On the Distributional Implications of Climate Change:

A Methodological Framework and Application to Rural India

Hanan Jacoby (DECRG)Emmanuel Skoufias (PRMPR)

Mariano Rabassa (PRMPR)World Bank

March 19, 2009

Motivation & Scope-1

General consensus is that the main effect of climate change will be to reduce agricultural productivity. Given that the poor are concentrated in developing country agriculture, they are likely to suffer the most.

Within rural areas of developing countries there is likely to be a great deal of heterogeneity in the vulnerability to climate change.

Studies to date useful for identifying vulnerable countries or regions.

Motivation & Scope-2

Studies on the Impacts of CC:Neo-Ricardian approach - Focus on

impacts of CC on agricultural productivity (land value, net revenues etc) taking into account adaptation

Crop models - little or no adaptationIndia: CC impacts range from + to –

and depend on crop and region studied

Impacts more modest when adaptation is taken into account

Motivation & Scope-3

Yet, policy (e.g. targeting interventions) must also be guided by information on which types of households are more vulnerable (e.g. according to physical and/or human capital)

Household level data are essentialThis study is the first attempt towards

estimating the distributional impacts of climate change.

Poverty Rate (headcount)

Proportion of hh income from land - lamda

FrameworkWelfare measured by consumption per

capita

Consumption determined by resource endowments (land and labor) and returns from activities (farm and off-farm).

We use a comparative statics approach to estimate the impacts of climate change on returns to land (agricultural productivity)

taking into account adaptation on the returns to off-farm activities

We trace the impacts of these productivity changes on consumption in rural areas

Caveats -1

Cross-sectional variability in climate and land values defines set of adaptation possibilities in the Long Run Technological envelope of present is the same as in the future.

Climate scenario: uniform +1°C increase in temperature (holding rainfall constant) Include higher-resolution climate change scenarios for India

(IITM, Pune). PRECIS model predicts higher temperature monotonously spread over the country but substantial spatial differences in projected rainfall changes

Ignore potential change in rainfall variability

Caveats -2Evolution of the distribution of

endowments (land, physical and human capital) over time is not taken into consideration, Impacts of CC derived based on current tock and

distribution of endowments . But different scenarios of such changes could potentially be accommodated into the framework (e.g. more educated hh members)

Lanjouw & Murgai (2009): table 3: distribution of occupations in rural India between 1983-2004 (period of trade reforms and economic expansion) practically constant

Caveats -3

River basin flows and irrigation supply not modeled in detail

Impact of climate change on prices not considered

Typical in all applications of the neo-ricardian approach

Methodology: basic model

climate

wageannual )(

members actively economical of proportion

landonreturnnet annualized )(

gslandholdincapitaper

nconsumptiocapitaper

where

:constraintbudget

w

p

a

c

pwac

Note: HH labor optimally allocated between own and off-farm activities

Vpwa

a

d

wdw

d

d

d

cdc

where

/)1(

//

Comparative Statics of Climate

Note: Shifts in hh labor allocation due to CC have no welfare consequences as a result of Envelope Theorem.

Extension: Land heterogeneity

Irrigated (i) and nonirrigated (n) land with different returns and responses to climate. (Need to assume increasing, convex cost of installing irrigation)

nnii

ii

nnii

aa

a

d

d

d

d

d

d

where

/)1(

//

Note: Change in calculation of λ, i.e., nnii aaa

Proportion Irrigated

Extension: Labor heterogeneity If skilled labor (requiring greater human

capital investment) and unskilled labor earn different wages and have different climate responses.

uuss

ss

uuss

nwnw

nw

d

wdw

d

wdw

d

wdw

where

/)1(

//

Note: Change in calculation of λ.

Empirical ImplementationEstimate marginal effects of θ on log-

endowment prices: log(πk) and log(wm).

Calculate λ, φ, σ for each household (these weights depend on household endowments and on the associated endowment prices).

Predict change in log(c) for given Δθ for each household using formula. [Why not directly estimate log(c) as function of θ?]

Neo-Ricardian Approach

Reduced-form relation between land productivity (net revenue or value) and climate normals.

Assumes cross-sectional relationship will continue to hold into future farmers will adapt to CC along today’s technological envelope.

Only way to quantify the economic costs of CC in agriculture while taking adaptation fully into account. (Crop modeling takes only limited account of adaptation).

Estimation Issues What to control for in Ricardian regressions?

Infrastructure (e.g., irrigation, roads) versus ‘immutable’ characteristics (e.g., soil, topography, irrigation potential). Will infrastructure remain fixed as climate changes? Will infrastructure adjust as it has in the past, as reflected in the current long-run equilibrium?

How to estimate log πk (θ) k =i,n ? Irrigation investment is largely irreversible Estimate log πi(θ) using data on irrigated plots only. Estimate log πn(θ) using data on both irrigated and

nonirrigated plots, thus allowing for the option of new irrigation investment as climate changes. Using only nonirrigated plots artificially holds irrigation infrastructure fixed at zero.

More Estimation Issues Panel versus cross-section: Dechenes and

Greenstone (2007) purge all locational characteristics using fixed effects estimate short-run response of farm revenue to weather shocks. Since little adaptation occurs from year to year, the SR impact is upper bound on LR impact of CC. How informative is upper bound for, e.g., India?

Tradeoff between more heterogeneous marginal effects of climate and danger of overparameterization. (E.g., quadratic terms and interactions in quarterly temp/precip.)

Land values versus net revenues. Each subject to measurement error of a different kind.

Existing Estimates for India

Sanghi et al. (1998) using data from 271 districts find that 1.0 °C warming would reduce net farm revenue by 9%. But Kumar and Parikh (1998) estimate only a 3% decline using similar data and methodology.

Guiteras (2008) uses district-panel data to estimate the impact of weather shocks on gross productivity. Medium term CC scenario (?) crop yield will decline by 4.5-9%, but again this is an upper bound. A 1.0 °C temp. increase would reduce rural wages by 2%.

For both approaches, negative effect of temperature rise far outweighs positive impact of precipitation increase.

Description of Key Variables District-level analysis of endowment prices (~500 districts)

based on household and plot level data from 59th (2002-03) & 61st (2004-05) rounds of nationally representative National Sample Survey.

Cropland values: 59th round gives data on area, value, and irrigation of 100+ thousand plots. We use log of district means. (“For assessing the value of land acquired by the household through inheritance or otherwise…the informant, if necessary, may be asked to take the help of the knowledgeable persons of the village to ascertain the current market price of the type of land. This may be determined on the basis of the transactions made within the village or in its vicinity during the recent past” NSS Field Manual).

Net crop revenue: 59th round has info for ~40 thousand farm households. Caveat: 2002-03 was a very poor harvest.

Rural wages: 61st round has daily wages earned in last week for ~50 thousand individuals. (~10k in skilled occupations: education, health, public administration). Residuals of log wage regression on age-gender dummies are averaged at district level.

Land value/ha vs. Net Revenue/ha

Poverty & Unskilled Rural Wages

Covariates Climate:

- Temperature: From 391 Indian weather stations (1951-1980; average 26 yrs/station). We take average of 3 nearest stations weighted by inverse squared distance to district centroid.

- Precipitation: Gridded data from +1800 stations for 1960-2000 interpolated by IMD on 1° cells (CRU data is on 0.5 ° cell but based on much fewer stations, including those outside India).

Immutable characteristics:- Soil (FAO, soil map of world, 34 categories)- Topography (% of district with slope in 3 categories)- Elevation (% of district with elevation in 3 categories)- Rivers/km2 in district- Groundwater in thousands m3/km2 (state level)- Straight-line distance to nearest city of +1 million & +5 million.

(Not really immutable, but formation of lots of new big cities as a result of CC seems unlikely in foreseeable future).

Temperature (annual average)

Rainfall (annual average)

Marginal effect of Temp on returns to endowments

Marginal effect of Temp on returns to irrigated land

Changes in PCE-Linear vs. Quadratic

Baseline Poverty & Impacts on Poverty-Linear

Climate Change Incidence Curves

Rural India

-2.90

-2.80

-2.70

-2.60

-2.50

-2.40

-2.30

-2.20

-2.10

Ch

ange

rat

e

0 10 20 30 40 50 60 70 80 90 100Percentile

Climate Change Incidence Curve - Linear Model

CChange Incidence Curves-Linear

Rural Andhra Pradesh vs Punjab

-2.65

-2.60

-2.55

-2.50

-2.45

-2.40

-2.35

-2.30

-2.25

-2.20

-2.15

Ch

ange

rat

e

0 10 20 30 40 50 60 70 80 90 100Percentile

Andhra Pradesh

Climate Change Incidence Curve - Linear Model

-3.60

-3.40

-3.20

-3.00

-2.80

-2.60

-2.40

-2.20

-2.00

-1.80

Ch

ange

rat

e

0 10 20 30 40 50 60 70 80 90 100Percentile

Punjab

Climate Change Incidence Curve - Linear Model

Take-away messages

We have proposed a flexible framework for quantifying distributional implications of climate change in the rural economies worth applying in, e.g., Mexico, Brazil.

Distributional impacts in India depend primarily on proportion of household income derived from land. Wealthier households will suffer proportionally greater consumption declines because they hold more land (and they are also concentrated in more affected areas).

Changes in poverty rates are not highly localized (e.g., Punjab proportionally harder hit but richer to start with).

Overall, the impacts on rural household income in the medium term seem modest. It remains to be seen whether impacts are robust to extensions such as modeling increased rainfall variability.

Thank you