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Rural Land Rental Markets in 
Southern Africa: 
trends, participation and impacts on 
household welfare in Malawi & Zambia...
Outline 
• Motivation 
• Theory 
• Context: Malawi & Zambia 
• Study objectives & contribution 
• Methods 
• Conceptual mo...
Motivation 
• Land is a key productive resource 
• Especially important in agrarian economies with limited 
non-farm secto...
Role of land markets? 
• Rental and sales markets should enable net transfers 
of land 
• From land-rich to land-poor 
• F...
This study 
• Malawi & Zambia: 
• Most land under customary tenure 
• High levels of land inequality and rural poverty 
• ...
6 
Persons per km2 
0 - 5 
6 - 25 
26 - 50 
51 - 100 
101 - 200 
> 200 
Malawi 
Zambia
Household model: participation 
• Rental regime decision: ordered probit 
푇푒푛푎푛푡 
푟푖푡 
퐴푢푡푎푟푘푦 
푟푖푡 
퐿푎푛푑푙표푟푑 
푟푖푡 
= 푓 푖 ...
Household model: impacts 
• Welfare: 
푇 + 훾퐿 푟푖푡 
푦푖푡 = 훾푇 푟푖푡 
퐿 + 휁푎푏푖푙푖푡푦푖 + 풙푖푡휷 + 휖푖푡 
8 
alt. specifications: binary...
Endogeneity in welfare model 
• Concern that self-selection into rental market 
participation may be an issue 
• Omitted v...
Endogeneity in welfare model 
Mundlak-Chamberlain device 
Correlation between covariates and unobserved 
heterogeneity 휇푖 ...
Endogeneity in welfare model 
푳 , 푹풊풕 
• What about correlation between 푹풊풕 
푻 and 푢푖푡? 
• Omitted variable bias time-vary...
Data 
Malawi household panel data Zambia household panel data 
3 rounds: 2003/4, 2007, 2009 
1,375 households in all waves...
This presentation 
• Motivation 
• Theory 
• Context: Malawi & Zambia 
• Study objectives 
• Methods 
• Conceptual model 
...
Rental status of the sample 
14 
7.5% 
4.3% 
13.4% 
5.3% 
15.4% 
8.9% 
% renting in % renting out 
0.9% 
0.1% 
1.2% 
0.7% ...
Rental status of the sample 
15 
7.5% 
4.3% 
13.4% 
5.3% 
15.4% 
8.9% 
% renting in % renting out 
0.9% 
0.1% 
1.2% 
0.7% ...
HH characteristics by rental status 
Tenants 
More education 
More assets 
More labor 
Less land 
Immigrants 
Landlords 
L...
Determinants of rental market participation: Malawi 
17 
Partial effects from ordered probit model 
(1) (2) (3) 
Renting i...
Determinants of rental participation extent: Malawi 
18 
Coefficient estimates from one-stage & two-stage Tobit 
(1) (2) (...
Welfare impacts: Malawi 
19 
Value of 
crop production 
(USD) 
(1) (2) 
Tenant (=1) 153*** 
(0.000) 
Landlord (=1) -70** 
...
Welfare impacts: Malawi [2] 
20 
Tenant (=1) 
Landlord (=1) 
Ha rented in 
Ha rented out 
Number of months 
staples expect...
Summarizing… 
21 
• Land rental markets more active in Mwi than Zmb 
• Likely driven by necessity with much higher PD 
• M...
Summarizing… 
• Even with more participation in Malawi, transactions 
costs are higher in Malawi than in Zambia 
• More pa...
Summarizing… 
• Even if renting in impacts are positive on average in 
Malawi, cost of renting and other costs of producti...
Policy recommendations 
• Our findings suggest some key policy stances: 
• Focus on creating enabling environment for rent...
Next steps for this work 
• Joint modeling of Mwi & Zmb panel data 
• Pooled panels 
• More nuanced view of distributional...
26 
Jordan Chamberlin, Michigan State University 
chamb244@msu.edu 
Jacob Ricker-Gilbert, Purdue University 
jrickerg@purd...
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What are the Drivers of Rural Land Rental Markets in sub-Saharan Africa, and how do they Impact Household Welfare? Evidence from Malawi and Zambia by Jordan Chamberlin

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We use nationally representative survey data from two neighboring countries in Southern Africa – Zambia and Malawi – to characterize the current status of rural land rental market participation by smallholder farmers. We find that rural rental market participation is strongly conditioned by land scarcity, and thus is more advanced in Malawi than in lower-density Zambia. In both countries, we find evidence that rental markets contribute to efficiency gains within the smallholder sector by facilitating the transfer of land from less-able to more-able producers. However, we also find evidence of significant transactions costs, which may hamper such gains. Evidence of welfare impacts of rental market participation is mixed, with generally positive impacts accruing to tenants and (to a lesser extent) landlords in Malawi, but negligible impacts in Zambia, where rental markets are still in their infancy.

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What are the Drivers of Rural Land Rental Markets in sub-Saharan Africa, and how do they Impact Household Welfare? Evidence from Malawi and Zambia by Jordan Chamberlin

  1. 1. Rural Land Rental Markets in Southern Africa: trends, participation and impacts on household welfare in Malawi & Zambia Jordan Chamberlin (Michigan State University) Jacob Ricker-Gilbert (Purdue University) ECAMA Research Symposium • Lilongwe, 8th-10th October 2014 MICHIGAN STATE U N I V E R S I T Y
  2. 2. Outline • Motivation • Theory • Context: Malawi & Zambia • Study objectives & contribution • Methods • Conceptual model • Estimation issues • Data • Results • Conclusions and next steps 2
  3. 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. 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. 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? Malawi 5 Zambia
  6. 6. 6 Persons per km2 0 - 5 6 - 25 26 - 50 51 - 100 101 - 200 > 200 Malawi Zambia
  7. 7. Household model: participation • Rental regime decision: ordered probit 푇푒푛푎푛푡 푟푖푡 퐴푢푡푎푟푘푦 푟푖푡 퐿푎푛푑푙표푟푑 푟푖푡 = 푓 푖 , 퐴 푖푡, 풙푖푡 • Rental amount decision (ha): tobit 푃 = 푓 , 퐴 푖푡 , 풙푖푡 , P ∈ [푇, 퐿] 푅푖푡 7 • Ability: from Cobb-Douglass production function: log(푄푖푡 ) = log(풙푖푡 )휷 + 푢푖 + 휀푖푡 we recover 푎푏푖푙푖푡푦푖 = 푢 푖 from FE estimation Rents in Rents out 푎푏푖푙푖푡푦푖 푎푏푖푙푖푡푦푖 Jin & Jayne 2013
  8. 8. Household model: impacts • Welfare: 푇 + 훾퐿 푟푖푡 푦푖푡 = 훾푇 푟푖푡 퐿 + 휁푎푏푖푙푖푡푦푖 + 풙푖푡휷 + 휖푖푡 8 alt. specifications: binary vs continuous measures 푦푖푡 = 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 MWI ZMB X X X X X X X X X X X X X
  9. 9. Endogeneity in welfare model • Concern that self-selection into rental market participation may be an issue • Omitted variable bias: • Positive impact of “social capital” or something similar on both participation decisions and on welfare outcomes 푦푖푡 = 풙푖푡휷 + 휇푖 + 푢푖푡 9 FE? FD? Okay, but would lose key time-invariant regressors of interest…
  10. 10. Endogeneity in welfare model Mundlak-Chamberlain device Correlation between covariates and unobserved heterogeneity 휇푖 controlled for using MC device: Auxiliary model: 휇푖 = 휓 + 풙푖 흃 + 푎푖 where 푎푖 = 0, 휎2 the estimating equation is: 푦푖푡 = 풙푖푡휷 + 휓 + 풙푖 흃 + 푎푖 + 푢푖푡 10
  11. 11. Endogeneity in welfare model 푳 , 푹풊풕 • What about correlation between 푹풊풕 푻 and 푢푖푡? • Omitted variable bias time-varying? • Still need an instrumental variable (IV) strategy… • We use village share of renters as an instrument • Control function approach (Blundell 1986) • CF residuals are not significant, suggesting this is not a problem (so CF results not reported here) 11
  12. 12. Data Malawi household panel data Zambia 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 12
  13. 13. This presentation • Motivation • Theory • Context: Malawi & Zambia • Study objectives • Methods • Conceptual model • Estimation issues • Data • Results • Conclusions and next steps 13
  14. 14. Rental status of the sample 14 7.5% 4.3% 13.4% 5.3% 15.4% 8.9% % renting in % renting out 0.9% 0.1% 1.2% 0.7% 3.0% 0.5%
  15. 15. Rental status of the sample 15 7.5% 4.3% 13.4% 5.3% 15.4% 8.9% % renting in % renting out 0.9% 0.1% 1.2% 0.7% 3.0% 0.5% USD/ha rental rate
  16. 16. 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 16 > > > < ≠
  17. 17. Determinants of rental market participation: Malawi 17 Partial effects from ordered probit model (1) (2) (3) Renting in Autarky Renting out APE p-value APE p-value APE p-value Ability 0.0235 *** (0.000) -0.0101 *** (0.000) -0.0134 *** (0.000) Land owned (ha) -0.0367 *** (0.000) 0.0158 *** (0.000) 0.0209 *** (0.000) Adult equiv. 0.0126 *** (0.000) -0.0054 *** (0.000) -0.0072 *** (0.000) Female (=1) -0.0029 (0.684) 0.0012 (0.685) 0.0016 (0.684) Education (yrs) 0.0046 *** (0.000) -0.0020 *** (0.000) -0.0026 *** (0.000) Age of head -0.0005 * (0.072) 0.0002 * (0.085) 0.0003 * (0.068) Assets (USD) 0.0014 (0.671) -0.0006 (0.664) -0.0008 (0.677) Immigrant (=1) 0.0835 *** (0.000) -0.0519 *** (0.000) -0.0316 *** (0.000) Mortality (=1) 0.0028 (0.823) -0.0012 (0.838) -0.0015 (0.812) Matrilineal (=1) -0.0111 (0.263) 0.0049 (0.288) 0.0062 (0.247) Lag. mz price (rainy) -0.1502 (0.542) 0.0647 (0.548) 0.0855 (0.540) Lag. mz price (harv.) 0.4842 ** (0.043) -0.2085 ** (0.047) -0.2756 ** (0.044) Log rainfall 0.0283 (0.523) -0.0122 (0.533) -0.0161 (0.519) Log pop. dens. 0.0163 ** (0.019) -0.0070 ** (0.029) -0.0093 ** (0.015) Km to road 0.0002 (0.208) -0.0001 (0.236) -0.0001 (0.194) Central 0.0358 *** (0.000) -0.0131 *** (0.001) -0.0228 *** (0.002) South 0.0254 * (0.050) -0.0080 ** (0.036) -0.0174 * (0.064) N 6946 6946 6946
  18. 18. Determinants of rental participation extent: Malawi 18 Coefficient estimates from one-stage & two-stage Tobit (1) (2) (3) (4) Land rented in (ha) Land rented in (ha) Land rented out (ha) Land rented out (ha) Ability 0.2491*** 0.1763*** -0.0105 -0.0887** (0.000) (0.000) (0.853) (0.015) Land owned (ha) -0.1800** -0.1486*** 0.2742*** 0.2550*** (0.013) (0.008) (0.000) (0.000) Adult equiv. 0.0956*** 0.0897*** -0.0566** -0.0609*** (0.000) (0.000) (0.036) (0.001) Female (=1) -0.0976 -0.1155 0.0775 0.0954 (0.532) (0.264) (0.671) (0.382) Education (years) 0.0337*** 0.0296*** -0.0251** -0.0227*** (0.000) (0.000) (0.021) (0.000) Age of head -0.0071*** -0.0029* -0.0015 0.0014 (0.002) (0.074) (0.551) (0.359) Assets ('000*USD) 0.0043 0.0113 -0.1102 -0.0201 (0.805) (0.601) (0.126) (0.514) Immigrant (=1) 0.4062*** 0.4083*** -0.6420*** -0.4279*** (0.000) (0.000) (0.000) (0.000) N 6946 6946 6946 6946 Model Tobit Two-stage Tobit Tobit Two-stage Tobit
  19. 19. Welfare impacts: Malawi 19 Value of crop production (USD) (1) (2) Tenant (=1) 153*** (0.000) Landlord (=1) -70** (0.032) Ha rented in 432*** (0.000) Ha rented out -58 (0.315) Net crop income (USD) (3) (4) 83** (0.014) -0.72** (0.020) 227** (0.025) -100** (0.026) Net off-farm income (USD) (5) (6) 272*** (0.007) -174** (0.050) 34 (0.378) 66 (0.2824) Net total household income (USD) (7) (8) 286*** (0.005) -44 (0.403) 258** (0.020) -23 (0.788)
  20. 20. Welfare impacts: Malawi [2] 20 Tenant (=1) Landlord (=1) Ha rented in Ha rented out Number of months staples expected to last (9) (10) 0.39*** (0.009) 0.13 (0.567) 0.543** (0.025) 0.537** (0.020) Subjective wellbeing (score: 1-5) (11) (12) 0.65*** (0.000) -0.36 (0.146) 0.33*** (0.003) 0.016 (0.826) Probability of poverty (13) (14) -0.11*** (0.006) 0.10*** (0.000) -0.07*** (0.000) 0.02 (0.355)
  21. 21. Summarizing… 21 • Land rental markets more active in Mwi than Zmb • Likely driven by necessity with much higher PD • Market participation growing in both countries • Land 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
  22. 22. Summarizing… • Even with more participation in Malawi, transactions costs are higher in Malawi than in Zambia • More participation ≠ lower TCs • 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? 22
  23. 23. 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 23 At the median, rental rates in Malawi equal 1/3 the gross value of production
  24. 24. 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 24
  25. 25. Next steps for this work • Joint modeling of Mwi & Zmb panel data • Pooled panels • More nuanced view of distributional effects • Quantile regression; other ideas? • 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 25
  26. 26. 26 Jordan Chamberlin, Michigan State University chamb244@msu.edu Jacob Ricker-Gilbert, Purdue University jrickerg@purdue.edu

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