J4ao8 THE WORLD BANK ECONOMIC REVIEW, VOL. 4, NO. 2: 1 35-1 S 6 OnteFILE 0py IY (Y1jO On the Accuracy of conomic Observations: Do Sub-Saharan Trade Statistics Mean Anything? Alexander J. Yeats African governments are being urged to promote commodity exports, yet without reliable trade statistics it is difficult to formulate appropriate policies to achieve this goal. This article assesses the accuracy of U.N. trade statistics by comparing the declared value of African exports, plus a transport and insurance cost factor, with partner countries' reported import values. The results show that major discrepancies often exist between the two, with false invoicing and smuggling apparently responsible for much of the difference. Although major disparities exist in data on trade with developed countries, the average differences in intra-African trade statistics are substantially larger. Statistical tests show that these data cannot be relied on to indicate the level, composition, or even direction and trends in African trade. For more than thirty years economists have been aware of and attempted to correct discrepancies in developed countries' trade data observed in matched export and import statistics (Allen and Ely 1953; Ely 1961; Morgenstern 1963; U.N. Economic and Social Council 1974; Yeats 1978; OECD 1985). Much less attention has been given to the quality of developing countries' trade data because the potential for such analysis was limited by the lack of comparable disaggregated time-series information. It is clear, however, that there are im- portant reasons why such investigations should be undertaken. For example, efforts have been made to increase trade among developing countries through regional arrangements, such as the Andean Group, or through the recent plan for a Global System of Trade Preferences (GSTP) under which tariff concessions could be exchanged among all developing countries. The design and evaluation of these integration efforts require accurate and up-to-date information on participating countries' trade. On a broader scale, errors in developing country trade data could adversely influence government policies relating to investment, balance of payments, initiatives for the liberalization of trade barriers, ex- The author is an economist in the International Economics Department of the World Bank. He greatly benefited from an early discussion with Wolfgang F. Stolper on the methodological approach to be used in and the need for this study. The author would like to thank Jong-Goo Park, Paul Meo, Alfred Tovias, and Bela Balassa for comments and suggestions. @1990 The International Bank for Reconstruction and Development / THE WORLD BANK. 135 136 THE WORLD BANK ECONOMIC REVIEW, VOL. 4, NO. 2 change rate policy, and a host of other factors that affect a nation's industrial- ization. In an attempt to evaluate the quality of statistics for one important group of developing countries, this study applies the trade reconciliation and evaluation procedures used on statistics from the Organisation for Economic Co-operation and Development (OECD) (Ely 1961; OECD 1985; Yeats 1978) to reported data on trade among Sub-Saharan African countries and between them and devel- oped countries. This study draws on previous research which identified eco- nomic and statistical factors that contribute to discrepancies in partner coun- tries' trade data. Bhagwati's pioneering studies (1964, 1967) showed that subsidies may encourage exporters to "overinvoice" shipments, whereas high tariffs create an incentive to underreport imports. The result may be that reported exports exceed matched imports (see also Sheikh 1974, Wulf 1981, and Gulati 1987). Overvalued exchange rates and foreign exchange controls may have the opposite effect. When the domestic currency is overvalued and exporters must turn in foreign exchange at the official (low) rate, there is an incentive to "underinvoice" and sell the unreported currency balance on the black market. Similarly, when the importing country has an overvalued ex- change rate and foreign exchange controls, the importer may overinvoice to obtain excess foreign currency and sell the balance on the black market. In this case, reported export values and quantities may be much smaller than reported imports. Higher import values than matched exports may also be the result of capital flight. Restrictions on private holdings of foreign assets may be adopted to induce domestic capital investment, and to reduce the demand for scarce for- eign exchange (sometimes in response to erosion of the value of the domestic currency). If an exporter is able to make necessary arrangements overseas, by underreporting foreign exchange earnings the excess can be placed in accounts or assets abroad. Similarly, by overinvoicing imports, the excess "paid" to the supplier can be deposited in accounts abroad. Although false invoicing often can be detected in matched trade data, a disquieting fact is that combinations of incentives may actually be self-disguis- ing. Trade data may show few discrepancies if an exporter earns subsidies while an importer is trying to accrue "extra" foreign exchange; both face incentives to overreport the value of shipments. Or, if an exporter wants to avoid export taxes while the importer faces import tariffs, both may underreport transac- tions. If the partners recognize their mutual interests in such false reporting and collude in it, the data may look quite consistent. A further important point is that all inconsistencies in trade data should not be attributed to illicit activities; a range of legitimate factors can cause differ- ences. Shipping costs, diversion en route, re-export of goods, differential time lags in reporting, multiple exchange rates, and differences between countries in commodity classification and valuation procedures all may cause discrepancies between matched trade data. Because exports are reported "free on board" Yeats 137 (f.o.b.), whereas imports normally include "cost, insurance, and freight" (c.i.f.), imports should exceed exports by the value of transport and insurance charges. In cases in which importers pay in advance (providing credit for delivery), the exporter may deduct finance charges from reported (f.o.b.) values, although these costs may be included in the c.i.f. import value. This would further widen the margin resulting from f.o.b. (export) and c.i.f. (import) reporting practices. Variations in exchange rates may cause trade data discrepancies. Exports and imports are first recorded in their respective national currencies, and if different official exchange rates are used to convert them to a uniform currency (say U.S. dollars), or if exchange rates change over the period of reporting, a disparity in the export and import figures will result. Another problem is that declared invoice values may be adjusted by customs authorities ("up-lifted") for assessing import duties and other taxes. Because these adjusted values are used in the importing country's official statistics, they may not correspond with those recorded by the exporting country. A problem that seems to be particularly prevalent in the African countries studied here is reporting discrepancies for transshipments in which goods are routed through countries bordering the exporter or importer. In these cases, the country of origin may inaccurately list a routing country as the importer, or the country of final destination may report the routing country as the exporter. A range of discrepancies may thus appear between the three (or more) parties to the transactions. Finally, the U.N. Statistical Office has followed some procedures that cause discrepancies in the African data to be underestimated. In several cases, the exact figure reported as imports by a partner country was inserted in the African country's export records and then designated as an estimate of trade. In these cases, there would be no differences between partner country trade data because the matched reported import figures were being used for both exports and imports. It was also clear that, in some instances, reported bilateral African trade for a given year was merely a reproduction of records relating to a different year. For example, the 1982 and 1983 records on Zimbabwe's imports from Malawi were generally identical down to the three-digit level of the Standard International Trade Classification (SITC). I. DATA AVAILABILITY: COVERAGE OF PERIOD AND PRODUCT The exclusive focus of this analysis is U.N. trade statistics, including the U.N. Series D Commodity Trade Tapes. They are the sole sources of developing and developed country export and import statistics which use a common clas- sification system-sITc-and are the most widely used source of data on South- South trade. Although national government publications may be available, they use varying product classifications that preclude accurate comparisons across countries. The United Nations reclassifies the government data to the SITC system using available concordances. Many policy or research studies are shaped 138 THE WORLD BANK ECONOMIC REVIEW, VOL. 4, NO. 2 as much by the period and level of aggregation of the data as by the specific theoretical or empirical question being addressed. In addition, studies are con- strained by the interval or time span for which national trade statistics are available, particularly if trends or long-term changes in the commodity struc- ture of trade are being examined. Trade statistics for the developed countries with market economies are normally available from U.N. sources with a one- year lag. A continuous time series for these countries is available back to 1962 or 1963, with a product breakdown to the five-digit SITC level. In contrast to the situation for the OECD countries, Sub-Saharan African trade data as of mid-1989 generally extended back to 1962, but there are important gaps in the historical record (see table 1). In three cases, Botswana, Lesotho, and Swaziland, no U.N. trade data exist because the trade of these countries is included in U.N. records for the South African Customs Union. In addition, the African countries' records show a much greater reporting lag: only six of the thirty-nine countries' records extend beyond 1983. Table 1. Trade Statistics for Sub-Saharan African Countries Available from U.N. Records as of July 1989 Years Years Region and country available Region and country available Customs and Economic Union Economic Community of the of Central Africa Great Lakes Countries Cameroon 1962-83 Burundi 1968-83 Central African Rep. 1962-83 Rwanda 1963-83 Chad 1962-83 Zaire 1962-83 Congo 1962-85 Other Africa Gabon 1962-83 Botswana Economic Community of West Djibouti 1969-83 African States Ethiopia 1962-85 Benin 1962-83 Kenya 1962-83 Burkina Faso 1962-83 Lesotho C6te d'lvoire 1962-85 Madagascar 1962-86 Gambia 1962-83 Malawi 1964-83 Ghana 1962-83 Mauritius 1962-83 Guinea 1979-83 Mozambique 1962-83 Liberia 1962-84 Seychelles 1967-86 Mauritania 1962-83 Somalia 1962-83 Mali 1962-83 Sudan 1962-83 Niger 1962-83 Swaziland Nigeria 1962-83 Tanzania 1962-83 Senegal 1962-83 Uganda 1962-83 Sierra Leone 1962-83 Zambia 1964-83 Togo 1962-83 Zimbabwe 1979-83 -Not available: classified under South African Customs Union. Note: Records are not available for the following years: Burundi, 1979 (1965 data available); Cote d'lvoire, 1984; Gambia, 1967, 1978; Mauritania, 1976-78; Rwanda, 1977; Senegal, 1977-78; Sey- chelles, 1969-70; Sierra Leone, 1977-78; Zaire, 1971; and Zimbabwe, 1966-78 (1963-65 data available). Data for six of the countries extended beyond 1983. Source: Compiled from U.N. Series D Commodity Trade Tapes. Yeats 139 The problems reflected in table 1 for the African countries do not exist for all, or even most, developing countries, although there have been persistent problems with some, such as India and Indonesia. Most Latin American coun- tries have had data available with a two- or three-year time lag (Mexico and Venezuela are important exceptions), whereas most of the Asian, newly indus- trializing countries have records that are as current as those of the developed countries. In addition, these countries' records normally extend back into the 1960s down to the four- and five-digit SITC level. The level of product detail in official trade statistics is often important in trade and commodity studies. Trade data for developed countries with market economies and many developing countries are compiled at very detailed levels. Many African countries' trade statistics lack this degree of precision and incom- pletely cover total trade even at the three- and four-digit level. In these cases the U.N. trade tapes allocate all trade possible given the available data, but this leaves some (possibly large) portion of trade unallocated. As shown in table 2, most African countries have relatively complete cover- age of total trade down to the three-digit SITC level. Mali is an exception: about one-quarter of its total exports are not reflected in the two-digit classification. But only eleven of the thirty-six countries retain full coverage at the four-digit level. The three-digit product groups are often too aggregated for product- specific studies, such as analyses of the influence of trade barriers (particularly tariff and nontariff barriers), or for investigations which require export and import unit values for fairly homogeneous products. Some problems in the U.N. trade tapes are clearly internal to the compilation process itself. For example, trade reported in component three- or four-digit SITC groups some- times exceeded the total reported at a higher level (see Ethiopia, table 2). II. THE ACCURACY OF AFRICAN TRADE STATISTICS To assess the quality of the African trade statistics, African export data were compared with corresponding partner import statistics. Total exports of the African countries were tabulated for 1982-83, the last period for which data were available for all countries (see table 1). A two-year period was used to reduce the influence of time lags in recording trade flows and irregularities associated with a single year's statistics. Reported imports for the African countries' trading partners were matched with the corresponding African ex- port statistics. The percentage differences, P, between the matched data were computed as: (1) P = [(Ij - Eji) - Ej] x 100 where Ej, are reported f.o.b. export values of African country j to destination i, and Iij are reported c.i.f. imports of destination i from j. 140 THE WORLD BANK ECONOMIC REVIEW, VOL. 4, NO. 2 African Exports to All Partners The total reported exports of the thirty-six African countries and the per- centage difference between the corresponding reported partner countries' im- ports are summarized in table 3. The measured disparities are often large and Table 2. Level of Product Detail in African Countries' Trade Statistics, 1983 Total trade Proportion of total trade recorded (percent) (millions of dollars) Two-digit SITC Three-digit SITC Four-digit SITC Country Imports Exports Imports Exports Imports Exports Imports Exports Benin 348.5 79.4 100.0 100.0 99.6 99.5 - - Burkina Faso 287.5 57.0 100.0 100.0 100.0 100.0 100.0 100.0 Burundi 116.1 99.4 100.0 95.1 99.0 95.0 0.1 0.0 Cameroon 1,187.6 1,836.8 100.0 99.8 97.6 99.2 0.1 0.0 Central African Rep. 71.1 109.4 100.0 99.5 99.5 99.5 - 0.0 Chad 70.6 131.6 100.0 100.0 99.6 100.0 - 0.0 Congo 629.0 639.9 100.0 100.0 100.0 100.0 100.0 100.0 C6te d'lvoire 1,813.5 2,067.7 100.0 100.0 100.0 100.0 100.0 100.0 Djibouti 252.4 33.6 100.0 100.0 99.4 97.2 - 0.0 Ethiopia 587.0 422.6 100.0 98.7 139.9 98.2 0.2 0.0 Gabon 685.6 1,475.4 100.0 100.0 100.0 100.0 100.0 100.0 Gambia 79.4 45.0 100.0 97.4 99.7 97.4 0.1 0.0 Ghana 599.9 512.5 100.0 99.8 98.1 99.6 0.4 0.0 Guinea 252.6 420.5 100.0 99.4 99.3 99.3 0.1 - Kenya 1,379.1 947.3 100.0 100.0 100.0 100.0 100.0 100.0 Liberia 411.6 422.6 100.0 100.0 100.0 100.0 100.0 100.0 Madagascar 411.5 310.3 100.0 100.0 100.0 100.0 100.0 100.0 Malawi 310.5 239.2 100.0 100.0 100.0 100.0 100.0 100.0 Mali 303.8 98.2 100.0 76.9 99.5 76.3 - - Mauritania 350.5 290.7 100.0 100.0 99.8 100.0 0.1 0.0 Mauritius 441.6 360.8 100.0 100.0 100.0 100.0 100.0 100.0 Mozambique 500.1 239.8 100.0 100.0 99.8 99.9 0.1 0.0 Niger 209.3 261.6 100.0 100.0 99.7 100.9 0.1 0.0 Nigeria 7,008.4 12,381.8 100.0 100.0 97.9 99.9 0.4 - Rwanda 148.1 96.9 100.0 99.7 99.2 99.7 0.1 0.0 Senegal 790.1 440.8 100.0 99.7 98.9 98.8 0.1 - Seychelles 87.8 3.7 100.0 100.0 100.0 100.0 100.0 100.0 Sierra Leone 165.7 90.7 100.0 100.0 100.0 100.0 100.0 100.0 Somalia 352.3 149.9 100.0 100.0 99.5 99.8 0.3 0.0 Sudan 1,424.0 601.1 100.0 100.0 99.5 98.9 0.1 0.0 Tanzania 537.7 425.0 100.0 99.6 98.2 99.3 0.4 0.0 Togo 479.9 225.6 100.0 99.3 99.6 99.2 - 0.0 Uganda 257.6 360.1 100.0 99.9 99.6 99.9 0.1 0.0 Zaire 841.7 1,387.9 100.0 98.4 98.5 98.1 0.2 0.0 Zambia 560.8 825.4 100.0 100.0 99.4 99.9 0.2 - Zimbabwe 449.6 672.2 100.0 100.0 100.0 100.0 - - -Negligible (less than 0.5 percent of total trade). Note: All countries reported 100 percent of total trade at the one-digit SITC level. Trade data are not available for Botswana, Lesotho, and Swaziland, which therefore are omitted from this and subsequent analyses. Source: Author's calculations, based on U.N. Series D Commodity Trade Tapes. Yeats 141 far exceed the average 3-6 percent differences observed for trade between developed countries (see Yeats 1978; OECD 1985). Differences of 100 percent or more are frequently observed, and disparities of more than 600 percent are calculated on the exports of the Gambia, Liberia, Niger, and Seychelles. Some proportion of the difference between f.o.b. export and c.i.f. import prices is accounted for by the costs of transport. Because the most accurate and comprehensive data for calculation of transport correction factors are available from U.S. Customs invoice data, the factors and analysis here are based on African exports to the United States (see the appendix). In general, U.S.-based transport margins should be between 5 and 15 percent of the value of f.o.b. exports (the margins for the Gambia, Guinea and Somalia are higher). But, as table 3 demonstrates, there are numerous bilateral trade flows in which the differences greatly exceed these transport cost margins. For example, the high- est recorded nominal freight rate for Gabon's exports to the United States over 1982-87 was about 9 percent, yet table 3 reports a difference in matched f.o.b.-c.i.f. partner data of 75 percent. Niger is the most extreme case: ship- ments to the United States have a maximum freight factor of 9 percent, but the difference between the matched trade data exceeds 300 percent. Given the extremely limited foreign exchange reserves of most African coun- tries, the fact that the reported value of their exports in importing countries exceeds domestic reported values by more than $100 million in several cases takes on special importance. For example, Cote d'Ivoire reported exports of $2.3 billion (billion = 1,000 million) to the EEC, whereas the latter reported imports valued at more than $500 million higher. Discrepancies of more than $500 million also occur on several other bilateral trade flows (for example, Cameroon-EEC, the Congo-the United States, Gabon-the United States, Ni- geria-EEC). Matched export-import data were compiled at the three-digit SITC level for every total bilateral trade flow reported in table 3 that showed a difference of at least $20 million. When possible, matched quantity and unit value statistics were also computed for each of the partner countries. This procedure was adopted to identify specific product groups that generate the overall discrepan- cies, and to indicate if price or quantity differences might cause them. Table 4 summarizes the results of this analysis for twenty-five bilateral trade flows. As indicated, several common factors appear responsible for many of the statistical discrepancies. For oil-exporting countries such as Cameroon, the Congo, and Gabon, the data suggest purposeful underreporting of export quan- tities and values of shipments, possibly to conceal noncompliance with inter- national agreements on production and export quotas. Similarly, quantity and value discrepancies in the coffee and cocoa shipments of Cote d'Ivoire, Ghana, Kenya, and Madagascar may result from attempts to evade quotas established under international commodity agreements. These situations may also reflect, however, false invoicing by exporters to evade foreign exchange controls. The discrepancy for Burundi and the Central African Republic is almost entirely accounted for by precious stones, items that can easily be smuggled out of a Table 3. Reported Exports from African Countries and Their Relation to Matched Partner-Country Imports, 1982-83 Exports to (millions of dollars): Percentage difference between reported imports and exports' All Sub- All Sub- developed United Saharan developed United Saharan Exporting country countries Canada EEC' EFTA' Japan States Africa countries Canada EECb EFTA' Japan States Africa All Sub-Saharan Africa 50,482.1 405.7 27,216.1 1,538.0 1,522.2 17,497.0 3,016.2 14.4 -2.9 15.2 17.5 22.3 11.6 13.1 Benin 91.1 - 50.4 7.5 3.5 28.7 29.2 13.0* 154.5* 20.1* 0.4 5.1 5.1 -96.2 N Burkina Faso 47.3 0.6 39.4 1.0 5.4 0.1 30.8 82.7 -100.0 63.4 141.1 238.8 242.2 -60.2 Burundi 197.0 0.0 113.1 21.7 8.8 47.4 2.2 16.7* 0.0 29.0* -2.5 0.0 0.0 52.9 Cameroon 2,607.5 1.2 1,547.3 15.0 40.6 947.8 91.6 39.8* -1.9 35.9* 55.8* 80.1 " 43.3 36.6 Central African Rep. 135.1 0.0 95.3 2.4 18.5 8.6 2.0 49.3* 0.0 67.8* -29.1 6.2 1.3 11.0 Chad 146.7 0.0 39.4 1.3 3.0 70.6 21.7 3.8 0.0 9.6 0.0 0.0 0.0 -50.3 Congo 1,623.5 0.0 483.1 3.4 12.0 995.2 5.5 46.7* 0.0 36.4* 37.1' 47.8* 54.2* 85.0 C6te d'lvoire 3,176.9 15.0 2,251.2 20.5 100.3 578.5 727.9 25.5* 23.2' 23.8* 207.3' 12.5 20.4* -3.5 Djibouti 10.7 0.0 9.8 0.7 0.0 0.1 38.7 1.3 0.0 1.4 0.0 d 0.0 -81.2 Ethiopia 541.6 2.3 272.0 11.2 50.8 198.2 74.7 11.3 57.2 7.8 50.6* 23.4* 2.8 47.5 Gabon 2,475.7 72.5 1,401.7 64.7 12.8 752.5 55.6 27.2' -88.3 6.5 -30.1 58.6* 75.4* 75.2 Gambia 54.8 0.1 34.1 14.2 0.0 0.4 1.0 19.3 0.0 30.9 0.0 0.0 0.0 1,138.2 Ghana 1,005.9 2.6 516.3 41.1 122.9 280.9 19.8 31.0* 116.5' 4.8 79.6* 15.8* 76.4* -43.1 Guinea 758.1 34.9 281.3 14.6 0.3 304.1 37.3 1.6 0.0 2.5 -1.2 0.0 0.0 -34.4 Kenya 941.7 15.2 710.3 65.1 18.8 115.4 466.2 28.0* 33.1' 24.3* 52.1* 43.8* 27.2' 12.3 Liberia 842.1 2.3 625.2 1.2 9.6 158.7 18.3 82.4 * -95.2 47.8' 1,024.4* 2,011.8* 39.0 137.9 Madagascar 457.9 0.5 275.4 3.1 49.2 118.0 5.7 19.2' 62.0* 6.7 139.5* 72.1* 18.7* -48.4 Malawi 366.3 2.3 242.5 18.4 20.7 32.5 53.1 6.5 160.6'" 8.1 15.5 5.4 51.3" 46.6 Mali 129.3 0.0 106.8 2.2 11.2 0.7 140.1 -12.0 0.0 -15.1 -5.3 4.4 166.1* -87.4 Mauritania 469.5 - 290.7 0.1 132.4 1.3 39.6 14.2 0.0 22.9 7.4 0.0 0.0 -4.5 Mauritius 698.6 8.9 613.1 10.1 0.1 60.7 3.2 7.1 -14.8 8.2 -23.5 -309.1* 10.8 -41.2 Mozambique 292.7 1.0 107.5 3.9 38.2 87.4 56.6 3.3 0.0 7.3 37.3 0.0 0.0 93.3 Niger 532.4 0.1 527.3 - 2.5 1.2 21.8 -3.8 -100.0 -5.3 -100.0 120.6* 327.2* 641.5 Nigeria 25,542.0 208.8 12,285.0 935.5 14.7 11,156.2 624.8 4.1 0.0 7.4 0.1 0.0 0.0 -13.7 Rwanda 154.2 0.0 70.4 14.7 4.1 64.8 8.3 3.6 0.0 7.5 0.7 0.0 0.0 12.1 Senegal 844.4 1.8 663.0 10.0 82.5 21.5 256.1 -19.1 -38.7 -10.2 -68.6 -67.6 -84.1 45.0 Seychelles 1.1 - 0.3 - 0.7 - 0.2 3,806.2 e 8,043.6* 600.0* 125.8* f 89.4 Sierra Leone 182.2 0.0 136.1 5.1 0.8 39.5 3.8 42.1* 40.4* -54.2 59.3* 52.1* 134.9 Somalia 51.3 0.3 49.5 - 0.1 1.3 2.6 7.3 0.0 7.6 3.8 -1.3 -5.1 50.7 Sudan 365.4 3.0 271.7 10.6 41.5 30.3 3.8 37.7* -58.6 23.3* 50.3* 122.3* 24.9* -17.8 Tanzania 598.4 3.7 487.3 35.5 29.3 33.0 47.6 0.3 4.4 -10.0 26.2* 24.2* 45.3 * 177.2 Togo 256.5 0.1 201.3 10.2 4.1 30.9 72.2 12.2 -26.6 14.8 -1.5 5.9 2.8 65.0 Uganda 669.0 1.0 280.5 3.6 42.4 277.2 5.2 3.5 0.0 1.8 430.6" 0.0 0.0 11.9 Zaire 1,805.8 25.8 746.9 31.5 155.4 802.1 26.1 51.9 0.1 124.6* 2.0 0.0 -0.1 -33.7 Zambia 1,308.4 1.6 728.9 97.2 394.0 84.4 26.6 4.2 0.0 6.9 1.4 0.0 0.0 12.0 Zimbabwe 1,100.3 0.0 661.8 61.0 127.0 166.3 65.6 7.7 h 6.0 11.8 0.0 0.0 -16.1 -Negligible. Note: An asterisk (*) indicates that the partner country trade difference exceeded by at least 5 percentage points the maximum recorded nominal freight rate for exports of the African country to the United States at any time between 1982 and 1987. For the African-European data, both a shorter distance and generally larger shipment volumes to Europe suggest that freight costs should be lower than on shipments to the United States, and thus this underestimates the number of cases in which disparities could not reasonably be attributed to transport costs (see the appendix). a. The difference between the value of total reported imports and exports divided by the value of exports, times 100. b. European Economic Community (ten member countries). c. European Free Trade Association. d. Djibouti reported no exports to Japan during 1982-83; Japan reported imports from Djibouti. e. Seychelles reported $6,000 in exports to Canada; Canada reported $33,863,000 in imports from Seychelles. f. Seychelles reported $7,000 in exports to the United States; the United States reported imports of $3,348,000 from Seychelles. g. Sierra Leone reported no exports to Canada; Canada reported $56,000 in imports from Sierra Leone. h. Zimbabwe reported no exports to Canada; Canada reported $7,817,000 in imports from Zimbabwe. Source: Author's calculations, based on U.N. Series D Commodity Trade Tapes. Table 4. Analysis of Differences of More than $20 Million between Matched Reported Import and Export Values, 1982-83 Reported differencea Value (millions Exporter-importer Major commodities traded (percent share) Observations Percent of dollars) Burkina Faso-European Oilseeds (32), cotton (16), hides (10) Roughly half the discrepancy is in oilseeds, for 63.4 25.0 Economic which unit values differ by more than 40 Community (EEc) percent Burundi-EEc Coffee (61), precious stones (16), natural Precious stones imports exceeded exports by 29.0 32.8 abrasives(14) $22.2 million Cameroon-EEC Petroleum (39), cocoa (19), coffee (15) Discrepancy almost entirely due to 35.9 554.8 underreporting petroleum shipment volume Cameroon-U.S. Petroleum (89), petroleum products (4) More than 80 percent of discrepancy caused by 43.3 409.9 underreporting crude petroleum shipments Central African Precious stones (42), coffee (40), cotton (7) Precious stones imports exceeded exports by 67.8 64.6 Republic-EeC $98.2 million ai~ Congo-EEC Petroleum (63), wood (9), precious stones (7) Difference caused by underreporting volume of 36.4 175.6 petroleum exports Congo-U.S. Crude petroleum (94), petroleum products (4) Petroleum imports exceeded exports by $494 54.2 540.0 million Cote d'lvoire-EEc Coffee (26), cocoa (26), wood (16) Coffee import unit value exceeded exports by 24 23.8 536.6 percent C6te d'Ivoire-European Cocoa (41), coffee (17), fruit (16) Cocoa imports exceeded exports by $45 million. 644.1 74.6 Free Trade Differences of $5 million to $10 million in Association (EFrTA) coffee and fruit trade Gabon-U.S. Petroleum (99) Underreported petroleum exports 75.4 567.9 Ghana-U.S. Aluminum (73), cocoa (11), petroleum (7) Aluminum imports exceeded exports by $159 76.4 214.7 million. Major differences in reported quantities traded Ghana-EFTA Cocoa (93), nonferrous ore (5) Cocoa imports exceeded exports by $31.4 94.8 34.6 million. EFTA omits quantities so source of error could not be determined Kenya-EEc Coffee (39), tea (27), fruit (19) Tea and coffee account for about $80 million of 24.3 172.8 the total discrepancy Kenya-U.S. Coffee (51), crude vegetable material (16), Tea imports exceeded exports by $11 million 21.7 31.4 tea (19) Kenya-EFTA Coffee (85), fresh fruit (2) Coffee imports exceeded exports by $34 million. 68.2 41.7 EFTA omits quantities so source of error could not be determined Liberia-Japan Special transactions (88) No "special transactions" (SITC 931) exports 2,001.8 193.9 reported. Japan's imports were $179 million for this item Liberia-EEC Iron ore (66), precious stones (11) Iron ore import unit value exceeds exports by 47.8 298.6 more than 40 percent Liberia-EFTA Ships and boats (98) Liberia failed to report quantities so source of 1,024.4 127.0 error could not be determined Madagascar-Japan Fish (60), coffee (22), spices (7) Over half of the discrepancy is due to fish: 72.1 35.4 exports unit value more than 40 percent below import unit value Madagascar-U.S. Spices (58), coffee (28) A discrepancy of $15 million exists in the 18.7 22.1 reported coffee trade 41 Mauritania-EEC Iron ore (80), fresh fish (15) Difference caused almost entirely by iron ore. 22.9 66.6 Quantity information was not reported so unit values could not be computed Seychelles-Canada Sugar and honey (98) Sugar imports exceeded exports by $33 million b 33.9 Sierra Leone-EEC Nonferrous metals (28), pearls and precious Approximately 44 percent of the total 40.4 55.1 stones (27) discrepancy is accounted for by pearls and precious stones Sudan-Japan Cotton (61), oilseeds (21) Cotton imports exceeded exports by 80 percent. 122.3 50.7 Oilseeds imports exceeded exports by 460 percent Sudan-EEC Crude vegetable material (21), cotton (20), Oilseeds imports exceeded exports by $31 23.3 63.4 oilseeds (15) million. Unit values could not be computed because quantity information was not available a. Value reflects the amount by which reported imports exceed reported exports. Percent expresses this difference relative to reported exports. b. Canada reported $33.8 million in imports; Seychelles reported $6,000 in exports to Canada. Source: Author's calculations, based on U.N. Series D Commodity Trade Tapes. 146 THE WORLD BANK ECONOMIC REVIEW, VOL. 4, NO. 2 country in order to evade taxes and secure foreign currencies. Rationales for the discrepancies for most other commodities are less obvious, with the excep- tion of one or two products which constitute special situations. Differences between official and black market dollar exchange rates were compiled for as many countries listed in table 3 as possible. The differences in exchange rates were found not to be significantly correlated with discrepancies in trade values, possibly because many smuggled goods were not reported in either export or import statistics. If smuggled goods were accurately reported in one of the partners' data, the correlations might achieve statistical signifi- cance. Trade among African Countries Because the discrepancies in data on intra-African trade were generally found to be considerably larger than those on trade with developed countries, more detailed partner country statistics were compiled and analyzed for these ex- changes. Table 5 shows the total reported value of each country's exports to all Sub-Saharan Africa and to its largest trading partner and gives the partner's reported imports. Similar information is also shown for African trade in man- ufactured goods. Major discrepancies in the African data are apparent from table 5. An average difference of 64 percent occurs in the matched total trade data of thirty-five of the countries (109 percent if the Gambia is included); the differ- ences with each exporter's largest single trading partner average 61 percent. A peculiarity revealed by the table is the fact that reported imports are less than reported exports for the total trade of eighteen of the thirty-six countries, and for the largest trading partner of twenty-one of the countries. These findings are unexpected because intra-African transport and insurance costs, which would be excluded from exporter f.o.b. values but included in importer c.i.f. import values, have often been found to reach 50 percent or more of a product's export value (Livingstone 1986). Table 5 shows the African statistics may be of limited use for identifying directions of trade or even trading partners. For example, Benin reported 1982-83 exports of $19 million to its major trading partner, Nigeria, but the latter reported no trade between them. A similar situation occurred for Dji- bouti-Somalia ($30 million in exports not accounted for) and for Zaire-Togo ($12 million). More broadly, only six of the thirty-six countries' export data reveal disparities of less than 50 percent in all of the categories analyzed. Benin, the Congo, Djibouti, the Gambia, Mozambique, Niger, Seychelles, Sierra Leone, and Tanzania showed discrepancies of greater than 50 percent in all three export categories. Information on the composition of trade among African countries may be needed for purposes such as formulating plans for regional or international trade agreements. Matched partner trade data could be used to verify infor- mation on goods exchanged down to the three-digit SITC level (most countries Table 5. Discrepancies in Reported Partner Country Statistics on Trade between African Countries, 1982-83 Manufactured exports to all All commodities (millions of dollars) Sub-Saharan Africa (millions of All Sub-Saharan Africa Largest African import market' dollars) Difference Different Difference Exporting country Exports Imports (percent) Partner Exports Imports (percent) Exports Imports (percent) Benin 29.2 1.1 -96.2 Nigeria 19.0 0.0 -100.0 20.6 0.5 -97.6 Burkina Faso 30.8 12.2 -60.2 Cote d'lvoire 16.9 2.0 -88.2 9.1 5.0 -45.1 Burundi 2.2 3.3 52.9 Kenya 0.7 0.8 14.3 0.2 - -100.0 Cameroon 91.6 125.2 36.6 Chad 20.3 40.5 99.5 45.3 58.2 28.5 Central African Rep. 2.0 1.8 -11.0 Cote d'lvoire 1.0 0.5 -50.0 0.8 0.6 -25.0 Chad 21.7 10.8 -50.2 Cameroon 21.1 10.6 -49.8 19.1 9.4 -50.8 Congo 5.5 10.1 85.0 Zaire 1.8 3.8 111.1 2.8 5.6 100.0 Cote d'lvoire 727.9 702.2 -3.5 Somalia 187.4 145.8 -22.3 290.0 225.4 -22.3 Djibouti 38.7 7.2 -81.2 Djibouti 30.2 0.0 -100.0 14.8 1.3 -91.2 Ethiopia 74.7 110.2 47.5 Nigeria 37.8 75.0 98.4 25.9 32.0 23.6 Gabon 55.6 97.4 75.1 Senegal 29.7 16.6 -44.1 9.3 15.4 65.6 Gambia 1.0 12.6 1,138.2 Togo 0.8 0.1 -87.5 0.1 - -100.0 Ghana 19.8 11.3 -43.1 Cameroon 14.6 0.7 -95.2 1.7 1.2 -29.4 Guinea 37.3 24.5 -34.4 Burkina Faso 36.6 18.5 -49.5 0.1 23.4 b Kenya 466.2 523.6 12.3 Uganda 189.1 200.9 6.2 139.5 185.2 32.8 Liberia 18.3 43.6 137.9 Nigeria 7.8 8.9 14.1 2.8 32.1 1,046.6 Madagascar 5.7 2.9 -48.4 Mauritius 2.1 1.6 -23.8 2.1 1.0 -52.4 Malawi 53.1 77.9 46.6 Zimbabwe 29.7 44.3 49.2 12.0 36.2 201.7 Mali 140.1 17.6 -87.4 C6te d'lvoire 91.7 4.9 -94.7 7.7 5.7 -26.0 Mauritania 39.6 37.8 -4.6 Cote d'lvoire 36.9 36.9 0.0 2.2 - -100.0 Mauritius 3.2 1.8 -41.2 Seychelles 2.0 2.7 35.0 1.8 2.4 33.3 (Table continues on the following page.) Table 5 (Continued) All commodities (millions of dollars) Manufactured exports to all _______________________________________________________________ Sub-Saharan Africa (millions of All Sub-Saharan Africa Largest African import market, dollars) Difference Different Difference Exporting country Exports Imports (percent) Partner Exports Imports (percent) Exports Imports (percent) Mozambique 56.6 109.4 93.2 Kenya 37.3 18.6 -50.1 12.8 4.4 -65.6 Niger 21.9 162.2 641.5 Burkina Faso 10.7 0.6 -94.4 1.0 11.3 1,030.0 Nigeria 624.7 539.0 -13.7 Ghana 229.2 205.4 -10.4 58.5 15.0 -74.4 Rwanda 8.3 9.3 12.1 Kenya 8.2 9.2 12.1 - - n.a. Senegal 256.1 371.2 45.0 Burkina Faso 104.9 8.2 -92.2 149.0 120.1 -19.4 Seychelles 0.4 0.2 -89.4 Liberia 0.2 0.3 50.0 - 0.1 b Sierra Leone 9.1 3.9 -134.9 Mauritius 2.9 8.5 193.1 0.4 0.7 75.0 a4 Somalia 2.6 3.9 50.7 Tanzania 2.6 2.8 7.7 2.4 0.9 -62.5 Sudan 3.8 3.1 -17.8 Ethiopia 2.1 0.4 -80.9 1.3 0.6 -53.8 Tanzania 47.6 132.0 177.2 Kenya 11.3 3.0 -73.4 21.0 47.3 125.2 Togo 72.2 119.1 65.0 C6te d'Ivoire 36.4 38.7 6.3 54.5 80.7 48.1 Uganda 5.2 5.8 11.9 Kenya 4.9 3.8 -22.4 0.4 0.2 -50.0 Zaire 26.1 17.3 -33.7 Togo 12.1 - -100.0 20.2 8.7 -56.9 Zambia 26.6 29.8 12.0 Malawi 10.3 19.8 92.2 6.5 5.8 -10.8 Zimbabwe 64.5 54.1 -16.1 Malawi 42.2 35.8 -15.2 45.7 39.5 -13.6 -Negligible. n.a. Not applicable. Note: The difference between the value of total reported imports and exports divided by the value of exports, times 100. a. C6te d'lvoire is the largest single destination for all other Sub-Saharan African exports. b. Because no (or very few) exports were reported, the percentage difference between reported imports and exports could not be computed. Source: Author's calculations, based on U.N. Series D Commodity Trade Tapes. Yeats 149 do not report data at lower levels of aggregation). Reported values of intra- African trade for the leading fifty manufactured and agricultural exports are shown in tables 6 and 7. The rank of each product in the value of total intra- African trade (imports and exports) is also shown. Although in both product groups there is a positive rank correlation for export and import statistics that is significant at the 90 percent confidence level, variations for specific products could create important biases in analytical studies. As an illustration, differences of over 18 million dollars exist in the trade of woven textile fabrics (the difference is about 160 percent of reported imports); this item ranks fifth in manufactured exports but only twenty-second in imports. Footwear, road motor vehicles and parts, iron and steel bars, and ships and boats are other manufactured goods for which major differences occur in the rank and value of the partner country data (table 6). In the Table 6. Commodity Composition of Reported Trade in Manufactures among African Countries, 1982-83 Reported value (thousands of Rank in value of dollars) Difference reported tradea SITC Description Exports Imports (percent) Imports Exports 661 Lime and cement 135,736 162,832 20.0 1 1 652 Woven cotton fabrics 113,776 143,432 26.1 2 2 554 Soaps and cleaning preparations 45,338 40,883 -9.8 13 3 851 Footwear 32,243 19,154 -40.6 17 4 653 Woven textile fabrics 29,594 11,289 -61.8 22 5 711 Power-generating machinery 27,735 31,170 12.4 6 6 684 Aluminum 27,544 32,903 19.5 3 7 541 Medicinal products 27,389 19,117 30.2 14 8 561 Fertilizers, manufactured 27,003 30,324 12.3 5 9 599 Chemicals 27,001 28,602 5.9 9 10 631 Plywood and veneers 26,704 30,866 15.6 8 11 642 Articles of paper 24,681 24,088 -2.4 10 12 732 Road motor vehicles and parts 23,777 32,425 36.4 4 13 673 Iron and steel bars 22,502 14,487 -35.6 24 14 893 Articles of plastic 18,481 15,502 -16.1 20 15 718 Machines for special industries 17,667 17,612 0.3 15 16 735 Ships and boats 16,431 29,188 77.6 7 17 719 Machinery and appliances 16,078 22,841 42.1 11 18 729 Other electrical machinery 16,037 17,391 8.4 16 19 651 Textile yarn and thread 15,568 18,215 17.0 23 20 629 Articles of rubber 13,785 9,849 -28.5 28 21 581 Plastics and resins 12,192 13,928 14.2 18 22 733 Road vehicles other than motor vehicles 10,776 8,288 -23.1 25 23 691 Finished structural parts 8,938 7,195 -19.5 27 24 678 Iron and steel tubes and pipes 8,128 6,746 -17.0 29 25 a. Rank by value in 103 three-digit SITC manufactured products (excluding U.N. special codes for which no data are available). Source: Author's calculations, based on U.N. Series D Commodity Trade Tapes. 150 THE WORLD BANK ECONOMIC REVIEW, VOL. 4, NO. 2 agricultural products group, a difference of more than 100 million dollars occurs for manufactured tobacco-it is first in imports but twelfth in exports. There are also major differences in the ranks and reported values for regener- ated fibers, textile waste, and unmilled maize. For many of these products, additional information on the underlying bilat- eral trade flows indicates that transshipment through neighboring countries is a source of major data errors. Whereas the country of final destination may simply list the routing country as the original exporter, the routing country may not list the products transported across its borders either as imports (when entering) or as exports (when exiting). As an example, in 1983, Sudan reports 69 million dollars in tobacco imports from Tanzania, although Tanzania re- ported no exports to Sudan. Because Tanzania only reported 58 million dollars in tobacco imports from all sources, it is likely that Sudan listed Tanzania as the origin of shipments that were transported through Tanzania but originated elsewhere. Similarly, Mali reported 1982 cotton exports to Cote d'lvoire, which Table 7. Commodity Composition of Reported Trade in Food and Agricultural Raw Materials among African Countries, 1982-83 Reported value (thousands of Rank in value of dollars) Difference reported trade, siTc Description Exports Imports (percent) Imports Exports 044 Maize, unmilled 116,271 23,705 -79.6 15 1 001 Live animals 103,320 132,305 28.1 2 2 031 Fresh fish 98,533 116,293 18.0 3 3 071 Coffee 64,755 69,597 7.5 4 4 263 Cotton 54,453 26,382 -51.5 11 5 074 Tea and mate 47,252 29,087 -38.4 9 6 061 Sugar and honey 43,595 33,809 -22.4 7 7 121 Tobacco, unmanufactured 39,834 30,885 -22.5 8 8 422 Other vegetable oils 33,091 62,530 89.0 5 9 051 Fresh fruit and nuts 31,835 25,004 -21.5 13 10 122 Tobacco manufactures 30,135 138,267 358.8 1 11 292 Crude vegetable material 29,672 34,537 16.4 6 12 099 Food preparations 26,067 25,873 -0.7 12 13 054 Fresh or frozen vegetables 22,437 27,878 24.2 10 14 048 Cereal preparations 21,936 22,195 1.2 16 15 062 Sugar confectionery 16,977 9,241 -45.6 20 16 267 Textile waste 12,448 2,043 -83.6 40 17 243 Shaped wood 10,653 24,987 134.6 14 18 221 Oilseeds and nuts 10,021 4,092 -59.2 28 19 045 Cereals, unmilled 9,831 3,426 -65.2 31 20 112 Alcoholic beverages 8,753 7,208 -17.7 23 21 421 Fixed vegetable oils, soft 8,366 19,348 131.3 17 22 042 Rice 8,273 11,604 40.3 18 23 266 Synthetic and regenerated fibers 5,823 232 -96.0 53 24 242 Wood in the rough 3,980 9,742 144.8 19 25 a. Rank by value in 53 three-digit SITC food and agricultural raw material products (excluding U.N. special codes, for which no data are available). Source: Author's calculations, based on U.N. Series D Commodity Trade Tapes. Yeats 151 reported no imports from Mali. Again, the available evidence suggests that Mali's cotton was transshipped through C6te d'Ivoire to other destinations. Numerous improperly reported transshipments appear to be causing large er- rors in the U.N. statistics. Table 8. Trends in Intra-African Trade: Reported Partner Country Export and Import Statistics All Sub-Saharan Percentage change in country exports and reported Africa, 1983 imports from the country (thousands of dollars) 1979-83 1980-83 1981-83 Country All exports All imports Exports Imports Exports Imports Exports Imports Benin 3,701 514 -54.1 -96.1 -55.1 -88.5 -54.3 -94.6 Burkina Faso 13,303 4,837 -64.4 -11.2 -64.8 -87.3 -52.4 -63.4 Burundi 396 2,863 -74.2 -16.5 -40.0 561.2 -76.9 296.5 Cameroon 5,007 58,951 -92.4 76.2 -84.9 32.0 -80.9 -15.1 Central African Rep. 571 174 -56.3 -84.5 -71.3 -89.5 -65.4 -81.0 Chad 10,695 238 62.7 -96.4 -2.9 -98.7 -2.5 31.9 Congo 3,994 2,815 3.2 -65.5 -25.3 -60.3 47.8 -51.3 C6te d'lvoire 351,298 332,761 49.2 -87.9 116.3 16.8 3.9 5.7 Djibouti 18,118 1,542 742.7 -90.2 -6.6 -92.6 0.2 -91.4 Ethiopia 35,144 45,991 10.9 2.7 -35.9 -33.4 56.0 -16.0 Gabon 24,322 53,650 1,386.7 8.8 32,767.6 69.7 -51.5 1.3 Gambia 400 92 426.3 -62.9 30.3 -77.0 -33.2 -99.1 Ghana 7,955 2,760 18.2 -79.9 133.3 -79.8 -26.5 -71.3 Guinea 18,665 1,161 46.8 -90.9 6.9 -94.2 0.0 209.6 Kenya 237,772 279,853 18.0 277.2 -22.5 -18.3 -22.2 -18.9 Liberia 8,679 32,512 -29.9 406.3 -22.4 125.5 -18.6 102.0 Madagascar 3,279 2,012 -39.5 -52.4 80.4 -18.0 -22.5 25.1 Malawi 51,792 39,190 193.7 88.8 43.3 8.3 30.8 -8.8 Mali 9,278 8,148 -63.9 -46.0 -48.7 -70.7 35.7 -11.6 Mauritania 20,881 20,280 5,120.3 2,419.3 1,858.8 2,913.3 382.0 412.0 Mauritius 1,462 2,332 -0.8 42.6 -26.5 -48.9 38.7 64.7 Mozambique 27,219 78,133 156.3 458.6 -59.5 16.8 62.5 480.5 Niger 9,094 81,439 -77.0 1,975.9 -87.4 17.1 -88.8 -1.3 Nigeria 200,434 190,111 -28.9 -38.9 -48.2 -63.6 -29.7 -62.5 Rwanda 4,153 5,163 83,060.0 -41.3 -41.5 -27.3 -49.7 -37.4 Senegal 180,514 174,769 97.4 68.4 40.5 34.3 1.2 -10.4 Seychelles 50 155 -86.8 -54.1 -89.5 -82.9 -69.3 -84.7 Sierra Leone 1,665 6,400 -34.2 122.1 -11.7 378.3 -61.1 55.3 Somalia 904 1,238 59.4 177.6 -39.2 -24.4 -44.3 -10.7 Sudan 521 1,163 -11.8 32.3 -64.1 45.9 -74.2 9.7 Tanzania 18,941 70,013 -67.7 315.5 66.4 8.7 -41.8 63.6 Togo 35,939 45,244 31.8 292.9 -59.1 -52.4 9.4 -24.5 Uganda 2,562 3,258 9,388.9 33.7 -74.2 -66.5 9.0 38.1 Zaire 8,508 13,195 21.0 28.3 -46.6 -9.9 -57.0 25.2 Zambia 11,703 15,737 -62.1 -57.4 25.4 -57.7 147.8 -44.8 Zimbabwe 34,057 26,273 344.8 330.8 -35.8 34.2 16.6 -12.6 n.a. Not applicable. Source: Author's calculations, based on U.N. Series D Commodity Trade Tapes. 152 THE WORLD BANK ECONOMIC REVIEW, VOL. 4, NO. 2 Product supply and demand and broader trade policy analyses also require correct identification of trends or changes in the level of trade. Measuring long- term trends is not easily accomplished because of the major gaps in the histor- ical records (see table 1), but data for all Sub-Saharan African countries were available for the 1979-83 period. Table 8 shows intra-African export and import totals for 1983, their percentage change, and the proportion of coun- tries for which partners' trade data show opposing changes in the direction of intra-African trade. This would occur, for example, if a country's reported exports rose while the matched reported imports of its trading partners de- clined. The information in table 8 strongly suggests that these data are unreliable as an indicator of changes in the level of trade. The data showed conflicting changes in direction in more than half of the thirty-six countries for 1981-83 and more than 40 percent of the countries over the 1979-83 period. In addi- tion, even when the data showed the same direction of change in trade, there were often major differences in the magnitude. As an indication, African im- porters of Togo's exports reported increases nine times larger than those shown in Togo's export data. Correlations between changes in partner countries' ex- port and import statistics were not statistically significant for any of the three time periods reported in table 8. It was not possible to determine if some of these data discrepancies arose because of attempts by other Sub-Saharan Africa countries to conceal trade with South Africa. From 1979 to 1982 the South African Customs Union reported no exports to any of the Sub-Saharan countries, although twenty-two out of the thirty-six countries reported imports from South Africa that totaled about $900 million. In 1983, the U.N. trade tapes show $116 million in South African exports to Malawi, $9 million to Seychelles, $3 million to Kenya, and minor exports (under $1 million) to six other African countries. Coal and petroleum accounted for about one-third of these exports. South Africa re- ported little trade in those products in which major differences exist in Sub- Saharan partner country data (see tables 6 and 7). III. THE ANALYTICAL AND POLICY IMPLICATIONS The key question that emerges from this study concerns the utility of African trade data for research and policy studies on intra-African trade. Five general findings bear directly on this question. First, the data cannot be used to assess the overall level of trade among African countries; the average discrepancy between matched export and import values is more than 60 percent for thirty- five of the countries (and more than 100 percent when the Gambia is included). Second, the data are probably useless for assessing the direction of intra-African trade because countries listed by the exporters as the largest markets for exports often fail to report any corresponding imports. Third, the data appear to be Yeats 1S3 equally deficient for determining the composition of trade because major dis- crepancies are revealed between partner country statistics at greater levels of detail. Fourth, there are large and persistent differences in the trends in both the magnitude and direction of intra-African trade as reflected in reported exports and matched imports. Fifth, the fact that reported f.o.b. exports fre- quently exceed matched reported c.i.f. imports suggests that smuggling is wide- spread in trade among African countries or that importers are intentionally underinvoicing to avoid high tariffs or quotas. Given these points, it is difficult to see how any confidence could be placed in the official U.N. data or the underlying national data upon which they are based. Similar conclusions about the reliability and utility of African trade data emerge from a comparison of these statistics with matched OECD data. Discrep- ancies between the African export and OECD import data are far greater than differences in statistics on trade among developed countries. Analyses of under- lying quantity and unit values indicate several factors are responsible. First, discrepancies in reported quantities traded of products such as petroleum, coffee, and cocoa suggest that exporters have intentionally been underreporting shipments in order to circumvent international commodity agreement quotas. Second, for high-value, low-volume products like pearls and precious stones, reported imports greatly exceed reported exports, suggesting that smuggling is occurring on a large scale. Third, large differences in the reported unit values for some products, particularly oilseeds and iron ore, suggest that exporters are purposefully underinvoicing (possibly to avoid government foreign exchange controls or restrictions on foreign asset holdings), or are not receiving full value for these items. Because most of the data errors appear to originate on the export side, this study suggests that any North-South analyses of African trade should primarily rely on OECD data. Because export subsidies and similar incentives are not widely used in the subject countries, the excess of reported exports over imports is consistent with underinvoicing by importers or smuggling on a fairly massive scale. The very high import tariffs in most African countries provide a strong incentive for such activities. Without further analysis it would be difficult to estimate the magni- tude of smuggling in African trade from data drawn from partner countries because there is no way to determine quantities and values that are not reported by either the exporter or importer as opposed to (smuggled) trade that is recorded by one of the countries involved. On a more general level, the results of this study accent the need for more information about the basic quality of official U. N. trade statistics. It would appear useful, for example, to extend the general approach employed in this analysis to other groups of developed and developing countries and make the findings of such investigations generally available. Until this is done, a strong possibility exists that basic research could be seriously biased, and inappropri- ate policy decisions made because of substantial errors in official trade data. 154 THE WORLD BANK ECONOMIC REVIEW, VOL. 4, NO. 2 APPENDIX. THE TRANSPORT COSTS CORRECTION FACTOR One factor which produces a discrepancy between partner country trade data is international transport and insurance costs. Exporters typically value ship- ments on an f.o.b. basis, whereas imports are normally tabulated on a c.i.f. basis. As such, a key question is how large a difference should be expected between partner country statistics as a result of freight and insurance costs. Because the United States now tabulates international transport costs actually paid for all imports directly from customs vouchers, these data can be used to derive transport correction factors for partner country trade data with the United States (Finger and Yeats 1976; Brodsky and Sampson 1979; Yeats 1981, 1990). The U.S. Customs information reflects freight charges actually paid, including all discounts and surcharges. (Studies have often had to rely on published liner conference charges. The conferences, organized by unincorpor- ated associations of ocean liner owners, formally establish freight rates, sailing schedules, and regulations that affect competition among members. These are often unreliable as guides to actual transport payments.) Transport and insurance costs for each African country's exports were com- piled and converted to nominal equivalents. The formula used for estimating these nominal freight costs, which serve as "correction factors" for partner country data (Cij), was: (A-1) Co f Vf, where f represents freight and insurance charges and Vf,1 the U.S. f.o.b. value of total imports from each African country. The resulting statistics show the percentage by which c.i.f. imports should exceed partner country f.o.b. data (see appendix table 1). For total trade the data suggest that most African countries' f.o.b.-c.i.f. correction factor would be in the range of 5 to 10 percent, although there are several exceptions. Guinea, whose exports are highly concentrated in low-grade metallic ores, has a nominal freight rate that ranges from 21 to 37 percent; the corresponding freight factors for Liberia and Somalia reach 25 and 28 percent. Although the data reported in appendix table 1 are based solely on U. S. statistics, they should be useful for assessing African-European partner country trade figures. Specifically, numerous studies of maritime transport costs show that freight rates are generally positively' associated with distance, and for commodities like wood, ores, or petroleum which involve economies of scale in transport, freight rates are inversely related to volumes shipped. Because distance is smaller to European ports, and quantities transported are generally larger, the f.o.b.-c.i.f. correction factors reflected in appendix table 1 would probably serve as upper limits for those that should be applied to most Afri- can-European partner country trade data. Equation A-1 will likely understate the correction factor for landlocked African countries, however, because the Yeats 155 Appendix Table 1. Import Values and Related Nominal Transport Costs for African Countries' Total Exports to the United States 1983 U.S. import value (millions of dollars) Nominal freight costs (percent) Exporting country fo.b. c.i.f. 1982 1983 1984 1985 1986 1987 Benin 26.9 28.7 7.1 6.7 n.a. n.a. n.a. 5.3 Burkina Faso 0.1 0.1 n.a. n.a. n.a. n.a. n.a. n.a. Burundi 2.8 3.0 7.S 7.1 5.3 9.0 6.0 6.4 Cameroon 515.0 535.9 4.1 4.1 4.1 4.5 9.3 7.0 Central African Rep. 3.5 3.6 4.1 2.9 n.a. n.a. 8.1 4.2 Chad 67.6 70.6 n.a. 4.4 n.a. 33.3 n.a. n.a. Congo 820.8 859.4 3.6 4.7 5.4 5.9 9.3 6.1 C6te d'lvoire 342.7 371.3 7.4 8.3 6.4 6.3 6.7 8.5 Djibouti n.a. n.a. n.a. n.a. n.a. n.a. 50.0 n.a. Ethiopia 86.8 93.9 7.4 8.2 7.9 7.7 4.2 6.0 Gabon 657.1 685.1 4.1 4.3 4.0 4.3 8.9 6.0 Gambia 0.2 0.2 n.a. n.a. 16.6 33.3 20.0 n.a. Ghana 119.8 125.3 2.3 4.6 7.8 6.0 5.2 4.3 Guinea 104.4 138.6 36.7 32.7 25.7 21.4 25.8 27.7 Kenya 65.0 70.2 7.4 8.0 8.0 7.8 6.5 7.4 Liberia 90.5 107.5 25.2 18.8 20.1 25.0 19.3 14.5 Madagascar 70.7 74.2 5.6 5.0 4.1 4.2 2.9 3.4 Malawi 14.5 15.5 9.8 6.9 8.2 9.0 10.6 8.0 Mali 0.7 0.7 9.1 n.a. 9.1 8.5 9.6 4.3 Mauritania 0.8 0.8 n.a. n.a. 10.0 25.0 10.5 12.4 Mauritius 31.5 33.9 10.2 7.6 8.1 9.3 9.6 10.2 Mozambique 28.5 31.0 10.8 8.8 9.2 8.8 6.4 6.0 Niger 4.2 4.3 n.a. 2.4 n.a. 8.8 5.1 5.6 Nigeria 3,736.0 3,882.7 3.2 3.9 3.9 3.5 5.9 5.4 Rwanda 28.4 29.9 6.1 5.3 4.7 6.3 5.6 6.4 Senegal 1.9 2.1 18.2 10.5 20.8 12.5 7.5 5.7 Seychelles 2.9 3.1 4.9 5.2 5.6 4.2 3.6 5.3 Sierra Leone 21.5 22.8 6.2 6.1 5.1 6.8 7.5 4.4 Somalia 0.1 0.2 22.2 n.a. 28.5 22.2 n.a. n.a. Sudan 19.0 20.4 10.2 7.4 11.5 8.1 3.6 3.5 Tanzania 14.3 16.2 9.7 13.3 10.4 11.5 8.5 5.9 Togo 19.9 21.0 6.9 5.5 5.1 7.0 12.4 13.1 Uganda 103.9 110.6 7.2 6.4 5.4 5.7 4.9 5.8 Zaire 366.3 378.2 4.0 3.2 3.2 3.5 4.1 8.0 Zambia 52.1 53.7 3.7 3.1 3.6 2.1 2.4 3.2 Zimbabwe 73.8 79.5 7.1 7.7 6.6 6.3 5.8 6.5 n.a. Not applicable (no exports or imports recorded in that year). Note: The figures in this table are used to calculate correction factors for exporter (f.o.b.) and importer (c.i.f.) trade values. Source: Author's calculations, based on U.S. reported trade data. U.S. transport data do not account for shipping costs from the border of the landlocked country to the ocean port of export. The ratios could also be higher for such countries as Guinea, Liberia, and Sudan. 156 THE WORLD BANK ECONOMIC REVIEW, VOL. 4, NO. 2 REFERENCES Allen, R. G. D., and J. Edward Ely. 1953. International Trade Statistics. New York: Wiley. Bhagwati, Jagdish. 1964. "On the Underinvoicing of Imports." 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