Development Economics II: Plan

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Development Economics II: Plan

1. Technology adoption (MK)

2. Agricultural contracts & markets (JS)

3. Social networks (MK)

4. Political Economy (MK)

5. Conflict (MK)

6. Corruption (JS)

7. Education (TB)

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Assignments

• Mock referee report

• Term paper

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Stockholm Doctoral Course Program in Economics

Development Economics II — Lecture 1

Technology Adoption

Masayuki Kudamatsu IIES, Stockholm University

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Motivations

• Adopting more productive production technology

⇒ Poverty reduction

• Examples for agriculture: High-yielding seeds varieties, fertilizer

• Examples for health:

insecticide-treated bed nets,

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Motivations (cont.)

• BUT many poor people do not

adopt such technologies (especially in Africa)

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This lecture

1. Reasons for non-adoption

2. Duflo, Kremer, & Robinson (2011)

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1. Reasons for non-adoption

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a. It’s actually unprofitable

• Duflo-Kremer-Robinson (2008)

• Ask Kenyan farmers to use fertilizer on a randomly chosen plot of their own

• Fertilizer does increase yields, only if appropriate amount is used

• But harvest labor cost ignored

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b. Profitable on average, but

heterogenous across people

• So non-adoption is rational for those farmers who would lose

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c. Lack of information

• Two types of information

• profitability

• how to use properly

• Learning by doing

• Learning from friends

• Evidence: mixed (Conley & Udry

2010: yes; Duflo et al 2006: no)

• Likely depends on type of technology

(e.g. not new or easy to use)

• Also who has information and the

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• If lack of information is the key reason for non-adoption, one-shot subsidy will be effective

• Some evidence on this:

• Dupas (2013) for antimalarial bed

nets in Kenya

• Matsumoto et al. (2011) for hybrid

maize seeds & fertilizer in Uganda

• Bryan, Chowdhury, and Mobarak

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d. Lack of education

• Access to information • Ability to learn

• Foster & Rosenzweig (1996): return to new technology is higher for the educated

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e. Lack of insurance / credit

• New technology is risky ⇒ Need insurance

• New technology may involve the fixed cost upfront ⇒ Need credit

• Difficult to disentangle one from the

other

• Gine & Yang (2009): RCT on rainfall insurance

• They find insurance reduces

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f. Behavioral reasons

• Inattention to the important aspect of how to use new technology

(Hanna et al. 2011)

• Self-control (Duflo, Kremer, & Robinson 2011)

cf. Banerjee & Mullainathan (2010)

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cf. Quick notes on manufacturing

/ macro

• Very under-studied topics

• An exception: Bustos (2011) on impact of tariff reduction

• For macro, starting point is

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2. Duflo, Kremer & Robinson

(2011)

• Does the self-control problem

explain why Kenyan farmers fail to use fertilizer?

• Earn income at harvest

• Fertilizer needed after planting

• Credit-constraint⇒ Need to save for

buying fertilizer

• But tempted to consume the saving

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Recap of Self-control problem

• Present-biased preference (β < 1)

u(ct) +β[δu(ct+1) + δ2u(ct+2) +...]

⇒ Procrastinate profitable actions w/ immediate cost and distant benefit

• Sophisticated vs Naive

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2. Duflo, Kremer & Robinson

(2011) (cont.)

• Evaluate SAFI by RCT

• Right after harvest, offer farmers w/

fertilizer voucher at a small discount (ie. free delivery)

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Evaluate counter-factual policies

• RCT cannot evaluate every policy

• Too costly (e.g. big subsidies)

• Politically infeasible

• An alternative: calibration /

structural estimation with RCT on feasible policies (cf. Todd & Wolpin 2006)

1. Build a model of behavior

2. Use treatment group outcomes to

calibrate/estimate the model

3. Validate the model against control

group outcomes

4. Predict outcomes under alternative

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Model: Players (3 types)

1. Always patient: γ of population

2. Always impatient: ψ

3. Stochastically impatient: φ

• γ + ψ + φ = 1

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Model: Preference

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Model: Preference (cont.)

Interpretation of stochastically impatient agents:

• Partially naive

• Consumption opportunity (e.g.

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Model: Period 1 (1st harvest)

• Receive income x > 2 from harvest

• Decide how much to buy fertilizer,

z ∈ {0,1,2}

• Discrete choice for tractability and

using experimental results on return to fertilizer

• Price per unit: pf1

• Utility cost f incurred if z > 0

⇐ Sample farmers use either no

fertilizer or a lot

* f > 0 creates procrastination in this

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Model: Period 1 (1st harvest)

(cont.)

• Also decide how much to save

• In this lecture, for simplicity, set return

to savingR = 0

• R: relevant only for SAFI with ex ante choice of timing, which is skipped due to time constraint in this lecture

⇒ Only reason to save is to buy fertilizer

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Model: Period 2 (planting)

• Decide how much to buy fertilizer,

z ∈ {0,1,2}

• Price per unit: pf2

• Utility cost f incurred if z > 0

• No borrowing possible

⇐ Consistent w/ the report by sample

farmers

• Fertilizer bought at period 1: cannot be resold

⇐ Consistent w/ very high transaction

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Model: Period 3 (2nd harvest)

• Receive income from harvest, Y(z)

• Denote return to additional unit of

fertilizer by

• y(1) ≡ Y(1)−Y(0)

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Model: Parametric assumptions

(consistent w/ experimental evidence on return to

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Analysis

Solve the model for

a. pf1 = pf2 = 1 (control group)

b. pf1 < 1,pf2 = 1 (SAFI treatment)

c. pf1 = 1,pf2 < 1 where period 2

discount is unanticipated in period 1 by backward induction

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Role of SAFI in this model

• Induce stochastically patient

farmers with βH in period 1 and βL

in period 2 to buy fertilizer

• They would otherwise procrastinate & end up not buying fertilizer in

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a. Behavior under

p

_f₁

=

p

_f₂

=

1

• All farmers prefer z = 1 to z = 2

• βLy(2) < βHy(2) < 1 = pft by (3)

• Empirically consistent (Appendix

Table 2, panel D:y(2) < 1)

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a. Behavior underpf1=pf2=1 (cont.)

γ

farmers (always patient)

• Period 2: Prefer z = 1 to z = 0

• βHy(1)−f > 1 by (1)

• Period 1: Prefer z = 0 & save 1 to

z = 1 & save 0

• 1+ βHf < 1+f

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ψ

farmers (always impatient)

• Period 2: Prefer z = 0 to z = 1

• 1+ f > 1+βLf > βLy(1) by (2)

• Period 1: Prefer z = 0 & save 0

• Better to consume now than later

(βL < 1)

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φ

farmers in Period 2

If saved 1 in period 1:

• z = 1 if β2 = βH

• z = 0 if β2 = βL

(by the arguments above) If didn’t save 1 in period 1:

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φ

farmers in Period 1

• If β1 = βL, save 0 & z = 0

• Buy fertilizer at period 1:

x −1−f +βLy(1) < x by (2)

• Save 1 to buy fertilizer at period 2 (in

case of β2 = βH):

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• If β1 = βH in period 1,

• Buy fertilizer in period 1 (which

dominates consumex, by (1)):

x −1−f +βHy(1)

• Buy fertilizer in period 2 by saving 1:

x −1+βH[˜p(y(1)−f) + (1−p˜)]

⇒ The latter dominates w/ sufficiently

highp˜ (“projection bias”, which is

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Summary of optimal behavior

φ farmers buy fertilizer at period 2 iff

β1 = β2 = βH. Therefore:

• γ + φp2 farmers: buy 1 fertilizer at period 2

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b. Behavior under

p

_f₁

<

1 ,

p

_f₂

=

1 (SAFI)

If pf1 is close enough to 1,

• Patient farmers: still prefer z = 1 to

z = 2 by (3)

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b. Behavior underpf1 <1,pf2=1 (SAFI) (cont.)

• φ farmers with β1 = βH now prefer z = 1 at period 1 if

p_f1 +f < 1 + βHf (if p˜ → 1)

or

pf1 < 1 − (1 − βH)f

• Subsidy can be very small

⇐ No need to compensate the foregone

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b. Behavior underpf1 <1,pf2=1 (SAFI) (cont.)

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c. Unanticipated discount at

period 2

• For φ famers with β1 = βH, β2 = βL

to buy fertilizer in period 2, we need

pf2 < βLy(1) − f

which is smaller than 1 − (1 − βH)f

if βL is sufficiently low

⇐ Need to compensate the foregone

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⇒ Same level of discount at period 2 as SAFI: no effect

• To reach same adoption rate as

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Experimental design

• Season 1: Offer randomly selected farmers with SAFI

• Season 2: Offer randomly selected farmers (stratified by season 1 treatment status) with

1. SAFI

2. Free delivery at planting

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Main results (Table 3)

Fraction of farmers using fertilizer for SAFI vs control

•

45

% vs

34

% in season 1

(difference significant at 1%)

•

38

% vs

28

% in season 2

(difference significant at 10%)

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Methodological Digression:

control for covariates in RCT

• Beauty of RCT: OLS is unbiased

• Once control for covariates, this is

no longer true (still consistent, though)

⇐ See Freedman (2008). Also Deaton

(2010, p. 444); Imbens (2010, p. 411)

• Report the estimated treatment

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Comparison to other discount

schemes (Table 4B)

• Free delivery at planting

• Usage up by 9-10 % pt. (not

significant)

• Size of impact significantly

smaller

than SAFI (p-value 0.08)

• 50% discount at planting

• Usage up by 13-14 % pt.

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Impact of Malawian subsidy

Estimate the impact of heavy

subsidization of fertilizer as in Malawi

a. Use some RCT outcomes to calibrate the model

b. Validate the model against other RCT outcomes

c. Predict outcomes under

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a. Calibration

Obtain γ, φ, ψ,p from adoption rates

• Control (γ + φp2): 0.24

• SAFI treated (γ + φp): 0.402

• Control over 3 seasons (γ +φp6): 0.14

⇒ γ = 0.14, φ = 0.69, ψ = 0.17,

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b. Model validation

• Never adopt over 3 seasons among control

• Simulated: ψ +φ(1−p2)3 = 0.60

• Observed: 0.57

• ψ farmers never adopt because 1 + f > βLy(1) (typo in the paper)

• y(1) ≤ 1.227 (Appendix Table 2)

• If f →0, 1+f > βLy(1) ⇒ βL < 0.81

• Estimates from Laibson et al. (2007):

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c. Evaluate Malawian subsidy

program

If pf1 = pf2 = 1/3, the model predicts

• z = 2 by γ +φp2 = 24% of farmers

• By ineq. (3)

• z = 1 by ψ +φ(1 − p2) = 76% of farmers

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Cost of program

• Assume marginal cost of public funds: 0.2

• Amount of fertilizer used: 2*0.24+1*0.76 = 1.24

⇒ 0.2*0.67*1.24=0.166

Loss due to too much use of fertilizer

• y(2) −1 = −0.525 (Appendix Table 2)

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Benefit of program

• 76% farmers: now use 1 unit of

fertilizer

• y(1) −1 ∈ [0.15,0.227] (Appendix Table 2)

⇒ In the range of 0.114 to 0.173

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Evaluate SAFI (Table 5 (2))

Cost of public funds

• Assume pf1 = 0.9

• 40% use fertilizer

• 0.2*(1-0.9)*0.40 = 0.008 Benefit

• φ(p − p2) farmers now use fertilizer

• (y(1) − 1)φ(p − p2) ∈ [0.024,0.037]

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Summary

SAFI outperforms heavy subsidization

• To help stochastically impatient farmers to adopt fertilizer for sure

• When too much use of fertilizer

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Response to criticism

• Calibration: sensitive to parameter /

functional form assumptions

• But several aspects of the model favor heavy subsidy

• Low marginal cost of public funds

• Heavy subsidy does induce impatient

farmers to use fertilizer

• No env. cost of fertilizer overuse

• SAFI outperforms laissez-faire if at

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Unsolved issues

• How to induce always impatient farmers to adopt?

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3. Suri (2011)

• Take heterogenous treatment effect

seriously

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Research question

Can heterogeneous return explain why not all farmers in Kenya use the high average return production technology (hybrid maize & fertilizer)?

• Some farmers may have negative return to such technology

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Data

• Panel surveys of over 1200

households in 1997 & 2004

• Representative of rural

maize-growing areas in Kenya (Figure 1)

cf. Duflo-Kremer-Robinson’s sample:

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Some descriptive stats

• Hybrid maize adoption rates: stable over sample period (Figure 2)

• Hybrid maize available since 1960s

⇒ Lack of information unlikely to explain

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Some descriptive stats (cont.)

• Very few plant both hybrid and non-hybrid

⇒ Technology adoption is modeled as a

binary choice

• 50% always adopt; 30% switch in and out of hybrid use over sample period (Table IID)

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Some descriptive stats (cont.)

• Data on profits: unavailable

⇐ Hard to impute the wage of family

labor

• Input costs: comparable across technologies except for fertilizer (Table IIB)

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Empirical strategy

We want to estimate βi in the following

yield function

yit = δt +βihit + αi +εit

• yit: Yield for farmer i in year t

• δt: Year fixed effect

• hit: Hybrid maize adoption indicator

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Empirical strategy (cont.)

By setting βi ≡ β + φθi, αi ≡ θi + τi

yit = δt +βihit + αi + εit

= δt + (β + φθi)hit + (θi +τi) +εit

= δt +βhit +τi + (1 + φhit)θi + εit

• τi: absolute advantage

• θi: comparative advantage

(orthogonal to τi by construction)

• how much more productive in hybrid

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Empirical strategy (cont.)

yit = δt +βhit +τi + (1 + φhit)θi + εit

• φ > 0: More productive farmers gain more from adoption

• φ < 0: Less productive farmers gain more (comparative advantage)

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Empirical strategy (cont.)

yit = δt +βhit +τi + (1 + φhit)θi + εit

• How to estimate φ and the distribution of θi?

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Empirical strategy (cont.)

1. Linearly project θi on adoption

history (ie. decompose θi into the

parts correlated and uncorrelated with adoption)

θi = λ0 +λ1hi1 + λ2hi2 +λ3hi1hi2 + υi

• where �_i θi = 0 (so that β is

average return & λ0 is a function of

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Empirical strategy (cont.)

2. Substitute θi into the yield equation

yit = δt + βhit + τi + (1 + φhit)θi + εit

to obtain:

yi1 = δ1 + γ1hi1 + γ2hi2 +γ3hi1hi2 + ξi1

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Empirical strategy (cont.)

where

γ1 = (1 +φ)λ1 +β +φλ0

γ2 = λ2

γ3 = (1 +φ)λ3 +φλ2

γ4 = λ1

γ5 = (1 +φ)λ2 +β +φλ0

γ6 = (1 +φ)λ3 +φλ1

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Empirical strategy (cont.)

3. Regress yit on hi1,hi2,hi1hi2 to estimate γ’s by SUR

4. Estimate λ’s, β, and φ from the estimated γ’s by minimum distance

• 5 unknowns, 6 equations

5. Predict θi for each adoption history,

using estimated λ’s

ˆ

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Intuition behind this method

• γ2: difference in period-1 yields between joiners & non-adopters

• γ1 and γ5 then allow you to estimate

φ (and β)

• Also estimate θi for always-adopters

from their yields (and refine the estimates for φ)

• Assuming φ also applies to non-adopters, recover

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Empirical strategy (cont.)

• This method can be modified to allow (i) covariates in yield

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Identifying assumption

ξit ≡ υi +φυihit +τi +εit: uncorrelated w/

adoption history (hi1,hi2,hi1hi2)

• Otherwise the estimated γ’s are biased

• By construction, υi is uncorrelated

with adoption history

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Identifying assumption (cont.)

ξit ≡ υi +φυihit +τi +εit: uncorrelated w/

adoption history (hi1,hi2,hi1hi2)

• τi is safely assumed to be

uncorrelated

• τi affects yield in the same way

irrespective of adoption

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εit is uncorrelated with adoption history?

• Shocks between t = 1 (1997) &

t = 2 (2004) may affect both hi2 &

εi2

• Household structure controlled for

• Drop two HIV-prevalent districts from

the sample (⇒ Results stronger)

• Survey evidence suggests: hit

largely driven by availability of seed & fertilizer, which is uncorrelated w/

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Results

• φ <ˆ 0 (Tables VIII)

• θˆi < 0 for non-adopters (Figure 5A)

⇒ Return to hybrid (β + φθi): highest

for non-adopters (Figure 5B)

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Results (cont.)

• Non-adopters face huge cost of

adoption (high cost of transport to fertilizer seller) (Table IX)

• Price of hybrid seeds: fixed across

Kenya until 2004

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Results (cont.)

• Adopters: return is high but smaller than non-adopters

• Switchers: small θi & return to

hybrid around zero

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Unresolved issues

• How to reconcile

Duflo-Kremer-Robinson and Suri?

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