Effects of Local Regulation on Neighboring Jurisdictions: Evidence from Mining Ordinances*

Alexey Kalinin

Dominic Parker

Daniel Phaneuf

Department of Agricultural and Applied Economics

University of Wisconsin, Madison

February 16, 2017

Preliminary and incomplete. Please do not cite or distribute

Abstract:

The environmental federalism literature describes local regulatory control as a double-edged sword. It

empowers jurisdictions to solve their local problems, but to discount spillover impacts on neighboring

jurisdictions. We study this tradeoff in the context of a regional ‘frac sand’ mining boom in Wisconsin,

which began around 2010 and was induced by the hydraulic fracturing surge across the U.S. We exploit a

2012 state Supreme Court ruling, permitting township-level mining ordinances, to study the effects of

local regulation on mining activity, resident exposure to disamenities, and property values. Consistent

with complaints of heightened traffic congestion and roadway risks, we find large effects of mine

openings on accidents involving industrial trucks ranging from 9 to 13% per mine but also positive effects

of mine openings on township-level property values, ranging from 3 to 5% per mine. Township

ordinances significantly reduce the accident effects of mine openings, but in a way that is only neutral

with respect to town-level property values. Mine openings under ordinances increase truck accidents and

decrease property values in neighboring jurisdictions. The results, although preliminary, suggest the net

value of local regulatory authority may be negative once spillover impacts are considered.

*Kalinin and Parker share lead authorship. Parker is the corresponding author. For comments on earlier drafts,

we thank Lucija Muehlenbachs, Reed Watson, and participants at the 2016 Annual ASSA Conference. Parker

gratefully acknowledges Lone Mountain Fellowship funding from the Property and Environment Research Center in

summer 2016 and helpful comments from participants at a seminar hosted by that organization. All authors

gratefully acknowledge grant support from the USDA through its AFRI program.

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1. Introduction

Natural resource booms boost local economies but also generate concerns about external health,

safety, and environmental costs. This is particularly so for mining and drilling, where complaints about

pollution and traffic congestion are common. The narrative surrounding the recent oil and gas boom in

the US provides a salient example. Technological advances in hydraulic fracturing have made extraction

from shale possible in areas of the country not previously involved in the energy sector. The novelty and

rapid expansion of production has, in many cases, raised questions about the legality of different

jurisdictions’ efforts to regulate the new activity. Political factions have emerged to promote and contest

unregulated extraction, pitting those who gain economically against locals who fear losses in quality of

life.

The contentious ‘boomtown’ environment raises several questions of interest to economists.

What are the advantages and costs of granting local government clear authority to regulate a booming

industry? Will such authority reduce local exposure to disamenities? If so, will this reduction come at

the expense of economic benefits? Will local regulatory authority displace disamenities onto neighboring

jurisdictions? More generally, are the aggregate effects of local regulation positive or negative?

We study these questions in the context of the ‘frac sand’ mining boom occurring in rural areas of

Wisconsin, Minnesota, Illinois, and elsewhere (figure 1). These states contain silica sand that is ideal for

use in the water-chemical mixture that drilling companies inject into shale wells to fracture shale. The

silica is a propellant in the process, which means that it props open cracks, allowing oil and gas to seep

out of shale and into wells. The same technology advances driving the shale boom in places such as

North Dakota, Pennsylvania, and Texas are also fueling an increase in demand for fracture-grade sand,

which has largely been sourced from our study area in western Wisconsin (table 1).1 As such, the growth

in sand mining has been rapid and large: in 2010 there were 13 mining and processing sites including

1 This area of the state is endowed with large quantities of accessible silica particles and loose cementation

that are ideal for processing into the propellant needed for hydraulic fracturing.

2

some that were opened long before the shale boom. By 2015, the number of permitted mines had

increased to 115.

This rapid expansion of extraction generates opportunities and challenges similar to those

associated with the larger shale boom. The sand boom is portrayed as infusing communities with jobs

and wealth (Prengaman, 2012), but it is also opposed by groups concerned about local quality of life and

health risks (Loeb, 2016). The sand boom is similar to the shale boom in that regulatory authority of

different jurisdictions – local, county, and state – is debated. Some states, such as New York, have placed

moratoria on fracking, and through this have deprived some areas of potentially substantial economic

benefits (Boslett et al., 2016). At the other extreme, the Texas legislature recently declared local-level

fracking bans illegal, depriving local communities of collective choice through their elected leaders.2

In our study area – the state of Wisconsin – regulatory authority is controversial, and lies at the

township level. 3 Initially, at the boom’s onset, townships lacked authority to directly regulate frac sand

mining. Townships with a zoning apparatus in place prior to the boom could use that apparatus to steer

the location of mining, but many townships lacked any zoning system at the onset of the boom. In these

townships, sand facilities could operate freely in accordance with their profit motive.

The regulatory landscape changed in early 2012, when the WI Supreme Court ruled in Zwiefelhor

v. Town of Cooks Valley that towns could directly regulate non-metallic mining through ‘police powers.’4

Many townships quickly passed mining ordinances in response. The ordinances specify terms such as

permissible trucking routes, truckload size limits, mine operating hours, and related activity.

There is now rich variation in local regulation across our study area. Fifty-one sand facilities

2 See http://thinkprogress.org/climate/2015/05/19/3660369/texas-prohibits-local-fracking-bans/

3 Townships are a prominent unit of local governance in most of the US; they roughly correspond to the 36

square mile (6 mile by 6 mile) Public Land Survey System grids (see Libecap and Lueck, 2011).

4 The court decision is available at

https://www.wicourts.gov/sc/opinion/DisplayDocument.pdf?content=pdf&seqNo=77767. It is controversial and

there are political attempts to make it more difficult for local governments to regulate frac sand mining. (See See

www.wpr.org/walker-says-hed-balance-between-local-control-frac-sand-mines).

3

spanning 34 townships were permitted prior to the court ruling, and are therefore unregulated by

ordinances. After the ruling, 64 facilities were permitted, with 32 of these facilities governed by

ordinances. The 115 permitted facilities span 58 townships (see tables 1 and 2). In some cases, single

townships contain separate mines that were permitted before and after the decision. Also, post-decision

mine openings governed by an ordinance exist concurrently with post-decision mine openings that are

not. This feature enables difference-and-difference comparisons of mine-opening impacts, based on

ordinance status in the post-decision period.

We use this variation to examine the local and spillover impacts of the township-level

regulations. To do so, first we construct an annual panel of local jurisdictions (townships, villages, and

cities) with information on ordinances and zoning, mine openings, and various outcome variables. On the

benefit side, we test for the effects of sand facility openings and ordinance passage on local population

growth and tax revenues, and note that annual measures of local employment and income are not

available. We perform the same tests on the cost side, focusing on traffic congestion and accident rates

involving cars and industrial trucks. We focus on disamenities from sand trucking, rather than near-mine

effects of light, noise, and air pollution, for two reasons.5 First, disamenities from sand trucking – which

may involve 20,000 truckloads from mines to rail lines per day in our study area – are garnering much of

the media attention, where allegations of extreme congestion and roadway risk on rural roads are

common.6 Second, ordinances are uniquely suited for dealing with this disturbance. Whereas near-mine

effects are mitigated by zoning rules that quarantine land uses or by voluntary Coase (1960) style

5 By testing for property-value effects near drilling sites, most of literature on the impacts of the oil and gas

boom implicitly focuses attention on the near-site disturbances (see, e.g., Gopalakrishnan and Klaiber, 2013 and

Muehlenbachs et al., 2014). The potential for mining activities to create more spatially extensive disamenities,

however, has received less attention.

6 Beiser (2016) discusses the environmental costs of trucking sand in general, and Prengaman (2012)

estimates the number of truck trips per day. Unlike oil and gas, these transport costs cannot be avoided by

constructing pipelines, because sand is not fluid.

4

agreements between mine operators and adjacent residents,7 transport-based disamenities are spatially

dispersed and hence more difficult to internalize, especially given the open access nature of road networks

(Duranton and Turner, 2011; Liebowitz and Margolis, 1994).8

After examining specific cost and benefit indicator, we measure the net effects by examining how

township-level property values change in response to mining and ordinances. These analyses make use of

both aggregate (township level) data, as well as records of individual property sales.

In terms of econometric methodology, we measure the effects of mine openings on outcomes

with and without ordinances using difference-in-differences specifications. The main specifications

estimate an average ‘treatment’ effect for the subset of townships that chose ordinances. These estimates

quantify the best-case scenario for locals, and the un-internalized spillovers to neighboring jurisdictions,

if we assume that jurisdictions pass ordinances only when they project positive net benefits. We make no

claim that the average effect could be transferred to townships not choosing ordinances, if such

ordinances were imposed from top-down.

To preview results, we find that mine openings boost local economies. Another mine is

associated with an increase of 0.7 to 0.9 percent in population growth, and a 4 percent increase in tax

revenues. However, a sand facility opening is also associated with a 12 to 18 percent within-township

increase in accidents involving industrial trucks on local and county roads, even after controlling for

population growth. The passage of an ordinance eliminates up 10 to 12 percent of the otherwise predicted

7 Some of the agreements involve cases in which mining operators offer to buy adjacent land at a price-

floor to mitigate concerns about dis-amenity effects on property values near sites. [Cite Wisconsin watch article]

This observation adds to other case-studies of how resource use disputes have been settled by a rich set of private

negotiations (see, e.g. Libecap 2014, Anderson and Hill 2004).

8 These factors raise the transaction costs of forming ex ante compensation agreements between mine

operators and affected parties but liability can help remedy the problems. We view the execution of liability to

imperfectly and incompletely compensate for increase accident risks and congestion. That is, we agree with other

researchers who conclude that vehicle accidents are an economically important outcome in spite of liability and

insurance protections (see Makowsky and Stratmann 2011, Burger et al. 2011).

5

impact of facility openings on accident occurrences. We find limited evidence – if any – that the

ordinances impair population growth or tax revenue generation or property values. In short, the

preliminary evidence suggests that local regulations are neutral or a net benefit, at least at the local

aggregate level.

We also estimate the effects of mine openings on adjacent townships and these estimates point to

a fuller narrative about the effects of local regulation. We find no evidence that mine openings cause

accidents in adjacent jurisdictions in the absence of ordinances. This finding makes intuitive sense in that

mining companies seek to minimize costs of trucking sand to rail load out facilities, and hence have

strong incentives to minimize route lengths in an unregulated environment. By contrast, we find evidence

that mines governed by ordinances do increase roadway accidents in adjacent townships by 4 to 5 percent.

A plausible mechanism is that the ordinances intentionally or unintentionally redirect truck traffic towards

other jurisdictions.

There are two broad takeaways. First, resource booms create economic benefits and quality-of-

life disturbances and, if given the authority, local jurisdictions can affect collective outcomes through

targeted regulation. Second, the regulatory instrument that succeeds at improving conditions locally can

cause more dispersed effects on a broader population base. These takeaways relate to the literature on

environmental federalism, which asks whether local, state, or federal authorities should govern natural

resources (Oates, 2002; Helland and Whitford, 2003; Sigman, 2002; Sigman, 2005; Anderson and

Watson, 2011; Banzhaf and Chupp, 2012; Olmstead and Sigman, 2014; Monogan et al., 2015). While the

local regulator is more responsive to local heterogeneity in the policy setting, the central regulator will

account for spatial spillover from local extraction.9 Our study contributes to this literature by identifying

9 The local regulator puts less weight on disamenities that migrate out of jurisdiction and empirical studies

find that this can lead to more pollution at jurisdictional boundaries. Helland and Whitford (2003) find that pollution

generating activity concentrates near state borders, and Olmstead and Sigman (2014) find evidence that damns in

international rivers disproportionately locate at country borders. Sigman (2002, 2005) finds evidence of more

pollution in international rivers, and in interstate rivers within the United States. There is also evidence that local

6

roadways which, like rivers that carry pollution, are a conduit through which local regulation can cause

spillovers on neighboring jurisdictions. Our study deviates in a fundamental way from the environmental

federalism literature because we compare spillovers in unregulated versus regulated settings. We find

provocative evidence suggesting it may be better to have unregulated neighbors.

2. Study Area

Our study area is shown on the map in figures 2A and 2B, which displays the counties and

townships in western and central Wisconsin that hosted sand mines as of 2015. The area is largely rural,

with the majority of land lying in unincorporated areas. Incorporated towns are small, and there are few

cities. The population in the study area was 666,144 as of 2015, which represents 12 percent of

Wisconsin’s total population. Agriculture is a large economic sector, and tourism is important in some

areas. The western counties bordering the Saint Croix and Mississippi Rivers, in particular, are

characterized by scenic landscapes.

The frac sand industry in Wisconsin has been controversial since the beginning of the mining

boom. Some landowners, truck drivers, and business owners have received an economic windfall from

sand development, but it has left others concerned about air and water quality impacts, loss of

environmental amenities, the integrity of transport infrastructure, and noise from truck and rail traffic.

Especially prior to Zwiefelhor v. Town of Cooks Valley, some communities expressed concerns about

their ability to control and plan extractive land use in the wake of a rapidly developing industry that is

offering to buy land and create local jobs (Chapman et al., 2014; Locke, 2015).

While concerns about the external effects of frac sand mining have garnered media attention,

there are relatively few objective studies that have measured their magnitude. Instead, most analysis is

qualitative and descriptive. For example, Power and Power (2013) describe the overall economic impacts

regulators respond to local preferences, in some cases by imposing more stringent land use and pollution regulations

than centralized jurisdictions (see Chupp, 2011; Gray and Shadbegian, 2004).

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of sand mining in Wisconsin, with attention given to both market and nonmarket aspects. They note the

potential for surface and ground water contamination based on the large amount of water used in mining

operations, as well as well as the potential for abandoned pits and residual contamination once extraction

is complete. In addition, Chapman et al. (2014) highlight concerns about fine particulate matter in mining

areas. Sand mining, processing, and transportation produce silica dust containing particles smaller than

2.5 micrometers (PM2.5), which are known to have human health consequences. With relatively little

systematic monitoring, however, it is not known if mining operations have increased ambient exposure in

nearby populations. Power and Power and Chapman et al. provide anecdotal evidence of property value

decreases in sand mining areas, but to date there has been little systematic study of this effect.

It is noteworthy that most of the disamenity concerns referenced above, with the exception of

roadway traffic disturbances, are highly localized. The reasons for this are likely twofold. First, the

existence of concentrated disturbances from other types of mining operations has been well-established.

For example, Olmstead et al. (2013) show that shale gas mining affects surface water quality in

Pennsylvania, and Muehlenbachs et al. (2014) demonstrate that property values are affected by proximity

to natural gas mines. Second, the existence of a mine at a point in space provides a natural focal point for

these concerns. In contrast, dispersed external effects from frac sand transport may be less concentrated,

and therefore less likely to spur action by local residents, officials, and activists.

To understand the impact of sand mining on roadway use, we consider an example from

Chippewa County. In 2008, Chippewa County produced 137,000 tons of frac sand annually. By 2012,

the county was producing 2.5 to 4 million tons annually (Hart et al. 2013). To translate this into truck

traffic, consider that it takes 40,000 trucks to move one million tons of sand, assuming 25 tons per truck

capacity – the typical size used (Hart et al., 2013; Orr and Krumenacher, 2015). This is equal to 110 daily

one-way truck trips from a mine to a processing or rail shipping facility. To put these numbers in context,

Chippewa County expects sand production to increase to 5 to 7 million tons per year as new mines are

developed, which will generate 550 to 770 daily one way trips.

The regulatory environment for frac sand mining in Wisconsin is also relevant for understanding

8

the existence and spatial distribution of external effects. In general, authority to regulate the industry is

decentralized, as state-level oversight is relatively limited: operators need to obtain state permits to

discharge wastewater and, in some cases, for air emissions. They also need to provide a land reclamation

plan for when mining operations cease.

Prior to Zwiefelhor v. Town of Cooks Valley, more stringent regulation was possible at the local

level primarily through zoning regulations, due to constitutional and statutory features of Wisconsin law.

Several levels of local government in the state are allowed to implement zoning. Specifically,

incorporated cities and villages possess ‘home rule’ authority, which provides a large degree of discretion

to manage local affairs, within the limits of the state constitution. In contrast, townships are

unincorporated areas that provide a more limited set of services. Wisconsin has 1256 townships that

roughly correspond to the grid of the Public Land Survey System (PLSS), and they represent the mostly

rural areas not contained in city and village boundaries. Townships are uniquely located in one of the 72

counties in the state.

Townships in Wisconsin dominate the rural landscapes in our study area. Out of 500

jurisdictions, 347 are townships, 57 cities and 96 villages (Figure 2A). They are similar to townships in

other states, in that they have ‘police power’ jurisdiction over basic law enforcement, fire protection, and

road maintenance services. Additionally, a township can choose to adopt the zoning ordinance of a

county that it is in, pass a zoning ordinance that is a modification of the county’s ordinance, with the

county’s approval, or elect to have no zoning ordinance aside from a few specific regulations mandated

by the state. The absence of a mandate for local zoning is a unique feature of Wisconsin local governance

and means there is potential for a variety of local regulatory environments to exist across our study area.

Indeed, out of 347 townships in our sample, 166 adopted county zoning, 42 wrote their own zoning

ordinances, and 139 have no zoning regulations.

Zoning laws primarily sort and quarantine land uses, and so are an indirect mechanism for

regulating frac sand mining and its transport externalities. Zoning laws specify types of land uses that are

generally allowed in a given zone and those that can be permitted. These land use regulations can also be

9

used to indirectly implement additional safety measures, including the adoption of speed limit

requirements for industrial traffic (Center for Land Use Education, 2007). This means that townships

with zoning in place at the time of the frac sand boom had more regulatory levers regarding mining, when

compared to unzoned jurisdictions.10

The Zwiefelhor v. Town of Cooks Valley decision gave townships another regulatory lever to

manage the frac sand industry. The 2012 court ruling made clear that in addition to zoning, townships

can exercise their police powers to pass non-zoning ordinances to regulate mining activities. In contrast

to zoning, non-zoning ordinances target a specific land use and regulate it on a case by case basis, based

on operating standards. As a result, ordinances regulate how an activity takes place, as opposed to where

it happens. Hence, the ruling gave townships greater flexibility in regulating sand mining, and this

particularly benefited unzoned townships. Had the decision not been rendered, the unzoned township

options for regulating sand mining were twofold. They could give up their independence in land use

planning and submit to top-down county zoning, or they could go through the time-consuming and costly

procedure of designing their own zoning ordinances.11

Beginning in 2012, many townships passed non-zoning mining ordinances to regulate new mines

(see table 2). Mining ordinances describe minimum operating standards and outline procedural steps

required for mine operators to receive a license from a town. Existing facilities are generally

grandfathered under the pre-ordinance regime and do not need a license to continue operating, unless they

expand or introduce substantial changes to their operation. Typical standards restrict hours of mine

operation, require that haul routes used in transport of sand not interfere with commuter or school traffic,

10 One objective of zoning rules is the reduction of local exposure to land use externalities (see Fischel

2004, Ihlanfeldt 2004, Glaeser and Ward 2009) but old zoning rules may be too rigid to adapt to novel land uses.

11 Both options have drawbacks. Adopting a county zoning ordinance means giving up independence, with

few opportunities to regain independence from the county later if the town so desiress. Passing an independent

zoning ordinance is costly as it requires towns to (i) develop a comprehensive landuse plan to guide development for

the next 20 years; (ii) design a comprehensive zoning ordinance; (iii) hire a zoning administrator; and (iv) maintain a

zoning board of appeals and legal counsel to arbitrate disputes over zoning decisions (citation).

10

specify use of tarps on trucks and screening around mines sites to reduce air pollution, mandate property

value guarantees to landowners adjacent to the mine site, set limits on onsite noise and lighting levels, and

require air quality monitoring. There are also standards relating to protection of surface and ground water

quality, requirements for road use agreements to compensate for damage to local roads from mine trucks,

and cumulative limits for the amount of mining activity or sand traffic that can occur in any one place.

Importantly, ordinances generally reserve the right of townships to make any additional restriction

deemed necessary to protect health and safety of town citizens.12

3. Theoretical Motivation

Although our main questions are largely empirical, here we provide some theoretical motivation.

The starting point, prior to the boom, is a rural landscape of farms and small towns that sit above sand of

homogenous physical quality. Traversing the landscape is a network of local, county, and state roads as

well as a network of railroad lines and railheads. The landscape is also subdivided into J small

jurisdictional boundaries (e.g., townships). We assume the jurisdictions lack regulatory authority over

unanticipated frac sand mining that will shortly emerge. The jurisdiction does, however, collect tax

revenue directly from mining facilities and other properties that it can use to finance local public goods.

At the start of the boom, there is a large demand shock for frac sand, which increases the return to

setting up and operating mines in the area. Mining companies need to acquire land, and minable parcels

near rail networks are particularly desirable. This is because the cost of hauling a ton by truck is

significantly more than the costs of shipping by rail – perhaps nine times as expensive (Glaeser and

Kohlhase, 2004). We expect land costs will reflect this differential, and so the cost of siting a mine and

producing sand will depend on the distances to railheads. We denote these production costs by

Cj(Xj,Djj,Djk), where j indexes the jurisdiction, Xj is the quantity of sand, Djj is the road miles a mine

12 The information in this paragraph is based on the authors’ assessment of a sample of mine ordinances

that we have obtained. These are available upon request.

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located in jurisdiction j uses in j, and Djk is the road miles a mine located in j uses in an adjacent

jurisdiction k. The unit cost of road transportation is Tj(Djj,Djk), so that total costs are given by

( , , ) ( , ).j j jj jk j jj jkC X D D X T D D (1)

We assume the following about the cost functions:

( ) ( ) ( )0, 0, 0,

.

j j j

j jj jk

C T T

X D D

(2)

and

( ) ( )

0, 0.j j

jj jk

C C

D D

(3)

The assumptions in (2) are intuitive – they simply imply positive marginal production costs, and that

additional transport miles (in either jurisdiction) lead to higher per-unit transport costs. The assumption

in (3) provides a useful means of isolating the location/transport cost tradeoffs that are an important

aspect of the sand mining industry in our study area.

Unregulated regime

We define an unregulated regime as one in which the firm can freely choose Djj and Djk, along

with output to maximize profit. In this case the objective function is

, ,

max ( , , ) ( , ) . . 0, 0.j j k

u

j j j j jj jk j j jj jk jj jkX D D

pX C X D D X T D D s t D D (4)

The first order conditions for the three choice variables are

( , , )( , )

( , , ) ( , ), 0

( , , ) ( , ), 0,

j j jj jk

j jj jk

j

j j jj jk j jj jk

j jj jj jj

jj jj

j j jj jk j jj jk

j jk jk jk

jk jk

C X D Dp T D D

X

C X D D T D DX D

D D

C X D D T D DX D

D D

(5)

where jj and jk are the multipliers on the non-negatively constraints. The conditions have an intuitive

12

interpretation. Conditional on operating in township j with necessary transport miles Djj and Djk, the firm

selects output Xj so that the sum of marginal production and marginal transport costs equals the price.

Simultaneously, it selects Djj and Djk to balance the production cost savings available from committing to

road transport miles, with the additional transport costs these miles create. The complementary slackness

conditions imply the optimal decision may involve using both, neither, or one jurisdiction’s road

networks. Denote the solution in the unregulated regime by ( , , )u u u

j jj jkX D D and the conditional profit

from setting up in county j by .u

j Note that a firm considering opening a single mine in the study area

will begin operations in township j if 0u

j and u u

j k for all k≠c.

In the unregulated equilibrium, we observe mines operating at different points in the landscape,

generating economic activity through their production decisions and externalities through their transport

decisions. Denote the transport externalities (e.g., accidents) in townships j and k by ( , )u u

j jj kjA D D and

( , ),u u

k kk jkA D D respectively, where k and j are adjacent townships in which one or both may be hosting

mining operations. Note that these functions describe the physical externalities in general terms, in that

they accommodate both own and cross-jurisdictional spillover effects that may emerge from firms’

location decisions. We expect externalities such as accidents and road degradation are increasing in both

arguments.

If land markets are well-functioning, we expect both the market and external impacts of sand

mining to be reflected in agricultural land and residential property markets. Denote the equilibrium

(hedonic) price functions in these markets by

, , , ;

, , , ; ,

u u u u

j j k jj kj j

u u u u

j j k jj kj j

P X X D D z

F X X D D z (6)

where Fj(∙) is the hedonic price function for agricultural land, Pj(∙) is the hedonic price function for

residential properties, and zj denotes other characteristics of township j that affect property values. Note

that these functions illustrate the idea that sand mines can have an ambiguous effect on property values.

13

Local and neighboring economic activity, and the resulting changes in factor demands, may increase

prices. This is related to the positive impacts such as higher population, increased income, and improved

local finance that are often associated with resource booms. At the same time, local land use and

transport externalities may decrease prices, meaning the net effect on land markets of expanded mining

activity is ambiguous.

Regulated regime

Consider now a regulated environment in which the local jurisdiction can influence the location

outcome for sand mines within its borders. Here we focus on regulations aimed at reducing transport

externalities, which may include restricted driving times, of the required use of alternative routes that

avoid residential areas. We model this by defining a policy parameters j>0 that proportionately

increases the marginal cost of transport miles within jurisdiction j. Specifically, under regulation the unit

transport cost function is ( , ),j jj jkT D D and the marginal cost of a road mile in township j is

,j jj jk

jj

T D D

D

(7)

Under this regulatory regime, the conditional profit maximizing problem becomes

, ,max ( , , ) ( , ) . . 0, 0.j jj jk

r

j j j j jj jk j j jj jk jj jkX D D

pX C X D D X T D D s t D D (8)

Importantly, does not enter the production cost function, since the miles variables in Cj(∙) represent

location-driven aspects of the sand mining costs, rather than transportation. The first order conditions in

this case are

( , , )( , )

( , , ) ( , ), 0

( , , ) ( , ), 0.

j j jj jk

j jj jk

j

j j jj jk j jj jk

j jj jj jj

jj jj

j j jj jk j jj jk

j jk jk jk

jk jk

C X D Dp T D D

X

C X D D T D DX D

D D

C X D D T D DX D

D D

(9)

14

We denote the solutions to this problem by ( ),r

jX ( ),r

jjD and ( ),r

jkD where the superscript r indicates

the regulated outcome, and we have made explicit the dependency of outcomes on the regulation. A firm

considering placement of a single mine will set up operations in jurisdiction j if 0r

j and profits in

other jurisdictions are not greater.

In the regulated equilibrium, we again observe mines operating at different points in the

landscape, including in regulated and unregulated jurisdictions. For the case of adjacent regulated and

unregulated townships j and k, respectively, the transport externalities are now

( ),

, ( ) .

r c

j jj kj

c r

k k jk

a D D

a D D

(10)

Through the term ( )r

jjD in the externality function for jurisdiction j, we allow the regulation to have a

(presumably negative) direct effect on local roadway outcomes. Through the term ( )r

jkD in the

externality function for jurisdiction k, we make explicit the notion that regulation in jurisdiction j can

spillover to jurisdiction k, via the optimal response of firms to the regulation. In our empirical section we

will test the extent to which accidents in jurisdiction k are differentially affected by regulation in

jurisdiction j, relative to the non-regulation regime.

Similar to the unregulated landscape, we once again expect both the market and external impacts

of sand mining to be reflected in agricultural land and residential property markets. We now denote the

equilibrium (hedonic) price functions in these markets by

( ), , ( ), ;

( ), , ( ), ; ,

r c r c

j j k jj kj j

r c r c

j j k jj kj j

P X X D D z

F X X D D z

(11)

and

, ( ), , ( );

, ( ), , ( ); .

c r c r

k k j k jk j

c r c r

k k j k jk j

P X X D D z

F X X D D z

(10)

15

Other impacts

Our model has focused on basics of firms’ behavior, and how responses to regulation may spill

over into other jurisdictions. Other responses are likely in the ‘boom’ environment of sand mining during

our study period. To attract labor for mining, facilities must offer higher wages if we assume that labor

was fully employed prior to the boom. If mining requires specialized labor, or if demand for extraction is

high relative to the pre-boom population of the local economy, then the mining sector will draw migrant

labor from outside the community. Thus, during the boom, we expect local population growth along with

expansion employment in the mining sector. This expansion should also raise the demand for goods and

services in construction, services, and retail industries. Therefore, we expect wages and employment in

other sectors to rise during the boom period through ‘multiplier effects.’13 There may also be economic

benefits through fiscal channels as found elsewhere in fracking communities (see Weber et al. 2016).

Jurisdictions tax mining directly, and the tax base may expand as a result mine openings. These revenues

are used to finance local public goods.

Testable hypotheses

Our formal and narrative theory motivate the following propositions:

P1: Mine openings in an unregulated jurisdiction should increase employment, population, wages,

and tax revenues within the jurisdiction, at least in the short run.14

P2: Mine openings in an unregulated jurisdiction should increase traffic and accident rates within

the jurisdiction, especially those involving industrial trucks.

P3: The effect of mine openings on property values is theoretically ambiguous. Property values

should positively capitalize the economic boom but negatively capitalize the disamenities.

13 The sand boom effects just described are positive, and they are consistent with empirical findings on the

local effects of energy booms. This literature has generally found short-run positive effects on employment, wages,

and income through the channels described above (Carrington 1996, Black et al. 2005, Marchand 2012, Weber

2012, Allcott and Keniston 2014, Maniloff and Mastromonaco 2014, Jacobsen and Parker 2016, Jacobsen 2016).

14 There is a large ‘resource curse’ and ‘Dutch Disease’ literature suggesting mining booms may reduce

longer run growth. Papers that study this issue within the U.S. include Deller et al. (2001), James and Aadland

(2011), Allcott and Keniston (2014), and Jacobsen and Parker (2016).

16

P4: Mine openings in a jurisdiction under ordinance will increase within-jurisdiction traffic flows

and accidents to a lesser extent than openings not under ordinance.

P5: Mine openings in a jurisdiction under ordinance will increase adjacent-jurisdiction traffic

flows and accidents to a greater extent than openings not under ordinance.

P6: Mine openings in a jurisdiction under ordinance will increase employment, population, wages,

and tax revenues, although the effects may be smaller than openings not under ordinance.

P7: The effect of mine openings under ordinance on within-jurisdiction property values is

theoretically ambiguous. However, the effect of mine openings on adjacent jurisdiction

property values should be more negative if the mines are governed by ordinances.

4. Data for Empirical Analysis

We have thus far collected jurisdiction-level data that allow us to empirically evaluate some, but

not all, of the propositions listed above. We develop a 2005-2015 panel data set that characterizes sand

mine facilities, population levels, tax revenues, property tax values, roadway accidents, ordinances, and

zoning. There are 500 jurisdictions in the study area; 347 are townships and the remainder are villages

and cities (see figure 2A).

Mine and Processing Facilities

We obtained information on the locations of frac sand mines and associated facilities from the

Wisconsin Department of Natural Resources (DNR). The associated facilities include standalone mines,

locations that combine mining and processing facilities, and standalone processing sites. For each

location the geo-coordinates and address are available, as well as an indication of whether a given site has

been permitted and is active, or is permitted and remains undeveloped. Because the DNR assembled

these data from a mix of sources, including investigative journalism reports, we verified the validity of the

data by checking address locations with Google Maps satellite imagery. We contacted county and

township zoning and land use offices to obtain the permitting and dates for each site.

Table 2 presents a summary of facilities over time. There were 87 active facilities through 2015

and 28 permitted but inactive facilities. The active facilities span 49 jurisdictions, generally townships,

17

and the permitted but inactive facilities span 17. There are 32 facilities under ordinance in 17

jurisdictions. The growth in facilities was concentrated over 2011-2014, and stalled when oil prices and

the number of fracking wells collapsed in 2015 (see table 1).

In terms of our main panel data set for empirical analysis, table 3 shows the number of operating

facilities in a township ranged from zero to 7 over 2005-2015 at the township level. We also construct a

variable indicating the number of sand facilities operating in adjacent townships. In constructing this

variable, we use the ‘queen’ rule for adjacency. This rule means that each township that that touches a

given township in any direction is considered adjacent. As figure 3 shows, the number of adjacent

facilities ranges from 0 to 14 in our sample. We include this variable to measure possible spillover effects

of mining in one jurisdiction on neighboring jurisdictions.

Mining Ordinances and Zoning

There is no comprehensive source for data on sand mining ordinances. We obtained information

about ordinances by directly contacting township clerks, who are charged with administrative and book

keeping duties. For all townships with permitted or active mines, we were able to determine the year that

an ordinance was passed if a township had one, and in many cases obtain a copy of the ordinance. Figure

3 maps the location of ordinance towns. Table 3 gives summary statistics for the two key variables in our

regression analysis. The first is the number of sand facilities in a township governed by an ordinance.

The second is the number of sand facilities in neighboring jurisdictions governed by an ordinance.

We obtained zoning data from a survey of Wisconsin counties, townships, cities, and villages

regarding their implementation and adoption of land use regulations and zoning ordinances, conducted by

the Wisconsin Department of Administration (DOA) in 2007. While the DOA survey provides additional

information about land use regulations and approaches to land use planning, for this draft, we simply

employ an indicator for whether or not a township had implemented zoning as of 2006 (WI DOA 2008).

Note that zoning existed prior to the sand boom and therefore was not caused by it. The variable is time

invariant, as it is measured prior to the boom, in 2006. To our knowledge, there is no comprehensive

18

source of zoning changes after the sand boom occurred.

Outcome Variables

The jurisdiction level population data come from the US Census Bureau annual population

estimates for minor civil divisions (US Census Bureau A, B). These population data are estimates

produced by the Bureau relying on interpolation between census years using housing stock data. Data on

property tax revenue are collected for sub county level jurisdictions by the Wisconsin Tax Payer Alliance,

a private government research organization (WISTAX A, B). Data on aggregate property values come

from Wisconsin Tax Payer Alliance and Wisconsin Department of Revenue (WISTAX B, WDOR).

Aggregate property values represent the full (assessed) value of all taxable property in the jurisdiction, as

reported by the Wisconsin Department of Revenue.

We obtained vehicle accident data from Traffic Operations and Safety (TOPS) Laboratory at the

University of Wisconsin, which includes all car accidents reported to the police. The accidents are geo-

referenced, with an indication of the road type on which accidents occurred as well as detailed

information about the accident type. We aggregate the accident data to the jurisdiction level for analysis,

by road type, allowing us to tabulate both aggregate and road type-specific accidents. Additionally, we

separately tabulate truck related accidents, non-truck related accidents, and total accidents, at the

township and county road levels. Table 3 summarizes the accident data.

5. Empirical Estimates of Facility Openings and Ordinances

We pursue two goals in this section. First, we exploit variation in the timing and number of

facility openings across townships to estimate the local effects of mining on population magnitudes, tax

revenues, vehicle accidents, and property values in a difference-in-difference framework. We estimate

both within-township and adjacent-township effects. Second, we exploit variation in ordinance passage

after 2011 to estimate how the within and adjacency effects of facility openings are moderated or

enhanced by ordinance governance. The estimates in this section provide one way of assessing the

19

theoretical propositions described in section 3. They are, fundamentally, comparisons of township-level

responses to sand mining activity with and without ordinance regulation.

We recognize that townships self-select ordinances and make no claim that estimates here

identify an overall average treatment effect that would be realized if ordinances were imposed on all

townships. Instead, the estimates here are best interpreted as average ordinance effects for townships

selecting ordinances. These are ‘best-case’ scenario effects if we assume that townships pass ordinances

only when their councils project the net benefits to be positive, for their local jurisdictions. The ‘best-

case’ scenario estimates are of central policy relevance because they demonstrate what will happen when

small local jurisdictions can flexibly regulate activity in a federalist system.

We rely on two main identifying assumptions in order to draw causal inferences. The first is that

post-boom changes in non-mining township outcomes represent good counterfactuals for post-boom

changes in mining township outcomes. The second is that post-court ruling changes in outcomes of

mining but non-ordinance townships represent good counterfactuals for post-court ruling changes in

outcomes of mining townships under ordinance. As support for the validity of these assumptions, we

offer graphical and econometric evidence in the following two sub-sections. We then present our main

econometric tests.

Graphical Evidence

Figure 4 plots mean population and fiscal outcomes for mining and non-mining townships. The

mining category includes all townships with at least one frac sand facility by 2015. The non-mining

category includes all townships without a facility. The mining jurisdictions persistently had higher means

for population but lower means for property values and tax revenue per capita. Visually, there is evidence

that mean outcomes were following common trends prior to 2009 or 2010, when the frac sand boom

began. After the boom, mean population began to diverge and property values began to converge. The

divergence and convergence in outcomes appears more pronounced after 2012, when the jurisdictions

started passing mining ordinance. There is less visual evidence of convergence in tax revenues, but we

20

note that the fiscal lag between mining activity and tax receipts (see Cummings and Schulz 1978)

confounds the visual diagnosis for that outcome.

Figure 5 plots mean accident counts in mining and non-mining jurisdictions for truck and non-

truck accidents on all roads and local and county roads alone. In all cases, the mean level of accidents in

mining jurisdictions is higher than the mean level in the non-mining jurisdictions. Visually, there is

evidence that mean accidents were generally following similar trends across the two jurisdiction types

prior to the boom. Once the boom occurred, mean accidents in mining jurisdictions clearly increased

relative to mean accidents in non-mining jurisdictions for every category but ‘all accidents.’ For truck

accidents, there is some suggestive evidence that the difference in mean accidents shrank after 2012

although this difference is not visually pronounced.

Figure 6 plots mean population and fiscal outcomes for two subsets of the mining category, which

are ordinance and non-ordinance townships. The ordinance category includes all townships that passed

an ordinance by 2015. Here we compress the plots to the 2009-2015 period, in order to focus visually on

boom period trends before and after ordinances were permitted by the court ruling. We see that, in

general, the pre-2012 trends were similar across the categories for all outcomes. There is also a

prominent spike in property values and tax revenue in 2013 in the ordinance towns. However, there is not

in general obvious visual evidence of convergence or divergence in outcomes post-2012.

Figure 7 plots mean accident counts for the same categories and time span. There is less support

for the common trends in ordinance and non-ordinance jurisdictions prior to 2012. The non-ordinance

townships had higher mean levels in all categories, and this difference tended to grow after 2012.

To summarize the figures just described, they provide graphical evidence to support the first

identifying assumption: that post-boom changes in outcomes in non-mining townships are good

counterfactuals for post-boom changes in mining townships. This is because outcomes in mining and

non-mining townships were on common trends prior to the boom. The figures provide mixed evidence to

support the second identifying assumption. Some, but not all, outcomes in mining townships were

following common trends in ordinance and non-ordinance townships. Because the graphical evidence is

21

inconclusive, particularly for the ordinance versus non-ordinance distinction, we turn next to econometric

assessments of the validity of this identifying assumption.

Tests for Validity of Identifying Assumptions

The key assumption under question is whether the mean township-level outcome reactions to a

mine opening not under ordinance is a good counterfactual for the mean township-level outcome reaction

to a mine opening under ordinance. To test for the potential validity of this assumption, here we exploit

the fact that no township could validly pass an ordinance prior to 2012. Hence, our test for assumption

validity is a test for whether or not outcomes reacted differently to mine openings in ordinance and non-

ordinance townships before 2012.

The econometric model is

1 2

3 4

2012

2011 2011

jt j t jt jt j

jt jt j jt

y Facilities pre Facilities Ordinance Town

post Facilities post Facilities Ordinance Town

(11)

Here yjt denotes the outcome variable in jurisdiction j and year t. The model includes jurisdiction ( j )

and year ( t ) fixed effects. The coefficient 1 measures the relationship between another sand mining

facility and the outcome of interest. This should be interpreted as the effect in a jurisdiction that never

obtains an ordinance. The effect is allowed to differ before and after 2011 so that the effect after 2011 for

non-ordinance townships is 1 + 3 . The key coefficient of interest is 2 . It measures any difference in

the pre-2012 response to a sand facility between ordinance and non-ordinance townships, before

ordinances could be passed. A coefficient estimate of 2̂ = 0 provides evidence in support of our

identifying assumption. The coefficient 4 measures any difference in response to facilities across

ordinance and non-ordinance townships once townships could (and did) pass ordinances. Hence, the

coefficients on 4 need not be zero and instead represent preliminary and crude tests of the impacts of

ordinances on outcomes.

Table 4 shows results for five outcomes: population, tax revenues, truck accidents on local roads,

22

all truck accidents, and property values. The population, tax, and property value variables are logged.

The truck accident variables are transformed by the inverse hyberbolic sine function (IHS). This

transformation is similar to a log transformation except the IHS function is defined at zero. This is

important because, in some township-year combinations there were zero accidents, particularly on only

local and county roads. We also note that, in the column 2 specifications that estimate tax revenues, we

lag the right-hand side variables one period to account for fiscal lags in timing between mining in a boom

town and tax receipts (see Cummings and Schulz 1978).

The key takeaway from table 4 is that 2̂ is not statistically different from zero in any

specification. That is, there is no evidence that outcomes in townships that later passed ordinances

responded differently to sand facility openings before ordinances were authorized for passage. This

finding adds empirical support for the identifying assumption that outcome responses to the opening of

facilities not under ordinance are valid counterfactuals for outcome responses to openings under

ordinance.

Econometric Analysis

We now turn to our main estimates of the effects of facilities and ordinances on outcomes. The

econometric model is

1 2 1

2

.

. 2011

2011 .

jt j t jt jt jt jt

jt jt jt jt

jt jt

y Facilities Facilities Ordinance Adj Facilities

Adj Facilities under Ordinance X Ordinance post Facilities

post Adj Facilities

(12)

The subscripts have the same interpretation as equation (11). There are two key differences between this

model and the model in equation (11). First, this model exploits variation in the timing of ordinance

passage to identify the within-jurisdiction effects of facility openings without ordinance governance ( 1 )

and with ordinance governance ( 1 + 2 ). Second, this model estimates the effects of adjacent facilities

on outcomes when those facilities are not governed by an ordinance ( 1 ) and when they are governed by

23

an ordinance (1 +

2 ).

Some specifications also control for township-level time varying covariates ( ) and possible

effects from passing an ordinance when no further facilities open after its passage ( ). Other

specifications also accommodate different effects for facility openings before and after 2011, as

represented by and . In all estimations we cluster standard errors at the township level and, in some

specifications, we more flexibly control for time effects by allowing year effects to differ for each of the

19 counties in the sample.

Table 5 presents results for population and tax revenues. Moving from column 1 to 4, we

sequentially add controls for the population regressions. In column 4, we control for county-specific year

effects. The specifications for the tax revenue estimates follow the same sequence moving from columns

5 to 8 but there are two differences. First, we lag the right-hand side variables one year to accommodate

delayed tax revenue response to new economic activity. Second, we control for population as covariate in

the tax revenue specifications to generate a more interesting structural relationship between sand mining

and tax revenue that controls for the population effects of the sand boom.

Turning to the results, there is a positive within-jurisdiction effect of sand facilities on population

and property tax revenues ( 1̂ >0). There is also a positive adjacent facility effect on both outcomes ( 1̂

>0). The estimates have log-level interpretations so the column (1) coefficient means that each additional

facility is associated with a 0.7 percent increase in population within township and a 0.3% increase across

townships. An additional facility is associated with a 4 percent increase in tax revenue within township,

controlling for population, and 1.1 percent increase across townships. Ordinance governance does not in

general mitigate the effects within township because the coefficient estimates on 2̂ are not statistically

distinct from zero. There is evidence, however, that ordinance governance reduces the population effect

on adjacent townships because the estimates on 2̂ are negative in 3 of 4 specifications.

Table 6 looks for evidence of negative, disamenity effects, by estimating the model for truck

24

accidents. In columns 1-4 the dependent variable is accidents on local and county roads and in columns

5-8 the dependent variable is truck accidents on all roads. The sequence of specifications mimics the

sequence in table 5. Here we control for population to focus on the more structural effect of sand

facilities on accidents, after controlling for the impact of facilities on population.

The results in table 6 indicate a strong positive relationship between within-township accidents

and increased sand facilities ( 1̂ >0). The results mean that, at a minimum, a facility opening is

associated with a 12.2 percent increase in truck accidents on county and local roads and a 9.4 percent

increase in truck accidents on all roads. This within-township effect is mitigated by ordinance

governance, as 2̂ <0 in all specifications meaning the within-township truck accident effects of another

facility is smaller when the facility is governed by an ordinance.

The adjacent-township effects of ordinance governance over sand facilities in table 6 are positive,

however. In 6 of 8 specifications we find that 2̂ is greater than zero and statistically significant. This is

evidence that ordinance governance induces spillover problems related to sand trucking congestion and

industrial truck accidents. The magnitudes strike us as economically important: an increase of one

adjacent sand mining facility under ordinance is associated with a 5.8 – 1.1 = 4.7 percent increase in truck

accidents in columns 5 and 6.

Table 7 shows estimates of the model when the outcome variable is the sum of township-level

equalized property values. Here there is evidence that more sand mining facilities increases within-

township property values ( 1̂ >0) by an amount ranging from 2.8 percent per facility to 5.2 percent. The

coefficients on 2̂ are all positive but none are statistically significant. Hence, there is no evidence that

ordinances governing within-township facilities have raised within-township property values unless

increases in property values proceeded the opening of a mine under ordinance. This is a perhaps

surprising result because the ordinances have succeeded in reducing disamenities related to trucking

accidents (table 6) without lowering population growth and tax revenues (table 5).

25

Table 7 also shows that the opening of sand facilities in adjacent townships has a positive effect

on property values ( 1̂ >0) unless those facilities are under ordinance governance. Based on the column 4

estimates, an additional facility in an adjacent township raises property values by 0.6 percent when the

facility is not under ab ordinance. When it is under an ordinance, the facility lowers property values by

0.6 – 0.9 = -0.3%. Performing the same calculation based on column 1 results, an additional facility in an

adjacent township raises property values by 1.3% when not under ordinance and lowers property values

by 1.3 – 2.3 = -1% when under ordinance.

How do we interpret these results? Consider that each township has an average of about six

‘queen’ neighboring townships once we account for unusual shapes of some jurisdictions. An opening of

mine under ordinance will lower property values by 0.3 to 1.0% in six adjacent townships and raise

within-township property values by 2.8 to 5.2%. This implies there are estimation scenarios in which the

effect of local regulation over a mine has a net negative effect, once one accounts for neighbor effects.

The net negative effects on property values in some estimation scenarios are plausibly caused by

local regulatory displacement of disamenities. Consider results from the trucking regressions of table 6.

In column 1, a sand facility under ordinance leads to a 9.9% decrease in the within-township effect of a

mine opening on trucking accidents on local roads but 5% increase in the mine impacts on accidents in

adjacent townships. If there are six adjacent townships that experience this effect, then the increased

accidents from the regulation in adjacent jurisdictions exceeds in aggregate the decreased accidents in the

target jurisdiction, at least on a percentage basis.

6. Preliminary Conclusions

Economic impact analyses indicate the US Fracking boom has boosted local economies in places

far removed from drilling sites, due to upstream and downstream linkages between oil and gas and related

industrial sectors. Our study of the frac sand industry highlights a specific example of this, and it also

draws attention to an issue not often discussed in commentary on the widespread benefits from the oil and

26

gas boom. Due to upstream and downstream linkages, the boom is also creating positive and negative

disturbances in locales far removed from shale deposits, in our case because of its demand for industrial

‘frac sand’ mining.

Specifically, Wisconsin communities at the epicenter of the local and mining boom are portrayed

as enjoying increased economic activity but also rising roadway congestion, rising accident risks, and

diminished local air quality along with noise and light pollution. We provide the first quantitative

evidence that some of the alleged impacts are real: jurisdictions with new sand mines have experienced

large proportional increases in accident rates on local roads, estimated at 9 to 12 percent per mine for

accidents involving large industrial trucks.

We also report preliminary evidence suggesting these external effects are exacerbated by the

presence of mining ordinances at the township level. We find that jurisdictions with lax regulation – as

measured by a lack of an ordinance – experience more accidents per sand mining facility but that these

transportation disamenities occur primarily within township hosting the sand mines. By contrast,

townships with stricter regulatory rules experience fewer within-township accidents per facility, but

traffic problems and accidents spillover to neighboring townships. These spillover effects are likely a

combination of unintentional and intentional side-effects of local regulator attempts to retain benefits

from hosting sand mining facilities while reducing the costs, to local constituents.

We attempt to estimate the net effects of the mining boom and of ordinances, by testing for the

effects of facility openings in the different governance regimes on township-level property values. Here

we find evidence that mining is a net benefit; an additional facility raises property values by 2.8 to 5.2%.

We fail to find evidence that ordinance passage has raised the per-mine effect on property values, within

township. But we do find evidence that ordinance passage has caused a negative per-mine effect on

property values in adjacent townships.

The finding that ordinance governance of mines in adjacent townships lowers property values

makes intuitive sense for two reasons. First, ordinances likely reduce the profitability of mining in

adjacent townships and hence reduce the positive economic spillover effects relative to unregulated

27

mines. Second, ordinances may re-direct property-value diminishing disamenities towards adjacent

jurisdictions as our evidence suggests. Hence, local regulation of mining is a double-whammy in terms of

net benefits for adjacent jurisdictions.

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U.S. Census Bureau A. “Annual Population Estimates for the United States: April 1, 2010 to December 1,

2014 .” http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk

(accessed October 10th, 2016)

U.S. Census Bureau B. “Intercensal Estimates of the Resident Population for Incorporated Places and

Minor Civil Divisions: April 1, 2000 to July 1, 2010.”

http://www.census.gov/popest/data/intercensal/cities/cities2010.html (accessed October 10th,

2016)

U.S. Geological Survey. “Industrial sand and gravel in Metals and minerals: U.S. Geological Survey

30

Minerals Yearbooks 2005-2014” accessed October 15, 2016

http://minerals.usgs.gov/minerals/pubs/usbmmyb.html andhttp://minerals.usgs.gov/minerals/pubs

/commodity/silica/.

Wisconsin Tax Payer Alliance (WISTAX A) . “2015-16 Municipal Levies and Rates.”

https://wistax.org/facts/municipalities (accessed October 12th, 2016)

Wisconsin Tax Payer Alliance B (WISTAX B). “Municipal Property Tax History.”

https://wistax.org/facts/municipalities (accessed October 12th, 2016)

Wisconsin Department of Revenue (WDOR). “Town, Village and City Taxes Bulletin 2014-2015.”

https://www.revenue.wi.gov/report/t.html (accessed October 16th, 2016)

Wisconsin Department of Natural Resources (WI DNR). 2015. “Locations of industrial sand

mines and processing plants in Wisconsin.”[internet] Available:

http://dnr.wi.gov/topic/Mines/ISMMap.html

Wisconsin Department of Natural Resources (WI DNR). 2001. “Groundwater Contamination

Susceptibility Model, Bedrock Depth.” [internet] Available: ftp://dnrftp01.wi.gov/geodata/gcsm/

Wisconsin Department of Administration (WI DOA). 2008. “Wisconsin local land use regulations

and comprehensive planning status report” [Internet]. Available:

http://cdm16119.contentdm.oclc.org/cdm/search/field/title/searchterm/regulations

Wu, J. and A.J. Plantinga. 2003. Open Space Policies and Urban Spatial Structure. Journal of

Environmental Economics and Management 46(2): 288-309.

Table 1: Sand Mining Facilities, Production, and Prices

Year Active

Facilities

Cumulative

Active Facilities

Count

WI Frac Sand

Annual

Production

(millions tons)

Active

Horizontal Oil

& Gas Wells

in U.S.

Avg US Frac

Sand Price

(usd/ton)

Brent Oil

Price

(usd/barrel)

2005 2 2 0.48 181 52.79 66.22

2006 2 4 0.69 285 52.08 76.60

2007 1 5 0.80 393 56.66 82.82

2008 2 7 1.21 553 54.42 106.71

2009 2 9 1.15 456 54.00 68.22

2010 4 13 1.89 822 49.16 86.51

2011 30 43 3.59 1074 57.75 117.25

2012 19 62 8.00 1151 64.72 115.23

2013 9 71 15.36 1102 65.25 110.42

2014 14 85 24.00 1275 60.20 99.31

2015 2 87 not available 744 50.00 52.32

Notes: Data on mine opening years were obtained by contacting townships and counties in our study area. Frac sand production and prices are

from the USGS Minerals Year Book (USGS). Active wells are derived from Baker Hughes Rig Count data.

Table 2: Count of Sand Mining Facilities

Mining Facilities Mining Facilities under Ordinance

Year Active Permitted-Not

Active

Total Active

Permitted –

Not Active -

Total

Panel A: Mine Level

2005 2 0 2 0 0 0

2006 4 0 4 0 0 0

2007 5 1 6 0 0 0

2008 7 1 8 0 0 0

2009 9 1 10 0 0 0

2010 13 1 14 0 0 0

2011 43 8 51 0 0 0

2012 62 15 75 12 2 14

2013 71 22 93 18 3 21

2014 85 24 109 25 5 30

2015 87 28 115 25 8 33

Panel B: Jurisdiction Level

2005 1 0 1 0 0 0

2006 3 0 3 0 0 0

2007 4 1 5 0 0 0

2008 6 1 7 0 0 0

2009 8 1 9 0 0 0

2010 11 1 12 0 0 0

2011 31 6 34 0 0 0

2012 40 11 47 6 2 8

2013 44 14 52 10 3 13

2014 48 15 56 12 4 15

2015 49 17 58 12 6 17

Notes: Not shown here are the x number of jurisdictions that have an ordinance but not mines. Township level

ordinance information was obtained by directly contacting townships.

Table 3: Summary of Township-Level Panel Data

Variable Definition Mean Sd Min Max

Sand Facilities # of sand facilities operating 0.0919 0.4901 0 7

Adj. Facilities # of sand facilities operating in adjacent towns 0.6659 1.6412 0 14

Ordinance =1 if town has passed ordinance, otherwise zero 0.0199 0.1397 0 1

Sand Facilities x Ordinance Interaction between variables defined above 0.0347 0.3429 0 7

Adj. Facilities under Ordinance # of adjacent facilities governed by ordinance 0.1176 0.7404 0 7

No Zoning = 1 if there was no zoning in 2006, otherwise zero

Population Population of jurisdiction 912.42 858.24 37 7710

Property Tax Revenue Aggregate revenue from property taxes, 2015 $s 177192 160616 0 1591693

Accident Count # of annual accidents 22.312 22.068 0 222

Truck Accidents # of accidents involving commercial trucks 5.6240 5.6987 0 49

Truck Acc. on Local Roads # of truck accidents on local & county roads 2.6958 2.7587 0 22

Non Truck Accidents # of accidents non involving trucks 17.356 0 183 17.356

Non Truck Acc. on Local Roads # of non-truck accidents on local & county 7.6133 0 66 7.6133

Note: There are n= 347 townships and t=11 years in the sample. Zoning data were obtained from a 2007 Wisconsin Department of Administration survey (WI DOA 2008). .

Population data come from the US Census Bureau annual population estimates for minor civil divisions (US Census Bureau A, B). Data on property tax revenue are collected

for sub county level jurisdictions by the Wisconsin Tax Payer Alliance, a private government research organization (WISTAX A, B). Data on aggregate property values come

from Wisconsin Tax Payer Alliance and Wisconsin Department of Revenue (WISTAX B, WDOR). We obtained vehicle accident data from Traffic Operations and Safety

(TOPS) Laboratory at the University of Wisconsin

Table 4: Tests of Assumption of no Pre-2012 Difference in Reaction to Facilities

Population Tax Rev Local Truck

Accidents

All Truck

Accidents

Property

Value

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

Sand Facilities ( 1̂ ) 0.008*** 0.030* 0.139*** 0.092** 0.037**

(0.004) (0.017) (0.043) (0.054) (0.015)

Facilities x Ordinance Town 0.007 0.001 0.084 0.109 0.007

x Pre 2012 Indicator ( 2̂ ) (0.005) (0.023) (0.096) (0.104) (0.019)

Facilities x Post 2011 Indicator 0.002 0.003 -0.030 -0.030 0.034***

( 3̂ ) (0.002) (0.010) (0.056) (0.052) (0.011)

Facilities x Ordinance Town -0.006*** 0.001 -0.137* -0.160* -0.003

x Post 2011 Indicator ( 4̂ ) (0.002) (0.016) (0.068) (0.097) (0.021)

Township FE (i =347) x x x x x

Year FE (t = 11) x x x x x

Observations 3817 3470 3817 3817 3817

Adj. R2 (within) 0.038 0.001 0.058 0.089 0.380

Notes: * p<0.1, ** p<0.05, *** p<0.01. Standard errors are in parentheses, and are clustered at the township

level. The dependent variables are logged; in the case of accidents, we perform and inverse hyperbolic sine

transformation rather than dropping zeroes that are undefined under a log transformation. The right-hand side

variables are lagged one period for the tax revenue specifications, to account for lags between economic activity

and tax returns. The Facility x Ordinance Town x Pre 2012 Indicator is an interaction between sand facilities,

and indicator for whether or not a township passed an ordinance after 2011, and an indicator for the all year

prior to 2012. The Facility x Ordinance Town x Post 2011 Indicator is an interaction between sand facilities, and

indicator for whether or not a township passed an ordinance after 2011, and an indicator for the all year after

2012.

Table 5: Fixed Effects Estimates of Facilities on Population and Tax Revenue

Log of Population Log of Property Tax Revenue

(1) (2) (3) (4) (5) (6) (7) (8)

Sand Facilities (1̂ ) 0.007*** 0.007*** 0.009** 0.007*** 0.040** 0.039** 0.045** 0.025

(0.004) (0.004) (0.003) (0.003) (0.018) (0.018) (0.019) (0.021)

Sand Facilities x Ordinance (2̂ ) -0.002 -0.006 -0.005 -0.001 -0.009 -0.012 -0.013 -0.006

(0.004) (0.004) (0.005) (0.005) (0.015) (0.016) (0.016) (0.017)

Adj. Facilities (1̂ ) 0.003*** 0.003*** 0.003*** -0.001 0.011* 0.011* 0.011* 0.003

(0.001) (0.001) (0.001) (0.001) (0.006) (0.006) (0.006) (0.005)

Adj. Facilities under Ordinance (2̂ ) -0.005*** -0.005*** -0.005*** -0.002 0.006 0.005 0.005 0.016**

(0.001) (0.001) (0.001) (0.001) (0.007) (0.007) (0.007) (0.008)

Covariate Controls

Log of Population x x x x

Ordinance x x x x x x

Post 2011 x Sand Facilities x x x x

Fixed Effects

Township FE (i =347) x x x x x x x x

Year FE (t = 11) x x x x x x

County-Year FE (c=18, t=11) x x

Observations 3817 3817 3817 3817 3817 3817 3817 3817

Adj. R2 (within) 0.043 0.044 0.044 0.284 0.043 0.044 0.044 0.284

Notes: * p<0.1, ** p<0.05, *** p<0.01. Standard errors are in parentheses, and are clustered at the township level. The right-hand side variables are lagged one period for the

tax revenue specifications, to account for lags between economic activity and tax returns. The coefficients on Ordinance, Population, and the Post 2011 x Sand Facilities

variables are statistically insignificant in all cases.

Table 6: Fixed Effects Estimates of Facilities on Accidents involving Trucks

Y = Truck Accidents on Local Roads (Inv. Hyb. Sine)

Y = Truck Accidents on All Roads (Inv. Hyb. Sine)

(1) (2) (3) (4) (5) (6) (7) (8)

Sand Facilities 0.141*** 0.143*** 0.178*** 0.122*** 0.094*** 0.095*** 0.133*** 0.102**

(0.030) (0.031) (0.042) (0.044) (0.032) (0.033) (0.050) (0.050)

Sand Facilities x Ordinance -0.099** -0.125** -0.115** -0.118** -0.097** -0.105** -0.094* -0.100*

(0.039) (0.050) (0.056) (0.059) (0.039) (0.050) (0.053) (0.057)

Adj. Facilities -0.004 -0.005 -0.004 -0.011 -0.011 -0.011 -0.010 -0.012

(0.012) (0.012) (0.013) (0.013) (0.014) (0.014) (0.014) (0.012)

Adj. Facilities under Ordinance 0.050* 0.048* 0.047* 0.022 0.058** 0.058** 0.057** 0.014

(0.027) (0.027) (0.027) (0.031) (0.028) (0.028) (0.028) (0.031)

Covariate Controls

Log Population x x x x x x x x

Ordinance x x x x x x

Post 2011 x Sand Facilities x x x x

Fixed Effects

Township FE (i =347) x x x x x x x x

Year FE (t = 11) x x x x x x

County-Year FE (c=18, t=11, n=198) x x

Observations 3817 3817 3817 3817 3817 3817 3817 3817

Adj. R2 (within) 0.061 0.061 0.061 0.089 0.091 0.091 0.090 0.151

Notes: * p<0.1, ** p<0.05, *** p<0.01. Standard errors are in parentheses, and are clustered at the township level. The dependent variables are transformed by the inverse

hyberbolic sine function. This transformation is similar to a log transformation, except the variable is defined at zero. The coefficients on Ordinance and the Post 2011 x Sand

Facilities variables are statistically insignificant in all cases. The coefficient on the log of population is negative and statistically significant in three of the eight specifications.

Table 7: Fixed Effects Estimates of Facilities on Property Values

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

Sand Facilities 0.052*** 0.052*** 0.033*** 0.028**

(0.019) (0.019) (0.011) (0.011)

Sand Facilities x Ordinance 0.017 0.013 0.008 0.020

(0.022) (0.025) (0.028) (0.028)

Adj. Facilities 0.013*** 0.013*** 0.013*** 0.006**

(0.003) (0.003) (0.003) (0.003)

Adj. Facilities under Ordinance -0.023*** -0.024*** -0.023*** -0.009*

(0.004) (0.004) (0.004) (0.005)

Covariate Controls

Log Population x x x x

Ordinance x x x

Post 2011 x Sand Facilities x x

Fixed Effects

Township FE (i =347) x x x x

Year FE (t = 11) x x x

County-Year FE (c=18, t=11) x

Observations 3817 3817 3817 3817

Adj. R2 (within) 0.403 0.403 0.405 0.637

Notes: * p<0.1, ** p<0.05, *** p<0.01. Standard errors are in parentheses, and are clustered at the township

level. The dependent variable is logged. The coefficients on the Ordinance are statistically insignificant in all

cases. The coefficients on Population are positive and statistically significant in 3 of 4 cases. The Post 2011 x

Sand Facilities variables are positive and statistically insignificant in two of four cases.

Table 8: Fixed Effects Estimates of Facilities in Zoned and Unzoned Townships

Trucks Accidents on

Local and County Roads

All Truck Accidents Property Values

Unzoned

Zoned

Unzoned

Zoned

Unzoned

Zoned

(1) (2) (3) (4) (5) (6)

Sand Facilities 0.168*** 0.133*** 0.166** 0.075** 0.116*** 0.032***

(0.062) (0.033) (0.066) (0.034) (0.029) (0.012)

Sand Facilities x Ordinance -0.118** -0.000 -0.151** 0.073 -0.043 0.036

(0.048) (0.120) (0.062) (0.125) (0.030) (0.027)

Adj. Facilities -0.023 0.003 -0.065** 0.005 0.011* 0.015***

(0.024) (0.014) (0.025) (0.014) (0.006) (0.003)

Adj. Facilities under Ordinance 0.040 0.070** 0.096** 0.065* -0.021*** -0.024***

(0.047) (0.033) (0.046) (0.034) (0.007) (0.005)

Controls and Fixed Effects

Log Population x x x x x x

Township FE (i =347) x x x x x x

Year FE (t = 11) x x x x x x

Observations 1529 2288 1529 2288 1529 2288

Adj. R2 (within) 0.043 0.071 0.082 0.101 0.467 0.410

Notes: * p<0.1, ** p<0.05, *** p<0.01. Standard errors are in parentheses, and are clustered at the township

level. The dependent variables are transformed by the inverse hyberbolic sine function in columns 1-4. This

transformation is similar to a log transformation, except the variable is defined at zero.

Table A1: Robustness of Table 6 Results

Y = Truck Accidents on Local Roads (Inv. Hyb. Sine) Y = Truck Accidents on All Roads (Inv. Hyb. Sine)

(1) (2) (3) (4) (5) (6) (7) (8)

A. Baseline

Sand Facilities 0.124*** 0.128*** 0.175*** 0.122*** 0.075** 0.077** 0.130** 0.102**

Sand Facilities x Ordinance -0.080** -0.118** -0.105** -0.122** -0.076** -0.097** -0.083 -0.104*

Adj. Facilities 0.012 0.010 0.011 -0.004 0.008 0.007 0.008 -0.008

B. Include Cities and Villages (N =5412)

Sand Facilities 0.108*** 0.110*** 0.155*** 0.142*** 0.068** 0.068** 0.119*** 0.109**

Sand Facilities x Ordinance -0.079** -0.101** -0.089* -0.099* -0.084** -0.088* -0.074 -0.093*

Adj. Facilities 0.013 0.013 0.014 0.009 0.008 0.008 0.008 0.008

C. Control for Facility Acres

Sand Facilities 0.131*** 0.140*** 0.186*** 0.127** 0.136*** 0.140*** 0.188*** 0.139**

Sand Facilities x Ordinance -0.079** -0.083** -0.070 -0.089* -0.085** -0.086** -0.073 -0.089*

Adj. Facilities 0.011 0.012 0.013 -0.002 0.008 0.009 0.009 -0.006

D. Add Permitted but Inactive Facilities

Sand Facilities 0.075*** 0.079*** 0.090** 0.080* 0.048** 0.050** 0.073** 0.057*

Sand Facilities x Ordinance -0.044 -0.044 -0.039 -0.056 -0.053* -0.054* -0.042 -0.051

Adj. Facilities 0.010 0.011 0.011 -0.003 0.007 0.008 0.008 -0.007

Notes: The sequence of controls and fixed effects follows that of table 5. Panel B includes the full sample of jurisdictions, townships, cities and villages. Panel C adds 347

township specific linear trends. Panel D controls for the acreage of each facility in operation. Panel E adds to the sand facilities variable the mines and processing facilities

that are permitted but not active, beginning in the year of the permit. Panel F includes only the townships that had zoning laws in place prior to the sand boom.

Table A2: Robustness of Table 7 Results

Y = Log of Equalized Property Values

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

A. Baseline

Sand Facilities 0.052*** 0.052*** 0.033*** 0.028**

Sand Facilities x Ordinance 0.017 0.013 0.008 0.020

Adj. Facilities 0.013*** 0.013*** 0.013*** 0.006**

Adj. Facilities under Ordinance -0.023*** -0.024*** -0.023*** -0.009*

B. All Jurisdictions (N = 5412)

Sand Facilities 0.049*** 0.050*** 0.035*** 0.032***

Sand Facilities x Ordinance 0.017 0.014 0.010 0.021

Adj. Facilities 0.014*** 0.014*** 0.014*** 0.009***

Adj. Facilities under Ordinance -0.025*** -0.025*** -0.025*** -0.015***

C. Control for Facility Acres

Sand Facilities

Sand Facilities x Ordinance

Adj. Facilities Need to create variable for Adj. Facility Acres

Adj. Facilities under Ordinance

D. Add Permitted but Inactive Facilities

Sand Facilities

Sand Facilities x Ordinance

Adj. Facilities

Adj. Facilities under Ordinance

Controls and Fixed Effects

Log Population x x x x

Ordinance x x x

Post 2011 x Sand Facilities x x

Township FE (i =347) x x x x

Year FE (t = 11) x x x

County-Year FE (c=18, t=11) x

Notes: * p<0.1, ** p<0.05, *** p<0.01. Standard errors are in parentheses, and are clustered at the jurisdiction

level. The sequence of controls and fixed effects follows that of table 5. Panel B includes the full sample of

jurisdictions, townships, cities and villages. Panel C controls for the acreage of each facility in operation. Panel

E adds to the sand facilities variable the mines and processing facilities that are permitted but not active,

beginning in the year of the permit. Panel F includes only the townships that had zoning laws in place prior to

the sand boom.

Figure 1: Producing and Potential Frac Sand Deposits in the US

Figure 2A: Active and Permitted Mines in Wisconsin with Distribution of Frac Sand Deposits

Figure 2B: Location of Frac Sand Mine Facilities, Sand Deposits and Rail Roads

Figure 3: Zoning Status, Ordinance Passage and Mine Facility Openings by Time Period

Note: the zoning indicates zoning status of townships as of 2006, prior to the sand boom.

Figure 4: Mean Populaton and Fiscal Outcomes in Mining and non-Mining Jurisdictions

Note: The first vertical bar is at 2009, the last year preceding the sand boom. The second vertical bar is at 2012, the year of the state Supreme Court ruling. The mining category includes all townships with at least one frac sand facility by 2015. The non-mining category includes all townships without a facility.

900

950

1000

2005 2010 2015

mining non-mining

A. Population

7000

085

000

1000

00

2005 2010 2015

mining non-mining

B. Equalized Property Values 000s70

8510

011

5

2005 2010 2015

mining non-mining

C. Equalized Property Values Per Capita 000s

190

210

230

2005 2010 2015

mining non-mining

D. Tax Revenues Per Capita

Figure 5: Mean Accident Counts in Mining and non-Mining Jurisdictions

Note: The first vertical bar is at 2009, the last year preceding the sand boom. The second vertical bar is at 2012, the year of the state Supreme Court ruling. The mining category includes all townships with at least one frac sand facility by 2015. The non-mining category includes all townships without a facility.

2426

2830

32

2005 2010 2015

mining non-mining

A. All Accidents

67

89

10

2005 2010 2015

mining non-mining

B. All Truck Accidents12

1314

1516

17

2005 2010 2015

mining non-mining

C. All Local & County Accidents

33.

54

4.5

55.

52005 2010 2015

mining non-mining

D. Truck Local & County Accident

Figure 6: Mean Populaton and Fiscal Outcomes in Mining Townships with and without Ordinances

Note: The vertical bar is at 2012, the year of the state Supreme Court ruling. Graphed here is the subset of townships with at least one mine by 2015. The ordinance category includes all of the mining townships with and ordinance by 2015

800

1000

1200

2009 2011 2013 2015

ordinance non-ordinance

A. Population

6000

075

000

9000

0

2009 2011 2013 2015

ordinance non-ordinance

B. Equalized Property Values 00075

8085

9095

2009 2011 2013 2015

ordinance non-ordinance

C. Equalized Property Values Per Capita 000

175

200

225

250

2009 2011 2013 2015

ordinance non-ordinance

D. Tax Revenues Per Capita

Figure 7: Mean Accident Counts in Mining Townships with and without Ordinances

Note: The vertical bar is at 2012, the year of the state Supreme Court ruling. Graphed here is the subset of townships with at least one mine by 2015. The ordinance category includes all of the mining townships with and ordinance by 2015

2025

3035

2009 2011 2013 2015

ordinance non-ordinance

A. All Accidents

78

910

2009 2011 2013 2015

ordinance non-ordinance

B. All Truck Accidents10

1214

1618

20

2009 2011 2013 2015

ordinance non-ordinance

C. All Local & County Accidents

34

56

2009 2011 2013 2015

ordinance non-ordinance

D. Truck Local & County Accident