when water runs out evidence from gujarat, india chhokrao baddha ahmedabad chala gaya. aapko koi...

When Water Runs Out...Evidence from Gujarat, India

Ram Fishman 1 Meha Jain 2 Avinash Kishore 3

1George Washington University

2Columbia University

3IFPRI

December 18, 2013

4th IGC-ISI India Development Policy Conference

We acknowledge funding by the International Growth Center India Central program.

We thank Saciwaters and the Columbia Water Center (India) for their support.

1 / 42

Overview

1 Introduction

2 Data and The Study Area

3 Empirical Strategy

4 Results

5 Robustness check - geographical controls

6 Conclusion

2 / 42

Introduction

Chhokrao baddha Ahmedabad chala gaya. Aapko koi nahin milegayahan. Kya karenge yahan? Paani-baani hai nahin.

Young lads all live in Ahmedabad [Nearest Big City] now. What willthey do here? There is no water.

Sarpanch (Mayor) of Chadasana Village

Introduction 3 / 42

Background

Increasing water scarcity is predicted to threaten the agriculturalproductivity of hundreds of millions of smallholder farmers indeveloping countries (Vrsmarty et al 2000). How will farmers adapt?

Environmentally induced migration is predicted to greatly intensify inthe 21st century (Myers and Kent, 1994; Mcleman and Smit, 2006;Warner et al, 2009; Myers, 2002), but there is limited empiricalevidence to assess these claims.

Introduction 4 / 42

This Study

Our approach utilizes spatial variation in the presence of a geologicalfeature that accelerates groundwater depletion. Comparing villages thatoverlie this feature with those that do not we find:

Increased indicators of water depletion

Lower cropping intensity (gross irrigated area) and a higher fractionof rain-fed farmers

Higher incidence of migration and exit from farming by young males(household sons)

Lower asset holdings

No evidence of significant investment in improved water use efficiency.

In the past, when the geological impact should not have had a strongeffect, these differences are weak or nonexistent.

Responses and impacts are caste dependent.

Introduction 5 / 42

Environmental Migration

The environment-migration link is complex and hard to isolate, and mostevidence on the causes is anecdotal/self-reported (Warner et al 2010).

Feng, Krueger and Oppenheimer (2010) find increases in Mexico-U.S.migration rates following crop yield declines resulting from short termweather shocks.

Hornbeck (2012) finds population declines in areas affected by thedust bowl of the 1930s in the American west - a log-term but rapidonset environmental change.

This study: increasing water scarcity and migration - a slow onsetenvironmental change.

Introduction 6 / 42

Groundwater Depletion in India

Groundwater depletion - a global problem (Konikowand Kendy 2005, Wada et al 2010)

India - world’s largest consumer and the countryprobably most vulnerable to this threat (Fishman etal, 2011; Sekhri, 2011; Shah, 2010; World Bank,1998; World Bank, 2010).

What will happen when groundwater ”runs out”?

Introduction 7 / 42

The Study Area

North Gujarat is one of the most groundwater dependent and depletedregions of India (UNDP, 1976; Kavalanekar and Sharma, 1992; Moench,1992; Postel, 1999; Prakash, 2005; Dubash 2008; CWC 2010).

The first alluvial aquifer region in India to enter into the ”decline” phase(Shah 2007)

The Groundwater Economy of South Asia 23

development mindset to a resource management mode. Forty years of Green Revolution and mechanized tube well technology have nudged many regions of South Asia into stages 2–4. However, even today, there are pockets that exhibit characteristics of stage 1, but the areas of South Asia that are at stage 1 or 2 are shrinking by the day. Many parts of western India were in this stage in the 1950s or earlier, but have advanced into stage 3 or 4. An oft-cited case is North Gujarat where groundwater depletion has set off a long-term decline in the booming agrarian economy; here, the well-off farmers who foresaw the impending doom forged a generational response and made a planned transi-tion to a non-farm, urban livelihood. The resource-poor have been left behind

Stages

Stage 1 Stage 2 Stage 3 Stage 4

The rise of GreenRevolution and tube welltechnologies

Groundwater-based agrarianboom

Early symptoms ofgroundwater overdraftand degradation

Decline of the groundwatersocio-ecology withimmiserizing impacts

Examples North Bengal, NorthBihar, Nepal Terai,Orissa

Eastern Uttar Pradesh,western Godavari,central and South Gujarat

Haryana, Punjab, westernUttar Pradesh, centralTamilnadu

North Gujarat, coastalTamilnadu, coastalSaurashtra, southernRajasthan

Characteristics Subsistence agriculture;protective irrigationtraditional crops;concentrated ruralpoverty; traditionalwater-lifting devicesusing human and animalpower

Skewed ownership oftube wells; access to pumpirrigation prized; rise ofprimitive pump irrigation‘exchange’ institutions;decline of traditional water-lifting technologies; rapidgrowth in agrarian incomeand employment

Crop diversification;permanent decline in watertables. The groundwater-based ‘bubble economy’continues booming, buttensions between economyand ecology surface aspumping costs soar andwater market becomeoppressive; private andsocial costs of groundwateruse part ways

The ‘bubble’ bursts; agriculturalgrowth declines;pauperization of the poor isaccompanied bydepopulation of entireclusters of villages; waterquality problems assumeserious proportions;the ‘smart’ begin moving outlong before the crisisdeepens; the poor get hit thehardest

Interventions Targeted subsidy onpump capital;public tube wellprogrammes;electricity subsidies andflat tariff

Subsidies continue;institutional credit for wellsand pumps; donors augmentresources for pump capital;NGOs promote small farmerirrigation as a livelihoodprogramme

Subsidies, credit, donor andNGO support continueapace; licensing, sitingnorms and zoning system arecreated but are weaklyenforced; groundwaterirrigations emerge as a huge,powerful vote bank thatpolitical leaders cannotignore

Subsidies, credit and donorsupport reluctantly go;NGOs and donors assumeconservationist posture;zoning restrictions begin toget enforced with frequentpre-election relaxations;water imports begin fordomestic needs; variety ofpublic- and NGO-sponsoredameliorative actions start

Groundwater abstraction

Pump density Percent of pump irrigation sold

Size of the agrarian economy

Premonsoon water table

Fig. 2.12. Rise and fall of groundwater socio-ecologies in South Asia where economies follow a four-stage progression.

Figure : Rise and fall of groundwater socio-ecologies in South Asia (Shah 2007)

Data and The Study Area 8 / 42

Study Area - Falling Water Tables (Depth to Water)

Farmers have a reasonable sense of water table levels and trends.


Study Area


Data

Survey60 villages were selected for surveying.

10 were identified by well drillers as especially water scarce50 others were randomly selected within a box surrounding the scarcityhotspot.Villages served by irrigation canals were excluded.

In each village, 5% of households were randomly selected for surveying.Respondent were asked about water usage and agricultural parameters,and the place of residence and primary activity of each son and brotherof the head of the HH. Consistency .

Additional DataSurveys of 1-3 prominent farmers in same + additional villagesInterviews with Well Drillers.Geological data from lithologs of Govt. drinking water wells.Census data (2001).Village level data on adoption of drip irrigation, 2005-2012


Villages Surveyed


The analysis of responses to water scarcity is disaggregated along threelines:

Time: present vs. past (2001)Water more scarce, agriculture shrunk, but asset holdings up

Generation: Current vs. Previous Sons and brothers of the HHhead both migrate (brothers less, and less recently), but sons exitagriculture more

Caste: The dominant Patel caste vs. the rest.Patels are richer, own wells, migrate and leave agriculture more


Time Trends

Table : Changes in irrigation, agriculture and assets (2001-Present)

N Now Past Diff. p

Time to Irrigate a Parcel (Hours) 1088 5.82 3.53 2.29∗∗∗ 0.00Days between irrigations 1080 16.31 11.78 4.52∗∗∗ 0.00Bore HP 985 53.23 33.24 19.99∗∗∗ 0.00Bore Depth (100 ft) 1034 5.78 3.58 2.20∗∗∗ 0.00

Land Cultivated, Rainy (Bg) 1166 6.94 7.03 -0.09 0.77Land Cultivated, Winter (Bg) 1166 5.19 5.60 -0.41 0.11Land Cultivated, Summer (Bg) 1166 2.31 2.82 -0.51∗∗∗ 0.00No. Irrigations, Rainy 1022 5.83 5.49 0.34∗∗ 0.02No. Irrigations, Winter 1068 6.13 6.36 -0.23∗ 0.09No. Irrigations, Summer 837 6.45 6.77 -0.32 0.11

Rain-fed 1116 0.04 0.02 0.01∗∗ 0.04Well co-owners 1116 0.58 0.58 -0.00 0.95Water buyers 1116 0.38 0.39 -0.01 0.53

Land (Bigha) 1529 5.10 5.50 -0.40 0.18Permanent House 1503 0.59 0.51 0.07∗∗∗ 0.00Ceiling Fans 1529 2.15 2.11 0.04 0.34Cows 1529 1.04 1.43 -0.40∗∗ 0.01Buffaloes 1529 1.25 1.98 -0.74∗∗∗ 0.00Tractors 1529 0.07 0.05 0.02∗∗ 0.05Motorcycles 1529 0.39 0.18 0.21∗∗∗ 0.00Cars 1529 0.07 0.03 0.04∗∗∗ 0.00


Current vs. Past Generation

Brothers Sons Diff.

Number 2.38 1.58 0.80***At least one migrant 0.20 0.22 -0.02Number of migrants 1.41 1.50 -0.09At least one non-farmer 0.10 0.25 -0.15***Number of non-farmers (in village) 1.37 1.36 0.00Migrated 0.16 0.16 -0.01Not Farming 0.08 0.18 -0.10***Years Migrated 17.65 7.58 10.07***


Caste Divisions

Table : Patels and non-Patels

Others Patel (Difference) SE

Share of rainfed farmers 0.04 -0.01 (0.01)Share of well co-owning farmers 0.47 0.30*** (0.03)Share of water buying farmers 0.49 -0.29*** (0.03)Did Any Sons Migrate? 0.12 0.31*** (0.02)Did Any Sons Exit Agri.? 0.22 0.10*** (0.03)Did Any Brothers Migrate? 0.15 0.17*** (0.03)Did Any Brothers Exit Agri.? 0.10 -0.00 (0.02)Land Holding 3.62 4.92*** (0.43)Permanent House 0.44 0.50*** (0.02)Ceiling Fans 1.90 0.81*** (0.07)Cows 0.88 0.52** (0.23)Buffaloes 1.24 0.03 (0.10)MotorCycles 0.29 0.32*** (0.03)Tractors 0.04 0.10*** (0.01)Cars 0.04 0.09*** (0.01)

Standard errors in parentheses

Errors are clustered by village

* p < 0.1, ** p < 0.05, *** p < 0.01


Correlations between water tables and migration rates

0

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Correlations between water tables and migration rates

Correlations between water tables and migration rates (and otherresponses) can be interpreted in several ways.

Adaptation to growing water scarcityImpacts of greater past extraction of the resourceOther unobservable factors

To disentangle causality, we exploit localized variation inhydro-geological conditions (work in progress).

Empirical Strategy 18 / 42

Regional Hydro-Geology

The North Gujarat aquifer is a complex mixture of permeable (e.g. sands) andimpermeable (e.g. clay) layers.

for certain zones the aquifer has a high proportion of the morepermeable sandy horizons; at other locations the horizons contain moreclay; there is no distinct continuous layering in the aquifer.

Kavalanekar and Sharma, 1992

Over-exploitation of an alluvial aquifer in India

333

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Regional Hydro-Geology

Changes in the water table and yield are determined by a balance ofwithdrawals and recharge.

In the regional aquifer, most of the recharge is lateral (Bradley andPhadtare 1989)

The presence of impermeable layers can lead to locally faster drops inwater tables.

Discussions with local well drillers and government geologistssuggested the presence of a highly impermeable dark clay layer isresponsible variation in water conditions.

The dark clay layer occurs at depths typically ranging from 450-650ft. Its only likely impact on village economics is through impacts onwater availability.


Dark Clay

The main local well driller identified, for about 100 villages (in Mansa and Vijapur),whether there was a dark clay layer or not.

Lithologs were obtained for Govt. Drinking water wells from GWSSB (Mahesana Dist.only). Data was found to be in perfect agreement with driller’s reports (data collection isongoing).

Dark Clay?

Dark Clay?

Dark Clay?0

0

01

1

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‘Balance’

Table : Impacts of a Dark Clay Layer - Balance

Mean OLS

Land Holding 5.50 -1.92***(0.55)

966

Land per Son 3.84 -1.15**(0.44)

871

Number of sons 1.60 -0.03(0.07)

966

Number of brothers 1.44 -0.12(0.07)

966

Land Owning Caste 0.31 0.12(0.08)

966



* p < 0.1, ** p < 0.05, *** p < 0.01


Study Area - Chasing the Water Table

Most bore wells probably only hit dark clay in the (late?) 1990s.


‘Balance’ - Secondary Census Data

Table : Regressions at Village Level - Census Indicators

N OLS Taluka Spatial Soil Drop Shl Lith

Share, Scheduled Castes (2001) 35 0.01 0.02 0.01 0.02 0.01(0.02) (0.02) (0.02) (0.02) (0.02)

Share, Cultivators (2001) 35 -0.02 -0.04 -0.05 -0.05 -0.02(0.03) (0.03) (0.04) (0.04) (0.03)

Share, Ag. Laborers (2001) 35 0.01 0.03 0.01 0.04 -0.02(0.04) (0.05) (0.05) (0.05) (0.04)

Share, Manufacturing (2001) 35 -0.00 -0.00 -0.00 -0.01 -0.00(0.00) (0.00) (0.00) (0.01) (0.00)

Share, Other (2001) 35 0.02 0.02 0.03 0.01 0.03(0.04) (0.05) (0.05) (0.05) (0.05)



Share, Ag. Laborers (1991) 35 0.03 0.03 0.04 0.01 0.05(0.04) (0.03) (0.04) (0.04) (0.04)

Share, Manufacturing (1991) 35 0.00 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00) (0.00)

Share, Other (1991) 35 -0.00 0.00 -0.00 0.00 0.00(0.04) (0.05) (0.05) (0.05) (0.05)

Populaion Growth (1991-01) 35 -0.08* -0.06 -0.06 -0.06 -0.08**(0.04) (0.04) (0.05) (0.05) (0.04)

Standard errors in parentheses* p < 0.1, ** p < 0.05, *** p < 0.01

Results 24 / 42

Results

We estimate regressions

Iv ,i = ωGv + αXi + ei ,v

I is an impact of interest, v is a village index, and i is a household index. Gv is a dummy variable

indicating whether the local geology contains a dark clay layer. Xi is a vector of household

characteristics (caste, land holding, number of sons and brothers). The errors ei,v are clustered

at village level.

We examine impacts in four categories:

Access to and Usage of Irrigation

Agricultural practices

Migration and Labor Shifts

Asset holdings

Results 25 / 42

Access to Irrigation

Table : Impacts of a Dark Clay Layer - Access to Irrigation

Mean Now Past Patels Others

Bore Failures 1.65 0.78*** 0.81*** 0.71***(0.17) (0.25) (0.18)

560 560 301 259

Share of rainfed farmers 0.02 0.07** 0.02 0.03 0.10*(0.03) (0.02) (0.03) (0.05)

640 625 322 318

Share of well co-owning farmers 0.55 0.04 0.03 -0.00 0.09(0.06) (0.06) (0.08) (0.07)

640 625 322 318

Share of water buying farmers 0.43 -0.11* -0.06 -0.04 -0.19**(0.06) (0.06) (0.07) (0.08)

640 625 322 318

Well depth (100 feet) 5.86 1.62** 0.82* 2.75*** 0.34(0.73) (0.42) (0.79) (0.72)

572 559 302 270

Pump HP 59.44 11.56* 5.42 20.94*** -0.38(5.85) (3.47) (7.28) (4.54)

531 512 283 248

Days between water access 16.05 1.71** 0.49 3.62*** -0.25(0.84) (0.56) (1.10) (0.71)

612 610 301 311

Time to irrigate a plot 4.26 0.88** -0.01 1.40*** 0.38(0.33) (0.17) (0.37) (0.37)

612 615 303 309

Energy used per bigha (HP Hour / Bigha) 259.20 92.17** 13.66 159.78*** 11.51(34.62) (9.66) (38.92) (36.84)

527 509 280 247

Standard errors in parenthesesErrors are clustered by village* p < 0.1, ** p < 0.05, *** p < 0.01

Results 26 / 42

Agriculture and Dairy

Table : Impacts of a Dark Clay Layer - Agriculture


Cropping Intensity 2.25 -0.35*** -0.15** -0.37*** -0.32**(0.09) (0.07) (0.09) (0.13)

649 643 315 334

Cropping Intensity, Summer 0.39 -0.12** -0.06 -0.12** -0.13*(0.05) (0.05) (0.05) (0.07)

649 643 315 334

Number of Irrigations 12.81 -0.24 0.14 -2.68*** 1.79(1.61) (1.56) (0.80) (2.60)

453 456 221 232

Cows 1.29 -0.23 0.58 -0.99 0.27(0.45) (0.48) (1.00) (0.31)

966 755 367 599

Buffaloes 1.19 -0.40** 0.26 -0.55* -0.33*(0.15) (0.26) (0.28) (0.17)

966 823 367 599



* p < 0.1, ** p < 0.05, *** p < 0.01

Results 27 / 42

Agriculture - Crop Choice

Table : Crop Choice

Season Cotton Castor Wheat Potato Sorghum Millet Other

Rainy 0.05 -0.01 0.00 0.00 -0.03 0.00 -0.01Winter 0.00 0.00 0.07 -0.06 0.00 0.00 -0.01Summer 0.00 0.02* 0.00 0.01 0.19* -0.21* 0.00Standard errors in parentheses

* p < 0.1, ** p < 0.05, *** p < 0.01

Results 28 / 42

Stated Reason for Migration

Table : Stated Reason of Migration

Land Scarcity Water Scarcity Better Employment

Sons

Mean 0.12*** 0.09*** 0.72***(0.02) (0.01) (0.02)

Decades since migrated 0.00 0.01 0.02(0.03) (0.03) (0.04)

Dark Clay 0.08 0.14*** -0.21**(0.06) (0.04) (0.09)

Brothers

Mean 0.09*** 0.03*** 0.85***(0.02) (0.01) (0.02)

Decades since migrated -0.03 -0.02** 0.05**(0.02) (0.01) (0.02)

Dark Clay 0.14*** 0.01 -0.16***(0.04) (0.02) (0.05)

Sample consists of all individual migrants for whom the reason of migration was stated.Standard errors in parenthesesErrors are clustered by household in the second row, by village in the third row* p < 0.1, ** p < 0.05, *** p < 0.01

Results 29 / 42

Migration and Labor Shifts

Mean Now Patels Others

Did Any Sons Migrate? 0.22 0.09* 0.16* 0.03(0.05) (0.09) (0.04)

701 303 398

Did Any Brothers Migrate? 0.21 0.07* 0.06 0.08*(0.04) (0.08) (0.04)

653 247 406

Did Any Brothers Migrate (Less than 15 Years Ago)? 0.10 0.04* 0.06 0.03(0.03) (0.06) (0.03)

653 247 406

Did Any Brothers Migrate (More than 15 Years Ago)? 0.12 0.03 0.02 0.03(0.04) (0.07) (0.04)

653 247 406

Did Any Sons Exit Agri.? 0.23 0.12** 0.09 0.13**(0.05) (0.07) (0.06)

565 192 373

Did Any Brothers Exit Agri.? 0.08 0.06 0.05 0.06(0.04) (0.05) (0.05)

550 198 352


Results 30 / 42

Asset Holdings


Land Holding 5.50 -2.56*** -1.55** -4.09*** -1.77***(0.54) (0.71) (1.08) (0.61)

966 861 367 599

Permanent House 0.60 0.01 0.15** -0.02 0.03(0.04) (0.08) (0.02) (0.07)

942 912 353 589

Ceiling Fans 2.36 -0.36** 0.05 -0.23 -0.44**(0.14) (0.12) (0.15) (0.19)

966 914 367 599

MotorCycles 0.37 -0.11 -0.03 -0.11 -0.11*(0.07) (0.05) (0.13) (0.06)

966 691 367 599

Tractors 0.05 -0.01 0.02 -0.04 0.00(0.02) (0.02) (0.05) (0.01)

966 671 367 599

Cars 0.09 -0.05** -0.02 -0.04 -0.05***(0.02) (0.02) (0.06) (0.02)

966 669 367 599


Results 31 / 42

Village level regressions

Table : Regressions at Village Level

N OLS Taluka Spatial Soil Drop Shl Lith

Access to IrrigationWell Depth 37 169.71** 182.86*** 148.55* 206.12** 126.37

(71.18) (61.87) (83.73) (86.93) (76.76)

Columns (Depth to Water) 51 14.56*** 13.22*** 10.62** 12.54*** 10.77**(3.96) (3.61) (4.37) (3.98) (4.03)

Time to Irrigate a Plot 37 0.80** 0.94*** 0.84** 0.89** 0.68*(0.33) (0.34) (0.33) (0.42) (0.36)

Days Between Irrigations 37 2.43*** 1.89* 1.25 2.49** 2.32**(0.87) (0.95) (0.97) (1.06) (0.98)

Frequency of Bore Failure 36 0.82*** 0.69*** 0.75*** 0.70*** 0.91***(0.15) (0.17) (0.18) (0.19) (0.15)

Pump HP 37 10.65* 10.29*** 7.20 12.97** 5.20(5.72) (3.63) (6.35) (6.23) (5.77)

AgricultureCropping Intensity 37 -0.28*** -0.32*** -0.35*** -0.27*** -0.28***

(0.08) (0.09) (0.09) (0.10) (0.09)

Number of Irrigations 37 -0.34 1.42 1.59 0.58 -0.26(1.54) (1.60) (1.74) (1.68) (1.72)

Drip Irrigation Purchases 35 0.10 0.43 1.26 0.84 -0.34(2.94) (3.05) (3.26) (3.41) (1.58)

Migration and Labor ShiftsHH has Migrant Son 37 0.11** 0.17*** 0.16** 0.13* 0.09

(0.05) (0.06) (0.06) (0.07) (0.06)

HH has Non-Farming Son 37 0.11** 0.10* 0.11* 0.12** 0.11**(0.05) (0.05) (0.06) (0.06) (0.05)

HH has Migrant Uncle 37 0.08* 0.07 0.07 0.07 0.07(0.04) (0.04) (0.05) (0.05) (0.05)

HH has Non-Farming Uncle 37 0.02 0.04 0.04 0.04 0.05(0.04) (0.04) (0.04) (0.04) (0.03)


* p < 0.1, ** p < 0.05, *** p < 0.01

Results 32 / 42

Access to Irrigation - Robustness Checks

Table : Impacts of a Dark Clay Layer - Access to Irrigation

OLS Taluka Geog Soil Deep Lith Spatial

Bore Failures 0.78*** 0.84*** 0.60*** 0.73*** 0.99*** 0.78***(0.17) (0.16) (0.17) (0.16) (0.16) (0.15)

560 560 461 447 507 560

Share of rainfed farmers 0.07** 0.07* 0.06* 0.06** 0.06** 0.07***(0.03) (0.04) (0.04) (0.03) (0.03) (0.02)

640 640 534 520 577 640

Share of well co-owning farmers 0.04 0.10 0.04 0.08 0.12 0.04(0.06) (0.08) (0.07) (0.09) (0.08) (.)

640 640 534 520 577 640

Share of water buying farmers -0.11* -0.18** -0.11 -0.14 -0.18** -0.11**(0.06) (0.07) (0.07) (0.09) (0.08) (0.05)

640 640 534 520 577 640

Well depth (100 feet) 1.62** 1.09* 0.97 1.98** 0.95 1.62(0.73) (0.59) (0.61) (0.89) (0.75) (1.22)

572 572 470 460 517 572

Pump HP 11.56* 6.25 8.29** 15.37** 4.70 11.56(5.85) (4.96) (3.57) (6.62) (5.86) (11.59)

531 531 432 423 480 531

Days between water access 1.71** 1.33* 1.17 1.95* 1.87** 1.71**(0.84) (0.77) (0.93) (1.00) (0.90) (0.83)

612 612 508 494 550 612

Time to irrigate a plot 0.88** 0.94*** 0.68 0.95** 0.78** 0.88***(0.33) (0.32) (0.43) (0.41) (0.35) (0.22)

612 612 508 494 550 612

Energy used per bigha (HP Hour / Bigha) 92.17** 75.30*** 68.23** 106.95** 57.02 92.17*(34.62) (27.04) (33.23) (45.65) (35.36) (53.38)

527 527 428 419 476 527


Robustness check - geographical controls 33 / 42

Agriculture - Robustness Checks

Table : Impacts of a Dark Clay Layer - Agriculture


Cropping Intensity -0.35*** -0.38*** -0.32*** -0.35*** -0.35*** -0.35***(0.09) (0.09) (0.10) (0.10) (0.09) (0.07)

649 649 542 527 588 649

Cropping Intensity, Summer -0.12** -0.14*** -0.11** -0.13** -0.15*** -0.12***(0.05) (0.05) (0.04) (0.05) (0.05) (0.04)

649 649 542 527 588 649

Number of Irrigations -0.24 0.27 1.63 0.15 -0.45 -0.24(1.61) (1.54) (2.16) (1.59) (1.23) (1.31)

453 453 385 364 406 453



Migration and Labor Shifts - Robustness Checks

Table : Impacts of a Dark Clay Layer - Migration and Labor Shifts


Did Any Sons Migrate? 0.09* 0.16*** 0.18*** 0.19*** 0.15*** 0.09*(0.05) (0.05) (0.06) (0.05) (0.05) (0.05)

701 701 603 589 636 701

Did Any Brothers Migrate? 0.07* 0.07* 0.08 0.08** 0.07 0.07*(0.04) (0.04) (0.06) (0.04) (0.04) (0.04)

653 653 557 545 595 653

Did Any Brothers Migrate (Less than 15 Years Ago)? 0.04* 0.04 0.05 0.06** 0.06* 0.04***(0.03) (0.03) (0.04) (0.02) (0.03) (0.01)

653 653 557 545 595 653

Did Any Brothers Migrate (More than 15 Years Ago)? 0.03 0.03 0.05 0.04 0.02 0.03(0.04) (0.04) (0.05) (0.04) (0.04) (0.04)

653 653 557 545 595 653

Did Any Sons Exit Agri.? 0.12** 0.14*** 0.10** 0.14*** 0.15*** 0.12**(0.05) (0.05) (0.05) (0.04) (0.05) (0.05)

565 565 484 475 505 565

Did Any Brothers Exit Agri.? 0.06 0.08** 0.04 0.05 0.08** 0.06**(0.04) (0.03) (0.04) (0.03) (0.03) (0.03)

550 550 463 457 498 550



Asset Holdings - Robustness Checks

Table : Impacts of a Dark Clay Layer - Asset Holdings


Land Holding -2.56*** -1.80*** -1.44*** -1.83*** -1.86*** -2.56***(0.54) (0.58) (0.47) (0.61) (0.61) (0.27)

966 966 832 812 882 966

Permanent House 0.01 0.09 0.12 0.09 0.11* 0.01(0.04) (0.06) (0.08) (0.07) (0.05) (0.04)

942 942 811 791 860 942

Ceiling Fans -0.36** -0.15 -0.18 -0.28 -0.23 -0.36***(0.14) (0.13) (0.18) (0.17) (0.16) (0.13)

966 966 832 812 882 966

Cows -0.23 -0.24 -0.10 -0.40 0.17 -0.23(0.45) (0.42) (0.60) (0.59) (0.26) (0.36)

966 966 832 812 882 966

Buffaloes -0.40** -0.27 -0.46** -0.35* -0.34* -0.40***(0.15) (0.18) (0.20) (0.18) (0.17) (0.15)

966 966 832 812 882 966

MotorCycles -0.11 -0.06 -0.07 -0.03 -0.05 -0.11***(0.07) (0.07) (0.10) (0.09) (0.07) (0.03)

966 966 832 812 882 966

Tractors -0.01 0.00 -0.00 -0.01 -0.01 -0.01(0.02) (0.02) (0.03) (0.02) (0.02) (0.02)

966 966 832 812 882 966

Cars -0.05** -0.02 -0.02 -0.04 -0.02 -0.05***(0.02) (0.03) (0.04) (0.03) (0.02) (0.01)

966 966 832 812 882 966



Conclusion

Assessing the eventual impacts of gradual water depletion (and other slowonset environmental changes) on agriculture and farmers in developingcountries is an important policy question, but requires an understanding offarmers’ adaptation strategies.As water resources become depleted, the sustainability of agriculture requiresinvestments in technologies which increase the efficiency of water use. Wefind evidence to suggest farmers are unable to maintain irrigation coverage,and instead adapt by migrating and/or leaving agriculture.North Gujarat is “ahead” of other parts of India where groundwater isextracted beyond sustainable limits and water tables are falling, and provides avaluable setting in which to observe these impacts. On the other hand, ourresults are derived from a relatively small study area and external validity maybe limited.Migration can be a natural part of the development process, but additionalresearch is needed to estimate migrants’ well-being and economic trajectories.

Conclusion 37 / 42

Extent of Under-Reporting

Are results driven by under-reporting?

Table : Reporting of Sons’ Properties

No Clay N Clay N Diff. p

Number of sons 1.60 452 1.57 514 0.03 0.60Sons Reporting Place of Residence 0.79 418 0.77 453 0.01 0.62Sons Reporting Primary Activity (In Village) 0.99 297 0.97 277 0.03 0.02Number of brothers 1.44 452 1.32 514 0.12 0.09Brothers Reporting Place of Residence 0.99 357 0.97 367 0.02 0.01Brothers Reporting Primary Activity (In Village) 0.98 317 0.98 289 0.00 0.96

38 / 42

Land Owning vs. Landless Castes

Table : Land-Owner Caste (Patel) vs. Others

Land-Owners N Others N Diff. p

Number of sons 1.51 459 1.61 828 -0.09∗ 0.07Did Any Sons Migrate? 0.43 373 0.09 566 0.34∗∗∗ 0.00Did Any Sons Migate for Education 0.07 161 0.08 51 -0.00 0.93Did Any Brothers Migrate? 0.32 334 0.14 636 0.17∗∗∗ 0.00Did Any Sons Exit Agri.? 0.32 253 0.20 543 0.12∗∗∗ 0.00Did Any Brothers Exit Agri.? 0.10 263 0.10 577 0.00 0.93Land Holding 8.54 459 3.15 828 5.39∗∗∗ 0.00Permanent House 0.94 445 0.38 819 0.56∗∗∗ 0.00Ceiling Fans 2.72 459 1.77 828 0.95∗∗∗ 0.00Cows 1.40 459 0.74 828 0.66∗∗∗ 0.01Buffaloes 1.27 459 1.19 828 0.08 0.39Livestock 2.67 459 1.93 828 0.74∗∗∗ 0.01Tractors 0.14 459 0.03 828 0.11∗∗∗ 0.00MotorCycles 0.61 459 0.25 828 0.36∗∗∗ 0.00Cars 0.14 459 0.03 828 0.11∗∗∗ 0.00Relatives in City 0.41 438 0.07 797 0.34∗∗∗ 0.00Borewells, Has Access to 2.06 401 1.39 489 0.68∗∗∗ 0.00Shortage of Water? 0.84 369 0.88 473 -0.05∗ 0.05Co-owns Borewell 0.77 422 0.45 519 0.32∗∗∗ 0.00Cultivates his Own Land 0.59 447 0.50 808 0.09∗∗∗ 0.00

39 / 42

Are Migrants Different then Other Sons?

Table : Properties of Migrants

(1) (2) (3) (4) (5)Age Education, primary Education, secondary Education, higher secondary Education, higher

Migrated -0.530 0.000 0.036 0.173∗∗∗ 0.179∗∗∗

(0.468) (.) (0.034) (0.050) (0.048)Observations 2330 2258 2096 1936 1769

Standard errors in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01



Not Farming 1.907∗∗∗ 0.000 0.039 0.093 0.124∗∗

(0.532) (.) (0.044) (0.061) (0.055)Observations 1855 1815 1678 1536 1379




Migrated 0.224 0.000 0.082∗ 0.188∗∗ 0.198∗∗

(0.912) (.) (0.045) (0.092) (0.096)Observations 760 724 702 687 674


40 / 42

Instrumental Variables Estimation

Table : Probability of Having a Non-Farming Son (OLS)

(1) (2) (3) (4)Migration IV Non-Farming IV

Well Depth, Now 0.022∗∗∗ 0.062∗ 0.020∗∗ 0.037(0.006) (0.033) (0.008) (0.028)

Land Holding 0.017∗∗∗ 0.031∗∗∗ 0.006 0.008(0.004) (0.010) (0.007) (0.011)

Relatives in City 0.174∗∗∗ 0.160∗∗ 0.102 0.104(0.056) (0.062) (0.067) (0.079)

Did Any Brothers Migrate? 0.125∗∗∗ 0.124∗∗∗ 0.042 0.092(0.043) (0.047) (0.056) (0.074)

Land per Son -0.022∗∗∗ -0.037∗∗∗ -0.011 -0.016(0.006) (0.011) (0.007) (0.010)

Land Owning Caste 0.202∗∗∗ 0.242∗∗∗ 0.093∗∗ 0.092∗

(0.041) (0.051) (0.039) (0.049)

Constant -0.212∗∗∗ -0.362∗ 0.072 0.032(0.049) (0.191) (0.061) (0.159)

Taluka FE Yes Yes Yes Yes

Observations 600 358 506 278widstat 5.285 9.076


41 / 42

Village Level Regressions

Table : Water Related and Agricultural Impacts of a Dark Clay Layer

N OLS Taluka FE Spatial Controls Soil FE

Water Scarcity

AgricultureCropping Intensity 37 -0.28*** -0.32*** -0.35*** -0.27*** -0.28***

(0.08) (0.09) (0.09) (0.10) (0.09)

Number of Irrigations 37 -0.34 1.42 1.59 0.58 -0.26(1.54) (1.60) (1.74) (1.68) (1.72)

Drip Irrigation Purchases 35 0.10 0.43 1.26 0.84 -0.34(2.94) (3.05) (3.26) (3.41) (1.58)


42 / 42

Village Level Regressions

Table : Labor and Migration Related Impacts of a Dark Clay Layer

N OLS Taluka FE Spatial Controls Soil FE

Migration and Labor

Labor Force Composition 1991-2001Share, Scheduled Castes (2001) 35 0.01 0.02 0.01 0.02 0.01

(0.02) (0.02) (0.02) (0.02) (0.02)


Share, Ag. Laborers (2001) 35 0.01 0.03 0.01 0.04 -0.02(0.04) (0.05) (0.05) (0.05) (0.04)

Share, Manufacturing (2001) 35 -0.00 -0.00 -0.00 -0.01 -0.00(0.00) (0.00) (0.00) (0.01) (0.00)

Share, Other (2001) 35 0.02 0.02 0.03 0.01 0.03(0.04) (0.05) (0.05) (0.05) (0.05)



Share, Ag. Laborers (1991) 35 0.03 0.03 0.04 0.01 0.05(0.04) (0.03) (0.04) (0.04) (0.04)

Share, Manufacturing (1991) 35 0.00 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00) (0.00)

Share, Other (1991) 35 -0.00 0.00 -0.00 0.00 0.00(0.04) (0.05) (0.05) (0.05) (0.05)

Populaion Growth (1991-01) 35 -0.08* -0.06 -0.06 -0.06 -0.08**(0.04) (0.04) (0.05) (0.05) (0.04)


43 / 42

when water runs out evidence from gujarat, india chhokrao baddha ahmedabad chala gaya. aapko koi...

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