![Rural-urban linkages
1) Dynamics in farming systems
2) Land tenure changes
3) Emergence of labour market
4) Dynamism in rural – urban linkages
5) Changes in consumption patterns
Trends in the expansion of rice processing industry [number of rice
processors] in Fogera area
1 1 2 2 3
8 9 11 13 16
36 40 44
50
57
64
69
83
97
123
0
20
40
60
80
100
120
140
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Numerofoperationalprocessors
Year](https://image.slidesharecdn.com/thompsonlongitudinalpanelandcross-sectionalstudiesindevelopmentresearchv3plaasjul19-190806111931/85/Analysing-the-Dynamics-of-Change-Using-longitudinal-panel-and-cross-sectional-studies-in-development-research-44-320.jpg)
Analysing the Dynamics of Change: Using longitudinal, panel and cross sectional studies in development research.
- 1. John Thompson - j.thompson@ids.ac.uk Future Agricultures Consortium / APRA CEO Senior Research Fellow, Institute of Development Studies, UK Seminar for Institute for Poverty, Land and Agrarian Studies, University of the Western Cape, South Africa 30 July 2019 Analysing the Dynamics of Change: Using longitudinal, panel and cross-sectional studies to investigate complex social, environmental and technological processes Lessons from Future Agricultures and the APRA Programme
- 2. Focus of presentation • Introduce 3 types of studies that examine dynamic social, environmental and technological change over time at individual, household and community level: 1. Longitudinal 2. Panel 3. Cross-sectional studies • Case studies from Future Agriculture’s APRA Programme – collaborative, multi-country, mixed-methods (quant-qual) studies – tracing change over time and highlighting the differential outcomes on local livelihoods • Compare the basic elements of each approach and highlight their strengths and weaknesses
- 3. What are longitudinal studies? Observational studies that follow the same subjects (‘study participants’) repeatedly over a period of time • Some longitudinal studies (LS) track the same people throughout their lives – e.g. from birth to old age • Some follow individuals, while others follow households or social groups, or even organisations • Some will also compare more than one case / location / context (multi-locational or multi-sited studies)
- 4. Why conduct longitudinal studies? To understand… • how key life transitions change the course of a person’s life (e.g. marriage or divorce, or entering or retiring from the labour market) • how earlier life circumstances affect later life (e.g. exposure to disease, education, wealth) • how patterns of behaviour change as people grow older • how different areas of lives are linked (e.g. health, wealth, family, education, employment and social attitudes) • how different aspects of life vary for different people • how human-environment interactions change over space and time
- 5. Common types of longitudinal studies 1. Cohort studies: chart the lives of groups of individuals who experience the same life events in a given period 2. Panel studies: follow the same households or individuals over time – e.g. the World Bank’s Living Standards Measurement Study (LSMS), USAID’s Demographic and Health Surveys (DHS), UNICEF’s Multiple Indicator Cluster Surveys 3. Tracker studies: follow the same households or individuals over time, but track the ‘movers’ to other locations to reduce attrition rates (‘drop outs’)
- 6. Cohort studies • Cohort studies chart the lives of groups of individuals who experience the same life events within a given time period • Most are birth cohort studies which follow groups of people born within a specific time period Richter, et al. (2007) Cohort Profile: Mandela's children: The 1990 birth to twenty study in South Africa. International Journal of Epidemiology 36(3): 504–511 https://doi.org/10.1093/ije/dym016 • Largest cohort study in Africa is the ‘Birth to Twenty Study’ (BT20) – began in 1990 and tracked a cohort of over 3,000 children born in the weeks following Nelson Mandela's release from prison (‘Mandela’s children’)
- 7. Panel studies • Panel studies follow the same individuals over time – over a number of ‘waves’ – and vary considerably in scale and scope • ‘Snapshot’ vs. ‘Film strip’ – showing how individuals or households changed over time • In a household panel study, one or more members of the household participate in the study over two or more waves • Can also be used to study changes in organisations and institutions
- 8. Tracker studies • People who move from their home areas are unlikely to be a random subset of the baseline respondents • They are likely to have certain characteristics that differ from those who remain • Tracker studies follow these ‘movers’ – as well as the ‘remainers’ • This can help to minimise the ‘attrition’ rate and address potential selectivity biases
- 9. Tracker studies Tracking: following respondents wherever they move… Wave 1 • Josiah (male head of household) • Gloria (female head of household) • Mary (daughter 1) • Thomas (son 1) • Hannington (son 2) • Eliud (male elder) • Josiah (male head of household) • Gloria (female head of household) • Mary (daughter 1) – left household • Thomas (son 1) – left household • Hannington (son 2) • Eliud (male elder) – deceased • Andrew (male head of household) • Mary (female head of household) • Charity (daughter 1) • Japhet (son 1) Wave 2 • Thomas (male head of household) HH 1, Village 1 HH2, Village 2 HH1, Village 1 HH3, Town 1
- 10. Cross-sectional studies • Cross-sectional studies – can compare different samples at a specific point in time – thus they provide a ‘snapshot’ • Multiple variables can be studied – e.g. age, gender, ethnicity, income, education, etc. • They are typically quicker and cheaper to conduct than multi- wave longitudinal panels • Limits of CS studies – harder to pin down clear cause-and- effect relationships
- 11. Prospective vs. retrospective studies • Prospective studies – follow individuals over time (i.e. into the future) and data about them is collected as their characteristics or circumstances change • Panel studies and birth cohort studies are good examples start
- 12. Prospective vs. retrospective studies • Retrospective studies – select from a sample of individuals and information is collected about their past • Recall studies have significant limitations and therefore require extra rigour (cross-checking/triangulation) to confirm statements by informants start
- 13. Prospective vs. retrospective studies • In reality, many studies use both prospective and retrospective methods • In birth cohort studies, participants are often asked to retrospectively provide information on their lives since the previous interview • In household panel and cross-sectional studies often collect an array of retrospective information about past events start
- 14. Combining elements over time 6 Year Olds 8 Year Olds 12 Year Olds10 Year Olds 10 Year Olds8 Year Olds Yearofbirth 2008 2006 2014 2016 2018 Cohort Comparisons Cross-Sectional Comparisons Longitudinal Comparisons
- 15. Funded by UK aid from the UK Government www.future-agricultures.org/apra A Brief Overview of the Programme’s Aims and Approach APRA: Agricultural Policy Research in Africa: Analysing Pathways to Commercialisation
- 16. Pathways to ‘inclusive’ commercialisation • Led by Future Agricultures Consortium (FAC) • Five-year grant from UK DFID to establish APRA – Agricultural Policy Research for Africa • Focus: analysing the differential impacts of agricultural commercialisation on local livelihoods and rural economies – in 8 countries • APRA involves large network of existing FAC partners, including PLAAS and IDS – plus new collaborators in Africa, UK, Sweden and USA (100+ contracted researchers)
- 17. A typology of agricultural commercialisation 1. Large-Scale Estate 2. Medium-Scale Commercial 4. Small-Scale Independent3. Outgrower/Contract
- 18. 1. ‘Stepping in’ – returning / moving into commercial agriculture from a non-farm base; rise of medium-scale ‘investor’ farmers 2. ‘Stepping out’ – accumulating, diversifying and creating alternative, non-farm economic activities 3. ‘Stepping up’ – improving and investing in existing agricultural activities and engaging in the market 4. ‘Hanging in’ – maintaining subsistence level 5. ‘Dropping out’ – moving out of productive agriculture or slipping into destitution due to shocks and stresses Analysing 5 livelihood trajectories through agricultural commercialisation
- 19. Funded by UK aid from the UK Government www.future-agricultures.org/apra Early Insights from the First Round of a Multi-Country Panel Study of Agricultural Commercialisation in Sub-Saharan Africa Amrita Saha, Rachel Sabates-Wheeler and John Thompson Agricultural Policy Research in Africa (APRA) Programme Future Agricultures Consortium
- 20. APRA panel studies Panel Studies of different commercialisation pathways and people’s ‘selection choices’ over two waves • Combining quantitative and qualitative elements • Ghana – oil palm commercialisation models • Tanzania – rice and maize commercialisation • Nigeria – role of medium scale commercial producers and interactions with smallholders • Zimbabwe – tobacco and maize commercialisation channels
- 21. APRA panel studies • Mixed methods research carried out in Ghana, Nigeria, Tanzania and Zimbabwe, each comprising representative data on highly commercialised areas (‘hot spots’) • Panel survey – first wave conducted in 2017-18 • A total of 3,993 farmer households interviewed • Farmers in each country grow a variety of different crops, e.g. maize, oil palm, rice, tobacco, mostly for commercial sales, and some home consumption • A second wave is planned for 2019-2020 42%22% 12% 17%
- 22. APRA livelihood outcome indicators Analysing livelihood outcomes indicators resulting from engagement with the different commercialisation types in 5 key areas: 1. Agricultural commercialisation choices 2. Women’s empowerment 3. Labour and employment 4. Food and nutrition security 5. Poverty and inequality
- 23. APRA panel research process Purposively selected study areas where farmers were increasingly engaging with markets • Used Computer-assisted personal interviewing (CAPI) interviewing technique – WB’s ‘Survey Solutions’ • Structured core questionnaire + country-specific modules for randomly selected households on: plots cultivated, crops grown and sold, and incomes calculated ‘household commercialisation index’ (HCI) • Plus data on the other APRA livelihood outcome indicators • Combined with secondary data and in-depth qualitative research Some emerging insights from the analysis of the poverty and inequality indicator data – but only preliminary findings
- 24. Agricultural commercialisation & income poverty Ghana Note: Based on adult equivalised total net cash income from crops and non-farm income. 500 10001500200025003000 lowessEquiv.incomeHCI 20 40 60 80 100 HCI 120013001400150016001700 lowessEquiv.incomeHCI 0 20 40 60 80 100 HCI Zimbabwe 0 200000400000600000800000 lowessEquiv.IncomeHCI 0 20 40 60 80 100 HCI Nigeria 400000600000800000 10000001200000 lowessEquiv.IncomeHCI 0 20 40 60 80 100 HCI Tanzania • Nigeria & Tanzania: increasing relationship between HH equivalised income (agri+non- agri) and HCI • Ghana: Initially decreasing, then increasing up to a certain threshold • Zimbabwe: Initially increasing at a decreasing rate, then increasing at an increasing rate up to a threshold Lowess: Running locally weighted least squares regression (no control variables) increasing increasing increasing Increasing at decreasing rate Increasing at increasing rate Threshold? Threshold? Poverty & Household Commercialisation Index (HCI) decreasing
- 25. Agricultural commercialisation & income poverty Ghana Note: Based on adult equivalised total net cash income from crops and non-farm income. ZimbabweNigeria Tanzania • Tanzania & Nigeria: increasing relationship between HH equivalised income (agri+non- agri) and HCI • Ghana: Initially decreasing, then increasing up to a certain threshold • Zimbabwe: Initially increasing at a decreasing rate, then increasing at an increasing rate up to a threshold Lowess: Running locally weighted least squares regression (no control variables) 0 500 100015002000 0 20 40 60 80 100 HCI adult equivalent net income in 2011 PPP$ lowess aenyppp HCI 0 200000400000600000800000 0 20 40 60 80 100 HCI pp_income_equiv lowess pp_income_equiv HCI 0 10002000300040005000 20 40 60 80 100 HCI pp_income_equiv lowess pp_income_equiv HCI 0 500000 10000001500000 0 20 40 60 80 100 HCI HH Equiv. Income lowess HH Equiv. Income HCI Scatterplot for HH Equiv. Income
- 26. Agricultural commercialisation & inequality 0 .2.4.6.8 1 cumulativeoutcomeproportion 0 20 40 60 80 100 population percentage 1st quartile (Gini = .604) 2nd quartile (Gini = .5) 3rd quartile (Gini = .444) 4th quartile (Gini = .347) Ghana=0.52 0 .2.4.6.8 1 cumulativeoutcomeproportion 0 20 40 60 80 100 population percentage 1st quartile (Gini = .485) 2nd quartile (Gini = .336) 3rd quartile (Gini = .3) 4th quartile (Gini = .477) Nigeria=0.62 0 .2.4.6.8 1 cumulativeoutcomeproportion 0 20 40 60 80 100 population percentage 1st quartile (Gini = .509) 2nd quartile (Gini = .317) 3rd quartile (Gini = .292) 4th quartile (Gini = .462) Zimbabwe=0.54 0 .2.4.6.8 1 cumulativeoutcomeproportion 0 20 40 60 80 100 population percentage 1st quartile (Gini = .606) 2nd quartile (Gini = .504) 3rd quartile (Gini = .517) 4th quartile (Gini = .483) Tanzania=0.63 Nigeria & Zimbabwe: Most commercialised & least commercialised HHs with highest inequality Ghana & Tanzania: Least commercialised HHs are most unequal Note: Lorenz curves on a regular-grid of 20 equally spaced points across the population (plus a point at the origin) Inequality declines with Rise in commercialisation Inequality declines with Rise in commercialisation High inequality across commercialisation High inequality across commercialisation
- 27. Exploring the relationship between commercialisation & assets 5678 Commercialisation(LogSales) 0 .2 .4 .6 .8 1 lpoly smoothing grid 95% CI lpoly smooth Local polynomial for pooled data Local polynomial smooth 5.5 6 6.5 7 7.5 Commercailisation(Logsales) 0 .2 .4 .6 .8 1 lpoly smoothing grid 95% CI lpoly smooth local polynomial for pooled data Local polynomial smooth 5678 Commercialisation(LogSales) 0 .2 .4 .6 .8 1 lpoly smoothing grid 95% CI lpoly smooth Local polynomial for pooled data Local polynomial smooth 5.5 6 6.5 7 7.5 LogCommercialisation 0 .2 .4 .6 .8 1 lpoly smoothing grid 95% CI lpoly smooth local polynomial for pooled data Local polynomial smooth Pooling the four country samples and running a local polynomial non-parametric regression (without any control variables) a slightly upward gradient with commercialization (sales) emerges for the production equipment & consumer durables assets. Production Assets Consumer Durables Housing Services Positive correlation between ownership of consumer durables & production equipment with household’s commercialisation 0.44 0.26 0.45 0.59 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 GHANA TANZANIA NIGERIA ZIMBABWE All Assets Production Assets Consumer Durables Housing Services
- 28. Commercialisation, assets & subjective perceptions of poverty 0 .2.4.6 0 .2.4.6 0 5 10 15 0 5 10 15 Ghana Nigeria Tanzania Zimbabwe Richer Poorer kdensitylog_comm x Graphs by country HHs that perceive themselves as richer Ghana: comparable sales but marginally higher assets Tanzania: have only marginally higher sales and assets Nigeria: have only marginally higher sales and assets Zimbabwe: marginally higher sales but much higher assets 02460246 0 .5 1 0 .5 1 Ghana Nigeria Tanzania Zimbabwe Richer Poorer kdensitynormalAsset_Index x Graphs by country HHSalesHHAssetIndex -Richer than Average (Amongst the richest in the village; Richer than most households; The richest in the village) -Poorer than Average (A little poorer than most households; Amongst the poorest in the village; The poorest in the village) Only marginal differences in commercialisation with differences in subjective welfare outcomes; Differences in asset ownership by subjective welfare outcomes.
- 29. Observations • First wave gave us first impression of commercialisation choices and pathways, but not full picture of outcomes • Qualitative research was essential – used to conduct initial reconnaissance of sites and then to understand the changing commercialisation contexts • Aiming to conduct analyses of first wave data for all APRA outcome areas • While we are awaiting those results, we have commissioned a study of WB Living Standards Measurement Survey (LSMS) data to analyse the trends in commercialisation and its related outcomes in our APRA study countries • Now preparing for second wave, which should allow us to get a better picture of commercialisation choices and outcomes
- 30. Experiences conducting the first round panels • Implementing a multi-themed survey with numerous modules was technically challenging for the country teams • Large investments were made in supporting the teams to design the studies, collect the data and write-up their results – but still found capacity gaps – major resources and commitment required for backstopping! • Lots of pre-testing was done, but still there were issues – e.g. with confirming local units of measure, sales figures – test and test again! • Datasets were large and took months to clean, despite using CAPI approach - there are no shortcuts!
- 31. Longitudinal Study of Rice Commercialisation and Agrarian Change on the Fogera Plain of Ethiopia Dawit Alemu, Abebaw Asaye, Degu Addis, Tilahun Tadesse, Agajie Tesfaye, John Thompson, Rachel Sabates-Wheeler and Shewaye Abera Funded by UK aid from the UK Government www.future-agricultures.org/apra
- 32. APRA longitudinal studies Longitudinal Studies of pathways to agricultural commercialisation • 6 countries: Ethiopia, Ghana, Malawi, Nigeria, Tanzania and Zimbabwe • Analysing how different pathways of commercialisation evolve over time from a wider historical assessment of the dynamics of agrarian change • Examining how these dynamics influence the different livelihood trajectories of rural men and women in different contexts • Focusing on processes of ‘stepping up’ and ‘stepping out’, in particular
- 33. Rice commercialisation as a driver of change • Agricultural commercialisation is generally viewed as an essential part of structural transformation of the economy • Introduction of rice in 1980s changed Fogera Plain from one of the most food insecure areas of the country to a thriving commercialisation ‘hot spot’ • With increased commercialisation of rice, there have been diverse agricultural and socio-economic changes in the region – some negative, many positive
- 34. Fogera Plain Bahir Dar Fogera Plain Ethiopia Lake Tana
- 35. Methodology • Secondary data sources • Primary data: – 3 linked formal surveys – 3 rounds of KIIs and FGDs using participatory methods with stakeholders • Retrospective and prospective analysis
- 36. Surveys and sampling Survey Sample size Sampling method Smallholder rice producers 723 Stratified random samples Rural labourers 265 Stratified random samples Rice processors 123 Population census
- 37. Results and discussion 1) Dynamics in farming systems 2) Land tenure changes 3) Emergence of labour market 4) Dynamism in rural – urban linkages 5) Changes in consumption patterns
- 38. Farming system changes 1) Dynamics in farming systems 2) Land tenure changes 3) Emergence of labour market 4) Dynamism in rural – urban linkages 5) Changes in consumption patterns • The introduction and expansion of rice in the Fogera plain has brought considerable land use changes: – Shift towards to more commercially viable commodities like rice, pulses and vegetables – Decline in other cereals and livestock • Different trends in the two major rice agro-ecologies (lowland and upland systems) – but increasing rice production in both
- 39. Farming system changes Start of the fall of Derge Regime (1990s) Ten years ago (2000s) Current period (2017) Upland Lowland
- 40. Changes in land tenure 1) Dynamics in farming systems 2) Land tenure changes 3) Emergence of labour market 4) Dynamism in rural – urban linkages 5) Changes in consumption patterns • Increased rice commercialisation has been among the key factors behind the emergence of different forms of land transfers • Within the policy framework prevailing in the Amhara region, the land markets followed three approaches: 1) Land sharing 2) Leasing 3) Public land allocation • Prevalent land disputes over sharing and leasing have forced the regional government to establish mandatory registration of any land transfers at local level • The directive stipulates that rural land is restricted to agricultural use
- 41. Changes in land tenure 1) Dynamics in farming systems 2) Land tenure changes 3) Emergence of labour market 4) Dynamism in rural – urban linkages 5) Changes in consumption patterns • With increased landlessness esp. among rural youth, there is administrative land allocation mainly distributing communal lands (previously used for grazing) • The number of youth beneficiaries and total land allocated in Fogera shows a considerable increase over 2008-2018: ‒ 1,600 ha of communal land was distributed to about 2,950 youth in Fogera woreda alone ‒ Many have become rice farmers
- 42. Changes rural labour markets 1) Dynamics in farming systems 2) Land tenure changes 3) Emergence of labour market 4) Dynamism in rural – urban linkages 5) Changes in consumption patterns • With increased rice production and high labour requirements, there has been the emergence of dynamic rural labour market • Over 55% of rice farmers reporting hiring labourers • The major farm operations for hired labour use were ploughing, weeding, harvesting and/or threshing • Majority of hired labour is from the local area, though some are migrants from more distant semi-arid regions
- 43. Rural-urban linkages 1) Dynamics in farming systems 2) Land tenure changes 3) Emergence of labour market 4) Dynamism in rural – urban linkages 5) Changes in consumption patterns • Increased rice commercialisation has led to the rapid emergence of a growing processing industry in nearby towns • This has created dynamic rural–urban interactions and multipliers • These are related with marketing related linkages for: ‒ Rice processing and marketing – value addition ‒ Transportation ‒ Asset accumulation and increased household consumption ‒ New services (banking, hotel, education, etc) ‒ Seasonal and annual labour with increased wage rates
- 44. Rural-urban linkages 1) Dynamics in farming systems 2) Land tenure changes 3) Emergence of labour market 4) Dynamism in rural – urban linkages 5) Changes in consumption patterns Trends in the expansion of rice processing industry [number of rice processors] in Fogera area 1 1 2 2 3 8 9 11 13 16 36 40 44 50 57 64 69 83 97 123 0 20 40 60 80 100 120 140 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Numerofoperationalprocessors Year
- 45. Rural-urban linkages Aspirations of rice processors in Fogera Business type % of processors Type of expansion businesses % of processors Plan to expand rice business 88.5 Purchase more processing machines 74 Utilise the full potential processing capacity of the same machines 74 Employ more labour 69.9 Import modern processing machine 57.7 Establish new processing facility in the same town 44.7 Establish new processing facility in other towns 32.5 Engage in direct rice production 19.5 Plan to expand to non-rice business 59.0 Trading of other products 35.8 Transport sector 30.1 Processing of other agricultural products 27.6 Building construction (town residence or business house) 22 Hotel industry 15.4
- 46. Changes in consumption patterns • The increased production and marketing of rice has resulted in major changes in household consumption patterns • Food and nutrition security has improved in terms of composition and frequency of consumption – from 1-2 to 2-3 meals per day; greater dietary diversity • But some decline in the consumption of milk and dairy products linked with the reduction of livestock in the area • Injera (traditional Ethiopian flatbread) now made from mix of rice and teff or rice and millet
- 47. Observations • Rice commercialisation having a multiplier effect in Fogera ‒ Increased household incomes ‒ Improved food and nutrition security ‒ Creation of casual and permanent labour opportunities ‒ Dynamic land market – but with displacement of livestock ‒ Growing rural-urban linkages ‒ Enhanced investment in other economic sectors • Changing livelihood trajectories – from ‘hanging-in’ to ‘stepping-up’ and ‘stepping-out’ • Observed changes were not induced by interventions from public programmes, but mainly farmer-led innovations
- 48. A Tracker Study of Groundnut Commercialisation and Livelihood Trajectories in Malawi Blessings Chinsinga, Loveness Msofi, Mirriam Matita, Jacob Mazalale, Masautso Chimombo and Ephraim Chirwa APRA Work Stream 2 Longitudinal Study Funded by UK aid from the UK Government www.future-agricultures.org/apra
- 49. In memory of Prof Ephraim Wadonda Chirwa
- 50. Tracking change in Malawi • Assessing long-term changes in livelihoods associated with the differential engagement with agricultural commercialisation • Focus is on groundnut commercialisation in Malawi’s central districts of Ntchisi and Mchinji • Goal is to understand the drivers of groundnut commercialisation in these two districts, from both a historical and contemporary perspective, and their impacts on livelihood trajectories
- 51. APRA Tracker research methods The APRA Malawi Tracker is based on a survey that was conducted in the 2006/2007 growing season, which focused on the Farm Input Subsidy Programme (FISP) The team revisited individuals from the original survey, including any off-shoot households established in the last decade • Tracked 216 original HHs out of total sample of 240 HHs • Total sample 519 HHs interviewed • 25 outside country (Zambia, Mozambique, SA) • Qualitative data (28 FGD and 6 KII interviews completed)
- 52. APRA Malawi team in the field
- 53. Observations 1. Agricultural Extension (ADMARC) service non-existent – reduced to a vehicle for FISP (providing vouchers for purchase of fertiliser and hybrid seeds) 2. Private vendors are biggest buyers of farm produce 3. Less progress in HH farming enterprises; largely ‘hanging in’, few ‘stepping out’ cases @ rural trading centres 4. Mchinji farmers more exposed to market forces – NGOs, export markets to Mozambique and Zambia • APRA Malawi team have not fully analysed their quantitative data; data cleaning is underway; further qualitative work planned • Several stakeholder engagements & meetings undertaken
- 54. Experiences tracking households • Transfers/failure to locate HH, death of eligible members • Some ‘ghost’ HH members in 2006/7 roster in anticipation of handouts • Few refusal cases • Excitement among respondent to be tracked after 10 years increase response rate • Unexpected community events slowed work • Long distance travel to conduct interviews of split HHs • Some HH members who moved are still dependent on original HH heads • 2 blogs specifically written on tracker expectations and experiences available on the Future Agricultures APRA website: 1. https://www.future-agricultures.org/blog/malawi-tracker-study-experiences-from-the-field/ 2. https://www.future-agricultures.org/blog/conducting-a-tracker-study-a-tough-nut-to-crack/
- 55. Reflections on strengths and weaknesses of longitudinal studies
- 56. Strengths of longitudinal studies • Detail over the life course: Value increases as each wave builds on what is already known about the study participants • Contain far more detailed information than could be collected through a one-off study (but danger of data overload!!) • Reducing recall bias: Prospective LS help reduce the impact of recall error or bias, which occurs when people forget or misremember events when asked about them later
- 57. Strengths of longitudinal studies Many of the advantages of LS relate to the analytical questions their data can help address – e.g.: • Exploring patterns of change and the dynamics of individual behaviour • Understanding how people follow particular livelihood trajectories (e.g. hanging in’, ‘stepping up’ and ‘stepping out’…)
- 58. Strengths of longitudinal studies LS can provide insights into causal mechanisms and processes: • LS data cannot definitively ‘prove’ causality, but can give more insights into the causal processes that might be involved • Can allow events to be ordered correctly in time understanding the order in which events occur is important in assessing causation
- 59. Weaknesses of longitudinal data Attrition • Over time, participants may cease to take part in a longitudinal study. This ‘attrition’ can result from a range of factors – e.g. migration, death, dropping out Conditioning • It is possible that participants may be ‘conditioned’ – i.e. answer questions differently or even behaving differently as a result of their participation in the study
- 60. Weaknesses of longitudinal data • Limits of representivity: Because longitudinal cohort studies focus on following a specific group (rather than a representative sample of the population as a whole), they are only representative of that group • Complexity: Datasets can be more complex to manage and analyse than the data for cross-sectional surveys (again, data overload!) • Cost: LS with a number of waves of data collection tend to be more expensive than cross-sectional studies
- 61. Longitudinal vs. cross-sectional studies Comparators Longitudinal Cross-Sectional Time / sequence Several points in time – ‘film strip’ One point in time – ‘snap shot’ Sampling Same sample Different sample Level of analysis Change at the individual / hh level Snapshot of a given point in time Case examples APRA tracker study APRA WS1 two-wave panel study APRA WS1 first wave panel study
- 62. Some sources of information on longitudinal studies in international development
- 63. Other examples of longitudinal studies • Multi-wave, multi-country, panel study – ‘Afrint: Smallholder Agriculture, Diversification and Gender in Africa’ http://fdslive.oup.com/www.oup.com/academic/pdf/openaccess /9780198799290.pdf • Retrospective longitudinal study – ‘More People, Less Erosion: Environmental Recovery in Kenya’ https://www.odi.org/sites/odi.org.uk/files/odi- assets/publications-opinion-files/4600.pdf • Repeat, multi-country, cross-sectional study – ‘Drawers of Water: 30 Years of Domestic Water Use and Environmental Change in East Africa’ https://pubs.iied.org/pdfs/9049IIED.pdf
- 64. Other examples of longitudinal studies • Repeat, prospective, longitudinal study – ‘EthiopiaWIDE: Tracking Ethiopian rural communities since 1994’ http://ethiopiawide.net/ • Tracker study – ‘Migration and Economic Mobility in Kagera, Tanzania’ http://documents.worldbank.org/curated/en/3207214687820970 29/pdf/WPS4798.pdf • Repeat, cross-sectional study – ‘Evaluating 35 years of Green Revolution Technology in Villages of Bulandshahr District, Western UP, North India’ https://doi.org/10.1080/00220380601125180
- 65. Sources of information on longitudinal studies http://surveys.worldbank.org/lsms
- 66. http://surveys.worldbank.org/lsms/programs/c4d2 Sources of information on longitudinal studies
- 67. https://www.dhsprogram.com/ Sources of information on longitudinal studies
- 68. Sources of information on longitudinal studies http://mics.unicef.org/
- 69. Test your knowledge: A longitudinal studies quiz
- 70. Test Your Knowledge Longitudinal Studies Quiz 1. True or false? A panel study contacts a fresh sample of people each time a new wave or sweep is carried out. Answer: False. Unlike cross-sectional studies, panel studies revisit the same group of people repeatedly over time. Each wave collects new information about participants’ lives, adding rich new data to what is already known about them.
- 71. Test Your Knowledge Longitudinal Studies Quiz 2. Which of the following is a strength of longitudinal studies? (select all that apply) A. They establish the order in which events occur B. They reduce the attrition of study participants C. They are cheaper than cross-sectional studies Answer: ‘A’ only. Longitudinal data collection allows researchers to build up a reliably ordered account of the key events and experiences in the lives of study participants.
- 72. Test Your Knowledge Longitudinal Studies Quiz 3. Which of the following are characteristics of the household panel studies described today? (select all that apply) A.The sample changes over time as people leave households and join new ones B.They are representative of a particular generation of people, not the population as a whole C. They collect information from whole households at each wave D.They don’t collect data through survey interviews; instead they rely on information from secondary sources, such as administrative records Answer: ‘A’ and ‘C’. Household panel studies follow whole households of people over time. New participants join the study as households break up and new ones form.
- 73. Test Your Knowledge Longitudinal Studies Quiz 4. Common data collection methods in longitudinal studies include which of the following? (select all that apply) A.Structured questionnaire survey B.Secondary administrative records – e.g. census records and benefit receipts C. Clinical measurements – e.g. height, weight and blood pressure D.Participant observation E. Focus group discussions guided by semi-structured checklists Answer: All but ‘D’. Participant observation is not normally a part of the methodological repertoire of longitudinal studies.
- 74. Test Your Knowledge Longitudinal Studies Quiz 5. What can longitudinal studies show us? (select all that apply) A.How key life transitions change the course of a person’s life B.How early circumstances affect later life C. How different areas of our lives are linked and how those relationships change over time D.How different aspects of life vary for different people throughout their lives E. How patterns of behaviour change as people get older Answer: All of the above. The breadth of data collected over time allows researchers to explore all of these areas.
- 75. Test Your Knowledge Longitudinal Studies Quiz 6. How does a tracker study differ from a conventional longitudinal study? A.It follows the same households in the same location over a long period of time (10 or more years) B.People who move are unlikely to be a random subset of the baseline respondents and therefore following the movers and interviewing them minimises the attrition rate and addresses potential selectivity biases C. The financial cost of tracking individuals can be considerably lower than in standard household longitudinal studies Answer: ‘B’ only. Trackers follow individuals over time, whether they remain in their original locations/households or move elsewhere. They can be expensive, but add additional insights that help improve our understanding of people’s livelihood trajectories.
- 76. APRA Contacts Directorate/IDS John Thompson j.thompson@ids.ac.uk Oliver Burch o.burch@ids.ac.uk Rachel Sabates-Wheeler r.sabates-wheeler@ids.ac.uk Amrita Saha a.saha@ids.ac.uk Lesley White l.white@ids.ac.uk East Africa Regional Hub/CABE Hannington Odame hsodame@gmail.com Southern Africa Regional Hub/PLAAS Cyriaque Hakizimana chakizimana@plaas.org.za Ruth Hall rhall@plaas.org.za West Africa Regional Hub/ Univ of Ghana Joseph Yaro yarojoe@yahoo.com www.future-agricultures.org/apra APRA Webpages www.future-agricultures.org/apra Funded by UK aid from the UK Government
Editor's Notes
- #7: ‘BT20’ – Mandela’s children cohort study – From its inception, BT20 was planned to be multidisciplinary, tracking the growth, health, well-being and educational progress of urban children across the first decade of their life. To do so, the research team had to innovate in a number of areas, such as establishing tracking systems under circumstances where few people had a street address or telephone, create a flexible dataset because names were inaccurately translated and transcribed from record to record, translating and adapting questionnaires and establishing norms for many of the measures used. Original planned for 10 years, the study ended up running from 1990-2010. ITV/BBC ‘7-Up’ series - Begun in 1964, the BBC provided an amazing insight, not just into Britain through the decades, but also into the journey of the lives of those who took part. The children were selected to represent the range of socio-economic backgrounds in Britain at that time, with the explicit assumption that each child's social class predetermines their future. Every seven years, the director, Michael Apted, films material from those of the fourteen who choose to participate. The most recent installment, ‘63 Up’, premiered in the UK on ITV on 4 June 2019 over 3 nights.
- #25: In Lowess, the regression is weighted so that the central point (xi; yi) gets the highest weight and points that are farther away receive less weight. The estimated regression line is then used to predict the smoothed value of yi for yi only. The procedure is repeated to obtain the remaining smoothed values, which means that a separate weighted regression is performed for every point in the data.
- #26: Adding scatterplot to Lowess to examine concentration
- #27: The curve above shows the income distribution, compared to a straight diagonal representing perfect equality.
- #28: Illustrates the relationship between asset ownership and commercialisation
- #29: Illustrates differences for sales and asset ownership by subjective perceptions of poverty
- #32: Please prepare a brief presentation of your study design, methodology, preliminary findings and next steps. The APRA Work Stream 2 studies aim at exploring the dynamic impacts of commercialisation on livelihoods over a longer period of time, understanding the pathways and drivers of different livelihood trajectories. In particular, WS2 is interested in providing evidence on the transition of households into different livelihoods as a result of commercialisation. These livelihood trajectories include dropping-out (moving out of agriculture due to destitution and other challenges), hanging-in (subsistence agriculture for survival), stepping-up (expanding their existing agricultural activities with the aim of increasing production and commercial activities) and stepping-out (accumulating wealth and diversifying into non-farm activities in both rural and urban areas).
- #49: Please prepare a brief presentation of your study design, methodology, preliminary findings and next steps. The APRA Work Stream 2 studies aim at exploring the dynamic impacts of commercialisation on livelihoods over a longer period of time, understanding the pathways and drivers of different livelihood trajectories. In particular, WS2 is interested in providing evidence on the transition of households into different livelihoods as a result of commercialisation. These livelihood trajectories include dropping-out (moving out of agriculture due to destitution and other challenges), hanging-in (subsistence agriculture for survival), stepping-up (expanding their existing agricultural activities with the aim of increasing production and commercial activities) and stepping-out (accumulating wealth and diversifying into non-farm activities in both rural and urban areas).
- #52: 24th October to 13th November--- round one 26 days; round two 19 days FGD with male and female club and non-club members separately 25 Original HH not tracked Total of 519 quantitative interviews Initial estimate of 829 HH members above 18 years of age// 303 represent 37% Engagements – Agribusiness conference; Meetings by CISANET, MAAFAS,
- #53: 24th October to 13th November--- round one 26 days; round two 19 days FGD with male and female club and non-club members separately 25 Original HH not tracked Total of 519 quantitative interviews Initial estimate of 829 HH members above 18 years of age// 303 represent 37% Engagements – Agribusiness conference; Meetings by CISANET, MAAFAS,
- #54: ‘ we can do nothing apart from farming’
- #55: Refusals – non response to phone call; keep changing location of interview; not available at agreed times; husband refusing wife to particpate