Big question in this lecture Does infrastructure

  

Stockholm Doctoral Course Program in Economics

Development Economics I — Lecture 8

Infrastructure

  Masayuki Kudamatsu

  IIES, Stockholm University

Big question in this lecture

  

Does

infrastructure

promote It’s NOT easy to empirically identify the impact of infrastructure Endogenous placement of

• infrastructure

  Infrastructure may be a response to

• rising economic opportunities (reverse causality) Govt. may target poor areas to
• improve their economic conditions

  What’s the mechanism?

Evaluation of infrastructure: emerging field in development

• Dams (Duflo & Pande 2007 QJE)
• Mobile phones (Jensen 2007 QJE)
• Excellent example of when DID works best
• Electricity (Dinkelman 2008)
• Careful analysis on heterogenous treatment effect
• Railroads (Donaldson 2008)

  Duflo and Pande (2007, QJE)

• Using geography as instrument to
• estimate the impact of infrastructure

  1-1 Research questions

  What’s the impact of irrigation dams

• on agricultural production and rural poverty? What’s the distributional
• consequence of building irrigation dams?

  1-2 Data

  Annual agricultural production,

• 1971-1999, for 271 districts Poverty data in 1973, 83, 87, 93, 99
• for 374 districts Dams: location (nearest city) and
• date of completion from World Registry of Large Dams

Data (cont.)

  Ratio to district area of river area with gradient more than 6%, 3-6%, 1.5-3% Data source: GTOPO30 (elevation

• at 30-arc second grid space) & Digital Chart of World (river drainage network) Identify GTOPO30 cells where
>rivers flow Calculate gradient in such c

  

1-3 Empirical strategy: 1st stage

_!

  

4

• D = ν + µ α (RGr ∗ ¯ D )

  ist i st k ki st k

=2

_!

  4

• β(M ∗ ¯ ) + α (RGr ∗ l )

  i D st k ki t k =2

• ω ist D ist : # of dams in district i of state s in

RGr ki : fraction of river areas with gradient falling in category k

• k : 2 for 1.5 to 3%; 3 for 3-6%; 4 for above 6%
• River flowing at some gradient: ideal for irrigation dams
• Very steep river: ideal for power generation dam

  D st : # of dams in India in year t (Figure III) multiplied by fraction of dams in state s in 1970

• ¯
• Why not the actual # of dams in state
• i

  ν : district FE

• st

  µ : state-year FE (different trends

  across states)

  M

• i

  : area, elevation, overall gradient,

  river length _{i st}

  Why (M ∗ ¯ D ) included?

• l
• t : year dummies

  

(RGr ∗ l ) included?

_{ki t} Why

  1st stage results (Table II)

  Dictricts w/ more river gradient

• 1.5-3% or above 6%: more dams built Dictricts w/ more river gradient
• 3-6%: less dams built

  

Empirical strategy: 2nd stage

U U

  y ist = γ i + η st + δD ist + δ D _{Z U U}

  ist

• Z ist δ + Z ist δ Z + ε ist _U

  U

  w/ ˆ D ist , ˆ D , Z ist , Z ist as instruments

  ist

• ist : outcome variable

  y

• i : district FE

  γ

• st U

  η : state-year FE

  D : # of dams in all upstream

  Z

• _U

  ist i st ki t

  : vector of M ∗ ¯ D , RGr ∗ l

  Z

• ist : vector of M i ∗ ¯ D st , RGr ∗ l

  ki t for

  upstream districts ˆ

• ist U

  D ist ,: fitted value for D

  ˆ D

• ist : the sum of fitted values for

  D over all upstream districts

  ist Empirical strategy: Method

  Feasible optimal IV with S.E. robust

• to arbitrary covariance of the residual w/i state (see ft. 15 for how to implement this) Why?
• Autocorrelation at state level • Feasible GLS: more efficient than • OLS with S.E. clustered ⇒ Small effect more likely to be detected (Power of test ր)

Digression: IV estimates & heterogenous treatment effect

  IV estimates: treatment effect for

• compliers (“Local Average Treatment Effect”) cf. Angrist and Imbens (1994), Imbens (2007) Dinkelman (2007) takes this issue
• seriously

  1-4 Results 1: Impact on agriculture (Table III)

  1 additional dam in upstream ⇒ Irrigated areas ր by 0.33%

• Production/Yield of 6 major crops
• ր by 0.34/0.19% Production of water-intensive crops
• ր by 0.47% No significant change in
• non-water-intensive crops

Results 2: Interaction w/ rainfall shocks (Table VI)

  Rainfall shocks (deviation from 1971-99 mean) on agricultural production: Mitigated if dams built upstream

• Amplified if dams built in own
• districts

  ⇐ Water use restricted by govt to keep reservoir full Results 3: Impact on rural welfare (Table VIII)

  Head count ratio: 0.77% pt ր by 1 more dam in own

• district 1.5% pt ց by 1 more dam upstream
• No impact on district-level
• population or in-migration (Table

  VII)

Results 4 (Table IX)

  Impact of dams on poverty in own districts: mitigated if tax collection in colonial days done by farmers, not by landlords cf. Banerjee and Iyer (2005): non-landlord districts ⇒ public goods ր

• agricultural productivity ր
• ⇒ Compensation for losers works w/

  1-5 Taking Stock

  Use geography interacted with

• nation-wide trends & inter-state variation in infrastructure-building to credibly estimate the impact of infrastructure Distributional consequences of
• infrastructure and how losers can be compensated: Important topic

  Dolandson (2008)

• Moving beyond reduced-form
• evidence

  2-1 Research questions

  Did the expansion of railroads in

• colonial India promote agricultural development? If so, was it due to gains from trade
• caused by reduced trade costs?

  2-2 Data

  Sample: 239 districts in colonial

  India Annual panel, 1861-1930

• Outputs & retail prices of 17
• principal crops Bilateral trade flows for 85
• commodities, 1880-1920 Daily rainfall from 3614 stations,
• 1891-1930

  2-3 Background

  Transportation means in colonial India Bullocks on roads (<20-30km/day)

• River (65km/day downstream,
• 15km/day upstream) Coast (>100km/day)
>Railroads (600km/

  2-4 Model (Eaton-Kortum 2002)

  D districts, each denoted by d or o

• K commodities, each w/ a
• continuum of varieties Unit mass of identical agents in
• each district Each owns L
• d units of land,

  immobile & supplied inelastically Land: only factor of production

• Land rental rate r
• d

of income spent on commodity k

  k

  dj ^$

  = 1

  k

  µ

  k

  where ^%

  σk σk −1

  σk −1 σk

  Model: Preference

  (j))

  k d

  (C

  1

  µ k ln ^{" #}

  K _! k =1

  ln U d =

• Cobb-Douglas over commodities ⇒ µ

Model: Preference (cont.)

  ⇒ CES over varieties (j) of each k

• Indirect utility per acre, W , is given

  d

  by (cf. equation (9) on p.15): _! µ r

  k d

  ln W d = µ k ln

  k

  ˜ p

  d k _!

  r d = ln µ ln µ + _& k k k

  µ _k

  (˜ p )

  

k d

_{" $} k _{1 '}

  1 ¹ k k 1 −σk −σ Model: Production k

  z (j): amount of variety of j of

  d

  commodity k produced by 1 unit of land in district d Follows type-II extreme value

• distribution _k

  −θk k z −A _d

  (z) = e F

  d k

• d

  A : how likely productivity is high

• k

  θ : how variable productivity is Model: Commodity market

• Many competitive firms w/i district

  ⇒ Each firm makes zero profit ⇒ Domestic price of k (j) produced at home: p

  k dd

  (j) = r

  d

  /z

  k d

  (j) Model: Trade

• k

  To export 1 unit of k from district o

  ≥ 1 units must be to d , T

  od produced in o (iceberg trade cost). _{k k k od md k} ≤ T _om T T

• T = 1 (normalization) • _{oo k} Railroads reduce T • _od

  ⇒ Import price of k from o:

  k k k k k

  p (j) = T p (j) = r T /z (j)

  o oo o od od od Model: Trade (cont.)

  Agents: indifferent about where

• each k (j) is made

  k

  ⇒ They pay the cheapest p (j),

  od k

  denoted by p (j)

  d

  ⇒ Its distribution is given by _{" $ % D k k}

  −θk θk A T p − (r ) _{o o od} k o=1

  G (p) = 1 − e

  d Model: Trade (cont.)

  ⇒ k (j)’s expected price in district d: _{" $ − !} D ₁

k k k k

  −θ θk _k

  E [p (j)] = λ A (r T )

  o d 1 o od

o

  =1 k

• d

  E [p (j)] is also the average price of

  commodity k varieties, denoted by

  k

  p

  d This is the price of each commodity

• observed in the data.

Eaton-Kortum’s result no. 1

• (see fn. 16 of Eaton-Kortum)

  Prob. for district d to import k (j)

  from o:

  k k −θ _k

  A (r T )

  o

o

k od

  π =

  od _{% D} k k −θ _k

  A (r o T )

  o o od

=1

k

• od

  π is also the fraction of varieties of

  k that district d imports from o Eaton-Kortum’s result no. 2

• k

  Price of a variety that district d

  imports from o: distributed by G (p)

  d See ft. 17 of Eaton-Kortum

• ⇒ District d’s expenditure for imports from o: same across o for each k

  k k k

  ⇒ π = X /X where

  od od d _{k od}

X : Trade flow from o to d for

• commodity k _k X : d ’s total expenditure on • _d

Model: land market

• k k

  Land: inelastically supplied

• o od k

  If A UP or T DOWN

  ⇒ π UP & demand for land in o UP

  od

  ⇒ Rental price r o should go up Land rental prices r d ’s solve the

• following system of equations _{! !}

  k

  = π µ , ∀o ∈ {1, ..., D} r o L o k r d L d

  od k d

  2-5 Taking Model to Data

  6 empirical steps to estimate the impact of railroads:

  1. Trade costs

  2. Trade flows

  3. Market integration

  4. Mean income

  5. Income volatility

  6. Quantitive assessment of the model Prediction 6

  Indirect utility per acre for agents in

• d , W , is given by

  d _{! !}

  µ µ

  k k k k

  ln W = Ω+ ln A − ln π

  d d dd

  θ k θ k

  k k k

• dd

  π : (inverse of) trade openness

  Trade costs & other districts’

• productivity and land affect welfare

  k

  only via π Prediction 6 can be used for identifying the mechanism of the railroad impact on welfare _{% µ} Regress ln W

• dt on RAIL dt w/ _k

  k

  A as control (reduced-form

  k d θ _k

  estimation) _{% µ}

_k

k Then add π as additional

• k dd

  θ

  _k

  regressor Extent to which coeff. on RAIL

• dt

  gets small: how much the model Testing Prediction 6 _{! !}

  µ k µ k

  k k

  ln W = Ω + ln A − ln π

  d d dd

  θ θ

  k k k k

• d : real agricultural income per

  W

  acre

  Observed from each commodity’s

• yield per acre and price & land areas
• k : k ’s consumption share

  µ

  Observed from outputs and trade

Testing Prediction 6 (cont.) _{! !}

  µ k µ k

  k k

  ln W = Ω + ln A − ln π

  d d dd

  θ θ

  k k k k

• k k

  We need to estimate unobserved

  A & π as functions of exogenous

  d dd

  variables We also need to estimate θ

• k

Step 1 k

• od

  Estimate the trade cost T in the

  model Check whether railroads really

• reduced the trade cost

Step 1: Prediction 1

  Remember average price of

• commodity k in d is

  D _{1 " $ !} − k k k k −θ θk _k

  p = λ A (r o T )

  d 1 o od o =1 k

• od

  We can infer T from commodity k

  produced only in one district Denote this commodity by o. Then

Step 1: Specification o o o o

  ln p = β + β + φ t

  d ot dt od o

• δ ln TC(R ) + ε

  t odt odt

  Commodity o: salt produced only in

• a particular district

  R

• t

  : Railway network in year t Step 1: Specification (cont.) o o o o

  ln p = β + β + φ t

  d ot dt od o

• δ ln TC(R ) + ε

  t odt odt

  Prediction 1 tells us:

  o o

• ot ot

  β = ln p

• t ) odt : time-variant

  δ ln TC(R

  component of trade cost btw. o & d

  o Step 1: Measuring TC (R ) t odt

  LCR (R t , α) odt : lowest-cost route distance in railway-equiv. km _α road river coast = (α , α , α ): trade cost

• per km relative to railroad Existing transportation network + R
• t

  ⇒ shortest-distance btw. o & d for each α ^α : estimated by NLS together with δ

Step 1: Results (Table 2) _α

  Railroads did reduce trade cost per

• ˆ km ( > 1)

  More than reported relative freight

• rates (α = (4.5, 3.0, 2.25)) suggest
• _{t odt}

  Over & above linear trends

  Important as LCR (R , α) ↓ over

• time

  Elasticity of trade cost to distance in

• rail-equiv. km: 0.247 Robust to railroad link d

Step 2

  Check whether railroads increased

• trade flows

  k

• k

  o

  θ Estimate A & in the model Step 2: Prediction 2

• k k k

  −θ _k

  Remember

  X A (r o T )

  k o

od od

  π = =

  od k D _% k k −θ _k

  X (r )

  d A o T o o =1 od

• k k k

  So trade flow is given by

  ln X = [ln A − θ ln r ] − θ ln T

  k o k od o od _! D k k k −θ _k

• [ln (r ) + ln X ]

  A o T

  o od d

• β
• β
• φ

  = ln A

  −θ

  k d

  −θ _k

  )

  k od

  (r o T

  

k

o

  A

  D o =1

  = ln ^%

  k dt

  − θ k ln r o

  k o

  k ot

  Prediction 2 suggests:

  k odt

  ˆ δ ln LCR(R t , ˆ ^α ) odt + ε

  t −θ k

  k od

  k od

  

k

dt

  k ot

  = β

  k odt

  ln X

  Step 2: Specification

• β
• ln X
• β

  k ln T k od

• Other terms:

Step 2: Specification (cont.)

• k

  Estimate for each k to obtain ˆ θ ’s

  S.E.: bootstrapped • See Deaton (1997) for references on • bootstrap

• k k

  Then we obtain

  ln ˆ A = ˆ β + ˆ θ k ln r ot

  o ot

  where r ot is measured by nominal agri. GDP per acre Step 2: Results (Table 3)

  ˆ −θ δ: significantly negative on

• k

  average (column 2) ⇒ Shorter railway-equiv. distance increased trade flow

  No significant heterogeneity in this

• coefficient across commodities by (1) weight per unit value & (2) railroad freight class (column 3)

Step 2b k

  Extract exogenous component in ln ˆ A :

  o k k k

  ˆ β + ˆ θ k ln r ot = β + β + β ot

  o t ot k k

• κRAIN + ε

  ot odt k

• ot

  RAIN : total rainfall between

  sowing and harvest dates for k in o κ ˆ : 0.441 (se: 0.082)

• k

  k
• Check if railroads integrate markets

• Remember p

  )

  k o

  depends more on A

  k d

  if T od ↓ 2. p

  k d

  depends less on A

  k d

  1. p

  −θ _k ^{$ − 1} θk

  k od

  Step 3: Prediction 3

  T

  o

  (r

  k o

  A

  1 ^" D _! o =1

  k

  = λ

  k d

  if T od ↓

• χ

• χ

  dt

  k t

  k d

  × RAIL odt

  k ot

  RAIN

  o ∈N _d

  ) ^!

  1 #N d

  4 (

  k ot

  RAIN

  

o

∈N _d

  ) ^!

  d

  1 #N

  (

  3

  dt

  × RAIL

  2 RAIN k dt

  1 RAIN k dt

  = χ

  k dt

  ln p

  Step 3: Specification

• χ
• β
• β
• β
• ε

Step 3: Specification

  k dt

  Prediction 3: χ

  1 < 0, χ 2 > 0, χ

3 < 0, χ

4 < 0 k k k

  ln p = χ

  2 RAIN × RAIL dt dt dt dt _!

  1

  k

• χ ( ) RAIN

  3 ot

  #N d

  

o

_! ∈N _d

  1

  k

• χ ( ) × RAIL

  4 RAIN odt ot

  #N

  d

o

∈N _d

k k k

• β + β + β dt + ε

  

d t dt Step 3: Results (Table 4) ∗∗∗

• 1 ∗∗

  χ ˆ = −0.402

• 2

  χ ˆ = +0.375 : railroad link reduces

  the dependence of price on own district rainfall χ

• 3 = −0.021: w/o railroad link,

  ˆ

  neighboring districts’ rainfall does not affect price

  ∗∗∗

• 4

  χ ˆ = −0.082 Step 3b: Model evaluation

• k

  If model correct, predicted

  commodity price ˆ p should be

  dt k

  close to observed p

  dt

  Solving the model & plugging

• estimated parameters & observed

  k

  exogenous variables to obtain ˆ p

  dt

  ⇒ “out-of-sample” test Step 3b: Model evaluation (cont.)

• Then estimate ln p

  k dt

  = β

  k d

  k t

• β
• β
• ω ln ˆ p
• ε

  dt

  k dt

  k dt

  which yields ˆ ω = 0.913. Step 4: Prediction 4

  Solve system of equations (6) to

• obtain r for the case D = 3, K = 1

  d

  Then conduct comparative statics

• on W d w.r.t. T od W
• d ↑ if T od ↓: arrival of railroads

  increase welfare

• ′

  ↓ if T ↓: railroads in other W

  d oo

  districts decrease welfare Step 4: Specification

  ln (W ) = β + β + γRAIL

  o o t ot _!

  1

• ψ( ) RAIL + ε

  dt ot

  #N

  

o

d ∈N _o

  Prediction 4: γ > 0, ψ < 0

• Estimated by OLS, assuming
• exogenous railroad placements

Step 4: OLS Results (Table 5)

  Arrival of railroad

• ⇒ Real agri GDP per acre ↑ by 18.2% Railroads in N
• o

  ⇒ Real agri GDP per acre ↓

  Treatment externality: ignoring this

• yields understimation (column 1) Distributional consequences of
• railroads

Step 4: validity checks

  1. Placebo tests: estimate impact of proposed but never built railroads ⇒ No effect (Table 6)

  2. IV estimation: 1876-78 rainfall deviation from long-run mean as IV ⇒ IV estimate: similar magnitude to

  OLS (Table 7)

  3. Bounds check: estimate coefficients separately for each Step 5: Prediction 5 k

  1. A UP ⇒ W UP

  d d k

  2. T DOWN ⇒ W less responsive

  d od k

  to A

  d

  ⇒ Trade cost reduction reduces volatility of income

• ψ

  dt

  θ k ˆ κ RAIN

  ˆ µ k ˆ

  k

  × ^{" !}

  3 RAIL

ot

  k ot ^$

  ˆ κ RAIN

  ˆ θ k

  k

  ˆ µ

  2 ^{" !} k

  RAIL

  d ∈N _o

  ) ^!

  1 #N o

  (

  1

  ot

  ) = γRAIL

  o

  ln (W

  Step 5: Specification

• ψ

  k ot ^$

• ψ
• β o + β t + ε ot

  2 > 0, ˆ ψ 3 < 0

  Step 5: Results (Table 9)

• Indeed ˆ ψ

  ⇒ Railroads reduce income volatility

• ψ

  2 ^{" !} k

  ln ˆ π

  k

  ˆ µ

  k ot ^$

  ˆ κ RAIN

  k

  θ

  ˆ µ k ˆ

  k

  × ^{" !}

  k ot ^$

  θ k ˆ κ RAIN

  ˆ µ k ˆ

  dt

  RAIL

  d ∈N _o

  ) ^!

  o

  1 #N

  (

  1

  ot

  ) = γRAIL

  o

  ln (W

  Step 6: Specification

• ψ
• ψ

  k oot

• η ^!
• β o + β t + ε ot

Prediction 6: γ = ψ

  1 = ψ 3 = 0, ψ 2 = 1, η = −1 _!

  1 ln (W o ) = γRAIL ot + ψ

  1 ( ) RAIL dt

  #N o

  d _{" ! $} ∈N _o

  µ ˆ

  k k

• ψ κ ˆ RAIN

  2 ot

  ˆ θ k

  k _{" ! $}

  µ ˆ

  k k

• ψ RAIL × κ ˆ RAIN

  3 ot ot

  ˆ θ

  k _! k

  µ ˆ k

  k

• η ln π ˆ + β + β + ε

  o t ot

Step 6: Results (Table 10)

  Once the openness term is included as a regressor, Railroad coefficients become

• insignificant and close to zero.

  ˆ ψ

• 2 is close to 1, η is close to -1! ˆ

  ⇒ Model explains a very large portion of the railroad welfare impact seen in Steps 5 & 6

  

Future research in the literature

(my own view)

  Impact on industrial development

• Distributional consequences of
• infrastructure construction Non-economic impact of
• infrastructure

  Public service delivery should be

• affected by infrastructure, too.

  Political economy of infrastructure

  References for the lecture on infrastructure Banerjee, Abhijit V., and Lakshmi Iyer. 2005. “History, Institutions and Economic _Review Performance: The Legacy of Colonial Land Tenure Systems in India.” American Economic 95(4): 1190-1213. ! Deaton, Angus. 1997. The analysis of household surveys. World Bank Publications. ! Dinkelman, Taryn. 2008. “The effects of rural electrification on employment: New evidence from South Africa.” Donaldson, Dave. 2008. “Railroads of the Raj: Estimating the Impact of Transportation