Development Economics II: Plan

  

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) Assignments

• Mock referee report
• Term paper

  Deadline: 31 January 2014

  

Stockholm Doctoral Course Program in Economics

Development Economics II — Lecture 1

  

Technology Adoption

Masayuki Kudamatsu

  

IIES, Stockholm University

30 October, 2013

  Motivations

  Adopting more productive

• production technology ⇒ Poverty reduction Examples for agriculture:
• High-yielding seeds varieties, fertilizer Examples for health:
• insecticide-treated bed nets, contraception methods, deworming

Motivations (cont.)

  BUT many poor people do not

• adopt such technologies (especially in Africa)

This lecture

  1. Reasons for non-adoption

  

2. Duflo, Kremer, & Robinson (2011)

  3. Suri (2011)

1. Reasons for non-adoption

  For more detailed literature survey,

• see Foster & Rosenzweig (2010) and Jack (2013).

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
• (Foster & Rosenzweig 2010)

  

b. Profitable on average, but

heterogenous across people

  So non-adoption is rational for

• those farmers who would lose Suri (2

c. Lack of information

• profitability

  Two types of information

• 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
• incentive to disseminate (BenYishay

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
• (2011) for seasonal migration in Bangladesh

d. Lack of education

  Access to information

• Ability to learn
• Foster & Rosenzweig (1996): return
>to new technology is higher for the educated But not crystal-clear

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

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)

  For behavioral issues in development in general, see Karlan & Appel (2011)

  

cf. Quick notes on manufacturing

/ macro

  Very under-studied topics

• An exception: Bustos (2011) on
• impact of tariff reduction For macro, starting point is
• Acemoglu, Aghion, and Zilibotti (2006)

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

• before fertilizer is needed

Recap of Self-control problem

• 2

  Present-biased preference ( β < 1)

  u (c ) + β[δu(c ) + δ u (c ) + ...]

  

t t t

• 1 +2

  

⇒ Procrastinate profitable actions w/

  immediate cost and distant benefit Sophisticated vs Naive

• cf. O’Donoghue-Rabin (1999)

2. Duflo, Kremer & Robinson

  (2011) (cont.)

• Right after harvest, offer farmers w/

  Evaluate SAFI by RCT

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

  Use the RCT results on SAFI to

  evaluate counter-factual policy

  large subsidy for fertilizer purchase, as in Malawi) Evaluate counter-factual policies

• Too costly (e.g. big subsidies)

  RCT cannot evaluate every policy

• Politically infeasible
• An alternative: calibration /
• structural estimation with RCT on

  (cf. Todd & Wolpin 2006)

  feasible policies

  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 Model: Players (3 types)

  γ of population

  1. Always patient:

  2. Always impatient: ψ

  3. Stochastically impatient: φ

• (cf.

  γ + ψ + φ = 1

  γ and ψ farmers needed for calibration purpose) Model: Preference

  Every player is present-biased, but its extent differs:

  1. Always patient ( β < 1) H T

  � u + β u

  

t H

τ τ =t+1

  β < β

  2. Always impatient ( L H ) T

  � u u

  

t + β L

τ

3. Stochastically impatient

  t

  w/ prob. 1 − p and over estimate p: ˜ p > p

  τ

  u

  τ =t+1

  �

  L T

  w/ prob. p u

  Model: Preference (cont.)

  τ

  u

  τ =t+1

  �

  t + β H T

  u

• β

Model: Preference (cont.)

  Interpretation of stochastically impatient agents:

• Partially naive
• Consumption opportunity (e.g. party) occasionally arrives

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

• f 1

  Price per unit: p

• ⇐ Sample farmers use either no

  Utility cost f incurred if z > 0

  fertilizer or a lot

• f > 0 creates procrastination in this

Model: Period 1 (1st harvest) (cont.)

• In this lecture, for simplicity, set return

  Also decide how much to save

• to saving R = 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 at period 2 Model: Period 2 (planting)

• Decide how much to buy fertilizer, z ∈ {0, 1, 2}
• Price per unit: p f 2
• 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

costs in the study area (fn. 4) 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)

• y (2) ≡ Y (2) − Y (1)

• f (2) 1 > β

  H

  3 (4)

  1

  y (2) <

  L

  β

  3 (3)

  1

  y (2) >

  3

  Model: Parametric assumptions

  1

  y (1) >

  L

  f > β

  L

  y (1) > 1 + f (1) 1 + β

  H

  β

  (consistent w/ experimental evidence on return to

  Analysis

  Solve the model for p

  a. = p = 1 (control group) f 1 f 2

  p

  b. < 1, p = 1 (SAFI treatment) f 1 f 2

  p

  c. = 1, p < 1 where period 2 f 1 f 2

  discount is unanticipated in period 1 by backward induction

  ⇐

  Sophisticated present-biased players: fully anticipate their present-biased behavior in the Role of SAFI in this model

  Induce stochastically patient

• farmers with β in period 1 and β

  H L

  in period 2 to buy fertilizer They would otherwise procrastinate

• & end up not buying fertilizer in period 2

a. Behavior under p

  f 1 = p f 2

  = 1

  = 1 to z = 2

• All farmers prefer z
• β L y (2) < β H y (2) < 1 = p ft by (3)
• Empirically consistent (Appendix Table 2, panel D: y (2) < 1)

  ⇒ Look at whether each farmer

  prefers z = 1 or z = 0 below

a. Behavior under p

  _{f 1 f 2} = p = 1 (cont.)

  γ farmers (always patient)

• _H

  Period 2: Prefer z = 1 to z = 0

  β y (1) − f > 1 by (1)

• = 0 & save 1 to
• z = 1 & save 0

  Period 1: Prefer z

• β < 1 + f _{H f}

  1

• ⇒ Save at period 1 & buy 1 unit of

  fertilizer at period 2

a. Behavior under p

  _{f 1} = p _{f 2}

  = 1 (cont.) ψ farmers (always impatient)

• Period 2: Prefer z = 0 to z = 1

  > β _{L y} (1) by (2)

• 1 + f > 1 + β _{L f}
>Period 1: Prefer z = 0 & sa
• Better to consume now than later

  ( β _L < 1) ⇒ Never buy fertilizer

a. Behavior under p

  _{f 1 f 2} = p = 1 (cont.)

  φ farmers in Period 2

  If saved 1 in period 1: z = 1 if β

• 2 = β H

• 2 L

  z = 0 if β = β

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

  = 0

a. Behavior under p

  _{f 1 f 2} = p = 1 (cont.)

  φ farmers in Period 1

• 1 L

  If β = β , save 0 & z = 0

  Buy fertilizer at period 1: • − 1 − f + β (1) < x by (2) x L y Save 1 to buy fertilizer at period 2 (in • case of β = β ): _{2 H} x − 1 + β (y (1) − f ) < x by (2) _L

a. Behavior under p

  _{f 1 f 2} = p = 1 (cont.)

• 1 = β H in period 1,

  If β

  Buy fertilizer in period 1 (which

• dominates consume x, by (1)): − 1 − f + β (1) x H y Buy fertilizer in period 2 by saving 1: • x − 1 + β [˜ p (y (1) − f ) + (1 − ˜ p )] _H

  ⇒ The latter dominates w/ sufficiently high ˜ p (“projection bias”, which is

a. Behavior under p

  _{f 1 f 2} = p = 1 (cont.)

  Summary of optimal behavior

  φ farmers buy fertilizer at period 2 iff β = β = β

  1

2 H . Therefore:

  2

  γ + φp farmers: buy 1 fertilizer at

• period 2

  2

  φ(1 − p ) + ψ farmers: do not buy

• fertilizer

b. Behavior under p < 1, p = 1 f 1 f 2

  (SAFI)

  If p is close enough to 1,

  f 1

  Patient farmers: still prefer z = 1 to

• z

  = 2 by (3) Impatient farmers: still prefer z = 0

• to z = 1 by (2)

b. Behavior under p

  _{f 1}

< 1, p

_{f 2}

  = 1 (SAFI) (cont.)

  

1

  = β

  H

  now prefer z = 1 at period 1 if p

• φ farmers with β
• f < 1 + β H f (if ˜ p
• Subsidy can be very small

  f 1

  → 1) or p

  f 1

  < 1 − (1 − β

  H

  )f

  ⇐ No need to compensate the foregone

b. Behavior under p

  _{f 1} < 1, p _{f 2}

  = 1 (SAFI) (cont.) ⇒

  Adoption rate w/ SAFI: γ + φp

c. Unanticipated discount at

  period 2

• 1 H

  For φ famers with β = β , β = β

  2 L

  to buy fertilizer in period 2, we need p y < β (1) − f

  f 2 L

  which is smaller than 1 − (1 − β )f

  H

  if β is sufficiently low

  L ⇐ Need to compensate the foregone

consumption (ie. 1 − β L y (1))

  ⇒ Same level of discount at period 2

  as SAFI: no effect To reach same adoption rate as

• SAFI, p f 2 needs to be very low (e.g. 50% discount)

Experimental design

  Season 1: Offer randomly selected

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

  farmers with

  1. SAFI

  2. Free delivery at planting

  3. Unexpected 50% discount at planting Main results (Table 3)

  Fraction of farmers using fertilizer for SAFI vs control

• (difference significant at 1%)

  45 % vs 34 % in season 1

• (difference significant at 10%)

  38 % vs 28 % in season 2

  Regression analysis confirms these

• findings (Table 4)

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

• effect both w/o & w/ covariates

Comparison to other discount schemes (Table 4B)

• Usage up by 9-10 % pt. (not

  Free delivery at planting

• significant)

  Size of impact significantly

• than SAFI (p-value 0.08)

  smaller

• Usage up by 13-14 % pt.

  50% discount at planting

• Size of impact similar to SAFI

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

  (hypothetical) heavily-subsidized

a. Calibration

  Obtain γ, φ, ψ, p from adoption rates

2 Control ( γ + φp ): 0.24

• SAFI treated ( γ + φp): 0.402
• 6

  Control over 3 seasons ( γ + φp ):

• 0.14

  ⇒ γ = 0.14, φ = 0.69, ψ = 0.17,

  p = 0.38

b. Model validation

• Never adopt over 3 seasons among control
• Simulated: ψ + φ(1 − p
² ) ³ =
• Observed: 0.57
• ψ farmers never adopt because 1 + f > β

  L

  y (1)

  (typo in the paper)

• y (1) ≤ 1.227 (Appendix Table 2)

  y (1) ⇒ β _L < 0.81

• If f → 0, 1 + f > β _L
• Estimates from Laibson et al. (2007): around 0.7

c. Evaluate Malawian subsidy program

  If p

  f 1

  = p

  f 2

  = 1/3, the model predicts

• z

  = 2 by γ + φp

  2

  = 24% of farmers

• By ineq. (3)

  2

  ) = 76% of farmers

• z = 1 by ψ + φ(1 − p
• By ineq. (2) & (4)

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)

  0.525*0.24=0.126

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

  ⇒ Net welfare gain: -0.119 to -0.178 (Table 5 (1))

  Evaluate SAFI (Table 5 (2))

  Cost of public funds

  f 1

  = 0.9

• Assume p
• 40% use fertilizer
• 0.2*(1-0.9)*0.40 = 0.008

  Benefit

  2

  ) farmers now use fertilizer

• φ(p − p

  2

  ) ∈ [0.024, 0.037] ⇒ Net welfare gain: 0.016 to 0.029

• (y (1) − 1)φ(p − p

Summary

  SAFI outperforms heavy subsidization To help stochastically impatient

• farmers to adopt fertilizer for sure When too much use of fertilizer
• hurts

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
• least 24-40 % of farmers are

Unsolved issues

  How to induce always impatient

• farmers to adopt?

  ⇐ Adoption rate under SAFI:

  still below 50%

3. Suri (2011)

  Take heterogenous treatment effect

• seriously One of the best job market papers
• in development in 2005/06

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

  ⇒ No adoption is a fully rational

  decision for them 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:

only Busia District in Western Kenya

  Some descriptive stats

  Hybrid maize adoption rates: stable

• over sample period (Figure 2)

  Hybrid maize available since 1960s

• ⇒ Lack of information unlikely to explain

  non-adoption

  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)

  ⇐ Crucial for estimation strategy Some descriptive stats (cont.)

• ⇐ Hard to impute the wage of family

  Data on profits: unavailable

  labor

  Input costs: comparable across

• technologies except for fertilizer (Table IIB)

  ⇒ Estimate the yield function, not the profit function

• β
• α
• ε

  i

  t : Year fixed effect

  

it : Yield for farmer i in year t

  it

  i

  

it

  h

  t

  = δ

  it

  in the following yield function y

  i

  We want to estimate β

  Empirical strategy

• y
• δ
• h

  it : Hybrid maize adoption indicator Empirical strategy (cont.)

  By setting β i ≡ β + φθ i , α i ≡ θ i + τ i y = δ + β h + α + ε

  it t i it i it

  = δ + (β + φθ )h + (θ + τ ) + ε

  t i it i i it

  = δ + βh + τ + (1 + φh )θ + ε

  t it i it i it

• i

  τ : absolute advantage

• i

  θ : comparative advantage

  (orthogonal to τ by construction)

  i Empirical strategy (cont.)

  y = δ + βh + τ + (1 + φh )θ + ε

  

it t it i it i it

  φ > 0: More productive farmers

• gain more from adoption φ < 0: Less productive farmers gain
>more (comparative advantage) φ = 0: No heterogeneity in re

Empirical strategy (cont.)

  y = δ + βh + τ + (1 + φh )θ + ε

  

it t it i it i it

  How to estimate φ and the

• distribution of θ ?

  i

  Below illustrate this in the case of 2

• periods, no covariates, & decision

Empirical strategy (cont.)

1. Linearly project θ i on adoption

  θ history (ie. decompose i into the parts correlated and uncorrelated with adoption) h h

  θ = λ + λ + λ

  i 1 i1 2 i2

  h h

• λ + υ

  3 i1 i2 i

  � where θ = 0 (so that β is

• i i

  average return & λ is a function of

2. Substitute θ

  = δ

  Empirical strategy (cont.)

  i

  into the yield equation y

• βh
• τ
• (1 + φh
• ε

  it

• γ
• ξ
• γ
• γ
• φλ

  i1

  i2

  h

  5

  i1

  h

  4

  2

  = δ

  i2

  y

  i1

  i2

  h

  

3

  h

  t

  i2

  h

  i1 + γ

  h

  1

  i1 = δ

1 + γ

  to obtain: y

  it

  i

  )θ

  it

  i

  it

  2

  it

  5

  i

  it

  h

  i

  i

  = υ

  it

  ξ

  1

  3

  = (1 + φ)λ

  6

  2

  = (1 + φ)λ

  γ

  Empirical strategy (cont.)

  

1

  = λ

  4

  γ

  2

  3

  = (1 + φ)λ

  3

  γ

  

2

  2 = λ

  γ

  1 = (1 + φ)λ 1 + β + φλ

  where γ

• β + φλ γ
• φλ
• φυ
• τ
• ε

Empirical strategy (cont.)

  h

  3. Regress y on h , h , h to it i1 i2 i1 i2

  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 θ i for each adoption history,

  using estimated λ’s ˆ h h

  θ = ˆ λ + ˆ λ + ˆ λ

  i 1 i1 2 i2 Intuition behind this method

• 2

  γ : difference in period-1 yields

  between joiners & non-adopters Yield equation

• y

  it = δ t + βh it + τ i + (1 + φh it )θ i + ε it

  tells you that γ

  2 is essentially

  joiner’s θ (relative to non-adopters)

  i Intuition behind this method (cont.)

• 5

  γ : difference in period-2 yields

  between joiners & non-adopters Yield equation

• y

  = δ + βh + τ + (1 + φh )θ + ε

  it t it i it i it

  tells you that γ

  5 is essentially

  joiner’s φθ i (plus β) Intuition behind this method (cont.)

• 2

  If γ > 0 and γ < 0, for example,

  

5

  this means φ < 0 (as long as β is not too negative). But we don’t know β.

• We need more information. S

Intuition behind this method (cont.)

• 4 : difference in period-2 yields

  γ

  between leavers & non-adopters Yield equation

• y = δ + βh + τ + (1 + φh )θ + ε

  it t it i it i it

  tells you that γ is essentially

  4

  leaver’s θ

  i Intuition behind this method (cont.)

• 1

  γ : difference in period-1 yields

  between leavers & non-adopters Yield equation

• y

  = δ + βh + τ + (1 + φh )θ + ε

  it t it i it i it

  tells you that γ

  1 is essentially

  leaver’s φθ i (plus β) Intuition behind this method (cont.)

• 1 and γ

  γ

  5 then allow you to estimate

  φ (and β)

• i

  Also estimate θ for always-adopters

  from their yields (and refine the estimates for φ) Assuming φ also applies to

• non-adopters, recover

Empirical strategy (cont.)

  This method can be modified to

• allow (i) covariates in yield equation, (ii) more than 2 periods, and (iii) use of fertilizer as another adoption decision

Identifying assumption

  ξ h

  

it ≡ υ i + φυ i it + τ i + ε it : uncorrelated w/

  , h , h h adoption history (h i1 i2 i1 i2 ) Otherwise the estimated γ’s are

• biased By construction, υ is uncorrelated
• i

  with adoption history h h θ = λ + λ + λ

  i 1 i1 2 i2

  h h

• λ + υ

  3 i1 i2 i Identifying assumption (cont.)

  ξ ≡ υ + φυ h + τ + ε

  

it i i it i it : uncorrelated w/

  adoption history (h , h , h h )

  i1 i2 i1 i2

• i

  τ is safely assumed to be

  uncorrelated

  τ affects yield in the same way • _i irrespective of adoption

• it

  ε is uncorrelated with adoption

  history?

  ε is uncorrelated with adoption history?

  it

• t

  Shocks between t = 1 (1997) &

  = 2 (2004) may affect both h i2 & ε

  i2 Household structure controlled for

• Drop two HIV-prevalent districts from
• ⇒ Results stronger) the sample (
• it

  Survey evidence suggests: h

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

  ˆ φ < 0 (Tables VIII)

• ˆ
• i

  θ < 0 for non-adopters (Figure 5A)

  ⇒ + φθ

  Return to hybrid (β i ): highest for non-adopters (Figure 5B)

  ⇒ Less productive farmers (θ < 0) i

  who would gain more from adoption (φ < 0) DO NOT adopt hybrid maize

  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

  Very large IV estimates when * distance to fertilizer shop used as instruments (Table IV): refer to these non-adopters Results (cont.)

  Adopters: return is high but smaller

• than non-adopters Switchers: small θ & return to
• i

  hybrid around zero Nearly zero FE estimates (Table *

  IIIA): refer to switchers Unresolved issues

  How to reconcile

• Duflo-Kremer-Robinson and Suri? More generally, what are the
• relative contributions of different factors for non-adoption?

  _{of the Poor. NBER Working Paper 15973.}

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