The industrial wage effects of Croatia’s accession to the EU

in an applied specific factors model of production

Josip Funda

Croatian National Bank

josip.funda@hnb.hr

Mia Mikiç

University of Zagreb

mmikic@efzg.hr

Henry Thompson

Auburn University

thomph1@auburn.edu

Abstract. One effect of Croatia’s accession to the European Union will be price changes across industries and the subsequent adjustments at the industrial level. With increased international trade, the increased national income will be redistributed with some industries and factors of production winning but others losing. The present paper gauges these industrial adjustments using a specific factors model of production and trade that focuses on labor specific to each of 23 industries in agriculture, manufacturing, and services. There are large impacts on sector specific wages but the shocks for mobile labor would be much smaller, suggesting policy should begin to encourage labor market flexibility to ease EU accession.

Introduction

Croatia’s accession to the European Union will increase but redistribute national income with some industries enjoying increased export opportunities while others suffer import competition. Croatia is in the midst of a transition from socialism, magnifying the political maneuvering of industry, labor, and regional groups. Political pressure promises to crescendo as the 2006 date for full accession nears.

EU accession will open Croatian industries to European markets and to world markets through the common external tariff (CET). Already almost three quarters of Croatian import spending goes to EU-25 products. The ongoing process of EU accession began with the 2001 Stabilization & Association Agreement stipulating asymmetric trade liberalization, and tariffs are falling in the move to qualify for EU membership.

The present application of the specific factors model of production and trade simulates the effects of anticipated price changes across the economy organized into 23 industries. The specific factors model of Jones (1971), Mayer (1974), Mussa (1974), and Neary (1978) has a rich tradition in trade theory although it has not been applied extensively. Output and factor markets adjust in the general equilibrium and the present focus is on what the effects would be if labor were specific to each industry. The other input in the model is aggregate residual “capital” assumed perfectly mobile across industries.

The model assumes constant elasticity production functions with constant returns to scale and the paper examines sensitivity to various degrees of substitution. The focus is the model’s comparative static elasticities of projected industrial price changes on industrial wages and outputs. Similar applications of the specific factors model include Thompson (1994) for Japan, Thompson (1996) for Alabama manufacturing, and Thompson and Toledo (2001, 2005) for Bolivia and Colombia.

Labor immobility is in fact a recognized economic and political issue in Croatia. There are strong regional preferences, a legacy of firm loyalty and “life-long” employment during socialism, restrictive labor laws, and mismatched labor skills. Much local industry has been subsidized and labor intensive under socialism and mounting political opposition is anticipated. A good share of the population might still be classified as peasants now facing integration into EU agricultural policy. Recognizing the potential impact on labor demand across industries, including the resource and service industries, might stimulate policy to ease Croatia’s transition into a more open economy.

Factor and industry shares

Factor and industry shares are the building blocks of the general equilibrium model based on employment and pricing conditions, and specifying factor substitution and intensity. Table 1 presents the industrial level data for 2001 from the Croatian National Bank (2003) with the number of workers L, value added x, and the yearly net wage w in kuna. The average $/kuna exchange rate in 2001 was 0.12. Value added x is revenue less the cost of intermediate inputs, and value added beyond the labor bill x – wL is attributed to an aggregate “capital” input.

Table 1. Data, 2001

HRK mil HRK mil HRK

	NCEA	L	w	wL	x	%GDP
1. Agriculture	A	30,376	37,836	1,149	12,198	8.8
2. Fishing	B	1,214	31,301	38	285	0.2
3. Mining	C	7,696	46,116	355	952	0.7
4. Mfg. Food	D15	43,919	41,484	1,822	5,147	3.7
5. Mfg. Apparel	D18	28,633	24,828	711	1,398	1.0
6. Mfg. Publishing	D22	11,476	43,884	504	1,646	1.2
7. Mfg. Refining	D23	4,513	51,564	233	3,356	2.4
8. Mfg. Chemicals	D24	14,620	50,856	744	3,796	2.7
9. Mfg. Minerals	D26	13,804	40,632	561	1,786	1.3
10. Mfg. Metal Products	D28	16,968	33,660	571	1,677	1.2
11. Mfg. Transport Equip	D35	18,018	48,060	866	1,472	1.1
12. Mfg. Other		99,906	37,680	3,764	8,702	6.3
13. Utilities (elec, gas, water)	E	27,655	46,716	1,292	3,926	2.8
14. Construction	F	65,782	33,996	2,236	6,832	4.9
15. Trade	G	159,479	35,004	5,582	15,955	11.5
16. Hotels & Restaurants	H	40,954	35,328	1,447	4,761	3.4
17. Transport & Telecom	I	82,138	46,716	3,837	13,821	9.9
18. Finance	J	28,637	62,952	1,803	6,710	4.8
19. Real Estate	K	52,408	42,480	2,226	14,549	10.5
20. Public Administration	L	121,305	51,660	6,267	11,645	8.4
21. Education	M	83,646	46,104	3,856	6,725	4.8
22. Health & Social Work	N	71,598	51,960	3,720	7,312	5.3
23. Other services	O	31,396	43,200	1,356	4,305	3.1
Total		1,056,141	42,072	44,434	138,957	100.0

The specification separates the eight National Classification of Economic Activities (NCEA) manufacturing industries that account for at least 1% of GDP. Trade, Real Estate, Transport & Telecom, and “natural resource” activities (Agriculture, Fishing, Mining) each account for about 10% of GDP, followed closely by Public Administration. The sizeable wage variation across industries is consistent with the present assumption of labor immobility and reflects both capital intensity and labor skills.

Labor industry shares λ_Lj in Table 2 come directly from the number of workers L_j in industry j, λ_Lj ≡ L_j/L_tot where L_tot = Σ_jL_j. The largest labor industry shares are in Trade with 15.1% (0.151) of the labor force, Public Administration 11.5%, Other Manufacturing 9.5%, Education 7.9%, and Transport & Telecom 7.8%. The smallest are in Fishing 0.1%, Mining 0.7%, and Refining 0.4%.

Table 2. Industry Shares λ_Lj & λ_Kj, Factor Shares q_Lj, and Capital Intensity a_Kj/a_Lj

λ_Lj λ_Kj q_Lj a_Kj/a_Lj

1. Agriculture	0.029	0.117	0.094	3.23
2. Fishing	0.001	0.003	0.133	0.20
3. Mining	0.007	0.006	0.373	0.08
4. Mfg. Food	0.042	0.035	0.354	0.08
5. Mfg. Apparel	0.027	0.007	0.509	0.02
6. Mfg. Publishing	0.011	0.012	0.306	0.10
7. Mfg. Refining	0.004	0.033	0.069	0.69
8. Mfg. Chemicals	0.014	0.032	0.196	0.21
9. Mfg. Minerals	0.013	0.013	0.314	0.09
10. Mfg. Metal Products	0.016	0.012	0.341	0.07
11. Mfg. Transport Equip	0.017	0.006	0.588	0.03
12. Mfg. Other	0.095	0.052	0.433	0.05
13. Utilities	0.026	0.028	0.329	0.10
14. Construction	0.062	0.049	0.327	0.07
15. Trade	0.151	0.110	0.350	0.06
16. Hotels & Restaurants	0.039	0.035	0.304	0.08
17. Transport & Telecom	0.078	0.106	0.278	0.12
18. Finance	0.027	0.052	0.269	0.17
19. Real Estate	0.050	0.130	0.153	0.24
20. Public Administration	0.115	0.057	0.538	0.04
21. Education	0.079	0.030	0.573	0.03
22. Health & Social Work	0.068	0.038	0.509	0.05
23. Other services	0.030	0.031	0.315	0.09

Assuming perfect capital mobility, capital r is the same across industries and the capital industry share λ_Kj in Table 2 is the ratio of industrial to total capital payment, λ_Kj = rK_j/rK = (x_j – w_jL_j)/(Σ_j(x_j – w_jL_j)) where K is capital input. The largest capital industry shares are Real Estate 13.0% and Agriculture 11.7%, which both include large land shares, as well as Trade 11.0%, and Public Administration 5.7%. The smallest capital industry shares are in Fishing 0.3%, Mining 0.6%, Transport Equipment 0.6%, and Apparel 0.7%.

The derived labor factor shares θ_Lj in Table 2 are the share of value added paid to labor in each industry, θ_Lj ≡ w_jL_j/x_j. For instance, the value added x of Agriculture is 12,198 million kuna, the labor bill 1,149 million kuna, and the labor factor share θ_L1 = 1,149/12,198 = 0.094 = 9.4%. Labor factor shares reflect labor intensity as well as relative wages. The largest labor factor shares are in Transport Equipment 58.8%, Education 57.3%, Public Administration 53.8%, and Health and Apparel 50.9%. The smallest are in Refining 6.9%, Agriculture 9.4%, Fishing 13.3%, Real Estate 15.3%, and Chemicals 19.6%.

The capital factor share is θ_K1 = 1 – θ_L1. The large capital factor shares in Agriculture and Real Estate in Table 2 are due to implicit land inputs. Land ownership is a critical issue in the transition from socialism and the ownership of large tracks of land is disputed.

Factor intensity anticipates the relative sizes of general equilibrium adjustments. Let a_Kj and a_Lj represent the cost minimizing inputs in industry j. The theoretical ranking of industries according to capital intensity is

α_m ≡ a_Km/a_Lm > … > a_Kn/a_Ln ≡ α_n. (1)

Assume a unit price of capital input to derive α_m. In Agriculture, the payment to capital is x_A – w_AL_A = 12,198 – 1,149 = 11,049 million kuna. Rescaling capital input r = 1 and there are 11,049 units of capital. With competition, value added is paid to the inputs x_j = w_jL_j + rK_j and K_j = x_j – w_jL_j. In Agriculture, the 3,376 workers imply the capital/labor ratio α_A = 11,049/3,376 = 3.23.

The most capital intensive industries in Table 2 is the outlier Agriculture 3.23 with its large implicit land input, followed by Refineries 0.69, Real Estate 0.24, Chemicals 0.21, and Fishing 0.20. The most labor intensive industries are Apparel 0.02, Transport Equipment 0.03, Education 0.03, Public Administration 0.04, and Other Manufacturing 0.05. The mean 0.07 and standard deviation 0.05 indicate high variation and there is a low peak with a skew toward labor intensity. Thompson (1995) shows that the influence of factor intensity outweighs substitution in the comparative static properties of these general equilibrium models, and the high variation in factor intensity implies there would be large differences in adjustments under any industrial structure. With similar rising prices, more capital intensive industries would attract proportionally more capital and enjoy higher wage and output increases.

Wages should be higher in capital intensive industries due to the positive effect of capital on labor productivity, and the correlation between α_m and w_m is 0.275. According to national statistics, workers differ quite a bit in education levels across industries. As an example, agriculture employs the highest percentage of workers with only primary education at 21% while financial intermediation at the other extreme employs only 0.6%. Working conditions and job security also influence wages.

Labor unions remain very much part of the economic and political landscape in Croatia. As an example, the education union has repeatedly won wage increases above average inflation while public administration has fallen behind. The present model focuses on the potential underlying forces across industries without delving into issues of union power. A “successful” industrial union would be able to translate falling labor demand from lower wages into less employment. It is possible to modify the basic model to allow such restricted factor market adjustments as suggested by Thompson (2003).

A specific factors model of Croatia

Input substitution plays a role in affecting the size of adjustments in industrial outputs and wages. Substitution elasticities describe adjustments in cost minimizing inputs to factor price changes as developed by Jones (1965), Chang (1979), Takayama (1982), and Thompson (1994). The cross price elasticity between the input of factor i and the payment to factor k in industry j is

E_ij^k = a_ij^/w_k^ = θ_kjS_ij^k (2)

where ^ represents percentage change and S_ij^k is the Allen (1938) partial elasticity of substitution. With constant elasticity of substitution (CES) production, Allen elasticities are constant and factor shares are sufficient to derive cross price elasticities. Cobb-Douglas is a special case of CES with unit Allen elasticities. Linear homogeneity implies Σ_kE_ij^k = 0 and the own price elasticity E_ijⁱ is the negative of the sum of the cross price elasticities. Aggregate substitution elasticities for the model are the weighted sum of cross price elasticities,

σ_ik = Σ_jλ_ijθ_kjS_ij^k . (3)

Estimates of the Allen partial elasticities in (2) with translog production functions in the applied production literature reveal little difference in the derivation of the aggregate substitution elasticities. For example, see Thompson (1997).

The Cobb-Douglas substitution elasticities are in Table 3. The own labor elasticities range from -.103 in Trade to -.001 in Fishing, with most in the interval (-.02, -.04). There is inelastic capital input with weak substitution of capital when w_j changes in the s_Kj column. There is somewhat stronger substitution of labor for capital when r changes in the s_jK column. The most elastic term is the own capital elasticity -.281.

Table 3. Substitution Elasticities, s_ik

Labor	sjj	sjK	sKj
1. Agriculture	-0.026	0.026	0.011
2. Fishing	-0.001	0.001	0.000
3. Mining	-0.005	0.005	0.002
4. Mfg. Food	-0.027	0.027	0.012
5. Mfg. Apparel	-0.013	0.013	0.004
6. Mfg. Publishing	-0.008	0.008	0.004
7. Mfg. Refining	-0.004	0.004	0.002
8. Mfg. Chemicals	-0.011	0.011	0.006
9. Mfg. Minerals	-0.009	0.009	0.004
10. Mfg. Metal Products	-0.011	0.011	0.004
11. Mfg. Transport Equip	-0.007	0.007	0.004
12. Mfg. Other	-0.054	0.054	0.023
13. Utilities	-0.018	0.018	0.009
14. Construction	-0.042	0.042	0.016
15. Trade	-0.098	0.098	0.038
16. Hotels & Restaurants	-0.027	0.027	0.011
17. Transport & Telecom	-0.056	0.056	0.029
18. Finance	-0.020	0.020	0.014
19. Real Estate	-0.042	0.042	0.020
20. Public Administration	-0.053	0.053	0.031
21. Education	-0.034	0.034	0.017
22. Health & Social Work	-0.033	0.033	0.019
23. Other services	-0.020	0.020	0.010
Capital K		-0.281

Constant elasticity substitution (CES) would scale these cross price elasticities, doubling them with CES = 2 for instance. Even with CES = 2, however, inelasticity would be prevalent in the aggregate substitution elasticities and there is no evidence of such high substitution in the applied production literature. Implications of inelasticity in production are a flatter production frontier and a more convex contract curve, implying larger adjustments in outputs and factor prices as pictured by the simulations of Ford and Thompson (1997).

Let x_j represent the output of product j, v_k the endowment of factor k, w_i the price of factor i, and p_m the price of product m. Behavioral assumptions are competitive pricing p_m = Σ_ia_imw_i and full employment v_k = Σ_ja_kjx_j. Fully differentiate these two conditions using the substitution elasticities and the cost minimizing envelope property to find

Σ_i σ_kiw_i^ + λ _kjx_j^ = v_k^ (4)

Σ_i θ_imw_i^ = p_m^ (5)

The comparative static model is the 47 equations in (4) and (5) arranged into matrix format

σ λ w^ = v^

θ’ 0 x^ p^ (6)

where s is a 24x24 matrix of substitution elasticities, l a 24x23 matrix of industry shares, ^' a 23x24 matrix of factor shares, and 0 a 23x23 null matrix. The comparative static general equilibrium model (6) solves for the effects of exogenous changes in prices p holding endowments v constant and assuming cost minimizing substitution and full employment. Inverting (6) the w^/p^ vector describes how changing prices affect industrial wages and the return to capital, and x^/p^ describes the local production possibility surface.

Derived comparative static elasticities

Table 4 summarizes the w^/p^ matrix, identical for any CES production function. The “own” price elasticities of wages are positive and elastic, ranging from 13.1 in highly capital intensive Refining to 1.70 in labor intensive Transport Equipment. Capital intensity makes labor more productive, leading to larger effects on labor demand. The correlation between capital intensity and own wage elasticity is 0.650. Each industry wage depends directly on the price in its industry but changing prices across the economy create capital industrial movements affecting labor productivities. The net effect across industrial wages will depend on the vector of price changes working through the w^/p^ elasticities in Table 4.

Table 4. Comparative Static Price Elasticities

Economic Activity	Own labor	Capital	Own Output
1. Agriculture	8.07	0.265	7.07
2. Fishing	7.52	0.004	6.52
3. Mining	2.68	0.004	1.68
4. Mfg. Food	2.79	0.021	1.79
5. Mfg. Apparel	1.96	0.003	0.96
6. Mfg. Publishing	3.25	0.008	2.25
7. Mfg. Refining	13.1	0.102	12.06
8. Mfg. Chemicals	4.96	0.035	3.96
9. Mfg. Minerals	3.17	0.009	2.17
10. Mfg. Metal Products	2.92	0.007	1.92
11. Mfg. Transport Equip	1.70	0.002	0.70
12. Mfg. Other	2.28	0.026	1.28
13. Utilities	3.00	0.018	2.00
14. Construction	2.99	0.032	1.99
15. Trade	2.73	0.067	1.73
16. Hotels & Restaurants	3.23	0.025	2.23
17. Transport & Telecom	3.39	0.081	2.39
18. Finance	3.61	0.041	2.61
19. Real Estate	5.53	0.182	4.53
20. Public Administration	1.84	0.023	0.84
21. Education	1.74	0.011	0.74
22. Health & Social Work	1.95	0.016	0.95
23. Other services	3.13	0.021	2.13

Shared capital input is much less sensitive to price changes since mobility lessens the price impact. Capital return elasticities in the second column of Table 4 range from 0.265 for Agriculture to 0.003 for Apparel. A price increase in a capital intensive industry has a relatively large impact on the market for shared capital as illustrated by the correlation of 0.483. Cross price effects on other wages are negative and inelastic, ranging from -.02 to -.05 but somewhat larger for the capital intensive outliers. Comparative static elasticities in Table 4 suggest wage adjustments will be large and uneven, while the impact on the return to shared capital will be minimal.

Table 4 also summarizes price elasticities of output in the last column. A higher price raises output along the production frontier drawing capital from other industries. Capital intensive industries with rising prices are magnets for capital with own elasticities of 12.06 in Refining, 7.63 in Agriculture, and 6.52 in Fishing. The correlation between capital intensity and these own elasticities is 0.650. The typical own price elasticity of output is greater than one. Assumptions of competition and efficient production lead to these large output effects.

Cross price elasticities of output are not reported in Table 4 but small and negative, ranging from -.01 to -.10 with more impact for capital intensive industries. An industrial price increase pulls small amounts of capital from other industries, and capital intensive industries have more pull.

The exogenous vector of price changes

Various influences, including trade with the rest of the world outside the EU, will affect industrial level price changes. The simple average MFN tariff in the EU is somewhat lower than in Croatia, implying the new trade regime will be more open. The EU tariff schedule will apply to about a quarter of Croatia’s imports and will generate moderate price changes. The reduction of MFN tariffs in the Doha round will lower prices of imported manufactures. These price changes will implicitly induce both trade creation and trade diversion although the effects will not be large.

EU regulatory and tax influences suggest higher food costs with GMO and pesticide regulations, higher chemical costs with environmental regulations, and higher costs in various industries with EU consumer protection. There will be less government support for traditional heavy industries but more support for Agriculture. Apparel prices will fall with global competition and elimination of the Multifiber Agreement in 2005.

There are moderate projected price changes in Table 5. A study by the World Bank (2003) indicates that the move to free trade between Albania and Macedonia did not cause significant price changes, although EU entry will generate somewhat larger price changes in Croatia. The Bank of Greece reports increases in consumer prices of about 10% in the switch to the euro although increased spending for the Olympics may have contributed.

Table 5. Projected Price Changes and Adjustments

	Prices	Wages	Outputs
1. Agriculture	2%	7.3%	5.3%
2. Fishing	5%	28.2%	23.2%
3. Mining	-1%	-5.1%	-4.1%
4. Mfg. Food	5%	11.5%	6.5%
5. Mfg. Apparel	-5%	-11.2%	-6.2%
6. Mfg. Publishing	-2%	-9.8%	-7.8%
7. Mfg. Refining	0	-19.5%	-19.5%
8. Mfg. Chemicals	1%	-0.9%	-1.9%
9. Mfg. Minerals	-3%	-12.7%	-9.7%
10. Mfg. Metal Products	-3%	-11.6%	-8.6%
11. Mfg. Transport Equip	-5%	-9.5%	-4.5%
12. Mfg. Other	0	-1.9%	-1.9%
13. Utilities	0	-3.0%	-3.0%
14. Construction	0	-3.0%	-3.0%
15. Trade	0	-2.7%	-2.7%
16. Hotels & Restaurants	3%	6.5%	3.5%
17. Transport & Telecom	-3%	-14.6%	-11.6%
18. Finance	-1%	-7.7%	-6.7%
19. Real Estate	5%	24.6%	19.6%
20. Public Administration	5%	8.0%	3.0%
21. Education	0	-1.1%	-1.1%
22. Health & Social Work	3%	4.5%	1.5%
23. Other services	0	-3.2%	-3.2%

The vector includes zero price changes for industries that have no clear direction. For instance, Refining and Utility prices depend mostly on fuel costs and the simulation holds their prices constant. Construction, Education, and Trade are not traded and their prices changes are set to zero. Other Manufacturing and Other Services are a mixed bag and their prices are unchanged.

The largest price increases are in Fishing and Food due to anticipated increased export demand. Real Estate should see increased activity will falling restrictions on property ownership. Public Administration will see a boost due to increased EU activity and support. These largest price increases are set at 5%, not overwhelming but noticeable at the industry level.

Hotels & Restaurants will enjoy increased tourism, poised to become a major industry. Health & Social Work will move toward a market system and will enjoy increased activity and higher prices. These two price increases are conservatively set at 3%.

Agriculture is a mixed bag with some products clearly gaining but others losing. The general level of support for agriculture promises to increase with the EU Common Agricultural Policy (CAP). There is international pressure, however, to lower support and there will be EU budget pressure with the entry of Central European countries. The agriculture price increase is set at a moderate 2%.

Chemicals will gain due to its location advantages and increased export opportunities, although there will be EU regulations. Its price increase is set at 1%.

The largest price decreases are set at -5% in Apparel and Transport Equipment. Apparel is highly protected and facing both EU and international competition. Transport Equipment is a traditional socialist industry producing carriages and other labor intensive transport components.

Three other losing industries have price declines set at -3%. Minerals and Metal Products are protected industries that will face increased import competition. Transport & Telecom will also face EU competition. In a similar situation, Telecom prices in Greece have fallen 10% over the past 5 years. Publishing and Finance will face competition and their price declines are set at -2% and -1%.

Projected wage and output adjustments

Multiply the matrix of price elasticities of factor prices sampled in Table 3 by the projected price changes to find the vector of factor price adjustments in Table 5. The wage adjustments for industry specific labor are large. The biggest winners are in Fishing 28.2%, Real Estate 24.6%, Transport & Telecom 14.6%, and Food Manufacturing 11.5%. The biggest losers are Refining -19.5%, Transport & Telecom -14.6%, Minerals -12.7%, Metal Products -11.6%, and Apparel -11.2%. The return to mobile capital rises slightly across the economy.

To derive effects on industrial outputs, multiply the matrix of price elasticities of output in Table 3 by the vector of price changes. Industrial outputs and wages move together but the output effects are somewhat smaller. The largest output gains are for Fishing 23.2%, Real Estate 19.6%, Food 6.5%, and Agriculture 5.3%. The largest output declines are for Refining -19.5%, Transport & Telecom -11.6%, Minerals -9.7%, Metal Products -8.6%, Publishing -7.8%, Finance -6.6%, and Apparel -6.2%. Smaller output effects occur in industries with no projected price change except for capital intensive Refining which loses capital to expanding industries. These output adjustments are generally large and represent considerable economic reorganization.

Adjustments are proportional to the price change vector. With price changes twice as large as in Table 5, adjustments in factor prices and outputs would also be twice as large. This high degree of output sensitivity arises from assumptions of full employment and competitive pricing. Employment slack, idle capital, or monopoly power imply smaller output adjustments but the present model provides a benchmark.

Varying the degree of CES substitution scales output adjustments accordingly but wage adjustments are unchanged. For instance, CES = 2 implies output adjustments twice as large as in Table 5. At the extreme of no input substitution, outputs are frozen. Elasticities of substitution are smaller in the short run implying small immediate output adjustments. Regardless of the degree of CES input substitution, the projected wage changes in Table 5 are identical. The basic lesson of the present model is that labor tied to its industry will experience large shifts in demand.

Conclusion

There will be substantial economic adjustment at the industrial level in Croatia as it opens to trade as an EU member. Wage adjustments in the present general equilibrium model are large for labor tied to its industry but labor mobility would dampen the impacts greatly. A lump sum subsidy for workers to relocate or change industries would encourage mobility and lessen the impact of EU accession.

Beyond the present model, investment in a more open and efficient Croatian economy will raise wages. Foreign investment is expected to transform the economy, especially in some key industries. Industry specific foreign investment in the specific factors model has magnified effects on industrial wages, and raises the wage of mobile labor. The present model focuses on industrial adjustments short of foreign investment in some short term with labor tied to its industry.

The projected declining industrial wages and outputs are no indictment of EU accession. With increased trade, gains will outweigh losses. Local firms facing import competition can increase their chance of survival with partnerships or mergers. In the highly aggregated industries of the present study, there is substantial room for specialization and trade. Nevertheless, particular labor groups and industries will attempt to insulate themselves from EU and international competition but our advice is to focus on becoming more competitive.

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