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Monetary Policy, Natural Resources, and Federal Redistribution

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Abstract

Can monetary policy shocks induce redistribution across natural resource rich and poor states within a federation? We conjecture that resource-rich states are capital intensive, hence their investment is more responsive to changes in monetary policy. Consequently, contractionary monetary policy shocks (e.g., increases in the interest rate) may induce redistribution from resource-poor states to resource-rich ones, via an equalizing federal transfer scheme, because investment is reduced more strongly in the latter. We test these hypotheses using a panel of U.S. states covering several decades, and find that: (1) resource-rich states are significantly and persistently more capital intensive; (2) contractionary monetary policy shocks induce a relative drop (increase) in investment (federal transfers) in resource-rich states, over the course of four years; (3) these patterns are driven by resource-induced differences in the capital share in the economy. We estimate that a one standard deviation contractionary monetary shock induces, within the first year, federal redistribution of approximately \(\$2.5\) billion from the resource-poor to the resource-rich states, representing about \(11\%\) of the total average annual federal transfers received by the latter states.

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Fig. 1
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Notes

  1. 1.

    See Ramey (2016) for a detailed review.

  2. 2.

    See, e.g., Ledoit (2011), Brunnermeier and Sannikov (2013), Doepke et al. (2015), Gornemann et al. (2016), and Coibion et al. (2016). Notably, this was also accompanied by a shift in policymakers’ attention to the relation between monetary policy and economic inequality (see, e.g., Mersch 2014; Bullard 2014; Bernanke 2015; Forbes 2015).

  3. 3.

    Previous research studied the heterogeneous impacts of monetary policy across different intra-federal (cross-state) dimensions, including for instance the extent of price rigidities, and the characteristics of the housing market. We elaborate on this when reviewing the related literature in the next section.

  4. 4.

    Notably, such a scheme is a standard feature of virtually all federations, via which federal governments forward fiscal transfer payments to local governments, partially in an attempt to redistribute resources equitably across regions. Importantly, it represents the primary, direct federal redistribution mechanism [see, e.g., Martinez-Vazquez and Searle (2007)], and consequently, the focus of our analysis.

  5. 5.

    Capital in this context refers to reproducible capital, namely the component of capital that is financed through investment, and thus does not include natural capital related to mineral production (i.e., capital from which point-source resources, on which we focus, are produced).

  6. 6.

    For instance, data from the EUKLEMS data set (O’Mahony and Timmer 2009) indicate that the mining sector has the largest average share of capital compensation in total compensation among the major NAICS industries, over the period of 1970–2007.

  7. 7.

    Evidence for these input–output linkages between oil and gas and various manufacturing sectors at the U.S. county level is provided by Allcott and Keniston (2018).

  8. 8.

    Grants under the Medicaid system are allocated to the states in accordance with the Federal Medicaid Assistance Percentages (FMAP) formula, where: \(FMAP=1-(\frac{[state\_per\_capita\_income]^{2}}{[U.S.\_per\_capita \_income]^{2}})*0.45\).

  9. 9.

    In the empirical part we show that such reactions are observed as quickly as within the same fiscal year as the income-affecting shocks, and up to several years after their occurrence. This persistence is also a feature of some of the programs; for instance, Medicaid’s formula often adopts income measures from the precedent three years, thus impacting the patterns of redistribution both in the short and medium terms.

  10. 10.

    We undertake a sectoral-level analysis to be able to isolate the mining sector, which as will be evident is important for effectively excluding measures related to natural capital.

  11. 11.

    Severance taxes are levied by U.S. states on the exploitation of natural resources located in their territories.

  12. 12.

    This set of additional controls includes the following state indicators, in addition to the capital share: real GSP per capita, population, real per capita government expenditures, and the unemployment rate.

  13. 13.

    Various studies, however, have shown that the interest rate is an important transmission channel of redistribution, including Bassetto (2014), Costinot et al. (2014), and Hurst et al. (2016).

  14. 14.

    Ferrero and Seneca (2015) examine the connection between monetary policy and resource richness; however, they do so within the context of deriving the optimal monetary policy under a shock to the oil price. In contrast, we consider the implications of exogenous resource-richness and monetary policy shocks to federal redistribution and other economic indicators at the local level.

  15. 15.

    Notably, the share of capital compensation in value added is a direct measure of \(\alpha\) in a standard CRS aggregate production function, \(F(K,L)=K^{\alpha }L^{\beta }\), which in turn points at the extent of the intensity of capital usage in production. In addition, it is consistent with the measure employed by related studies [see, e.g., Karabarbounis and Neiman (2014)].

  16. 16.

    Garofalo and Yamarik (2002) applied their methodology to construct a state-level panel data series of capital stocks.

  17. 17.

    Notably, the EUKLEMS methodology regards capital compensation as the value added residual after subtracting labor compensation from it. This definition, in turn, suggests that the capital share measure is equivalent to one minus the value added share of labor compensation. Consequently, it excludes taxes and accounts for amortization.

  18. 18.

    This includes all the states that have an average share of severance tax income in total tax income of at least \(10\%\) over the sample period.

  19. 19.

    While our focus is on U.S. states, this observation is also more generally applicable across countries. Figure A1 in the Online Appendix presents a similar graph for a sample of 217 countries, which points at a similar difference in the capital share of resource rich and poor countries (see the Online Appendix for the list of countries, classifications, and sources).

  20. 20.

    We are concerned with the natural capital that is related to mineral production specifically because the latter represents the key difference between the resource rich and poor states, by construction.

  21. 21.

    The mining sector represents the sector in which subsoil minerals are produced.

  22. 22.

    This measure may, to some extent, capture differences in tax and extraction rates, technologies, or characteristics related to the mobile tax bases (labor and capital). However, these differences are relatively marginal in terms of affecting the cross-state differences in natural endowments, given the federal context (which exhibits significant convergence, mobility, and tax competition across states), and the long term perspective that considers averages over several decades. Nonetheless, we also examine different resource measures, and consider the impact of cross-state differences in more detail, when testing the robustness of the baseline specifications.

  23. 23.

    This methodology is based on the notion that changes in the oil prices induce differential impacts across resource abundance levels, hence yielding concurrent cross-sectional and time variation that is not swept away by the fixed-effects. This approach has been adopted by various studies in the related literature, including James (2015) and Perez-Sebastian and Raveh (2016), among others.

  24. 24.

    Albeit being absorbed by the state and year fixed effects, CS and Oil are outlined separately in (4) for completeness.

  25. 25.

    Notably, 29 states have coastal access, 17 states are located in the American South, and 14 states have no gubernatorial term limits. See the Data Appendix for the list of states included in each case. Together with states’ land size, the plausible exogeneity of each measure is motivated by its cross-sectional nature.

  26. 26.

    We execute this procedure utilizing lagged levels of the explanatory variables, dated \(t-1\) to \(t-3\), as instruments. We assume that the endogenous variables are persistent in the medium term, and that level lags affect the dependent variable only via their impact on the corresponding endogenous variables, suggesting that the proposed instruments yield a viable first stage, and that they follow the exclusion restriction. The results are robust to employing different lagged periods for the set of instruments, including the maximum available lags provided by the sample.

  27. 27.

    The period covered is limited by the availability of the main measure of monetary policy shocks employed, as we describe below.

  28. 28.

    We annualize their raw quarterly series by summing up the corresponding quarterly observations.

  29. 29.

    Romer and Romer (2004) estimate these shocks in two steps. First, they derive intended changes in the Federal Funds Rate from narrative records of internal briefings to the Federal Open Market Committee. Second, they regress predicted developments in interest rates on changes in the Federal Reserve’s Greenbook forecasts to derive a typical response function. The policy shocks are then deviations from this function, expressed in percentage points.

  30. 30.

    Importantly, motivated by the plausible exogeneity of the international price of oil, the extent of state mineral production is not endogenous to the monetary policy. We provide supportive evidence for this conjecture in Table A4 in the Online Appendix which shows that, controlling for real GSP per capita, state fixed effects, and the international oil price (and with the exclusion of year fixed effects, which otherwise absorb the monetary shocks and oil price) monetary policy shocks are not directly associated with the resource abundance proxies tested in the main analysis.

  31. 31.

    Notably, this methodology follows that of other recent studies that have also examined the heterogeneous state effects of national shocks, by testing the impact of an interaction between state factors and a national shock, including de Ridder and Pfajfar (2017), Perez-Sebastian et al. (2019), and Williams and Liu (2019), among others.

  32. 32.

    Considering the unequal burden of federal taxation (Albouy 2009), we control for income in the analysis presented in a later sub-section. As will be evident, this addition does not alter the main results.

  33. 33.

    States’ capital expenditures are financed mostly through (state) public debt, which is responsive to contemporaneous changes in the interest rate. By being matched to these local capital expenditures, related federal transfers are hence similarly affected by changes in the interest rate, despite financing long-term projects.

  34. 34.

    Albeit being absorbed by the state fixed effects, CS is outlined separately in (6) for completeness.

  35. 35.

    Similar to monetary, T is added separately in (7), despite being absorbed by the time fixed effects, for completeness.

  36. 36.

    We employ annualized versions of these shocks, by either aggregating their quarterly values in the cases of the TFP, tax, and fiscal changes, or considering a recession in case one is reported in at least one of the quarters within the given year. Similar to the case of monetary, the plausible exogeneity of these shocks is motivated by their national perspective, under which no specific state is sufficiently large to induce changes.

  37. 37.

    For instance, Perez-Sebastian et al. (2019) report that federal tax shocks induce systematically different impacts on the vertical tax reactions of resource rich and poor U.S. states.

  38. 38.

    The length of the examined horizon is based on the repeated observation in the related literature that the effects of monetary policy are observed, and measurable, over the medium-term horizon of approximately four years, after which additional long-term factors (e.g., technology) play a dominant role [see, e.g., Christiano et al. (2005)].

  39. 39.

    Notably, consistent with the observation that there is convergence to zero over the given horizon [see, e.g., Christiano et al. (2005)], the observed differential patterns imply that there are persistent differences in the rate of convergence across the state-groups.

  40. 40.

    See, e.g., Auray and Eyquem (2014) for a reminiscent illustration within a standard New-Keynesian framework of a monetary union.

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Correspondence to Ohad Raveh.

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Data Appendix

Data Appendix

We use an annual-based, state-level, panel that covers the 50 U.S. states over the period 1969–2007. Unless otherwise specified, variables are based on data from the U.S. Bureau of Economic Analysis and the U.S. Census Bureau. Descriptive statistics for all variables appear in Table 7.

Variable Definitions

Real per capita GSP Real Gross State Product divided by state population.

Hours worked per capita Number of hours worked per week divided by state population. The sample excludes Alaska and Arizona, and covers the period 2000–2007 (Source: variable ’UHRSWORK’, IPUMS-USA).

Resource income Share of severance tax revenues in states’ total tax revenues.

Real per capita capital stock State-level measure of capital stock, divided by state population, in constant prices (Source: Garofalo and Yamarik 2002, including an extension of it available at the second author’s homepage).

Mining share GSP share of the mining sector.

Price measure An interaction of the real international oil price in year t and the average share of severance tax revenues in total state tax revenues over the complete sample period (1969–2007).

Resource dummy A dummy variable that captures the states that have an average share of severance tax revenues in total state tax revenues (computed over 1969–2007) above 10%; these include: AK, LA, MT, ND, NM, OK, TX, and WY.

Monetary policy shocks Monetary policy shocks a-la Romer and Romer (2004) [Source: Tenyero and Thwaites (2016)].

Sims–Jha shocks Contractionary monetary policy shocks [Source: Sims and Zha (2006)], available for the period 1963–2003.

Capital share Capital compensation divided by value added, available for the period 1970–2000 (Source: constructed by the author, as outlined in the text).

Real federal transfers per capita Real transfers from the federal the state governments, divided by state population, examined in total, as well as in the following categories: public welfare, health and hospitals, employment security administration, education, highways, and natural resources.

Real investment per capita Real investment, divided by state population [Source: Garofalo and Yamarik (2002), including an extension of it available at the second author’s homepage].

Real consumption per capita Real consumption, divided by state population. Sample covers the period 1997–2007.

Real income per capita Real income (wages and salaries) divided by state population.

Population State population.

Unemployment rate The number of unemployed individuals in the state, divided by state population.

Real government expenditures per capita Real state government expenditures, divided by state population.

Land State size in square miles.

North–South (NS) An indicator for whether the state is located in the American South, as defined by the U.S. Census Bureau, including: AL, AR, DE, FL, GA, KY, LA, MD, MO, MS, NC, OK, SC, TN, TX, VA, WV.

Coast An indicator for whether the state has a coast, including: AL, AK, CA, CT, DE, FL, GA, HA, IL, LA, ME, MD, MA, MI, MN, MS, NH, NJ, NY, NC, OH, OR, PA, RI, SC, TX, VA, WA, WI.

Term limits (TL) An indicator for whether the state has no gubernatorial term limits, including: CT, IA, ID, IL, MA, MN, ND, NH, NY, TX, UT, VT, WA, WI.

TFP shocks Aggregate, national TFP shocks, aggregated to an annual level [Source: Fernald (2014)].

Federal tax shocks (Fed) Narrative-based federal tax shocks, aggregated to an annual level, and normalized by U.S. GDP [Source: Romer and Romer (2010)].

Business cycles (Cycle) An indicator for whether the U.S. economy is in a recession (Source: U.S. Federal Reserve).

Defense shocks Narrative-based national defense news shocks, in real $ billions, aggregated to an annual level [Source: Ramey and Zubairy (2018)].

Table 7 Descriptive statistics

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Raveh, O. Monetary Policy, Natural Resources, and Federal Redistribution. Environ Resource Econ (2020) doi:10.1007/s10640-020-00400-9

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Keywords

  • Monetary shocks
  • Natural resource abundance
  • Redistribution
  • Capital share

JEL Classification

  • E52
  • Q32
  • H77