Investments Under Regulatory Uncertainty: Evidence from Renewable - - PowerPoint PPT Presentation

K I R A R . F A B R I Z I O M A Y 1 0 , 2 0 1 1

Investments Under Regulatory Uncertainty:

Evidence from Renewable Energy Generation

• Avg. Annual Post-RPS MW Investment

27 271 226 CT: 4 600 MA: 7 MD: 34 ME: 23 830 100 16 NH: 8 NJ: 3 44 33 NY: 222 105 165 1585 119

Policy Impact Theory

 Similar RPS policies

were passed in similar states with widely different results. Why?

 How does uncertainty

about future policy stability affect firms’ response to policies?

Two ways to view this paper

This is related to TCE theory and empirical studies on ―political risk‖

 International business literature includes studies of the

impact of political risk on FDI

 Governance of transactions takes place within a particular

institutional environment  determines transaction hazards (Williamson 1991)

 ―Political risk‖ in a country determines firm’s willingness to invest in

long-lived, sunk assets and therefore impacts the governance structures that firms select.

 Cross-national empirical evidence that greater restraints on regulators

/ political actors are associated with more infrastructure investment (Levy and Spiller 1994, Henisz and Zelner 2001).

 The risk of future change to regulatory policy creates the

potential for ex post expropriation of return on firms’ investments.

Regulatory instability impedes investment in renewable generation assets

 RPS policies seek to promote investment in new

renewable generation assets.

 Large, sunk capital investments  Location specific (most valuable in-state) and expensive to

relocate

 Much less valuable in ―next-best‖ use, i.e. in the absence of

RPS policy

 Where there is more uncertainty about the stability

• f the RPS policy, firms will be less willing to invest

in long-lived, specific generation assets.

Empirical test

 Prediction: Firms invest less in renewable generation assets in

response to RPS policy in states with more regulatory uncertainty.

 Diff-in-Diff Model annual state-level MWs of new renewable

generation as a function of:

 RPS policy (or StateGap)  RPS policy * Regulatory Uncertainty  State-year controls :  Electricity price  Non-RPS Demand for renewable energy: GSP, population, elec. sales and growth,

sierra club membership, % democrats in legislature

 Existing supply of renewable energy: state and region eligible renewable generation

capacity, other policies

 State (conditional) & Year (unconditional) fixed effects

 Method: Quasi-Maximum-Likelihood Poisson w/ conditional state

FEs (Wooldridge 1999, Simcoe 2007)

Measuring RPS Policy

 Policy indicator variable (0/1) turns on in policy

effective year.

 Up to 10 years after the RPS policy was enacted.  Allows for construction lead time for generation projects.  If projects come online early (before the effective year), creates

conservative bias in estimates.

 Measure unmet portion of RPS-created demand.

 Required % X Target year sales = Required MWhs of

renewable energy.

 Eligible installed MWs X Capacity Factor = Eligible MWh

Supply of renewable energy.

 Required Sales – Eligible Supply = Unmet demand (StateGap)

How to measure regulatory uncertainty?

 Want to capture the perceived likelihood of repealing

• r modifying the RPS policy.

 Option 1: Use prior repeal of electricity industry deregulation

policy as an indicator of policy instability in the state.

 24 states passed restructuring legislation between 1996 & 2001  8 later repealed it between 2001 & 2007

Source: Energy Information Administration Website

How to measure regulatory uncertainty?

 Want to capture the perceived likelihood of repealing

• r modifying the RPS policy.

 Option 1: Use prior repeal of electricity industry deregulation

policy as an indicator of policy instability in the state.

 Option 2: Instrument using institutional characteristics that

determine likelihood of repeal:

 Elected PUC commissioners (13 states), formalized consumer

advocate (30 states) (Holburn & Vanden Bergh 2006)

 Party of governor and alignment in executive & legislature (Henisz

2000)

Employ novel state-year level dataset

 Annual MW investments in renewable generation assets, 1990-2010

 Platts (UDI) North American database of electric power producers.  Plant level data; on-line year, location, status, plant type, company type

 RPS policy histories: US Dept. of Energy DSIRE database

 Effective date, target, annual requirements for each RPS policy

 Restructuring policy histories: US Energy Information

Administration (EIA)

 Dates restructuring policy passed / repealed

 State-level institutions:

 Consumer Advocate (Holburn & Vanden Bergh 2006), Elected commissioners

(NARUC)

 Executive—Legislative party & alignment (Census Bureau)

 Annual controls from a variety of sources

 Census Bureau, Bureau of Economic Analysis, EIA, DSIRE, Sierra Club

Summary statistics suggest that investment increased more post-RPS in states w/o repeal

States Without Repeal of Restructuring States With Repeal of Restructuring Pre-RPS Annual MW Investment in new Renewable Generation Assets 28.31 (5.41) 44.95 (10.84) Post-RPS Annual MW Investment in new Renewable Generation Assets 203.93 (57.93) 108.40 (27.75) Pre-to-Post Increase in MW Investment 175.62 (28.71)** 63.45 (25.49)*

Includes observations only for states that passed RPS policies. N=366 (w/o, pre), 75 (w/o, post), 100 (w/, pre), 26 (w/, post). SE in parentheses, *significant at 5 percent level, **significant at 1 percent level.

T6(2) T6(4) RPS 0.812 0.863 (0.288)** (0.440)* Repeal X RPS

• 1.349

(0.413)** ISO 0.748 0.693 (0.376)* (0.420) Post Interconnect

• 0.578
• 0.614

(0.228)* (0.243)* Restructure 1.190 0.856 (0.466)* (0.535) Restructure X RPS 0.322 (0.451) Observations 1029 1029 Log Likelihood

• 26435
• 26053

Results suggest regulatory uncertainty is an important determinant of investment

Dep Var.: MWs of new renewable gen in state-year

Results suggest regulatory uncertainty is an important determinant of investment

7.03 0.4 8.15

1 2 3 4 5 6 7 8 9

RPS (model 2)* RPS w/ Repeal RPS w/o Repeal* MWs Renewable Generation * Predicted effect statistically significant at the 5% level.

Investments to meet ―State Gap‖ are sensitive to uncertainty, those to meet ―Region Gap‖ are not.

T7(2) T7(3) StateGap 0.078 0.093 (0.027)** (0.030)** Repeal X StateGap

• 0.070
• 0.082

(0.022)** (0.024)** RegionGap 0.022 (0.011)* Repeal X RegionGap 0.007 (0.015) ISO 1.000 1.106 (0.480)* (0.477)* Post Interconnect

• 0.537
• 0.633

(0.253)* (0.248)* Restructure

• 0.475
• 0.434

(0.518) (0.531) Restructure X RPS

• 0.635
• 0.795

(0.408) (0.436) Observations 1008 1008 Log Likelihood

• 22980
• 22692

Interpretation: Policy instability

 Results demonstrate that investments increased less

post-RPS in states with prior repeal, relative to other

• states. Is policy instability the culprit?

 Predict repeal of restructuring as a function of

institutional characteristics of the state.

 Elected PUC commissioners, formalized consumer advocate,

Democratic governor.

 Two-stage Poisson estimates generate results

consistent with those reported here (in appendix).

Concerns & Limitations

 Are there pre-existing trends in investment that

differ across RPS and non-RPS states? No.

 Are states that repealed restructuring systematically

different in a way that impacts investments? No.

 Are RPS policies different in states that repealed

restructuring? No.

 Small number of observations (49 states).  Do results from electricity industry generalize?

What do we learn?

 Uncertainty about future regulatory change impacts firm

investments…

 Especially when investments are specific to the regulatory policy, long-

lived, expensive to relocate.

 Policy makers will struggle to have an impact with initiatives

when they can’t credibly commit to policy stability.

 This generalizes to many other contexts where investments are specific

to policy regimes: healthcare, intellectual property, carbon policy.

 Credible commitments to policy initiatives may be required to

get firms to respond.

 Create mutual dependence: Invest to in assets specific to policy (e.g.

transmission) or limit alternatives (e.g. natural gas)

 Decrease ability to change policy: Create procedural hurdles that reduce

risk of policy renegotiations (e.g. Federal & State APA)

 OR ―vertically integrate‖ to achieve the policy goals [in this case, the

government investing in renewable generation assets themselves]