I.

The purpose of this study is to examine the prospects for export-led growth in tropical Africa based on agricultural raw materials. Specifically, the article analyzes whether there is a significant non-price induced preference in the world market for non tropical agricultural raw materials. Also examined is the extent to which there is a non-price-induced preference for non-African sources in the market for tropical agricultural raw materials.

Since 1981 there have been ongoing discussions regarding the relative merits of propositions made separately by the World Bank and the Organization for African Unity (OAU) for the acceleration of economic growth in sub-Saharan Africa. According to the World Bank’s propositions, tropical African countries should emphasize export-led growth based on their comparative advantage in the production of agricultural raw materials. In a contrary proposal, the leaders of OAU member states rejected the suggestions of the World Bank. Instead, they proposed an internally propelled self-sufficiency growth strategy.

It has been ten years since the propositions of the OAU and World Bank were made. Therefore, the analysis and discussion in this article deal with the fundamental problem of the constraints surrounding export-led growth, and especially in the narrower context of agriculture-based export-led growth. Since the OAU and World Bank propositions provide an opportunity to discuss an important problem, the following paragraphs will make considerable reference to them. The competing propositions were made in two separate documents after extensive analysis and studies by the two institutions. The OAU made its recommendations in a document titled, The Lagos Plan o f Action for The Economic Development of Africa, 1980-2000. 1 The World Bank made its own recommendations in a document titled, Accelerated Development In Sub Saharan Africa: An Agenda for Action. 2 Both the OAU and the World Bank studied the causes of Africa’s economic stagnation and recommended ways to accelerate development in order to end the crisis. The World Bank came to the conclusion that Africa’s problems were due, primarily, to internal mismanagement and misguided policies by the leaders of the continent. Problems such as misaligned exchange rates that discriminate against exports, distorted incentive structures for factors of production, and price subsidies were cited to support the World Bank’s claim. On the other hand, the OAU claimed that Africa’s problems were due, primarily, to external constraints beyond the control of African leaders and policy makers. In particular, the effects of colonialism, trade barriers in the advanced countries, and discriminatory trade practices by the industrial countries that buy agricultural raw materials, among others, were cited as factors that constrain the ability of African countries to participate in the international economic system.

In addition to other policy recommendations, the World Bank suggested that African countries adopt an export-led growth strategy based on the export of agricultural raw materials. Since the OAU believed that African countries face external constraints beyond the control of their leaders, it recommended that these countries adopt an internally propelled economic growth strategy. Such an approach to development would include self-sufficiency in foodstuffs production, and increased intra-African trade. It is, then, the purpose of this article to determine if there is a basis for the claim that there are external constraints against an export growth strategy, based on agricultural exports.

The balance of the article is organized as follows: The OAU-World Bank debate on development in Africa is examined in Section II. Specifically, this section will take the World Bank suggestions as given, and deal primarily with the objections of the OAU.3 In Section III the empirical model is specified and discussed. Section IV discusses the empirical estimation. The regression results are also presented in this section. A summary and concluding remarks appear in Section V.

II. OBJECTIONS OF THE OAU: A DISCUSSION

In the final analysis, the fundamental issues to be addressed in the OAU-World Bank debate is how to generate the necessary resources to accelerate economic growth in Africa. This objective can be achieved in several ways, including an export-led growth strategy.

Assuming export-led growth, each country could choose a strategy based on the export of manufactured goods, minerals, food, or agricultural raw materials. The World Bank has suggested a strategy based on the export of agricultural raw materials. This suggestion is probably justified by the expectation that African countries would have a comparative advantage in the production of agricultural exports, based on climate and the availability of low cost labor. However, the leaders of the OAU have expressed reservations about the World Bank proposals on the basis of historical experience. According to the OAU, the export of agricultural raw materials by African countries is a colonial legacy, which has relegated African countries to a dependency position in the world economy. Furthermore, from the experience of the last ten years, Africa’s terms of trade in world trade have declined substantially. This decline stems, in large part, from the fact that most African countries depend largely on the export of raw materials, agricultural as well as nonagricultural. According to the World Bank, the average decline in Africa’s terms of trade between 1965 and 1981 was 2.27%.4 This average includes the data for oil exporting countries, whose terms of trade rose substantially during the same period. If the latter group is excluded, the data would show a more substantial decline.⁵

Another reason why African leaders reject an export-led strategy to pursue development on the continent is the progressive development of technologies by the advanced industrial countries that are biased against the use of agricultural raw materials. In recent years, the industrial countries have developed substitutes for some African commodities. For example, between 1954 and 1985, the production of synthetic rubber significantly affected the price of natural rubber, an important commodity for several African countries. The price ratio of synthetic rubber to natural rubber increased persistently, and significantly. At the same time, the consumption of synthetic rubber rose from 29 percent to 68 percent of total world consumption of raw rubber. Specifically, the consumption of synthetic rubber increased twelve-fold form 740,000 tons in 1954 to 9,000,000 tons in 1985 (See table 1 ). On the other hand, the consumption of natural rubber increased only two and half times, from 1,700,000 tons in 1954 to 4,300,000 tons in 1985. 6 It appears that there has been a change in the preference function of the users of raw rubber, and that the shift to synthetic rubber is not primarily price-induced.

In addition to developing temperate substitutes for tropical raw materials, the industrial market economies have systematically substituted away from a number of African raw materials and in favor of the same raw materials from other regions of the world. The implication is that Africa is being pushed gradually into the fringes of world trade, with its share of world trade diminishing very rapidly. As a consequence, the OAU argues that Africa cannot lay emphasis on world trade as the primary engine of growth, and must seek internal solutions to its economic problems. This article investigates the claim that the industrialized countries are developing temperate substitutes for African primary commodities. In addition, the extent to which African commodities are being discriminated against in the international market place is assessed. If the industrialized countries are developing temperate substitutes or preferring non-African sources, does this mean that export-led growth is no longer appropriate for African development?

The buyers of tropical raw materials may develop temperate substitutes due to cost considerations for strategic and security reasons, or due to unstable supply from traditional sources. The analysis in this article will ascertain if the advanced industrial countries are developing alternative sources of agricultural raw materials or showing a preference for non-African sources of tropical agricultural raw materials. If so, are the developments due to cost considerations or due to a deliberate shift in the preference functions of the buyers of agricultural raw materials? These questions are explored by estimating the input demand functions of a translog cost function.

III. THE EMPIRICAL MODEL

Consider the one product, translog, production function:⁷

Application Of The Trans log Function In The Present Study

The demand for a subset of inputs is analyzed via a cost function model. Specifically, a set of agricultural raw materials is chosen and their demand functions in the international export market are studied. The specific commodities studied are vegetable oils from oil seeds and their derivative, oil seed cakes. All the commodities studied are those in which tropical African countries have a significant interest. In addition, these African products face significant competition from temperate substitutes. The specific cakes studied are groundnut cake, palm kernel cake, cotton seed cake, and copra cake. Their temperate region competitors are rape seed cake, sunflower seed cake, and soybean cake. The vegetable oils studied are groundnut oil, palm oil, copra oil, and palm kernel oil. Their temperate region competitors are soybean oil, cottonseed oil, and sunflower seed oil. Furthermore, the model takes account of nonneutral shifts in the underlying utility or production function of the decision makers in a manner similar to nonneutral technical change.

In the context of the present study, the concept of nonneutral technical change needs further clarify-cation. It is assumed that the agricultural raw materials are used in some production process, to produce final or other.

TABLE 1
World Production, Consumption and Price of Raw Rubber

intermediate goods. An optimization decision is made about using them in the same manner that the primary inputs of labor and capital are made. In this framework, it is understood that the various agricultural inputs are not perfect substitutes. For example, palm oil from different regions, or even from the same region, has different qualities and characteristics. There may be long-term contracts that do not expire at the same time. Also, locational differences and implied transportation costs, as well as the reliability of sources could be factors that affect the production function. It is therefore conceivable that progressive biases could be built into the substitution process to favor particular raw materials or regional sources of particular raw materials.

Once a decision is made to use a particular raw material that is a member of a subset, the next decision is about the source. For example, consider the production of livestock feed. The first decision is to determine whether or not to use an oil seed cake. The next action then is to determine whether to use a tropical or temperate oil seed cake. The final step is to determine the source <‘f the cake.

Data Sources

Data for vegetable oils and oil seed cakes are taken from several issues of the FAO (Food and Agricultural Organization) Trade Year Book. The data include values and quantities of imports and exports from various regions of the world. Prices are determined by dividing the quantities into the values. All quantities are measured in pounds, and prices are in U.S. cents per pound. Data for rubber is taken from The Survey of Current Business, published by the U.S. Department of Commerce and from the Commodities Year Book, published by the Commodity Research Bureau.

IV. EMPIRICAL ESTIMATION

In the present study, a total variable cost function (See equations 3, and 3a) is specified and estimated for each commodity or group of commodities. Total variable cost is taken to be the total expenditure on the imports of the relevant commodity by the world market. The inputs into the production process are the agricultural raw materials from Africa, the advanced industrial countries, and from other non-African developing regions of the world. ⁹

The model is specified to enable the determination of whether:
1. There is a substitution bias in favor of temperate raw materials and against competing tropical raw materials.
2. There is further bias against African sources of tropical raw materials. All models are estimated using the joint generalized least squares (JGLS) procedure through share equations.

The Cost Equation:

The cost equation of the model is as follows:
C=C(P,Q,t); P=(Pr,P₀, Pd,Pw,Pv); Where,
Pr = Price of some tropical raw material from African sources.
Po = Price of some tropical raw material from non-African developing regional sources.
Pd = Price of some tropical raw material form advanced industrial countries. (Note that this could be true for a derivative product like seed cakes and oils)
Pv = Price of competing temperate raw material from the advanced industrial countries.
Pw = Price of some competing temperate raw material from non- African developing regions.

The total cost then is of the form:
(4) C = PrXr + PoXo + PdXd + PwXw + PvXv
In equation 4, Xi is the quantity of a raw material from some source I. P; is the price per unit of the given raw material from that source. “C” then represents the total expenditure by the world import market on the given raw material.

The Share Equations:

Let Si be the share in the total cost function of the ith source of agricultural raw material in the world market, where Si=(PiC/ C); Ci is the partial derivative of the total cost function, C, with respect to the price of the ith input, and 1:Si=l , since the share of all inputs that exhaust the total cost must necessarily sum to one. Note also that cost shares to buyers represent revenue shares to sellers.
Let “Q” be some activity index, and “t” is time. Then:

1. SF = α_f + Σα_fjLnP_j + δ_fLnQ + o_fLnτ
2. SO = α_o + Σα_ojLnP_j + δ_oLnQ + o_oLnτ
3. SD = α_d + Σα_djLnP_j + δ_dLnQ + o_dLnτ
4. SV = α_w + Σα_vjLnP_j + δ_wLnQ + o_vLnτ
5. SW = α_w + Σα_wjLnP_i + δ_wLnQ + o_wLnτ

SF is the cost share of African sources of some tropical raw material.
SO is the cost share of non-African developing countries sources of some tropical raw material.
SD is the cost share of advanced industrial countries sources of some tropical raw material.
SV is the cost share of advanced industrial sources of some competing temperate raw material.
SW is the cost share of non-African developing country sources of some competing temperate raw material.

If technical change is induced and neutral, then Si!Ct)=0 for all i. The implication of factor-neutral technical change is that there is an equiproportionate change in the elasticity of substitution between factors of production for any changes in their price ratios. In biased technical innovation, technical change overcompensates for the change in relative prices. Therefore, the price of a factor of production may rise while its share of total cost rises, because technology is designed to use more of it and still be efficient. In this case dS/dt>0. If technical change is factor I saving, dS/dt<0. If it is determined that technology has changed in favor of any subset of factors (in tcis case, a subset of regional sources of raw material inputs) this implies a progressive redistribution of returns to production in favor of this subset.
Using the model above, the following hypotheses are tested:

la. Have the users of agricultural raw materials replaced raw materials from Africa (SF) with temperate substitutes (SV) and (SW)?
If so, have the sources been primarily from non-African developing countries (SW) or from the advanced industrial countries (SV)?
2a. If not, then tropical raw materials must still have a competitive advantage over temperate agricultural raw materials. If tropical materials still maintain their comparative advantage, has this comparative advantage shifted to other non-African developing countries? In other words, have the sources of supply shifted from SF to SO?

The results of the empirical estimation are presented in Tables 2 to 9. The analysis for each tropical commodity is first presented along with one or more competing temperate commodities, and then separately. The period of analysis is from 1959 to 1985/1986 depending on the availability of data.

Oil Seed Cakes:

All the tropical oil seed cakes are studied jointly with all the temperate oil seed cakes. This model is referred to as the integrated model. The integrated model tests the hypothesis that buyers of agricultural raw materials are shifting their preferences from tropical to temperate raw materials. In the international market, the demand for oil seed cakes arises from the demand for animal feed. They are, therefore, in direct competition with each other. The structure of competition here is different from the competition in vegetable oils, where each oil has specific direct and close competitors. Against this background, the integrated model for oil seed cakes assumes an aggregate demand model for all commodities. However, the joint model is followed, as explained earlier, by separate analysis for each tropical commodity, referred to as the separate commodity model. The separate commodity model tests the hypothesis that buyers of tropical raw materials are shifting their preferences to non-African developing country sources. For the integrated model the share equations are such that SF+SO+SD+SW+SV=l. For the tropical commodity model the share equations are such that SF+SD+S0=1.

Vegetable Oils:

Each tropical vegetable oil is first analyzed jointly with its closest temperate competitor, and then separately. This approach is taken because different vegetable oils are used for slightly different purposes. For example, palm kernel oil is used for making margarine, but palm oil is used for making shortening. It is also used directly for cooking, and for making soap. For industrial as well as for alimentary purposes, the closest competitor for palm oil is soybean oil. Since palm oil is not a derivative product, no palm oil is exported from the advanced industrial countries. Therefore the sum total of the value of palm and soybean oil is distributed into four shares. The shares are SO, SF, SW, and SD, where SD is the share in total value of the exports of soybean oil from the advanced industrial countries. SW is the share of the exports of soy-bean oil from the non-African developing world. SO is the share of the exports of palm oil from non-African developing regions. African export of soybean oil, if any, is sporadic and insignificant.

Regression Results:

Below are the results of regression estimates, using share equations for commodities from the various regions. To recapitulate: Si is the share of some commodity from region i in the total revenue of a group of commodities such that S i=l . Where Si = Si(P1, •••• Pk,t), P1, …. ,Pk are the prices of commodities from various regions, and t is time. The models presented below do not include an output or activity variable, which imposes a maintained hypothesis of homotheticity on the underlying production function. 10 If Si1>0 this implies a bias in favor of commodity i, or some commodity from region i. Si1<0 implies a bias against region i, or some commodity from region i. Si1=0 implies neutral substitution. Finally, it should be noted that the slope coefficients of the input share equations of the translog function do not have much intuitive economic meaning.11 They are, however, related to the elasticities of substitution. In this study, the main problem is not to estimate elasticities of demand or substitution between input variables. Rather the purpose is to determine if there have been any nonneutral shifts in the underlying preference functions from which the input demands are derived. Therefore no elasticities of substitution are reported.

Unless otherwise stated, all equations reported are estimated using an iterative SUR procedure. This is done to mitigate the problem of collinearity due to the possibility that the prices of competing commodities track each other. This approach is also used in other studies. 12 One share equation is dropped, and S;1 for the dropped equation is reported only partially as Sd(P) + OLnt. Sd refers to the dropped share equation. 13 “T” statistics are reported in brackets. An asterisk indicates significance at the 10 percent level or better.

The Oil Seed Cakes:

The results (see Table 2) support the conclusion that there is a positive substitution bias in favor of temperate oil seed cakes, with SV(t)=.06208, and SW(t)=.0581. The parameters of substitution bias for the other commodities and regions are SF(t)= -.0384, SD(t)= -.0341, SO(t)= -.0447. During the period studied, the world market shifted its preference in favor of temperate oil seed cakes. As a result of this shift, oil seed cakes from Africa or those cakes in which Africa participates significantly lost revenue shares.

Tropical Seed Cakes:

In the market for tropical cakes (Tables 3 and 4), there is a negative bias against African sources of all cakes except groundnut cake. The parameter of substitution bias is -.0073 for copra cake, -.0587 for cottonseed cake, -.111 for palm kernel cake, and +.0321 for groundnut cake. During the same period, non-African developing regions gained revenue shares, with positive bias parameters of .0882 for copra, .0597 for cotton seed oil, .447 for palm kernel cake. The advanced countries lost revenue shares in this market. Their parameters are -.0562 for copra
cake, -.01004 for cottonseed cake, -.336 for palm kernel cake, and -.0041 for groundnut cake. The evidence leads to the important conclusion that the advanced countries yielded comparative advantage in the processing of tropical cakes. Their time shift parameters were negative for all tropical seed cakes.

Vegetable Oils:

In the market for soybean oil and palm oil (Table 5), there is a substitution bias in favor of non-African developing regions. The bias favors

 TABLE 2
Oilseed Cakes (Cost Share Equations) Integrated Model

these regions for palm oil with So(t)=+.139 as well as for soybean oil, with SW(t)=.0425. At the same time Africa and the advanced industrial regions lost revenue shares, with SF(t)= -.116, and SD(t)-.0655 respectively. In the market for palm oil (Table 6), the substitution bias favored non-African developing regions; SF(t)=-.242. The model for groundnut oil and competing vegetable oils sunflower

TABLE 3
Tropical Seed Cakes (Cost Share Equations)

Seed oil and cottonseed oil), when estimated in the same form as other commodity groups, does not perform very well. As can be seen from Table 7, the “F” and “t” statistics are very low except in the case of Africa (F=9.282, R2=.688). The bias parameters also have very low “t” statistics, except for Africa, with a bias parameter of -.0860, t=-7.101.

To improve the analysis, the empirical model was estimated in a modified form. In the modified structure, the value of the demand for ground nut oil and related oils from each region is combined into one system. In this system then:

SF is the share of the value of groundnut oil from Africa in the total export value of all related oils.

SO is the share of the value of the sum of groundnut oil and related oils exported from non-African developing regions.

SD is the share of the value of groundnut oil and related oils exported from the advanced industrial countries.

The results of the regression estimates, using iterative SUR, are presented in Table 8. The conclusion from this modified system is that there is a bias in favor of non-African developing regions and the advanced countries as sources of vegetable oils of this group. The bias parameters are -.082 for Africa; +.072, and +.0102 for non-Africa and the advanced countries respectively. In the groundnut oil market there has also been a substitution away from African sources, with a bias parameter of -.143. The parameters for non-African developing regions is +.085, while it is +.058 for the advanced countries. In the market for palm kernel oil and copra oil, (Table 9), there is also a bias in favor of other non-African developing regions SO(t)=+.084) against African sources SF(t)=-.042, and advanced industrial sources SD(t)=-.042. In the market for palm kernel oil the bias parameters are SO(t)=.161Lnt, SF(t)=-.058, and SD(t)=-.102 for non-African developing countries, African, and the industrial countries respectively.

V. SUMMARY AND CONCLUSIONS

The purpose of this article has been to extend the discussion on the prospects for export-led growth in sub-Saharan Africa. African leaders expressed reservations about the World Bank’s proposal for an agriculture-based export-led growth. Such a strategy was considered not to be a viable approach to African development.

This article has shown that there is a bias in favor of importing raw materials from other regions of the world, and against buying them from Africa. As such, a change in the relative price of agricultural raw materials will lead to a distribution of income in favor of other regions of the world, and against Africa. It must be noted particularly that the preference for other sources of supply over Africa is not price-induced. Indeed this is what the translog input demand functions with time shift parameters are supposed to explain away. The shifts in sources of supply are

TABLE 4
Tropical Seed Cakes (Cost Share Equations)

therefore explained by the changing structure of preferences as revealed by the production isoquants. The results of this study also lead to other important conclusions. It does not appear that temperate raw materials represent formidable competition for tropical raw materials. Although temperate seed cakes have gained market shares over tropical oil seed cakes, the preferred supply sources for these inputs are not the advanced industrial countries, but non-African developing countries. Palm oil, a tropical product, gained

TABLE 5
Vegetable Oils (Cost Share Equations) Soybean and Palm Oil: Integrated Model

revenue share over soybean oil. Purchasers of this product in the industrial countries, however, still preferred non-African developing country sources. The pattern of supply is not clear in the case of groundnut oil. Overall, though, it appears that Africa is the only clear loser (see Table 10 for a summary of results).

Despite the fact that Africa may have a comparative advantage in the production of several tropical commodities, the preference, by industrialized countries, for non-African sources of supply implies that Africans have not been able to successfully market their products in the global

TABLE 6
Vegetable Oils (Cost Share Equations)

economy. It appears then that the export of agricultural raw materials does not represent a viable development strategy for African countries. The observed bias against the purchase of raw materials from African countries and the marginalization of Africa in the international economic order is a cause for concern, and should stimulate further discussion and research. The export-led approach has been used, and sometimes with success, by other developing regions of the world. The question that is being asked now is whether it will work for Africa. The results of this study show a preference bias against African sources of raw materials. In light of these findings, a growth strategy based on the export of agricultural raw materials may not be a very promising answer to Africa’s problems. It must be noted however, that an export-led strategy still represents the most attractive approach to development in Africa.

TABLE 7
Vegetable Oils (Cost Share Equations)

It may be necessary for African countries to explore other sources of comparative advantage and find ways to make their products more attractive to international buyers. The establishment of industrial sectors based on the processing of agricultural commodities for export should be a first step, as these countries attempt to diversify their economies and reduce their dependence on the sale of raw materials abroad. Since many African economies are small and poorly developed, regional cooperation

TABLE 8
Vegetable Oils (Cost Share Equations)

should be used to grant access to markets that are large enough to provide economies of scale in industrialization. Increased trade between African countries should help in the development of products that can compete effectively in the international markets. Although export-led growth is still a viable alternative, its success requires a substantial amount of diversification on the part of African economies.

TABLE 9
Vegetable Oils (Cost Share Equations)

NOTES
This article has benefited tremendously from valuable comments and insights by two anonymous referees, the journal editor, as well as Adesina Fadairo and Ki-Ho Kim. All remaining errors are mine.

1. OAU, The Lagos Plan of Action for Economic Development in Africa, 1980-2000 (Geneva: International Institute for Labor Studies, 1981).

TABLE 10
Summary of Changes in Cost Shares by Region and Commodity

2.) World Bank, Accelerated Development in Sub Saharan Africa: An Agenda for Action (Washington, DC: World Bank, 1981).

3.) It would be useful to raise and discuss several issues about the cause of and possible solutions to African economic stagnation. Such issues should include alternatives to agriculture-based, export-led growth such as the export of manufactured goods and mineral exports. In addition one would examine the pros and cons of an internally propelled growth strategy. Given the space limitations in a short article of this type, however, it is not possible to discuss these other issues thoroughly. Besides, other researchers have already examined these issues. See, for example, John Ravenhill, “Adjustment With Growth: A Fragile Consensus,” Journal o f Modern African Studies, Vol. 26(2) (June 1988), pp. 179-210. See also Timothy M. Shaw, “The African Crisis: Debates and Dialectics Over Alternative Development Strategies For The Continent,” in John Ravenhill, ed., Africa In Economic Crisis (New York: Columbia University Press, 1986), pp. 108-126. See also Barbara Jamieson, “Agricultural Development and Self Reliance,” in D.F. Luke and T.M. Shaw, eds., Continental Crisis: The Lagos Plan o f Action and Africa’s Future (New York: University Press of America, 1984), pp. 13-32.

4.) World Bank, World Development Report 1987 (New York: Oxford University Press, 1987), p. 48.

5.) Ibid, p. 176; See also E. Grilli and M. Yang, “Primary Commodity Prices, Manufactured Goods Prices and the Terms of Trade of Developing Countries: What the Long Run Shows,” The World Bank Economic Review (January 1988), pp. 1-48.

6.) See Grilli and Yang, “Primary Commodity Prices,” p. 18; See also E. Grilli, R. Helterline, and P. Pollack, “An Econometric Model of the World Rubber Economy,” World Bank Staff Papers, #3 (1979), and E. Grilli, M. Yang, and C. Hoof”t Welvaars, “The World Rubber Economy: Structure, Change, and Prospects,” World Bank Occasional Papers (Washington DC: World Bank, 1980). C. E. Egbe, “Non Neutral Technical Change, Import Demand Functions, and Export Led Growth in Sub Saharan Africa,” paper presented at the Western Economic Association Conference (1988); also shows there is a technological bias in favor of domestic factors like labor, capital and synthetic rubber, against natural rubber, in the U.S. rubber goods industry.

7.) L.R. Christensen, D.W. Jorgensen, and L.J. Lau, “Transcendental Logarithmic Production Frontiers,” Review of Economics and Statistics, Vol. 55 (February 1973),pp. 28-45.

8.) Earns! Berndt, and Mohamed Khaled, “Parametric Productivity Measurement and The Choice Among Functional Forms,” Journal of Political Economy, Vol. 87, No.6 (1979), pp. 1220-1245. See also H.P. Binswanger, ” A Cost Function Approach to the Measurement of Factor Demand and Elasticity of Substitution,” American Journal of Agricultural Economics, Vol. 56 (May 1974), pp. 378-386. H.P. Binswanger, “The Measurement of Technical Change Biases With Many Factors of Production,” American Economic Review, Vol. 64 (December 1974), pp. 964-976.

9.) The other developing regions of the world are South America, Southeast Asia, Oceania, The Pacific Islands, and The Caribbean Islands.

10.) I have used a model similar to that of U. Kohli and E. Morey, in “The U.S. Demand For Foreign Crude Oil: A Translog Approach,” The Journal of Energy and Development, Vol. 11, No. 2 (1986), pp. 115-133. In their study of United States import of foreign crude oil, Kohli and Morey estimate translog cost-based import demand functions. Their demand functions depend only on prices of the various sources of foreign crude oil, with no parameters for technical change (change in preference functions) or for some measure of output from which the demand for foreign crude oil is derived, e.g., refined gasoline or petroleum products in general. There is no theoretical justification for a Hicks neutral shift in their model. However, there could be some justification for the non-inclusion of domestic petroleum or other fuels and factors of production. Once the decision has been made to use foreign crude petroleum, then decisions about domestic factors, or the level of output become irrelevant.

11.) See for example, Binswanger, “The Measurement of Technical Change Biases.” See also, K.A. Mohabbat, A. Dalla!, and M. Williams, “Import Demand Function For India: A Translog Approach,” Economic Development and Cultural Change, Vol. 32 (April 1984), pp. 593-605.

12). See D.F. Burgess, “Production Theory and The Derived Demand for Imports,” Journal of International Economics, Vol. 4, No. 1 (1974), pp. 103-117.

13.) I have chosen not to report all the parameters of the dropped equation, since I am interested only in their time shift parameters.