The Role of Bargaining Costs in

Addressing the Depletion of Natural

Resources

The Case of California’s Groundwater

Andrew B. Ayres

UCSB, Bren School and Economics

Gary D. Libecap

UCSB, Bren School and Economics

Eric C. Edwards

Utah State, Applied Economics

Outline

• Application: California provides opportunities for differential management adoption; critical basins

1.Introduction and Background

2.Model Predictions

3.Empirical Strategy

4.Results and Discussion

5.Conclusion

Overview 1/17

California’s Puzzle

2/17Background

Common-pool Resource Management

• Common-pool Resources (CPR)– Rivalry without excludability; Depletion

• Alleviation of CPR problem requires new institutions (Ostrom, 1990; Barzel, 1987)– Rational opposition (Grainger and Costello,

2015;Libecap, 1989)

• Groundwater: shared – yet renewable – CPR– Excessive pumping results from open-access

framework

3/17Background

Research Agenda

• Research Questions:– Which basin and user characteristics explain the adoption of management?

– What determines the difficulty users will have in reaching agreement?

– When and why do basins adopt management?

• Few empirical investigations of returns to new institutions and bargaining costs

• Sustainable Groundwater Management Act (SGMA): Implementation can benefit from a better understanding of costs and benefits

4/17Background

Rights and Regulation in California

• Correlative Rights– Overlying and Appropriative Rights– Retains important open-access characteristics: No mechanism for

internalizing full costs of pumping

Alternatives:• Groundwater Management Plans (GMPs)

– AB 3030 (1992): The original Groundwater Management Act– They may not restrict or redefine water rights

• Adjudication to Define Property Rights– Typically a stipulated judgement—bargaining among parties– Total pumping is often capped and pumping rights are assigned

5/17Background

Twenty-four adjudications cover 32 groundwater basins (as defined by

Bulletin 118).

In addition, 91 basins have at least one GMP under AB 3030, SB 1938,

or AB 359.

Basins not pictured here (307) have adopted neither remedy.

Several basins are listed as critically overdrafted by CA DWR yet have not been adjudicated. This will be key for

our analysis.

6/17

Model

Analytic Model Predictions

• For basin-wide adjudication to be welfare enhancing– Aggregate Benefits of Adoption > Aggregate Costs of Implementation and Bargaining

• Private tradeoff of implementing management– Private cost of reduced pumping vs. private benefits of neighbors’ reduced pumping

Aggregate benefits increase with:• Hydraulic conductivity• Aridness or lack of precipitation• Well density• More valuable uses (sometimes

due to longer planning horizons)• Risk of collateral impacts

Bargaining costs increase with:• Number of Users/Logistics• Variance in Distribution of

Private Benefits

7/17

Empirics

Data

8/17Empirics

Variable Units Source

Precipitation (1950 - 2014 mean) Millimeters PRISM

Precipitation (1950 - 2014 spatial variance)

Millimeters PRISM

Basin Surface Area Acres DWR

Coastline Dummy Dummy DWR

Well Yield (Max/Average) Gal/min DWR

Number of Farms Count USDA

Number of Wells Count DWR

Number of Drinking Water Wells Count or Percentage DWR

Number of Agricultural Wells Count or Percentage DWR

Well Density Count/Acre DWR/Author

Number of Wells within 1000m of Coast Count or Percentage DWR/Author

Urban Population Growth (1950 - 2010) Decadal Average Growth Rate CA. Dept of Finance/ Author

State Water Project Connection Dummy Author's Data

Number of GMPs Number (also: Dummy if >0) DWR

Adjudication Dummy Author’s Data

Length of Adjudication Years Author’s Data

Empirical Strategy

• Benefits: Ordered Logit Model to examine management choice: None, GMPs, Adjudication

𝑌𝑖∗ = 𝑋𝑖

′𝛽 + 𝑢𝑖• Costs:

– (1) Critical, unadjudicated basins: counterfactual for adjudicated basins

• Low vs. high transaction costs, holding benefits constant

– (2) Estimate Determinants of Adjudication Duration

• Cox Proportional-Hazard Model

9/17Empirics

Results

Ordered Logit Results - Benefits

Results 10/17

(1) (2) (3) (4) (5)

Mgt Type Mgt Type Mgt Type Mgt Type Mgt Type

Baseline Spec. SAD in Tier 3 SWP

Avg. Well Yield0.000721** 0.000565* 0.000655* 0.000535**

(0.000332) (0.000342) (0.000368) (0.000261)

Max. Well Yield0.000291***

(0.0000892)

Mean Precipitation 1950-2014

-0.0000684 -0.000442 -0.000154 -0.0000575

(0.000458) (0.000419) (0.000480) (0.000470)

Coastline Dummy-0.380 -0.233 -0.563 -0.655* -0.980*

(0.408) (0.363) (0.400) (0.393) (0.535)

Well Density46.21*** 38.37*** 47.07*** 37.84*** 42.79***

(12.25) (13.87) (13.71) (12.89) (12.56)

Percentage Ag Wells-0.501 -0.261 -0.719 -0.185

(0.629) (0.585) (0.643) (0.711)

Average Urban Pop. Growth (1950-2010)

0.0155** 0.0241** 0.0219**

(0.00703) (0.0101) (0.0101)

SWP Connection1.672***

(0.372)

Kappa 10.767** 0.925*** 0.829** 0.897*** 1.218***

(0.322) (0.310) (0.338) (0.329) (0.246)

Kappa 22.340*** 2.435*** 2.504*** 2.147*** 2.982***

(0.397) (0.396) (0.415) (0.360) (0.307)

N 184 209 184 184 184

(Standard errors) = * p<.1, ** p<.05, *** p<.01

Ordered Logit Results - Benefits

Results 10/17

(1) (2) (3) (4) (5)

Mgt Type Mgt Type Mgt Type Mgt Type Mgt Type

Baseline Spec. SAD in Tier 3 SWP

Avg. Well Yield0.000721** 0.000565* 0.000655* 0.000535**

(0.000332) (0.000342) (0.000368) (0.000261)

Max. Well Yield0.000291***

(0.0000892)

Mean Precipitation 1950-2014

-0.0000684 -0.000442 -0.000154 -0.0000575

(0.000458) (0.000419) (0.000480) (0.000470)

Coastline Dummy-0.380 -0.233 -0.563 -0.655* -0.980*

(0.408) (0.363) (0.400) (0.393) (0.535)

Well Density46.21*** 38.37*** 47.07*** 37.84*** 42.79***

(12.25) (13.87) (13.71) (12.89) (12.56)

Percentage Ag Wells-0.501 -0.261 -0.719 -0.185

(0.629) (0.585) (0.643) (0.711)

Average Urban Pop. Growth (1950-2010)

0.0155** 0.0241** 0.0219**

(0.00703) (0.0101) (0.0101)

SWP Connection1.672***

(0.372)

Kappa 10.767** 0.925*** 0.829** 0.897*** 1.218***

(0.322) (0.310) (0.338) (0.329) (0.246)

Kappa 22.340*** 2.435*** 2.504*** 2.147*** 2.982***

(0.397) (0.396) (0.415) (0.360) (0.307)

N 184 209 184 184 184

(Standard errors) = * p<.1, ** p<.05, *** p<.01

Ordered Logit Results - Benefits

Results 10/17

(1) (2) (3) (4) (5)

Mgt Type Mgt Type Mgt Type Mgt Type Mgt Type

Baseline Spec. SAD in Tier 3 SWP

Avg. Well Yield0.000721** 0.000565* 0.000655* 0.000535**

(0.000332) (0.000342) (0.000368) (0.000261)

Max. Well Yield0.000291***

(0.0000892)

Mean Precipitation 1950-2014

-0.0000684 -0.000442 -0.000154 -0.0000575

(0.000458) (0.000419) (0.000480) (0.000470)

Coastline Dummy-0.380 -0.233 -0.563 -0.655* -0.980*

(0.408) (0.363) (0.400) (0.393) (0.535)

Well Density46.21*** 38.37*** 47.07*** 37.84*** 42.79***

(12.25) (13.87) (13.71) (12.89) (12.56)

Percentage Ag Wells-0.501 -0.261 -0.719 -0.185

(0.629) (0.585) (0.643) (0.711)

Average Urban Pop. Growth (1950-2010)

0.0155** 0.0241** 0.0219**

(0.00703) (0.0101) (0.0101)

SWP Connection1.672***

(0.372)

Kappa 10.767** 0.925*** 0.829** 0.897*** 1.218***

(0.322) (0.310) (0.338) (0.329) (0.246)

Kappa 22.340*** 2.435*** 2.504*** 2.147*** 2.982***

(0.397) (0.396) (0.415) (0.360) (0.307)

N 184 209 184 184 184

(Standard errors) = * p<.1, ** p<.05, *** p<.01

Ordered Logit Results - Benefits

Results 10/17

(1) (2) (3) (4) (5)

Mgt Type Mgt Type Mgt Type Mgt Type Mgt Type

Baseline Spec. SAD in Tier 3 SWP

Avg. Well Yield0.000721** 0.000565* 0.000655* 0.000535**

(0.000332) (0.000342) (0.000368) (0.000261)

Max. Well Yield0.000291***

(0.0000892)

Mean Precipitation 1950-2014

-0.0000684 -0.000442 -0.000154 -0.0000575

(0.000458) (0.000419) (0.000480) (0.000470)

Coastline Dummy-0.380 -0.233 -0.563 -0.655* -0.980*

(0.408) (0.363) (0.400) (0.393) (0.535)

Well Density46.21*** 38.37*** 47.07*** 37.84*** 42.79***

(12.25) (13.87) (13.71) (12.89) (12.56)

Percentage Ag Wells-0.501 -0.261 -0.719 -0.185

(0.629) (0.585) (0.643) (0.711)

Average Urban Pop. Growth (1950-2010)

0.0155** 0.0241** 0.0219**

(0.00703) (0.0101) (0.0101)

SWP Connection1.672***

(0.372)

Kappa 10.767** 0.925*** 0.829** 0.897*** 1.218***

(0.322) (0.310) (0.338) (0.329) (0.246)

Kappa 22.340*** 2.435*** 2.504*** 2.147*** 2.982***

(0.397) (0.396) (0.415) (0.360) (0.307)

N 184 209 184 184 184

(Standard errors) = * p<.1, ** p<.05, *** p<.01

Ordered Logit – Marginal Effects

Results 11/17

Average Well Yield

Mean Precipitation

Well DensityPercentage

Ag Wells

Average Urban

Growth

One Standard Deviation around Mean

None -7.32% 1.47% -4.51% 4.10% -9.89%

GMP 4.44% -0.89% 2.77% -2.46% 6.03%

Adj. 2.89% -0.57% 1.74% -1.64% 3.86%

Critical Basin Comparison

VariableCritical,

UnadjudicatedAdjudicated

Significance Level

Fitted Values (Benefits) 0.8213 0.8142 -

Basin Area 1,219,700 145,651 **

Mean Spatial Precip. Variance 1950-2014

5459.1 4027.3 -

Number of Wells 3269.4 342.9 ***

Well Heterogeneity 0.15415 0.09209 **

Percentage Drinking Wells 0.561 0.566 -

Percentage Ag Wells 0.399 0.192 **

N 8 32

* p<.1, ** p<.05, *** p<.01

Results 12/17

• We wish to hold benefits constant, explore determinants of bargaining costs• Critical basins are proper counterfactual if benefits scores are similar

Mean Std. Dev.

Adj. Duration (Yrs) 7.6 5.8

13/17Results

Predictions – Adjudication Duration

Results 14/17

Measure Predicted SignPrediction from Conceptual

Model

Basin Size - Number of Users/Logistics

Spatial Variance in Recharge - Variance in Private Benefits

Number of Wells - Number of Users

Well Heterogeneity - Variance in Private Benefits

Wells within 1000m of Coastline - Variance in Private Benefits

Wells within 1000m: Quadratic + Variance in Private Benefits

Results – Adjudication Duration(1) (2) (3) (4)

Adj Duration Adj Duration Adj Duration Adj Duration

Basin Area-0.00000111 -0.000000810 -0.00000292*** -0.00000272***

(0.000000988) (0.000000950) (0.000000899) (0.000000767)

Mean Spatial Precip. Variance 1950-2014

-0.0000509 -0.0000483 -0.0000698 -0.0000601

(0.0000496) (0.0000400) (0.0000588) (0.0000510)

Avg. Well Yield-0.000244 -0.000128 -0.000304 -0.000166

(0.000356) (0.000376) (0.000335) (0.000358)

Number of Wells-0.00129*** -0.00127*** -0.00117*** -0.00109***

(0.000229) (0.000258) (0.000202) (0.000211)

Proportion of Wells within 1000m of Coastline

-5.845 -5.279 -107.3*** -114.5***

(4.037) (4.333) (29.08) (35.26)

Proportion of Wells within 1000m Squared

433.9*** 466.0***

(122.5) (146.4)

Well Heterogeneity-10.24*** -17.02** -10.34*** -17.60**

(3.523) (7.585) (3.690) (7.581)

Controls

Coastline Dummy0.354 -0.0792 1.340*** 0.987**

(0.782) (0.930) (0.455) (0.400)

Average Number of Farms 1940-19590.00343*** 0.00342*** 0.00430*** 0.00425***

(0.000704) (0.000746) (0.000866) (0.00100)

Percentage Drinking Wells-0.0677 0.118

(0.868) (0.776)

Percentage Ag Wells3.160 3.480

(2.785) (2.512)

N 23 23 23 23

(Standard errors) = * p<.1, ** p<.05, *** p<.01

Results 15/17

Results – Adjudication Duration(1) (2) (3) (4)

Adj Duration Adj Duration Adj Duration Adj Duration

Basin Area-0.00000111 -0.000000810 -0.00000292*** -0.00000272***

(0.000000988) (0.000000950) (0.000000899) (0.000000767)

Mean Spatial Precip. Variance 1950-2014

-0.0000509 -0.0000483 -0.0000698 -0.0000601

(0.0000496) (0.0000400) (0.0000588) (0.0000510)

Avg. Well Yield-0.000244 -0.000128 -0.000304 -0.000166

(0.000356) (0.000376) (0.000335) (0.000358)

Number of Wells-0.00129*** -0.00127*** -0.00117*** -0.00109***

(0.000229) (0.000258) (0.000202) (0.000211)

Proportion of Wells within 1000m of Coastline

-5.845 -5.279 -107.3*** -114.5***

(4.037) (4.333) (29.08) (35.26)

Proportion of Wells within 1000m Squared

433.9*** 466.0***

(122.5) (146.4)

Well Heterogeneity-10.24*** -17.02** -10.34*** -17.60**

(3.523) (7.585) (3.690) (7.581)

Controls

Coastline Dummy0.354 -0.0792 1.340*** 0.987**

(0.782) (0.930) (0.455) (0.400)

Average Number of Farms 1940-19590.00343*** 0.00342*** 0.00430*** 0.00425***

(0.000704) (0.000746) (0.000866) (0.00100)

Percentage Drinking Wells-0.0677 0.118

(0.868) (0.776)

Percentage Ag Wells3.160 3.480

(2.785) (2.512)

N 23 23 23 23

(Standard errors) = * p<.1, ** p<.05, *** p<.01

Results 15/17

Results – Adjudication Duration(1) (2) (3) (4)

Adj Duration Adj Duration Adj Duration Adj Duration

Basin Area-0.00000111 -0.000000810 -0.00000292*** -0.00000272***

(0.000000988) (0.000000950) (0.000000899) (0.000000767)

Mean Spatial Precip. Variance 1950-2014

-0.0000509 -0.0000483 -0.0000698 -0.0000601

(0.0000496) (0.0000400) (0.0000588) (0.0000510)

Avg. Well Yield-0.000244 -0.000128 -0.000304 -0.000166

(0.000356) (0.000376) (0.000335) (0.000358)

Number of Wells-0.00129*** -0.00127*** -0.00117*** -0.00109***

(0.000229) (0.000258) (0.000202) (0.000211)

Proportion of Wells within 1000m of Coastline

-5.845 -5.279 -107.3*** -114.5***

(4.037) (4.333) (29.08) (35.26)

Proportion of Wells within 1000m Squared

433.9*** 466.0***

(122.5) (146.4)

Well Heterogeneity-10.24*** -17.02** -10.34*** -17.60**

(3.523) (7.585) (3.690) (7.581)

Controls

Coastline Dummy0.354 -0.0792 1.340*** 0.987**

(0.782) (0.930) (0.455) (0.400)

Average Number of Farms 1940-19590.00343*** 0.00342*** 0.00430*** 0.00425***

(0.000704) (0.000746) (0.000866) (0.00100)

Percentage Drinking Wells-0.0677 0.118

(0.868) (0.776)

Percentage Ag Wells3.160 3.480

(2.785) (2.512)

N 23 23 23 23

(Standard errors) = * p<.1, ** p<.05, *** p<.01

Results 15/17

Results – Adjudication Duration(1) (2) (3) (4)

Adj Duration Adj Duration Adj Duration Adj Duration

Basin Area-0.00000111 -0.000000810 -0.00000292*** -0.00000272***

(0.000000988) (0.000000950) (0.000000899) (0.000000767)

Mean Spatial Precip. Variance 1950-2014

-0.0000509 -0.0000483 -0.0000698 -0.0000601

(0.0000496) (0.0000400) (0.0000588) (0.0000510)

Avg. Well Yield-0.000244 -0.000128 -0.000304 -0.000166

(0.000356) (0.000376) (0.000335) (0.000358)

Number of Wells-0.00129*** -0.00127*** -0.00117*** -0.00109***

(0.000229) (0.000258) (0.000202) (0.000211)

Proportion of Wells within 1000m of Coastline

-5.845 -5.279 -107.3*** -114.5***

(4.037) (4.333) (29.08) (35.26)

Proportion of Wells within 1000m Squared

433.9*** 466.0***

(122.5) (146.4)

Well Heterogeneity-10.24*** -17.02** -10.34*** -17.60**

(3.523) (7.585) (3.690) (7.581)

Controls

Coastline Dummy0.354 -0.0792 1.340*** 0.987**

(0.782) (0.930) (0.455) (0.400)

Average Number of Farms 1940-19590.00343*** 0.00342*** 0.00430*** 0.00425***

(0.000704) (0.000746) (0.000866) (0.00100)

Percentage Drinking Wells-0.0677 0.118

(0.868) (0.776)

Percentage Ag Wells3.160 3.480

(2.785) (2.512)

N 23 23 23 23

(Standard errors) = * p<.1, ** p<.05, *** p<.01

Results 15/17

How Costs Block Management

• Examples

– San Joaquin and the Central Valley: Large, many users, heterogeneous demand

– Seaside and Salinas: Disparate bargaining positions result from spatial distribution of pumpers

Discussion 16/17

Conclusions

• Reliable determinants of management benefits include– Well Yields (Commonality) – Precipitation (Value) – Well Density (Heterogeneity/Commonality) – Population Growth/ Ag Users (Value)

• Bargaining costs robustly controlled by– Basin Size (Logistical, Informational Costs)– Number of Wells (Number of Actors)– Spatial Distribution of Actors Near Coastline (User/Resource Heterogeneity)– Well Type Heterogeneity (User Heterogeneity)

• SGMA implementation can be informed by this work: demonstrate where adjudication will be costly or shed light on types of CPR problems for which less stringent restrictions are appropriate

Conclusions 17/17

THANK YOU!

aayres@bren.ucsb.edu

Summary Statistics

Appendix 18/17

Adjudicated GMP None TotalCritical,

UnadjudicatedNumber of Basins 32 91 307 430 8

Precipitation (mean)337 469 533 505 369

(161) (294) (445) (405) (201)

Precipitation (spatial variance)4,027 3,427 2,945 3,127 5,454

(6,113) (5,580) (5,127) (5,300) (5,094)

Basin Size (acres)145,652 207,126 52,540 92,184 1,094,380

(204,795) (1,002,996) (125,798) (479,070) (2,745,233)

Coastline Dummy0.28 0.23 0.28 0.27 0.5

(0.46) (0.42) (0.45) (0.45) (0.52)

Well Yield (maximum)2,450 1,816 1,150 1,493 2,824

(2,313) (1,705) (1,288) (1,633) (1,496)

Well Yield (average)710 565 432 502 777

(495) (541) (556) (550) (322)

Avg. Number of Farms (1940-1959)216 567 38 163 3,282

(464) (3,442) (156) (1,601) (9,346)

Number of Wells342 631 28 179 1,764

(571) (3,481) (93) (1,623) (4,898)

Percentage Drinking Wells 56.6 49.6 34.8 40 35.2

(29.2) (36.9) (40) (39.4) (32.5)

Percentage Agricultural Wells 19.2 17.3 12 13.7 29

(17.6) (23.5) (24.5) (23.9) (28.1)

Well Density (per acre)0.00171 0.00358 0.00136 0.00185 0.00123

(0.00212) (0.00751) (0.00315) (0.00448) (0.00169)

Well Heterogeneity0.092 0.0631 0.0347 0.045 0.00169

(0.0698) (0.0754) (0.0669) (0.0711) (0.0914)

Urban Population Growth (%)20.28 12.12 4.12 7.01 26.37

(33.24) (24.33) (17.33) (21.04) (19.68)

SWP Connection0.718 0.274 0.104 0.186 0.2

(0.456) (0.448) (0.306) (0.389) (0.421)

Benefits of Groundwater Management

For basin-wide adjudication to be welfare enhancingAggregate Benefits of Adoption > Aggregate Costs of Implementation and Bargaining

𝑉𝑖0 = max

𝑤𝑖

0

∞

𝜋𝑖 𝑤𝑖 , ℎ𝑖 𝑒−𝛿𝑡 𝑑𝑡

𝑠. 𝑡 ℎ𝑖 = 𝑟𝑖 − 𝑤𝑖 − 𝜃 ℎ𝑖 − ℎ−𝑖 and 𝜃 =𝑘

𝑑

Define: 𝑉𝑀 ≥ 𝑉0

• We show: aggregate net benefits of management increase with– Hydraulic conductivity (𝑘)

– Aridness or lack of precipitation (𝑟)

– Well density (𝑑)

– More valuable uses (sometimes due to longer planning horizons) (𝜕𝜋𝑖

𝜕𝑤𝑖)

– Risk of collateral impacts (f𝑖 ∙ ℎ)

7/17Conceptual Model

Conceptual Model: Costs of Adjudication

Private tradeoff of implementing managementPrivate cost of reduced pumping vs. private benefits of neighbors’ reduced pumping

Δ𝑖 = 𝑉𝑖𝑀 − 𝑉𝑖

0

𝐶 = 𝑔 𝑣 Δ𝑖

𝑣 Δ𝑖 =

𝑖=1

𝑁

(Δ𝑖 − 𝜇∆)2

• Benefits must be positive for some users in order for management to be considered– Some aquifers are so critical (drawdown, intrusion, subsidence, etc.) that all expect positive net benefits– Sometimes recalcitrant parties must be brought on board: institutional rule changes or side payments

• Bargaining to assign water rights and control extraction may be difficult due to:– Number of Actors: Number of Rights Holders, Number of Wells (Agrawal and Goyal, 2000; Libecap and Wiggins, 1984)– Resource Size and Characteristics: Basin Area, Conductivity, Coastline, Confinement, Variance in Recharge (Libecap

and Smith, 1999)– User Heterogeneity: Type of Use (Urban/Ag), Relative Position (to Coast, Other Users, Density), Consolidation (Ruttan,

2008; Libecap and Wiggins, 1987)– Monitoring and Enforcement: Metering, Watermaster– Asymmetric Information: Difficult to Measure

Conceptual Model 8/17