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Who works in agriculture? 2019 re sakss_ppt-kashi 4

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by Kashi Kafle
for the 2019 ReSAKSS Annual Conference

Publié dans : Économie & finance
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Who works in agriculture? 2019 re sakss_ppt-kashi 4

  1. 1. Who Works in Agriculture? Exploring the Dynamics of Youth Employment in the Agri- food System (AFS) in Malawi and Tanzania Kashi Kafle1 , Neha Paliwal 2, Rui Benfica 3, Soumya Balasubramanya1 1IWMI, Colombo, Sri Lanka 2International Fund for Agriculture Development (IFAD), Rome, Italy 3IFPRI, Washington DC, USA 2019 ReSAKSS Conference, 11-13 November, Lome, Togo
  2. 2. IWMI RESEARCH IN AFRICA 1: WATER, FOOD & ECOSYSTEMS IMPROVE FOOD SECURITY CONSERVE ECOSYSTEMS & WATER RESOURCES 2: WATER, CLIMATE CHANGE & RESILIENCE ADAPT TO & MITIGATE CLIMATE CHANGE / BUILD RESILIENCE TO SOCIETAL DISRUPTION 3: WATER, GROWTH & INCLUSION PROMOTE SUSTAINABLE GROWTH ACHIEVE GENDER EQUALITY & INCLUSIVE SOCIETIES 4: STRATEGIC FOCUS 1. Raise smallholder water productivity 2. Transform basin & aquifer management for sustainability 3. Optimize basin infrastructure & ecosystems 1. Adapt water systems to climate change 2. Reduce water- related disaster risk & fragility 1. Cut pollution & optimize resource reuse in the circular economy 2. Reform water governance & increase gender equality 3. Assess economics of water solutions & incentives for change DIGITAL INNOVATION Apply data analytics & tools to support decision making & policy ChangeR4D MISSION – PROBLEM SCOPE Generating innovative water solutions for sustainable development: Food. Climate. Growth Diverse products, tools, models, maps, publications WWW.iwmi.org Youth and agri-food system: Cross- cutting
  3. 3. • It is believed that young people are leaving agriculture. But little evidence exists in support of the claim. • Especially, youth migration out of rural agricultural areas in sub- Saharan Africa has been a policy concern. Even though no conclusive evidence to back this up. • Questionable statistics: Average farmer in sub-Saharan Africa is ‘above 60 years old’ (FAO 2013, IFAD 2016) • Reported adverse perceptions:  Farming is ‘dirty’, unattractive to youth! • Concerns over labor replacement:  ‘Who will grow our food?’ • But, are youth really leaving agriculture in SSA? MOTIVATION
  4. 4. Scope of youth involvement in Farming/Agri-food system • Unmet demand for food indicates substantial opportunity  High imports / domestic supply lacking • Opportunities to participate in agricultural transformation  Youth to adopt modern technologies to commercialize the agri. sector • Policy strategy anticipates youth participation MOTIVATION
  5. 5. • Policymakers are concerned about increasing youth unemployment and declining agricultural sector • Tanzania:  National Strategy for Involvement of Youth in Agriculture 2016- 2021 identifies youth employment as the priority policy and includes provisions for youth engagement in agriculture • Malawi:  Launched National Youth Policy 2013 which identifies agriculture as a major pillar for youth employment • But, lack of evidence to guide policymaking POLICY CHANGES
  6. 6. RESEARCH QUESTIONS 1. What is the average age of a ‘farmer’? 2. What is the distribution of employment outcomes for Rural Youth? 3. How stable is rural youth participation in agriculture and the agri- food system (AFS) over the short-term? 4. What are the differences in participation between youth and young adults? 5. How does the youth employment in agriculture differ by gender?
  7. 7. KEY DEFINITIONS • Youth  UN (15-24) vs some Gov’t Definition (15-35)  Our definition: Youth (15 – 24) ; Young Adults (25-35) • Farming  Anyone with positive hours of labor in family farm is considered `engaged in farming’  We consider farming participation instead of intensity • Agri-food enterprises (AFE)  Self-Employed work in an agricultural enterprise defined by the ISIC codes  Wage Employed work in an agricultural enterprise defined by the ISIC codes (including casual/ganyu labor) • Agri-food Systems (AFS)  Participation in Farming + Employment in AFE
  8. 8. CATEGORIZATION OF EMPLOYMENT STATUS • Wage employment Had wage employment in the last 12 months (including casual/ganyu labor) • Self-employment Worked as an owner or manager of a non-farm enterprise in the last 12 months • Single-occupation farming To avoid double counting, we make employment categories mutually exclusive in this order: Wage employment => Self-employment => Farming Identify individuals who use farming as their only form of income generation • Unpaid household work: Worked in non-farm household activities in the last 7 days • Unemployment: Residual category
  9. 9. • Means and trends • Transition matrices:  Proportion of people consistently staying in a certain sector of employment over time  Proportion of people moving into a different sector of employment over time • Regression methods:  Conditional lagged model: to estimate the probability of stability in a certain sector or mobility into and out of the sector or other sectors given baseline status • 𝑌𝑖𝑡2 = 𝛼0 + 𝛼1 𝑌𝑖𝑡1 + 𝛽1 𝐴𝑔𝑒𝑖𝑡1 + 𝛽2 𝐴𝑔𝑒𝑖𝑡1 2 + Θ𝑋𝑖𝑡1 + 𝜀𝑖𝑡  Panel data estimators: Panel fixed effects to estimate the probability of employment by age • 𝑌𝑖𝑡 = 𝛼0 + 𝛽1 𝐴𝑔𝑒 + 𝛽2 𝐴𝑔𝑒2 + Θ𝑋 + 𝜇𝑖 + 𝜀𝑖𝑡 • Y is outcome of interest, X is a vector of control covariates that includes household demographics, individual characteristics, and other controls METHODOLOGY
  10. 10. • Data from World Bank’s LSMS-ISA surveys from Malawi and Tanzania Data available for multiple waves, but we use only the two waves to study the short term employment dynamics DATA Country Baseline Endline Panel Year Sample Year Sample Sample Tanzania Household 2010/11 3924 2012/13 5010 3786 Individual 20,559 25,412 16,164 Malawi Household 2010/11 3246 2013 4000 3104 Individual 15,597 20,220 14,165
  11. 11. RURAL POPULATION STRUCTURE: 2011 31.5 28.2 12.9 22.7 4.8 34.5 23.3 17.0 20.9 4.3 0 5 10 15 20 25 30 35 40 6 to 14 15 to 24 25 to 34 35 to 64 65 and up Percentages Age group Tanzania Malawi
  12. 12. RURAL POPULATION STRUCTURE: TANZANIA
  13. 13. Average age of the farmer: -30 years in both Malawi and Tanzania -No gender differences (1) AVERAGE AGE OF THE FARMER Tanzania All Females 2010/11 2012/13 2010/11 2012/13 Farming >0 hours/year 29.5 30.5 29.9 30.9 Farming as the only occupation > 0 hours/year 27.3 28.5 28.1 29.3 > 10 days/year 31.1 31.7 31.5 32.3 > 25 days/year 32.6 33.2 32.9 34.1 > 50 days/year 34.6 35.1 34.5 35.8 Malawi All Females 2010/11 2013 2010/11 2013 Farming >0 hours/year 30.4 29.4 30.7 29.7 Farming as the only occupation > 0 hours/year 29.1 28.5 30.1 29.1 > 10 days/year 32.9 31.6 33.9 32.3 > 25 days/year 34.7 33.5 35.3 33.9 > 50 days/year 36.3 35.5 36.9 35.9 No Evidence to support the statistics that average age of African farmer is above 60.
  14. 14. (2) RURAL YOUTH EMPLOYMENT 0 10 20 30 40 50 60 70 Percentge Tanzania 2010/11 2012/13 0 10 20 30 40 50 60 70 80 Percentage Malawi 2010/11 2013
  15. 15. • Limited view (in some instances) is that agricultural sector is only farming • Agri-food system includes farming as well as activities related to agriculture and food value addition. • Policies looking to attract youth into agriculture should consider both farming and the agri-food system together EXTENDING TO AGRI-FOOD SYSTEM (AFS)
  16. 16. Similar patterns in both countries PARTICIPATION IN THE AGRI-FOOD SYSTEM 0 20 40 60 80 100 2010/11 2012/13 2010/11 2012/13 Youth Young adult Percentage Tanzania Unemployed Employed outside of the agri-food system Employed in the agri- food system
  17. 17. Tanzania Malawi Males Females Males Females Movement into farming (%) Stayed in farming 45.1 52.1 53.9 57.4 Wage labor to farming 19.2 24.3 30.8 40.9 Self-employment to farming 18.5 18.9 29.4 32.1 Unemployed to farming 10.9 16.1 39.7 35.5 Movement out of farming (%) Farming to wage labor 28.9 16.3 35.5 28.2 Farming to self-employment 10.8 15.6 6.1 7.7 Farming to unemployed 15.3 16.0 4.3 6.4 (4) MOVEMENT INTO AND OUT OF AGRICULTURE, AGES 15 TO 34 GIVEN BASELINE OCCUPATION Key message: Youth and young adult’s involvement in Agriculture is stable. Female participation in farming is more stable than males. Movement into farming outnumbers movement out of farming.
  18. 18. • Regression analysis
  19. 19. Farming in 2012/13 AFE employment in 2012/13 Tanzania Malawi Tanzania Malawi 0.88*** 0.62*** 0.71*** 0.39*** 0.072*** 0.071*** 0.079*** 0.050*** -0.001*** -0.001*** -0.001*** -0.001*** 0.77*** 0.59*** 0.14*** 0.12*** 0.028 -0.09*** -0.032 0.054** 0.002 0.24*** -0.37*** -0.033*** 0.29*** 0.66*** -0.12*** 0.014 15,919 11,360 15,919 11,360 ECONOMETRIC RESULTS [1] CONDITIONAL LAGGED MODEL First lag (2010/11) -𝜶 𝟏 Age Age2 Agricultural households (1=Yes, 0=No) Gender (1=Male, 0=Female) Current student (1=Yes, 0=No) Rural area (1=Yes, 0=Urban) Observations Any employment in 2012/13 Tanzania Malawi 0.76*** 0.47*** 0.12*** 0.104*** -0.0013*** -0.0012*** 0.32*** 0.51*** 0.24*** 0.022 -0.21*** 0.29*** -0.014 0.56*** 15,919 11,360 Key message: Involvement in farming is more stable than AFE employment. Farming and AFE involvement more increase with age but at a decreasing rate. Employment more stable in Tanzania than Malawi. Males more likely to be employed in AFE and less likely to participate in farming in Malawi.
  20. 20. Farming AFE employment Tanzania Malawi Tanzania Malawi 0.14*** 0.44*** 0.17*** 0.23*** -0.0012*** -0.0057*** -0.002*** -0.0019*** 1.42*** 1.03*** -0.047 0.071 -0.73** 0.099 0.32 -0.094 -0.068 -0.008 -0.37*** -0.20*** 0.005 0.94*** 0.012 -0.025 -3.76*** -3.60*** -1.63*** -1.92*** 31,819 22,521 31,819 22,521 ECONOMETRIC RESULTS [2] PANEL FIXED-EFFECTS MODEL Age Age2 Agricultural households (1=Yes, 0=No) Gender (1=Male, 0=Female) Currently attending school Rural area Constant Observations Any employment Tanzania Malawi 0.16*** 0.49*** -0.001*** -0.004*** 0.63*** 0.77*** -0.22 0.23 -0.19*** -0.14*** -0.084 0.74*** -2.36*** -2.91*** 31,819 22,521
  21. 21. PROBABILITY TO ENGAGE IN FARMING, BY AGE Probability of participation in farming declines after mid forties
  22. 22. 1. The average age of the farmer in Tanzania and Malawi is 30 years, NOT over 60. No gender differences in age of the farmer 2. More than 60% of Youth and Young adults are engaged in single-occupation farming, and a significant majority of them remain in farming over the period of 3 years. 3. Female’s participation in farming is more stable than that of males. 4. Considerable mobility towards single-occupation farming from both wage - and self-employment, i.e. youth are moving into farming, but unlikely that it is due to choice – could be poor prospects outside farming! 5. Males more likely to be employed in Agri-food enterprise and less likely to participate in farming, than females 6. When youth diversify away from single-occupation farming, they are most likely to enter into wage employment 7. Compared to Youth, Young adults less likely to stay/enter into farming CONCLUSIONS
  23. 23. 1. Policy change needed to include/consider agri-food system in the broader ‘agriculture’ sector. 2. Policy needs to focus on creating jobs for youth and young adults in the AFS and beyond (… get them ready to take up opportunities!) while supporting farming investments 3. More research needed to validate the findings. Policy changes are unlikely until a critical mass of evidence is produced IMPLICATIONS
  24. 24. Thank You! • Questions or comments? • Drop a line to k.kafle@cgiar.org
  25. 25. 0 10 20 30 40 50 60 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 GDP Level (Billion $) and Agricultural Share in GDP (%) TZ GDP (Billion $) MLWI GDP (Billion $) TZ Ag % MLWI Ag % Agricultural GDP per Capita (2011): Tanzania: $229.48 Malawi: $160.03 Data Source: World Bank IMPORTANCE OF THE AGRICULTURAL SECTOR IN TANZANIA AND MALAWI
  26. 26. RURAL POPULATION STRUCTURE: MALAWI
  27. 27. • Transition Matrices
  28. 28. 2010/11 2013 Wage labor Self-employment Single-Occupation farming Unemployed Total Wage Labor 4.5 1.1 8.5 1.5 15.6 Self-Employment 0.7 1.0 2.5 0.2 4.5 Single-Occupation farming 8.8 4.4 42.2 5.3 60.7 Unemployed 2.7 0.9 10.7 4.9 19.2 Total 16.7 7.4 64.0 11.9 100.0 (3) RURAL YOUTH EMPLOYMENT TRANSITIONS: MALAWI
  29. 29. 2010/11 2012/13 Wage labor Self-employment Single-Occupation farming Unemployed Total Wage Labor 7.6 1.5 4.9 1.9 15.9 Self-Employment 1.8 1.6 2.4 0.7 6.5 Single-Occupation farming 10.5 5.9 24.3 10.8 51.5 Unemployed 4.1 2.6 8.3 11.0 26.1 Total 24.0 11.6 39.9 24.5 100.0 RURAL YOUTH EMPLOYMENT TRANSITIONS: TANZANIA
  30. 30. Similar patterns in both countries PARTICIPATION IN THE AGRI-FOOD SYSTEM 0 20 40 60 80 100 2011 2013 2011 2013 Youth Young adult Percentage Malawi Unemployed Employed outside of the agri-food system Employed in the agri-food system
  31. 31. [1] CONDITIONAL LAGGED MODEL • Conditional lagged model to estimate the probability of stability in a certain sector or mobility into and out of the sector or other sectors given baseline status • 𝑌𝑖𝑡2 = 𝛼0 + 𝛼1 𝑌𝑖𝑡1 + 𝛽1 𝐴𝑔𝑒𝑖𝑡1 + 𝛽2 𝐴𝑔𝑒𝑖𝑡1 2 + Θ𝑋𝑖𝑡1 + 𝜀𝑖𝑡 • 𝛼1- stability coefficient, if positive and significant, employment is stable over time. If negative and significant, indicates mobility out of the sector
  32. 32. [2] PANEL FIXED-EFFECTS MODEL • Panel data estimators: Panel fixed effects to estimate the probability of employment by age • 𝑌𝑖𝑡 = 𝛼0 + 𝛽1 𝐴𝑔𝑒 + 𝛽2 𝐴𝑔𝑒2 + Θ𝑋 + 𝜇𝑖 + 𝜀𝑖𝑡 • 𝛽1- probability of employment/involvement in certain sector by individual’s age • 𝛽2- Tells whether the probability of employment in the sector is non-linear in age
  33. 33. Farming AFS employment Tanzania Malawi Tanzania Malawi Children, 10-14 0.16*** 0.16*** -0.015** -0.006 (0.012) (0.015) (0.0060) (0.0075) Youth, 15-24 0.26*** 0.34*** -0.016 0.0007 (0.020) (0.023) (0.012) (0.015) Young adult, 25-34 0.26*** 0.29*** 0.085*** 0.037 (0.027) (0.031) (0.021) (0.024) Adult, 35-64 0.23*** 0.26*** 0.13*** 0.062** (0.032) (0.036) (0.027) (0.031) Elderly, 65 and up 0.26*** 0.29*** 0.10*** 0.025 (0.036) (0.039) (0.035) (0.037) Agricultural household (1=Yes, 0=No) 0.35*** 0.31*** -0.029* 0.024* (0.017) (0.017) (0.015) (0.014) Rural (1=Yes, 0=Urban) 0.0072 0.17*** 0.0007 0.0081 (0.012) (0.032) (0.011) (0.031) Observations 26346 20384 26346 20384 R2 0.487 0.47 0.140 0.09 RELATIONSHIP BETWEEN AGE GROUPS AND AFS PARTICIPATION
  34. 34. • Include most recent data to see shifts in occupation over longer periods of time • Assess whether trends hold over time, and as Youth age out into Young Adults FUTURE RESEARCH

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