Case Selection in the Textiles, Apparel, Furniture, and

43 Pages·2006·0.33 MB·English

by  Ryan Denniston

Case Selection in the Textiles, Apparel, Furniture, and Computer Industries: An
Assessment of Industry Location and County Dependence upon Manufacturing
Employment

Ryan Denniston
Department of Sociology
Duke University

January 2006

Paper #3

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Abstract

The decline of relatively well-paid manufacturing employment raises concern among
policymakers and academics as to whether comparable employment opportunities surface
and how to best promote their creation. North Carolina is disproportionately geared
toward manufacturing, with proficiencies in several manufacturing industries, and many
communities rely on these industries for substantial employment. Finally, workers in
these face especially severe hardships due to the sizable decline in pay and the
obsolescence of skills in an era where most manufacturing activities are in decline. This
paper describes statewide trends in manufacturing employment and selected industries
over the 1990s in an attempt to gauge the actual and potential severity of these
nationwide trends to local communities. This effort is used to guide future case study
research in communities and industries of particular interest to North Carolina.

Using multiple data sources and measures of industry strength and trends, case counties
are chosen for future exploration in the furniture, textiles, and apparel industries. Cases
chosen for the furniture industry are Catawba, Randolph, Guilford, Caldwell, and
Davidson counties. For the textiles industry, these cases are Guilford, Rockingham,
Vance, Alamance, and Gaston counties. Forsyth and Catawba counties are chosen for the
apparel industry, and Durham County is important in the computers and electronics
industry.
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Introduction

The decline of many manufacturing industries throughout North Carolina, especially the
textiles and furniture industries, raises concern among communities and policymakers
regarding future employment opportunities and sustainability of prevailing living
standards. From an academic standpoint, the decline of manufacturing activities raises a
host of interesting questions regarding the geographical aspects of economic activity, and
the potential social causes and consequences of geographical specialization. Are there
differences across communities in the severity of the impacts of economy-wide changes?
Do communities depart from these general trends by large magnitudes, and if so, why?
Do new employment opportunities emerge to replace declining industries, and do
workers proactively seek to alleviate hardship entailed by these general trends by
commuting or moving?

This paper seeks to gauge differences in the experiences of communities facing decline in
traditional manufacturing industries through a quantitative comparison of counties in
North Carolina from 1990 to 2004. Specifically, counties are assessed according to their
importance to the selected industry in North Carolina, the degree to which this
importance has grown or declined throughout the 1990s, and the degree to which they are
critical places for manufacturing activity in general. Second, the importance of the
community and community-level factors necessitates an exploration of the importance of
these industries to various communities as sources of employment and as proxies for
specializations in certain productive activities. Finally, an assessment of worker
characteristics is presented to more fully elaborate on differences across communities in
this key aspect of economic activity.

Data and Methods

A variety of data sources were used to compile the information in this paper, at to the
greatest extent possible, consistency was sought when working with multiple data
sources. However, the number of data sources and the data items they cover entail
differences in counts of populations. As such, information from different sources, which
are contained in different tables, should not be compared directly. Comparisons are made
within data sources in the tables, but the analysis in the text also compares across the
sources to provide a general understanding of the activities studied.

Primary employment data comes from the Employment and Wages series, maintained by
the North Carolina Employment Security Commission (NCESC) and the Quarterly
Census of Employment and Wages, maintained by the Bureau of Labor Statistics. These
data sets are generated through the same program and are virtually identical, but both are
used to correct for small anomalies that occur in the data. Employment information is
provided at the county and state level, and is provided on a North American Industrial
Classification System (NAICS) basis. This data also provides information on the number
of establishments and the average wage by industry and is finally available from 1990
through 2004.

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The Announced Business Closings series, also provided by the NCESC, is the primary
source used for layoff information. This series is a survey of business closings and
layoffs announced through North Carolinian newspapers. While not inclusive of all
layoffs and plant closings, it provides information not available through other data sets,
specifically information on layoffs and the companies associated with these activities.
However, industries are classified according to the Standard Industrial Classification
(SIC) system at the two digit level. Three of the four industries studied are roughly
comparable across the SIC and NAICS systems at this level of aggregation, but the
computers and electronics industry is not. This series is available from 1990 through
2005.

Finally, migration and commuting information were obtained from the decennial
Censuses 1990 and 2000. This information allows for some assessment of county-level
populations and how movement relates to employment. Ultimately, worker skills,
training, and government efforts aimed at allowing workers to more easily adjust to
changing economic conditions must be undertaken, but migration and commuting data
encompass a first attempt at understanding how workers cope with employment losses.

The analysis involves several steps. First, employment and manufacturing patterns are
established in order to form a basis for comparison using the case industries: furniture,
textiles, apparel, and computers and electronics. Comparisons between counties are
made in order to discern those counties most important to manufacturing employment
and employment in the case industries of furniture, textiles, apparel, and computers and
electronic equipment. Additionally, an attempt to assess the most significant locations
for layoffs and the companies involved for the case industries is made. Second, a county-
centric approach is used to assess the importance of manufacturing and selected
industries to employment at the county level. Finally, a worker-centric approach is used
to more fully establish how workers adjust to employment shifts. Specifically, migration
and commuting data is used to assess the movement of workers over the period of study.

Communities and Industries, Industry Focus

Introduction

The key motivation behind this paper is the informed selection of appropriate cases for
the study of manufacturing decline. North Carolina possesses considerable specialization
in the textiles, apparel, and furniture industries at the national level, and these industries
have been crucial as sources of employment for many years. Case selection is focused on
counties that have lost employment in key industries and counties of particular
importance to these state-level industries. However, aggregate manufacturing is included
in order to establish whether the trajectories of these key industries parallel the wider
declining trend in manufacturing seen in industrialized states, or whether these industries
have fared differently from manufacturing in general. Finally, the computers and
electronics industry is included in order to capture an industry widely perceived to be
important to future employment and different from traditional manufacturing industries.

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First, the largest and most rapidly changing counties in terms of total employment and
manufacturing employment are identified and discussed. This is presented in order to
provide a general picture of employment patterns and to identify those counties who are
relatively specialized in manufacturing industries. Second, a similar effort at the industry
level is presented. Critical counties to the industries of interest are identified and
discussed. The information provided in this step is compared with the information from
the first step, allowing for some analysis of the differences between the trajectories of
industries in which North Carolina specializes and manufacturing in general. Finally, an
assessment of layoff events at the county level is presented to further identify differences
across counties and to identify companies responsible for these closings and layoffs.

Manufacturing, Employment, and Industry Location

Table 1 displays the largest and most rapidly changing counties in terms of
manufacturing employment and total employment. Because small counties often display
large percentage changes over time due to small denominators, a threshold of 1% of
statewide employment was chosen for the selection of the most rapidly changing
counties.

[Table 1 about here]

Four of the top five counties for manufacturing employment in 2004 are also top five
counties for total employment, which is expected. Additionally, four out of the top five
counties for manufacturing employment were also top five counties in 1990, with
Durham replacing Gaston County over the period. In fact, Durham County is one of the
fastest growing locations for manufacturing in the state, 40% growth over the period. In
contrast, all other top locations for manufacturing employment met with decline since
1990. Growth rates for top manufacturing employment counties are roughly similar, with
the exceptions of Gaston and Durham counties.

The fastest growing and declining counties are relatively small compared with leading
counties, again with the exceptions of Durham and Gaston counties. Interestingly, in all
cases, manufacturing employment has declined from 1997 to 2004. For counties
experiencing manufacturing decline, the pace of decline has generally increased after
1997. To what extent this is due to import competition versus economic recession in the
last five years is not known at this point, but the general decline in manufacturing has
accelerated in recent years.

A comparison with total employment confirms a decline specific to manufacturing,
though differences point to larger problems for specific counties. The five largest
counties by employment have grown at least 15% and by as much as 56% between 1990
and 2004. Only Durham County experienced growth in the manufacturing sector, all of
which indicates that the largest counties are able to attract new employment sources.
However, a number of counties have experienced total employment declines over the
period. Stanly, Gaston, and Richmond counties are among the fastest declining counties
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in terms of both manufacturing and total employment. These counties have been unable
to create jobs to completely replace manufacturing losses, indicating particularly severe
impacts of the statewide economic trends visible. As such, they will be of key interest in
the county-focus section.

Specific industries depart from these general trends in terms of rates of growth or decline,
and in the major locations of employment across the state. A quick glance across the
industries displayed in Table 2 indicates variation in the importance of each county to
statewide manufacturing employment and variation in where employment takes place at
the industry level. Textile mills represent the largest employer in North Carolina
manufacturing, responsible for 20% of manufacturing employment in 1990 and 10% in
2004. The top five counties by employment do not change between 1990 and 2004, and
the rates of decline of these counties fall between the fastest and slowest declining in the
state. However, the decline of textile mill employment at the state level is nearly 63% of
1990 levels. The set of the most rapidly changing counties are dominated by very small
players in the industry, and only one county has grown in absolute employment.

[Table 2 about here]

Non-Apparel textile products are relatively small compared with both apparel and
textile products, and interestingly, only two of the top five counties in 2004 were also top
employers in the textiles industry, which runs counter to the implicit relationship between
these two industries. Moreover, the change in the top counties over the period is striking,
with only one top county in 1990 surviving to 2004, Vance County. Similar to the textile
mills industry, this industry experienced relatively minor declines between 1990 and
1997. After 1997, a combination of factors intervened to bring most of the employment
declines observed and to make these declines widespread across all or most counties.

Most rapidly growing or slowly declining counties are small, with Rockingham and
Catawba counties the only exceptions. In contrast, rapidly declining sites for this
industry tend to be larger players in 1990. Guilford stands out as a site where growth
existed prior to 1997. Vance and Guilford are interesting cases in their importance to this
industry and, in the case of Guilford, its importance to the related textile industry.

In the set of important counties displayed for the apparel industry, only Forsyth County
has experienced growth. With nearly three-quarters of employment lost between 1990
and 2004, percentage losses at the county level are generally high. Additionally, several
counties are also important to the textile mills and non-apparel textile products industries,
entailing losses across a broad group of industries and potential erosion of resources and
cluster advantages that exist at the inter-firm level. Only Forsyth County could be added
to the set of potential cases established thus far, for it is interesting in two respects. First,
it has expanded employment in this industry over the period. Second, it is not a
significant player in the two textiles industries, which is a contrast from the cases selected
to this point.

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The computers and electronics industry contains few important participants, making
unavailable a meaningful assessment of rapidly changing counties. However, a glance at
important counties by employment points to the importance of large counties to the
industry, as well as some optimism for counties important to the textiles and apparel
industries. Durham County dominates the industry, followed by Wake and Mecklenburg
counties. Guilford and Forsyth counties have risen to importance for this industry by
2004, and as these contain smaller, more manufacturing-oriented labor forces, there is
some evidence that ability in manufacturing, and not the specific industry, is essential to
the replacement of employment opportunities with other manufacturing industries.
However, these two counties still contain relatively small shares of state employment,
and the industry has declined in size between 1997 and 2004.

Finally, the furniture industry most closely resembles the textile mills industry, though
with smaller declines in employment at the state level. Two of the top five counties have
increased their shares of the industry, Catawba and Randolph. Additionally, many of
these counties also are important employers in the industries discussed above, an aspect
worth further study given the overall objective of understanding job shifts and local
capabilities of workforces. Rapidly changing counties are all very small in the furniture
industry, with the exception of Davidson County, the fifth-fastest declining county in the
industry.

Layoffs
The above information portrays general employment patterns, but do not provide specific
information on patterns of layoffs and plant closings. Data on announced layoffs and
plant closings are available at the county level, but several important caveats warrant
mention. First, the list of plant closings and layoffs is not comprehensive, but instead
relies upon a survey of announcements in newspapers. Second, not every closing or
layoff contains an estimate of the number of workers affected, so I have chosen to select
important counties both by the number of events and the listed number of affected
employees. Finally, the data have only recently been classified according to the NAICS
industry classification system used throughout this paper. Instead, 2-digit SIC codes are
used, and I have chosen the four most appropriate categories for the analysis. However,
the industries studied in this section will not exactly match the NAICS definitions used in
the rest of the paper. Appendix Table 1 lists the corresponding NAICS and SIC codes
used for each industry in this paper.

Table 3 details top locations for layoffs in general, layoffs in the manufacturing sector,
and layoffs in each of the industries selected. Not surprisingly, top layoff counties are
among the largest counties in the state. In addition, these same counties appear on the list
of top counties for layoffs in the manufacturing sector. The number of workers affected
generally corresponds to the sheer number of layoffs, but the number of these affected
workers employed in manufacturing industries varies widely by county. Counties with
high numbers of laid off manufacturing workers are generally large counties, Gaston
County leads this list and Guilford County places fourth. These two counties have
experienced disproportionate impacts on manufacturing workforces. More generally, the
percentages of layoffs composed of manufacturing workers is higher than the percentage
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of total layoff events, which is not surprising given the large establishment sizes present
in the manufacturing sector.

[Table 3 about here]

At the level of specific industries, Table 3 identifies the most severely impacted counties
as measured by events in normal typeface, and the most severely impacted in terms of the
number of workers affected in italics. Nearly 10% of all layoff events and 20% of all
affected workers were in the textile industry at the state level. However, with the
exception of Guilford, these counties have experienced a large share of their layoffs in
this industry. This industry is responsible for one-third of all layoffs in Gaston county
and nearly two-thirds of all layoffs in Rutherford and Cabarrus counties. Textile
products do not appear to have lost as many workers over the same time period, but a
greater number of counties have either experienced a high number of layoff events or a
high number of affected workers. Many of these counties are relatively small, and this
industry composes a large proportion of total layoffs.

The data on the information industry further supports the existence of specialization at
the county level, as shown by differences in the proportion of all layoffs composed by
this industry. Guilford and Catawba have experienced sizable losses in this industry, but
they have the potential to fare better than Davidson and Caldwell counties, where these
industries are responsible for a very large share of total layoffs at the county level.
Finally, the last two industries listed are used as proxies for the computers and electronics
industries, and not surprisingly, most layoffs take place in the same counties previously
identified as centers of activity. However, the number of total layoffs composed of
layoffs in these two industries is somewhat large, especially in Durham and Wake
counties. This may be related to the downturn in the industry over the last five years, or
it might simply be the result of company turnover. As Durham, a county experiencing
growth in this industry, is a leading county in terms of layoffs, the reasons behind the
layoffs are not clearly tied to permanent shifts in the location of this industry.

Over time, layoffs in the manufacturing sector closely follows layoffs in total economic
activity. Chart 1 displays statewide layoff and plant closing events by year, while Chart 2
displays the number of employees affected by these layoffs and plant closings.
Manufacturing composes a substantial share of total employees lost to layoffs and plant
closings, though this percentage has generally declined, as evidenced by the dashed line
in Chart 2. Textile mill layoffs most closely follow the pattern displayed in
manufacturing and are the most substantial in number of the industries displayed. Across
all of the categories displayed, layoffs and plant closings increased substantially in 2000
and 2001, and declined by 2004.

[Chart 1 about here]

[Chart 2 about here]

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Layoff Companies
Tying the aggregate information to specific companies and actions above is crucial to the
construction of appropriate cases, and a special advantage of the Announced Business
Closings series. Table 4 displays the companies with the most numerous layoffs and
plant closings in each industry. The computers and electronic products industry is not
discussed due to the aforementioned industry classification problems, but is available on
request. Additionally, this table is not discussed at great length, but should be used to
further assist in the identification of appropriate companies in case counties.

[Table 4 about here]

The most interesting finding in the data is that the top companies generally compose a
small share of total layoffs, especially in the textiles industry, and that these layoffs affect
a surprisingly diverse set of counties. In most cases, most layoffs are centered on a few
counties, but business activities appear to be widespread across counties with both high
and low levels of employment in the industry. Across companies, many of the same
counties appear frequently, all of which have been previously identified as central
locations for industry activity. However, companies also differ in where they have
chosen to locate lower levels of employment, assuming layoffs are fairly evenly
distributed across counties in which they locate activities.

Clusters may exist where many companies choose to locate productive activities, leading
to pools of specialized labor and local resources. In this event, counties where
employment and layoffs are widespread are locations where these clusters have formed.
On the other hand, if clusters are better able to withstand declines at the industry level,
these locations will be underrepresented in the layoff data. This note of caution should be
kept in mind when reading through this table. As previously stated, the strength of this
data series is its ability to tie larger employment trends in the state to specific industries.

Conclusion
Counties differ with respect to their presences in manufacturing activities, and further
specializations are present in the specific industries analyzed. Manufacturing as a whole
has experienced a dramatic decline, evidenced by employment losses throughout the
state. The bulk of these losses have taken place since 1997, a period which corresponds
to intensified trade, higher productivity, and economic recession.

Interestingly, while the largest counties in terms of population are generally the largest
employers in the manufacturing sector, they are not the most crucial for most of the
industries studied. At this point, case selection should include both large counties and
large employing counties in any given industry. But a central concern to these smaller,
more specialized counties is how to cope with employment losses from locally dominant
industries. In the next section, I attempt to assess how specialized these counties are by
taking a county-centered focus to the analysis.
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Counties and Concentration of Employment in Select Industries

As manufacturing has declined, have some counties experienced sharper employment
losses than others? Are some counties hurt more easily than others due to the relative
preponderance of manufacturing employment in the county? As losses have mounted,
what has happened to wages in the county, and for the manufacturing jobs that remain?

Not surprisingly, counties with large amounts of employment do not have this
employment concentrated in manufacturing activities, as shown in Table 5. Only three of
the largest 10 counties have more than 15% of their workforces employed in
manufacturing activities. Further, only Durham County has increased its employment in
manufacturing, and only Gaston County has lost employment across all sectors between
1990 and 2004. In the case of Gaston, manufacturing employment has declined by more
than 50%. Finally, a quick scan of the largest counties indicates that the share of each
county’s total employment composed of manufacturing employment is generally lower
than the state average, and the proportion of any given county’s employment composed
of manufacturing activities has generally declined over the period.

[Table 5 about here]

Counties with the highest amounts of employment composed of manufacturing
employment are generally small, with Catawba County the only outlier from this pattern.
In a number of cases, employment rose between 1990 and 1997, but fell after 1997,
though by smaller numbers. In contrast to the largest counties, these counties appear to
generally have had more difficulty replacing manufacturing employment with other types
of employment. However, while overall job growth has been generally less than the state
average, only 2 out of these 10 counties performed worse than the state average in terms
of manufacturing. These two counties, Caldwell and Catawba, lost a greater percentage
of their manufacturing jobs than the percentage losses incurred by the state as a whole
and were among the largest of those counties displayed. Finally, 4 out of these 10
counties experienced increases in manufacturing employment, though all four are
relatively small in terms of employment. There does appear to be some ability for
counties with high proportions of their workforces employed in manufacturing to weather
more general trends better than counties that specialize in other activities, though overall
job growth also appears to suffer.

Counties with rapidly increasing or decreasing manufacturing workforces display no
discernable pattern. Only 10 counties have experienced manufacturing employment
growth, and only three contain more than 1 percent of statewide employment. Declining
counties are small, and only two exceed the statewide average for employment growth.

With these general trends in mind, further analysis specific to the industries of interest
aims to further identify potential case studies. Of key interest are counties with rapidly
changing numbers of employment in the specific industries and with high proportions of
total employment in these same industries. Table 6 summarizes counties that exhibit
these traits for each industry.
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