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China's Foreign Trade Behavior in the 1980's

Author(s):
Adi Brender
Published Date:
January 1992
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I. Introduction 1/

Since the adoption of outward-oriented reform policies, beginning in 1978, China’s foreign trade has expanded at a dramatic rate. Between 1980 and 1990, the annual value of trade tripled (from $39 billion to $115 billion), and China’s share of world exports nearly doubled (from 1.0 percent to 1.9 percent) (Tables 1 and 2).

Table 1:China: Trade Growth, 1978-90
1978198119821984198519861987198819891990
Total exports (Y bn.)1737415881109147177195297
Total imports (Y bn.)19373661126150162206220255
National income (Y bn.)3053944265257037899321,1771,3231,430
Percent of national income
Exports5.59.39.710.311.513.815.815.014.820.8
Imports6.29.38.410.917.919.017.317.516.617.8
Sources: IMF, International Financial Statistics, June 1990; and IMF, International Financial Statistics Yearbook, 1989.
Sources: IMF, International Financial Statistics, June 1990; and IMF, International Financial Statistics Yearbook, 1989.
Table 2:Growth and Share in World Trade, 1980-90(In billions of U.S. dollars)
1980198119821983198419851986198819891990
China:
Total exports18.322.022.226.127.430.939.447.552.562.0
Percent change--20.5-0.417.64.613.127.520.510.618.1
Exports excluding petroleum--17.317.920.720.627.635.444.048.957.6
Percent change----2.01524-0.534.228.424.111.117.8
Total imports19.622.021.427.442.342.943.255.359.153.3
Percent change--12.610.928.154.11.50.727.97.0-9.8
World:
Total exports, f.o.b.1,8981,8651,6821,7841,8081,9912,3422,6832,8943,303
Total imports, c.i.f.1,9451,9301,7481,8631,8782,0562,4082,7742,9963,413
China’s share of world
trade (percent):
Exports1.01.21.31.51.51.61.71.81.81.9
Imports1.01.11.21.52.22.11.82.02.01.6
Sources: For China: General Administration of Customs, various issues; and for World: IMF, International Financial Statistics Yearbook, 1989 and IMF, International Financial Statistics, May 1991.
Sources: For China: General Administration of Customs, various issues; and for World: IMF, International Financial Statistics Yearbook, 1989 and IMF, International Financial Statistics, May 1991.

This paper uses Chinese customs data to analyze the behavior of China’s trade during the 1980s. The disaggregated data are used to construct, for the first time, quarterly unit value and volume series that are then used to estimate foreign trade price and income elasticities. Close relations are found between the sequence of economic reforms and trade developments. Administrative controls and changes in the management system of Chinese enterprises and foreign trade corporations (FTCs) are also shown to have an important effect on the behavior of the Chinese entities involved in foreign trade. The results suggest that the decentralization of decision making in the absence of strong links between managerial performance and rewards may lead managers to reduce the volume of exports as prices increase. The results also suggest that, despite the far-reaching reforms, China in 1990 was still far from being a free market economy. One of the reasons for this is that much of the delegation of decision making was not to enterprises but to local governments.

The high savings rate in China 2/ allowed for large investments without a large current account deficit. Still, the need for imported technology required the development of exportable products to finance technology imports. Primary products led export growth early in the decade (in volume terms) while industrial products exports grew faster since 1985. Some diversification of industrial exports towards home electronics and other mechanical and electronic products began to take place in the late 1980s after a stage of import substitution earlier in the decade. Direct foreign investment was another important source of foreign exchange earnings, while the deteriorating terms of trade, and especially the decline in oil prices, levied a heavy burden on the development process.

Chapter II of this paper lays the background for the trade analysis with an overview of the reforms, particularly in the external sector. Chapter III presents an analysis of unit values and volume developments and the relations between domestic production and trade. Chapter IV derives and analyzes income and price elasticities and the effects of other factors on China’s trade. Chapter V offers some concluding remarks. Finally, Annex I describes the data base, its coverage, and its limitations, and explains the methods used to calculate the indices presented in this paper.

II. Background: Economic Reforms

1. Decentralization of decision making

Beginning in 1978, the Chinese economy entered a phase of reforms that featured the relaxation of direct planning controls, decentralization of decision making in state-owned enterprises, and the emergence of a non-state sector. 1/ The reforms had important implications for the external sector, and a short description of them is therefore in order prior to the discussion of China’s foreign trade. 2/

Reforms began in the rural areas in 1979. The reforms allowed more independence to households in the agricultural sector regarding production decisions and gradually permitted the long-term private leasing of land. 3/ Households were still required to enter into contracts with state or local government agencies, but any surplus belonged to the household and could be sold directly in the market. At the same time, the operations of nonagricultural enterprises in the rural sector were also liberalized. 4/ A large number of both collectively and privately owned enterprises were established and, by 1987, these accounted for 25 percent of industrial output and 17 percent of industrial employment.

By 1984, the focus of reforms shifted to the state enterprise sector. 5/ Managers received greater responsibility for operations of their enterprise, and profits above an agreed level remained in the enterprise. However, a number of factors--adjustment taxes on profitable enterprises, renegotiation of contracts under the management responsibility system, and limitations on bonus payments--potentially diminish the connection between managerial performance and rewards.

The decade of reforms did increase the autonomy of enterprises, but the Chinese enterprise is still far from operating only according to market signals. State ownership and government (especially local) administrative controls, which vary in intensity, still play a major role in economic decisions, especially in the industrial sector. Managers still have to negotiate with the authorities about enterprise operations, and government officials have a final say in many decisions. The state also controls the supply of certain crucial inputs, a large number of input and output prices, and exerts substantial influence on the allocation of credit. As a result, managerial decisions are not necessarily aimed at growth or profit maximization. 1/

2. Reforms in the external sector

The reforms in China’s external sector had a significant impact on the structure and size of foreign trade. This section describes the developments in three major fields: (a) access to foreign markets; (b) access to foreign exchange; and (c) exchange rate determination. These developments, in turn, affected the behavior of the trading entities and the evolution of China’s trade itself.

a. Access to foreign markets 2/

Prior to 1978, China’s trade was conducted by 12 foreign trade corporations (FTCs), all under the Ministry of Foreign Economic Relations and Trade (MOFERT). 3/ These corporations traded the quantities directed by the central plan and all their losses or profits were covered or absorbed by the state. Enterprises, retailers, and local governments did not have access to foreign markets. As part of the production plan, enterprises were assigned target quantities that they had to supply to the FTCs for export.

After 1978, the number of entities involved in foreign trade increased substantially and their discretion in choosing traded goods increased as well. The central government introduced export and import licensing and duties as part of steps to reduce the role of mandatory planning in economic management. The plan still assigned mandatory quantitative targets for trade in some commodities, but for others only value targets were set to local governments and FTCs, and they had flexibility in determining how to achieve the targets. Over the years, the number of products subject to quantitative or value targets set by the central government declined and a system based on value quotas for total exports by the local governments was introduced. However, local governments still tend to assign value targets to their enterprises in order to achieve their aggregate target.

After 1978, ministries other than MOFERT were also allowed to establish FTCs. The existing FTCs were also permitted to delegate authority to their branch offices. In 1984, the monopolistic positions of most FTCs were abolished and the right to engage directly in trade was granted to some enterprises. In addition, a system allowing FTCs to operate as agents of enterprises was introduced. In this agency system the FTCs charge a fee for their services but they do not assume responsibility for profits or losses. In early 1988 the system was extended, and at the same time, more domestic enterprises were given the authority to export directly and import inputs.

In early 1988, local branches of most of the FTCs became independent profit units under the management contract system and were made accountable to the local governments. This, together with the establishment of many new FTCs, increased the number of FTCs to over 5,000. However, during 1989-90, as regulations on their activities were tightened, the number of FTCs was reduced to about 4,000 (World Bank, 1990).

The increased access to foreign markets has the potential to increase the efficiency of the trade sector and improve the flow of information from foreign markets to Chinese enterprises. However, since imports are still, in general, subject to quantitative restrictions, the direct import of inputs by enterprises may be motivated in part as a means of bypassing restrictions. 1/ In the presence of these and other distortions in the domestic and external sectors, it is unclear whether partial trade liberalization increases the efficiency of the economy. 2/

b. Access to foreign exchange

Until 1984, all foreign exchange earnings in China were transferred to the central government. The government then allocated the foreign exchange according to the trade plan. In early 1984, local governments were granted the right to a share (usually 12.5 percent) of the foreign exchange earned in their region, and by early 1985 enterprises were allowed to retain a similar proportion of their foreign exchange earnings. These shares were subject to negotiations and changes throughout the years and varied between provinces and enterprises.

To encourage exports, enterprises that exported more than their target received a larger share (up to 100 percent) of their incremental foreign exchange earnings. In 1988, the retention rate was increased, especially for high domestic value-added products, and reached 60 percent for garments and 100 percent for home electronics. Also in 1988, access to foreign exchange adjustment centers (FEACs) 3/ was permitted for all entities with retention quotas. This allows them to realize larger profits from exports since the exchange rate at the FEACs is more depreciated than the official rate. It also allows enterprises with low priority at the allocation process of foreign exchange to attain the currency they need to import inputs.

The retention system was modified in early 1991 and a uniform rate was set throughout the country. The retention rate is 60 percent to the FTC and 10 percent to the supplier for general commodities, and 90 percent and 10 percent, respectively, for mechanical and electronic products. The central government and local governments receive 20 percent and 10 percent, respectively, of foreign exchange earnings on general commodities. The central government is eligible to purchase additional foreign exchange from the FTCs and enterprises at the FEACs exchange rate.

c. Exchange rate determination and developments

The existence in China of many import and export duties and quantitative controls, as well as dual exchange rates through most of the 1980s, made it difficult to determine the exchange rate effectively faced by entities engaged in foreign trade. Indeed, in the late 1980s the variation of retention rates between industries and regions led to an infinite number of effective exchange rates 1/ between the administered and the FEACs’ exchange rate. The narrowing of the difference between the administered and FEAC rates since the December 1989 and November 1990 devaluations (27 and 11 percent respectively), and the more uniform retention rates established in 1991 reduced the problem, but trade duties and quantitative controls still play an important role in determining the relevant price to the trading entities.

From 1981 until 1984, China had dual exchange rates. The official rate depreciated gradually while the secondary rate was fixed at a more depreciated rate. The secondary rate, termed the internal settlement rate, was used for settlement of payments between the FTCs and the enterprises.

The internal settlement rate was abolished in 1985 but in 1986, with the establishment of the FEACs, dual exchange rates prevailed again in China. Between July 1986 and December 1989 the administered (official) exchange rate was fixed at 3.72 yuan per U.S. dollar, leading to a real appreciation of the yuan in the face of rising inflation. 2/ At the same time, the exchange rate in the FEACs depreciated, reflecting the excess demand for foreign currency at the official rate. For exporting enterprises, the ability to sell retention quotas in the FEACs allowed them to maintain the profitability of exports. The devaluations in December 1989 and November 1990 returned the real exchange rate to the level prevailing in 1986. Beginning in April 1991, the administered rate was adjusted more frequently through small periodic adjustments.

d. Summary

Despite the substantial reforms since 1978 the Chinese economy is still characterized by much government intervention, both in the external and the domestic sectors of the economy. Decentralization of decision making stopped, in many cases, at the local government level and was not passed on to the enterprises. The enterprises remained under state ownership and this raises doubts whether managers seek maximization of growth and profits, or want to maximize slack to ensure that targets can easily be met. The results presented in Section IV.4 provide support for the latter hypothesis.

In the external sector minimum targets still prevail for some enterprises and this, in combination with the ownership structure, may lead to suboptimal behavior. The effects of the contract system are explored in Chapter IV. However, the reforms in the external sector were substantial and the increased liberalization facilitated the fast growth of China’s foreign trade during the 1980s. Although several partial reversals of the process took place in periods of balance of payments difficulty, China’s trade in 1991 is much more liberalized than in 1978.

III. Developments in China’s Trade From 1980 to 1991

1. Some general observations

China’s international trade grew rapidly during the 1980s. The dollar value of total trade increased between 1980 and 1990 at an annual rate of 12 percent while the value of world trade increased by 6 percent annually. This led to an increase in China’s share of world exports from less than 1 percent in 1980 to 1.9 percent in 1990. The growth rate of exports was similar to those of the fast-growing economies of Hong Kong, Korea, Singapore, and Taiwan Province of China. While the absolute figures for China’s share in world trade are still small, the dynamics are an indication of its growing importance in world markets.

Chapter II of this paper described the reforms that took place in China’s foreign trade during the period. Policy changes and liberalization of trade were not a continuous process but occurred in discrete steps. The effect of these steps on trade is evident from the import data in Table 2. Following reform measures in 1984 and 1988, one observes increases of 64 percent and 61 percent, respectively, in the value of imports. 1/ In the periods between the adoption of new policy measures, imports grew at a very slow rate. 1/ The fast growth of imports in reaction to policy measures may be an indication of constrained demand. Whenever the restrictions were eased, the demand for imports surfaced immediately and filled the new “quota”. Until a new reform is introduced, all the allowed value is imported and then we see a new “jump” when new reforms are introduced. This possibility is supported by the estimated price elasticities of imports in Chapter IV.

The effects of policy measures on China’s trade were also demonstrated during 1988-91. After the imposition of restraints on imports, together with the austerity plan in 1988 and the devaluation in December 1989, imports declined by 15 percent in the first three quarters of 1990 compared to the same period a year before. Exports continued to grow at an annual rate of 14 percent. These trends continued early in 1991.

2. Unit values

This paper uses, for the first time, unit value indices to analyze China’s foreign trade. The use of unit values allows price and quantity developments in China’s foreign trade to be separately identified. The unit value data thus facilitate a better understanding of the sources of growth in China’s trade, the sources of trade deficits, and the performance of Chinese exports in world markets. Some of these issues were discussed in the past 2/ but the availability of unit value data offers new dimensions to the analysis.

a. Terms of trade developments

China’s terms of trade index (Table 3) shows a deterioration of 30 percent during the 1981-90 period. Most of the decline, 23 percentage points, took place in 1986 as a result of the drop in world oil prices and an increase in import prices. However, excluding petroleum prices, the data show a decline of only 13 percent in the terms of trade during 1981-90. This decrease took place after 1985 and was associated with a sharp increase in import prices. The terms of trade of non-oil exporting developing countries improved by 5 percent during 1981-88. China’s terms of trade, excluding petroleum exports, decreased by 2 percent during the same period owing to the much smaller decrease in China’s import prices relative to these countries. The effect of the decline in oil prices on the import unit value index (UVI) of non-oil exporting developing countries can explain the difference. The increase in China’s import UVI during 1985-90 is also in line with the increase in export prices of the industrial economies, which are the sources of most of China’s imports.

Table 3.Terms of Trade Developments 1981-901/(1981=100)
198219831984198519861987198819891990
China:
Export dollar unit values:
Total exports948683836970737878
Excluding petroleum9487889190889510198
Real yuan export unit values: 2/
Total exports1039610512311211710394117
Excluding petroleum10297111135147147134121147
Import dollar unit values:97868285939198110112
Real yuan import unit values: 2/10696104127151152139133168
Terms of trade (export/import)97100101977477757170
Excluding petroleum971011071079797979187
Non-oil exporting developing economies:
Export dollar unit values:9190918482879294--
Import dollar unit values:9291888581868889--
Terms of trade (export/import)989910399101101105106--
Industrial economies
Export dollar unit values:96939190104116124124136
Sources: Calculated from data in General Administration of Customs, various issues; and from IMF, International Financial Statistics.

Yearly indices were calculated as the average of the quarterly indices.

Calculated using the official exchange rate and the retail price index.

Sources: Calculated from data in General Administration of Customs, various issues; and from IMF, International Financial Statistics.

Yearly indices were calculated as the average of the quarterly indices.

Calculated using the official exchange rate and the retail price index.

Since the indices were calculated using sample weights 1/ the effects of using the weights of each category in total trade should also be mentioned. Adjusting the weight of petroleum products in the export data 2/ turns out to be the most important factor and increases the terms of trade index in 1990 from 70 to 75. However, in the most significantly under-sampled category, “Machinery and Electronics,” it seems, based on the data about industrial countries’ trade, that the sample commodities underestimate the price increases in this category and therefore China’s terms of trade index, as presented in Table 3, is biased upward. 3/

Unit value data, for export and import commodities, appear in Tables 4 and 6, respectively, and are used in the following subsection to focus on the commodity categories that contributed to the changes in the aggregate indices. Particularly, the export analysis investigates, using also the data in Table 5, whether there had been an upgrading of China’s export products and an improvement in the perceived quality of Chinese goods in world markets. This should be reflected in increased prices for China’s industrial exports relative to the prices of other products in the same markets.

Table 4.China: Export UVI of Selected Commodity Categories 1981-90(1981 =100)
Commodity19821984198519861987198819891990
Agricultural products9277706868758080
Live animals and frozen meat10287798688879297
Live pigs (excluding for breeding)998577818699105116
Aquatic products98787796106116113124
Cereals10067605551526157
Rice9258565046637368
Maize 1/----100916984105100
Soybean7779655960718167
Vegetables9590908798109109109
Fruits, sugar, nuts, and honey7874677174858382
Tea95941018895939395
Edible oil seeds7368575252566462
Cotton 1/----1007380122125143
Canned food99102112111113140144144
Furs and animal skins898498971131069183
Filature silk9485838989115163170
Inedible crude materials, except fuels8887777662647064
Tungsten ore8260605141413933
Crude drugs10512788936479109102
Chemicals and related products8778747563666467
Coal11084878775707781
Crude petroleum oil9477743748424760
Refined petroleum products9680794652485456
Yarns and fabrics98969293101107106109
Cotton yarn9587777588999288
Cotton woven fabrics9610098102106108111117
Polyester/cotton woven fabrics103948887100108108106
Silk piece goods9288100108110117149160
Carpets106128141150147192184166
Steel products9199122109114132144128
Machinery and electronics7064728069727779
Sewing machines (including industrial)10581786176798073
Garments, embroidery, and related products100100126129127127131148
Garments, not knitted or crocheted10097123127126127133150
Garments, knitted or crocheted99107139147137144140148
Of cotton10096134139125123113116
Of other fibers (excluding silk)101113132139136152159187
Footwear99101113110109127146176
Source: Calculations based on data in General Administration of Customs, various issues.

1985=100.

Source: Calculations based on data in General Administration of Customs, various issues.

1985=100.

Table 5.China: Performance in the U.S. Textile and Apparel Import Market, 1979-88
1979198219841985198619871988
China:
1. Export value (US$ mn.)1167901,2291,3212,0512,3732,240
2. Percent of total U.S. imports1.88.67.67.410.410.19.6
3. Export volume (mn. sq. yards eq.)2316701,0531,0491,6751,7381,607
4. Percent of total U.S. imports5.011.39.99.212.812.712.5
5. Unit value (US$ per sq. yard eq.)0.501.181.171.261.221.371.39
6. Relative unit value (2/4)0.360.760.770.800.810.800.77
Four largest exporters 1/
7. Market share in value48.658.955.252.851.150.748.0
8. Market share in volume46.454.248.546.245.143.041.1
9. Hong Kong’s market share (value)21.520.218.918.714.915.114.3
10. Hong Kong’s market share (volume)17.514.211.711.39.08.88.4
11. Hong Kong’s relative unit value (9/10)1.231.421.621.651.661.721.70
12. Relative unit value of other three largest exporters 2/ ((7-2/(8-4))1.131.171.231.261.261.341.35
Source: Calculated from Peltzman (1990).

China, Hong Kong, Korea, and Taiwan Province of China.

Hong Kong, Korea, and Taiwan Province of China.

Source: Calculated from Peltzman (1990).

China, Hong Kong, Korea, and Taiwan Province of China.

Hong Kong, Korea, and Taiwan Province of China.

Table 6.China: Dollar-Based Import Unit Value Indices of Selected Commodity Categories, 1981-90(1981 = 100)
Commodity19821984198519861987198819891990
Food, beverages, and tobacco9071806353618383
Cereals9476896349568278
Wheat9676756148558079
Sugar6845344137536276
Inedible crude materials (excluding fuel)867369757995102100
Rubber791067572821049179
Log and pulp7763646563798687
Textile fibers8687848391111122122
Cotton791014345114907391
Synthetic fibers suitable for spinning1008788869410710892
Yarn of synthetic fibers918185799010411290
Wool99758792101136146125
Metal ores100898884859410599
Fuels and electricity 1/100113103113123
Animal and vegetable oils and fats8775654140485046
Chemicals8792958082108108100
Organic, inorganic, and dyes66102100118119147156134
Manufactured fertilizers and pesticides8778916961748177
Plastic materials101157114112144216192152
Goods classified by material10175757784108125126
Plywood paper and paperboard101739098108126122118
Iron and steel10571717480106130133
Aluminum, copper, and zinc7281747585122125124
Machinery and electronics 2/10081737069
Machine tools10854106285168110111107
Household refrigerators or deep
freezers 1/94100106120122128111
Color televisions134174171185198220230278
Household washing machines 1/106100114130186324348
Motor vehicles and chassis15995120178169176178185
Saloon car (including CKD and SKD)9793848883104130128
Truck and dumper (including CKD and SKD)139154166235198248230263
Buses 1/100112173197202210
Source: Calculations based on data in General Administration of Customs, various issues.

1985 = 100.

1986 = 100.

Source: Calculations based on data in General Administration of Customs, various issues.

1985 = 100.

1986 = 100.

b. Export and import unit values by commodity category

Table 4 shows opposing trends in the export prices of Chinese primary and manufactured products. While the prices of primary products declined during 1981-90, the prices of manufactured products increased. This was a general phenomenon in world markets, that limited the ability to increase foreign exchange earnings by expanding primary exports.

The major contributors to the positive effects on China’s export UVI were the textile and apparel industries, especially since 1985. The UVI of these industries increased by 35 percent between 1981 and 1990 and their significance in China’s trade makes it important to find out whether the increase in the unit values of these commodities during 1981-90 was part of a general phenomenon in world markets or specific to Chinese exports.

While a broad analysis of this question is beyond the scope of this paper, some insights can be gathered from data on the U.S. textile and apparel market reported by Peltzman (1990). Since the U.S. market is an important destination for China’s garment exports, an analysis of the developments in this market can help to evaluate the change in China’s unit values.

Between 1982 and 1988 China enjoyed an increase of 18 percent in the unit value of its products in the U.S. textile and apparel market (Table 5). 1/ However, this increase was due to the general price increase in the market. Unlike the other major exporters in this market, China did not experience an increase in the relative unit value of its products. This suggests that there was no perception of improved relative quality of Chinese products in this market and that China had not diversified its exports to higher-priced commodities. A shift to higher-quality and higher-priced products would be the key to continued export growth in the future because many of the quotas in the market are in volume terms. 2/

The increase in China’s import unit values during 1981-90 (Table 6) was a result of a rise in the prices of industrial products in the “Machinery and electronics” and “Goods classified by material” categories. However, during 1986-90, the rise in import unit values was due to increases in the prices of primary products and “Goods classified by material” while the prices of “machinery and electronic products” decreased. 3/

3. Volume of trade

China’s export volume increased during 1981-90 at a higher rate than import volume (Table 7). 4/ The current account deficits between 1987 and 1989 also reflected the deterioration in the terms of trade after 1983, as China’s export volume grew faster than that of imports between 1983 and 1987. 5/ The comparison with world trade volume indicates that China’s increasing share in world trade resulted from faster quantity increases in its trade relative to world trade.

Table 7.China: Trade Volume Indices, 1981-90(1981 = 100)
198219831984198519861987198819891990
Total exports107117143150205256295307362
Excluding petroleum109120137131177233268281340
Total imports90113152225210215256244216
World imports98100109112117125136145--
Sources: Calculated from data in General Administration of Customs, various issues; and IMF, International Financial Statistics.
Sources: Calculated from data in General Administration of Customs, various issues; and IMF, International Financial Statistics.

The developments in trade volume (Tables 8 and 9) reflected the sequence of the reforms in the Chinese economy. When the rural sector was reformed during the early 1980s, its production increased (see Appendix Table 15), and exports of agricultural products rose rapidly while imports of these products declined.

Table 8.China: Export Volume Indices, 1981-90(1981=100)
Commodity19821984198519861987198819891990
Agricultural products96137221260279289273293
Live animals and frozen meat103105112137164199201245
Live pigs (excluding for breeding)10297939895959394
Aquatic products89107103144188228250302
Cereals76285628644523615529489
Rice792001741641761225553
Maize 1/1008962625553
Soybean1075938149861,2211,064893671
Vegetables107112118148163203225247
Fruits, sugar, nuts, and honey10292129169181185250269
Tea118162152192194221221221
Edible oilseeds5385100132148133110160
Cotton 1/1001612171357948
Canned food109110109122144140142147
Furs and animal skins92143127104911339884
Filature silk195160204176173176212140
Inedible crude materials, except fuels8793106119151200237231
Tungsten ore427079888610211561
Crude drugs998595115159156174189
Coal93101112141195226221249
Crude petroleum oil106159217206196195176173
Refined petroleum products105122133117106103101113
Chemicals and related products100125151179266286421451
Yarns and fabrics99156152195241238255241
Cotton yarn102351324479509431385369
Cotton woven fabrics93136131161183175183174
Polyester/cotton woven fabrics119153156207244221244244
Silk piece goods85119134155175245202203
Carpets78828183109114131132
Steel products14736253762132143324
Machinery and electronics118105861602716077691018
Sewing machines (including industrial)988168128126170262460
Garments, embroidery, and related products10714394129163243298296
Garments, not knitted or crocheted10714696130164207255255
Garments, knitted or crocheted10814585118162214262278
Of cotton97131109150241359465534
Of other fibers (excluding silk)10817280126147155177161
Footwear106105102139202262489596
Source: Calculations based on data in General Administration of Customs, various issues.

1985 = 100.

Source: Calculations based on data in General Administration of Customs, various issues.

1985 = 100.

Table 9.China: Import Volume Indices, 1981-90(1981=100)
Commodity19821984198519861987198819891990
Food, beverages, and tobacco12478446511714112296
Cereals11272355311210611595
Wheat10676414710211211496
Sugar218124193119184374160114
Inedible crude materials (excluding fuel)81781191039312311395
Rubber106135138171223225230216
Log and pulp144198264207208306184131
Textile fibers6540705949677055
Cotton595----146552
Synthetic fibers suitable for spinning4854986048917974
Yarn of synthetic fibers488915310244576559
Wool14512725834734742723876
Metal ores110179295557435441578728
Fuels and electricity 1/------100100160317222
Animal and vegetables oils and fats13210719452090878217962185
Chemicals140208195199252403323309
Organic, inorganic, and dyes133143180289260397328261
Manufactured fertilizers and pesticides128260192126263370348395
Plastic materials151117177192159300197152
Goods classified by material117351578570414359375243
Plywood, paper, and paperboard70121185288437338304366
Iron and steel107361608590381278283135
Aluminum, copper, and zinc324604923463243298404220
Machinery and electronics 1/------100124204211182
Machine tools1113725136351019177416431727
Household refrigerators or deep freezers 2/--231004945444512
Color televisions2649193531418163
Household washing machines 2/--341003110520.3
Motor vehicles and chassis39358851361217227206153
Saloon car (including CKD and SKD)791,5457,5503,4322,1802,3683,2122,431
Truck and dumper (including CKD and SKD)42164573344153939081
Buses 2/----1001641075
Source: Calculations based on data in General Administration of Customs, various issues.

1986 = 100.

1985 = 100.

Source: Calculations based on data in General Administration of Customs, various issues.

1986 = 100.

1985 = 100.

The focus of reforms shifted to the urban sector by the mid-1980s. At the same time, the growth rate of exports of industrial products increased and was higher than that of primary products. The increase in industrial production (see World Bank 1990) led to higher incomes in the urban sector and, as a result, domestic demand for food rose. This led to a decrease in cereal exports after 1985 and an increase in imports. The production of other agricultural products continued to grow after 1985, facilitating increases both in exports and domestic consumption.

An important process that can be inferred from the data on quantities is that of import substitution. The data in Appendix Table 15 show a decline in the market share of imported durable consumer goods and steel products. In some cases (e.g. television sets and sound recorders), China became a net exporter of products that were imported on a large scale earlier in the decade.

China’s export growth was based on exporting a small proportion of production in many sectors and the development of a few sectors that export a large proportion of their output. The latter is true especially for the textile and apparel industries and for some consumer durable goods. In addition, the processing industries operate mostly for export.

IV. Price and Income Elasticities

In this section, estimated price elasticities are derived for imports and exports and estimated income elasticities are derived for imports. This may help to assess the responsiveness of China’s trade to the different variables and the extent to which market-oriented behavior has developed in China.

Interpreting the estimated elasticities is not an easy task without a complete structural model. Since the data available for this paper were not sufficient to estimate structural equations, only simplified equations for China’s trade were estimated. 1/ Therefore, a discussion of the interpretation of the coefficients is required before presenting the results.

1. Price elasticities

The estimation of export and import price elasticities using reduced form equations is correct only if we accept the “small country assumption” for China. This implies that the demand price elasticity for exports and the supply price elasticity for imports are infinite, and therefore that all the effects of prices on trade volume that one observes are supply effects in the export equations and demand effects in the import equations. Since China’s share in total world trade is still small and this is also true for most of the individual commodities, 1/ it seems reasonable to adopt the small country assumption as a preliminary working hypothesis. Attempts to test this hypothesis are made in section 4.b., but it will be useful to test this assumption in the future along the lines of Bond (1987) and Goldstein and Khan (1978, 1984) using a more complete model.

The interpretation of the import elasticities requires caution. It is not clear to what extent the estimation reflects the demand for imports, and to what extent it reflects the willingness of the Government to allow imports. Some attempts are made to explore the authorities’ reaction to foreign currency constraints but only a complete structural model can separate the government and private responses.

Saracoglu and Zaidi (1986) estimated a system for developing countries 2/ that includes the government “supply” of imports by calculating the probability of restrictions. In China’s case, we know that restrictions exist; hence, a similar method should be used to estimate the probability of liberalization in each period. 3/ Since we have information on the dates of the reforms, slope dummy variables 4/ are used to test if there are any changes in the price elasticities after the reforms. Some other variables that may affect government policy are also included in the regressions, but a more complete model should be tried, when more data become available, in order to separate the components of the import elasticities.

The export price elasticities may overestimate the responsiveness of FTCs and enterprises to price changes. The assignment of overall export value targets to local governments 5/ allowed them to shift export targets to enterprises whose products enjoyed an increase in world prices. The extent to which local governments used this ability increased the estimated export price elasticity even if there has been no change in the price responsiveness of FTCs and enterprises.

Another issue is the choice of the price variable. As discussed in Chapter II.c, the relevant prices for the trading entities in China may differ from the dollar unit values and even from the real yuan prices in Table 3. 1/ However, while transparency of world prices in China is far from perfect, the use of retention quotas for FTCs, local governments, and enterprises that are set as a percentage of total revenue creates a positive correlation between world and domestic prices. 2/ The effects of retention quotas may be especially important because they were very high at the margin (i.e., for exports above the target). 3/

An additional factor that may affect the results follows from the way in which the UVI is calculated. Dividing the trade value by the quantity for each commodity creates a negative correlation between the volume index and the UVI if there is a measurement error in the data on quantities. 4/ A possible solution for this problem is to use world prices of the commodities as substitutes for the calculated unit values. However, since products, especially in the manufacturing sector, are not homogeneous, this method is unreliable. The comparison in Chapter III between the estimated UVI for China’s textile and apparel exports and the prices reported by Peltzman (1990) shows that the two sets of prices do not differ significantly, at least for these commodities.

2. Income elasticities

The income elasticities for imports, like the price elasticities, have two components. One is the income elasticity of the economic entities’ demand, and the other is the willingness of the government to allow imports. As mentioned above, only a system of structural equations would permit the separate identification of these effects.

The effect of domestic production in the export equations can be positive or negative. As Bond (1987) has shown, the sign may be positive because an increase in production represents a greater ability to export, or negative if it results from an exogenous increase in domestic demand for exported or non tradable products. In the single commodity equations it may be more likely that the coefficient will reflect income effects unless production of the commodity is highly correlated with the total size of production.

3. Estimation

Regression equations were estimated in order to obtain the required elasticities. For the export elasticities, two sets of equations were used: including and excluding petroleum.

The dependent variable in the export equations was the log of the seasonally adjusted volume index. The right-hand side variables (in logs) were the comparable real unit value index, a seasonally adjusted index of real industrial production, 1/ and an index of world imports as a proxy for world demand. Slope dummy variables were used to test for changes in the price elasticity after the reforms in 1985 and 1988 (as discussed in Chapter II). In addition, lagged values of the price variables were included in order to test for the length of the adjustment process.

In the import equations, two different dependent variables were used to explore the possible effects of sampling bias on the results. These variables were (1) the log of seasonally adjusted imports for the UVI sample commodities and (2) the log of the seasonally adjusted index of total import volume. 2/ The right-hand side variables were the log of the UVI, log of the index of real industrial output, and slope dummy variables to test for changes in the price elasticity since 1985 and 1988. The import coverage of reserves (in quarters), the level of export value, and the terms of trade in current values and with lags were also included. The latter variables were introduced as possible components that affect government restrictions on imports. A complete list of the variables and their definitions appears in Annex III. 3/ However, in the future, it may be useful to experiment with other variables that may affect the government’s policies.

The regressions were estimated for 40 quarters between the first quarter of 1981 and the last quarter of 1990. In order to correct the observed serial correlation in the data, corrections for moving average and autoregressive processes were implemented using the Cochrane-Orcutt (1949) method. 4/

4. Results

a. Imports

The equations for the two different import volume indices are reported in Table 11 and reveal different estimates for the income elasticity-- an expected result owing to the different commodity content of the indices.

Table 10.China: Import Regressions (OLS)
Dependent Variable: Seasonally Adjusted Import Volume 1/, 2/
Variables 1/TOIVOSTOIVOSIVOSESIVOSESIVOSESIVOSESIVOSES
Constant4.35**2.74**6.94**13.36**7.62**6.61**6.06**
(3.49)(3.22)(6.57)(6.00)(4.30)(6.70)(7.89)
SINOUT1.22**1.47**0.30**0.150.140.47**0.71**
(12.2)(12.7)(2.63)(0.67)(0.67)(4.85)(14.3)
IRDUP-1.10**-0.79**
(4.26)(3.88)
RDUP85-0.05**0.08**
(2.87)(4.38)
RESQUA-0.17-0.16
(4.99)(5.24)
RESQUA(-2)0.11**0.12**
(3.01)(4.94)
IUP-1.00**-1.02**-0.82**
(4.80)(3.67)(2.70)
DUP85-0.06**0.09**0.10**
(3.83)(3.99)(4.56)
DUP880.06**0.04**
(2.67)(2.07)
IRYUPI-0.93**
(4.57)
DYUP850.12**
(6.43)
IEYUPI-1.01**
(6.44)
TERMTR(-1)-1.02**
(2.73)
Adjusted R Square0.930.930.860.860.890.910.88
D.W.1.981.982.011.941.941.992.07
S.E.0.1010.0980.1150.1180.1030.0950.106
F Statistic83.288.249.233.267.1661.273.6

For complete definitions of variables see Annex III.

* - Significant at the 10 percent level. ** - Significant at the 5 percent level.

Absolute values of t statistics in parentheses. All the regressions are corrected for moving average serial correlation. The degrees of freedom are adjusted to account for seasonal adjustments of the data.

Variables List: TOIVOS: Total import volume; IVOSES: Import volume for UVI sample; SINOUT: Industrial output; IRDUP: Import prices in real dollars; RDUP85: Slope dummy for IRDUP since 1985; RESQUA: Value of foreign exchange reserves in terms of quarters of imports; IUP: Import prices in dollars; DUP85, DUP88: slope dummies for IUP since 1985 and 1988 respectively; IRYUPI: Import prices in real yuans; DYUP85: Slope dummy for IRYUPI since 1985; IEYUPI: Imports prices in real yuan using the secondary exchange rate until 1984; TERMTR: China’s terms of trade, calculated by dividing imports prices by exports prices.

Numbers in parentheses after variable names indicate lagged values of the variable.

For complete definitions of variables see Annex III.

* - Significant at the 10 percent level. ** - Significant at the 5 percent level.

Absolute values of t statistics in parentheses. All the regressions are corrected for moving average serial correlation. The degrees of freedom are adjusted to account for seasonal adjustments of the data.

Variables List: TOIVOS: Total import volume; IVOSES: Import volume for UVI sample; SINOUT: Industrial output; IRDUP: Import prices in real dollars; RDUP85: Slope dummy for IRDUP since 1985; RESQUA: Value of foreign exchange reserves in terms of quarters of imports; IUP: Import prices in dollars; DUP85, DUP88: slope dummies for IUP since 1985 and 1988 respectively; IRYUPI: Import prices in real yuans; DYUP85: Slope dummy for IRYUPI since 1985; IEYUPI: Imports prices in real yuan using the secondary exchange rate until 1984; TERMTR: China’s terms of trade, calculated by dividing imports prices by exports prices.

Numbers in parentheses after variable names indicate lagged values of the variable.

Table 11.China: Export Regressions (OLS): Dependent Variable: Seasonally Adjusted Export Volume Excluding Petroleum (SEVNOP) 1/, 2/
Variables(1)(2)(3)(4)(5)(6)(7)(8)
Constant-1.86**-1.35-2.33**-0.810.290.40-0.59
(4.56)(1.17)(3.96)(0.42)(0.12)(0.94)(0.57)
SINOUT0.89**0.62**0.88**0.74**0.67**0.69**1.42**1.42**
(11.4)(6.60)(8.80)(3.78)(8.09)(2.98)(23.3)(16.2)
ERDUPI-0.38-0.25-0.38**-0.26
(1.48)(0.84)(4.19)(0.79)
ERDUPI(-2)-0.20
(0.70)
SWORTR0.84**1.04**0.62**0.68**0.70**0.70**
(8.28)(7.80)(5.38)(5.00)(5.61)(4.31)
RDUP85-0.05**-0.04**-0.03**-0.04**
(3.70)(2.47)(3.01)(2.49)
RDUP85(-2)0.03**0.03**0.03**0.03**
(2.58)(2.76)(3.09)(2.93)
RDUP880.02**0.03**0.03**0.02**
(2.13)(2.20)(3.94)(2.27)
RYUPNP-0.33**-0.53**-0.32*
(3.61)(4.22)(1.71)
RYUP85-0.05**
(2.62)
RYUP85(-2)0.04**
(2.70)
Adjusted R Square0.980.970.980.980.980.980.960.96
D.W.1.991.961.992.001.991.981.941.97
S.E.0.0620.0700.0550.0550.0540.0550.0860.084
F Statistic471.0372.8398.6347.1416.7277.4242.2168.4

For complete definitions of variables, see Annex III.

* - Significant at the 10 percent level. ** - Significant at the 5 percent level.

Absolute values of t statistics in parentheses. All the regressions are corrected for moving average serial correlation. The degrees of freedom are adjusted to account for seasonal adjustments of the data.

Variables List: SEVNOP: Exports volume excluding petroleum; SINOUT: Industrial output; ERDUPI: Exports prices in real dollars; SWORTR: Value of world imports, RDUP85; RDUP88: Slope dummy for ERDUPI since 1985 and 1988 respectively; RYUPNP: Exports prices in real yuans; RYUP85: Slope dummy for RYUPNP since 1985.

Numbers in parentheses after variable names indicate lagged values of the variables.

For complete definitions of variables, see Annex III.

* - Significant at the 10 percent level. ** - Significant at the 5 percent level.

Absolute values of t statistics in parentheses. All the regressions are corrected for moving average serial correlation. The degrees of freedom are adjusted to account for seasonal adjustments of the data.

Variables List: SEVNOP: Exports volume excluding petroleum; SINOUT: Industrial output; ERDUPI: Exports prices in real dollars; SWORTR: Value of world imports, RDUP85; RDUP88: Slope dummy for ERDUPI since 1985 and 1988 respectively; RYUPNP: Exports prices in real yuans; RYUP85: Slope dummy for RYUPNP since 1985.

Numbers in parentheses after variable names indicate lagged values of the variables.

The first volume index includes only the UVI sample commodities. This sample excludes most of the advanced technology imports into China and represents the more basic commodities. The income elasticity for these commodities is 0.30 when we use real dollar unit values in the equation and increases to 0.47 when we use real yuan prices. 1/ In other specifications the value increased to as much as 0.71 but statistically, in all cases, the elasticity is significantly lower than 1. When one uses dollar prices, the inclusion of the last period’s terms of trade or lags of the foreign exchange reserves drives the value of the estimated coefficients close to zero. 2/

This suggests that the demand for standardized goods did not increase as fast as income. This may be due to the substitution of local production for imports of these goods, as shown in Appendix Table 15, and the increase in demand for other goods; that is, technologically more advanced goods. The negative values of income elasticity estimated for many commodities in Appendix Table 17 also suggest that import substitution was an important factor in China’s trade.

The income elasticity for total imports is much larger than that estimated for the UVI sample. The elasticity estimates ranged from 1.22 to 1.47 1/ (Table 10). Thus, the income elasticity for total imports shows a tendency for the share of imported goods in domestic expenditure to increase with income. This increase applies mostly to imports of mechanical and electronic goods other than consumer goods. For most of these products the industrial production index reflects only income effects because there are no domestically produced substitutes, a factor that creates the large difference in income elasticities between total imports and the UVI sample.

Attempts to estimate the effect of foreign exchange availability on the Government’s tendency to allow increased imports yielded significant results only with regard to imports of goods in the UVI sample. Those imports depend positively on the value of foreign reserves with a lag of two quarters, and the terms of trade with a lag of one quarter. No effects were found on the volume of total imports or even on the size of the income elasticity in their equations. The coefficients were insignificantly different from zero or had the wrong signs. While more work is required in order to estimate the Government’s reaction function, these results may indicate that the Government’s determination of the volume of technology imports is based primarily on its perception of domestic needs, while the imports of other inputs and consumer goods are determined also in conjunction with balance of payments considerations. Indeed, most of the controls over trade during the 1980s were on imports of consumer goods and basic inputs.

The estimated price elasticities of imports are significantly different from zero and very close to -1. The price elasticities for the UVI sample goods were between -0.79 and -1.02 regardless of the price variables we chose. Statistically, in all cases, the price elasticity estimator was insignificantly different from -1. A price elasticity of -1 reflects fixed-budget importers who have to adjust the volume of their purchases exactly in an opposite magnitude to changes in the price. This is consistent with the trade system in China where import quotas are allocated to local governments and enterprises. The price elasticity of those products became significantly smaller (in absolute values) after the liberalization in 1985, reflecting more discretion for the importing entities in determining the value of trade.

For total imports, the dollar price elasticity is also very close to -1 and became more negative after 1985. The yuan price elasticity is smaller (between -0.36 and -0.60). The reason may be that for technology imports, which constitute most of the difference between the two volume indices, there are no local substitutes and therefore their import volume is not affected by the yuan price. The difference between the dollar price elasticities of the two volume indices is statistically insignificant.

Price and income elasticities for imported commodities are reported in Appendix Table 17. Equations that also include slope dummies for changes in the price elasticity since 1985 and/or the effect of foreign exchange reserves on imports are reported only if the inclusion of these variables reduced the standard error of the regression.

b. Exports

The export equations are reported in Table 11 and reveal the strong reaction of export volume to output. The coefficients, including or excluding petroleum, 1/ are between 0.62 and 0.89. Without seasonal adjustment or when the value of world trade is excluded from the equations, the elasticity is significantly larger than 1. 1/ As production grows the tendency to export increases. One of the reasons may be that exports react to increases in the demand for imports as income increases. The increasing demand for imports requires more exports in order to finance the increased foreign currency expenditure. Future estimation of a complete macroeconomic model of the Chinese economy can help to sort out these effects. 2/

The volume of China’s exports is also responsive to the size of world trade. As world imports expand, China increases its exports. The elasticity estimates ranged from 0.62 to 1.04 and statistically are always significantly different from zero. In most specifications the coefficient is smaller than 1. 3/ This may indicate that the direct reaction of China’s exports to world demand would have led to a decreasing or constant share of China in world trade. Only the additional effect of growth in production caused the increase in China’s share.

The price elasticity of total export volume is not significantly different from zero. For export volume excluding petroleum, we observe a price elasticity of about -0.3 (Table 11). There had been a small but statistically significant change in this elasticity in 1985, and again in 1988 as reflected by the slope dummies coefficients. The negative supply price elasticity may mean that the phenomenon of export value quotas is significant and strongly affects the responsiveness of China’s exports to their unit values. As unit values decrease, exporters are required to export larger quantities in order to achieve their value quota. As unit values increase, the trading entities have more slack and they can reduce their efforts to export larger volumes. As there had been a shift toward more value targets, rather than volume targets, the elasticity became more negative after 1985. The increase in retention quotas in 1988 may be the cause for the correction in the opposite direction in 1988-90.

The positive lagged price elasticity during 1985-90 may suggest a mechanism in which enterprises and FTCs reduce their export volume as prices increase to avoid larger value targets in the future (the ratchet effect). 4/ When the authorities (usually the local governments) observe the price increase they indeed raise the export target, and this causes the lagged price elasticity to be positive. 1/ This may be a result of the separation between management and ownership. Managers received more control over operations during the 1980s, but they have little financial incentive to increase profits beyond the contract level and may, therefore, prefer to minimize the risk of failure to meet contract targets.

Another possibility is that for China’s exports the “small country” assumption is incorrect and the observed price elasticity is dominated by the demand price elasticity. To correct this problem, two-stage least squares (TSLS) models for China’s exports were estimated. In this method only price changes that result from demand variables are included in the second stage equation and therefore, the estimated price elasticity reflects only the supply reaction. 2/ The estimation of the TSLS equations also yields negative supply price elasticities, although the equation with dollar prices was very inefficient (Table 12).

Table 12.Two-Stage Least Squares Dependent Variable: Seasonally Adjusted Export Volume
(1)(2)
Constant3.0439.4
(1.59)(0.84)
SINOUT (seasonally adjusted industrial production)1.72**0.53
(6.98)(0.68)
RYUPNP (export prices excluding petroleum in real yuan)-1.39**
(2.16)
ERDUPI (export prices excluding petroleum in real dollars)-8.19
(0.87)
Adjusted R square0.930.52
D.W. statistic1.971.68
S.E. of regression0.1140.290
F statistic118.211.2
Source: Organization for Economic Cooperation and Development (OECD), (1991).Note: The price variables are instrumented by consumption in the OECD countries. The numbers in parentheses are t values.
Source: Organization for Economic Cooperation and Development (OECD), (1991).Note: The price variables are instrumented by consumption in the OECD countries. The numbers in parentheses are t values.

Another explanation for the negative export price elasticities may be that even if China is a “small economy” its supply curve for exports may shift simultaneously with that of other countries because of changes in input prices. Controlling for this possibility in addition to the TSLS process is difficult because it requires a knowledge of the inputs that are used for the exported commodities. However, oil and cotton are two important inputs in China’s production and therefore their world prices were used to control for shifts in world supply. This procedure maintained the negative supply price elasticities and increased their t values.

In order to control for the possibility that the negative supply price elasticity results from the effect of a few commodities that have large market shares in their export markets, or from errors in the calculation of the aggregate unit value index, separate equations were estimated for each commodity that had sufficient data. The results are presented in Appendix Table 16 and discussed in the next section.

c. Export price elasticities by commodity

Estimation of the price elasticities reveals that most of the commodities have negative export price elasticities. This occurs even for commodities for which China’s world market share is negligible. Only in the case of a few agricultural products is the export price elasticity positive. This supports the assumption that the observed export price elasticities are indeed supply elasticities and supports the notion that the negative export price elasticity is a result of the quota system. In addition, since aggregate export value targets are assigned to local governments which have to allocate them among the products they export, this is a test for efficient substitution between commodities at the local government level. The results indicate that such substitution effects are not very strong in China.

The estimated values for the price elasticity are usually negative and in most cases significantly different from zero. In some cases, we cannot reject the hypothesis that the elasticity is -1, but the elasticities are generally between zero and -1. In the garment industry, the elasticities became significantly negative only after the reforms in 1984-85. The result is that, for most commodities, the value of exports is positively correlated with unit values. As unit values decrease, some increase in export volume takes place in order to reach the targeted quota, but export supply does not adjust fully to the price change. As unit values increase, some of the increase is used to reduce the export effort. This is true at the commodity level as well as at the national level.

V. Conclusion

The developments In China’s external sector during 1981-90 were examined in this paper in light of the reforms in the external and internal sectors of the economy. Close links were found between the implementation of different stages in the reforms and trade developments. The institutional environment seemed to have an important effect on the enterprises and corporations involved in China’s trade.

China’s trade grew rapidly during the decade--faster than world trade, and also faster than the rest of the economy. This rapid growth was achieved in spite of a decline in the terms of trade attributable to the decline in world oil prices and an increase in import prices. Export unit values (excluding petroleum) remained almost unchanged while import unit values increased by 12 percent between 1981 and 1990 (37 percent between 1984 and 1990). The export unit values of manufactures increased, but, at least for the textile and apparel industries, this increase is shown to be a result of general price increases in the market and not a result of higher relative prices for Chinese products.

Growth in China’s imports took place in large discrete steps following import liberalization, possibly reflecting excess demand. The analysis of import volumes also suggests that imports were used to release bottlenecks in the economy when the demand for certain commodities exceeded supply. The analysis of export and import price elasticities shows the strong effect of trade restrictions and the management system on the development of trade. It seems that many of the enterprises and FTCs involved in China’s trade are still oriented toward meeting administrative targets rather than exploiting opportunities in the market to increase profits. This results from the way in which local governments implement the diversification of microeconomic decisions and may offer important lessons for other countries engaged in efforts to decentralize decision making. While each country’s circumstances differ, the Chinese experience may suggest that privatization, rather than delegation of power to local governments, would be a more effective way to achieve market-oriented behavior and efficiency at the microeconomic level in formerly centrally planned economies.

The availability of quarterly data on China’s trade, including unit values, should facilitate future work in developing a more complete model of the country’s trade. However, development of a complete macroeconomic model that will be able to forecast trade developments would require information (on a quarterly basis) about macroeconomic policy variables, national accounts, and the factors that underlie the Government’s policy toward trade. The large degree of government involvement in the Chinese economy, even after the reforms, makes such an analysis an important component of any trade model for China.

Analysis at the industry and commodity level may also be useful especially for business planning purposes both in China and in its trade partners. In particular, it may be useful to relate production data by commodity to the trade data.

In order to further investigate the accuracy of China’s negative export price elasticities as estimated in this paper, more work is required using world unit value data instead of the prices calculated for China’s export commodities. The use of disaggregated data on trade of other developing economies can also be very useful. Further development of a theoretical framework to explain the existence of negative export supply price elasticities for the entities involved in China’s trade is needed, and is currently being developed by the author along the lines of the ratchet effect literature.

APPENDIX
Table 13.China: Share of Selected Products and Product Categories in Exports, 1981-90

(In percent) 1/

Commodity19811984198519861987198819891990
Agricultural products11.410.916.313.811.010.910.810.2
Live animals and frozen meat5.34.04.44.44.34.24.14.6
Live pigs (excluding for breeding)1.41.01.00.80.70.60.60.6
Aquatic products2.01.41.52.02.32.52.42.8
Cereals2.13.47.55.43.23.22.92.2
Rice1.31.31.30.80.60.50.20.2
Maize----4.12.51.01.01.10.9
Soybean0.31.11.51.21.21.00.90.5
Vegetables1.21.01.21.11.11.31.31.2
Fruits, sugar, nuts, and honey1.50.91.31.31.21.11.31.2
Tea1.11.41.71.41.21.11.00.9
Edible oilseeds2.01.01.11.00.90.70.60.7
Cotton----2.42.12.51.91.10.6
Canned food1.91.82.21.81.71.71.71.5
Furs and animal skins1.31.31.61.00.80.90.50.3
Filature silk0.91.01.41.00.80.81.30.8
Inedible crude materials, except fuels3.62.42.82.32.12.22.82.1
Tungsten ore1.30.50.60.40.30.30.30.1
Crude drugs0.90.80.70.70.50.50.70.7
Chemicals and related products2.92.53.22.72.32.12.82.8
Coal1.91.31.71.71.51.41.41.4
Crude petroleum oil 2/14.915.421.09.09.06.66.16.6
Refined petroleum products 2/6.35.25.82.72.21.71.71.7
Yarns and fabrics7.39.29.79.510.18.68.47.1
Cotton yarn0.71.81.61.81.71.41.00.8
Cotton woven fabrics4.55.15.55.35.04.03.93.4
Polyester/cotton woven fabrics1.51.82.01.92.11.61.71.4
Silk piece goods1.41.21.71.61.51.81.81.6
Carpets1.00.91.10.90.91.01.10.8
Steel products1.00.30.30.30.40.80.91.5
Machinery and electronics
(Only the amounts reported by commodity)2.81.51.62.62.96.27.49.0
Sewing machines (including industrial)0.10.10.10.10.10.10.10.1
Garments, embroidery, and related products8.810.59.910.410.212.615.514.9
Garments, not knitted or crocheted6.07.06.67.07.07.28.68.3
Garments, knitted or crocheted2.53.22.73.13.13.53.83.7
Of cotton0.90.91.21.31.51.82.02.0
Of other fibers (excluding silk)1.01.71.01.31.21.11.21.1
Footwear0.90.81.01.01.11.92.73.4
Source: Calculations based on data in General Administration of Customs, various issues.

Percent of exports, excluding petroleum and goods after inward processing.

Percent of exports, excluding goods exported after inward processing.

Source: Calculations based on data in General Administration of Customs, various issues.

Percent of exports, excluding petroleum and goods after inward processing.

Percent of exports, excluding goods exported after inward processing.

Table 14.China: Share of Selected Commodities and Commodity Categories in Imports, 1981-90

(In percent) 1/

Commodity19811984198519861987198819891990
Food, beverages, and tobacco16.57.33.23.75.96.47.06.5
Cereals14.46.32.52.74.63.95.75.3
Wheat12.85.92.22.13.63.65.04.8
Sugar2.00.90.70.50.81.80.80.8
Inedible crude materials (excluding fuel)19.48.98.78.48.210.39.49.1
Rubber0.91.10.50.71.01.00.80.8
Log and pulp2.82.72.62.12.13.01.81.6
Textile fibers14.54.04.74.03.74.95.24.8
Cotton6.80.3------0.11.41.6
Synthetic fibers suitable for spinning3.71.41.71.11.01.61.31.2
Yarn of synthetic fibers2.51.41.81.10.60.70.80.7
Wool0.70.50.91.31.41.81.00.3
Metal ores0.50.70.81.41.11.01.41.9
Fuels and electricity0.20.20.21.11.31.53.12.7
Animal and vegetable oils and fats0.40.30.30.50.90.81.72.2
Chemicals7.211.07.36.48.514.110.610.9
Organic, inorganic, and dyes0.80.90.81.51.42.11.71.3
Manufactured fertilizers and pesticides4.16.83.92.03.85.14.96.2
Plastic materials2.13.12.32.52.86.23.42.4
Goods classified by material9.219.421.622.618.416.118.013.8
Plywood paper and paperboard1.20.81.11.83.22.21.82.5
Iron and steel7.114.716.817.412.59.511.16.3
Aluminum, copper, and zinc0.93.53.31.71.01.41.91.2
Machinery and electronics
Reported by commodity 2/20.19.917.522.422.425.224.024.8
(Excluding complete sets of equipment)4.77.213.722.422.425.224.024.8
Machine tools0.10.20.41.21.21.10.91.1
Household refrigerators or deep freezers--0.10.40.20.20.20.2--
Color televisions1.40.82.50.80.20.20.20.1
Motor vehicles and chassis1.23.36.84.42.62.21.91.7
Saloon car (including CKD and SKD)--0.41.20.60.40.40.60.5
Trucks and dumpers (including CKD and SKD)0.61.23.12.71.00.60.50.6
Buses--1.31.40.20.10.20.10.1
Source: Calculations based on data in General Administration of Customs, various issues.

Percent of total imports excluding goods imported for inward processing.

Includes all the commodities in this category that are reported separately, and not only those that are used in the construction of the unit-value index.

Source: Calculations based on data in General Administration of Customs, various issues.

Percent of total imports excluding goods imported for inward processing.

Includes all the commodities in this category that are reported separately, and not only those that are used in the construction of the unit-value index.

Table 15.China: Exports and Imports as Percent of Local Production, 1981-89
CommodityUnit1981198419851986198719881989
Pork, beef, and mutton1,000 tons12,60015,40017,60019,20019,90021,90023,300
Exports(percent)0.90.90.90.80.80.70.8
Aquatic products1,000 tons4,6006,2007,1008,2009,60010,60011,500
Exports(percent)2.52.01.72.02.32.52.6
Cereals10,000 tons32,50040,73037,91039,15040,30039,41040,700
Exports(percent)0.30.82.52.41.81.81.6
Imports(percent)4.52.61.32.04.03.94.1
Wheat10,000 tons5,9608,7808,5809,0008,5908,6409,080
Imports(percent)21.811.26.36.815.416.816.4
Rice10,000 tons14,40017,83016,86017,22017,43016,91018,010
Exports(percent)0.40.70.60.60.60.40.2
Imports(percent)----------0.20.7
Maize10,000 tons5,9207,3406,3907,0907,9207,7407,890
Exports(percent)----9.98.04.95.14.4
Imports(percent)1.10.10.10.81.90.10.1
Soybean10,000 tons9309701,0501,1601,2501,1701,020
Exports(percent)1.58.610.911.913.712.712.3
Imports(percent)6.0----2.42.21.3--
Fruits10,000 tons8001,0001,2001,3001,7001,7001,800
Exports(percent)2.51.71.81.71.41.71.4
Sugar 1/10,000 tons3,6104,7806,0405,8505,5506,1905,800
Exports(percent)0.30.10.30.50.80.40.7
Imports(percent)2.72.63.22.03.36.02.7
Tea1,000 tons343414432461509545535
Exports(percent)26.235.131.737.334.236.437.1
Edible oilseeds10,000 tons1,0001,1901,5801,4701,5301,3201,300
Exports(percent)3.12.82.63.43.53.93.0
Imports(percent.)0.20.00.00.0--0.0--
Cotton10,000 tons300620410350420410380
Exports(percent)----8.515.918.011.46.8
Imports(percent)26.70.60.00.00.10.813.0
Coalmn. tons6627898728949289801,040
Exports(percent)1.00.90.91.11.51.61.5
Imports(percent)0.70.30.30.30.20.20.2
Crude petroleum100,000 tons1,0121,1461,2491,3071,3411,3711,382
Exports(percent)13.719.224.021.820.319.717.6
Imports(percent)----------0.62.4
Electricitybn. kwh309377411450497539582
Imports(percent)------0.20.30.30.3
Cotton yarn10,000 tons320320350400440470480
Exports(percent)1.55.24.45.75.54.43.8
Cementmn. tons83123146166186210207
Exports(percent)1.00.10.10.10.10.10.2
Imports(percent)--2.02.52.11.10.70.6
Manufactured fertilizers10,000 tons1,2401,4601,3201,4001,6701,7401,800
Imports(percent)30.563.257.636.365.484.577.4
Pesticides1,000 tons484299211203161179208
Imports(percent)3.919.87.63.76.219.117.6
Steel products100,000 tons6237728379281,0021,0631,102
Exports(percent)1.00.30.20.20.40.80.8
Imports(percent)5.715.923.919.812.48.68.6
Sewing machines10,000 units1,040930990990970980970
Exports(percent)4.54.13.26.16.18.212.7
Imports(percent)----------2.32.7
Machine tools1,000 units1,0261,3351,6721,6371,7221,9171,787
Exports(percent)3.21.21.03.210.722.633.8
Imports(percent)0.20.70.81.01.52.32.3
Television sets1,000 units5,40010,00016,70014,60019,30025,10027,700
Exports(percent)----------10.514.2
Imports(percent)73.814.730.49.55.75.14.7
Sound recorders10,000 units1507801,3901,7601,9802,5402,420
Exports(percent)----------48.1111.3
Imports(percent)101.38.910.73.011.36.719.2
CKD and SKD(percent)--------9.86.319.1
Home refrigerators1,000 units1005001,4002,3004,0007,6006,700
Imports(percent)--45.669.921.011.05.76.5
Home washing machines1,000 units1305808808909901,050830
Imports(percent)--34.566.820.76.12.91.5
Motor vehicles1,000 units176316437370472645584
Exports(percent)1.00.10.020.10.40.20.5
Imports(percent)23.747.081.040.619.114.614.7
Bicycles10,000 units1,7502,8603,2303,5704,1204,1403,680
Exports(percent)4.11.81.71.12.03.66.7
Wrist watches10,000 units2,8703,8005,4307,3206,1406,6607,280
Exports(percent)9.746.68.823.144.947.564.3
Imports(percent)25.94.89.63.92.91.6--
Exports as a percent of local consumption
Cloth (fabrics)(percent)18.723.321.227.330.4----
Knitted garments(percent)37.445.026.637.852.8----
Shoes(percent)12.919.011.016.416.0----
Sources: Trade data are based on data in General Administration of Customs, various issues. Output data are based on: World Bank, 1990. Local consumption data are based on State Statistical Bureau, 1988.

Production data are for sugar beets and sugarcane while trade data are for sugar. Therefore, only the trend should be referred to.

Sources: Trade data are based on data in General Administration of Customs, various issues. Output data are based on: World Bank, 1990. Local consumption data are based on State Statistical Bureau, 1988.

Production data are for sugar beets and sugarcane while trade data are for sugar. Therefore, only the trend should be referred to.

Table 16.China: Export Price and Output Elasticities by Commodity
CommodityPrice

elasticity
Change

since

1985
Output

elasticity
Adj.

R sq.
D.W.N
Live pigs (excluding those for breeding)0.06-0.070.092.0040
Live poultry0.28*0.81**0.901.9240
Fresh or frozen beef0.361.86**0.751.8940
Fresh or frozen pork1.23**-0.280.171.8540
Frozen chicken-0.89**-0.16**1.46**0.681.8340
Frozen rabbit-0.48**-0.07-0.070.701.9640
Fresh eggs0.06-0.24*0.372.0140
Aquatic products0.32**-0.07**1.44**0.922.0040
Live fish-0.110.040.301.7328
Frozen fish or fish fillet0.76**2.12**0.702.0228
Fresh or frozen prawns0.532.86**0.711.9628
Other-0.141.96**0.931.9128
Cereals-1.41**1.27**0.921.8740
Rice-1.16**-0.230.751.8540
Maize0.30-1.07**0.611.8624
Soybean-0.330.20*0.620.782.2040
Other (including maize)-1.11**0.42**0.330.941.9140
Other (excluding maize)-1.12**1.09**0.451.8224
Vegetables-0.30**0.81**0.771.7640
Fresh vegetables-0.23-0.030.67**0.531.9040
Day lily-1.01*0.220.152.0032
Dried Ksiang ku-0.58*2.41**0.771.8540
Fungus-0.62**0.71**0.751.8040
Black moss-0.61-0.05--1.8032
Hot pickled mustard tuber0.190.29**0.401.6832
Other-0.271.23**0.871.9540
Fruits0.330.080.222.0340
Mandarins and oranges0.510.850.172.0340
Apples0.98-0.08--1.9840
Other0.29-0.18--1.9840
Bitter apricot kernels0.72**0.36**0.192.1240
Chestnuts0.58-0.040.071.8934
Walnuts in shell-1.76**0.86*0.291.7040
Walnut meat-0.480.19**-0.050.391.9940
Sugar0.93*0.46**-1.170.482.1040
Natural honey-0.470.11*-0.120.482.3440
Tea-0.010.710.731.9740
Dried chilies-0.170.590.221.8040
Canned food-0.090.47**0.521.9840
Canned pork0.480.020.071.9040
Canned vegetables-0.240.58**0.592.0340
Canned fruits-0.35*0.56**0.351.7240
Other-1.55**1.72**0.472.1140
Bean expellers or cakes0.14-0.830.192.1716
Beer-0.140.65**0.531.9640
Flue-cured tobacco-0.66*0.53*0.031.9040
Goatskins-0.59**0.07*-0.94**0.721.9840
Fur skins, raw0.11-0.04-0.080.162.1140
Mink skins, raw-0.270.87**0.111.9440
Weasel skins-0.650.22**-1.85**0.142.0732
Other-0.77**0.09-0.520.281.9740
Edible oilseeds0.120.86**0.701.9540
Peanuts, shelled or not0.641.04**0.571.9540
Other (including sesame)-0.96**0.410.811.9640
Filature silk-0.84**0.86**0.191.8640
Cotton-1.31*-1.080.471.8324
Cashmere-0.050.140.411.9240
Rabbit furs-0.65**0.190.401.7240
Salt-0.12**0.07-1.19**0.611.8632
Fluorspar-0.37**0.83**0.672.0640
Barium sulfate-0.38**0.82**0.661.9640
Talc-0.44**0.88**0.811.9740
Aluminum ore2.00**4.04**0.661.8940
Tungsten ore0.490.770.141.9240
Bristle-0.040.57**0.561.9540
Salted sheep casings-0.17-0.10*0.78*0.081.9032
Salted goat casings0.010.010.001.8932
Salted pig casings-0.35**0.68**0.241.8832
Feathers or down of ducks or geese0.421.43**0.741.7640
Crude drugs-0.58**0.76**0.721.8240
Coal-0.40**1.02**0.841.7840
Coke, semi-coke-0.60*1.61**0.382.0840
Crude petroleum oil-0.080.06**0.110.861.9440
Refined petroleum products0.150.03-0.130.041.9840
Paraffin wax-0.53*0.78**0.591.7340
Petroleum coke-0.99*0.16**-2.19**0.481.8632
Edible vegetable oil-1.65**0.16-1.150.511.8340
Tung oil-0.101.09**0.471.9640
Furfural-0.39*0.37**0.091.9640
Barium carbonate-0.101.29**0.862.0340
Synthetic organic dyes-0.47**2.01**1.891.8940
Lithophone-0.111.91**0.662.0340
Toilet soap@@-1.36**0.12**-1.35**0.901.8432
Detergent0.08-0.09**0.66**0.151.9840
Mosquito coils0.520.070.042.1440
Rosin-0.38**0.59**0.602.1840
Furskin plate-0.39*-0.91**0.792.2040
Rubber tire-0.49**1.56**0.772.0840
Paper and paperboard-0.06**0.10*0.430.8033
Cotton yarn0.200.950.812.2040
Spun rayon yarn-0.390.91**0.651.9540
Flax or ramie yarn-0.98**-2.89**0.411.9916
Cotton woven fabrics0.070.65**0.732.0640
Polyester/cotton woven fabrics-0.020.84**0.802.0640
Rayon woven fabrics-0.34*0.110.281.7440
Spun rayon woven fabrics0.270.74**0.852.2240
Silk piece goods-0.58**1.40**0.822.0740
Woolen piece goods-0.510.050.46*0.551.7440
Gunny bag-0.170.14**-0.91**0.322.1840
Woolen blanket-0.80**1.50**0.222.0040
Carpet-0.68**1.08**0.592.0140
Cement-0.210.19*-2.490.872.0940
Plain glass0.751.98**0.851.8540
Steel products-1.38**0.20**0.870.862.0640
Steel shapes and sections-1.82**3.40**0.851.7227
Steel sheets and plates-2.53*8.76**0.801.8824
Steel plate (more than 3 mm.)-3.26*10.00**0.852.3219
Other (including shapes and sections)-1.70**3.53**0.821.7427
Other (excluding shapes and sections)-0.784.13**0.841.8124
Iron or steel wire-0.85*1.12**0.632.2140
Cast iron tubes0.26-0.15**0.67**0.161.9640
Copper products-1.50*3.04**0.721.7140
Aluminum products@@-2.55**2.91**0.621.9640
Zinc and its alloys, inwrought-0.580.36-0.170.171.9040
Tin and its alloys, inwrought-0.701.13**0.501.9240
Tungsten, inwrought-0.83**0.740.352.0540
Antimony, inwrought-0.051.40**0.661.8040
Kerosene cooking stoves-0.42--0.662.1032
Locks-0.250.76**0.692.0940
Electric motors (not direct current)-0.38**2.13**0.841.9320
Sewing machines-1.10**1.54**0.672.1040
Industrial sewing machines-0.324.80**0.621.7115
Household sewing machines-1.71**1.71**0.772.0027
Machine tools-0.93**2.69**0.912.0840
Lathes-0.49**3.63**0.891.7827
Grinding machines-0.74**4.89**0.902.0027
Shaping machines0.820.060.331.6819
Gear processing machines-0.47*3.72**0.552.0219
Other-0.68**5.26**0.911.8324
Electric fans@@-1.83**-0.13**3.66**0.921.8540
Primary cell of zinc-manganese dioxide-1.23**1.63**0.881.9740
Storage batteries for motor vehicles0.84**0.470.381.9640
Graphite electrodes-0.91**0.200.291.9440
Motor vehicles and chassis-0.283.61**0.462.1040
Bicycles-0.662.23**0.782.1440
Electrical measuring instruments-0.73**2.96**0.862.1540
Cameras-0.61*4.76**0.661.8323
Wrist watches-0.32**-0.17**3.71**0.871.7640
Mechanical wrist watches-1.11**-6.86*0.831.7020
Electronic wrist watches0.182.51**0.751.9320
Clocks-1.12**0.93**0.721.8732
Flashlights-0.66*0.57**0.352.2040
Travel goods, bags, similar containers-1.56**-0.86**0.721.8732
Garments, other than knitted or crocheted-0.05-0.12**1.65**0.911.9940
Garments knitted of wool-0.17-0.21**1.12**0.582.1640
Garments knitted of cotton-0.51**2.10**0.951.9040
Garments knitted of other fibers0.08-0.16**1.21**0.712.0340
Plastic slippers-0.72**0.79**0.401.8940
Leather shoes0.19-0.12**2.53**0.931.9940
Cloth shoes with rubber/plastic soles-0.52*1.92**0.931.9840
Footballs, basketballs, and volleyballs-0.04-0.07*1.04**0.581.9140
Pencils-0.49*1.12**0.832.0640
Umbrellas-0.302.63**0.932.2940
Bristle brushes-0.27-0.07**0.73**0.531.9140
Vacuum flasks0.100.290.352.1440
Explanatory notes:

- Significantly different from zero at the 10 percent level.

- Significantly different from zero at the 5 percent level.

- Significantly different from -1 at the 10 percent level (only commodities with price elasticity lower than -1).

- Significantly different from -1 at the 5 percent level (only commodities with price elasticity lower than -1).

The degrees of freedom are adjusted to account for seasonal adjustments. Regressions with fewer than 20 observations are not seasonally adjusted. All the regressions are corrected for autoregressive or moving average serial correlation.

The commodities are presented with the names assigned to them at General Administration of Customs.

Explanatory notes:

- Significantly different from zero at the 10 percent level.

- Significantly different from zero at the 5 percent level.

- Significantly different from -1 at the 10 percent level (only commodities with price elasticity lower than -1).

- Significantly different from -1 at the 5 percent level (only commodities with price elasticity lower than -1).

The degrees of freedom are adjusted to account for seasonal adjustments. Regressions with fewer than 20 observations are not seasonally adjusted. All the regressions are corrected for autoregressive or moving average serial correlation.

The commodities are presented with the names assigned to them at General Administration of Customs.

Table 17.China: Import Price and Output Elasticities by Commodity
CommodityPrice

elasticity
Change

since

1985
Foreign

currency

reserves
Output

elasticity
Adj.

R sq.
D.W.N
Cereals-0.68*-0.16**-0.22**1.00**0.551.9938
Wheat-0.53-0.20**-0.19**1.31**0.531.9838
Maize $@@-5.92**0.35-0.470.161.4928
Sugar-0.630.310.331.9640
Coffee and coffee extracts@@-1.58**-0.351.450.602.0438
Cocoa beans@@-0.04-0.61**-0.320.092.0035
Cigarettes@@-0.380.010.662.1220
Bovine and calf skins, raw@@ 0.89**-1.96**0.681.8420
Natural rubber@@-0.07-0.07*1.23**0.602.0440
Synthetic rubber-0.92**0.26**-0.560.571.8840
Log-0.050.410.711.9840
Pulp-0.280.30*0.510.371.7538
Cotton@@ 2.80**-1.09**7.700.792.1340
Synthetic fibers for spinning@@ 0.170.23**5.27**0.752.0338
Polyamide fibers-1.27*0.660.270.561.6826
Polyester fibers-0.460.107.70**0.682.1734
Acrylic fibers-0.860.17**0.080.401.8440
Yarn of synthetic fibers-0.200.060.010.641.6940
Yarn of continuous polyamide-1.97**2.680.601.8840
Yarn of polyester fibers-1.30*-0.260.511.9540
Regenerated fiber for spinning@@ 0.55-0.190.480.632.0438
Yarn of regenerated fibers-1.06**6.47**0.782.0040
Rayon@@-2.34**6.940.871.5740
Wool-0.060.270.440.462.0237
Sulphur-0.89-9.56**0.431.9828
Diamonds suitable for industry@@-0.42**0.52**-0.340.512.2429
Iron ore@@ 1.28**0.160.620.541.6340
Copper ore0.61-1.760.042.0120
Alumina (aluminum oxide)@@ 0.260.19-0.130.252.0418
Chromium ore@ -0.261.18**0.252.0137
Crude drugs-0.95**0.050.290.881.9837
Coal@@-0.71**-0.17*0.200.812.1738
Refined petroleum products $@@-2.59**1.25**-0.030.501.9120
Animal oils/fats, unprocessed-0.040.950.002.2035
Edible vegetable oils@@-3.50**3.82**0.842.0240
Other vegetable oil-0.86**-0.27**2.94**0.861.9138
Oil seeds/oleaginous fruit $@@-2.39**-0.47-1.260.362.1132
Butyl alcohols@ -0.170.55*-8.44**0.872.0818
Octyl alcohols@@-4.23**0.96-2.310.561.8017
Ethylene glycol@@-0.09-0.21-0.030.472.0218
Phthalic anhydride $@@ 3.04*0.95-1.240.361.9120
Esters of terephthalic acid@@-5.34**-5.250.831.8920
Caprolactam $@@ 0.620.251.28**0.401.9840
Sodium hydroxide@ -0.21-0.040.432.0740
Neutral sodium carbonate-0.66-0.180.531.8940
Synthetic organic dyes-0.93**1.06**0.871.9740
Manufactured fertilizers@@ 0.01-0.14**1.69**0.711.8835
Urea $@@ 0.160.501.64**0.501.9040
Ammonium sulfate@@-2.96**-0.73-1.740.571.9626
Plastic materials in primary form
Polyethylene-1.26**0.25*1.29**0.271.6338
Polypropylene@@ 1.08**0.22**0.030.371.8738
Polystyrene/its copolymers-1.31*3.73**0.792.2840
Polyvinyl chloride-1.040.21**1.40**0.581.9540
Pesticides@ -0.48*-0.40**0.54**3.11**0.682.0538
Bovine or equine leather@@-0.72**0.22**0.010.711.9218
Plywood@@ 1.68**1.06**0.571.8820
Paper and paperboard-0.76**0.28**-0.130.381.7040
Newsprint-0.450.64**-4.06**0.231.8940
Wool top-0.430.34-0.080.121.8018
Cement-0.19-0.15-0.400.911.8326
Pig iron and cast iron@@ 1.64-2.29**0.482.0220
Steel billets/roughly forging-1.78**-5.10*0.702.2531
Steel products-0.85**-0.01-1.030.931.4738
Steel wire rods-0.35-4.22**0.911.9527
Steel bars and rods-1.70-8.20**0.792.0815
Steel shapes and sections-0.05-4.60**0.861.9627
Steel plate (over 3 mm. thick)-0.67-0.740.441.9227
Steel plate (4.75 mm. or more)-2.23**1.130.341.9815
Steel plate (3 to 4.75 mm.)-1.63**-0.580.622.0216
Steel plate (less than 3 mm.)-1.29*-0.030.100.671.7326
Seamless steel pipes-1.59**-1.110.691.9327
Iron or steel wire@@-2.46**0.48**-0.660.821.9040
Nonferrous metals, inwrought
Copper and its alloys@ -4.50**1.03**3.66**0.412.3838
Aluminum and its alloys0.27-1.390.452.4340
Zinc and its alloys-2.55**0.27*-3.08**0.672.1436
Stranded iron and steel cables@@ 0.73-3.61**0.721.9820
Electric motors and generators@@-0.77**-0.402.19**0.812.0018
Open-end spinning frames-0.60-1.990.001.9115
Knitting machines $@@-0.22-0.140.170.601.3620
Electronic color scanners0.100.03-0.020.002.1314
Machine tools@@-0.39**0.16**2.43**0.941.9740
Numerically controlled $@@-0.230.31-0.010.282.0316
Household refrigerators/freezers-0.73-3.31**0.691.6627
Air conditioners-0.92**0.360.842.1220
Electronic calculators $@@-0.280.79**-1.000.481.6732
Copying apparatus@@-2.05**0.55**-2.88**0.792.1376
Complete digital data processing machines
Word length 16 bites or less@@-0.65**0.27*-2.84**0.852.1324
Word length more than 16 bites $-1.06**0.38-1.000.822.0020
Central processing units-0.96**-1.66**0.482.2220
Television sets
(including video monitors)@@ 0.203.690.681.7240
Color@ -2.02**0.82**-2.31**0.891.8425
Black and white@@-3.02**-6.54**0.802.1918
Video recorders or reproducers@@-1.50**-1.68*0.752.0524
Television cameras@@-0.53**0.02-1.25**0.312.0418
Sound recorders-1.06**0.140.670.652.1438
CKD and SKD@@-0.15-1.29**1.000.452.0014
Household washing machines@@-3.00**0.53-1.800.911.9826
Television picture tubes, cathode ray-0.480.070.710.782.1818
Motor vehicles and chassis-0.84**-0.200.850.861.7838
Saloon cars (including CKD and SKD)@@ 0.82--1.310.911.9138
Jeeps-1.71**-1.550.702.1327
Minibuses (9 or fewer seats)-0.771.960.292.1620
Combined passenger-cargo cars-1.58**0.32-0.060.591.9918
Trucks (including CKD and SKD)-0.200.220.640.682.0738
Dampers (including CKD and SKD)@@ 0.050.850.442.2640
Buses (30 seats or more)-0.010.82**-0.160.451.7622
Buses (10-29 seats) $@@-2.78**1.05**0.500.741.8624
Chassis with engines $0.181.26**1.610.302.0834
Motorcycles $@@-4.52**-1.29**-0.420.722.0620
Aircraft@@-0.12-0.21-0.270.242.2518
Ships@@-0.52**0.341.38**0.522.0338
Watches@@-0.45**0.060.751.7231
Cameras-1.18**-3.94**0.821.9424
Professional $@@ 0.08-0.11-0.140.392.0120
General-1.51**-2.82**0.431.8220
Explanatory notes: The commodities are presented with the names assigned in General Administration of Customs, various issues.The variable used to estimate the price elasticities and their change since 1985 are IRYUPI and DYUP85, respectively. Output elasticities are estimated using SINOUT. The degrees of freedom are adjusted to account for seasonal adjustments. All the regressions are corrected for autoregressive or moving average serial correlation.For variables definitions see Annex III.

- Significantly different from zero at the 10 percent significance level.

- Significantly different from zero at the 5 percent significance level.

- Significantly different from -1 at the 10 percent significance level.

- Significantly different from -1 at the 5 percent significance level.

- Indicates that the foreign reserves value (RESQUA) is not lagged. In all the other regressions the variable is included with a lag of two quarters.

Explanatory notes: The commodities are presented with the names assigned in General Administration of Customs, various issues.The variable used to estimate the price elasticities and their change since 1985 are IRYUPI and DYUP85, respectively. Output elasticities are estimated using SINOUT. The degrees of freedom are adjusted to account for seasonal adjustments. All the regressions are corrected for autoregressive or moving average serial correlation.For variables definitions see Annex III.

- Significantly different from zero at the 10 percent significance level.

- Significantly different from zero at the 5 percent significance level.

- Significantly different from -1 at the 10 percent significance level.

- Significantly different from -1 at the 5 percent significance level.

- Indicates that the foreign reserves value (RESQUA) is not lagged. In all the other regressions the variable is included with a lag of two quarters.

ANNEX I Data

The data used in this study are Chinese customs quarterly data on imports and exports, covering the period from the first quarter of 1981 to the fourth quarter of 1990. The first section of this annex describes the data base, the second discusses its advantages relative to other existing data bases, and the third section describes the indices calculated in this s tudy.

a. Data description

The data are classified by UN Standard International Trade Classification (SITC) (Rev. 2) one-to-five digits with some modifications. However, especially in the early period, data were mainly reported at the two-and-three digits level of disaggregation. Even in the later period some commodities are only reported at SITC three digits. The number of commodities reported at the more detailed level increased substantially over the period, especially with regard to imports. This reflects, in part, the increased variety of traded goods. However, it also reflects the refinement of statistical reporting in parallel with the increased value of trade. As the value of trade in a commodity increased, it began to be reported separately and not only as part of its commodity category.

The coverage of the data is comprehensive, especially for exports (Table 18). Large additions to the number of reported commodities took place in 1985 and 1988 when the volume of trade increased significantly. Coverage excluding trade of the processing industries is also reported, because trade by these industries inflated the value of trade as described in Annex II.

Table 18.China: Coverage of the Unit Value Indices Sample, 1981-90(In percent)
198119831984198519861987198819891990
Share of sample commodities in total exports625959655451585857
Excluding exports after inward proccessing625959716057676969
Share of sample commodities in total imports736856555045525244
Excluding imports for inward proccessing736856585451595952
Source: Calculations based on data in General Administration of Customs, various issues.
Source: Calculations based on data in General Administration of Customs, various issues.

The data include all the commodities passing Chinese customs, and the report includes both value and volume. In this way one can calculate unit values for the different commodities as well as for exports and imports as a whole.

The data exclude goods under entrepôt trade without entering China’s territory and goods entering China only for transportation purposes. They also exclude temporary imports and exports with a subsequent re-export or re-import contract within a specified period of time.

Imports are reported at c.i.f. value at the first port of entry into China, while exports are reported at f.o.b. value at China’s port of shipment. Data are converted between U.S. dollars and yuan using the official exchange rate. Trading partners are determined by the last location of processing for imports and by the location of consumption or further processing for exports. The timing for each transaction is determined by the date the good was released by customs.

b. Comparison with alternative data bases

The publication of the Chinese customs data base allows one to analyze the trade structure, quarterly unit values and income and price elasticities in a more detailed and accurate way than was possible in the past, although some limitations still exist.

Until 1978, China did not report trade statistics as part of a policy not to publish vital national statistics. Between 1978 and 1981 the Foreign Trade Department (FTD) began to publish statistics on the total value of trade. However, the data suffered from several deficiencies that made their comparability to other countries’ data limited.

The FTD data reported transactions based on the date contracts were signed and not on the date that goods actually crossed the border. As a result, foreign aid data or imports for processing were excluded from the data. The data were only annual and therefore limited the possibilities for analysis.

Until 1981 the only data available for analyzing China’s trade structure was based on reports by trading partners. Several organizations, including the Central Intelligence Agency, the Institute of Developing Economies in Tokyo, the International Monetary Fund, and the United Nations, calculated trade figures by commodity from reports of China’s trade partners. There were, however, some shortcomings to this method that impeded the accuracy of the data. First, China’s trade with other centrally planned economies was not accounted for, because these countries did not report their trade figures. Second, the need to rely on reports from many countries creates compatibility problems in fitting the data to identical periods and commodity categories. A number of countries, especially developing ones, do not report their data on a regular basis or by standard commodity classifications and this limits the availability of data for the series. Finally, since China’s exports are reported as the trading partners’ imports from China, the data are usually reported as c.i.f. This increases the value of Chinese exports relative to other countries that report exports as f.o.b., and can also bias trends over time if the c.i.f./f.o.b. ratio changes. In the same way, the generated import figures for China were too low.

The large trade of many countries with China via Hong Kong and Macao presents another problem for data collection. Many shipments are not directed to China but to Hong Kong and Macao and they appear in this way in the reporting country’s statistics. However, the commodities are transferred directly to China without being recorded in Hong Kong’s or Macao’s customs. In this way, some of the value of trade with China “disappears” along the way. 1/

However, some limitations should be noted about the Chinese customs data base as well. The level of disaggregation is limited compared with reports of other countries. Volume data for some SITC two-digit sections are not reported at all and for others they cover only a small fraction of the traded products. This may affect the accuracy of the indices calculated using these data. Reporting more data at SITC four- and five-digit levels and the use of finer volume units would help to improve the data base.

c. Computation methods

The significant share of trade covered by the customs data base allows one to calculate unit value indices for total of imports and exports. Those indices were calculated by using unit value indices for the individual commodities and then combining them into an aggregate.

The computation of an aggregate price index raises some theoretical problems when the weights of the included goods change over time. An even more complicated problem is that of introducing new goods into the index. When the composition of goods changes over time, the exclusion of the new goods from the index creates a missing picture of price changes. If the change is large (as in the case of China), 2/ adaption of the weights and inclusion of the new goods is required.

Divisia (1925), Roy (1927), and later Stuval (1957), offered proof that the use of a continuously chained index with adjusted weights will lead to an index that will satisfy the conditions:

Where V is the value index, P is the price index, and Q is the quantity (volume) index.

Where 0 is the base period.

Condition 2, the circular condition suggested by Stuval (1957), imposes a requirement on P(t) and Q(t) separately. Stuval shows that the chained index satisfies this condition while fixed weights indices do not. Condition 2 gives:

Allen (1975) shows that the use of chained indices in discrete time leads to good approximation. The adoption of yearly chained indices allows one to look at price indices that reflect the current composition of trade, referring both to changes in weights and the introduction of new goods.

Unit value and volume indices were calculated by using a Laspeyres- type chained index. Weights were adjusted annually and new goods were included from the year they appeared in the data. There is still a loss of information because the change in volume from zero to the first reported level is not accounted for. This affects the volume index significantly because the number of new commodities that were introduced into the data was large. Therefore, at the analysis stage, derived volume indices were used, which were obtained by dividing the value index by the unit value index. 1/ The problem of composition does not affect the analysis of single commodities. 2/ However, since most of the reported commodities are aggregates of several commodities, changes in unit values can reflect both a change in price or a change in the category’s composition. As the data become more disaggregated, the importance of category composition declines.

Unit values were calculated by dividing the value reported for each commodity by the reported volume. Since, for some commodities, the reported volume units were too crude, some information about unit values was lost.

The quarterly indices used in the regressions were calculated by using yearly weights in the same method as for the yearly indices. In some specifications the data were adjusted for seasonal changes that, in the case of China’s trade, were quite large. The adjustment was made on a quarterly basis using the trended regression method. 3/

The weights used for calculating the indices were value weights. Since only commodities with reported volume could be used to calculate unit values, each commodity’s weight was its value divided by the total value of commodities with reported volumes. Since the export unit value index was strongly affected by oil prices, separate export unit value indices were calculated, including and excluding petroleum products.

The use of sample weights instead of weights in total trade may bias the indices and therefore they should be used with caution. One method to overcome this problem is to estimate unit value indices for the different SITC categories and then calculate an aggregate index using total trade weights. However, this method assumes that other commodities in the same SITC category predict better the change in the price of an unreported commodity than the average change in price for all the commodities. In the case of China’s trade, the most under sampled SITC categories are the “machinery and electronics” category and several categories of “goods classified by material.” A comparison of changes in world prices of these products to the prediction achieved by reported commodities in these categories shows large differences and therefore this method was not used to adjust the weights of under sampled commodities. A discussion of the possible effects of this problem appears in Chapter III.

ANNEX II Inward Processing of Products

The category defined as “products exported after inward processing” is singled out because of its large size and because it represents an export procedure that differs from other commodities. The size of this category hints at the importance of the special economic zones and foreign investment in the development of China’s trade through the 1980s.

Exports in this category have appeared in the trade statistics since 1985. They mainly reflect processing in the special economic zones along the border and involve enterprises operated primarily by investors from Hong Kong (and also Taiwan Province of China and Macao). Their value and share in total exports are reported in Table 19. The composition of this category is reported only since 1988 and is about 30 percent machinery and electronics, 27 percent textiles and garments, and 12 percent toys. The rest of this category is unknown.

Table 19.China: Trade By Inward-Processing Industries, 1985-90(In millions of U.S. dollars)
198519861987198819891990
Exports of goods after inward processing2,3863,4604,7466,4868,23010,454
As percent of total exports8.711.112.013.616.116.8
Imports of goods for inward processing2,0013,5614,8916,6747,0028,708
As percent of total imports4.78.211.312.111.816.3
Source: Calculated from data in General Administration of Customs, various issues.
Source: Calculated from data in General Administration of Customs, various issues.

For China the fast growth of inward processing and assembling can be a source of technological advancement without a cost in foreign currency to the economy. The foreign investors employ Chinese labor force in these enterprises, and then use their existing marketing channels in the developed world to export the processed goods. In this way, Chinese labor force is trained at these enterprises, with secured markets for the output.

However, the size of value added generated to the Chinese economy from inward processing is not clear. There is a comparable commodity category in the import statistics, 1/ but since the ownership of factories is mostly foreign, and management, sometimes even at intermediate levels, 2/ is also largely foreign, the difference between exports and imports of the processing industries is an overestimation of the value added to the Chinese economy. In 1986-88, the value of imports of these industries was even larger than that of exports, reflecting the fast growth of these industries. Owing to the sequence of production, imports grew before exports and this resulted in an excess of imports over exports. During 1989-90, exports in these industries exceeded imports and in 1990 the trade surplus of these industries was $1.7 billion.

ANNEX III Variable List (All Variables in Natural Logs of an Index Where 1982 Q-1= 100)
DUMM85- A dummy variable, equals 1 for 1985 Q-1 to 1990 Q-4.
DUMM88- A dummy variable, equals 1 for 1988 Q-1 to 1990 Q-4.
DUP85,DUP88 - IUP multiplied by DUMM85 and DUMM88, respectively.
DYUP85,DYUP88 - IRYUPI multiplied by DUMM85 and DUMM88, respectively.
ERDUPI- Export unit-price index, excluding petroleum, in dollars deflated by the U.S. producer price index.
EUPNOP- Export unit prices, excluding petroleum, in dollars.
EVONOP- Export volume excluding petroleum.
IEYUPI- Real import prices in yuan. Calculated like IRYUPI using the secondary exchange rate for the period 1981-84.
INOUTP- Industrial output in fixed prices.
IRDUP- Import unit price in 1981 Q-1 dollars deflated by the U.S. producer price index.
IRYUPI- Real import prices in yuan. Calculated from IUP like RYUPNP.
IUP -Import unit prices in dollars.
IVOSES- Seasonally adjusted import volume, only for commodities with reported volume.
RDUP85,RDUP88 - IRDUP multiplied by DUMM85 and DUMM88, respectively, in the import equations. ERDUPI multiplied by DUMM85 and DUMM88, respectively, in the export equations.
RESQUA- The size of foreign exchange reserves in import-quarter equivalents (not in logs).
RYUPNP- Real export prices in yuan, excluding petroleum. Multiplied by the official exchange rate and divided by the retail price index.
RYUP85,RYUP88 - RYUPNP multiplied by DUMM85 and DUMM88, respectively.
SEVNOP- Seasonally adjusted EVONOP.
SINOUT- Seasonally adjusted INOUTP.
STOEVO- Seasonally adjusted total export volume.
SWORTR- WORTR, seasonally adjusted.
TERMTR- Terms of trade. Calculated as IUP/EUPNOP.
TOEUPI- Total export unit prices in current dollars.
TOIVOS- Seasonally adjusted total import volume.
UPN085, UPN088- EUPNOP multiplied by DUMM85 and DUMM88, respectively.
WORTR- World import value in 1981 Q-1 dollars, deflated by the U.S. producer price index.
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1/Work on this paper began during my summer internship in the then Asian Department. Comments of and discussion with Messrs. Michael Bell, David Bloom, David Burton, David Card, Martin Fetherston, Ronald Findlay, Hoe Ee Khor, and Jeffrey Taylor are greatly acknowledged. The paper also benefited from discussions in the Applied Economics Dissertation Seminar at Columbia University. Yael Brender and Natalie Hairfield provided research assistance, Rosanne Heller editorial advice, and Florence Lee secretarial help. The conclusions and all remaining errors are solely mine.
1/It should be noted that the term “country” used in this report does not in all cases refer to a territorial entity that is a state as understood by international law and practice. The term also covers some territorial entities that are not states but for which statistical data are maintained and provided internationally on a separate and independent basis.
2/More than thirty five percent of GNP in 1989 (World Bank, 1990).
1/Many of the town and village enterprises (TVEs), which are an important part of this sector, are owned by local governments.
2/For a comprehensive discussion of all the components of the reforms see: Blejer and others (1991), Harding (1987), and Riskin (1987).
3/For discussion of rural reforms see also Lardy (1986) and Perkins (1988).
5/See also Perry and Wong (1985), World Bank (1989a, 1989b).
1/See Hsu (1989a)Walden (1989) and discussion in Chapter IV.
3/The name then was The Ministry of Foreign Trade.
1/See Pitt (1981) for similar discussion.
2/See Hsu (1989b) for discussion.
3/These centers were originally established in 1986 to permit foreign exchange transactions between foreign investment enterprises.
1/The term effective exchange rate is defined here as the amount of domestic currency received by a domestic enterprise, or FTC, for a unit of foreign currency. This definition of the effective exchange rate is distinct from the often used definition of the effective exchange rate expressed in terms of a weighted basket of foreign currencies.
2/The real exchange rate is calculated by dividing the official exchange rate against the U.S. dollar by the retail price index. The retail price index is used because it is the only price index in China that is reported quarterly. It may not be the best indication of local prices of producer goods.
1/In 1988, comparing the last three quarters of 1988 to 1987.
1/Imports increased by less than 3 percent annually during 1980-83, and by 1 percent during 1985-87, and decreased between 1988 and 1990.
1/See discussion in Annex I.c.
2/This adjustment is performed by weighing petroleum products by their share in total exports, instead of their share in the sample.
3/That is because the share of unreported commodities in this category in imports is larger than their share in exports.
1/This rate is almost identical to the change in unit value of textiles and garments that is reported in the Chinese customs data base.
2/The importance of such diversification is discussed by Blackhurst and others (1977), Hughes and Newberry (1986), and Kruger (1980).
3/The reported UVI for the machinery and electronics category in Table 6 is only suggestive. The index was calculated only since 1986 because the coverage in the early years of the decade was not sufficient to calculate an aggregate index for this category. Therefore, only selected commodities are reported in this category before 1986. In addition, price changes of many commodities in this category do not represent genuine price changes but changes in quality or even size of the imported product. Many commodities were not included in the index because only the value of their imports was reported, or because price changes were related to changes in the essence of the commodity (for example a decrease of 98 percent in the price of ships).
4/For calculation methods and discussion of limitations, see Annex I.
5/This result holds when one adjusts the weights in the unit value indices from sample weights to weights in total trade.
1/In particular, quarterly data about fiscal policy, tariffs and other trade restrictions, and domestic production by industry were not available.
1/For a few exported commodities, China’s market share in some of its export markets may be large enough to affect the price. For further discussion and corrections of this problem see section 4.b.
2/The countries used in the empirical analysis were El Salvador, Greece, India, Korea, and Thailand.
3/See Corden (1987) for theoretical discussion.
4/These variables are constructed by multiplying the price variable by a variable that has a value of 1 for the relevant period and 0 elsewhere.
2/See also Hsu (1989b) for indications to this correlation.
3/Byrd (1989) discusses the importance of marginal prices in the Chinese economy if firms are maximizing profits.
4/Since unit values are calculated by dividing the value of trade by the traded quantity, any measurement error in the volume data will affect the unit value data in the opposite direction.
1/The industrial production index is used as a proxy for income since quarterly GDP or national income data for the whole period are not available. The possible effects of the approximation are discussed in section 4.b.
2/The UVI sample includes only commodities for which both value and volume data were reported. The volume index for these commodities was calculated, as described in Annex I, using only the import value of these commodities. The volume index for total imports is based on the total value of imports and the UVI calculated using the sample commodities. The difference in commodity content between the two indices is mostly in the “Machinery and Electronics” category.
3/For computation methods see Annex I.c.
4/For a discussion of the possible effects of serial correlation, see Granger and Newbold (1974).
1/The statistical difference between the elasticities estimated using yuan and dollar prices is not significant.
2/This may be interpreted as an indication that the income elasticity of these commodities reflects, to a large extent, the Government’s willingness to allow imports.
1/However, in some specifications of the equation it could not be determined that the elasticity is larger than 1 at the 5 percent significance level.
1/The equations that include petroleum are not reported in the tables. They did not differ from the reported equations with regard to output elasticities. Price elasticities in these equations were close to zero.
1/This results from the correlation between seasonal changes in industrial production and exports.
2/The estimation of a two-stage least squares models where the volume of imports is instrumented by industrial production and then used as a right-hand side variable in the estimation of export volume supports this possibility. The coefficients for the import volume index in the second stage ranged between 0.38 and 0.65 and the adjusted R square values were between 0.97 and 0.98 (D.W. statistics ranged from 2.05 to 2.18).
3/In many cases, the difference is not statistically significant.
4/For discussion of the ratchet effect see Dearden and others (1990), Freixas and others (1985), Holmstrom (1982), Laffont and Tirole (1988), and Weitzman (1980).
1/However, in all cases the sum of the current and lagged price elasticities remains negative. When only lagged prices are included in the equations the sign is negative and in many specifications is significantly different from zero.
2/For discussion of two-stage least squares models, see Maddala (1977).
1/This problem also affects the determination of the directions of trade using the Chinese customs data base but not the total value of trade.
2/For example, all the imported commodities that were reported in the Chinese customs data base in 1981 constituted only 42 percent of total imports in 1989.
1/However, this procedure assigns all the change in value at the first period the product is introduced in the data to volume. Economically, this may not be a plausible assumption.
2/Changes in the quality of a commodity may still be the cause of price changes even for single commodities.
1/The title of this category is: “Materials and component parts imported for inward processing or assembling.” It does not include imported equipment for the production process.

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