br>|(0.01)<br>0, ogee<br>|(o.01)<br>Ogee"<br>||
|HousehoHownsanCD/DVDplayer||fo.014<br>O.088**|0.001<br>OOS|foul<br>0.016|fo.01i<br>O.0gsee|fO.014<br>Ose|fO.01i<br>Gea|0.014<br>O08]||
|<br>Householown ateevision||(0.02)<br>0,14g she<br>|(0.01)<br>O0546k<br>|fol<br>0.0G4ee<br>|<br>(0.02)<br>O.L3aee<br>|(0.02)<br>0, La Ree<br>|(0.02)<br>0.Lage<br>|(0.0)<br>0.132<br>||
|HousehoHowns acommuter<br>||(O02<br>0.18]**<br>|fO.01i<br>O111<br>|foul<br>0.061*<br>|fO.02<br>OLLager<br>|0.021<br>O.1ash*<br>|(0.021<br>0.174<br>|(O01<br>O.17aee*<br>||
|HousehoHownawfizerator||(0.08%)<br>0,1)0-66|(0.01;<br>O.05.36k|(0.02)<br>0.016|fo.)<br>0.052|(0.08)<br>0,Lape rk|(0.03)<br>0. Taqeke|(0.08)<br>O11gee||
|<br>HousehoHowns aamconditioner||<br>(0.02)<br>-0.002<br>|(0.01)<br>4.012<br>|(0.02)<br>-0.008<br>|(o.02)<br>0.011<br>|<br>0.02)<br>0.001<br>|<br>(0.02)<br>-0.007<br>|<br>(0.02<br>O05<br>||
|||(0.113<br>|(0.08)<br>|(0.08)<br>|(0.115<br>|(0.11)<br>|(0.11)<br>|(0.115<br>||
|HousehoH cowry afin||OOFee<br>(0.02)|OD.geehk<br>(0.013|0.068<br>(0.02)|OOSgRRRE<br>(0.02)|O.0FLe<br>(0.02)|OOF ark<br>£0.02)|OO gARRE<br>(0.02)||
|HousehoHowns a vras hingmachme||0.28566"<br>|Cader<br>|0.061<br>|O.200Fe*<br>|0.336<br>|0.3016<br>|Oagae*<br>||
|Log of resdentialareaper capita||(O.09)<br>0.30546<br>|(0.05)<br>O.21 (Fe<br>|(0.08)<br>-O.021<br>|(os)<br>O25seh<br>|(0.08)<br>O314<br>|(0.09)<br>O30<br>|(0.064<br>O27<br>||
|HouwsehoHdwellinghasimprovedwalk||(0.02)<br>O.02geeee|(0.013<br>0.007|(O01<br>0.004|(0.02)<br>O.020Fe*|(0.02)<br>0.025|£0.02)<br>0.0|(0.02)<br>OU||
|||(0.013|(0.00)|(O01|(0.013|(0.01)|(0.01)|(0.015||
|Househodwelimehas mmoroved 2o0f||O06 Ae<br>|O02<br>|0.001<br>|OOSSee<br>|OOeer aRE<br>|OO<br>|O.0eere<br>||
|Household dwelling has mmproved floor<br>||(0.01)<br>O.08<br>|(0.00)<br>OLD gree<br>|(O01)<br>0.003<br>|(o.01)<br>O06 Ree<br>|(0.01)<br>OO<br>|(0.01)<br>0.0gee<br>|(o.01)<br>Oona<br>||
|HousehoHvratersourceisoroved||(0**.**013<br>O055**|(**0.**01)<br>005|(O01<br>o.o14*|(0.013<br>O.0e*|(0.01)<br>OOSARE|(0**.0**1)<br>O<br>Gaee|(0**.**015<br>O0Gae*||
|||<br>(0.014|(0.01)|fol|(0.01)|<br>(0.01)|<br>(0.01)|<br>fo.01)||
|HousehoH toilet facility nupnoved||0.10546<br>|O.0g3k<br>|O.0G3 ie<br>|OO RRR<br>|OOSRARE<br>|0. 10g RR<br>|OO<br>||
|Househohasmosauitonets||(0.02)<br>O.052***|(0.01)<br>0.004|(O01<br>0.04|(0.02)<br>OO|(0.02)<br>OOS|(0.02)<br>O.OR4ee|(0.02)<br>O.0Gae*||
|<br>cone|13.2446*|(0.014<br>13.4se*|(0.00)<br>3gsek|(O00)<br>5.414eRe|(0.01)<br>12.131"|(0**.**01)<br>12soa|<br>(0.01)<br>13.0|fo**.**01)<br>La290s|12.e726"|
||O04)<br>|<br>(0.04)<br>|<br>(0.05)<br>|<br>(0.07)<br>|(0.05)<br>|<br>(0.04)<br>|(0.04)<br>|<br>(0.05)<br>|<br>(0.05)<br>|
|sigma e|O41|036|ols|0.25|ost|036|0.35|0.35|a4|
|sige1|0.16|O14|0.05|O.08|O12|Ola|0.14|0.13|O15|
|<br>tho|O.14|O13|0.08|0.08|O11|O11|0.15|o.12|0.12|
|ao<br>|0.53<br>|064<br>|O92<br>|0.84<br>|0.69<br>|0.66<br>|0.65<br>|0.68<br>|0.37<br>|
|H|12446,|12446|12446,|12446|12446|12446|12446|12446|12446,|
<!-- Start of picture text --> Household sim Model- 0.069 1 Modd= 00g 2ae Model-0 O15 43 Model+ 0.038 +4 Model101g gtk:§ Modd010185 HAé Model=O) O11 7 Ae Model0.08 54§ Modd= 0.01839HA<br>(0.003 (o.oo) oon) (0.003 10.004 0.00) o.007 (0.003 ro.003<br>Head's age 0.00 1+ “0.0 05 0.000 “0.002 +8" 0.00 34 “0.003 4 =O 01g. te 0.00 34* -O.002**<br>(0.007 o.oo 0.00) fO.00% (0.005 m.00) 0.00% 0.00) M.007<br>Head & femak -0.052%* -0.002 0.007 -O.015 0.005 -0.001 “0.007 -0.004 0.053%"<br>(0.024 (O02) mon (0.024 0.02) 0.02) 0.02) (0.023 (0.025<br>Head has primary education O.10g% O.039 o.016* -0.011 0.043 0.035 * ona7 0.038% 0.106 #4"<br>(0.024 (02) mol (0.013 (0.02) 0.02) (0.02) (0.024 (0.024<br>Headhas secondary education O 24s eat O.115 4% 0.033 se Old O.117 4k 0.115 + 0.09 tea 0.12] 4 O25 +e<br>(0.027) mo.o2) mg. (0.024 (0.024 0.02) 0.024 0.02) MO.02)<br>Headhas sacondary vocational education ad higher 05 Sg 0.250 = 0.110 +* 0.026 0.237 =r 0.243 ee 0.22 yake 0.246 sk 0.55] #e<br>(0.034 mo.o7) mon (0.024 0.03) m.03) (0.03) (0.034 (0.03)<br>Share of household members in 0-14 -0.439 sem “0.321% -0.131 -0.041 -0.32 55" -0.322"" “0.310 -0.320"* “0.435 88<br>(0.044 (ood) mo (0.034 (0.04) .04) O.04) (O44 (0.04)<br>Share of household members in 25-59 O 205s 0.181 44% -0.027* 0.1654 0.174 Ase OLR Ae 0.23 a5 0.180) #% O20 Ae<br>(0.057 moody 0.02) (0.03% (0.044 0.04) 044 (0.04) CO.05)<br>Share of housmemb e rsholdi 60 and oiler O06 0.175 -0.006 0.134 0.173 #0 0.165 *e* Q31ae* 0.1 724 O04<br>(0.054 (0.05) (02) (0.044 0.05) mos 05) (0.054 (0.05)<br>Headdid any work im bet } days O15 748 0.113 #4 0.009 0.0 70 0.109 sae O14 se O.1llas O.L 11 #9 O53 #8<br>(0.024 (o.02) mol (0.024 (0.02) 0.02) 0.02) (0.024 (0.024<br>Urban +033 7 “0.1304 0.037 + -0.029 “0.126 “0.137 “0.126 -0.12 5" 0.324ee<br>(0.037 mo.o2) mg. (0.024 (0.024 0.02) 0.024 0.02) O.03)<br>Log of parcafood conammption 0.8550<br>fo)<br>Log of perca nor-food consumption Osage"<br>(0.013<br>Logof perca nfftequent notefood consmmption 0 029 Ak<br>0.009<br>Lof of perca health expendboes 0.025 46%<br>mon<br>Logof parca education ¢npanidinmes O.015***<br>0.004<br>Log of parca utilities 0.0 13 ee 015 4%<br>(0.003 ro.003<br>Household ome amotorcycb 0.086 4% 0.02 7 Aer O.as 0.08] 4% OO 7g ea OP gee 0.083 sek<br>mo.oa) mg. (0.024 0.024 02) 0.024 (0.02)<br>Household ome abirck 0.167 0.092 se Oud? 0.175 se O16 7 se 016g 0.1 Fass<br>Household ome amobik phone (o.0. 08 6) 024-0 06a* (0.044on se (0.0. 0 816) ( 0.0 918) (0.06)ones (0o .0 9964<br>(0.104 044 (0.084 (0.104 0.105 0.10) (0.104<br>Household ome a DVD player -0.013 -0.011* o.n0s -0.016 -O.017 -0.014 -O.013<br>(02) mon (o.01) (0.02) m.o7 02) (0.024<br>Household ome atekvicin 0.065 0.013" ool 0.062 #4 0.062 se 0.06 5 0.064 a<br>mon mon (0.013 (0.01) m.01 mon (0.013<br>Household ovms a computer 0.214 ae 0.09 1 tobe o.n38* 0201 ae 0.212 0.2114 0.2 14 tek<br>cO.0s4 mon (0.024 M034 0.03) 0.034 (0.034<br>Household ome a refrigerator 0.109 se 0.024 se O37 O.111) 0.106 + 0.104 se 0.10 #9<br>mo.oa) mg. (0.024 0.024 02) 0.024 (0.02)<br>Household ome a air conditiovr 0.063 se 0.021%" O15 0.062 se 0.06] se 0.06 ge 0.061**<br>(O02) mon (0.024 0.02) 0.02) 0.02) (0.023<br>Household ome a washing machine 0.136 #4 0.09 78 Ona4 O.1a7 se 0.13] + 0.12 7a 0.133"<br>(ood) mol (0.034 (0.04) .04) o.04) CO.044<br>Household ome a car 0.09] 4k o.011 Onde 0.093 Ae 0.033 se 0.08 Gee O.093 sek<br>mo.o2) mg. (0.024 (0.024 0.02) 0.024 0.02)<br>Household ovme afar 0.133 0.04 § oe -0.02 7 0.132 = O.1ag 4 0.124% 0.132%<br>o.02) mol (o.014 (0.02) m.o7 02) (0.024<br>Household ovmes a sate Tite O.05¢* -0.022* o.046* O.056* O.058* oss O.054*<br>(o.03) mo (0.024 (0.034 0.03) 0.03) (0.034<br>Log of residertia] area 0.145 ae O05 7 Ae ool 0.151 #4 0.150 4 1.14 [4 0.14 tack<br>mo.oa) mg. O.013 0.024 02) 0.024 (0.02)<br>Roof & made of comcretetmatal sheets tbe 0.090 = 0.025 * O12 0.093 sr 0.085 +e 0.08 take 0.089 sk<br>o.0a) mon (0.013 0.02) m.02) 0.02) (0.023<br>Walls made of burnt bricks‘: omerete Ametal sheets 0.049 0.016 0.004 0.050 sa 0.050 se 0.04 4% 0.045"<br>(o.02) mol (0.013 (0.02) 0.02) 0.02) (0.024<br>Pad vater/Trick 0.022 “0.005 On 0.021 0.029 On22 0.020<br>mo.o2) mg. (0.024 (0.024 0.02) 0.024 0.02)<br>Arey vre Lvrater O.016 -0.001 o.n19 0.008 0.015 o.021 O.017<br>o.02) mol (o.014 (0.02) m.o7 02) (0.024<br>On water/FlshVIP to ikt 0.153 #1 0.07 1 0.026 0.156 0.133 4 0.151" 0.1Sg<br>(oo) mo (0.024 (0.034 0.03) (0.03) (0.034<br>Other todet 0.0504 O01 7a 0.036% O05 4 0.036 * Od Ga o.os1s*<br>m.o2) mon 0.024 0.024 02) 0.024 0.024<br>Todet is not shared 0.063 4% 0.03 5 O.al 0.0.59 4 O07 7 ea 0.06 4 aoe 0.062<br>(02) mon (o.01) (0.02) m.o7 02) (0.024<br>cone 12.270" 11.544 #4 1.8554 6.5240" 11.554 11.4 90*3 11.49] #* 11.434 12,1099"<br>(0.084 (0.08) 0.08) (0.113 mor) m.07) 0.08) (0.084 (0.084<br>sigma e Odd o40 old O31 0.39 o40 O40 O40 Odd<br>sigma u O23 0.16 0.05 0.13 0.16 0.16 0.16 0.16 0.23<br>Tho O21 0.14 0.12 0.13 0.15 O14 0.15 Old o.20<br>Tao O45 0.58 O95 O76 0.59 0.59 0.58 0.58 O46<br>H 4405 4405 4405 4405 4405 4405 4405 4405 4405<br><!-- End of picture text -->

|Householdsim|Model 1<br>-0.069|Modd 2<br>=00gae|Model 3<br>-0O154|Model 4<br>+0.038+|Model §<br>101ggtk:|Modd é<br>010185HA|Model7<br>=O)O11Ae|Model §<br>0.0854|Modd9<br>=0.0183HA|
|---|---|---|---|---|---|---|---|---|---|
|<br>Head's age|<br>(0.003<br>0.00 1+|<br>(o.oo)<br>“0.005|<br>oon)<br>0.000|<br>(0.003<br>“0.002 +8"|<br>10.004<br>0.0034|<br>0.00)<br>“0.0034|<br>o.007<br>=O 01g.te|<br>(0.003<br>0.00 34*|<br>ro.003<br>-O.002**|
||(0.007|o.oo|0.00)|fO.00%|(0.005|m.00)|0.00%|0.00)|M.007|
|Head&femak|-0.052%*|-0.002|0.007|-O.015|0.005|-0.001|“0.007|-0.004|0.053%"|
|<br>Head hasprimaryeducation|(0.024<br>O.10g%<br>|(O02)<br>O.039<br>|mon<br>o.016*<br>|(0.024<br>-0.011<br>|0.02)<br>0.043<br>|0.02)<br>0.035*<br>|0.02)<br>ona7<br>|(0.023<br>0.038%<br>|(0.025<br>0.106 #4"<br>|
|Headhas secondary education|(0.024<br>O 24s eat<br>(0.027)|(02)<br>O.1154%<br>mo.o2)|mol<br>0.033 se<br>mg.|(0.013<br>Old<br>(0.024|(0.02)<br>O.1174k<br>(0.024|0.02)<br>0.115+<br>0.02)|(0.02)<br>0.09 tea<br>0.024|(0.024<br>0.12]4<br>0.02)|(0.024<br>O25 +e<br>MO.02)|
|Headhas sacondary vocational education ad higher|05Sg<br>(0.034|0.250=<br>mo.o7)|0.110+*<br>mon|0.026<br>(0.024|0.237 =r<br>0.03)|0.243ee<br>m.03)|0.22 yake<br>(0.03)|0.246sk<br>(0.034|0.55] #e<br>(0.03)|
|Share of household members in 0-14|-0.439 sem<br>|“0.321%<br>|-0.131<br>|-0.041<br>|-0.32 55"<br>|-0.322""<br>|“0.310<br>|-0.320"*<br>|“0.43588<br>|
|Share of household members in 25-59|(0.044<br>O205s<br>(0.057|(ood)<br>0.181 44%<br>moody|mo<br>-0.027*<br>0.02)|(0.034<br>0.1654<br>(0.03%|(0.04)<br>0.174Ase<br>(0.044|.04)<br>OLR Ae<br>0.04)|O.04)<br>0.23a5<br>044|(O44<br>0.180)#%<br>(0.04)|(0.04)<br>O20 Ae<br>CO.05)|
|Share ofhous**e**hold<br>memb rs i 60 and oiler|O06<br>|0.175<br>|-0.006<br>|0.134<br>|0.173 #0<br>|0.165 *e*<br>|Q31ae*<br>|0.1724<br>|O04<br>|
||(0.054<br>|(0.05)<br>|(02)<br>|(0.044<br>|0.05)<br>|mos<br>|05)<br>|(0.054<br>|(0.05)<br>|
|Head<br>did any work im bet } days|O15748<br>|0.113#4<br>|0.009<br>|0.070<br>|0.109sae<br>|O14se<br>|O.1llas<br>|O.L 11#9<br>|O53 #8<br>|
|Urban|(0.024<br>+0337<br>(0.037|(o.02)<br>“0.1304<br>mo.o2)|mol<br>0.037+<br>mg.|(0.024<br>-0.029<br>(0.024|(0.02)<br>“0.126<br>(0.024|0.02)<br>“0.137<br>0.02)|0.02)<br>“0.126<br>0.024|(0.024<br>-0.125"<br>0.02)|(0.024<br>0.324<br>ee<br>O.03)|
|Log ofparca<br>food conammption|||0.8550<br>|||||||
|Log of perca nor-food consumption|||fo)|Osage"<br>(0.013||||||
|Log<br>of perca nfftequent notefood consmmption|||||0 029Ak<br>|||||
||||||0.009|||||
|Lof of perca health expendboes||||||0.02546%<br>mon||||
|Log<br>of parca education ¢npanidinmes|||||||O.015***<br>0.004|||
|Log ofparca utilities||||||||0.0 13ee<br>|0154%<br>|
|Household ome amotorcycb||0.0864%<br>|0.02 7Aer<br>|O.as<br>|0.08]4%<br>|OO7gea<br>|OPgee<br>|(0.003<br>0.083 sek<br>|ro.003|
|Householdomeabirck||mo.oa)<br>0.167|mg.<br>0.092se|(0.024<br>Oud?|0.024<br>0.175se|02)<br>O167se|0.024<br>016g|(0.02)<br>0.1Fass||
|<br>Household||(o.**08**)<br><br>|<br>024<br>|(0.044<br>|<br>(0.**0**6)<br>|<br>(**0.0**8)<br>|(0.06)<br>|<br>(0**.0**64<br><br>||
|ome amobik phone||0.<br>6<br>|-006a*<br>|onse<br>|0. 81<br>|91<br>|ones<br>|o<br>99<br>||
|Household ome aDVD player||(0.104<br>-0.013<br>(02)|044<br>-0.011*<br>mon|(0.084<br>o.n0s<br>(o.01)|(0.104<br>-0.016<br>(0.02)|0.105<br>-O.017<br>m.o7|0.10)<br>-0.014<br>02)|(0.104<br>-O.013<br>(0.024||
|Household ome atekvicin||0.065<br>|0.013"<br>|ool<br>|0.062 #4<br>|0.062se<br>|0.065<br>|0.064a<br>||
|Household ovms acomputer||mon<br>0.214 ae|mon<br>0.09 1 tobe|(0.013<br>o.n38*|(0.01)<br>0201ae|m.01<br>0.212|mon<br>0.2114|(0.013<br>0.2 14tek||
|Household ome arefrigerator||cO.0s4<br>0.109se<br>mo.oa)|mon<br>0.024se<br>mg.|(0.024<br>O37<br>(0.024|M034<br>O.111)<br>0.024|0.03)<br>0.106+<br>02)|0.034<br>0.104se<br>0.024|(0.034<br>0.10 #9<br>(0.02)||
|Household ome aairconditiovr||0.063 se<br>|0.021%"<br>|O15<br>|0.062se<br>|0.06] se<br>|0.06ge<br>|0.061**<br>||
|Household ome awashing machine||(O02)<br>0.136 #4|mon<br>0.0978|(0.024<br>Ona4|0.02)<br>O.1a7se|0.02)<br>0.13]+|0.02)<br>0.127a|(0.023<br>0.133"||
|Household ome a car||(ood)<br>0.09]4k<br>|mol<br>o.011<br>|(0.034<br>Onde<br>|(0.04)<br>0.093Ae<br>|.04)<br>0.033se<br>|o.04)<br>0.08 Gee<br>|CO.044<br>O.093 sek<br>||
|||mo.o2)<br>|mg.<br>|(0.024<br>|(0.024<br>|0.02)<br>|0.024<br>|0.02)<br>||
|Household ovme afar||0.133<br>|0.04 § oe<br>|-0.027<br>|0.132 =<br>|O.1ag4<br>|0.124%<br>|0.132%<br>||
|HouseholdovmesasateTite||o.02)<br>O.05¢*|mol<br>-0.022*|(o.014<br>o.046*|(0.02)<br>O.056*|m.o7<br>O.058*|02)<br>oss|(0.024<br>O.054*||
|<br>Log of residertia] area||(o.03)<br>0.145ae<br>|mo<br>O05 7Ae<br>|(0.024<br>ool<br>|(0.034<br>0.151 #4<br>|0.03)<br>0.1504<br>|0.03)<br>1.14[4<br>|(0.034<br>0.14 tack<br>||
|Roof &made of comcretetmatal sheets tbe||mo.oa)<br>0.090=|mg.<br>0.025 *|O.013<br>O12|0.024<br>0.093sr|02)<br>0.085+e|0.024<br>0.08take|(0.02)<br>0.089sk||
|Wallsmade of burnt bricks‘:omerete Ametal sheets||o.0a)<br>0.049<br>(o.02)|mon<br>0.016<br>mol|(0.013<br>0.004<br>(0.013|0.02)<br>0.050sa<br>(0.02)|m.02)<br>0.050se<br>0.02)|0.02)<br>0.04 4%<br>0.02)|(0.023<br>0.045"<br>(0.024||
|Pad vater/Trick||0.022<br>|“0.005<br>|On<br>|0.021<br>|0.029<br>|On22<br>|0.020<br>||
|||mo.o2)<br>|mg.<br>|(0.024<br>|(0.024<br>|0.02)<br>|0.024<br>|0.02)<br>||
|AreyvreLvrater||O.016|-0.001|o.n19|0.008|0.015|o.021|O.017||
|||o.02)|mol|(o.014|(0.02)|m.o7|02)|(0.024||
|Onwater/FlshVIPtoikt||0.153#1|0.071|0.026|0.156|0.1334|0.151"|0.1<br>Sg||
|<br>||<br>(oo)<br>|<br>mo<br>|(0.024<br>|(0.034<br>|<br>0.03)<br>|(0.03)<br>|(0.034<br>||
|Other todet||0.0504|O01 7a|0.036%|O05 4|0.036 *|Od Ga|o.os1s*||
|Todetisnotshared||m.o2)<br>0.0634%|mon<br>0.035|0.024<br>O.al|0.024<br>0.0.594|02)<br>O077ea|0.024<br>0.064aoe|0.024<br>0.062||
|<br>cone|12.270"<br>|<br>(02)<br>11.544#4<br>|<br>mon<br>1.8554<br>|(o.01)<br>6.5240"<br>|<br>(0.02)<br>11.554<br>|<br>m.o7<br>11.490*3<br>|<br>02)<br>11.49]#*<br>|(0.024<br>11.434<br>|12,1099"<br>|
||(0.084|(0.08)|0.08)|(0.113|mor)|m.07)|0.08)|(0.084|(0.084|
|sigma<br>e|Odd|o40|old|O31|0.39|o40|O40|O40|Odd|
|sigma u<br>Tho|O23<br>O21|0.16<br>0.14|0.05<br>0.12|0.13<br>0.13|0.16<br>0.15|0.16<br>O14|0.16<br>0.15|0.16<br>Old|0.23<br>o.20|
|Tao|O45|0.58|O95|O76|0.59|0.59|0.58|0.58|O46|
|H<br>|4405<br>|4405<br>|4405<br>|4405<br>|4405<br>|4405<br>|4405<br>|4405<br>|4405|
|Hote:**p0.01,ps0.05,ps0.)Standardcrorsa|reiparentheses.Al|lestimationse|mmys¢lesterr|andomeffectsm|dekandcordro|kfortheregio|naldummyvaris|bks.||
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Lgg 0.135 * 0.1804" o.161* 05h Peak<br>0.084 mo) cO.04) c.0F) mos) (0.08) 1.095 (O08) C008)<br>Share of owsehold members age 0-14 “0,735 eae -0 AggHaee -O,135 48" -0, 25) eH “0 4 ogi - 0,54] wee “0,530 "He -O.S00 Hee -O,. Fag Hee<br>moo mor CO. .08 mo fo.09%) 0.105 moo) CO.09y<br>Share of oucehol members age 15-24 0207 Hak 0 224 Hebe -0.032 -0.14] 4 -0 2egHee -0.21] ee 0.235 Hee -O 206g Hee Oe Hee<br>(0.084 moon 0.034 (0.067 moon (O07 mor roon 0.08<br>Share of houcehold members aze 60 and oMer 0.104 0.100 -0.024 0.164 Oss 0.083 0.113 0.093 O.109<br>mo.114 ron ros roo roo ro ro. ron ron<br>Head was workingfor waze/sabry hist7 davs 0143 Hoek 0.1199 Hatake 0.026 O.064 ee 086 0.104 Hoe 0.095 Hoth: O10 Hoke O13 Sree<br>(04) 04) (0.02) RES] mo4y C104) (04) coos) co.04)<br>Head vrag seF-empleyped (on-farm) lst F days 0.16546 O.049 O.022 o.010 o.n4g 0.056 O.048 0.040 0.12 Seve<br>m.0+) moa) (0.02) 034 mney (0.044, m.0+) moa) CO.0e)<br>Tar es Sakon 0.83]se<br>Ta<br>Restof whan O27 Hee<br>t.01)<br>Lauber O.0gg eH<br>oon<br>Log of food ¢ oremmmtion Derae a 0.020 tek<br>ro.007,<br>Log of norfood consmmption peraeg 0.003<br>0.004<br>Logof fuméhings avd houseWl experces peraeg OO L P Hate 0.04 gueek<br>mol o.o1y<br>Logof healh expendinures peraeq 0.210 Habe O.135 Hee O.0 gg Hee O24 Heke 0.215 hk O.2130ee O.213Heke<br>mos (0.02) mo.04) mos) ros) (0.05) coos)<br>Logof education expendinmes persed, 0.029 -O.0a7" O.0so% 0.020 O.039 0.029 0.031<br>Lof of utilities peraea rooF4onde roO . 011oz) ro.0340.176 ros)“0001 ros)-0.126 rO0 .0 3459 roo30.156<br>Household ove a motorcyck mae0.14] razO06 Hoe mah-0.000 maeO.121"* moanO.13] #4 mas0.139 Hebe roaey0.141] 4ke<br>mos) (0.02) m.04) mos 0.05) m.05) os)<br>Househol owe a biyck 0.1254" O01 O.094 4 o.110#* 0.126 + 0.1254" 0.1204<br>fo 05) (0.02) ro.04) mos) (os) (0.05) rosy<br>Household owns a desk phone 0.061 0.035 ool O.061 0.064 0.059 o.054<br>Howseholl owns a mobile phone 05)o.1gs" ( 0 ,146.02) Hae t.0540.078 mos)0.176 (1.05)0.22] ee (0O . 05317gue coos)o.1gse"<br>Househiol ove an CD DVD plevrer mosO.130"e# ro.04O.085 Have mon0.065 mos)0.136 Hee 10.08)0.130 + roO . 084124 4 roos40.121"<br>mos fO.021 O.04) oon m.os1 mos mos<br>Howehol one a television 0.200 Hak O07 Hae O10 7a OL Pg teen 0.178 Hee 0.200 Hoek 01g Hoke<br>fo 05) (0.02) ro.04) mos) (os) (0.05) rosy<br>Household owns a compute 0.027 0.001 o.004 0.022 0.030 0.027 0.022<br>mos) Co.o1y 0.034 mos) (0.034 m.034 RES<br>Househiol ovme a refrigerator 0.0824 O.016 O.04s 0.084 He 0.069 * 0.084 4 0.081434<br>mos (0.02) 0.03) moa) roe) 1.04) roa)<br>Howeholome a ait conditioner/fan 0.4.29 Hak O06 Haak O35 Peek Og 15 Hoe 0.417 tek 0.423 Hoek O42 Hoke<br>moor 0.034 0.064 mon (0.064 mori roori<br>Householi owns a Tadio O.ng54 o.007 O.045 0.100 0.097 He O94 oor<br>mos roo ro044 most moos) mo.0s4 roo<br>Householdomy 4 moss ret -0,133" -0.044 -0.063 “0.162 -0,122 0,139" -O.127<br>mos) co. .07y mos) (0.087) m.084 moos)<br>Log of residential area per capita -O.L6H Og a 0.083 -0.130 -0.157 0.166 4 -O.1L5744<br>fos) co.04) co.07) m.o8) (0.08) 10.08) cooe)<br>Roof # made of concreAnetal shee t sstik:s 0.056 O57 Hae -0.041 onss 0.054 0.054 O.043<br>04) (0.02) to.04) mo4y C104) (04) coos)<br>Walk made of bunt bricks ‘stores 0.161] 4 0.024 0.126 ee 0. 1g He 0.18] 4H 0.16] #4 0. 14g Hoke<br>mon room mo.044 mos) 1.05) moos moos<br>Wallk made of noid bricksAnod stones 0.026 -0.014 O.030 0.024 0.042 0.026 0.032<br>Flor © made of c omcretecementtike mosO.La 7H (0o .02 9) mo.04)Ogg ee mos)0.129 Heke ros)0.119 #4 ( 0 .05).12646 coos)O.L154e*<br>mo) (0.02) 0.03) mo4) roe) 1.04) rood)<br>Ppedvrater (0.083O4 164 O.094" 0.0 Fg Hae 0.006 O.12za"* 0.106 o.09s O.076 0.294 tae<br>Ayoy rel vrater 0 280 Hoek dog)0.091 4 (0.03)O01 5d Hae 0.053o.011 05)O11] een (0.0. 0 825) 4 (0O .0 83ag Hee cog)O07 gH (0.0.2 0 6)) Sree<br>o.04) mos (0.02) 0.03) moa) roe) 1.04) roa) co.04)<br>FlshWIP toikt O.Ltgee -0098" - 0.064 -0.030 “0009 -0.105* “0.090 -0.113" ong<br>come tory14.135" foe)13 693 Hee (0.03)2505 Hee 0.05)10. 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|Househiolsine|Modd 1<br>0.03]ek|Model?<br>029Hebe|Modd 3<br>-0.006"|Modd 4<br>0.01geee|Mo del §<br>-00316"|Modd 6<br>-0.03]4k|Model?<br>-0.030ek|Model £<br>-O.027Hebe|Modd9<br>0.026Hee|
|---|---|---|---|---|---|---|---|---|---|
|<br>|<br>mw.01)<br>|<br>mol<br>|(0.00)<br>|<br>0.01)<br>|<br>mol<br>|<br>rool)<br>|<br>m.01)<br>|<br>rool<br>|<br>rool<br>|
|Head's age|0.003#<br>|-00054<br>|-0.000<br>|0.004eH<br>|-00054<br>|0.0054<br>|0.0054"<br>|“0.0054<br>|00104Hate<br>|
|Head i female|0.004<br>O neg vee<br>|foo}<br>o.o7ge<br>|(0.00)<br>0.013<br>|0.00)<br>0.029<br>|moo)<br>O65"<br>|(0.00)<br>o.068<br>|0.005<br>oor"<br>|ooo<br>o.o7ae<br>|(0.00)<br>o.03g4"<br>|
|Headha:primate¢ducation|rod<br>0062|roa<br>-0.026|ro.02)<br>O.013|rO.034<br>0.024|roe)<br>“0.020|roe)<br>-0,032|rod<br>“0,026|rood<br>-0.032|ro.044<br>0.032|
||mos)|mo|fO.021|O.047|oo|<br>ro.0471|m.0447|movi|051|
|Head has secondary ordinary education|O 355s<br>|O.L143<br>|O.039<br>|0.053<br>|0.103<br>|O.093 *<br>|O.117*<br>|0.110"<br>|O30 gees<br>|
|Head hassecondary advanced education ard higher|(0.06)<br>0 649 dalek<br>|mos)<br>ORG<br>|(0.03)<br>0.145 Haak<br>|0.05)<br>O.041<br>|mos)<br>0.Lgg<br>|ros)<br>0.135 *<br>|(0.06)<br>0.1804"<br>|coos)<br>o.161*<br>|(0.06)<br>05h Peak<br>|
|Share of owsehold members age 0-14|0.084<br>“0,735 eae<br>moo|mo)<br>-0 AggHaee<br>mor|cO.04)<br>-O,13548"<br>CO.|c.0F)<br>-0,25) eH<br>.08|mos)<br>“0 4ogi<br>mo|(0.08)<br>-0,54] wee<br>fo.09%)|1.095<br>“0,530"He<br>0.105|(O08)<br>-O.S00Hee<br>moo)|C008)<br>-O,.FagHee<br>CO.09y|
|Share of oucehol members age 15-24|0207Hak<br>(0.084|0 224Hebe<br>moon|-0.032<br>0.034|-0.14]4<br>(0.067|-0 2egHee<br>moon|-0.21] ee<br>(O07|0.235 Hee<br>mor|-O 206gHee<br>roon|Oe Hee<br>0.08|
|Share of houcehold members aze 60 and oMer|0.104<br>|0.100<br>|-0.024<br>|0.164<br>|Oss<br>|0.083<br>|0.113<br>|0.093<br>|O.109<br>|
|<br>|mo.114<br>|ron<br>|ros<br>|roo<br>|roo<br>|ro<br>|ro.<br>|ron<br>|ron<br>|
|Head wasworking<br>for waze/sabry hist7davs|0143 Hoek|0.1199 Hatake|0.026|O.064ee|086|0.104Hoe|0.095 Hoth:|O10 Hoke|O13 Sree|
||(04)|04)|(0.02)|RES]|mo4y|C104)|(04)|coos)|co.04)|
|Headvrag seF-empleyped(on-farm)lst F days|0.16546<br>m.0+)|O.049<br>moa)|O.022<br>(0.02)|o.010<br>034|o.n4g<br>mney|0.056<br>(0.044,|O.048<br>m.0+)|0.040<br>moa)|0.12 Seve<br>CO.0e)|
|Tar es Sakon|||0.83]<br>se<br>Ta|||||||
|Restofwhan||||O27 Hee<br>||||||
|Lauber||||t.01)|O.0ggeH<br>oon|||||
|Logoffood¢oremmmtionDeraea||||||0.020tek||||
|||||||<br>ro.007,||||
|Log ofnorfoodconsmmptionperaeg|||||||0.003<br>|||
|Logof fuméhings avdhouseWl expercesperaeg|||||||0.004|OO L PHate<br>mol|0.04 gueek<br>o.o1y|
|Log<br>of healh expendinures peraeq||0.210 Habe<br>mos|O.135 Hee<br>(0.02)|O.0ggHee<br>mo.04)|O24 Heke<br>mos)|0.215 hk<br>ros)|O.2130ee<br>(0.05)|O.213Heke<br>coos)||
|Log<br>of educationexpendinmes persed,<br>||0.029<br>|-O.0a7"<br>|O.0so%<br>|0.020<br>|O.039<br>|0.029<br>|0.031<br>||
|||||||||||
|of utilities peraea<br>||onde<br>|O 011<br>|0.176<br>|“0001<br>|-0.126<br>|0 59<br>|0.156<br>||
|Householdoveamotorcyck||mae<br>0.14]|raz<br>O06Hoe|mah<br>-0.000|mae<br>O.121"*|moan<br>O.13]#4|mas<br>0.139Hebe|roaey<br>0.141]4ke||
|||mos)|<br>(0.02)|m.04)|mos|<br>0.05)|<br>m.05)|<br>os)||
|Househol owe a biyck||0.1254"<br>fo05)|O01<br>(0.02)|O.0944<br>ro.04)|o.110#*<br>mos)|0.126 +<br>(os)|0.1254"<br>(0.05)|0.1204<br>rosy||
|Household owns a desk phone<br>||<br>0.061<br>|0.035<br>|ool<br>|O.061<br>|0.064<br>|0.059<br>|o.054<br>||
|Howseholl owns a mobile phone<br>||05)<br>o.1gs"<br>|(**0**.02)<br>,146 Hae<br>|t.054<br>0.078<br>|mos)<br>0.176<br>|(1.05)<br>0.22] ee<br>|(0**.**053<br>O 17gue<br>|coos)<br>o.1gse"<br>||
|Househiol ove an CD<br>DVD plevrer||mos<br>O.130"e#<br>mos|ro.04<br>O.085 Have<br>fO.021|mon<br>0.065<br>O.04)|mos)<br>0.136Hee<br>oon|10.08)<br>0.130 +<br>m.os1|ro**.**084<br>O 1244<br>mos|roos4<br>0.121"<br>mos||
|Howehol one a television||0.200Hak<br>|O07Hae<br>|O107a<br>|OL Pg teen<br>|0.178 Hee<br>|0.200 Hoek<br>|01g Hoke<br>||
|||fo05)<br>|(0.02)<br>|ro.04)<br>|mos)<br>|(os)<br>|(0.05)<br>|rosy<br>||
|Household owns a compute||0.027<br>|0.001<br>|o.004<br>|0.022<br>|0.030<br>|0.027<br>|0.022<br>||
|Househiol ovme arefrigerator||mos)<br>0.0824<br>mos|Co.o1y<br>O.016<br>(0.02)|0.034<br>O.04s<br>0.03)|mos)<br>0.084 He<br>moa)|(0.034<br>0.069*<br>roe)|m.034<br>0.0844<br>1.04)|RES<br>0.081434<br>roa)||
|Howehol<br>ome a aitconditioner<br>/fan||0.4.29 Hak<br>|O06 Haak<br>|O35 Peek<br>|Og 15 Hoe<br>|0.417 tek<br>|0.423 Hoek<br>|O42 Hoke<br>||
|Householi owns aTadio||moor<br>O.ng54<br>|0.034<br>o.007<br>|0.064<br>O.045<br>|mon<br>0.100<br>|(0.064<br>0.097 He<br>|mori<br>O94<br>|roori<br>oor<br>||
|Household<br>omy 4mossret||mos<br>-0,133"<br>mos)|roo<br>-0.044<br>co.|ro044<br>-0.063<br>.07y|most<br>“0.162<br>mos)|moos)<br>-0,122<br>(0.087)|mo.0s4<br>0,139"<br>m.084|roo<br>-O.127<br>moos)||
|Log of residential areaper capita||-O.L6H<br>fos)|Oga<br>co.04)|0.083<br>co.07)|-0.130<br>m.o8)|-0.157<br>(0.08)|0.1664<br>10.08)|-O.1L5744<br>cooe)||
|Roof#madeofconcre**t**:<br>Anetalsheesstiks||0.056|O57Hae|-0.041|onss|0.054|0.054|O.043||
|<br> <br>||04)<br>|<br>(0.02)<br>|to.04)<br>|mo4y<br>|C104)<br>|(04)<br>|coos)<br>||
|Walk made of bunt bricks‘stores||0.161]4<br>|0.024<br>|0.126ee<br>|0.1gHe<br>|0.18] 4H<br>|0.16]#4<br>|0.14gHoke<br>||
|||mon|room|mo.044|mos)|1.05)|moos|moos||
|Wallkmade ofnoidbricksAnod stones<br>||0.026<br>|-0.014<br>|O.030<br>|0.024<br>|0.042<br>|0.026<br>|0.032<br>||
|Flor © made of c omcretecementtike||mos<br>O.La7H<br>|(0**.02**)<br>o<br>9<br>|mo.04)<br>Ogg ee<br>|mos)<br>0.129Heke<br>|ros)<br>0.119 #4<br>|(**0**.05)<br>.12646<br>|coos)<br>O.L154e*<br>||
|Ppedvrater<br>|O4164<br>(0.083|mo)<br>O.094"<br>|(0.02)<br>0.0 Fg Hae<br>|0.03)<br>0.006<br>|mo4)<br>O.12za"*<br>|roe)<br>0.106<br>|1.04)<br>o.09s<br>|rood)<br>O.076<br>|0.29<br>4tae<br>|
|Ayoyrelvrater|0280Hoek|dog)<br>0.0914|(0.03)<br>O015dHae|0.053<br>o.011|05)<br>O11]een|(0.**0**5)<br>0.824|(0**.0**83<br>OagHee|cog)<br>O07gH|<br>(0.**0**6)<br>0.2)Sree|
|<br>FlshWIP toikt<br>|<br>o.04)<br>O.Ltgee<br>|<br>mos<br>-0098"<br>|<br>(0.02)<br>-0.064<br>|0.03)<br>-0.030<br>|<br>moa)<br>“0009<br>|<br>roe)<br>-0.105*<br>|<br>1.04)<br>“0.090<br>|roa)<br>-0.113"<br>|<br>co.04)<br>ong<br>|
|come|tory<br>14.135"|foe)<br>13693Hee|(0.03)<br>2505Hee|0.05)<br>10.S36Hee|mod)<br>13539Hole|(0.06)<br>13.6114|(0.065<br>13.715ee|ood)<br>13StirHt|cory<br>13.609ee|
||.105|<br>m133|<br>C019)|<br>oO.|<br>0.13)|(0.13)|<br>0.133|<br>cols|<br>0.10)|
|signa<br>e<br>signau|O41<br>O19|O46<br>O06|0.23<br>0.00|o40<br>ood|O46<br>0.06|O45<br>0.03|O46<br>ong|O46<br>O07|0.50<br>0.15|
|<br>tho|0.13|0.03|0.00|ool|o.0a|O.01|ona|0.03|0.08|
|m0<br>H|0.40<br>1179|O55<br>1179|0.90<br>1179|os?<br>1179|0.56<br>1179|0.5?<br>1179|O45<br>1179|O55<br>1179|0.43<br>1179|
<!-- Start of picture text --> Model 1 Model? Model 3 Model 4 Model 5 Model 6 Model 7 Model 3 Model9<br>Household size -0.065*** -0.152*** -0.068*** -0.014*** -0.101*** -0.128*** -0.154** 0-011 5e** -0.046%**<br>(0.00) 10.007) 0.00) 00) 0.00) (0.00) (0.004 moo} 0.00)<br>Head's age o.o002*** -0.001 -0.000 -0.000** o.000 -0.001** -0.001 -0.001 o.001*<br>(0.007 0.007 .007 moo01 (0.007 0.007 0.004 ooo 0.007<br>Head is female o.019 0.05 7*** O.0356** * -0.01 1* 0.030% 0.031] 0.05 9% * 0.050% ** o006<br>(0.01) 0.014 (1.013 00) 0.01) (0.01) (0.01) fool fool)<br>Headbelongs to ethuc minority group -O.419%e* 0.187 0 Oo -0.015** -O.174*** -0.155*** 0.175 0.146 0. age<br>(0.02) (0.02) (O.013 mmol) (0.01) (0.01) (0.02) moo2) 0.02)<br>Head completed primary school O.1T7(0.01) ee O.046***0.01) 0.053***(0.013 -0.005moo) Oost(0.01) Oost(0.01) 0.04(0.01) 5+" * 0mol 059*** 0.102***0.01)<br>Head completed lower secondary 0.28] *** O.OT4*** O.047e** -0.003 O.05e + 0.060% O.07 1+** 0.064% ** 0.165%<br>school (0.01) 0.01) (0.01) mol) (0.01) (0.01) (0.01) wooly 01) **<br>Head comple tedupper secondary 0.4635 *** O.145*** OOse*** oo08 0.120% O.12ans 0.15 5*** 0.126% ** 0.505*<br>school (0.02) 0.01) (0.013 mol) 0.01) (0.01) (0.014 wooly 0.02) **<br>Head has (some) college education 0.729%(0.02) O.216***(0.02) o.090***(O.013 O058***mmol) O.172***(0.02) O.209%**(0.02) 0.206***(0.02) 0moo2) 200*** 0.510.02) 5***<br>Share of household members age 0-14 -0(0.04286" ** -O.324(0.031 * -0.14(0.021 54 -O.05coon 5% * -0(0.031255% #4 -O.26£0,031 7% #* -0.505***(0.031 -O.280***cos) -0.1‘oon954#*<br>Share of household members age 12-24 O.115***in yay (0.03)o.029 (0.02)0.026 oonooo7 0.06(0.03) 1** O.082s+*(0.03) -O.09G£0.03)a 0.064(0.03) ** O.182***(0.03)<br>ShareHead workedof householdin the lastmembers12 monthsage 19-24 0.275%-0020ga) O05-0(0 .0 152)Te -0.027***(0.01)0.003 O(001)ooo?044s -00.04(0 .02 2**)0* O.084(0.02)0.004#4 O.046**-0.(0. 0 162) 0-( 0.0 033***112) O.207**-( 0.0 2)15<br>(0.02) (0.01) (0.017 fO.01) (0.01) (0.01) (0.014 fOO1y (0.014<br>Log of food comumptionper capita O86 Tate *<br>(0.01)<br>Logofronfood consumptionper capita OT 4g<br>(0.00)<br>Logofduirables consumption per capita 0.134<br>(0.00)<br>Log oflealthexpenditures per capita 0.07044<br>0.007<br>Log of education expenditures percapit: O.01 S++<br>(0.00)<br>Log ofelectricity, water& garabage 0.1034 +4 0 2A a<br>expenditires per capita wooly mol)<br>Household owns acar O609*** 0.540*** 0.050 ** O.274 O.6204** O.601*** 0 4590***<br>(0.03) 0.02) mol) 0.03) (0.03) (0.034 03)<br>Household owns amo torbike 0.167 * O.105*** -O.02 5% + -O.002 0.16] *** O.164*** O.164***<br>(0.01) (0.01) mol) (0.01) (0.01) (0.01) wooly<br>Household owns abic pele -0.04] -0.010* -0.01 F# * -0 033" ** - 0.044% -0.058 *** -0.04 544%<br>0.011 011 moo01 (0.011 O.011 0.014 fool<br>Household owns adeskh phone 0.067" * O.042*** o.o09* o.o7o#** O.052*** 0.06 7*** 0 0so0***<br>(0.014 (O.013 mmol) (0.01) (0.01) (0.014 mol<br>Household owns acell phore O.128*** 0.063*** ooo1 0.055*** O.111*** O.124*** 0.1135***<br>(0.01) (0.017 fO.01) (0.01) (0.01) (0.014 fOO1y<br>Household owns aD¥D player O.0451*** O.021*** 0.007 ** 0.01 5** 0.050% O.0452*** 0.045%**<br>(0.01) (0.01) mmo0) (0.01) (0.01) (0.01) wooly<br>Household owns atelevision 0.06 5*** O.0356** * -O.009 -O.009 0.05444 O.0458*** -0.003<br>(0.02) O01) mol) (0.014 (O01) (0.02) moo2)<br>Household owns acomuuter O.16e*"* 0.105*** ooo? C.090 O.165** 0.14 g%8% 0.1 56*4%<br>(0.014 (O.013 mmol) (0.01) (0.01) (0.014 mol<br>Household owns arefrigerator 0.160*** O.0 944 * oool O.0 74 0.147 0.145 45*** O.110***<br>(0.01) (0.01) mmo0) (0.01) (0.01) (0.01) wooly<br>Household owns an airconditioner O.207*** O.121*** O034*4 0.1 3a 0.201] ** 0.2] 5*** 0.1 76***<br>0.011 011 fool) (0.011 O.011 0.014 fool<br>Household owns awashine machire 0.109% 0.055*** O013*+ 0.0524 C.O9 7 0.102*** 0095"<br>(0.014 (O.013 00) (0.01) (0.01) (0.014 mol<br>Household owns an electric fan O.06a*** O.054*** ooo2 O.03o%** O.04o%** 0.06 5*** 0.0350 **<br>(0.014 .01) mol) (O01) (0.01) (0.014 mol)<br>Log of residential area 0.18 7*** O.141*** -0.006* 0.15] *+* O.1835s+* 0.18 4*** O17e***<br>(0.01) (0.01) mmo0) (0.01) (0.01) (0.01) wooly<br>House wall materials O.026*** O.017*** -O.00 6 + 0.020% 0.024 0.02 5*** 0 .020***<br>(0.00% (1.00) 00) 0.00) (0.00) (0.005 moo}<br>Access to drinkine water o.oo Te * O.004e* * oooo 008+ o.oo7e** 0.00 7*** 0.003<br>(0.007 (1.00) M00) (0.00) (0.00) (0.00% moo}<br>Trre of toilet O.04o%** O.016*** O008*+* 0.031 *** Doss 0.039%" * 0032***<br>(0.007 (1.00) moo) 0.00) (0.00) (0.004 moo}<br>Urban 0.269 #4 0.06 3*** O.042*** ooo04 0.1035*** CLOTe 0.049% * O 047% ** 0.159%#<br>0.014 0.011 011 fool) (0.011 O.011 0.014 fool 0.011<br>Co ns tart BAT 5 tt 842 2%%% B.S 4 gre 2.70] *+% 7.611 +++ S.084 sa 8.470++% 8 O2g+ +4 8 08a*+4<br>(0.05) 0.05) (0.06) mmo4) (0.05) (0.05) (0.05) moo5) 0.05)<br>oe 0.59 0.30 0.21 o.14 028 0.29 0.30 0.29 O35<br>Gu 0.25 a.19 0.11 0.08 0.18 0.18 a.19 a.19 025<br>p O.29 0.28 0.21 o.24 o29 0.28 0.29 0.29 o31<br>iad 0.46 0.69 0.86 o.94 0.73 O71 0.69 0.70 046<br>H 3300 3300 3300 3300 3300 3300 S300 S300 S300<br>Mote: * p-0.10,** peo 045 *** p<0.01 .5 tandard errors are in parentheses. Allestimatonemploys commune random effects models and control for regional dinuny<br>wariables. House wall material is assigned numerical values using the following categories: 6 ‘bement', 3 "brick", 4 “ironfwood') 3 "earth'staw', 2 "bamboo/board", and 1<br>‘others.The trpes of toilet are assigned mamerical values using the following categories: & "septic') 5 'suilabh') 4 ‘double septic")3 “fish bridge’) 2 ‘others') and 1] ‘hone".<br><!-- End of picture text -->
**Table A.7. Predicted Poverty Rates Based on Imputation (percentage)**

|**i**|||||**Second perio**|**d**||||**True**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Indcators**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|**Model 7**|**Model 8**|**Model 9**|**rates**|
|**_Panel A: Bangladesh 2015-2018/19_**|||||||||||
|Hdt t t|12.5|**7.5***|**8.2**|**6.7**|**7.7***|**6.8***|**7.6***|N/A|N/A|7.3|
|eacoun povery rae|(0.7)|(0.6)|(0.8)|(0.7)|(0.6)|(0.6)|(0.7)|||(0.6)|
|Nt t|13.1|9.7|**11.8***|10.5|10.1|9.4|9.7|13.1|9.7|12.2|
|ear-povery rae|(0.6)|(0.6)|(0.7)|(0.6)|(0.6)|(0.6)|(0.6)|(0.6)|(0.6)|(0.6)|
|Et t t|1.9|**0.9**|**0.8**|**0.5***|**0.8**|**0.7***|**0.9**|1.9|**0.9**|0.6|
|xreme povery rae|(0.3)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.3)|(0.2)|(0.2)|
|Pt|<br>2.4|**1.3**|<br>**1.3**|<br>**1.0***|<br>**1.3**|**1.1***|**1.3**|2.4|**1.3**|<br>1.1|
|overy gap|(0.2)|(0.1)|(0.2)|(0.1)|(0.1)|(0.1)|(0.1)|(0.2)|(0.1)|(0.1)|
|USAID Prt|18.8|17.0|**16.1**|**15.1***|**16.5**|**16.3**|17.0|18.8|17.0|15.2|
|ovey gap<br>|(0.7)|(0.9)|(0.9)|(0.9)|(0.9)|(0.9)|(0.9)|(0.7)|(0.9)|(0.8)|
|N|||||||||||
|<br>**_Panel B: Malawi 2016/17-2019/20_**|||||||||||
|Hd|53.5|**52.8**|60.8|52.8|53.9|**52.7**|**52.7**|**52.3**|**52.7**|51.1|
|eacount poverty rate|(0.9)|(1.0)|(1.1)|(1.2)|(1.0)|(1.0)|(1.0)|(1.0)|(0.9)|(0.9)|
||**14.0***|**14.2***|12.3|**14.3***|**14.3***|**14.4***|**14.2***|**14.4***|**14.3***|14.1|
|Near-poverty rate|(0.4)|(0.4)|(0.5)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|
|E|22.7|**21.5**|29.6|**21.6**|**21.9**|**21.5**|**21.7**|**21.1***|**21.8**|20.6|
|xtreme poverty rate|(0.7)|(0.8)|(1.1)|(0.9)|(0.8)|(0.8)|(0.8)|(0.8)|(0.7)|(0.8)|
|P|18.2|**17.6**|22.6|**17.5**|18.0|**17.5***|**17.7**|**17.3***|**17.7**|17.1|
|overty gap|(0.4)|(0.5)|(0.6)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.4)|(0.4)|
|USAID P|<br>**34.1**|<br>**33.4***|<br>37.2|<br>**33.2***|<br>**33.3***|<br>**33.3***|<br>**33.6***|<br>**33.2***|<br>**33.6***|<br>33.5|
|overty gap|(0.4)|(0.4)|(0.6)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.5)|
|N|11,432|11,432|11,432|11,432|11,432|11,432|11,432|11,432|11,432|11,432|
|**_Panel C: Nigeria 2012/13-2018/19_**|||||||||||
|Headcount poverty rate|33.4|33.5|51.3|27.6|24.7|29.7|33.4|34.4|34.6|46.4|
||(2.0)|(2.0)|(2.4)|(1.9)|(1.8)|(1.9)|(2.0)|(2.0)|(2.0)|(1.9)|
|Near-poverty rate|**12.5**|**12.7***|**13.4***|**13.3***|11.5|**12.6**|**12.6**|**12.6**|**12.4**|13.7|
||(0.9)|(0.9)|(1.0)|(1.0)|(0.9)|(0.9)|(0.9)|(0.9)|(0.9)|(1.0)|
|Extreme poverty rate|14.7|14.8|26.4|9.0|10.0|12.2|14.9|15.4|15.5|22.2|
||(1.4)|(1.5)|(2.1)|(1.1)|(1.2)|(1.3)|(1.5)|(1.5)|(1.4)|(1.4)|
|Poverty gap|10.9|10.9|18.0|7.4|7.6|9.3|11.0|11.3|11.4|<br>15.3|
||(0.9)|(0.9)|(1.2)|(0.7)|(0.7)|(0.8)|(0.9)|(0.9)|(0.9)|(0.8)|
|USAID poverty gap|**32.6***|**32.7***|35.1|26.9|30.7|31.2|**33.0***|**33.0***|**33.0***|33.0|
||(1.1)|(1.3)|(1.1)|(1.1)|(1.5)|(1.3)|(1.3)|(1.2)|(1.1)|(0.8)|
|N|4,976|4,976|4,976|4,976|4,976|4,976|4,976|4,976|4,976|4,976|

**_Panel D: Tanzania 2019/20-2020/21_**

52
|Headcount poverty rate|**17.1***|**17.3***|**19.4**|**16.4**|**17.1***|**16.5**|**17.3***|**17.6***|**18.2***|17.8|
|---|---|---|---|---|---|---|---|---|---|---|
||(1.1)|(1.3)|(1.5)|(1.3)|(1.2)|(1.3)|(1.3)|(1.3)|(1.1)|(1.1)|
|Near-poverty rate|**10.5***|**10.9***|**10.7***|**11.3**|**10.8***|**10.3***|**10.8***|**10.8***|**10.5***|10.2|
||(0.7)|(0.8)|(0.8)|(0.8)|(0.8)|(0.8)|(0.8)|(0.8)|(0.7)|(0.7)|
|Extreme poverty rate|**9.8***|**9.4***|**10.9**|**8.4**|**9.3***|**9.0**|**9.5***|**9.6***|**10.4***|9.8|
||(0.8)|(1.0)|(1.1)|(0.9)|(0.9)|(1.0)|(1.0)|(1.0)|(0.9)|(0.8)|
|Poverty gap|**4.6***|**4.5***|**5.2**|**3.9**|**4.4***|**4.2**|**4.5***|**4.5***|**4.9***|4.6|
||(0.4)|(0.4)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.3)|
|USAID Poverty gap|**26.9***|**25.8***|**27.0***|**24.0**|**25.6***|**25.5***|**25.8***|**25.7***|**26.8***|25.9|
||(1.1)|(1.1)|(1.4)|(1.1)|(1.1)|(1.2)|(1.1)|(1.1)|(1.1)|(1.1)|
|N|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|**_Panel E: Viet Nam 2014-2016_**|||||||||||
|Headcount poverty rate|14.7|13.1|6.0|8.3|**10.3**|12.3|13.1|11.2|**9.0**|9.6|
||(0.5)|(0.6)|(0.5)|(0.6)|(0.6)|(0.6)|(0.6)|(0.6)|(0.5)|(0.4)|
|Near-poverty rate|9.4|8.6|4.9|6.1|**7.2***|8.0|8.6|7.8|**6.9***|6.9|
||(0.4)|(0.4)|(0.3)|(0.4)|(0.3)|(0.4)|(0.4)|(0.4)|(0.3)|(0.3)|
|Extreme poverty rate|1.9|1.9|0.6|**1.2***|**1.5**|2.0|2.0|1.9|1.9|1.2|
||(0.2)|(0.3)|(0.1)|(0.2)|(0.2)|(0.3)|(0.3)|(0.3)|(0.4)|(0.2)|
|Poverty gap|3.9|3.6|1.5|**2.3**|**2.8**|3.4|3.6|3.1|**2.7**|2.5|
||(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.3)|(0.3)|(0.2)|
|USAID Poverty gap|**26.5***|**27.2**|**24.5**|**27.4**|**26.9***|27.9|**27.4**|28.0|30.0|26.1|
||(0.7)|(1.0)|(1.3)|(1.3)|(1.0)|(1.0)|(1.0)|(1.4)|(1.9)|(0.9)|
|N|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347||
|**_Control variables_**|||||||||||
|Food expenditures|||Y||||||||
|Non-food expenditures||||Y|||||||
|Furnishings and household expenses|||||Y||||||
|<br>Health expenditures||||||Y|||||
|Education expenditures|||||||Y||||
|Utilities expenditures||||||||Y|Y||
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Empirical distribution of the error terms model with bootstrapped SEs is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed poverty rates for 2019/20 use the estimated parameters based on the 2016/17 data. 100 simulations are implemented. ’Near poor’ status is defined as living on an income between 100 and 125% of the poverty line. All indicators are expressed in %. True rate is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

53
# **Table A.8. Predicted Mean (Log) Consumption Based on Imputation**

|||||**Se**|**cond rou**|**nd**|||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Model**<br>**1**|**Model**<br>**2**|**Model**<br>**3**|**Model**<br>**4**|**Model**<br>**5**|**Model**<br>**6**|**Model**<br>**7**|**Model**<br>**8**|**Model**<br>**9**|**True rates**|
|Bldh f 2015 2018/19|10.7|10.8|**10.8**|10.8|10.8|10.9|10.8|N/A|N/A|10.8|
|angaes, rom to|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|||(0.0)|
||12.1|12.1|**12.0**|12.1|12.1|12.1|12.1|12.2|12.1|12.1|
|Malawi, from 2016/17 to 2018/19|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
||11.3|11.3|11.0|11.4|11.4|11.3|11.3|11.2|11.2|11.0|
|Nigeria, from 2012/13 to 2018/19|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
||<br>13.7|<br>13.7|<br>13.7|<br>**13.7**|<br>**13.7**|<br>**13.7***|<br>**13.7**|<br>13.7|<br>13.7|<br>13.7|
|Tanzania, from 2019/20 to 2020/21|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
||<br>9.6|<br>9.7|<br>10.0|<br>9.8|<br>**9.8***|<br>9.7|<br>9.7|<br>9.7|<br>**9.8***|<br>9.6|
|Viet Nam, from 2014 to 2016|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
|Food expenditures|||<br>Y||||||||
|<br>Non-food expenditures||||Y|||||||
|<br>Furnishings and household expenses|||||Y||||||
|<br>Health expenditures||||||Y|||||
|<br>Education expenditures|||||||Y||||
|<br>Utilities: water, kerosene, lighting||||||||Y|Y||
|<br>Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Empirical distribution of the error terms model with bootstrapped SEs is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed consumption per capita for the second round uses the estimated parameters based on the data from the first round. 100 simulations are implemented. True consumption per capita is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

54
||Hew oni <br>Speci|poverty rate<br>Spee2|New pov<br>spect|ertyrate<br>Spec?|Extreme powerty rate<br>spect<br>spec?|Powe<br>spect|atymp<br>Specs|LAIDPo<br>spend|verty7p<br>Spec2|Canin<br>Spect|one<br>Sree|
|---|---|---|---|---|---|---|---|---|---|---|---|
|TMode]<br>2:Demographics,employanent,assets,house|o34¢4|<br>-0.103|-O.127|-2435|-0.192<br>1264|-0.000|1.184|-0.000|<br>2836*|<br>0000|2.135|
|<br>characteristics|(L14+]|tLio1|(0.14)|(2.37)|tog]<br>(2051|tL]|(207)|(0.43||(L601|(089)|(2.06)|
|IMade1|Lagatee|0.284|1014|-$,185|Lagle+*<br>sogre|1Ls620**|4.903|<br>o213|S.liree|Z0L6+"*|go7gee|
|(adds<br>food exqs.taIfode]2]<br>|<br>(O.19]<br>|(2.60)<br>|(090)<br>|(6.33)<br>|(O56)<br>(3451<br><br>|(0.71)<br>|(322)<br>|(020)<br>|(3.328)<br>|(043)<br>|(422)<br>|
|IMadel+|O34¢4|-O.756|o3g)|-4+.419|0.537<br>+042|-0.000|2446|-O410|S.7gsee|“0000|4735|
|<br>(addsnoncodex.<br>toModel2]|(0.34)|<br>(2.031|(0.72)|420]|(0.661<br>(3011|(0.34)|(2.63)|(0.33)|(277)|(0.00)|(324)|
|<br> <br>TMade1§|o34¢4|-0.190|O.1a5|<br>-2.307|-0497<br>1215|-0.000|1.369|-0207|3.120*|O.2g9|2733|
|<br>(addsdurablesexp.<br>toModke12]|(100)|ULF41|093]|(3.66)|(O34)<br>(220)|(Ll|(246)|(051)|tL7oy|UF)|(229)|
|<br> <br>Tok6|o34¢¢|-0.155|-0.127|-2.663|-0.192<br>14230|0.195|1.339|-O.511*|2407+|“0000|24353|
|<br>(addshealthexp.toModel2]|(0.76)|(143)|(0.14)|<br>(2.68]|(O97)<br>(2.16]|(0.93)|(202)|(033)|tL42]|(102)|(195)|
|<br>IMadel7|0664|O311|-O.1E7|-2452|-O.193<br>Laie|-0.000|Lid|-0.000|2a76*|O.2g9|2472|
|<br>(adi<br>¢dwationesg9.toIfode]2]|(1.14)|(Leo)|(0.141|(233)|togry<br>(2071|(1.123)|(203)|(0.58)|(Ld)|(O77)|(2.12)|
|<br>IMode1 3<br>|0220<br>|0486<br>|0300<br>|-1929<br>|0.184<br>1925<br><br>|O31<br>|2.186<br>|o213<br>|3.200*+<br>|O.2g9<br>|2475<br>|
|(addswilitiesoq.toModel2)|(0.72)|(149)|(043)|(244)|(0.78)<br>(191)|(0.77)|(183)|(O47)|(LS)|(O77)|(223)|
|<br>Madk1?|Ls¢ot*|2.099e«|asin|-0.002|Ogsee<br>Ligeeee|o204*|1s01*|-0.000|0939|O41|1l2agere|
|(addsutilitiesoq.todemoaraiic&employment]|(0.73)|(Loi|(031)|(1.18)|to34)<br>(O44)|(0.49)|(0.33)|(0.53)|(0.981|(036)|(0.14)|
|<br>IMode110|o249|-0270|-O438|-2.302|<br><br>-O0561<br>0.66c|“0.081|OogT|-02355|2611|“0463|1473|
|<br>(addsdistancetofacilities<br>toModel?)|CL]|(L321|(0.60)|(2.61)|(1431<br>(2441|(1.60)|(246)|(0.96)|(L921|(L493)|(253)|
|<br> <br>Titode1 11<br>|o465<br>|on<br>|-0438<br>|-2.797<br>|-0461<br>O33<br><br>|-0.361<br>|org<br>|-0255<br>|24574<br>|O.0G1<br>|2074<br>|
|(addsagpiculowalsoil<br>qmlirytoMode]2]|(1.30)|(136)|(0.60)|(2.62]|(142)<br>(246]|CL.67]|(245)|(0.96)|(Lary|(105)|(222)|
|<br> <br>IMadelLZ|L323|2.055|O465|-0.054|0.020<br>O437|O471|OS4|-O.413|O541|O415|1290|
|<br>(addsdistmeetofacilities<br>toModel3)|(1.13)|(Ld|(0421|<br>(1.27)|(O64)<br>(1131|tL.)|(144)|(0.33)|(L320)|(122)|(L112)|
|<br> <br>Todel13|Logge++|24g5ee%|0465|0004|0.020<br>OS57|02271|O.6e1|-O255|O47o|O415|1L10ge**|
|<br>(adie apriculbwalsoil<br>gmbtytoMod]9)|(0.67)|(O79)|042]|(1.23)|(0541)<br>(L161|(LO)|(L4F]|(0.96)|(L246)|(O41)|(O17)|
|<br>Trye eschnaies||||||||||||
|Head ont poarerty rate||0.033<br>(0.04)||||||||||
|Newpovertyrate||||O48¢||||||||
|||||0.37]||||||||
|Exdreme powertyrate|||||OL<br>+ee|||||||
||||||<br>(0.03)|||||||
|Poverty ap|||||||O21L3+t+|||||
||||||||(0.01)|||||
|USAIDpovertyam|||||||||OO81|||
||||||||||(0.08)|||
|Come<br>ptign<br>2 or||||||||||||
||||||||||||169gt*|
|Gthermodelparameters|||||||||||(O74)|
|<br>Lozatstaplesizofbaseceey||-O927"""||4.164**|O4gg||o.0g9||-0.083||-OG22|
|||(030)||<br>(Le)|(0.431||(0.60)||(O.74]||(039)|
|Intervallengthbetveen<br>base&targetseteys||-0.942+*||-0.105|-LL||-O.785+*||-0.279||-0337|
|<br>Nomnallieareayessignmok]||(039)<br>Os39ee||(0.53)<br>0065|<br>(02**5**1<br>D12||<br>(O37)<br>0.026||(0.251<br>-O0.042||(040)<br>O.LS7|
|<br>Muanber of round wed<br>||(O24)<br>-0.089<br>||(0.08]<br>- LOLOS**<br>|<br>(0.15)<br>Odagee<br>||(0.09)<br>OooLs**<br>||(O07 ]<br>O1s7<br>||(037)<br>-Layyeee<br>|
|d||3.682||670|-8.559||-6941||-17.439+*||-15941|
|||(5.13)||(16.351|(3491||(3.15)||UFS11||<br>(1009)|
|Coun<br>FP||||||||||||
|Tommi|g037||O220+t+||Lagat*|Loglet*||2205¢**||LRgdstee||
|Malawi<br>|(0**.**0 |<br>-Q 397 eee<br>||(0.06)<br>ladgere<br>||<br>(O**.**11)<br>0 929ee"<br>|(0.07 |<br>ogrzeee<br>||(0.07]<br>OL +++<br>||<br>(L44]<br>Lz.g30++*<br>||
|Nigeria<br>|(0.02)<br>-O.455++*<br>||(006)<br>34505+++<br>||(oor<br>1.084 ee<br>|(0.041<br>Os45***<br>||(0.**0**0)<br>20 5+++<br>||(L481<br>12.2335++*<br>||
|Constant|(**0.**02)<br>-<br>673|TOsatte|(0.111<br>-O404+++|29451++*|(O03)<br>-LB1l¢++*<br>-L691|00.04]<br>-L.Sa+|-0.195|(**0.**04]<br>-<br>502|Tarate|(L49]<br>-LOSTLEte|-L333|
|Log Lie lihood<br>|(0.75<br>-202 33<br>|(130<br>-L39.22<br>|<br>(0.15<br>-17590<br>|<br>(3.**3**6<br>-13 36<br>|<br>(O61<br>(4.14<br>-LT9.25<br>- 14099<br><br>|<br>(0.37<br>-18126<br>|(390<br>-L6L47<br>|<br><br><br>(043<br>-17555<br>|<br>(3.83<br>- 17420<br>|<br>{L327<br>-132.22<br>|(3.04<br>-118.82<br>|
|PoeudoRt|0.07|O27|0.16|O36|013<br>O31|0.12|O21|0.13|og|0.19|0.28|
|<br>N|314|314|314|314|314<br>314|314|314|314|314|314|314|
<!-- Start of picture text --> Headk ovat Povety rate Wear Teowety pute Eohreane Poverty rate Povety pb USA Poverty ta Connon Wes<br>Model 2: Dennogyaphics, suphymmant, assets , house 0231 0.130 -0.030 0.822 -0.299 1133 -O.16L L2gg “0.027 L.z3s* 0.156 2275<br>characterBtis (L321 (2.051 (0.341 (1.1 (LOL (2.14) (L320) (231) (0.34) (029) 10.4] (236)<br>Modk13 1044" 0.699 Latd** -O.05¢ O9g5 S446 0935 S497 0203 S.Lyree* 20ggeee 950"<br>(adds food e:g9. to Model2) (0.611 (2221 (0.391) UF.281 (0.59) (3.361 (L021 (3361 (0.12) (L193) (0.331 [434]<br>Model ¢ - 0.084 -0 389 O.335 “1050 0.100 2.887 -0425 2414 0.635 3076 0.000 4553<br>(adds nanfood e299. to Model3) (0.45) (2.14] (0.67 ] (B7L] (0.37) (3.11) (O97) (3.02) (0.29) (2.02) i (322)<br>Model § oar? O321 O.6FL oo -O507 Lit6 -0.100 1408 -O.L67 Lar O79 2.735<br>(adds durables ex. to Model3) (1.29) (2.18) (0.87) (2.30) (105) (242) (140) (2.75) (0.43) (1.18) (0.81) (240)<br>Model 6 oo3+ -O.1L%4 O.0e9 -O.479 -0.389 Lis -0.108 Laay -O.454 154" O.257 23833<br>(adds health esp. to Midel2) (O.94] (L92] (0.24) (1.82) (L051 (2.34) (L.14] (2 39] (0.33) (092) (1051 (233]<br>Model 7 0429 0331 -0.030 -O.G1L7 O41 0928 026) 1.109 “0.027 Lise O39 252<br>(adds edie ation eg. to Model2) (L211 (2.001 (0.341 (LaF (O91) (1921 (L111 (2.111 (O31 (0.92) (0.811 (233)<br>Mod1 ¢ oro O745 O17 “0.455 0086 1.g02 O6S1 Badd 0263 2.15e* O456 2243<br>(adds utilities e299. to Model?) (0.90) (L341 (0.251 (LaF (O94) (2.301 (O94) (2231 (541 (LO? (0.311 (2451<br>Mode19 1405+ 149) O436 0230 O47a+* Laglt+ 0535 Led -O.0E o409 O.6d0+* 13ratt*<br>(adds utilities exp. to demogranhic & enpbyment) (0.76) (O94) (0.430) (0.751 (o271 (0.60) (0.63) (1.13) (0.451 (0.70) (0.25) (0.16)<br>Mod] 10 0120 0.010 -0.153 0.606 0.735 0.403 0233 LOs4 0.562 12340 -0.257 1356<br>(adds distance tofaciltiesto Model 2) (1.33) (236) (0.70) (1.33) (143) (2.60) (1.59) (221) (0.71) (124) (1.65) (291)<br>Model 11 o307 0.187 -0.153 0.630 0.735 O6e6 0428 O.8ss “0.285 1464 O17 22399<br>(adds agricubial soil qualryto Mlodk1 2) (LAz] (225] (0.70) (L811 (158] (2.62 ] (L.76] (233] (0.83) (L34] (LLL (250)<br>Mok] 2 L1Lds 1225 O.304 0.190 “0.033 0.252 0350 OTTL “0.611 0082 OF1L5 1.155<br>(adds distance tofaciltiesto Model 9) (1031 (L241 (0.331 (L021 (059) (1K (L111 (L271 (0.611 (0.33) (0.99) (0.25)<br>Mok1 1G L193 1.184 O.304 d.19 -0.033 O.330 0.132 0.382 -O.211 0319 OTS 1L4as ete<br>(adds agricubwal soil qualityto Modk1?) (0.731 (O39) (0.531 (OE (0591 (1031 (O39) (L44] (0.811 (0.30) (0.55) (030)<br>Tre eaiimedes<br>Headcount poverty rate 0021<br>(0.03)<br>Near powerty rate 0399<br>(0.23)<br>Externe powerty rate O164eee<br>(0.03)<br>Poverty zap Olyiee*<br>(0.02)<br>USAID poverty aan 0.073<br>(0.08)<br>Consumption me a 13m ttt<br>(0431<br>Gther mock? parcone ters<br>Logzaf sammpk size of base anvey “OG Latet -0.979 O.271 -0.003 0.140 “0423<br>(024) (0.82) (O.42] (041) (0.55) (045)<br>Intervallmgth betereen basa 4 target surveys aor eee -O.184 -LOoLe+* -O.741*" -0.102 -0323<br>Noma] Ine ar regression model (030)-0 093 (O31-O.193 (0.231O35 (03O4 1 6*1 (0271O31¢ (0471-LSgaeee<br>(O25) (O.4L] (O.23] (O25] (.19] (041)<br>Munwber af rounds wed OF9L** 9020 0.153 O.01¢ -0.202 0227<br>Ri soured (O.L710632 (O.LF]4307 (O-9 . 1+1595 (005)-9411 -1L(0.131 Odette - 15.233(0231<br>($40) (2.441 (é41] (6.07) (4.13) (10.52)<br>Coury FF<br>Tanzania 0022 -O.151* 1LT§o* ++ 1 Bar ++ 202 ++* 14.305+**<br>(0.00) (0.021 (0.13) (0.10) (0.14) CLL]<br>Malar -O.650++" Ooryeee O.7z2e* O77 tte O27reee 1La.9g4+ee<br>Nigeria (0.051-O41L5e6" (0.01Lage ee (0.0511 Oe2eee (0.04)Os¢seee (0.021Lyageee (LYLYJay e e<br>cutL (0.02O3Ts] -G SSL ee" (0.111Os “3.80 (0.0511502** - Lg4 (0.04114633 -2576 (0.1810564 “E48 (LLle4rgt ee 1020<br>ound (0.9511620+ (2321-£007** (0.331O24 + +e (ad)-2427 (O71)2412*+* (2.350)-O.0K7 (12 . 00)74 tee (233)-1408 (O42)13325++* (“ 3. 131008 CLL13.3304 +* (229)1972<br>(0.90 (220 (O27 (at (o33 (3.09 (993 (FLL (0.33 (293 (Li (230<br>Log lee thood -309 21 - 24797 -29862 -ETL39 - 240.02 “ERE 32 - 263 39 “Hie -28331 -385.77 “L782 - 14 33<br>Psoeuds Rt 003 ou oo9 ol 16 O14 0.07 g12 o10 o.07 ols o.17<br>N 314+ 314 314 314+ 314+ 314 314 314 314+ 314+ 314 314<br><!-- End of picture text -->

||Headk ovat|Povety rate|Wear Te|owety pute|Eohreane P|overty rate|Pov|ety<br>pb|USA Po|verty ta|Conn|on Wes|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Model2:Dennogyaphics,suphymmant,assets,house|0231|0.130|-0.030|0.822|-0.299|1133|-O.16L|L2gg|“0.027|L.z3s*|0.156|2275|
|<br>characterBtis|(L321|(2.051|(0.341|(1.1|(LOL|(2.14)|(L320)|(231)|(0.34)|(029)|10.4]|(236)|
|Modk13|1044"|0.699|Latd**|-O.05¢|O9g5|S446|0935|S497|0203|S.Lyree*|20ggeee|950"|
|(addsfoode:g9.toModel2)|(0.611|(2221|(0.391)|UF.281|(0.59)|(3.361|(L021|(3361|(0.12)|(L193)|(0.331|[434]|
|<br>Model ¢<br>(addsnanfoode299.toModel3)|-0.084<br>(0.45)|-0 389<br>(2.14]|O.335<br>(0.67]|“1050<br>(B7L]|0.100<br>(0.37)|2.887<br>(3.11)|-0425<br>(O97)|2414<br>(3.02)|0.635<br>(0.29)|3076<br>(2.02)|0.000<br>i|4553<br>(322)|
|<br>Model§|oar?|O321|<br>O.6FL|oo|-O507|Lit6|-0.100|1408|-O.L67|Lar|O79|2.735|
|<br>(addsdurablesex.toModel3)|(1.29)|(2.18)|(0.87)|(2.30)|(105)|(242)|(140)|(2.75)|(0.43)|(1.18)|(0.81)|(240)|
|<br>Model6|oo3+|-O.1L%4|O.0e9|-O.479|-0.389|Lis|-0.108|Laay|-O.454|154"|O.257|23833|
|<br>(addshealthesp.toMidel2)|(O.94]|(L92]|(0.24)|(1.82)|(L051|(2.34)|(L.14]|(239]|(0.33)|(092)|(1051|(233]|
|<br>Model7|0429|0331|-0.030|-O.G1L7|O41|0928|026)|<br>1.109|“0.027|Lise|O39|252|
|<br>(addsedieationeg.toModel2)<br>|(L211<br>|(2.001<br>|(0.341<br>|(LaF<br>|(O91)<br>|(1921<br>|(L111<br>|(2.111<br>|(O31<br>|(0.92)<br>|(0.811<br>|(233)<br>|
|<br>Mod1¢|oro|O745|O17|“0.455|0086|1.g02|O6S1|Badd|0263|2.15e*|O456|2243|
|<br>(adds utilities e299.to Model?)<br>|(0.90)<br>|(L341<br>|(0.251<br>|(LaF<br>|(O94)<br>|(2.301<br>|(O94)<br>|(2231<br>|(541<br>|(LO?<br>|(0.311<br>|(2451<br>|
|Mode19|1405+|149)|O436|0230|O47a+*|Laglt+|0535|Led|-O.0E|o409|O.6d0+*|13ratt*|
|(addsutilitiesexp.todemogranhic&enpbyment)|(0.76)|(O94)|(0.430)|(0.751|(o271|(0.60)|(0.63)|(1.13)|(0.451|(0.70)|(0.25)|(0.16)|
|<br>Mod]10|0120|0.010|-0.153|0.606|0.735|0.403|0233|LOs4|0.562|12340|-0.257|1356|
|<br>(adds distance tofacilties<br>toModel2)|(1.33)|(236)|(0.70)|(1.33)|(143)|(2.60)|(1.59)|(221)|(0.71)|(124)|(1.65)|(291)|
|<br>Model11|o307|0.187|-0.153|0.630|0.735|O6e6|0428|O.8ss|“0.285|1464|O17|22399|
|<br>(addsagricubialsoilqualry<br>toMlodk12)|(LAz]|(225]|(0.70)|(L811|(158]|(2.62]|(L.76]|(233]|(0.83)|(L34]|(LLL|(250)|
|<br> <br>Mok]2|L1Lds|1225|O.304|0.190|“0.033|<br>0.252|0350|OTTL|“0.611|0082|OF1L5|1.155|
|<br>(adds distance tofacilties<br>toModel9)|(1031|(L241|(0.331|(L021|(059)|(1K|(L111|(L271|(0.611|(0.33)|(0.99)|(0.25)|
|<br>Mok1 1G<br> <br>|L193<br>|1.184<br>|O.304<br>|d.19<br>|-0.033<br>|O.330<br>|0.132<br>|0.382<br>|-O.211<br>|0319<br>|OTS<br>|1L4asete<br>|
|(addsagricubwalsoilquality<br>toModk1?)|(0.731|(O39)|(0.531|(OE|(0591|(1031|(O39)|(L44]|(0.811|(0.30)|(0.55)|(030)|
|<br> <br>Tre eaiimedes<br>|||||||||||||
|Headcountpovertyrate||0021|||||||||||
|||(0.03)|||||||||||
|Nearpowertyrate||||0399|||||||||
|||||(0.23)|||||||||
|Externe powerty rate||||||O164eee<br>|||||||
|Poverty zap||||||(0.03)||Olyiee*<br>|||||
|USAIDpovertyaan||||||||(0.02)||0.073|||
|||||||||||(0.08)|||
|Consumption mea||||||||||||13m ttt<br>(0431|
|Gthermock?parconeters|||||||||||||
|<br>Logzafsammpksizeofbaseanvey||“OGLatet||-0.979||O.271||-0.003||0.140||“0423|
|||<br>(024)||(0.82)||(O.42]||(041)||(0.55)||(045)|
|Interval<br>lmgth betereen basa 4 target surveys<br>||aoreee<br>||-O.184<br>||-LOoLe+*<br>||-O.741*"<br>||-0.102<br>||-0323<br>|
|Inearregressionmodel||-0093||-O.193||O35||O46*||O31¢||-LSgaeee|
|||<br>(O25)||(O.4L]||(O.23]||<br>(O25]||(.19]||(041)|
|Munwberafroundswed||OF9L**||9020||0.153||O.01¢||-0.202||0227|
|<br>Ri||(O.L71<br>||(O.LF]<br>||(O**.**1+1<br>||(005)<br>||(0.131<br>||(0231<br>|
|soured||0632<br>($40)||4307<br>(2.441||-9 595<br>(é41]||-9411<br>(6.07)||-1LOdette<br>(4.13)||-15.233<br>(10.52)|
|Coury FF|||||||||||||
|Tanzania|0022||-O.151*||1LT§o*++||1Bar++||202++*||14.305+**||
||(0.00)||(0.021||<br>(0.13)||<br>(0.10)||<br>(0.14)||CLL]||
|Malar|-O.650++"||Ooryeee||O.7z2e*||O77<br>tte||O27reee||1La.9g4+ee||
|Nigeria|(0.051<br>-O41L5e6"||(0.01<br>Lageee||(0.051<br>1Oe2eee||(0.04)<br>Os¢seee||(0.021<br>Lyageee||(LY<br>LYJay**e**e||
|cutL|(0.02]<br>O3Ts|-GSSLee"|<br>(0.111<br>Os|“3.80|<br>(0.051<br>1502**|-Lg4|(0.041<br>14633|-2576|(0.181<br>0564|“E48|(LL<br>le4rgtee|1020|
|ound|<br><br>(0.951<br>1620+<br>|<br>(2321<br>-£007**<br>|(0.331<br>O24 + +e<br>|(ad)<br>-2427<br>|(O71)<br>2412*+*<br>|<br>(2.350)<br>-O.0K7<br>|(1**.**00)<br>2 74 tee<br>|(233)<br>-1408<br>|(O42)<br>13325++*<br>|(**3.**131<br>“<br>008<br>|<br>CLL<br>13.3304 +*<br>|(229)<br>1972<br>|
||(0.90|(220|(O27|(at|(o33|(3.09|(993|(FLL|(0.33|(293|(Li|(230|
|Logleethood|-30921|-24797|-29862|-ETL39|-240.02|“ERE32|-26339|“Hie|-28331|-385.77|“L782|-1433|
|<br>Psoeuds Rt|<br>003|<br>ou|oo9|ol|<br>16|<br>O14|<br>0.07|g12|o10|o.07|ols|<br>o.17|
|N|314+|314|314|314+|314+|314|314|314|314+|314+|314|314|
# **Table A.11. Comparison of utility expenditure variables in the BIHSs 2010/11 to 2018/19**

|**Item name**|**2010/11**<br>**BIHS**|**2015**<br>**BIHS**|**2018/19**<br>**BIHS**|
|---|---|---|---|
|**_Module P1 (monthly recall)_**||||
|1. Firewood|+|+|+|
|2. Cow dung/cakes/bhushi/wood-powder|+|+|+|
|3. Jute stick|+|+|+|
|4. Kerosene|+|+|+|
|5. Agriculture by-products used for fuel: paddy, hag, pressed<br>sugarcane, and dried com plants|+|+|+|
|6. Gas (natural, bio-gas) or liquified petroleum gas_(_LPG)|+|+|+|
|7. Electricity|+|+||
|8. Pit coal, char coal, wood coal|+|+|+|
|9. Other fuels and light (e.g., matches and candles)|+|+|+|
|10. Electricity (national grid)|||+|
|11. Electricity (generator)||+|+|
|12.Electricity (solar)||+|+|

Notes: The inconsistent variables are marked in red. The sign “+” indicates the variable is included in the specific survey round. For item 8, very few households (8 households in 2015 BIHS and 1 household in 2018/19 BIHS) reported use of “Pit coal, char coal, wood coal”.

57
**Figure A.1. Number of Models with Predicted Estimates of (Log) of Consumption That Are Statistically Insignificantly Different from the True Estimates**

<!-- Start of picture text --> Bangladesh 2015-2018/19 Malawi 2016/17-2019/20<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 75th 90th 95th<br>Percentiles Percentiles<br>Nigeria 2012/13-2018/19 Tanzania 2019/20-2020/21<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 75th 90th 95th<br>Percentiles Percentiles<br>Vietnam 2014-2016<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>5th 10th 25th 50th 75th 90th 95th<br>Percentiles<br>Number of models Number of models<br>Number of models Number of models<br>Number of models<br><!-- End of picture text --> 58
# **Figure A.2. Predicted consumption distribution, Model 9**

<!-- Start of picture text --> Malawi 2019/20 Nigeria 2018/19<br>5 10 25 50 75 9095 5 10 25 50 75 9095<br>Percentile Percentile<br>Tanzania 2020/21 Vietnam 2016<br>5 10 25 50 75 9095 5 10 25 50 75 9095<br>Percentile Percentile<br>95% CIs of true value True percentile value<br>95% CIs of imputed value Imputed percentile value<br>13.5 12.5<br>13 12<br>12.5 11.5<br>12 11<br>Log of consumption Log of consumption<br>11.5 10.5<br>11 10<br>15 11<br>14.5 10.5<br>14 10<br>13.5 9.5<br>Log of consumption 13 9 Log of consumption<br>12.5 8.5<br><!-- End of picture text --> 59
# **Figure A.3. Predicted USAID FGT indexes, Tanzania 2019/20- 2020/21**

<!-- Start of picture text --> Panel 1: FGT 1 Panel 2: FGT 2<br>14<br>30<br>28 12<br>26<br>10<br>24<br>8<br>22<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Panel 3: FGT 3 Panel 4: FGT 4<br>5<br>7<br>4<br>6<br>5 3<br>4 2<br>3 1<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Panel 5: FGT 5<br>3<br>2.5<br>2<br>1.5<br>1<br>.5<br>m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model<br>Estimated poverty gap (%) Estimated poverty gap (%)<br>Estimated poverty gap (%) Estimated poverty gap (%)<br>Estimated poverty gap (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true poverty gap. Dashed lines represent the true poverty gap. Dotted lines represent confidence intervals of the true poverty gap. Estimates are obtained using the normal linear regression models.

60
# **Figure A.4. Predicted Headcount Poverty Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>11 50<br>10<br>45<br>9<br>8<br>40<br>7<br>6<br>35<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>22 54<br>20 52<br>18 50<br>16 48<br>14 46<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>55 12<br>11<br>50<br>10<br>45<br>9<br>8<br>40<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true poverty rates. Dashed lines represent the true poverty rates. Dotted lines represent confidence intervals of the true poverty rates. Estimates are obtained using the normal linear regression models.

61
# **Figure A.5. Predicted Near - Poverty Rates Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>13 16<br>12 15<br>14<br>11<br>13<br>10<br>12<br>9 11<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>16<br>12<br>11 15<br>10<br>14<br>9<br>13<br>8<br>7 12<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>18 9<br>16 8<br>14 7<br>12 6<br>10 5<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true near- poverty rates. Dashed lines represent the true near-poverty rates defined as living on an income between 100 and 125% of the poverty line. Dotted lines represent confidence intervals of the true nearpoverty rates. Estimates are obtained using the normal linear regression models.

62
**Figure A.6. Predicted Extreme Poverty Rates Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>2<br>16<br>1.5<br>14<br>1<br>12<br>.5<br>10<br>0 8<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>23<br>12<br>22<br>11<br>21<br>10<br>9 20<br>8 19<br>7 18<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>30<br>2.5<br>25 2<br>1.5<br>20<br>1<br>15 .5<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true extreme poverty rates. Dashed lines represent the true extreme poverty rates. Extreme poverty line is defined as US$1.25 (2011 PPP) per day per capita in Bangladesh and Nigeria and as half of the national poverty line in Viet Nam and Ethiopia. National food poverty lines are used as extreme poverty line in Malawi and Tanzania. Dotted lines represent confidence intervals of the true extreme poverty rates. Estimates are obtained using the normal linear regression models.

63
# **Figure A.7. Predicted Poverty Gap Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>2 18<br>1.5 16<br>1 14<br>.5 12<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>6 19<br>5.5<br>18<br>5<br>17<br>4.5<br>4 16<br>3.5<br>15<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>20 3.5<br>18<br>3<br>16<br>2.5<br>14<br>2<br>12<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicate that the estimates are statistically insignificantly different from the true poverty gap. Dashed lines represent the true poverty gap. Dotted lines represent confidence intervals of the true poverty gap. Estimates are obtained using the normal linear regression models.

64
# **Figure A.8. Predicted USAID Poverty Gap Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>20<br>40<br>18<br>38<br>16<br>36<br>14<br>34<br>12 32<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>36<br>30<br>35<br>28<br>34<br>26<br>24 33<br>22 32<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>35<br>38<br>36<br>30<br>34<br>25<br>32<br>30 20<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicate that the estimates are statistically insignificantly different from the true poverty gap. Dashed lines represent the true poverty gap. Dotted lines represent confidence intervals of the true poverty gap. Estimates are obtained using the normal linear regression models.

65
# **Figure A.9. Predicted (Log) Consumption Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>10.8 9.4<br>10.78<br>9.35<br>10.76<br>9.3<br>10.74<br>10.72 9.25<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>12.1<br>13.8<br>12.08<br>13.75 12.06<br>12.04<br>13.7<br>12.02<br>13.65 12<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>10.22<br>11.1<br>10.2<br>11.05<br>10.18<br>11<br>10.16<br>10.95<br>10.14<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicate that the estimates are statistically insignificantly different from the true consumption mean. Dashed lines represent the true consumption mean. Dotted lines represent confidence intervals of the true consumption mean. Estimates are obtained using the normal linear regression models.

66
# **Appendix B: Machine Learning Results**

# **Table B.1. ML Estimates, Tanzania 2020/21**

|**Indicators**|**LASSO**|**Elastic**<br>**Net**|**Random**<br>**Forest**|**True**<br>**rates**|
|---|---|---|---|---|
|Headcount overt rate|9.8|9.7|5.8|17.8|
|py|(0.9)|(0.9)|(0.7)|(1.1)|
|Near-overt rate|12.0|12.0|16.6|10.2|
|py|(0.8)|(0.8)|(1.0)|(0.7)|
|Extreme overt rate|3.8|3.8|0.1|9.8|
|py|(0.6)|(0.6)|(0.0)|(0.8)|
|Povert a|1.9|1.8|0.4|4.6|
|y gp|(0.3)|(0.3)|(0.4)|(0.3)|
|USAID Pt|18.9|19.0|6.6|25.9|
|overy gap|(1.3)|(1.3)|(0.5)|(1.1)|
|Log of consumption mean|**13.7***|**13.7***|**13.7***|13.7|
||(0.0)|(0.0)|(0.0)|(0.0)|
|N||4,6|44||

67
# **Table B.2. ML Estimates, Vitenam 2016**

|**Indicators**|**LASSO**|**Elastic**<br>**Net**|**Random**<br>**Forest**|**True**<br>**rates**|
|---|---|---|---|---|
|Headcount overt rate|25.2|25.2|21.5|9.5|
|py|(0.6)|(0.6)|(0.6)|(0.4)|
|Near-overt rate|14.8|14.8|16.3|6.9|
|py|(0.4)|(0.4)|(0.5)|(0.3)|
|Extreme overt rate|4.7|4.7|3.5|1.2|
|py|(0.4)|(0.4)|(0.3)|(0.2)|
|Povert a|7.3|7.3|5.9|2.5|
|y gp|(0.3)|(0.3)|(0.2)|(0.2)|
|USAID Povert a|29.1|29.1|27.3|26.1|
|y gp|(0.7)|(0.7)|(0.7)|(0.9)|
|Log of consumption mean|9.7|9.7|9.7|9.8|
||(0.0)|(0.0)|(0.0)|(0.0)|
|N||4,6|44||

68
# **Table B.3. The list of selected variables in Lasso and Elastic Net models, Tanzania**

||**LASS**|**O**|**Elastic**|**Net**|
|---|---|---|---|---|
||**Non-standardized**|**Standardized**|**Non-standardized**|**Standardized**|
|Head has primary education|-0.050|-0.025|-0.049|-0.024|
|Head has secondary ordinary education|0.024|0.010|0.025|0.010|
|Head has secondary advanced education and higher<br>|0.003|0.001|0.004|0.001|
|Household size|-0.051|-0.167|-0.050|-0.164|
|Dependency Ratio|-0.023|-0.021|-0.023|-0.022|
|Gender Ratio|-0.046|-0.051|-0.046|-0.050|
|Household Head worked in unpaid apprentice in the last 12 months|0.048|0.003|0.045|0.003|
|<br>Household Head worked in farm in the last 12 months|-0.008|-0.004|-0.008|-0.004|
|Household Head employed in mining, manufacturing, construction|-0.014|-0.003|-0.012|-0.003|
|Household Head employed in retail, transportation|0.105|0.028|0.103|0.027|
|Household Head employed in information and communication|0.209|0.020|0.208|0.020|
|Household Head employed in technical, administrative, education|0.084|0.019|0.083|0.019|
|Proportion of adult males that worked for a wage, salary, or commission|-0.053|-0.025|-0.051|-0.024|
|Proportion of adult males that engaged in casual/ganyu labor in the last 12 months|-0.003|-0.001|-0.000|0.000|
|Proportion of adult males that worked in unpaid apprentice in the last 12 months|0.091|0.005|0.083|0.005|
|Proportion of adult males that worked in farm in the last 12 months|-0.004|-0.002|-0.005|-0.002|
|Proportion of adult males that worked in electricity or water supply|0.000|0.000|||
|Proportion of adult males that worked in information and communication|0.075|0.006|0.072|0.006|
|<br>Overcrowded|-0.032|-0.043|-0.033|-0.045|
|Household dwelling roof materials|-0.092|-0.030|-0.091|-0.030|
|Household dwelling floor materials|-0.042|-0.021|-0.043|-0.021|
|Burnt bricks/Concrete walls|0.007|0.003|0.007|0.003|
|Piped water/Truck water|0.075|0.038|0.075|0.038|
|Flush/VIP toilet|0.061|0.031|0.062|0.031|
|Electricity for lighting|0.072|0.035|0.072|0.035|
|Household owns a chair or sofa|-0.037|-0.018|-0.037|-0.018|
|Household owns a sewing machine|-0.072|-0.019|-0.070|-0.019|
|Household owns an electric/gas stove|0.174|0.068|0.173|0.068|
|Household owns a refrigerator/freezer|0.051|0.018|0.050|0.018|
|<br>Household owns a bicycle|-0.014|-0.006|-0.013|-0.006|
|Household owns a motor vehicle|0.030|0.010|0.031|0.010|
|Household owns a computer|0.166|0.032|0.164|0.032|
|<br>Household owns a mobile phone|0.042|0.014|0.040|0.013|
|Household owns an iron|0.010|0.005|0.010|0.005|
|Household owns an air c/fans|0.120|0.041|0.119|0.041|
|Anyone in the household owns livestock|-0.039|-0.019|-0.038|-0.019|
|Household owns cows|0.033|0.010|0.030|0.009|
|Household consumed spaghetti, macaroni|0.042|0.012|0.042|0.012|
|Household consumed onions, tomatoes, carrots|-0.138|-0.044|-0.135|-0.043|
|Household consumed sweets|-0.035|-0.008|-0.031|-0.007|

69
|Household consumed biscuits, buns, scones|0.047|0.023|0.047|0.023|
|---|---|---|---|---|
|Household consumed potato|0.080|0.040|0.079|0.040|
|Household consumed beef|0.130|0.062|0.129|0.062|
|Household consumed eggs|0.105|0.040|0.104|0.040|
|Household purchased cigarettes or other tobacco|0.005|0.001|0.001|0.000|
|Household purchased matches|-0.024|-0.010|-0.023|-0.010|
|Household purchased toothpaste, toothbrush|0.131|0.063|0.130|0.063|
|Household purchased personal products|0.058|0.029|0.058|0.029|
|Household purchased petrol or diesel|0.167|0.047|0.164|0.046|
|Household purchased cleaning products|0.162|0.042|0.161|0.042|
|Household spent on taxes|0.032|0.008|0.032|0.008|
|Household spent on wedding|0.076|0.035|0.075|0.035|
|Household purchased education|0.078|0.012|0.074|0.011|
|Household purchased schoolbooks|-0.044|-0.017|-0.043|-0.017|
|Cereals, Grains, and Cereal Products|-0.089|-0.022|-0.088|-0.022|
|Fruits|0.077|0.038|0.077|0.038|
|Meat, Fish and Animal Products|0.041|0.014|0.039|0.013|
|Milk/Milk Products|0.124|0.056|0.123|0.056|
|Nuts and Pulses|0.060|0.024|0.059|0.023|
|Root, Tubers, and Plantains|0.045|0.020|0.044|0.019|
|Spices/Condiments|-0.290|-0.066|-0.282|-0.064|
|Sugar/Sugar Products/Honey|0.080|0.032|0.080|0.032|
|Vegetables|-0.000|-0.000|-0.005|-0.001|
|_cons|14.281|0.000|14.277|0.000|
|MSE|0.18||0.18||
|R squared|0.63||0.63||
|N|1182||1,182||

70
# **Table  B.4. The list of selected variables in Lasso and Elastic Net models, Viet Nam**

||**LASS**|**O**|**Elastic**|**Net**|
|---|---|---|---|---|
||**Non-standardized**|**Standardized**|**Non-standardized**|**Standardized**|
|Head`s age|0.003|0.039|0.003|0.039|
|Head`s ethnicity|-0.125|-0.046|-0.125|-0.046|
|Primary education<br>|0.037<br>|0.016<br>|0.036<br>|0.016<br>|
|Lower secondary education|0.051|0.023|0.050|0.023|
|Upper secondary education|0.121|0.043|0.120|0.043|
|College|0.220|0.055|0.219|0.055|
|Household size|-0.151|-0.236|-0.150|-0.236|
|Dependency Ratio|-0.087|-0.060|-0.087|-0.060|
|Gender Ratio|-0.015|-0.013|-0.015|-0.013|
|Household Head worked for a wage, salary, or commission in the last 12 months|-0.029|-0.010|-0.029|-0.010|
|<br>Household Head engaged in casual/ganyu labor in the last 12 months|-0.001|0.000|-0.001|-0.000|
|Household Head employed in industry 1|-0.018|-0.007|-0.018|-0.007|
|Household Head employed in industry 2|0.036|0.012|0.035|0.012|
|Household Head employed in industry 3|0.028|0.004|0.028|0.004|
|Household Head employed in industry 5|0.033|0.003|0.032|0.003|
|Proportion of adult males that engaged in casual/ganyu labor in the last 12 months|-0.007|-0.004|-0.007|-0.004|
|Proportion of adult males that worked in farm in the last 12 months|-0.019|-0.009|-0.019|-0.009|
|Proportion of adult males that worked in industry 1|-0.008|-0.003|-0.008|-0.003|
|Proportion of adult males that worked in industry 4|0.012|0.003|0.012|0.003|
|Proportion of adult males that worked in industry 5|0.019|0.002|0.019|0.001|
|log of residential area|0.196|0.113|0.196|0.112|
|Roof: cement|0.017|0.007|0.017|0.007|
|Wall: bricks|0.029|0.012|0.029|0.012|
|Wall:cement|0.051|0.007|0.050|0.007|
|Improved water source|0.006|0.017|0.006|0.017|
|Improved toilet source|0.029|0.049|0.029|0.049|
|Lighting source - Electricity|-0.108|-0.015|-0.107|-0.015|
|<br>Dwelling_cookfuel|0.016|0.006|0.016|0.006|
|Household owns a car|0.625|0.080|0.624|0.080|
|Household owns a motorbike|0.094|0.035|0.094|0.035|
|Household owns a bicycle|-0.048|-0.024|-0.048|-0.024|
|Household owns a DVD|0.060|0.030|0.060|0.030|
|Household owns a TV|0.054|0.014|0.054|0.014|
|Household owns a computer|0.182|0.072|0.182|0.072|
|<br>Household owns a refrigerator|0.124|0.061|0.125|0.061|
|<br>Household owns a sewing machine|0.063|0.013|0.063|0.013|
|<br>Household owns an electric/gas cooker|0.122|0.042|0.122|0.042|
|Anyone in the household cultivate any plot|-0.072|-0.036|-0.072|-0.036|
|Anyone in the household earn revenues from husbandry, hunting, trapping and dome|-0.023|-0.012|-0.023|-0.012|
|Household obtains goat/sheep|0.027|0.002|0.026|0.002|

71
|Household obtains chickens|-0.023|-0.011|-0.022|-0.011|
|---|---|---|---|---|
|Household consumed Noodle last 30 days|0.026|0.009|0.026|0.009|
|Household consumed Peas, beans last 30 days|0.047|0.024|0.047|0.024|
|Household consumed tomatoes last 30 days|0.064|0.028|0.064|0.028|
|Household consumed tea, coffee last 30 days|0.022|0.010|0.064|0.024|
|Household consumed potatoes last 30 days|0.033|0.015|0.022|0.010|
|Household consumed beef last 30 days|0.117|0.056|0.033|0.015|
|Household consumed ice cream & yogurt last 30 days|0.045|0.019|0.117|0.056|
|Household consumed chicken last 30 days|0.078|0.038|0.045|0.019|
|Household purchased matches|-0.040|-0.014|0.078|0.038|
|Household purchased petrol or diesel|0.068|0.026|-0.040|-0.014|
|Household purchased cleaning products|0.059|0.014|0.068|0.026|
|Household purchased soap|0.055|0.023|0.059|0.014|
|Household spent on wedding last 12 months|0.252|0.040|0.055|0.023|
|Household spent on building housing accommodation|-0.039|-0.015|0.251|0.039|
|Household spent on house repair and maintenance over the past 12 months|0.038|0.011|-0.039|-0.014|
|Household purchased tuition fee|0.080|0.039|0.038|0.011|
|Household purchased school uniform|-0.011|-0.006|0.080|0.039|
|Consumption category last 30 days: Fruits|0.064|0.024|-0.011|-0.006|
|Consumption category last 30 days: Milk/Milk Products|0.042|0.021|0.042|0.021|
|Consumption category last 30 days: Peanuts & sesame|0.014|0.006|0.014|0.006|
|Consumption category last 30 days: Sugar/confectionery/ molasses|0.053|0.019|0.052|0.019|
|Consumption category last 30 days: Vegetables|-0.052|-0.006|-0.052|-0.006|
|Household has living conditions improved in 5 years|0.002|0.001|0.002|0.001|
|_cons|8.494|0.000|8.494|0.000|
|MSE|0.12||0.12||
|R squared|0.71||0.71||
|N|9,296||9,296||

72
# **Table  B.5. Variable importance scores in Random Forest, Tanzania**

|**Variable Importance**||
|---|---|
|Head`s age|0.0546|
|Head is literate|0.0409|
|Head has primary education|0.0462|
|Head has secondary ordinary education|0.0450|
|Head has secondary advanced education and higher|0.0640|
|Household size|0.1974|
|Dependency Ratio|0.1003|
|Gender Ratio|0.0620|
|Household Head worked as an employee in the last 12 months|0.0396|
|Household Head worked as self-employed in the last 12 months|0.0388|
|Household Head worked in unpaid apprentice in the last 12 months|0.0318|
|<br>Household Head worked in farm in the last 12 months|0.1056|
|Household Head employed in mining, manufacturing, construction|0.0492|
|Household Head employed in retail, transportation|0.0452|
|Household Head employed in electricity or water supply|0.0371|
|Household Head employed in information and communication|0.0474|
|<br>Household Head employed in technical, administrative, education|0.0768|
|Proportion of adult males that worked for a wage, salary, or commission|0.0412|
|Proportion of adult males that self-employed|0.0397|
|<br>Proportion of adult males that worked in unpaid apprentice|0.0524|
|Proportion of adult males that worked in farming|0.0443|
|Proportion of adult males that worked in mining, manufacturing, construction|0.0516|
|Proportion of adult males that worked in retail, transportation|0.0445|
|Proportion of adult males that worked in electricity or water supply|0.0348|
|<br>Proportion of adult males that worked in information and communication|0.0495|
|Proportion of adult males that worked in technical, administrative, education|0.0708|
|Number of rooms|0.0549|
|Overcrowded|0.1625|
|Household dwelling roof materials|0.0631|
|<br>Household dwelling floor materials|0.3701|
|Burnt bricks/Concrete walls|0.0521|
|Piped water/Truck water|0.2013|
|Flush/VIP toilet|0.6522|
|Charcoal for cooking|0.1125|
|<br>Electricity for lighting|1.0000|
|Household owns a chair or sofa|0.0440|
|Household owns a radio|0.0433|

73
|Household owns a tv|0.4729|
|---|---|
|Household owns a DVD|0.1763|
|Household owns a sewing machine|0.0475|
|Household owns an electric/gas stove|0.9191|
|Household owns a refrigerator/freezer|0.1640|
|Household owns a bicycle|0.0443|
|Household owns a motor vehicle|0.0803|
|Household owns a computer|0.1311|
|Household owns a mobile phone|0.0618|
|Household owns an iron|0.1057|
|Household owns an air c/fan|0.1585|
|Household owns decoder|0.0770|
|Anyone in the household owns livestock|0.0521|
|Household owns goat|0.0542|
|<br>Household owns chicken|0.0455|
|Household owns cows|0.0532|
|Household consumed spaghetti, macaroni|0.0774|
|Household consumed beans|0.0696|
|Household consumed onions, tomatoes, carrots|0.0782|
|Household consumed fruits|0.0938|
|Household consumed sugar|0.1569|
|Household consumed sweets|0.0553|
|Household consumed tea|0.0209|
|Household consumed biscuits, buns, scones|0.1033|
|Household consumed potato|0.0627|
|Household consumed beef|0.1249|
|Household consumed yogurt|0.0600|
|Household consumed chicken|0.0757|
|Household consumed eggs|0.1615|
|Household purchased cigarettes or other tobacco|0.0459|
|Household purchased matches|0.0514|
|<br>Household purchased toothpaste, toothbrush|0.1203|
|Household purchased personal products|0.0659|
|<br>Household purchased petrol or diesel|0.0998|
|Household purchased cleaning products|0.2526|
|<br>Household purchased soap|0.0623|
|Household spent on taxes|0.0842|
|<br>Household spent on construction|0.0480|
|Household spent on wedding|0.0548|
|Household spent on repair|0.0397|

74
|Household purchased education|0.0623|
|---|---|
|Household purchased schoolbooks|0.0541|
|Household purchased uniform|0.0545|
|Cereals, Grains, and Cereal Products|0.0920|
|Oil/fats|0.0643|
|Fruits|0.1559|
|Meat, Fish and Animal Products|0.0886|
|Milk/Milk Products|0.0677|
|Nuts and Pulses|0.0906|
|Root, Tubers, and Plantains|0.0705|
|Spices/Condiments|0.1161|
|Sugar/Sugar Products/Honey|0.1868|
|Vegetables|0.1040|

Note: The values are scaled proportional to the largest value in the set.

75
# **Table  B.6. Variable importance scores in Random Forest, Viet Nam**

|**Variable Importance**||
|---|---|
|Head`s age|0.0086|
|Head`s ethnicity|0.2932|
|Primary education|0.0069|
|Lower secondary education|0.0072|
|Upper secondary education|0.0102|
|College|0.0309|
|<br>Household size|0.0493|
|Dependency Ratio|0.0218|
|<br>Gender Ratio|0.0099|
|Household Head worked for a wage, salary, or commission in the last 12 months|0.0094|
|<br>Household Head worked as self-employed in the last 12 months|0.0083|
|Household Head engaged in casual/ganyu labor in the last 12 months|0.0086|
|Household Head employed in industry 1|0.0080|
|<br>Household Head employed in industry 2|0.0110|
|Household Head employed in industry 4|0.0119|
|<br>Household Head employed in industry 3|0.0115|
|Household Head employed in industry 5|0.0137|
|Proportion of adult males worked for a wage, salary, or commission|0.0136|
|<br>Proportion of adult males that engaged in casual/ganyu labor in the last 12 months|0.0103|
|Proportion of adult males that worked in farm in the last 12 months|0.0121|
|<br>Proportion of adult males that worked in industry 1|0.0094|
|Proportion of adult males that worked in industry 2|0.0107|
|Proportion of adult males that worked in industry 3|0.0120|
|<br>Proportion of adult males that worked in industry 4|0.0126|
|Proportion of adult males that worked in industry 5|0.0138|
|log of residential area|0.0212|
|<br>Roof: cement|0.0087|
|Roof: cement|0.0140|
|Wall: bricks|0.0144|
|Wall:cement|0.0161|
|Improved water source|0.0208|
|Improved toilet source|0.1869|
|<br>Lighting source- Electricity|0.0115|
|<br>Dwelling_cookfuel|0.0089|
|Household owns a car|0.1665|
|Household owns a motorbike|0.0253|
|Household owns a bicycle|0.0109|

76
|Household owns a DVD|0.0117|
|---|---|
|Household owns a TV|0.0183|
|Household owns a computer|0.4517|
|Household owns a refrigerator|1.0000|
|Household owns a sewing machine|0.0100|
|Household owns an electric/gas cooker|0.2177|
|Anyone in the household cultivate any plot|0.0560|
|Anyone in the household earn revenues from husbandry, hunting, trapping and dome|0.0169|
|Household obtains goat/sheep|0.0112|
|Household obtains chickens|0.0120|
|Household obtains pigs|0.0101|
|Household consumed Noodle last 30 days|0.0118|
|<br>Household consumed Peas, beans last 30 days|0.0134|
|Household consumed tomatoes last 30 days|0.0166|
|Household consumed fruits last 30 days|0.0239|
|<br>Household consumed sugar last 30 days|0.0125|
|Household consumed tea, coffee last 30 days|0.0164|
|<br>Household consumed potatoes last 30 days|0.0114|
|<br>Household consumed beef last 30 days|0.2596|
|Household consumed ice cream & yogurt last 30 days|0.0147|
|<br>Household consumed eggs last 30 days|0.0177|
|Household consumed chicken last 30 days|0.0117|
|<br>Household purchased matches|0.0111|
|<br>Household purchased petrol or diesel|0.0291|
|Household purchased cleaning products|0.0190|
|<br>Household purchased soap|0.0131|
|<br>Household spent on wedding last 12 months|0.0220|
|Household spent on building housing accommodation|0.0125|
|<br>Household spent on house repair and maintenance over the past 12 months|0.0104|
|Household purchased tuition fee|0.0143|
|<br>Household purchased schoolbooks|0.0108|
|<br>Household purchased school uniform|0.0109|
|<br>Consumption category last 30 days: Fruits|0.0321|
|<br>Consumption category last 30 days: Milk/Milk Products|0.0131|
|Consumption category last 30 days: Peanuts & sesame|0.0107|
|<br>Consumption category last 30 days: Sugar/confectionery/ molasses|0.0138|
|<br>Consumption category last 30 days: Vegetables|0.0169|
|Householdhasliving conditionsimprovedin5 years|0.0117|

77