Free Trade with Cuba, Henry Thompson

Free Trade with Cuba:

The Effects of a Lifted Embargo in Alabama

Curtis M. Jolly

Henry Thompson

Auburn University

Published in the Southern Economics and Business Journal

Trade with Cuba will provide ex= port and investment opportunities, and ultimately some competition for Alabama.<= span style=3D'mso-spacerun:yes'> Cuba is a large neighbor with substanti= al economic potential that will provide trading opportunities missing with the embargo. The present paper examine= s the potential impacts of lifting the on Alabama at the aggregate level of manufacturing, services, and natural resource sectors. The effects on output levels, wages, ca= pital returns, and energy prices are examined in a simulated general equilibrium model of production.

This paper presents some backgr= ound information on the Cuban economy and gauges the potential effects in Alabam= a of a lifted embargo. The first sectio= n briefly reviews Cuban economic history and the second summarizes available economic data.

The following sections present a model of the Alabama economy adjusting to a lifted embargo with price chang= es simulating the impact of free trade. The sectors in the model are manufacturing, services, and natural resources, and the inputs are capital, labor, and energy. Output levels and input prices adjust to trade prices in the competi= tive production model.

1. History and Trade Potential of Cuba=

The US became involved with Cuba following its independence from Spain in the early 1900s. Cuba began a period of growth but junta= s vied for power in the 1930s leading to US military intervention to protect agricultural investment. There was= some political stability in the 1940s under Batista but he became an unpopular dictator supported by the US during the 1950s leading to the rise of Castro= by 1959 (Library of Congress, 2006).

Prior to the 1962 embargo there= was substantial US investment in sugar and tobacco production for export. In 1926, US companies owned 60% of the = Cuban sugar industry and 95% of the crop was exported to the US. In 1958, the US accounted for 67% of Cu= ba’s total exports and 70% of its imports. In the other direction, Cuba accounted for 3% of US exports and 4% of imports,= not a trivial amount.

Castro became politically promi= nent during the Cold War. The collapse = of communism ended Soviet subsidies in 1991 leading to substantial economic adjustment. Some private business = has developed, especially in agriculture, and there is limited foreign investme= nt mainly from Europe. Sugar has rema= ined the top export but cigars and fish have replaced citrus and are more competitive internationally as pointed out by Messina, Bonnett, and Taylor (2007).

Cuba has limited but normal rel= ations with the world outside the US. Cuba rejects globalization, however, and investment controls remain tight as sta= te enterprises do not want international competition, consistent with Alvarez’s (2007) finding of higher productivity of non-state relative to state agricultural production.

There is little political rationale for the embargo as the US traded with other Communist countries throughout the Cold War. The embargo not only failed to reach any political objective but also spotlighted and strengthened Castro. With no embargo, Castro would have had no publicity and shrinking support within Cuba. The Southeast= and Alabama in particular suffered due to the embargo. The US International Trade Commission e= stimates the embargo costs the US $1.2 billion annually in lost export revenue, less than 0.1% of total US export revenue but focused on particular industries a= nd regions.

The rela= xed embargo in 2001 for humanitarian exports of food and medicine to Cuba catapulted Alabama trade to over $126 million by 2004. Political pressure from US agribusiness contributed to the relaxed t= rade embargo. By 2006, Cuba accounted f= or 1/4 of Alabama agricultural export revenue. <= /span>

Cuba has substantial production potential. Cuba is the largest isl= and in the Caribbean, about as large in land area as Alabama, and 2/3 of the land = can be cultivated. Cuba’s population o= f 11 million is about twice that of Alabama, and about equal to Georgia or the combination of Mississippi, Louisiana, and Arkansas.

Cuba’s major agriculture export= s are sugar, citrus fruit, fish, cigars, and coffee, while Alabama’s are poultry, cotton, peanuts, soybeans, and feed grains. There would be little immediate direct competition in agricultural t= rade between Alabama and Cuba, and opportunity for profitable trade on both sides. Cuba also has mineral depos= its of nickel (world’s second largest reserves), cobalt, iron, copper, chromite, manganese, zinc, and tungsten, not to mention unexplored petroleum potentia= l. Cuba has no potential to export manufac= tures at present but that will change with foreign investment.<= /p>

Cuba trades with the US through= third countries and smuggling. Exports f= rom Europe, South America, and Asia to Cuba have higher transport costs than fr= om the US. Mobile is only 600 nautical miles or a two day sail from Havana, and was the dominant port prior to the embargo. Under the present relaxed embargo, Alabama ships poultry, catfish, soybeans, and eggs to Cuba. About $30 million of poultry was shipped during 2006. Other major exports a= re utility poles, lumber, and cotton.

In 2006, about 1/3 of US export= s to Cuba were from Alabama. There is potential to increase agricultural trade simply with relaxed travel and financial restrictions (ITC, 2007). Florida is more advanced in trade negotiations and operations, but t= he product mix may favor Alabama.

Increased political pressure to liberalize trade can be expected as more US firms and workers become aware = of the potential gains. Most Cubans i= n Miami now favor diplomatic relations with Cuba as well as limited trade (Institute for Public Opinion Research, 2007). US tourism, cruises, and retirement in Cuba will become an important part of t= he Cuban economy when the embargo is lifted.

2. Historical Eco= nomic Performance of Cuba

Real output has grown continuou= sly in Cuba over recent decades as shown in Figure 1 although the smoothly increas= ing output looks artificial. The bump = in the early 1990s was due the lost Soviet subsidy and Hernández-Catá (2000) quest= ions the quick recovery that follows. <= o:p>

Figure 1 Real Output in Cuba= (UN)

Figure 2 shows the negative out= put growth during the collapse of the early 1990s. The uneven performance since then is due to inefficient production a= nd lack of investment. Other Caribbean countries are much healthier, and less developed countries open to foreign investment have had consistent growth rates over 10%.

Figure 2 The 1990s Collapse = of Real Output Growth (IMF)

Income per capita remains near = the level of the 1950s although the economy grew slowly during the 1970s and 19= 80s as shown in Figure 3. The collapse of= the early 1990s is apparent. Cuba has = about 10% of the per capita income of developed countries and is near the bottom = in the hemisphere. By comparison, real income per capita in the US is close to $40,000 and in Mexico $8,000. The task of converging with the develop= ed countries seems daunting but international investment can raise income per capita quickly as in the Pacific Rim and Eastern Europe over recent decades= .

Figure 3 Cuban Real Income p= er Capita ($2000, Lexus-Nexus)

All sectors of the Cuban econom= y grew slowly during the 1970s and 1980s before faltering during the 1990s as show= n in Figure 4. Trade and manufacturing = have been growing faster than agriculture and construction. Agricultural output was higher than manufacturing but has lagged behind since the mid 1980s. Cuba holds potential for manufacturing = with foreign investment. As economies modernize, service industries tend to become larger and the same can be expected in Cuba. Tourism will bec= ome a major industry when the embargo is lifted.

Figure 4 Cuban Economy by Se= ctor ($2000, UN)

Figure 5 shows the steady growt= h of trade from the 1930s up to the embargo and beyond to the 1970s. Trade grew at a very fast rate from the middle of the 1970s but then collapsed with the lost Soviet subsidies. Soviet support is reflected during the = 1980s with import spending consistently higher than export revenue. Trade has rebounded since the mid 1990s.= Cuba’s trading partners have changed fr= om the Soviet era to a mix of Latin American, European, and Asian countries.

Figure 5 Cuban Exports & Imports ($2000, International Histo= rical Statistics)

Sugar remains Cuba’s primary agricultural export product although it has become less dominant. Figure 6 compares Cuban export revenue = by product in 1985 and 1999. The larg= est categories are now sugar, cigars, fish, and citrus.

Figure 6 Cuban Export Revenue ($1999, IMF) Sugar 1985 =3D $68 bil<= /span>

Citrus accounts for almost 10% = of Cuban export revenue. Cuba is the world’s third largest grapefruit producer following the US and Israel. These products do not represent import competition for Alabama agricultural producers. Kost (2002) points out that the Florida citrus industry stands to ga= in through investment in Cuba, supplying rootstock, technology, and entrepreneurial talent.

González, Spreen, and Jáuregui = (2007) make the point that the Cuban citrus industry is undergoing adjustment with abandoned marginal production areas, new plantings, new fruit varieties, cl= oser tree spacing, and new processing operations. Exports to Europe are the most important, and white grapefruit is exported to Japan. Cuba can also e= xport grapefruit in late August before Florida. The Caribbean is a potential market for fresh and processed Cuban oranges and limes.

Pertolia (2007) simulates the e= ffects of increased imports of Cuban sugar to the US assuming the US eliminates its tariff. Imports would generate a w= elfare gain of over $500 million in the US, about $2 per capita.

The recent history of US agricu= ltural exports to Cuba under the relaxed embargo is shown in Figure 7. Cereals and meats are the leading US agricultural exports. Given its production potential, Alabama will enjoy increased export demand for agricultural products when the embargo is lifted.

Figure 8 takes a closer look at= US agricultural exports to Cuba in 2006. Wheat, soybean products, chicken, corn, and rice are leading US expo= rts to Cuba. Given this demonstrated d= emand for agricultural products, it is safe to say that a lifted embargo will increase demand for Alabama agricultural products.

Figure 7 US Agricultural Exp= orts to Cuba (TradeStat Express)

Figure 8 US Agricultural Exp= orts to Cuba (2006, US-Cuba Trade & Economic Council)<= /p>

In the 1930s the US accounted f= or about 1/3 of Cuban import spending and 3/4 of Cuban export revenue as point= ed out by Messina, Brown, Ross, and Alvarez (2007). Cuba’s imports were mainly rice, flour, vegetable oils, and lard, and exports were mainly sugar, tobacco, fish, and minerals. Their prediction is that= US trade with Cuba will revert to this historical pattern when the embargo is lifted. Figure 9 shows that the US= was the major trading partner with Cuba before the embargo, and Cuba’s trade partners seem likely to revert to this historical pattern.

Figure 9 Pre-Embargo Cuban T= rade Partners, 1957 (International Histo= rical Statistics)

3. Potential Price Effects of Free Trade in Alabama

This section discusses the pote= ntial impact of trade with Cuba on prices of agriculture, manufactures, and servi= ces in Alabama. There will be increased exports as well as import competition across individual industries but the present focus is these aggregated sectors. The effects are gauged in a general equilibrium production model wit= h competitive product and input markets adjusting to changing product prices.

Agricultural prices are likely = to rise in Alabama with increased demand from Cuba. Messina, Spring, Moseley, and Adams (19= 96) make the point that Cuban agricultural products do not generally compete wi= th Alabama. Messina (2001) describes = the transition of Cuba in the 1990s to a market based agricultural economy concerned prima= rily with feeding its own population. C= uba can compete in only a few international agricultural markets including suga= r, citrus, and tropical fruits. Incre= ased demand can be expected for major Alabama exports of poultry, meats, soybean= s, and grains. The price effects of t= rade with Cuba would vary across products but present simulations focus on aggre= gate agricultural output assuming price increases of 1% and 2% in model simulati= ons.

The effect on the price of aggr= egate manufacturing should be similar. T= here will be increased demand and higher prices for Alabama’s major exports including transport equipment and chemicals. There will be very limited import competition in manufacturing for y= ears until investment in Cuba improves its infrastructure and capital stock to t= ake advantage of cheap labor. Competit= ion will be in labor intensive products, and Alabama has already adjusted to cheap l= abor intensive imports from Mexico in NAFTA and from Asia in the WTO. Price increases of 1% and 2% for aggreg= ate manufacturing are included in the simulations.

There will also be increased de= mand for Alabama business services including engineering, construction, shipping, transport, banking, finance, insurance, consulting, and higher education. Service industries supporting Alabama industry will also enjoy a positive spillover with increased manufacturing = and agricultural production. The prese= nt simulations include price increases of 1% and 2% for services.

In the following general equili= brium models, input payments and output levels adjust to these projected price changes. Adjustments across factor payments and outputs depend on relative price changes, input intensities ac= ross sectors, and input substitution. P= rice changes of 1% and 2% are simulated to gauge sensitivity of the Alabama econ= omy to free trade with Cuba.

4. A Competitive Model of Production and T= rade for Alabama

The foll= owing model of the Alabama economy is based on full employment and competitive pr= icing. Outputs of natural resources N, manufac= tures M, and services S are produced with inputs of capital K, labor L, and energ= y E (a composite Btu energy equivalent). Paper products are added to agriculture in the natural resource outp= ut N. Capital input is derived as the residual of labor and energy bills from value added. Publicly available input data is from t= he US Census of Manufactures, USDA, and Department of Energy.

Table 1 reports the shares of the three inputs employed across sectors. The relatively small natural resource s= ector N (agriculture and forest products) employs less than 2% of the labor force= L and its 4.5% share of capital K implicitly includes land. The large service sector S employs two = thirds of labor L and over half of capital K. Manufacturing M employs almost one third of labor L and almost 90% of energy E.

Table 1 Alabama industry sha= res λ_ij

	N	S	M
L	0.017	0.667	0.316
K	0.045	0.520	0.434
E	0.033	0.091	0.876

Table 2 reports the shares of value added paid to each factor. The price of each factor is assumed equ= al across sectors. Labor L receives a= lmost 60% of the value added in services S but only a quarter in natural resources N. Energy E receives almost a quar= ter of value added in manufacturing M, high relative to the US due to energy inten= sive production of chemicals and primary metals. Land input is included in the high capital K factor share for natural resource output N. Based on the la= rgest factor shares, natural resource output N is capital intensive and services S is la= bor intensive. Manufacturing M is intermediate and has the highest energy share.

Table 2 Alabama factor shares θ_ij

	N	S	M
L	0.259	0.579	0.342
K	0.615	0.401	0.417
E	0.128	0.020	0.241

Table 3 reports factor intensity comparisons. Natural resource outp= ut N is the most capital intensive relative to labor while services S is by far = the most capital intensive relative to energy. Services is the most labor intensive relative to both capital and energy. Manufacturing M is the mos= t energy intensive relative to labor and capital.

Table 3 Alabama factor inten= sities

	N	S	M
K/L	2.38	0.69	1.22
K/E	4.81	20.1	1.73
E/L	0.49	0.04	0.71

Substitution elasticities describe flexibility in cost minimizing inputs with respect to input prices as developed by Takayama (1982). The cross price elasticity between the input of factor i and the payment to factor k in sector j is E_ij^k =3D q_kjS_ij^{k<=
/sup>
where S_ij^k is the underlying Allen (1938) partial
elasticity of substitution. The present simulations assume =
Allen
elasticities of 0.1 following the applied production literature that genera=
lly
finds weak substitutes. Constant elast=
icity
of substitution CES would scale substitution elasticities and other
degrees of substitution are discussed. Linear homog=
eneity
of cost functions imply S_kE_ij^{k<=
/sup>
=3D 0 and own price elasticities E_ijⁱ are derived as =
the
negative of the sum of cross price elasticities. Aggregate substitution elasticities are=
the
weighted average of cross price elasticities for each sector,}=
s_ik =3D =
å_jl_ijE_ij^{k<=
/sup>
=3D}å_j=
l_ijq_kjS_ij^{k<=
/sup>. The derived CES =3D 0.1 substitution
elasticities are in Table 4 where w is the wage, r the return to capital, a=
nd e
the price of energy input.}}

Table 4 Substitution elastic= ities, CES =3D 0.1

	L	K	E
w	-.050	.041	.009
r	.046	.058	.012
e	.036	.043	-.079

The comparative static model is= built as in Jones and Scheinkman (1977) and Thompson (1990) with the substitution elasticity matrix matrix s, industry share matrix λ, and factor share matrix θ in (1). The first eq= uation in (1) is based on full employment and the second on competitive pricing, a= nd differentials represent percentage changes. Endowments are held constant by the null vector dv =3D 0 and price c= hanges in the vector dp represent percentage changes. Comparative static partial derivatives are solved by inverting =

σ λ dw dv 0 = (1)

=3D =3D

θ^T0 dx dp dp .

Table 5 reports the elasticitie= s of factor prices and outputs with respect to product prices derived by inverti= ng the system matrix (1). When the pr= ice of a product increases, demands for factors increase in that sector attracting inputs and forcing adjustments in factor prices and outputs. These factor price elasticities are ide= ntical for any degree of substitution.

Table 5 Factor price and out= put elasticities

	p_N	p_S	p_M
w	-1.95	2.09	0.86
r	2.92	0.15	-1.52
e	2.30	-2.25	5.56
x_N	19.4	-5.82	-13.6
x_S	-0.33	0.40	-0.07
x_M	-0.96	-0.09	1.06

Output elasticities in Table 5 = scale proportionately with the degree of CES substitution. These output elasticities are based on = full employment with fixed supplies of labor, capital, and energy, and one output can increase only if others fall along the production frontier. A higher price raises output in that in= dustry drawing resources from the others and lowering their outputs.

5. Simulated Adjustments to Projected Price Changes

Table 6 presents adjustments in factor prices and outputs to the various combinations of 1% and 2% price increases. Percentage price change= s are first multiplied by factor price and output elasticities, and then summed to arri= ve at the total effects for each scenario. <= /span>

Table 6 Free trade price sce= narios

Scenario	p_N	p_S	p_M	%Dw	%Dr	%De	%Dx_N	%Dx_S	%Dx_M
1	1%	1%	2%	1.86	-0.52	6.56	-13.7	-0.07	1.06
2	1%	2%	1%	3.09	0.58	-1.25	-5.85	0.40	-0.09
3	2%	1%	1%	-0.94	3.92	-1.30	19.4	-0.33	-0.96
4	2%	2%	1%	1.15	3.51	-3.55	13.6	0.07	-1.05
5	2%	1%	2%	-0.08	2.40	4.26	5.75	-0.40	0.09
6	1%	2%	2%	3.95	-0.93	4.31	-19.5	0.33	0.97

With prices increases of 1% or = 2% the real return to a factor has to rise by more than 2% for an unambiguous incr= ease its real income. An increase betwe= en 1% and 2% results in an uncertain effect on that factor’s real income. For instance, in Scenario 1 the wage ri= ses by 1.86% and the real wage depends on the product mix consumed by labor. Given that services represent the bulk = of output and consumption and the price of services rises 1%, it is likely the real wage would increase in Scenario 1. <= /span>

Any increase of less than 1% im= plies a decrease in the real income of that input. For instance, in Scenario 2 the capital return rises 0.58% implying a loss for capital owners. Of the 18 possible outcomes for factor prices, there are 8 clear winners and 8 clear losers with 2 uncertain outcomes.

The wage impact ranges from nea= rly 4% to almost -1%. Labor clearly wins = in Scenarios 2 and 6 but loses in Scenarios 3 and 5, and is in an intermediate position in Scenarios 1 and 4. Lab= or generally enjoys higher prices for services but not higher prices for the natural resource output. In Scenar= io 2 with the relatively large 2% increase in the price of services, the wage ri= ses over 3% as labor is attracted from natural resources and manufacturing. In Scenario 6 the price of manufactures= also increases 2% leading to a nearly 4% increase.

Capital clearly gains in Scenar= ios 3, 4, and 5 and clearly loses in the other scenarios. Capital enjoys an increase in the relat= ive price of the natural resource output. Energy input is affected the most, with large gains in Scenarios 1, = 5, and 6 but losses in the others. En= ergy input benefits from an increase in the relative price of manufactures where 90% of energy is consumed. =

Output adjustments in the last = three columns of Table 5 are small except for natural resource output x_{N. The natural resource sector is small an=
d any
induced factor movements have relatively large output effects. Output adjustments in manufacturing xM} and services x_S are negligible and just as likely positive as negative.

These factor price and output e= ffects scale in price changes. If price c= hanges were twice as large as those in Table 6, factor price and output changes wo= uld be twice as large. At a more detai= led industrial level there would be larger price changes and more variation in output adjustments.

6. Short Run Adjustment in a Specific Capi= tal Model

Capital = input is less mobile across sectors than labor and energy in the short run. For instance, turret lathe machines can= not readily move from manufacturing. A= lack of capital mobility is captured by the specific factors model with capital input specific to its sector. The = effects of price changes on capital returns differ across sectors. Elasticities of factor prices and output= s with respect to product prices are in Table 7.

Adjustments in factor prices and outputs to the price scenarios are in Table 8. There is more impact on the capital returns when capital is specific= in the short run. Capital returns in services r_S and manufacturing r_M are tied to prices in those sectors. The return to capit= al in natural resources r_N is less dependent on the price p_{N of
natural resource output due to the small size of that sector. The real return to capital r_{N in
natural resources falls except in Scenario 5 when both p_N and p<=
sub>M}
increase by 2%. In contrast, the r=
eturn
to capital r_S in the large service sector clearly gains in Scena=
rios
2, 4, and 6 when the price of services p_S increases 2%. The same is true for the return r_M=
to capital in manufacturing, and both r_S and r_M gain =
in
real terms when both p_S and p_M increase by 2% in Scen=
ario
6.}

Table 7 Sector specific capi= tal elasticities

	p_N	p_S	p_M
r_N	5.30	-1.56	-2.74
r_S	-0.01	1.39	-0.38
r_M	-0.03	-0.44	1.47
w	0.02	0.50	0.48
e	0.01	-0.28	1.28
x_N	2.15	-0.78	-1.37
x_S	-0.01	0.19	-0.19
x_M	-0.01	-0.22	0.24

Table 8 Specific capital tra= de scenarios

Scenario	p_N	p_S	p_M	%Dr_N	%Dr_S	%Dr_M	%Dw	%De	%Dx_N	%Dx_S	%Dx_M
1	1%	1%	2%	0.65	0.60	2.51	1.23	2.21	-0.04	-0.04	0.05
2	1%	2%	1%	0.73	2.41	0.52	1.76	0.77	-0.03	0.04	-0.05
3	2%	1%	1%	0.22	1.01	0.94	1.25	1.10	0.06	-.002	-.002
4	2%	2%	1%	-0.05	2.42	0.45	2.01	0.87	0.03	0.04	-0.05
5	2%	1%	2%	2.26	0.59	2.48	1.24	2.23	0.03	-0.04	0.05
6	1%	2%	2%	0.38	2.02	2.02	1.99	1.97	-0.06	.002	.002

Labor is generally in an interm= ediate position although the wage rises about 2% when the price of services p_=
S increases by 2% in Scenarios 2, 4, and 6. The real wage never clearly falls although it only clearly rises in Scenario 4. Energy is more closely= tied to manufacturing and the price of energy e rises about 2% when the price of manufacturing p_M increases 2%. In Scenarios 2 and 4 the real return to energy clearly falls. Outputs increase in their relative pric= e but the effects are not large in the specific factors model as capital immobili= ty hinders output adjustment.

A change in the capital return = alters investment in the sector. Suppose = the capital stock changes in proportion to the capital return. For instance, in Scenario 1 the stock o= f capital in manufacturing would increases over time by 2.51%. Sector output changes about proportiona= tely to the capital stock. The implicat= ion is that outputs will adjust in the long run according to the %Dr columns in Table 8. These long run output adjustments are larger than the short run adjustments in Table 8 with capital specific to the sectors. <= o:p>

7. Conclusion

The aggregate Alabama economy h= as a moderate amount at stake when the trade embargo with Cuba is lifted but cha= nging prices will affect both outputs and income distribution. There will be winners as well as losers= but the adjustments are not large, stressing the merit of the general equilibrium production model with its broad perspective. At a more detailed level, particular industries will have more at stake. Overall gains will outweigh losses.

Subsequent studies can examine disaggregated models with industrial detail leading to larger industry effe= cts but the wage and energy price effects will be similar to the present model. The model can be modified to include industry supply and demand between Cuba and Alabama, or Cuba and the Southeast. The structure of the en= ergy market can be modified to allow for an exogenous world price of imported oil and gas. Special attention can be = paid to the industries in manufacturing, services, and agriculture that will fac= e higher degrees of export opportunities and import competition from Cuba.

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