How long will they stay?

The Bakken and migrants’ expected duration of residence in affected areas

Jack DeWaard1

1 Department of Sociology, Minnesota Population Center, University of Minnesota. 909 Social

Sciences, 267 19th Ave. South, Minneapolis, MN, 55455, USA. E: [email protected], P: (612)

624-9522, F: (612) 624-7020.

ABSTRACT

In places that have experienced relatively recent and unprecedented migration, a common concern is whether and for how long migrants will remain in the affected areas(s). These cases, however, also present challenges to estimating migrants’ duration of residence. In this paper, with the idea that the present is a reasonable starting point for understanding the future, I use county-to-county migration data and multiregional life tables to develop period estimates of migrants’ expected duration of residence in western and eastern Montana during the recent Bakken oil boom in the late 2000s. To assess change over time, I compare these to similar estimates for the late 1990s. While my concern is with migrants’ expected duration of residence in the Bakken, the insights and methods can be applied to any case that has experienced similar changes that make it difficult to determine how long migrants will reside in the affected area(s).

KEYWORDS

Bakken; Oil; Migration; Duration of Residence; Multiregional; Multistate INTRODUCTION

Although the situation has begun to change in light of the falling price of oil in the past year or so (Grunewald and Batbold 2015; Healy 2016; Shaffer 2016), the recent oil “boom” in western North Dakota and eastern Montana—hereafter, the Bakken—that began in the mid- to late-2000s was accompanied by unprecedented migration to the region (Brown 2013). This change prompted concerns on the part of state and local planners and policy makers

(Bohnenkamp et al. 2011). On one hand, there emerged a need for new and/or affordable housing, infrastructure, and services in Bakken communities like Williston, ND, and Sidney, MT

(Bangsund and Hodur 2013). On the other hand, despite the estimated seven billion barrels of recoverable oil that will take several decades to extract given current production levels (Demas

2013), there were reservations about making such sizeable investments given no guarantees that migrants would set down long-term roots in the region. Planners and policy makers therefore had and continue to have a strong interest in migrants’ “length of time of residence” in the Bakken

(De Laporte 2015:22; Wernette 2015).

Cases like the Bakken that are characterized by very recent and unprecedented migration make it difficult to estimate migrants’ duration of residence. Given the recency of migration, retrospective estimates are of little use. Likewise, prospective estimates require intimate knowledge of, and likely a host of assumptions about, the drivers of migration and of retention, including whether and how these will change over time. Accordingly, in this paper, I take a different and quintessentially demographic approach.

Guided by the idea that the present is a reasonable starting point for understanding the future, I use county-to-county migration flow data from the 2006-2010 American Community

Survey and multiregional life tables to develop period estimates of migrants’ expected duration of residence in the Bakken and in each Bakken county. A type of conditional life expectancy, these estimates summarize the average number of years that migrants could be expected to live in the Bakken and in each Bakken county during their lives based on prevailing age patterns of county-to-county migration and mortality observed in the late 2000s. To assess change over time, I compare these estimates to similar estimates for the late 1990s, constructed using county- to-county migration flow data from the 2000 decennial census. While the focus of this paper is with migrants’ expected duration of residence in the Bakken, the insights and methods can be applied to others cases that have experienced similar changes that make it difficult to determine how long migrants will reside in the affected area(s).

BACKGROUND

Migration to the Bakken

Due to the rising price of oil in the early 2000s and controversial extraction techniques, the United States is currently the top oil producing country in the world, producing upwards of

10 million barrels of oil per day (U.S Energy Information Administration 2015). While Texas and the Federal Offshore Gulf of Mexico account for the majority of oil production in the United

States, since 2005, North Dakota has accounted for largest growth in oil production, going from about 90,000 barrels per day to about 1.2 million barrels per day by the end of 2015, an increase of nearly 1,200%. Presently, North Dakota ranks second behind Texas as the top oil producing state in the country (Brown 2013; Moore 2011; Shaffer 2014).

As I show in Figure 1, North Dakota’s black is a highly localized phenomenon that actually extends beyond the state’s borders. The so-called Bakken oil patch is located in the Williston Basin in a 12-county area in western North Dakota and eastern Montana,1 as well as in the southern parts of the Canadian provinces of Saskatchewan and Manitoba. The Bakken oil patch is estimated to hold upwards of 7 billion barrels of recoverable oil given current extraction technologies (Demas 2013), most especially horizontal drilling and , also known as hydrofracking or, simply, fracking. Fracking is a highly controversial practice that involves drilling, first vertically and then horizontally, into rock formations—in the case of the

Bakken oil patch, into shale formations—and injecting a pressurized liquid solution that fractures

(i.e., creates fissures in) the rock, releasing both oil and natural gas. Fracking requires large amounts of water, which is contaminated in the process and must be safely disposed of. It also results in a number of other potential externalities, including groundwater contamination, earthquakes, and, in the absence of the ability to capture the release of natural gas, flaring and associated threats to air quality. Presently, flaring is so prevalent in the Bakken that, despite being one of the least populated areas in the United States, the region can clearly be distinguished in nighttime satellite imagery.

---FIGURE 1 ABOUT HERE---

As I show in Figure 2, Panel A, despite the falling price of oil in the past year or so

(Grunewald and Batbold 2015; Healy 2016; Shaffer 2016), oil production in the Bakken has increased steadily since the mid-2000s (Grunewald 2016; Dalrymple 2016b). This is due, in part, to greater efficiencies in oil well design and drilling. These production gains have, in turn, fueled increases in wages and employment in the Bakken (Vachon 2015). As I show in Panel B in

Figure 2, the average weekly wage in the Bakken increased from $584 in the first quarter of 2004 to $1,400 in the first quarter of 2015, an increase of nearly 140%. In contrast, corresponding increases for the rest of North Dakota and Montana were 30% and 16%, respectively. Likewise, as I show in Panel C in Figure 2, with the exception of increasing at the tail end of the Great

Recession, the unemployment rate in the Bakken fell by 55% between the first quarters of 2004 and 2015. Corresponding decreases for the Rest of North Dakota and Montana were 16% and

1%, respectively.

---FIGURE 2 ABOUT HERE---

Prevailing economic theory posits a strong positive association between, on the one hand, wages and employment and, on the other hand, migration (Bodvarsson and Van den Berg 2013).

At the individual level, persons are theorized to compare the utility of remaining in their current place of residence—with expected earnings and the likelihood of employment over some time horizon figuring centrally into this function—to the utility of migrating, with this difference discounted by the economic and psychic costs that would be incurred by migrating (Sjaastad

1962; Todaro 1976). If this balance favors the destination, persons choose to migrate. At the macro level, this translates into larger migration flows in the presence of more pronounced wage and employment gaps favoring receiving (versus sending) areas (Greenwood 1997). As is evident in Panel D in Figure 2, estimates of net-migration in the Bakken are consistent with this intuition. Between 2003 and 2004, net-migration in the Bakken was negative, and stood at -2.2 persons per thousand. Net-migration turned positive between 2006 and 2007, and, after falling slightly during the , increased dramatically to +64.1 persons per thousand between 2011 and 2012. Net-migration has since fallen to +53.9 persons per thousand, with corresponding estimates for the rest of North Dakota and Montana at +5.7 and +4.9 persons per thousand, respectively.

How Long Will They Stay? Speaking at the recent Bakken Researchers Convening, held in Dickinson, ND, in the summer of 2015, the now former Mayor of Dickinson, Dennis Johnson, remarked that “there’s a large transitory labor force, [and] you have increased criminal activity and increased social issues” in places throughout the Bakken region (Wernette 2015; see also De Laporte 2015). This statement is telling for several reasons.

First, consistent with previous theoretical, empirical, and policy research, it clearly connects migrants’ duration of residence to a range of issues under the banners of, for example, social and community cohesion, integration and assimilation, and quality of life (Huddleston et al. 2011; Sampson and Groves 1989). As Alexander (2005:653) noted some time ago in his study of return migration to the U.S. South after the Great Migration out of the region in the early twentieth century, “[a] highly transient migrant stream can inhibit the development of migrant community…[and] draw at least some sort of antipathy from both long-term settlers and other local residents alike.” Pronounced population mobility, or churning, is thought to disrupt existing relations and arrangements that govern, for example, shared expectations, trust, and reciprocity

(Kornhauser 1978). Likewise, processes of integration and assimilation, which are inherently temporal phenomena (Hirschman 2001; Kasarda and Janowitz 1974), are undermined by short

(versus long) durations of stay (Geddes et al. 2005).

Second, and relatedly, Bakken residents are concerned about the potentially negative consequences of a highly transient migrant stream to and within the region. Given that many of these migrants are single men (Angel 2014; Krogstad 2014), there are growing concerns about prostitution, sexually transmitted infections, human trafficking, and sexual violence (Dodds

2014; Westwood and Orenstein 2015). Rates of sexual assault in Bakken communities like

Williston and Watford City, ND, have spiked since the early 2000s, as have rates of other types of crime (e.g., aggravated assault, theft, robbery, and burglary) (Archbold 2015; Ruddell et al.

2014). Some of these increases are potentially tied to the growing illegal drug trade, and includes an increasing role played by drug cartels (Horwitz 2014; Valencia 2014). As a consequence of these changes, police and social services are operating well above capacity and are consistently overwhelmed (Archbold et al. 2014; Weber et al. 2014). Lastly, all of the above has yet to reference another important change, which is increased mortality from sources that include workplace accidents, suicides, and homicides (Archbold 2015; Golan 2015).

Third, there is a lot on the line for state and local planners and policy makers who must balance emerging needs (e.g., for new and/or affordable housing, infrastructure, and services), limited resources, and anxious constituencies in the face of a burgeoning population (Bangsund and Hodur 2013). In places where decisions to move forward have been made and resources committed, it is undoubtedly a risky gamble that cities like Dickinson and Watford City, ND, for example, have made considerable investments in the construction of new single family homes, primary and secondary schools, community recreational facilities, and other amenities

(Dalrymple 2016a). Not only do these investments assume that migrants to the region are tied to partners/spouses and children, perhaps more importantly, they assume that these individuals and households will set down long-term roots in the region.

Presently, however, little is known about migrants’ “length of time of residence” in the

Bakken (De Laporte 2015:22). Cases like the Bakken that have experienced very recent and unprecedented migration present a number of challenges to conventional approaches to the estimation of migrants’ duration of residence. Retrospective approaches using survey questions and/or migration life histories are problematic given the recency of migration to the region. After all, if these data existed (which they do not), they would likely show that most migrants to the Bakken have lived about 5-10 years in the region, consistent with the net-migration reversal from negative to positive since mid-2000s displayed earlier in Panel D in Figure 1. Prospective estimates likewise require detailed information on the drivers of migration and of retention, including whether and how these will change over time. In the case of the Bakken, this is problematic because some of these potential drivers (e.g., the price of oil) are fairly volatile, especially as of recent (Grunewald and Batbold 2015; Healy 2016; Shaffer 2016; Vachon 2015).

It is also difficult to predict future economic shocks (e.g., recessions) and related changes that would likely play a role in how long migrants reside in the affected area(s).

Given these problems, as a place to start, I take a quintessentially demographic approach.

Following previous research in which total life expectancy is partitioned into any number of qualitative life states (happiness states, health and disability states, etc.) in order to estimate the average total life lived in each state over the life course or some segment of the life course

(Crimmins and Saito 2001; Crimmins et al. 1997; Yang 2008; Yang and Waliji 2010), I develop estimates of migrants’ “expected” duration of residence in the Bakken and in each Bakken county (Palloni 2001:265). In my case, qualitative life states take the form of the Bakken region and counties therein. My estimates of migrants’ expected duration of residence are a type of conditional expectation of life that summarize the average number of years that migrants could be expected to live in the Bakken and in each Bakken county over the course of their lives based on prevailing age patterns of county-to-county migration and mortality observed in the late

2000s. I subsequently compare these estimates to similar estimates for the late 1990s in order to examine change over time.

APPROACH

Quantities and Models

Standard multiregional life tables (Rogers 1995; Schoen 1988) provide a useful point of entry for estimating migrants’ expected duration of residence in the Bakken and in each Bakken county. These quantities can be derived from the following, more general conditional expectations of life at age x.

(1)

(2)

The first quantity, , summarizes the average number of years that persons living outside of the Bakken region ( ) at exact age x could expect to live in the Bakken region ( ) beyond age x. This quantity is calculated as the ratio of total person-years lived in the Bakken beyond age x by those living outside of the region at exact age x, , to the set of all persons living outside of the Bakken at exact age x, . The second quantity, , is similar to the first and summarizes the average number of years the persons living outside of the Bakken region at exact age x could expect to live in Bakken county ( ) beyond age x. Across all Bakken counties

( , the quantities in (2) are additive and sum to the quantity in (1).

(3)

The quantities in (1) and (2) are estimated from a standard multiregional model, a diagram of which is shown in Figure 3 for four hypothetical counties—two non-Bakken counties, and

, and two Bakken counties, and —for a single age interval. First, one envisions a cohort—real or synthetic, a point to which I will return in the next subsection—that is living outside of the Bakken region (i.e., in non-Bakken county or ) at exact age x. Between the ages of x and x+n, members of this cohort can remain in their non-Bakken county of residence, migrate to another county that is located outside of or in the Bakken region, or die according to a set of age-specific probabilities of county-to-county migration and death, calculated from my data (discussed below). This process is then repeated at each subsequent age until the cohort has died out. Total person-years lived each Bakken county (i.e., in and ) are calculated by adding up the number of person-years lived in each county in each age interval, with person-years lived in each age interval usually calculated by averaging the number of persons at the beginning and end of the interval (Palloni 2001). Dividing this quantity through by the initial size of the cohort yields an estimate of the expected duration of residence each Bakken county among persons who lived outside of the Bakken region (i.e., non-Bakken residents) at exact age x. Summing these quantities across all Bakken counties gives the corresponding estimate for the Bakken region as a whole.

---FIGURE 3 ABOUT HERE---

To estimate migrants’ (versus non-Bakken residents’) expected duration of residence in the

Bakken and in each Bakken county, following DeWaard (2013), I restrict the denominator in (1) and (2) to persons who live outside of the Bakken region at exact age x and migrate to the region at least once beyond age x, . Migrants’ expected duration of residence in the Bakken region and in each Bakken county can therefore be written as follows:

(4)

(5) where an asterix (*) is used to distinguish the quantities in (4) and (5) from the respective and more general quantities in (1) and (2). The quantities in (4) and (5) summarize the average number of years that migrants could expect to live in the Bakken region and in each Bakken county, respectively, beyond age x.

As I show in Figure 4, calculating these quantities requires modifying the demographic accounting shown earlier in Figure 3 in order to isolate, and thereby count, those who migrate to the Bakken region. In Figure 4, as persons living outside of the Bakken region at exact age x migrate to the Bakken, they are absorbed (see the bolded transitions in Figure 4) into a parallel system of transitions among counties and to death. While total-person years lived in the Bakken region in (4) and in each Bakken county in (5) are calculated as before, the denominator in (4) and (5) is calculated by adding up persons on the right-hand side of the transition diagram in

Figure 4 who have ever migrated to the Bakken region by the end of the closing age in the life table. As before, the quantities in (5) are additive, such that:

(6)

---FIGURE 4 ABOUT HERE---

The quantities in (4) and (5) are the primary quantities of interest in this paper. Given my data (discussed below), I develop period (versus cohort) estimates of migrants’ expected duration of residence in the Bakken and in each Bakken county. As with any period life expectancy or quantity derived from it, my estimates can be viewed “as rendering a ‘snapshot’ of current

[demographic] experience and showing the long-range implications of a set of age-specific

[migration and mortality] rates” (Arias 2014:1). In other words, my estimates summarize what would happen to a cohort and, ultimately, in a population if a set of age-specific migration and mortality conditions (discussed below) persisted into the indefinite future. Given this assumption of stationarity, my approach is therefore to view the present as a reasonable starting point for understanding the future. Of course, as I discuss below, in order to examine change over time, I compare my estimates of migrants’ expected duration of residence in the Bakken and in each

Bakken county for late 2000s to similar estimates for the late 1990s.

Data

The primary data used in this paper are county-to-county migration flow data, disaggregated by age, taken from the 2006-2010 American Community Survey (ACS).2 These data provide the necessary age breakdowns of county-to-county migration in the form of age- specific counts of persons who migrated from county i to county j over a one-year period prior to the survey. Age-specific counts of non-migrants are also provided. With these counts and additional information on age-specific mortality taken from U.S. life tables from the Centers for

Disease Control and Prevention, one can calculate age-specific probabilities of county-to-county migration and death, a key input into the multiregional models discussed in the previous subsection.

In contrast to the hypothetical and highly simplified state space shown earlier in Figures 3 and 4, the actual state space in this paper consists of 3,134 U.S. counties and county equivalents

(I discuss this number further below), plus death, for a total of 9,828,225 transitions at each age.3

Unfortunately, given no information on international out-migration in the ACS data, the state space is closed to international migration. In cases where a given county-to-county migration flow is two or less persons, the ACS withholds identifying information for the sending (but not the receiving) county, with these flows allocated to a residual count for the sending state to the receiving county in question. In an attempt to utilize this information, I allocated these flows to sending counties in the sending state that were not already represented as valid sending counties to the receiving county in question. These allocations were made on the basis of the relative population size of the sending county given that population size is a strong determinant of the size of migration flows (Bodvarsson and Van den Berg 2013; Greenwood 1997).

In order to provide a point of comparison for my estimates of migrants’ expected duration of residence in the Bakken and in each Bakken county in the late 2000s, constructed using the

ACS data described above, I construct a similar set of estimates for the late 1990s using county- to-county migration flow data from the 2000 decennial census. In doing so, it was necessary to reconcile several issues of comparability between these two data sources. First, I combined counties and county equivalents that experienced a boundary change between the late 1990s and the late 2000s.4 For example, despite the fact that the Skagway-Hoonah-Angoon Census Area in

Alaska was split in 2007 to create the Skagway Municipality and the Hoonah-Angoon Census

Area, I treat these as a single geographic unit in my analysis. Second, in order to reconcile discrepancies in the age range and age intervals between the ACS and decennial census data, my analysis is restricted to ages five and older. All but two of the age intervals are five years.

Exceptions include the first age interval (5-19) and the last age interval (75+).

Finally, the ACS and decennial census do not use the same migration window. The ACS data provide age-specific counts of the number migrants from county i to county j over a one-year period prior to the survey. The decennial census data provide similar counts over a five-year period prior to the census. To adjust for this difference and bring the migration window in the decennial census in line with that used in the ACS, after calculating age-specific probabilities of county-to-county migration using the decennial census data, I utilize the following transformation to convert these five-year probabilities into one-year probabilities. (7) where is the age-specific probability of migrating from county i to county j over five years, calculated using the decennial census data, and is the corresponding probability of migrating over one-year that is implied from the life table survivor function (see DeWaard and

Raymer 2012:544). With this transformation, the latter probability is always less than the former, excluding cases where the former is equal to zero or one, in which case the latter is equal to the former.

RESULTS

Non-Bakken Residents’ Expected Duration of Residence in the Bakken

To follow the organization in the previous section, in Figure 5, I display estimates of non-

Bakken residents’ expected duration of residence in the Bakken ( ) and in each Bakken county ( ). Based on prevailing age patterns of county-to-county migration and mortality observed in the late 2000s, persons living outside of the Bakken region could expect to live an average 0.0039 years—or just 1.4 days—in the Bakken over the course of their lives. Of this time, the majority (36.3%) could be expected to be lived in Stark County, followed by Williams

County (21.6%), Richland County (12.8%), Mountrail County (10.5%), and McKenzie County

(8.4%). Interestingly, only about 2.0% of this time could be expected to be lived in Dunn

County, which, along with the five counties above, the Federal Reserve Bank of Minneapolis considers a “core oil economic activity” county.5

---FIGURE 5 ABOUT HERE--- As I discussed in the previous section, the estimates in Figure 5 are very low because most persons living outside of the Bakken ( ) do not migrate to the region. Accordingly, the estimates in Figure 5 help to further motivate the need for the adjustments detailed earlier in (4) and (5) using the revised demographic accounting shown in Figure 4. However, before turning to migrants’ (versus non-Bakken residents’) expected duration of residence in the Bakken and in each Bakken county, it is worth connecting the estimates in Figure 5 to the age patterns that produced them. To see this, in Figure 6, I display age patterns of in- and out-migration to and from Stark and Williams Counties, respectively. Clearly, the reason that non-Bakken residents’ expected duration of residence is higher in Stark County than in Williams County (see Figure 5) is that the risk of in-migration in the former is higher than in the latter.6 What is more is that, relative to Williams County, the risk of in-migration to Stark County is particularly high at younger (versus older) ages. Like any measure of life expectancy or quantity derived from it, the estimates displayed in Figure 5 are more sensitive to conditions at younger (versus older) ages because younger persons simply have more years ahead of them to live than do older persons.

The end result is that non-Bakken residents’ expected duration of residence in Stark County

(0.0014 years) is nearly double that for Williams County (0.0008 years).

---FIGURE 6 ABOUT HERE---

To conclude this subsection, in Figure 7, I display estimates of the change in non-Bakken residents’ expected duration of residence in the Bakken and in each Bakken county between the late 1990s and the late 2000s. Overall, non-Bakken residents’ expected duration of residence in the Bakken region increased by 0.0014 years, or about one-half of one day. Despite the very small magnitude of this change, it the general take-away is that non-Bakken residents could expect to live a greater portion of their lives in the Bakken in the late 2000s. In other words, they could expect to live more (versus less) time in the region. Of course, this change was neither evenly distributed nor positive across all counties. Of the 12 counties that comprise the Bakken region, only six—Stark, Williams, Mountrail, Richland, McKenzie, and Burke—posted increases in non-Bakken residents’ expected duration of residence. With the exception of Burke County— and Dunn County, which experienced a decline in non-Bakken residents’ expected duration of residence—the majority of additional time that non-Bakken residents could expect to live in the region was concentrated in five core oil economic activity counties.

---FIGURE 7 ABOUT HERE---

Migrants’ Expected Duration of Residence in the Bakken

Transitioning to migrants’ (versus non-Bakken residents’) expected duration of residence in the Bakken ( ) and in each Bakken county ( ), I display these estimates for the late 2000s in Figure 8. On average, migrants could expect to live about 36.0 years in the Bakken over the course of their lives. Of this time, 13.1 years could be expected to be lived in Stark

County, followed by in Williams County (7.8 years), Richland County (4.6 years), Mountrail

County (3.8 years), and McKenzie County (3.0 years). With respect to the ordering of counties in

Figure 8, it is important to remember that the only difference between these estimates and the estimates presented earlier in Figure 5 is the denominator ( in Figure 8 versus in Figure

5). Consequently, the ordering of counties is the same across the two figures. What differ are the magnitudes of the estimates and their interpretations. The estimates displayed earlier in Figure 5 are akin to asking how long a given person could expect to live in the Bakken and in each

Bakken county conditional on residing outside of the Bakken at exact age five. The estimates displayed in Figure 8 further condition on migrating to the Bakken at least once over the life course (i.e., beyond age five). In this way, the risk set shrinks from to . Consequently, migrants’ expected duration of residence in the Bakken and in each Bakken county is higher than the corresponding estimate for non-Bakken residents’.

---FIGURE 8 ABOUT HERE---

In Figure 9, I display estimates of the change in migrants’ expected duration of residence in the Bakken and in each Bakken county between the late 1990s and the late 2000s. Migrants’ expected duration of residence in the Bakken increased by 4.2 years; however, as is evident, positive changes were recorded in only five Bakken counties. These include Stark County (2.6 years), Williams County (2.0 years), Mountrail County (1.6 years), McKenzie County (1.4 years), and Richland County (1.0 year), each of which is a core oil economic activity county. The remaining seven counties in the Bakken region experienced negative changes in migrants’ expected duration of residence ranging from 0.1 years in Burke County to 1.7 years in Roosevelt

County.

---FIGURE 9 ABOUT HERE---

The estimates displayed in Figure 9 do not support the idea that migrants to the Bakken have become increasingly transitory between the late 1990s and late 2000s. On average, based on prevailing age patterns of migration and mortality in each period, migrants could expect to live more (versus less) time in the Bakken region as a whole. These positive changes were concentrated in five (out of six) core oil economic activity counties, including in Stark County despite statements to the contrary (Wernette 2015). However, the estimates displayed in Figure 9 do support the idea that migrants have become increasingly transitory in counties outside of the above five core oil economic activity counties. Beyond the mechanics of multiregional life tables, one substantive reason for the differentially positive and negative changes in Figure 9 may be that most migrants to the region choose to live in counties with, for example, sufficient density and/or availability of housing, infrastructure, services, and other amenities. Naturally, this would include relatively more (versus less) populated cities and towns, such as Dickinson,

ND, in Stark County, Williston, ND, in Williams County, and Sidney, MT, in Richland County.

And, while not exactly major population centers by any means, Stanley, ND, in Mountrail

County and Watford City, ND, in McKenzie County have emerged as important centers given the dearth of alternatives in these areas (Dalrymple 2016a).

DISCUSSION

Motivated by the fact that it is difficult to estimate migrants’ “length of time of residence” in places that have experienced relatively recent and unprecedented migration (De

Laporte 2015:22), I developed the first period estimates of migrants’ expected duration of residence in western North Dakota and eastern Montana during the recent Bakken oil boom that began in the mid- to late-2000s. Based on age patterns of county-to-county migration and mortality observed in the late 2000s, I showed that migrants could be expected to live about 36.0 years in the Bakken region over the course of their lives, with the majority of this time concentrated in five core oil economic activity counties. The same estimate, based on age patterns of migration and mortality observed in the late 1990s, was 31.8 years. On average, migrants to the Bakken region have therefore become less transitory over time; however, this conclusion does not hold for each individual county within the region. In the majority of counties within the Bakken region, migrants have become more transitory over time. Only in five core oil economic activity counties have migrants become less transitory over time. For reasons discussed earlier, cases like the Bakken present challenges to the estimation of migrants’ duration of residence in retrospective and/or prospective fashion. Accordingly, guided by the idea that the present is a reasonable starting point for understanding the future

(Arias 2013), I developed estimates of migrants’ “expected” duration of residence (Palloni

2001:265), and, in the process, provided a convenient way to simultaneously summarize millions of unique age patterns of county-to-county migration and mortality. However, as with any period life expectancy or quantity derived from it, the underlying assumption of stationarity is one of the key limitations of this paper. Clearly, age patterns of migration and mortality change over time (Winkler et al. 2013). This is likely to be especially true in the case of the Bakken given recent declines in the price of oil (Grunewald and Batbold 2015; Healy 2016; Shaffer 2016) and, to lesser extent, oil production (Grunewald 2016; Dalrymple 2016b). My estimates of migrants’ expected duration of residence must therefore be understood for what they are, namely summaries of what would happen to a cohort and, ultimately, in the implied stationary population if a set of age-specific migration and mortality conditions persisted into the indefinite future. The above said, while this is a restrictive assumption, given my data, it was ultimately a necessary one.

Another limitation with this paper concerns the ACS county-to-county migration data.7

Despite the fact that these are the only available data that provide the necessary age breakdowns of county-to-county migration among all counties in the United States in the late 2000s, these data are problematic because they are classified only by previous/next county of residence. Prior research shows that other characteristics like place of birth are also important to consider when estimating probabilities of county-to-county migration (Rogers 1995). Unfortunately, however, the ACS data are not further cross-classified by these sorts of characteristics. The ACS data are also limited by the fact that, for each county-to-county flow, there is inherent uncertainty in the point estimate of migration given that the ACS is a rolling sample. Finally, the ACS county-to- county migration data, disaggregated by age, presently stop in 2010, well before the price of oil and net-migration began to fall (see Figure 2).

The above said, my estimates of migrants’ expected duration of residence in the Bakken and in each Bakken county break new ground and provide a useful starting point for quantifying the “transitory” nature of migration to and within the region (Wernette 2015). This is an important need that has been explicitly identified by researchers, planners, and policy makers working in this area (De Laporte 2015). Migrants’ expected duration of residence is one part of a complicated mosaic of information that state and local planners and policy makers need at their disposal if they are to make informed decisions about short- and long-run investment priorities.

With this in mind, toward building on the work in this paper, future research might pursue one or more of the following agenda items. First, toward thinking prospectively about migrants’ expected duration of residence in the Bakken and in each Bakken county, researchers might, as an initial next step, consider developing plausible assumptions about the future course of the age-specific migration schedules observed in the ACS data. As an exercise in comparative statics, this would permit developing one or more scenarios in which migrants’ expected duration of residence changes over time. Second, the measure of migrants’ expected duration of residence in the Bakken and in each Bakken county might be incorporated into subsequent analyses in order to examine associations with other outcomes (e.g., county-level crime rates). Finally, the insights and methods of this paper can be applied to study similar cases (e.g., other natural resources booms) within and outside of the United States.

ENDNOTES

1. This is the definition that is used by the Federal Reserve Bank of Minneapolis, which serves

North Dakota, Montana, and four additional states in the Ninth Federal Reserve District.

2. These data are publicly available online at

http://www.census.gov/hhes/migration/data/acs/county-to-county.html.

3. 9,828,225 = (3,134 sending counties + death) * (3,134 receiving counties + death).

4. See https://www.census.gov/geo/reference/county-changes.html.

5. See https://www.minneapolisfed.org/publications/special-studies/bakken/oil-production.

6. In a multiregional model, there are only risks of out-migration, not in-migration. As I note at

the bottom of Figure 6, mean probabilities of in-migration to Stark and Williams Counties

are, in fact, mean probabilities of out-migration from all other U.S. counties to Stark and

Williams Counties.

7. Some, but not all, of these are also issues with the county-to-county migration flow data from

the 2000 decennial census.

ACKNOWLEDGEMENTS

This research is supported by center grant #R24 HD041023 awarded to the Minnesota

Population Center at the University of Minnesota by the Eunice Kennedy Shriver National

Institute of Child Health and Human Development. The author is grateful for the invitation and funding provided by the Federal Reserve Bank of Minneapolis and the Strom Center at

Dickinson State University to attend the Bakken Researcher’s Convening on May 18-19, 2015, in Dickinson, ND. The author has benefitted from numerous conversations with Bruce Braun, and wishes to thank Rob Grunewald, RayAnn Kilen, and Kathryn Albrecht for early assistance in securing and preparing some of the data. REFERENCES

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in the U.S. Population Research and Policy Review, 29, 775-795. Figure 1. The Bakken Region and Counties

Panel A. Bakken Region in eastern Montana and western North Dakota

Panel B. Bakken Counties and County Names

Figure 2. Oil, Wages, Unemployment, and Net-Migration in the Bakken, the Rest of North Dakota, and the Rest of Montana since 2004

Sources: Data in Panels A-C provided by the Federal Reserve Bank of Minneapolis. Data in Panel D taken from U.S. Census Bureau’s Population Estimates Program. Notes: In Panels A-C, the Federal Reserve Bank of Minneapolis uses a definition of the Bakken region using the following 12 counties: In North Dakota, these include Billings, Burke, Divide, Dunn, Golden Valley, McKenzie, Mountrail, Stark, and Williams Counties. In Montana, these include Richland, Roosevelt, and Sheridan Counties. I used this same definition to construct the figure in Panel D. Figure 3. Hypothetical Transition Diagram of Age-Specific Migration and Death: Standard Multiregional Model

Non-Bakken County i (~B i ) Bakken County i (B i )

Non-Bakken County i (~B j ) Bakken County j (B j )

Death (D )

Figure 4. Hypothetical Transition Diagram of Age-Specific Migration and Death: Revised Multiregional Model with Absorbing Regional Transitions

Non-Bakken County i (~B i ) Bakken County i (B i ) Non-Bakken County i (~B i )

Non-Bakken County j (~B j ) Bakken County j (B j ) Non-Bakken County i (~B j )

Death (D ) Death (D )

State space for those having never lived in Bakken region State space for those having ever lived in Bakken region

State space

Figure 5. Non-Bakken Residents’ Expected Duration of Residence in the Bakken Region and Bakken Counties: Late 2000s

Source: Author’s calculations.

Figure 6. Age Patterns Migration to and from Selected Bakken Counties: Late 1990s and Late 2000s

Source: 2006-2010 American Community Survey (ACS) County-to-County Migration Flow Files. Notes: Mean probabilities refer to county-to-county migration probabilities averaged across all sending counties and across all receiving counties in the case of in- and out-migration, respectively. Given the age intervals in the ACS files, the first age interval is necessarily wider than subsequent age intervals. Displayed age patterns are smoothed.

Figure 7. Change in Non-Bakken Residents’ Expected Duration of Residence in the Bakken Region and Bakken Counties: Late 1990s to Late 2000s

Source: Author’s calculations.

Figure 8. Migrants’ Expected Duration of Residence in the Bakken Region and Bakken Counties: Late 2000s

Source: Author’s calculations.

Figure 9. Change in Migrants’ Expected Duration of Residence in the Bakken Region and Bakken Counties: Late 1990s to Late 2000s

Source: Author’s calculations.