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Land expropriation, peri-urbanization and income diversification: Evidence from peri-urban Tigray, northern Ethiopia
- 1. Land Expropriation, Peri-urbanization and Income Diversification: Evidence from Peri-urban Tigray, Northern Ethiopia tsegagi@gmail.com1 IFPRI/ESSP Seminar at EDRI , May 8, 2015 Tsega G. Mezgebo and Catherine Porter
- 2. Outline tsegagi@gmail.com2 1. Motivation 2. Peri-urbanization in Ethiopia: overview 3. Conceptual and Empirical Framework 4. Data and Main findings 5. Conclusions 6. Policy implications
- 3. 1. Motivation tsegagi@gmail.com3 Urban areas in many developing countries expand rapidly by incorporating the nearby rural villages. Squeezes land for farming, eventually farming livelihoods vanished over time and replaced by urban-based activities Necessitates rural to urban livelihood transitions i.e. simple to complex in terms of social and economical issues Rural-urban livelihood transitions could be far from smooth due to asset fixity and missing markets. Livelihood adaptation could be negative, e.g. shift from cash crop production to unskilled nonfarm employments. Not only poor but also better-off farm households could be marginalized.
- 4. Cont. tsegagi@gmail.com4 Rural–urban livelihoods transitions have such social cost. The current literature on income diversification focus on rural areas. Peri-urban areas differ from rural No uniform definition for peri-urban areas. Dynamic urban and rural livelihoods co-exits where the later vanish with time. Diversification to nonfarm is crucial to the farm households to make a living. Given the peculiar feature, applying the common assumptions of rural livelihood diversification could be less effective and appropriate to policy.
- 5. This study Objective: investigate factors that influence the household’s decision to adopt specific income diversification strategies. Also compare if the strategies and associated factors depend on the administration the household belongs to – urban versus rural. Aim: to contribute to policy in designing effective interventions to the poor in peri-urban areas to improve access to assets and productivity of the assets they already own. 5 tsegagi@gmail.com
- 6. 2. Peri-urbanization in Ethiopia: overview Urban Ethiopia is expanding rapidly with annual rate of 4% between 1994 and 2007 (Bane and Alemu, 2012) and land is owned by the sate. The process: First, urban development plan is prepared and endorsed. To implement the plan, the nearby rural villages incorporated to the respective town administrations (FDRE, 2008). New urban boundary is demarcated and then land is expropriated for investments. Affected households receive compensation equivalent to the lost farm-income and property damages (FDRE, 2005). New investments effected then after. This is the stage where peri-urbanization (urban expansion) starts formally. 6 tsegagi@gmail.com
- 7. 3. Conceptual and Empirical Framework tsegagi@gmail.com7 Causes to diversify income sources For instance Ellis (2000) Choice or pull factors to accumulate wealth Necessity or push factors to survive We argue that peri-urban farm households diversify to alternative income sources likely of Necessity. The farmers lost farmland due to urban expansion and likely forced to diversify to nonfarm to sustain the lost farm income.
- 8. Cont. tsegagi@gmail.com8 Incentives and constraints to diversify to nonfarm sector depend on Experiences, skills and asset ownership (Haggabled et al, 2007) In contexts where markets for credit and insurance are missing, A hypothetical household has to possess the necessary capital (skill, tools, and/finance) to enter high-niche nonfarm HHs endowed with nonfarm skills have better opportunity to access better-paying nonfarm activities compared to those equipped with farm skills
- 9. Estimation Model tsegagi@gmail.com9 Assumption: the HH is expected to choose one strategy from mutually exclusive strategies to maximize utility. Maddala’s (1983) random utility model is applied to examine the associated factors. Multinomial logit is used and the covariates consist of household capital, shocks and towns (as proxies for the local economy).
- 10. 4. Data and Main Findings tsegagi@gmail.com10
- 11. Data tsegagi@gmail.com11 The rate of urban expansion in Tigray Regional State is above the national average. Urban population change between 1994 and 2007 is 85% in Tigray while 59% national (CSA). Data from Tigray can represent features of the fast growing towns in Ethiopia. Towns with a population of 20,000 and above were identified first. Mekelle, Adigrat, Axum and Alamata were purposely selected for the study considering the expansion rate but FHS selected randomly.
- 12. Cont. tsegagi@gmail.com12 Each survey site has two groups – in urban versus rural, we call them rur-urban and rural Rur-urban: sub-villages included to urban admin between 2006 and 2009. Rural: under rural admin but adjacent to rur-urban Panel data was collected in 2011 and 2012 from FHS in peri-urban areas of the towns. Peri-urban, in this study, is the sub-villages within 15kms from edge town built up. First round, the sample was 478 HH ( 240 rur-urban & 238 rural). Second round, attrition rate 3% and seeming random
- 13. Figure 1: Map of survey sites tsegagi@gmail.com13
- 14. Terms used tsegagi@gmail.com14 Income is collected from all sources. Income earned by member of the FH is considered as income of the FH Income sources classified in to farm, nonfarm and unearned A FH is assumed engaged in the category, if at least one member has earned from the source. Nonfarm is further classified to skilled and unskilled employments.
- 15. i. Summary Statistics tsegagi@gmail.com15 Distribution of time-invariant variables
- 17. Distribution of time-varying variables tsegagi@gmail.com17 Rur-Urban Rural Year 2011 Year 2012 Year 2011 Year 2012 Variable Mean (StD) Mean (StD) Mean (StD) Mean (StD) Head main job farming (%) 46 37 70 62 Real value of livestock (000 ETB) 6.64 (1.15) 6.26 (9.62) 6.77 (6.99) 8.14 (9.34) Share to total HH income Farm income 0.28 (0.36) 0.33 (0.35) 0.51 (0.31) 0.50 (0.32) Nonfarm income 0.66 (0.37) 0.65 (0.35) 0.35 (0.29) 0.33 (0.30) PSNP income 0.05 (0.12) 0.02 (0.08) 0.13 (0.17) 0.17 (0.24)
- 18. Percentage distribution of income sources, Rur-urban tsegagi@gmail.com18 2011 2012 Q1 Q2 Q3 Q4 Average Use Q1 Q2 Q3 Q4 Average Use Farm income On-farm (crop and livestock) 43 31 17 24 27 61 48 31 28 23 25 76 Nonfarm Skilled employment 1 21 49 50 44 40 5 25 25 45 39 35 Unskilled employment 5 17 18 9 12 18 12 29 31 18 18 32 Productive safety net 13 5 2 1 2 41 5 2 1 1 1 37 Rent, remittance, etc. 38 22 13 15 12 56 28 14 14 12 16 46 Mean income (ETB) 604 1774 3311 8251 1077 2673 4501 10862 Observation (N) 60 60 60 60 240 59 58 59 58 234
- 19. Percentage distribution of income sources, Rural tsegagi@gmail.com19 2011 2012 Q1 Q2 Q3 Q4 Average Use Q1 Q2 Q3 Q4 Average Use Farm income On-farm (crop and livestock) 40 42 55 60 49 92 42 54 49 52 49 91 Off-farm 2 2 3 3 2 14 3 2 2 0 1 7 Nonfarm Skilled employment 3 16 26 29 18 41 7 9 31 35 20 38 Unskilled employment 19 21 8 3 12 34 14 20 10 7 13 30 Productive safety net 21 13 8 3 11 73 14 11 7 4 9 63 Rent, remittance, etc. 15 5 1 2 6 35 19 3 2 2 7 26 Mean income (ETB) 1431 2622 3821 10358 1537 3023 4479 9255 Observation (N) 60 59 59 60 238 57 56 57 56 226
- 20. Percentage distribution of diversification strategies tsegagi@gmail.com20 2011 2012 Q1 Q2 Q3 Q4 Total Q1 Q2 Q3 Q4 Total Rur-urban farm households Farm and skilled nonfarm 3 17 43 44 27 3 24 27 55 28 Farm and unskilled nonfarm 3 17 16 9 11 5 38 37 16 24 Skilled nonfarm 0 12 24 23 14 5 10 15 9 7 Unskilled nonfarm 3 9 9 5 6 10 5 12 7 8 Farm and transfer income 52 28 3 12 24 58 16 15 10 24 Transfer income only 38 17 3 7 16 19 7 3 3 8 Observation (N) 60 60 60 60 240 59 58 59 58 234 Rural households Farm and off-farm 0 5 2 2 2 0 0 0 0 0 Farm and skilled nonfarm 22 32 46 61 40 11 41 49 50 38 Farm and unskilled nonfarm 42 27 27 22 30 37 34 25 18 28 Skilled nonfarm 2 0 2 2 1 2 2 5 2 3 Unskilled nonfarm 5 7 5 0 4 0 2 4 4 2 Farm and transfer income 28 24 14 14 20 49 21 18 27 29 Transfer income only 3 2 0 2 2 5 0 0 5 3 Observation (N) 60 59 59 60 238 57 56 57 56 226
- 21. tsegagi@gmail.com21 Percentage distribution of diversification strategies (re-grouped) Rur-urban Rural 2011 2012 Average 2011 2012 Average Farm and skilled nonfarm (FSNF) 42 35 38 44 40 42 Farm and unskilled nonfarm (FUNF) 18 31 25 34 31 33 Farm and transfer income (FTI) 40 33 37 22 29 25 Observations (N) 240 234 238 227
- 22. tsegagi@gmail.com22 Estimation results for rur-urban HHs FSNF FUNF FTI variable dy/dx Std. err. dy/dx Std. err. dy/dx Std. err. hsex* -0.114* 0.063 -0.021 0.052 0.135** 0.068 hage -0.005** 0.002 -0.003* 0.002 0.008*** 0.002 hjob* -0.180*** 0.059 -0.186*** 0.048 0.366*** 0.061 hedu* -0.014 0.067 -0.093 0.062 0.107 0.069 nadult 0.047 0.032 0.016 0.029 -0.063** 0.029 lwelm 0.065 0.062 0.045 0.057 -0.109* 0.063 upelm 0.018 0.040 0.003 0.035 -0.021 0.036 rlval 3.88E-07 0.000 3.69E-06** 0.000 3.30E-06 0.000 fland -0.001 0.020 0.019 0.017 -0.017 0.020 comp 1.95E-06*** 0.000 -4.58E-07 0.000 -1.49E-06** 0.000 nonf* 0.114** 0.055 0.056 0.049 -0.170*** 0.052 other* -0.185*** 0.059 -0.023 0.055 0.208*** 0.067 health* -0.014 0.071 -0.135*** 0.049 0.149** 0.076 crime* -0.187** 0.091 0.072 0.098 0.116 0.113 climate* -0.143** 0.061 -0.004 0.058 0.148** 0.066 price* -0.038 0.055 0.101** 0.049 -0.063 0.054 mekelle* -0.229** 0.107 0.252*** 0.092 -0.023 0.090 adigrat* -0.267*** 0.092 0.232 0.149 0.035 0.117 axum* -0.059 0.127 0.206 0.151 -0.147* 0.088
- 23. tsegagi@gmail.com23 Estimation results for rural HHs FSNF FUNF FTI variable dy/dx Std. err. dy/dx Std. err. dy/dx Std. err. hsex* -0.238*** 0.084 0.131* 0.079 0.107 0.083 hage 0.001 0.003 -0.005* 0.003 0.004** 0.002 hjob* -0.039 0.086 -0.038 0.079 0.077 0.052 hedu* 0.091 0.074 -0.130* 0.070 0.039 0.054 nadult 0.061** 0.032 -0.056* 0.031 -0.005 0.018 lwelm 0.012 0.050 0.032 0.049 -0.043 0.035 upelm -0.003 0.040 0.044 0.038 -0.041* 0.024 rlval 0.13E-04 ** 0. 1E-04 0.2E-04 *** 0. 1E-04 0.1E-04*** 0.000 fland -0.036 0.021 0.038* 0.021 -0.002 0.012 nonf* 0.338*** 0.063 0.118* 0.063 -0.457 0.065 other* 0.019 0.086 -0.134* 0.071 0.116 0.073 health* -0.143 0.088 0.086 0.086 0.057 0.061 climate* -0.019 0.076 0.108 0.071 -0.089** 0.044 crime* 0.056 0.121 -0.054 0.119 -0.002 0.082 price* -0.186*** 0.063 0.146** 0.059 0.040 0.044 mekelle* 0.051 0.095 0.076 0.088 -0.126** 0.055 adigrat* -0.063 0.118 0.199* 0.119 -0.137*** 0.041 axum* -0.218 0.110 0.201 0.119 0.017 0.060
- 24. 5.Conclusion tsegagi@gmail.com24 The results show that: Agriculture is an important source of livelihood, although access to farmland is limited. Almost all households earn income from diversified sources, diversification is a norm. Participation in nonfarm employments is higher for the rural compared to the rur-urban. Production behaviour of the better-off FHs is similar regardless of the administration they belong but not the poor. Associated factors depend the administration the FHs belongs to. Labor-poor FHs are marginalized in the emerging employment opportunities. Experience in nonfarm play crucial role to engage in rewarding activities but money has negligible effect.
- 25. 6. Policy implications tsegagi@gmail.com25 Targeted interventions should be designed to improve the productive capacity of the poor. The land compensation packages should be revised and incorporate capacity building trainings with special emphasis to labor-poor FHs. Further studies to understand the poverty dynamics and risk perceptions of FHs in peri-urban areas.
Editor's Notes
- Refer page 30 why Ravallion