How Does Population Density Affect Agricultural Intensification and - - PowerPoint PPT Presentation

How Does Population Density Affect Agricultural Intensification and Household Well-being in Africa? Insights from 5 countries

Rui Benfica, Jordan Chamberlin, Derek Headey, Thom Jayne, Anna Josephson, David Mather, Milu Muyanga, Jacob Ricker-Gilbert

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• Population in SSA expected to grow by 500

million in next 20 years.

• Many rural households live in areas of relatively

high population density.

• large tracts of un-used land in many countries.
• What does this mean for agriculture and food

security?

Zambia Kenya

Why does Population Density Matter?

Relationships between farm size and household income

ETHIOPIA

Ha

L

• g

( P e r C a p i t a I n c

• m

e ) Per Capita Land Access (Ha)

.25 .5 .75 1 5.4 5.8 6.2

KENYA

Ha .25 .5 .75 1 9.0 9.4 9.8

RWANDA

Ha .25 .5 .75 1 3.8 4.0 4.2 4.4

MOZAMBIQUE

Ha .25 .5 .75 1 3.0 3.5 4.0

ZAMBIA

Ha .25 .5 .75 1 3.2 3.4 3.6 3.8

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Descriptive evidence reveals that larger farms have higher incomes.

Boserupian theory predicts that population density drives intensification

Research questions: 1) Through what channels does population density drive intensification? 2) What are the constraints to smallholder intensification in areas of high population density? 3) Is there a population density threshold beyond which farmers are no longer able to intensify production?

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Population density Output prices Wage rates Landholding Input demand Output supply Income Information flow, Institution development, Transaction costs Land prices Non-market institutions unobserved

• bserved
• bserved
• bserved

Pathways Through which Population Density Affects Household Outcomes

INDIRECT EFFECTS DIRECT EFFECTS

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Household Intensification Under Population Density

Yit = α1Dt + α2D2

t + Ptβ + ρLit + Xit𝜖 + ci + vit

Y: Outcome measure of interest D: population density and its square. P: factor and output prices L: landholding X: other household, and community factors c: unobserved time-constant effects v: unobserved time-varying effects H0: α 𝟐, α 𝟑= 0, tests the direct effect of pop den on Y

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Indirect Partial Effect + Total Partial Effect

Landholding (farm size) Lit = η1Dt + η2D2

t + Mit𝜖 + μit

Prices Pit = ζ1Dt + ζ2D2

t + Zit𝜖 + εit

Calculating Total Effect (combine D and D2) Yit = αDt + (Dt)Pitβ + ρLit(Dt) + Xit𝜖 + ci + vit

Total Partial Effect

𝜖Yit 𝜖Dt = 𝜖Yit 𝜖Dt + 𝜖Yit 𝜖Pit ∗ dPit dDt + 𝜖Yit 𝜖Lit ∗ dLit dDt

• r

𝜖Yit 𝜖Dt = α

+ β (ζ1+ 2ζ2Dt) + ρ (η1+ 2η2Dt)

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DIRECT EFFECT INDIRECT EFFECT + = TOTAL EFFECT

Is population density endogenous?

• Maybe

– Due to omitted variables – Or due to reverse causality

• correlation between covariates and unobserved

heterogeneity ci controlled for using the correlated random effects (CRE) estimator. ci = Ψ + 𝑌𝑗 δ + ai; where ai = (o, σ2)

• Some countries used IV methods with control

function approach.

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Estimation Procedure

• All models estimated linearly.
• Pooled CRE
• Estimate via Seemingly Unrelated Regression

(SUR).

– Models are linked through their error terms, so allows us to directly compute total partial effects. – Efficiency gain over equation-by-equation.

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Case Studies

Ethiopia Kenya Zambia Malawi Mozambique

“Land abundance” “Land constrained” (high land/labor ratios) (low land/labor ratios) Zambia Northern Mozambique Southern Malawi Kenya

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Gridded Population Data

• High-resolution gridded estimates of rural

population distributions

– GRUMP (Global Rural-Urban Mapping Project, Balk and Yetman 2004) – AfriPop project (Linard et al. 2012)

• Significant improvements over earlier databases

– input statistical data are at fairly high levels of disaggregation – reporting units further disaggregated spatially

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INDIVIDUAL COUNTRY CASE STUDIES