metadata: authors: 0: author: Amankwah,Akuffo
1: author: Johnson,Darcey Jeanne Genou
2: author: Ofori Adofo,Josephine
3: author: Gul,Maryam
4: author: Palacios-Lopez,Amparo
count: Tanzania
docna: 0: docna: Measuring Poverty in Tanzania : Comparison
            of Diary and Recall Approaches to Food Consumption Data Collection
docty: Policy Research Working Paper
theme: Inclusive Growth,Human Development and Gender,Data Development and Capacity Building,ICT,Economic Policy,Skills Development,ICT Solutions,Economic Growth and Planning,Institutional strengthening and capacity building,Private Sector Development,Labor Market Policy and Programs,Public Sector Management,Data production, accessibility and use,Spatial Growth
abstracts: cdata!: Consumption data from household surveys
            continue to be the main source for poverty and inequality
            statistics in low- and middle-income countries. Although
            recent research has demonstrated that the choice of diary-
            versus recall-based methods for consumption data collection
            can directly impact poverty measurement, the available
            evidence stems from small-scale, subnational survey
            experiments. This study uses data from a nationally
            representative randomized survey experiment in Tanzania to
            provide a comparative assessment of how household
            consumption and poverty measures may be impacted by relying
            on a 14-day food consumption diary versus two different
            variants of 7-day-recall-based food consumption data
            collection. The analysis reveals significant differences in
            food consumption expenditures across the diary and recall
            arms, and these differences result in differences in total
            consumption expenditures as well. The results further show
            that the diary method captures more diverse food consumption
            items, but the overall consumption expenditure appears
            significantly lower than in the recall arms, even at
            different percentiles. Despite these disparities, the paper
            finds little statistically significant difference in poverty
            rates between the diary and recall arms, even at different thresholds.
display_title: Measuring Poverty in Tanzania :
            Comparison of Diary and Recall Approaches to Food
            Consumption Data Collection
listing_relative_url: /research/2024/08/34379094/measuring-poverty-tanzania-comparison-diary-recall-approaches-food-consumption-data-collection
topicv3: Poverty m1326687 695,Growth, Poverty and Inequality m1327633 1091
publishtoextweb_dt: 2024-08-19T00:00:00Z
dois: 10.1596/1813-9450-10871
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 46
author_keys: 0: author_key: 1390066
1: author_key: 1236593
2: author_key: 1589389
3: author_key: 2037742
4: author_key: 1043558
model_extractions: Policy Research Working Paper 10871

# Measuring Poverty in Tanzania

Comparison of Diary and Recall Approaches to Food Consumption Data Collection _Akuffo Amankwah Darcey Jeanne Genou Johnson Josephine Ofori Adofo Maryam Gul Amparo Palacios-Lopez_ Development Economics Development Data Group August 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10871

# **Abstract**

Consumption data from household surveys continue to be the main source for poverty and inequality statistics in lowand middle-income countries. Although recent research has demonstrated that the choice of diary- versus recall-based methods for consumption data collection can directly impact poverty measurement, the available evidence stems from small-scale, subnational survey experiments. This study uses data from a nationally representative randomized survey experiment in Tanzania to provide a comparative assessment of how household consumption and poverty measures may be impacted by relying on a 14-day food consumption diary versus two different variants of 7-day-recall-based food consumption data collection. The analysis reveals significant differences in food consumption expenditures across the diary and recall arms, and these differences result in differences in total consumption expenditures as well. The results further show that the diary method captures more diverse food consumption items, but the overall consumption expenditure appears significantly lower than in the recall arms, even at different percentiles. Despite these disparities, the paper finds little statistically significant difference in poverty rates between the diary and recall arms, even at different thresholds.

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 aamankwah@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
# **Measuring Poverty in Tanzania: Comparison of Diary and Recall Approaches to Food Consumption Data Collection**

Akuffo Amankwah<sup>1</sup> , Darcey Jeanne Genou Johnson<sup>1</sup> , Josephine Ofori Adofo<sup>2</sup> , Maryam Gul<sup>1</sup> , Amparo Palacios-Lopez<sup>1</sup> 1 Living Standards Measurement Study, World Bank Group, Washington DC, USA.

2 Africa Chief Economist Office, World Bank Group, Washington DC, USA.

**Key Words** : Food consumption expenditure; Recall design; Diary design; Poverty; Inequality; Tanzania.

**WB Thematic Areas:** Poverty; Inequality and Growth.

**JEL Codes** : C83; D12
# **1. Introduction**

In most low- and middle-income countries, household surveys continue to be the main data source for measuring and monitoring poverty and inequality. At the center of poverty and inequality measurements are income and consumption expenditure approaches (Deaton and Zaidi 2002; Mancini and Vecchi 2022). Given the challenges of accurately measuring income in household surveys in low-income settings with a high proportion of the population working in informal and seasonal jobs, the consumption expenditure approach has dominated poverty and inequality measurements over the years and continues to garner attention in the development economics literature. A critical component of household consumption expenditure is food, which is estimated to contribute about 40% of total household spending in Sub-Saharan Africa (SSA) and 17% in advanced economies (Bogmans et al. 2022). There is growing interest in improving the accuracy and quality of food consumption expenditure measurement in household surveys (Beegle et al. 2012; Christiaensen et al. 2022; Battistin et al. 2023; Brzozowski et al. 2017), including understanding the sources of measurement errors in these variables (Friedman et al. 2017).

Two methods are globally used to generate household food consumption data – diary and recall – though the duration and reference periods vary by the countries adopting each approach. Generally, the method used by a country is based on their own discretion and is a function of what they have done in the past and have the human capital to continue with such. While this may pose no problem with national poverty and inequality analysis in respective countries across time, international comparison becomes difficult, both cross-sectional and temporal. Although crosscountry differences in these indicators could be attributed to _actual_ changes in the standard of living of the countries or variations in methodology, or both, international comparisons generally do not consider methodological disparities.

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That notwithstanding, there is a growing push for harmonizing the methodology for producing poverty statistics to ensure regional and global comparability (Mancini and Vecchi 2022; Zezza et al. 2017), including the transition from diary to recall methods, due in part to the high cost and human capital requirements (in the form of literacy and supervision) in diary implementation (Beegle et al. 2012). The East African Community (EAC) is one such regional body that is recommending to member countries to use the 7-day recall methodology for food consumption data as part of a broader effort to harmonize statistics in the region in general. Mancini and Vecchi (2022) recommend that any methodological change (e.g., from diary to recall or vice versa) should be preceded by an experiment to ensure that there is no break in the poverty series.

These harmonization efforts and the experimental recommendation by Mancini and Vecchi provide an interesting avenue for this study in Tanzania. Since the work of Beegle et al. (2012) on understanding how different methods of collecting food consumption data could impact consumption aggregates and ultimately, living standards measurements, several other studies have been conducted in this area (see for e.g., Christiaensen et al. 2022; Battistin et al. 2023; Brzozowski et al 2017). These studies generally focus on understanding measurement errors in food consumption data, and different approaches for improving the quality of measurement. There are other studies that have fielded country-specific food consumption data collection methods to compare them with potentially new methods to be adopted by the respective country (Durazo et al. 2017; Backiny-Yetna et al. 2017).

This study adds to the growing body of literature on measuring household food consumption expenditure by using nationally representative randomized household survey data from Tanzania to compare two methods of data collection – recall and diary. The study compares 3
the 14-day diary approach used in the Household Budget Survey (HBS) and the 7-day recall approach used in the National Panel Survey (NPS), both surveys implemented by the Tanzania National Bureau of Statistics (NBS) and the Office of the Chief Government Statistician (OCGS) Zanzibar. This study is vital and timely, especially as regional and global bodies push for the harmonization of poverty statistics to ensure comparability. While Beegle et al. (2012) provide general guidance on how the diary and recall methods work, the data used is not nationally representative in nature. This study builds on their findings but uses existing modules and new methods of data collection, following the evolution of methods in this area.

We use nationally representative household survey data from a randomized experiment in Tanzania to explore differences in household food consumption expenditure between 14-day diary and 7-day recall designs. We employ both descriptive and regression analytical approaches. The results provide evidence of differences in food and total consumption expenditure across the diary and recall arms. We observe very little significant differences in non-food consumption expenditure, allowing for attributing variations in total consumption expenditure to differences in food expenditure emanating from the disparities in methodological designs. The results show that while the diary method captures more diverse food consumption items, the overall consumption expenditure appears significantly lower than the recall arms, even at different percentiles. Poverty rates varied significantly across the designs and are very much conditional on the poverty line chosen.

The rest of the paper is organized as follows. In Section 2 we present country context for the study. Section 3 provides a description of the data and study design. Section 4 presents the analytical framework employed to generate the results of the study, while Section 5 contains the discussion of results. The conclusion and implications of the study are presented in Section 6.

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# **2. Country context – macroeconomic indicators and recent poverty surveys in Tanzania**

Tanzania has experienced substantial improvements in macroeconomic indicators in recent years. At the national threshold, poverty headcount has decreased from 34.4% in 2007, to about 26.4% in 2018, and it stood at 44.9% in 2018 using the 2017 international poverty line of US$2.15. Poverty in Tanzania, like other countries in the region, is predominantly rural. Similar to other SSA countries, food constituted about 59.9% of the consumption basket in 2017 in Tanzania, about 4 percentage points increase from 2011, with rural and urban shares being <mark>63.2% and 52.6%, respectively</mark> (Ministry of Finance, NBS, and World Bank 2020). Food security is a major developmental challenge in Tanzania. The FAO (2021) estimates that about 34.4 million Tanzanians were moderately or severely food insecure in 2021, an increase from 25.1 million in 2014. As an agrarian economy, the agriculture sector continues to be critical to Tanzania, especially the rural poor. The sector contributed about 30% to GDP in 2021 (NBS 2022a), employed nearly 62% of the population in 2021 (NBS 2022b), and generated about 30% of foreign exchange earnings (Government of Tanzania 2017).

The apex government agencies, NBS in Mainland and OCGS in Zanzibar, are mandated with the production of official statistics, including poverty and inequality in the country. The NBS and OCGS have, in the past, independently published official poverty statistics for their jurisdictions using data from the HBS where food consumption is captured using diaries. Besides the HBS, the NBS and OCGS also implement the NPS as part of the Living Standards Measurement Study Integrated Survey on Agriculture (LSMS-ISA) project, which uses 7-day recall for food consumption data collection.

Both the NPS and HBS are multi-topic household surveys. Though the HBS and NPS are implemented by the NBS and OCGS, the focus of each survey is different – HBS provides data 5
for official poverty statistics while the NPS helps monitor welfare dynamics in the country – and they are always implemented separately and in different years. The HBS is implemented every 5 years, while the NPS is implemented more frequently. Historically, the HBS used a 30-day diary until 2017/18 when the institutions switched to a 14-day diary. The nature of a diary implementation means that the respondent is not restricted by an item list in the HBS, while in the NPS the respondent is limited to a list of both food and non-food items (though variations of smaller and larger lists have been utilized in the NPS). Both surveys are collected over a 12-month period, to capture seasonality.

# **3. Data and study design**

# **3.1. Data**

This paper uses data from the Tanzania Household Consumption Methodological study conducted in May-June 2022 by the National Bureau of Statistics (NBS) and Office of the Chief Government Statistician (OCGS) Zanzibar, with funding and technical assistance from the World Bank. The methodological study adopted a multi-stage sampling approach designed to provide estimates at the national level, as well as rural and urban stratification. The first stage involved the selection of 143 enumeration areas (EAs) spread across all regions of the United Republic of Tanzania (both Mainland and Zanzibar) using a probability proportional to size (PPS) approach. The selection ensured the inclusion of both urban and rural EAs in each region. Once the EAs were selected, a fresh household listing was conducted to get the total household composition in each of the EAs. Following the listing exercise, 15 households were selected using a systematic random sampling procedure for inclusion in the study, with 5 households assigned to each of the three arms of the study. In the end, a total of 2,119 households were included in the analysis. The final sample was 6
weighted to adjust for non-response and other sampling errors. See **Table 0** for the response rates by treatment.

The survey was implemented using the World Bank’s _Survey Solutions_ software for Computer Assisted Personal Interviews (CAPI). This allowed for daily upload of completed interviews by the field teams, as well as frequent data download and review by the NBS and OCGS headquarters teams to ensure the quality of the data being collected, while providing feedback to the enumerators for further quality control.

# **3.2. Study design**

Three approaches to collecting household food consumption data – 14-day diary, 7-day recall with short items list, and 7-day recall with long items list – are compared in this study. To better understand the differences in household food consumption data collection, this methodological study replicated the HBS 2017/18 (14-day diary) and the NPS (7-day recall with short list) methodologies, as well as a variant/combination of the NPS (7-day recall with long list). Given that the HBS is the survey that NBS and OCGS use to produce official poverty statistics, the diary methodology served as the benchmark and households assigned to this group served as the control group, and the two 7-day recall arms are compared to the diary. In addition, all households in the study received a Food Away from Home (FAFH) module implemented at the individual level to capture expenditure on food consumed outside the home, such as from restaurants, joints, etc. Households were randomly assigned to each study arm in all clusters to ensure non-contamination (Beegle et al. 2012; Brzozowski et al 2017). The design details of each method are described below.

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# **3.2.1. Treatment 0: 14-day diary**

The design and implementation of the 14-day diary (henceforth, diary or T0) in this study followed exactly the methodology in the 2017/18 HBS (see Ministry of Finance, NBS, and World Bank 2020). The methodology required administering the 14-day diary as part of a multi-topic questionnaire with multiple visits to the household. During the first visit, the enumerator introduced the food consumption diary to the household, gave them training on how to complete it, and left it with the household/individuals to complete on a daily basis. Daily expenditure information on food items purchased, the quantity purchased and corresponding unit (including non-standard units), value of the quantity purchased, source and destination of the quantity purchased were recorded. In addition, households were asked to record separately the actual daily food consumption, as well as FAFH. The enumerator revisited the household at different times during the 14 days to supervise and transfer the information from the daily record book into the main CAPI questionnaire. At the end of the 14 days, the enumerator revisited the household (on the 15<sup>th</sup> day) to close the diary with them. Once the information was transferred into the main questionnaire, COICOP codes were assigned to each of the food items that the household indicated to have consumed/purchased. The other modules in the main questionnaire were administered anytime between the first and the last visit to the household. Thus, the number of food items, the number and phrasing of questions, and methodology of diary implementation in this study (including frequency of enumerators visits to the household) were exactly the same as those used in the 2017/18 HBS. A separate FAFH module was also implemented at the individual level for this group. Food consumption aggregates were then generated using the quantity consumed and price information collected in this module, while adjusting for temporal (within survey months) 8
and spatial price variabilities. Resident enumerators (stationed in the EAs) were used to implement this arm, thus making diary implementation and supervision more feasible.

# **3.2.2. Treatment 1: 7-day recall with short-list of food items**

The food consumption module in this arm (henceforth, recall short list or T1) was implemented as part of a multi-topic questionnaire in a “single visit” to the household. During the interview, households were asked to recall their food consumption over the 7 days preceding the day of the interview (excluding the day of interview). A total of 75 food items were included in this module. The respondent of the food consumption module is generally an adult female household member responsible for food purchases and preparation. The respondent is first presented with a YES/NO filter question for all 75 food items included in the module. For those items that the respondent indicated their household consumed within the last 7 days (YES response in the first filter question), additional questions on the quantity consumed, sources of consumption (purchased, own production, and gifts), and total value of purchases if any of the household’s food consumption came from purchases.

Information on quantity consumed was collected only in standard units (except for eggs where “piece” was allowed). In addition to food consumed within the household, information on FAFH at the individual level was also collected. Food consumption aggregates were then generated using the quantity consumed and price information collected in this module, while adjusting for temporal (within survey months) and spatial price variabilities. Mobile teams that operate in a roving manner were used for data collection in this study arm.

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# **3.2.3. Treatment 2: Modified 7-day recall with long list of food items**

This module (henceforth, recall long list or T2) was designed to follow the most widely used 7- day food consumption recall in countries that are part of the LSMS-ISA project. Households in this group were administered a modified version of the 7-day food consumption recall module in T1. The module captures 7-day food consumption recall of quantity consumed, and the sources of the food consumed (purchases, own-production and gifts). Households were also asked if they purchased a given food item in the last 30 days, and where applicable, the quantity and value of their most recent purchase within the last 30 days were collected. Respondents were allowed to provide quantity consumed and purchased in non-standard units (NSU), and enumerators used NSU photo albums where applicable.

Given the unavailability of conversion factors for some of the NSUs, efforts were made to only allow respondents to provide responses in units for which conversion factors were available. In addition, for those items where respondents reported in NSUs, they were asked to provide an estimate of that in standard units (kilograms, grams, liters, or centiliters as applicable) as well. A long list of food items (167) that constituted the largest share of consumption in HBS 2017/18 were included in this group. FAFH at the individual level was also implemented for this group. Food consumption aggregates were then generated using the quantity consumed and price information collected in this module, while adjusting for temporal (within survey months) and spatial price variabilities. Similar to T1, mobile teams that operate in a roving manner were used for data collection in this study arm.

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# **3.3. Additional implementation considerations**

# **3.3.1. Questionnaire design**

In addition to the treatment-specific food consumption modules described earlier, all households included in the three treatment groups also answered questions on household roster, education, employment, non-food consumption (7-day, 30-day, and 12-month reference periods), household participation in agriculture (crops and livestock), asset, labor (an abridged version), credit, group formation, housing, cognitive skills, and other relevant balancing variables. For each of the modules included in the questionnaire, information was collected on the main respondent, as well as the start and end times.

# **3.3.2. Ensuring comparability**

Given that different sets of team arrangements were used for the diary and recall components of the study, and the different reference periods, it was vital to ensure that there was overlap in the reference period between the diary and the recall arms. In each EA, the mobile team responsible for that EA liaised with their resident enumerator counterpart to ensure they visit the EA and start administering the food consumption module at least 7 days after the diary has been open for the diary households in that EA. This ensured an overlap in the days for the food consumption diary and the recall modules implementation. This was made possible since the EA-specific resident enumerator and mobile team had the same supervisor.

# **3.4. Construction of consumption aggregates and treatment of outliers**

The consumption aggregates were constructed in an identical manner and guided by the same theoretical and practical considerations for all three designs. This includes the components, the 11
treatment of outliers, the inclusion of benefits beyond food and non-food (i.e., rent, housing, durables), and adjustments for both adult equivalent, and spatial and temporal price differences. For all three designs, the final consumption aggregate is represented by the sum of all its components: i) food consumed inside the home – whether purchased, self-produced, or received in-kind as gift; ii) food consumed outside the home; iii) non-food expenditures of various reference periods – one week, one months, 12 months in our case; iv) education expenditure; v) health expenditure – excluding hospitalization costs; vi) the imputed value of consumer durables; and vii) the imputed value of dwelling benefits.

Construction of the food component was guided by three principles. First, that all possible sources of food consumption were included (i.e., purchases in market, produced by household, received as gift or payment, etc.). Second, non-purchased food that was consumed must be assigned a value. Finally, only food that was consumed – not necessarily the total amount of food purchased – is included. Non-food consumption is slightly more difficult to measure, and we relied on a few widely accepted assumptions to do so. First, that the costs of acquisition of a non-food item is equal to its consumption, and second, that the value of consumption of non-food goods and services from own production or gifts is negligible. Finally, unlike food, non-food expenditures were valued at the purchase or self-reported acquisition cost, as most non-food items are too heterogeneous to collect quantities and calculate prices. Expenditure on some non-food goods and services was excluded from the calculation of the consumption aggregate, including payments of mortgages or debts (financial transactions), losses to theft (neither consumption nor expenditure), expenditure on marriages, dowries, births, and funerals (too irregular). Given that consumption data from household surveys is both extensive and complex, data errors and outliers are expected regardless of data collection method. The following rules were applied to the management of 12
outliers for all three designs: i) outliers were dealt with similarly across treatment arms; ii) outliers were identified at the item and rural-urban level for greatest accuracy; and iii) outliers were defined as falling outside of _two_ predetermined tolerance intervals (the variable-specific interquartile range (IQR) and three times the standard deviation from the logarithmic mean). For both cost and quantity values, moderate outliers were preserved while extreme outliers were replaced with the strata-level median value or some factor thereof, i.e., for large outliers the value was replaced with four times the strata-level median value. All consumption expenditure components are converted to one month using an appropriate adjustment factor depending on the reference period and then aggregated to generate the final monthly consumption aggregate.

# **4. Analytical approach**

The study uses both descriptive and regression approaches. The descriptive approach involves basic t tests and the construction of density functions to understand differences in consumption expenditures and treatments. Following the descriptive analysis, we estimate two sets of Ordinary Least Squares (OLS) regressions. In the first specification, we run the logarithm of food consumption expenditure and log total consumption expenditure (both food and non-food) on dummies for the two recall arms (long and short), using the diary as the comparison category. We estimate the following model: estimate the following model: i i (1) ii ii YY = ββ DD + ee where, i is the logarithm of total per capita food consumption expenditure and log total per capita consumption expenditure of household YY for treatment . is a vector of dummy variables for ii treatment types (0 for the diary, 1 for the recall short list, and 2 for the recall long list). ii jj DD jj= 0, 1, 2 13
captures the differences in expenditure of the short and long list designs relative to the diary, ii which is the excluded category. ββ i is the error term which captures unobserved consumption expenditures. The results from equation 1 will help understand the percentage change in food and ee total consumption expenditures emanating from diary versus the recall designs.

In the second specification (equation 2), we estimate the model including household, head, and respondent characteristics that could potentially influence the performance of household consumption expenditure. These covariates include education, employment type, gender, access to improved water and sanitation.

i i i i (2) ii ii ii ii ii YY = ββ DD + γγ XX + δδ DD XX + ee where i represents a single household characteristic and is the coefficient of the interaction ii between a treatment dummy and a given household characteristic. We first estimate the model XX δδ controlling for and interacting treatment type with household head’s characteristics, and then repeat the same analysis for the main respondent.

# **5. Results**

# **5.1. Descriptive results**

# **5.1.1. Demographic characteristics by design**

Demographic characteristics in this study in general mirror those from other nationally representative surveys in Tanzania (Ministry of Finance, NBS, and World Bank 2020). With minor exceptions, **Table 1** shows that household characteristics relevant for consumption appear balanced across the three study arms, as do individual characteristics of both the household head 14
and the main respondent of the food consumption module. In all three study arms, the average household size is 4.8, while the number of adults (age 18 years and older) is 2.4.

Household heads are predominantly male across all arms (T0: 72.6%; T1: 72.7%; T2: 72.8%) and approximately one-quarter were employed in wage work (T0: 23.1%; T1: 24.2%; T2: 26.8%). There is no statistically significant difference in either of these two characteristics across designs. We see, however, that significantly fewer household heads in the diary arm are employed in non-farm enterprise work than in the recall short list (T0: 19.9%; T1: 24.6%). While a majority of household heads have below secondary school education (T0: 60.5%; T1: 61.8%; T2: 59.5%) with no significant difference between the diary and recall designs, the table shows that significantly fewer household heads in the recall short list have completed at least _secondary_ school (11.8%) than the recall long list (18.6%).

Though the main respondent for the food consumption module is predominantly female in each of the three treatment groups, there are significantly fewer female respondents in the two recall designs than in the diary arm (T0: 77.4%; T1: 72.4%; T2: 70.9%). The share of wage employment of the respondents to the food consumption module is similar across treatment groups, but significantly fewer diary respondents were employed in non-farm enterprise work than in either of the two recall designs. We observe no significant differences in the proportion of respondents who have completed at least secondary school. The low level of education of the main respondent has implications for the cognitive development and ability to recollect consumption information, especially for long reference periods.

We also observe limited differences in housing characteristics – including building materials, water sources, sanitation facilities, and number of rooms – across study arms ( **Table 2** ). A majority of the households have an improved roof, and more than half have improved walls, 15
regardless of design. Approximately half of the households in the sample have an improved floor, although significantly fewer households in the diary arm have an improved floor than in the recall short list (47.4% and 51.4%, respectively). In addition, a substantial share of households have access to an improved drinking water source in the rainy season (T0: 69.2%; T1: 71.9%; T2: 72.7%), with no significant difference across study arms. Overall, access to improved drinking water sources in the dry season is slightly less common, and those in the diary arm appear to have significantly better access than either of the two recall designs (T0: 61.1%; T1: 55.9%; T2: 54.7%). More than 9 of every 10 households in each of the three designs have access to basic sanitation, with no significant differences observed.

On average, it took about 24 minutes to complete the food consumption module in the recall short list, while that of the recall long list took about 43 minutes to complete.

# **5.1.2. Household consumption patterns**

This section assesses household consumption patterns across the different designs. First, it analyzes concrete consumption activities in the food component – including average number of items consumed as well as most common food items – and then it analyzes expenditures (food, non-food, and total). Expenditure data is also presented with a variety of disaggregates, including by food group, expenditure type, and daily values for the diary sample.

# **_5.1.2.1. Diversity of food consumption_**

The results show significant differences in the average number of food items consumed across the three designs. As expected, the diversity of food consumption is higher in the diary arm ( **Table 3** ), corroborating similar studies (Beegle et al. 2012; Durazo et al. 2017). This means that open ended 16
lists generally produce a higher number of final food consumption items. The recall long list design, which includes an extended list of food items, has the second highest number of food items consumed, potentially indicative of the need for a more comprehensive and detailed item list, if using a recall design.

In the diary and recall long list designs, 19 food items account for half of all food consumption, while in the recall short list design, 12 food items account for half ( **Table 4)** . This is again expected, as longer lists tend to produce higher number of consumed items than the more aggregated form. In each treatment group, tomatoes, onions, and salt are independently found in the top four most consumed food items. Likewise, sweet potato, maize flour, sugar, beans, and cooking oil – staples in Tanzania – are in the top twelve most consumed food items in each treatment group, with remarkably similar proportions of households in each group reporting consumption of each of these items. The similarities are glaring, and little is lost when utilizing a recall short list or recall long list in comparison to a diary.

# **_5.1.2.2. Variations in consumption expenditure_**

In addition to diversity of food consumption, we examine food consumption expenditure at different levels of disaggregation. Regardless of treatment assignment, the food component of consumption comprises eleven aggregated food groups ( **Table 5** ). While there is no significant difference in average expenditure across treatments in either the _largest_ expenditure group (breads and cereals) or the _smallest_ expenditure group (spices and salts), expenditures for some of the other food groups do vary significantly.

In general, average consumption expenditure _per food group_ in the diary tend to be lower than the recall designs, although the number of food items consumed in the former is higher ( **Table** 17
**3** ). The recall long list appears to have the highest expenditures per _food group_ . Among the eleven food groups, average expenditure reported in the diary are lower than those in both recall designs for eight of the food groups; these differences were statistically significant for five of the food groups (vegetables, meat, dairy, fruit, and sugars).

While vegetables account for the second largest expenditure in all three designs, households in the diary arm report significantly lower average expenditure on this food group than the recall designs (T0: 48,580 Tsh; T1: 54,142 Tsh; T2: 63,650 Tsh). Similarly, average expenditure on meat – the third largest expenditure on a food group – is significantly lower in the diary arm than either of the two recall designs (T0: 23,632 Tsh; T1: 47,075 Tsh; T2: 52,422 Tsh).

The generally low level of expenditure recorded for most food items in the diary arm warrants further examining how daily consumption fared in the diary arm ( **Figure 1).** Expectedly, expenditure on food consumed at home is considerably larger than expenditure on food consumed away from home, both in the daily and aggregated form. We also observe that total expenditure on food for Day 1 was larger compared to the rest of the days, a finding consistent with Durazo et al (2017) and many others. Despite slight increases noted around Day 4 and Day 5 and again at the end of the diary period, the general downward trend of consumption expenditure – driven by a decrease in number of transactions recorded – could be attributed to fatigue in diary completion by the household, though not exclusively.<sup>1</sup>

**Table 6** presents _monthly_ expenditure for food and non-food consumption, as well as their sub-components. Overall, total consumption expenditure in the diary was lower than the recall designs (T0: 424,252; Tsh; T1: 458,847; Tsh; T2: 482,538 Tsh), driven by significantly lower

> 1 Durazo et al. (2017) found that estimates of food consumption expenditure for Day 1 of the diary module were larger than Day 2 through Day 7, in Indonesia. However, they argue that this result is due to an overestimation of the Day 1 food consumption expenditure relative to the rest of the days.

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expenditure on food (T0: 237,367 Tsh; T1: 279,253 Tsh; T2: 301,524 Tsh). Despite consuming a greater number of food items ( **Table 3** ), estimates of expenditure for all three sub-components of food – food consumed at home, both purchased and non-purchased,<sup>_2_</sup> and food consumed away from home – were also significantly lower in the diary arm.

Across the recall designs, however, we see similar expenditure on both food away from home (T1: 105,974 Tsh; T2: 107,113) and _purchased_ food consumed at home (T1: 134, 496; T2: 143,367). In the case of non-purchased food consumption, the data shows significantly higher expenditure in the recall long list.

In terms of non-food consumption, the data shows no significant difference in total nonfood expenditure across treatments. For three sub-components of non-food, however, we do see statistically significant differences, which are driven by variations in frequently purchased (weekly) non-food items between the three groups. In contrast, expenditures on education and health, which are less frequent expenses, are far greater in the diary arm than either of the two recall designs. That notwithstanding, given the remarkable similarities in total non-food expenditure and the heftier share of expenditure on food than non-food, we can confidently attribute divergence in total consumption expenditure to those emanating from food.

In **Table 7** we compare the percentiles distribution of food consumption expenditure across designs. In addition to average expenditure, median estimates of food consumption expenditure are also lowest in the diary arm but highest in the recall long list, mitigating the notion of any improbable or unlikely values skewing the results. Only in the tenth percentile do we see larger (albeit, marginally) expenditure in the diary than a recall design. To further test the differences in these distributions, we present p-values from a two-sample Kolmogorov-Smirnov test for equality

> 2 Food consumed at home (not purchased) includes food from own-production and gifts.

19
of distribution functions<sup>3</sup> ; that is between the diary and recall short list, and between the diary and recall long list. The p-value (0.00) results between the diary and the recall designs indicate that food consumption expenditure across diary and each of the two recall designs have different distribution functions and explain the variations in the results observed.

# **_5.1.2.3. Distribution of consumption expenditure shares_**

**Tables 8** and **9** show the expenditure shares of different consumption components. Over 98% of households in Tanzania consumed two food groups: bread and cereal products, and vegetables, and expenditure on those accounts for over 60% of total food consumption, regardless of treatment assignment. In contrast, although food items like spices and salt were also consumed by majority of households, expenditure on these was small, and thus forms a smaller share of total food consumption expenditure, as expected.

While the proportion of households consuming fruit is similar in both recall designs (77% and 81.4% for T1 and T2 respectively), only 59% of the diary households consumed fruit, a divergence clearly reflected in the average expenditure and share of total food consumption expenditure. For food items such as “fish” and “mineral waters, soft drinks and juices”, the share in total food consumption expenditure was similar across all three designs (8%-11% for the former and approximately 5% for the latter), despite the percentage of households consuming them being more than twice as high in the diary arm compared to the recall groups.

As noted earlier, households spend more on food than non-food items. Therefore, it appropriately follows that the _share_ of total expenditure on food is also higher than the share on

> 3 This is a two-sample Kolmogorov-Smirnov test for equality of distribution functions. The combined p-values, which are reported here, test the null hypothesis that both samples are from a population with the same distribution function.

20
non-food ( **Table 9** ). This is consistent with the general evidence that poor households tend to spend a large share of their expenditure on food (Bogmans et al. 2022).

# **5.1.3. Poverty and inequality distributions**

**Table 10a and 10b** outlines the incidence of poverty for each of the three designs at four different thresholds: the 2017/18 national poverty line (1,876 Tanzanian shilling per person per day, USD $1.90 (2011 PPP), USD $2.15 (2017 PPP), and USD $3.25). The incidence of poverty, while clearly sensitive to the method of consumption measurement, is also reflective of the poverty threshold being used. For three out of the four poverty thresholds, the incidence of poverty is lowest for households in the diary design – despite earlier findings that their actual consumption was generally the lowest. At these three thresholds, the incidence of poverty is also consistently highest for those using the recall short list. However, when we examine poverty at the $3.25 per person per day international poverty threshold, the incidence of poverty becomes highest for households in the diary design. At this same threshold, the incidence of poverty for households in the recall long list, which at all other thresholds is remarkably similar to the diary design, is the lowest. Figure 2 throws further light on these differences. Thus, at different poverty thresholds, the results show little statistically significant difference between the diary and recall arms, though poverty rates varied significantly across the two recall designs at lower thresholds.

The Lorenz curve in Figure 2 shows the distribution of inequality across the three arms. The diagonal of the Lorenz curve (45 degrees line) represents a distribution with zero Gini value. The figure shows that inequality is higher in the recall designs than the diary arm, consistent with the findings of Beegle et al (2012) and Belghith et al (2018). This could be explained by the fact that the two recall arms use a restricted set of food items, while the diary arm has an open-ended 21
item list and thus captures more diversity in food consumption which is more common among wealthy households.

# **5.2. Regression results of differences in consumption expenditure**

This section provides further insights into the differences in consumption expenditure (food and total) between the treatment groups using the regressions discussed earlier in **Section 4** . Equations 1 and 2 were estimated using the logarithm of total food consumption and the logarithm of total consumption expenditure as dependent variables.

The regression results in **Tables 11a & 11b** report the differences in per capita food expenditure between the diary arm (the reference category) and the recall groups. Whereas **Table 11a** includes characteristics of the household head as controls, **Table 11b** controls for the characteristics of the main respondent. In both tables, model 1 presents regression estimates from the basic model without controls (equation 1). Relative to the diary, the use of recall short list and recall long list modules increases reported per capita food consumption expenditure by 11% and 21% ( **Table 11a** ), respectively. Similar to Beegle et al. (2012), we see that the recall short list tends to report relatively lower percent change in food consumption expenditure than the long list.

The remaining models present results from estimating equation 2, which includes household and individual characteristics. In model 2 of **Table 11a** , we see that the inclusion of household and head covariates has little impact on the percentage change in total food consumption expenditure emanating from the two recall designs in comparison to the results in model 1. Model 3, and models 5 to 8 show that the education level of the household head, type of employment (non-wage), the household’s access to improved floor material, safe drinking water during the 22
rainy and dry seasons, affect the coefficient of reported food consumption expenditure between the diary arm and the recall long-list. On the contrary, the level of education of the head has no significant effect on the differences in food consumption expenditure between the diary arm and the recall short-list ( **Table 11a** ). Across all models, the magnitude of the difference between the reference category and the two recall designs reduces when the treatment dummy is interacted with household characteristics.

In model 2 of **Table 11b** where we control for respondent characteristics, we see a marginal percentage decrease in food consumption expenditure from 11.2% in **Table 11a** to 9.5% in **Table 11b** for the recall long list. The results reveal further that the gender of the respondent does not matter in explaining the differences in food consumption expenditure between the recall and diary arms. That notwithstanding, respondents with any form of employment tend to report higher percentage change in food consumption in the recall long list, compared to the diary arm.

**Tables 12a** and **12b** report differences in total per capita consumption expenditure (which includes food and non-food expenditure). Model 1 shows that households report a higher total consumption expenditure (9.5%) under the recall long list compared to the diary arm. The recall short list also shows higher percentage change in total consumption expenditure, but the difference is not statistically significant. Except for the type of employment of the household head (model 5), all other household head’s characteristics do not influence the accuracy of reporting under the recall long list ( **Table 12a)** . In the case of the recall short list, however, households with improved floor materials report statistically significant higher total consumption expenditure. The results are similar when we include the main respondent’s characteristics as controls **(Table 12b).** Like food consumption, the sex of the main respondent does not influence reporting under both the recall short list and recall long list.

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# **6. Conclusion**

Household surveys remain the only source of data for generating official poverty and inequality statistics in most low- and middle-income countries. Differences in food consumption data collection make global and cross-country poverty and inequality comparisons challenging. This study uses nationally representative household survey data from Tanzania that was fielded as a randomized experiment to understand differences in household consumption, poverty, and inequality measures by comparing three alternative methodological designs.

The randomized nature of the study ensured that households were assigned to three treatments: 14-day diary, 7-day recall with short list of food items, and 7-day recall with long list of food items. The paper used both descriptive and regression approaches to explore the differences in consumption and poverty emanating from the three methods. The results show that the number of food items consumed is highest in the diary treatment, compared to the recall designs. Food diversity was similar across the three designs, with relatively common items forming the food consumption basket. To capture more diverse food items consumed in the recall design requires fielding an expanded list of relevant food items in the questionnaire. It is worth noting, however, that a very long item list comes at a cost; respondent fatigue, long interview time, and cost could compromise quality of data collected from such.

This notwithstanding, food consumption expenditure was higher in the recall designs than the diary treatment. Non-food consumption expenditure is relatively similar across the three designs, with very little significant differences. This indicates that differences in total consumption expenditure are highly driven by variations in food consumption. On the poverty measures, the results provide evidence that the poverty line matters; at lower poverty threshold, we observe higher poverty rates in the recall designs, but the reverse is observed at higher poverty thresholds.

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At different poverty thresholds, the paper finds little statistically significant difference between the diary and recall arms. Household characteristics such as employment and education are vital in explaining observed differences in food and total consumption expenditure.

The study is limited in the following ways. First, the implementation of the survey used separate teams for the diary and recall arms, which could explain some of the observed differences in consumption expenditure across the three designs. The analysis did not correct for this. Second, the non-availability of conversion factors forced respondents to report food consumption in standard units, which is very restrictive and difficult, especially for respondents and enumerators with low cognitive prowess.

# **References**

- <mark>Battistin, E., De Nadai, M., & Krishnan, N. (2023). The insights and illusions of consumption measurements.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>161</mark>_ <mark>, 102991.</mark>

- <mark>Bee, A., Meyer, B. D., & Sullivan, J. X. (2012).</mark> _<mark>The validity of consumption data: are the consumer expenditure interview and diary surveys informative?</mark>_ <mark>(No. w18308). National Bureau of Economic Research.</mark>

- Backiny-Yetna, P., Steele, D., & Djima Y.I. (2017). The impact of household food consumption data collection methods on poverty and inequality measures in Niger. _Food Policy_ , 72, 7-19

- Beegle, K., De Weerdt, J., Friedman J., Gibson, J. (2012) **.** Methods of household consumption measurement through surveys: Experimental results from Tanzania. _Journal of Development Economics_ , 98(1), 3-18.

- Belghith, B.H.N., Lopera, A.M., Ndip, E.A., & Karamba, W. (2018). Analysis of the Mismatch between Tanzania Household Budget Survey and National Panel Survey Data in Poverty and Inequality Levels and Trends.  Policy Research Working Paper 8361, World Bank, Washington DC, USA.

- <mark>Bogmans, C., Kearns, J., Pescatori, A., & Prifti, E. (2022). War-fueled surge in food prices to hit poorer nations hardest.</mark> _<mark>IMF blog</mark>_ <mark>,</mark> _<mark>16</mark> ._

- <mark>Brzozowski, M., Crossley, T. F., & Winter, J. K. (2017). A comparison of recall and diary food expenditure data.</mark> _<mark>Food policy</mark>_ <mark>,</mark> _<mark>72</mark>_ <mark>, 53-61.</mark>

- <mark>Christiaensen, L., Ligon, E., & Sohnesen, T.P. (2022). Consumption subaggregates should not be used to measure poverty. The World Bank Economic Review, 36(2), 2022, 413–432.</mark> https://doi.org10.1093/wber/lhab021

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- <mark>De Weerdt, J., Gibson, J., & Beegle, K. (2020). What can we learn from experimenting with survey methods?.</mark> _<mark>Annual Review of Resource Economics</mark>_ <mark>,</mark> _<mark>12</mark>_ <mark>, 431-447.</mark>

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- <mark>Durazo, J., Herewait, Y., Pattinasarany, G.D.V., & Jolliffe, D. (2017) bridging Surveys: A methodological experiment of food consumption data in the Indonesian Social Economic Survey (Susenas). Contribution presented at the XV EAAE Congress, “Towards Sustainable Agri-food Systems: Balancing Between Markets and Society”, August 29 – September 1, 2017, Parma, Italy</mark>

- <mark>FAO, IFAD, UNICEF, WFP and WHO. (2021). The State of Food Security and Nutrition in the World 2021.</mark> <u><mark>https://data.unicef.org/resources/sofi-2021/</mark></u>

- <mark>Friedman, J., Beegle, K., De Weerdt, J., & Gibson, J. (2017). Decomposing response error in food consumption measurement: Implications for survey design from a randomized survey experiment in Tanzania.</mark> _<mark>Food Policy</mark>_ <mark>,</mark> _<mark>72</mark>_ <mark>, 94-111.</mark>

- Government of Tanzania. (2017). Agricultural Sector Development Strategy Phase – “Agricultural Sector for Industrial Development.”

- <mark>Mancini, G., & Vecchi, G. (2022). On the construction of a consumption aggregate for inequality and poverty analysis.</mark> _<mark>World Bank Group, Washington, DC</mark>_ <mark>.</mark>

- Ministry of Finance and Planning - Poverty Eradication Division (MoFP- PED) [Tanzania Mainland], National Bureau of Statistics (NBS) and the World Bank. (2020). Tanzania Mainland Household Budget Survey 2017/18. Final Report Dodoma, Tanzania MoFP-PED, NBS and Washington DC USA, and WB.

- National Bureau of Statistics (NBS). (2022a). _National Accounts of Tanzania Mainland (2015 – 2021)._ NBS, Tanzania

- National Bureau of Statistics (NBS). (2022b). _Integrated Labour Force Survey 2020/21_ . NBS, Tanzania.

- <mark>Sharp, M., Buffiere, B., Himelein, K., & Gibson, J. (2019). Effects of data collection methods on estimated household consumption and poverty, and on survey costs: evidence from an experiment in the Marshall Islands. In</mark> _<mark>IARIW-World Bank Conference on New Approaches to Defining and Measuring Poverty in a Growing World, Washington DC, November</mark>_ <mark>.</mark>

- <mark>UNICEF. (2021). The state of food security and nutrition in the world 2021</mark> . <u>https://data.unicef.org/resources/sofi-2021/</u>

- <mark>Zezza, A., Carletto, C., Fiedler, J. L., Gennari, P., & Jolliffe, D. (2017). Food counts. Measuring food consumption and expenditures in household consumption and expenditure surveys (HCES). Introduction to the special issue.</mark> _<mark>Food Policy</mark>_ <mark>, 72, 1-6.</mark>

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**Figure 1.** Average daily reported food consumption expenditure in diary over 14 days

<!-- Start of picture text --> 12,000<br>Day 1<br>Day 2<br>10,000<br>Day 3<br>Day 4<br>8,000 Day 5<br>Day 6<br>6,000 Day 7<br>Day 8<br>4,000 Day 9<br>Day 10<br>Day 11<br>2,000<br>Day 12<br>Day 13<br>0 Day 14<br>Total food expenditure Food consumed at home Food away from home<br>Daily consumption expenditure<br><!-- End of picture text --> 27
**Figure 2.** CDF of total household expenditure, by treatment

# CDF Curves

<!-- Start of picture text --> T0 T1 T2<br>0.30<br>0.28<br>0.26<br>0.24<br>0.22<br>0.20<br>0.18<br>0.16<br>0.14<br>0.12<br>0.10<br>50000 51000 52000 53000 54000 55000 56000 57000 58000 59000 60000<br>Total hh expenditures, monthly, per aeq<br>CDF<br><!-- End of picture text --> 28
**Figure 3.** Lorenz curves of inequality, by treatment

<!-- Start of picture text --> T0 T1 T2<br>0 10 20 30 40 50 60 70 80 90 100<br>Percentage of population<br>1<br>.8<br>.6<br>.4<br>Cumulative expenditure<br>.2<br>0<br><!-- End of picture text --> 29
**Table 0:** Response rates by study arm

|**Study arm**|**Expected HH**|**Interviewed HH**|**Of which**<br>**replacements**|**Response rate**<br>**(excl. replacements)**|
|---|---|---|---|---|
|T0|715|702|34|93.43%|
|T1|715|711|30|95.24%|
|T2|715|706|28|94.83%|

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**Table 1.** Basic demographic characteristics

|**Demographic characteristics**|**T0**|**T1**|**Sig**<sup>**a**</sup>|**T0**|**T2**|**Sig**<sup>**a**</sup>|**T1**|**T2**|**Sig**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|---|---|
|Household size|4.8|4.8|n/s|4.8|4.8|n/s|4.8|4.8|n/s|
|Adult equivalent household size<br>|3.9|3.9|n/s|3.9|3.9|n/s|3.9|3.9|n/s|
|Number of adults<sup>b</sup>|2.4|2.4|n/s|2.4|2.4|n/s|2.4|2.4|n/s|
|Number of males|2.4|2.4|n/s|2.4|2.4|n/s|2.4|2.4|n/s|
|Number of females|2.5|2.5|n/s|2.5|2.5|n/s|2.5|2.5|n/s|
|**Head of household**||||||||||
|Age (years)|43.0|44.1|n/s|43.0|43.8|n/s|44.1|43.8|n/s|
|Female (%)|27.4|27.3|n/s|27.4|27.2|n/s|27.3|27.2|n/s|
|Secondary education or higher (%)|17.1|14.1|n/s|17.1|18.5|n/s|14.1|18.5|**|
|Employed (wage and non-farm, %)|36.9|43.5|**|36.9|46.7|***|43.5|46.7|n/s|
|Wage employment|23.1|24.2|n/s|23.1|26.8|n/s|24.2|26.8|n/s|
|Non-farm enterprise employment|19.9|24.6|**|19.9|23.9|n/s|24.6|23.9|n/s|
|**Main respondent for food consumption**||||||||||
|Age (years)|39.2|39.4|n/s|39.2|39.0|n/s|39.4|39.0|n/s|
|Female (%)|77.4|72.4|*|77.4|70.9|**|72.4|70.9|n/s|
|Secondary education or higher (%)|17.5|14.4|n/s|17.5|18.6|n/s|14.4|18.6|n/s|
|Employed (%)|29.0|36.6|***|29.0|41.0|***|36.6|41.0|*|
|Wage employment|16.2|17.3|n/s|16.2|18.3|n/s|17.3|18.3|n/s|
|Non-farm enterprise employment|16.8|22.3|**|16.8|24.1|***|22.3|24.1|n/s|
|Mean time of completion of food module|||||||24.3|42.6|***|
|**Number of households**|**702**|**711**||**702**|**706**||**711**|**706**||

_a Differences found to be statistically significant are indicated by level: * p<0.10, ** p<0.05, *** p<0.01; n/s = not significant b Number of adults is defined as an individual 18 years of age or older_ 31
**Table 2** . Basic housing characteristics, by treatment

|**Housing characteristics**|**T0**|**T1**|**Sig**<sup>**a**</sup>|**T0**|**T2**|**Sig**<sup>**a**</sup>|**T1**|**T2**|**Sig**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|---|---|
|Improved floor materials (%)|47.4|51.5|*|47.4|50.1|n/s|51.5|50.1|n/s|
|Improved wall materials (%)|57.8|59.4|n/s|57.8|58.7|n/s|59.4|58.7|n/s|
|Improved roofing material|86.5|84.1|n/s|86.5|85.5|n/s|84.1|85.5|n/s|
|Improved drinking water source, rainy season (%)<sup>e</sup>|73.3|78.4|*|73.3|80.6|***|78.4|80.6|n/s|
|Improved drinking water source, dry season (%)|61.1|55.9|**|61.1|54.7|**|55.9|54.7|n/s|
|Clean drinking water source, rainy season (%)f|48.1|40.8|***|48.1|41.0|**|40.8|41.0|n/s|
|Clean drinking water source, dry season (%)|58.9|54.1|*|58.9|52.3|**|54.1|52.3|n/s|
|**Drinking water source (%)**||||||||||
|Piped water|41.3|34.3|***|41.3|35.4|**|34.3|35.4|n/s|
|Tubewell or borehole|1.1|5.4|***|1.1|7.4|***|5.4|7.4|*|
|Protected dugwell|6.8|6.4|n/s|6.8|5.6|n/s|6.4|5.6|n/s|
|Unprotected dugwell|11.0|9.6|n/s|11.0|8.0|n/s|9.6|8.0|n/s|
|Protected spring|3.5|1.1|*|3.5|0.6|**|1.1|0.6|n/s|
|Unprotected spring|2.8|0.7|**|2.8|1.2|n/s|0.7|1.2|n/s|
|Rainwater collection|20.5|29.7|***|20.5|29.9|***|29.7|29.9|n/s|
|Bottled water|0.1|1.0|n/s|0.1|1.5|*|1.0|1.5|*|
|Cart with small tank/drum|0.0|0.4|n/s|0.0|0.2|n/s|0.4|0.2|n/s|
|Tanker-truck|0.1|0.1|n/s|0.1|0.0|n/s|0.1|0.0|n/s|
|Surface water (river, dam, lake, pond)|12.2|11.3|n/s|12.2|10.1|n/s|11.3|10.1|n/s|
|Other|0.7|0.0|**|0.7|0.0|**|0.0|0.0|n/s|
|**Use basic sanitation facilities (%)**<sup>**g**</sup>|90.6|91.6|n/s|90.6|90.6|n/s|91.6|90.6|n/s|
|**Number of habitable rooms (main dwelling)**|3|3|n/s|3|3|n/s|3|3|n/s|
|**Number of households**<br>_aDifferences found to be statistically significant are indi_|**702**<br>_cated by le_|**711**<br>_vel: * p_|_<0.10,_|**702**<br>_** p<0.0_|**706**<br>_5, ***_|_p<0.01_|**711**<br>_; n/s =_|**706**<br>_not sign_|_ificant_|

> _a_ _<mark>Differences found to be statistically significant are indicated by level: * p<0.10, ** p<0.05, *** p<0.01; n/s = not significant</mark>_

> _b_ _<mark>Improved floor materials include concrete slab, sand/cement, tiles</mark>_

> _c_ _<mark>Improved wall materials include baked/burnt bricks, concrete slab, sand/cement blocks, stones</mark>_

> _d_ _<mark>Improved roof materials include concrete slab (flat roof), iron sheets (GCI), asbestos sheets, tiles</mark>_

_e Improved drinking water source include piped water, tubewell or borehole, protected dugwell, protected spring, unprotected spring, bottled, cart with small tank/drum water f_ _<mark>Clean drinking water includes piped water inside the dwelling or to public or private standpipes (taps), and protected wells</mark> g Basic sanitation facilities include pit latrine with/without slab, pit latrine washable, ventilated improved pits (VIP), pour flush, flush toilet_ 32
**Table 4.** Most common food items consumed, by treatment

|||**Per**|**cent of food item in overall food i**|**tems c**|**onsumed**|**(%)**|||
|---|---|---|---|---|---|---|---|---|
|**T0**|||**T1**|||**T2**|||
|||**C**|||**C**|||**C**|
|**Food Item**|**%**|**um**<br>**%**|**Food Item**|**%**|**um**<br>**%**|**Food Item**|**%**|**um**<br>**%**|
|Maize flour (white, loose)|4.8|4.8|Salt|6.4|6.4|Salt|5.2|5.2|
|Tomatoes|4.5|9.3|Tomatoes|5.8|12.2|Tomatoes|4.7|9.9|
|Salt|4.3|13.6|Onions|5.6|17.8|Round onions|4.7|14.5|
|Onions|4.1|17.7|Cooking oil|5.0|22.8|Brown sugar|3.1|17.7|
|Dried sardines|3.7|21.4|Other green vegetables|4.9|27.7|Maize flour (white, loose)|3.1|20.8|
|Sunflower oil or seeds|3.0|24.4|Other beans, lentils, and pulses|4.3|32.0|Dried sardines|3.0|23.8|
|Beans, dry|2.8|27.2|Sugar|4.3|36.3|Mchicha|2.6|26.4|
|Mchicha|2.7|29.9|Maize (flour)|4.1|40.4|Sweet potato|2.3|28.7|
|Brown sugar|2.7|32.6|Rice (husked)|3.3|43.7|Beans, dry|2.2|30.9|
|Sweet potato|2.3|34.9|Sweet potato|3.0|46.7|Maize grain (white)|2.1|33.1|
|Mineral waters|2.1|37.0|Maize (grain)|2.5|49.2|Sunflower oil or seeds|2.1|35.1|
|Pumpkin leaves|2.0|39.1|Dagaa (fresh)|2.4|51.6|Pumpkin leaves|2.1|37.2|
|Beef - with bones|1.8|40.8||||Tea, tea leaves|2.1|39.3|
|Cassava, fresh|1.7|42.5||||Other oil|1.9|41.2|
|Potato leaves|1.6|44.2||||Bitter tomatoes|1.8|43.0|
|Wheat buns, scones|1.6|45.8||||Potato leaves|1.8|44.8|
|Dried fish|1.6|47.4||||Okra|1.8|46.6|
|Tea (tea leaves)|1.5|49.0||||Oranges|1.7|48.4|
|Cassava leaves|1.4|50.3||||Bananas|1.7|50.0|

34
**Table 5.** Average monthly expenditure of food groups, by treatment

|**Food groups**|**T0**|**T1**|**Sig**<sup>**a**</sup>|**T0**|**T2**|**Sig**<sup>**a**</sup>|**T1**|**T2**|**Sig**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|---|---|
|Bread and cereals|82,154|75,959|n/s|82,154|76,203|n/s|75,959|76,203|n/s|
|Vegetables|48,580|54,142|*|48,580|63,650|***|54,142|63,650|***|
|Meat, meat products|23,632|47,075|***|23,632|52,422|***|47,075|52,422|n/s|
|Fish|19,186|20,613|n/s|19,186|24,587|***|20,613|24,587|**|
|Milk, cheese and eggs|14,832|20,674|*|14,832|25,568|***|20,674|25,568|*|
|Oils and fats|14,315|17,512|*|14,315|15,308|n/s|17,512|15,308|*|
|Fruits|14,022|24,745|**|14,022|22,564|*|24,745|22,564|n/s|
|Sugar, jam, honey, chocolate and confectionery|7,297|12,119|***|7,297|10,007|***|12,119|10,007|***|
|Coffee, tea and cocoa|8,196|3,535|**|8,196|16,168|*|3,535|16,168|***|
|Mineral waters, soft drinks, fruit & vegetable juices|<br>9,629|16,703|**|9,629|12,420|n/s|16,703|12,420|n/s|
|Other foodproducts n.e.c.(spices,salt)|2,385|1,921|n/s|2,385|2,132|n/s|1,921|2,132|n/s|

_a Differences found to be statistically significant are indicated by level: * p<0.10, ** p<0.05, *** p<0.01; n/s = not significant *Estimates are based on monthly total consumption *The sum of the reported expenditure on the food categories will not sum to the total reported food expenditure as only non-zero consumption values are included in the calculation of expenditure. This rule applies to the total food expenditure for households who did not eat any of the food groups and to expenditure on specific food categories for households who may have not consumed one or more food groups._ 35
**Table 6.** Average monthly food and non-food consumption expenditure, by treatment

|**Monthly expenditure**|**T0**|**T1**|**Sig**<sup>**a**</sup>|**T0**|**T2**|**Sig**<sup>**a**</sup>|**T1 **|**T2**|**Sig**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|---|---|
|**Food expenditure, total**|**237,367**|**279,253**|*******|**237,367**|**301,524**|*******|**279,253**|**301,524**|*****|
|Food at home, purchased|114,234|134,496|***|114,234|143,367|***|134,496|143,367|n/s|
|Food at home, not purchased<sup>b</sup>|85,194|108,030|**|85,194|123,293|***|108,030|123,293|**|
|Food away from home|74,773|105,974|***|74,773|107,113|***|105,974|107,113|n/s|
|**Non-food expenditure, total**|**186,884**|**179,593**|**n/s**|**186,884**|**181,014**|**n/s**|**179,593**|**181,014**|**n/s**|
|Non-food (week)|15,972|25,561|***|15,972|23,049|***|25,561|23,049|n/s|
|Non-food (month)|55,263|54,804|n/s|55,263|56,417|n/s|54,804|56,417|n/s|
|Non-food (year)|18,252|19,103|n/s|18,252|19,094|n/s|19,103|19,094|n/s|
|Education|40,926|29,915|*|40,926|40,014|n/s|29,915|40,014|*|
|Health|53,483|42,164|n/s|53,483|38,511|**|42,164|38,511|n/s|
|Durables|10,269|11,826|n/s|10,269|9,565|n/s|11,826|9,565|n/s|
|Rent|24,939|24,812|n/s|24,939|26,444|n/s|24,812|26,444|n/s|
|**Total cons. expenditure**<sup>**c**</sup><br>_aDifferences found to be statistic_|**424,252**<br>_ally signific_|**458,847**<br>_ant are indic_|**n/s**<br>_ated by_|**424,252**<br>_level: * p<_|**482,538**<br>_0.10, ** p<0_|******<br>_.05, ***_|**458,847**<br>_p<0.01; n/s_|**482,538**<br>_= not sign_|**n/s**<br>_ificant_|

> _a_ _<mark>Differences found to be statistically significant are indicated by level: * p<0.10, ** p<0.05, *** p<0.01; n/s = not significant</mark>_

> _b_ _<mark>Food consumed at home (not purchased) includes food from own-production and gifts</mark>_

> _c_ _<mark>The sum of the reported expenditure on the three food sources (purchased for home consumption, non-purchased for home</mark> consumption, consumed away from home) will not sum to the total reported food expenditure as only non-zero consumption values are included in the calculation of expenditure._

36
**Table 7.** Percentile of monthly food consumption expenditure, by treatment

|**Treatment**|**P10**|**P25**|**P50**|**P75**|**P90**|**Mean**|**p-value**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|
|**T0**|90,207|127,750|197,671|295,005|444,413|237,367||
|**T1**|88,615|142,084|222,081|361,041|533,813|279,253|0.00|
|**T2**|98,122|144,158|239,302|368,518|583,653|301,524|0.00|

_*Food consumption expenditure includes total monthly expenditure on food consumed at home (both purchased and non-_ _<u>purchased) and food away from home</u>_ _a p-values are a two-sample Kolmogorov-Smirnov test for equality of distribution functions i.e., between T0-T1, and T0-T2._ 37
**Table 8.** Distribution of food group consumption and expenditure shares, by treatment

||**% of ho**|**useholds co**|**nsuming**|**Share in to**|**tal food con**|**sumptionb**|
|---|---|---|---|---|---|---|
|**Food Group**<sup>**a**</sup>|||||||
||**T0**|**T1**|**T2**|**T0**|**T1**|**T2**|
|Bread and cereals|99.4|99.2|98.4|39.3|36.7|33.2|
|Meat, meat products, fish|48.6|61.1|45.4|11.2|16.7|16.4|
|Fish|88.0|43.4|80.7|10.9|8.0|10.1|
|Milk, cheese and eggs|31.9|44.2|40.2|6.9|8.3|8.6|
|Oils and fats|89.0|79.5|82.0|7.3|7.7|6.7|
|Fruits|59.3|77.0|81.4|5.9|9.9|9.6|
|Vegetables|99.8|99.3|98.0|25.9|25.7|28.1|
|Sugar, jam, honey, chocolate and confectionery|64.4|69.5|71.0|3.6|5.3|4.4|
|Food products n.e.c.|87.1|98.2|96.5|1.3|1.0|1.0|
|Coffee, tea and cocoa|37.2|40.1|44.7|4.0|1.5|4.9|
|Mineral waters, soft drinks, fruit/vegetable juices<br>_a Share of total food consumption on the food group_|56.0<br>_"Alcohol" is_|25.0<br>_not shown fo_|22.6<br>_r T1 as the o_|5.1<br>_ther two treat_|5.0<br>_ments did not_|4.9<br>_specifically_|

_a Share of total food consumption on the food group "Alcohol" is not shown for T1 as the other two treatments did not specifically include this food category in their data collection instruments b The sum of the reported shares of consumption on the food categories will not sum to 100 percent as only non-zero consumption values are included in the calculation of expenditure, and therefore are also not included in the calculation of shares_ 38
**Table 9.** Shares of monthly household food and non-food expenditure, by treatment

||**Share of**|**total expendit**|**ure**<sup>**b**</sup>**(%)**|
|---|---|---|---|
|**Expenditure type**|**T0**|**T1**|**T2**|
|**Food expenditure, total**|**61.9**|**65.5**|**67.6**|
|Food at home, purchased|30.0|29.5|30.7|
|Food at home, not purchased<sup>a</sup>|25.1|32.0|31.5|
|Food away from home|15.5|19.3|21.2|
|**Non-food expenditure, total**|**38.1**|**34.5**|**32.4**|
|Non-food (week)|3.2|4.7|4.1|
|Non-food (month)|11.5|10.3|10.1|
|Non-food (year)|4.3|4.3|3.9|
|Education|6.1|4.9|4.9|
|Health|10.7|7.6|7.3|
|Durables|1.9|1.8|1.4|
|Rent|6.1|5.9|5.6|

> _a_ _<mark>Food consumed at home (not purchased) includes food from own-production and gifts</mark> b The sum of the reported shares of consumption on the three food sources will not sum to 100 percent as only non-zero consumption values are included in the calculation of expenditure, and therefore are also not included in the calculation of shares._

39
**Table 10a** . Prevalence of poverty at different thresholds with confidence intervals

|**Treatment**|Natio<br>( <br>|nal poverty line<br>TSh 1,876)<br>|( <br>|US$ 1.90<br>TSh 1,644)<br>|( <br>|US$ 2.15<br>TSh 1,860)<br>|( <br>|US$ 3.2<br>TSh 2.8<br>|5<br>12)<br>|
|---|---|---|---|---|---|---|---|---|---|
||**%**|**[95% C.I.]**|**%**|**[95% C.I.]**|**%**|**[95% C.I.]**|**%**|**[95%**|**C.I.]**|
|**T0**|**20.1**|[15.1 – 25.2]|**13.1**|[9.4 – 16.7]|**19.7**|[14.8 – 24.6]|**49.6**|[44.3|– 54.9]|
|**T1**|**23.9**|[19.2 – 28.6]|**17.6**|[13.7 – 21.6]|**23.0**|[18.4 – 27.6]|**48.3**|[42.9|– 53.7]|
|**T2**|**20.7**|[16.3 – 25.1]|**14.9**|[10.6 – 19.3]|**20.4**|[15.9 – 24.8]|**44.8**|[39.3|– 50.2]|

* _all US$ converted to local values using 2017 purchasing power parity (PPP), expressed in 2022 prices_

**Table 10b.** Prevalence of poverty at different thresholds with statistical significance tests

|**Poverty**|**T0**|**T1**|**Sig**<sup>**a**</sup>|**T0**|**T2**|**Sig**<sup>**a**</sup>|**T1**|**T2**|**Sig**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|---|---|
|National poverty line (TSh 1,876)|20.1|23.9|n/s|20.1|20.7|n/s|23.9|20.7|n/s|
|US$ 1.90 (TSh 1,644)|13.1|17.6|*|13.1|14.9|n/s|17.6|14.9|n/s|
|US$ 2.15 (TSh 1,860)|19.7|23.0|n/s|19.7|20.4|n/s|23.0|20.4|n/s|
|US$ 3.25 (TSh 2,812)|49.6|48.3|n/s|49.6|44.8|n/s|48.3|44.8|n/s|

_a Differences found to be statistically significant are indicated by level: * p<0.10, ** p<0.05, *** p<0.01; n/s = not significant_ 40
**Table 11a:** Regression of log per capita food expenditure by treatment – household head characteristics

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
|Recall short-list|0.110***|0.114***|0.120***|0.059|0.091*|-0.054|0.083|0.030|-0.116|
||(0.038)|(0.038)|(0.043)|(0.086)|(0.048)|(0.057)|(0.056)|(0.064)|(0.096)|
|Recall long-list|0.214***|0.209***|0.176***|0.082|0.198***|0.134**|0.196***|0.145**|0.012|
||(0.041)|(0.040)|(0.043)|(0.091)|(0.044)|(0.056)|(0.057)|(0.063)|(0.096)|
|Recall short-list*educated|||0.009||||||-0.061|
||||(0.114)||||||(0.108)|
|Recall long-list*educated|||0.178*||||||0.126|
||||(0.104)||||||(0.096)|
|Recall short-||||||||||
|list*employed||||0.062|||||0.073|
|||||(0.095)|||||(0.093)|
|Recall long-list*employed||||0.161|||||0.096|
|||||(0.112)|||||(0.097)|
|Recall short-list*non-||||||||||
|wage|||||0.038||||-0.026|
||||||(0.101)||||(0.106)|
|Recall long-list*non-wage|||||0.031||||-0.016|
||||||(0.107)||||(0.097)|
|Recall short-list*floor||||||||||
|material||||||0.296***|||0.296***|
|||||||(0.074)|||(0.080)|
|Recall long-list*floor||||||||||
|material||||||0.144*|||0.082|
|||||||(0.074)|||(0.075)|
|Recall short-list*water||||||||||
|(rainy)|||||||0.112||-0.070|
||||||||(0.082)||(0.113)|
|Recall long-list*water||||||||||
|(rainy)|||||||0.087||0.008|
||||||||(0.087)||(0.107)|
|Recall short-list*water||||||||||
|(dry)||||||||0.175**|0.126|
|||||||||(0.085)|(0.113)|
|Recall long-list*water||||||||||
|<br>(dry)||||||||0.161**|0.113|
|||||||||(0.081)|(0.097)|
|Constant|10.791***|10.430***|10.727***|10.767***|10.749***|10.650***|10.671***|<br>10.672***|<br>10.571***|
||(0.034)|(0.046)|(0.034)|(0.057)|(0.035)|(0.039)|(0.044)|(0.053)|(0.066)|
|Observations|2,118|2,108|2,118|2,118|2,118|2,118|2,118|2,108|2,108|
|R-squared|0.017|0.179|0.079|0.023|0.041|0.135|0.072|0.074|0.192|

Note: Dependent variable is the log of per capita food consumption expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes household head and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

41
**Table 11b:** Regression of log per capita food expenditure by treatment – main respondent characteristics

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
|Recall short-list|0.110***|0.096**|0.058|0.071|0.097**|-0.054|0.083|0.030|-0.169|
||(0.038)|(0.039)|(0.087)|(0.068)|(0.049)|(0.057)|(0.056)|(0.064)|(0.127)|
|Recall long-list|0.214***|0.200***|0.283***|0.085|0.200***|0.134**|0.196***|0.145**|0.118|
||(0.041)|(0.041)|(0.074)|(0.077)|(0.047)|(0.056)|(0.057)|(0.063)|(0.118)|
|Recall short-list*female|||0.060||||||0.059|
||||(0.107)||||||(0.105)|
|Recall long-list*female|||-0.113||||||-0.115|
||||(0.088)||||||(0.084)|
|Recall short-||||||||||
|list*employed||||0.062|||||0.092|
|||||(0.078)|||||(0.086)|
|Recall long-||||||||||
|<br>list*employed||||0.182*|||||0.083|
|||||(0.096)|||||(0.093)|
|Recall short-list*non-||||||||||
|wage|||||0.017||||-0.071|
||||||(0.121)||||(0.123)|
|Recall long-list*non-||||||||||
|wage|||||0.004||||-0.067|
||||||(0.104)||||(0.103)|
|Recall short-list*floor||||||||||
|material||||||0.296***|||0.293***|
|||||||(0.074)|||(0.075)|
|Recall long-list*floor||||||||||
|material||||||0.144*|||0.150**|
|||||||(0.074)|||(0.072)|
|Recall short-list*water||||||||||
|(rainy)|||||||0.112||-0.084|
||||||||(0.082)||(0.117)|
|Recall long-list*water||||||||||
|(rainy)|||||||0.087||-0.009|
||||||||(0.087)||(0.103)|
|Recall short-list*water||||||||||
|(dry)||||||||0.175**|0.113|
|||||||||(0.085)|(0.114)|
|Recall long-list*water||||||||||
|<br>(dry)||||||||0.161**|0.096|
|||||||||(0.081)|(0.094)|
|Constant|10.791***|10.651***|10.933***|10.836***|10.758***|10.650***|10.671***|<br>10.672***|<br>10.762***|
||(0.034)|(0.057)|(0.070)|(0.055)|(0.038)|(0.039)|(0.044)|(0.053)|(0.087)|
|Observations|2,118|2,100|2,114|2,110|2,114|2,118|2,118|2,108|2,100|
|R-squared|0.017|0.178|0.038|0.020|0.032|0.135|0.072|0.074|0.189|

Note: Dependent variable is the log of per capita food consumption expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes main respondent and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

42
**Table 12a:** Regression of log total per capita consumption expenditure – household head characteristics

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
|Recall short-list|0.020|0.024|0.052|-0.105|-0.002|-0.115**|0.019|-0.021|-0.229**|
||(0.038)|(0.038)|(0.045)|(0.097)|(0.049)|(0.058)|(0.059)|(0.067)|(0.103)|
|Recall long-list|0.095**|0.092**|0.062|-0.130|0.077*|0.037|0.124*|0.071|-0.137|
||(0.038)|(0.039)|(0.044)|(0.109)|(0.043)|(0.055)|(0.063)|(0.066)|(0.100)|
|Recall short-list*educated|||-0.101||||||-0.138|
||||(0.120)||||||(0.108)|
|Recall long-list*educated|||0.139||||||0.110|
||||(0.104)||||||(0.100)|
|Recall short-list*employed||||0.166|||||0.192*|
|||||(0.111)|||||(0.101)|
|Recall long-list*employed||||0.290**|||||0.215*|
|||||(0.137)|||||(0.111)|
|Recall short-list*non-wage|||||0.036||||-0.041|
||||||(0.111)||||(0.110)|
|Recall long-list*non-wage|||||0.027||||-0.037|
||||||(0.107)||||(0.098)|
|Recall short-list*floor||||||||||
|material||||||0.217***|||0.240***|
|||||||(0.075)|||(0.082)|
|Recall long-list*floor||||||||||
|material||||||0.088|||0.039|
|||||||(0.073)|||(0.081)|
|Recall short-list*water||||||||||
|(rainy)|||||||0.075||-0.029|
||||||||(0.094)||(0.113)|
|Recall long-list*water||||||||||
|<br>(rainy)|||||||-0.000||-0.013|
||||||||(0.097)||(0.135)|
|Recall short-list*water||||||||||
|(dry)||||||||0.116|0.056|
|||||||||(0.088)|(0.110)|
|Recall long-list*water||||||||||
|<br>(dry)||||||||0.098|0.078|
|||||||||(0.089)|(0.131)|
||11.334**|10.876**|11.233**|11.393**|11.279**|11.066**|11.145**|<br>11.112**|<br>11.029**|
|Constant|*|*|*|*|*|*|*|*|*|
||(0.040)|(0.047)|(0.038)|(0.080)|(0.042)|(0.039)|(0.048)|(0.058)|(0.070)|
|Observations|2,118|2,108|2,118|2,118|2,118|2,118|2,118|2,108|2,108|
|R-squared|0.003|0.281|0.102|0.009|0.036|0.215|0.084|0.096|0.291|

Note: Dependent variable is the log of per capita total expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes head of household and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

43
**Table 12b:** Regression of log per capita total expenditure - main respondent characteristics

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
|Recall short-list|0.020|0.003|-0.092|-0.049|-0.001|-0.115**|0.019|-0.021|-0.289**|
||(0.038)|(0.039)|(0.094)|(0.084)|(0.050)|(0.058)|(0.059)|(0.067)|(0.133)|
|Recall long-list|0.095**|0.078**|0.065|-0.083|0.080*|0.037|0.124*|0.071|-0.088|
||(0.038)|(0.038)|(0.072)|(0.088)|(0.045)|(0.055)|(0.063)|(0.066)|(0.121)|
|Recall short-list*female|||0.137||||||0.146|
||||(0.115)||||||(0.107)|
|Recall long-list*female|||0.019||||||0.021|
||||(0.089)||||||(0.085)|
|Recall short-list*employed||||0.118|||||0.120|
|||||(0.098)|||||(0.093)|
|Recall long-list*employed||||0.261**|||||0.135|
|||||(0.115)|||||(0.102)|
|Recall short-list*non-wage|||||0.045||||-0.047|
||||||(0.130)||||(0.119)|
|Recall long-list*non-wage|||||-0.004||||-0.096|
||||||(0.110)||||(0.105)|
|Recall short-list*floor||||||||||
|material||||||0.217***|||0.216***|
|||||||(0.075)|||(0.076)|
|Recall long-list*floor||||||||||
|material||||||0.088|||0.110|
|||||||(0.073)|||(0.071)|
|Recall short-list*water||||||||||
|(rainy)|||||||0.075||-0.054|
||||||||(0.094)||(0.118)|
|Recall long-list*water||||||||||
|<br>(rainy)|||||||-0.000||-0.039|
||||||||(0.097)||(0.133)|
|Recall short-list*water||||||||||
|(dry)||||||||0.116|0.042|
|||||||||(0.088)|(0.112)|
|Recall long-list*water||||||||||
|<br>(dry)||||||||0.098|0.069|
|||||||||(0.089)|(0.131)|
||11.334**|11.156**|11.560**|11.460**|11.290**|11.066**|11.145**|<br>11.112**|<br>11.302**|
|Constant|*|*|*|*|*|*|*|*|*|
||(0.040)|(0.061)|(0.084)|(0.075)|(0.045)|(0.039)|(0.048)|(0.058)|(0.094)|
|Observations|2,118|2,100|2,114|2,110|2,114|2,118|2,118|2,108|2,100|
|R-squared|0.003|0.278|0.026|0.009|0.025|0.215|0.084|0.096|0.284|

Note: Dependent variable is the log of per capita total consumption expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes head of household and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

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