Living Standards Measurement Study Integrated Surveys on Agriculture: Main Features, Challenges and Next Steps

Transcription

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) RakaBanerjee (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 Computer Assisted Personal Interviews (CAPI) Open data access policy Micro-data publicly available within 12 months of data collection 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/ / /13 Uganda 2009/ / / /14 Malawi 2010/ /13 Nigeria 2010/ /13 Mali 2012/ /15 Ethiopia 2011/ /14 Niger 2011/ /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 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 Why is it important? Fundamental component of agricultural statistics Priority #1 of Global Strategy Current methods inaccurate or impractical What are the different methods available? Traversing (compass and rope method) P 2 /A Self-reporting GPS

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 Deciles Area of HH landholding Nb. of plots per hh Mean farm area using GPS Mean farm area using Self-Reported Farm Discrepancy (GPS-Self Reported) Discrepancy in % terms % % % % % % % % % % Total %

18 Systematic bias in reporting Acres Land Deciles (GPS)

19 Yields and farmsize Landholding Average land areas Yield (GPS) Yield (Self Reported) Bias in yield (GPS-Self Reported) Acres Acres Kg/acres Kg/acres % Small Farms % Medium Farms % Large Farms %

20 The models Plot level: Discrepancy in measurement HH head characteristics Plot size, squared Control for rounding Involvement in disputes, Slope, PSU dummies e = bx + j j u j 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 Yi ln A i = b0 + b1 ln Ai + b2 X i + b3 R + u i

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

22 Smallholders are more efficient however you measure it UGANDA : Inverse Farm Size Productivity Relationship Yield Deciles of Land Cultivated Land Self-Reported Land GPS

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 Why is it important? Fundamental driver of productivity in Africa remains a key unobserved variable for analysis What are the different methods available? Conventional soil analysis (CSA) Farmers subjective evaluations Spectral soil analysis (SSA)

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 Why is it important? Continuously / extended harvest crops are major staple crops in many African countries Inaccuracy of recall May extend across seasons What are the different methods available? Recall (6 month or 12 month) Crop cards (with crop card monitors) Ongoing work with the Uganda Bureau of Statistics CATI (data collection / supervision)

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

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

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 Non-standard units (cont d) 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

35 Non-standard units (cont d) State of crop 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.

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

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 V2 V3 V4 Assumption Assumption Assumption Preferred 100 to Head 50/50 Split a la HH Child: Male ** Woman's Share of Household Income x Male Child *** Observations 2,522 2,522 2,522 2,522 R note: *** p<0.01, ** p<0.05, * p<0.1

42 Concurrent Data Entry The tale of the missing plot measurements Missing Plot Measurements Percent of Plots 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% High initial rates of missing gps data in months 1 & Month

43 Concurrent Date Entry (cont d) The tale of the missing plot measurements Missing Plot Measurements 10.0% Percent of Plots 8.0% 6.0% 4.0% 2.0% Intervention - High rate of missing data observed and new instructions to field disseminated. 0.0% Month

44 Concurrent Data Entry The tale of the missing plot measurements >> Missing Plot Measurements Percent of Plots 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Substantial decrease in missing data. Because of revisit of households in month 4-6, part of the missing data was now captured Month

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 Dissemination Strategy NOT proposing to disseminate actual household GPS coordinates ARE proposing to disseminate modified EA centerpoints, 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

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