Are Land Policies Consistent Are Land Policies Consistent with Agricultural Productivity with Agricultural Productivity

and Poverty Reduction and Poverty Reduction Objectives?Objectives?

T.S. Jayne, Jordan Chamberlin, Milu Muyanga, Munguzwe Hichaambwa

World Bank Land and Poverty Conference

Washington, DC, March 24, 2015

MICHIGAN  STATEU  N  I  V  E  R  S  I  T  Y 

BotswanaBurkina Faso

Namibia

Senegal

Cameroon

Congo, Rep.Cote d'Ivoire

Guinea

Guinea-Bissau

Madagascar

Malawi

Mozambique

Zambia

Ethiopia

Ghana

Mali

Nigeria

Rwanda

South Africa

TanzaniaTogo

-.04

-.02

0.0

2.0

4A

vg a

nnua

l agr

icul

tura

l gro

wth

-.06 -.04 -.02 0 .02Avg annual change in rural poverty

Growth without poverty reduction Growth with poverty reduction

.04 .06

Countryag.

growthpov.

growthstart year

end year years

Botswana -1.0% -5.3% 2003 2009 6Burkina Faso -1.2% -1.9% 2003 2009 6Cameroon 2.9% 0.4% 2001 2007 6Congo, Rep. 1.0% 1.0% 2005 2011 6Cote d'Ivoire 1.1% 1.2% 2002 2008 6Ethiopia 2.0% -1.6% 2004 2011 7Ghana 1.0% -1.0% 2006 2012 6Guinea 0.9% 0.3% 2002 2012 10Guinea-Bissau 1.3% 0.4% 2002 2010 8KenyaMadagascar 0.6% 0.2% 2001 2010 9Malawi 2.7% 0.1% 2004 2010 6Mali 2.1% -1.2% 2001 2010 9Mozambique 0.3% 0.2% 2003 2009 6Namibia -0.4% -2.3% 2004 2009 5Nigeria 0.6% -0.5% 2004 2010 6Rwanda 3.2% -2.1% 2006 2011 5Senegal -2.6% -0.2% 2005 2011 6South Africa 2.3% -1.4% 2006 2011 5Tanzania 2.0% -0.6% 2001 2012 11Togo 1.7% -0.2% 2006 2011 5Zambia 2.3% 0.0% 2004 2010 6

Ag. growth = avg. annual change in value added per capita (agriculturally active population)Source: FAOStatPov. growth = avg. annual change in rural poverty headcount, using national poverty linesSource: WDI

Purpose of study:Purpose of study:• To explore the role of land inequality in affecting how economic growth

occurs

• To explore how land inequality affects labor productivity in agriculture and non-farm sectors

Purpose of study:Purpose of study:• To explore the role of land inequality in affecting how economic growth

occurs (in areas that are still primarily agrarian)

• To explore how land inequality affects labor productivity in agriculture and non-farm sectors

Main hypothesis:Main hypothesis:• the initial distribution of assets affect the rate of economic growth

• it also affects the poverty-reducing effects of the growth that does occur

TheoryTheory

• Why should land concentration affect the link between ag growth and poverty reduction?

• Concept of “multiplier effects”

Applied evidenceApplied evidence

• Ravallion and Datt (2002)• the initial percentage of landless households significantly affected the

elasticity of poverty to non-farm output in India.

• Vollrath (2007)• Rate of agricultural productivity growth inversely related to the gini

coefficient of landholdings

• Gugerty and Timmer (1999)• (n=69 countries); in countries with an initial “good” distribution of assets,

both agricultural and non-agricultural growth benefitted the poorest households.

• In countries with a “bad” distribution of assets, economic growth was skewed toward wealthier households

GINI coefficients in farm landholding

8

Period Movement in Gini coefficient:

Ghana (cult. area) 1992 2013 0.54 0.54 0.70 0.70

Kenya (cult. area) 1994 2006 0.51 0.51 0.55 0.55

Zambia (landholding) 2001 2012 0.42 0.42 0.49 0.49

Source: Jayne et al. 2014 (JIA)

MethodsMethods

• Dependent variables: (household-level)

• agricultural output per family adult labor time on farm (15-64 yrs)

• non-farm output per family adult labor time in non-farm activities

• total household income per family adult labor

MethodsMethods

• Estimated reduced form models of labor productivity• Particular interest in the coefficient of land inequality measures at district-

level• Gini coefficient• Measure of skewness• Gugerty and Timmer’s measure

• Yit = f ( Xit, Cit, LandIneqjt-1 ) + eit

• Pooled OLS with Mundlak-Chamberlin device / fixed effects, applied to panel data

DataData

• Nationwide panel data sets:

• Kenya (1997, 2000, 2004, 2007, 2010) (n=1,169)

• Tanzania (2009, 2011, 2013) (n=2,123)

• Zambia (2001, 2004, 2008) (n=3,398)

Table 1 Labor productivity in agriculture (Dep var: ln farm labor productivity) (1) (2) (3) (4) (5) (6) loglabprod_f loglabprod_f loglabprod_f loglabprod_f loglabprod_f loglabprod_f coef. p-value coef. p-value coef. p-value coef. p-value coef. p-value coef. p-value ha_cult 0.1520 (0.000)*** 0.1618 (0.000)*** 0.1487 (0.000)*** 0.1556 (0.000)*** 0.1520 (0.000)*** 0.1520 (0.000)*** headage 0.0409 (0.217) 0.0493 (0.126) 0.0463 (0.164) 0.0461 (0.158) 0.0409 (0.217) 0.0409 (0.217) hhsize -0.0828 (0.210) -0.0623 (0.341) -0.0730 (0.269) -0.0680 (0.302) -0.0828 (0.210) -0.0828 (0.210) headeduc 0.0255 (0.556) 0.0259 (0.539) 0.0214 (0.618) 0.0204 (0.633) 0.0255 (0.556) 0.0255 (0.556) femhead 0.6124 (0.459) 0.8737 (0.304) 0.7174 (0.397) 0.7823 (0.347) 0.6124 (0.459) 0.6124 (0.459) nplots 0.2968 (0.017)** 0.3490 (0.004)*** 0.2971 (0.017)** 0.2880 (0.018)** 0.2968 (0.017)** 0.2968 (0.017)** logassets 0.0488 (0.425) 0.0469 (0.431) 0.0465 (0.446) 0.0441 (0.459) 0.0488 (0.425) 0.0488 (0.425) oxplough 0.0819 (0.747) 0.0261 (0.918) 0.0642 (0.800) 0.0090 (0.972) 0.0819 (0.747) 0.0819 (0.747) tractor -0.0249 (0.938) -0.0092 (0.977) -0.0479 (0.883) -0.0235 (0.944) -0.0249 (0.938) -0.0249 (0.938) logfertkg 0.0747 (0.023)** 0.0702 (0.032)** 0.0703 (0.033)** 0.0707 (0.031)** 0.0747 (0.023)** 0.0747 (0.023)** kmroad 0.0057 (0.199) 0.0061 (0.170) 0.0056 (0.208) 0.0027 (0.541) 0.0057 (0.199) 0.0057 (0.199) kmroad2 -0.0000 (0.725) -0.0000 (0.721) -0.0000 (0.846) 0.0000 (0.613) -0.0000 (0.725) -0.0000 (0.725) avgrain -0.0007 (0.001)*** -0.0006 (0.001)*** -0.0007 (0.001)*** -0.0004 (0.028)** -0.0007 (0.001)*** -0.0007 (0.001)*** elev 0.0002 (0.235) 0.0003 (0.066)* 0.0001 (0.375) -0.0001 (0.654) 0.0002 (0.235) 0.0002 (0.235) slope 0.0066 (0.503) 0.0026 (0.797) 0.0082 (0.409) 0.0099 (0.321) 0.0066 (0.503) 0.0066 (0.503) 2013 -0.0711 (0.665) -0.0672 (0.666) -0.1919 (0.229) -0.3040 (0.057)* -0.0711 (0.665) -0.0711 (0.665) L2.gini -1.7007 (0.049)** -2.0638 (0.013)** -2.1911 (0.009)*** -1.4684 (0.075)* -1.7007 (0.049)** -1.7007 (0.049)** N 2123 2123 2123 2123 2123 2123 * p<0.10, ** p<0.05, *** p<0.01

Tanzania

Magnitude of effect on labor productivity, Kenya

LAND INEQUALITY Evaluated at

GINI 10th pct of gini 90th pct of gini % difference

log: hh farm labor productivity

10.59 10.40 -19.4

log: hh non-farm income per adult

11.48 10.74 -73.7

Kenya results: w/ gini*asset wealth interaction terms Log of farm income Log of off-farm income Log of household income Coef. P>t Coef. P>t Coef. P>t hh head age (years) -0.01 0.34 0.00 0.90 0.00 0.81 Sq. hh head age- ‘00 0.02 0.09 0.02 0.52 0.01 0.51 hh head sex (1=male) -0.13 0.05 0.09 0.69 0.03 0.64 Head education (years) 0.03 0.01 0.09 0.00 0.04 0.00 Landholding (ha) 0.06 0.00 -0.03 0.13 0.03 0.00 Sq. landholding- ‘00 -0.05 0.00 0.03 0.34 -0.02 0.00 hh productive assets- ‘million 0.17 0.06 0.39 0.55 0.21 0.06 hh own plough (1=yes) 0.12 0.15 0.11 0.63 0.06 0.25 hh own tractor (1=yes) -0.15 0.31 1.00 0.23 -0.06 0.65 hh own radio (1=yes) 0.25 0.00 0.60 0.00 0.35 0.00 Fertilizer use (’00 kg/ha cultivated) 0.11 0.02 0.09 0.39 0.09 0.00 Distance to motorable road (‘0 km) -0.12 0.36 0.34 0.40 0.04 0.67 Distance to electricity (‘0 km) 0.03 0.47 -0.19 0.18 -0.08 0.02 Distance to health center (‘0km) -0.12 0.08 -0.57 0.01 -0.06 0.12 Parliament seat won by incumbent (1=yes) 0.07 0.06 -0.11 0.33 0.08 0.01 Proportion of ruling party votes 0.23 0.00 0.33 0.07 0.20 0.00 Rainfall '00mm 0.04 0.34 0.17 0.08 -0.01 0.77 Rainfall variability -0.28 0.50 2.17 0.13 -0.06 0.86 District land inequality: gini coefficient -0.16 0.64 -3.84 0.00 -0.51 0.06 Gini* asset quintiles [base: 5th quintile – wealthiest]

gini*1st quintile [poorest] -1.02 0.00 0.22 0.63 -0.66 0.00 gini*2nd quintile -0.63 0.00 0.23 0.57 -0.43 0.00 gini*3rd quintile -0.25 0.01 0.33 0.40 -0.18 0.04 gini*4th quintile -0.26 0.00 0.31 0.35 -0.22 0.00

Kenya results: w/ gini*asset wealth interaction terms Log of farm income Log of off-farm income Log of household income Coef. P>t Coef. P>t Coef. P>t hh head age (years) -0.01 0.34 0.00 0.90 0.00 0.81 Sq. hh head age- ‘00 0.02 0.09 0.02 0.52 0.01 0.51 hh head sex (1=male) -0.13 0.05 0.09 0.69 0.03 0.64 Head education (years) 0.03 0.01 0.09 0.00 0.04 0.00 Landholding (ha) 0.06 0.00 -0.03 0.13 0.03 0.00 Sq. landholding- ‘00 -0.05 0.00 0.03 0.34 -0.02 0.00 hh productive assets- ‘million 0.17 0.06 0.39 0.55 0.21 0.06 hh own plough (1=yes) 0.12 0.15 0.11 0.63 0.06 0.25 hh own tractor (1=yes) -0.15 0.31 1.00 0.23 -0.06 0.65 hh own radio (1=yes) 0.25 0.00 0.60 0.00 0.35 0.00 Fertilizer use (’00 kg/ha cultivated) 0.11 0.02 0.09 0.39 0.09 0.00 Distance to motorable road (‘0 km) -0.12 0.36 0.34 0.40 0.04 0.67 Distance to electricity (‘0 km) 0.03 0.47 -0.19 0.18 -0.08 0.02 Distance to health center (‘0km) -0.12 0.08 -0.57 0.01 -0.06 0.12 Parliament seat won by incumbent (1=yes) 0.07 0.06 -0.11 0.33 0.08 0.01 Proportion of ruling party votes 0.23 0.00 0.33 0.07 0.20 0.00 Rainfall '00mm 0.04 0.34 0.17 0.08 -0.01 0.77 Rainfall variability -0.28 0.50 2.17 0.13 -0.06 0.86 District land inequality: gini coefficient -0.16 0.64 -3.84 0.00 -0.51 0.06 Gini* asset quintiles [base: 5th quintile – wealthiest]

gini*1st quintile [poorest] -1.02 0.00 0.22 0.63 -0.66 0.00 gini*2nd quintile -0.63 0.00 0.23 0.57 -0.43 0.00 gini*3rd quintile -0.25 0.01 0.33 0.40 -0.18 0.04 gini*4th quintile -0.26 0.00 0.31 0.35 -0.22 0.00

Kenya results: w/ gini*asset wealth interaction terms Log of farm income Log of off-farm income Log of household income Coef. P>t Coef. P>t Coef. P>t hh head age (years) -0.01 0.34 0.00 0.90 0.00 0.81 Sq. hh head age- ‘00 0.02 0.09 0.02 0.52 0.01 0.51 hh head sex (1=male) -0.13 0.05 0.09 0.69 0.03 0.64 Head education (years) 0.03 0.01 0.09 0.00 0.04 0.00 Landholding (ha) 0.06 0.00 -0.03 0.13 0.03 0.00 Sq. landholding- ‘00 -0.05 0.00 0.03 0.34 -0.02 0.00 hh productive assets- ‘million 0.17 0.06 0.39 0.55 0.21 0.06 hh own plough (1=yes) 0.12 0.15 0.11 0.63 0.06 0.25 hh own tractor (1=yes) -0.15 0.31 1.00 0.23 -0.06 0.65 hh own radio (1=yes) 0.25 0.00 0.60 0.00 0.35 0.00 Fertilizer use (’00 kg/ha cultivated) 0.11 0.02 0.09 0.39 0.09 0.00 Distance to motorable road (‘0 km) -0.12 0.36 0.34 0.40 0.04 0.67 Distance to electricity (‘0 km) 0.03 0.47 -0.19 0.18 -0.08 0.02 Distance to health center (‘0km) -0.12 0.08 -0.57 0.01 -0.06 0.12 Parliament seat won by incumbent (1=yes) 0.07 0.06 -0.11 0.33 0.08 0.01 Proportion of ruling party votes 0.23 0.00 0.33 0.07 0.20 0.00 Rainfall '00mm 0.04 0.34 0.17 0.08 -0.01 0.77 Rainfall variability -0.28 0.50 2.17 0.13 -0.06 0.86 District land inequality: gini coefficient -0.16 0.64 -3.84 0.00 -0.51 0.06 Gini* asset quintiles [base: 5th quintile – wealthiest]

gini*1st quintile [poorest] -1.02 0.00 0.22 0.63 -0.66 0.00 gini*2nd quintile -0.63 0.00 0.23 0.57 -0.43 0.00 gini*3rd quintile -0.25 0.01 0.33 0.40 -0.18 0.04 gini*4th quintile -0.26 0.00 0.31 0.35 -0.22 0.00

% change in household income per adult % change in household income per adult (with change in land gini from 25(with change in land gini from 25thth to 75 to 75thth percentile), Kenyapercentile), Kenya

% change in household income per adult % change in household income per adult (with change in land gini from 25(with change in land gini from 25thth to 75 to 75thth percentile), Kenyapercentile), Kenya

SummarySummary1. Landholding distribution influences both the rate and nature of

household income growth – in both farm and non-farm sectors• All other factors equal, households in districts at the 10th percentile of farmland

inequality (relatively low level of inequality) have • 19 to 50% higher farm incomes• 28 to 90% higher non-farm incomes• significantly higher total incomes per resident adult member than households in districts at the 90th percentile of

farmland inequality (high inequality)• Large majority of alternate specifications are highly statistically significant• When switching from lagged measures of land concentration to contemporaneous

measures, the effect of land inequality becomes more negative and statistically significant

2. Effects of land concentration are most adverse on the rural poor

Policy Questions:Policy Questions:

• Farm structure in many African countries is changing rapidly

• becoming more concentrated

• should governments want to influence this?

GINI coefficients in landholding

21

Period Movement in Gini coefficient:

Ghana (cult. area) 1992 2013 0.54 0.54 0.70 0.70

Kenya (cult. area) 1994 2006 0.51 0.51 0.55 0.55

Zambia (landholding) 2001 2012 0.42 0.42 0.49 0.49

Source: Jayne et al. 2014 (JIA)

Policy questions:Policy questions:

1. Farm structure in many African countries is becoming more concentrated – should governments want to influence this?

2. Is rising land inequality contributing to concentration of marketed farm output? Can agric development still be small-farm led?

3. Implications for poverty reduction strategies?

4. Implications for structural transformation processes?

23

T.S. Jayne: jayne@msu.edu

Jordan Chamberlin: chamb244@msu.edu

Milu Muyanga: muyangam@msu.edu

M. Hichaambwa: munguzwe@gmail.com