An Analysis of the Effect of Reserve 

Activation on Small Business 

John B. Hope, Douglas B. Christman 

and Patrick C. Mackin 

SAG Corporation 

Annandale, VA 20003 

by 

for 

Under contract no. SBAHQ-07-F-0306 

Release Date: October 2009

This report was developed under a contract with the Small Business Administration, Office 

of Advocacy, and contains information and analysis that was reviewed and edited by 

officials of the Office of Advocacy. However, the final conclusions of the report do not 

necessarily reflect the views of the Office of Advocacy. 
Abstract 

The authors use Department of Defense (DOD) data on the employment and activation of 

military Reserve personnel and Dun and Bradstreet data on sales and firm size to examine the 

impact of Reserve activation on employers. The study includes an analysis of the size and 

industry characteristics of firms employing activated reservists as well as the impact of the length 

of activation on the firm. The authors find that small businesses are disproportionately affected at 

the margin. Adding one more employee increases the percent activated in small firms from 

0.36% to 2.6% but increases the percent activated in large firms by only 0.05% to 0.06%. There 

is a corresponding negative effect of 0.30% on the change in sales for small firms, about 15 

times greater than the 0.02 percentage for large firms. 

The authors also find that the length of activation has a small but significant negative effect on 

the firm’s revenues. The econometric model found a 1.9 percentage point decrease in sales for 

small firms relative to larger firms for those with reservist employees activated 30 days or more 

and a 3.0 percentage point decrease in sales for those with employees activated 180 days or 

more. 
Table of Contents

Abstract....................................................................................................................................... 1 

1. Introduction............................................................................................................................. 1 

2. Literature Review.................................................................................................................... 3 

Retention Effects..................................................................................................................... 3 

Reservist Family and Community Effects .............................................................................. 4 

Employer Effects .................................................................................................................... 5 

Firm Demographics ................................................................................................................ 6 

Employee Absenteeism........................................................................................................... 6 

Findings................................................................................................................................... 7 

3. Data Description ..................................................................................................................... 7 

Personnel Data Overview ....................................................................................................... 8 

Personnel Demographics ...................................................................................................... 10 

Activation Data Overview .................................................................................................... 12 

Employment Data Overview................................................................................................. 14 

Firm Data .............................................................................................................................. 15 

4. Hypothesis............................................................................................................................. 17 

5. Model Description ................................................................................................................ 17 

6. Results................................................................................................................................... 19 

7. Conclusion ............................................................................................................................ 26 

8. References............................................................................................................................. 27 

Appendix A – Demographics and Length of Activations for Reservists.................................. 29 

Appendix B – Alternative Analysis to Check Consistency ...................................................... 44 

Table of Figures

Figure 1: Number of Reservists by Service and Year..................................................................... 9 

Figure 2: Number of Reserve Activations by Month.................................................................... 13 

Figure 3: Total Employees of Combined HQ and DUNS by Firm Size....................................... 16 

Figure 4: Firm Sales of Combined HQ and DUNS by Year......................................................... 17 

Figure 5: Percent of Total Force by Service and Year.................................................................. 29 

Figure 6: Education Level by Service........................................................................................... 30 

Figure 7: Percent Female per Service ........................................................................................... 31 

Figure 8: Enlisted Personnel’s Marital Status by Grade............................................................... 31 

Figure 9: Officers’ Marital Status by Grade ................................................................................. 32 

Figure 10: Warrant Officers’ Marital Status by Grade ................................................................. 32 

Figure 11: Enlisted Personnel’s Dependents by Grade................................................................. 33 

Figure 12: Officers’ Dependents by Grade................................................................................... 33 

Figure 13: Warrant Officers’ Dependents by Grade..................................................................... 34 

Figure 14: Enlisted Personnel’s Pay Grade by Year..................................................................... 34 

Figure 15: Officers’ Pay Grade by Year....................................................................................... 35 

Figure 16: Warrant Officers' Pay Grade by Year ......................................................................... 36 

Figure 17: Length of Activation – Air National Guard ................................................................ 36 

Figure 18: Length of Activation - Air Force Reserve................................................................... 37 

Figure 19: Length of Activation - Navy Reserve.......................................................................... 37 
Figure 20: Length of Activation - Coast Guard Reserve .............................................................. 38 

Figure 21: Length of Activation - Army Reserve......................................................................... 38 

Figure 22: Length of Activation - Marine Corp Reserve.............................................................. 39 

Figure 23: Length of Activation - Army National Guard............................................................. 39 

Figure 24: Length of Activation - All Services ............................................................................ 40 

Figure 25: Total Activations by Service ....................................................................................... 40 

Figure 26: Reservists’ Employment by Industry Group and Self-employment Status................. 41 

Figure 27: Reservists’ Employment by Industry Group............................................................... 42 

Figure 28: Reservists’ Employers by Firm Size ........................................................................... 42 

Figure 29: Length of Reservists’ Employment............................................................................. 43 

Table of Tables

Table 1: Ratio of Active to Reserve Component Personnel ........................................................... 9 

Table 2: Change in Reservist Count by Component and Year..................................................... 10 

Table 3: Percent of Total Force by Component and Year............................................................ 10 

Table 4: Education by Service ...................................................................................................... 11 

Table 5: Length of Activation by Component .............................................................................. 13 

Table 6: Count of DUNS and HQ DUNS Numbers..................................................................... 15 

Table 7: Firm Industry Distribution.............................................................................................. 20 

Table 8: Variable Description and Means .................................................................................... 21 

Table 9: Change in Sales - Filtered Interactive............................................................................. 22 

Table 10: Change in Sales – Filtered ............................................................................................ 23 

Table 11: Probit Bankruptcy......................................................................................................... 24 

Table 12: Probit 5% Loss.............................................................................................................. 25 

Table 13: Crosswalk of Variables for Education.......................................................................... 30 

Table 14: Percent of Total Activations by Component ................................................................ 41 

Table 15: Extended Variable Descriptions................................................................................... 44 

Table 16: Change in Sales............................................................................................................. 45 

Table 17 Change in Sales Lagged................................................................................................. 45 

Table 18: Change in Sales – 180................................................................................................... 46 

Table 19: Change in Sales - 180 Lagged ...................................................................................... 46 

Table 20: Change in Sales - 180 Year .......................................................................................... 47 

Table 21: Change in Sales – Interactive ....................................................................................... 47 

Table 22: Change in Sales – Filtered 180 ..................................................................................... 48 

Table 23: Probit Bankruptcy 180.................................................................................................. 48 
1. Introduction 

Since 2003, thousands of military reservists have been called to active duty; whether that duty 

station is overseas or near home, these reservists are not available to work at their civilian jobs 

during the period of activation. Businesses that experience employee absences due to Reserve 

component activation must adjust staffing and are likely to experience productivity losses and 

decreased performance. To the extent that these lost employees possess unique job-specific or firmspecific skills or work in a team environment, the loss of productivity may substantially exceed the 

employee’s foregone wages and lower the firm’s profitability. Moreover, the firm may face 

increased costs due to mandatory requirements of the Uniformed Services Employment and 

Reemployment Rights Act of 1994, also known as USERRA.1 These costs can include the 

continuation of certain benefits for the absent employee and higher management costs necessary to 

reallocate work and ensure that a comparable position is available upon the employee’s return. 

The costs of job absence also fall on the activated employee; absences may adversely affect career 

progression, for example. However, Reserve participation is a voluntary decision on the part of the 

employee. Other things being equal, those who volunteer for Reserve duty are in jobs where such 

absences are less likely to affect career progression and earnings. Firms have little latitude or choice 

regarding Reserve participation by their employees. Indeed, employers are prohibited by Federal 

law from discriminating against employees who participate in the Reserves. 

Reservist activation may disproportionately affect small businesses in at least two ways. First, a 

higher number of activated reservists may be drawn from small firms than would be expected given 

their relative share of the total civilian workforce. Second, the average activation may have a greater 

relative impact on smaller firms in terms of productive hours lost. 

Previous work has shown that productivity losses from absenteeism are larger for small firms than 

larger firms. Employees who have no perfect substitutes within the local production unit cause 

productivity losses to their firms in excess of their wages.2

 In firms with a small number of 

employees, those employees may be more likely to perform a large number of tasks and be more 

difficult to replace. 

The main challenge in examining this issue is obtaining data. The Department of Defense (DOD) 

maintains a database with employee-provided information on their employers. Reporting for this 

database is now mandatory and includes a unique identifier called a DUNS number assigned by the 

Dun and Bradstreet Corporation (D&B) for most of the firms. DOD estimates that its database 

currently covers about 90 percent of members of the Selected Reserve, that portion of each service’s 

Reserve components that trains regularly and is expected to be ready for quick mobilization at any 

time. This database can be linked to other DOD data that identify which reservists have been 

activated and for how long. 

The Dun and Bradstreet Corporation has provided credit information on private businesses and 

corporations since 1841. D&B is best known for its Data Universal Numbering System (DUNS) and 

 

1

 38 U.S.C. § 4301 et seq. 

2

 See Nicholson, et al. (2006). 
its database with over 140 million business records with approximately 1,500 data elements. The 

DOD employer database includes DUNS numbers that can be matched to D&B’s database. 

Using these two data sources, we can construct a database of firms to identify differences in the 

characteristics of employers of reservists. Compared with the employers of non-activated reservists 

and otherwise similar civilians, are the employers of activated reservists more or less likely to be 

small businesses? Are activated reservists concentrated in particular occupations? Are there any 

differential impacts on firms in particular industries or regions of the country? 

Small businesses are expected to face greater difficulties adjusting to extended absences of activated 

reservists because they have fewer employees. They must either work shorthanded or hire 

replacements, and there is an additional problem redistributing work when reservists return. The 

model of firm performance will determine if there are measurable differences between the 

productivity effects of small and large firms in terms of income and thus profitability when reservist 

employees are called to active duty. 

Since the first Gulf War began in 1990, the DOD has increased the operational tempo of the 

Reserve components of the armed forces. In 2004 alone, over one-third of the service members 

deployed in Iraq and Afghanistan were from the Reserves. Generally speaking, the department 

includes National Guard components with the individual services’ Reserve components. 

The Uniformed Services Employment and Reemployment Rights Act of 1994: 

 Guarantees the right of reservists to be reemployed by their civilian employers after 

serving on active duty; 

 Prohibits employers from discriminating against individuals in any aspect of 

employment because of their service in the Reserves; and 

 Mandates some continuation of benefits to reservists who have been activated. 

There is little information about the type and magnitude of the disruption that firms experience 

when their reservist employees or reservist owners are activated. 

The Congressional Budget Office (CBO) conducted a study in 2005 using survey information 

collected by the Departments of Defense and Labor and by other organizations. CBO found that 

most employers were unaffected by the activation of reservists; in fact, only about 6 percent of 

business establishments employed reservists, and fewer than one-half of one percent of selfemployed people served in the Reserves. While this number may seem small when viewed from the 

perspective of the economy as a whole, for those firms that are affected by Reserve activation, it is 

important to know the degree of inconvenience to both the reservist and the firm. 

Several studies have postulated that Reserve call-ups will be more severe for: 

 Small businesses that lose essential (key) employees; 

 Businesses that require workers with highly specialized skills; and 

 Self-employed reservists. 

2 
Among reservists who held civilian jobs, including government jobs, CBO found that small 

businesses with fewer than 100 employees employed about 18 percent, and 35 percent were 

employed by businesses with fewer than 500 employees or were self-employed. The CBO also 

estimated that, of the 860,000 members of the Selected Reserves (who constitute the majority of 

active Reserve personnel), 8,000 to 30,000 held key positions in small businesses and an additional 

55,000 were self-employed. 

The existence of laws such as USERRA could in fact cause additional difficulties for employers 

with activated reservist employees. Not only could they experience a loss of productivity, but they 

may also incur ongoing costs to maintain benefits for the absent employees. Policymakers are 

required to balance conflicting goals in order to help DOD recruit and retain military personnel 

while minimizing the effect on the civilian economy and avoiding harm to small business. 

This analysis will proceed with a brief literature review chapter followed by descriptive analysis of 

the data used. Next, we state the hypothesis to be tested, present an economic model of firm 

performance, and report the results of our econometric analysis. Finally, the report provides a set of 

conclusions and recommendations for further analysis. 

2. Literature Review 

The economic effects of National Guard and Reserve activation and deployment on small business 

employers (or any size business for that matter) have not been extensively studied. Few, if any, 

studies exist that directly or indirectly examine this relationship. This dearth of previous analysis is 

partially attributable to the fact that large-scale activations of reservists did not occur prior to the 

early 1990s. Before Desert Shield/Desert Storm (sometimes referred to as the first Gulf War), the 

last major mobilization of reservists occurred nearly 40 years previously during the Korean conflict. 

The Selected Reserve components before 1990 had practically no experience with any large-scale 

force mobilization, and the likelihood of activation or deployment probably played almost no role in 

enlistment and retention decisions. 

Since that time, there have been few studies of the economic effects of Reserve deployment. These 

studies tend to fall into one of three categories: retention effects; personal and economic effects on 

the reservist, their family and community; and only recently, the economic effects on employers of 

activated reservists. In addition to these military-specific studies, there are also some studies on the 

general economic effects of long-term employee absenteeism for reasons other than military 

activation – usually sick leave or sabbaticals – that would, in theory, have similar economic effects 

on employers. 

Retention Effects 

The Department of Defense (DOD) commissioned the first survey of reservists’ attitudes toward 

their military service in 1986: the Status of Forces Survey (Reserve) SOFS(R). In 1992, DOD 

included in the SOFS(R) questions relating to how mobilizations affect reservists. DOD cited three 

reasons for this focus: 

3 
1. Increased reliance on the Guard and Reserves had increased the likelihood of 

mobilization.

2. Little empirical research existed concerning the effects that mobilizations have on 

reservists' attitudes and those of their employers and families. 

3. Reservists' stay/leave decisions are dependent on their own attitudes and perceptions and 

those of employers and families. 

Kirby, et al. (1992) studied the 1986 and 1992 SOFS(R) and found a shift in the motivation for 

staying in the Guard and Reserve from purely pecuniary reasons to more of an emphasis on the 

educational benefits available. This was accompanied by an encouraging favorable shift when asked 

to describe their immediate civilian supervisors’ overall attitude toward their participation in the 

Guard/Reserve. 

The study used a sample from the 1991 Guard/Reserve Survey of Officers and Enlisted Personnel of 

3,269 part-time enlisted reservists with 4-12 years of service, of whom 1,752 were mobilized. These 

survey responses were matched to individual-level administrative data from the Quarterly Master 

Personnel Files of the Reserve Common Component Personnel Data System (RCCPDS) maintained 

by the Defense Manpower Data Center (DMDC). Kirby et al. examined differences in retention 

among various subgroups and estimated the net effect of different variables on the probability of 

retention. They found no statistically significant difference in retention between mobilized and nonmobilized reservists. 

Reservist Family and Community Effects 

Angrist and Johnson (2000) analyzed the effects of deployment on military families in the first Gulf 

War. Using DOD’s 1992 Survey of Officers and Enlisted Personnel, they found that deployments 

increased the divorce rate for both male and female soldiers. Deployments also decreased spousal 

employment. Children were not found to have an increase in disability due to parental deployment. 

In a later study by the Rand Corporation, Loughran, et al. (2006) examined the impact of activations 

and deployments on the lives of reservists, their families, and their communities. The authors 

hypothesized that activation of reservists affects the supply of labor to the community. In addition, 

the demand for goods and services decreases when reservists are removed from the community. In 

order to test this, the authors generated counts of activated and deployed Reserve and active-duty 

personnel from the DMDC Global War on Terrorism (GWOT) Contingency File. The research 

showed that, although employment declines initially during the first month of activation, it recovers 

over the subsequent three months. There was no conclusive evidence that Reserve activation 

disproportionately drew from small communities. An additional interesting result was that police 

employment decreased after activation, but the magnitude was difficult to gauge. 

In a separate Rand study, Loughran, et al. (2006) examined the effect of activation on reservist 

earnings using administrative data from DMDC and the Social Security Administration. Contrary to 

earlier findings based on survey data, the authors found that most reservists did not suffer earnings 

losses attributable to activation, but actually had earnings increases, particularly when the value of 

certain tax advantages was considered. Still, about 17 percent of reservists did experience a loss in 

earnings, and 11 percent experienced a loss of more than 10 percent of their base-year earnings. 

4 
Employer Effects 

Doyle, et al. (2004) attempted to measure the costs incurred by businesses as a result of Reserve 

activation. Using estimates of costs based on analysis of provisions in the Uniformed Services 

Employment and Reemployment Rights Act (USERRA), and supported by interviews of a nonrandom sample of 8 companies applying for the Small Business Administration’s Military Reservist 

Economic Injury Disaster Loans, or MREIDLs, (selected precisely because they had suffered 

serious economic consequences), they identified several sources of these costs. These included not 

only direct financial costs associated with activated reservists’ retirement and health benefits and 

transition costs for their return to the job they left, but also the cost of replacement employees, such 

as hiring and training costs, and costs from lost productivity due to delays. Among the costs that 

they were able to objectively measure were health and retirement benefits which they extrapolated 

using the 2000 Survey of Reserve Component Personnel and data from the Employee Benefit 

Research Institute (EBRI) using a weighted average by sector and firm size. They concluded that 

the employer cost for a reservist’s retirement plan averaged $372 per month and that for an 

employer-provided health insurance plan averaged $215 for singles and $550 for families. Other 

long-term costs that may also result from the loss of key personnel include losing clientele and 

effects on other employees in a team environment. They found that small businesses were 

particularly affected by Reserve activations. The authors concluded that further research was 

necessary to more specifically define the costs associated with Reserve mobilization and the 

businesses incurring those costs. 

Golding, et al. (2007) examined the combined effects of reservists’ activation and federal job 

protections on civilian employers using interviews with 19 employers of 28 reservists; 12 of these 

employers were MREIDL recipients. Using evidence from Nieva (1999), which found that about 6 

percent of businesses employed reservists, along with anecdotal evidence, the authors concluded 

that small businesses that lose essential employees, businesses that employ highly skilled workers 

and self-employed reservists were most strongly impacted. Of the 860,000 reservists in the Selected 

Reserves, about 85,000 were in positions where activation could severely affect their employers or 

themselves as self-employed individuals. The study presented four options to mitigate the effects of 

Reserve activation: compensation of affected businesses through tax credits or direct payments, 

subsidized loans to employers, provision or subsidization of call-up insurance, and exemption of 

certain reservists from call-up. 

Gotz (2003) examined available information on general employer support of the Guard and Reserve 

based upon the assumption that employer opinion of Reserve activities affects the decision of 

current and potential Reserve participants. He also found a lack of hard statistical evidence but 

plenty of anecdotal evidence. In order to improve access to relevant data, Gotz recommended that 

DOD institute five initiatives to increase employer support for Reserve participation: 

1. Establish a mandatory-reporting employer database linked to Reserve Personnel file. 

(This policy was approved in August 2004 when the voluntary DOD Reserve 

Employer Survey became mandatory.) 

2. Obtain timely information for early warning of problems. 

3. Provide more timely information to employers. 

4. Decrease uncertainty about call-up frequency and duration. 

5. Offset employer costs. 

5 
As a direct result of these recommendations, the Civilian Employer Information (CEI) database was 

created, and it is this repository that we hope will provide crucial data in our study of Reserve 

activation effects. 

Firm Demographics 

The Dun and Bradstreet Corporation has provided credit information on private businesses and 

corporations since 1841. Often referred to as D&B, it is best known for its Data Universal 

Numbering System (DUNS) and its database with over 140 million business records with 

approximately 1,500 data elements. 

Evans (1987) examines the relationship between firm growth, firm size, and firm age for a sample 

of manufacturing firms between 1976 and 1982. Data from the Small Business Data Base (SBDB), 

which was originally collected by D&B for credit reporting, provides data on firm age, number of 

employees, sales, and various corporate demographics. Evans found several problems with the data: 

under-representation of very small firms; data that did not apply to the file year for which the data 

were held; and a lack of information on firm acquisitions and mergers. As with most survey 

datasets, the SBDB data had various errors due to firms reporting bad data, firms’ misunderstanding 

questions, or D&B making various clerical errors. Overall, Evans found that the quality of the data 

was reasonable compared with other datasets used by economists. The paper found that firm growth 

decreases with firm age and size. 

Struyk (1972) examines the impact of industry location on the Boston, Cleveland, Minneapolis, and 

Phoenix metropolitan areas over a 32-month period. He found that industry locations significantly 

changed the existing spatial configuration of the metropolitan areas. This research was conducted 

by using D&B DUNS Market Identifier files to determine Standard Metropolitan Statistical Areas 

(SMSAs) and which firms either relocated within an SMSA, entered the SMSA, ceased operating 

within the SMSA or remained at their current location. He found that there were some biases toward 

firms that ceased to operate in certain SMSAs, though other SMSAs provided highly accurate 

accounts of entry and exit through crosschecks against secondary data. 

Employee Absenteeism 

There exists a small amount of research pertaining to the more general case of employees who are 

absent from work for other reasons (whether for a major illness, a sabbatical, or Family Medical 

Leave Act (FMLA) reasons). We can extrapolate that the firm effects will likely be similar to the 

temporary loss of a reservist employee due to activation or deployment. 

De Kok (1997) constructed an explanatory model of involuntary absence from work based on a 

telephone inquiry of 900 small and medium-sized (fewer than 200 employees) Dutch firms in 6 

different sectors (industry, construction, trade and catering, transport, financial and business 

services).3 Due to a small survey sample that increases the possibility of biased parameter estimates, 

he modeled the impact of a firm’s precautionary measures on involuntary employee absences using 

a two-stage estimation technique. The first endogenous measurement equation estimated a set of 

 

3

 The data were originally collected in a previous study, Bosch and de Kok (1997). 

6 
latent variables that measure a firm’s efforts to curb involuntary absences by using several indicator 

variables as proxies. These were in turn used in the second-stage maximum likelihood structural 

equations estimation that measured the impact of these efforts on the employee’s involuntary 

absences. He found that small and medium-sized enterprises are less likely to consider absenteeism 

a significant problem and less likely to take precautionary actions against involuntary absenteeism. 

Pauly et al. (2002) constructed a general theoretical model without using any empirical analysis. 

This model examined the production function effects of employee absenteeism for the firm in 

different situations. Starting with a baseline model where all labor is homogeneous, the study 

examined the effects of varying the production function, including team production, employee 

penalties for output shortfalls, full employment and less than full employment. They found that 

small businesses are more likely than large businesses to suffer losses due to absenteeism. This is 

due to the inability to hire additional employees to offset small probabilities of absence for their 

small worker pool. The team production scenario and the penalty for shortfalls scenario are viable 

possibilities for use in this study. 

Navarro and Bass (2006) provided an overview of the problem of employee absence and its 

associated costs by summarizing several surveys, including the 2005 CCH Business Compliance 

Group survey and the 2003 survey by the Society of Human Resource Management. They 

suggested that absenteeism costs could amount to nearly 15% of payroll for some companies. They 

also noted that only 20% of large companies track the cost of absenteeism and only a third track 

leave utilization. 

Nicholson et al. (2006) examined the effect of absenteeism on employee productivity as a 

percentage of employee wages. The authors hypothesized that the loss of productivity would exceed 

the absent workers’ wages for two reasons. First, it may be difficult to find perfect substitutes for 

the absent employees, particularly on short notice. Second, the employees’ jobs may involve 

teamwork and thus adversely affect the productivity of other employees. Using data collected 

through surveys of over 800 managers and covering 57 jobs in 12 industries, the authors found 

significant evidence to support their hypothesis. The average productivity loss was 1.28 times the 

absent employees’ wages, although there was significant variation across occupations. 

Findings 

There is little existing evidence in the literature about the type and magnitude of disruption that 

firms experience when their reservist employees or reservist owners are activated and most of what 

there is uses less than rigorous econometric techniques to analyze the results. While some studies do 

hint at a differential impact of activation and/or other forms of employee absence by firm size, 

much of this is based on anecdotal evidence. In our analysis we provide an empirical starting point 

from which future analysis may expand and refine these findings. 

3. Data Description 

The analysis uses data from two sources: 1) the DOD’s Defense Manpower Data Center (DMDC) 

Civilian Employer Information (CEI) database that matches firm-level data to up-to-date, 

individual-level data on reservists, including activation/deactivation dates; and 2) the Dun and 

7 
Bradstreet Data Universal Numbering System (DUNS) which provides financial and firm 

demographic data. The DMDC data were provided through the sponsorship of the Office of the 

Assistant Secretary of Defense for Reserve Affairs [OASD(RA)].4

DOD established a database of employer information that is submitted by Reserve members. When 

the database was established in 2001, participation was voluntary; however, a new DOD initiative 

made reporting mandatory beginning in August 2004. By August 2006, the compliance rate for 

reporting data to the database was 91 percent for the Selected Reserve and 30 percent for the 

Individual Ready Reserve and Inactive National Guard. There are some concerns about the validity 

of the information contained in the database, particularly concerning the updating process. 

Employment changes of reservists and their employers may not be updated in a timely fashion, and 

although Dun and Bradstreet verifies the accuracy of new firm data submitted, it is unable to verify 

changes in many small businesses or to update any reservist job changes until reported in a new 

survey. 

Using these data sources, we can identify differences in the characteristics of reservist employers. 

Compared with the employers of non-activated reservists and otherwise similar civilians, are the 

employers of activated reservists more or less likely to be small businesses? Are activated reservists 

concentrated in particular occupations? Are there any differential impacts on firms in particular 

industries? 

Personnel Data Overview 

 

The Reserve Personnel dataset consists of yearly demographic information on all reservists between 

September 11, 2001, and the end of fiscal year 2007. In total there are 9,590,659 records with 

2,391,221 unique individuals. The dataset contains individuals from all Guard/Reserve components, 

including the Air National Guard, Navy Reserve, Army Reserve, Coast Guard Reserve, Air Force 

Reserve, Marine Corps Reserve and Army National Guard. The Reserve components are under the 

command of the Federal Government, while the Guard components fall under the authority of 

individual states. 

Figure 1 shows the number of reservists for each component by year. The Army Reserve and the 

Navy Reserve both decreased over the period, while the other components stayed relatively the 

same. 

 

4

 The Civilian Employer Information database, the Reserve Activations database and the Reserve Master File all require 

a DOD sponsor for access. 

8 
Number of Reservists by Service and Year

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

2001 2002 2003 2004 2005 2006 2007

Air National Guard

Navy Reserve

Army Reserve

Coast Guard Reserve

Air Force Reserve

Marine Corps Reserve

Army National Guard

Figure 1: Number of Reservists by Service and Year 

Table 1 shows the ratio of total active component members to total Reserve component members 

from 2002 until 2007. The Uniformed Services Almanac was used to determine the total for active 

duty military personnel. In 2002 the Army’s force was split nearly evenly between Active and 

Reserve members, while the Coast Guard had over twice as many Active as Reserve members. 

Overall, the Army, Navy and Coast Guard have seen a decrease in the proportion of reservists in 

their total force. The Marine Corps and Air Force have maintained relatively stable proportions in 

the number of active duty to Reserve personnel. 

Table 1: Ratio of Active to Reserve Component Personnel 

Year Army Navy Marine Corps Air Force Coast Guard 

2002 1.08 1.84 1.66 1.86 2.20 

2003 1.13 1.96 1.66 1.96 2.34 

2004 1.18 1.99 1.64 1.97 2.46 

2005 1.23 2.00 1.66 1.92 2.50 

2006 1.28 2.04 1.67 1.82 2.55 

2007 1.40 2.03 1.67 1.79 2.67 

The change in the number of Reserve and Guard members between 2002 and 2007 can be seen in 

Table 2. Overall, there was a decline in all components, with the Navy Reserve and Army Reserve 

decreasing the most. The Marine Corps Reserve experienced the largest increases, primarily in the 

two years following the start of Operation Iraqi Freedom. 

9 
Table 2: Change in Reservist Count by Component and Year 

Year 

Air 

National 

Guard 

Navy 

Reserve 

Army 

Reserve 

Coast 

Guard 

Reserve 

Air Force 

Reserve 

Marine 

Corps 

Reserve 

Army 

National 

Guard 

2002 3.31% -5.18% -2.11% 1.64% -2.67% -1.10% -0.47%

2003 -3.51% -4.14% -3.75% 0.57% -0.97% 1.07% -0.28%

2004 -1.22% -2.00% -2.16% -1.79% 1.54% 2.94% -2.51%

2005 -0.37% -3.06% -4.07% -0.08% 2.92% -1.26% -2.81%

2006 -0.73% -4.72% -5.40% -0.28% -0.99% 0.79% 4.00%

2007 0.56% -3.11% -5.51% -3.60% 0.27% 0.26% 1.98%

Total -1.95% -22.20% -23.00% -3.53% 0.11% 2.71% -0.08%

Average -0.32% -3.70% -3.83% -0.59% 0.02% 0.45% -0.01%

On average over all years, the Army Reserve and Army National Guard make up the largest 

portions of the Reserve and Guard forces, 30.02% and 25.52%, respectively. The Air Force Reserve 

and Navy Reserve make up 13.89% and 13.81% of total Reserve force, and the Air National Guard 

and Marine Corps Reserve make up 7.87% and 7.76%, respectively. The Coast Guard Reserve is 

the smallest component of the total Reserve and Guard forces at 1.14%. These data are reflected in 

Table 3 below. 

Table 3: Percent of Total Force by Component and Year5

Year 

Air 

National 

Guard 

Navy 

Reserve 

Army 

Reserve 

Coast 

Guard 

Reserve 

Air Force 

Reserve 

Marine 

Corps 

Reserve 

Army 

National 

Guard 

2001 7.52% 14.74% 31.43% 1.08% 13.31% 7.27% 24.65%

2002 7.90% 14.22% 31.30% 1.11% 13.18% 7.31% 24.97%

2003 7.79% 13.93% 30.79% 1.15% 13.34% 7.55% 25.44%

2004 7.79% 13.83% 30.52% 1.14% 13.72% 7.88% 25.12%

2005 7.93% 13.69% 29.90% 1.16% 14.43% 7.94% 24.94%

2006 7.99% 13.23% 28.69% 1.18% 14.49% 8.12% 26.31%

2007 8.15% 13.00% 27.49% 1.15% 14.74% 8.26% 27.21%

Average 7.87% 13.81% 30.02% 1.14% 13.89% 7.76% 25.52%

Personnel Demographics 

The data provided by DMDC originally had 25 different education categories. These were 

aggregated into 6 values, which include: less than high school, high school diploma, some college, 

BA/BS degree, graduate school or more, and unknown. A crosswalk between the 25 original 

education categories and the 6 aggregated categories can be viewed in Table 13 in Appendix A. The 

Coast Guard Reserve data is from 2004 until 2007. 

 For the period of this study, the Air National Guard had the highest level of education, with about 

10% of their force having only high school diplomas and approximately 90% having at least some 

college education. On the other hand, the Marine Corps Reserves had the lowest level of education, 

 

5

 Can also be seen in Figure 5 in Appendix A. 

10 
with approximately 84% of their force having only high school degrees and about 15% having at 

least some college. It is important to note that the Marine Corps Reserves also have the highest ratio 

of enlisted personnel to officers, with approximately 10 enlisted to each officer. Generally speaking, 

enlisted personnel are required to have a high school diploma, and officers are required to have a 

four-year degree, respectively. The educational breakout by service is presented in Table 4 below. 

Table 4: Education by Service 

Year 

Air 

National 

Guard 

Navy 

Reserve 

Army 

Reserve 

Coast 

Guard 

Reserve 

Air Force 

Reserve 

Marine 

Corp 

Reserve 

Army 

National 

Guard 

Less than High School 1.15% 1.80% 3.45% 1.31% 0.51% 0.46% 7.05%

High School Diploma 9.69% 51.74% 56.15% 52.68% 55.22% 83.95% 67.66%

Some College 69.32% 12.09% 9.39% 23.02% 9.51% 2.85% 11.42%

BA/BS Degree 13.72% 19.30% 15.91% 14.16% 20.41% 9.44% 11.12%

Graduate School 4.61% 8.29% 7.63% 2.78% 13.37% 2.70% 2.66%

Unknown 1.50% 6.77% 7.47% 6.06% 0.98% 0.59% 0.08%

Marital status shows very little variation between 2001 and 2007. On average over all years, 

38.32% of all reservists were never married, 52.15% were married, and 6.61% were divorced. On 

average, females made up 16.49% of the Reserve and Guard total force.6 By component, the Marine 

Corps Reserve had the lowest percent of females, 5.49%. The Air Force Reserve had the largest 

proportion of females, 20.88%. 

The percentage of enlisted reservist and Guard members who are married increases with pay grade, 

which we assume is due to the fact that pay grade is a close proxy for age. For example, enlisted 

members are overwhelmingly new high school graduates (age 18) at pay grade E01, so it is not 

surprising that almost 90% of these individuals are unmarried.7 In contrast, nearly 85% of members 

at pay grade E09 (age 40+) are married. Officers show less variation in marital status across pay 

grade.8 This is most likely because of a higher starting age due to the college degree requirement. 

Warrant officers probably have higher levels of marriage due to the increased age that is required to 

hold these positions.9

On average, 39.34% of reservists had no children, 19.68% had one child, 13.28% had two children, 

13.66% had three children, 5.72% had four children, 1.81% had five children, 0.68% had six or 

more children, and 5.84% had an unknown number of children. Overall, each reservist had an 

average of 1.24 children. As age increases, indicated by higher pay grades, so does the prevalence 

of dependents. Enlisted personnel show the greatest degree of variation: almost 85% of E-1s have 

no dependents, while less than 10% of E-9s have none.10 The number of dependents also increases 

for commissioned and warrant officers, but the variation across pay grades is much more modest.11

 

6

 Can be seen in Figure 7 in Appendix A. 

7

 Can be seen in Figure 8 in Appendix A. 

8

 Can be seen in Figure 9 in Appendix A. 

9

 Can be seen in Figure 10 in Appendix A. 

10 Can be seen in Figure 11 in Appendix A. 

11 Can be seen in Figures 12 and 13 in Appendix A. 

11 
Enlisted pay grades for the Reserve and Guard range from E01 to E09. The primary composition for 

the enlisted ranks is E04s through E06s, with the E04s being the largest single grade for enlisted 

personnel.12 The Air National Guard and Air Force Reserve, which require their enlisted personnel 

to have stronger technical skills, have a personnel composition that weighs more heavily on E05s 

through E07s. Other components, such as the Marine Corps Reserve, have a larger proportion of 

more junior enlisted personnel. 

The officer and warrant officer pay grades range from O01 to O08 and W01 to W05, respectively, 

with some services excluding some or all of the warrant officer grades. Most commissioned officers 

are in pay grades O03 through O05.13 The Army National Guard has a higher percentage of O01 

and O02 than the other components. The majority of warrant officers in the Reserve and Guard 

units are WO2s through WO4s.14 

Activation Data Overview 

The activation dataset contains 1,047,773 records of 633,893 unique individuals in the Reserve and 

Guard who were activated between September 1, 2001, and February 29, 2008.15 This data provide 

the start and end dates of each activation. A length of activation field was created using the 

difference in the start date and the end date for each record. Records for individuals with activation 

periods of less than one month were censored. Finally, we assumed that individuals without end 

dates were still activated and they were given an end date of February 29, 2008 (the end of our 

sample period). 

Figure 2 shows the distribution of activations by month. The average number of activations per 

month was 14,752 and the median was 12,000. The largest number occurred in February 2003 

(75,165 activations), a month before the commencement of Operation Iraqi Freedom. The smallest 

number occurred in June 2002 (5,602 activations). The average length of activation was 267 days, 

while the median was 256 days. The maximum length of activation was 2,362 days. It is policy to 

restrict Reserve activations to two years or less.16 

 

12 Can be seen in Figure 14 in Appendix A. 

13 Can be seen in Figure 15 in Appendix A. 

14 Can be seen in Figure 16 in Appendix A. 

15 44 records were deemed to be duplicates and were deleted. 16 www.Army.com, June 9, 2008. 

12 
Number of Reserve Activations by Month

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

Sept_2001

Dec_2001

Mar_2002

June_2002

Sept_2002

Dec_2002

Mar_2003

June_2003Sept_2003

Dec_2003

Mar_2004

June_2004

Sept_2004

Dec_2004

Mar_2005

June_2005Sept_2005

Dec_2005

Mar_2006

June_2006Sept_2006

Dec_2006

Mar_2007

June_2007Sept_2007

Dec_2007

Figure 2: Number of Reserve Activations by Month 

Table 5 shows the length of activation by component from 2001 through 2007. There is a large 

disparity in length of activation across components, mainly depending on the needs of the 

component service.17 The Air National Guard and Air Force Reserve both have numerous short 

activations, with over 75% of all activations being for less than 6 months. The Navy Reserve and 

Marine Corps Reserve primarily have activations between 6 months and 1 year in duration, both 

with over 45% of all activations within this time frame. The Army Reserve and Army National 

Guard have the longest activations, with approximately 40% of all activations between 1 year and 

1.5 years. The Coast Guard Reserve has the greatest number of activations over two years long, 

with over 17% within this time frame. 

 

Table 5: Length of Activation by Component 

Time Frame 

Air 

National 

Guard 

Navy 

Reserve 

Army 

Reserve 

Coast 

Guard 

Reserve 

Air Force 

Reserve 

Marine 

Corp 

Reserve 

Army 

National 

Guard 

Less than 6 Months 76.85% 18.75% 20.03% 32.16% 75.96% 16.98% 17.17%

6 Month to 1 Year 13.44% 55.06% 24.73% 13.52% 11.34% 46.78% 24.20%

1 Year to 1.5 Years 5.87% 15.42% 39.35% 29.20% 8.41% 23.73% 44.54%

1.5 Years to 2 Years 2.77% 8.07% 10.65% 7.32% 1.80% 6.67% 9.71%

Greater than 2 Years 1.08% 2.69% 5.24% 17.79% 2.49% 5.86% 4.37%

 

17 Can be seen in Figures 17 through 24 in Appendix A. 

13 
The average length of activation increased in 2002, coincident with the beginning of operations in 

Afghanistan. The average length of activation increased from 273 days in 2000 to about 295 days in 

2002. Increases in activations occurred almost immediately after September 11, 2001, in the Air 

National Guard, Air Force Reserve and Coast Guard Reserve.18 Activations in the Army National 

Guard, Army Reserve and Marine Corps Reserves began to lengthen in 2003 as force levels in Iraq 

grew. 

Employment Data Overview 

The Civilian Employer Information (CEI) database used in this study is a dataset containing 

1,151,763 records from a self-reported survey on employment information of Reserve and National 

Guard component members. DOD began collecting data after September 11, 2001, subsequent to an 

increase of activations of reservists. Participation was voluntary until it became mandatory in 

August 2004. Since becoming mandatory, the participation rate of reservists has increased, reaching 

60% in 2005, and 77% in 2006, including 91% participation by the Selected Reserves. 

Information collected through surveys is known to contain errors, and therefore it is important to 

scrutinize the validity of this dataset. Several errors are observed in the CEI data. First, there are 

only end dates of employment for 39,521 individuals. It appears that end dates are not updated 

when new employment is inserted for an individual who is already in the database. Second, total 

firm size fluctuates greatly between individuals who are employed by firms with the same DUNS 

number, which could be related to changes over time. Third, only 14% of records possess DUNS 

numbers, which could be a result of a large number of employers without DUNS Numbers. 

Since checking the validity of each record would be cumbersome, broad rules were applied to create 

a subset of the total population of 698,853 records that have a valid Standard Occupational Code 

(SOC), Begin and End employment dates, and unique ID.19 

 

The U.S. Census Bureau industry and occupation crosswalk was used to translate the SOC codes 

into Census Occupation Codes (COC).20 Adding the COC consolidates the population to 460,451, 

which were divided into 3 industry groups as follows: 30.54% worked in professional settings, 

40.53% in services and 28.93% in manufacturing. Overall, self-employed individuals make up only 

6.5% of the all respondents with valid COC values. Self-employed individuals are more likely to 

work in the professional group than those not self-employed.21 Self-employed individuals were also 

less likely to work in service industries, as opposed to those not self-employed. 

 

18 Can be seen in Figure 25 in Appendix A. 

19 The actual logic on CEI restrictions follows. Records in the CEI data must agree with the all of the following rules to 

be considered valid. 

Where SOC <> (‘WW-WWWW’ or ‘ZZ-ZZZZ’ or ‘00-0000’ or ‘99-9999’) 

And Begin Date < 02/29/2008 

And End Date < 02/29/2008 

And (Begin Date < End Date or End Date = ‘00000000’) 

And ID > 0 

20 http://www.census.gov/hhes/www/ioindex/cens_900_975.html , 06/18/2008. 21 Can be seen in Figure 26 in Appendix A. 

14 
Smaller firms (fewer than 100 employees) were the primary employers of those Selected Reserve 

members working in the private sector. According to DoD, nearly 70% of the employers of Selected 

Reserve members fit this definition of small businesses.22 DoD data also indicate that 62% of 

private sector employers of reservists had fewer than 50 employees; 39% employed fewer than 10; 

and 28% had from 1 to 4 employees.23 

The length of employment was calculated by finding the difference between the start and end date 

of employment. Individuals with null end dates were not included. In total, 39,521 individuals had 

both start and end dates, with an average length of employment of 4.2 years. Almost 50% of 

employment lasted 2 years or less, with the maximum employment being 52 years.24

Firm Data 

The Dun and Bradstreet database consists of seven yearly datasets from 2001 until 2007. In each 

year there are 172,971 records with unique DUNS numbers. Many of these records, however, show 

zero employees and sales, and are filtered from the estimation dataset. Other records are 

consolidated with a Headquarters (HQ) DUNS record associated with a parent organization. The 

average number of records represented by a DUNS number was 91,359, of which on average 

49,315 were attached to a HQ DUNS number. On average, there were 16,181 unique HQ DUNS 

numbers per year. This data are broken out by year in Table 6 below. 

Table 6: Count of DUNS and HQ DUNS Numbers 

Year DUNS HQ DUNS 

numbers 

Unique HQ 

DUNS Numbers 

2001 75,659 41,276 14,718 

2002 81,843 45,143 15,558 

2003 86,509 48,215 15,944 

2004 92,419 50,416 16,499 

2005 99,248 52,553 16,739 

2006 102,092 53,646 16,903 

2007 101,744 53,957 16,903 

Average 91,359 49,315 16,181 

 

Approximately 78% of the records represent small businesses with 100 or fewer employees, 14% 

were of medium-sized businesses with between 101 and 500 employees, and 9% represented large 

businesses with more than 500 employees.25 The data for the HQ DUNS numbers provided much 

 

22 March 2007 DMDC data used by DOD in its Interim Report to Congress of the Special Working Group on Transition 

to Civilian Employment of National Guard and Reserve Members Returning from Deployment in Operation Iraqi 

Freedom and Operation Enduring Freedom, November 2007 (http://www.defenselink.mil/ra/html/publications.html). 

While an imperfect comparison, the Census Bureau’s March 2007 Supplement to its Current Population Survey

reported that 49% of private-sector employees work for firms with fewer than 100 employees 

(http://dataferrett.census.gov). 23 August 2006 DMDC data as reported by the U.S. Government Accountability Office (GAO) in “Additional Actions 

Needed to Improve Oversight of Reserve Employment Issues,” GAO-07-259, February 2007. 

24 Can be seen in Figure 29 in Appendix A. 25 The Office of Advocacy, U.S. Small Business Administration, for research purposes usually defines small firms as 

those having fewer than 500 employees. 

15 
different size ratios as firms with fewer than 100 employees are much less likely to have multiple 

locations, with 18% being from small businesses, 30% from medium-sized businesses, and 52% 

from large businesses. To get a comprehensive view of the data, records were assigned HQ DUNS 

data when available and regular DUNS data when the headquarters data was absent. This was done 

in order to group all subsidiary businesses with their parent organization and to include firms 

without parent organizations. The combined data, seen in Figure 3, show that 72% of the records 

were from small businesses, 16% were from medium-sized businesses, and 13% were from large 

businesses. 

Total Employees of Combined HQ and DUNs by Firm Size

0%

10%

20%

30%

40%

50%

60%

70%

80%

2001 2002 2003 2004 2005 2006 2007

Year

Percent of Firms

Small

Medium

Large

Figure 3: Total Employees of Combined HQ and DUNS by Firm Size 

Firm sales were classified into categories of $100,000 or less, between $100,000 and $1 million, 

between $1 million and $10 million, between $10 million and $100 million, and $100 million or 

greater. When looking at data from records with individual DUNS numbers, approximately 76% of 

businesses had sales of $10 million or less per year, while only 7% had sales of over $100 million. 

Using the HQ DUNS data, approximately 78% of firms had sales of $10 million or more per year, 

while less than 4% had sales of less than $1 million. The combined data in Figure 4 show that 80% 

of firms had annual sales of between $100,000 and $100 million. 

16 
Firm Sales of Combined HQ and DUNs by Year

0%

5%

10%

15%

20%

25%

30%

35%

40%

2001 2002 2003 2004 2005 2006 2007

Year

Percent of Firms

$100K and Less

$100K-$1M

$1M-$10M

$10M-$100M

$100M and Greater

Figure 4: Firm Sales of Combined HQ and DUNS by Year 

4. Hypothesis 

The hypothesis that this study addresses is that extended absences by employees because of Reserve 

activation have a disproportionate impact on small businesses. Because they have fewer employees, 

small businesses face greater difficulties adjusting to extended absences and redistributing work 

when reservists return. There are measurable differences between the productivity effects of small 

and large firms in terms of income, profitability, or both when reservist employees are called to 

active duty. We address the hypothesis by comparing the characteristics of firms employing 

activated reservists with the characteristics of firms employing non-activated reservists and a 

sample of similar employees with no Reserve experience. We also estimate a model of firm 

performance as a function of Reserve activation. 

5. Model Description 

The Department of Defense maintains a database for reservists with employee-provided information 

on their employers. Reporting for this database was initially voluntary but is now mandatory for all 

Reserve personnel, and the Office of the Secretary of Defense (OSD) estimates that the database 

currently covers about 90 percent of members of the Selected Reserve. This database can be linked 

to other DOD data that identify which reservists have been activated and for how long. 

17 
In order to address the hypothesis that extended absences by employees because of Reserve 

activation have a disproportionate impact on small businesses we must analyze the differences in 

the characteristics of reservist employers. We assume that because they have fewer employees, 

small businesses face greater difficulties adjusting to extended absences and redistributing work 

when reservists return and that we shall be able to measure differences between the productivity 

effects of small and large firms in terms of income, profitability, or both when reservist employees 

are called to active duty. The DOD data do not contain a measurable performance metric for the 

employers affected by Reserve activation but OSD has attempted to merge the data with firm data 

from Dun & Bradstreet. 

The Dun & Bradstreet Corporation (D&B) provides credit information on businesses and 

corporations using its Data Universal Numbering System (DUNS) identifiers, assigned to over 100 

million global companies. These numbers are linked to business information reports that include 

data such as: credit terms, principal's history, financials, liens, sales, number of employees, etc. The 

DUNS system is used by all major banks and finance companies as well as by Federal agencies. A 

DUNS number is required for many U.S. Federal Government transactions and corporate research. 

Using these two data sources, we can analyze differences in the characteristics of reservist 

employers as compared with the employers of non-activated reservists and otherwise similar 

civilians. Are the employers of activated reservists more or less likely to be small businesses? Does 

reservist activation have a proportionately more negative effect on small business? Are activated 

reservists concentrated in particular occupations? 

Reservist activation may disproportionately affect small businesses in at least two ways. First, a 

higher number of activated reservists may be drawn from small firms than would be expected given 

their relative share of the total civilian workforce. Second, the average activation may have a greater 

relative impact on smaller firms in terms of productive hours lost. Previous work has shown that 

productivity losses from absenteeism are larger for small firms than larger firms. Employees who 

have no perfect substitutes within the local production unit cause productivity losses to their firms 

in excess of their wages. In firms with a small number of employees, those employees may be more 

likely to perform a large number of tasks and be more difficult to replace. 

We develop a model of firm performance with particular emphasis on the effects of Reserve 

activation and firm size. 

The initial step is to determine the percent activated by firm (i) and year(t) 

it,

it,

it, Employees ofNumber Total

Reservists Activated ofNumber 

%Activated  

where the Total Number of Employees is the reported total in the Dun & Bradstreet DUNS dataset. 

These numbers can be adjusted by changing the annual length of time activated (either 30 days or 

180 days). Firm sizes of zero were removed from the sample. Next, we calculate the percent change 

in sales by firm and year. 

18 
it,

i1,t it,

it, Sales

Sales Sales

 Salesin %Change   

The general model is shown below, where dummy variables will be used for industry and firm size. 

Both are binary variables with a value of 1 for industry X or if the firm is a small business and 0 

otherwise. Firm size can be adjusted by changing the definition of a small business (either under 

100 employees or under 500 employees). 

i i i i i i %ChangeinSales  FirmSize  %Activated Ind  Ind .....21   1  1 1 2 

The dependent variable will be the change in sales during the relevant time period, and the 

independent variables will be firm size, percent activated, and industry-related demographic dummy 

variables. The model will also include an interaction variable between the percent activated and the 

small business term in order to determine whether the effect of Reserve activation is 

disproportionate on small firms. We will proceed to determine the effect of industry, firm size and 

percent activated on the financial well-being of the firms. The model is estimated using Ordinary 

Least Squares (OLS) regression. 

We hypothesize that firm size has a positive effect on percent change in sales (i.e., small firms will 

show a greater share change in sales, which is most likely a sales loss, than large firms due to 

activation). We also expect that the percent activated will have a positive effect on percent change 

in sales (i.e., the more reservists activated as a percent of the firm’s total employees, the greater the 

change in sales). Industry effects are less predictable, but we would expect that those industries 

which have higher activation rates, such as construction, security, and medical, will have greater 

percent changes in sales relative to other industries. 

6. Results 

Two primary data sources are used to explore the impact of reserve activations on businesses. These 

sources are Dun & Bradstreet, which provides the Data Universal Numbering System (DUNS) 

database, and the Defense Manpower Data Center (DMDC) which provides the Civilian Employer 

Information (CEI) database and the Activations database. The data in these databases ranges from 

2001 to 2007. 

The Dun & Bradstreet database provides firm information defined by DUNS numbers that are 

categorized by hierarchy. Local data are used for firms with a single location, but those firms with 

several locations are consolidated into one firm-wide data point for number of employees and sales 

figures. The variables of interest are year, total sales, and number of employees. Unique DUNS 

numbers extract these for the model for each year. 

The Dun & Bradstreet data are used to create several variables. The first variable, DELTA_SA, is 

defined as the percentage change in sales this year compared with the sales of the previous year. 

The second variable, AVE_EMP, represents the average number of employees over the range of 

data where the firm has a positive number of employees. This variable is then used to define a 

dummy variable indicating whether the firm qualifies as a small business (SMALL100). The last 

19 
variable is BANKRUPT, a dummy variable that uses the bankruptcy date to determine if the firm 

filed for bankruptcy during that particular year. 

The CEI database is filtered to determine which jobs individuals held each year. It is assumed that 

each individual holds only one job at a time; therefore, data for the current job are used for each 

corresponding year. The CEI-filtered data are cross-referenced to the Reserve Personnel database in 

order to confirm that individuals are active in the Reserves in each corresponding year. 

 

The data from the CEI are used for two purposes: first, to connect the Dun & Bradstreet and 

Activation databases, and second, to determine the industry classification for each firm. Each firm is 

assigned a dummy variable to indicate the self-reported industry based on Standard Industrial 

Classification (SIC) codes.26 Table 7 presents the distribution of industries by the firms in our 

sample. 

Table 7: Firm Industry Distribution 

Variable Percent Description 

IND_1 2% Agriculture, Forestry, and Fishing 

IND_2 1% Mining 

IND_3 5% Construction 

IND_4 16% Manufacturing 

IND_5 7% 

 Transportation, Communications, 

Electric, Gas, and Sanitary Services 

IND_6 8% Wholesale Trade 

IND_7 15% Retail Trade 

IND_8 6% Finance, Insurance, and Real Estate 

IND_9 41% Services 

The Activations database is used to create two variables indicating the count of individuals who are 

activated each year for at least 30 or 180 days. The Activations database is connected with the CEI 

and Dun & Bradstreet databases to provide these counts by each individual firm. The variables for 

percent activated (PERACT30 and PERACT180) are calculated by dividing the firm’s number of 

activated individual reservists by its number of employees. 

When examining the percent change in sales variable (DELTA_SA), we found what were assumed 

to be two validity problems. First, some sales totals had no variation over several years. We 

assumed that this occurred because no new data were reported for the current year. Second, some 

sales totals showed extreme variation across years, resulting in a change of several hundred percent 

or more. We assume that there was an error in either reporting or recording the magnitude of sales 

figures for firms with excessively large changes in sales. Therefore, we filtered records that had no 

change in sales or that more than doubled over a one-year period in order to eliminate some of these 

validity problems. 

Table 8 presents the primary variables used in the model and their means. Results are provided for 

both an unfiltered and filtered dataset. The unfiltered dataset has average sales of approximately 

 

26 The CEI database reports both NAICS codes and the older SIC codes. SIC was chosen due to the availability of a 

crosswalk table that was used in a previous SBA report. 

20 
$141 million and an average number of employees of 2,640. When the filters described above are 

applied to the data, average sales and average employees increased to approximately $181 million 

and 3,878 employees per firm. The bankruptcy variable shows that approximately one in every 

1,000 businesses filed for bankruptcy over the range of the dataset, and small businesses of 100 or 

fewer employees made up approximately 32% of the sample. About two out of every 1,000 

employees were activated for 30 days or more in the unfiltered data, with about one out of 1,000 in 

the filtered data. The interactive variable has a mean that was approximately the same for both the 

unfiltered and filtered datasets. The change in sales was approximately 45 times the previous year’s 

sales in the unfiltered data, which caused concern over the validity of this variable. The change in 

sales variable, now normalized between 1 and –1 in the filtered data, has an average change of 

positive .1%. 

Table 8: Variable Description and Means 

Variable Name Description Unfiltered Mean Filtered Mean 

SALES Sales ($K) 141,060,916 181,279,616 

DELTA_SA Change in sales 45.1856 0.0013 

BANKRUP0 Bankruptcy dummy variable 0.0097 0.0012 

EMP Number of employees 2,640.1 3,878.5 

PERACT30 Percent activated 30 days and over 0.0025 0.0014 

INTACT30 Equal to PERACT30*SMALL100 0.0021 0.0036 

SMALL100 Businesses with 100 or less employees 0.3161 0.3057 

The OLS model is used to estimate the percent change in sales (DELTA_SA) as the dependent 

variable. The model includes the following explanatory variables: percent activated for 30 days or 

more (PERACT30), small business with 100 employees or fewer (SMALL100), the interaction term 

(INTACT30), and industry dummy variables. Industry 9 is assigned as the omitted dummy for all 

model runs. The interaction term is included to determine whether the effect of activations is 

different for small firms. 

The results of the estimation, shown in Table 9, provide support for our initial hypothesis. First, we 

find that the percentage of employee activation of 30 days or more had a negative effect on change 

in sales. Second, businesses of 100 or fewer employees are more likely to experience negative sales 

growth compared with large firms. Third, we see a positive, significant coefficient on the interaction 

term, meaning that activations have a smaller effect on small firms than on large firms. The typical 

small business saw a 3.2 percentage point decrease in sales relative to an otherwise similar large 

firm. The marginal effects of reserve activations were very small in absolute terms: a 100% increase 

in the percent activated results in a .05 percentage point decrease in sales. 

21 
Table 9: Change in Sales - Filtered Interactive 

Variable Coefficient Standard Error P[|Z|>z] 

Constant 0.0035078 0.0031891 0.2714 

PERACT30 -0.9188452 * 0.2895303 0.0015 

SMALL100 -0.0324184 * 0.0057890 0.0000 

INTACT30 0.7856073 * 0.3255108 0.0158 

IND_1 -0.0779210 * 0.0242496 0.0013 

IND_2 0.0010921 0.0310107 0.9719 

IND_3 0.0148720 0.0104076 0.1530 

IND_4 -0.0044524 0.0057328 0.4374 

IND_5 0.0334771 * 0.0077838 0.0000 

IND_6 0.0114311 0.0083053 0.1687 

IND_7 0.0031103 0.0074029 0.6744 

IND_8 0.0190667 * 0.0094441 0.0435 

 R-squared = 0.0027139 

* Significant at the .05 level 

The coefficient on the interaction term suggests that small firms are less affected by Reserve 

activations than are large firms. That is, the positive coefficient on the interaction term partially 

offsets the negative effects of the coefficient on PERACT30. However, recall that Reserve 

activations are measured in relative terms (percent of workforce activated), and one of the reasons 

for our original hypothesis was that each reservist activated had a larger relative impact on a small 

business employer than on a large business. To test this contention, we simulated the effects of a 

single reservist activation on typical small and large firms from our sample. Using the means of the 

explanatory variables for small and large firms in the sample, we calculated the marginal effect on 

sales of activating one additional employee for each type of firm. 

Not surprisingly, the change in percent activated was much larger for the average small firm. Small 

firms have an average of 0.15 employees per firm activated in a given year; larger firms have about 

2.4 employees per firm activated. However, in relative terms, a higher proportion of small-firm 

employees (0.36% versus 0.05%) are activated. Adding one more employee per firm increases the 

percent activated in small firms to 2.6% but only increases the percent activated in large firms to 

0.06%. There was a correspondingly large impact on sales of small firms: an increase of one 

employee activated had a negative effect of 0.30 percentage points on the change in sales. The 

impact on large firms was also negative, but only 0.02 percentage points. In other words, the 

negative sales impact of activating an employee is about 15 times greater for the typical small firm 

compared with the average large firm in our sample. 

In addition to the main specification discussed above, we tested several alternative specifications of 

the Change in Sales model. We also estimated two binary probability models to determine whether 

activations had an effect on the probability that a firm would declare bankruptcy or realize at least a 

5% decrease in sales. These two models are referred to as the Probit Bankruptcy model and the 

Probit 5% Loss model. 

Several OLS models were run using the unfiltered data and can be viewed in Table 16 through 

Table 20. These models used the normal OLS model, an OLS lagged model, OLS with the 180 

22 
percent activated variable, OLS with year variable, and OLS with the interactive variable. None of 

the models using the unfiltered data returned significant results for the explanatory variables. 

The filtered data provided much more significant results. An OLS Change in Sales model, shown in 

Table 10, had similar significant coefficients as the primary model, which included the interaction 

term. Again, we find that percentage of employee activation of 30 days or more has a negative 

effect on change in sales and that businesses with 100 or fewer employees are more likely to 

experience negative sales growth. The marginal effect of the explanatory variables was calculated 

by inserting the mean values into the model. This indicated that small businesses had a 1.9 

percentage point decrease in sales relative to larger firms, while an increase in percent activated of 

10% had a –3.7 percentage point change in sales. 

Table 10: Change in Sales – Filtered 

Variable Coefficient Standard Error P[|Z|>z] 

Constant 0.0103138 * 0.0030879 0.0008 

PERACT30 -0.3703531 * 0.1279291 0.0038 

SMALL100 -0.0191772 * 0.0055720 0.0006 

IND_1 0.0079956 0.0245763 0.7449 

IND_2 0.0086260 0.0301768 0.7750 

IND_3 0.0101819 0.0100822 0.3125 

IND_4 -0.0027402 0.0055623 0.6223 

IND_5 0.0302667 * 0.0075357 0.0001 

IND_6 0.0091315 0.0080557 0.2570 

IND_7 0.0047118 0.0071880 0.5121 

IND_8 0.0187088 * 0.0091599 0.0411 

 R-squared = 0.0015436

* Significant at the .05 level 

An additional OLS Change in Sales model with filtered data was estimated using the percent 

activated variable of 180 days or greater. The results shown in Table 22 are similar to our findings 

for the other specifications using filtered data. Both the percent activated and small business 

variables show significant negative coefficients. In this excursion, the marginal effect of being a 

small business caused a 3.0 percentage point decrease in sales. While the coefficient of percent 

activated at 180 days or more was significant, it would take a 1,000% increase in activations at this 

level to cause a 0.04 percentage point decrease on change in sales. 

The Probit Bankruptcy model is estimated using the binary dependent variable, BANKRUPT, 

which indicates whether the firm declared bankrupt during the time period. Similar to the OLS 

Change in Sales model, it uses the following independent variables: PERACT30, SMALL100, and 

the industry dummy variables. 

Table 11 shows the results of the Probit Bankruptcy model. Only two explanatory variables, 

Industries 4 and 5, were found to have significant results. The positive coefficient indicates that 

firms in manufacturing and transportation have an increased probability of filing for bankruptcy. 

23 
Table 11: Probit Bankruptcy 

Variable Coefficient Standard Error P[|Z|>z] 

Constant -3.0966664* 0.1227597 0.0000 

PERACT30 0.8593904 0.7760742 0.2681 

SMALL100 -0.2312401 0.1778953 0.1936 

IND_1 -4.6950045 191,654.8060000 1.0000 

IND_2 -4.7021193 212,262.4870000 1.0000 

IND_3 0.1102880 0.3275048 0.7363 

IND_4 0.3971233* 0.1542231 0.0100 

IND_5 0.5488454* 0.1671664 0.0010 

IND_6 -0.0282511 0.3168516 0.9290 

IND_7 0.1463795 0.2152213 0.4964 

IND_8 0.3360701 0.2237248 0.1331 

 Chi Squared 19.0675900

* Significant at the .05 level 

An additional Bankruptcy excursion, seen in Table 23, was run with the variable PERACT180 

substituted in place of PERACT30. This was done in order to determine the effect of extended 

activations. Industry 4 and 5 remained the only significant explanatory variables. Both retained their 

positive coefficients. 

The Probit 5% Loss model creates a dummy variable indicating whether a firm had a 5% loss in the 

DELTA_SA variable or not. Similar to the previous models, the explanatory variables were: 

PERACT30, SMALL100, and the industry dummy variables. 

Table 12 shows the results for the Probit 5% Loss model. Several of the explanatory variables 

returned significant results. PERACT30 returned a positive coefficient indicating that firms with 

larger percentages of activation have a higher probability of having change in sales of 5% loss or 

greater. SMALL100 returned a negative coefficient indicating that small businesses had a lower 

probability of having change in sales of 5% loss or greater. Industries 1, 7 and 8 had significant 

negative coefficients while industries 3 and 4 had significant positive coefficients. 

24 
Table 12: Probit 5% Loss 

Variable Coefficient Standard Error P[|Z|>z] 

Constant -1.3728556 * 0.0090091 0.0000 

PERACT30 1.3715580 * 0.2160303 0.0000 

SMALL100 -0.2031728 * 0.0128598 0.0000 

IND_1 -0.3300386 * 0.0545459 0.0000 

IND_2 -0.0924595 0.0741500 0.2124 

IND_3 0.1142412 * 0.0257341 0.0000 

IND_4 0.1390919 * 0.0152927 0.0000 

IND_5 -0.0016587 0.0221866 0.9404 

IND_6 0.0091607 0.0211749 0.6653 

IND_7 -0.2018023 * 0.0183054 0.0000 

IND_8 -0.0946447 * 0.0247309 0.0001 

 Chi Squared = 712.8361000

* Significant at the .05 level 

 

Final Comments 

We discovered several problems with the available data that required us to censor a number of 

observations. The CEI database did not have consistent quit dates for the employment records and 

the self-employed indicator was inconsistent. There also may have been some confusion by the 

reservists on some survey questions, as many indicated that they were employed full-time by the 

Reserves but reported a civilian work address and vice-versa. 

The DUNS database also had several gaps in the sales data annual coverage, many of which were 

amended by simply carrying over the previous year’s data. The absence of small firms and selfemployed firms that do not belong to the DUNS subscription service also leaves a large amount of 

unreported data that could be useful. 

To remedy these and other shortcomings it may be necessary to obtain a more complete dataset. 

One promising alternative is the Longitudinal Employer-Household Dynamics (LEHD) database, 

which contains employer-employee data linked by SSN and elements of state unemployment data. 

Rather than rely on voluntarily self-reported data, the LEHD includes firm-reported data on sales 

figures, start and quit dates of employment, self-employment, periods of unemployment, and annual 

updates on all data. This would resolve most problems that we encountered pertaining to gaps in the 

data and inaccuracies in employee-reported firm characteristics. Additionally, the use of state 

unemployment data substantially improves the ability to track individual job tenure. 

While we have covered some new and unexplored territory with this analysis, several questions 

remain to be answered before major policy changes should be proposed. Our results must be 

qualified because of significant concerns regarding the quality of the data available for this research. 

We were unable to determine the extent of job changes for many reservists due to gaps in the selfreported employment data. There are missing firms in the DUNS data, mostly small businesses, 

since many have no interest in or use for subscribing to that database. There is also very limited 

25 
information on the number of self-employed reservists in the CEI employer database and no 

information at all on the effect of activation on employers’ sales receipts. 

We recommend additional study in order to determine the proper level of government policy 

adjustment to offset the detrimental effects of Reserve activation on small business. Further study 

should include special emphasis on the self-employed and those reservists who either change jobs 

or work at multiple jobs during the study period. 

7. Conclusion 

The results of this research could have immediate policy relevance due to the continued need for 

reservist deployment in Iraq and Afghanistan. The analysis indicates that smaller firms are the 

primary private-sector employers of Reserve and National Guard component members, with about 

70% of these employers having fewer than 100 employees. We also show that activation of Reserve 

employees causes a negative effect on business overall, but that the activation of an additional 

employee has a disproportionately larger (negative) impact on small businesses than on large 

businesses. 

We began with the hypothesis that extended absences by employees because of Reserve activation 

had a disproportionate impact on small businesses, and our econometric model found a 1.9 

percentage point decrease in sales relative to larger firms for those activated 30 days or more and a 

3.0 percentage point decrease in sales for those activated 180 days or more. 

We also estimated a model of firm performance as a function of Reserve activation with an 

interaction variable to account for the effect of activation on small business and found that the 

marginal effect of one additional employee being activated was a decrease of 0.30 percentage points 

on small businesses and a decrease of 0.02 percentage points for large businesses. This 15-fold 

difference in the magnitude of the activation effects would seem to indicate that small businesses 

bear a heavier burden than large businesses when reservist employees are activated. 

This study represents one of the first attempts to analyze the effects of Reserve activation from the 

employer side of the equation using reservist data from the Department of Defense and employer 

data from Dun & Bradstreet. By combining activation data from DOD with sales data from D&B 

we were able to show the relationship between Reserve activation and changes in the firm’s 

finances. In subsequent studies we hope to avoid some of the self-reporting problems that were 

encountered in the data by using data from databases such as the LEHD where reporting is 

mandated by law. 

26 
8. References 

Abowd, John M., John Haltiwanger and Julia Lane. “Integrated Longitudinal EmployerEmployee Data for the United States.” The American Economic Review, Vol. 94, No.2 

Papers and Proceedings of the One Hundred Sixteenth Annual Meeting of the American 

Economic Association San Diego, CA, January 3-5, 2004. (May, 2004), pp. 224-229. 

 

Angrist, Joshua D. and John H. Johnson IV. “Effects of Work-Related Absences on Families: Evidence from 

the Gulf War.” Industrial and Labor Relations Review, Vol. 54, No.1. (Oct., 2000), pp. 41-58. 

 

Coffey, Kenneth J., “The Economic Cost of Recall” working paper for the U. S. Navy 

Bureau of Personnel. 1992, BuPers Research Professor, Department of Economics, U.S. Naval 

Academy. 

 

De Kok, Jan M. P., “Involuntary Absence from an Organizational Point of View” paper for EIM small 

business research and consultancy, Zoetermeer, the Netherlands and Tinbergen Institute, Erasmus 

University Rotterdam, the Netherlands, Tinbergen Institute Discussion Papers 97-126/3. 

 

Doyle, Colin M., Glenn A. Gotz, Neil M. Singer and Karen W. Tyson “Analysis of Employer Costs from 

Reserve Component Mobilization.” Institute for Defense Analysis paper for The Office of the 

Assistant Secretary of Defense (Reserve Affairs). 2004. 

 

Evans, David S. “Test of Alternative Theories of Firm Growth.” The Journal of Political Economy. Vol. 95, 

No. 4 (Aug. 1987), pp. 657-674. 

 

Golding, Heidi L. W., J. Michael Gilmore and Matthew S. Goldberg “The Effects of Reserve Call-Ups on 

Civilian Employers.” (May 2005). Congressional Budget Office (CBO) report to the Senate 

Committee on Small Business and Entrepreneurship. 

 

Gotz, Glenn A., “Strengthening Employer Support of the Guard and Reserve” Task BE-6-2117, for the 

Office of the Under Secretary of Defense (Personnel and Readiness), (Jan.2003). 

 

Kirby, Sheila Nataraj, David Grissmer, Stephanie Williamson and Scott Naftel (1992). Costs & Benefits of 

Reserve Participation. Santa Monica, CA: RAND Corporation, MR-812-OSD.

 

Lipsey, Robert E. “The Creation of Microdata Sets for Enterprises and Establishments.” Annales de L’insee. 

No. 30/31, The Econometrics of Panel Data (Sep., 1978) pp/ 395-422. 

Loughran, David S., Jacob Alex Klerman and Craig Martin (2006a). Effect of Activation on Reservist 

Earnings. Santa Monica, CA: RAND Corporation, MG-474. 

 

Loughran, David S., Jacob Alex Klerman and Bogdan Savych (2006b). The Effect of Reserve Activations and 

Active-Duty Deployments on Local Employment During the Global War on Terrorism. Santa 

Monica, CA: RAND Corporation, Technical Report, TR-321. 

 

McAlister, W.H., R.M. Handley, T.A. Wingert “U.S. Army Reserve optometry mobilization in support of 

Operations Desert Shield and Desert Storm.” Military Medicine 1994; 159: 442-4. 

 

Navarro, Chris and Cara Bass “The Cost of Employee Absenteeism” Compensation & Benefits Review 2006 

38: 26-30. 

27 
 

Nicholson Sean, Mark V. Pauly, Daniel Polsky, Claire Sharda, Helena Szrek and Marc L. Berger “Measuring 

the effects of work loss on productivity with team production.” Health Economics 15: 111–123 

(2006). 

 

Pauly, Mark V., Sean Nicholson, Judy Xu, Dan Polsky, Patricia M. Danzon, James F. Murray and Marc L. 

Berger “A General Model of the Impact of Absenteeism on Employers and Employees.” Health 

Economics 11: 221-231 (2002). 

 

Struyk, Raymond J. “Evidence on the Locational Activities of Manufacturing Industries in Metropolitan 

Areas.” The University of Wisconsin Press Vol. 48, No. 4 (Nov., 1972), pp. 377-382. 

 

Nieva, Veronica et al., 1999 Employer Reservist Survey: Final Report (Rockville, Md.: Westat, Inc., August 

2000). 

 

U.S. Department of Defense, Defense Manpower Data Center “May 2003 Status of Forces Survey of 

Reserve Component Members.” DMDC Report No. 2003-010 November 2003. 

 

U.S. Government Accountability Office (GAO), “Additional Actions Needed to Improve Oversight of 

Reserve Employment Issues.” GAO-07-259 February 2007. 

U.S. Department of Defense, “Interim Report to Congress of the Special Working Group on Transition to 

Civilian Employment of National Guard and Reserve Members Returning from Deployment in 

Operation Iraqi Freedom and Operation Enduring Freedom.” November 2007. 

28 
Appendix A – Demographics and Length of Activations for Reservists 

Percent of Total Force by Service and Year

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

2001 2002 2003 2004 2005 2006 2007

Percent

Army National Guard

Marine Corps Reserve

Air Force Reserve

Coast Guard Reserve

Army Reserve

Navy Reserve

Air National Guard

Figure 5: Percent of Total Force by Service and Year 

29 
Table 13: Crosswalk of Variables for Education 

 Secondary Credential Near Completion 

 Less than High School Non-High School Graduate 

Attending High school, Junior or Less 

 Attending High School, Senior 

 High School Diploma 

 Completed High School, No Degree 

 Adult Education Diploma 

 Non-Traditional High School Credential 

High School Diploma Home Study Diploma 

 ARNG Challenge Program GED Certificate 

 Test-Based Equivalent Diploma 

 Correspondence School Diploma 

 High School Certificate of Attendance 

 Associate Degree 

 Occupational Program Certificate 

Some College 1-2 Year College, No Degree 

 Professional Nursing Diploma 

 Completed One Semester of College, No High School Diploma 

BA/BS Degree Baccalaureate Degree 

 Doctorate Degree 

 Master's Degree 

Graduate School First Professional Degree 

 Post Doctorate Degree 

 Post Master's Degree 

Unknown Unknown 

Education Level by Service

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Air

National

Guard

Navy

Reserve

Army

Reserve

Coast

Guard

Reserve

Air Force

Reserve

Marine

Corp

Reserve

Army

National

Guard

< HS

HS Diploma

Some College

BA/BS Degree

Graduate School

Unknown

Figure 6: Education Level by Service 

30 
Figure 7: Percent Female per Service 

Percent Female per Service

0%

5%

10%

15%

20%

25%

Air National

Guard

Navy

Reserve

Army

Reserve

Coast

Guard

Reserve

Air Force

Reserve

Marine Corp

Reserve

Army

National

Guard

Enlisted Personnel’s Marital Status by Grade

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

E01 E02 E03 E04 E05 E06 E07 E08 E09

Never Married

Married

Divorced

Unknown

Other

Figure 8: Enlisted Personnel’s Marital Status by Grade 

31 
Officers’ Marital Status by Grade

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

O01 O02 O03 O04 O05 O06 O07 O08

Never Married

Married

Divorced

Unknown

Other

Figure 9: Officers’ Marital Status by Grade 

Warrant Officers’ Marital Status by Grade

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

 WO1 WO2 WO3 WO4 WO5

Never Married

Married

Divorced

Unknown

Interlocutory

Figure 10: Warrant Officers’ Marital Status by Grade 

32 
Enlisted Personnel’s Dependents by Grade

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

E01 E02 E03 E04 E05 E06 E07 E08 E09

None

One

Two

Three

Four

Five

Six or more

Unknown

Figure 11: Enlisted Personnel’s Dependents by Grade 

Officers’ Dependents by Grade

0%

5%

10%

15%

20%

25%

30%

35%

40%

O01 O02 O03 O04 O05 O06 O07 O08

None

One

Two

Three

Four

Five

Six or more

Unknown

Figure 12: Officers’ Dependents by Grade 

33 
Warrant Officers’ Dependents by Grade

0%

5%

10%

15%

20%

25%

30%

35%

 W01 W02 W03 W04 W05

None

One

Two

Three

Four

Five

Six or more

Unknown

Figure 13: Warrant Officers’ Dependents by Grade 

 

Enlisted Personnel's Pay Grade by Year

0%

5%

10%

15%

20%

25%

30%

35%

2001 2002 2003 2004 2005 2006 2007

E01

E02

E03

E04

E05

E06

E07

E08

E09

Figure 14: Enlisted Personnel’s Pay Grade by Year 

34 
 

Officers’ Pay Grade by Year

0%

5%

10%

15%

20%

25%

30%

2001 2002 2003 2004 2005 2006 2007

O01

O02

O03

O04

O05

O06

O07

O08

Figure 15: Officers’ Pay Grade by Year 

35 
Warrant Officers' Pay Grade by Year

0%

5%

10%

15%

20%

25%

30%

35%

40%

2001 2002 2003 2004 2005 2006 2007

W01

W02

W03

W04

W05

Figure 16: Warrant Officers' Pay Grade by Year 

Length of Activation - Air National Guard

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

0-30

61-90

121-150

181-210

241-270

301-330

361-390

421-450

481-510

541-570

601-630

660-690

721-750

781-810

>841

Length of Activations in Days

Number of Activations

Figure 17: Length of Activation – Air National Guard 

36 
Length of Activation - Air Force Reserve

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0-30

61-90

121-150

181-210

241-270

301-330

361-390

421-450

481-510

541-570

601-630

660-690

721-750

781-810

>841

Length of Activation in Days

Number of Activations

Figure 18: Length of Activation - Air Force Reserve 

Length of Activation - Navy Reserve

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0-30

61-90

121-150

181-210

241-270

301-330

361-390

421-450

481-510

541-570

601-630

660-690

721-750

781-810

>841

Length of Activations in Days

Number of Activations

Figure 19: Length of Activation - Navy Reserve 

37 
Length of Activation - Coast Guard Reserve

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

0-30

61-90

121-150

181-210

241-270

301-330

361-390

421-450

481-510

541-570

601-630

660-690

721-750

781-810

>841

Length of Activations in Days

Number of Activations

Figure 20: Length of Activation - Coast Guard Reserve 

Length of Activation - Army Reserve

0

5,000

10,000

15,000

20,000

25,000

30,000

0-30

61-90

121-150

181-210

241-270

301-330

361-390

421-450

481-510

541-570

601-630

660-690

721-750

781-810

>841

Length of Activations in Days

Number of Activations

Figure 21: Length of Activation - Army Reserve 

38 
Length of Activation - Marine Corp Reserve

0

2,000

4,000

6,000

8,000

10,000

12,000

0-30

61-90

121-150

181-210

241-270

301-330

361-390

421-450

481-510

541-570

601-630

660-690

721-750

781-810

>841

Length of Activations in Days

Number of Activations

Figure 22: Length of Activation - Marine Corp Reserve 

Length of Activation - Army National Guard

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0-30

61-90

121-150

181-210

241-270

301-330

361-390

421-450

481-510

541-570

601-630

660-690

721-750

781-810

>841

Length of Activations in Days

Number of Activations

Figure 23: Length of Activation - Army National Guard 

39 
Length of Activation - All Services

0

50,000

100,000

150,000

200,000

250,000

0-30

61-90

121-150181-210241-270301-330

361-390

421-450481-510541-570601-630660-690721-750

781-810

>841

Length of Activations in Days

Number of Activations

Figure 24: Length of Activation - All Services 

Total Activations by Service

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

2001 2002 2003 2004 2005 2006 2007

Year

Army National Guard

Air National Guard

Army Reserve

Naval Reserve

Coast Guard Reserve

Air Force Reserve

Marine Reserve

Figure 25: Total Activations by Service 

40 
Table 14: Percent of Total Activations by Component 

Year 

Air 

National 

Guard 

Navy 

Reserve 

Army 

Reserve 

Coast 

Guard 

Reserve 

Air Force 

Reserve 

Marine 

Corps 

Reserve 

Army 

National 

Guard 

2001 36.36% 7.53% 5.25% 18.16% 31.46% 0.30% 0.93%

2002 33.08% 6.62% 10.68% 0.76% 22.34% 3.81% 22.72%

2003 13.00% 4.10% 28.37% 1.77% 10.77% 8.28% 33.71%

2004 11.88% 2.32% 20.99% 0.53% 11.00% 6.01% 47.26%

2005 18.33% 2.96% 21.18% 0.58% 16.62% 4.56% 35.76%

2006 21.18% 4.52% 19.91% 0.32% 15.92% 5.31% 32.84%

2007 23.83% 5.16% 23.00% 0.00% 19.06% 4.92% 24.02%

Average 22.52% 4.74% 18.48% 3.16% 18.17% 4.74% 28.18%

Reservists' Employment by Industry Group 

and Self-employment Status

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

Self Non-Self

Professional

Manufacturing

Service

Figure 26: Reservists’ Employment by Industry Group and Self-employment Status 

41 
Figure 27: Reservists’ Employment by Industry Group 

Reservists’ Employment by Industry Group

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Professional Service Manufacturing

Reservists’ Employers by Firm Size

0%

10%

20%

30%

40%

50%

60%

70%

1 - 100 101-

200

201-

300

301-

400

401-

500

501-

600

601-

700

701-

800

801-

900

901-

1000

> 1001

Firm Size

Figure 28: Reservists’ Employers by Firm Size 

42 
Length of Reservists’ Employment

0%

5%

10%

15

20%

25%

30%

35%

1 Year 2

Years

3

Years

4

Years

5

Years

6

Years

7

Years

8

Years

9

Years

10

Years

> 10

Years

Figure 29: Length of Reservists’ Employment 

43 
Appendix B – Alternative Analysis to Check Consistency 

Table 15: Extended Variable Descriptions 

Variable Name Description Unfiltered Mean Filtered Mean 

SALES Sales 141060916.0000000 181279616.0000000 

DELTA_SA Change in sales 45.1856509 0.0012785 

BANKRUP0 Bankrupt dummy variable 0.0097264 0.0011718 

EMP Number of employees 2640.0695400 3878.4790600 

PERACT30 Percent Activated 30 days and over 0.0025733 0.0014370 

PERACT180 Percent Activated 180 days and over 0.0014658 0.0008128 

SMALL100 Businesses with 100 or less employees 0.3161006 0.3056810 

INTACT30 Equal to PERACT30*SMALL100 0.0021338 0.0035954 

INTACT180 Equal to PERACT180*SMALL100 0.0011990 0.0005997 

IND_1 Agriculture, Forestry, and Fishing 0.0153087 0.0186031 

IND_2 Mining 0.0060417 0.0065184 

IND_3 Construction 0.0460042 0.0475941 

IND_4 Manufacturing 0.1563030 0.1558190 

IND_5 

 Transportation, Communications, Electric, 

Gas, and Sanitary Services 

0.0710057 

0.0743390 

IND_6 Wholesale Trade 0.0825763 0.0837138 

IND_7 Retail Trade 0.1508089 0.1520349 

IND_8 Finance, Insurance, and Real Estate 0.0635383 0.0662948 

IND_9 Services 0.4081524 0.3947658 

44 
Table 16: Change in Sales 

Variable Coefficient Standard Error P[|Z|>z] 

Constant 7.8012333 80.4831540 0.9228 

PERACT30 -11.8115786 1395.6667400 0.9932 

SMALL100 -36.0408381 153.6146240 0.8145 

IND_1 28.4718466 600.0848270 0.9622 

IND_2 2.2159203 690.1232760 0.9974 

IND_3 4.7724689 271.0362770 0.9860 

IND_4 273.8319310 * 143.1202070 0.0557 

IND_5 2.7261412 196.3849450 0.9889 

IND_6 5.2442629 210.9461550 0.9802 

IND_7 4.2340117 170.6745460 0.9802 

IND_8 6.4445522 252.8026870 0.9797 

R-squared = 0.0002049 

* Significant at the .10 level 

Table: 17 Change in Sales Lagged 

Variable Coefficient Standard Error P[|Z|>z] 

Constant 7.9024005 67.5848208 0.9069 

PERACT30 265.2160340 1105.9847100 0.8105 

SMALL100 -44.7104788 123.8758700 0.7182 

IND_1 23.8356895 496.9428200 0.9617 

IND_2 -0.4831966 577.9162010 0.9993 

IND_3 3.6662055 223.7831870 0.9869 

IND_4 233.3471810 * 120.4302140 0.0527 

IND_5 1.7211251 164.8983380 0.9917 

IND_6 4.0952666 173.1420350 0.9811 

IND_7 2.6986454 140.2582330 0.9846 

IND_8 2.0122128 209.8891790 0.9924 

R-squared 0.0001970 

* Significant at the .10 level 

45 
Table 18: Change in Sales – 180 

Variable Coefficient Standard Error P[|Z|>z] 

Constant 9.8261189 124.3653840 0.9370 

PERACT180 -11.7952684 2345.1027200 0.9960 

SMALL100 -51.5959333 252.9824010 0.8384 

IND_1 39.6338942 882.8620570 0.9642 

IND_2 4.9407918 1106.5856600 0.9964 

IND_3 5.7449038 440.9905650 0.9896 

IND_4 419.6722090 * 221.3923010 0.0580 

IND_5 3.7019096 301.1567890 0.9902 

IND_6 6.7887376 338.7077750 0.9840 

IND_7 4.7792861 268.0079040 0.9858 

IND_8 9.7057171 405.5053770 0.9809 

R-squared 0.0003114 

* Significant at the .10 level 

Table 19: Change in Sales - 180 Lagged 

Variable Coefficient Standard Error P[|Z|>z] 

Constant 9.8135243 104.8664440 0.9254 

PERACT180 161.0611780 1872.1334200 0.9314 

SMALL100 -52.6645945 203.8168330 0.7961 

IND_1 34.6851206 728.8231770 0.9620 

IND_2 0.8696514 925.6639280 0.9993 

IND_3 4.6279499 361.2652680 0.9898 

IND_4 359.2625760 * 187.0766250 0.0548 

IND_5 -2.0458269 254.3277450 0.9936 

IND_6 5.0688348 279.0374000 0.9855 

IND_7 3.9576617 222.1107570 0.9858 

IND_8 4.6538899 336.7835450 0.9890 

R-squared 0.0002989 

* Significant at the .10 level 

46 
Table 20: Change in Sales - 180 Year 

Variable Coefficient Standard Error P[|Z|>z] 

Constant 64.6355329 308.4192390 0.8340 

YEAR -12.5860713 64.8097635 0.8460 

PERACT180 -17.7375797 2345.3847200 0.9940 

SMALL100 -50.5314305 253.0506630 0.8417 

IND_1 38.6423088 882.9078360 0.9651 

IND_2 8.1350475 1106.7467700 0.9941 

IND_3 6.3218231 441.0160620 0.9886 

IND_4 418.2259130 * 221.5253010 0.0590 

IND_5 3.0793320 301.1844310 0.9918 

IND_6 7.0819492 338.7230390 0.9833 

IND_7 8.0481133 268.5453560 0.9761 

IND_8 10.4430483 405.5373960 0.9795 

R-squared 0.0002989 

* Significant at the .10 level 

Table 21: Change in Sales – Interactive 

Variable Coefficient Standard Error P[|Z|>z] 

Constant 10.7967453 124.7317550 0.9310 

PERACT180 -591.1778420 6133.4500600 0.9232 

SMALL100 -58.1331647 260.9478390 0.8237 

INACT180 679.0019800 6641.8442100 0.9186 

IND_1 39.4829942 882.8951840 0.9643 

IND_2 4.7260765 1106.6276300 0.9966 

IND_3 7.2481491 441.2515710 0.9869 

IND_4 419.9022770 * 221.4117360 0.0579 

IND_5 5.1447022 301.4981640 0.9864 

IND_6 7.8905638 338.8914390 0.9814 

IND_7 5.1326459 268.0398730 0.9847 

IND_8 9.1473868 405.5568010 0.9820 

R-squared 0.0003122 

* Significant at the .10 level 

47 
48 

Table 22: Change in Sales – Filtered 180 

Variable Coefficient Standard Error P[|Z|>z] 

Constant 0.0031654 0.0031850 0.3203 

PERACT18 -0.5040024 * 0.1696011 0.0030 

SMALL100 -0.0304789 * 0.0056615 0.0000 

IND_1 -0.0777262 * 0.0242502 0.0013 

IND_2 0.0009979 0.0310112 0.9743 

IND_3 0.0143206 0.0104062 0.1688 

IND_4 -0.0045386 0.0057329 0.4285 

IND_5 0.0327381 * 0.0077779 0.0000 

IND_6 0.0110063 0.0083042 0.1850 

IND_7 0.0029483 0.0074026 0.6904 

IND_8 0.0190873 * 0.0094435 0.0433 

R-squared = 0.0026429 

* Significant at the .10 level 

Table 23: Probit Bankruptcy 180 

Variable Coefficient Standard Error P[|Z|>z] 

Constant -3.0961954 * 0.1496594 0.0000 

PERACT180 -0.4578350 2.6197680 0.8613 

IND_1 -4.6950871 225649.9070000 1.0000 

IND_2 -4.7029183 264360.6730000 1.0000 

IND_3 0.2452296 0.3497650 0.4832 

IND_4 0.4218674 * 0.1849948 0.0226 

IND_5 0.5835889 * 0.1985133 0.0033 

IND_6 0.0871292 0.3374465 0.7963 

IND_7 0.1325636 0.2627981 0.6140 

IND_8 0.3325928 0.2733614 0.2237 

SMALL100 -0.0936195 0.2272484 0.6804 

Chi Squared 13.1594500 

* Significant at the .10 level