From Plows to Horizontal Fracking:
Anti-commons and Unintended Consequences of Land Privatization
Bryan Leonard and Dominic Parker
January 30, 2015
Incomplete and Preliminary, Do not Distribute
Abstract: Land contains multiple natural resources that are efficiently managed at different
spatial scales, either concurrently or over time. We model the use of two such resources
under common land ownership, individually owned parcels, and hybrid regimes. The model
shows how enclosing the commons for one resource can create anticommons problems for
another. We provide empirical tests in the context of American Indian reservations, which
are mosaics of private, tribal, and fragmented ownership interests due to U.S. government
allotment policies during the late 19th and early 20th centuries. Using spatial data of historical
and modern resource endowments, we show how the policies intentionally enclosed
commons to agricultural land but inadvertently fragmented land interests over oil and gas
shale deposits that are efficiently extracted by horizontal drilling spanning up to two miles.
Based on a detailed case study of Fort Berthold reservation – which sits atop the highly
productive Bakken oil field – and corroborating data for all western reservations with
deposits, we find evidence that deposits under tribal lands are more quickly and fully
exploited than are deposits under land fragmented with multiple exclusion rights. The results
are a cautionary tale about top-down land privatization schemes intended to benefit
impoverished indigenous populations; our results show how privatization can inadvertently
raise the transaction costs of coordinated resource use if not designed for flexibility.
I.
Introduction
Much of the world’s indigenous populations lack formal property rights to land and many
economists consider this a hindrance to development. The main argument is that informal
property rights are generally too insecure and lack the necessary transferability to encourage
current users to invest in land improvements that would increase future income streams (see
Demsetz 1967, Alchian and Demsetz 1973, Feder and Feeny 1991, Besley 1995, Goldstein and
Udry 2008, Besley and Ghatak 2010).
Land-titling and privatization programs attempt to address underinvestment problems in
tribal areas of Africa, South America, and elsewhere, and are now being debated for indigenous
populations in Canada as described in more detail below. Through subdivision and codification
of land rights, privatization programs seek to enclose the “commons”, which are areas or
resources for which individual users lack fully defensible exclusion rights (Gordon 1954, Hardin
1968, Barzel 1997). In theory, having title over a specific parcel lowers the costs of enforcing
exclusion rights and hence makes future claims on prior investments more secure (Alston et al.
1996). The empirical evidence, although somewhat mixed, suggests that individual land rights
tend to raise agricultural productivity.
1
In this paper, we study a potential unintended consequence of top-down privatization. Even
when subdividing and privatizing land successfully encloses the commons for one type of land
use (e.g., agriculture), the subdivision process can create anticommons problems for other
valuable types of resource use. Anticommons problems arise when too many exclusion rights are
granted, raising the transaction costs of resource use and leading to underutilization (Heller 1998,
Buchanan and Yoon 2000). The concern here is that subdividing and codifying land rights may
raise the transaction costs of managing larger-scale resources such as oil shale plays, wind
energy, and wildlife herds that are best managed at spatial scales exceeding those required for
agriculture (see Lueck 1989, Fennell 2011). Moreover, in some areas of the world with large
indigenous populations the potential value of large-scale natural resources—if managed well—
may dominate the value of farming.
1
Galiani and Shcargrodsky (2012) provide a review of empirical studies. A set of recent studies find that private
land ownership rights have generally stimulated productivity-enhancing investments in land and agriculture (see,
e.g., Banerjee et al. 2002, Field 2005, Do and Lakshmi 2008, Galiani and Schargrodsky 2010, Libecap and Lueck
2011). Other studies, however, fail to find significant improvements in agricultural investment after titling (see
Brasselle et al. 2002 and Jacoby and Minten 2007).
1
We study this issue by examining the legacy of the U.S. government’s sweeping program for
allotting Native American land over 1887-1934. During this period, roughly 41 million acres of
Indian land was subdivided into typically 160 or 320 acre parcels and allotted to individual
Native American families with the stated goal of encouraging productive farming (Carlson
1981). 2 Some of the allotted lands were fully privatized and others remain held in trust by the
U.S. government with multiple owners retaining exclusion rights as we explain in more detail in
section 3. Other tribal lands were never allotted and remain governed by informal use rights
determined by tribal members and their governments. The upshot is that modern Indian
reservations are a patchwork of commonly owned land, individually owned parcels, and minute
inherited subdivisions of allotted lands (see Trosper 1978, Anderson 1995, Banner 2005). This
patchwork enables comparisons of long-run investments in land under different tenure
arrangements. Empirical research to date suggests that trusteeship has limited investment in land
(Akee 2009) and lowered agricultural productivity, especially on tribal lands that were never
allotted (Anderson and Lueck 1992).
We contribute to the literatures on land privatization, anticommons, and Native American
land allotment by examining how land tenure affects the timing and density of modern oil and
gas extraction under reservation lands. Our focus on oil and gas is important for at least three
reasons. First, modern oil and gas extraction is executed by drilling a set of wells along a
horizontal line extending up to three miles from the main well pad. Hence, an oil company
wanting to drill horizontally will need to get consent from owners of adjacent parcels (e.g., there
would be six separate square 160 acre parcels along a three-mile line). This drilling technology
generates land assembly transaction costs that are not relevant with agriculture, or even with the
drilling of a conventional vertical oil well, especially because state-level forced pooling and oil
unitization laws do not generally apply on Indian reservations (Slade 1996). As our theoretical
framework in section 2 elaborates upon, the land assembly transaction costs are plausibly lower
under tribally governed, common lands.
The second reason for focusing on oil and gas is that shale endowments were exogenous to
the land allotment process whereas agricultural potential was not. As we describe in section 3,
high quality agricultural lands were the main targets of allotment and this explains why arid
2
A less charitable interpretation is that land allotment policies were devised to transfer land from Native Americans
to white settlers (see Carlson 1981, Banner 2005).
2
Indian reservations remain largely intact, in tribal common ownership. Oil and gas reserves in
general, and certainly shale deposits in particular, were largely undiscovered at the height of
allotment and are either weakly related to or uncorrelated with agricultural potential. For this
reason, shale deposits are largely uncorrelated with modern land tenure patterns.
The third reason for focusing on oil and gas is that its value dwarfs that of other natural
resources on reservations, and royalties from drilling could significantly mitigate poverty on
some reservations. For context, consider that income per capita for American Indians living on
reservations over 2006-2010 was only $11,454 compared to $27,344 for the total U.S.
population. 3 As we show in section 5, the drilling of an additional well per capita on Indian
reservations has generated increases of about $2,670 per capita based on regression analysis of
panel data spanning 1915-2010. 4 This striking relationship between incomes and oil
development highlights the importance of identifying transaction cost barriers to consensual
drilling.
Unfortunately for tribes with allotted and fragmented land, we find evidence of substantial
contracting barriers due to that form of land tenure (see section 4). Our main set of findings are
based on a detailed case study of drilling on North Dakota’s Fort Berthold reservation, which
combines detailed shape files of land tenure from the Bureau of Indian Affairs with proprietary
oil and gas drilling data from a private company called iHS. The Fort Berthold reservation sits
atop the highly productive Bakken oil field and contains a mosaic of tribal land, allotted land,
and fully privatized parcels. Within this reservation, we find that horizontal drilling is less likely
to occur under clusters of allotted land when compared to clusters of tribally owned land.
Moreover, regression analysis shows that the average number of days elapsed between the
permitting of well and its spudding (i.e., when drilling commences) is much higher under allotted
land. This detailed result supports our theoretical claim that land assembly transaction costs,
rather than confounding factors, are responsible for the relatively sparse amount of drilling under
allotted land. We complement the case study results with corroborating but less detailed data for
all western reservations over oil and gas fields. There we find evidence that the increase in
3
The data on American Indian reservation incomes come from the U.S. Census Bureau, which beginning in 2000,
gave survey respondents the option to select a single race or multiple races. Throughout the analysis we employ data
for respondents who reported a single American Indian race.
4
Several factors beyond oil and gas drilling and casino gaming help explain differences in per capita income across
reservations (see Cornell and Kalt 2000, Anderson and Parker 2008, Akee et al. 2012, Dippel 2013, Cookson 2009).
3
transactions costs associated with drilling a horizontal instead of a vertical well is greater on
reservations than off.
II.
Theoretical Framework
The starting point for our analysis is recognizing two challenging facts about land, natural
resources, and property rights. First, a swath of land harbors multiple resources that are
efficiently managed at different spatial scales, either concurrently or over time. Second, it is not
possible to simultaneously match the scale of property rights with the optimal management scale
of all resources unless use and exclusion rights are unbundled for different natural resources
(Lueck 1989, Barzel 1997). We model a fully bundled regime but we also discuss the unbundled
case below. In the fully bundled regime, an owner of a tract of land also has rights to the
subsurface (e.g., oil and gas) and the super-surface (e.g., wind). 5
A. Basics
We imagine a swath of land of area L that could be held in common by N users or privatized
and subdivided in to L/N parcels, where N is the number of users. The land is of homogenous
quality for agriculture and it sits atop a subsurface resource—an oil shale—of homogenous
quality and depth that is extracted using horizontal drilling technology that spans the entire
length of L.
The income generated from the land is the sum of agricultural and oil income, because we
assume that these resource uses do not compete. 6 Abstracting from the dynamics of agricultural
investment and oil extraction, we have:
(1) YA p A g ( L; K ( p A , L, r , N )) − C ( K (⋅)) and
=
(2) YO F ( pO , L, N ) ⋅ W ( F (⋅); pO , L) .
=
Equation (1) indicates that collective agricultural income, YA , is equal to agricultural revenue
minus costs, where p A is the output price and g (⋅) is the production function. Agricultural output
5
The owner may also have rights to resources that migrate through the land (e.g., wildlife, stream water, etc).
6
In our view, this assumption of non-competing uses is approximately realistic for agriculture and oil drilling, and
for agriculture and wind turbines, but less realistic for agriculture and wildlife management as we discuss below.
4
and costs are a function of aggregate investment, K, which includes digging ditches, planting
trees, fertilizing soil, etc. Investment is a function of output price, land area, the price of
investment (r), and N, the number of claimants with use rights.
Equation (2) indicates that collective oil income is the revenue earned from charging oil
companies a fee of F for each oil well, W. The fee is itself a function of the price of oil ( pO ),
land area, and the number of individuals with exclusion rights (N). The number of wells chosen
by the oil company is also a function of the fee it will be charged. The key assumption about oil
drilling, however, is that consent of all landowners is necessary before a horizontal line can be
drilled.
B. Agricultural Income
To keep things simple, and to highlight our key points expeditiously, we assume the
following specific functional form for agricultural income:
A
(3) Y=
p A ( LK (⋅))1/2 − rK (⋅)
If the land is subdivided into L/N parcels, then each individual solves the following
1/2
 Lki 
(4) =
max y A,i p A  N 


ki
− rki .
Optimizing with respect to ki and solving for ki we have:
(5) kiSD =
p A2 L
.
4 Nr 2
The superscript SD denotes the “subdivided” regime. In aggregate, we have
(6)=
K SD
=
kiSD
∑
p A2 L
p A2 L
SD
and
.
Y
=
A
4r
4r 2
When land is subdivided and privatized, we see that aggregate agricultural income depends on
land area and output and input prices. Aggregate agricultural income, however, does not depend
on N.
Next, consider the case when land is not subdivided into private parcels but instead remains
held in common. Under this common property regime, each of the N individuals has use rights
but lacks exclusion rights. Hence, returns on agricultural investments are not excludable. Each
individual user solves
5
p A ( LK (⋅))
(7) max
=
y A ,i
=
− rki
N
ki
1/2
p A ( L(ki + ∑ k j ))1/2
j ≠i
N
− rki .
The individual users optimize by choosing ki , taking as given the investment choices of all other
users. If we assume symmetric behavior, the solutions for ki are:
(8) kiCP =
p A2 L
.
4N 2r 2
In aggregate, we have
p A2 L
p A2 (2 LN 1/2 − L)
CP
(9)=
and YA =
.
K
=
k
∑
4 Nr 2
4 Nr
CP
CP
i
Comparing the outcomes, we see that K SD ≥ K CP and YASD ≥ K CP . The equality holds only if N =
1. This is the well-known prediction that investment in agriculture will be lower when other
users cannot be excluded from the returns on investments.
C. Oil Extraction
We assume that income earned from oil development comes from charging a fee on each
well that is drilled, like a per-unit tax. 7 This system means that an outside oil company bears the
risk of fluctuations in the world price of oil. The revenue earned from fees on oil wells is given
by equation (2) above, which
is YO F ( pO , L, N ) ⋅ W ( F (⋅); pO , L) and F is the fee determined by
=
land owners.
The oil company’s demand for oil-producing wells is derived from their profit function. For
simplicity, we assume the oil company has the following profit function
(10)
pO [W − (W / L) 2 ] − FW .
max p =
W
The amount of oil extracted is W − (W / L) 2 ; this eventually diminishes for any given L, due to
congestion costs of crowding oil wells next to one another. Optimizing with respect to W yields
the following demand function for wells:
(11)
=
W L2 [ pO − F ] / 2 pO .
This demand curve is decreasing in F, the fee charged per oil well.
7
Alternatively, we could assume there is a royalty payment based on the amount of oil extracted. In either case, we
would arrive at the same conclusion with respect to the effect of the number of exclusion rights on extraction levels.
6
In the case of common property, there are N users but no one individual has exclusion rights
and hence cannot holdup oil extraction. In the common property regime, the collective seeking to
maximize their income from mineral development solves the following problem
(12)
YO FW
= FL2 [ pO − F ] / 2 pO .
max =
F
Solving this problem yields the following solutions
(13)
F CP = p0 / 2 , W CP = L2 / 4 , and YOCP = pO L2 / 8 .
If the land is subdivided into L/N parcels, then the solution is complicated by the fact that N
landowners have N exclusion rights. We follow Buchanan and Yoon (2000) and assume that an
exclusion right entitles each landowner the right to charge his own fee, fi , such that the total per
N
well fee paid by the oil company is F = ∑ fi .
i =1
Under subdivided land, each owner solves the following optimization problem
(14)
2
max yO ,i = fiW = fi L [ pO − ( fi + ∑ f j )] / 2 pO .
j ≠i
F
Each individual takes as given the fees charged by others. If we assume symmetric behavior, the
subdivided regime solutions are:
(15)
fi SD =
pO
NpO
NpO L2
L2
, F SD =
, W SD =
, and YOSD =
.
N +1
N +1
2( N + 1)
2( N + 1) 2
Comparing the outcomes, we see that W CP ≥ W SD and YOCP ≥ YOSD . The equality holds only if
N = 1. 8
D. Implications and Extensions
The main implications from the model are that K SD ≥ K CP and W CP ≥ W SD . That is, the
framework predicts there will be underinvestment in agriculture under common property, but
underutilization of oil under subdivided privatization. Holding constant the land area, an increase
in N will exacerbate the differences in these outcomes. Whether or not aggregate income—
agricultural plus oil income—is higher under subdivided privatization depends on the relative
8
This is a version of the anti-commons prediction, that resources will be underutilized if too many people hold
exclusion rights (Heller 1998, Buchanan and Yoon 2000, Fennell 2004). The result also highlights the importance of
oil unitization agreements in getting pools of separate landowners to act as a single owner (see Libecap and Wiggins
1984).
7
prices of oil and agricultural output. Privatization makes more sense when agricultural output is
relatively valuable and less sense when oil is relatively valuable. 9 The key mechanism at work in
our model is the marginal cost to firms of drilling a well, which is F CP = p0 / 2 under the
common property regime and F SD =
NpO
under divided ownership. For 𝑁𝑁 > 1 the marginal
N +1
contracting costs of drilling a well are higher on a divided parcel. This difference grows as the spatial
scale of the resource (and hence the total number of users) grows.
III.
Native American Land Allotment: An Experiment with Privatization
To test the theoretical framework, we study the U.S. Allotment Act of 1887. By 1887,
nearly all tribes had signed treaties with the U.S. government relegating them to reservations
located primarily in the Western United States (see figure 1). The main wave of western
migration had yet to occur, and most reservations were remote from non-Indian populations.
The Act authorized the U.S. government to survey and subdivide communal Native
American reservations and allot individual parcels to families. The Act was promoted as a way to
encourage agricultural investment, as reflected in sponsor Henry Dawes’ assessment that Indians
had not “…got as far as they can go because they own their land in common, and under that
[system] there is no enterprise to make your home any better than that of your neighbors.” 10
Allotment happened sequentially and some reservations were not allotted before the process was
halted by the Indian Reorganization Act of 1934. Other reservations were fully allotted, and
many were partially allotted creating a mosaic of land tenure types within reservations. As we
show in this section, the scale and timing of allotment was determined primarily by agricultural
land endowments. Although underground shale oil and gas deposits were either undiscovered
during 1887-1934 or not considered valuable, we demonstrate how the allotment process
unintentionally subdivided and fractionated ownership over these deposits on many reservations.
We also demonstrate specifically how the allotment process unintentionally subdivided
ownership over shale oil deposits on North Dakota’s Fort Berthold reservation, which is the
subject of our case study.
2
2
1/2
In particular, Y SD ≥ Y CP if p A ( N − 2 N − 1) ≥ pO L (1/ 4( N + 1) − N ) .
4 Nr
2( N + 1) 2
10
This quote is cited from Ambler (1990, p. 10).
9
8
A. The Political Economy of Allotment
In his book, How the Indians Lost their Land, Banner (2005) says there were two kinds of
people involved in setting Indian reservation policies during the 19th century. Those were nonIndians trying to protect Indians by isolating them and non-Indians trying to get Indian land. The
Allotment Act of 1887 played into both motives giving the “protectors” a way of assimilating
Indians into non-Indian society by making Indians citizens of the United States and good yeoman
farmers and giving the “takers” a way of transferring reservation land to western settlers. The
implication in either case is that land allotment patterns will be endogenous to agricultural land
quality but not necessarily subsurface endowments.
The Act authorized the president to allot communal reservation land to individual Indians
with the intention of granting private ownership including the right to alienate after 25 years or
once the allottee was declared “competent” (the words in the act) by the secretary of the interior.
For arable agricultural land the Indian head of a household was to receive 160 acres and for
grazing land 320 acres. Indians would become U.S. citizens upon receiving their allotments and,
in practice, Bureau of Indian Affairs agents would help Indians select their allotments and would
determine whether the Indian was “competent.” On reservations for which total acreage
exceeded that necessary to make the allotments, the excess could be ceded to the federal
government and opened for white settlers with the proceeds deposited in a trust fund managed by
the Department of Interior through the Bureau of Indian affairs. A 1903 U.S. Supreme Court
ruling, however, allowed surplus land to be opened to non-Indian settlement without tribal
consent.
Through a combination of land sales once allotments owners were declared competent
and title was alienable, and the declaration of surplus land, millions of reservation acres were
transferred from Indians to non-Indians. 11 The Indian Reorganization Act (IRA) of 1934 halted
such transfers, declaring those acres not already alienated to be held in trust by the Bureau of
Indian Affairs, either as individual trust land or as tribal lands. Table 1 reports that the number of
reservation acres was cut from 136 million in 1887 to 69 million in 1934. This implies that 66
million acres of surplus lands were sold to white settlers or retained by the federal government.
Of the land that was retained within Indian reservations, 22 million acres was fully privatized
11
Some of the land cleared for fee simple ownership remains owned by Indian, but there are no systematic sources
on how much this is.
9
and out of trust status by 1934; most of these non-trust acres were owned by non-Indians in 1934
(U.S. Dept. of Interior 1935).
Table 1: Reservation Acres in 1887 and 1933
Acres
1. Reservation Land, 1887
2. Reservation Land, 1933
Tribal trust , 1933
Individual trust, 1933
Allotments no longer in trust (i.e. fee simple)
3. Surplus land surrendered, 1933
136,394,895
69,588,411
29,481,685
17,829,414
22,277,342
66,806,454
Source: U.S. Dept. of Interior (1935).
Figure 1 illustrates the distribution and timing of allotted reservations, which is an
important determinant of whether land on reservations today is fully privatized with one owner
or held in individual trust with multiple owners. In general, reservations today have more private
land if a) the reservation was opened to settlement, and b) the reservation was allotted at an early
date. Land on reservations that were allotted early had a greater likelihood of being privatized
because there was more time to be declared “competent”. Allotted lands that were not privatized
prior to 1934 are held in trust to this day, and interests in the land are divided among the heirs of
the allottee at the time of the IRA. Hence, the “individual trust” plots of land on reservations
today have multiple owners, sometimes more than 100 (Strattman 20xx). Figure 1 shows that
many reservations that were allotted or opened for settlement overlap modern shale deposits.
10
Figure 1: The Timing and Distribution of Allotted Reservations
Notes: This map is based on our digitization of an 1890 Office of Indian Affairs map of 97 reservations that were west of
the Mississippi River and clearly visible in the original map. With the exception of the Osage Reservation, we exclude
Oklahoma because reservations in that state are no longer federally recognized. The data on surplus land and the timing of
allotment come from Indian Land Tenure, Economic Status, and Population Trends prepared by the Office Indian Affairs of
the U.S. Department of Interior in 1935. Based on that report, 68 of the reservations in our sample were allotted to some
extent, and surplus land was given to white settlers in 21 reservations. Of the 68 reservations that were allotted, some land
was alienated and sold out of trust on 56 reservations.
To demonstrate how the demand for agricultural land during 1887 to 1934 is a dominant
determinant of land tenure patterns on reservations today, we created a historical crossreservation data set. We first digitized reservation shapefiles from an 1890 Office of Indian
Affairs map, tracing the boundaries of all 97 reservations west of the state of Michigan and
visible on the original map. 12 To measure endowments of agricultural land, we calculate average
annual precipitation across the 1890 reservation acreage. We divide the 1890 reservation acreage
into four categories which are “arid” (less than 10 inches of annual precipitation), “semi-arid”
12
The year 1890 is the closest to 1887 for which we could find a high-quality map of reservations.
11
(10-25 inches), “moderate” (25-50 inches), and “wet” (greater than 50 inches). 13 To measure
population pressures near reservations, we calculated the proximity (in kilometers) of a
reservation’s boundary to a major railroad line, based on 1893 rail lines and the population
density of the state. We also digitized historical maps of inventoried coal deposits in 1919 and
overlaid that map with reservation boundaries. Figure 2 displays the rainfall categories, coal
deposits, and rail lines with the 1890 reservation boundaries.
Figure 2: Historical Resource Endowments and 1890 Reservation Boundaries
Notes: This map is based on our digitization of an 1890 Office of Indian Affairs map of 98 reservations that were west of
the Mississippi River and clearly visible in the original map. We exclude Oklahoma because reservations in that state are no
longer federally recognized, with the exception of the Osage Reservation. [Need to add sources for GIS shapefiles].
Table 3 shows relationships between resource endowments and the current percentage of
reservation acreage that is privately owned (“fee-simple”), allotted but not fully privatized
(“allotted”), and remaining in tribal ownership. The results complement Carlson (1981) who
finds that reservations in states with faster population growth and more rainfall were more likely
13
Like Haber (2012), we assume that growing conditions increase with rainfall as we move from arid to moderate
categories, but the relationship is less clear as rainfall increases from moderate to plentiful.
12
to be allotted first. In columns 1 and 4, we find that having more arable acres, being closer to
1893 railroads, and being in a densely populated state in 1890 are all associated with more feesimple land today. 14 Fee simple lands include those opened for surplus settlement and lands that
were allotted and alienated after “competence” was declared. In columns 2 and 5, we find that
larger endowments of semi-arid acres and shorter distances to railroad lines correlate with higher
percentages of allotted (but not fully privatized) acres today. In columns 4 and 6, we see the
determinants of tribal land tenure are opposite to those of fee simple. Having more arid acres,
being further to 1893 railroads, and being in a sparsely populated state in 1890 are all associated
with more tribal land today. Although these findings do not rule out the possibility that the
pattern of allotment was intended to benefit Native Americans, they are consistent with the view
that allotment was administered in a way that let non-Indians acquire quality and accessible
agricultural lands that were otherwise unattainable.
Most accounts of the Allotment Act point to agricultural land quality as the main determinant
of allotment (Banner 2005, Carlson 1981) but subsurface minerals, particularly coal, also played
a role. The general Allotment Act of 1887 presumably gave the allottee full rights to minerals
(Ambler 1990). Later legislation, however, sometimes gave mineral rights to either the tribe or
the federal government when the surface was allotted or settled by non-Indians. As Ambler
(1990, p. 42-43) notes:
The complicated land and mineral pattern of energy reservations today results from such
decisions made over the years about whether a tribe, an allottee, a non-Indian settler, or the
U.S. government should get the minerals under a parcel of land. Those decisions seemed to
depend upon four factors: the national sentiment about leasing versus selling public land
minerals; the national sentiment about whether tribal governments should continue to exist;
demand for land; and what was then known about mineral potential. In most cases the federal
government seemed to adopt Indian policies as afterthoughts to public land policies, whether
or not they were appropriate.
In general, allottees and settlers who acquired surplus lands on reservations before 1911 also
acquired subsurface rights even if minerals were yet undiscovered. After 1910, the Coal Lands
Act split mineral ownership from surface ownership on homesteaded land in the U.S. West
14
The coefficients have the following interpretations. In column 1, for example, an increase of 100,000 acres of
arable land is associated with a 4.1 increase in the percent of fee simple land today. Similar, an increase in the
distance of a reservation boundary to an 1893 rail line of 100 kilometers is associated with a decrease the percent fee
simple today of 24.2 percent.
13
reserving coal rights to the federal government. 15 This law affected ownership of coal on parcels
within surplus reservation lands that were opened for settlement but not yet settled. Reservation
lands that were not yet opened for settlement were inventoried for subsurface endowments and,
in 1917, Congress opened reservation coal lands for surplus settlement with the federal
government retaining coal ownership (Ambler 1990).
Table 3: Cross-Sectional OLS and Tobit Estimates of Land Tenure
Y = Percent Fee
Simple
OLS
Y = Percent
Allotted
Y = Percent
Tribal
Y = Percent
Fee Simple
(4)
Tobit
Y=
Percent
Allotted
(5)
Y=
Percent
Tribal
(6)
(1)
(2)
(3)
Arid and Wet Acres, in 1000s
(< 10 and > 50 inches of precip.)
-0.008
(0.122)
-0.007**
(0.005)
0.014***
(0.005)
-0.017
(0.109)
-0.007***
(0.007)
0.014***
(0.004)
Semi-Arid Acres, in 1000s
(10 – 25 inches of precip.)
0.005
(0.150)
0.005**
(0.021)
-0.011**
(0.012)
0.006*
(0.085)
0.007***
(0.004)
-0.011**
(0.012)
Arable Acres, in 1000s
(26 – 50 inches of precip.)
0.041***
(0.006)
0.006
(0.438)
-0.047***
(0.001)
0.054***
(0.001)
0.011
(0.151)
-0.047***
(0.001)
Distance from Reservation
Boundary to RR Line, 1893 (km)
-0.242***
(0.001)
-0.090**
(0.028)
0.331***
(0.000)
-0.302**
(0.011)
-0.098*
(0.077)
0.332***
(0.000)
State Pop. Density, 1890
732.5**
(0.030)
-98.73
(0.465)
-638.6*
(0.096)
877.6**
(0.033)
-24.24*
(0.887)
-638.6*
(0.096)
Δ in State Pop. Density 1890 to
1930
-267.4
(0.253)
186.25
(0.177)
71.67
(0.822)
-591.9*
(0.098)
147.8
(0.438)
71.67
(0.822)
Constant
25.72***
(0.000)
11.09**
(0.001)
63.62***
(0.000)
23.73***
(0.000)
6.27
(0.145)
63.62***
(0.000)
N
R-squared
F-Stat
97
0.194
6.21
97
0.099
2.56
97
0.201
8.65
97
-5.14
97
-2.56
97
0.201
8.65
25
72
26
71
0
97
No. of left censored obs.
No. of uncensored obs.
Notes: Standard errors are robust to heteroskedasticity. P-values are in parentheses. * p<0.1, ** p<0.05, ***p<0.01. The dependent variables come
from Anderson and Parker (2008).
Similar split estate policies began to be adopted for reservations with recently inventoried
minerals. For some reservations, coal rights under allotments were reserved for tribes by specific
15
The Coal Lands Act of 1909 was push forth by president Theodore Roosevelt who was concerned about coal
companies gaining monopolies over coal supplies throughout the U.S. West. This law was passed at the time when
homestead entries were reaching a peak, and lands that were thought to potentially have minerals were withdrew
from homestead entry until the federal government classified the land (Ambler 1990).
14
laws: for example, Blackfeet in 1919; Crow in 1920; Fort Peck in 1920 and 1927; Fort Belknap
in 1921; Northern Cheyenne ain 1926; and Wind River in 1928. 16 Importantly, however, some
of these reservations had already been allotted and the split estates applied only to new
allotments. As a result, only a few energy-endowed reservations have their communal mineral
interests fully intact today and most reservations are mosaics of both land and surface tenure due
to the allotment era and related policies.
In summary, allotment policies created rich variation in land tenure but the political economy
of this era limits causal inferences one can make about the effects of tenure on land use. On one
hand, it would be difficult to infer the effects of tenure on agricultural-investments because the
allotment process was clearly endogenous to the quality of agricultural lands. On the other hand,
the variation in mineral tenure induced by the allotment era has exogenous determinants
allowing us to infer the effects of tenure on oil and gas extraction today. Although some crossreservation variation in mineral tenure may be endogenous to the political strength of tribes or to
the quality of mineral endowments known at the time, the history of allotment suggests that
within-reservation variation is exogenous to the quality and accessibility of oil and gas shale
deposits today. We study this claim in the context of North Dakota’s Fort Berthold reservation in
the next section.
B. Determinants of Mineral Tenure on the Fort Berthold Reservation
To understand the micro-level determinants and effects of land and mineral tenure, we
focus on the Fort Berthold Indian Reservation in North Dakota, which sits atop the Bakken Oil
Formation—one of the largest in the world. 17 The variation of land and mineral tenure on the
Fort Berthold reservation is plausibly exogenous to the quantity and quality of shale oil and gas
because the reservation was allotted - and opened for surplus settlement – long before oil and gas
was discovered. In fact, the reservation was allotted and opened for settlement even before the
reservation’s full coal potential was known. As Ambler (1990, 42-43) notes:
16
Why did the federal government permit tribes to retain their mineral ownership? One reason is that federal
agencies exerted considerable control over the use and revenues from tribally owned minerals through leasing laws
and hence apt to prefer tribal to allottee ownership (Ambler 1990).
17
By 2012, North Dakota had surpassed California and Alaska to become the second largest oil producing state after
Texas. By the end of 2012, the Bakken accounted for 10 percent of the entire nation’s oil production (Zuckerman
2013).
15
When it surveyed [Fort Berthold] in the 1910s, the U.S. Geological Survey discovered
only about half of the actual coal potential. It found no oil and gas potential, which is not
surprising because oil and gas was not discovered in the state until 1951. As was true on
other reservations, [Department of Interior] attorneys later determined that the early
mineral classifications were final, and the tribes had no claims on minerals under
allotments or homesteads that were not recognized during that classification. That early
ignorance had lasting financial and political impacts at Fort Berthold. Instead of the
tribes’ owning all minerals on the reservation, non-Indian descendants of the settlers
owned the coal and oil and gas in the homesteaded area and allottees owned most of the
oil and gas on the rest of the reservation.
Moreover, the reservation provides an ideal case study because there is significant tenure in each
category with approximately xx% in fee simple, xx% in allotted, and xx% in tribal ownership.
We obtained parcel-level GIS data on mineral tenure for allotted and tribal parcels from
the Bureau of Indian Affairs for Fort Berthold in addition to GIS data on which areas of the
reservation have fee simple mineral rights. Before analyzing the effect of each type of tenure on
the costs and density of oil and gas extraction in section 4, we first study the determinants of
mineral tenure on Fort Berthold to identify any selection biases. The primary identification
concern is that land tenure may be correlated with the quality or accessibility of mineral
resources
The micro-spatial data we collected for this reservation measure agricultural suitability,
the precise location of modern-day oil and gas fields, and roads and railroads infrastructure. We
collected GIS data on soil type from the United States Geological Survey’s surface geology
database; soils are classified as tillable, silt, sand, sandstone, clay, or other. 18 We also calculate
the average slope within each PLSS section (640 acres) from the National Elevation Dataset.
Data on historical railroad lines were obtained from (??). GIS data for active oil fields, PLSS
sections, and major rivers were obtained from the North Dakota GIS portal. Because we do not
observe parcel boundaries for areas held in fee simple, we use PLSS sections as our unit of
analysis and measure the percentage of each PLSS section that is held in each type of tenure. 19
We calculate the distance from each PLSS section to the 1892 railroad line, the closest major
river, and the boundary of the reservation. We create a dummy that is equal to 1 if a section
contains tillable soil.
18
We focus on this broad characterization of soil type to avoid concerns that finer measures of soil quality may be
endogenous to tenure if, as our model predicts, investment in agricultural productivity varies with tenure.
19
Parcels are primarily created as subdivisions of PLSS sections, so it is rare for a parcel to not fall completely
within a section.
16
Figure 3 is a map of the Fort Berthod Reservation with separate colors indicating which
areas are allotted but remain in individual trust, along with tribal and fee simple zones. The
figure also shows the PLSS sections.
Figure 3 : Tenure on the Fort Berthold Indian Reservation
Figure 4 depicts each PLSS section shaded by soil type—darker areas are more suited to
agriculture. Because soil type may endogenous to farm management, we also control for the
topography of the reservation, something that is clearly exogenous (see Figure 6). Figures 5 and
6 show that land that in fee simple systematically has better soil for agricultural and is generally
much flatter. This is suggestive that variation in tenure within the Fort Berthold reservation was
driven by attempts to privatize agriculturally productive land, as was the generally the case
across reservations.
17
Figure 4: Tenure and Soil Type on Fort Berthold Reservation
Figure 6: Tenure and Topography on Fort Berthold Reservation
Table 5 presents results of linear regressions of tenure type on resource endowments. The
dependent variables in columns 1, 2, and 3, are percent fee simple, percent allotted, and percent
tribal, respectively. The results suggest that flatter land that was closer to the railroad (the
18
northeast corner of the reservation) was much more likely to be fee simple. On the other hand,
land further from the railroad was more likely to remain in trust. Tillable land is more likely to
be allotted and more likely to end up in fee simple, and substantially less likely to be held by the
tribe. Land on the interior of the reservation is more likely to be held by the tribe. While the
distribution of tenure is not random with respect to oil fields, it is not perfectly determined by it
either. As can be seen in figure 4, there is plenty of within-oil field variation to estimate the
effect of land tenure on oil extraction activity. Moreover, as we will show in section 4, the fact
that tribal mineral rights are less likely to overlay oil fields works in the opposite direction of our
main result, strengthening our finding.
Table 5: Determinants of Mineral Tenure on Ft. Berthold Reservation
Unit of obs. is a PLSS
Section
Y= Percent Fee Simple
Y= Percent Allotted
Y= Percent Tribal
Section is on
an oil field
(1)
-0.442
(3.220)
(2)
20.45*
(5.993)
(3)
-19.58***
(2.845)
Distance to the
nearest river
81.53
(74.85)
13.10
(50.16)
-127.6
(60.33)
Tillable soil
12.96*
(4.199)
13.14*
(3.620)
-20.63**
(4.584)
-0.0179***
(0.00148)
0.0237
(0.00938)
-0.00287
(0.00792)
Distance to
railroad
-226.3*
(71.30)
242.5***
(15.89)
-26.44
(39.45)
Distance to
reservation boundary
90.63*
(26.47)
-79.65
(32.52)
81.59**
(11.99)
Constant
75.16*
(19.15)
-39.79*
(10.14)
46.48*
(17.29)
Average slope
N
1689
1689
1689
R2
0.521
0.434
0.260
Standard errors are clustered by county and given in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001
IV.
Effects of Land Tenure on Modern Oil and Gas Extraction
As described in section 2, our main theoretical argument is that divided ownership and
multiple exclusion rights over shale access will repress oil and gas extraction by raising the costs
of contracting to drill wells on tracts of land with many owners. The allotment of Native
19
American Reservations created a rich setting for testing this hypothesis because it resulted in
three distinct types of tenure with different numbers of exclusion rights per parcel of land. Fee
simple parcels are basic private property and typically have a sole owner. Allotted parcels were
subject to fractionation over time (cite Stratmann), so that each time an owner died the parcel
was divided among all their living heirs. This has resulted in fractionated and subdivided
ownership among sometimes hundreds of claimants of individual parcels who all have formal
exclusion or land use veto rights. Finally, tribal land is communally owned with individuals
having use rights but the tribal government ultimately holds exclusion rights. Our model predicts
that these three different types of regimes will have different costs of contracting to drill and
hence different well densities for each type of tenure. 20
Our model holds the spatial scale of development fixed at 𝐿𝐿, but for the empirical setting it is
useful to consider the rate of increase in the number of exclusion rights as the scale of extraction
expands to include multiple parcels. This is especially relevant for the multi-staged drilling of
horizontal wells, which usually extend about two miles and pass through multiple mineral claims
and entail surface disturbances along the fracking line. As fee simple parcels are added, the
contracting costs increase by one owner for each parcel added. Assuming there are on average 𝛼𝛼
owners per allotted parcel, each additional allotted parcel requires contracting with an additional
𝛼𝛼 users. In stark contrast, adding tribal parcels to a project already taking place on tribal land
adds zero new holders of exclusion rights to contract with. For projects requiring spatially
coordinated resource use such as fracking, it may be less costly to contract with multiple tribal
parcels rather than with multiple fee parcels, depending on the costs of initial coordination with
the tribe.
Our reasoning is that the costs of contracting increase with the number of holders of
exclusion rights, and that the number of these holders increases at a different rate for each type of
tenure as the spatial scale of development expands. One way to evaluate this hypothesis is to
compare vertical drilling to horizontal drilling. Vertical drilling only requires contracting over
20
Need to mention the policy change on Fort Berthold in 2004 (?) that allowed companies to contract with a simple
majority of individual trust owners rather than requiring unanimous consent—still much higher contracting costs
than for fee simple.
20
mineral rights for the parcel on which the well is drilled, whereas horizontal drilling requires
contracts with mineral rights holders over a broader area. 21
Table 6 shows the density of vertical and horizontal wells for each type of tenure on the Fort
Berthold Reservation for wells drilled since 2000. The well data come from from iHS’
proprietary database on gas and oil wells in the western region. The database includes
information on every well drilled in the region dating back to the beginning of the 1900s,
including the well’s location (longitude and latitude), permit date, and spud date. 22
There are two results in Table 6 that are consistent with our theoretical reasoning. First,
vertical wells are most dense on fee land, where there is only one exclusion right holder. Second,
vertical wells are denser on allotted land than on tribal land, but horizontal wells are denser on
tribal land. This suggests that fixed costs associated with permitting may be higher when dealing
with the tribe but that the marginal contracting costs associated with coordinating resource use
over space are lower with the tribe.
Table 6: Well Density by Tenure on the Fort Berthold Reservation, for wells since 2000
Fee
Allotted
Tribal
269.74
448.38
63.85
Total Wells
925
260
32
Vertical Wells
239
46
1
Horizontal Wells
686
214
31
Wells/Sq. Mile
3.4292
0.5799
0.5012
Vertical/Sq. Mile
0.8860
0.1026
0.0157
Sq. Miles On Oil
2.5432
0.4773
0.4855
Horizontal/Sq. Mile
Note: Land that is either underneath the Missouri River or does not overlap a known oil field has been excluded from the area
calculations.
In an attempt to isolate the impact of landowner contracting costs from other factors that
may influence the density of well drilling, we test for the effects of tenure on the number of days
between the permitting of a well (by state oil and gas commissions) and the spudding of that
well. For wells spudded in North Dakota over 2000-2012, the mean number of days from permitto-spud was 85.8 days for vertical wells and 120.3 days for horizontal wells since 2000.
21
This is where we should nod to the literature on pooling and unitization. There are spatial issues even with vertical
drilling, but they the key thing is that they are worse for horizontal drilling.
22
The iHS data have been used in other research, including Jacobsen and Parker (2014).
21
Although some of the landowner contracting costs must be incurred by the drilling company
prior to obtaining a permit, we argue that the permit-to-spud variable still captures some of the
landowner contracting costs based on our reading of the legal literature. After a permit has been
obtained but before drilling has commenced, individual landowners along the proposed drilling
line still have opportunities to holdup drilling. For example, the oil company must still obtain
agreement on specific drilling plans and non-participating landowners can refuse to allow surface
access to stages of a multi-staged fracking plan. 23 For these reasons, we predict longer durations
for horizontal wells than for vertical wells because more parties are necessarily involved raising
the possibility of holdups. Moreover, because drilling a horizontal well requires first drilling a
vertical well, it is unlikely that differences in permit-to-spud duration between vertical and
horizontal wells are driven by technical constraints or costs of moving equipment.
Table 7 assesses these predictions first in the context of all wells drilled in the western
U.S. since 2000. (We return to the Fort Berthold case below). The results show that horizontal
wells entail longer permit-to-spud durations, even after including individual oil and gas field
fixed effects. This result is consistent with our reasoning that horizontal drilling is more subject
to holdups and delays, even after permits are granted. Wells on Indian reservations also have
significantly longer permit-to-spud durations. As column 3 shows, however, horizontal wells do
not appear to generally have longer durations on Indian reservations than off reservations. This is
possibly because reservations are a mix of tribal and allotted trust land with one property regime
(tribal land) reducing permit-to-spud delays and the other (allotted trust land) increasing the
delays.
23
In the case of allotted Indian reservation parcels with multiple owners, the oil company will have to notify the
surface landowners before drilling but some may be difficult to locate.
22
Table 7: OLS Estimates of Days between Permitting and Spudding of Well
(for all wells permitted in the Western Region since 2000)
(1)
(2)
(3)
Horizontal Well
7.759**
(0.023)
16.67***
(0.000)
48.70***
(0.000)
On Indian Reservation
26.91***
(0.000)
-3.357
(0.596)
20.43**
(0.010)
Indian Res. X Horizontal Well
26.87***
(0.003)
28.91***
(0.003)
6.240
(0.280)
x
x
x
x
x
x
x
x
x
0.098
79,469
0.125
79,469
0.161
79,469
Year Fixed Effects
41 Basin Fixed Effects
290+ County Fixed Effects
2000+ Oil/Gas Field FE
R-squared
Observations
Notes: Standard errors are robust to heteroskedasticity. P-values are in parentheses. * p<0.1, ** p<0.05, ***p<0.01. The western
region includes Idaho, Nevada, Arizona, Montana, Utah, Wyoming, Colorado, North Dakota, South Dakota, and part of New
Mexico. The source of the well data is the iHS proprietary database.
To test more specifically for the impact of land tenure on our measure of contracting
costs, we return to the Fort Berthold case study. Table 8 shows our OLS estimates, which are
based on horizontal wells since 2000. (We focus on horizontal wells here because there have
been too few vertical wells on the reservation to evaluate empirically). There are 3867
observations, representing the number of wells spudded on the Fort Berthold reservation and
within a 10-mile buffer of the reservation since 2000. We include a measure of road density
within a 5-mile radius of each well in all regressions to control for potential differences in the
costs of moving equipment to the well pad. 24 We also cluster standard errors by oil field to
account for potential correlation in observed characteristics that affect the timing of spudding.
Figure 8 shows road density within and around Fort Berthold.
24
Our result could be confounded if the extent of transportation infrastructure varies substantially by land tenure
type. In this case, longer permit-to-spud times may just reflect the cost of moving equipment across worse
infrastructure. On the other hand, the contracting problems associated with multiple exclusion rights depicted in our
model are just as likely to hold-up infrastructure projects as oil development. Hence, infrastructure simply provides
another channel through which our mechanism could operate.
23
Figure 8: Road Density on Fort Berthold
Across columns in table 8, we see that allotted land is associated with the longest permitto-spud durations and tribal land is arguably associated with the shortest because there is never a
statistically significant increase in permit-to-spud timing on tribal land. These results are
consistent with our reasoning, which predicts that increases in the number of landowners to a
contract will slow down the drilling process. Moreover, there is some evidence that being
surrounded by allotted land (in a one-mile radius) further slows drilling, presumably because the
number of potential holdups expands when surrounded by allotted land. By contrast, there is also
evidence that drilling is faster when the wellhead is surrounded by tribally owned land, although
these coefficient estimates are not always statistically significant. Finally, the result that drilling
on fee-simple land takes longer on the reservations when compared to the buffer is also
consistent with our theory. Off reservations, owners of fee simple land can be forced into oil
extraction pooling agreements minimizing their ability to holdup drilling. On reservations,
however, these forced pooling agreements are not enforceable (Slade 1996).
24
Table 8: Permit to Spud Regression Results
Y= Days from Permit to Spudding
(1)
(2)
**
Allotted
37.05
39.42**
(11.48)
(11.97)
(3)
25.22
(17.75)
(4)
43.53**
(15.60)
(5)
46.33**
(14.84)
Tribal
17.03
(28.91)
21.27
(29.85)
39.74
(33.04)
43.26
(34.54)
48.31
(33.07)
Fee Simple
21.10*
(8.653)
20.66*
(8.418)
11.92
(10.68)
24.69*
(10.50)
24.83*
(9.648)
Road Density
-0.167**
(0.0614)
-0.177**
(0.0586)
-0.168**
(0.0634)
-0.159*
(0.0617)
-0.155*
(0.0651)
Allotted Area
In 1-Mile Radius
1.458
(0.740)
0.963
(1.102)
0.790
(1.114)
Tribal Area
In 1-Mile Radius
-2.187*
(1.078)
-1.291
(1.525)
-1.394
(1.511)
41.94***
(11.34)
X
X
-63.00
(35.54)
X
X
-42.83
(22.02)
X
X
X
3867
0.127
3867
0.135
147.0***
(6.414)
Constant
Year Effects
Month Effects
Field Effects
69.16***
(5.119)
X
N
3867
3867
3867
R2
0.010
0.039
0.051
Standard errors(clustered by field with 155 clusters) in parentheses
*
p < 0.05, ** p < 0.01, *** p < 0.001
To summarize, we find preliminary evidence that the subdivision and privatization of
reservation land has delayed and sometimes stunted oil and gas extraction on American Indian
reservations.
V.
Income Effects of Oil Wells
To highlight the importance of oil and gas extraction for income generation on reservations,
we estimate the historical relationship between per-capita income on reservations and wells in a
panel of reservations spanning certain years from 1917 to 2010. The 1917 data actually report the
average per capita incomes of Indians on reservations during 1915-1918. The 1917, 1938, and
1945 data come from reports of the Bureau of Indian Affairs. The 1938 and 1945 data are from
25
the U.S. National Archives and the 1917 data are available online. The 1969, 1979, 1989, 1999
data come from decadal U.S. Census reports, and the 2010 data are from American Community
Surveys. 25 From these data, we construct the 1917-2010 per capita income panel, with incomes
presented in 2010 dollars adjusted by the CPI. Our balanced panel is based only on the 49
reservations for which data are available for each of the eight time periods. Our unbalanced panel
employs a larger set of reservations for which data are available for at least seven of the eight
time periods. Figure 9 summarizes and displays the per capita income data. It is clear that
populations are relatively poor, especially compared to the U.S. per capita income of $27,344 in
2010.
Figure 9: Mean Per Capita Income of American Indians on Reservations
(in 2010 $s)
0
5,000 10,000 15,000
Panel A: Balanced Panel
1915-1918
1938
1945
1969
1979
1989
1999
2006-2010
1999
2006-2010
0
5,000 10,000 15,000
Panel B: Unbalanced Panel
1915-1918
1938
1945
1969
1979
1989
To scale the extent of energy development, we calculate the oil and gas wells drilled on
reservations divided by the reservation’s Native American population in each of the relevant
years. More specifically, this variable aggregates the number of oil and gas wells drilled on
reservations in the five year period preceding each year for which we have income data. We use
this procedure because oil and gas drilling may have lagged effects on income due to the flow of
25
The 2010 data come from the American Community Survey (ACS) which differs from the earlier decennial
reservation census reports in certain ways. For geographic areas with populations less than 20,000, the ACS reports
5-year estimates (i.e. 2006-2010 averages). Because of this, the only data available for most reservations are the 5year estimates which are what we use in our analysis.
26
oil and gas that subsequently occurs. For 1999, for example, the variable indicates the number of
well drilled during 1995-1999 divided by a reservation’s 1999 American Indian population.
We estimate the following regression model:
(16)
yit = a i + θt + η oilwells. percapit + l slots. percapit + β st. jurit + δ state. pcist + e it .
Here y = per capita income, i=reservation, s=state, and t=time period. Each model controls for
time shocks affecting all reservations ( θt ), and allows each reservation to have its own income
intercept ( α i ). The use of reservation-specific fixed effects controls for time-invariant differences
across reservations (e.g., geographic location, reservation size, and land quality) that may cause
persistent cross-sectional differences in income.
In addition, we introduce time varying covariates to control for economic shocks that
might affect income growth. The first covariate measures casino gaming activity on reservations
with the number of slot machines per American Indian in 1999 and 2010. The casino variable is
zero prior to 1999 because reservations in these samples did not have casinos prior to 1999. 26 We
predict this variable to be positively correlated with income. The second covariate is whether or
not a reservation is subject to state judicial jurisdiction under P.L. 280. We include this variable
because previous research suggests that state jurisdiction has increased access to credit,
investment, and income on Indian reservations (Anderson and Parker 2008, Parker 2012,
Cookson 2013). This variable is equal to zero for all reservations through 1945, and then it is
equal to 1 for a subset of reservations from 1969-2010. The third covariate we include is the per
capita income of the state surrounding the reservation. 27 We expect this variable to positively
correlate with the per capita income of American Indians on reservations.
Table 9 presents the panel regression results. Column 1, which employs the balanced
panel of 392 reservations (i = 49, t = 8), shows that oil and gas wells are strongly related to per
capita income. To put the 4248.7 coefficient into perspective, consider that the mean number of
26
Prior to the Indian Regulatory Gaming Act of 1988, casino gaming on reservations was virtually non-existent
(Cookson 2010).
27
Ideally we would prefer to include the per capita incomes of counties adjacent to reservations as we did in
Anderson and Parker (2008) but the census first reports per capita income at the county level in 1959. The data on
per capita income at the state level are from the Bureau of Economic Analysis and are available for 1929 to 2010.
27
wells drilled per capita changed from 0.065 in 1999 to 0.186 in 2010 for the eleven oil endowed
tribes in this sample. The 4248.7 coefficient therefore implies an increase of $514 in the per
capita income of residents on reservations with oil and gas endowments. This is relative to a
mean per-capita income of only $11,921 in the balanced panel. For comparison, consider the
coefficient of 1662.6 on the slot machines variable. For perspective, consider that the mean
number of slot machines per capita increased from 0.23 to 0.37 from 1999 to 2010. The 1662.6
coefficient therefore implies an increase of $232.8 in per capita income. The coefficients on P.L.
280 and state per capita income are also positive and statistically significant, as expected.
Table 9: Panel Regressions of Per Capita Income on American Indian Reservations
BALANCED PANEL
(1)
(2)
UNBALANCED PANEL
(3)
(4)
Oil wells drilled per capita
4248.7***
(0.011)
2696.9***
(0.003)
4104.9***
(0.001)
2670.5***
(0.004)
Slot machines per capita
1662.6**
(0.017)
1701.5**
(0.022)
1642.5***
(0.000)
1360.1
(0.113)
P.L. 280 Reservation
(=1 if yes, otherwise =0)
1234.4***
(0.007)
2771.0***
(0.001)
1302.7***
(0.001)
2007.1**
(0.015)
State per capita income
0.273***
(0.000)
0.348***
(0.000)
0.280***
(0.000)
0.355***
(0.000)
Year fixed effects
Reservation fixed effects
Reservation time trends
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Observations
Adjusted (within) R2
343
0.82
343
0.89
487
0.78
487
0.84
Notes: * p < 0.10, ** p < 0.05, and *** p < 0.01. P-values are reported in parentheses and are based on standard errors that are
clustered at the reservation level. The number of observations is lower in the specifications that control for state-level per capita
income because we lack 1917 data on state per capita incomes. The state-level per capita income data come from the U.S. Bureau
of Economic Analysis. The slot machines variable takes on a value of zero for all reservations prior to the 1989 Census. The data
on slot machines for 1989 and 1999 were compiled by Anderson and Parker (2008) and also used in Cookson (2010). The data on
slot machines in 2010 were compiled by the authors from www.500nations.com/Indian_Casinos.asp. This site provides the
number of slots/gaming machines for all American Indian casinos in the U.S. Each casino can be tied to a reservation by looking
at which tribe owns the casino and where the casino is located. We downloaded gaming machine data from the site in 2013, so
our measure may include casinos built after 2010. The data on the number of oil and gas wells drilled come from our merge of
ArcGis Shapfiles of U.S. Indian reservations boundaries with iHS data on the longitude and latitude of oils and gas wells drilled
in the Western Region of the U.S. For this variable, we aggregate the number of oil and gas wells drilled over the four year
period prior to the year in which income data are reported. For example, our 1999 measure aggregates the number of wells drilled
over 1995-1999 and divides this number by the 1999 American Indian population of the reservation.
28
Adding reservation specific time trends in column 2 helps control for pre-existing income
growth prior to the construction of new wells and may therefore generate more credible
estimates. In column 2, we see that the coefficient on oil wells decreases to $2696 but remains
highly statistically significant.
Columns 3-4 show the estimated coefficients for equivalent specifications using the
unbalanced panel, which enables a larger number of observations. The results are qualitatively
similar to those in columns 1-2. In both samples, the coefficients on oil wells are positive and
economically large, and precisely estimated. These regression results suggest that tribes have
much to gain economically from overcoming transaction cost obstacles to utilizing their oil and
gas endowments.
VI.
Conclusion
•
•
•
Discuss proposal to subdivide and privatize Canadian First Nations
Discuss policy implications and land consolidation rules on Fort Berthold and
majority
Discuss implications for utilization of wind, wildlife and other natural
resources
29
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