sophisticated model to estimate that NAFTA compliance costs averaged only 3.0 percent. This
estimate is in line with Cadot et al. (2005), who calculate that Mexican goods shipped to the
United States in sectors eligible for NAFTA preferences are priced 4–5 percent higher than
exports to non-preferential markets. Cadot et al. estimate that only half of this price differential
(2–2.5 percentage points) is due to RoO compliance costs.
The compliance costs of textile and apparel RoO appear to be much higher than these
average estimates. Anson et al. note that textiles and apparel have slightly below-average
utilization rates but higher than average RoO restrictiveness, implying that the costs of RoO in
textiles and apparel are higher than average. Carrère and de Melo (2004) support this assertion,
estimating the average compliance cost to be 9.2 percent in these sectors, close to the average
textile and apparel tariff preference rate of 10.4 percent.
4
They also find that technical operations,
which require products to undergo specific manufacturing operations in the originating country,
are the most costly type of RoO.
5
These technical operations apply to Mexican apparel but not
textiles.
Our paper explicitly incorporates reduced prices for imported textiles and apparel and
reduced foreign demand for U.S. goods as part of the liberalization scenario, accounting for two
important features of preferential RoO absent in previous studies. This paper suggests that these
outcomes of RoO policy are important in evaluating the welfare consequences of preferential
RoO, as our estimates imply that RoO compliance costs are high enough to reduce aggregate U.S.
welfare. These effects are even more important in understanding the effect of potential
liberalization on sectoral activity: in sectors subject to preferential RoO, reductions in foreign
demand account for 52–99 percent of the output reduction from liberalizing all restraints.
Because these two forces have opposite effects on welfare and imports and reinforcing negative
effects on exports, it is important to include them both.
This paper is organized as follows. Section 2 quantifies the restrictiveness of quantitative
restraints, tariffs, and RoO, which provide price and quantity shocks for the liberalization
scenario. Section
3 describes the model, and section 4 provides estimates of changes in welfare
4
In detail, they estimate that RoO compliance cost are actually slightly higher than preference margins for sectors
with positive but not complete preference utilization, and compliance costs average 61.7 percent of the preference
margin in textile and apparel sectors with complete utilization.
5
Their classification of RoO types was introduced by Estevadeordal (2000).
3
and sectoral activity from liberalizing the shocks quantified in section 2. This section also
contrasts the welfare and sectoral impacts of liberalizing quantitative restraints, tariffs, and RoO
separately. Section
5 concludes.
2 Restrictiveness of U.S. Import Restraints
2.1 Introduction
Trade in textiles and apparel in the United States has been subject to quantitative
restriction since the 1960s to the present day, most notably under the terms of the Multifibre
Arrangement (MFA, 1974-1994) and its successor, the Agreement on Textiles and Clothing
(ATC, 1995-2005), established as part of the Uruguay Round negotiations.
6
ATC set as its goal
the orderly elimination of quantitative restraints in textiles and clothing by January 1, 2005. The
ATC succeeded in eliminating these quotas in 2005, although countries remain free to impose
quotas on non-WTO countries.
China has been the largest beneficiary (by value) from global quota elimination and the
resulting market share reallocation. Chinese exports to the United States rose from $12.8 billion
to $27.7 billion between 2002 and 2005, an increase of 115.5 percent. This rapid increase led to
the establishment of 10 safeguards (quantitative restraints) on selected imports of Chinese textile
and apparel articles in 2005, as provided for under China's WTO Protocol of Accession. U.S.
imports under these safeguards accounted for approximately 5.9 percent of all textiles and
apparel from China in 2005.
7
All 10 safeguards filled at rates higher than 90 percent, and eight of
the safeguards filled in their entirety, effectively preventing U.S. importers and retailers from
receiving ordered goods.
Disruptions and uncertainties associated with the safeguards led to the negotiation of a
Memorandum of Understanding (MOU), a three-year agreement that established quotas on U.S.
imports of selected textile and apparel products from China. The MOU went into effect on
January 1, 2006 and extends through December 2008, at which time the United States' right to
invoke safeguards under the textile provision of China's WTO Membership Accession
6
Spinanger (1999) describes the development and demise of the Multifibre Agreement and the ATC. He also
provides historical trade data that detail the rise of China to world number one exporter of apparel by 1996.
7
On a calendar year basis, total U.S. imports of the 10 categories subject to safeguards in 2005 represented 14.7
percent of total U.S. imports of textiles and apparel from China, but most safeguards were not in place for the entire
year.
4
Agreement expires. The MOU established 21 quotas covering 34 categories of textile and
apparel products (table 2), which accounted for 37.0 percent by value of imported Chinese
textiles and apparel in 2005. Although the MOU covers more products, for most sectors that
were subject to safeguards, the MOU allows higher quantities and higher annual growth rates
than the minimums specified in the safeguard provision.
2.2 Nature of Quantitative Restraints
To export to the United States, a firm in a quota-constrained country must buy an export
license or otherwise obtain the right to use a portion of the quota. Given that quotas impose a
cost on exporting firms that is analogous to an export tax, one common way to measure the
restrictiveness of a quota is to compute an export tax equivalent (ETE), which measures the
degree to which the quota increases the export price. More restrictive quotas lead to more
valuable export licenses, which in turn produce higher ETEs.
8
We estimated ETEs for all Chinese safeguard sectors and all sectors in non-WTO
countries that were subject to binding quotas in 2005. Using a quota fill rate of 90 percent to
indicate a binding quota, exports were restrained in 10 sectors from China, 10 sectors from
Vietnam, and one sector from Belarus (table 3).
9
Total imports under Chinese safeguards during
the safeguard periods totaled $1,646 million, and imports in restrained sectors with non-WTO
countries totaled $723 million; together these accounted for only 2.4 percent of total U.S. textile
and apparel imports. The incidence of these quotas has declined significantly since the expiration
of the ATC, and hence ETEs (and their economic importance to the United States) have also
declined relative to earlier estimates. The ETEs, however, remain important to the countries with
quantitative restrictions and to their foreign competitors.
10
8
As noted by Krishna and Tan (1997), large U.S. retailers, which increasingly source directly from foreign suppliers,
may extract a portion of these rents. The extent of such rent sharing is unknown; however, these ETEs may overstate
import price increases and associated welfare reductions in the U.S. economy.
9
An alternative fill rate of 80 percent is sometimes employed in studies of trade restrictiveness. Using this
alternative rate, only three additional sectors would be considered restrained. Because U.S. imports in these three
sectors were low, the choice of fill rate has very little effect on trade-weighted ETEs and consequently has very little
effect on the simulation results.
10
In 2005, Chinese imports under safeguards were 5.9 percent of $27.9 billion c.i.f. total Chinese imported textiles
and apparel; Vietnamese restrained imports were 24.3 percent of $3.0 billion; Belarusian restrained imports were 1.4
percent of $42 million; and none of the 65 million of Ukrainian imports were deemed restrained.
5
2.2.1 Chinese ETEs
Under the ATC, the Chinese government auctioned a portion of export licenses in each
restrained sector, and these prices have been used in a number of studies to estimate ETEs.
However, no export licenses were sold in 2005, because safeguards on Chinese imports were
administered on a first-come-first-served basis. The Chinese government resumed its
administration and auctions of export licenses under the MOU in 2006. Ten of the 21 MOU
sectors were nearly identical to the corresponding 2005 safeguard sectors, so the January 2006
monthly average license prices were used as the best proxy for the 2005 license prices.
11
The
per-unit production cost in each sector was estimated as the difference between the f.o.b. export
price per unit to the United States and the per-unit price of an export license.
12
The ETE in each
sector was calculated as the license price divided by the estimated production cost.
Table 3
presents estimates of Chinese ETEs, which range from 6.5–93.3 percent. Because the sectors
with the largest import volumes (cotton trousers, cotton shirts, and brassieres) have intermediate
ETEs, the trade-weighted and unweighted averages are both about 42 percent.
2.2.2 Vietnamese ETEs
Vietnam does not report license prices, so the ETEs cannot be calculated as with China.
In this case, the license price can be estimated as the difference between the export price and the
production cost, if an estimate of the per-unit production cost in each sector is available.
However, production costs are difficult to estimate and may differ from product to product and
even factory to factory within a country. Trade journals estimate that Vietnamese production
costs are 20–30 percent higher than Chinese costs for comparable products, although other
industry sources estimate that Vietnamese costs are the same as Chinese costs in some
industries.
13
Comparison to Chinese costs is further complicated by recent Vietnamese quality
upgrading to avoid direct competition with low-cost commoditized goods from China. This
quality upgrading is reflected by recently increasing Vietnamese unit values (table 3); in 2005
11
License prices at the beginning of 2006 are likely to reflect the prices of 2005 licenses, had they been sold,
because the set of restricted countries exporting to the United States did not change and the quota and MOU limits in
2006 are close to the quantities traded in 2005. January prices were used instead of the average prices in 2006
because prices in 2006 declined considerably after January, reflecting quota fill rates considerably below the levels
seen in previous years. (The low fill rates indicate that some U.S. importers switched to non-Chinese sources, likely
due to the uncertainty associated with the safeguards in 2005, although the initially higher quota prices indicate that
importers were not able to change sources immediately.) The January license prices were typically slightly lower
than average 2004 prices in comparable sectors.
12
The f.o.b. price per unit is derived from official U.S. Customs data for customs value and quantity.
13
See, for example, Just-style (2005).
6
these values were about 30 percent higher than Chinese unit values in comparable sectors.
Because the portion of the Vietnamese-Chinese price differential attributable to rent capture,
quality upgrading, and higher production costs cannot be reliably distinguished for each sector,
we choose a cost value such that Vietnamese ETEs that are on average equal to Chinese ETEs
for comparable products.
14
Table 3 presents estimates of Vietnamese ETEs, which range from 0
to 71.8 percent. Because the sector with the highest trade—cotton knit shirts—has the highest
estimated ETE, and the sector with the lowest trade—synthetic filament fabric—has the lowest
ETE, the trade weighted average of 43.9 percent is considerably higher than the unweighted
average of 33.5 percent.
15
2.2.3 ETEs in Model Sectors
The ETEs for individual restrained sectors must be combined to determine the ETE in
each USAGE-ITC model sector. For each model sector, a trade-weighted average ETE is
calculated using the ETE for each restrained subsector in that model sector, and an ETE of zero
for all other trade in that sector.
16
Table 4 gives the ETE for each model sector along with trade-
weighted average tariff rates. ETEs are considerably lower than tariff rates in all sectors except
for socks.
17
The ETEs in 2005 are also considerably lower than those estimated in previous
studies; for example, the current ETE for all textiles and apparel is less than one-third of the
average ETE reported in USITC (2004). ETEs declined because the elimination of import quotas
from most countries in 2005 as specified by the ATC considerably reduced the share of imports
that were restrained by quotas.
14
This is equivalent to assuming that Vietnamese costs are 28 percent higher than Chinese costs. This cost
differential is higher than the 10 percent differential assumed in USITC (2007), which relied more heavily on
industry sources and minimized the role of quality differences. The higher cost differential leads to lower ETE
estimates in the present paper, alhough these ETEs may still by overstated if greater-than average quality upgrading
has occurred in sectors such as cotton knit shirts.
15
Trade with Belarus is also restricted in one sector, heavyweight glass fiber fabric. To calculate this ETE, we
assumed that Belarusian costs were 50 percent higher than Chinese costs in the glass fiber fabric MOU sector.
16
The ETE in model sector k is calculated as
∑ ∑
∈
=
ki
k
j
ijijk
METEMETE ,/)(
where M
ij
is the value
of U.S. imports in restrained sector i from country j, and M
k
is the value of U.S. imports in model sector k.
17
The sock sector is officially denoted “hosiery, not elsewhere classified.” In addition to socks, it includes three
small hosiery sectors: nonsurgical, nonsynthetic-fiber pantyhose; tights without soles; and a few types of legwarmers.
The “women’s hosiery” sector includes all remaining types of pantyhose, tights, and legwarmers, and excludes
socks.
7
2.3 Tariffs and RoO
Textiles and apparel imports are subject to some of the highest U.S. tariffs, although a
substantial portion now enter duty free. The trade-weighted average ad valorem tariff on U.S.
textile and apparel imports in 2005 was 9.4 percent (
table 4). In general, tariffs on textiles and
apparel increase with each stage of manufacturing (i.e., the duty rates are usually higher on
apparel than on its yarn or fabric inputs). The trade-weighted average tariffs were 4.4 percent for
textile mills, 6.4 percent for textile products, and 10.6 percent for apparel.
18
These average rates
are not representative for many products and partners, however. Tariffs for many heavily traded
apparel articles were much higher than these average tariffs.
19
Further, a significant portion of
textile and apparel imports either enter duty free under FTAs and trade-preference programs or
are eligible for a partial duty exemption under the production-sharing provisions of HTS chapter
98. In 2005, 28.0 percent of total U.S. textile and apparel imports entered duty-free.
20
The prevalence of duty-free textiles and apparel imports highlights the importance of
accounting for RoO in any analysis of trade liberalization.
21
In most textile and apparel sectors,
imports must fulfill certain RoO criteria to enter free of duty. These criteria require the use of
U.S. or regional fabric in the production of apparel items. RoO are influential in directing trade
flows because they create demand for U.S. exports of textile articles for use in the production of
apparel, which is then re-exported to the United States free of duty.
Although the United States granted preferential access to dozens of countries in 2005,
most trade occurred with Mexico, Canada, CAFTA, and the Caribbean basin. These countries
received 95.3 percent of U.S. textile and apparel exports to all preferential trading partners, or
74.7 percent of total U.S. exports of these goods. Not all of this trade is driven by RoO, however;
18
These tariff values are based on the NAICS nomenclature. NAICS code 313 contains textile mills, which
primarily include yarn, thread, and fabric mills. NAICS code 314 contains textile products, which include carpets
and rugs, bed and bath linens, canvas products, rope and twine, tire cord, and other miscellaneous textile products.
NAICS code 315 contains apparel, which includes knit-to-shape apparel as well as apparel assembled from cut
fabric.
19
For example, the 2005 Normal Trade Relations (formerly, MFN) duty rates on certain women's and girls' man-
made fiber pants and blouses were 28.2 percent and 32.0 percent, respectively.
20
The following are the largest suppliers of duty-free imports: NAFTA countries (36.0 percent of the total), United
States–Caribbean Basin Trade Partnership Act countries (25.7 percent), African Growth and Opportunity Act
countries (5.5 percent), and Andean Trade Promotion and Drug Eradication Act countries (5.1 percent). Goods
entered under the production-sharing provisions of HTS chapter 98 accounted for an additional 18.4 percent of the
duty-free value.
21
We thank Andrea Boron for valuable assistance identifying RoO sectors, and Kim Freund for encouraging us to
investigate textile and apparel RoO by highlighting implausible results in simulations that exclude them.
8
the prevalence and effects of RoO vary considerably by textile sector. RoO have the greatest
effect on foreign demand for U.S. products in apparel and textile mill sectors, and have little
effect on most textile products. Consultation with industry analysts, examination of FTA texts,
and analysis of preferential trade patterns identified the following 10 sectors with significant
preferential RoO: broadwoven fabric, narrow fabric, knit fabric, yarn mills, thread mills, coated
fabric, pleating, women’s hosiery, socks, and apparel.
22
Industry analysts estimate that RoO are
responsible for 95 percent of U.S. exports to these partners in most of these sectors, which
amounts to 44.3 percent of total U.S. textile and apparel exports.
23
As noted in the introduction, RoO have high compliance costs, particularly for apparel
products which face the most restrictive types of RoO. These costs are passed along to U.S.
consumers when they buy imports from preferential trading partners. No studies exist that
estimate compliance costs by detailed sector and trading partner. We estimate that compliance
costs are equal to 40 percent of preferential tariff margins in textile sectors and 80 percent in
apparel sectors.
24
This is a fairly conservative estimate because it is below Carrère and de Melo
(2004) estimates for NAFTA compliance costs in most sectors, and because it does not accord
any compliance cost to textiles that are re-exported to the United States in a non-RoO sector.
25
Further, we do not estimate compliance costs in non-textile-and-apparel sectors, because
estimated RoO compliance costs are much lower in other sectors of the economy.
Examination of trade flows shows that preferential trading partners tend to have high
exports to the United States, and thus high compliance costs, in the same sectors in which RoO
22
We thank Kim Freund for valuable assistance identifying these sectors, and indeed, for encouraging us to begin
this investigation of textile and apparel RoO by highlighting the implausible results of simulations that exclude them.
Auto appliqué and trim is also subject to some RoO-based preferences, but this sector was not included because
foreign producers rarely utilize these preferences, and only 1.1 percent of U.S. output in this sector is exported.
23
Industry analysts noted that some textiles, particularly narrow fabric, have industrial uses that would generate
trade even in the absence of RoO. Also, considerable trade with Canada, like U.S. apparel trade with other
developed countries, would likely continue without preferential status. Thus we assume that RoO drive only 50
percent of U.S. exports in these sectors and partners.
24
We also impose a 10 percent maximum compliance cost in all sectors. Because we use collected duties for FTA
and non-FTA partners to calculate AVE preferential tariff rates, this procedure implicitly incorporates preference
utilization rates. For example, because CBERA and CBTPA have relatively low utilization rates of knit fabric
preferences, these countries’ estimated compliance costs are lower than Mexican and Canadian costs in this sector.
25
This choice also reflects calculations by Estevadeordal and Suominen (2006) that other U.S. FTA RoO are
somewhat less restrictive than NAFTA RoO, although no compliance cost estimates are available for these other
partners.
9
drive U.S. exports.
26
Table 5 summarizes the partners and sectors in which RoO generate U.S.
exports, and the estimated compliance costs in these sectors.
3 Model Description
USAGE-ITC is the latest in a series of models developed by the Centre of Policy Studies
and the Impact Project over the last 30 years, beginning with the ORANI model and moving
through to the dynamic MONASH model of Australia.
27
The USAGE-ITC model is large scale,
dynamic CGE model of the United States developed in collaboration with the U.S. International
Trade Commission. USAGE-ITC is capable of conducting both static and dynamic CGE
simulations, in the second case with recursive or forward-looking expectations. The dynamic
components of USAGE-ITC involve, most importantly, the accumulation of various real and
financial stocks and inter-temporal optimization by economic agents. USAGE-ITC distinguishes
523 commodities, 521 industries, 23 foreign regions, and a detailed handling of margins and
taxes.
28
Other features of the model include a detailed modeling of government expenditures and
foreign liabilities.
USAGE-ITC follows the MONASH approach to CGE in being designed to conduct
several broadly-defined types of simulation analysis. Historical simulations estimate the paths of
unobservable variables over a historical period, such as changes in technology and consumer
preferences. Forecasting simulations generate baselines consistent with outside macroeconomic
forecasts and model-consistent historical structural processes that are derived from the historical
simulations. Policy simulations impose policy and other structural changes to calculate
deviations from a forecast simulation baseline. In this paper, we report the results of both
forecast and policy simulations. However, the historical simulation is essential to estimating
trends that are applied to the forecast, as described below.
3.1 Generating the forecast and policy simulations
In creating a forecast for the period 2005–11, we first create a complete dataset with 2005
values. These data come from a number of sources. Production data are based on the 2005
26
Except for CAFTA and Caribbean basin countries, which typically do not export upstream textile products
(including thread, yarn, narrow fabric, and broadwoven fabric) back to the United States. When these countries do
export these products to the United States, they typically receive the same tariff rate as non-preferential trading
partners, leading to low estimated RoO compliance costs in these sectors with these partners.
27
For more detail on USAGE as a MONASH style of model, see Dixon and Rimmer (2002).
28
Changes in foreign economies are not modeled endogenously but the model does incorporate changes in foreign
productivity and shifts in foreign demand and supply schedules based on historical trends.
10
national income and product accounts published by the Bureau of the Census and on the 1992
input-output accounts from the Bureau of Economic Analysis. Trade flows and U.S. tariff rates
for 2005 come from the U.S. Department of Commerce. Foreign tariff rates come from the
UNCTAD TRAINS database.
Then we apply shocks to exogenous variables to represent movements from their 2005
values to their forecast values for 2011. Some exogenous values are taken from forecasts made
by U.S. government agencies, including the Bureau of Economic Analysis, the U.S. Department
of Agriculture and the Energy Information Administration. A careful assessment is made to
reconcile the macroeconomic forecasts with the model's structure and to determine the suitability
of the forecasts themselves. For example, some of the macro forecasts implied a US current
account deficit in excess of global savings within a decade of the start of the forecast period, a
situation easily ruled out as unrealistic. Along with the macroeconomic forecasts, pre-negotiated
or pre-announced trade policy changes are also included in the forecast. These include future
tariff rates for U.S. free trade agreements, based on the final texts provided by the USTR.
Shocks to technology, consumer preferences, foreign supply, and foreign demand for U.S.
products are derived from extrapolations in the historical simulation. The historical simulation is
used to generate information about conventionally unobservable variables. The approach
involves (a) exogenizing many of the naturally endogenous variables (i.e., those usually
explained in a CGE model), (b) imposing shocks on these variables calculated from data
provided by the historical record, and (c) endogenizing the otherwise naturally exogenous or
unobservable variables, allowing them to accommodate these data. For example, given
information such as historical movements in relative commodity prices and household disposable
income, it is possible to make a model-consistent estimate of the implied movements in
consumer preferences over the same period.
Policy simulations are conducted by perturbing USAGE-ITC away from the forecast path
by shocking policy variables. The results we report are calculated as the deviation, in percentage
terms, away from the dynamic baseline forecast.
11
3.2 Model details
3.2.1 Demand and production
Consumers use a three stage procedure to allocate expenditure across goods that are
differentiated by country of origin. In the first stage, expenditure for each sector is determined by
a linear expenditure system, without regard to the origin of goods.
29
In the second stage,
consumers choose the relative expenditure on domestic and imported varieties of each good. The
substitution possibility is specified with a constant elasticity of substitution (CES) parameter,
commonly called the Armington elasticity. In the third stage, consumers allocate expenditure
across multiple imported varieties, again with CES utility.
All sectors are assumed to be perfectly competitive. In the forecast, however, sector
productivity may change due to exogenous shifts in a range of technological-change variables
consistent with changes in the historical simulation. Firms engage in a multi-stage process that
determines the relative expenditure on primary factors, domestic intermediates, and imported
intermediates. Use of individual primary factors (labor, capital and land) is determined by a
multi-level CRESH nesting structure. For each intermediate input, firms determine the
expenditure on domestic and imported varieties using a CES function (the "Armington"
approach). The primary factor bundle and the intermediate goods bundles are then combined to
produce output using a CES function, for which parameters are chosen to allow very little
substitution, resulting in a combination that is close to fixed proportions.
3.2.2 Primary factors
Capital stocks evolve with a lagged adjustment process driven by dynamic investment
behavior. Firms that increase output in response to increased product demand also increase their
demand for capital. In the current period capital is in fixed supply, as investment augments the
capital stock with a lag of one period. In response to the increase in demand for capital, the
rental price of capital rises which, ceteris paribus, leads to an increase in the expected rate of
return on capital. Larger expected rates of return lead to an increase in investment as the firm
attempts to increase the rate of capital accumulation with the objective of reducing the scarcity of
capital in the subsequent period. Furthermore, investors' required rates of return are an
increasing function of capital growth, reflecting risk aversion by suppliers of investment funds.
29
The linear expenditure system allows consumers to change their relative preferences for goods and services at
different levels of income.
12
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