Jurisdictional Tax Competition and the Division of Nonrenewable Resource Rents

Abstract

This paper presents a model of nonrenewable resource extraction across multiple jurisdictions which engage in strategic tax competition. The model incorporates rents due to both resource scarcity and capital scarcity as well as intra-region Ricardian rents. Regions set taxes on nonrenewable resource production strategically to balance tax revenues and local benefits from investment conditional on other regions’ tax rates. A representative extraction firm then allocates production capital across regions and time to maximize the present value of profits. Generally, we find that the division of resource rent between firms and regional governments ultimately depends on the relative scarcity of natural and production capital, relative costs across space, and the value regional governments place on economic activity. This theoretical result provides policymakers with information on the determinants of optimal tax rates and motivates future empirical research on the factors influencing the division of resource rent in practice.

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Notes

  1. 1.

    Authors’ calculation based on 2014 U.S. Census Bureau Annual Survey of State Government Tax Collection. To date, Pennsylvania has used a per-well fee instead of a severance tax. State officials are discussing a severance tax, but it would only raise approximately 3% of state revenues (EIA Today In Energy 08/21/15 http://www.eia.gov/todayinenergy/detail.cfm?id=22612 retrieved 08/24/15).

  2. 2.

    These states also have other taxes and fees, such as environmental cleanup taxes. These are typically much smaller in magnitude and have also been stable over our study period. There are also a variety of incentives. For examples, Texas has a 2% point tax reduction for enhanced oil recovery projects and tax credits ranging from 0 to 100% for low-producing wells. (Texas Administrative Code 34.1.3.C.3.37 and 34.1.3.C.3.39, http://texreg.sos.state.tx.us/public/readtac$ext.ViewTAC?tac_view=5&ti=34&pt=1&ch=3&sch=C&rl=Y, retrieved 09/28/15).

  3. 3.

    This simplification allows us to focus on the cross-sectional strategic interaction with varying nonrenewable resource deposits. It does limit the applicability of this model to the case where tax rates changes are rare. This describes the U.S.—of the top ten oil producing states in the continental U.S., only California has changed their tax rate (aka conservation tax) since 2007. These ten states accounted for over 94% of oil production from the continental U.S. in 2014 (author’s calculations based on EIA data). The simplification may not apply as well in locations such as Alberta, Canada, where tax rates are adjusted more frequently (thanks to an anonymous reviewer for pointing this out).

  4. 4.

    The stock \(x_{it}{} \) measures the geologic stock of resources in a state. This is a broader classification than “economic” or “proven” reserves which depend on contemporary price.

  5. 5.

    Mineral rights holders and firms may in practice negotiate over cash payments for mineral rights, a royalty rate or share of production value, and a variety of potential operational practices such as well siting and noise restrictions (Timmins and Vissing 2014). We discuss the implications of the assumption that the industry controls mineral rights in Sect. 5.

  6. 6.

    In practice, wells have varying lifespans. Unconventional wells, which are typically the high-cost or marginal wells in the current environment, produce the bulk of their oil in the first 12–24 months. Unconventional wells can be ‘re-fracked,’ which increases production. To the extent that this is cheaper than drilling and fracking a new well (because it does not entail drilling expenses), it will be inframarginal to new unconventional wells. Conventional wells produce at substantial rates for longer periods. Approximately 10% of U.S. oil production comes from “stripper wells”, which may produce at a rate of several barrels per day for many years.

  7. 7.

    This cost function captures the desired relationship between investment, resource stocks, and cost. Specifically, marginal costs increase as stock are depleted, under the assumption that lower cost stocks are exploited first. Also, within a time period and conditional on a resource stock, there are increasing marginal costs of investment. Specifying the cost function allows us to solve the model but means that our results may be limited to the chosen functional form.

  8. 8.

    Author’s calculations based on EIAa (2017); EIAb (2017), respectively.

  9. 9.

    This range is based on Weber (2014), which finds a multiplier for oil and gas jobs of 2.4. The U.S. Census Bureau reports that the payroll per employee in the oil and gas industry (NAICS 2111) was $98,544 in 2012, which is higher than the mean and median income for the economy as a whole. As the expected local payroll increment is the number of additional jobs times their average salary, it likely falls substantially below $500,000.

  10. 10.

    The price paid for oil as it leave the property on which it was produced, in an arm’s length transaction.

  11. 11.

    We use 10 times the proven reserve amount for parameterization because the state variable represents the quantity of ultimately recoverable oil. EIA estimates of that U.S. technically recoverable stocks are approximately 7 times proven reserves (Table 9.1, EIAa 2017).

  12. 12.

    The transfer of oil rights is often described as a “lease”, with a limited primary term (often 5 years), and a secondary term for the duration of active oil production.

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Correspondence to Peter Maniloff.

Additional information

Lead authorship is shared between the two authors. We would like to thank seminar participants at the Colorado Energy Camp as well as our research assistants Jeremy Miller and Brian Scott.

Appendix

Appendix

Derivation of 3-region model with 2 competitive regions.

$$\begin{aligned} H= & {} \left( 1-\gamma _{1} \right) pq_{1}-\frac{A_{1}q_{1}^{2}}{2x_{1}}+\left( 1-\gamma _{2} \right) pq_{2}-\frac{A_{2}q_{2}^{2}}{2x_{2}}+\left( 1-\gamma _{3} \right) p\left( \bar{q}-q_{1}-q_{2} \right) \\&-\frac{A_{3}\left( \bar{q}-q_{1}-q_{2} \right) ^{2}}{2x_{3}}+\lambda _{1}\left( -q_{1} \right) +\lambda _{2}\left( -q_{2} \right) +\lambda _{3}\left( -\left( \bar{q}-q_{1}-q_{2} \right) \right) \\ \frac{dH}{dq_{1}}= & {} \left( 1-\gamma _{1} \right) p-\frac{A_{1}q_{1}}{x_{1}}-\left( 1-\gamma _{3} \right) p+\frac{A_{3}\left( \bar{q}-q_{1}-q_{2} \right) }{x_{3}}-\lambda _{1}+\lambda _{3}=0\\ \frac{dH}{dq_{2}}= & {} \left( 1-\gamma _{2} \right) p-\frac{A_{2}q_{2}}{x_{1}}-\left( 1-\gamma _{3} \right) p+\frac{A_{3}\left( \bar{q}-q_{1}-q_{2} \right) }{x_{3}}-\lambda _{2}+\lambda _{3}=0\\ \dot{\lambda }_{1}= & {} r\lambda _{1}-\left( \frac{A_{1}q_{1}^{2}}{2x_{1}^{2}}+ \right) \\ \dot{\lambda }_{2}= & {} r\lambda _{2}-\left( \frac{A_{2}q_{2}^{2}}{2x_{2}^{2}} \right) \\ \dot{\lambda }_{3}= & {} r\lambda _{3}-\left( \frac{A_{3}\left( \bar{q}-q_{1}-q_{2} \right) ^{2}}{2x_{3}^{2}} \right) \end{aligned}$$

Obtain system of ODEs in state and co-state:

Step 1 Use \(q_{2}{} { foctosolvefor}\hat{q}_{2}\left( q_{1} \right) \)

$$\begin{aligned} \hat{q}_{2}=\frac{\left( p\left( \gamma _{3}-\gamma _{2} \right) +\frac{A_{3}\bar{q}}{x_{3}}-\frac{A_{3}q_{1}}{x_{3}}-\lambda _{2}+\lambda _{3} \right) }{\frac{A_{3}}{x_{3}}+\frac{A_{2}}{x_{2}}} \end{aligned}$$

Step 2 Plug \(\hat{q}_{2}{} { into}q_{1}{} { focandsolvefor}q_{1}^{*}{} \).

$$\begin{aligned} q_{1}^{*}=\, \frac{\left( \frac{A_{3}\bar{q}}{x_{3}}-\frac{A_{3}}{x_{3}}\left( \frac{\left( p\left( \gamma _{3}-\gamma _{2} \right) +\frac{A_{3}\bar{q}}{x_{3}}-\lambda _{2}+\lambda _{3} \right) }{\frac{A_{3}}{x_{3}}+\frac{A_{2}}{x_{2}}} \right) -\lambda _{1}+\lambda _{3}+p\left( \gamma _{3}-\gamma _{1}\right) \right) }{\frac{A_{1}}{x_{1}}+\frac{A_{3}}{x_{3}}-\frac{\frac{A_{3}^{2}}{x_{3}^{2}}}{\frac{A_{3}}{x_{3}}+\frac{A_{2}}{x_{2}}}} \end{aligned}$$

Step 3 Plug \(q_{1}^{*}{} { into}\hat{q}_{2}{} { toget}q_{2}^{*}\):

$$\begin{aligned} q_{2}^{*}=\, =\frac{\left( p\left( \gamma _{3}-\gamma _{2} \right) +\frac{A_{3}\bar{q}}{x_{3}}-\frac{A_{3}q_{1}^{*}}{x_{3}}-\lambda _{2}+\lambda _{3} \right) }{\frac{A_{3}}{x_{3}}+\frac{A_{2}}{x_{2}}} \end{aligned}$$

System of ODEs with 3 regions:

$$\begin{aligned} \dot{x}_{1}= & {} -q_{1}^{*}\\ \dot{x}_{2}= & {} -q_{2}^{*}\\ \dot{x}_{3}= & {} -\left( \bar{q}-q_{1}^{*}-q_{2}^{*} \right) \\ \dot{\bar{q}}= & {} \alpha \left( \left( 1-\gamma _{1} \right) p-\frac{A_{1}}{x_{1}}q_{1}^{*}-\lambda _{1} \right) \\ \dot{\lambda }_{1}= & {} r\lambda _{1}-\left( \frac{A_{1}q_{1}^{2}}{2x_{1}^{2}}\right) \\ \dot{\lambda }_{2}= & {} r\lambda _{2}-\left( \frac{A_{2}q_{2}^{2}}{2x_{2}^{2}}\right) \\ \dot{\lambda }_{3}= & {} r\lambda _{3}-\left( \frac{A_{3}\left( \bar{q}-q_{1}-q_{2} \right) ^{2}}{2x_{3}^{2}} \right) \\&\lambda _{T1},\, \lambda _{T2},\, \lambda _{T3},\\&x_{01},\, x_{02},\, x_{03},\, \bar{q}_{0}\, \, \, { given} \end{aligned}$$

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Maniloff, P., Manning, D.T. Jurisdictional Tax Competition and the Division of Nonrenewable Resource Rents. Environ Resource Econ 71, 179–204 (2018). https://doi.org/10.1007/s10640-017-0143-6

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Keywords

  • Strategic tax competition
  • Severance tax
  • Hotelling
  • Oil production
  • Dynamic optimization