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Living Standards Measurement Study – Integrated Surveys on Agriculture: Main Features, Challenges and Next Steps Gero Carletto Development Research Group.

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Presentation on theme: "Living Standards Measurement Study – Integrated Surveys on Agriculture: Main Features, Challenges and Next Steps Gero Carletto Development Research Group."— Presentation transcript:

1 Living Standards Measurement Study – Integrated Surveys on Agriculture: Main Features, Challenges and Next Steps Gero Carletto Development Research Group The World Bank February 27th, 2012

2 Outline The Living Standards Measurement Study The LSMS-ISA project – Main features – Progress to date Methodological validation/research Challenges Next steps

3 The LSMS Flagship initiative in DECRG since 1980 Evolution … – Poverty monitoring and measurement: the “McNamara anecdote” – Technical assistance, capacity building – Back to the “roots”: “wholesale” research – Focus on agriculture: LSMS-ISA

4 The LSMS-ISA project Started in early 2009 Tanzania pilot in mid-2008 Funding from the Bill and Melinda Gates Foundation, with additional funding from USAID, DFID and a number of other sources, including governments Working on four fronts: Household survey data collection Methodological validation/research/tool development Capacity Building Dissemination

5 The team Gero Carletto Luc Christiaensen (from Nov 2011) Kinnon Scott (on DAIS until Sept. 2012) Kathleen Beegle (WDR until June 2012) Diane Steele (LSMS Database coordinator) Talip Kilic Kristen Himelein (sampling) Mimi Oseni (50% with AFTAR) Alberto Zezza (50% with LDIA project) Siobhan Murray (GIS; part-time) Raka Banerjee (ETC; project coordinator) Jon Kastelic (formerly in Malawi; data processing) Prospere Backiny-Yetna (in Mali; formerly in Niger) Bjorn Campenhout (in Uganda, 50% with IFPRI) Colin Williams (formerly in Nigeria, now on STC)

6 Main Features 6+ year program ( ) Panel household surveys with emphasis on agriculture in 7 Sub-Saharan African countries Sample: 3-5,000 households – Population-based frame (Global Strategy) – Representative at national- and few sub-national levels Tracking – Movers – Subsample of individual split-offs

7 Main Features (cont’d) Multi-faceted “integration” – Multi-topic survey instrument Farm plus nonfarm, consumption, nutrition, inter alia – Build on existing/planned surveys country ownership Sustainability … but large trade-offs – Link to major initiatives National Strategy for the Development of Statistics (NSDS) Global Strategy to Improve Agricultural and Rural Statistics – Improved links to other data sources GIS, censuses, surveys, etc.

8 Main Features (cont’d) Gender-disaggregated data Use of technology – GPS for households and plots (area) – Concurrent field-based data entry Concurrent field-based data entry – Computer Assisted Personal Interviews (CAPI) Open data access policy – Micro-data publicly available within 12 months of data collection – GPS data dissemination GPS data dissemination

9 Main Features (cont’d) Inter-institutional partnerships – In-country Government (NSO, MoA, other line ministries) WB country offices Other development partners – Thematic GPS and crop production: FAO (GS Action Plan) Livestock: WB-ARD/ILRI/FAO Food Security: WFP Empowerment: IFAD Agricultural Income: FAO/RIGA Climate Change: WB/ENV Fishery: WorldFish Centre Analysis: IFPRI/Harvest Choice, IFAD CGIAR: technology adoption

10 Surveys  Tanzania National Panel Survey  Uganda National Panel Survey  Malawi Integrated Panel Household Survey  Nigeria General Household Survey Panel:  Niger Enquête National sur les Conditions de Vie Des Ménages  Ethiopia Rural Socio-Economic Survey  Mali Integrated Agricultural Survey

11 Survey Schedule Tanzania 2008/092010/112012/13 Uganda 2009/102010/112011/122013/14 Malawi 2010/ /13 Nigeria 2010/ /13 Mali 2012/132014/15 Ethiopia 2011/122013/14 Niger 2011/122013/14

12 Data release Tanzania X X X Uganda X X X Malawi X X Nigeria O O X Niger X X Ethiopia X X Mali X X

13 Project outputs Documented public-access microdata Sourcebooks, best practice guidelines Tools – Computer Assisted Personal Interviews (CAPI) software Computer Assisted Personal Interviews (CAPI) software – ADePT-Agriculture and ADePT-Livestock – Comparative Living Standards Project (CLSP) Research and analysis for policy and programming Methodological validation – Research pillar of Action Plan of Global Strategy – DFID grant

14 Experiments on Survey Methodology Identification process  Alignment with Global Strategy  Field experience  Consultation  Peer review Research topics  Land area measurement  Soil fertility  Water resources  Labor input in agriculture  Continuous/extended harvest crops  Non-Standard Units  Livestock  Post-harvest losses

15 Land Area Measurement

16 Impact of GPS on IR  Old controversy in development economics, with recent reprise  Friends: Barrett (1996), Benjamin and Brandt (2002), … Binswanger, Pingali, …  Foes: Bhalla and Roy (1988); Benjamin (1995); Collier and Dercon (2009), …\  Binswanger et al (1995); Eastwood et al. (2010).  Possible explanations  Factor market imperfections  Omitted variables (land quality)  Measurement errors  Low correlation bet/w measurements (Udry and Goldstein, 1999)  Recent findings:  Lamb (2003)  Factor market imperfections and land quality differences  Barrett et al (2010)  Lab soil test. Marginal impact of land quality. Measurement error?  De Groote and Traore (2005)  Systematic bias in reporting (small over-report)  Objective: Test robustness of IR to land measurement error

17 Systematic bias in reporting

18 Land Deciles (GPS) Acres Systematic bias in reporting

19 Yields and farmsize

20 The models  Plot level: Discrepancy in measurement  HH head characteristics  Plot size, squared  Control for rounding  Involvement in disputes, Slope, PSU dummies  Household level: Farm profits/acre  Farm size  HH char’s (age, edu, gender)  Inputs:  Family labor (interacted with land)  Hired labor  Variable inputs  Land quality (soil, slope, irrigation)  PSU dummies

21 Main findings  Plot level: Small farmers over-report  Household level: IR strengthened by GPS measurement Area Self-ReportedArea GPS Log Land Size-0.62***-0.83*** [0.000]

22 Smallholders are more efficient however you measure it

23 Experiments on Survey Methodology Research topics  Land area measurement  Soil fertility  Water resources  Labor input in agriculture  Continuous/extended harvest crops  Non-Standard Units  Livestock  Post-harvest losses

24 Soil Fertility

25 Water Resources Why is it important? Agriculture in Africa is predominantly rainfed – water is a key input to production Large discrepancies across data sources Low resolution/idiosyncratic risk Lack of access to data What are the different methods available? Satellite imagery Weather stations Self-reporting Community rain gauges

26 Labor Inputs Why is it important? Labor inputs are fundamental to labor productivity measurement Very poorly measured What are the different methods available? Recall (6 month or 12 month, by activity, by demographics) Computer-assisted telephone interviews (CATI) Labor input diaries

27 Experiments on Survey Methodology Research topics  Land area measurement  Soil fertility  Water resources  Labor input in agriculture  Continuous/extended harvest crops  Non-Standard Units  Livestock  Post-harvest losses

28 Extended Harvest Crops

29 Diary vs. recall (Uganda) Compare recall and diary methods for crop production estimates (and consumption from own production) in Uganda Lack of gold standard – “well-administered” diary? – Crop cutting? Does it vary by crop type? – Extended-harvest crops » Cassava » Banana

30 Frequencies of crops reported RecallDiary % Coffee Maize Cassava Tomatoes Avocado Diary vs. recall (Uganda) (cont’d)

31 Output values (usd) RecallDiary Consumption plus sales Ratios (1) (2)(3) (2)/(1) (3)/(1) Cash crops Food crops seasonal Food crops continuous Fruit & Vegetables Total Diary vs. recall (Uganda) (cont’d)

32 Experiments on Survey Methodology Research topics  Land area measurement  Soil fertility  Water resources  Labor input in agriculture  Continuous/extended harvest crops  Non-Standard Units  Livestock  Post-harvest losses

33 Non-Standard Units 33

34 Weight in kgs of a 50 kg sack Maize 50.0 Cassava 41.7 Sweet potato 36.2 Irish potato 42.6 Groundnut 44.2 Ground bean 43.2 Rice 56.2 Finger millet 50.5 Sorghum 49.6 Pearl millet 50.5 Bean 77.6 Soyabean 53.1 Pigeonpea 57.1 Non-standard units (cont’d)

35 Maize: A 50 kg size sac weighs only 29.0 kilograms when it is filled with fresh maize. This translates into 14.8 kgs of maize grains. Cassava: A 50 kg size sac weighs 37.8 kilograms when it is filled with dried cassava. To attain these 37.8 kgs of dried cassava, one would have had to start with 108 kgs of fresh cassava. Rice: A 50 kg size sac weighs 38.5 kgs when filled with rice which has not been husked. These 38.5 kgs translate into 24.3 kgs of grain when husked. State of crop …

36 Land area by zone (Nigeria) Zone Specific Conversion Factors into Hectares Zone Conversion Factor HeapsRidgesStands Non-standard units (cont’d)

37 Challenges Integration comes with trade-offs Data preparation and data release – Time consuming and not enough “control” Resident advisor in bet/w years – Dissemination of GPS information protocol Sample – Too small? Representative at crop level? Livestock? – Linking to Ag census and large Ag surveys CAPI – More sustainable solution Frequency of surveys – Moving to every other year Tracking – Still a challenge in some countries

38 Next steps More tools? – Mobile phones Ag input/labor use, climate data; Listening to Africa More countries ? – in Africa: Burkina Faso – beyond Africa? More analysis Gender and Agriculture Agriculture and Nutrition Myths and Facts in African Agriculture

39

40 The End

41 Is women’s control of income important for child nutrition? Dependent Variable: Z-Score of Height-for-Age Definitions of Woman’s Share of Household Income V1 Assumption 100 to Head V2 Assumption 50/50 Split V3 Assumption a la HH V4 Preferred Child: Male ** Woman's Share of Household Income x Male Child *** Observations2,522 R note: *** p<0.01, ** p<0.05, * p<0.1 >>

42 Concurrent Data Entry The tale of the missing plot measurements High initial rates of missing gps data in months 1 & 2

43 Concurrent Date Entry ( cont’d) The tale of the missing plot measurements Intervention - High rate of missing data observed and new instructions to field disseminated.

44 Concurrent Data Entry The tale of the missing plot measurements Substantial decrease in missing data. Because of revisit of households in month 4-6, part of the missing data was now captured. >>

45 CAPI “Comparative Assessment of Software Programs for the Development of Computer- Assisted Personal Interview (CAPI) Applications”, with the University of Maryland IRIS Center Implementation in LSMS-ISA partner countries relying on readily available commercial software products Plans to create a public, freely available CAPI software package >>

46 GPS: What we are collecting Household location with current devices coordinate precision is within 5-10m (atmospheric interference, vegetation canopy, buildings.. may effect precision )

47 GPS: What we are collecting Plot area Plot location Plot outline

48 NOT proposing to disseminate actual household GPS coordinates ARE proposing to disseminate modified EA center- points, offset to prevent identification of communities AND a set of geovariables generated using the true locations true household GPS locations maintained in the Statistics Office, to be used for continuation of the panel or at the discretion of the NSO Dissemination Strategy

49 Distance HH to Plot HH to Market HH to Major Road Dissemination: geovariables Environmental Climatology Landscape typology Soil Elevation Terrain Time Series Rainfall Vegetation Indices >>


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