1. Drivers of Rural Land Rental Markets in sub-Saharan Africa, and their Impact Household Welfare. Evidence from Malawi and Zambia Jordan Chamberlin (Michigan State University) Jacob Ricker-Gilbert (Purdue University) World Bank Conference on Land and Poverty 2015: Linking Land Tenure and Use for Shared Prosperity. - Washington D.C. – March 23-27. MICHIGAN STATE U N I V E R S I T Y

2. Outline Motivation Theory Context: Malawi & Zambia Study objectives & contribution Methods Conceptual model Estimation issues Data Results Conclusions and next steps 2

3. Motivation Land is a key productive resource Especially important in agrarian economies with limited non- farm sectors (e.g. Jayne et al. 2014) High and rising land scarcity in many parts of SSA Smallholders report limited expansion potential even in low density areas! (e.g. Chamberlin 2013, for Zambia) High and rising inequality in landholdings Even within the smallholder sector (e.g. Jayne et al. 2003, 2014) 3

4. Role of land markets? Rental and sales markets should enable net transfers of land From land-rich to land-poor From less-able to more-able farmers Enable productive livelihoods Especially for households with insufficient land… Such gains are conditional on efficient rental prices, transactions costs of participation, etc. 4  equity gains  efficiency gains  welfare gains Mixed evidence in the empirical literature (e.g. Holden et al. 2009)

5. This study Malawi & Zambia: Most land under customary tenure High levels of land inequality and rural poverty Similar agroecological, socioeconomic & legal contexts Vary significantly in rural pop. density & market access Research questions What are the trends in rental market development? Who is participating? What are the benefits? Efficiency Equity Implications for a variety of welfare outcomes Do participation and/or benefits vary with level of mkt dev’t? 5 Malawi Zambia

6. 6 Persons per km 2 Malawi Zambia

7. Household model: participation 7 Rents in Rents out Jin & Jayne 2013

8. Household model: impacts 8 alt. specifications: binary vs continuous measures Value of crop production Net crop income Net off-farm income Net total household income Probability of expected deficit # months staples expected to last Subjective wellbeing (score: 1-5) Probability of poverty MWIZMB XXXXXXXXXXXXXXXX XXXX XXXXX X

9. Endogeneity in welfare model 9 FE? FD? Okay, but would lose key time-invariant regressors of interest…

10. Endogeneity in welfare model 10

11. Data Malawi household panel dataZambia household panel data 3 rounds: 2003/4, 2007, 2009 1,375 households in all waves Nationally representative 2 rounds: 2001, 2008 3,736 households in both waves Nationally representative Geospatial controls (both countries) Rural population density Access to markets Rainfall 11

12. This presentation Motivation Theory Context: Malawi & Zambia Study objectives Methods Conceptual model Estimation issues Data Results Conclusions and next steps 12

13. Rental status of the sample 13 % renting in% renting out 7.5% 4.3% 13.4% 5.3% 15.4% 8.9% 0.9% 0.1% 1.2% 0.7% 3.0% 0.5%

14. Rental status of the sample 14 % renting in% renting out 7.5% 4.3% 13.4% 5.3% 15.4% 8.9% 0.9% 0.1% 1.2% 0.7% 3.0% 0.5% USD/ha rental rate

15. HH characteristics by rental status Tenants More education More assets More labor Less land Immigrants Landlords Less education Fewer assets Less labor More land Local households 15 > > > < ≠

16. Determinants of rental market participation: Malawi 16 Partial effects from ordered probit model (1)(2)(3) Renting inAutarkyRenting out APEp-valueAPEp-valueAPEp-value Ability0.0196 *** (0.000)-0.0090 *** (0.000)-0.0106 *** (0.000) Adult equivalents0.0126 *** (0.000)-0.0058 *** (0.000)-0.0068 *** (0.000) Landholding (ha)-0.0354 *** (0.000)0.0162 *** (0.000)0.0192 *** (0.000) Female head (=1)-0.0010(0.882)0.0005(0.876)0.0005(0.888) Education of head (years)0.0054 *** (0.000)-0.0025 *** (0.000)-0.0029 *** (0.000) Age of head-0.0004 * (0.072)0.0002 * (0.081)0.0002 * (0.071) Assets ('000*USD)0.0017(0.575)-0.0008(0.563)-0.0009(0.587) Immigrant (=1)0.0859 *** (0.000)-0.0561 *** (0.000)-0.0299 *** (0.000) Mortality (=1)0.0013(0.927)-0.0006(0.932)-0.0007(0.923) Matrilineal (=1)-0.0073(0.432)0.0034(0.454)0.0038(0.416) Lagged maize price (rainy)-0.0982(0.652)0.0450(0.655)0.0533(0.650) Lagged maize price (harvest)0.4250 * (0.083)-0.1946 * (0.087)-0.2304 * (0.085) Log rainfall0.0233(0.190)-0.0107(0.181)-0.0126(0.201) Population density0.0001(0.335)-0.0000 ** (0.326)-0.0000(0.347) Km to road0.0002(0.193)-0.0001(0.209)-0.0001(0.188) Central0.0338 *** (0.001)-0.0134 *** (0.002)-0.0204 *** (0.003) South0.0201 * (0.077)-0.0066 ** (0.048)-0.0135(0.102) N of Obs6942

17. Determinants of rental market participation: Zambia 17 Partial effects from ordered probit model (1)(2)(3) Renting inAutarkyRenting out APEp-valueAPEp-valueAPEp-value Ability0.0034 ** (0.019)-0.0016 ** (0.035)-0.0018 * (0.059) Adult equivalents0.0002(0.845)-0.0001(0.851)-0.0001(0.849) Landholding (ha)-0.0002 * (0.099)0.0001(0.137)0.0001(0.150) Female head (=1)-0.0042(0.196)0.0013(0.659)0.0029(0.545) Education of head (years)-0.0000(0.944)0.0000(0.946)0.0000(0.946) Age of head-0.0002(0.116)0.0001(0.230)0.0001 * (0.096) Assets (1000s USD)0.0006(0.364)-0.0003(0.353)-0.0003(0.413) Immigrant (=1)0.0125 ** (0.028)-0.0092 * (0.088)-0.0033 *** (0.000) Mortality (=1)0.0031(0.719)-0.0018(0.758)-0.0012(0.708) Matrilineal (=1)0.0030(0.271)-0.0015(0.332)-0.0015(0.273) Lagged maize price0.0000(0.379)-0.0000(0.407)-0.0000(0.405) Log lagged rainfall (mm)0.0095(0.229)-0.0045(0.229)-0.0049(0.288) Population density-0.0001(0.304)0.0000(0.342)0.0000(0.331) Km to road0.0001(0.311)-0.0000(0.341)-0.0000(0.325) N of obs.7,698

18. Welfare impacts: Malawi 18 Value of crop production (USD) (1)(2) Tenant (=1) 118.2320 (0.000)*** Landlord (=1) -83.7949 (0.033)** Ha rented in 208.1458 (0.002)*** Ha rented out -105.8355 (0.012)** Net crop income (USD) (3)(4) 72.3953 (0.001)*** -95.0686 (0.018)** 180.5940 (0.002)*** -135.8716 (0.006)*** Net off-farm income (USD) (5)(6) -33.0360 (0.463) -26.5993 (0.467) -23.6094 (0.662) -17.7318 (0.764) Net total household income (USD) (7)(8) 42.3451 (0.389) -145.1319 (0.027)** 152.9715 (0.061)* -169.7044 (0.068)*

19. Welfare impacts: Malawi [2] 19 Tenant (=1) Landlord (=1) Ha rented in Ha rented out Number of months staples expected to last (9)(10) 0.7085 (0.027)** 0.1100 (0.782) 1.2905 (0.003)*** 0.9751 (0.041)** Subjective wellbeing (score: 1-5) (11)(12) 0.1855 (0.031)** -0.0017 (0.988) 0.0574 (0.546) -0.0857 (0.572) Probability of poverty (13)(14) -0.0943 (0.000)*** -0.0023 (0.913) -0.0781 (0.000)*** -0.0088 (0.674)

20. Quantile regression results - Malawi 20 (1)(2)(3)(4)(5)(6) 10 th 25 th 50 th 75 th 90 th Mean Tenant (=1) -8.0530-3.526410.912542.360995.832772.3953 (0.091)*(0.375)(0.086)*(0.001)***(0.000)***(0.001)*** Landlord (=1) -1.4830-5.3225-7.2555-19.2919-44.0652-95.0686 (0.803)(0.285)(0.361)(0.210)(0.154)(0.018)** Dependent variable: Net crop income (USD) Returns to renting in and losses from renting out are skewed towards the top of the income distribution. Mean for tenants higher than 75 th percentile for renting in.

21. Welfare impacts: Zambia 21 Value of crop production (USD) (1)(2) Tenant (=1) 156.31 (0.039)** Landlord (=1) -4.39 (0.950) Ha rented in 138.44 (0.000)*** Ha rented out -5.45 (0.122) Net crop income (USD) (3)(4) 68.51 (0.194) 7.92 (0.905) 47.05 (0.002)*** -4.76 (0.131) Net off-farm income (USD) (5)(6) -335.55 (0.226) 356.77 (0.159) -10.18 (0.898) 6.77 (0.245) Net total household income (USD) (7)(8) 266.49 (0.639) 471.39 (0.084)* 160.60 (0.226) 29.41 (0.007)***

22. Welfare impacts: Zambia [2] Probability of poverty (9)(10) Tenant (=1) -0.0347 (0.534) Landlord (=1) -0.4657 (0.000)*** Ha rented in -0.1604 (0.050)** Ha rented out -0.4481 (0.092)* 22

23. Rental rate as a proportion of gross value of crop production per hectare 23 ( Malawi ) Rental rates are high relative to production under current productivity levels percentile 10th25th50th75th90th tenants only0.060.120.230.470.95 full sample0.100.170.310.591.19

24. Quantile regression results - Malawi 24 (1)(2)(3)(4)(5)(6) 10 th 25 th 50 th 75 th 90 th Mean Tenant (=1) -8.0530-3.526410.912542.360995.832772.3953 (0.091)*(0.375)(0.086)*(0.001)***(0.000)***(0.001)*** Landlord (=1) -1.4830-5.3225-7.2555-19.2919-44.0652-95.0686 (0.803)(0.285)(0.361)(0.210)(0.154)(0.018)** Dependent variable: Net crop income (USD) Returns to renting in and losses from renting out are skewed towards the top of the income distribution. Mean for tenants higher than 75 th percentile for renting in.

25. Summarizing… 25 Land rental markets more active in Mwi than Zmb Likely driven by necessity with much higher PD Market participation growing in both countries Land possibly being rented in by smallholders from outside sector Mkt participation results very similar in Mwi & Zmb Efficiency gains: more able farmers rent in, less able rent out Equity gains: land-rich rent to land poor, and labor-poor rent to labor-rich

26. Summarizing… Welfare impacts differ between Malawi & Zambia Malawi: Clear evidence of positive impacts on renting in, on average Small or negative impact from renting out, on average -- potential evidence for distress rentals? Zambia: Smaller or no welfare impacts -- due to lower participation rates? 26

27. Summarizing… Even if renting in impacts are positive on average in Malawi, cost of renting and other costs of production are high relative to output Most returns to renting in captured at top of the distribution Raises questions about who has access to these rental markets & liquidity required for up-front rental arrangements 27 At the median, rental rates in Malawi equal 1/3 the gross value of production

28. Policy recommendations Our findings suggest some key policy stances: Focus on creating enabling environment for rental market participation Clarifying rights within customary tenure systems Complementary investments Productivity growth on small farms Welfare investments 28

29. Next steps for this work Joint modeling of Mwi & Zmb panel data Pooled panels Better measures of soil quality May affect land available to rent and thus impacts Take a closer look at rental rates Determinants of rental rates over space Determinants of rental participation at community level Account for spillovers via spatial econometric model 29

30. 30 Jordan Chamberlin, Michigan State University chamb244@msu.edu Jacob Ricker-Gilbert, Purdue University jrickerg@purdue.edu