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

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

1. Dams

• Duflo and Pande (2007, QJE) • Using geography as instrument to

1-1 Research questions

• What’s the impact of irrigation dams on agricultural production and rural poverty?

• What’s the distributional

1-2 Data

• Annual agricultural production, 1971-1999, for 271 districts

• Poverty data in 1973, 83, 87, 93, 99 for 374 districts

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

• RGrki: 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

• νi: district FE

• µst: state-year FE (different trends

across states)

• M_{i: area, elevation, overall gradient,}

river length

• Why (M_{i ∗D}¯_{st) included?}

• lt: year dummies

1st stage results (Table II)

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

Empirical strategy: 2nd stage

yist = γi + ηst + δDist +δUDistU

+Z_{istδZ +} ZU_istδZU _{+ εist}

w/ Dˆist,Dˆ_istU ,Zist,ZUist as instruments

• yist: outcome variable

• γi: district FE

• ηst: state-year FE

• Z_{ist: vector of} M_{i ∗ D}¯_{st,RGrki ∗lt}

• ZU_{ist: vector of} M_{i ∗ D}¯_{st,RGrki ∗ lt for}

upstream districts

• Dˆist,: fitted value for Dist

• DˆU

ist: the sum of fitted values for

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)

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%

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

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

1-5 Taking Stock

• Use geography interacted with nation-wide trends & inter-state

variation in infrastructure-building to credibly estimate the impact of

infrastructure

2. Railroads

• Dolandson (2008)

2-1 Research questions

• Did the expansion of railroads in colonial India promote agricultural development?

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,

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 Ld units of land,

immobile & supplied inelastically • Land: only factor of production • Land rental rate rd

Model: Preference

Model: Preference (cont.)

• CES over varieties (j) of each k ⇒ Indirect utility per acre, Wd, is given

by (cf. equation (9) on p.15):

Model: Production

z_dk(j): amount of variety of j of

commodity k produced by 1 unit of land in district d

• Follows type-II extreme value distribution

F_dk(z) = e−Akdz−

θ

k

• Ak_d: how likely productivity is high • θk: how variable productivity is

Model: Commodity market

• Many competitive firms w/i district ⇒ Each firm makes zero profit

Model: Trade

• To export 1 unit of k from district o

to d, T_odk ≥ 1 units must be

produced in o (iceberg trade cost). • T_odk ≤T_omk T_mdk

• T_ook = 1 (normalization)

• Railroads reduceT_odk

⇒ Import price of k from o:

Model: Trade (cont.)

• Agents: indifferent about where each k(j) is made

⇒ They pay the cheapest pk_od(j), denoted by p_dk(j)

⇒ Its distribution is given by

Gk_d(p) = 1 − e−

"

%D

o₌1Ako(roT_odk )−θk

$

Model: Trade (cont.)

commodity k varieties, denoted by

p_dk

Eaton-Kortum’s result no. 1

• Prob. for district d to import k(j)

from o: (see fn. 16 of Eaton-Kortum)

π_odk = A

k

o(roTodk )−θk

%D

o=1Ako(roT_odk )−θk

• π_odk is also the fraction of varieties of

Eaton-Kortum’s result no. 2

• Price of a variety that district d

imports from o: distributed by G_dk(p)

• See ft. 17 of Eaton-Kortum

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

⇒ π_odk = X_odk /X_dk where

• X_odk : Trade flow from o to d for commodity k

Model: land market

• Land: inelastically supplied • If Ak_o UP or T_odk DOWN

⇒ π_odk UP & demand for land in o UP ⇒ Rental price ro should go up

• Land rental prices rd’s solve the

following system of equations

roLo =

!

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

Prediction 6

• Indirect utility per acre for agents in

d, Wd, is given by • Trade costs & other districts’

Prediction 6 can be used for identifying the mechanism of the railroad impact on welfare

d as control (reduced-form

estimation)

dd as additional

regressor

• Extent to which coeff. on RAILdt

Testing Prediction 6

• Wd: real agricultural income per

acre

• Observed from each commodity’s yield per acre and price & land areas

• µk: k’s consumption share

Testing Prediction 6 (cont.)

• We need to estimate unobserved

Ak_d & π_ddk as functions of exogenous variables

• We also need to estimate θk

Step 1

• Estimate the trade cost T_odk in the model

Step 1: Prediction 1

• Remember average price of commodity k in d is

p_dk = λk₁"

produced only in one district

Step 1: Specification

lnp_do = β_oto +β_dto +φo_odt

+δ lnTC(R_{t)odt + ε}o

odt

• Commodity o: salt produced only in a particular district

Step 1: Specification (cont.)

lnp_do = β_oto +β_dto +φo_odt

+δ lnTC(R_{t)odt + ε}o

odt

Prediction 1 tells us: • β_oto = lnpo_ot

• δ lnTC(R_{t)odt: time-variant}

component of trade cost btw. o & d

Step 1: Measuring

TC

(

R

_t

₎

_odt LCR(R_t,α)_odt: lowest-cost route

distance in railway-equiv. km

• α = (αroad, αriver, αcoast): 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)

• Railroads did reduce trade cost per km (αˆ > 1)

• More than reported relative freight rates (α = (4.5,3.0,2.25)) suggest

• Over & above linear trends

• Important as LCR(R_t,α)_odt ↓ over time

• Elasticity of trade cost to distance in rail-equiv. km: 0.247

Step 2

• Check whether railroads increased trade flows

Step 2: Specification

lnX_odtk = β_otk + β_dtk + β_odk + φk_odt

−θkδˆlnLCR(Rt,αˆ)_odt + εk

odt

Prediction 2 suggests: • β_otk = lnAk_o − θk lnro

• β_dtk = ln%D

o=1Ako(roTodk )−θk +lnXdk

• Other terms: −θk lnT_odk

Step 2: Specification (cont.)

• Estimate for each k to obtain θˆk’s

• S.E.: bootstrapped

• See Deaton (1997) for references on bootstrap

• Then we obtain lnAˆk

o = ˆβotk + ˆθk lnrot

where rot is measured by nominal

Step 2: Results (Table 3)

• −θkδˆ: significantly negative on

average (column 2)

⇒ Shorter railway-equiv. distance increased trade flow

Step 2b

Extract exogenous component in lnAˆk o:

• RAIN_otk : total rainfall between

sowing and harvest dates for k in o

• κ: 0.441 (se: 0.082)ˆ

Step 3: Prediction 3

• Check if railroads integrate markets • Remember

p_dk = λk₁"

D

!

o=1

Ak_o(roT_odk )−θk

$−_θ1

k

1. p_dk depends less on Ak_d if Tod ↓

Step 3: Specification

Step 3: Results (Table 4)

• χ1ˆ = −0.402∗∗∗

• χˆ2 = +0.375∗∗: railroad link reduces the dependence of price on own district rainfall

• χˆ3 = −0.021: w/o railroad link, neighboring districts’ rainfall does not affect price

• χˆ4 = −0.082∗∗∗

Step 3b: Model evaluation

• If model correct, predicted commodity price pˆ_dtk should be close to observed p_dtk

• Solving the model & plugging

estimated parameters & observed exogenous variables to obtain pˆ_dtk

Step 3b: Model evaluation (cont.)

• Then estimate

lnpk_dt = β_dk + β_tk +βdt +ωlnpˆ_dtk + εkdt

Step 4: Prediction 4

• Solve system of equations (6) to obtain rd for the case D = 3,K = 1

• Then conduct comparative statics on Wd w.r.t. Tod

• Wd ↑ if Tod ↓: arrival of railroads

increase welfare

• Wd ↓ if Too′ ↓: railroads in other

Step 4: Specification

ln(Wo) = βo + βt + γRAILot

+ψ( 1 #N_o)

!

d∈N_o

RAILdt + εot

• Prediction 4: γ > 0, ψ < 0 • Estimated by OLS, assuming

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)

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 category of railroads

Step 5: Prediction 5

1. Ak_d UP ⇒ Wd UP

2. T_odk DOWN ⇒ Wd less responsive

to Ak_d

Step 5: Results (Table 9)

• Indeed ψ2ˆ > 0,ψ3ˆ < 0

Step 6: Results (Table 10)

Once the openness term is included as a regressor,

• Railroad coefficients become insignificant and close to zero. • ψ2ˆ is close to 1, ηˆ is close to -1! ⇒ Model explains a very large portion

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.

References for the lecture on infrastructure

Banerjee, Abhijit V., and Lakshmi Iyer. 2005. “History, Institutions and Economic

Performance: The Legacy of Colonial Land Tenure Systems in India.” American Economic Review 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 Infrastructure.”

Duflo, Esther, and Rohini Pande. 2007. “Dams.” Quarterly Journal of Economics 122(2): 601-646. !

Eaton, Jonathan, and Samuel Kortum. 2002. “Technology, Geography, and Trade.”

Econometrica 70(5): 1741-1779. !

Hansen, Christian B. 2007. “Generalized Least Squares Inference in Panel and Multilevel Models with Serial Correlation and Fixed Effects.” Journal of Econometrics 140(2): 670-694.

!

Imbens, Guido W. 2007. “Instrumental Variables with Treatment Effect Heterogeneity: Local Average Treatment Effects.” Available at: http://www.nber.org/minicourse3.html.

Imbens, Guido W., and Joshua D. Angrist. 1994. “Identification and Estimation of Local Average Treatment Effects.” Econometrica 62(2): 467-475. !