South Asia’s Potential Share of China’s Apparel Trade

Developing countries seeking to gain a foothold in the apparel industry, seen as one of the most internationally mobile industries, often lament the intense competitive pressure between countries.The competition is driven by the constant threat of shifting production across countries. In the early 2000s, many developing countries feared that the end of the Multifibre Arrangement (MFA)—a trade pact that restricted their textile and apparel exports to developed countries—would allow China to capture a significant share of global apparel production. Their fears were not unwarranted: China’s share of U.S. apparel imports increased dramatically from about 13 percent in 2000 to 38 percent in 2013. Since then, however

South Asia’s Potential Share of

China’s Apparel Trade

(MFA)—a trade pact that restricted their textile and apparel exports to devel-oped countries—would allow China to capture a significant share of global

apparel production Their fears were not unwarranted: China’s share of U.S

apparel imports increased dramatically from about 13 percent in 2000 to

How much of the global apparel production can they hope to capture? That is the question that this chapter tries to answer—specifically how much South Asian apparel exports would increase for a given increase in Chinese apparel prices and how this sum would compare to the estimated sum for South Asia’s most likely competitors Although estimates of how much pro-duction might shift in response to rising Chinese prices are important for policy makers, few accurate estimates of the magnitude of production shifts across countries are available because the paradigm of production shifting in value chains is relatively new

Our approach is based on a model in which the developed countries are characterized as “buyers” who can choose how much to source from each developing country It is grounded in the foundations of a traditional gravity model (examines trade volumes), directly calculating elasticities (measures how much import and export quantities will change if relative prices change), and Feenstra (1994) (measures gains from trade in differentiated products) In our model, buyers use several criteria to make their decisions, such as logistics, quality, and prices And, because prices are not the only

variable that buyers care about, the countries are imperfect substitutes—

which in economics jargon means that buyers do not completely shift their orders (and therefore, in effect, production) between countries when the prices in one country change

The degree to which buyers shift their orders in response to price changes

(holding all other variables constant) is called the elasticity of substitution

It is this figure that we focus on for four South Asian countries: Bangladesh, India, Pakistan, and Sri Lanka We then compare it with the elasticity of substitution for three potential competitors: Vietnam and Cambodia in Southeast Asia, and Mexico in Latin America The target markets are the two largest apparel buyers: the United States and the European Union (EU) Vietnam and Cambodia have become increasingly important in the global apparel market because Chinese investors have been attracted by lower wages and the proximity to China Since 2000, Mexico’s share of the U.S apparel market has fallen as China’s has risen, although some recent anec-dotal evidence suggests that some production may be returning to Mexico (Agren 2013)

Our results suggest that a 10 percent increase in Chinese apparel prices will result in a 13–25 percent (depending on country) rise in South Asian countries’ apparel exports to the United States, and a 37–51 percent increase in Southeast Asian countries Thus, unless South Asia successfully identifies and removes bar-riers to apparel exports—such as barriers to importing manmade fibers (MMF) and poor exporting logistics—other countries, such as Cambodia and Vietnam, stand to gain even more

a Snapshot of U.S and eU Imports

Office of Textiles and Apparel (OTEXA)—which posts monthly U.S import

values and the volume of apparel products dating back to 1989—shows that

China’s share has increased dramatically over time (figure 3.1) In addition,

Source: U.S Department of Commerce’s Office of Textiles and Apparel (OTEXA)

Note: We define apparel as HS 61 and 62 HS stands for Harmonized System Code 61 includes articles of apparel and clothing

accessories that are knitted or crocheted, and Code 62 includes articles of apparel and clothing accessories that are not

knitted or crocheted

tries between 1990 and 2014 (table 3.1) The first period (1990–94) was domi-nated by China, India, and Mexico, which all exported in excess of $800 million

That said, the U.S import apparel story has varied greatly for our focus coun-to the United States per year Of our competitor countries, Cambodia and Vietnam exported the least, reflecting the fact that their apparel industries were not yet export oriented as they transitioned away from communist regimes China was the top exporter with an average value of $4.30 billion per year, offered the greatest variety with 1,397 different apparel products, and posted the highest mean price per square meter of apparel at $3.93 At the other extreme, Vietnam exported only 34 products at a mean-weighted price of $1.24 per square meter

In the second period (1995–99), Cambodia and Vietnam markedly increased not only the value of their apparel exports and product variety but also their prices China also saw a large increase in value but a drop in product variety and

a rise in price It is worth noting that India and Mexico made large gains as well, with Mexico seeing the largest value increase—putting it on par with China, a phenomenon that would persist until the mid-2000s The cheapest apparel in this period came from Vietnam, and the most expensive came from China.Period three (2000–04) saw Vietnam top the $1 billion mark—a dramatic increase from $0.5 billion in the first period—coupled with a price per square

Figure 3.2 U.S and Chinese apparel prices Move together

100 120 140 160 180 200

Source: U.S Department of Commerce’s Office of Textiles and Apparel (OTEXA) and the U.S Bureau of Labor

Statistics

Note: Data shown are the seven-month rolling average of monthly price indexes (for the U.S apparel CPI)

and import unit values CPI = consumer price index; m1 = January (the first month of the monthly data)

meter just above $3.00 China continued its steady growth and maintained a

sharply China was also producing 1,680 different products by the 2000s,

table 3.1 Value of U.S apparel Imports Shot Up as prices Fell

(Summary Statistics of U.S Imports from Specified Countries)

Source: World Bank calculations based on data from U.S Department of Commerce’s Office of Textiles and Apparel (OTEXA)

Note: This analysis uses OTEXA data (rather than COMTRADE), which provide detailed information on unit quantities and prices; certain categories

with missing quantities were dropped Value/year is given in millions of dollars per year, and price data are in 1990 dollars The mean weighted price is weighted by exported product shares per period Products are identified by a 10-digit Harmonized Tariff System (HTS) code, and the quantities are measured in different units (such as pounds, dozens, or pieces) To harmonize the quantity measurements, we apply OTEXA- provided conversion factors to convert the various units into square meter equivalents

just 110 products short of what the United States imported in period four Pakistani prices fell in period four, making it the cheapest source of apparel The fact that buyers care about issues besides price is cast into sharp relief in the Pakistani case because, although Pakistan’s prices were the lowest, it did not capture the majority of apparel production.

Period five (2010–14) saw a continuation of robust Chinese export growth with value reaching $25.45 billion per year—and nearly every apparel product imported by the United States produced by China (1,568 of 1,619 different products) India, Mexico, Pakistan, and Sri Lanka all saw a reduction in apparel exports Pakistan was the cheapest source of apparel whereas Mexico and Sri Lanka were the two most expensive exporters

Over these five periods, the key driving variable was the change in average apparel prices, which could reflect two different types of forces at work One

type of change is referred to as between products It occurs when countries change

price products); even if the prices of those products remain constant, the average

the mix of products they export (for example, moving from low-price to high-prices would appear to rise The other type of change is referred to as within

products It occurs when countries produce the same product but experience a change in the price of those products

So which type of price change dominated? To answer this question, we broke

down the price changes into changes within products and between products for

two periods: 2000–04 and 2010–14 The main message of table 3.2 is that the price of apparel generally rose, and there was an overall shift into lower-priced products The net result was a drop in overall apparel prices Comparing the last two columns supports this finding in that the average individual prices of new products are generally lower than the overall average prices Starting with price drops, China and India were the only two countries where this occurred (table 3.2) China’s price drop of $1.22 between the two periods can be

table 3.2 a tendency for higher apparel prices and Lower-priced products

(Decomposition of Price Changes between 2000–04 and 2010–14)

Country Within Between Total New price Overall price

by the mean share, by product Between is equal to the change in the share from the two periods analyzed multiplied by the

mean price, by product The new price is the weighted price of the products that were exported only in period 2 The overall price is the weighted price of all products in period 2 Period 1 represents 2000–04, and period 2 represents 2010–14

by $0.66 between products The largest decline within products was from

Cambodia ($0.12) Of the countries that saw price growth, Vietnam had the

largest decline in price growth between products while Pakistan had the largest

increase, with an increase in price of $0.30

European Union Apparel Imports

In Europe’s apparel market, we can see several trends that vary from what

Model and estimation approach

Now that we have detailed data on U.S and EU apparel imports over time, we can estimate the relationship between Chinese prices and U.S and EU apparel imports from countries other than China Our approach and model are described fully in Annex 3A Candidate estimation approaches include a standard gravity model, direct estimation of elasticities, and Feenstra’s model

table 3.3 Values of eU apparel Imports Grow even as prices rise

(Summary Statistics of European Imports from Specified Countries)

Source: World Bank elaboration based on data collected and provided by EUROSTAT

Note: This analysis uses EUROSTAT data (rather than COMTRADE), which provide detailed information on unit quantities and

prices; certain categories with missing quantities were dropped Value/year is given in millions of dollars per year The mean weighted price is weighted by quantities by period The mean weighted price is 1990 real price per kg The values in the table are given in real 1990 dollars The total product measure is given by a six-digit Harmonized System (HS) code, a slightly broader version than the 10-digit Harmonized Tariff System (HTS) code given in the U.S import summary statistics European import data range from 2000 to 2014 (U.S data begin in 1990)

It assumes that trade volumes can be modeled as a function of the size of

the trading economies (often measured as gross domestic product [GDP]

per capita), the distance between the two countries, and a varied list of other

factors that might affect trade (such as sharing a common border and a

common language, resource differences, and trade agreements) However,

because of several shortcomings, we cannot apply the gravity model directly

(see annex 3B)

In our robustness section, we compare our elasticity estimates with those

produced using the gravity approach as well as estimates produced following

Feenstra’s (1994) method The main difference between our results and

A China,t = g0 + d1P it + b2P China,t + d3P LatinAmerica,t + g1Y t + e it

A LatinAmerica,t = l0 + d2P it + d3P China,t + b3P LatinAmerica,t + l1Y t + e it (3.1)

d 1 captures the change in imports from India if Chinese prices increase The d

terms from these equations are used to calculate the elasticity of substitution for each country, which are the main estimates we are interested in for this chapter The dependent variable is the share of imports in each 10-digit Harmonized Tariff System (HTS) good from the country specified in each of the three equa-tions in each system

China is the most vulnerable in terms of quantity drops if its prices rise.

When comparing China’s own-price elasticities to those of other countries, it

is clear that a rise in Chinese prices would result in a larger fall in Chinese production than a rise in prices in other countries This result is important because it suggests that rising Chinese prices will result in production leaving China in relatively large amounts, although it is not clear where the production would go

Currently, an increase in global apparel demand favors China This can be

seen in the rows marked “Q World,” which show how the United States would respond toward each country given a general increase in apparel demand The coefficients for the South Asian and Southeast Asian countries (shown in the row marked “1: Q World”) are negative (except Vietnam) whereas the coefficients for China (shown in the row marked “2: Q World”) are all positive This reveals a preference for China while prices are held constant—that is, unless they rise

Rising world prices could shift demand to our focus countries The uniformly

positive and relative large values for the “Rest of World P” suggest that rising prices in the rest of the world will cause the United States to import more from our focus countries, including China and Latin America These values are small-est (in absolute value) for Vietnam, whose apparel production increased later than that in the other countries

table 3.4 higher Chinese prices Will Benefit China’s Competitors

(SUR Weighted Fixed Effects Using Shares)

Note: Standard errors in parentheses Table 3.4 shows the regression results Each column represents the results from a

three-equation system with homogeneity and symmetry constraints imposed The first equation (with the “1” prefix) is for the

country “X” listed at the top of each column The second equation (with the “2” prefix) represents China The third equation

(with the “3” prefix) represents Latin America P represents prices Q represents quantities LAM represents Latin America

The dependent variable is the share of imports in each 10-digit HTS (Harmonized Tariff System) good from the country

specified in each of the three equations in each system The “A” prefix represents variables that appear in, and are constrained

across, equations 1 and 2 The “B” prefix represents variables that appear in, and are constrained across, equations 1 and 3

The “C” prefix represents variables that appear in, and are constrained across, equations 2 and 3 (China and Latin America)

The “Rest of World P” variable appears in all three equations and is constrained to have the same coefficient in all three

equations This variable is a proxy for all other possible input factors available to the buyers when making purchasing

decisions SUR = seemingly unrelated regression

***p<.01, **p<.05, *p<.1