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State-level equity and the demise of the Agreement on Textiles and Clothing
North American Journal of Economics and Finance 17 (2006) 17–33
State-level equity and the demise of the Agreement on Textiles and Clothing Soamiely Andriamananjara a , Edward J. Balistreri b,∗ , Martin T. Ross c a
World Bank Institute, The World Bank, 1818 H Street, NW Washington, DC 20433, USA Division of Economics and Business, Colorado School of Mines, 1500 Illinois Street, Golden, CO 80401, USA c Research Triangle Institute (RTI) International, Research Triangle Park, 3040 Cornwallis Road, NC 27709-2194, USA
b
Received 11 March 2005; received in revised form 17 May 2005; accepted 16 June 2005 Available online 18 July 2005
Abstract As of January 1, 2005 all quantitative restrictions on textile and apparel commodities were removed in accordance with the Agreement on Textiles and Clothing (ATC). This paper examines the economic impacts of the liberalization at the U.S. state level. The methodology utilizes a regional model of the U.S. economy built up from individually consistent IMPLAN social accounts for each state. The model incorporates forward-looking dynamic responses and equilibrium unemployment. The results are useful in quantifying the geographic distribution of the benefits and costs of the ATC’s expiration on the U.S. economy. © 2005 Elsevier Inc. All rights reserved. JEL classification: F10; F13; C69 Keywords: ATC; MFA; Textiles; Clothing; Quotas; Simulations
∗
Corresponding author. Tel.: +1 303 384 2156; fax: +1 303 273 3416. E-mail address: [email protected] (E.J. Balistreri).
1062-9408/$ – see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.najef.2005.06.004
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1. Introduction As required by the Uruguay Round Agreement on Textiles and Clothing (ATC), the United States, along with the European Union and Canada, eliminated all remaining quantitative restraints on textile and clothing imports from WTO countries in 2005. Prior to the January 2005 deadline, numerous studies were produced regarding the likely economic effects of the quota liberalization on different developing country suppliers. Most simulation studies predicted that Asian suppliers are likely to experience the largest exports expansions, while Latin American and African countries are likely to see relatively small expansions (and possibly reductions) in exports.1 Andriamananjara, Dean, and Spinanger (2004) conduct an econometric analysis of the factors determining the market shares of different suppliers. They conclude that while the degree of restraint imposed by the quotas themselves is an important influence, other factors including production costs, tariffs, quality of infrastructure, and transport costs also play major roles. The USITC (2004a) also provides a detailed assessment of the competitiveness of different suppliers of textile and apparel products to the U.S. market. In contrast, relatively few papers have looked at the likely economic effects of lifting the Multifiber Arrangement (MFA) quotas on the United States.2 Even fewer have studied the distribution of those impacts across different U.S. regions. Using newly computed estimates of the restrictiveness of the quotas, we make an important contribution to that scarce literature by providing the first (to our knowledge) examination of the economic impacts of the 2005 liberalization at the U.S. state level.3 The methodology is relatively novel and utilizes a bottom-up regional model of the U.S. economy based on individually consistent IMPLAN social accounts for each state. The model incorporates forward-looking dynamic responses and equilibrium unemployment. The results provide a useful quantification of the geographic distribution of the potential benefits and costs of the ATC’s expiration on the U.S. economy. The framework introduced here could be of use for both policymakers and researchers in determining the implications of different trade policies. Indeed, knowing the regional impacts of a given policy change can provide extremely valuable political–economy insights by identifying the potential opponents and proponents of the change. The paper has the following structure. Section 2 briefly provides an overview of the Agreement on Textiles and Clothing as well as some measures of the degrees of restrictiveness of the quantitative restraints lifted in January 2005. Section 3 presents the analytical framework as well as the simulation design used to study the removal of the quotas. Section 4 presents the central simulated state-level impact of the ATC phase-out. Section 5 provides some diagnostics and sensitivity analysis around the central formulation. Section 6 concludes.
1
See for example, Avisse and Fouquin (2001) and Diao and Somwaru (2001). USITC (2004b) estimates that the elimination of the quotas would result in an overall U.S. welfare gain of between 0.10% ($7 billion) and 0.17% ($12 billion). 3 USITC (2004b) uses a “top-down” approach to study the state-level impact of removing all significant U.S. import restraints. However, the specific effects of the quota removal are only reported at the national level. 2
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2. The Agreement on Textiles and Clothing In 1995, the Agreement on Textiles and Clothing (ATC) called for the progressive elimination of the quantitative restraints established under the MFA, which had since 1974 governed international trade in textiles and apparel. The quotas were negotiated bilaterally and were administered in the form of “voluntary export restraints” (VERs), so that the product coverage and the degree of restrictiveness vary by supplier.4 In 2002, the United States had quotas on 45 countries, which accounted for about 78% of the total value of U.S. imports of textiles and apparel. The ATC requires the United States (along with other major importers like Canada and the European Union) to bring textile and apparel articles under the GATT discipline in four stages (1995, 1998, 2002, 2005) over a 10-year transition period ending on January 1, 2005: any quotas on such goods are to be eliminated and new quotas may not be established, except as provided under normal GATT rules. The specific schedule of integration varied greatly among the different importing economies. In the case of the United States, none of the articles integrated in the first stage was under quota, and most of the articles integrated in the second and third stages either were not under quota or had low quota usage. As a result, 89 and 47% of the U.S. apparel imports and textile imports, respectively, remained to be integrated on January 1st, 2005.5 This suggests that the 2005 removal of the remaining quotas might lead to major changes in the textile and apparel sector. In order to quantify the potential impact of lifting the remaining quotas, one has to determine the degree of restrictiveness of those quotas, which is not a trivial exercise. One common approach is to compute the export tax equivalents (ETEs), which measure the degree to which the quota increases the export price (i.e., the price before entry into the U.S. market).6 In fact, the quota can be viewed as an implicit tax on exports of textiles and apparel from restrained countries to the United States; in order to export, a firm in a quota-constrained country has to obtain or buy the right to use the quota (or an export license). Note that even in countries where quotas are distributed without charge, the system is still costly to exporters who must forgo the opportunity to sell the valuable quotas to other suppliers. A binding quota effectively limits the supply to the U.S. market of a given product, making its price higher in the United States than in the world market. Given the resulting wedge between the U.S. and world prices, the limited and scarce supply of quotas becomes valuable and benefits accrue to the exporting firms (or individuals). In other words, exporters who have licenses to export the products to the United States are able to capture economic rents by increasing the export prices of their products. In this paper, we use a set of newly estimated ETEs for the year 2002.7 These were computed and then averaged across all suppliers. They were also computed for all products 4 For detailed discussions of the numerous and complex economic implications of the discriminatory nature of VERs, see Krishna (1989) and Dean and Gangopadhyay (1991). 5 For a detailed discussion on this, see USITC (2004a). 6 See, for example, Francois and Spinanger (2002). 7 These estimates were produced by the staff of the USITC. They are based on actual license prices for Bangladesh, Cambodia, China, Hong Kong, India, Indonesia, Macau, Pakistan, and Taiwan. For detailed description of how they were calculated, see USITC (2004b).
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Table 1 Estimated ad valorem export tax equivalents and import-weighted average tariffs, for textiles and apparel by sectors, percent, 2002 Sector
Export tax equivalents (ETEs)
Average tariff rates
Broadwoven fabric mills and fabric finishing plants Narrow fabric mills Yarn mills and finishing of textiles, n.e.c. Thread mills Coated fabrics, not rubberized Cordage and twine Textile goods, n.e.c. Women’s hosiery, except socks Hosiery, n.e.c. Apparel made from purchased materials Curtains and draperies House furnishings, n.e.c. Fabricated textile products, n.e.c.
4.78 0.01 0.54 1.97 0.05 0.28 0.01 0.52 0.81 12.88 6.03 12.57 0.96
7.86 4.18 4.81 7.08 2.22 3.10 2.28 6.55 9.38 10.88 8.95 6.26 2.43
Source: USITC (2004b).
that could be subject to binding quotas. The ETEs used in this paper are reported in Table 1, along with the ad valorem average tariff rates. The estimated ETEs are relatively high for apparel and finished textile products (e.g., house furnishings and other processed textile articles), but are relatively low for textile mill articles, which consist mostly of intermediate inputs such as yarns and fabrics.
3. Model description and simulation design The model employed to simulate the impacts of eliminating the MFA restrictions represents a multi-region U.S. economy operating on a dynamic growth path. Agents are assumed to be forward-looking and invest to support a growing stock of capital. In a given simulation, five regional economies within the U.S. are assumed. These are defined by labor endowments (which grow in each time period), initial capital endowments, and regional consumption patterns and production technologies. The primary data for calibrating the consumption and production technologies are the 1998 IMPLAN state-level social accounts.8 These state-level accounts are aggregated to the desired product and regional level largely using tools developed by Rutherford (2004). Rutherford’s tools are modified to allow flexible-batch processing of the regional aggregation, such that a single state of focus can be designated and modeled relative to the other regions. Four primary regions are designated (Eastern, Great Lakes, Plains, and Western), and the fifth region is any desired state. For example, if South Carolina is the focus of analysis in a particular simulation, the regions are South Carolina, the Eastern region less 8
Data purchased from the Minnesota IMPLAN Group Inc., 1725 Tower Drive West, Suite 140, Stillwater, MN 55082 (http://www.implan.com).
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South Carolina, the Great Lakes region, the Plains region, and the Western region. This structure of automated regional aggregation yields a system that is dimensionally tractable yet flexible enough to generate consistent multi-regional results for any desired state of interest. The aggregated social accounts are assumed to represent a snapshot of the multi-region, multi-sector U.S. economy on an infinite horizon growth path. For simplicity, the results presented below assume steady-state growth in the benchmark equilibrium.9 We follow Lau, Pahlke, and Rutherford (2002) in approximating the infinite-horizon intertemporal equilibrium as a complementarity problem. The model is simulated for 1-year time intervals from 2005 to 2015. The terminal constraint on physical capital suggested by Lau et al. (2002) is employed to get an accurate approximation to the intertemporal problem, and infinitehorizon welfare in each region is calculated using the approximation suggested by Harrison and Rutherford (1999). In this case, the finite problem is a good approximation of the infinite horizon, because the relatively small macroeconomic impacts of the simulated removal of the ATC restrictions generate consumption growth paths very close to the new steady state even with our relatively short horizon. A representative household in each region is assumed to maximize intertemporal utility, subject to income derived from each year’s endowment of labor and the first period’s endowment of capital. Aggregate regional saving (equal to investment) is chosen such that capital stocks react optimally to the future set of equilibrium prices. The model is completely deterministic; all periods of the modeled horizon are solved simultaneously and no consideration is given to uncertainty. The household utility function in each region is assumed to exhibit constant relative risk aversion, with an intertemporal elasticity of substitution of 0.5 and a discount rate of 5%. In each time period, the household is assumed to get utility from a constant-elasticity-ofsubstitution (CES) composite of leisure and goods and services. From a static perspective, the labor–leisure calibration is chosen such that the uncompensated-labor-supply elasticity is 0.1 and the compensated-labor-supply elasticity is 0.35. This indicates an elasticity of substitution between leisure and the composite consumption of other goods and services close to unity and a labor-endowment to labor-supply ratio of about 1.4–1.5 (the exact values of these calibrated statistics depend on the particular state’s or region’s labor share of total income). See Ballard (1999) for a detailed analysis of the relationship between the calibration parameters and the compensated- and uncompensated-labor-supply elasticities. Utility over goods and services is assumed to be Cobb–Douglas, with the value shares determined by the social accounts. Thus, the Cobb–Douglas aggregate of goods and services is combined with leisure to generate total utility in a given time period. This series of instantaneous utility is then aggregated according to a CES of 0.5 and discounting at 5% per year.
9 The relative sizes of the textile and apparel sectors of the U.S. economy have fallen in recent years. The current results presented here do not consider this important structural trend. We intend to develop the model further to incorporate differential growth rates across sectors in the medium run. Because the textile and apparel sectors are growing relatively slowly, one might expect the quantitative impacts presented here to have an upward bias (i.e., we probably overstate the size of the impacts).
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Production technologies for the goods and services produced in each region are assumed to exhibit constant returns to scale and perfect competition. The 528 industries available in the IMPLAN data are aggregate to the following 17 focus industries: • • • • • • • • • • • • • • • • •
Broadwoven fabric mills and finishing Narrow fabric mills Yarn mills and finishing of textiles n.e.c. Thread mills Coated fabrics (not rubberized) Cordage and twine Other textile goods Women’s hosiery except socks Hosiery n.e.c. Apparel made from purchased material Curtains and draperies Housefurnishings n.e.c Other fabricated textile products Agriculture and food Manufacturing Services (private) Services (public)
The detailed textile and apparel industries were chosen to match the products with estimated MFA-related distortions. The other industries were aggregated (into four broad categories) to reduce the overall dimensions of the model. Following the Lau et al. (2002) formulation of partial putty–clay adjustment dynamics, production in each regional industry is assumed to be the sum of two distinct processes. In the first process, installed first-period sector-specific capital is combined in fixed proportions (Leontief) with other inputs to produce output. Over time, the quantity of extant capital diminishes due to geometric depreciation. Thus, output from the first process approaches zero as time approaches infinity. In contrast, the second process, which replaces the first over time, is characterized by a general CES production function in which new vintage capital is combined with other inputs to produce output. The CES technology is assumed to include a Cobb–Douglas value-added nest in which labor and capital substitute with each other, and subsequently the value-added composite is combined with intermediate commodity inputs in fixed proportions. The putty–clay production technology is an intuitively appealing formulation that slows adjustment dynamics by limiting short-run factor substitution possibilities. The other major feature of the model that incorporates short-run adjustment frictions is a formulation of aggregate equilibrium unemployment. Following Balistreri (2002), unemployment is incorporated through an aggregate labor market matching technology. Changes in the demand for labor and income changes, which affect labor supply, indicate changes in the matching opportunities of unemployed workers. Currently, the model only considers matching in each region’s market for homogeneous labor. An important extension of the model might examine the impact of the ATC on labor matching in multiple markets that delineate workers of different skills.
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The formulation of international and inter-regional trade within the model relies on products differentiated by geographic origin. From the perspective of each region, there are potentially three distinct sources (or sinks) for each commodity consumed (or produced). There is a domestic variety, a national (U.S. market) variety, and a foreign variety. For all products, the elasticity of substitution between the foreign and national variety is assumed to equal 8, and the elasticity of substitution between the domestic and foreign-national composite is assumed to equal 4. The elasticity of transformation between producing output for the different markets is assumed to equal 3. The foreign import-supply and export-demand elasticities are assumed to be infinite. That is, the U.S. is a price taker on world markets. This is probably a reasonable assumption for the relatively limited scope of the ATC experiment, but it is noted that terms-oftrade effects might mitigate some of the impacts shown. The overall trade equilibrium is closed by specifying no change in net capital flows for the modeled horizon. This allows regions to borrow or lend in international financial capital markets within the horizon, but any net accumulation of debt (or loans) must be paid off by the terminal period. In the sensitivity section of the paper, we relax this constraint and examine the case of perfect capital markets utilizing an approximation suggested by Lau et al. (2002). Timing the counter-factual shock within our central horizon of 2005–2015 is a bit problematic in terms of how we are dealing with expectations. In the end, we model the quota removal as an unanticipated shock that occurs in 2005. This is not entirely satisfying given the phase-out outlined in the ATC, but it greatly simplifies model calibration, eliminating the need to make assumptions about how policy anticipation might have altered agents’ behavior. We could have expanded the horizon to include the phase-out period; however, this opens a number of calibration issues that detract from our central demonstration of state-level equity impacts. The quota expansion schedules were known, but the actual and expected ETEs depend on demand shifts. It seems a difficult empirical task to delineate which of these demand shifts are induced by expectations about policy and which are induced by structural changes. We simply warn the reader that our steady-state benchmark approach skirts the difficulties associated with estimating the expectations that agents held at the point that we observe the data. Thus, we simulate of the removal of MFA restrictions by reducing the world price for textile and apparel products by the amount of the export-tax equivalent shown in Table 1 over the entire modeled horizon. This is consistent with the rents from the quantitative restrictions accruing to the exporting countries. It is assumed that the existing import tariffs on textile and apparel remain at their current levels.
4. Simulation results Our primary goal is to examine the simulated impacts of the removal of MFA quotas on different U.S. states. Fig. 1 presents these results. The costs of ATC liberalization are concentrated in a limited number of states. These include a number of southern states and New York. To understand this distribution it is useful to isolate income and demand influences. Table 2 presents the simulated welfare impacts and some data features that
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Table 2 State-level changes in welfare and unemployment rates, and selected benchmark data State (1)
Welfare change (%) (2)
Benchmark gross-product share (%) (3)
Benchmark foreign-import share (%) (4)
Unemployment rate change in 2005 (%) (5)
AL SC NC MS KY GA TN NY VA CA SD HI NJ NM PA TX AR MO LA MA DE ND VT ME OK CT MD WA RI FL NH NV UT CO ID KS IA IL AK NE OR WI MN WV MI OH AZ IN MT WY
−0.27 −0.20 −0.15 −0.06 −0.02 −0.01 −0.01 −0.01 0.01 0.02 0.05 0.05 0.05 0.06 0.07 0.08 0.09 0.09 0.10 0.10 0.12 0.12 0.13 0.13 0.13 0.13 0.14 0.14 0.14 0.14 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.16 0.16 0.16 0.17 0.17 0.17 0.18 0.18 0.18 0.18 0.19 0.19 0.20
1.90 4.00 3.24 1.16 1.01 2.20 0.99 0.59 0.69 0.49 0.20 0.17 0.43 0.13 0.56 0.27 0.59 0.33 0.24 0.49 0.42 0.07 0.35 0.79 0.22 0.24 0.17 0.17 1.19 0.20 0.75 0.06 0.19 0.10 0.05 0.15 0.19 0.15 0.02 0.11 0.16 0.25 0.16 0.19 0.32 0.21 0.11 0.20 0.10 0.05
2.35 4.61 0.38 11.18 11.51 9.32 8.98 21.61 22.49 26.06 56.52 55.44 41.45 63.48 27.40 42.46 32.57 41.00 50.68 46.94 50.98 73.10 46.61 44.24 46.99 59.37 64.15 60.20 42.20 59.15 50.40 72.65 60.63 66.25 73.78 60.91 56.98 64.41 79.92 66.51 61.98 57.64 65.15 63.17 59.58 62.00 70.13 61.48 73.91 75.63
0.14 0.35 0.19 0.04 0.00 0.07 0.01 0.02 0.05 0.01 −0.02 −0.04 0.01 −0.01 0.04 0.03 0.05 0.01 0.03 −0.02 −0.04 −0.03 0.00 0.00 0.04 −0.03 −0.01 −0.01 0.05 0.03 0.01 −0.03 −0.02 −0.02 0.00 0.00 0.02 −0.01 −0.06 0.00 0.00 −0.01 −0.02 0.05 −0.04 −0.02 0.00 −0.02 0.03 0.03
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Fig. 1. Distribution of state-level welfare changes from simulated ATC removal.
indicate the source of these impacts. Column (3) of Table 2 reports the share of total state value-added attributed to the textile and apparel industries. The correlation coefficient between columns (2) and (3) is −0.81. As one might expect, there is a strong negative relationship between the concentration of a state’s income in the import-competing industry and the welfare impact of liberalization.
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In addition to the degree of textile and apparel production concentration, there could be important demand idiosyncrasies that cause states to be affected differently. Column (4) of Table 2 reports the state’s foreign import share of textile and apparel products. The correlation between columns (2) and (4) is 0.87. The correlation between relatively high foreign demand and welfare changes is very strong. The data seem to be constructed in a way that ensures this effect, however, as the foreign import share is very small when textile and apparel production is intense. For example, the foreign import share is only 2.35% for Alabama when the simple average import share across all states is over 48%. Our prior is relatively less variation in the foreign import shares across states, but it is possible that import shares might vary widely for individual textile products used as intermediate inputs (as opposed to apparel products destined for final demand). To explore the sensitivity of our results to the import shares, we made a set of model runs in which we adjusted the social accounts under the ad-hoc assumption that all regions source a common share of textile and apparel imports from abroad. This adjustment has little effect on the overall welfare results. The change in welfare for Alabama drops by 2%, from an absolute loss of −0.270% to an absolute loss of −0.266%. The welfare impacts for Tennessee and New York switch from negative to positive; however, since their central results are very close to zero, the absolute changes are not very important. The overall correlation between the set of welfare impacts under common import shares and the set of central results is 0.98. This high level of correlation indicates that the demand idiosyncrasies are relatively unimportant in driving the distribution of impacts across states. The final column in Table 2 reports the simulated initial (2005) change in the unemployment rate in each of the states. As one might expect, there is a strong negative relationship between welfare changes and the change in the unemployment rate; the correlation between columns (2) and (5) is −0.71. Although increases in the unemployment rate are found in those states that show a negative impact from the policy, many states that gain are also observed to have increases in their unemployment rates. There are two driving forces that might increase the short-run unemployment rate. First, decreases in the demand for labor may be consistent with welfare increases in a particular state. Decreased demand for labor reduces matching efficiency, indicating increased unemployment. At the same time, however, the household’s real income is changing and this causes changes in labor supply decisions. Decreases in labor supply, all else equal, also reduce matching efficiency. Overall, the change in the short-run unemployment rate will be determined by the influences of both demand and supply shifts on the matching equilibrium. Over time the unemployment rate settles close to the benchmark rate, as there is no significant change in long-run labor payment shares (see Balistreri, 2002). The model reports many details on how a particular state economy reacts to the liberalization experiment. Fig. 2 shows the time paths for changes in consumption, investment, gross output, and labor earnings for South Carolina, one of the most heavily impacted states. Consumption is measured gross of leisure. Given the small nature of the shock on the overall state economy, the consumption path is stable over the horizon. Little intertemporal substitution is indicated in the consumption time series. Notice that the drop in gross output shown in Fig. 2 is slightly higher in the first year of the shock (relative to the new steady state). This reflects the adjustment costs associated with our formulation of equilibrium unemployment. Fig. 3 reports the changes in the unemployment rate for South Carolina.
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Fig. 2. Macroeconomic effects in South Carolina.
Initially, the unemployment rate in South Carolina increases by 0.35 percentage points (the benchmark is 5% unemployment; so simulated 2005 unemployment is 5.35% in South Carolina), then settles at a new equilibrium that is about 0.05 percentage points higher than in the benchmark. The model also indicates changes in the mix of industries in each state economy. Fig. 4 indicates the percent change in revenue for each of the textile and apparel sub-aggregates 5
Fig. 3. Changes in the South Carolina unemployment rate.
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Fig. 4. Textile and apparel revenue in South Carolina (2010).
years after the shock (2010). The most heavily impacted sectors are household furnishings n.e.c., broadwoven fabric mills, and yarn mills and finishing of textiles n.e.c. These sectors show a simulated drop in revenue of well over 10%. Unlike South Carolina, which has a relatively large share of its economy devoted to textiles, many states benefit from the ATC liberalization. Fig. 5 shows output revenues for all industries in Colorado, a state with little reliance on textile manufacturing. The 13 textile and apparel industries in the model are combined into a bar in the graph for simplicity. However, 90% of the decline in revenue shown in this bar is from a drop in apparel manufacturing
Fig. 5. Output revenue in Colorado (2010).
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Fig. 6. Macroeconomic effects in Colorado.
from purchased materials, rather than from textiles. All non-textile industries experience an increase in revenues, most notably manufacturing and private services. Although changes in these sectors are small in percentage terms, they more than offset the decrease in apparel manufacturing in absolute terms. Macroeconomic impacts on Colorado, shown in Fig. 6, indicate that households are investing more to take advantage of their improved economic condition, especially in the initial years when their capital stock is below new desired levels. Overall labor earnings
Fig. 7. Changes in the unemployment rate in Colorado.
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rise as production expands due to a slight decline in unemployment and an increase in wage rates. This translates into additional consumption by households that, along with minor changes in leisure time, causes the welfare increase of 0.15% shown in Table 2. Fig. 7 shows that the unemployment rate for Colorado falls slightly. Initially the unemployment rate drops by 0.016 percentage points, then settles at a new equilibrium that is about 0.004 percentage points lower than in the benchmark.
5. Model sensitivity The central results presented in the previous section adopt a particular set of structural and parametric assumptions. It is useful, and prudent, to examine alternative assumptions to give an indication of robustness. Generally, we find that the geographic distribution of welfare impacts is robust across a number of alternative structural formulations. Individual state-level impacts might vary, and the dynamic trajectories of detailed results will change, but the overall welfare analysis is largely consistent. We summarize some key structural sensitivities in this section, and offer the full set of sensitivity results upon request.10 The first area that we examine is our method of generating a complete set of state-level results, while maintaining a dimensionally tractable model. It is reasonable to question the validity of the flexible aggregation process that we propose, because each set of state results comes from essentially different assumptions concerning regional definitions. We measure the approximation error associated with our method by comparing summaries of the disaggregate results to a benchmark that maintains fixed regional definitions. For a benchmark, we run the model with the full aggregates of the four regions (Eastern, Great Lakes, Plains, and Western) and no focus state broken out.11 We then compare these results with the sum of their components from the state-level analysis. Because we are comparing across regions, we use a consistent summary measure of nominal income (gross product) measured in consistent units. When measured as the sum of the component-state results, nominal income under ATC removal is within 0.02% of regional nominal income in the case where no state was broken out (for any of the regions and in any of the simulated years). For the U.S. as a whole, the sum of the 50 states’ scenario incomes is 0.01% higher than the sum of the four regions’ scenario incomes. These relatively small absolute errors are, of course, compounded when we look at differences in incomes (relative to not liberalizing). This is especially true in the case of ATC removal, which has relatively minor aggregate impacts. The change in nominal income for the U.S., measured as the sum of the 50 states’ change in nominal incomes, is as much as 5% higher than the sum of the four regional changes in nominal incomes. So, although the absolute errors are compounded when we examine errors in the differences, none of the errors seem unreasonably large. Given these results, we 10 Although we feel that parametric sensitivity analysis is important, it is not central to our goals for this paper. Rather than add a substantial section on parametric sensitivity, we offer the reader the opportunity to contact us and request any of our specific sensitivity reports, or to generate new reports over specific parameters. 11 In fact, we exclude the District of Columbia, so that the benchmark regional analysis is consistent with the U.S. being composed of just the 50 states.
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feel that our decomposition method strikes a workable balance between tractability and accuracy. The second key sensitivity we examine concerns the dynamic formulation of the model. Generally, we find little impact on the qualitative welfare analysis and little impact on the distribution of welfare changes across states of alternative assumptions about the adjustment dynamics. Of course, there are quantitative differences and the dynamic trajectories of specific variables are directly affected by the dynamic formulation. We focused our sensitivity analysis of the dynamic formulation around the specific structural assumptions about external capital markets and the adjustment costs associated with putty–clay capital and unemployment. In our central set of assumptions, we restrict external capital markets by requiring no change in regional net indebtedness over the horizon. This is a natural formulation that allows external borrowing and lending of financial assets within the computed horizon. Alternatively, one might assume a more restrictive case by imposing period-by-period balance of payments, or a less restrictive case by assuming debt servicing over the infinite horizon (perfect capital markets). We approximate the perfect capital markets case following the terminal asset adjustment method proposed by Lau et al. (2002).12 The effect of this is a shift in the capital-flow trajectories, such that net accumulated debt is serviced but not retired. The additional flexibility offered by this relaxation increased the average quantitative welfare impact (equivalent variation or EV) across the states from 0.094 to 0.109%. In only one case, Tennessee, did the added flexibility have a qualitative impact, changing the welfare impact from −0.0124 to +0.0012%. The simple correlation between the central welfare impacts and the welfare impacts including the terminal asset adjustment is 0.96. There seems to be relatively little impact on the overall welfare analysis of different assumptions about capital flows. That said, the alternative assumptions do have important implications for detailed reports on the capital account and the trade equilibrium across the horizon. We also find that the qualitative and distributional welfare analysis is robust to our specific unemployment formulation. For example, turning off the unemployment module (fixing the matching rate at its benchmark level) results in a set of welfare impacts from ATC removal that are 99.8% correlated with the central results reported in Table 2. There is a slight decrease in the average welfare impact when unemployment is turned off. This is consistent with the general pattern that ATC removal reduces the unemployment rate for most states. Most states benefit when the unemployment rate is allowed to fall under our central assumptions. The relative insensitivity of the overall welfare analysis to the unemployment formulation masks many of the important dynamic responses. Welfare is impacted similarly either by increases in the wage rate, or by decreases in the unemployment rate. The unemployment formulation is important in that it distinguishes the price and quantity effects, essentially incorporating the stylized fact that wages are slow to adjust. As a method of communicating with policy makers, it is important to distinguish employment and unemployment effects relative to wage effects. So, although the welfare analysis is largely unaffected by our 12 We were unable to directly use the adjustment suggested by Lau et al. (2002), because their method is only applied in the case of putty–putty capital. Our method includes an additional approximation on the asset value of sector-specific, post-terminal “clay” capital.
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unemployment formulation, we feel that it is an important innovation that enhances the model’s credibility in the policy forum. Similar to unemployment impacts, the reported changes in returns to stranded sectorspecific capital offered by our model inform the sectoral distribution of policy, despite having relatively minor impacts on the aggregate welfare analysis. Our method of formulating putty–clay capital allows us to continuously vary the proportion of “clay” capital at the beginning of the horizon. With the proportion of beginning “clay” capital at zero, the model reverts to the standard putty–putty model. Under the putty–putty formulation, the welfare impacts of ATC removal are 99.9% correlated with the central results presented in Table 2. Although the average welfare impact only moves slightly, for states adversely impacted by the liberalization the putty–clay formulation substantially increases the cost of the policy. For those eight states, the average ratio of EV impact under putty–clay to the EV impact under putty–putty is 80%, indicating that the average welfare cost to those negatively impacted states is 20% higher when we consider extant sector-specific capital. Again, the intertemporal dynamic formulation is important for specific impacts, but the overall distribution and qualitative pattern of the welfare analysis is robust.
6. Conclusion This paper makes at least two important contributions. The first one is of a policyrelated nature. Using newly estimated measures of the restrictiveness of the MFA quotas, we examine the distribution of the economic impacts of removing the quotas across U.S. states. Overall, the simulated welfare impacts are modest with a simple average impact across states of a 0.1% increase (the median change is 0.13%). The key advantage that our modeling system offers is a decomposition of the modest aggregate results into heterogeneous state results. We find that the costs of liberalization (in terms of welfare) are concentrated in a few states that rely heavily on the domestic textile and apparel industry, while the benefits of liberalization are spread across many states. The second contribution is of a technical nature. In particular, we use a relatively novel state-level dynamic model of the U.S. economy built up from individually consistent social accounts for each state. The simulation model incorporates important dynamic adjustment features. It includes the standard perfect-foresight activities of new capital formation through investment. A partial-putty-clay formulation of production is also included, however, which offers an explicit tracking of captured extant capital. In addition, an equilibrium unemployment formulation is included to capture changes in labor market matching. There are many areas for future research that might utilize and improve the model presented. Data on interstate trade flows are limited, and the model would benefit from a more refined treatment of bilateral interstate trade. Currently we do not allow migration of households across regional boundaries in order to focus on policy implications for the welfare of existing households. Furthermore, our assumption of a single household and single labor type within each region masks important equity impacts. Clearly, some skill classes and some income groups are likely to be impacted in a way that is not reflected in the average. To generate more useful statements about the impacts of policy on equity across these additional dimensions, there is a great deal of model development warranted. For the
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current analysis, however, we take a small step toward examining equity across geography within the U.S. economy.
Acknowledgements The views expressed herein are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent; or of RTI. Although not directly involved in this project, Thomas F. Rutherford provided essential software, and methodological guidance, which form the foundation of our computational model. Helpful comments from an anonymous referee are gratefully acknowledged.
References Andriamananjara, S., Dean, J., & Spinanger, D. (2004). Trading textiles and apparel: Developing countries in 2005. Paper presented at the 7th Annual Conference on Global Economic Analysis, Washington DC. Avisse, R., & Fouquin, M. (2001, Feb.). Textiles and clothing: The end of discriminatory protection. La Lettre du CEPII, No. 198. Balistreri, E. J. (2002). Operationalizing equilibrium unemployment: A general equilibrium external economies approach. Journal of Economic Dynamics and Control, 26(3), 347–374. Ballard, C. L. (1999). How many hours are in a simulated day? The effects of time endowment on the results of tax-policy simulation models (Mimeo). Department of Economics, Michigan State University. Dean, J., & Gangopadhyay, S. (1991). Market equilibrium under the ‘threat’ of a VER. Journal of International Economics, 20, 137–152. Diao, X., & Somwaru, A. (2001, October). Impact of the MFA phase-out on the world economy: An intertemporal global general equilibrium analysis (TMD Discussion Paper No. 79). Trade and Macroeconomics Division, International Food Policy Research Institute. Francois, J., & Spinanger, D. (2002). ATC export-tax equivalents. In B. V. Dimaranan & R. A. McDougall (Eds.), Global trade, assistance, and production: The GTAP 5 data base, center for global trade analysis. Ashland, OH: Purdue University Press. Harrison, G. W., & Rutherford, T. F. (1999). Burden sharing, joint implementation, and carbon coalitions. In C. Carraro (Ed.), International environmental agreements on climate change (pp. 77–108). Amsterdam: Kluwer. Krishna, K. (1989). Trade restrictions as facilitating practices. Journal of International Economics, 26, 251–270. Lau, M. I., Pahlke, A., & Rutherford, T. F. (2002). Approximating infinite-horizon models in a complementarity format: A primer in dynamic general equilibrium analysis. Journal of Economic Dynamics and Control, 26(4), 517–706. Rutherford, T. F. (2004). Tools for building national economic models using state-level IMPLAN social accounts (Mimeo). University of Colorado, http://debreu.colorado.edu/implan98.htm. USITC. (2004a, January). Textiles and apparel: Assessment of the competitiveness of certain foreign suppliers to the U.S. market (inv. No. 332–448). USITC Publication 3671. USITC. (2004b, June).The economic effects of significant U.S. import restraints: Fourth update. USITC Publication 3701.
State-level equity and the demise of the Agreement on Textiles and Clothing Soamiely Andriamananjara a , Edward J. Balistreri b,∗ , Martin T. Ross c a
World Bank Institute, The World Bank, 1818 H Street, NW Washington, DC 20433, USA Division of Economics and Business, Colorado School of Mines, 1500 Illinois Street, Golden, CO 80401, USA c Research Triangle Institute (RTI) International, Research Triangle Park, 3040 Cornwallis Road, NC 27709-2194, USA
b
Received 11 March 2005; received in revised form 17 May 2005; accepted 16 June 2005 Available online 18 July 2005
Abstract As of January 1, 2005 all quantitative restrictions on textile and apparel commodities were removed in accordance with the Agreement on Textiles and Clothing (ATC). This paper examines the economic impacts of the liberalization at the U.S. state level. The methodology utilizes a regional model of the U.S. economy built up from individually consistent IMPLAN social accounts for each state. The model incorporates forward-looking dynamic responses and equilibrium unemployment. The results are useful in quantifying the geographic distribution of the benefits and costs of the ATC’s expiration on the U.S. economy. © 2005 Elsevier Inc. All rights reserved. JEL classification: F10; F13; C69 Keywords: ATC; MFA; Textiles; Clothing; Quotas; Simulations
∗
Corresponding author. Tel.: +1 303 384 2156; fax: +1 303 273 3416. E-mail address: [email protected] (E.J. Balistreri).
1062-9408/$ – see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.najef.2005.06.004
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1. Introduction As required by the Uruguay Round Agreement on Textiles and Clothing (ATC), the United States, along with the European Union and Canada, eliminated all remaining quantitative restraints on textile and clothing imports from WTO countries in 2005. Prior to the January 2005 deadline, numerous studies were produced regarding the likely economic effects of the quota liberalization on different developing country suppliers. Most simulation studies predicted that Asian suppliers are likely to experience the largest exports expansions, while Latin American and African countries are likely to see relatively small expansions (and possibly reductions) in exports.1 Andriamananjara, Dean, and Spinanger (2004) conduct an econometric analysis of the factors determining the market shares of different suppliers. They conclude that while the degree of restraint imposed by the quotas themselves is an important influence, other factors including production costs, tariffs, quality of infrastructure, and transport costs also play major roles. The USITC (2004a) also provides a detailed assessment of the competitiveness of different suppliers of textile and apparel products to the U.S. market. In contrast, relatively few papers have looked at the likely economic effects of lifting the Multifiber Arrangement (MFA) quotas on the United States.2 Even fewer have studied the distribution of those impacts across different U.S. regions. Using newly computed estimates of the restrictiveness of the quotas, we make an important contribution to that scarce literature by providing the first (to our knowledge) examination of the economic impacts of the 2005 liberalization at the U.S. state level.3 The methodology is relatively novel and utilizes a bottom-up regional model of the U.S. economy based on individually consistent IMPLAN social accounts for each state. The model incorporates forward-looking dynamic responses and equilibrium unemployment. The results provide a useful quantification of the geographic distribution of the potential benefits and costs of the ATC’s expiration on the U.S. economy. The framework introduced here could be of use for both policymakers and researchers in determining the implications of different trade policies. Indeed, knowing the regional impacts of a given policy change can provide extremely valuable political–economy insights by identifying the potential opponents and proponents of the change. The paper has the following structure. Section 2 briefly provides an overview of the Agreement on Textiles and Clothing as well as some measures of the degrees of restrictiveness of the quantitative restraints lifted in January 2005. Section 3 presents the analytical framework as well as the simulation design used to study the removal of the quotas. Section 4 presents the central simulated state-level impact of the ATC phase-out. Section 5 provides some diagnostics and sensitivity analysis around the central formulation. Section 6 concludes.
1
See for example, Avisse and Fouquin (2001) and Diao and Somwaru (2001). USITC (2004b) estimates that the elimination of the quotas would result in an overall U.S. welfare gain of between 0.10% ($7 billion) and 0.17% ($12 billion). 3 USITC (2004b) uses a “top-down” approach to study the state-level impact of removing all significant U.S. import restraints. However, the specific effects of the quota removal are only reported at the national level. 2
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2. The Agreement on Textiles and Clothing In 1995, the Agreement on Textiles and Clothing (ATC) called for the progressive elimination of the quantitative restraints established under the MFA, which had since 1974 governed international trade in textiles and apparel. The quotas were negotiated bilaterally and were administered in the form of “voluntary export restraints” (VERs), so that the product coverage and the degree of restrictiveness vary by supplier.4 In 2002, the United States had quotas on 45 countries, which accounted for about 78% of the total value of U.S. imports of textiles and apparel. The ATC requires the United States (along with other major importers like Canada and the European Union) to bring textile and apparel articles under the GATT discipline in four stages (1995, 1998, 2002, 2005) over a 10-year transition period ending on January 1, 2005: any quotas on such goods are to be eliminated and new quotas may not be established, except as provided under normal GATT rules. The specific schedule of integration varied greatly among the different importing economies. In the case of the United States, none of the articles integrated in the first stage was under quota, and most of the articles integrated in the second and third stages either were not under quota or had low quota usage. As a result, 89 and 47% of the U.S. apparel imports and textile imports, respectively, remained to be integrated on January 1st, 2005.5 This suggests that the 2005 removal of the remaining quotas might lead to major changes in the textile and apparel sector. In order to quantify the potential impact of lifting the remaining quotas, one has to determine the degree of restrictiveness of those quotas, which is not a trivial exercise. One common approach is to compute the export tax equivalents (ETEs), which measure the degree to which the quota increases the export price (i.e., the price before entry into the U.S. market).6 In fact, the quota can be viewed as an implicit tax on exports of textiles and apparel from restrained countries to the United States; in order to export, a firm in a quota-constrained country has to obtain or buy the right to use the quota (or an export license). Note that even in countries where quotas are distributed without charge, the system is still costly to exporters who must forgo the opportunity to sell the valuable quotas to other suppliers. A binding quota effectively limits the supply to the U.S. market of a given product, making its price higher in the United States than in the world market. Given the resulting wedge between the U.S. and world prices, the limited and scarce supply of quotas becomes valuable and benefits accrue to the exporting firms (or individuals). In other words, exporters who have licenses to export the products to the United States are able to capture economic rents by increasing the export prices of their products. In this paper, we use a set of newly estimated ETEs for the year 2002.7 These were computed and then averaged across all suppliers. They were also computed for all products 4 For detailed discussions of the numerous and complex economic implications of the discriminatory nature of VERs, see Krishna (1989) and Dean and Gangopadhyay (1991). 5 For a detailed discussion on this, see USITC (2004a). 6 See, for example, Francois and Spinanger (2002). 7 These estimates were produced by the staff of the USITC. They are based on actual license prices for Bangladesh, Cambodia, China, Hong Kong, India, Indonesia, Macau, Pakistan, and Taiwan. For detailed description of how they were calculated, see USITC (2004b).
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Table 1 Estimated ad valorem export tax equivalents and import-weighted average tariffs, for textiles and apparel by sectors, percent, 2002 Sector
Export tax equivalents (ETEs)
Average tariff rates
Broadwoven fabric mills and fabric finishing plants Narrow fabric mills Yarn mills and finishing of textiles, n.e.c. Thread mills Coated fabrics, not rubberized Cordage and twine Textile goods, n.e.c. Women’s hosiery, except socks Hosiery, n.e.c. Apparel made from purchased materials Curtains and draperies House furnishings, n.e.c. Fabricated textile products, n.e.c.
4.78 0.01 0.54 1.97 0.05 0.28 0.01 0.52 0.81 12.88 6.03 12.57 0.96
7.86 4.18 4.81 7.08 2.22 3.10 2.28 6.55 9.38 10.88 8.95 6.26 2.43
Source: USITC (2004b).
that could be subject to binding quotas. The ETEs used in this paper are reported in Table 1, along with the ad valorem average tariff rates. The estimated ETEs are relatively high for apparel and finished textile products (e.g., house furnishings and other processed textile articles), but are relatively low for textile mill articles, which consist mostly of intermediate inputs such as yarns and fabrics.
3. Model description and simulation design The model employed to simulate the impacts of eliminating the MFA restrictions represents a multi-region U.S. economy operating on a dynamic growth path. Agents are assumed to be forward-looking and invest to support a growing stock of capital. In a given simulation, five regional economies within the U.S. are assumed. These are defined by labor endowments (which grow in each time period), initial capital endowments, and regional consumption patterns and production technologies. The primary data for calibrating the consumption and production technologies are the 1998 IMPLAN state-level social accounts.8 These state-level accounts are aggregated to the desired product and regional level largely using tools developed by Rutherford (2004). Rutherford’s tools are modified to allow flexible-batch processing of the regional aggregation, such that a single state of focus can be designated and modeled relative to the other regions. Four primary regions are designated (Eastern, Great Lakes, Plains, and Western), and the fifth region is any desired state. For example, if South Carolina is the focus of analysis in a particular simulation, the regions are South Carolina, the Eastern region less 8
Data purchased from the Minnesota IMPLAN Group Inc., 1725 Tower Drive West, Suite 140, Stillwater, MN 55082 (http://www.implan.com).
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South Carolina, the Great Lakes region, the Plains region, and the Western region. This structure of automated regional aggregation yields a system that is dimensionally tractable yet flexible enough to generate consistent multi-regional results for any desired state of interest. The aggregated social accounts are assumed to represent a snapshot of the multi-region, multi-sector U.S. economy on an infinite horizon growth path. For simplicity, the results presented below assume steady-state growth in the benchmark equilibrium.9 We follow Lau, Pahlke, and Rutherford (2002) in approximating the infinite-horizon intertemporal equilibrium as a complementarity problem. The model is simulated for 1-year time intervals from 2005 to 2015. The terminal constraint on physical capital suggested by Lau et al. (2002) is employed to get an accurate approximation to the intertemporal problem, and infinitehorizon welfare in each region is calculated using the approximation suggested by Harrison and Rutherford (1999). In this case, the finite problem is a good approximation of the infinite horizon, because the relatively small macroeconomic impacts of the simulated removal of the ATC restrictions generate consumption growth paths very close to the new steady state even with our relatively short horizon. A representative household in each region is assumed to maximize intertemporal utility, subject to income derived from each year’s endowment of labor and the first period’s endowment of capital. Aggregate regional saving (equal to investment) is chosen such that capital stocks react optimally to the future set of equilibrium prices. The model is completely deterministic; all periods of the modeled horizon are solved simultaneously and no consideration is given to uncertainty. The household utility function in each region is assumed to exhibit constant relative risk aversion, with an intertemporal elasticity of substitution of 0.5 and a discount rate of 5%. In each time period, the household is assumed to get utility from a constant-elasticity-ofsubstitution (CES) composite of leisure and goods and services. From a static perspective, the labor–leisure calibration is chosen such that the uncompensated-labor-supply elasticity is 0.1 and the compensated-labor-supply elasticity is 0.35. This indicates an elasticity of substitution between leisure and the composite consumption of other goods and services close to unity and a labor-endowment to labor-supply ratio of about 1.4–1.5 (the exact values of these calibrated statistics depend on the particular state’s or region’s labor share of total income). See Ballard (1999) for a detailed analysis of the relationship between the calibration parameters and the compensated- and uncompensated-labor-supply elasticities. Utility over goods and services is assumed to be Cobb–Douglas, with the value shares determined by the social accounts. Thus, the Cobb–Douglas aggregate of goods and services is combined with leisure to generate total utility in a given time period. This series of instantaneous utility is then aggregated according to a CES of 0.5 and discounting at 5% per year.
9 The relative sizes of the textile and apparel sectors of the U.S. economy have fallen in recent years. The current results presented here do not consider this important structural trend. We intend to develop the model further to incorporate differential growth rates across sectors in the medium run. Because the textile and apparel sectors are growing relatively slowly, one might expect the quantitative impacts presented here to have an upward bias (i.e., we probably overstate the size of the impacts).
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Production technologies for the goods and services produced in each region are assumed to exhibit constant returns to scale and perfect competition. The 528 industries available in the IMPLAN data are aggregate to the following 17 focus industries: • • • • • • • • • • • • • • • • •
Broadwoven fabric mills and finishing Narrow fabric mills Yarn mills and finishing of textiles n.e.c. Thread mills Coated fabrics (not rubberized) Cordage and twine Other textile goods Women’s hosiery except socks Hosiery n.e.c. Apparel made from purchased material Curtains and draperies Housefurnishings n.e.c Other fabricated textile products Agriculture and food Manufacturing Services (private) Services (public)
The detailed textile and apparel industries were chosen to match the products with estimated MFA-related distortions. The other industries were aggregated (into four broad categories) to reduce the overall dimensions of the model. Following the Lau et al. (2002) formulation of partial putty–clay adjustment dynamics, production in each regional industry is assumed to be the sum of two distinct processes. In the first process, installed first-period sector-specific capital is combined in fixed proportions (Leontief) with other inputs to produce output. Over time, the quantity of extant capital diminishes due to geometric depreciation. Thus, output from the first process approaches zero as time approaches infinity. In contrast, the second process, which replaces the first over time, is characterized by a general CES production function in which new vintage capital is combined with other inputs to produce output. The CES technology is assumed to include a Cobb–Douglas value-added nest in which labor and capital substitute with each other, and subsequently the value-added composite is combined with intermediate commodity inputs in fixed proportions. The putty–clay production technology is an intuitively appealing formulation that slows adjustment dynamics by limiting short-run factor substitution possibilities. The other major feature of the model that incorporates short-run adjustment frictions is a formulation of aggregate equilibrium unemployment. Following Balistreri (2002), unemployment is incorporated through an aggregate labor market matching technology. Changes in the demand for labor and income changes, which affect labor supply, indicate changes in the matching opportunities of unemployed workers. Currently, the model only considers matching in each region’s market for homogeneous labor. An important extension of the model might examine the impact of the ATC on labor matching in multiple markets that delineate workers of different skills.
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The formulation of international and inter-regional trade within the model relies on products differentiated by geographic origin. From the perspective of each region, there are potentially three distinct sources (or sinks) for each commodity consumed (or produced). There is a domestic variety, a national (U.S. market) variety, and a foreign variety. For all products, the elasticity of substitution between the foreign and national variety is assumed to equal 8, and the elasticity of substitution between the domestic and foreign-national composite is assumed to equal 4. The elasticity of transformation between producing output for the different markets is assumed to equal 3. The foreign import-supply and export-demand elasticities are assumed to be infinite. That is, the U.S. is a price taker on world markets. This is probably a reasonable assumption for the relatively limited scope of the ATC experiment, but it is noted that terms-oftrade effects might mitigate some of the impacts shown. The overall trade equilibrium is closed by specifying no change in net capital flows for the modeled horizon. This allows regions to borrow or lend in international financial capital markets within the horizon, but any net accumulation of debt (or loans) must be paid off by the terminal period. In the sensitivity section of the paper, we relax this constraint and examine the case of perfect capital markets utilizing an approximation suggested by Lau et al. (2002). Timing the counter-factual shock within our central horizon of 2005–2015 is a bit problematic in terms of how we are dealing with expectations. In the end, we model the quota removal as an unanticipated shock that occurs in 2005. This is not entirely satisfying given the phase-out outlined in the ATC, but it greatly simplifies model calibration, eliminating the need to make assumptions about how policy anticipation might have altered agents’ behavior. We could have expanded the horizon to include the phase-out period; however, this opens a number of calibration issues that detract from our central demonstration of state-level equity impacts. The quota expansion schedules were known, but the actual and expected ETEs depend on demand shifts. It seems a difficult empirical task to delineate which of these demand shifts are induced by expectations about policy and which are induced by structural changes. We simply warn the reader that our steady-state benchmark approach skirts the difficulties associated with estimating the expectations that agents held at the point that we observe the data. Thus, we simulate of the removal of MFA restrictions by reducing the world price for textile and apparel products by the amount of the export-tax equivalent shown in Table 1 over the entire modeled horizon. This is consistent with the rents from the quantitative restrictions accruing to the exporting countries. It is assumed that the existing import tariffs on textile and apparel remain at their current levels.
4. Simulation results Our primary goal is to examine the simulated impacts of the removal of MFA quotas on different U.S. states. Fig. 1 presents these results. The costs of ATC liberalization are concentrated in a limited number of states. These include a number of southern states and New York. To understand this distribution it is useful to isolate income and demand influences. Table 2 presents the simulated welfare impacts and some data features that
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Table 2 State-level changes in welfare and unemployment rates, and selected benchmark data State (1)
Welfare change (%) (2)
Benchmark gross-product share (%) (3)
Benchmark foreign-import share (%) (4)
Unemployment rate change in 2005 (%) (5)
AL SC NC MS KY GA TN NY VA CA SD HI NJ NM PA TX AR MO LA MA DE ND VT ME OK CT MD WA RI FL NH NV UT CO ID KS IA IL AK NE OR WI MN WV MI OH AZ IN MT WY
−0.27 −0.20 −0.15 −0.06 −0.02 −0.01 −0.01 −0.01 0.01 0.02 0.05 0.05 0.05 0.06 0.07 0.08 0.09 0.09 0.10 0.10 0.12 0.12 0.13 0.13 0.13 0.13 0.14 0.14 0.14 0.14 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.16 0.16 0.16 0.17 0.17 0.17 0.18 0.18 0.18 0.18 0.19 0.19 0.20
1.90 4.00 3.24 1.16 1.01 2.20 0.99 0.59 0.69 0.49 0.20 0.17 0.43 0.13 0.56 0.27 0.59 0.33 0.24 0.49 0.42 0.07 0.35 0.79 0.22 0.24 0.17 0.17 1.19 0.20 0.75 0.06 0.19 0.10 0.05 0.15 0.19 0.15 0.02 0.11 0.16 0.25 0.16 0.19 0.32 0.21 0.11 0.20 0.10 0.05
2.35 4.61 0.38 11.18 11.51 9.32 8.98 21.61 22.49 26.06 56.52 55.44 41.45 63.48 27.40 42.46 32.57 41.00 50.68 46.94 50.98 73.10 46.61 44.24 46.99 59.37 64.15 60.20 42.20 59.15 50.40 72.65 60.63 66.25 73.78 60.91 56.98 64.41 79.92 66.51 61.98 57.64 65.15 63.17 59.58 62.00 70.13 61.48 73.91 75.63
0.14 0.35 0.19 0.04 0.00 0.07 0.01 0.02 0.05 0.01 −0.02 −0.04 0.01 −0.01 0.04 0.03 0.05 0.01 0.03 −0.02 −0.04 −0.03 0.00 0.00 0.04 −0.03 −0.01 −0.01 0.05 0.03 0.01 −0.03 −0.02 −0.02 0.00 0.00 0.02 −0.01 −0.06 0.00 0.00 −0.01 −0.02 0.05 −0.04 −0.02 0.00 −0.02 0.03 0.03
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Fig. 1. Distribution of state-level welfare changes from simulated ATC removal.
indicate the source of these impacts. Column (3) of Table 2 reports the share of total state value-added attributed to the textile and apparel industries. The correlation coefficient between columns (2) and (3) is −0.81. As one might expect, there is a strong negative relationship between the concentration of a state’s income in the import-competing industry and the welfare impact of liberalization.
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In addition to the degree of textile and apparel production concentration, there could be important demand idiosyncrasies that cause states to be affected differently. Column (4) of Table 2 reports the state’s foreign import share of textile and apparel products. The correlation between columns (2) and (4) is 0.87. The correlation between relatively high foreign demand and welfare changes is very strong. The data seem to be constructed in a way that ensures this effect, however, as the foreign import share is very small when textile and apparel production is intense. For example, the foreign import share is only 2.35% for Alabama when the simple average import share across all states is over 48%. Our prior is relatively less variation in the foreign import shares across states, but it is possible that import shares might vary widely for individual textile products used as intermediate inputs (as opposed to apparel products destined for final demand). To explore the sensitivity of our results to the import shares, we made a set of model runs in which we adjusted the social accounts under the ad-hoc assumption that all regions source a common share of textile and apparel imports from abroad. This adjustment has little effect on the overall welfare results. The change in welfare for Alabama drops by 2%, from an absolute loss of −0.270% to an absolute loss of −0.266%. The welfare impacts for Tennessee and New York switch from negative to positive; however, since their central results are very close to zero, the absolute changes are not very important. The overall correlation between the set of welfare impacts under common import shares and the set of central results is 0.98. This high level of correlation indicates that the demand idiosyncrasies are relatively unimportant in driving the distribution of impacts across states. The final column in Table 2 reports the simulated initial (2005) change in the unemployment rate in each of the states. As one might expect, there is a strong negative relationship between welfare changes and the change in the unemployment rate; the correlation between columns (2) and (5) is −0.71. Although increases in the unemployment rate are found in those states that show a negative impact from the policy, many states that gain are also observed to have increases in their unemployment rates. There are two driving forces that might increase the short-run unemployment rate. First, decreases in the demand for labor may be consistent with welfare increases in a particular state. Decreased demand for labor reduces matching efficiency, indicating increased unemployment. At the same time, however, the household’s real income is changing and this causes changes in labor supply decisions. Decreases in labor supply, all else equal, also reduce matching efficiency. Overall, the change in the short-run unemployment rate will be determined by the influences of both demand and supply shifts on the matching equilibrium. Over time the unemployment rate settles close to the benchmark rate, as there is no significant change in long-run labor payment shares (see Balistreri, 2002). The model reports many details on how a particular state economy reacts to the liberalization experiment. Fig. 2 shows the time paths for changes in consumption, investment, gross output, and labor earnings for South Carolina, one of the most heavily impacted states. Consumption is measured gross of leisure. Given the small nature of the shock on the overall state economy, the consumption path is stable over the horizon. Little intertemporal substitution is indicated in the consumption time series. Notice that the drop in gross output shown in Fig. 2 is slightly higher in the first year of the shock (relative to the new steady state). This reflects the adjustment costs associated with our formulation of equilibrium unemployment. Fig. 3 reports the changes in the unemployment rate for South Carolina.
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Fig. 2. Macroeconomic effects in South Carolina.
Initially, the unemployment rate in South Carolina increases by 0.35 percentage points (the benchmark is 5% unemployment; so simulated 2005 unemployment is 5.35% in South Carolina), then settles at a new equilibrium that is about 0.05 percentage points higher than in the benchmark. The model also indicates changes in the mix of industries in each state economy. Fig. 4 indicates the percent change in revenue for each of the textile and apparel sub-aggregates 5
Fig. 3. Changes in the South Carolina unemployment rate.
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Fig. 4. Textile and apparel revenue in South Carolina (2010).
years after the shock (2010). The most heavily impacted sectors are household furnishings n.e.c., broadwoven fabric mills, and yarn mills and finishing of textiles n.e.c. These sectors show a simulated drop in revenue of well over 10%. Unlike South Carolina, which has a relatively large share of its economy devoted to textiles, many states benefit from the ATC liberalization. Fig. 5 shows output revenues for all industries in Colorado, a state with little reliance on textile manufacturing. The 13 textile and apparel industries in the model are combined into a bar in the graph for simplicity. However, 90% of the decline in revenue shown in this bar is from a drop in apparel manufacturing
Fig. 5. Output revenue in Colorado (2010).
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Fig. 6. Macroeconomic effects in Colorado.
from purchased materials, rather than from textiles. All non-textile industries experience an increase in revenues, most notably manufacturing and private services. Although changes in these sectors are small in percentage terms, they more than offset the decrease in apparel manufacturing in absolute terms. Macroeconomic impacts on Colorado, shown in Fig. 6, indicate that households are investing more to take advantage of their improved economic condition, especially in the initial years when their capital stock is below new desired levels. Overall labor earnings
Fig. 7. Changes in the unemployment rate in Colorado.
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rise as production expands due to a slight decline in unemployment and an increase in wage rates. This translates into additional consumption by households that, along with minor changes in leisure time, causes the welfare increase of 0.15% shown in Table 2. Fig. 7 shows that the unemployment rate for Colorado falls slightly. Initially the unemployment rate drops by 0.016 percentage points, then settles at a new equilibrium that is about 0.004 percentage points lower than in the benchmark.
5. Model sensitivity The central results presented in the previous section adopt a particular set of structural and parametric assumptions. It is useful, and prudent, to examine alternative assumptions to give an indication of robustness. Generally, we find that the geographic distribution of welfare impacts is robust across a number of alternative structural formulations. Individual state-level impacts might vary, and the dynamic trajectories of detailed results will change, but the overall welfare analysis is largely consistent. We summarize some key structural sensitivities in this section, and offer the full set of sensitivity results upon request.10 The first area that we examine is our method of generating a complete set of state-level results, while maintaining a dimensionally tractable model. It is reasonable to question the validity of the flexible aggregation process that we propose, because each set of state results comes from essentially different assumptions concerning regional definitions. We measure the approximation error associated with our method by comparing summaries of the disaggregate results to a benchmark that maintains fixed regional definitions. For a benchmark, we run the model with the full aggregates of the four regions (Eastern, Great Lakes, Plains, and Western) and no focus state broken out.11 We then compare these results with the sum of their components from the state-level analysis. Because we are comparing across regions, we use a consistent summary measure of nominal income (gross product) measured in consistent units. When measured as the sum of the component-state results, nominal income under ATC removal is within 0.02% of regional nominal income in the case where no state was broken out (for any of the regions and in any of the simulated years). For the U.S. as a whole, the sum of the 50 states’ scenario incomes is 0.01% higher than the sum of the four regions’ scenario incomes. These relatively small absolute errors are, of course, compounded when we look at differences in incomes (relative to not liberalizing). This is especially true in the case of ATC removal, which has relatively minor aggregate impacts. The change in nominal income for the U.S., measured as the sum of the 50 states’ change in nominal incomes, is as much as 5% higher than the sum of the four regional changes in nominal incomes. So, although the absolute errors are compounded when we examine errors in the differences, none of the errors seem unreasonably large. Given these results, we 10 Although we feel that parametric sensitivity analysis is important, it is not central to our goals for this paper. Rather than add a substantial section on parametric sensitivity, we offer the reader the opportunity to contact us and request any of our specific sensitivity reports, or to generate new reports over specific parameters. 11 In fact, we exclude the District of Columbia, so that the benchmark regional analysis is consistent with the U.S. being composed of just the 50 states.
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feel that our decomposition method strikes a workable balance between tractability and accuracy. The second key sensitivity we examine concerns the dynamic formulation of the model. Generally, we find little impact on the qualitative welfare analysis and little impact on the distribution of welfare changes across states of alternative assumptions about the adjustment dynamics. Of course, there are quantitative differences and the dynamic trajectories of specific variables are directly affected by the dynamic formulation. We focused our sensitivity analysis of the dynamic formulation around the specific structural assumptions about external capital markets and the adjustment costs associated with putty–clay capital and unemployment. In our central set of assumptions, we restrict external capital markets by requiring no change in regional net indebtedness over the horizon. This is a natural formulation that allows external borrowing and lending of financial assets within the computed horizon. Alternatively, one might assume a more restrictive case by imposing period-by-period balance of payments, or a less restrictive case by assuming debt servicing over the infinite horizon (perfect capital markets). We approximate the perfect capital markets case following the terminal asset adjustment method proposed by Lau et al. (2002).12 The effect of this is a shift in the capital-flow trajectories, such that net accumulated debt is serviced but not retired. The additional flexibility offered by this relaxation increased the average quantitative welfare impact (equivalent variation or EV) across the states from 0.094 to 0.109%. In only one case, Tennessee, did the added flexibility have a qualitative impact, changing the welfare impact from −0.0124 to +0.0012%. The simple correlation between the central welfare impacts and the welfare impacts including the terminal asset adjustment is 0.96. There seems to be relatively little impact on the overall welfare analysis of different assumptions about capital flows. That said, the alternative assumptions do have important implications for detailed reports on the capital account and the trade equilibrium across the horizon. We also find that the qualitative and distributional welfare analysis is robust to our specific unemployment formulation. For example, turning off the unemployment module (fixing the matching rate at its benchmark level) results in a set of welfare impacts from ATC removal that are 99.8% correlated with the central results reported in Table 2. There is a slight decrease in the average welfare impact when unemployment is turned off. This is consistent with the general pattern that ATC removal reduces the unemployment rate for most states. Most states benefit when the unemployment rate is allowed to fall under our central assumptions. The relative insensitivity of the overall welfare analysis to the unemployment formulation masks many of the important dynamic responses. Welfare is impacted similarly either by increases in the wage rate, or by decreases in the unemployment rate. The unemployment formulation is important in that it distinguishes the price and quantity effects, essentially incorporating the stylized fact that wages are slow to adjust. As a method of communicating with policy makers, it is important to distinguish employment and unemployment effects relative to wage effects. So, although the welfare analysis is largely unaffected by our 12 We were unable to directly use the adjustment suggested by Lau et al. (2002), because their method is only applied in the case of putty–putty capital. Our method includes an additional approximation on the asset value of sector-specific, post-terminal “clay” capital.
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unemployment formulation, we feel that it is an important innovation that enhances the model’s credibility in the policy forum. Similar to unemployment impacts, the reported changes in returns to stranded sectorspecific capital offered by our model inform the sectoral distribution of policy, despite having relatively minor impacts on the aggregate welfare analysis. Our method of formulating putty–clay capital allows us to continuously vary the proportion of “clay” capital at the beginning of the horizon. With the proportion of beginning “clay” capital at zero, the model reverts to the standard putty–putty model. Under the putty–putty formulation, the welfare impacts of ATC removal are 99.9% correlated with the central results presented in Table 2. Although the average welfare impact only moves slightly, for states adversely impacted by the liberalization the putty–clay formulation substantially increases the cost of the policy. For those eight states, the average ratio of EV impact under putty–clay to the EV impact under putty–putty is 80%, indicating that the average welfare cost to those negatively impacted states is 20% higher when we consider extant sector-specific capital. Again, the intertemporal dynamic formulation is important for specific impacts, but the overall distribution and qualitative pattern of the welfare analysis is robust.
6. Conclusion This paper makes at least two important contributions. The first one is of a policyrelated nature. Using newly estimated measures of the restrictiveness of the MFA quotas, we examine the distribution of the economic impacts of removing the quotas across U.S. states. Overall, the simulated welfare impacts are modest with a simple average impact across states of a 0.1% increase (the median change is 0.13%). The key advantage that our modeling system offers is a decomposition of the modest aggregate results into heterogeneous state results. We find that the costs of liberalization (in terms of welfare) are concentrated in a few states that rely heavily on the domestic textile and apparel industry, while the benefits of liberalization are spread across many states. The second contribution is of a technical nature. In particular, we use a relatively novel state-level dynamic model of the U.S. economy built up from individually consistent social accounts for each state. The simulation model incorporates important dynamic adjustment features. It includes the standard perfect-foresight activities of new capital formation through investment. A partial-putty-clay formulation of production is also included, however, which offers an explicit tracking of captured extant capital. In addition, an equilibrium unemployment formulation is included to capture changes in labor market matching. There are many areas for future research that might utilize and improve the model presented. Data on interstate trade flows are limited, and the model would benefit from a more refined treatment of bilateral interstate trade. Currently we do not allow migration of households across regional boundaries in order to focus on policy implications for the welfare of existing households. Furthermore, our assumption of a single household and single labor type within each region masks important equity impacts. Clearly, some skill classes and some income groups are likely to be impacted in a way that is not reflected in the average. To generate more useful statements about the impacts of policy on equity across these additional dimensions, there is a great deal of model development warranted. For the
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current analysis, however, we take a small step toward examining equity across geography within the U.S. economy.
Acknowledgements The views expressed herein are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent; or of RTI. Although not directly involved in this project, Thomas F. Rutherford provided essential software, and methodological guidance, which form the foundation of our computational model. Helpful comments from an anonymous referee are gratefully acknowledged.
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