Poliquin Dissertation - Harvard University
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Essays in Strategy and Microeconomics
CitationPoliquin, Christopher W. 2018. Essays in Strategy and Microeconomics. Doctoral dissertation, Harvard Business School.
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Essays in Strategy and Microeconomics
A dissertation presentedby
Christopher W. Poliquinto
The Strategy Unit at Harvard Business School
in partial fulfillment of the requirementsfor the degree of
Doctor of Business Administrationin the subject of
Business Administration
Harvard UniversityCambridge, Massachusetts
March 2018
©2018 – Christopher W. Poliquinall rights reserved.
Dissertation Advisor: Professor Shane Greenstein Christopher W. Poliquin
Essays in Strategy and Microeconomics
Abstract
This dissertation consists of three essays.
In Chapter 1, I study the beneficiaries of technology adoption in the workplace. I
combine worker-level wage data with information on broadband adoption by Brazilian
firms to estimate the effects of broadband on wages. Overall, wages increase 2.3 per-
cent following broadband adoption. Consistent with the theory of biased technological
change, wages increase the most for workers engaged in non-routine cognitive tasks and
returns are negative for routine cognitive tasks. There is no effect of broadband adop-
tion on wages for either routine or non-routine manual tasks. Additionally, I estimate
the effect of broadband on selected quantiles of the within-firm wage distribution and
find evidence that within-firm wage inequality increases following broadband adoption.
Both new hires and the firm’s existing employees benefit from broadband adoption,
which indicates that broadband’s effects are not driven only by better recruitment of
new employees.
Chapter 2 presents three main findings about the impact of mass shootings on gun
policy in the United States. First, mass shootings evoke large policy responses. A
single mass shooting leads to a 15 percent increase in the number of firearm-related bills
iii
Dissertation Advisor: Professor Shane Greenstein Christopher W. Poliquin
introduced within a state in the following year. This effect increases with the number
of fatalities. Second, mass shootings account for a small portion of all gun deaths, but
have an outsized influence relative to other homicides. Our estimates suggest that the
per-death impact of mass shootings on bills introduced is about 80 times as large as the
impact of individual gun homicides in non-mass shooting incidents. Third, when looking
at enacted laws, the impact of mass shootings depends on the political party in power.
A mass shooting increases the number of enacted laws that loosen gun restrictions by
75 percent in states with Republican-controlled legislatures. There is no statistically
significant effect of mass shootings on laws enacted when there is a Democrat-controlled
legislature.
Chapter 3 directly studies the extent and drivers of internal labor markets in multi-
business firms. Leveraging a rich employer-employee matched dataset from Brazil, we
track all worker movements across firm units. We find that multi-business firms source
a large share of their workers internally, especially managers and workers with more
firm-specific experience. Redeployed workers earn a large wage premium over otherwise
comparable workers hired through external labor markets. Geographic proximity and
resource relatedness between establishments play an important role in facilitating rede-
ployment. In contrast to prevailing views of internal labor markets as a means to avoid
external labor market frictions, our findings are consistent with internal labor markets
as conduits of knowledge.
iv
Contents
1 The Effect of the Internet on Wages 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Anecdotal Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2 The Impact of Mass Shootings on Gun Policy 282.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.2 Background and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3 Internal Labor Markets in Multi-business Firms 523.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.2 Theory and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . 593.3 Data and Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . 713.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
Appendix A Construction of O∗NET Task Measures 89
Appendix B Variable Definitions for Chapter 2 92
Appendix C Coding Gun Laws 94
Appendix D Effect of Mass Shootings in Neighboring States 96
Appendix E Effect of Mass Shootings on Enacted Laws 100
v
Appendix F Predicting Mass Shootings 102
Appendix G Mass Shootings and State-Specific Time Trends 106
Appendix H Placebo Mass Shooting Analyses 108
Appendix I Excluding States from Mass Shooting Analyses 110
Appendix J Gun Ownership, Shootings, and Enacted Laws 114
Appendix K Mass Shootings as an Instrument for Gun Policy 116
References 125
vi
Acknowledgments
I am grateful to Shane Greenstein, Michael Luca, Raffaella Sadun, and Deepak Mal-
hotra for their advice and guidance. I also thank the HBS Latin America Research
Center, Partners of the Americas, Harvard Business School, the Harvard Economics
Department, and Brazil’s Ministério do Trabalho e Emprego for supporting this project.
I also acknowledge the immense support of my friends and family.
Chapter 2 is co-authored with Michael Luca and Deepak Malhotra. We thank Joseph
Hall and Jessica Li for excellent research assistance.
Chapter 3 is co-authored with Jasmina Chauvin.
vii
1The Effect of the Internet on Wages
1.1 Introduction
Who benefits from technology adoption in the workplace? Technology can substitute
for some workers while complementing others. Specifically, the “task approach” to
labor markets highlights the potential for digital technologies to substitute for workers
in performing routine tasks, while complementing workers in non-routine tasks (Autor,
Levy, and Murnane, 2003). To date, empirical work on this hypothesis has largely relied
on industry-, region-, or to a lesser extent, firm-level data. In contrast, this paper uses
worker-level wage data in conjunction with firm-level information on technology use
over time. Specifically, I study how broadband Internet technology affects the wages of
individual workers within firms.
1
I find that wages increase 2.3 percent following firm broadband adoption, but the
effect of broadband is heterogeneous. Regressions of wages on the task profile of jobs
suggest that broadband complements employees performing non-routine cognitive tasks,
while substituting for workers in routine cognitive tasks. Intuitively, both routine and
non-routine manual tasks are unaffected by broadband.
The differences in the returns to broadband across tasks have implications for within-
firm wage inequality. I examine changes to the entire wage distribution within firms fol-
lowing broadband adoption using a grouped quantile regression estimator (Chetverikov,
Larsen, and Palmer, 2016). Wage increases following broadband are concentrated in the
right tail of the wage distribution; in other words, within-firm wage inequality increases
after broadband adoption. This result contributes to a literature that emphasizes the
role of firms in determining pay inequality (e.g. Cobb, 2016; Gartenberg and Wulf,
2017b; Nickerson and Zenger, 2008), and provides the first direct evidence connecting
adoption and use of advanced information technology to a widening pay gap within an
organization.
As evidence of broadband enhancing the productivity of existing workers, rather
than only improving the recruitment of new workers, I show that wages increase for
both new hires and existing employees following broadband adoption. Furthermore,
firm directors—who are most likely to also be firm owners—appear to capture large
rents from the introduction of broadband, a pattern consistent with increased firm
productivity post-adoption.
2
The analysis combines an employer-employee matched dataset from Brazil with firm-
level data on technology use over time. By linking information on which firms use
broadband with data on their individual workers, I can estimate the effect of broadband
within firms over time. Additionally, I can examine changes in the wages of individual
workers while controlling for worker characteristics and unobserved firm heterogeneity.
This paper is the first to combine within-firm variation on technology use with large-
sample microdata on the wages and characteristics of individual workers. While other
research has examined the impact of technology —including the Internet—on wages,
prior studies have not observed changes in the technology used at individual firms over
time. Recent research on the effects of the Internet in Brazil (Almeida, Corseuil, and
Poole, 2017; Dutz et al., 2017), Africa (Hjort and Poulsen, 2017), Norway (Akerman,
Gaarder, and Mogstad, 2015), and the United States (Forman, Goldfarb, and Green-
stein, 2012; Gillett et al., 2006; Kolko, 2012) relies on geographic variation in Internet
availability and/or cross-sectional variation in firm adoption. In contrast, I observe the
same firm and workers before and after the adoption of broadband. The results of this
paper are consistent with prior work, which shows broadband substitutes for workers
engaged in routine tasks while complementing workers engaged in non-routine tasks.
Broadband technology is especially worthy of study because of the Internet’s perva-
siveness and policymakers’ interest in public investments in broadband infrastructure.
Nearly 50 percent of people worldwide now access the Internet. The transformation of
the Internet from a technology used by fewer than one percent of people in the mid-
3
1990s to the ubiquitous network of today has potentially large effects on firm operations
and jobs.
Although a number of studies suggest that broadband, and Internet access generally,
is a skill-biased technological change, few if any provide concrete examples of how or why
this might be the case. The next section provides anecdotal evidence from interviews
with Brazilian managers suggesting that broadband use in firms can assist workers
with non-routine cognitive tasks while substituting for workers in performing routine
cognitive tasks.
1.2 Anecdotal Evidence
This section provides examples, through interviews with managers in Brazilian firms, of
how broadband can affect firm operations. Although several papers suggest broadband
complements workers in performing non-routine tasks, while substituting for routine
tasks, few are specific about how high-speed Internet access might do this.
Managers I interviewed described using broadband to facilitate information exchange
both within and between firms and customers. A manufacturer of industrial equipment
explained how broadband provided constant connectivity with their suppliers that al-
lowed them to automate routine aspects of inventory management:
“We scan the barcode on the kanban card and new part orders are sentdirectly to the supplier. This has saved time for the logistics people tospend more time on other tasks, like inventory optimization. It also means
4
we’ve had some layoffs. We need fewer people to do ordering, and a differentset of skills.”
The same firm also used broadband to facilitate communication between workers
directly involved in production and engineers and managers higher in the organizational
hierarchy. Broadband, therefore, complemented the skills of engineers in the non-routine
task of reviewing product design issues and communicating solutions:
“The [machine] operator scans the production order and the computer down-loads the CAD drawing from our database. We can share designs worldwide.If there is a problem, he can hit a button on the screen and report it to anengineer, who can diagnose and solve it.”
A provider of medical imaging services explained using broadband to automate ap-
pointment scheduling, therefore eliminating the routine task of finding open dates. At
the same time, this firm leveraged broadband to unify databases across multiple work
sites in a single location so that important documents could be shared and accessed
from anywhere. This made it easier for doctors to access patient medical records across
facilities.
A manufacturer of bottled water used broadband to connect its machines to the
company that supplied them so that their performance could be monitored remotely.
This change obviated the need for someone who could monitor the machine’s controls,
eliminating the routine task of documenting and recording information.
In addition to these examples, managers reported using broadband to stay in closer
5
contact with their customers, research competitors, and communicate with subordi-
nates.
Two aspects of these examples are especially noteworthy. First, the examples em-
phasize that broadband is a tool that firms use in conjunction with other software and
hardware to enable changes in production. Second, the examples illustrate that work-
ers can benefit from the introduction of broadband without using it in their own work.
The changes in inventory management described above benefited workers with skills in
optimization even though broadband was being used to facilitate part orders, not solve
optimization problems. The wage effects reported in this paper are not the treatment
effects of assigning broadband to particular workers. Rather, the analysis examines
the impact of firm broadband adoption and concomitant changes in production on the
wages of all the firm’s workers.
1.3 Data
The data used in this study are richer than data used in prior studies of broadband
adoption because they include information on individual workers and their employers
over time. This allows me to examine how wages change for different types of workers
following firm adoption of broadband.
Data on individual workers come from the Relação Anual de Informações Sociais
(RAIS) for the years 2000 to 2009. RAIS is an establishment-employee matched survey
6
of all employers in Brazil’s formal economy conducted annually by the Ministério do
Trabalho e Emprego (MTE). Participation is mandatory. Unique identifiers for workers
and establishments in RAIS allow records to be linked across years. Employee records
include data on wages, occupation, education, experience, age, gender, and contract
hours (but not hours actually worked).
I combine the employer-employee matched data from RAIS with firm-level data on
broadband adoption from the Latin American version of the Ci Technology Database
(CiTDB) from Aberdeen Group.1 The European and U.S. versions of CiTDB have been
used in prior studies to measure technology adoption (Bloom et al., 2014). CiTDB
contains information on communication technologies used by the firm (e.g. xDSL, T1,
etc.), which I use to measure broadband adoption.
I limit my study to manufacturing firms—which is the largest group of businesses in
the data—with technology adoption information in Harte Hanks and wages in RAIS so
that analyses of the task content of jobs and occupational hierarchy can be more easily
interpreted.
Figure 1.1 shows that broadband use increased substantially from 2000 to 2009; fewer
than 20 percent of the sample firms used broadband in 2000, but more than 70 percent
had a broadband connection by 2009. Note that these numbers are not necessarily repre-
sentative of all Brazilian manufacturing firms. The firms surveyed by Harte Hanks—my
1CiTDB and Aberdeen Group were formerly owned by Harte Hanks; Halyard Capital acquiredAberdeen and CiTDB in April 2015.
7
Figure 1.1: Adoption of High-Speed Internet
010
2030
4050
6070
80
Firm
s w
ith H
igh-
Spe
ed In
tern
et (
%)
2000 2003 2006 2009Year
source of technology data—are larger than the typical firm in Brazil.
To examine how the effects of broadband vary for different types of workers, I use
measures from the U.S. Department of Labor’s O∗NET database to characterize the
importance of various tasks for each occupation.2 O∗NET contains hundreds of scales
that rate the importance of various activities, skills, abilities, and work contexts for
each job. For consistency with prior research and to limit researcher degrees of freedom
in picking from hundreds of O∗NET scales (Autor, 2013), I use the same variables as
Acemoglu and Autor (2011) and computer code from David Autor’s website3 to produce
2I use O∗NET version 9.0, which was released in December 2005 and is the most recent versionto use the SOC 2000 occupation codes. I use this version because I rely on a crosswalk betweenSOC 2000 and ISCO 88 to match the Brazilian occupation codes with O∗NET.
3Available at https://economics.mit.edu/faculty/dautor/data/acemoglu (archived at
8
Table 1.1: Task Summary Statistics
Task measure mean sd p5 p10 p50 p90 p95
Non-routine cognitive -0.72 0.79 -1.61 -1.60 -0.85 0.30 0.70Non-routine manual 0.32 0.74 -0.94 -0.90 0.39 1.30 1.40Routine cognitive -0.19 0.77 -1.36 -1.02 -0.43 1.13 1.17Routine manual 0.79 0.99 -0.68 -0.46 0.59 2.16 2.16
Note: Table shows the distribution of occupation task measures acrossindividual workers.
four measures of the extent to which each occupation involves various tasks:
1. Non-routine cognitive
2. Non-routine manual
3. Routine cognitive
4. Routine manual
Each of these variables is standardized across occupations so that a unit increase
equals a one standard deviation increase in the extent to which an occupation depends
on the given tasks relative to other occupations. Appendix A lists the specific O∗NET
scales used for each task measure. Table 1.1 shows the distribution of the task measures
across Brazilian workers. The means and medians for the cognitive (manual) scales are
negative (positive), reflecting the greater prevalence of workers engaged in manual-task-
intensive occupations in Brazil’s manufacturing sector.
O∗NET scales were developed to measure features of U.S. occupations. I adapt
these measures to Brazil by merging both the U.S. and Brazilian occupation codes
https://perma.cc/B7SK-VKUV).
9
Table 1.2: Wage Distribution by Hierarchy Level
Director Manager Supervisor Worker
mean 18,085 8,679 3,763 1,476p5 1,593 1,053 735 391p10 3,030 1,692 984 468p25 7,573 3,458 1,674 636p50 16,617 7,144 2,953 961p75 26,403 11,767 4,979 1,648p90 35,531 17,166 7,365 2,937p95 40,745 21,779 9,145 4,222
Note: Wages are mean monthly wage in 2008 reais.
to the International Standard Classification of Occupations (ISCO 88). This results in
instances where a single Brazilian occupation code matches multiple U.S. codes; in these
cases I assign the Brazilian occupation to a simple average of the U.S. task measures.
Additionally, I use occupation codes from RAIS to divide each establishment’s work-
force into hierarchical layers. My approach mirrors the method used by Caliendo, Monte,
and Rossi-Hansberg (2015) in their study of French manufacturers. Specifically, I assign
each worker to one of the following four layers:
1. Directors (e.g. Chief Executive Officer, Chief Financial Officer)
2. Managers (e.g. Sales Manager, Branch Manager)
3. Supervisor (e.g. Foreman, Logistics Supervisor)
4. Workers (e.g. Welder, Production Line Feeder, Fish Cooker)
Like Caliendo, Monte, and Rossi-Hansberg (2015), I find the grouping of occupations
into layers reflects meaningful differences between employees. Table 1.2 shows the mean
10
and selected percentiles of the wage distribution by layer. Directors and managers have
higher wages than supervisors, who have higher wages than workers (at all percentiles).
1.4 Methodology
I use a staggered difference-in-differences research design that identifies the effect of
broadband adoption on wages by comparing firms that did and did not adopt broadband
over the ten-year period between 2000 and 2009.
The main models of interest examine the effect of broadband adoption on workers,
allowing for the effect of broadband to differ by occupation:
lnwijt = β0Djt + β′1Djt ∗Kit + θ′Kit + δ′Xijt + γLjt + αj + λκ(j)t + ϵijt (1.1)
where wijht is the real wage of worker i at firm j in year t. Djt is an indicator variable
for broadband use by firm j and Kit is a vector of continuous measures representing the
task content of worker i’s occupation in year t. The task measures capture the extent to
which the worker’s job involves routine vs. non-routine and cognitive vs. manual tasks.
The vector Xijt is a set of time-varying worker covariates that includes education, current
job experience, sex, age, age squared, and log contract hours.4 Some specifications also
include log employment, Ljt, to control for the possibility that larger firms pay higher
wages and are more likely to adopt broadband (Oi and Idson, 1999). Employment,
4The data do not include actual hours worked, but do include hours in the labor contract.Full-time work in Brazil is 44 hours per week.
11
however, could itself be affected by broadband adoption; I therefore use employment
as the dependent variable in other analyses and omit it from most models. The model
includes both firm (αj) and industry-year (λκ(j)t, where κ(j) is the industry of firm
j) fixed effects that control for unobserved firm heterogeneity and annual shocks that
affect all workers within an industry equally.
Combining employer-employee matched data with information on technology use over
time allows me to examine how the entire wage distribution within firms changes fol-
lowing broadband adoption. To do so, I implement the grouped quantile regression
approach from Chetverikov, Larsen, and Palmer (2016). Specifically, I estimate:
Qlnwijt|Djt,ηjt(τ) = αj(τ) + λκ(j)t(τ) + γ′(τ)zij + β(τ)Djt + ϵ(τ, ηjt) (1.2)
where Q(τ) selects the τth quantile of log wages for firm j in year t, Djt is an indicator
for firm broadband adoption, zij is a vector of individual-level covariates, and αj and
λκ(j)t are firm and industry-year fixed effects.
The grouped quantile approach allows me to estimate how broadband adoption affects
inequality within firms. Greater effects of broadband in the upper quantiles of the wage
distribution than in lower quantiles imply that inequality within firms increases following
broadband adoption.
In addition to studying the effect of broadband on wages, I also examine how employ-
12
Table 1.3: Summary Statistics
mean sd p5 p10 p50 p90 p95
High-speed Internet 0.52 0.50 0 0 1 1 1Log wage 7.05 0.81 6.0 6.2 6.9 8.2 8.6Log contract hours 3.77 0.09 3.7 3.7 3.8 3.8 3.8Tenure in months 60.45 70.92 1.9 3.4 32.7 161.9 211.9Age 33.11 10.10 20.0 21.0 32.0 47.0 52.0Female 0.24 0.43 0 0 0 1 1Education DummiesBelow Elementary 0.08 0.27 0 0 0 0 1Elementary 0.09 0.28 0 0 0 0 1Some Middle School 0.14 0.35 0 0 0 1 1Middle School 0.15 0.35 0 0 0 1 1Some High School 0.10 0.31 0 0 0 1 1High School 0.31 0.46 0 0 0 1 1Some College 0.05 0.21 0 0 0 0 0Higher Ed Degree 0.08 0.28 0 0 0 0 1
Note: Log wages are log of mean monthly wage in 2008 reais.
ment changes at the firm level following broadband adoption:
Ljt = βDjt + αj + λκ(j)t + ϵjt (1.3)
Table 1.3 presents summary statistics of variables used in the analyses. Just over half
of observations are for people working in firms that use broadband.
1.5 Results
1.5.1 Wage Effects
Overall, wages increase 2.3 percent following firm adoption of broadband. Table 1.4
shows the effect of broadband adoption without distinguishing between occupations or
13
Table 1.4: Wage Effects of Broadband
(1) (2) (3) (4) (5)
Broadband 0.034∗∗∗ 0.026∗∗∗ 0.026∗∗∗ 0.023∗∗∗ 0.023∗∗∗(0.009) (0.009) (0.009) (0.008) (0.008)
Log employees 0.015∗∗ -0.007(0.007) (0.008)
Worker Controls • • • •Fixed EffectsFirm • • • • •Year • • •Industry-Year • •
Adj-R2 0.45 0.69 0.69 0.69 0.69Firms 3,333 3,333 3,333 3,332 3,332N 6,949,890 6,949,890 6,949,890 6,949,887 6,949,887
Note: Standard errors in parentheses are clustered by firm.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
types of employees. The results in columns 2–3 include firm and year fixed effects,
while columns 4–5 include firm and industry-year fixed effects. The estimates are stable
across specifications and show a positive average effect of broadband adoption on wages.
Comparing the results of columns 2 and 4 with those of columns 3 and 5 shows that the
estimate of the broadband effect is insensitive to controlling for firm size. The increase
in wages following broadband adoption, therefore, is not explained by bigger, growing
firms paying both higher wages and simultaneously choosing to adopt broadband.
There are several caveats to a causal interpretation of these results. First, firms might
increase wages for other reasons that happen to coincide with broadband adoption.
Without controlling for these factors, wage increases will be erroneously attributed to
broadband. Second, even if broadband causes wages to increase, the firms most likely
14
to benefit from the technology will be more likely to adopt, in which case estimates
from the sample of adopters will be greater than the effect of introducing other firms
to broadband. Third, trends in wages prior to broadband adoption might be different
from trends in wages at firms that do not adopt. In this case, firms that do not adopt
broadband are a poor control group for the adopters.
I cannot correct for omitted variables without an instrument. The problem of firms
selecting into broadband use, however, is partially mitigated by the ten-year sample
period. Figure 1.1 shows that most firms in the sample eventually adopt broadband.
Additionally, the long sample period allows me to examine wage trends prior to broad-
band adoption. Figure 1.2 shows coefficient estimates from a modified version of the
model in column 4 of Table 1.4 that includes separate dummy variables for years before
and after adoption. These single year estimates are imprecise, but show that the largest
wage increases happen in the years following broadband adoption. There is, however,
some evidence that wages at adopting firms begin increasing relative to non-adopting
firms in the year before broadband adoption.
The effect of broadband is heterogeneous; workers in occupations that require more
non-routine cognitive tasks see larger wage gains than workers in occupations that are
intensive in routine cognitive tasks. Table 1.5 shows regressions in which broadband
adoption is interacted with occupation-specific measures of task intensity. The coeffi-
cients on non-routine cognitive and routine cognitive tasks have opposite signs, suggest-
ing that broadband complements workers performing non-routine cognitive tasks and
15
Figure 1.2: Wages Before and After Broadband Adoption
-0.02
0.00
0.02
0.04
0.06
0.08
Coe
ffici
ent E
stim
ate
3 YearsPrior
2 YearsPrior
1 YearPrior
AdoptionYear
1 YearAfter
2 YearsAfter
3+ YearsAfter
Note: Values along the x-axis represent time relative to broadband adoption; e.g. “2 YearsAfter” refers to the second year following adoption.
16
substitutes for workers in routine cognitive tasks. A one unit increase (roughly one
standard deviation) in the intensity of non-routine cognitive tasks implies an additional
3.5–4.5 percent wage increase following broadband adoption. In contrast, a one unit in-
crease in the intensity of routine cognitive tasks implies a 4–5 percent decrease in wages,
which cancels out the baseline increase of four percent from broadband adoption.
Table 1.5 also indicates that the effect of broadband adoption does not vary in the
intensity of manual tasks. This is consistent with the intuition that broadband ought
to have small, if any, effect on tasks that require interaction with equipment and using
one’s hands.
The use of four, continuous task measures interacted with broadband complicates
interpretation of the results in Table 1.5. Table 1.6 therefore presents the distribution
of wage effects (across workers) implied by the task regressions. For each worker, I
use the coefficients from the regressions in Table 1.5 and the task intensities of the
worker’s occupation to calculate the hypothetical impact of broadband for that worker.
I then calculate the distribution of these wage effects across all workers. The results in
Table 1.6 show that the effect of broadband on real wages is positive for the majority
of workers and that wage gains in the right tail of the distribution are much larger in
magnitude than wage losses in the left tail.
Overall, the broadband/task interaction effects of Table 1.5 are consistent with the
routinization hypothesis (Autor, Levy, and Murnane, 2003) that computer technology
complements and increases demand for non-routine tasks while substituting for routine
17
Table 1.5: Wage Effects of Broadband by Tasks
(1) (2)
Broadband ×Intercept 0.041∗∗∗ 0.040∗∗∗
(0.011) (0.012)
Non-routine cognitive 0.044∗∗ 0.036∗(0.018) (0.019)
Non-routine manual -0.004 -0.009(0.009) (0.009)
Routine cognitive -0.050∗∗∗ -0.041∗∗(0.016) (0.018)
Routine manual 0.006 0.006(0.008) (0.008)
Non-routine cognitive 0.145∗∗∗ 0.150∗∗∗(0.017) (0.017)
Non-routine manual -0.059∗∗∗ -0.056∗∗∗(0.007) (0.007)
Routine cognitive -0.018 -0.023(0.016) (0.016)
Routine manual -0.006 -0.007(0.007) (0.007)
Worker Controls • •Fixed EffectsFirm • •Year •Industry-Year •
Adj-R2 0.70 0.71Firms 3,333 3,332N 6,871,375 6,871,372
Note: Standard errors in parentheses are clustered by firm.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
18
Table 1.6: Summary of Wage Effects of Broadband by Tasks
Model mean sd p5 p10 p25 p50 p75 p90 p95
(1) 0.023 0.036 -0.020 -0.015 0.005 0.015 0.035 0.079 0.115(2) 0.024 0.029 -0.013 -0.000 0.010 0.019 0.033 0.072 0.096
Note: Table shows the distribution of wage effects across individual workers forthe models in Table 1.5.
tasks. In the case of broadband, this pattern is pronounced for cognitive tasks, but not
present for manual tasks.
My estimates for the wage effects of broadband are larger, although roughly similar
in magnitude, to those of Dutz et al. (2017), who examine the regional wage effects
of Brazil’s Internet (but not specifically broadband) rollout. They report a two-year
cumulative wage increase of 4.1–4.8 percent for middle- and high-skill occupations in
manufacturing in response to an increase in Internet access, but no wage effect for low-
skill occupations.5 A possible explanation for the larger effect estimates in this paper
is that, unlike Dutz et al. (2017), I observe the adoption decisions of individual firms
instead of relying on measures of regional broadband availability.
1.5.2 New Versus Existing Employees
The effect of broadband on new employees is the same as the effect on existing employees.
This suggests that wage increases from broadband adoption are not driven only by
firms recruiting better workers post-adoption. Table 1.7 shows the effect of broadband
5Internet access in Dutz et al. (2017) is measured using the share of schools with Internet ineach municipality. The reported effects are based on increasing Internet access from 0 to 100percent (i.e. going from no access to every school having access).
19
adoption on wages allowing for the effect to differ by whether an employee is in his first,
first two, or first three years of working at the firm. The results show that newly hired
employees do not earn an additional wage premium from broadband adoption over that
earned by existing employees.
1.5.3 Wage Effects and Organizational Hierarchy
Wage increases following broadband adoption are greatest for workers higher in the
organizational hierarchy: directors and managers see larger increases than lower-level
workers. Columns 1 and 4 of Table 1.8 show that directors and managers earn 8–9
percent more following broadband adoption compared to a main effect of just over two
percent for all employees.
The effect of broadband is especially large for directors at the top of the organiza-
tional hierarchy. Columns 2–3 and 5–6 split the managers and directors group into
two separate coefficients, and columns 3 and 6 add another coefficient for supervisors,
who are grouped with workers in the other columns. The estimates suggest that di-
rectors earn 18–19 percent more following firm adoption of broadband. This is about
nine percentage points more than the increase for managers. Most firms in the sample
are private companies. The directors in this sample are therefore more likely to have
an ownership stake in the firm than if the firms were public. The wage increases for
directors are consistent with firm owners capturing large gains as a result of broadband
increasing firm productivity. Unfortunately, I do not have data on revenue or non-labor
20
Table 1.7: Wage Effects, New vs. Existing Employees
(1) (2) (3) (4) (5) (6)
Broadband ×Intercept 0.023∗∗∗ 0.023∗∗∗ 0.024∗∗∗ 0.021∗∗∗ 0.020∗∗ 0.022∗∗
(0.008) (0.008) (0.008) (0.008) (0.008) (0.009)
Hiring year 0.009 0.008(0.007) (0.007)
First 2 years 0.003 0.001(0.008) (0.007)
First 3 years 0.001 -0.002(0.008) (0.008)
Hiring year -0.136∗∗∗ -0.136∗∗∗(0.005) (0.005)
First 2 years -0.155∗∗∗ -0.154∗∗∗(0.005) (0.005)
First 3 years -0.152∗∗∗ -0.151∗∗∗(0.006) (0.005)
Worker Controls • • • • • •Fixed EffectsFirm • • • • • •Year • • •Industry-Year • • •
Adj-R2 0.69 0.69 0.69 0.69 0.70 0.70Firms 3,333 3,333 3,333 3,332 3,332 3,332N 6,949,890 6,949,890 6,949,890 6,949,887 6,949,887 6,949,887
Note: Standard errors in parentheses are clustered by firm.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
21
Table 1.8: Wage Effects of Broadband by Hierarchy Level
(1) (2) (3) (4) (5) (6)
Broadband ×Intercept 0.024∗∗∗ 0.024∗∗∗ 0.024∗∗∗ 0.022∗∗∗ 0.022∗∗∗ 0.023∗∗∗
(0.009) (0.009) (0.009) (0.008) (0.008) (0.008)
Director/Manager 0.052∗∗ 0.063∗∗∗(0.023) (0.021)
Director 0.141∗∗∗ 0.141∗∗∗ 0.153∗∗∗ 0.153∗∗∗(0.042) (0.043) (0.042) (0.043)
Manager 0.050∗∗ 0.051∗∗ 0.061∗∗∗ 0.061∗∗∗(0.023) (0.024) (0.021) (0.022)
Supervisor 0.002 0.004(0.014) (0.013)
Director/Manager 0.723∗∗∗ 0.718∗∗∗(0.021) (0.019)
Director 1.163∗∗∗ 1.230∗∗∗ 1.152∗∗∗ 1.220∗∗∗(0.033) (0.034) (0.033) (0.034)
Manager 0.688∗∗∗ 0.741∗∗∗ 0.683∗∗∗ 0.737∗∗∗(0.021) (0.022) (0.019) (0.019)
Supervisor 0.469∗∗∗ 0.467∗∗∗(0.010) (0.010)
Worker Controls • • • • • •Fixed EffectsFirm • • • • • •Year • • •Industry-Year • • •
Adj-R2 0.70 0.70 0.71 0.71 0.71 0.72Firms 3,333 3,333 3,333 3,332 3,332 3,332N 6,949,890 6,949,890 6,949,890 6,949,887 6,949,887 6,949,887
Note: Standard errors in parentheses are clustered by firm.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
22
inputs to explore this hypothesis. Akerman, Gaarder, and Mogstad (2015), however,
report that firms in Norway earn large rents from broadband adoption, and Jung and
López-Bazo (2017) find a positive effect of broadband on regional productivity in Brazil.
The greater effect of broadband for directors and managers implies that within firm
wage inequality increases following adoption. To more thoroughly examine this pattern,
I use the grouped quantile regression estimator from Chetverikov, Larsen, and Palmer
(2016) to assess how broadband adoption affects the distribution of wages within firms.
Figure 1.3 plots the effect of broadband on selected quantiles of the wage distribution.
Figure 1.3a shows results from a model without worker-level controls, while Figure 1.3b
is based on a model that controls for experience, age, and two education dummies (high
school and college completion). The sample in the latter model is also restricted to
firms with at least 10 employees to allow for the inclusion of the worker-level controls.
Although the estimates for the individual quantiles are imprecise, the pattern of point
estimates in Figure 1.3 suggests that broadband has larger effects on the right tail of
the wage distribution than on wages in the left tail. In other words, high wage workers
benefit more than low wage workers from broadband adoption and inequality within
firms increases.
Broadband’s effect in widening the within-firm wage distribution is noteworthy for
the literatures on vertical pay comparisons within firms (e.g. Gartenberg and Wulf,
2017a,b; Kacperczyk and Balachandran, 2018), the antecedents of compensation poli-
cies (e.g. Chin and Semadeni, 2017; Fredrickson, Davis-Blake, and Sanders, 2010), and
23
Figure 1.3: Quantile Effects of Broadband Adoption
-0.010
0.000
0.020
0.040
0.060
0.080B
road
band
Coe
ffici
ent
0 20 40 60 80 100
Percentile
(a) Without worker micro-covariates
-0.020
0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
Bro
adba
nd C
oeffi
cien
t
0 20 40 60 80 100
Percentile
(b) With worker micro-covariates
24
the role of firms in determining pay inequality (e.g. Cobb, 2016). This paper provides
the first direct evidence connecting adoption and use of advanced information technol-
ogy to a widening pay gap within an organization. Furthermore, this paper provides
estimates of broadband’s effect across the entire wage distribution; existing research on
pay dispersion is largely focused on top-management teams and key employees.
Prior work suggests that pay inequality can have psychological costs (Larkin, Pierce,
and Gino, 2012), and can negatively impact performance (Fredrickson, Davis-Blake,
and Sanders, 2010; Siegel and Hambrick, 2005). Unfortunately, I do not have data to
assess either the first order effect of broadband on performance or any second order
effects operating through employee motivation. I also lack data on performance-linked
compensation that would allow me to examine how different components of pay change
in response to technology adoption.
1.5.4 Employment Effects
Broadband has positive effects on firm-level employment. Column 1 of Table 1.9 in-
dicates that employment increases roughly 5.4 percent following broadband adoption.
Columns 2 and 3 show separate regressions for managerial and non-managerial employ-
ees respectively. These estimates are not statistically different from zero at conventional
significance levels, and the point estimates do not suggest different employment effects
for workers and managers following broadband adoption. Columns 4–6, which include
industry-year fixed effects instead of just year fixed effects, show slightly larger esti-
25
Table 1.9: Employment Effects of Broadband
Total Managers Workers Total Managers Workers(1) (2) (3) (4) (5) (6)
Broadband 0.053∗∗ 0.044∗ 0.040 0.071∗∗∗ 0.054∗∗ 0.058∗∗(0.026) (0.026) (0.027) (0.027) (0.026) (0.026)
Worker Controls • • • • • •Fixed EffectsFirm • • • • • •Year • • •Industry-Year • • •
Adj-R2 0.84 0.79 0.85 0.84 0.81 0.76Firms 3,026 2,722 3,023 2,990 2,990 2,990N 17,722 15,348 17,696 17,310 17,310 17,310
Note: Standard errors in parentheses are clustered by firm.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
mates. Column 4 indicates that employment increases about seven percent following
broadband adoption, and columns 5–6 again suggest that the effect is similar for man-
ages and non-managers.
1.6 Conclusion
I combine data on firm adoption of broadband technology over time with data on indi-
vidual workers to estimate the effects of broadband on wages and employment. Overall,
wages increase 2.3 percent following broadband adoption, but the effects are heteroge-
neous. Consistent with the theory of skill-biased technological change, wages increase
the most for workers engaged in non-routine cognitive tasks. Returns for routine cog-
nitive tasks are negative, and intuitively, the effect of broadband does not vary in the
intensity of either routine or non-routine manual tasks. Quantile regressions measuring
26
the effect of broadband on the full wage distribution suggest that broadband increases
within-firm wage inequality.
Additionally, I am able to compare the returns of broadband adoption for new and
existing employees. I find that both new and existing employees benefit from broadband
adoption, which suggests the effect of broadband on wages is not solely the result of
recruiting better employees post-adoption. Overall employment increases 5–7 percent
following broadband adoption.
The results are useful for policymakers evaluating the potential impacts of public
investment in broadband infrastructure. Such investments are often predicated on the
hypothesis that high-speed Internet spurs economic and wage growth despite limited re-
search on this topic. I show that workers do not equally share the gains from broadband
adoption; workers engaged in higher paid occupations that require non-routine cogni-
tive tasks experience larger gains from adoption than workers in occupations intensive
in routine cognitive tasks.
27
2The Impact of Mass Shootings on Gun Policy
This chapter is co-authored with Michael Luca and Deepak Malhotra.
2.1 Introduction
Recent decades have witnessed a series of high-profile mass shootings throughout the
United States in towns ranging from Newtown, CT to Killeen, TX. While most homi-
cides receive little attention from the general public, mass shooting incidents are ex-
tremely salient. Nonetheless, a common and frequently articulated view is that despite
extensive discussion about mass shootings, they have little influence on policymaking.
Should we expect policymakers to propose new legislation in the wake of a mass shoot-
ing? Given that the vast majority of gun deaths do not result from mass shootings, it
28
would be difficult to reconcile large responses to mass shootings with basic models of op-
timal policy aimed exclusively at reducing gun violence. However, mass shootings may
have another effect—bringing attention to the issue of gun violence. Mass shootings
potentially lead to policy changes by focusing attention on gun violence, even if they
do not provide new information or change politicians’ preferences (which are generally
static and aligned with party preferences). Political scientists have noted the fact that
issues tend to rise and fall within a policy agenda, creating periods in which specific
policies change very rapidly and other periods in which they do not change at all (Baum-
gartner and Jones, 1993; Kingdon, 1984). In the context of gun violence, events like the
Columbine shooting have lead to both calls for new restrictions on guns and vehement
reaction from gun rights groups opposed to such changes (Goss, 2006; Spitzer, 2012).
More generally, mass shootings may create “policy windows” during which legislatures
become receptive to change—potentially due to shifts in the attention of constituents.
But the extent to which this occurs, and the direction of resulting changes are empirical
questions.
In this paper, we explore the impact of mass shootings on gun policy, constructing a
dataset of all U.S. gun legislation and mass shootings over a period of 25 years (1989–
2014), combining data from a variety of media and government sources. We begin by
looking at the extent of deaths resulting from mass shootings relative to other gun
deaths. Overall, there are more than 30,000 gun related fatalities in the United States
per year. Roughly 56 percent of these are suicides and 40 percent are homicides. The
29
remaining four percent are accidents or incidents of undetermined intent. Mass shoot-
ings accounted for about 0.13 percent of all gun deaths and 0.34 percent of gun murders
between 1989 and 2014.
Because mass shootings are salient and plausibly random occurrences, we are able
to implement a difference-in-differences strategy around the timing of mass shootings
to estimate their causal impact on gun regulation. Specifically, we compare gun laws
before and after mass shootings, in states where mass shootings occur relative to all
other states.
We then present three main findings about the impact of mass shootings on policy.
First, mass shootings evoke large policy responses. A single mass shooting leads to
an approximately 15 percent increase in the number of firearm bills introduced within
a state in the year after a mass shooting. This effect is largest after shootings with
the most fatalities and holds for both Republican-controlled and Democrat-controlled
legislatures.
Second, although mass shootings account for a small portion of all gun deaths, they
have an outsized influence relative to other homicides. Our estimates suggest that the
per-death impact of mass shootings on bills introduced is about 80 times as large as the
impact of gun homicides in non-mass shooting incidents.
Third, when looking at enacted laws, the impact of mass shootings depends on the
party in power. A mass shooting increases the number of enacted laws that loosen gun
restrictions by 75 percent in states with Republican-controlled legislatures. We find no
30
significant effect of mass shootings on laws enacted when there is a Democrat-controlled
legislature.
These findings contribute to the empirical literature that uses a political economy
lens to explore the determinants of policymaking (Makowsky and Stratmann, 2009;
Bardhan and Mookherjee, 2010). Our results show that salient events—such as mass
shootings—can lead to significant policy responses. Moreover, policymakers seem to
use mass shootings as an opportunity to propose bills that are consistent with their
ideology. This helps to shed light on the role of attention and salience in shaping policy
and the interaction between issue salience and existing political preferences in shaping
the degree and direction of enacted policies.
2.2 Background and Data
As described above, out of the roughly 30,000 annual gun deaths in the United States,
fewer than 100 occur in mass shootings. For the purpose of this paper, we define a “mass
shooting” as an incident in which four or more people, other than the perpetrator(s),
are unlawfully killed with a firearm in a single, continuous incident that is not related
to gangs, drugs, or other criminal activity. This definition closely matches the one
used by Krouse and Richardson (2015) and the FBI’s definition of “mass murder” as
four or more murders “occurring during the same incident, with no distinctive time
period between the murders…typically involv[ing] a single location” (Morton and Hilts,
31
2008). We further restrict our analysis to cases where at least three of the fatalities were
individuals unrelated to, and not romantically involved with, the shooter(s). We include
spree murders—homicides at multiple locations without a significant pause between
incidents—if they result in four or more deaths.
We assemble a list of mass shootings since 1989 from a variety of government and
media sources because there is no single, comprehensive government database of mass
murders. We extract all gun-related mass murders (four or more dead) that are not
felony related from the FBI supplementary homicide reports (SHR). We then verify each
incident in the SHR using media accounts; the SHR may contain errors in which separate
homicides in a month are reported as a single incident, which is why it is necessary to
verify the incidents with media coverage. Participation in the SHR program is voluntary
and many law enforcement agencies do not report detailed data to the FBI. We therefore
supplement the FBI data with mass shootings gathered from media accounts or compiled
by other researchers and journalists interested in the topic. We combine the SHR data
with mass shootings collected by the Mass Shootings in America (MSA) project at
Stanford University (Stanford Geospatial Center and Stanford Libraries, 2015) and a
list created by USA Today (2013). For each shooting, we determine the event location
as well as the number of victim fatalities and injuries. We also classify shootings based
on the relationship (if any) between the alleged shooter(s) and victims. Previous work
on mass shootings (Duwe, 2007; Krouse and Richardson, 2015) distinguishes between
public mass shootings that occur in places frequented by the public, felony-related
32
murders, and familicide. We categorize shootings by whether they are public events
or primarily related to domestic conflicts, and we focus on incidents in which at least
three people not related or romantically involved with the shooter died. This restriction
filters out family killings in residences as well as family-related murders in public places.1
Figure 2.1 shows the number of incidents and fatalities in mass shootings by year. The
data show a slight upward trend in the number of incidents and fatalities over time, but
both incidents and fatalities vary substantially from year to year.
2.2.1 Gun Legislation
State governments are the primary regulators of firearms. Federal laws establish a
minimum level of gun control, which is then augmented to varying degrees by state
and local policies. Federal government has limited commerce, the possession of guns by
potentially dangerous individuals, and some types of firearms and ammunition. States
decide a variety of gun policies ranging from who can purchase and possess a gun to
what types of guns are allowed in different situations to how guns should be stored
and what types of training should be undertaken by gun owners. Local ordinances
can also restrict firearm possession and use, but state statutes enacted in the past few
decades have limited the importance of local government in this arena by preempting
local regulations.
1A 2006 shooting at a church in Louisiana is one example. A man killed his wife and in-lawswhile abducting her and their children from a church. Only the wife’s family was present atthe church during the shooting.
33
Figure 2.1: Mass Shootings and Fatalities by Year, 1989–2014
0
20
40
60
80
Fata
litie
s
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Mass Shooting Fataliites
2468
1012
Num
ber o
f Sho
otin
gs
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Year
Mass Shooting Incidents
Note: The upper panel shows the number of fatalities in mass shootings in which at leastthree people not related or romantically connected to the shooter were killed. The bottompanel shows the number of these incidents. Washington, D.C. is not included in the sample.
34
We create a comprehensive dataset of gun legislation in all fifty states using the bill
tracking reports service from LexisNexis, which includes all bills introduced in state
legislatures since at least 1990 with a synopsis and timeline of each bill’s progress. This
allows us to determine whether bills pass the legislature and become law. We identify
firearm bills by searching for the firearm-related terms “firearm”, “handgun”, “pistol”,
“revolver”, “rifle”, “shotgun”, “long-gun”, and “assault weapon.” We identify 20,409
firearm bills and 3,199 laws between 1990 and 2014. In other words, there were 20,409
proposals introduced and 3,199 laws passed in the 25 year sample period across all fifty
states. This includes laws that loosen or tighten gun restrictions, and many that do
neither or both. We exclude resolutions, executive orders, and ballot initiatives from
the analysis.2
To explore whether gun control is tightened or loosened after mass shootings, we
hired eight people to manually code the summary of bills that became law. Coders
were given instructions explaining how to code legislation, but were otherwise blind
to the topic and design of the study. We presented bill summaries from LexisNexis
to coders in randomly chosen groups of 50. Two people coded each summary, and
no coder saw the same summary multiple times. For each summary, coders decided
whether the bill was tightening (stricter gun control), loosening (weaker gun control),
uncertain (insufficient information), both tightening and loosening, or neither tightening
2Legislators in some states first submit ideas for bills in the form of a draft request or similardocument. We exclude these from our analyses because they result in double counting somelegislation. We instead focus only on actual bills.
35
nor loosening (neutral). There were therefore five possible labels for bills: tighten,
loosen, both, neutral, or uncertain. Appendix C shows example bill summaries and
their expected labels.
To cross-validate (and incentivize) the bill coding, we coded a small fraction of bills
ourselves as a baseline comparison point. For this process, we blinded ourselves from
any information about when or where the bill was proposed. We then used our scores
to assess the quality of coders. Specifically, each group of 50 bills given to a coder
contained five bills that we had also coded (they did not know which bills were and
weren’t coded, and did not have access to any of our assessments of whether a bill was
looser or tighter). Coders were paid up to a 50 percent bonus based on the extent to
which their coding matched ours (which we simply told them was a “gold standard” of
known codes).
Across all five categories, coders agreed with each other 52 percent of the time (the
agreement rate would be 20 percent by chance) and agreed with the gold standard 71
percent of the time. Coders performed worst on the neutral category, and best on the
tighten-only and loosen-only categories; when a bill tightens gun control (according to
the gold standard), coders agree on tightening 67 percent of the time, and when a bill
loosens gun control, coders agree on loosening 60 percent of the time.
Most importantly for the purposes of our analysis: when coders agree with each other
on tightening, they also agree with our coding 93 percent of the time; when coders agree
on loosening, they are consistent with our scores 91 percent of the time. When analyzing
36
Figure 2.2: Comparison of Legislation Introduced by Political Party
l
l
l
l
ll
l
l
l
l
l
l
5
10
15
20
0.25
0.50
0.75
1.00
Bills Introduced Laws Passed Tightening Laws Loosening Laws
Mea
n Le
gisl
atio
n pe
r Ye
ar
Legislature
l
l
l
Republican
Democrat
Split
Note: Points represent the mean and lines are 95 percent confidence intervals. Legislaturecontrol means one political party includes both chambers of the legislature. The counts oftightening and loosening laws are based on laws with coder agreement (see section 2.2.1 foran explanation of coding legislation).
the direction of policy change, we leverage this high degree of reliability by restricting
our analysis to bills on which coders agreed that the law was designed to tighten or
loosen gun control. Because states can pass either, none, or both types of laws in a year,
our dependent variable is the count of laws in each direction.
Figure 2.2 shows mean bills introduced, laws enacted, and tightening and loosening
laws by political control of the state legislature. Republicans enact more laws loosening
gun control, and fewer laws tightening gun control, than Democrats. Republican, Demo-
cratic, and split legislatures enact a similar number of total gun laws. The coders who
classified the legislation were only given summaries of each bill; they were not provided
with the state, year, or any information on political affiliation.
37
2.2.2 Control Variables
While our empirical strategy allows us to control for all time invariant factors that may
affect gun legislation, we also add time varying controls. These include economic and
demographic factors such as unemployment, divorce rates, and rates of military service.
We also control for institutional differences between legislatures. First, we control for
the number of lawmakers as a measure of legislature size. Larger legislatures consider
more bills. Second, we create a dummy for legislatures that held a regular session in a
given year because not all legislatures meet annually. Third, we control for whether bills
in each year carryover into subsequent sessions; some chambers allow for carryover while
others kill all unpassed bills at the end of each session. Fourth, we control for years in
which bills were restricted to specific topics; seven states restrict the scope of legislation
(e.g. appropriations only) in specific years. Fifth, we control for the political party in
power. Table 2.1 contains summary statistics for all variables used in the analyses.
2.3 Methodology
We implement a difference-in-differences strategy that compares gun laws before and
after mass shootings, in states where mass shootings occur relative to all other states.
Our dependent variables are counts of bills or enacted laws at the state-year level. We
38
Table 2.1: Summary Statistics
Variable mean sd p5 p10 p50 p90 p95
LegislationBills introduced 16.3 22.0 0 1 10 38 53Laws enacted 2.56 3.35 0 0 1 6 9Tightening laws 0.70 1.29 0 0 0 2 3Loosening laws 0.25 0.62 0 0 0 1 1
Gun ViolenceMass shooting 0.12 0.32 0 0 0 1 1Fatalities 0.72 2.40 0 0 0 4 5Gun homicide rate 3.76 2.55 0.72 0.98 3.42 7.40 8.65
Political ControlsDemocratic legislature 0.42 0.49 0 0 0 1 1Republican legislature 0.34 0.47 0 0 0 1 1Republican governor 0.53 0.50 0 0 1 1 1
Institutional ControlsRegular session 0.94 0.24 0 1 1 1 1Bill carryover 0.27 0.44 0 0 0 1 1Limited leg. topic 0.06 0.24 0 0 0 0 1Legislature size 148 59.3 62 82.5 144 200 236
Demographic ControlsElderly (65+) % 12.9 2.0 9.8 10.7 13.1 15.2 15.7Under 25 % 35.1 2.7 31.4 32.2 34.8 38.0 39.5Black % 10.3 9.5 0.6 0.8 7.4 26.4 30.1Hispanic % 8.3 9.2 0.8 1.2 5.1 20.3 29.9Unemployment % 5.7 1.9 3.1 3.5 5.4 8.1 9.3Income per capita 19.1 3.3 14.1 15.0 18.7 23.3 25.8High school % 85.2 5.2 75.7 78.4 86.1 91.2 92.0Veteran % 11.8 2.4 7.9 8.8 11.8 15.0 16.1Divorced % 11.8 1.8 8.9 9.5 11.8 14.1 14.7
Note: Sample includes 1,250 state-year observations.See appendix B for a list of variable definitions.
39
study the effect of mass shootings using Poisson regressions with conditional mean:
E[ys,t | αs, λt, Shoots,t−1, Xs,t
]= exp
(αs + λt + β′Shoots,t−1 + γ′Xs,t
)where ys,t is a count of bills introduced or laws enacted in state s and year t; αs and
λt are state and year fixed effects; Shoots,t−1 is either an indicator for states with a
mass shooting or the fatality count in mass shootings, and Xs,t is a vector of time-
varying political, economic, and demographic factors. We estimate the parameters via
maximum likelihood by conditioning on the sum of ys,t within states and including year
indicators.
Our identification allows us to measure the impact of a mass shooting within that
state, controlling for other changes that are happening at the national level. Because
this identification strategy does not identify national responses to mass shootings (which
would be absorbed by our year effect), our estimates of changes in gun policy may under-
estimate the total impact of a mass shooting. We can account for potential spillovers
into neighboring states, but do not see significant spillover effects; these results are
presented in Appendix D.
40
2.4 Results
2.4.1 The Effect of Mass Shootings on Gun Bill Introductions
Table 2.2 shows that a mass shooting leads to a 15 percent increase in firearm bills
introduced. For the average state, this amounts to an additional 2.5 firearm bills in-
troduced in the year following a mass shooting. Mass shootings with more deaths lead
to larger effects. On average, each additional death in a mass shooting leads to a 2.5
percent increase in the number of gun bills introduced. This result holds both for
Republican-controlled and Democrat-controlled legislatures.3
2.4.2 Comparing Mass Shootings with Non-Mass Shootings
Table 2.3 shows that fatalities resulting from mass shootings lead to much larger in-
creases in gun bill introductions than gun homicides in non-mass shooting incidents.
We estimate the models in this table using mass shooting fatalities and ordinary gun
homicides per 100,000 people to facilitate comparison between the two types of murder.
It would take approximately 80 people dying in individual gun homicide incidents to
have as much impact on bills introduced as each person who dies in a mass shooting.
Our estimates imply that, on average, a single mass shooting has as much impact on
the number of bills proposed as would a 270 percent increase in the number of gun
3Results on bills proposed broken down by political affiliation are available upon request.
41
Tabl
e2.
2:Eff
ecto
fMas
sSho
otin
gson
Gun
Bill
Intro
duct
ions
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Mas
ssh
ootin
g0.
154∗
0.08
70.
155∗
∗0.
147∗
∗
(0.0
88)
(0.0
74)
(0.0
65)
(0.0
62)
Fata
litie
s0.
023∗
0.02
0∗∗
0.02
3∗∗∗
0.02
3∗∗∗
(0.0
13)
(0.0
09)
(0.0
07)
(0.0
07)
Reg
ular
sess
ion
1.94
6∗∗∗
1.93
1∗∗∗
1.95
6∗∗∗
1.93
9∗∗∗
(0.3
64)
(0.3
80)
(0.3
55)
(0.3
70)
Bill
carr
yove
r0.
500∗
∗∗0.
504∗
∗∗0.
498∗
∗∗0.
502∗
∗∗
(0.1
30)
(0.1
31)
(0.1
38)
(0.1
40)
Lim
ited
leg.
topi
c-0
.665
∗∗∗
-0.6
71∗∗
∗-0
.632
∗∗∗
-0.6
39∗∗
∗
(0.2
40)
(0.2
51)
(0.2
25)
(0.2
36)
Legi
slatu
resiz
e0.
007∗
∗0.
008
0.00
7∗∗
0.00
8(0
.004
)(0
.005
)(0
.003
)(0
.005
)D
em.l
egisl
atur
e-0
.136
∗-0
.134
∗
(0.0
74)
(0.0
76)
Rep
.leg
islat
ure
0.06
70.
071
(0.0
64)
(0.0
64)
Rep
.gov
erno
r-0
.022
-0.0
14(0
.054
)(0
.053
)
Dem
ogra
phic
Con
trol
s•
••
•St
ate
Fixe
dEff
ects
••
••
••
••
Year
Fixe
dEff
ects
••
••
••
N1,
250
1,25
01,
250
1,25
01,
250
1,25
01,
250
1,25
0
Not
e:T
hede
pend
ent
varia
ble
isth
enu
mbe
rof
firea
rm-r
elat
edbi
llsin
trod
uced
inth
est
ate
legi
slatu
re.
Rob
ust
stan
dard
erro
rscl
uste
red
byst
ate
inpa
rent
hese
s.∗p<
.10,∗
∗p<
.05,
∗∗∗p<
.01
42
Table 2.3: Comparing Mass Shootings and Non-Mass Shootings
(1) (2) (3) (4)
Mass shooting fatalities/100,000 1.678∗∗∗ 1.332∗∗∗ 1.303∗∗∗ 1.316∗∗∗(0.428) (0.240) (0.223) (0.202)
Ordinary gun homicides/100,000 -0.007 0.017 0.014 0.017(0.024) (0.032) (0.032) (0.035)
Political Controls •Institutional Controls • •Demographic Controls •State FE • • • •Year FE • • •N 1,250 1,250 1,250 1,250
Note: The dependent variable is the number of firearm-related bills introduced inthe state legislature. Robust standard errors clustered by state in parentheses. MassShooting Fatalities/100,000 is the number of deaths in mass shootings per 100,000 stateresidents. Ordinary gun homicides/100,000 is the number of gun homicides not in massshootings per 100,000 state residents.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
homicides in a state. Given the average number of gun homicides per year is roughly
260 per state, this would be equivalent to an additional 448 homicides per state-year.
2.4.3 The Role of Political Party on Laws Enacted
As mentioned previously, the two major political parties in the United States differ dra-
matically in their stances on how restrictive gun policy should be, with the Republican
Party favoring fewer gun restrictions.4 To look at the impact of political parties on gun
policy, we restrict our analysis to enacted laws, all of which were coded for whether
they loosened or tightened gun restrictions (see data description for more details).
Table 2.4 shows the effect of mass shootings interacted with Democrat and Repub-
4See https://www.gop.com/platform/ and https://www.democrats.org/party-platform.
43
lican control of state government (divided government, in which the legislature is not
controlled by a single party, is the omitted group). The results show that Democrats
and Republicans respond differently to mass shootings.
When there is a Republican-controlled legislature, mass shootings lead to more firearm
laws that loosen gun control. A mass shooting in the previous year increases the number
of enacted laws that loosen gun restrictions by 75 percent in states with Republican-
controlled legislatures. When there is a Democrat-controlled legislature, mass shootings
lead to a statistically insignificant reduction in laws that loosen gun control. We find
no significant effects of mass shootings on laws that tighten gun restrictions, but the
estimates are imprecise. Summing across all legislatures (Republican, Democrat, and
split), there is roughly a 10 percent increase in laws enacted after a mass shooting, but
this estimate is imprecise and statistically insignificant (Appendix E).
2.4.4 Robustness Checks
In this section, we present four sets of robustness checks. First, we provide support for
the exogeneity of mass shootings. Second, we show that our main results are robust to
the inclusion of state-specific time trends. Third, we perform a falsification exercise in
which we use randomly generated placebo shootings instead of actual shootings; we show
there are no effects using the placebo shootings, providing support for our identification
strategy. Fourth, we individually drop each state from the sample and re-estimate the
models to ensure our effect is not driven by a single state or shooting event.
44
Tabl
e2.
4:M
assS
hoot
ings
and
Enac
ted
Laws
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Mas
ssh
ootin
g-0
.030
0.24
6(0
.101
)(0
.181
)Fa
talit
ies
0.01
5∗0.
008
(0.0
08)
(0.0
22)
Shoo
ting×
Rep
.leg
islat
ure
-0.0
170.
733∗
∗∗
(0.2
38)
(0.2
55)
Dem
.leg
islat
ure
0.03
7-0
.250
(0.1
40)
(0.4
02)
Split
legi
slatu
re-0
.216
0.16
8(0
.262
)(0
.340
)Fa
talit
ies×
Rep
.leg
islat
ure
0.01
80.
152∗
∗∗
(0.0
50)
(0.0
31)
Dem
.leg
islat
ure
0.01
4-0
.047
(0.0
15)
(0.0
54)
Split
legi
slatu
re0.
015
-0.0
18(0
.013
)(0
.018
)D
em.l
egisl
atur
e0.
110
0.10
50.
070
0.10
5-0
.318
-0.3
03-0
.253
-0.2
59(0
.147
)(0
.148
)(0
.171
)(0
.165
)(0
.200
)(0
.198
)(0
.229
)(0
.222
)R
ep.l
egisl
atur
e0.
165
0.18
00.
134
0.17
80.
494∗
∗∗0.
496∗
∗∗0.
402∗
0.31
8(0
.137
)(0
.137
)(0
.147
)(0
.142
)(0
.187
)(0
.190
)(0
.213
)(0
.202
)R
ep.g
over
nor
-0.0
46-0
.045
-0.0
47-0
.045
-0.1
13-0
.111
-0.0
93-0
.084
(0.0
86)
(0.0
85)
(0.0
85)
(0.0
83)
(0.1
68)
(0.1
67)
(0.1
66)
(0.1
67)
Inst
itutio
nalC
ontr
ols
••
••
••
••
Dem
ogra
phic
Con
trol
s•
••
••
••
•St
ate
Fixe
dEff
ects
••
••
••
••
Year
Fixe
dEff
ects
••
••
••
••
N1,
250
1,25
01,
250
1,25
01,
175
1,17
51,
175
1,17
5
Not
e:T
hede
pend
ent
varia
ble
isth
enu
mbe
rof
firea
rm-r
elat
edla
ws
enac
ted
(i.e.
bills
that
beca
me
law
).R
obus
tst
anda
rder
rors
clus
tere
dby
stat
ein
pare
nthe
ses.
∗p<
.10,∗
∗p<
.05,
∗∗∗p<
.01
45
2.4.4.1 Determinants of Mass Shootings
Our ability to identify the causal impact of mass shootings on policy rests on the assump-
tion that they are plausibly exogenous to other factors that would drive gun control in
a given year. Given the erratic nature of mass shootings, this is a plausible assumption.
Nonetheless, one might be concerned that both mass shootings and gun policy are being
driven by a third variable. To provide support for our assumption and interpretation,
we regress an indicator for whether a mass shooting occurs on economic, demographic,
and policy variables.
Consistent with the assumption that mass shootings are exogenous with respect to
potential confounds, the results in Appendix F show that, out of 32 variables we con-
sider, only unemployment is significantly associated with a higher probability of mass
shootings. Because higher unemployment is also associated with a reduction in gun bill
introductions (Table 2.2), the potential bias of this would work in the opposite direction
of our finding—making it unlikely that this is driving our results. To further support
our interpretation, we control for unemployment in all models. Importantly, bills in-
troduced, laws enacted, and major gun policies do not predict future mass shootings
(Appendix F).
46
2.4.4.2 State-Specific Time Trends
Another potential concern is that states have differential trends in mass shootings, and
that these trends correlate with gun regulations, which would violate the parallel trends
assumption. As a robustness check, we run our main specifications with state-specific
trends. Appendix G shows the results of re-estimating the models in Tables 2.2 and 2.3
with state-specific time trends. The inclusion of state-specific trends does not change
our main results from Tables 2.2 and 2.3. We are unable to estimate models with state-
specific trends for our analyses of tightening and loosening laws because the likelihood
function is discontinuous when including the additional parameters due to some states
having very few laws that we can identify as tightening or loosening. We can, however,
conduct a placebo analysis to address any residual concerns.
2.4.4.3 Placebo Tests
We perform a falsification exercise based on the insights of Bertrand, Duflo, and Mul-
lainathan (2004) and Donald and Lang (2007).
Specifically, we randomly assign placebo mass shootings to state-years in which no ac-
tual shooting occurred with probability equal to each state’s frequency of shootings, and
randomly draw a fatality count from the empirical distribution of fatalities. Appendix H
shows percentiles of the test statistic based on 1,000 repetitions of this procedure and
our actual test statistics from Tables 2.2 and 2.4. The results suggest our tests do not
47
over-reject the null hypothesis that mass shootings have no effect on gun policy.
2.4.4.4 Excluding Individual States
To ensure our results are not driven by a single state or shooting, we separately remove
each state from the sample and re-estimate the models. Appendix I presents graphs
of the resulting 50 coefficients for the effect of mass shootings on bills and laws, and
coefficients for the Republican and Democrat interaction terms in our analysis of laws
that tightened or loosened gun policy. The results show that dropping individual states
has little effect on our estimates.
2.5 Discussion
Mass shootings account for a small fraction of gun deaths in the United States, but have
a significant impact on gun policy. More gun laws are proposed in the year following
a mass shooting, a result that holds for both Republican- and Democrat-controlled
legislatures. Notably, mass shootings have much larger effects on policy, per fatality,
than ordinary gun homicides.
However, we also find evidence that Democrat- and Republican-controlled legislatures
differ significantly when it comes to enacting gun laws. Republicans are more likely to
loosen gun laws in the year after a mass shooting. The effect for Democrats, which tends
toward less loosening of gun restrictions after a mass shooting, is statistically insignif-
48
icant. This is consistent with survey evidence suggesting that even when a majority
supports a gun control proposal, those opposed to increased gun control are more likely
to take actions like writing a letter or donating money to support their side (Schuman
and Presser, 1981).
Our results are consistent with qualitative research that has hypothesized the pos-
sibility of mass shootings precipitating change. For example, Godwin and Schroedel
(1998) argue that the Stockton schoolyard massacre in 1989 led to the enactment of
California’s assault weapons ban. We find empirical evidence that sporadic events such
as mass shootings can lead to major policy changes. This raises the question of other
factors that might drive policy, and conditions under which we might expect such ef-
fects. For example, Egan and Mullin (2012) show that extreme temperatures influence
beliefs about global warming in the short-term. Might we expect a greater impact of
random events in some policy contexts (e.g. the effect of a terrorist attack) than in
others (e.g. the effect of an Ebola outbreak)?
Our findings raise a number of additional questions, and suggest several directions for
future research. First, our estimates focus on the impact on policy within the state in
which each shooting took place. Some mass shootings get national media attention and
potentially affect policy nationwide, which would not be identified by our fixed effects
model. One direction for future research is to develop strategies to identify national
responses. With respect to our findings, this suggests that the total impact of mass
shootings on gun policy may be even larger than our estimates.
49
Second, future research might further explore the role of salience in shaping policy by
examining the conditions under which events are more or less influential. For example,
some types of events (e.g. school shootings) may have larger effects than others, some-
thing we could not test given the relative infrequency of such events. Salient events
might have a greater impact if they occur at a time when few other events are com-
peting for media attention (Eisensee and Strömberg, 2007), or during elections, when
public attention is more focused on such issues (Bouton et al., 2014). Finally, the role
of interest groups that try to promote their preferred policies in the aftermath of such
events deserves further exploration.
Third, future research might directly explore the preferences of politicians. Do Re-
publican legislatures loosen gun restrictions because Republican politicians themselves
prefer looser restrictions or due to pressure from constituents or interest groups? For
example, if constituent preferences are driving results, we might expect that results
differ in areas with high versus low rates of gun ownership. To provide exploratory
evidence, Appendix J shows the results from adding a proxy for gun ownership to the
models. Following Cook and Ludwig (2006), we calculate the percentage of suicides
that are firearm related as a proxy for gun ownership and interact this variable with
the mass shooting indicator. The coefficient on this variable is not significant either in
isolation or when added to the specification with all political interactions. This suggests
that the tendency of Republicans to loosen gun control is not entirely driven by high
rates of gun ownership (and presumably high rates of support for less gun control), but
50
represents a distinct effect of political affiliation.
Fourth, there is a large literature on the impact of gun policies on crime (Abrams,
2012; Duggan, 2001; Ludwig and Cook, 2000, 2003), which has yielded mixed results.
The relationship we find between mass shootings and gun policy raises the possibility
of using mass shootings as an instrumental variable to study the impact of gun laws
on gun deaths. Unfortunately, in our sample, mass shootings are not a sufficiently
strong instrument to estimate the effects of gun policy on gun deaths, due to their
relative infrequency. (Appendix K presents results of this analysis.) This leaves open
the possibility of using salient and plausibly random events to instrument for policy
changes in future research.
Our findings suggest that while much attention has been rightfully devoted to un-
derstanding the impact of policy, there is a lot to be learned from exploring the de-
terminants of policy change as well. We find that even random and infrequent events
that account for a relatively small portion of total societal harm in a domain might
nonetheless be crucial levers for policy consideration and change. This does not imply
that politicians and policy makers are over-reacting; it may be that on issues where
there is usually political deadlock, salient events create opportunities for change that
has been sought all along. Whether these changes reflect appropriate responses to the
problem remains an open question.
51
3Internal Labor Markets in Multi-business Firms
This chapter is co-authored with Jasmina Chauvin.
3.1 Introduction
In recent years, an active stream of research has developed around the theory of resource
redeployment, the view that firms can generate excess value by actively managing their
resources, withdrawing them from some business units and reallocating them to others
in response to changing conditions (Helfat and Eisenhardt, 2004; Levinthal and Wu,
2010; Sakhartov and Folta, 2014, 2015; Lieberman, Lee, and Folta, 2016). The theory
is attractive because it is able to explain a potential source of competitive advantage in
diversified firms as well as diversification decisions.
52
Despite these theoretical advances, with the exception of studies on internal capital
markets, we still have little evidence regarding how firms manage their internal resource
pools. Even simple descriptive statistics for the prevalence of resource redeployment
in multi-business firms are needed (Folta, Helfat, and Karim, 2016). We also lack
direct tests for key features of theory, in particular, studies showing which resources
are redeployed and how firms’ organizational features enable or constrain redeployment.
One key challenge for work in this area is the paucity of internal firm data showing how
resources are redeployed.
In this paper, we leverage a rich dataset to study how firms allocate one key resource—
their human capital—though internal labor markets. As production processes have
become more skill- and service-driven, human capital is a critical resource for many firms.
Although a rich literature exists on external labor markets (e.g. reviewed in Mawdsley
and Somaya (2016)), we know much less about how human capital is allocated and
reallocated within the firm though internal labor markets.
We develop a simple framework to predict when a firm with labor needs in a focal
business unit will staff a position by redeploying a worker internally instead of hiring a
worker in the external labor market. The resource redeployment literature has tended
to assume that some feature of a resource makes it uneconomical to transact in external
markets. Our framework allows us to be precise about when and why firms would want
to reallocate workers internally versus source externally. Specifically, we model the
possibility that internal workers are distinct from workers available in external labor
53
markets because they possess firm-specific human capital. In addition, we also model
the costs of using external labor markets, such as the costs of hiring and firing.
By incorporating these realistic features of internal and external labor markets, the
framework shows that two distinct types of motivations can drive internal redeploy-
ments. One is external labor market frictions, e.g. the costs of hiring new workers and
the costs of firing existing workers. Even if internal and external workers were other-
wise homogeneous, such frictions would create incentives for firms to redeploy workers
internally in order to avoid these transactions costs and institutional voids (Khanna
and Palepu, 2000).
A theoretically different possibility is provided by the view that workers are resources,
embodiments of knowledge (Kogut and Zander, 1992; Grant, 1996). Some of that knowl-
edge may be non-codifiable (Teece, 1981) and firm-specific (Barney, 1991). Such knowl-
edge is acquired over time and cannot be easily transferred outside the firm (Groysberg,
Lee, and Nanda, 2008). In these cases, internal and external workers are not perfect sub-
stitutes. Even in a world lacking external labor market frictions, we would still observe
redeployment motivated by the desire to allocate this firm-specific resource, embodied
in the worker, to its most productive use within the firm.
Beyond these two distinct motivations for redeployment, our framework also incorpo-
rates the idea that a firm’s organization—both its corporate strategy as reflected in the
relatedness of its activities and its geography, i.e. the location of its business units—can
enable or constrain the firm’s ability to engage in worker redeployment. We model the
54
relatedness of the firms’ activities, in particular the occupational similarity of the firm’s
different industries, as an enabler of redeployment. Greater similarity increases the
probability that the type of worker needed in a focal unit is actually available elsewhere
in the firm. We model geographic distance between the origin and destination plants as
an increase in the transfer costs involved in redeploying workers, for example relocation
expenses and incentives paid to workers to encourage them to relocate.
Guided by the framework, we study the extent and drivers of worker redeployment
leveraging a rich employer-employee matched dataset made available by the Government
of Brazil, the Relação Anual de Informações Sociais (RAIS). In it, we can observe
all workers employed at a firm, as well as their movements among the firm’s plants.
For the current analysis, we select a ten percent random sample of all multi-business
firms operating in Brazil from 2004–2014. During this time period, multi-business firms
accounted for 14–17 percent of the total formal sector labor force of Brazil.
In stylized facts, we observe that Brazilian multi-business firms source a substantial
share of their labor needs internally. On average 12.1 percent of workers hired in any
year come from other establishments of the same firm. At any point in time, redeployed
workers represent 5.5 percent of a plant’s workers. Among workers leaving an establish-
ment, 11.8 percent move to jobs within the same firm. This percentage is even higher
when firms close an establishment; 21.8 percent of workers in establishments that are
closing down move to new positions within the same firm.
We next analyze worker-level models to gain insight into which employees are more
55
likely to be redeployed and thus infer the motivations of redeployment. Two findings
emerge consistently. First, comparing otherwise similar workers employed at the same
plant and occupation group in a year, workers with more firm-specific experience are
more likely to be redeployed. All else equal, a worker with the average level of firm
experience (2.9 years) has a 7.4 percent higher likelihood of being redeployed compared
to a worker with no firm-specific experience. Second, within a given plant, workers
higher in the organizational hierarchy are more likely to be redeployed. In particular,
on average, 8.2 percent of an establishment’s managers are redeployed in any year. This
is nearly double the 4.4 percent of service and production workers that are redeployed. If,
as commonly thought, valuable firm-specific human capital tends to reside with workers
higher up in the hierarchy, these results are consistent with the hypothesis that firms
use redeployment as a tool to reallocate valuable human capital resources.
In order to better tease apart a hypothesis of firm-specific human capital from other
potential drivers of worker redeployment—for example, external labor market frictions
(e.g. higher search and information frictions for managers) or redeployments due to work-
ers’ personal motivations—we test whether internally redeployed workers earn a wage
premium over otherwise similar workers hired in the external labor market in the same
plant, occupation, and year. We find strong evidence of wage premia to internally rede-
ployed workers. Specifically, internally redeployed workers enjoy a nine percent higher
contractual wage compared to otherwise similar workers hired into the same occupation
and establishment though the external labor marker. Moreover, this premium is small
56
for internal workers without firm-specific experience (i.e. workers hired and immediately
redeployed) and rising steeply in a worker’s years of firm-specific experience. These find-
ings also are consistent with the existence of productivity-enhancing firm-specific human
capital which allows internal workers to generate (and capture) excess returns. They
are less consistent with pure frictions in external labor markets or moves motivated by
workers’ personal preferences.
Exploring to what extent large firing costs may be driving redeployments, we find
that while plant exits are associated with more workers being redeployed, overall, only 11
percent of the redeployments that we observe are concurrent with plants shutting down.
We do not find that plants shutting down become more likely to redeploy “blue-collar”
workers who may otherwise present additional firing costs (e.g. due to unionization)
(Cestone et al., 2017). Rather we find that when a plant exits, workers highest in the
hierarchy and those with more firm-specific experience are more likely to be redeployed.
Finally, we explore how the firm’s organizational features, in particular the related-
ness of activities and its geographic footprint, affect the extent of worker redeployment.
Here we model the volume of redeployments between all possible sets of origins and des-
tinations (dyads) in a firm as a function of their industry relatedness, their geographic
distance, and proxies for differences in the growth patterns of their respective industries.
We find evidence consistent with the view that greater labor relatedness between indus-
tries facilitates the redeployment of workers while greater geographic distance between
plants strongly discourages worker redeployment.
57
Taken together, the findings point to the conclusion that internal labor markets within
multi-business firms serve as a conduit though which firm-specific human capital is
transferred among the firm’s units. We find particularly strong evidence that firms
redeploy their managerial human capital, and especially those workers with higher levels
of firm-specific experience. We find strong evidence that workers with more firm-specific
human capital earn excess rents in the form of higher wages.
The view of internal labor markets supported by our findings is quite distinct from
other prevailing views. Until recently, much of the literature of internal labor markets
focused on “vertical” labor markets, or “career ladders”—i.e. the processes through
which workers move up the hierarchy within a given firm and the ways that firms can
design appropriate promotion mechanisms for workers over their careers (e.g. Doeringer
and Piore, 1971). Recently, a literature has begun to emerge which, rather than vertical
considers the unique aspects of horizontal internal labor markets, i.e. worker moves in
multi-plant and multi-business firms. Thus far, existing studies have focused primarily
on the potential of internal labor markets to avoid frictions and rigidities of external
labor markets (Belenzon and Tsolmon, 2016) and to enable firms to adjust to unexpected
shocks, e.g. by reallocating workers from plants that are shutting down to other parts
of the firm (Tate and Yang, 2015).
In this paper we propose that, beyond these possibilities, internal labor markets can
also play the role of allocating valuable, firm-specific human capital to the parts of the
firm where it is most needed. Our view is consistent with redeployment motivated by
58
the existence of rare and valuable resources which are otherwise not available or easy to
transact in external markets. Beyond offering evidence for this alternative motivation
for internal labor market activity, our study is also unique in exploring the organi-
zational enablers of worker redeployment. Our findings suggest that the relatedness
between the different industries of the multi-business firm and the geographic proximity
of the firm’s units facilitate workers redeployment. This implies that firms for which
worker redeployment is an important part of the strategy and a source of competitive
advantage, face a trade-off between the objectives of expanding their geographic and
product boundaries and facilitating the flows of workers though the firm’s internal labor
market.
3.2 Theory and Hypotheses
The resource-based view sees the firm as a collection of “those (tangible and intangible)
assets which are tied semipermanently to the firm” (Wernerfelt, 1984, p. 172). An
important emphasis in this view is embodied in the word tied, which implies that these
assets have features that make them difficult to transact in the open market. If assets
are homogeneous or perfectly mobile, they are not a resource, which are those assets
that are valuable, rare, imperfectly imitable, and not substitutable (Barney, 1991).1
1The resource-based view is one of serval theories of the firm. In alternative theories—forexample, firms as a “nexus of contracts” (Fama, 1980)—resource flows among the firm’s unitsare not a necessary feature.
59
As a firm learns and grows, some resources get freed up and “slack” is created (Penrose,
1959). Because slack resources are difficult to transact in the open market (Teece,
1982), assuming that they are fungible across activities and that the firm cannot expand
infinitely in its primary product, this provides an incentive the firm to diversify and thus
gives rise to the multi-business firm. Once diversified, the ability to generate synergies
through the simultaneous use of resources across multiple activities provides economies
of scope and is a source of competitive advantage for multi-business firms.
More recent additions to this literature highlight that while some internal resources
have the quality of being public goods within the firm (scale free) others are rival
and their use in one part of the firm limits their use in other parts (non-scale free)
(Levinthal and Wu, 2010). For example, a firm’s brand is a scale-free resource and can
be simultaneously leveraged in different units of the firm. However, the time and skill
of the firm’s managers is a non-scale-free resource. Although scale-free resources lend
themselves to the simultaneous use across business units and the generation of synergies,
non-scale-free resources do not.
However, firms can achieve competitive advantages in resource use even for resources
that are non-scale-free through resource redeployment (Helfat and Eisenhardt, 2004).
Rather than using resources simultaneously in two different business units, firms can
redeploy resources from activities where there are less valuable and toward activities
where they are more valuable. The benefits generated by a strategy of redeployment
are termed inter-temporal economies of scope, as they generate competitive advantage
60
through the ability to optimally adjust resource use across activities over time. Resource
redeployment can help firms exit businesses with declining prospects while lowering the
costs of starting or expanding operations in more promising areas (Lieberman, Lee, and
Folta, 2016). Note that the original business unit does not necessarily close as part of
a strategy of redeployment (Folta, Helfat, and Karim, 2016).
Although theoretically attractive, synergies and resource redeployment have been
very difficult to study empirically. A key reason is the rarity of data internal to firms
that show how they allocate resources among the different business units.2 Notable are
approaches based on the observations or resource reconfiguration within particular firms
(Karim and Mitchell, 2004), though those pose the question how generalizable strategies
are across firms. Other approaches induce redeployment by observing business unit
entries and exits (Lieberman, Lee, and Folta, 2016), though the actual flows of resources
are not observed.
In this paper, we directly observe the movement and reallocation of one important
type of resource across a large set of firms—workers. Human capital resources are one
of the three resource categories identified by Barney (1991) and include “the training,
experience, judgment, intelligence, relationships, and insight of individual managers
and workers in a firm” (Barney, 1991, original emphasis). However, not all workers
constitute resources. To the extent that a worker’s attributes are homogeneous, and
2An important exception is the literature on internal capital markets, which has documented theextensive use of internal allocation mechanisms and the relative advantages of internal versusexternal capital markets, e.g. Lamont (1997); Stein (1997); Shin and Stulz (1998).
61
thus easily substitutable, then this worker would not be considered a resource. On the
other hand, if a worker has some rare skills or has made certain firm-specific investments
and possesses firm-specific knowledge (Morris et al., 2017), then the worker constitutes
a resource. If workers possess firm-specific knowledge, they are not fully substitutable
though workers available in external labor markets. With the exception of a few types of
workers (e.g. the CEO), workers are a non-scale-free resource—their use in one activity
prevents their use in another.
How do multi-business firms decide how to optimally allocate this key resource, work-
ers, across their different business units? Both the theoretical and empirical literature
on this specific question is scarce. Existing studies have tended to focus on the internal-
to-external transitions of workers (e.g. see literature reviewed in Mawdsley and Somaya
(2016)) or internal labor markets as a means of vertically transitioning workers though
a firm’s hierarchy, i.e. “career ladders” (Doeringer and Piore, 1971).
We develop a simple model of the decision to fill labor needs in a business unit by
redeploying workers internally instead of hiring them in the external labor market. The
model incorporates the assumption that (at least some) workers have firm-specific hu-
man capital, i.e. indeed constitute a resource. The model also incorporates key features
of the theory of resource redeployment (Helfat and Eisenhardt, 2004; Sakhartov and
Folta, 2014, 2015), such as adjustment costs (in particular, industry relatedness) and
industry-level inducements, tailoring them to the specific context of internal labor mar-
kets. Finally, we incorporate the existence of external market frictions, in particular cost
62
and rigidities associated with hiring and firing of workers (Lafontaine and Sivadasan,
2009; Belenzon and Tsolmon, 2016). By explicitly modeling the choice of internal re-
deployment alongside the alternative of external market resource acquisition, we are
precise about the conditions under which internal market transactions are preferable,
which the literature has tended to not specifically address.
3.2.1 A Simple Model of Worker Redeployment
In this section, we propose a simple model to gain insights under what conditions a multi-
business firm will staff a labor need in a focal business unit via internal redeployment
versus the external labor market.
The firm’s objective in any period is to maximize the sum of profits across its business
units. We assume, for simplicity, that the firm operates two business units, one in
industry j and the other in k, such that πf = πj + πk. We assume that demand in each
business unit is exogenously given by dj = D̄j/Nj where D̄j is industry demand and
Nj is the number of firms in industry j, and that prices are perfectly competitive with
p = 1. Each business unit j requires one worker of a particular type o (think of type
as an occupation, e.g. a welder) who can produce any quantity of output at a constant
marginal cost. Labor is the only input into production and the constant marginal cost of
a unit of output is MCj = w/L̃ij where w is the wage and L̃ij is the labor productivity
of worker i in business unit j. A business unit employing worker i, thus has variable
63
profits of: πj = dj(1− wL̃ij
).3
The labor productivity of a worker of type o in business unit j is a function of two
terms: 1) the worker’s general skills (e.g. general expertise, education, experience) and 2)
the worker’s firm-specific human capital (e.g. tacit knowledge, internal social networks),
which we model as: L̃ij = hisif . Letting I represent an internal and X an external hire,
we assume that sI > 1 and sX = 1, meaning only internal workers have productivity-
enhancing firm-specific human capital. Thus, we have: L̃Iij = hisif and L̃X
ij = hi with
sif ≥ 1. In what follows, for brevity, we denote πIj as the profits generated in a business
unit when it employs an internal hire and πXj the profits generated by employing an
external hire.
A firm looking to hire a unit of labor in business unit j faces two options to fill the
position: hiring externally or redeploying the worker internally from k. Hiring for j in
the external labor market incurs a one-time hiring cost HCj (e.g. the costs of time,
search) while redeployment from k to j incurs a transfer cost TCkj . Brazilian labor
law, for example, guarantees certain rights to employees in the case of internal company
transfer to a different address, among them the payment of relocation expenses. If the
3Because we are focused on within-firm, rather than between-firm dynamics, in order to keepthe framework simple, we assume perfect competition on the demand side with cost differenceson the supply side, without specifying a general equilibrium model of competition betweenfirms where firms price at marginal cost. However, the model conclusions do not hinge onthese simplifying assumptions. The key aspects that are required for our analysis are thatfirms’ demand is determined to some extent by external industry conditions and that firms haveheterogeneous marginal costs with lower marginal costs mapping into higher profits. Specifyingconstant electricity of substitution demand with monopolistic competition among firms wouldalso deliver the conditions needed for the analysis.
64
firm has no slack in k (an assumption to be relaxed later), redeploying the worker also
implies replacing her with an external hire in the origin unit k, i.e. incurring a hiring
cost in k, HCk.
Finally, whether a worker of type o is available within the firm is a function of the
labor similarity of industries j and k, which we denote by γjk. For example, if j requires
a welder, than γjk denotes the likelihood that a welder is indeed available in business unit
k (e.g. if k is the firm’s marketing unit, this probability will be low). This probability
will range from zero to one, i.e. 0 ≤ γjk ≤ 1.4 Conditional on a worker of type o
being available within the firm (a probability that’s increasing in γij), then the firm will
choose the optimal hiring institution, H∗. The choice between hiring internally (HI)
and hiring externally (HX) is:
H∗ =
HI if πI
j + πXk − TCkj −HCk ≥ πX
j + πIk −HCj
HX otherwise
(3.1)
This inequality can also be expressed as, hire internally if:
(πIj − πX
j ) + (πXk − πI
k) ≥ TCkj − (HCj −HCk) (3.2)
Equation 3.2 shows that an internal redeployment will take place if the incremental
benefits that an internal worker generates over an external worker in destination unit j
(term 1) net of the benefits foregone by replacing an internal worker with external one
4Note that we assume that external labor markets are thick enough that a firm can always findthe worker of the needed type.
65
in the origin unit k (term 2) exceed the transfer cost net of any difference in the cost of
external hiring in j relative to k.
In the absence of prohibitive hiring costs in j, for a redeployment incentive to exist it
has to be the case that the first term of the equation is positive, πIj − πX
j > 0: internal
workers have an advantage over external ones in j. Omitting the worker subscript i, this
implies: dj(1− whsf
) > dj(1− wh ). Assuming for now that internal and external workers
with the same general skills are paid the same wage, wI = wX , this is true when sf > 1,
firm-specific human capital advantages exist. Therefore, in the absence of slack and
with zero differences in hiring frictions across locations, workers possessing firm-specific
human capital is a necessary condition for redeployment to take place.
At the same time, note that an internal worker with high levels of firm-specific human
capital will also be more valuable in the origin.5 Indeed, in order for the transfer to be
profitable, it has to be the case that the gains of the internal worker in j exceed the
opportunity cost of the internal worker at origin k. This condition is met when:
dj(1−w
hsf)− dj(1−
w
h) ≥ dk(1−
w
hsf)− dk(1−
w
h) (3.3)
which is true when dj ≥ dk—i.e. demand conditions in the destination are weakly better
than in the origin.
Thus a positive difference in demand conditions between the destination and origin
5Note that we assume that an internal worker is always at least as profitable as an externalworker in the origin business unit, i.e. sif ≥ 1 in the origin.
66
is a second necessary condition for worker redeployment to occur. These observations
allow us to formalize our first set of hypotheses:
Hypothesis 1: All else equal, a worker will more likely be redeployed the higher
their level of firm-specific human capital. (firm-specific human capital hypothesis)
Hypothesis 2: Redeployments will be higher, the more positive the industry con-
ditions of the destination relative to the origin. (inducement hypothesis)
Hypothesis 3: Redeployments will be higher, the greater the labor relatedness of
two industries. (relatedness hypothesis)
Revisiting equation 3.2, even if firm-specific knowledge and positive industry differ-
entials exist, the relative benefits of the internal worker in j have to be sufficiently large
to compensate for transfer costs. In the case of worker redeployment, transfer costs
will include things like reimbursements that a firm has to pay a worker for the cost
of moving, as well as any incremental incentives (e.g. bonuses) that the firm will pay
to encourage the worker to relocate. In general, geographic distance between plants is
likely to imply greater transfer costs, and thus all else equal, fewer redeployments. Fi-
nally, per equation 3.2, a differential in the external market hiring costs can also create
an incentive for redeployment. In particular, destinations where the external market
hiring costs are high, are likely to see more redeployments from within the firm. Seen in
a different light, the incremental advantages of an internal workers at the destination
67
can be lower for destinations where the external labor market hiring costs are high. We
formalize these observations:
Hypothesis 4: Redeployments will be will be higher the lower the geographic
distance between the origin and destination business units. (distance hypothesis)
Hypothesis 5: Redeployments will be higher the more unfavorable local labor
market conditions of the destination relative to the origin business unit. (external
labor market frictions)
We next consider a situation where the firm has slack, defined as a need of firing a
worker in the origin business unit.6 This could be due to learning-by-doing, or because
the firm is shutting down the origin business unit, for example, due to unfavorable
industry conditions.7 When a firm has a hiring need in j and a slack worker in k, it will
redeploy the worker internally if:
πIj − TCkj ≥ πX
j −HCj − FCk (3.4)
which can be rewritten as:
(πIj − πX
j ) ≥ TCkj − (HCj + FCk) (3.5)
6We assume that workers are indivisible and use the term “slack” to denote that the workeris superfluous, rather than that she has some excess capacity — i.e. we do not model thepossibility of synergies whereby a worker is active in more than one establishment at the sametime.
7While, to keep the framework simple, we have not modeled any fixed costs of operating, onecan imagine the existence of fixed overhead costs in each period, which can lead a firm to decideto shut down when demand is low.
68
Comparing equations 3.2 and 3.5, we see that the threshold for redeployment is always
lower when the firm has slack. Note also from equation 3.5, that in some scenarios, the
sum of hiring and firing costs may exceed the transfer cost and redeployment may occur
even when the right hand side of equation 3.5 is negative—i.e. when internal workers
do not have a productivity advantage over external workers in the destination unit.
Therefore, redeployments motivated by plant closures may lead to some “inefficient
hires” from the perspective of the receiving business unit, which would have been able
to attract a higher quality human capital in the absence of firing costs in the origin.
Note also that, keeping constant the left side of equation 3.5, a situation involving slack
and positive firing costs implies potential for higher levels of transfer costs, compared
to a situation of no slack. These observations lead to:
Hypothesis 6a: All else equal, a worker will be more likely to be redeployed if
their business unit is exiting. (slack hypothesis)
Hypothesis 6b: Redeployments occurring when a business unit is exiting will occur
at higher geographic distances, on average, than redeployments occurring when a
business unit is not exiting. (slack-distance trade-off hypothesis)
Finally, note that thus far, we have assumed that any productivity advantages that
internal workers generate due to their firm-specific human capital accrue to the firm via
higher profits. In reality, is it likely that firms and workers share the rents generated
via some form of Nash bargaining. Therefore, due to their firm-specific human capital,
69
internal hires may earn higher wages than external hires with the same level of general
skills: wI > wX | hI = hX .8 Combining the possibility of excess returns to firm-specific
capital with the insights reflected in the prior hypotheses we propose:
Hypothesis 7 : A redeployed worker is likely to earn higher wages compared to a
worker in the same position and comparable general skills hired externally. The
wage premium to the internal worker will be increasing in the worker’s level of
firm-specific human capital. (wage advantage hypothesis)
Overall, this simple model provides several insights into a dual role of internal labor
markets. We see that there are two distinct types on inducements to redeployment ac-
tivity: one, the desire to transfer the “best” workers to their most productive uses, for
example in response in differences industry prospects and two, the desire to reduce ad-
justment costs given slack in an existing business units. These two types of inducements
have different implications for which workers are transferred, the wage earnings of the
transferred workers, and the productivity advantage of internal versus external hires for
the firm. Although redeployments in the absence of slack incentivize the transfer of the
workers with the highest levels of firm-specific human capital, redeployments involving
slack will be associated with relatively lower levels of human capital and may even result
in some “inefficient redeployments” from the perspective of the destination unit. Thus,
8Although it’s also possible that internal hires may be willing to accept a lower wage thanexternal hires, which would also allow for instances of redeployment driven by this internalwage gap, such cases should not be part of an equilibrium as worker could always quit andreceive the (higher) market wage for their skills.
70
we see that internal labor markets can play both the function of allocating firm-specific
knowledge to its most valuable uses as well as providing an adjustment mechanisms to
weather negative shocks, with better-performing different business units absorbing slack
generated in business units that under-perform or experience a negative external shock.
3.3 Data and Empirical Strategy
Our primary data source is the Relação Anual de Informações Sociais (RAIS), a manda-
tory, annual census of all formal-sector employers and their employees in Brazil. These
data are collected by the Ministry of Labor to support various social insurance programs
and contain detailed information on the wages, occupation, and demographics of work-
ers along with the industry and location of employers. Importantly for our purposes,
RAIS is an establishment-level census with unique identifiers for each worker, establish-
ment, and firm. We can therefore link workers to firms and observe them moving both
between and within firms over time.
We take a ten percent random sample from the population of firms in RAIS that
operated establishments in multiple industries between 2004 and 2014. This results in
an initial sample of 31,428 establishments in 8,535 unique firms. The average (median)
multi-business firm has 3.6 (2) establishments in 2.1 (2) industries.
To identify instances of worker redeployment, we start with observations from the first
and last month that each worker was employed during the year and take the worker’s
71
highest paying job within each establishment-month pair. We code redeployment as
a worker switching establishments either between the first and last month of employ-
ment within a year or from one year to the following calendar year. When analyzing
redeployments, we exclude the first and last years of our sample because we cannot ob-
serve redeployments occurring between years for the initial and final sample year. This
procedure identifies 573,259 worker redeployments for 455,514 unique workers in 7,605
firms over the nine-year period from 2005 to 2013. The final sample of redeployments
contains fewer firms than the initial random sample due to the exclusion of the first and
last sample years.
Following recent literature (Sakhartov and Folta, 2014), our main measure of industry
resource relatedness is built from the similarity of industries’ occupational requirements.
Using data from the year 2000 from RAIS—five years before our sample period—we cal-
culate each of 2,331 different occupations’ share of total employment for each industry
and then calculate labor relatedness between any two industries as one minus the Eu-
clidean distance of their occupation shares. We then normalize this variable across all
the industry pairs to have mean zero and variance equal to one.
To measure the industry opportunities that may act as an inducement for redeploying
workers to activities with high returns, we use the two-year growth rate in total industry
employment. This measure assumes that greater employment growth within an industry
over time is indicative of better prospects for firms. This variable is calculated from
RAIS as the total number of unique workers with jobs within a given industry and year
72
across all firms in Brazil.
Tables 3.1, 3.2, and 3.3 show summary statistics for workers, establishments, and
destination-origin establishment pairs respectively.
We conduct two types of analyses, one at the level of individual workers and the other
at the level of business unit dyads. The first set of worker-level models take the form:
Redepikt = β + βssit +∑l
βlhit + βzzkt + ηk + τt + ϵikt (3.6)
where the dependent variable takes the value one if a worker was redeployed in the year
and zero otherwise. sit is a proxy for a worker’s level of firm-specific human capital in
year t, hit control for the worker’s general level of human capital (education, age, age
squared, gender), zkt is establishment size, η and τ are establishment and year fixed
effects, and ϵikt is a randomly-distributed error term. We perform the analysis using a
linear probability model.
The focus of the model is the coefficient on the firm-specific capital proxy, βs, which
conditional of the worker’s general skills, estimates the effect of a worker’s level of firm-
specific human capital on their probability of redeployment. Note that, due to the rich
set of fixed effects, the comparison is among workers with different levels of firm ex-
perience working in the same establishment in the same year. We employ two proxies
of a worker’s level of firm-specific human capital. The first is the worker’s years of
work experience with the firm. Our second proxy is the worker’s position in the occu-
pational hierarchy, i.e. whether the worker’s occupation falls in the director/manager,
73
Table 3.1: Worker Summary Statistics
Variable mean sd p5 p25 p50 p75 p95
Redeployment 0.04 0.18 0 0 0 0 0Log wage 0.77 0.72 0.01 0.28 0.60 1.09 2.23Firm experience 2.85 4.16 0 0 1 4 11Age 31.48 9.77 19 24 29 37 51Female 0.37 0.48 0 0 0 1 1
Occupation groupsManagers 0.03 0.18 0 0 0 0 0Professionals 0.04 0.20 0 0 0 0 0Technicians & Admin 0.27 0.45 0 0 0 1 1Service & Production 0.65 0.48 0 0 1 1 1
Education groupsBelow high school 0.40 0.49 0 0 0 1 1High school 0.51 0.50 0 0 1 1 1Higher education 0.09 0.29 0 0 0 0 1
Note: Redeployment is a dummy variable for worker redeployment in a given year.Firm experience and age are measured in years.
Table 3.2: Establishment Summary Statistics
Variable mean sd p5 p25 p50 p75 p95
Employees 66.4 323.1 1 4 11 33 249New hires 26.7 151.1 0 1 4 13 96Separations 23.0 120.9 0 1 3 12 85Closing year 0.06 0.3 0 0 0 0 1
Note: Separations refers to workers leaving the establishment. Closing year refers tothe last year an establishment operates with employees in RAIS.
Table 3.3: Destination-Origin Dyad Summary Statistics
Variable mean sd p5 p25 p50 p75 p95
Industry similarity 1.82 1.20 -0.57 1.41 2.44 2.44 2.44Distance (km) 824 816 18 203 545 1,145 2,683Difference in growth 0.00 0.12 -0.11 0.00 0.00 0.00 0.11Closing origin 0.06 0.25 0 0 0 0 1
Note: Difference in growth is calculated as the two-year employment growth rate inthe industry of the destination establishment less the same growth rate in the industryof the origin establishment. Closing origin refers to the last year that an origin operateswith employees in RAIS.
74
professional, technical and administrative personnel, or production and service worker
category. Our prior is that workers higher in the occupational hierarchy are likely to
posses rarer and more valuable firm-specific human capital.
Based on H1, the specific human capital hypothesis, we expect βs to be positive—
i.e. workers with higher levels of firm-specific human capital will have a higher likelihood
of being redeployed. We also use a slightly modified version of this model to test H6a
(the slack hypothesis) by adding to the model specified in equation 3.6 an indicator
variable for whether the worker’s current establishment exits in that year. Based on the
hypothesis, we expect the coefficient on the exit indicator to be positive and significant.
Our second worker-level model takes workers’ contractual wage as the dependent
variable. Our tests regarding the wage advantages of redeployed workers over workers
hired in the external labor market take the following form:
lnwiojt = β + βiRedepit + βssit +∑l
βlhit + θojt + ϵiojt (3.7)
The sample of observations for this model are all new employees joining business unit j
at time t, which are sourced from either the internal or the external labor market. redepit
is an indicator variable taking the value one if the worker was redeployed internally and
zero if hired externally. By including a fixed effect for each occupation-establishment-
year combination and the full set of worker controls, we are effectively comparing the
wage of an internal and an external hire entering the same occupation, in the same
establishment, in the same year with the same observable characteristics. Per H7 (the
75
wage advantage hypothesis), we expect βi to be positive. To further test whether it is
the worker’s firm-specific human capital that is driving the wage premium, we introduce
βssit. The hypothesis is that the wage premium of workers with little firm experience
will be small while βs will be positive.
The final set of models are estimated at the level of business unit dyads. We construct
the set of all possible origin- and destination- business units (dyads) within each firm
and measure the total amount of redeployments between them in each year (note that
each dyad is directional, thus a → b is not equal to b → a). We estimate the following
model:
Redepkjft = α+ β1△djkt + β2γjk + β3Geojkt + β4zjt + β5zkt + ξf + τt + ϵkjft (3.8)
where Redepkjft is the number of workers redeployed from origin business unit k to
destination j within firm f in year t, △djkt is a measure of the difference in prospects
of j and k’s industries, γjk is the industry relatedness, Geojkt is geographic distance
between the plants and the zs are establishment controls. The model also includes firm-
and year fixed effects. To test H2 (the inducement hypothesis), we focus on the sign of β1
which is expected to be positive. We test H3 (the relatedness hypotheses) by evaluating
the sign on β2 which is expected to be positive. H4 (the distance hypothesis) predicts
that the sign of β3 is negative. In other versions of this model, we also introduce an
indicator for whether the origin business unit exits at t and its interaction with the main
effects, to test H6b, whether redeployments occur at larger distances when the origin
76
Table 3.4: Percentage of Workers Redeployed by Establishment
Condition mean sd p50 p75 p90 p95 p99
Incoming redeployments as percentage ofEmployees 5.5 15.2 0.0 2.5 15.4 33.3 91.3New Hires 12.1 24.0 0.0 12.5 50.0 71.4 100New Plants 22.5 32.8 0.0 40.0 81.8 100 100
Outgoing redeployments as percentage ofEmployees 4.8 13.8 0.0 2.2 13.6 25.0 83.3Separations 11.8 23.9 0.0 12.5 44.4 68.8 100Closing Plants 21.8 35.2 0.0 33.3 100 100 100
Note: Numbers represent redeployments as a percentage of employees in each cat-egory. Incoming redeployments are workers joining an establishment from anotherestablishment owned by the same firm; outgoing redeployments are workers leavingan establishment for another establishment of the same firm. Numbers for hires andseparations (i.e. workers leaving the establishment) are conditional on anyone beinghired or exiting.
plant is exiting.
3.4 Results
Worker redeployment is pervasive. Table 3.4 shows that 12.1 percent of new hires
come from other establishments of the firm. Among workers leaving an establishment,
11.8 percent move to jobs within the same firm. This percentage is even higher when
firms close an establishment; 21.8 percent of workers in establishments that are ceasing
operations move to new positions within the same firm.
Employees higher in the organizational hierarchy and employees in professional roles
are more likely than others to be redeployed between establishments of the same firm.
Table 3.5 shows that on average, 8.2 percent of an establishment’s managers are rede-
77
Table 3.5: Percentage of Workers Redeployed by Occupation
Occupation All Years Closing Plants New Plants
Managers 8.2 27.7 41.6Professionals 6.4 30.2 34.1Technicians & Admin 5.0 23.4 25.4Service & Production 4.4 21.3 20.5Total 5.6 23.7 27.0
Note: Numbers represent redeployments as a percentage of employees in each cat-egory. Closing Plants refers to establishments in their final year of operation andnumbers represent the percentage of employees who move to another establishment ofthe same firm. New Plants refers to the first year of a new establishment and numbersrepresent the percentage of employees hired from other establishments of the samefirm.
ployed in any year. This is nearly double the 4.4 percent of service and production
workers that are redeployed. The gap in redeployment between managers and others
narrows when establishments close. On average, closing establishments redeploy 28 per-
cent of their managers, 30 percent of their professionals, and roughly 22 percent of other
workers. Managers’ increased likelihood of redeployment is suggestive evidence for the
hypothesis that firms use redeployment as a tool for reallocating valuable human capital
resources.
Consistent with the hypothesis that redeployment is increasing in firm-specific human
capital, Table 3.6 shows an additional year of experience working in a firm increases the
probability of redeployment by roughly 0.1 percentage points. About 3.5 percent of
all workers are redeployed in any given year, so a 0.1 percentage point increase repre-
sents a 2.9 percent increase in the probability of redeployment. Model 2 of Table 3.6
provides further support for the hypothesis. Workers who are more likely to have valu-
78
Table 3.6: Redeployment of Workers
(1) (2) (3) (4) (5)
Firm experience 0.0009∗∗∗ 0.0007∗∗∗ 0.0008∗∗∗(0.0003) (0.0002) (0.0003)
Managers 0.0481∗∗∗ 0.0466∗∗∗ 0.0466∗∗∗(0.0118) (0.0113) (0.0121)
Professionals 0.0091∗∗ 0.0086∗ 0.0076∗(0.0045) (0.0044) (0.0046)
Technicians & Admin 0.0069∗∗∗ 0.0064∗∗∗ 0.0059∗∗∗(0.0012) (0.0012) (0.0012)
Closing 0.1353∗∗∗ 0.1315∗∗∗(0.0321) (0.0309)
Closing ×Firm experience 0.0045∗∗
(0.0020)Managers 0.0815∗∗∗
(0.0248)Professionals 0.1137∗∗∗
(0.0313)Technicians & Admin 0.0609∗∗∗
(0.0226)High school 0.0047∗∗∗ 0.0029∗∗∗ 0.0031∗∗∗ 0.0047∗∗∗ 0.0028∗∗∗
(0.0008) (0.0008) (0.0008) (0.0008) (0.0008)Higher Ed 0.0161∗∗∗ 0.0071 0.0075∗ 0.0163∗∗∗ 0.0072
(0.0042) (0.0045) (0.0043) (0.0042) (0.0045)Age 0.0013∗∗∗ 0.0014∗∗∗ 0.0012∗∗∗ 0.0013∗∗∗ 0.0013∗∗∗
(0.0003) (0.0003) (0.0003) (0.0003) (0.0003)Age squared -0.0000∗∗∗ -0.0000∗∗∗ -0.0000∗∗∗ -0.0000∗∗∗ -0.0000∗∗∗
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)Female -0.0031∗∗∗ -0.0031∗∗∗ -0.0029∗∗∗ -0.0032∗∗∗ -0.0031∗∗∗
(0.0011) (0.0010) (0.0009) (0.0011) (0.0009)Log employment 0.0141∗∗∗ 0.0139∗∗∗ 0.0144∗∗∗ 0.0195∗∗∗ 0.0193∗∗∗
(0.0049) (0.0049) (0.0049) (0.0051) (0.0051)
Firms 7,474 7,474 7,474 7,474 7,474Observations 6,179,313 6,179,313 6,179,313 6,179,313 6,179,313
Note: Standard errors in parentheses are clustered by firm. All models include establishment andyear fixed effects. The excluded category for the education dummies is “Less than high school”; theexcluded category for the occupation categories is “Service & Production” workers.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
79
able firm-specific human capital—managers and professionals—are much more likely to
be redeployed. Controlling for worker characteristics and establishment and year fixed
effects, managers are redeployed at a rate that is nearly five percentage points higher
than the probability of redeployment for service and production workers.
Closing an establishment dramatically increases the probability that workers will be
redeployed. As hypothesized, Table 3.6 shows that closing an establishment is associated
with a 13.5 percentage point increase in the probability of redeployment; this is a more
than 300 percent increase in the probability of redeployment. Models 4 and 5 further
show that the impact of closing an establishment is even greater for employees with
more firm-specific work experience and those higher in the organizational hierarchy—
e.g. managers. Intuitively, an establishment closing represents an opportunity for the
loss of rents from firm-specific human capital. In these cases, redeployment allows the
firm to keep a worker within its boundaries and therefore maintain any benefits of
firm-specific human capital.
Workers who are redeployed (i.e. internal hires) earn a wage premium over workers
who are hired externally, and the premium is increasing in the firm-specific experience of
redeployed workers. Models 1–4 of Table 3.7 compare the contractual wage of internal
and external hires, controlling for an establishment-occupation-year fixed effect.9 This
9The RAIS data provide information on several different measures of worker compensation,including the contractual wage and the actual amounts paid out to workers in any given year.While the two are highly correlated, for our analysis we use the contractual wage in order tonot capture any non-recurring payments that may otherwise be included in that year’s wagefor redeployed workers, such as relocation bonuses.
80
Table 3.7: Wages of Redeployed Workers
(1) (2) (3) (4) (5)
Redeploy 0.093∗∗∗ 0.032∗∗∗ 0.036∗∗∗ 0.095∗∗∗ 0.012(0.011) (0.009) (0.010) (0.015) (0.011)
Firm exp. 0.024∗∗∗ 0.024∗∗∗ 0.016∗∗∗(0.004) (0.004) (0.001)
Closing origin -0.041∗∗∗(0.014)
Redeploy ×Firm exp. 0.006∗∗
(0.003)Managers 0.014
(0.021)Professionals -0.011
(0.025)Technicians and Admin -0.008
(0.017)High school 0.016∗∗∗ 0.017∗∗∗ 0.017∗∗∗ 0.016∗∗∗ 0.041∗∗∗
(0.002) (0.002) (0.002) (0.002) (0.004)Higher Ed 0.174∗∗∗ 0.177∗∗∗ 0.177∗∗∗ 0.174∗∗∗ 0.252∗∗∗
(0.013) (0.012) (0.012) (0.013) (0.013)Age 0.004∗∗∗ 0.004∗∗∗ 0.004∗∗∗ 0.004∗∗∗ 0.011∗∗∗
(0.001) (0.001) (0.001) (0.001) (0.001)Age squared -0.000∗ -0.000∗∗ -0.000∗∗ -0.000∗ -0.000∗∗∗
(0.000) (0.000) (0.000) (0.000) (0.000)Female -0.021∗∗∗ -0.021∗∗∗ -0.021∗∗∗ -0.021∗∗∗ -0.047∗∗∗
(0.004) (0.004) (0.004) (0.004) (0.004)
Firms 5,991 5,991 5,991 5,991 6,749Observations 1,833,356 1,833,356 1,833,356 1,833,356 3,421,007
Note: All models include establishment-occupation-year fixed effects so that comparisons betweenredeployed workers and other workers are within establishment-occupation-year. The excluded cate-gory for the education dummies is “Less than high school”; the excluded category for the occupationcategories is “Service & Production” workers. Standard errors in parentheses are clustered by firm.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
81
model compares workers hired for the same occupation, in the same establishment, and
in the same year. On average, redeployed workers — i.e. those hired internally from
another unit of the same firm — earn about nine percent more than workers hired from
other firms (model 1). Model 2 shows that this wage premium is increasing in the firm-
specific work experience of redeployed workers. Specifically, a worker redeployed in their
first year with zero years of firm-specific experience earns an average wage premium of
3.5 percent over external hires. This premium increases by roughly 2.4 percent for each
year of experience working within the firm. Model 3 suggests that workers hired from
closing establishments within the same firm, however, earn much lower wage premiums
over external hires than workers moving from establishments that are not shutting down.
The comparison between workers moving from closing versus continuing establishments
should be interpreted cautiously; the use of establishment-occupation-year fixed effects
in the model means that this comparison depends on establishments hiring internal
workers from both closing and ongoing establishments to perform the same job in the
same year.10
One possible explanation for the large wage premium of internal over external hires
is unobservable differences in skill between those who are hired internally through rede-
ployment and workers hired through external labor markets. To address this possibility,
Model 5 of Table 3.7 compares the wages of redeployed workers and other workers
10There are 1,690 establishment-occupation-year cells with this variation (out of approximately1.8 million total observations.
82
Table 3.8: Adjustment Costs and Redeployment
(1) (2) (3) (4)
Diff. in growth 0.017 0.018(0.017) (0.017)
Industry similarity 0.029∗∗∗ 0.037∗∗∗(0.010) (0.011)
Log distance -0.125∗∗∗ -0.125∗∗∗(0.013) (0.013)
Dest. log employment 0.074∗∗∗ 0.075∗∗∗ 0.082∗∗∗ 0.083∗∗∗(0.011) (0.011) (0.010) (0.010)
Origin log employment 0.138∗∗∗ 0.138∗∗∗ 0.149∗∗∗ 0.150∗∗∗(0.024) (0.024) (0.023) (0.023)
Firms 2,286 2,286 2,282 2,282Observations 59,266 59,264 59,219 59,217
Note: Observations are establishment dyads (i.e. a destination and originestablishment pair) with positive redeployment. The dependent variable isthe natural logarithm of redeployments. All models include firm, destinationindustry, origin industry, and year fixed effects. Standard errors in parenthe-ses are clustered by firm.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
at the destination who were not hired that year. In other words, unlike models 1–4,
model 5 compares redeployed workers to their peers who were not redeployed. These
results show no statistically significant wage premium for redeployment in the absence
of firm-specific experience. This suggests that redeployed workers in fact resemble other
workers in their destination establishment. The interaction of redeployment with firm-
specific experience, however, indicates that redeployed workers may earn an additional
premium for firm-specific work experience. One additional year of firm-specific expe-
rience increases the wages of workers who are not redeployed by roughly 1.6 percent,
versus a 2.2 percent increase for those who are redeployed.
Firms redeploy workers more intensively between establishments in related industries
83
and establishments that are geographically closer to each other. Table 3.8 shows that a
one standard deviation increase in industry similarity is associated with a three percent
increase in redeployments. Models 3 and 4 show that a one percent increase in distance
between establishments is associated with 0.13 percent fewer redeployments. Together,
these results support the relatedness and distance hypotheses (i.e. hypotheses 3 and 4).
The results for the inducement hypothesis that favorable industry conditions in a desti-
nation establishment relative to an origin establishment are equivocal. The coefficients
in models 1 and 4 of Table 3.8 have the expected sign, but are not statistically significant.
Consistent with the results of Table 3.6 showing an increased likelihood of redeploy-
ment when closing an establishment, models 1–3 of Table 3.9 show that the intensity
of redeployment is also greater when closing an establishment even after controlling for
industry similarity, geographic distance, differences in industry growth, and establish-
ment size. Specifically, a closing origin establishment is associated with a roughly 50
percent increase in the number of worker redeployments (see model 1). Models 2–3,
however, fail to support the hypotheses that redeployments occurring when an estab-
lishment closes (i.e. under slack conditions) will be less sensitive to industry relatedness
and geographic distance. The coefficients on the interactions between an establishment
closing and industry similarity or geographic distance do not have the expected sign; the
positive and negative coefficients on these interactions respectively suggest that firms
may be more sensitive to industry relatedness and geographic distance when redeploy-
ing workers from a closing establishment. Some caution is warranted, however, when
84
Table 3.9: Adjustment Costs and Redeployment When Closing
(1) (2) (3)
Closing origin 0.508∗∗∗ 0.421∗∗∗ 0.728∗∗∗(0.081) (0.053) (0.106)
Closing origin ×Industry similarity 0.050∗
(0.030)Log distance -0.060∗∗∗
(0.018)Industry similarity 0.036∗∗∗ 0.032∗∗∗ 0.036∗∗∗
(0.010) (0.011) (0.010)Log distance -0.124∗∗∗ -0.124∗∗∗ -0.121∗∗∗
(0.013) (0.013) (0.013)Diff. in growth 0.014 0.015 0.014
(0.018) (0.018) (0.018)Dest. log employment 0.080∗∗∗ 0.081∗∗∗ 0.081∗∗∗
(0.010) (0.010) (0.010)Origin log employment 0.174∗∗∗ 0.173∗∗∗ 0.174∗∗∗
(0.020) (0.020) (0.020)
Firms 2,282 2,282 2,282Observations 59,217 59,217 59,217
Note: Observations are establishment dyads (i.e. a destination andorigin establishment pair) with positive redeployment. The dependentvariable is the natural logarithm of redeployments. All models includefirm, destination industry, origin industry, and year fixed effects. Stan-dard errors in parentheses are clustered by firm.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
85
interpreting these results because workers redeployed when establishments close may be
unlike workers redeployed during normal business conditions. Specifically, such workers
may be less adaptable to new industries or more sensitive to moving large geographic
distances.
3.5 Conclusion
This study has explored the extent and drivers of internal labor market activity in
multi-business firms in the context of Brazil. We have presented a simple framework
where two distinct forces can give rise to an incentive to redeploy workers: external
labor market frictions (hiring and firing costs) and workers’ possession of valuable, firm-
specific knowledge.
We find that Brazilian multi-business firms source a meaningful share of their workers
from within the firm. In an average year, the typical establishment sources 12.1 percent
of new hires internally. Studying what predicts whether a worker is redeployed, we find
evidence that both workers higher in the occupational hierarchy and workers with more
firm-specific experience are more likely to be redeployed. Managers, in particular, are
redeployed more than twice as often as the average worker.
We also study the wages of workers hired into a position through internal redeploy-
ment versus the external labor market. Comparing two workers hired into the same
narrow occupation, in the same establishment, in the same year, with otherwise similar
86
characteristics, we find that redeployed workers earn a nine percent wage premium over
those hired externally. On the other hand, redeployed workers do not earn a substantial
premium over otherwise comparable workers at the destination, suggesting that these
results are not driven by selection on unobservable worker quality (redeployed workers
being of better quality relative to other internal workers). The wage premium is consis-
tent with firm-specific experience rather than worker’s personal motivations or external
hiring frictions driving redeployment.
Our paper contributes to existing theory of resource redeployment, which has theo-
rized but rarely observed actual redeployment. Our results show that redeployment is
pervasive in the context of internal labor markets in multi-business firms, and most of-
ten does not involve the simultaneous exit of the origin business unit. Furthermore, our
paper contributes to the broader literature on internal labor markets. Compared to the
existing focus on vertical labor markets and horizontal labor markets as a response to
external labor market frictions, the results of our paper support the view that internal
labor markets also serve as conduits of firm-specific knowledge inside the firm.
Our results also provide directions for future research. One feature that we have
observed in the data is that redeployments are especially high when firms first open
new establishments. Tables 3.4 and 3.5 show that in such cases, 22.5 percent of all
initial workers and 42 percent of all managers of the new plants are sourced from other
units of the same firm. Understanding the strategies multi-business firms use when
they engage in “intrapreneurship”—in particular the type and nature of the human
87
resources allocated to new businesses—and whether the option to leverage their internal
labor markets provides a competitive advantage over independent startups constitutes
an important and interesting area for future research.
88
AConstruction of O∗NET Task Measures
This appendix lists the O∗NET scales used to construct the occupation task measures
used in Table 1.5. The O∗NET scales used in this paper are based on those in Acemoglu
and Autor (2011) and computer code from David Autor’s website.1
Acemoglu and Autor (2011) use two sub-measures of non-routine cognitive tasks:
“analytical” and “interpersonal.” For simplicity, I combine these two measures into a
single non-routine cognitive measure.
The computer code provided by David Autor for constructing task measures includes
two non-routine manual scales: “physical” and “interpersonal”. The interpersonal scale
is not used in Acemoglu and Autor (2011). I combine the two non-routine manual scales
1Available at https://economics.mit.edu/faculty/dautor/data/acemoglu (archived athttps://perma.cc/B7SK-VKUV).
89
in the computer code into a single non-routine manual measure.
Non-routine cognitive
4.A.2.a.4 Analyzing data/information
4.A.2.b.2 Thinking creatively
4.A.4.a.1 Interpreting information for others
4.A.4.a.4 Establishing and maintaining personal relationships
4.A.4.b.4 Guiding, directing and motivating subordinates
4.A.4.b.5 Coaching/developing others
Non-routine manual
4.A.3.a.4 Operating vehicles, mechanized devices, or equipment
4.C.2.d.1.g Spend time using hands to handle, control or feel objects, tools or
controls
1.A.2.a.2 Manual dexterity
1.A.1.f.1 Spatial orientation
2.B.1.a Social Perceptiveness
Routine cognitive
90
4.C.3.b.7 Importance of repeating the same tasks
4.C.3.b.4 Importance of being exact or accurate
4.C.3.b.8 Structured v. Unstructured work (reverse)
Routine manual
4.C.3.d.3 Pace determined by speed of equipment
4.A.3.a.3 Controlling machines and processes
4.C.2.d.1.i Spend time making repetitive motions
91
BVariable Definitions for Chapter 2
The variables for chapter 2—listed in Table 2.1—are defined as follows:
• Bills introduced is the number of bills introduced in the legislature.
• Laws enacted is the number of bills that became law.
• Tightening and Loosening laws are numbers of enacted laws that tightened and
loosened gun control respectively.
• Mass shooting is an indicator for state-years with a mass shooting in which three
or more people not romantically involved with or related to the shooter(s) were
killed.
• Fatalities is the total number of deaths in mass shootings in a state-year.
92
• Democratic and Republican Legislature are indicators for party control of the state
legislature.
• Republican governor is an indicator for Republican governors.
• Regular session indicates whether the legislature convened a regular (as opposed
to special) session to consider bills; some state legislatures only meet every other
year.
• Bill carryover is proportion of chambers in which bills are eligible for carryover
to the next session.
• Limited leg. topic is an indicator for legislative sessions during which bills are
limited to specific topics (e.g. appropriations).
• Legislature size is the number of lawmakers serving in the state legislature.
• The demographic controls are percentages of the state’s population, except for
Income per capita, which is measured in thousands of 1987 U.S. dollars.
93
CCoding Gun Laws
In order to facilitate accurate coding of gun legislation, coders were given a full manual
to explain the meaning of “tighten”, “loosen”, “neutral,” and “uncertain” along with
the examples in Table C.1. The table mimics the appearance of the Excel workbooks
used by the coders. The first bill creates a new crime related to firearms. It tightens
restrictions on firearms. The second bill makes it easier for people to acquire guns; it
loosens restrictions on firearms. The third bill is exclusively about parole officers; it
is neutral because it does not affect the general public. The fourth bill is uncertain
because the summary is a generic description that does not specify whether the law
tightens or loosens restrictions on firearms. The fifth bill both tightens and loosens; it
regulates gun shows, but also eliminates a restriction on firearm purchasers.
94
Table C.1: Coding Gun Laws
ID Summary Tighten Loosen Uncertain
1 Creates a new felony for firing a gun within 1,000 feet of aneducational facility.
1 0 0
2 Reduces the age limit for purchase of a handgun from 21 to18.
0 1 0
3 Allows parole officers to carry a loaded firearm while commut-ing to and from work.
0 0 0
4 Relates to the use of firearms in state parks and campgrounds. 0 0 15 Requires a license to operate a gun show. Eliminates the
waiting period for firearm sales if the purchaser has a validpermit to carry a concealed weapon.
1 1 0
Note: Table shows examples of coding laws based on bill summaries.
95
DEffect of Mass Shootings in Neighboring States
96
Tabl
eD
.1:
Mas
sSho
otin
gsin
Neig
hbor
ing
Stat
es,B
illsan
dLa
ws
Bills
Intro
duce
dLa
wsEn
acte
d
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Mas
ssh
ootin
g0.
147∗
∗0.
158∗
∗0.
097
0.08
7(0
.064
)(0
.073
)(0
.067
)(0
.080
)N
eigh
bor
shoo
ting
-0.0
520.
028
(0.0
41)
(0.0
48)
Cen
.div
ision
shoo
ting
-0.0
180.
020
(0.0
42)
(0.0
49)
Fata
litie
s0.
023∗
∗∗0.
022∗
∗∗0.
014∗
0.01
1(0
.007
)(0
.008
)(0
.008
)(0
.009
)N
eigh
bor
fata
litie
s-0
.002
0.00
2(0
.004
)(0
.004
)C
en.d
ivisi
onfa
talit
ies
0.00
20.
004
(0.0
05)
(0.0
04)
Polit
ical
Con
trol
s•
••
••
••
•In
stitu
tiona
lCon
trol
s•
••
••
••
•D
emog
raph
icC
ontr
ols
••
••
••
••
Stat
eFi
xed
Effec
ts•
••
••
••
•Ye
arFi
xed
Effec
ts•
••
••
••
•N
1,25
01,
250
1,25
01,
250
1,25
01,
250
1,25
01,
250
Not
e:T
hede
pend
ent
varia
ble
isth
enu
mbe
rof
firea
rm-r
elat
edbi
llsin
trod
uced
(mod
els
1–4)
orla
ws
enac
ted
(mod
els
5–8)
inst
ate
legi
slatu
res.
Nei
ghbo
rre
fers
tost
ates
with
ash
ared
bord
er;
Cen
.di
visi
onre
fers
tost
ates
with
inth
esa
me
Cen
sus
divi
sion.
Rob
ust
stan
dard
erro
rscl
uste
red
byst
ate
inpa
rent
hese
s.∗p<
.10,∗
∗p<
.05,
∗∗∗p<
.01
97
Table D.2: Mass Shootings in Neighboring States and Directions of PolicyChange
Tightening Laws Loosening Laws
(1) (2) (3) (4)
Shooting ×Republican legislature -0.015 -0.104 0.732∗∗∗ 0.809∗∗∗
(0.223) (0.260) (0.257) (0.275)Democratic legislature 0.072 0.082 -0.294 -0.169
(0.129) (0.122) (0.379) (0.416)Split legislature -0.242 -0.057 0.174 0.090
(0.249) (0.224) (0.331) (0.383)Neighbor shooting ×Republican legislature 0.208 -0.059
(0.135) (0.236)Democratic legislature -0.315∗∗ 0.370
(0.131) (0.246)Split legislature -0.157 -0.095
(0.202) (0.234)Cen. division shooting ×Republican legislature 0.166 -0.153
(0.154) (0.232)Democratic legislature -0.071 -0.141
(0.112) (0.175)Split legislature -0.258 0.146
(0.170) (0.258)
Political Controls • • • •Institutional Controls • • • •Demographic Controls • • • •State Fixed Effects • • • •Year Fixed Effects • • • •N 1,250 1,250 1,175 1,175
Note: The dependent variable is the number of firearm-related laws enacted thatmake gun laws stricter (models 1 and 2) or less strict (models 3 and 4). Neighborrefers to states with a shared border; Cen. division refers to states within thesame Census division. Robust standard errors clustered by state in parentheses.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
98
Table D.3: Fatalities in Neighboring States and Directions of Policy Change
Tightening Laws Loosening Laws
(1) (2) (3) (4)
Fatalities ×Republican legislature 0.014 0.002 0.151∗∗∗ 0.150∗∗∗
(0.047) (0.049) (0.031) (0.043)Democratic legislature 0.016 0.023 -0.046 -0.018
(0.014) (0.014) (0.053) (0.054)Split legislature 0.013 0.029∗∗ -0.022 -0.023
(0.012) (0.014) (0.019) (0.029)Neighbor fatalities ×Republican legislature 0.017 -0.005
(0.013) (0.022)Democratic legislature -0.025∗∗ 0.015
(0.012) (0.017)Split legislature -0.008 -0.035∗∗
(0.022) (0.018)Cen. division fatalities ×Republican legislature 0.020∗ 0.002
(0.011) (0.029)Democratic legislature -0.009 -0.031∗∗
(0.008) (0.015)Split legislature -0.015 0.007
(0.021) (0.023)
Political Controls • • • •Institutional Controls • • • •Demographic Controls • • • •State Fixed Effects • • • •Year Fixed Effects • • • •N 1,250 1,250 1,175 1,175
Note: The dependent variable is the number of firearm-related laws enacted thatmake gun laws stricter (models 1 and 2) or less strict (models 3 and 4). Neighborrefers to states with a shared border; Cen. division refers to states within thesame Census division. Robust standard errors clustered by state in parentheses.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
99
EEffect of Mass Shootings on Enacted Laws
100
Tabl
eE.
1:Eff
ecto
fMas
sSho
otin
gson
Enac
ted
Laws
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Mas
ssh
ootin
g0.
074
0.02
00.
079
0.09
8(0
.118
)(0
.108
)(0
.075
)(0
.067
)Fa
talit
ies
0.01
10.
007
0.01
10.
014∗
(0.0
13)
(0.0
11)
(0.0
08)
(0.0
08)
Reg
ular
sess
ion
3.39
3∗∗∗
3.38
4∗∗∗
3.38
9∗∗∗
3.37
8∗∗∗
(0.7
72)
(0.7
82)
(0.7
80)
(0.7
91)
Bill
carr
yove
r-0
.074
-0.0
79-0
.081
-0.0
88(0
.096
)(0
.095
)(0
.096
)(0
.095
)Li
mite
dle
g.to
pic
-0.6
02∗∗
-0.6
16∗∗
-0.5
97∗∗
-0.6
09∗∗
(0.2
60)
(0.2
66)
(0.2
58)
(0.2
65)
Legi
slatu
resiz
e-0
.012
∗∗∗
-0.0
07-0
.012
∗∗∗
-0.0
07(0
.004
)(0
.004
)(0
.004
)(0
.005
)D
em.l
egisl
atur
e-0
.023
-0.0
20(0
.082
)(0
.085
)R
ep.l
egisl
atur
e0.
269∗
∗∗0.
275∗
∗∗
(0.0
87)
(0.0
87)
Rep
.gov
erno
r-0
.002
-0.0
00(0
.051
)(0
.050
)
Dem
ogra
phic
Con
trol
s•
••
•St
ate
Fixe
dEff
ects
••
••
••
••
Year
Fixe
dEff
ects
••
••
••
N1,
250
1,25
01,
250
1,25
01,
250
1,25
01,
250
1,25
0
Not
e:T
hede
pend
entv
aria
ble
isth
enu
mbe
roffi
rear
m-r
elat
edla
wse
nact
edby
the
stat
e.R
obus
tsta
ndar
der
rors
clus
tere
dby
stat
ein
pare
nthe
ses.
∗p<
.10,∗
∗p<
.05,
∗∗∗p<
.01
101
FPredicting Mass Shootings
Tables F.1 and F.2 show the results of trying to predict mass shootings with demographic
and gun policy variables. The policy variables in Table F.2 are defined as follows:
• Handgun waiting period is the number of days purchasers must wait before ac-
cepting delivery of a handgun.
• Long-gun waiting period is similarly defined for long-guns (e.g. rifles and shot-
guns).
• Age 18+ transaction is an indicator for laws that prevent vendors from selling
handguns to minors or prevent minors from purchasing handguns.
• Age 21+ transaction is defined the same way for persons under 21.
102
• Handgun Permit System is an indicator for states that require permits to purchase
a handgun.
• Background check, all handgun sales is an indicator for requiring a background
check for all handgun transactions (including private sales).
• Background check, all firearm sales is an indicator for requiring a background
check for all firearm transactions (including private sales).
• Assault weapons ban is an indicator for states that ban some types of assault rifles
or pistols.
• Shall issue concealed carry is an indicator for states that require the permitting au-
thority to grant a license to anyone meeting the minimum statutory qualifications
(i.e. do not permit law enforcement discretion in issuing permits).
• No permit needed concealed carry is an indicator for states that allow concealed
carry without a permit.
103
Table F.1: Linear Probability Model for Mass Shootings Using Control Variables
(1) (2) (3) (4) (5) (6) (7)Lag bills introduced 0.000
(0.001)Lag laws enacted 0.000
(0.005)Lag tightening laws -0.000
(0.009)Lag loosening laws 0.016
(0.014)Dem. legislature 0.006 0.011 0.010 0.010 0.010 0.012
(0.044) (0.043) (0.045) (0.046) (0.045) (0.045)Rep. legislature -0.030 -0.030 -0.027 -0.027 -0.027 -0.028
(0.036) (0.035) (0.037) (0.037) (0.037) (0.038)Rep. governor -0.012 -0.010 -0.010 -0.010 -0.010 -0.010
(0.020) (0.020) (0.021) (0.021) (0.022) (0.021)Regular session 0.121 0.122∗ 0.122∗ 0.121 0.128∗
(0.077) (0.072) (0.067) (0.073) (0.073)Bill carryovera 0.057∗∗ 0.056∗ 0.055∗∗ 0.055∗∗ 0.056∗∗
(0.026) (0.029) (0.027) (0.027) (0.027)Limited leg. topic -0.044 -0.062 -0.062 -0.062 -0.065
(0.062) (0.059) (0.060) (0.059) (0.060)Legislature size 0.002 0.002 0.002 0.002 0.002
(0.001) (0.002) (0.002) (0.002) (0.002)Log population -0.191 -0.131 -0.140 -0.210 -0.211 -0.210 -0.210
(0.279) (0.265) (0.273) (0.297) (0.298) (0.297) (0.299)Elderly 0.001 -0.001 -0.002 0.004 0.004 0.004 0.003
(0.025) (0.024) (0.024) (0.024) (0.024) (0.025) (0.025)Under 25 -0.002 0.001 0.001 0.002 0.003 0.003 0.002
(0.020) (0.019) (0.019) (0.020) (0.020) (0.020) (0.020)Black -0.009 -0.009 -0.008 0.000 -0.000 -0.000 0.002
(0.015) (0.015) (0.015) (0.017) (0.018) (0.018) (0.017)Hispanic -0.001 -0.003 -0.002 -0.001 -0.001 -0.001 -0.001
(0.015) (0.015) (0.015) (0.016) (0.016) (0.016) (0.016)Unemployment 0.025∗∗ 0.025∗∗ 0.025∗∗ 0.024∗∗ 0.024∗∗ 0.024∗∗ 0.024∗∗
(0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.012)Income 0.013 0.012 0.013 0.013 0.013 0.013 0.013
(0.013) (0.013) (0.014) (0.014) (0.014) (0.014) (0.014)High school -0.004 -0.003 -0.002 -0.003 -0.003 -0.003 -0.003
(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006)Veteran -0.003 -0.001 -0.001 -0.004 -0.004 -0.004 -0.004
(0.012) (0.012) (0.012) (0.013) (0.013) (0.012) (0.012)Divorced -0.004 -0.003 -0.002 0.001 0.001 0.001 0.001
(0.010) (0.010) (0.011) (0.011) (0.011) (0.011) (0.011)N 1,250 1,250 1,250 1,200 1,200 1,200 1,200
a There is no a priori reason to think bill carryover would be related to mass shootings; this correlationis insignificant when Virginia, which unlike most states, allows carryover in even years is dropped fromthe sample. Four of Virginia’s six mass shootings happened in even years.Note: All models include state and year fixed effects. Standard errors are clustered by state.∗ p < .10, ∗∗ p < .05
104
Table F.2: Linear Probability Model for Mass Shootings Using Policy Vari-ables
(1) (2)
Handgun waiting period (days) 0.002 0.002(0.005) (0.005)
Long-gun waiting period (days) -0.005 -0.005(0.019) (0.020)
Age 18+ for transaction 0.031 0.026(0.026) (0.028)
Age 21+ for transaction -0.067 -0.080(0.057) (0.056)
Handgun permit system -0.137 -0.136(0.094) (0.100)
Background check, all handgun sales -0.066 -0.066(0.080) (0.085)
Background check, all firearm sales 0.019 -0.005(0.116) (0.127)
Assault weapons ban 0.042 0.050(0.051) (0.053)
Shall issue concealed carry -0.010 -0.006(0.038) (0.039)
No permit needed concealed carry 0.162 0.181(0.188) (0.184)
Log population -0.494 -0.445(0.325) (0.310)
Political Controls •Demographic Controls • •N 1,250 1,250
Note: All models include state and year fixed effects. Standard errors are clus-tered by state.
105
GMass Shootings and State-Specific Time Trends
106
Table G.1: Effect of Mass Shootings on Gun Bill Introductions
(1) (2) (3) (4) (5) (6)
Mass shooting 0.074 0.151∗∗ 0.157∗∗(0.075) (0.069) (0.063)
Fatalities 0.020∗∗ 0.024∗∗∗ 0.024∗∗∗(0.010) (0.008) (0.007)
Institutional Controls • • • •Political Controls • •Demographic Controls • •State Fixed Effects • • • • • •State-Specific Trends • • • • • •Year Fixed Effects • • • • • •N 1,250 1,250 1,250 1,250 1,250 1,250
Note: The dependent variable is the number of firearm-related bills introduced in state legisla-tures. Robust standard errors clustered by state in parentheses. Variables are identical to thosein Table 2.2.∗∗ p < .05, ∗∗∗ p < .01
Table G.2: Mass Shootings, Ordinary Gun Homicides, and Bill Intro-ductions
(1) (2) (3)
Mass shooting fatalities / 100,000 1.504∗∗∗ 1.481∗∗∗ 1.409∗∗∗(0.323) (0.261) (0.195)
Ordinary gun homicides / 100,000 0.010 0.005 0.007(0.058) (0.055) (0.049)
Institutional Controls • •Political Controls •Demographic Controls •State Fixed Effects • • •State-Specific Trends • • •Year Fixed Effects • • •N 1,250 1,250 1,250
Note: The dependent variable is the number of firearm-related bills in-troduced in state legislatures. Robust standard errors clustered by state inparentheses. Variables are identical to those in Table 2.3.∗∗∗ p < .01
107
HPlacebo Mass Shooting Analyses
We randomly assign placebo mass shootings to state-years in which no actual shooting
occurred with probability equal to each state’s frequency of shootings, and randomly
draw a fatality count from the empirical distribution of fatalities. We then re-run the
models and calculate the test statistic for the placebo shooting and fatality coefficients.
The percentiles in Tables H.1 and H.2 are based on 1,000 replications. The “pooled”
rows in Table H.2 mirror the models in Table 2.4 without interaction effects.
108
Table H.1: Placebo Analysis for Bill Introductions
Percentiles of Placebo Test Statistic
Actual 1st 5th 10th 90th 95th 99th
Shooting Indicator (model 4) 2.37 -3.69 -2.80 -2.33 0.71 1.06 1.90Shooting Fatalities (model 8) 3.29 -4.01 -2.81 -2.40 0.94 1.35 2.32
Note: Models mirror those of Table 2.2.
Table H.2: Placebo Analysis for Enacted Laws
Percentiles of Placebo Test Statistic
Actual 1st 5th 10th 90th 95th 99th
Tightening LawsPooled shooting -0.30 -2.19 -1.40 -1.04 1.82 2.29 3.02Pooled fatalities 1.88 -2.39 -1.64 -1.24 1.80 2.28 3.44Shooting ×Republican legislature -0.07 -2.93 -1.71 -1.18 1.63 2.18 3.28Democratic legislature 0.26 -2.47 -1.63 -1.33 1.45 1.96 2.60Split legislature -0.82 -2.65 -1.46 -0.98 2.13 2.60 4.14
Fatalities ×Republican legislature 0.36 -2.80 -1.86 -1.33 1.68 2.46 3.72Democratic legislature 0.93 -2.81 -1.80 -1.47 1.52 2.02 3.49Split legislature 1.15 -2.79 -1.65 -1.09 2.26 2.94 4.82
Loosening LawsPooled shooting 1.36 -3.19 -2.28 -1.89 0.84 1.26 2.11Pooled fatalities 0.36 -3.04 -2.25 -1.82 0.93 1.46 2.58Shooting ×Republican legislature 2.87 -2.98 -2.27 -1.97 0.68 1.05 1.69Democratic legislature -0.62 -2.69 -1.79 -1.40 1.23 1.66 2.80Split legislature 0.49 -3.10 -2.12 -1.70 1.40 1.86 2.89
Fatalities ×Republican legislature 4.90 -2.80 -2.24 -1.89 0.93 1.35 2.22Democratic legislature -0.87 -2.89 -1.90 -1.48 1.47 1.94 2.87Split legislature -1.00 -3.22 -2.12 -1.75 1.47 2.04 3.32
Note: Models mirror those of Table 2.4. The models for tightening laws mirrormodels 1–4; models for loosening laws mirror models 5–8.
109
IExcluding States from Mass Shooting Analyses
These analyses exclude each state, one at a time, from our sample. Each graph plots
the resulting 50 regression coefficients (from smallest to largest) along with a 95 percent
confidence interval and estimates using the full sample of all states. The state abbrevi-
ations in the figures indicate the state that was dropped from the sample and mark the
resulting point estimate. Vertical bars represent 95 percent confidence intervals. The
solid, horizontal line indicates the point estimate from the complete sample (presented
in chapter 2), and dotted, horizontal lines represent the lower and upper bounds of the
95 percent confidence interval for the full sample estimate. Removing individual states
has little effect on the coefficient estimates, supporting the claim that the effect of mass
shootings on gun policy is not driven by an individual state or shooting.
110
Figure I.1: Effect of Mass Shooting on Bill Introductions
ALAKAZ ARCA COCT
DE FLGAHI ID ILIN IAKS KY LAME
MDMA
MIMNMSMO MT NENV NHNJ NM
NY
NC ND OH OKORPA
RISC
SD
TN
TX UT VTVAWA
WV WIWY
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Coe
ffici
ent E
stim
ate
Figure I.2: Effect of Mass Shooting on Laws Enacted
AL
AK AZ
ARCA CO
CT DEFL GA HI ID
IL
IN IAKS KYLA
MEMDMA
MI
MNMS MOMT NE NVNH NJ NM
NY
NCND OH
OKOR PARISC SD
TN
TX
UT VT
VA
WAWV WI WY
−0.10
−0.05
0.00
0.05
0.10
0.15
0.20
0.25
Coe
ffici
ent E
stim
ate
111
Figure I.3: Effect of Republican Legislature Mass Shooting on Loosening Laws Enacted
ALAK AZ
ARCA
COCT DE
FL
GAHIID
ILINIA
KS KYLAMEMD MAMI MN MSMO MT NE
NV
NH NJNMNY
NC
ND
OH
OKOR PA
RI SCSD
TN
TX
UT
VTVA
WA WV
WI
WY
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Coe
ffici
ent E
stim
ate
Figure I.4: Effect of Democratic Legislature Mass Shooting on Loosening Laws Enacted
AL
AK AZ
AR
CA
COCTDE FLGA HIIDIL
IN IA KSKYLA
MEMDMAMI MNMS MOMT NE NVNHNJ NM
NYNCND OH OK ORPARI
SCSD TNTX UTVT VA WAWVWI WY
−1.5
−1.0
−0.5
0.0
0.5
Coe
ffici
ent E
stim
ate
112
Figure I.5: Effect of Republican Legislature Mass Shooting on Tightening Laws Enacted
AL AK
AZ
AR
CACO CT DE
FL
GAHI IDIL INIA
KS KYLA
MEMD MA
MI
MN MSMOMTNENV NHNJ NMNY NC
ND
OHOKOR PA
RISCSD
TN
TXUTVT
VAWAWV
WIWY
−0.6
−0.4
−0.2
0.0
0.2
0.4
0.6
Coe
ffici
ent E
stim
ate
Figure I.6: Effect of Democratic Legislature Mass Shooting on Tightening Laws Enacted
ALAKAZ
AR
CA
COCT
DE
FLGA
HI IDIL
IN IAKSKY
LA
ME
MDMA MI MNMS MOMT NE NVNHNJ
NM NYNC
NDOH OKOR PARI SC
SDTN TXUTVT
VA
WAWV
WIWY
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
Coe
ffici
ent E
stim
ate
113
JGun Ownership, Shootings, and Enacted Laws
Table J.1 adds a proxy for gun ownership—the percentage of suicides that are firearm
related (Cook and Ludwig, 2006)—to the analysis of tightening and loosening laws
presented in Table 2.4. Models 1 and 4 of Table J.1 show that the main results do not
change when adding this control variable. The other models suggest that the respond
of Republican legislatures cannot be explained by rated of gun ownership.
114
Table J.1: Effects of Mass Shootings with Gun Ownership Proxy
Tightening Laws Loosening Laws
(1) (2) (3) (4) (5) (6)
Mass shooting -0.209 -0.469 -0.325 0.189 0.720 0.527(0.260) (0.584) (0.511) (0.347) (0.786) (0.710)
Shooting ×Rep. legislature 0.193 0.169 0.533 0.580
(0.399) (0.385) (0.484) (0.482)Dem. legislature 0.255 0.243 -0.385 -0.382
(0.308) (0.295) (0.467) (0.440)Gun suicide percent 0.005 0.005 -0.010 -0.005
(0.009) (0.010) (0.014) (0.014)Gun suicide percent 0.037 0.036 0.035 0.106∗ 0.109∗ 0.116∗
(0.027) (0.027) (0.027) (0.060) (0.061) (0.061)Dem. legislature 0.062 0.062 0.100 -0.255 -0.261 -0.322∗
(0.171) (0.170) (0.149) (0.229) (0.225) (0.194)Rep. legislature 0.145 0.149 0.177 0.423∗∗ 0.413∗ 0.510∗∗∗
(0.143) (0.143) (0.133) (0.216) (0.218) (0.192)Rep. governor -0.040 -0.046 -0.046 -0.092 -0.082 -0.108
(0.086) (0.088) (0.089) (0.164) (0.162) (0.166)
Institutional Controls • • • • • •Demographic Controls • • • • • •State Fixed Effects • • • • • •Year Fixed Effects • • • • • •N 1,250 1,250 1,250 1,175 1,175 1,175
Note: The dependent variable is the number of firearm-related laws enacted that makegun laws stricter (models 1–3) or less strict (models 4–6). Gun suicide percent is the five-year moving average of the percentage of suicides that are firearm-related and is used toproxy for gun ownership (Cook and Ludwig, 2006). Robust standard errors clustered bystate in parentheses.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01
115
KMass Shootings as an Instrument for Gun Policy
In this appendix we use mass shootings as an instrumental variable to study the impact
of gun laws on gun deaths. We start with the following model:
lnDst = αs + θt + βGun Controlst + δ′Zst + ϵst
where Dst is non-mass shooting gun deaths per 100,000 people in state s and year t,
αs and θt are state and year fixed effects, Gun Controlst is an index representing the
strictness of gun policy, and Zst is a vector of controls—demographic, political, and
economic factors-–-that potentially affect gun deaths. We use the same variables as
Levitt (1996) as controls, but also include dummies for Republican and Democratic
trifectas or legislatures, and a dummy for Republican governors.
We do not directly observe Gun Controlst; instead, we observe the enactment of new
116
laws that change gun policy. Therefore, we estimate the equation in first differences:
∆ lnDst = λt + βNew Gun Lawsst +∆Zstδ +∆ϵst
where New Gun Lawsst −∆Gun Controlst is negative for laws that loosen gun control
and positive for laws that tighten gun control (according to our coders, see data de-
scription). Based on our main results, we instrument for gun laws using the first lags
of mass shooting fatalities and the interaction of lagged mass shooting fatalities with
Republican control of state government. The former should be positively correlated
with new laws and the latter negatively correlated with new laws.
We estimate the model using Fuller’s (1977) modified LIML with α = 1 (Baum,
Schaffer, and Stillman, 2007). First stage results suggest the instruments are weak
despite being jointly significant (F = 5.98) with the expected sign (Stock and Yogo,
2005). The coefficients on the exogenous instruments in the reduced form equation
for firearm deaths are not significant, but also have the expected signs (negative for
lagged mass shooting fatalities and positive for the interaction of lagged fatalities with
Republican control of government). Our estimate β̂ is −0.016 with standard error
0.013. A conditional likelihood ratio test (Moreira, 2003; Andrews, Moreira, and Stock,
2006; Finlay and Magnusson, 2009) cannot reject the null hypothesis that β = 0 (p =
0.24). We also estimated models that include proxies for gun ownership. Including the
percentage of suicides committed with a gun (Cook and Ludwig, 2006) does not change
our inference.
117
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