National Textile Center
Year 11 Continuing Project Proposal
Project No.
I01-P13
Competency:
Intelligent Systems
Optimal Investment Strategies for Enhanced
Productivity in the Textile Industry
Project Team:
Leader:
Email:
Members:
S. Christoffersen, Ph.D., Philadelphia University, Economics / Industrial Organization
ChristoffersenS@PhilaU.edu Phone: (215) 951.2821
D.K. Malhotra, Ph.D., Philadelphia U, MalhotraD@PhilaU.edu, Finance/Quantitative Analysis,
Anusua Datta, Ph.D., Philadelphia U., DattaA@PhilaU.edu, Econometrics / Productivity
Moon Suh, Ph.D., NC State University, Moon_Suh@NCSU.edu, Statistics
Objective:
The object of this research is to increase the market share of the US textile industry by identifying investment
strategies to achieve maximum manufacturing productivity while satisfying dynamic consumer needs. Market share
can be gained if textile firms are more competitive, either through product development or improved productivity
resulting in lower costs. Identifying optimal investment strategies to achieve these goals is complex, dependent upon
foreign industry dynamics, tariff changes, uncertain R&D pay-offs, the type and timing of investment in information
technology (IT) and domestic industry dynamics, specifically economies of scale.
Progress Statement:
Traditionally, labor productivity (output per worker) has been used to measure productivity growth , however, this
measure exaggerates productivity gains as the textile industry becomes more high tech and therefore capital
intensive: stagnant output and a diminishing labor force would look like a productivity gain. In order to achieve our
main objective, we have gathered data on the multi-factor productivity (MFP) of the textile industry, which will be
analyzed to assess the impact of R&D investment, IT investment, capital and labor expenditures, and economies of
scale. Ultimately, this project will develop a model for deriving the optimal investment strategy, measuring the
relative impact of these expenditures on productivity.
The study will be conducted at three levels: using overall industry data ( SIC22), three-digit SIC level data and firm
level data. For the latter, we will use panel data regression analysis: the time series aspect will allow us to analyze
how parameters change over time and cross-section data will show variations across firms. For example, there are
learning curve effects associated with R&D and IT, causing pay-offs to increase over time, however there are
interactions with other factors of production, and these may vary across firms. This is important for firms
commercializing new products and processes as they must divert productive resources towards implementation. Such
adjustment costs for R&D related projects are estimated to be seven times the adjustment costs of investment in new
plant and equipment. This necessarily changes the way we view R&D (and IT) investment. Analyzing multi-factor
productivity will improve how one assesses productivity by including these costs of complexity.
Chart 1 presents the growth in MFP for both the textile and apparel industries. The tremendous growth in
productivity in textiles is not found in the apparel industry due to the inability of the apparel industry to shift away
from labor-intensive production. While the textile industry has demonstrated a greater ability to adopt advanced
technology, demand for textiles may be limited by the lagging productivity of the apparel industry. The data is
indexed at 100 in 1996; fifty years ago the textile industry was 35% as productive as it was in 1996 and between
1996 and 1999 productivity increased by 8 percent. While this data has been compiled on the two-digit level, it is
augmented in the study by data gathered on the three-digit level, as well as plant-level data.
Chart 1: Comparision of MFP for SIC 22 and SIC 23
120
INDEXES
100
SIC 22
80
SIC 23
60
40
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
1973
1971
1969
1967
1965
1963
1961
1959
1957
1955
1953
1949
20
TIME
Chart 2 shows the productivity trends for the textile industry, broken down to the level of three-digit SIC codes.
SIC 221 - Broadwoven Fabric mills, Cotton.
SIC 223 - Broadwoven fabric mills, wool
SIC 225 - Knitting mills
SIC 227 - Carpets and rugs
SIC 229 - Miscellaneous textile goods
1996
1995
1994
1993
TIME
1992
1991
1990
1989
1988
1987
INDEXES
Chart 2: Comparison: MFP's of SIC-221, 222, 223, 224, 225, 226, 227, 228 229
130
125
120
115
110
105
100
95
90
85
SIC 222 - Broadwoven fabric mills, manmade
SIC 224 - Narrow fabric mills
SIC 226 - Textile finishing, except wool
SIC 228 - Yarn and thread mills
This chart indicates which types of firms have had the greatest productivity advances. While these sectors require
greater study, the chart highlights the need to disaggregate. Indeed, on the industry level, shrinking employment is
discouraging, however plant level data tell a different story: Table 1 shows significant exit and entry in the textile
industry. Historically, firms that exit an industry are generally the more inefficient firms. Those that remain and the
ones that enter, on the other hand, are typically more productive and technologically advanced.
Table 1: Rates of Plant Entry and Exit
Year
Gross Rate of
Entry
Gross Rate of
Exit
Textiles
Gross Rate of
Entry
Gross Rate of
Exit
Apparel
1972-77
26%
32%
42%
44%
1977-82
31%
32%
48%
43%
1982-87
20%
38%
25%
55%
1987-92
28%
31%
49%
46%
An important aspect is foreign competition, which has intensified due to increased openness in trade, especially from
the newly industrializing countries (NICs), while also increasing outsourcing possibilities for U.S. firms. Chart 3,
presents the trends in textile exports and imports. Beginning around 1983 imports began a steady increase
outstripping exports by a sizeable margin. Imports in textiles came from both developing and developed countries,
although the share of the former is increasing. Exports declined for a while between 1982-85, and although they
increased thereafter, exports continued to fall short of imports. Clearly, 807A imports play an import role, rising
precipitously since NAFTA, but declining after 1998.
Imports ($millions)
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
Exports ($millions)
1972
$ MILLIONS
Chart 3: Comparison of Imports and Exports in $millions
12,000
10,000
8,000
6,000
4,000
2,000
0
TIME
The Annual Report presents more details on the data compiled to-date.
Next Year’s Goals:
Currently we are working on the consistency of the data. While data collection at the industry level SIC 22 is
complete, inconsistencies in three-digit SIC data exist due to changes in SIC definitions over time. Additionally,
nominal figures have to be deflated by consistent yet specific inflation indices: imports use a different deflator than
domestic output but the underlying inflation estimates must be the same. In the coming year we will use regression
analysis to analyze the data in stages. First we will analyze factors that enhance productivity. Next we will assess the
contribution of enhanced productivity to market share. The Approach section, following, details the analyses.
Approach:
Panel data will be used to econometrically estimate the Cobb-Douglas production function for textile firms:1
Qit = A CLK M.
The results will shed light on the impact and interactions of factors of production. K represents knowledge and
quantifies the contribution of R&D and IT as measured by a distributed lag effect of past R&D/IT investment; the
residual indicates productivity (MFP). The parameters and  represent the elasticities of output relative to
capital, labor and knowledge. For example, the elasticity of capital indicates the percentage increase in production to
expect from increased investment in new plant and equipment. Comparison of these measures reveals the relative
importance of various investments such as the impact on production of R&D relative to capital investment (.
Time series allows one to analyze how these parameters change over time. Once production functions are estimated,
contribution of other factors can be assessed, such as firm size, nature of the product (availability of close
substitutes), ownership characteristics, sources of financing, and offshore plants.
MFP = f(R&D, IT, Scale, Import Competition, Policy etc.)
To understand the factors that contribute to higher productivity, we will estimate the above model. Changes in MFP
will be regressed on changes in R&D and IT to determine the effectiveness of these investments, as well as a proxy
for size, to assess economies of scale versus customization. Understanding the sources of productivity growth will
then contribute to an analysis of market share:
Sales = f( MFP, X-M, apparel sales, K/Q, tariffs, etc.)
The market share will be analyzed as a function of productivity, foreign markets, apparel dynamics, tariff rates,
capital deepening and scale. Initial estimates will require adjustments for inflation, depreciation of both the capital
stock and R&D, the latter being very different from the depreciation of plant and equipment investment. Previous
studies pertain to pharmaceuticals and aeronautics where the importance of R&D is recognized, yet the potential to
enhance productivity remains great in the textile industry.
Outreach to Industry:
Burlington, UNIFI, and Cannon-Fldcrst were visited, with interviews of financial officers and product development
guys. Our research results, based upon rigorous research, will address decisions they face. Presentations at academic
conferences and publication in scholarly journals will be augmented with industry pieces on our findings.
New Resources Required: None
1
Q is the output of firm i at time t, as measured by sales deflated by the relevant price index. Gross plant, adjusted for inflation, is the measure
of capital stock, C, and L is the total number of employees and M is materials; the value of purchases. A is the rate of disembodied technical
change while is the perturbation or error term.
K= Ri (t-), where R is a deflated measure of R&D, and the subscripts t,tand i stand for current year, lagged year and firm.