Survey Methodologies: Measurement Experiments with the Living Standards Measurement Study (LSMS)

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2 Survey Methodologies: Measurement Experiments with the Living Standards Measurement Study (LSMS) November 19, 2014 Sydney Gourlay Living Standards Measurement Study, DECRG

3 Living Standards Measurement Study (LSMS) Surveys Started in 1980s Purpose: Measure poverty plus study household behavior, welfare, interactions with government policies: determinants of outcomes, and linkages among assets/ characteristics of households/livelihood sources/government interventions. Currently 104 surveys available online with more to worldbank.org/lsms

4 LSMS Integrated Surveys on Agriculture Multi-topic, nationally representative panel household surveys conducted in eight sub- Saharan countries Include collection of detailed data on agriculture as well as various indicators, including: HH composition Education Health Labor Migration Consumption HH enterprises Community characteristics, prices Credit Use Etc

5 The Methodological Research Mission LSMS: More than just household surveys Methodological research gains ground Improving Measurement of Agricultural Productivity through Methodological Validation and Research Key stakeholders and donors in support of the methodological mission FAO and the Global Strategy to Improve Agriculture and Rural Statistics UK Aid from the British People

6 The Methodological Research Mission UK Aid-funded research agenda includes methodological validation of: 1. Land area measurement 2. Soil fertility 3. Water resources 4. Labor inputs 5. Skill measurement 6. Production of continuous and extended-harvest crops 7. Computer-assisted personal interviewing for agricultural data

7 The Methodological Research Mission No such thing as a simple question How big is your plot? How much income did you earn last year? How much did you harvest last season? Is so-and-so a household member? Who you ask and how you ask matter. Data collection methods can significantly impact policy relevant outcomes.

8 Methodological Research Agricultural Land Other Areas Agricultural Production Household Consumption

9 Methodological Research Agricultural Land Land Area Measurement Soil Fertility Testing Other Areas Agricultural Production Household Consumption

10 Land Area Measurement How big is [PLOT]? The answer will impact yield estimates, land inequality figures, etc. Primary Methodological Options: Farmer self-reported estimate Compass and rope (aka traversing) -> the gold standard GPS Remote sensing (?)

11 Land Area Measurement Farmer selfreported estimate Compass and rope (aka traversing) GPS Remote sensing (?) Cheap Less missingness But Subjective Complicated by traditional units Potential ulterior motives for over/under estimation Traditional gold standard for accuracy Eliminates subjectivity But Time/labor intensive (leading to higher costs) Requires travel to plot Significantly quicker than traversing with advantages of objective measurement But Questions of accuracy on small plots (?) Requires travel to plot Potential to eliminate plot visits But Resolution limitations Feasibility of plot boundary identification

12 Farmer Estimates: Good Enough? LSMS-ISA Co. GPS (acres) Farmer SR (acres) Mean Bias (SR GPS) Bias as % of GPS Malawi % Uganda % Tanzania % Niger % Excerpt from WB Policy Research Working Paper 6550, From Guesstimates to GPStimates: Land Area Measurement and Implications for Agricultural Analysis. Calogero Carletto, Paul Winters, and Sydney Gourlay.

13 Farmer Estimates: Good Enough? When you take a closer look - strong evidence of systematic bias

14 Farmer Estimates: Good for Nothing? Not exactly. Kilic, et al use multiple imputation to illustrate that selfreported estimates can be used to compute improved area measurements where GPS measurements are missing Self-reported area estimates are statistically significant predictors of observed GPS areas in both Tanzania 2010/11 and Uganda 2009/10 See: Kilic, Zezza, Carletto, and Savastano. Missing(ness) in Action: Selectivity Bias in GPS-Based Land Area Measurements. World Bank Policy Research Working Paper No

15 Land Area Validation Exercise: Nigeria Objective Explain the dramatic differences between farmer self-reported plot areas and GPS measurements observed in the first wave of the LSMS panel through a small-scale land area verification exercise. Partnerships National Bureau of Statistics Tercile (CR) N* Panel Wave 2 Validation Exercise Panel - Validation SR GPS SR GPS SR - SR GPS - GPS ** 0.133*** *** *** *** Total *** *** **Observations without both GPS and SR in Wave 2 were dropped, *p<0.1; ** p<0.05; *** p<0.01

16 Land Area Validation Exercise: Nigeria So far, we have sees that GPS and farmer selfreported estimates are not simple substitutes, but how does GPS stack up to the gold standard?

17 Land and Soil Experimental Research (LaSER): Ethiopia Objective Test various measurement methodologies in order to: Validate the data quality associated with each method Determine the costs and benefits of each method Assess the feasibility and necessity of implementing each method in national household surveys Document best practices in data collection based on our findings from experiments in Ethiopia and beyond Partnerships Central Statistical Agency of Ethiopia World Agroforestry Centre (ICRAF) Status Fieldwork completed early March 2014 Soil testing and data analysis underway

18 LaSER: Ethiopia Elevation Rainfall AEZ 85 EAs 12 HH Each

19 Land and Soil Experimental Research (LaSER): Ethiopia Methodologies tested: Land area Traversing (i.e., compass and rope) GPS measurement (Garmin) GPS measurement (Android tablet) Farmer self-reported area Clinometer Farmer self-reported incline Soil fertility Spectral Soil Analysis Conventional Soil Analysis Farmer self-reported soil quality Maize production Crop-cutting using a 4m x 4m subplot and 2m x 2m subplot Farmer self-reported harvest Completed during the post-planting visit on up to two fields per household Samples collected during the postplanting visit, processed at regional labs and shipped to ICRAF Nairobi for analysis Completed by field teams when alerted by household 1018 households interviewed 1799 fields selected for objective measurement and soil testing 3791 soil samples collected* 205 fields with cropcutting *2 samples were collected from each field (different depths and sampling procedures), an additional sample was collected on fields with crop-cutting.

20 Land and Soil Experimental Research (LaSER): Ethiopia Measurement Duration (Minutes) (< 0.25 acres) ( acre) (> 1 acre) CR Plot Area Ethiopia Level (CR) Avg GPS - Avg N SR GPS CR CR / Avg CR 1 (< 0.25 acres) % 2 ( acre) % 3 (> 1 acre) % Total % ET GPS ET CR

21 Land and Soil Experimental Research (LaSER): Ethiopia Alternative Tablet GPS Measurements (Ethiopia Land and Soil Experimental Research) Acres Level (CR) LaSER Garmin Handheld GPS Mean bias: (Tablet - Garmin)/Garmi n Alternative N CR Tablet GPS 1 (< 0.05 acres) % 2 (< 0.15 acres) % 3 (< 0.35 acres) % 4 (< 0.75 acres) % 5 (< 1.25 acres) % 6 (>= 1.25 acres) % Total % *unweighted means Despite similar measurements, field experience revealed slow satellite acquisition (resulting in several 0 areas)

22 Land and Soil Experimental Research (LaSER): Ethiopia Much of the difference between GPS and SR estimates are explained by plot and household factors. Difference between GPS and CR much harder to explain due to small discrepancy. Preliminary results (please do not quote) Determinants of Bias (SR-GPS) OLS Regression, Error Clustered on Enumerator ID GPS Area (acres) *** GPS Area Per : Area Ratio (GPS) Distance from dwelling 0.011** Number cultivated plots in HH ** Slope (clinometer) Soil Quality (SR): Fair ** Poor Title or Certificate of Ownership 0.086*** Can sell or use as collateral Treecover: Partial 0.04 Heavy 0.182* HH Head Characterisitcs: Female Yrs education 0.014* Age Literate * Constant 0.286*** N 1716 R Enumerator Fixed Effects *p<.1; ** p<.05; *** p<.01

23 Land and Soil Experimental Research (LaSER): Ethiopia Why Soils? Research questions: Inverse-productivity relationship Gender issues in land allocation Effective and appropriate input use Impact of climate change and farmer response Objective: assess logistical feasibility of plot level soil testing and analytical and policy value

24 Land and Soil Experimental Research (LaSER): Ethiopia Currently, soil data comes largely in the form of spatial data (but at relatively low resolution for small Africa plots) and coarse subjective farmer assessments. Image source: ISRIC,

25 Land and Soil Experimental Research (LaSER): Ethiopia Soil results TBD! Samples are currently with ICRAF Nairobi for testing. Analysis of various agricultural relationships with the plot-level data Identify best subjective questions to predict laboratory results. Average soil collection time: approx. 38 minutes per plot

26 Land and Soil Experimental Research (LaSER): Ethiopia Challenges: LOGISTICS Soils can t be bagged for more than 5-7 days depending on moisture levels (leading to high drive time/fuel costs) Local labs to conduct initial processing before shipping Labeling, labeling, labeling Timing with crop: when can you sample? Mobile teams and crop-cutting Insert photo from ICRAF labs Next Steps: Soil Analysis at ICRAF Nairobi Data Analysis Capacity Building Workshop Analysis and Research Papers Ethiopia-specific handbook on soil sampling for household surveys

27 References and Other On-Going Research: Land Carletto, Savastano, and Zezza (2013). "Fact or Artifact: The Impact of Measurement Errors on the Farm Size Productivity Relationship." Journal of Development Economics, 103C, Carletto, Gourlay and Winters. From Guesstimates to GPStimates: Land Area Measurement and Implications for Agricultural Analysis. World Bank Policy Research Working Paper No Expected submission to Journal of African Economies: June Kilic, Zezza, Carletto, and Savastano. Missing(ness) in Action: Selectivity Bias in GPS- Based Land Area Measurements. World Bank Policy Research Working Paper No Expected submission to Agricultural Economics: June Kilic, Kim, and Yang. Exploring the Promise of Fractional Imputation to Predict Missing GPS-Based Area Measurements in Household Surveys: Evidence from Malawi. Draft manuscript expected by December Sourcebook: Land Area Measurement in Household Surveys: Empirical Evidence & Practical Guidance for Effective Data Collection.

28 Methodological Research Agricultural Land Other Areas Household Consumption Agricultural Production Production of continuous crops & use of mobile phones Seed identification Ag labor

29 Cassava Productivity: Zanzibar Objectives Validate methods for measuring cassava productivity by evaluating the data quality and costs vs. benefits for each method Assess the feasibility and necessity of implementing each method in national household surveys Document best practices in data collection Partnerships Ministry of Agriculture and Natural Resources, Zanzibar Office of the Chief Government Statistician, Zanzibar Status Fieldwork: June 2013 June 2014 Data cleaning and analysis underway

30 Cassava Productivity: Zanzibar Methodologies tested: Land area Traversing (i.e. compass-and-rope) GPS measurement Farmer self-reported area Cassava production Crop-cutting with balance scales Crop diaries with enumerator visits twice a week Crop diaries with telephone calls twice a week Farmer self-reported harvest (12-month recall) Farmer self-reported harvest (6-month recall)

31 Cassava Productivity: Zanzibar Level (CR) N GPS CR Bias Mean Bias / Mean CR 1 (< 0.05 acres) 2 (< 0.15 acres) 3 (< 0.35 acres) 4 (< 0.75 acres) 5 (< 1.25 acres) 6 (>= 1.25 acres) % % % % % % Total % Difference in mean GPS and mean SR: 148%

32 Cassava Productivity: Zanzibar Fieldwork Challenges: Differing incentive structures across treatment arms (for example, mobile phones) Connecting with farmers while not in the field (timing of phone prompting) Know the country context (for example, Ramadan)

33 DNA Fingerprinting for Varietal Identification - Pilot Ethiopian Institute for Agricultural Research (EIAR) Central Statistical Agency (CSA) Diversity Arrays Technology (Dart) With support from: International Food Policy Research Institute (IFPRI) Bill & Melinda Gates Foundation (BMGF)

34 DNA Fingerprinting for Varietal Identification - Pilot Why are varietal identification and adoption crucial? Considerable public investment in crop improvement in Ethiopia Tracking individual variety uptake is critical to ensuring the Ethiopian crop improvement program remains targeted and capable of accomplishing its goals This requires having a highly responsive system of monitoring the spread of improved varieties 34

35 DNA Fingerprinting for Varietal Identification - Pilot Traditional methods of estimating varietal diffusion include farmer identification (ID) of varietal use through HH surveys, with recent improvements to incorporate expert identification and consultation Challenges with farmer ID: Farmers may not be able to identify individual varieties in the field Prevalence of local names that cannot be matched to the official variety names Scientists have long discussed using DNA fingerprinting technology to verify farmer ID estimates, but this is the world s first field application pilot with preliminary results 35

36 DNA Fingerprinting for Varietal Identification - Pilot Objectives of the Pilot Project in Ethiopia: 1. Test the feasibility of estimating varietal adoption levels in specific pilot zones using DNA fingerprinting Wheat and Maize grain samples from farmer fields Reference library of breeder seed 2. Test the technical feasibility : Can DNA fingerprinting discriminate across improved varieties grown by farmers? 3. Test the logistical feasibility: Can the institutions partner for the collection and analysis of hundreds of data and grain samples and maintain identity preservation? 36

37 DNA Fingerprinting for Varietal Identification - Pilot Pilot survey covered a total of 108 Enumeration Areas in three zones in Oromia Region. Field Visits with CSA enumerators for sample collection Grain (seed) samples for DNA fingerprinting from AgSS crop cuts It is possible to use existing CSA crop cuts to collect grain samples to be fingerprinted? Logistically easier to collect and transport seed samples, including in potential scale-up Additional short survey asking farmers to identify the variety they were growing This information is to see if farmer s variety identification matches DNA fingerprinting results Data management and analysis Linking genetic analysis, short survey and AgSS data for additional analysis of seed use, inputs, etc.

38 DNA Fingerprinting for Varietal Identification - Pilot After lab analysis at DART(Australia), preliminary results for wheat find: 62% of the farmers reported using adopted improved wheat varieties Genetic analysis found that 96% of the farmers actually use improved wheat Only 9.3% of the farmers were able to correctly identify the improved wheat varieties cultivated in their fields

39 DNA Fingerprinting for Varietal Identification - Pilot Preliminary results for maize slightly more encouraging: 56% of the farmers reported using adopted improved maize varieties Genetic analysis found that 64% of the farmers actually use improved maize Only 47% of the farmers were able to correctly identify the improved maize varieties cultivated in their fields (all for hybrid users)

40 DNA Fingerprinting for Varietal Identification - Pilot Farmer reports consistently underestimate the use of improved varieties compared to DNA results with little knowledge of what specific varieties are being used Significant implications for monitoring varietal adoption rates, effectiveness of distribution programs, etc.

41 Measuring labor in farm households in Africa Objective Develop and validate methods on improving data collection on the quantity and demographics of family labor in farming in low-income settings Most small-holders do not hire labor - Characterizing farm labor productivity becomes a challenge

42 Measuring labor in farm households in Africa 7 day recall for each type of labor 12 month for each type (recall) LSMS-ISA: intensive recall PLOT ID HH ROSTER ID CODE #1 WEEKS DAYS / WEEK HOURS / DAY HH ROSTER ID CODE #2 WEEKS DAYS / WEEK LA N D P R EP A R A T ION A N D P LA N T IN G HOURS / DAY HH ROSTER ID CODE #3 WEEKS DAYS / WEEK HOURS / DAY HH ROSTER ID CODE #4 WEEKS DAYS / WEEK HOURS / DAY

43 Challenges: Measuring labor in farm households in Africa What is the truth? Need a benchmark/gold standard The price of getting the truth Alternatives that are feasible for large household survey efforts

44 Measuring labor in farm households in Africa Musoma District in the Mara region, Tanzania A district with high prevalence of maize 18 EAs 36 households per EA

45 Measuring labor in farm households in Africa Three alternative survey designs: Control: Standard agricultural labor module labor reported in the aggregate by recall for the entire season Truth Treatment: Weekly interviews for labor module for the duration of the main season. Practical Treatment: Weekly phone surveys for labor module for the duration of the main season.

46 Measuring labor in farm households in Africa Fieldwork Challenges: Some normal Mobile phones (charger, coverage) Measuring plot size Some linked to field work during the ag season Transport during rainy season Adjusted interview times to accommodate household labor activities Uncertainty of start of season (determined by rains) Some quite unexpected Free Mason panic in Mara region

47 Measuring labor in farm households in Africa Punchline TBD Analysis is underway!

48 Methodological Research Agricultural Land Other Areas Agricultural Production Issues of recall Food eaten away from home Consumption module design (x2) Household Consumption

49 Motivation Consumption Research Consumption expenditures are the core indicator to measure poverty, inequality and living standards in low income countries large national representative surveys used to compare welfare over time or across countries: Different countries use different methods Even within countries survey design changes over time Trends in poverty over time, or differences across countries could be due to differences in design, rather than reflecting any true change.

50 Challenges in Measuring Household Consumption Wide variety of consumption modules Differences across countries and over time Differences and weaknesses along many dimensions Diary vs. recall Length of reference period Specificity of list Nomenclature; Open vs. close; Prompting Consumption vs. acquisition Household vs. individual Non-standard units of measure Food consumed away from home (FAFH) Valuation of consumed own-production Method does matter and lack of standards results in poor/noncomparable data

51 Some previous attempts to assess differences Papua New Guinea: Diaries result in 26% more food consumption El Salvador: Long recall list results in 31% more consumption than shorter aggregated list Indonesia: Long recall yields 20% more consumption but no re-ranking of households Ghana: For every day added to recall period, total purchases fall by 2.9%, plateau of 20-25% loss Russia: Individual diaries gave 6-11% higher expenditure than a household diary 51

52 Testing Consumption Measures in Tanzania Compare benchmark (personal diary) with 7 alternatives: 2 household diaries, 5 recall modules Module Consumption measurement 1 Long list (58 items) 14 day 2 Long list (58 items) 7 day 3 Subset list (17 items) 7 day 4 Collapsed list (11 items) 7 day 5 Long list (58 items) Usual 12 month 6 HH diary with frequent visits 7 HH diary with infrequent visits (by literacy status) 8 Personal diary with daily visits 52

53 Testing Consumption Measures in Tanzania SHWALITA: Survey of Household Welfare and Labour in Tanzania Tanzania: 168 villages, in 7 districts Mix urban/rural EAs; agro-climactic zones Per EA: randomly sample 3 HHs for each module, i.e. N=500 for each of 8 modules 53

54 Testing Consumption Measures in Tanzania Regressions of per capita consumption expenditure Personal diary omitted C ik = β k M k + e ik (1) Ln total 1. Recall: Long, 14 day *** 2. Recall: Long, 7 day Recall: Subset, 7 day * 4. Recall: Collapse, 7 day *** 5. Recall: Long usual 12 month *** 6. Diary: HH, frequent *** 7. Diary: HH, infrequent *** Number of households 4,025 54

55 Testing Consumption Measures in Tanzania Isolating effect of Recall Period & Length of List (per capita consumption) (1) Long, 7 day omitted Ln total Length of recall period 1.Long, 14 day *** (0.037) 5.Usual 12 month *** (0.037) Number of households 1,511 Length of list 3.Subset, 7 day (0.036) 4.Collapse, 7 day *** (0.036) Number of households 1,512 55

56 Testing Consumption Measures in Tanzania There are important differences in means, but these may not result in: Differences in inequality Differences in poverty 56

57 Testing Consumption Measures in Tanzania Gini coefficient Total per capita 1. Long 14 day 0.512*** 2. Long 7 day Subset 7 day Collapse 7 day Long Usual 12 month 0.537*** 6. HH diary Frequent HH diary Infrequent 0.419* 8. Personal diary

58 Testing Consumption Measures in Tanzania Poverty headcount rate (%) at $1.25/person/day by module type Recall: Long, 14 day 2. Recall: Long, 7 day 3. Recall: Subset, 7 day 4. Recall: Collapse, 7 day 5. Recall: Long Usual 12 month 6. Diary: HH, Frequent 7. Diary: HH, Infrequent 8. Diary: Personal 58

59 Testing Consumption Measures in Tanzania Hunger Prevalence: (% of people in households with energy availability less than energy requirements) 80 0 Long list, 14 day Long list, 7day Subset list, 7day Usual month Diary, HH freq Diary, HH infreq Diary, Indiv

60 Minutes to complete consumption section Testing Consumption Measures in Tanzania COSTS: Personal diaries are 9 times more expensive than recall Household diaries are 3-7 times more expensive (depending on degree of supervision) Recall is cheaper, but which module to choose?

61 Testing Consumption Measures in Tanzania Recommendations & key take-aways: usual month or collapsed list approach (modules 4 & 5) perform badly (high cognitive demands) The long list, 14 day recall has much lower mean, higher inequality and higher poverty. All considered: Tie between 7 day recall with full or subset list (modules 2 or 3). Time stamps tell us that reducing the list saves only 8 minutes of interview time: doesn t seem worth loss in detail 61

62 Testing Consumption Measures in Tanzania All Questionnaire modules & diaries are available at: Also, see: Beegle, K, J. De Weerdt, J. Friedman and J. Gibson Methods of Household Consumption Measurement through Surveys: Experimental Results from Tanzania. Journal of Development Economics 98:

63 Indonesian SUSENAS & LSMS LSMS team is currently engaged with BPS to analyze the consumption module(s) of the SUSENAS Historically, SUSENAS has: Long Version: o 229 food items with 6 questions each o Ideally took 2 hours, in reality, it took minutes average o Conducted 1x/3 years to 2005, 1x/year to 2010, 4x/year current o Usage: Poverty Line, pce/poverty head count Short Version: o 21 food buckets with 1 question asking total expenses o Took 30 minutes in average o Conducted 1x/year until 2010 o Usage: pce/poverty head count Task: improve the design of the consumption module while minimizing damage to comparability over time.

64 Indonesian SUSENAS & LSMS 6 randomized groups Gold standard: 7-day diary, daily visits Status quo (1 visit) Status quo with prompting (1 visit) Shorter list (1 visit) Shortest list (1 visit) Shorter list, with bounding (2 visits) Funding from WB and BPS Fieldwork, using dedicated teams, in Sept-Oct, 2014

65 Improving FAFH in Peru: Establishment survey and lab analysis Food away from home is growing in the developing world, but very little information is collected in household surveys Incidence in Peru Source: ENAHO Household-level statistics

66 Improving FAFH in Peru We know 44% of households eat at least 1 meal outside of home 4 meals per week Type of establishment Type of meal (breakfast, lunch, dinner) Cost We DO NOT know Meal contents Caloric/nutritional information of meals

67 Improving FAFH in Peru Sample design Metropolitan Lima and Callao (48 districts) Representative survey of almost 1800 formal food establishments Stratified by 5 socio-economic strata Implementation Phase 1 (DONE) Identify 3 most frequently consumed meals per establishment For each: collect detailed information on ingredients Phase 2 (DONE) Laboratory analysis - caloric and nutritional information for top 50 menus (10 per strata same menus)

68 Improving FAFH in Peru Implementation (cont d) Phase 3 (UNDERWAY) Poverty analysis Calculate unit caloric cost of each meal/item Poverty simulations - poverty lines, consumption, poverty estimates Nutritional analysis By strata (are all meals created equal?) Eating-in vis a vis Eating-out Phase 4: Feeding back to the ENAHO and extensions Updated ENAHO survey module

69 Improving FAFH in Peru Although analysis is still underway, preliminary results suggest that after including FAFH roughly 20% of the at-home poor leave poverty

70 Methodological Research Respondent selection in asset identification Skills Measurement CAPI Other Areas Agricultural Land Agricultural Production Household Consumption

71 Skills Measurement: Kenya Objective Develop and validate methods for the measurement of functioning and skills in agriculture Methodologies tested: Cognitive - Raven - Forward and backward digit span - Math exercises - Reading test Partnerships Paris School of Economics (Karen Macours & Rachid Laajaj) Non-cognitive Technical Skills - Entrepreneurship (initiative, risk taking, tenacity, aspiration) - The Big Five - Mental Health and Self esteem (CESD) - Knowledge test - Self-assessment - What farmers do Adapted to local inputs and outputs with local expert(s). Status Fieldwork ongoing since January 2014 Data analysis underway

72 Skills Measurement: Kenya Timeline: Pilot November 2013 Training January 2014 Fieldwork January March 2014 Analysis April present 900 farmers 96 villages 16 sub-locations Siaya province in Western Kenya Quantitative pilot was programmed in Blaise; piloted on 120 farmers Two visits: January and March 2014 Sample equally divided between women and men

73 Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA) Survey experiment implemented in collaboration with the UN Evidence and Data for Gender Equality (EDGE) initiative & Uganda Bureau of Statistics Dual objective Provide an assessment of the effects of respondent selection decisions on the analysis of individual asset ownership & control To inform the 2015 operations in EDGE pilot countries & the UN guidelines on measuring individual asset ownership & control

74 Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA) 140 Enumeration Areas (EAs) selected with probability proportional to size across Uganda Actual EAs Visited: 137 Rural/Urban EA Split: 60/40 percent HH listing in each EA for random selection of sample HHs 20 HHs randomly selected in each EA, 4 randomly allocated to each treatment arm in each EA prior to field work

75 Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA) Treatment Arms: Arm Who? How? What? 1 Most Knowledgeable Household Member 2 Randomly Selected Member of Principal Couple Alone Alone Assets Owned Exclusively/ Jointly by Household Members Assets Owned Exclusively/ Jointly by Household Members 3 Principal Couple Together Assets Owned Exclusively/ Jointly by Household Members 4 Adult (18+) Household Members 5 Adult (18+) Household Members Alone, Simultaneous Alone, Simultaneous Assets Owned Exclusively/ Jointly by Household Members Assets Owned Exclusively/ Jointly by Respondent

76 Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA) Scope of data collection: (Individual-Level) Demographics Core Assets Dwelling & Residential Land Agricultural Land Non-Agricultural Land & Other Real Estate Livestock Non-Farm Enterprises & Assets Agricultural Equipment Consumer Durables Financial Assets & Liabilities Valuables

77 Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA) Implementing agency: Uganda Bureau of Statistics (UBoS) Implementation period: March-August 2014 Use of Android tablets & computer-assisted personal interviewing (CAPI) application designed in Survey Solutions 7 mobile field teams Arms 4 & 5 interviews attempted to be conducted simultaneously Female (male) respondents attempted to be matched with female (male) enumerators

78 Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA) Stay tuned Findings will be presented at the Kitakyushu Forum on Asian Women in Japan December 3-6, 2014.

79 Computer-Assisted Personal Interviewing Software Development SURVEY SOLUTIONS Objectives Develop a CAPI application suitable for collecting agricultural data as part of a complex integrated household survey Partnerships Computational Tools Unit, Development Research Group, The World Bank (DECCT) Food and Agricultural Organization of the United Nations Status Beta version released in September 2013 Survey Solutions 3.0 released Nov 10 th, 2014 New and improved features under development

80 Computer-Assisted Personal Interviewing Software Development SURVEY SOLUTIONS Package Elements Designer, a web-based console for constructing CAPI questionnaires Interviewer, an Android-based application for enumerators to collect data from assigned respondents Supervisor, an interface for supervisors to assign respondents and to review completed questionnaires Headquarters, a portal for aggregating survey data and reporting on survey progress. Unique features include: Survey management system Fluid questionnaire navigation Intuitive toolkit for collaborative questionnaire development Join Arthur Shaw this afternoon for a hands-on tutorial of Survey Solutions

81 Methodological Research Agricultural Land Other Areas Agricultural Production Household Consumption

82 In the pipeline Uganda: Crop cutting - Selected districts in Eastern Region soils, maize crop-cutting on pure and intercropped plots, variety identification use of high resolution remote sensing imagery for land area measurement and and yield estimation Malawi: Cassava Production & Productivity Fieldwork: Nov/Dec 2014 Oct/Nov 2015 Zanzibar, Tanzania: Measurement of Banana Production Water quality in partnership with WHO (?) Future research could include Rainfall measurement Validation of big data

83 Thank you! The search for objective measurements that are both scalable and more in line with gold standard precision continues For further information, please visit worldbank.org/lsms or contact me at

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