Business Age, another Source of Employment Growth1

 

* Corresponding author. 

1

 We thank Maryland Department of Labor, Licensing and Regulation (DLLR), Office of Workforce Information 

and Performance staff for their patient explanation of data field definitions and appropriate data processing steps for 

our analysis. 

Ting Zhang*, PhD 

Research Assistant Professor 

Jacob France Institute 

Merrick School of Business 

University of Baltimore 

Email: tzhang@ubalt.edu 

Phone: 410-837-6551 

David Stevens, PhD 

Research Professor 

Jacob France Institute 

Merrick School of Business

University of Baltimore 

1420 N. Charles Street 

Baltimore, Maryland 21201-5779 
Business Age, another Source of Employment Growth2

ABSTRACT 

This paper contributes new evidence using recent administrative data to advance what is 

known about an important, but still controversial, topic among economists—the expected 

contributions of old and new businesses to employment growth. Improved understanding of the 

relative place-specific contributions of old and new businesses to employment growth is needed 

to help policy makers make efficient and effective decisions to hasten and sustain recovery from 

the current recession. 

 While not an either-or decision, federal, state and local governments are encouraged by 

competing advocacy groups to favor new incentives for hiring by existing businesses, or to 

prefer offering of incentives for new business start-ups. Successful employment growth policies 

will remain complex, place specific and contentious, but new recent information about the 

origins of employment growth can add a common starting point for consideration of available 

policy options. 

 Our analysis uses Quarterly Census of Employment and Wages (QCEW) data at the 

county level for one state, covering business employment dynamics between January 2004 and 

September 2008. 

New transformations of business establishment start and end dates and employment sizeclass designations are introduced. Innovative transformations of published local unemployment 

rates incorporate both magnitude and propensity. 

 Based on estimates from multistage econometric modeling with inverse use of instrument 

variable technique and hierarchical panel data with autocorrelation correction, policy relevant 

findings include a positive employment growth advantage for older business establishments in 

more than half of local industry sectors. Established large businesses are typically found in a 

different spatial pattern and lower count than the distribution of smaller, often newer 

establishments. This means that the former locales are more dependent upon the success of 

fewer businesses, including merger and acquisition transactions. 

Local unemployment rates impact employment growth. However, it is the 

unemployment rate trend that consistently associates with employment growth, not the 

magnitude of the rate per se. Local demographics matter too, for both labor supply and 

consumer behavior reasons. 

 The local level administrative data source, the unique transformations and measurement, 

and the model specifications and statistical results presented in this paper offer a new guide for 

future researchers and policy decision-makers. Local emergence from the U.S. recession that 

began in December 2007 will be uneven in timing, magnitude and composition. The design and 

implementation of successful interventions to accelerate and advance local employment growth 

will depend in part on an improved understanding of the current local economic composition. 

 

2

 We thank Maryland Department of Labor, Licensing and Regulation (DLLR), Office of Workforce Information 

and Performance staff for their patient explanation of data field definitions and appropriate data processing steps for 

our analysis. 
1 

I. Introduction 

Recovery from a recession requires employment growth. With large and growing 

national, state and local fiscal challenges it is critical to create jobs smartly and effectively. 

When many businesses face enormous challenges of their own, what types of business should be 

targeted by governments for assistance? 

For many, pursuit of the ―American Dream‖ involves entrepreneurship and small 

businesses. The importance of small business has been emphasized for years as a major engine of 

economic growth and job creation. Entrepreneurship is believed by most observers to have been 

a major source of America‘s competitive edge in the world economy. 

However, small businesses also lose many jobs. Compared with many larger and more 

mature businesses, small businesses often contribute to limited net job growth. Considering the 

high failure rate among small businesses, focusing limited financial resources on smaller 

businesses could be a poor use of resources. 

To effectively utilize public resources and efficiently create more jobs, should tax payers‘ 

money be invested in older or newer businesses, in larger or smaller businesses, and in what type 

of industries? The ultimate judgment call depends on what type of businesses generates more 

jobs. 

There has been no consensus in previous literature about whether larg or small businesses 

generate more employment growth. The complexity of business size measurement has 

contributed to the absence of a consensus. Employment size-class assignment changes as a 

business grows. Facing competition,  ̳grow or out‘ is often the survival rule. This makes the 

timing of business size measurement crucial. Using base-time-period size to define business size 

employment gain must always be from smaller employment size classes to larger ones, while 

employment losses can only arise from larger business being reassigned to smaller employment 

size classes. Static plant level business employment size class measurement introduces bias. 

Unlike business size, business age does not directly define employment growth at the 

establishment level, though large businesses tend to have been active for a longer time. Our 

analysis has been motivated by a belief that the impact of business age, in addition to 

employment size, will provide new policy relevant insights about promising sources of job 

creation potential. Business age has been somewhat neglected in previous literature, as a factor in 

employment growth policy. 

This paper measures business establishment age and size dynamics by location and tests 

whether newer or older establishments tend to generate higher net employment growth. Our 

analysis uses a reliable administrative dataset, the Quarterly Census of Employment and Wages 

(QCEW). The analysis proceeds in the context of local economic conditions, industry mix and 

demographic characteristics. A simple economic accounting model using multistage regression 

estimation with inverse use of instrument variable technique and hierarchical panel data 

modeling with lagged time effect correction captures interrelationships among the different 

layers of data and models variable interdependence. To more accurately estimate the impact of 

local economic setting an innovative transformation of local unemployment rates is introduced. 

We begin with a review of pertinent literature in Section II. Section III introduces our 

research hypotheses. Section IV explains the paper‘s methodology. Section V presents results. 

We conclude with future research directions and policy implications. 
2 

II. Literature Review 

The role of business size on an economy‘s economic growth trajectory and speed has 

been discussed for many years.. Much of this literature responds to and tests the validity of what 

is known as ―Gibrat‘s Law‖ (Gibrat, 1931). ―Gibrat‘s Law‖ was a behavioral conjecture 

proposed to explain the observed business size distribution and speculated that business growth 

rates are independent of size. While many predecessors have focused on the contribution of 

smaller and younger businesses to employment growth, there have been counterarguments. 

Based on various datasets and analytical approaches from different countries the business size 

effect remains unclear and mixed. 

II.1 The Power of the Small and Young 

Many studies have found that smaller and younger businesses tend to perform better. 

Hansen (1992) used a data set on innovation output to assess the degree to which the level of 

innovation in manufacturing businesses is influenced by business size and business age. He 

found that both business size and business age tend to be inversely related to innovative output. 

He also indicated that it was possible to separate the effects of age and size in assessing the level 

of innovation. Dunne and Hughes (1994) examined growth and survival amongst quoted and 

unquoted UK companies in the period 1975-85 and compares the results with earlier UK and US 

studies. With careful attention paid to problems of sample selection bias, their study found that 

smaller companies grew faster than larger companies, that Gibrat's Law did not hold amongst 

smaller businesses, that age was negatively related to growth, and that these results were not an 

artifact of sample selection bias. Hart (2000) conducted empirical research on the growth of U.K. 

companies and found that smaller and younger businesses have been growing more quickly than 

larger and older businesses and thus generating proportionately more new jobs. Calvo (2006) 

drew upon a sample of 1272 manufacturing businesses in which only 967 of the businesses 

survived for the entire ten year period and tested the Gibrat‘s law. The results did not support the 

various theories of static and dynamic economies of scale that is consistent with main economic 

theories and empirical results on businesses' growth in the United States. The findings of all the 

above studies rejected Gibrat‘s law and supported the proposition that small businesses grew 

faster. Most of the studies use data from manufacturing businesses and many of them are 

addressing the business size impact in an economy that is not the most recent. 

II.2 Other Arguments 

Small businesses are not always believed to be so powerful. Davis, et al (1996) used a 

plant- level data source, the Longitudinal Research Data (LRD) constructed by the U.S. Census 

Bureau, for a study of the U.S. manufacturing sector from 1972 to 1988. Their findings suggest 

that the belief that small businesses are the major contributor of new jobs is largely based on 

methodological flaws. In particular, their reasoning about the "regression fallacy", i.e., those 

temporary fluctuations in size systematically biased estimates in favor of small business job 

creation. 

Two issues are related in this "regression fallacy". First is the type of size measure. Many 

studies use size-classes, instead of continuous size measure. This could overstate the change 

around the size-class defining threshold values. The second is the timing of size measures. Many 
3 

studies use base-time-period size measure. When a business experiences positive employment 

growth and crosses the size boundary, the increase is ascribed to its base-time-period category 

(i.e., smaller businesses); when the same firm shrinks back its base-year category, all of the job 

losses are ascribed to larger businesses. 

 Davis, et al (1996) also indicated that small businesses did not have higher net job 

creation rates. This research has caught on interests among researchers and concerns among 

policy makers. Considering the high failure rate of small businesses, Wren and Colin (1998) 

even believe that, ―wisdom has it that direct financial assistance to small businesses for 

employment creation may be poor use of resources.‖ However, this ―regression fallacy‖ effect 

was also argued to be very small and that correcting for it did not lead to qualitative change of 

the results (Davidsson, Lindmark, and Olofsson, 1998). The Davis, et al (1996) research also 

only analyzed manufacturing businesses. 

In contrast to the existing studies on the relationship between business growth and size 

(as well as) age, which typically focus on relatively mature industries in developed economies, 

Das (1995) analyzed business growth patterns for an infant industry in a developing economy. It 

was found that (a) age positively impacted growth, which is opposite to the result in many 

previous studies; (b) as in many previous studies, current size negatively impacted growth but 

the magnitude is much higher; and (c) lagged size negatively impacted growth suggesting that 

fixed factors became a hindrance to growth in rapidly growing infant industries. This study 

touched the timing issue of the size measure, which would give business age rather than business 

size a potential advantage to be used as an indicator for employment growth. 

As Haltiwanger and Krizan (1999) indicated, the more important factor to employment 

growth seemed to be business age, not size. They used the updated LRD U.S. manufacturing data 

from 1970s to early 1990s. Although this study showed that young businesses exhibited high 

average net employment growth rates, relative to mature businesses, among mature business (not 

for the young businesses), net employment growth rates increased with business size. This study 

also called for attention to idiosyncratic factors. Again this research only addressed 

manufacturing businesses and employment. 

Kaplan (2003) used the annual first job- and worker-flows data set of Mexico during 

1994-2000 and found that net job growth rates were higher in older businesses; during the 

economic crisis of 1995 large businesses outperformed small businesses. 

The results based on Persson (2004) study that used data from Sweden in 1987 and 1988, 

with the exception of the construction industry show that new businesses faced a high risk of 

closing down and the probability of business survival increased with the age and size of the 

business. This study also found that the initial business size had a negative effect on employment 

growth. 

II.3 The Impact of Unemployment 

While the roles of business size and age in generating employment growth have been 

discussed, estimation of economic cycle effects, represented by the unemployment rate, often 

play a deterministic role. Davis et al. (1996) indicated that job reallocation appears to be driven 

primarily by idiosyncratic shocks based on the LRD US manufacturing data from 1970s to late 

1980s. Stiglbauer, et al (2003) found that job creation increased significantly during cyclical 

upswings whereas job destruction rose in downturns, using a large Austrian administrative 
4 

dataset in the period of 1978 to 1998 where ―spurious‖ entries and exits of businesses were 

corrected for. 

The relationship between unemployment and employment growth can also be interpreted

through entrepreneurship. Previous literature has identified considerable ambiguities about the 

relationship between unemployment and entrepreneurship (Audretsch, et al., 2000). 

Unemployment on the one hand stimulates entrepreneurial activity, which was termed as a 

―refugee effect‖ or ―push effect‖; on the other hand, as identified by a very different strand in the 

literature, higher levels of entrepreneurship reduce unemployment, which was termed as a 

―Schumpeter effect‖ or ―pull effect‖. 

II.4 Industry Sector & Demographic Characteristic Effects 

Other factors that have been identified in the literature to impact employment growth 

include industry sectors and demographic characteristics. As indicated, there appear to be 

substantial differences between industry sectors, as competitive advantage in some was achieved 

through aggregation (upsizing) and in others through disaggregation (downsizing) of the 

productive process (Reynolds, 1997). Demographic factors were also found important in 

explaining firm survival and growth (Persson 2004). 

III. Hypotheses 

The still unclear contribution of business size to employment growth has been attributed 

in part to measurement related ―regression fallacy‖. Other contributing factors, such as 

unemployment rate, industry sectors, and demographic factors and business age could contribute 

to the controversy as well. Business age has not received as much attention as business size. This 

might be partially related to the data availability. The accuracy and policy value of business size 

and age contributions to employment growth depends on how the respective measurements are 

conducted. 

As addressed earlier, the timing of business size and age measurement is important. 

Previous studies have used a static base time period measure, a lagged time period measure, or a 

combination of lagged and current time period measure. We use a dynamic longitudinal measure 

specific to each month when the employment was reported. This measurement is also on a 

continuous scale1, instead of within size or age classes. 

Similarly important is where business size and age are measured. We use locationspecific measures to avoid the transitory size fluctuations, to include employment growth due to 

establishment births and deaths, and to capture net employment growth instead of only gross 

employment growth. 

So, this paper integrates QCEW administrative records with other economic data sets to 

generate dynamic measures of business age and size at the county level and conducts a 

longitudinal analysis. The QCEW data set also allows multiple levels of industry sector 

definition and analysis. 

 

1

 Only in the descriptive statistics for the figures, size and age classes are used. In the regressions, continuous size 

and age measurements are e used. 
5 

We test the following hypotheses: 

 Average business age specific to each month has a significant impact on 

local employment growth, but this establishment age impact differs among 

industry sectors; 

 Local unemployment rate dynamics also matter. 

IV. Methodology 

This section introduces our model, data and variable definitions. The analysis is 

conducted in the context of industry sectors, using both two-digit and three-digit North American 

Industry Classification System (NAICS) codes. The geographic unit of analysis adopted is 

county, and the temporal unit of the analysis is month2. This generates four layers of data—

month, county, NAICS two-digit industry code, and NAICS three-digit industry sector code. 

We use hierarchical panel data modeling to both interpret the relationship between the 

key variables and capture the interrelationship between the three cross-sectional layers of the 

data. For such a panel data, we consider the panel specific serial autocorrelation to capture the 

time effect. A series of correlograms are conducted to detect the serial autocorrelation. When 

serial autocorrelations are detected, appropriate time effect vectors are adopted in the models. 

The models used in this study also adopt a multi-stage regression approach, based on a 

simple economic accounting model. The multi-stage regression approach accommodates the 

nested relationship between the key variables and therefore instrumental variables are used. This 

specific nested relationship will be further explained in this methodological section. 

IV.1 The Data 

Availability of the Quarterly Census of Employment and Wages (QCEW) administrative 

records for authorized use honoring the confidentiality requirements imposed made this study 

possible. The Bureau of Labor Statistics QCEW program releases quarterly employment and 

payroll figures based on employer reports covering 98 percent of U.S. jobs. The BLS data 

releases are available at the county, MSA, state and national levels by industry. Published 

quarterly, with approximately a six-month lag after the end of each reference quarter, the QCEW 

combination of frequency, timeliness and detail is unmatched. 

Based on an Interagency Agreement between the Maryland Department of Labor 

Licensing and Regulation, Office of Workforce Information and Performance, and [affiliation 

omitted during review to ensure anonymity of authors], we received authorized access to 

Maryland QCEW cross-sectional micro data quarterly from 2004 through, at this writing, third 

quarter of 2008. Approval for use of the QCEW data is specific to each proposed study design, 

and preliminary results are subject to the state agency‘s review prior to public release. 

The QCEW data file provided us with a recent longitudinal administrative dataset and an 

opportunity to measure business establishment age specific to each month, including coverage of 

the 47 months leading up to the beginning of the current recession and the first 10 months of the 

recession. 

The QCEW data features comprehensive coverage of businesses subject to the state‘s 

unemployment insurance law and reporting requirements. Self-employed individuals and 

 

2

 Although QCEW data is a quarterly dataset, the employment information is offered monthly. 
6 

independent contractors are not included3. The QCEW data has limitations for economic analysis 

use. For example, accurate reporting of information for multi-establishment corporations across 

various geographic locations is an unresolved challenge, but there are recent and continuing 

improvements. We therefore use establishment level data only. 

Other data used in this study include Bureau of Labor Statistics local unemployment rate 

estimates and demographic data from the U.S. Census Bureau. The Census Bureau population 

data are defined by residence, while the unemployment rate and QCEW data are defined by work 

location. When we interpret our results, it is therefore necessary to note this difference in data 

reporting calibration, particularly when employment impacts of population attributes are 

considered. 

IV.2 Variable Measurement 

Before explaining our model we turn first to explain measurement caveats for major 

variables. As mentioned earlier, we focus on establishment level data instead of firm level. In a 

relatively high resolution research in terms of geography, i.e. at the county level in this study, 

focusing on establishments rather than firms is more accurate because establishments are more 

sensitive to local economic conditions. Three types of variables are used. The first type is 

macroeconomic variables—total employment and unemployment rate estimates. The key 

dependent variable, monthly total employment (E), is the total count of employment of a county 

in a reference month. When applied in a panel data regression model, this variable actually 

measures the net employment growth. 

We use an innovative way to transform Bureau of Labor Statistics unemployment rate

estimates. Previous literature has identified an uncertain relationship between unemployment 

rates and entrepreneurial actions. To further clarify the role of unemployment rates, we use two 

vectors to capture the change of unemployment rates. The level of unemployment rate (U)

captures the magnitude of unemployment rate changes; the differenced unemployment rate 

(dU) captures the direction and trend of unemployment rate changes, whether it is going upward 

or going downward. At the same magnitude, U, there could be two totally opposite economic 

situations: one is when the unemployment rate is rising (i.e. a slowing-down economy) and the 

other is when the unemployment rate is decreasing (i.e. the recovering or growing economy). 

This differenced term of unemployment rate (dU) in regression reflects the derivative measure of 

unemployment rates. These two vectors capture additional nuances that the traditional 

unemployment rate magnitude measure alone does not achieve. Endogeneity is addressed by 

including three-month lagged values. 

The second type of variable used in this study is the establishment level variables—

establishment size, establishment count, and establishment age. The establishment size (S) is 

the average number of employment of an establishment at a reference month in a county. The 

average is the unweighted arithmetic mean. As addressed earlier, this study measures 

establishment sizes specific and current to each month and also measures at the county versus 

plant level, which avoids the timing issue that caused fallacy in previous literature. Each 

establishment has many values for its size measure along the timeline, if its size changes over 

time; each county has an average establishment size measure at a specific month. 

 

3 Self-employment is not the focus of our analysis. We are more concerned with the job creation impact of business 

establishments. 
7 

Establishment count (C) is the total number of establishments of a county in a reference 

month. Again, due to the longitudinal nature of the QCEW, we are able to measure the 

establishment counts of a county specific and current to each month. 

Establishment age (A) refers to the average time duration (in months) of a county‘s 

establishments since their initial liability starting dates. The QCEW data offers information on 

the specific initial liability date of an establishment. However, some establishments have more 

than one initial liability dates due to situations like reactivation or just seasonal reopening. To 

simplify the study, we used the earliest initial liability date of an establishment. Again, the 

longitudinal nature of the QCEW data enables us to measure the establishment age of a county 

specific and current to each reference month. 

The third type of variables is population attribute variables4

:population count, 

population racial diversity index, population age, and gender ratio. These are control variables. 

Population count (P) measures the total number of people in a county of a reference year. The 

population age (PA) measures the arithmetic mean age of a county‘s population in a reference 

year. The gender ratio (PG) is the ratio of the number of male persons divided by the number 

of female persons. For the racial diversity index (PR). 

We use Simpson's Index of Diversity (Gibbs and Martin, 1962) to compose our racial 

diversity index (PR) , where n represents the total number of persons of a 

particular race and N indicates the total number of persons of all races5. 

There is also a time effect. We use the time effect to capture the serial autocorrelation. It 

could be a variety form of autocorrelation—autoregressive, moving average, or even 

differencing, depending on the serial autocorrelation detection diagnostics mentioned earlier. 

To ensure all the variables are measured at a standardized level, we use the log form for 

some variables. The models of this study mainly follow the log-linear format. This offers the 

advantage of identify elasticity between the dependent variables and explanatory variables. 

However, not all variables are transformed into a log format. For variables that are measured as 

proportions or falling between 0 and 1, the original values of the variables are retained. For those 

variables that are at a much larger value scales, a logarithm form is applied to standardize the 

coefficients. Appendix A summarizes the statistical characteristics of the variables. 

IV.3 The Model 

The basic model of this study starts with a basic employment accounting equation: the 

total employment of a county (E)6 is a multiplicative result of the average employment per 

establishment, i.e., establishment size (S) and the total number of establishments in the county, 

i.e., the count of establishments (C). 

E = S * C (1)

 

4

 The population attribute data are annual. The Census Bureau publishes population estimates annually. Timing of 

the population data estimate is not consistent with QCEW data collection, nor are the estimation methods. Those 

differences in data calibration pose some challenges for interpreting model estimation results.

5

 The value of this racial index ranges between 0 and 1. W ith this index, 1 represents infinite racial diversity and 0, 

no racial diversity. That is, the bigger the value of the diversity index, the higher the racial diversity level. To avoid 

the potential endogeneity issue, we use one-year lagged values for all population indicators.

6

 Business units with only self-employed individuals are excluded in the QCEW data set.


10

function of establishment age. To avoid issues related to categorization that are addressed 

earlier, we use the continuous measure of establishment age and size, specific to each month . 

<Insert Figure 1 here.> 

_ 

<Insert Figure 2 here.> 

_ 

<Insert Figure 3 here.> 

<Insert Figure 4 here.> 

_

Table 1 presents the empirical results of equation (6). Starting from the third row of the 

table, each horizontal row reports the regression coefficients for one model. The model results 

that test the all-industry aggregate data are presented the first and then the results for each 

individual industry sector (two-digit NAICS) are presented in turn. 

The vector T(t): l_lnE captures the time effect. Based on correlograms, we identify that 

the source of panel specific serial autocorrelation for our data is basically from the first order 

autoregressive term. Shown in Table 1, the lagged time effect is statistically significant at the 

0.01 level for the overall aggregate data as well as for data across all industries. 

The overall establishment age effect (A) for aggregate data is not large (0.0744), but 

positive and statistically highly significant at 0.01 level. For each 1% increase in a Maryland 

county‘s average establishment age (in months), there is a 0.07% increase in the county‘s total 

employment, controlling for other factors. With the hierarchical modeling, the all-industry 

aggregate effect has incorporated the interrelationships of business age effects between each 

two-digit and three-digit NAICS sector and subsector. 

For individual industries, the establishment age impacts are mostly highly significant as 

well, except for two sectors, Information (51) and Finance & Insurance (52). Among industries 

with a significant establishment age effect, more than half of the sectors display a positive 

establishment age effect. This means that for over half of industry sectors, older establishments is 

associated with a larger employment growth, controlling for other variables. This is particularly 

true with a coefficient of 0.20 or higher for Manufacturing (31-33) (0.1992), Wholesale Trade 

(42) (0.2726), Education Services (61) (0.2579), and Arts, Entertainment, and Recreation (71)

(0.2428). Establishments in those sectors tend to have a relatively longer history and tends to be 

reputation based. Reputation needs time to build. 

<Insert Table 1 here.> 

However, for slightly above one third7 of the industry sectors— Agriculture, Forestry, 

Fishing and Hunting (11), Mining, Quarrying, and Oil and Gas Extraction (21), Construction 

 

7

 8 out of 19. 
11

(23), Utilities (22), Retail Trade (44-45), Real Estate and Rental and Leasing (53), 

Accommodation and Food Services (72), and Other Services (except for Public Administration) 

(81)—older establishments are associated with lower employment growth at the county level, 

holding other variables constant. Among those sectors, Construction (23) exhibited the most 

evident negative establishment age effect. For Construction (23) establishments, each 1% 

increase in a county‘s average establishments‘ age (in months) is associated with 0.63% decrease 

in the county‘s total employment, controlling for other factors. Establishments in Construction 

(23) tend to be small, volatile, and young. This on one hand gives them flexibility in catering for 

local consumer needs, on the other hand make them more vulnerable to the economic change. 

Many establishments in this sector that remain active for a long time do not increase employment 

much over time. Many of those establishments are also subject to seasonality. As time goes by, 

those establishments do not generate higher employment growth, and instead display a negative 

establishment age effect. Their momentum of employment growth is most likely to occur at the 

early stage of their business. Similar situation occurs to establishments, in Retail Trade (44-45), 

Real Estate and Rental and Leasing (53), Accommodation and Food Services (72), and Other 

Services (81). Those sectors also tend to be the ones hit severely by the recession. Sectors like 

Agriculture, Forestry, Fishing and Hunting (11) and Mining, Quarrying, and Oil and Gas 

Extraction (21) relatively have lower labor skill requirements and older infrastructures. For those 

sectors, older establishments do not necessarily have more mature skilled workers or a better 

business model; therefore, not necessarily generate more employment. 

For the two unemployment rate measure, the magnitude of unemployment rates (U)

does not exhibit a consistent effect on total employment and the effect is small; however, the 

differenced the unemployment rates (dU) shows a consistently negative and mostly highly 

significant effect (at 0.01 level) on county employment growth. This means that when 

unemployment rate trends up (i.e., during economic down turn), the county employment shrink 

three months later, holding other variables constant. While the magnitude of unemployment rate 

does not necessarily offer a straightforward impact on employment, the trend of unemployment 

rate changes matters. Only in three sectors, Utilities (22), Health Care and Social Assistance 

(62), and Management of Companies and Enterprises (55), the trend of unemployment rate does 

not seem to matter and result in a statistically insignificant effect. Those three sectors are also, 

relatively speaking, economically acyclical. 

The results also show that the county population size is positively related to the 

employment growth. A larger size of population means a potentially larger labor pool for 

employment; it can also mean a larger consumer pool and thus a higher demand, which requires 

more employment as well. The positive population size effect is particularly evident in the sector 

of Administrative and Support and Waste Management and Remediation Services (56),

according to Table 1. More residents in a county often mean more people to manage and 

administrate and immediately more personnel might be needed. 

The average age of the county populations also display a positive effect. The county 

average age scales are all between age group value 7 and 9, corresponding to a range of age 30-

44. Within this age range, the younger ones, those in early 30s could be more likely than those in 

40s to be full-time or part-time students pursuing academic advancement instead of being fulltime employees; early 30s are also tend to be the ages that are most bound by child caring 

responsibilities; compared toearly 40s. Within this age range, the higher the average age, a 

potentially higher labor force participation rate. 
12

The racial diversity is an interesting variable in Table 1. Except for the sectors of Health 

Care and Social Assistance (62) and Professional, Scientific, and Technical Services (54), the 

coefficients of this variable are all negative if they are significant. As noted earlier, racial 

diversity as well as other population attributes from the Census Bureau are residential based, 

while employment data is workplace based. According to Table 1, Maryland counties with a 

higher residential racial diversity have lower employment growth, controlling for other variables. 

Some of the racial diversity effects are small, but for Manufacturing (31-33) and Real Estate and 

Rental and Leasing (53), there exist a large negative effect from racial diversity. Manufacturing 

(31-33) is a relatively old industry sector. Residential areas with a higher racial diversity level 

may tend to be relatively newer residential areas that attract new migrants and that might not 

have major manufacturing jobs available. Counties that have a higher residential racial diversity 

level have a higher proportion of minority residents. Many of the minority residents tend to be 

more vulnerable to employment and economic changes; this may help to explain the negative 

racial diversity effect on employment growth in many sectors. However, since minority is not an 

economically homogenous group, racial diversity would not be able to capture the full racial 

effect. 

The gender ratio exhibits negative impacts on employment in many industry sectors, and 

positive impacts in several others. Most of the negative impacts are relatively small, compared to 

the positive impacts. The most pronounced gender effect is exhibited in Mining, Quarrying, and 

Oil and Gas Extraction (21). There is a a large and positive gender effect in this sector. It means 

that a higher proportion of male residents in a Maryland county, the higher total employment 

count in Mining, Quarrying, and Oil and Gas Extraction (21) in that county, controlling for other 

factors8. This sector is mostly concentrated in non-urban area and nonresidential areas where this 

sector is a major local job engine. Counties relying on Mining, Quarrying, and Oil and Gas 

Extraction (21) could be more concentrated with male laborers and with relatively limited female 

residents. In this case, there is a higher concentration of males, and a higher number of employed 

persons in this sector, controlling for other variables. 

The regression diagnostics also show that hierarchical modeling is better than simple 

linear regression models, based on the likelihood ratio (LR) test. The hierarchical models using 

panel data captures both the overall establishment age and unemployment rate effects on 

employment growth, it also captures the interrelationship of establishments between three-digit 

NAICS industry codes, more general two-digit industry codes, and the county localities. 

Appendix B exhibits the results of the earlier stage regressions during the multi-stage 

regression estimation process. Those results offer additional nuances. For most industry sectors, 

there seems to be a significant and positive establishment age impact on establishment size, but a 

significant and negative establishment age effect on establishment counts of a county. This 

means that when controlling for other factors, older establishments tend to be larger and have 

more employees for most industry sectors, except for the limited few with no statistical 

significance. Older establishments have been in business longer. According to the ―grow or out‖

principle in the business world, most of those older establishments have grown from smaller 

establishments years ago to larger establishments. 

When holding other factors constant, counties with older establishments tend to have 

smaller numbers of establishments for all industry sectors but Education Services (61). Many 

 

8

 Please note again that the gender ratio is measured for residents and without distinction for industry sectors, while 

the employment is measured for workers with clear industry sector distinction. This situation made the interpretation 

of the gender ratio effect complex. At this moment, we can only try more evident explanations.
13

older establishments may have grown through merger and acquisition from other firms, which 

could shrink the total number of establishments. Since the relationship between establishment 

age and count is basically consistent across all industry sectors, industry mix seems not to be a 

deterministic factor resulting in this negative relationship. Education Services (61) becomes an 

exception possibly for two reasons. First, Education Services (61) tends to be a stable sector 

where majority of the establishments tend to last for a long time. This could include large 

educational institutions as well as small interest groups with special niche. 

VI. Future Research Directions 

This study delivers an exploratory analysis of the impact of business establishment age 

on employment growth using reliable and recent administrative records. Future refinements, in 

addition to obvious interest in updating through the end of the recession and into the next growth 

phase, should include selected three-digit NAICS sector diagnostics. 

Additional local labor force attribute data, versus population attribute data, are expected 

to be useful. The U.S. Census Bureau Longitudinal Employer-Household Dynamics (LEHD) 

Program longitudinal data files are likely to be the preferred choice for this refinement. Our next 

stage of analysis will respond to the multi-faceted challenges involved in grappling with merger, 

acquisition and other change-of-ownership events. This will refine our understanding of the 

complex relationship between business establishment age and firm age, as these have 

interdependent impacts on employment growth. 

VII. Conclusions 

Building on previous literature, this paper started with an economic accounting model 

and then constructed a multi-stage regression model to estimate the business establishment age 

impact on employment growth. This multi-stage regression model inversely uses the traditional 

instrument variable techniques to directly test the elasticity of business establishment age on 

employment. This transforms business establishment age from an instrumental variable to a key 

explanatory variable. This methodology also considered the error term correlations among the 

seemingly unrelated regressions. 

To explore the controversial role of business employment size, and overcome some 

previous limitations of business size and age measurement, we used longitudinal administrative 

data (QCEW data) to dynamically measure establishment age and size specific and current to 

each month when establishment employment was reported. This measure not only responds to 

the measurement timing challenge predecessors faced, but also uses a continuous measurement 

scale. Our business age and size measures also use county as the unit of analysis, instead of the 

traditional plant level. Using the county level data avoids plant level transitory business size 

class changes reported in previous studies, incorporates business birth and death related 

employment changes, measures the net employment growth in a regression model, and captures 

the local economic dynamics. 

This paper relies on longitudinal administrative records, QCEW, with four data 

dimensions: time, county, two-digit NAICS code level, and three-digit NAICS code level. We

therefore adopted hierarchical modeling techniques to capture the interrelationship between the 

different data dimensions. We also use panel data techniques to correct for location specific 

serial autocorrelations across time horizon. 
14

Our analysis has shown that business establishment age is associated with local 

employment growth. In the all-industry-aggregate model, counties with older establishments 

exhibit a higher employment level, controlling for other variables. This is also consistent with the 

establishment age effect in over half of industry sectors. Therefore, for those industry sectors, 

targeting public assistance to older establishments could be more effective as a job growth 

strategy. 

The establishment age effect on employment growth is not simply uni-directional across 

all industries. For over half of industry sectors, such as Manufacturing (31-33), Wholesale Trade 

(42, and Education Services (61), older establishments tend to have higher employment levels 

because scale, history, and reputation matter to them. Among other few industry sectors, such as 

Construction (23) and Other Services (81), investing in older establishments is less likely to be 

an effective employment growth strategy. For two sectors, Information (51) and Finance & 

Insurance (52), the establishment age effect is not statistically significant, and thus there is no 

direct evidence whether older establishments or newer ones contribute more to employment 

growth. 

We measure unemployment rates with a differenced vector as well as a magnitude vector. 

Our findings show that the differenced vector exhibit a more consistent and deterministic effect 

than the magnitude measure. It is the trend of unemployment rate that matters, not the magnitude

per se. When the monthly unemployment rates trend up, the county employment declines three 

months later in almost all industry sectors, except for three economically acyclical sectors—

Utilities (22), Health Care and Social Assistance (62), and Management of Companies and 

Enterprises (55). 

Our findings also reveal that for most industry sectors in Maryland counties with older 

establishments have a larger average establishment size and a smaller number of total 

establishments, holding other variables constant. We interpret this to mean that statewide 

employment growth policies are likely to be less efficient and effective as sub-state targeted 

policies that recognize the importance of differences in current local business size and age 

composition. 
15

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16

Figures to be inserted in the text 

Figure 1. Quarterly Total Employment Change by Establishment Age, 2004q1-2008q1 

 

Figure 2. Quarterly Total Employment Change by Establishment Age for Small Establishments 

(<50), 2004q1-2008q1 

 

-1000

-500

0

500

1000

-1000

-500

0

500

1000

2004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q12004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q12004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q1

Active for up to 1 Year Active for 1-2 Years Active for 2-3 Years

Active for 3-5 Years Active for 5-10 Years Active for 10+ Years

Negative Employment Change Positive Employment Change

Net Employment Change (+ or -)

Quarter

The Jacob France Institute

Data received from the Maryland Department of Labor, Licensing & Regulation

Quarterly Total Employment Change by Establishments Age, 2004q1-2008q1

-500

0

500

-500

0

500

2004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q12004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q12004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q1

Active for up to 1 Year Active for 1-2 Years Active for 2-3 Years

Active for 3-5 Years Active for 5-10 Years Active for 10+ Years

Negative Employment Change Positive Employment Change

Net Employment Change (+ or -)

Quarter

The Jacob France Institute

Data received from the Maryland Department of Labor, Licensing & Regulation

Quarterly Total Employment Change by Establishments Age for Small Businesses (<50), 2004q1-2008q1
17

Figure 3. Quarterly Total Employment Change by Establishment Age for Medium 

Establishments (50-99), 2004q1-2008q1 

Figure 4. Quarterly Total Employment Change by Establishment Age for Large 

Establishments (50-99), 2004q1-2008q1 

-200 -100

0

100 200

-200 -100

0

100 200

2004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q12004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q12004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q1

Active for up to 1 Year Active for 1-2 Years Active for 2-3 Years

Active for 3-5 Years Active for 5-10 Years Active for 10+ Years

Negative Employment Change Positive Employment Change

Net Employment Change (+ or -)

Quarter

The Jacob France Institute

Data received from the Maryland Department of Labor, Licensing & Regulation

Quarterly Total Employment Change by Establishments Age for Medium Businesses (50-99), 2004q1-2008q1 -400 -200

0

200 400

-400 -200

0

200 400

2004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q12004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q12004q12004q22004q32004q42005q12005q22005q32005q42006q12006q22006q32006q42007q12007q22007q32007q42008q1

Active for up to 1 Year Active for 1-2 Years Active for 2-3 Years

Active for 3-5 Years Active for 5-10 Years Active for 10+ Years

Negative Employment Change Positive Employment Change

Net Employment Change (+ or -)

Quarter

The Jacob France Institute

Data received from the Maryland Department of Labor, Licensing & Regulation

Quarterly Total Employment Change by Establishments Age for Large Businesses (>=100), 2004q1-2008q1
18

Table to be inserted in the text 

Table 1 Regression Results on Employment Growth, Model (6). 

 Regression Coefficients: for Dependent Variable--lnE 

 

T(t): 

l_lnE lnA U DU lnP lnPA PR PG

Overall 

- 

0.6217 

***

0.0744 

***

0.0189 

***

- 

0.0365 

***

1.0968 

***

1.6523 

***

- 

1.9667 

***

- 

2.8896 

***

Agriculture 

- 

1.5406

***

- 

0.1657 

***

0.0381

***

- 

0.0432 

***

0.2863

**

6.4476

***

- 

1.7059

***

- 

5.7374 

***

Mining 

- 

0.5640

***

- 

0.1720

***

-

0.0170

- 

0.0289

* 

0.5430

***

7.1530

***

- 

0.8729

15.9003

***

Construction 

- 

0.9528 

***

- 

0.6288

***

0.0309

***

- 

0.0474

***

1.0690

***

1.0297

**

- 

0.1345

- 

0.3183

Utilities 

- 

0.7526

***

- 

0.0506 

***

0.0039 - 

0.0065

0.8250

*** 0.3507 - 0.6995 

- 

7.5142 

***

Transp. & 

Wareh. 

- 

1.3982 

***

0.0416

***

0.0603

***

- 

0.0551

***

1.0942 

***

1.4140

** 0.1895 2.1614 

Manufacturing 0.0584 

***

0.1992 

***

0.0211 

***

- 

0.0187 

***

0.7712 

***

- 

0.0414 

- 

4.5839 

***

- 

7.4900

***

Wholesale 

- 

1.1169 

***

0.2726 

***

0.0335 

***

- 

0.0444 

***

1.1418 

***

3.2262 

***

- 

1.3096 

***

- 

3.5852 

***

Retail 

- 

0.5049 

***

- 

0.1964 

***

0.006 

6*** 

- 

0.0423 

***

1.1765 

***

0.7006 

***

- 

0.4688 

***

- 

2.3529 

***

Information 0.13387 

*** 0.0155 0.0019 

- 

0.0126 

* 

1.2891 

***

- 

3.5835 

***

- 

1.5684 

***

- 

1.6057 

Education 0.0922 

***

0.2579 

***

0.0307 

***

- 

0.0503 

***

2.7359 

***

6.5258 

***

- 

1.6045 

***

7.1552 

***

Health 

Services 

0.1466 

***

0.0729 

***

- 

0.0052 

***

0.0011 1.0304 

***

3.8514 

***

0.1805 

**

- 

2.1280

***

Finance & 

Insurance 

- 

0.6611 

***

0.0057 0.0025 

- 

0.0149 

**

1.4527 

***

5.9925 

***

- 

1.5457 

***

- 

2.6190

* 

Real Estate 

- 

0.9666 

***

- 

0.1506 

***

0.0261 

***

- 

0.0702 

***

1.5653 

***

9.6731 

***

- 

5.9370 

***

- 

6.7617 

***

Management 

- 

0.7300

***

0.1411 

***

- 

0.0071

- 

0.0055

1.1922

*** 1.6058 - 0.2987

6.3916

**

Professional 

Services 

0.0344 

**

0.0476 

***

-0.0069 

***

- 

0.0032 

***

1.1089 

***

1.0922 

***

1.6593 

***

0.8266 

***

Accommodati

on

0.0440 

***

- 

0.3061 

***

0.0482 

***

- 

0.1167 

***

0.7469 

***

8.1614 

***

- 

0.4189 

***

- 

3.2542 

***
19

Administration 0.0557 

***

0.1351

***

- 

0.0029 

* 

- 

0.0360

***

3.6985 

***

1.2891 

***

- 

2.1021

***

5.0899 

***

Arts & 

recreation 

- 

1.9417 

***

0.2428 

***

0.1660 

***

- 

0.1538 

***

1.3242 

*** 0.3131 0.3697 

6.5682 

***

Other Services 

- 

0.3745 

***

- 

0.3910 

***

0.0102 

***

- 

0.0228 

***

1.1854 

***

1.4106 

***

- 

0.5931 

***

- 

0.4307 

Table 1 (continued) 

 Regression Coefficients (cont.) 

No. 

of 

obs

Group 

Variable: 

No. of 

Groups

Log 

restrictedlikelihood 

Wald 

chi2(10) 

Prob > 

chi2 

 ei1 ei2 cons.

3-digit 

NAICS

Cou

-

nty

Overall 1.2900 

***

0.7647 

***

- 

8.4410

***

10210

1 

93 (2 

digit: 20) 1926 -10029 42159 0.0000 

Agriculture 2.3768 

***

0.8670

***

- 

7.3777

**

5113 5 99 -448 7190 0.0000 

Mining 1.2410

***

1.0168

***

- 

33.6340

***

1730 3 37 -558 10851 0.0000 

Construction 1.6964 

***

0.8181

***

- 

4.7573 

***

4032 3 72 2910 3006 0.0000 

Utilities 1.3846

**

0.4907

*** 2.0838 1324 - 24 515 612 0.0000 

Transp. & 

Wareh. 

2.1524

***

0.9359

***

- 

15.1511 

***

8730 11 172 -2492 6105 0.0000 

Manufacturing 0.9774 

***

0.9022 

***

2.8291 

*** 22206 21 424 19044 344789 0.0000 

Wholesale 1.8997 

***

1.1441 

***

- 

11.8944 

***

4023 3 72 1549 1 5620 0.0000 

Retail 1.1785 

***

0.8220

***

- 

6.0764 

***

15939 12 287 7848 10767. 0.0000 

Information 0.3840

***

0.6531 

***

-2 

.1328 6707 7 136 -1218 2080 0.0000 

Education 0.9202 

***

0.9531 

***

- 

47.1050 

***

1341 24 24 1698 18557 0.0000 

Health Services 0.7814 

***

0.7844 

***

- 

11.3120 

***

5264 4 94 8516 24314 0.0000 

Finance & 

Insurance 

1.3002 

***

0.5801 

***

- 

22.6491 

***

4873 5 96 -386 2027 0.0000 

Real Estate 1.7440

***

0.7442 

***

- 

25.134 

1*** 

3183 3 61 719 2617 0.0000 
20

Management 

1.5179 

***

0.9037

***

- 

18.6174

***

1310 - 24 -231 819 0.0000 

Professional 

Services 

0.9329 

***

0.9358 

***

- 

9.1790 

***

1344 24 24 3259 20684 0.0000 

Accommodation 0.9956 

***

1.0227 

***

- 

14.144 

***

2688 2 48 4291 39134 0.0000 

Administration 0.9551

***

0.9073

***

- 

44.7163

***

2632 2 47 3477 39218 0.0000 

Arts & 

recreation 

2.8329 

***

0.9439 

***

- 

20.1231 

***

3756 3 70 774 9804 0.0000 

Other Services 1.1225 

***

0.8156 

***

- 

8.6989 

***

5376 4 96 5549 4823 0.0000 

Notes: 

1. All the LR test finds that the hierarchical model is better than linear model. 

2. *** p<0.01 

 ** p<0.05 

 * p<0.1