A Tim E Series Production Function Analysis of Postw
Order Number 9307783
A time series production function analysis of postwar Romanian industry: Its branches and regions
Hunt, Scott Edward, Ph.D.
The Ohio State University, 1992
U MI 300 N. Zeeb Rd. Ann Aibor, M I 48106 A TIME SERIES PRODUCTION FUNCTION ANALYSIS OF POSTWAR ROMANIAN INDUSTRY: ITS BRANCHES AND REGIONS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of the Ohio State University
By
Scott Edward Hunt, B.S., M.A.
The Ohio State University
1992
Dissertation Committee: Approved by
W.W. Eason
W. Boal Advisor R. Steckel Department of Economics ACKNOWLEDGEMENTS
I wish to express my deepest appreciation to Dr. Warren W.
Eason for his guidance, patience, and direction throughout the research and writing of the dissertation. I also express my utmost appreciation to Dr. William Boal for his insightful comments throughout this entire endeavor. I also wish to thank Dr. Richard
Steckel for his suggestions and comments. To my parents, Richard and Lucille, I thank you for never questioning my decisions and always supporting my educational pursuits. To my wife, Jane, I truly appreciate your patience and I sincerely thank you for all your encouragement and your undying confidence in me. Finally, I wish to thank all those with whom I have shared my graduate experiences, thank you for making this a pleasant experience. VITA
May 8,1963 ...... Born - Geneva, Ohio
198 5 ...... B.S. John Carroll University, University Heights, Ohio
198 6 ...... M.A. Ohio State University, Columbus, Ohio
1990-1992 ...... Lecturer of Economics, Ohio State University, Lima, Ohio
1992-Present ...... Professor of Economics Columbus State Community College, Columbus, Ohio
FIELDS OF STUDY
Major Field: Economics
Primary Fields: Soviet and Eastern European Economics International Trade
Secondary Fields: Labor Economics Economic History TABLE OF CONTENTS
ACKNOWLEDGEMENTS i i
VTTA i i i
LIST OF TABLES...... v iii
LIST OF...... FIGURES...... xv
CHAPTER PAGE
I. INTRODUCTION: ROMANIA AND ITS POSTWAR INDUSTRIAL DEVELOPMENT...... 1
Why Romania? ...... 1 Industry’s Role in a Socialist Economy ...... 2 Postwar History of Romanian Industry ...... 4 A Comparison with the USSR ...... 12 Problem Statement ...... 13 Contributions ...... 15 Chapter Descriptions ...... 18
II. DATA, METHODOLOGY, AND PRODUCTION FUNCTIONS... 21
Introduction ...... 21 The Output Data Series ...... 22 The Labor Data Series ...... 23 The Capital Stock Data Series ...... 24 Data Reliability ...... 25 CHAPTER PAGE
Production Function Models ...... 29 The Cobb Douglas (CD) Production Function.. 29 The Constant Elasticity of Substitution Production Function ...... 30 The Trans-log (TL) Production Function 32 Total Factor Productivity ...... 33 Production Function Acceptance Criteria 34
III. THE PATTERN OF GROWTH OF THE ROMANIAN INDUSTRIAL BRANCH...... 37
Introduction ...... 37 Elements of the Methodology ...... 39 Data and Sources ...... 45 Output ...... 45 Inputs ...... 45 Labor Input ...... 46 Capital Input ...... 47 Total Factor Inputs ...... 48 Measures and Results ...... 49 Elasticity of Substitution ...... 53 Returns to Scale ...... 53 Total Factor Productivity ...... 54 Electrical Consumption ...... 58 Implications ...... 60 Structural Change ...... 82 Comparisons ...... 87 Schatteles and Mihailescu ...... 87 Soviet Industrial Production ...... 92 Summary of Conclusions ...... 95
v CHAPTER PAGE
IV POSTWAR GROWTH OF THE ROMANIAN INDUSTRIAL BRANCHES AND REGIONS...... 99
Introduction ...... 99 Data and Sources ...... 100 Output ...... 100 Labor...... 101 Capital ...... 102 Regional Gerrymandering ...... 106 Data Reliability ...... 110 Total Factor Inputs ...... 111 Measures and Results: Industrial Branches 112 Direct Effects ...... 112 Capital ...... 118 Labor...... 121 Summary of Direct Effects ...... 123 Indirect Effects: Industrial Branches ...... 126 Heavy Industrial Group ...... 126 Light Industrial Group ...... 140 Romania and Soviet Comparison: Industrial Branches ...... 155 Regional Analysis ...... 160 Output ...... 160 Capital ...... 166 Labor...... 169 Summary of Direct Effects ...... 172 Indirect Effects: Regions...... 172 Romania and Soviet Comparison: Regions 191 Implications ...... 194 Direct and Indirect Data Concerns ...... 198 Structural Change: Industrial Branches ...... 203 Conclusions...... 211
vi CHAPTER PAGE
V. CONCLUSIONS...... 218
APPENDICES PAGE
A. Graphical Appendix ...... 229 Growth Rates of Output, Labor and Capital: Industrial Branches ...... 229 Rates of Total Factor Productivity: Industrial Branches ...... 247 Growth Rates of Output, Labor and Capital: Regions ...... 272 Rates of Total Factor Productivity: Regions 290
B. Data Transformations ...... 303 Indexing the Data ...... 303 Output ...... 303 Capital ...... 305 Labor...... 306 Capital Stock Estimation ...... 306
C. Geographical Conversions ...... 316 Coversions ...... 316 Areas ...... 318
LIST OF REFERENCES...... 322
vi I LIST OF TABLES
TABLE PAGE
1. Percentage of contribution made by industrial branches to overall industrial production ...... 11
2. Possible data set combinations ...... 49
3. Cobb Douglas production function results Inputs: Romanian capital stock, wage earners Romanian industrial sector, 1951-85 and 1960-85 ...... 52
4. Cobb Douglas production function results Inputs: Romanian capital stock, wage earners electricity, Romanian industrial sector 1960-85 ...... 59
5. Population shares, urban and rural, industrial and agricultural sectors, Romania, 1930-85.. 67
6. The annual total employment and annual changes in total employment in the agricultural and industrial sectors (in 1000’s), Romania 1950-81 ...... 69
viii TABLE PAGE
7. Tractor horsepower per 1,000 hectares of agricultural land and per 1,000 workers in agriculture, Romania and Eastern Europe 1973-82 ...... 70
8. Terms of trade and percentage share of imports and exports of major branches of the Romanian economy, selected years ...... 73
9. Growth rates of output, capital, labor, electrical consumption and the Solow method estimates of total factor productivity Romanian industrial sector, 1951-85 and 1960-85 ...... 77
10. Percentage contributions of capital, labor and total factor productivity to the growth of Romanian industrial output, 1952-85 ...... 80
11. Cobb Douglas production function results for the test of structural change, Romanian industrial sector, 1951-65 and 1966-85 ...... 84
12. Cobb Douglas production function results Inputs: Romanian capital stock, total industrial employment, Romanian industrial sector, 1951-85 and 1960-85 Schatteles and Mihailescu (extended) 88
13. Cobb Douglass production function results reproductions of Schatteles and Mihailescu's study, Romanian industrial sector, 1951-60 and 1960-69...... 90
ix TABLE PAGE
14. Cobb Douglass production function results Inputs: Romanian capital stock, wage earners Romanian industrial sector, 1951-60 and 1960-69, Comparison to Schatteles and Mihailescu’s findings ...... 91
15. Cobb Douglass production function results Soviet industrial sector, 1950-79 and 1960-79, estimated by Padma Desai ...... 94
16. The industrial branches comprising the Romanian industrial sector ...... 113
17. Growth rates of industrial production, groups A and B, industrial branches, Romania, five year midpoint averages 1951-85 ...... 114
18. Percentage share of industrial production by industrial group and branch, Romania 1951.1961.1971.1981.198 5 ...... 117
19. Growth rates of industrial capital stock, groups A and B, industrial branches, Romania, five year midpoint averages 1951-85 ...... 119
20. Percentage share of industrial capital stock by industrial group and branch, Romania 1951.1961.1971.1981.198 5 ...... 120
x TABLE PAGE
21. Growth rates of industrial labor, groups A and B, industrial branches, Romania, five year midpoint averages 1951-85 ...... 122
22. Percentage share of industrial labor by industrial group and branch, Romania 1951,1961,1971,1981,1985 124
23. Accepted production function models, heavy industrial group, Romania, 1951-85 and 1960-85
Chemical ...... 127 Construction Material ...... 128 E lectrical ...... 129 Ferrous...... 131 Fuel ...... 133 MBMW...... 1 34 Mining ...... 135 Non-Ferrous ...... 136
24. Accepted production function models, light industrial group, Romania 1951-85 and 1960-85
Food Processing ...... 141 Fur...... 142 Glass ...... 143 Manufacturing ...... 144 Paper ...... 146 Printing ...... 147 Soap ...... 148 Textiles ...... 149 Woodworking...... 150
xi TABLE PAGE
25. Regression and Solow method estimates of total factor productivity of industrial branches characterized by a constant rate of total factor productivity (regression), five year midpoint averages ...... 154
26. Accepted production function models, Soviet industrial branches, estimated by Padma Desai Soviet Union, 1950-79 and 1960-79 ...... 156
27. The industrial regions comprising the Romanian territories ...... 161
28. Growth rates of industrial production, industrial regions, Romania, five year midpoint averages, 1960-85 ...... 163
29. Percentage shares of industrial production by industrial territory and region, Romania 1960,1970,1980,1985 164
30. Growth rates of industrial capital stock, industrial regions, Romania, five year midpoint averages, .1960-85 ...... 167
31. Percentage shares of industrial capital stock by industrial territory and region, Romania, 1960, 1970, 1980, 1985 ...... 168
32. Growth rates of industrial labor, industrial regions, Romania, five year midpoint averages, 1960-85 ...... 170
xii TABLE PAGE
33. Percentage shares of industrial labor by industrial territory and region, Romania, 1960, 1970, 1980, 1985 ...... 171
34. Accepted production function models, Southwest territory, Regions: Arges, Banat, and Olt, Romania, 1960-85 ...... 173
35. Accepted production function models, Central territory, Regions: Brasov, Hunedoara and Mures, Romania, 1960-85 ...... 176
36. Accepted production function models, Northwest territory, Regions: Cluj, Crisana and Maramures, Romania, 1960-85 ... 179
37. Accepted production function models, Northeast territory, Regions: Bacau, Iasi and Suceava, Romania, 1960-85...... 181
38. Accepted production function models, Southeast territory, Regions: Bucharest, Dobrogea, Galati, llfov, and Ploiesti Romania, 1960-85...... 184
39. Regression and Solow method estimates of total factor productivity of industrial regions characterized by a constant rate of total factor productivity (regression) five year midpoint averages ...... 190
40. Estimated growth rates of total factor productivity of industry, Solow method estimates, Soviet republics (Koropeckyj) Romanian regions (Hunt), 1960-85 ...... 193
xiii TABLE PAGE
41. Production functions used in the tests of structural change, industrial branches, Chemical, Ferrous, Glass, MBMW, Non-Ferrous Romania, 1951-65 and 1966-85 ...... 205
42. Percentage of industrial capital stock and the average annual percentage of gross industrial investment within each industrial branch of the Soviet Union, 1960-80 ...... 310
43. Percentage of industrial capital stock and the average annual percentage of gross industrial investment within each industrial branch for Hungary, Poland, Bulgaria, and the Soviet Union, 1960-64 ...... 311
44. Average annual percentage share of industrial gross investment in Romanian industrial branches and regions, 1951-60 .... 314
45. Listing of the new districts which comprise the old regions of Romania ...... 319
46. The percentages of production, investment and labor of the districts that lie between tow regions ...... 320
47. Comparison of the area of old regions and the summed areas of the new districts, Romania, 1962 and 1985, in square kilom eters ...... 321
xiv LIST OF FIGURES
FIGURE PAGE
1. Annual growth rates of national income agricultural and industrial production, Romania, 1952-85 ...... 7
2. Annual growth rates of output, labor and capital,Romanian industrial sector, 1952-85 9
3. Rate of total factor productivity, industrial sector, Romania 1951-85 ...... 56
4. Rate of total factor productivity, industrial sector, Romania 1960-85 ...... 57
5. Rate of total factor productivity, industrial sector with electrical consumption, Romania, 1960-85 ...... 61
6. Working age populations, Romania, 1964, 1977, and 1985 ...... 65
7. Rate of total factor productivity, industrial sector under the hypothesis of structural change, Romania, 1951-65 and 1966-85 ...... 86
8. Maps depicting the old regional and new district administrative systems, Romania, 1962 and 1977 ...... 107
xv FIGURE PAGE
9. Illustration of the procedure to transform district data into a regional form, labor example, Romanian regions 1965-6 ...... 109
Growth rates of output, labor and capital Romania, industrial branches, 1952-85
10. Chemical ...... 230
11. Construction Material ...... 231
12. Electrical ...... 232
13. Ferrous ...... 233
14. Fuel ...... 234
15. MBMW...... 235
16. Mining ...... 236
17. Non-Ferrous ...... 237
18. Food Processing ...... 238
19. Fur ...... 239
20. Glass ...... 240
21. Manufacturing ...... 241
22. Paper ...... 242
xvi FIGURE PAGE
23. Printing...... 243
24. Textiles ...... 244
25. Soap ...... 245
26. Woodworking ...... 246
Rates of total factor productivity, industrial branches, Romania, 1951-85 and 1960-85
27. Chemical 1951-85 ...... 248
28. Chemical 1960-85 ...... 249
29. Construction material 1951-85 ...... 250
30. Construction material 1960-85 ...... 251
31. Electrical 1951-85 ...... 252
32. Electrical 1960-85 ...... 253
33. Ferrous 1951-85 ...... 254
34. Ferrous 1960-85 ...... 255
35. MBMW 1951-85 ...... 256
36. MBMW 1960-85 ...... 257
37. Non-ferrous 1951-85 ...... 258
38. Non-ferrous 1960-85 ...... 259
39. Fuel 1951-85 ...... 260 xvii FIGURE PAGE
40. Mining 1951 ...... 261
41. Food processing 1951-85 ...... 262
42. Food processing 1960-85 ...... 263
43. Printing 1951-85...... 264
44. Printing 1960-85 ...... 265
45. Textiles 1951-85 ...... 266
46. Textiles 1960-85 ...... 267
47. Manufacturing 1951-85 ...... 268
48. Soap and Cosmetics 1960-85 ...... 269
49. Glassware 1960-85 ...... 270
50. Woodworking 1960-85 ...... 271
Growth rates of output, labor and capital industrial regions, Romania, 1961-85
51. Arges ...... 273
52. Banat ...... 274
53. O lt...... 275
54. Brasov...... 276
55. Hunedoara ...... 277
xviii FIGURE PAGE
56. Mures ...... 278
57. C lu j ...... 279
58. Crisana ...... 280
59. Maramures ...... 281
60. Bacau ...... 282
61. Ia s i ...... 283
62. Suceava ...... 284
63. Bucharest ...... 285
64. Dobrogea ...... 286
65. G a la ti ...... 287
66. Ilfo v ...... 288
67. P lo ie s ti ...... 289
Rate of total factor productivity, industrial regions, Romania 1961-85
68. Banat ...... 291
69. O lt ...... 292
70. Crisana ...... 293
xix FIGURE PAGE
71. Brasov ...... 294
72. Hunedoara ...... 295
73. Mures ...... 296
74. Bacau ...... 297
75. Ia si ...... 298
76. Suceava ...... 299
77. Bucharest ...... 300
78. Dobrogea ...... 301
79. G alati ...... 302
xx CHAPTER I
INTRODUCTION: ROMANIA AND ITS POSTWAR INDUSTRIAL DEVELOPMENT
Whv Romania?
The Romanian economy, especially the industrial sector, has experienced rapid growth from the 1950’s until the mid 1970’s. The purpose of this dissertation is to ascertain the factors behind the rapid growth of Romanian industry prior to 1973 and its subsequent slowdown through 1985. Romanian industrial growth and development is first analyzed at the aggregate level then at the level of the individual industrial branches and regions. The branches and regions are examined to determine what role they played in the the growth of industrial production (prior to 1973) and its subsequent slowdown.
Romania and its economy have been relatively neglected (prior to the reform movements in Eastern Europe) by Western acamedicians compared to the amount of research concerning the former Soviet Union, Poland, and China. China and the Soviet Union drew attention because of their potential “threat” to the US and
1 2 Central Europe has also caused additional attention to be focussed on the USSR and Poland. However, Romania and the other Balkan nations also face many of the same problems and questions that the
USSR and Poland have addressed. Hence, it is necessary to examine how the Romanian economy, particularly industry, developed.
Soviet troops have not been present on Romanian soil since the early 1950’s. Romania was never a “ true” participant in the Warsaw
Pact and had increasingly implemented its own policies with little regard to Moscow as time passed. Romania also acted as a buffer zone for the Balkans against the Soviet Union, as it protected
Yugoslavia and Greece from the overburdening pressures of the USSR.
Romania also has access to the Black Sea and these ports are strategic outlets for Romania’s products while being in close proximity to the most important commercial and naval ports of the
Soviet Union. Thus, Romania and its industry are an important and intregal part of the Balkan region.'
Industry’s Role in a Socialist Economy
The industrial sector plays an important role in determining the direction of any economy. In socialist economies the industrial sector takes on even greater significance than in a market economy.
Marx emphasized in his “Critique of the Gotha Programme,” that in
1 Knapp, David, “Why Study Romanian," in The Society for Romanian Studies Newsletter. (Huntington, IN:Huntington College) Vol. II, 1987-88, Nr. 3, 40-42. 3 the transformation of society from capitalism to socialism, the
State would use the capitalists’ means of production, improve upon the existing production process, and increase industrial production.
Increased industrial production and a change in the attitudes of consumers were the vehicles to transform society from socialism to communism. Thus, the basis of the transformation of society stems from economic development based upon industrial production.
Industry also plays a prominent role in the development of the economy prior to the transformation of society. The USSR faced an economic dilemma when confronted on how to achieve this desired transformation of society. It was forced to decide whether the transformation would be slower in coming by promoting agriculture, and increase savings prior to industrial development or to develop industry immediately. This dilemma is referred to as the great industrial debate in which Preobrazhensky argued that the USSR should promote the industrial sector, particularly the branches of heavy industry. It was believed that over the long run the investment in heavy industry would lead to greater benefits for the economy and the consumers. This pattern of investment in and promotion of heavy industrial development has become a “cornerstone” of economic policy in all of socialist Eastern Europe. As the nations in Central and Eastern Europe rose from the ashes and destruction of WWII, 4 under the watchful eye of Stalin’s Soviet Union, each nation pursued
an investment program that emphasized the development of heavy industry. Thus, the Soviet investment strategy of the 1920’s became the trademark of the socialist natrons’ development programs in the
1950’s.
Postwar History of Romanian Industry
Prior to WWII Romania operated under a market oriented economy and was ruled by a constitutional monarchy. During WWII,
Romania was allied with the Axis Powers through 1943, but thereafter it fought on the side of the Allies (because of the Soviet invasion). The period of Soviet occupation was characterized by a general recovery from WWII in which the Romanian citizenry received the basic necessities of life. The Romanian government was forced to pay enormous war reparations to the Soviets until the mid-1950’s.
Romania, after 1948, began to implement a Soviet-styled command economy, emphasizing heavy industrial development, in place of the market system that had prevailed prior to WWII. The agricultural sector of the Romanian economy was predominant in the pre-WWII era, as Romania was referred to as the “ Breadbasket of the Balkans.” This development pattern changed after WWII, since the Eastern European nations, including Romania, came under the 5 direction of Soviet policy. The nations of Eastern Europe, excluding
Romania and Albania, experienced rapid industrial growth shortly after WWII. Romania, however, did not since it was paying war reparations and was forced to turn over a portion of its mineral rights to the USSR.
Economic growth in Romania continued at a slow rate through the late 1940’s as the economy was transformed from an agrarian society to an industrial society. Georghe-Dej continued this effort through 1965 when he was replaced as premier of Romania in 1965 by Nicolai Ceausescu. Ceausescu intensified the effort to establish, not only an industrialized economy, but an economy that would be self-sufficient as well. In order to achieve this goal, Romania imported raw materials from Third World nations in exchange for its low quality machinery. Romania also received numerous loans from the West during the 1970’s to continue its industrial growth.
However, the value of Romanian industrial production fell on the world market as interest rates and oil prices rose (by this time
Romania was a net importer of energy), thus worsening Romania’s debt position. Ceausescu turned inward to lessen the burden of the debt by exporting any good that could be sold on the world market and by limiting imports. 6 The Romanian government promoted the growth of heavy industry (following the pattern of Soviet industry in the late 1920's and 1930’s) through forced collectivization of agriculture and nationalization of industry (completed in 1962). Much of the industrial growth through the mid-1970’s was accounted for by large capital investments in the industrial sector. In 1960 capital investment was approximately 20 percent of national income, but by the beginning of the 1970’s it had reached 30 percent and remained at this level until 1980. Romania constructed and placed over 1500 plants on-line during the 1960’s.
These policies led to drastic changes in the role played by the industrial sector in the development of the Romanian economy. By
1975 the industrial sector accounted for 65 percent of national income; while in 1969 it was responsible for 57 percent of national income, in 1950 43 percent, and in 1938 barely 30 percent of national income. Thus, after WWII, the Romanian industrial sector was the intregal factor in the development of the economy.
Figure 1 presents the annual average growth rates (midpoint) of total production in the Romanian economy’s industrial sector
(measured by the official value of industrial output in 1963 constant prices); of agricultural production (measured by the value of agricultural production in constant 1963 prices); and 18
16
14 / Nat. Inc. ' 12
10 % Rate of 8 Growth 6
4
2
0
2
Year Figure 1 Annual Growth Rates of National Income, Agricultural And Industrial Production, Romania 1952-85 Source: Annual Romanian Statistical Yearbook
■Sl 8 of national income (measured in constant 1963 prices). The growth rate of industrial production practically mirrors the growth rate of national income as it steadily declined from 15 percent in 1974 to 3 percent in 1982. However, the growth rate of agricultural production exhibits large fluctuations in five year intervals beginning in 1964.
The agricultural sector seems to expand at a rapid rate after 1964 and by a lesser rate by 1985.
The growth rate of industrial production follows a cyclical pattern over the period of 1953-19742. That cyclical pattern of production follows from the cyclical growth of industrial labor input (and that of the total labor force) is shown in Figure 2. This decline in the growth of industrial labor is partially due to the decrease in the birth rate during WWII3,as well as a WWII echo effect and the drying up a a agricultural labor surplus. The completion of agricultural collectivization, in 1962, meant that the largest labor transfers from the agricultural sector to the industrial sector had already taken place and that industry could not rely on the agricultural sector for its additional workers. Although Romania
2 In general, the growth rates of industrial production of the Western European nations do not follow a similar cyclical pattern. A number of these countries, including Austria, France, Norway, Portugal Switzerland and Spain have industrial growth rates that are stable or slowly declining over this period. Nations such as Germany, the United Kingdom and Sweden have much greater variation in their yearly industrial growth rates than the other European nations. Western European industrial growth rates occasionally are negative (Romania through 1985 did not experience a negative industrial growth rate). Also, each Western European nation experienced a sharp decline in industrial growth in 1974-5 primarily due to the Middle east oil crisis. Data was obtained from the U.N. Monthly Statistics, various volumes. 3 This will be specifically detailed in Chapter 3. 18
16 Oiitpui
14
12
10 N- % Rate Capital— of 8 Growth 6
4 Labor
2
0
2 70 80
Year Figure 2 Annual Growth Rates of Output, Labor and Capital, Romanian Industrial Sector, 1952-85 Source: Annual Statistical Yearbooks, Romania 10 has one of the highest percentages of labor working in the agricultural sector, the proportion of females has surpassed that males.
Since 1974, the growth rate of the industrial labor input continued to decline past its previous trough level until it reached a post-WWII low in 1980. From 1981-1984 the growth rate of output recovered somewhat, but it did not regain the high rates of growth it saw in the past. It is concluded that the decline in the growth rate of production over the period 1974-1981 was caused by decreases in the growth rates of inputs (both labor and capital) and a declining rate of total factor productivity.
Not only did industry take over as the leading sector of the economy, but there was also an impressive transformation taking place in the branch composition of the industrial sector. Table 1 indicates these changes as it depicts the branch composition of industrial production (valued in 1955 prices) for the years 1938,
1950, 1960, 1975, and 1985. The emergence of the Metallurgy
(Ferrous and Non-Ferrous), MBMW (Machine building metal working) and the Chemical branches are noteworthy. Each more than tripled its percentage contribution to industrial production between 1938 and 1975 to become the leading branches of the industrial sector, as well as of the Romanian economy as a whole. 11 Table 1 Percentage of Contribution Made by Industrial Branches to Overall Industrial Production
--Y e a r--
Branch 1938 1950 1960 1975 1980 1985
MBMW 10.2 13.3 24.0 32.4 35.3 29.2 Chemical 2.7 3.1 6.1 11.3 9.7 10.6 Metallurgy 6.7 7.5 8.4 7.9 14.7 15.1 Food 32.4 24.2 18.9 13.1 12.8 11.5 Fuel 16.8 11.3 9.1 3.6 3.4 9.4 T extiles 12.8 18.6 13.5 11.9 8.2 6.8 Woodworking 9.5 9.9 7.5 4.7 4.1 3.8 Electric 1.1 1.9 2.5 2.7 1.8 3.4 Construction 1.2 2.4 3.2 3.1 3.4 3.4 Other 6.6 7.8 6.8 9.3 6.8 6.9
Sources and Notes: Data was obtained from the Official Statistical Yearbooks of Romania 1955-86. 12 The importance of the Fuel, Woodworking, Textile and Food
Processing branches have steadily declined, but, the Fuel branch did recover somewhat between 1981-85. Domestic fuel resources (oil and gas) has dwindled during and since WWII as the Romanians supplied fuel to the Germans and then paid war reparations to the
USSR.
Though the percentage of the industrial production accounted for by the Electrical branch has been relatively low, a massive electrification project was undertaken between 1951 and 1960. The effort greatly increased the number of kilowatt hours produced, but by the late 1970's Romania had become an importer of Soviet electricity as it was cheaper to import electricity from the USSR than oil from the Middle East.
A Comparison with the USSR
The strategy of post-war Romanian economic growth was patterned after that of the Soviet Union, whose influence in Romania also included the appointment of political leaders and administrators congenial to Moscow. Both Romania and the Soviet
Union experienced the death and destruction of WWII, though the
Soviets endured much greater losses. After WWII, collectivization of agriculture produced surplus labor that was transferred to the industrial sector, in the same manner as the Soviets in the 1930’s. 13 Both countries implemented policies that promoted the growth and development of heavy industrial branches through increases in capital formation.
In a similar fashion, Romania experienced a dramatic decline in the growth rate of industrial production (shown in Figure 1) over the 1974-81 period, similar to that of the Soviet Union in 1970.
Given the common recent (post WWII) histories of Romania and the
USSR and the adoption of the “Soviet” model of economic development by Romania, one would expect that the causes of the slowdown in Soviet industrial production were at the root of the industrial decline in Romania.
Problem Statement
The primary focus of the dissertation is to describe and analyze the growth and development pattern of post war Romanian industry by means of production function analysis. The respective industrial branches and regions of Romania are also examined to determine the causes of the slowdown in industrial production after 1973. Since the Romania economy has experienced rapid growth and numerous changes, the industrial sector is tested for the occurrence of a structural change in its pattern of production. The tests indicate that the production of the industrial sector has always been dominated by the heavy industrial branches and 14 therefore it structure did not change over time. However, those individual branches of MBMW, Ferrous, Non-Ferrous, Chemical and
Glass experienced significant changes over the same period.
Romania has followed the Soviet command-type economic pattern, characterized by a pattern of decline in industrial production similar to that experienced by the USSR, according to which the growth rate of industrial production declined while the growth rate of the capital input exceeded that of the labor input.
Finally, the reasons or causes of the slowdown in Romanian industrial production are contrasted with those of the Soviet experience. To the extent that the Romanian experience is different from the Soviet, in these term, the implication would be that the
Soviet model may not be relevant. Evidence that the Soviet model is the correct model is provided if the same or similar reasons for the decline in both Romanian and Soviet industrial production are discovered and this would support the more or less universal relevance of the Soviet experience for the socialist countries of
Eastern Europe.
Production function analysis is used to investigate Romanian industrial production, development, growth and and subsequent slowdown. Similar methodology has been used in investigating
Soviet industry, Weitzman (1970) and Desai (1985) being the 15 prominent examples. Romanian industry is examined by the use of
three different production functions, the Cobb Douglas (CD), the
Constant Elasticity of Substitution (CES), and the Trans-log (TL)
production function. Each production function is tested with the
assumption of normal errors that are identically and independently
distributed. They are also examined for errors that may be serial
correlated. The production functions use capital and labor as inputs
and the value of production as the output4. Each of the 17 industrial
branches and 17 regions are investigated by the production functions
analysis. Finally, the Romanian industrial branches are contrasted
with their Soviet industrial branch counterparts.
Contributions
The main contribution to the literature is that the factors
which have caused the Romanian industrial branch to grow at such
rapid rates from 1950 to 1972 and those which have caused the
subsequent slowdown in industrial production are identified through
production function analysis. This analysis was duplicated for each
of the 17 industrial branches and 17 regions of Romania. The results
of this analysis identify the industrial branches and regions that
were the major contributors to slowdown in industrial production.
The industrial branches and regions with the greatest shares of
aggregate industrial production were also identified. These were the
4 The definitions of these are found in Chapter 2. 16 sectors of MBMW and Chemicals and the regions of llfov and
Bucharest. Similar factors have caused slowdowns in production in these sectors and regions. The analysis indicated that the slowdown of Romanian industrial production was due to the declining growth rates of inputs and a declining, even sometimes negative, rate of total factor productivity. Similar factors have been identified by
Desai as being the cause in the Soviet industrial slowdown.
The only production function study concerning the Romanian industrial sector, prior to this study, was conducted by Schatteles and Mihailescu (1971). Their study only analyzed Romanian aggregate industrial production in ten year intervals over the period of 1951-
1969. Thus, there was no research conducted over the entire period
(1951-85) and given the added years of data this study provides a more relevant description of Romanian industrial production.
Schatteles and Mihailescu only examined the industrial sector
(aggregate) itself and did not examine either the individual industrial branches or regions (this was because data at these levels were either unavailable or insufficient at the time of their study).
There has been no attempt to update their research. It seems that a lack of belief in production function analysis in socialist economies
(as stated by Schatteles and Mihailescu) meant that further research in this area was not appropriate. 17 Schatteles and Mihailescu confine their study of Romanian industry only to the CD production function. They use two specifications, the first having the restriction of constant returns to scale and a second which has no restrictions. They report the results of both specifications, but fail to indicate which is preferred. Schatteles and Mihailescu were correct in their use of the
CD production function, however, it is indicated here that the best specification includes both a constant and variable time trend with a constant returns to scale restriction.5 They did not test the CES or
TL production functions and therefore were not able to state that the CD production function was the appropriate model to be used in the description of Romanian industrial production. Nor did their study report any test statistics or standard errors of the estimated parameters, so their results are questionable. This dissertation employs all the production functions (CD, CES, TL) and tests for the best fit amongst these models and their different specifications for all levels of industrial production (sector, branch, region).
Given this finding, studies such as Scherer (1986, an efficiency study on Eastern European industry) and others may be reevaluated or may be given greater support. This new information concerning the “ micro” level of Romanian industry, especially
5 The author retested Schatteles and Mihailescu' work { only for the two ten year periods of 1951-60 and 1960-69) and found that the CD specification which included a variable time trend was the preferred specification. 18 concerning its industrial branches and regions can be used in other studies. Finally, the data that has been compiled from various sources and collected from the statistical yearbooks are now in a collective and more usable form.
Chapter Descriptions
This Chapter has introduced Romania, its post-war industrial history, its industrial growth and development pattern, and its subsequent slowdown in industrial production. It has posed questions that are to be answered by the dissertation and has shown how the dissertation contributes to and extends the current literature with respect to economic growth within a socialist economy.
Chapter Two explicitly describes the data series employed in the dissertation. The industrial value of production, the annual average number of workers in the industrial labor force and the capital stock series are defined. The formal description of the compilation of these series is left to Appendix B. These data series pertain to the aggregate industrial level, as well as the industrial branch and regional levels. The limitations of the data series are also described in this chapter. Finally a description of the production function models and their parameters, including the 19 concept of total factor productivity, are provided.
Chapter Three describes the pattern of Romanian industrial growth and development and identifies the factors that have affected the development of industry. The cyclical pattern of industrial growth, for the 1951-74 period, is shown to be influenced by the growth rate of the industrial labor force, which has also followed a cyclical pattern. This pattern was due to the effect of
WWII on the birth rate of the Romanian population, its echo effect, the mechanization of agriculture, and the worsening of the terms of trade in the agricultural sector and these factors led to the decreasing growth rate of the industrial labor force since 1974.
Couple this with the declining rate of capital growth, due to a worsening of the terms of trade primarily in the Fuel branch, and a decreasing growth rate of total factor productivity and the result was a severe slowdown in the growth rate of industrial production.
Chapter Three also indicates that the structure of Romanian industry has not changed since 1951, though the composition and contribution of different branches have changed. Finally, the results produced here are compared to the work of Schatteles and
Mihailescu, concerning Romanian industrial production, and to the work of Weitzman and Desai, concerning Soviet industrial growth.
Chapter Four extends the analysis of Chapter Three to the 20 industrial branches and regions. The growth rates of industrial production for the branches and regions are examined separately to identify the individual branches and regions which have played a significant role in the slowdown of industrial production. Finally, the results of the production function analysis of the branches and regions are compared to Desai’s results concerning Soviet industrial branches and to Koropeckyj’s research on the Soviet republics.
Chapter Five summarizes the findings of Chapters Three and
Four and draws the main conclusions concerning the development of post-war Romanian industry. Special emphasis is placed on the model of the production function and its interpretation of the factors that have caused the slowdown in Romanian industrial production. CHAPTER II
DATA, METHODOLOGY AND PRODUCTION FUNCTIONS
Introduction
This chapter briefly describes the data series and models used
in the production function analysis of Romanian industry. The
chapter contains the definitions of each data series used for output,
labor, and capital and it divulges the source of each data series, as well as describing how the data series were constructed into a
usable form for the use in the production functions. The bias of the
data and the possible errors introduced by forming the data series from the raw data are discussed. The chapter also provides a
description of the production function models utilized in this study
(CD, CES and TL) and the parameters contained within these models.
The concept of total factor productivity (TFP) and its inclusion within a production function is also discussed. Finally, the criteria
used to determine which model of the production function best
describes industrial production are disclosed.
21 22 The Output Data Series
The only output series available to describe Romanian industrial output is the official value of industrial production, valued in constant 1963 prices. It is reproduced in this study from data contained in the official Romanian statistical yearbooks. The actual value of industrial production is available over the 1975-85 period, however, the remaining years, 1951-74, were found by the use of index numbers’. In order to obtain an output series series for the industrial branches and regions the percentage of each year’s production (for the respective branch or region) was multiplied by the value of industrial production. The major drawback of using the value of industrial production as the output variable is that it is measured in official prices (prices are not determined by the market). This bias is found throughout the series. Socialist governemnts are in the practice of creating many “new” products,2 which commandeer higher prices, and this artificially inflates the value of production. This bias was eliminated by employing 1963 constant price weights throughout the entire series.
1 A formal description is contained in the Appendix. * These products may not be new, but have some different name or package and this is because enough to increase the product's price. 23 Data prior to 1965 is not available for the present day 41 administrative districts, however, it is available for the 17 regions (which were partitioned into the above districts). The percentage share of industrial output of the districts were combined to approximate the percentage share of industrial production for each region for data after 1965. The regional shares were then multiplied by the value of industrial output to arrive at the value of industrial output within each region. Maps (found in Chapter 4) and a detailed description of the transformation of the data from district to regional form is found in Appendix C.
The Labor Data Series
Two alternative labor series are available and suitable for implementation into the production function models. Each provides a measure of the labor involvement in the industrial production process. The first labor series, designated as total industrial employment (TIE), is measured by the combination of the annual average number of salaried personnel and wage earners. This series is disaggregated into the average annual number of industrial employees within each branch and region. It is available for the entire 1951-85 period. 24 The second labor series, designated as wage earners (WE), is
measured by the annual average number of wage earners in the
industrial sector. The WE is available over the 1951-85 period for
aggregate industry only. It has not been fully published at the level
of industrial branches or regions. Production function studies of this
type usually attempt to measure the labor input in either man-days
or man-hours, however, for Romania, data in these forms are not
available. Both the TIE and WE labor series were obtained directly
from data contained in the official Romanian statistical yearbooks. The Capital Stock Data Series
Two alternative capital stock data series are available for
implementation into the production function models. The first
capital stock series, designated as the Romanian capital stock (RK),
is the official value of Romanian industrial capital stock. It is
measured in constant 1963 prices3 and was obtained from the
official Romanian statistical yearbooks. Capital stock data series
for the industrial branches and regions are derived from this
aggregate series4. This capital stock series is available for
aggregate industry and the industrial branches for the 1951-85
period, but is only available over the 1960-85 period for the regions.
5------This was done for identical reason as those in the case of the valuation of industrial production. 4 The procedure is detailed in the Appendix. 25 The second capital stock data series, designated as the
Western estimate (WK), is a measurement of the value of Romanian
industrial capital stock as a percentage of Romanian GNP and is
measured in constant 1977 prices. This data series was obtained
from the L. W. Financial International Research Company via a telephone conversation with Gregor Lazarcik. No other details were
disclosed pertaining to the assumptions used to create this data series other than similar series are produced for Hungary and
Czechoslovakia. The WK series is available for aggregate industry, the industrial branches and regions for the 1960-85 period5.
Data Reliability
Data obtained from the official statistical yearbooks of the
USSR and Eastern European nations must be examined for the sources of possible errors and/or bias contained within the series. The first possible source of bias originates in the inconsistencies in the definitions used and publishing of the data over time. Secondly, the output and capital stock series are reported in official value
(prices) terms, not in the physical quantities. Therefore, the gross value of output and capital stock will be greater than the value- added figure (used in Western economies), since double counting and higher prices for “ new” products are common practices in socialist
i------A more detailed description of the capital stock data series is contained in the Appendix. 26 economies. The use of constant prices, valued in weights of a single year, eliminates some of this bias, however not all, since the prices used are also subject to these biases.
A third source of bias or error is introduced in the development of a constant price series for both the output and capital data sets since it was necessary to link indices with different base years. It was possible to convert the index number of years not in the base year of 1963 into index numbers with a base year of 1963 since the indices overlapped one another. This linkage, however, was based upon index numbers which were rounded to the nearest whole number and therefore an additional error of 0.5 percent (since index numbers represent percentage changes) is introduced by this procedure.
The fourth source of bias or error is created by the use of index numbers that are used to determine the values of output and capital stock. The official index numbers are rounded to the nearest whole number in the indices of industrial production and capital stock. The rounding of the index numbers introduces a possible error as large as 0.5 percent. There is an additional error as large as 0.05 percent when the index numbers are used to produce output and capital data of the industrial branches and regions since these index numbers are rounded to the nearest tenth of one percent. 27 Both capital stock series, RK and WK, are affected by the index number problem when the data is transformed into the actual figures measuring the value of the industrial capital stock. The capital stock series were converted to mid-year average figures to avoid the possible year-end bias. Finally, the capital stock series of the industrial branches and regions were estimated using the average rate of investment in the branch or region6. This estimation has introduced an additional error to these data series. The only limitation in the labor series data is its form, that is, it is measured by the average number of employees and not in man-days or man-hours.
Since Romania politically realigned its domestic territories from large regions into smaller districts in 1968, the data pertaining to these administrative areas was produced in two forms, regional and district. Given the available data, it was decided that it was best to proceed by converting the newer district data back into the regional form. For the most part, many of the newer districts were wholly contained within the older regions, but several districts were found to be lay across the border of two regions. It was necessary to estimate the assigned percentages of output, labor
t ------A detailed description of this conversion is presented in Appendix C. 28 and capital of the districts to each region7 . This estimation
introduces another possible error in the data series of the regions.
Thus, there are a number of possible sources of bias and errors contained within these data series. However, the fact remains that this is the best data available for the Romanian industrial sector
and it is impossible to “improve” the published data further than what has been presented here. The output and capital data series are presented in a constant price form to limit the bias of the socialist pricing policies. Rounding errors should have a relatively minor effect on the results produced here. Only the estimation of the capital stocks in the industrial branches and regions, as well as the conversion of output and labor data series from district to regional form, have been introduced by the author. Although these data series are not optimal in the western sense, they are comparable to those of other eastern European and Soviet data series which have been utilized in previous production function studies.
The official Romanian data series are as reliable as other
Balkan countries' data series, however, the Romanian data is not of the same quality of Poland or the Soviet Union. The Romanian data are not as precisely defined as in these other nations, as is the case with the labor data. It is also true that the Romanian data has not
i ------A detailed description of this conversion is presented in the Appendix. 29 been adjusted for quality or waste, however, these will be captured in the residual term of the production function. Finally, Romanian data, particularly in the late 1980’s has been adjusted8 to conform with the desired outcome, however, this is evident only in the agricultural branch during this time period only. Since this study pertains to industrial production prior to 1986 this should not affect the reliability of the data.
Production Function Models
The three production function models,specifications, underlying assumptions, and procedures implemented in this study are now examined.
The Cobb Douglas (CD) Production Function
The log-linear form of the CD production function is given by the expression:
1) log Y = intercept + a(log K) + p(log L) + error where Y represents output, K is capital, and L is labor. The CD production function is the most restrictive of the three functions because it implies that the elasticity of substitution is constant and
‘ Ben-Ner, Avner and J. Michael Monlias, T h e Introduction of Markets in a Hypercentralized Economy: The Case of Romania." The Journal of Economic Perspectives. Vol. 5. No. 4, Fall 1991, p. 163. 30 equal to unity. The terms a and P measure the output elasticity of
capital and labor, respectively.
The Constant Elasticity of Substitution (CES) Production Function
The CES production function is represented by the expression:
2) log Y = intercept + w{ log (aKp + pLp)} + error
where Y, L, K, a, and P are the same as in the CD function. The
combination of the intercept and the w term in the CES model serves
the same function of the intercept in the CD model. The P term is
known as the substitution parameter. The elasticity of substitution,
a, is found from the following relation:
3) a = {1 / (1+ p)>
The CD production function has no such term since it has the
property of a constant elasticity of substitution which is equal to
one. Thus, as p approaches zero the CES model will approach the
results of the CD model9.
Given the non-linear nature of the CES production function,
attempts were made (which were fruitless) to “guess” the
appropriate starting values (using the CD parameter estimates as
'The substitution parameter, p, is determined by the following expression: p = {(-2g)x(a+P)}/(axP) from which the elasticity of substitution can be found through the following relation: a - (1-p ) / p. (Kmenta, p. 463) 31 these starting values) of the CES parameter estimates so the
convergence criteria could be met. This coupled with the large
number of regressions which needed to be run and examined and that for each of these trials a minimization method of the error vector
needed to be chosen, made the use of the non-linear CES model
unpractical. It was therefore decided to use the linear approximation of the CES model in place of the non-linear model. The linearized CES model is expressed as follows:
4) Log Y = intercept + a(log K) + b(log L) + g((Log K - log L)2) + error'0
This specification is asymptotically identical to the non-linear CES model11 .
The third term, (log K - log L)2, can be rewritten as (log K/L)2 in the linearized CES production function. This term represents the square of the log of the capital-labor ratio. It can be interpreted as the relative capital intensity of industry (branch or region). Thus, if industry experiences growth in the capital-labor ratio while at the same time a slowdown in the growth rate of production, the parameter g should be negative. This suggests that further growth in
,0 See Kmenta, Jan Elements of Econometrics. New York: MacGraw Hill. 1971, pp.463-4. This form of the CES also solves for the elasticity of substitution as defined above. "This is done by expanding Y around r = 0 (where r is the substitution parameter) and by eliminating the terms which have r with powers greater than one. Kmenta states: “...if the appropriate assumptions about K, L and e (the error term) hold, the estimates of the production function parameters obtained in this way will be very nearly asymptotically efficient.” (Kmenta, pp.464-465.) 32 the capital stock without an accompanied growth in the labor input,
increasing the capital-labor ratio, will actually cause output growth
to slow. This is because capital usage is excessive. Authors such as
Weitzman and Brown have indicated that the acceptance of the CES
production function indicates that diminishing returns to a factor
have set in.12
The Trans-loo Production Function
The final production function model that is utilized in this
study is the Trans-log (TL) model. It has the following form:
5) Log Y = intercept + a(log K) + p(log L) +.5n(log K)2 + .5£(log L)2 +
v(log K) (log L) + error
The CD function is nested within the TL model if the second order
terms of p, £, and v are equal to zero. The CES (linearized) is also
nested within the TL model. If p and £ equal -v then the TL reduces to
the linearized CES model13. The parameter estimates of the TL
function do not represent the income share attributable to the factor
12 Weitzman argues that a low elasticity of substitution implies that “ capital growth has outstripped labor growth by such a proportion that diminishing returns to the capital factor are seriously affecting the rate of growth of output.” Weitzman had indicated the CES production function best described the production process of Soviet industry. (Weitzman 1970, p. 685.) ,sThe restrictions are derived as follows: g {log K -log L}2 = 0.5p(log K)2 + 0.5m(log L)2 + v(log K)(log L) so: g (log K)2 - 2g(logL)(log K) + g (log L)2 = 0.5p(log K)2 + 0.5n(log L)2 + v(log K)(log L) Thus, if g = .5p = .5n, then: -2g(logL)(log K) +g (log L)2 = v(log K)(log L) Therefore, -2g = n. Thus, the restrictions are g= .5p = ,5m =-.5v 33 nor the factor’s output elasticity since the quadratic parameters
include their second derivatives. The TL production function has the
characteristic of allowing the elasticity of substitution to vary, it therefore is the least restrictive model14.
Total Factor Productivity
The residual term, the intercept, of the production function
has become accepted, in the economic literature, as a measurement of the role played by technology in the production process. It is commonly referred to as total factor productivity (TFP). The
residual term of the production function is a measure of the effect on output by factors which are not directly measurable. The residual is often partitioned into two components, a new residual and a constant trend term, dependent upon time. The constant trend term is commonly referred to as a measure of technical progress or total factor productivity and is represented by the expression:
6) TFP = LogB +X.1t where Log B is the new residual, x is the constant rate of TFP, and t represents time (measured by the year). The constant rate of TFP is a measure of the constant growth of output due to technology each year.
14 The elasticity of substitution is found by the following relation: o - {6(K/L)/(K/L)| / {(6K/6L)/6(6K/6L) 34 Total factor productivity is also commonly partitioned into three components, a new residual, a constant rate of TFP and a variable rate of TFP. This specification of TFP is given by the following expression:
7) TFP = Log B + X.^ t + ^ t ^ where the first two terms are defined as in equation 6 and X2 represents the variable rate of TFP. The variable rate of TFP measures the changes in the of output growth due to technology from the base rate, the constant rate of TFP. The growth rate of TFP is expressed as:
8) Growth Rate of TFP = + (2 x ^ x t).
Even though TFP is actually a “catch-all” measuring the effect of factors not directly measurable by the actual production function parameters, it is generally accepted to measure the contribution of technology in the production process.
Production Function Acceptance Criteria
Each of the three production function models are tested against one another separately using both specifications of TFP
(from equation 6 and 7), and all possible combinations of the data series. There are four possible combinations of data series for the
1951-85 period while there are eight distinct combinations for the 35 1960-85 period15. Each specification is also examined for the
presence of constant returns to scale. The production function
chosen to best represent aggregate industry (or an industrial branch
or region) is the model that cannot be rejected as the the model
which minimizes the unexplained error16. This was accomplished by
the use of the F-test, which detects the model that produces the
lowest unexplained error. Finally, if the Durbin Watson statistic was
found to be low, indicating autocorrelation of the error terms, the
models were adjusted to correct this problem by applying an AR1
e rror scheme17.
,s For the 1951-85 period the possible combinations are: 1) TIE, RK, l^; 2) TIE, RK, , l2; 3) WK, RK, l-j; 4) WK, RK, l^, l2. The number of combinations is increased to eight for the 1960-85 period because of the availability of the WK capital stock series. 16 The specification resulting in the lowest sum of squared errors (SSE) are seen as the specification that best approximates the true relationship between inputs and output. A test statistic, F-test, is used to find the model which has the greatest probability of representing the true relationship. The test statistic is formed as follows: p = [{(SSEr -SSEh)/q} /{SSE^/ (n-k)}] where p is distributed as an F-statistic with degrees of freedom of q, n-k, n is the number of observation, k is the number of restrictions, h represents the unrestricted model and r represents the restricted model. The restricted model is accepted as the true model if lest statistic p is found to be below the tabled F-stalistic at the 5 percent level.
17 Correction for autocorrelation was made by the following procedures. The DW option in SAS programs produces a correction parameter which is applied to the parameters in the production function. The first observation is weighted by the square root of one minus the correction factor squared. The remaining observations were transformed by subtracting the observation in the t-1 period, multiplied by the correction factor, from the observation in period t. Thus, this portion of the adjustment takes the form of: Y, - rY,.., = a(1-r) -r^X, - X,^) + e, - reM where Y is the dependent variable, a the intercept term, X the independent variable and e the error term. Kmenta, Jan, Elements of Econometrics. (New York:MacMillan Press, 1971) p. 283. 36 This chapter has described the data series and production functions which are implemented in this study. The first chapter
raised the questions to be discussed by the use of these production functions. Thus, the stage is set to begin the investigation of the growth pattern of postwar Romanian industrial production. CHAPTER III
THE PATTERN OF GROWTH OF THE ROMANIAN INDUSTRIAL SECTOR
Introduction
The present chapter examines the growth pattern of the industrial sector of the Romanian economy over the thirty-five years of its existence, under seven successive five-year plans, from
1951 to 1985.
The point of departure for the analysis industrial growth is the general relationship between the direction of the growth of output and that of the principal inputs (labor and capital). This relationship is sometimes identified in the literature as expressing the direct effect of inputs on output, according to which, for example, generally increasing output is seen to result from generally increasing inputs. The Socialist Republic of Romania, as a socialist, centrally planned and administered system committed to economic growth and industrialization, would be a clear and consistent
37 38 Of principal interest analytically, however, are the indirect effects of inputs on output that result from the proportions by which various inputs are joined to produce the output. Over time, for example, the growth rate of output could be seen to be influenced by the growth rate of capital relative to labor, or by technological or other qualitative changes embodied in the growth of the inputs, taken collectively.
The core of the analysis of input growth in these terms is found in the nature of the production function through which the formal relationship between inputs and output is expressed, and the initial problem is to determine which variant of the production function is most appropriate in a given case. Early studies of economic growth (GNP, industrial or agricultural production) in the
Soviet Union and other countries of the Communist world 1 were based on simplifying assumptions and a relatively restricted view of the production function. Subsequent attempts to test econometrically for the appropriate variant of the production function were restricted by the number of observation on hand2. The present study, using an expanded set of data representing aggregate
1 Studies of the Soviet economy were made by: Bergson (1970), Holzman (1972), Weitzman (1970). Studies of other Communist countries were made by: Schatteles and Mihailescu [Romania (1970)] and Brown [ Hungary (1973)]. 2 For example, Weitzman (1970) indicated that the CES production function was the correct variant while Desai (1987) shows that with additional data the correct variant is the CD production function. 39 industry over a 35 year period, 1951-85, as well as new data series, has broadened the number of observations and increased the number of possible combinations of inputs. This has expanded the possibilities for testing for the appropriate variant or model of the production function with the strongest explanatory powers.
The methodology and subsequent results, applied to the
Romanian industrial sector, of this work are set forth in the present chapter. A comparison is made with the earlier work of the
Romanian economists, Schatteles and Mihailescu (1970), concerning
Romanian industrial production and with the works of Weitzman
(1970) and Desai (1985) on Soviet industrial production.
Elements of the Methodology
There are three basic categories of indirect effects that may be specified and identified through the implementation of the three alternative models of the production function.
The first category concerns the elasticity of substitution of factor inputs, defined as the change in the marginal rate of technical substitutions of capital for labor (designated as MRTS^ |_and is shown by the slope of the isoquant) associated with a change in the capital/labor ratio. The elasticity of substitution of factor inputs is a measure of the substitutability of one input for another as the quantity of production is held constant. Several production function 40 studies 3 of industrial growth in Eastern Europe and the USSR have indicated the presence of a low elasticity of substitution of factor inputs4. This implies that it is increasingly difficult to substitute capital for labor. These studies indicate that the slowdown in industrial production resulted primarily from the fact that the elasticity of substitution was less than one and from decreasing growth rates of inputs. The slowdown in the Romanian industrial sector would be explained by a low elasticity of substitution if the
CES or TL production function was accepted as the appropriate variant of the production function (provided a low elasticity of substitution was found). Though the Romanian industrial sector experienced rapid capital accumulation, it did not experience the phenomenon of having to increase the growth rate of capital in order to maintain a constant growth rate of output. As was previously stated, output growth was affected more by the growth rate of labor, but this did not affect the substitutability between the
3 Weitzman (1970) and Brown (1973). 4 Weitzman (1970) in his investigation ot Soviet industrial growth, finds that a low elasticity of substitution, coupled with declining growth rates of inputs (capital and labor) have caused the slowdown in Soviet industrial production. He states, “A low elasticity of substitution seems to imply that capital accumulation has outstripped labor growth by a wide enough margin that the drag due to diminishing returns is significantly cutting into output growth...We again stress that the present emphasis on diminishing returns is very different from the somewhat more usual factor productivity approach.” (1970, p.685) Weitzman also goes on to say that ”... it is useful to think of the elasticity of substitution as the measure of the rate at which diminishing returns sets in as one factor is increase relative to the other. A less than unit elasticity of substitution implies eventual difficulty in increasing output by primarily increasing one factor, because diminishing returns set in strongly and rapidly. Such a situation would have special relevance for the Soviet case because capital has grown so fast relative to labor.” (1970, p670.) Italics in original. 41 inputs. Thus, in effect, capital and labor could be seen as complements in the production process5.
The CD production function model was found to be the most appropriate variant6. Since the CD production function implies a constant elasticity of substitution that is equal to one, there is a one to one correspondence between the changes in the MRTSK L and the capital/labor ratio. Therefore, the Romanian industrial sector,
as a whole, did not encounter a situation where increasing amounts of capital were needed to substitute for labor to increase production, otherwise referred to as a low elasticity of substitution.
A second category of indirect effects arises from the phenomenon known as returns to scale, which may appear as increasing, constant or decreasing returns to scale. Increasing returns to scale exist where an increase in all inputs, to a given degree, lead to a greater than proportionate increase in output; constant returns to scale (CRS) exist where inputs and output increase by equal proportions; and decreasing returns to scale exist
5 Complements, here, refers to the relationship between labor and capital such that as the amount of labor increases additional capital is employed. Capital being used in the industrial sector was not of the labor-saving type, but was actually of the labor-augmenting type. • All results reported in this study are the “best" results defined by the acceptance criteria in Chapter 2. The CD production function was chosen over the CES and TL variants since the null hypothesis that the CD variant minimized the SSE could not be rejected The corresponding F- statisitics are as follows: 1951-85: CD v. CES F= 3.84 196085: CD v. CES F= 4.05 CD v. TL F=1.96 CDv. TL F=2.54 42 where increases in all inputs lead to a smaller than proportionate increase in output. Alternative variants of the production function do not imply a specific type of economy of scale, although earlier studies of the Soviet and Eastern European economies typically assume CRS in order to limit the number of factors that can affect output growth. In the present study, each variant of the production function was tested for the presence of CRS and it was determined that industrial growth in Romania is best characterized by constant returns to scale.
The tests of CRS were by two separate, but statistically identical methods. Some of the accepted specifications restricted the model to exhibit CRS. A t-statistic testing the validity of the restriction was produced for these specifications7. Secondly, where the SSE's were lower without the restriction of CRS, a F-test was used to determine if the unrestricted parameter values exhibited the presence of CRS.
The third category of indirect effects addresses the portion of industrial growth that is not explained by the measurable inputs employed in the production process. This indirect effect is referred to as total factor productivity (TFP). It is generally accepted that the residual term of the regression captures all remaining factors
7 ------This t-statistic is labeled Restrict in the following reports of the regression results. 43 that affect industrial production that are not independently measured. As stated in Chapter 2, the residual term can be partitioned into either two or three components. When specified in two components the residual term is comprised of a new residual and a constant term as in equation 6 (see Chapter 2). The constant term produces the growth rate of the original residual term over time and represents the rate of technological change or a constant rate of
TFP. The constant rate of TFP can be thought of as the base rate of output growth due to technology.
Should the accepted model have the original residual partitioned into three components, the original residual would be partitioned as in equation 7(see Chapter 2). The three components would be the new residual, the constant rate of TFP (as described above) and a variable rate of TFP. The variable rate of TFP captures the yearly changes of the effect of technology on output growth away from the constant rate of TFP. These changes away from the constant rate of TFP can arise for several reasons. Improvements in the quality of the measurable inputs in the form of machinery (with embodied technical change) or workers (with greater skills and/or education levels) will lead to a greater proportion of output growth being attributed to technological change. The variable rate of TFP 44 also includes changes due to increased (or decreased) efficiency.
New methods of production or new combinations of inputs can create increases in efficiency, whereas the lack of competitive pressures
(known as X-inefficiency) arising from poor incentive, organizational and institutional structures could decrease efficiency. The planning process in Romania (as well as most of the nations of Eastern Europe) creates bottlenecks and shortages of numerous goods and thereby can be seen as creating inefficiency.
Total factor productivity then is comprised of either the constant rate of TFP only or the constant rate combined with the variable rate of TFP depending upon the specification of the model.
The constant rate of TFP is a measure of the growth of production due to technological change over the entire period studied. The variable rate of TFP is a measure of the year to year changes in output growth due to changes in technology or its related factors.
The measurement and interpretation of these indirect effects on the industrial sector of the Romanian economy forms the substance of the present study. The methods employed, measures which are reproduced and the interpretation of the results follows a summarization of the data and its sources. 45 Data and Sources
This section summarizes the data available for the examination of Romanian industry by the use of production functions.
It briefly notes the definitions and reliability of the data used here and makes comparisons to other production studies.
Output
Industrial output, from 1951-85, is represented by the official value of industrial production in constant 1963 prices. This was obtained from the official Romanian statistical yearbooks 1955-86.
This is the only industrial output data series available and therefore is consistently used throughout the entire study. Official output data series and indices have been utilized in other production function studies of Eastern Europe [Kemme on Poland (1981)] and the Soviet
Union [Weitzman (1970), Desai (1985)]. Therefore, the Romanian output series is comparable to those data series in the above studies since it is also subject to the problems of non-scarcity pricing, index numbers, and double accounting.
Inputs
Input data is restricted only to the average number of
"workers” (labor) and the value of the capital stock within the industrial sector (as described below). Electrical consumption is available for the industrial sector over the 1960-85 period, 46 however, it is not available for either the industrial branches or regions. The omission of raw materials and energy from the production will bias the estimates of the measurable inputs6.
Labor Input
The industrial labor input is measured by two alternative data series, both taken directly from the official Romanian statistical yearbooks. The first labor data series, designated as the total industrial employment (TIE), represents the combination of the average annual number of salaried personnel (white collar) and wage earners (blue collar). The TIE series is available for the entire
1951-85 period.
The second industrial labor series, termed wage earners(WE), is measured by the average annual number of wage earners only. It is the “blue-collar” component of the TIE labor series. The WE series is also available over the entire 1951-85 period for the industrial
a The estimates of the labor and capital parameters will be biased in the following manner: E(a) - a + toj rm x RM E(P) - p +
The TIE and WE labor series are implemented separately in each variant of the production functions in order to discern which labor data set combines best with the capital stock data set
(described below). Data on man-days or man-hours, frequently used in production function studies of this type, are not available for
Romanian industry. Both labor series are based on the average annual number of industrial “workers” and are deemed fairly reliable since the average levels the deviations and errors over the year9. However, of course, these data series are not as accurate as those of man- days or man-hours would be.
Capital Input
The level of industrial capital stock is measured by two alternative data series. The first, denoted as the Romanian capital stock (RK), is represented by the official value of industrial capital stock, measured in 1963 constant prices. The RK series was derived from the capital stock indices contained in the official Romanian statistical yearbooks and is available over the entire 1951-85 period.
— t ------— The correlation coefficient between the TIE and WE labor series is 0.9992, indicating almost a perfect linear relationship. Thus, the salaried workers, when included, do not skew the TIE data set 48 The second capital stock series, designated as the Western capital stock estimated (WK) series, is a capital stock made by the
L. W. International Financial Research Company. It is based upon a percentage of Romanian GNP and is valued in 1977 constant prices10.
It is only available for the period of 1960-85. Both the RK and WK capital stock data series were originally year-end data series, but each has been centered to mid-year averages to attempt to eliminate any year-end bias in the production functional analysis. The RK series is subject to the same potential biases as the output series, namely, non-scarcity pricing, double accounting and the index number problem. Given the unknown nature of the WK series and that it is an estimate it is seen to be less reliable than the RK series11. Total Factor Inputs
Given the availability of the data, there are only two possible combinations of input data series which can be implemented in the production function models over the entire 1951-85 period, namely:
RK with TIE and RK with WE. The period of 1960-85, however, has the possibility of using all four data series as follows:
10 This data series was obtained in a phone conversation with Gregor Lezarcik. No further details were provided on how the estimates were derived. 11 However, the correlation coefficient between the RK and WK capital stock series is 0.9996, indicating an almost perfect linear relationship. 49 Table 2 Possible Data Set Combinations
Labor Data Set Capital Data Set TIE RK TIE WK WE RK WE WK
The present study concludes that the combination of the WE and the
RK data series produces the best estimates and approximately represents the true relationship between inputs and output for both the 1951-85 and 1960-85 periods.
Measures and Results
This portion of the paper first discusses the direct effects of the inputs on industrial production. It then proceeds to discuss the indirect effects of the elasticity of substitution, returns to scale and TFP on industrial growth and production.
The brief presentation of the historical development of
Romanian industry since 1950 in Chapter 1, specifically pertaining to Figure 1, indicates that industrial production followed a cyclical pattern from 1951-74. During this time frame there were two complete cycles, from 1953-63 and 1964-74. The slowdown in
Romanian industrial production began in 1974 as the growth rate of production continued to decline beyond the previous low until 1981.
The longer and deeper contraction is of major importance to this 50 study.
Since the annual growth rate of industrial production had decreased over the period of 1972-81, it is necessary to determine what role inputs had in this slowdown. The first portion of the slowdown, from 1972-74, reflects the latter portion of the cyclical contraction. The Middle East oil crisis had a major impact, since by this time Romania had become a net importer of oil. The decline in the growth rate of output continued until 1981. The growth rates of industrial output, the average annual number of industrial wage earners (WE), and the Romanian capital stock (RK) are depicted in
Figure 2 (see Chapter 1). The growth rates of labor and capital decreased from 1974 through 1984, though industrial production experienced a slight increase after 1982. Thus, the direct effect of the inputs on production is evident since both the growth rates of labor and capital declined during this period (1974-81). This may be due to the fact that higher oil prices meant cutbacks in obtaining or manufacturing new capital. Figure 2 also indicates that the growth rate of industrial output followed the same general cyclical pattern as that of the labor input. The growth rates of both output and labor decreased from 1953 to 1956; increased from 1957 to 1961; decreased from 1962 to 1966; increased from 1967 to 1971; and decreased from 1972 to 1974; despite that over the entire period 51 (1953-74) the growth rate of the capital input was increasing. Since labor and capital are seen as complements in production, output growth followed the growth of the slower growing input, labor.
The CD production function best describes Romanian industrial production in both periods studied, 1951-85 and 1960-85. The specifications of the CD function are presented in Table 3. The production function estimates of both periods utilize the identical combination of input data series, the average annual number of wage earners (WE) and the Romanian capital stock (RK).
The estimates of the contribution of labor and output, presented in Table 3, are represented by the output elasticities of labor, labeled as Wage Earners, and capital, labeled as Romstock.
Output elasticities measure the percentage increase in output coming from a one percent increase in the quantity of the input.
Thus, for the 1951-84 period, the output elasticity of labor of 0.84 indicates that a one percent increase in the quantity of labor would increase output by 0.84 percent. The output elasticity of labor is greater than that of capital for both the 1951-85 and 1960-85 specifications, suggesting that output is more sensitive to changes in the labor input than to changes in the capital input. However, the results are dependent upon the period studied, as is shown 52 Table 3 Cobb Douglas Production Function Results Inputs: Romanian Capital Stock, Wage Earners Industrial Sector, 1951-85 and 1960-85
1 9 5 1 -8 5 Parameter Estimate Stan. Error T-Statistic
Intercept 5.6090 2.880 1.948 Wage Earners 0.8400 0.1810 2.604 Romstock 0.2864 0.1100 4.640 Constant 0.0845 0.0093 9.086 Variable - 0.0011 0.0003 3.667
DW= 1.482 SSE = 0.0152 R-SQ = 0.9994 Obs. = 35 AR1 correction = 0.6347 CRS F-test = 0.1101 F-test CD v. CES = 3.84 F-test CD v. TL =1.98
1 9 6 0 -8 5 Parameter Estimate Stan. Error T-Statistic
Intercept 31.426 5.110 6.150 Wage Earners 0.8204 0.131 6.263 Romstock 0.1796 0.131 1.371 Constant 0.0639 0.0319 2.036
Variable - 0.0012 0.0007 1.714 Restrict 0.0396 0.0099 4.000
DW = 1.524 SSE = 0.0447 R-SQ = 0.9984 Obs. = 26 AR1 correction = 0.7190 F-test CD v. CES = 4.05 F-test CD v. TL = 2.54 Where: Wage earners are the WE series (in logs), Romstock the RK series (in logs), Constant the constant rate of TFP, Variable the variable rate of TFP, and Restrict refers to the restriction of the wage earners and Romstock parameter estimates summing to one (CRS).DW- Durbin Watson statistic SSE- sum of squared errors Estimation Method: OLSAR1. 53 in section 6 of this chapter, entitled Structural Change.
Elasticity of Substitution
Since the CD function best describes the production process of
Romanian industry, the elasticity of substitution is constant and equal to one. The economic consequence of accepting the CD production function as the correct model is that the ease of substitution of one input for another is not affected. It neither becomes more difficult nor easier to substitute one input in place of another. This is implied by the result of the elasticity of substitution equaling one, indicating that the change in the marginal rate of technical substitution (slope of the isoquant) is offset by an equal change in the capital/labor ratio.
Returns to Scale
As a further consequence of choosing the CD production function, the returns to scale can be found by simply summing the output elasticities of the inputs. Simple addition of the output elasticities (column 2 of Table 3) in the CD specification of 1951-
85 produces a sum greater than one, suggesting the possibility of increasing returns to scale. However, the hypothesis of constant returns to scale (CRS) is accepted because the test statistic (F- test) indicates that the sum of output elasticities is not 54 statistically different from one at the ten percent level. The hypothesis of CRS is also accepted in the 1960-85 specification. Total Factor Productivity
The CD production function specification listed in Table 3 also include a measure of total factor productivity (TFP)12. The parameters of Constant and Variable in Table 3 (column 2) pertain to the constant and variable rates of TFP respectively. Thus, TFP is described by the expression contained in equation 7 (Chapter 2). The growth rate of TFP was expressed in equation 8 is reproduced below:
8 ) Growth Rate of TFP = X-| + (2 x ^ x t) where X-j and are the estimates of the constant and variable rates of TFP and t refers to the year or time period. Note that the variable growth rate of TFP is negative for both CD specifications contained in Table 3, indicating that the growth of TFP is slowing 13. The linear estimates of the growth rate of TFP, using equation 8 , are presented in Figures 3 (1951-85) and 4 (1960-85).
12 See Chapter 2 for a description of the formation of TFP. ”ln both the 1951-85 and 1960-85 specifications the parameter, the variable rate of TFP, is negative. Specifically, the growth rates of TFP are: 1951-85 G(TFP) - 0.0845 - (0.0022 x t) 1960-85 G(TFP) = 0.0639 - (0.0024 x t) The second derivatives are negative indicating that when they are set equal to zero the resulting t indicates the year in which TFP will be maximized. TFP was maximized in 1988 in the 1951-85 specification and in 1987 for the 1960-85 specification if these trends continued into this time frame. 55 Figures 3 and 4 also include the Solow estimates of TFP. From the general relationship of the production function, given here:
9) Y = f(A,K,L) where Y is output, A is the residual (TFP), K is capital and L is labor, a yearly estimate of the TFP can be produced. Equation 9 can be transformed into the relationship between the growth rates of output, the inputs and TFP found below:
10) G(Y) = G(A) + a G(L) + 0 G(K) where G() is the growth rate and a and 0 are the output elasticities
(in the CD function) of the inputs.
Finally, the combination of the inputs, RK and WE, explain to a high degree (R-square term) the resulting level of production. The parameter estimates themselves are significant at the ten percent level. There was evidence of positive autocorrelation of the error terms since the Durbin Watson statistic was low. The models were corrected for first order autocorrelation and as a result the Durbin
Watson statistics improved slightly, however they remain low
(though the Durbin Watson statistics can not be rejected). These
Durbin Watson statistics are comparable to those found in other production function studies concerning Eastern European economies and the Soviet Union. 20
15 fpper 95% Cl
10
5 TFP % Rate ; v " ~ \ ...... of 0 j i i n i . ^ i \ TFP 5 156IOW -10 Estimate
-15
-20 Lower 95% Cl
Year Figure 3 Rate of Total Factor Productivity, Industrial Sector Romania, 1951-85
cn CD 40 Upper 95% Cl
% Rate of JEB, TFP -10 75
-20
-30 pwer 95% Cl
-40
Year Figure 4 Rate of Total Factor Productivity Industrial Sector, Romania, 1960-85 58 Electrical Consumption
The CD model was extended to include an input measuring the electrical usage by the industrial sector. The model was then expressed by:
11) Log Y = residual + a(log K) + p(log L) + ro(log E) +x 11 + \2t 2 + error where Y is output, L is labor, K is capital, E is electricity, t is time, a, p, and m are the output elasticities of capital, labor and electricity, X.-J and X 2 are the constant and variable rates of TFP respectively. The omission of relevant variables, as previously noted, results in biased estimates. The inclusion of electrical consumption eliminates a portion of that bias, however other relevant variables may still be missing (such as raw materials, other fuels and managerial skills).
The model only examines the Romanian industrial sector over the 1960-85 period since electrical data is not available prior to
1960. The results of the model with the inclusion of electrical consumption are presented in Table 4. Note that the output elasticity of the labor parameter has decreased sharply, while that of capital grew slightly so that there is approximately a 2:1 ratio between 59 Table 4 Cobb Douglas Production Function Results: Electrical Model Inputs: Romanian Capital Stock, Wage Earners, Electricity Industrial Sector, 1960-85
Parameter Estimate Stan. Error T-Statistic
Intercept 8.916454 3.858843 2.311 Wage Earners 0.557688 0.223583 2.494 Romstock 0.280834 0.277872 1.011 Electricity 0.161477 0.195439 0.826 Constant 0.051257 0.022753 2.253 Variable -0.000496 0.001004 -0.494 Restrict 0.001419 0.000892 1.591
DW « 1.524 SSE = 0.009 R-SQ = 0.9983 Obs. = 25
Where: All parameters are defined identically as before. Electricity is the log of the number of kilowatt hours consumed by the industrial sector per year. Estimation Method: OLS AR1. 60 them. The output elasticity of electricity is approximately 0.16. The rate of total factor productivity is declining as in the previous cases and constant returns to scale is also accepted in this case at the five percent level. The constant rate of TFP, “Constant” in Table
4, is 0.0512 and the variable rate of TFP, “Variable,” is -0.00049.
Given that the variable rate of TFP is negative, the growth rate of
TFP is declining over time. The estimates of the constant and variable rates of TFP in the electrical model are lower than those of
Table 3. This is because electrical consumption was a portion of the growth explained by the residual in the models of Table 3 and is now explicitly found. Figure 5 presents the growth rate of TFP, its 95 percent confidence intervals and the Solow estimates of TFP when electrical consumption is explicitly included in the model.
Implications
The previous sections developed measures for describing the direct and indirect effects of the inputs on production based on the acceptance of the CD production function. This section describes the
implications of these effects, using the above results, on Romanian
industry and its pattern of growth.
How did the growth rates of inputs affect the growth rate of
industrial production? Industrial production in Romania had followed
a cyclical pattern over the 1951-74 period in two complete cycles. ------a! SoIqw-Estimate % Rate 5 ./ >. / \ \
Lower 95%
Year Figure 5 Rate of Total Factor Productivity Industrial Sector With Electrical Consumption
Romania, 1960-85 O') 62 The mid-1970’s saw the cyclical contraction plunge deeper than ever before so that by 1981 the growth rate of industrial production reached its post-war low. The capital input experienced continued growth from 1951 through 1974, when it peaked. It declined steadily from this point through 1985. Thus, the increasing growth rate of capital did lead to increases in production and aided in the growth of industrial production. However, since 1974 the declining growth rate of capital has directly contributed to the slowdown the growth of industrial production.
Labor growth, on the other hand, also followed a cyclical growth pattern, which was mirrored by the growth rate of industrial production (see Figure 2, Chapter 1). A plausible explanation for the cyclical correlation of labor growth and output growth is that the capital that was being added to the industrial sector was in fact a labor-augmenting type and not a labor-saving type of capital. Thus, to increase industrial output increases in the number of workers was needed so they could man the additional capital. This may be one of the reasons Romania implemented a policy of collectivization of agriculture in the early 1960’s, that is to increase the industrial work force. Given this relationship between labor and capital meant that increases in production must follow the slower growing of the two inputs, that being labor. Figure 2 does indicate this relationship 63 as decreases in the growth of labor are closely followed by decreases in the growth of production.
One of the factors behind the substantial decline in the growth rate of industrial output between 1974 and 1981 were the declining growth rates of both the labor and capital inputs. The effects of a declining birth rate of the WWII cohorts, the echo effect and the infant industry argument explain the decline in the growth of the labor input.
Why would labor growth experience this cyclical pattern over this 35 year period? The first decline in the growth rate of labor,
1953-56, can be attributed to the demand-side argument originating from the assumption that at this point in time Romanian industry was in its “ infancy stage.” The 1950’s were the formulative years of the “new” Romanian industry. Only a few industrial branches could grow at any one time and construction of new facilities and factories took several years to plan and construct, therefore the implementation of new industrial labor slowed.
The second decline in the growth rate of industrial labor occurred from 1961-65, which was approximately 20 years after
WWII. Data concerning Romanian birth rates were not published in the statistical yearbooks for the years of WWII, however, most
European nations experienced a decline in their birth rates because 64 of the war. It is then assumed that Romania also shared in this experience. This contention is indirectly supported by the reduction in the ratio of men to women as in 1941 the ratio was 98 men for every 100 women, while by 1945 the ratio was 92 men for every 100 women14. Though this was less of a decrease than in other countries it still had an effect on the birth rate. The working age population
(ages 16-65), divided into three-year age brackets, for the years of
1964, 1976, and 1985 are presented in Figure 6. The figure for each of these years indicates that the three-year age bracket of WWII aged cohorts is smaller than the remaining three-year brackets.
Therefore, population growth slowed during WWII since the number of WWII aged cohorts entering the labor market in the 1958-
65 period decreased from previous years. Thus, a declining birth rate during the WWII period had a large effect on the population growth and in turn on the growth of industrial labor.
The arrows labeled “WWII echo” in Figure 6 also indicate that there was a significant WWII echo effect on the working age population of Romania. The WWII echo effect follows the entry of
WWII aged cohorts into the working aged population by approximately a single generation. This can be seen by the smaller increases of the working aged population in the years of 1958-63
14 Blanc (1967, p.237) 65
Population Age Brackets Romania, 1964, 1976, 1985
*mm if M M IH 4
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D*t» O t u m torn *■ ISIS lomirttfn SfMMrtJI T«
W o r trr^ A f t P qpuM tt i i i r t
4703
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*s- i f it- tv is* Si- !«- if* «s o * «(• «*• st- ss- ss- si* t*- ' • I I 14 IF JO | | M IS 41 « | <» s« S4 fF SO SS «f Af«S k im
**o<«vD m* itw >S|S«
Figure 6 Working Age Populations, Romania, 1964, 1975, and 1985
{ 66 and 1973-76. The combination of the infant industry argument and the declining birth rate during WWII are two of the factors causing the growth rate of industrial labor to decline. Finally, there is almost a perfect linear relationship between the industrial labor force (TIE) and the working age population15.
The demographic aspects of the urban migration of the population and the participation of females in the work force must be considered to obtain better understanding of the Romanian industrial work force and its growth and prospects for the future.
Table 5 presents the share of the urban population, industrial work force, and agricultural work force of the population. In less than 30 years, over the 1940-1964 period, the absolute number of urban dwellers doubled whereas the rural population grew by only twelve percent16. The industrial work force and the urbanized population continually increases their shares while the rural population and the agricultural work force’s shares decline. In the mid-1970’s the
Eastern European nations’ supply of young labor began to dwindle because of low birth rates in the 1960’s (WWII echo effect).
However, Romania, unlike most other Eastern European nations, was
,s The Pearson correlation coefficient between TIE and the working age population is 0 .9 7 0 2 . “ Blanc (1967, p.237). Table 5 Population Shares Urban and Rural Areas Industrial and Agricultural Sectors Romania, 1930-85
Urban/Rural
Year Urban Rural
1930 21.4 78.6 1946 23.8 76.2 1964 33.1 66.9
Industry/Agriculture
Year Industry* Agriculture
1950 14.4 74.3 1955 17.4 69.7 1960 20.0 65.6 1965 25.5 56.7 1970 30.8 49.3 1978 34.8 34.7 1985 37.2 30.5
Notes and Sources: Urban/rural data through 1964 taken from Blanc (1967, p.237). Industrial/agricultural data through 1970 taken from Turnock (1970, p. 18). Industrial/agricultural data of 1978 taken from Alton (1980, p.368). *- Industry includes the Construction sector. 68 able to rely on its labor reserves from the agricultural sector17.
Table 6 presents the annual industrial and agricultural labor forces18 and their respective annual changes (1000’s). It is evident that a significant portion of the agricultural work force had been transfer into industry. This transfer has been possible because Romania, like other Eastern European nations, moved to mechanized the agricultural sector directly following WWII. However, the rate of mechanization has, since the early 1970’s, significantly decreased, as is shown in Table 7, which present the amount of horsepower available in agriculture. In fact, by the 1970’s Romania labor productivity in agricultural was only 1/3 that in Poland or East
Germany19.
Though Romania has this excess labor available from the agricultural sector, this does not imply that the industrial labor force would experience increasing growth rates of labor. Romania has continually, since 1948, shifted workers from agriculture into the industrial sector and this has drained the majority of the excess labor reserves. In fact, major industrial areas, such as Brasov, have experienced labor shortages in the 1970’szo. Marvin Jackson has
17 Vais (1980, pp. 237-8). 11 Labor force here pertains to the total employment and thus includes both salaried personnel and wage earners. '“Lazarcik (1985, p.412). "Jackson (1980, p. 252). 69 Table 6 The Annual Total Employment and the Annual Changes in Total Employment in the Agricultural and Industrial Sectors (in 1000’s), Romania, 1950-8121
Year Aariculture Chanae in Aar. Industrv Chanae
1981 3002.8 -45.3 3748.6 69.9 1980 3048.1 -122.8 3678.7 96.7 1979 3170.9 -173.7 3582 135.4 1978 3344.6 -185 3446.6 84.6 19 77 3529.6 -111.2 3362 94.1 1976 3640.8 -196.6 3267.9 158.2 1975 3837.4 -174.7 3109.7 126.5 1974 4012.1 -194.2 2983.2 185.6 1973 4206.3 -176.2 2797.6 196.4 1972 4382.5 -219.7 2601.2 144.1 1971 4602.2 -246.4 2457.1 180.3 1970 4848.6 -192.4 2276.8 113.8 1969 5041 -187 2163 130 1968 5228 -62 2033 60 1967 5290 -124 1973 41 1966 5414 -62.5 1932 69.1 1965 5476.5 -126.5 1862.9 100.9 1964 5603 -126 1762 80 1963 5729 -63 1682 61 1962 5792 -252 1621 80 1961 6044 -189 1541 100.8 1960 6233 -402 1440.2 74.2 1959 6635 -75 1366 47 1958 6710 0 1319 29 1957 6710 0 1290 27 1956 6710 199 1263 42 1955 6511 174 1221 50 1954 6337 126 1171 65 1953 6211 38 1106 -64 1952 6173 131 1170 131 1951 6173 -35.7 1039 38.3 1950 6208.7 1000.7
21 Source: Scherer, raw data, no page number. 70 Table 7 Tractor Horsepower Per 1,000 Hectares of Agricultural Land and Per 1,000 Workers in Agriculture, Romania and Eastern Europe, 1973-8222
Romania
Year HP/1000 Hectares HP/1000 Workers
1973-76 496 1854 1976-79 571 2448 1979-82 634 3083
Eastern Europe=100(Index)
Year HP/1000 Hectares HP/1000 Workers
1973-76 76 64 1976-79 71 62 1979-82 64 60
“ Lazarcik, p.414. 71 estimated that 75 percent of the agricultural cooperative members actually worked and only 50 percent were full-time agricultural workers. Also, by 1977, approximately 67 percent of the agricultural work force was comprised of females23. Therefore, the combination of the feminization of the agricultural work force and that only one- half of this labor force works full-time (in agriculture) suggests that the amount of excess labor available for the industrial sector has been overestimated.
Thus, the WWII echo effect, the dwindling of the agricultural work force, the dwindling agricultural source of labor, the composition of the rural population and the increased feminization of the agricultural work force have slowed the growth of the industrial labor force.
The growth rate of capital in industry increased at very high rates from 1951 to the early 1970’s. During this period the terms of trade24, in agricultural foodstuffs worsened, but remained well above one, however its share of exports continued to decrease. At the same time the terms of trade in the industrial branches
23 (1980, p. 254) 24 Usually, the terms of trade is defined as the ratio of export prices to import prices, however, these are unavailable for Romania. Terms of trade, in this study, refer to the ratio of the value of exports to the value of imports. The terms of trade therefore reflect the what markets in which Romania was competitive. This is seen by a ratio greater than one which indicates that Romania was able to export the goods of these particular branches to the world market. Ratios of less than one, signifying greater imports, indicate the need for goods of a particular segment of the economy or the desire of the political planners. 72 continued to improve. Thus, as it happened, Romania directed its
capital to the sector from which it was to receive a higher return
(with respect to the terms of trade), the industrial sector. Although
specific terms of trade data (Export prices/import prices) are not
readily available for Romania, rough estimates, subject to
substantial error do exist. During the 1970’s it was estimated that
the overall terms of trade declined by over 8 percent for Romania,
including a 4-6% decline in their terms of trade with other CMEA
nations, but these are 'hazardous” estimates with a substantial
margin of error25. It was also estimated that Romania suffered
heavily due to price changes since it primarily trades with
underdeveloped countries and with the Middle East for oil26. The
terms of trade of the major branches of the Romanian economy are
estimated in Table 8, along with their respective shares of imports
and exports.
The growth rate of the capital input declined over the latter
period, 1972-1985, primarily due to the massive debt accumulated
during the 1970’s from the importation of foreign oil and Soviet
electricity and from Ceausescu’s need to increase his personal wealth. The terms of trade in petroleum and petroleum products, as
well as in agricultural foodstuffs, continued to worsen from 1950
” Marer, p.50. **lbtd..p. 53. Table 8 Terms of Trade27 and Percentage Share of Imports And Exports of Major Branches of the Romanian Economy, Selected Years
Branch Year TOT Import Share Export Share
Transport/ 1985 1.75 27 35 Machines 1980 0.87 25 25 1975 1.00 35 25 1970 0.94 40 23 1965 1.02 40 19 1950 0.87 38 4
Petroleum 1985 0.65 24 34 1980 0.51 31 26 1975 0.58 30 23 1970 0.70 39 22 1965 0.81 50 30 1950 1.25 51 25
Chemicals 1985 2.05 6 10 1980 1.30 6 10 1975 1.66 6 11 1970 0.98 7 7 1965 1.05 6 6 1950 0.32 4 2
Construction 1985 2.05 6 10 1980 1.30 6 10 1975 1.66 6 11 1970 0.98 7 7 1965 1.05 6 6 1950 0.32 4 2
27 Terms of trade here refer to the ratio of the value of exports to the value of imports. The data was derived from the annual statistical yearbooks. 74 Table 8 (cont.)
Branch Year TOT Import Share Export Share
Primary 1985 1.09 4 6 Industrial 1980 0.73 6 6 Raw Mat. 1975 0.75 8 8 1970 0.94 10 10 1965 1.28 11 14 1950 1.18 21 29
Primary 1985 0.64 3 1 Agricultural 1980 0.63 6 4 Raw Mat. 1975 1.14 5 6 1970 1.88 2 4 1965 9.35 1 8 1950 15.8 1 12
Foodstuffs 1985 4.12 2 7 1980 2.31 3 9 1975 4.75 2 11 1970 3.76 3 12 1965 5.92 2 14 1950 36 1 14 75 onward. The terms of trade of the petroleum-related branch fall below one indicating that imports had become more expensive relative to exports. The share of imports accounted for by petroleum-related goods steadily increased to over 50 percent and this was the major cause of the accumulation of Romania’s debt.
To lessen the debt, investment was reduced, except in the
Mining and Electrical branches, therefore it was only natural that growth rate of capital would decline. Thus, the increasing debt and personal obsession of Ceausescu coupled with the decline in the birth rate during WWII, the WWII echo effect, and the infant industry argument caused the declining growth rates of the capital and labor inputs over the 1972-81 period.
Since the CD production function was chosen as the most appropriate model, the elasticity of substitution is thought to be constant and equal to one. This implies that the percentage changes in the capital/labor ratio and the marginal rate of technical substitution are equal to one another. This also implies capital can replace labor at a constant rate without affecting the quantity of production. An elasticity of substitution less than one would imply increasing difficulty in substituting capital in the place of labor and may, in time, slow growth of output. This, however, is not the case for the Romanian industrial sector since the elasticity of 76 substitution is equal to one. Therefore, the elasticity of substitution cannot be looked upon as a factor contributing to the slowdown in industrial production.
Constant returns to scale is evident within the CD specification that describes the production of the Romanian industrial sector over both the 1951-85 and 1960-85 periods.
Constant returns to scale indicates that increases in the growth rates of labor and capital result in a identical percentage increase in output growth. In fact, constant returns to scale also suggests that output will grow at the rate of the slower growing input, labor in this case. This seen in Table 9, which presents the growth rates of output, capital, labor, electricity, and the residual (measuring
TFP). Decreasing returns to scale, if present, would explain a portion of the decline in the growth rate of industrial production. However, returns to scale cannot be seen as a factor which slowed the growth of industrial production since constant returns to scale is present.
Finally, the variable rate of total factor productivity (TFP) is negative, implying that the overall growth rate of TFP is declining over time. Therefore, the growth rate of TFP was a factor which contributed to the industrial slowdown in the 1972-1981 period. 77 Table 9 Growth Rates of Output, Capital, Labor, Electrical Consumption And the Solow Method Estimate of Total Factor Productivity Romanian Industrial Sector 1951-85 and 1960-85
1 9 5 1 -8 5 Growth Rates Period OutDUt Capital Labor Solow TFP 1951-85 10.98 8.25 4.14 5.05 1951-55 13.10 -0.06 4.84 13.77 1956-60 10.90 1.68 3.07 9.01 1961-65 14.10 8.95 5.79 4.06 1966-70 11.77 12.31 4.36 -2.01 1971-75 13.82 12.89 6.37 -0.61 1976-80 9.32 11.44 3.41 -3.49 1981-85 4.62 9.29 1.31 -6.14
1 9 6 0 -8 5 Growth Rates Period Output Capital Labor Solow TFP 1960-85 10.16 10.65 3.25 4.82 1961-65 14.10 8.95 5.79 7.73 1966-70 11.77 12.31 4.36 5.98 1971-75 13.82 12.89 6.37 6.27 1976-80 9.32 11.44 3.41 4.46 1981-85 4.62 9.29 1.31 1.51
1 9 6 0 -8 5 Growth Rates Period OutDut Capital Labor Electricitv Solow TFP 1960-85 10.16 10.65 3.25 9.97 3.59 1961-65 14.10 8.95 5.79 16.96 5.61 1966-70 11.77 12.31 4.36 13.67 3,76 1971-75 13.82 12.89 6.37 10.79 4.90 1976-80 9.32 11.44 3.41 6.00 3.23 1981-85 4.62 9.29 1.31 2.47 0.52 78 The rates of TFP were calculated by two separate methods. The first method used to estimate the effect of TFP on output growth was to calculate the regression estimates of TFP as a function of time. These estimates are found in Tables 3 and 4 under the parameters of “Constant" and “Variable.” These estimates were produced by using equation 8 of Chapter 2. They are also depicted graphically in Figures 3, 4 and 5.
The second method of estimating the rate of TFP was previously described as the Solow Method. By using the relation of equation 10 an estimate of the growth rate of the residual, or TFP, is produced. The Solow Method estimates of TFP are presented in
Table 9 and are shown graphically in Figures 3, 4 and 5.
The Solow Method estimates of TFP fall primarily within the
95 percent confidence intervals, in Figures 3, 4 and 5, of the regression estimates of TFP indicating a close fit. The regression estimates, as previously noted, have a negative variable rate of TFP indicating that the growth rate of TFP is declining over time. This is also indicated by the TFP estimates of the Solow Method in Table 9.
In considering each of the three instances the growth rate of the
Solow Method TFP estimates declines in over all periods, except from 1971-1975. The Solow method estimates support the fact the the growth rate of TFP of the Romanian industrial sector is 79 declining.
Table 10 presents the yearly contributions of labor, capital and total factor productivity to output growth of Romanian industry over the 1952-85 period. Table 10 indicates that both labor and total factor productivity made larger contributions to output growth than had capital, especially in the 1952-72 period. This was true despite enormous capital investments which created a growth rate of capital as high as three times that of labor (See Table 9).
Beginning in the 1970’s, capital contributions were equivalent to or greater than those made by labor and that of total factor productivity, however, it is evident that the contributions of labor, capital and total factor productivity to output growth had significantly declined from the 1970’s onward. The declines in the contributions made by labor and capital were primarily due to the drastic declines in their respective growth rates over the same period. 80 Table 10 Percentage Contributions28 of Capital, Labor and Total Factor Productivity to the Growth of Romanian Industrial Output, 1952-85
Year Caoital Labor Total Factor Productivity
1985 2.59 0.67 1.63 1984 2.69 0.02 3.97 1983 2.56 1.41 0.64 1982 2.66 1.73 -3.38 1981 2.70 1.69 -0.39 1980 2.77 2.33 -0.49 1979 2.90 3.51 1.28 1978 3.12 2.19 3.83 1977 3.55 3.49 5.74 1976 4.03 2.81 5.51 1975 3.97 4.93 7.44 1974 3.59 6.90 6.11 1973 3.38 4.81 4.63 1972 3.52 4.58 3.49 1971 3.98 5.57 2.18 1970 4.03 3.42 4.79 1969 3.53 5.11 1.74 1968 3.53 3.36 3.96 1967 3.54 3.04 8.29 1966 2.98 3.37 4.13 1966 2.91 4.23 5.79
28 The capital contribution to output growth is found through the Solow equation such that it is equivalent to the growth rate of capital multiplied by the output elasticity of capital from Table 3. The labor contribution to output growth is found similarly, such that it is equivalent to the product of the growth rate of labor and the output elasticity of the labor input from Table 3. 81 Table 10 (Cont.)
Year CaDital Labor Total Factor Productivity
1965 3.19 3.18 9.17 1964 2.81 3.90 4.93 1963 2.40 6.42 4.24 1962 1.52 6.60 9.21 1961 0.61 4.42 11.24 1960 0.39 2.82 7.04 1959 0.49 3.24 6.14 1958 0.55 -0.30 7.31 1957 0.38 2.71 7.45 1956 0.45 1.38 11.28 1955 0.24 2.76 3.02 1954 -0.37 5.36 10.31 1953 -1.01 6.82 12.91 1952 -1.21 7.23 13.52 82 Structural Change
Romanian industry has experienced extraordinary growth since
1951 and this section of the work investigates the possibility, over this 35 year period, that the relationship between inputs and output has changed. A change in this relationship is referred to as a structural change in production. The two periods used in this study to examine the possibility of a structural change in industry are the
15 year period of 1951-65 and the 20 year period of 1966-85. The year of 1965 was chosen as a separation point between these periods since it is the year in which Nicolai Ceausescu assumed the premiership of Romanian Communist Party. Thus, the test of structural change may also indicate that political policies of
Ceausescu have contributed to the structural change and hence the slowdown in the growth of the Romanian industrial sector.
Chapter 1 presented the historical development of the
Romanian industrial sector. It indicated that the branches of heavy industry continually increased their shares of industrial production since the 1950’s. The data contained in Table 1 (see Chapter 1) the
Romanian industrial sector was changing from one dominated by light industry to one which was dominated by heavy industry. 83 The hypothesis that the Romanian industrial sector
experienced no structural change was examined so that the
performed the F-test sought to determine whether the production
functions of the industrial sector over these two periods (1951-65
and 1966-85) were identical to one another29. If the hypothesis of no
structural change is accepted since the test statistic is significant
at the five percent level. The results of the production functions
used in testing for no structural change are contained in Table 11.
Thus, it is concluded that there has been no structural change in the
production of the industrial sector, however, several industrial have
experienced a structural change in their production (discussed in the
next chapter). Finally, Figure 7, presents the rate of TFP of both the
1951-65 and 1966-85 periods along with their respective 95
percent confidence intervals.
29 The test for structural change takes the model of the entire 1951-85 period as being the unrestricted model. The models of the specific sub-periods then are seen as the restricted models, so that: Unrestricted Model: Y = f(WE, RK, k j, * 2 ) f°r period of 1951-85.
Restricted Models : Y = ffWEgg, RKg 5 ( M ,65’ ^2,65^ for ^ Periocl °f 1951-65 and Y = f(WEg5 , RKgg> *-it85» ^2,85^ ^or Per'od ° f 1966-85, where all variables are identified as before and the 65 and 85 subscripts refer to the sub period. An F-test is performed using the sum of the SSE’s from the restricted models and the SSE of the unrestricted model to test the null hypothesis of no structural change. (Johnston, pp.207-211). The F-statistic was calculated by the following expression: F-calc = fs SSE fRl - SSEfUlV a SSE (U) / (n-k) where U is unrestricted, R is restricted, q is the number of restrictions and n-k is the degrees of freedom of the unrestricted model. 84 Table 11 Cobb Douglas Production Function Results For the Test of Structural Change Romanian Industrial Sector, 1951-65 and 1966-85
Unrestricted Models
1951-65
Parameter Estimate Stan. Error T-Statistic
Intercept 10.851 2.7178 3.992 Wage Earners 1.0354 0.2366 4.376 Romstock -0.0354 0.2366 0.150 Constant 0.0726 0.0249 2.915 Variable -0.0004 0.0002 1.192 R estrict -0.0006 0.0008 0.750
DW = 2.238 SSE = 0.005447 R-SQ= 0.9983 Obs. = 15 AR1 correction = 0.230
1 966-85
Parameter Estimate Stan. Error T-Statistic
Intercept 1.8297 4.310 0.424 Wage Earners 0.1835 0.4272 0.430 Romstock 0.8165 0.4272 1.911 Constant 0.0461 0.0057 0.807 Variable -0.0007 0.0010 0.779 R estrict 0.001 5 0.0007 2.028
DW = 1.282 SSE = 0.007717 R-SQ = 0.9960 Obs. = 20 AR1 correction = 0.805 85 Table 11 (Cont.)
Restricted Model
1951-85 Parameter Estimate Stan. Error T-Statistic
Intercept 7.8460 1.480 5.300 Wage Earners 0.7732 0.1298 5.955 Romstock 0.2267 0.1298 I.747 Constant 0.0901 0.0077 II.76 1 Variable -0.0009 0.00038 2.498 R estrict 0.0024 0.00204 1.222
DW = 1.469 SSE = 0.0149 R-SQ = 0.9992 Obs. = 35 ART correction = 0.727
Notes and Sources: F-test for no structural change at the 5 percent level= 0.671. F-tabled value with degrees of freedom of 5,26 = 2.47 at the 5 percent level. Where all variables are defined as before. Estimation Method : OLS ART 60
40 Upper 95% Cl
20
> Rate TFP of 0 — h - + TFP
-40
-60 Lower 95%
Year Figure 7 Rate of Total Factor Productivity, Industrial Sector Under the Hypothesis of Structural Change Romania, 1951-65 and 1966-85 87 Comparisons
Schatteles and Mihailescu
How do the results presented in this study compare to those of
Schatteles and Mihailescu in their inquiry of the production relationship of the Romanian industrial sector?
Schatteles and Mihailescu (1970) tested the CD production function over ten ten-year periods (1951-60,..., 1960-69) beginning in 1951 and ending in 1969. The output and capital data series used by Schatteles and Mihailescu are identical to those employed in this study over the 1951-85 period (official value of industrial production and the official value of Romanian industrial capital stock, RK). However, they only utilized what has been referred to as the TIE labor series, the average annual number of wage earners and salaried employees. This studied found that the best results were produced if the WE, wage earners, labor series was used.
Schatteles and Mihailescu used a specification of the CD function which contained only a constant rate of TFP. This study found that the production of the industrial sector is best described by the specification containing both a constant and variable rate of
TFP. Table 12 presents the results of Schatteles and Mihailescu’s production function study. Only the first and last ten-year periods estimates are reproduced. Note that the standard errors and 88 Table 12 Cobb Douglas Production Function Results Inputs: Romanian Capital Stock, Total Industrial Employment Romanian Industrial Sector, 1951-85 and 1960-85 Schatteles and Mihailescu (Extended)
1951-60 Restricted Unrestricted Parameter Estimate Parameter Estimate
Employment 0.8232 Employment 1.0504 Romstock 0.1768 Romstock 0.5004 Constant -0.0833 Constant 0.0437
1 960-69 Restricted Unrestricted Parameter Estimate Parameter Estimate
Employment 0.9823 Employment -0.5176 Romstock 0.0177 Romstock -0.0261 Constant 0.0734 Constant 0.1910
Notes and Sources: These are the point estimates reported by Schatteles and Mihailescu. Restricted refers to the specification being restricted for constant returns to scale. Unrestricted implies that the specification was not restricted so that CRS would hold. Employment refers to the TIE labor series (Combination of the annual average number of wage earners and salaried personnel) 89 t-statistics are omitted since they were not reported in the original
work. The author has reproduced their work for these two periods in
Table 13. The results contained in Tables 12 and 13 are
approximately the same. However, when the variable rates of TFP
are included, in Table 14, the parameter estimates of the inputs
change dramatically.
In their original article, Schatteles and Mihailescu show that
the constant rate of TFP was higher in each successive ten-year
period until the period ending in 1968 (second to last period). They
concluded that the constant rate of TFP changed distinctively after
1963, when it was estimated at 7 percent whereas prior to 1963 it
had been in the 3-4 percent range. The findings of this study also
indicate a high constant rate of TFP (although not as high as found by
Schatteles and Mihailescu) and that the output elasticity of capital
is greater than that of labor for the 1951-65 period (Schatteles and
Mihailescu’s are contrary to the findings here). The constant rates of
TFP grew higher in successive ten-year periods indicating that TFP
had a positive effect on output growth.
Finally, Schatteles and Mihailescu did not provide any t-
statistics or standard errors of their estimates to support their
findings, thereby reducing the reliability of their findings. The point 90 Table 13 Cobb Douglas Production Function Results Reproductions of Schatteles and Mihailescu’s Study Romanian Industrial Sector, 1951-60 and 1960-69
1951 -60
Parameter Estimate Stan. Error T-Statistic
Intercept 6.6464 18.166 0.366 Employment 1.1138 0.4405 2.529 Romstock 0.0897 0.5095 0.176 Constant 0.0674 0.0183 3.674
DW = 2.660 SSE = 0.01018 R-SQ = 0.9980 Obs. = 10
1 9 6 0 -6 9
Parameter Estimate Stan. Error T-Statistic
Intercept 10.269 5.110 6.426 Employment 0.9232 0.145 6.358 Capital 0.0707 0.145 0.529 Constant 0.0703 0.0078 8.991 R estrict 0.0006 0.0002 3.000
DW= 1.524 SSE = 0.0447 R-SQ = 0.9984 Obs. = 26
Notes and Sources: All variables are the same as previously defined. Employment refers to the use of the TIE labor series which is the combination of the average annual number of salaried workers and wage earners. Estimation Method: OLS ART Table 14 Cobb Douglas Production Function Results Inputs: Romanian Capital Stock, Wage Earners Romanian Industrial Sector, 1951-60 and 1960-69 Comparison to Schatteles and Mihailescu’s Findings
1951-60
Parameter Estimate Stan. Error T-Statistic
Intercept 48.237 44.615 1.081 Wage Earners 0.3066 0.7647 0.401 Romstock 1.1261 1.0067 1.018 Constant 0.0914 0.0223 4.101 Variable 0.0011 0.0019 0.550
DW = 2.251 SSE = 0.00105 R-SQ = 0.9989 Obs. = 10
1960-69
Parameter Estimate Stan. Error T-Statistic
Intercept 4.4108 2.8528 1.564 Wage Earners 0.3971 0.2568 1.568 Romstock 0.6029 0.2568 2.348 Constant 0.0771 0.0068 11.278 Variable -0.0031 0.0013 2.289 Restrict 0.0002 0.0001 2.000
DW = 2.229 SSE = 0.0011 R-SQ =0.9991 Obs. = 10
Notes and Sources: All variables are defined as before. Estimation method: OLS ART 92 estimates of Schatteles and Mihailescu are not the same identified in this study since a different labor data series was utilized and the periods studied are much longer here. They only utilized the CD production function and they did not examine the CES or TL models.
The CD function they did utilize only contained a constant rate of
TFP and ignored the possibility of a variable rate of TFP. Soviet Industrial Production
A final question posed in this chapter asks whether the same causes of the slowdown in Soviet industrial production are present within the Romanian industrial sector. One would expect that the causes found in both the Romanian and Soviet industrial experiences ought to be similar since the Romanian industrial sector was created on the Soviet model of industrial development. The Romanian and Soviet economies had similar economic institutions, comparable economic planning systems, and utilized similar methods to achieve their economic goals. Two such methods were the forced collectivization of the agricultural work force and the promotion of heavy industry to a dominant position in the development of industry and the economy. Both Romania and the Soviet Union suffered economic and human losses during WWII (though the Soviets endured much more than the Romanians and the Soviets were able to obtain war reparations from the Romanians after the war). The growth 93 rates of industrial production and national income for the Soviet economy have been decreasing since the mid-1970’s, very much like that of Romania.
Weitzman (1970) found that the CES production function best described Soviet industrial production over the 196-79 period. He stated that the elasticity of substitution was less than one so that the primary reason for the slowdown in Soviet industrial production was due to diminishing returns of the capital factor30. Today, however, through the work of Desai (1987), it is believed that the industrial sector of the Soviet economy is best described by the CD production function. She suggests that the declining rate of TFP coupled with decreases in the growth rates of the capital and labor inputs, led to the slowdown in Soviet industrial production. Thus, the causes of the decreasing growth rates of industrial production in the Soviet Union are also present in the Romanian industrial sector.
Desai’s results of her production function study of the Soviet union are reproduced in Table 15.
Though these fundamental similarities are evident, there are also a number of differences that are visible. First, the average
(midpoint) rate of TFP in Romania is much larger than that in Soviet industry. Secondly, the rapidity of the decline in the rate of TFP is slower in Romanian industry than it is in Soviet industry (This is
30 See footnote 12. Table 15 Cobb Douglas Production Function Results Soviet Industrial Sector, 1950-79 and 1960-79 Estimated By Padma Desai
1950-79
Parameter Estimate T-Statistic
Intercept -0.3408 1.043 Capital 0.6563 3.502 Labor 0.3437 Constant 0.0244 1.194 Variable -0.0038 1.656
DW = 1.685 SSE = 0.0063 R-SQ = 0.9905 Obs. = 30 AR1 correction = 0.6838
1 960-79
Parameter Estimate T-Statistic
Intercept -0.5419 1.943 Capital 0.9407 5.864 Labor 0.0593 Constant 0.0528 2.538 Variable - 0.0015 3.270
DW = 2.053 SSE = 0.0009 R-SQ = 0.9498 Obs. = 20 AR1 correction = 0.8358
Notes and Sources: Desai used the official Soviet data for output over the 1950-79 period while in the 1960-79 period Greenslade’s estimate of industrial production was utilized. The Rapawy man-hour labor data series and the CIA capital stock data was used in both periods studied. CRS is specified throughout and the regressions were corrected for autocorrelation. Estimate method: OLS ART 95 seen by comparing the variable rates of Tables 3 and 15). Thirdly,
Desai finds that the output elasticity of the capital input has is greater than that of labor in Soviet industry. This was true for the
Romanian industrial sector in its reconstruction years following
WWII, but when the entire 35 year period is examined the output elasticity of labor is greater than that of capital. This suggests that industrial production in Romania is relatively labor intensive when compared to Soviet industry.
Thus, the same underlying reasons, decreasing growth rates of inputs and a declining rate of TFP, have caused the slowdown in the growth of Soviet industrial production are also present in the
Romanian industrial sector. Again, this is exactly what one might conclude given the respective historical development patterns of the industrial sectors of the Soviet Union and Romania.
Summary of Conclusions
This chapter has investigated the growth pattern of the
Romanian industrial sector via the use of production function analysis. The major conclusions of this investigation are summarized below. 96 1. Romanian industrial growth followed a cyclical pattern over the 24-year period of 1951-74. The growth rate of industrial output continued to decline past the previous cyclical low point until it reached an all time low point in 1981. The growth rate of industrial production grew slightly after 1985.
2. The cyclical pattern, prior to 1974, of the growth rate of industrial output can be attributed, at least partially, to the cyclical pattern of labor growth. The pattern of labor growth was influenced from the demand side by the slow development of the industrial sector shortly after WWII during the 1953-6 period. The other decline in the growth rate of labor was caused by the decline in the birth rate during WWII. The growth rate of the capital input continued to increased until 1974.
3. The 1974-81 period is characterized by persistent decreases in the growth rates of industrial production, as well as the labor, capital, and electrical inputs. Labor growth was affected by the decline in the WWII birth rate and the subsequent echo effect.
The growth rate of capital decreased during this time frame because of the growing national debt and from attempts to limit imports. The rate of investment also slowed in order to retire a portion of the accumulated debt. The direct effects of the input growth rates play a large role in explanation of the changing patterns of development 97 of the Romanian industrial sector.
4. The CD production function was found to best describe the industrial production in Romania. A consequence of accepting the CD model is that the elasticity of substitution is seen as being constant and equal to one. Therefore, the elasticity of substitution, since it is not less than one, cannot be looked upon to help explain the decline in the growth rate of industrial production.
5. Romanian industrial production is characterized by constant returns to scale. Therefore, the periods in which industrial production suffers a slowdown cannot be attributed to decreasing returns to scale nor can the periods of increasing growth rates be attributed to increasing returns to scale.
6. Romanian industry exhibits a declining rate of TFP. This is evidenced by the negative parameter estimates of the variable rate of TFP (Variable, in Tables 3 and 4). The Solow Method estimates of
TFP also indicate that the growth rate of TFP is declining over time.
7. Romanian industry has not experienced a structural change within its production process over the 35 year period when partitioned into the two sub-periods of 1951-65 and 1966-85.
8. The factors that have affected the growth rate of industrial production in the Soviet Union, as noted by Desai, are also present in the Romanian industrial sector. The Soviet industrial rates of TFP, however, decline at a faster rate than those of Romanian industry.
This presents evidence that the problems faced by Romanian and
Soviet industry are systemic in their origin even though the two economies are different in size, population, and endowments. CHAPTER IV
POSTWAR GROWTH OF THE ROMANIAN INDUSTRIAL BRANCHES AND REGIONS
Introduction
The present chapter extends the analysis of the growth pattern
of the Romanian industrial sector of Chapter 3 to the industrial
branches and regions of Romania. Production function analysis is
used to determine whether the slowdown in industrial production is economy-wide or if it is centered in certain industrial branches or
regions. The investigation questions whether the growth rate of output and the growth rates of inputs are uniform throughout the
industrial branches and amongst the regions of Romania. If the growth rates are uniform then it maybe concluded that the each industrial branch and region contributed equally to the decreasing growth rate of industrial production. On the other hand, the possibility exists that certain branches and regions which have a relatively larger share of industrial production could be slowing the growth rate even though other branches/regions (which carry less weight) may be expanding. The chapter first analyzes the industrial branches and then proceeds to examine regional growth.
99 100 As in Chapter 3, the analysis begins by examining the direct effects of the relationship between the growth rate of output and the growth rates of the principal inputs, labor and capital. Following this discussion, the indirect effects, namely, the elasticity of substitution, returns to scale, and total factor productivity (TFP) are addressed, as defined in Chapter 3. The best variant of the production function models is chosen for each of the industrial branches as well as for each region. These production function results are then compared and contrasted with Padma Desai’s production function estimates of Soviet industrial branches and
Koropeckyj’s estimates of Soviet industrial regions.
Data and Sources
The data used in examining the industrial production of the industrial branches and regions of Romania is similar to the data that was utilized in the analysis of the Romanian industrial sector in Chapter 3. The similarities of the data between that of the industrial sector and the industrial branches and regions are noted.
The differences in the data are discussed at length.
Output
The output data series for both the industrial branches and the regions were derived from the official value of industrial production
(valued in constant 1963 prices) of the industrial sector used in 101 Chapter 3. The percentage share of the industrial output produced by each industrial branch is available for the entire 35 period, 1951-
85. However, the percentage shares of production of the regions are only available from 1960 onwards (26 years). The percentage share of the respective branch and region was multiplied by that year’s value of industrial sector’s output to arrive at the value of output produced by the branch or region. These output data series for the industrial branches and regions are the only output series available and therefore are used consistently throughout the investigation of branch and regional growth. Data pertaining to the industrial branches within each region is not available.
Labor
The labor input of both the industrial branches and regions is measured by the total industrial employment within the respective branches and regions. Total industrial employment (TIE series of
Chapter 3) consists of the average annual number of wage earners and salaried personnel. These labor data series were taken directly from the official Romanian statistical yearbooks. The labor series is referred to as total industrial employment by branch, TIEB. The regional labor series is referred to as the total industrial 102
employment by region, TIER. These are the only labor data series
available at the branch and regional level since the annual average
number of wage earners (the WE series of Chapter 3), alone (which
comprises nearly 90 percent of the TIE series), is not available.
Capital
Capital stock data series for the industrial branches and
regions are not directly available from Romanian sources. However, the statistical yearbooks provide enough information pertaining to the yearly investment flows into the respective branches and
regions so that reliable estimates of the capital stocks could be obtained. Each of the capital stock series were produced by following the procedure described below 1.
The first step in the estimation procedure was to obtain the yearly changes in the capital stock of industry. This was possible since the RK and WK series are available at the aggregate level. The second step was to obtain the yearly changes in the capital stocks of the branches and regions. The Romanian statistical yearbooks publish the percentage share of investment that each branch and region received during the year. The amount of investment which flowed to each respective branch and region was found by
1A detailed description of the procedure used to estimate the capital stock series is contained in the Appendix. 103 multiplying the industrial investment (the change in the industrial capital stock) by each branch’s and region’s share of investment.
Thus, this procedure produced the flow of capital into the industrial branches and regions.
Therefore, an estimate of the capital stock was needed to develop a capital data series. The capital stock for the year of 1955 was estimated for each of the industrial branches and regions. The third step is to find the average percentage share of investment over the 1951-60 period (1960-69 period for the regions) and use it as an approximation of the percentage share of the industrial capital stock. The assumption that the average rate of investment, within the branches and regions, over the ten year period of 1951-60
(1960-69 period for the regions) represented the percentage of the industrial sector’s capital stock held by the respective branches and regions in 1955 was used because industrial development did not truly begin until after the WWII in Romania. The fourth step is to multiply these averages (of each respective branch and region) by the capital stock of the industrial sector to arrive at a estimate of the industrial capital stock contained in each branch and region for
1955. The final step consists of adding (or subtracting, as the case may be) the respective yearly flow of capital to the industrial branches and regions to the 1955 estimate of the respective capital 104 stock.
This method was chosen because of its simplicity and because of the lack of other data and alternative estimation methods. This method was applied to several Soviet, Polish, Hungarian and
Bulgarian industrial branches for which the capital stock was known. The results produced a fairly close approximation to the actual capital stock contained within these branches3. What is important here is the relative level of capital stock between the individual branches and regions, since the study is primarily concerned with the growth rates of the inputs.
Why was the average percentage share of investment over the ten year period 1951-60 chosen? It was chosen under the assumption that during WWII the capital stock of Romania industrial branches and regions were destroyed, depleted, or made useless because of the destruction of the war3. Romania was bombed by the
Allies through 1943 and thereafter it was occupied by Soviet forces.
The Soviet occupation continued into the early 1950’s and the
Soviets forced Romania to pay large war reparations, took control of major industries, and claimed mineral rights on Romanian territory.
2 A detailed description of this procedure applied to these countries’ industrial branches is presented in the Appendix. 3 Andre Blanc states that Romania was ruined after the disastrous war in 1945. More than 85 percent of the fuel equipment was destroyed and 75 percent of the transportation equipment met the same fate. (1967, p.239). It should also be pointed out at this time the fuel industry comprised a majority of the industrial branch, followed by food processing. 105 Thus, the capital stock of all industry was relatively small and the
capital stock of the branches and regions relatively equal. Large
investments did not begin to flow back into the Romanian industrial
sector until the mid-1950’s. Thus, it is the contention of the author
that investment rates of the decade of the 1950’s actually do
reflect the percentage of capital stock held within the individual
branches and regions.
The first of the two estimates of the capital stock of the
industrial branches is referred to as the Romanian branch capital
stock, RBK, which was derived from the RK industrial sector capital stock series. The second estimate is denoted as the Western branch capital stock, WBK, and was derived from the WK industrial sector capital stock series. The first of the two estimates of the capital stock of the regions is referred to as the Romanian regional capital stock, RRK, which was derived from the RK industrial sector capital stock series. The second estimate is denoted as the Western branch capital stock, WRK, and was derived from the WK industrial sector capital stock series. 106 Regional Gerrymandering
Ceausescu, in 1968, implemented a redistricting program
which enable his faction of the Communist Party to contain the
rising opposition within certain areas of Romania. This program
increased the number of the existing 17 administrative regions to
40 districts in 1968 and in 1980 the 41st district was created.
Figure 8 presents maps of the older regional system and the newer
district system. Output, investment and labor data was reported in the regional form until 1968. The district form of these data were
available from 1965 onward. Hence, the data were not published in a
consistent form over the entire 26 year period (1960-85) and it was therefore necessary to transform these data into a single form.
The data in district form was transformed into the regional format, since the majority of districts lie entirely within the past borders of the regions. However, there were several exceptions,
namely, the districts of Alba, Arad, Mures and Olt straddle two
regions. But because of the availability of data in both the district
and regional forms, over the 1965-6 period, the percentage
contributions of these districts to the regional totals of output, labor and investment were able to be estimated. 107
m A * A M U * £ M M ara*
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tana H*
MACAU
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9V2*J SE C AAAf SCVtAlN mxAha l A c s J S Siobor* Stalina O o b ait-T f UIOMIJAJ V
I. Tirgoviita IlfO V ~ D O tJ Q Towns with status O of municipality
Administrative Systems of Romania
A: Regional system, 1951-68 B: District system 1968-present Figure 8 108 The task was more difficult when the regions included a portion of one of the four districts mentioned above, since these districts lie across the borders of two regions. A portion of the respective district’s data needed to be assigned to each of the two regions in question. Thus, it was necessary to estimate the percentage of output, labor, and investment of the district that would be allocated to each region4. The percentage contributed by the district to each of the regions was derived as follows.
The illustration in Figure 9 may help clarify the procedure5, which transforms the data from the district form into a regional form. For example, the sum of the Districts 1 and 2 total labor
(9000), which lie entirely within Region A, was subtracted from the
Region A’s total labor (10,000). Therefore, District 3, which lies across two Regions A and B, contributed the remainder (1000) to
Region A’s total labor. Therefore, 25 percent of District 3’s labor was applied to that of Region A in successive years to transform the district data into the regional form. The “ leftover” labor of the
District 3 was then applied to the Region B’s total labor. This procedure was repeated for the the amount of investment and output of District 3.
4 Of course, these percentages did not have to be equal to one another since it is possible, for example, that the district of Olt may contribute 10 percent of its output and 40 percent of its investment to the region of Oltania. Therefore it was necessary to estimate the percentage contributed to the region by the district for each input and for output. s A detailed examination of this procedure is found in the Appendix. 109
/ t f ^ Reqion A
District 1 District 2
District 3 Region A 1000
[District 3, Region B
Region A total labor = 10,000 District 1 (within Region A) = 3875 District 2 (within Region A) = 4125 District 3 (lies across Region A and region B) = 4 00 0 £ District 1 and District 2 labor = 9000 Regional labor - (District 1 labor + District 2 labor) = 10,000 -9000 = 1000 Remainder = 1000 = labor contributed to Region A Percentage of District 3’s labor contributed to Region A =1000/4000= 25% “Leftover" labor of District 3 assigned to Region B = 4 0 0 0 - 1000 = 3 0 0 0 Percentage of District 3's labor contributed to Region B =3000/4000= 75%
Figure 9
Illustration of the Procedure Used to Transform District Data Into a Regional Form Labor Example Romanian Regions, 1965-6 110 The procedure described above was conducted for the years of
1965 and 1966 since these were the only years that the data was published in both the regional and district form. The estimated percentages contributions made by the districts overlapping two regions were applied consistently over the period of 1968-85 in order to transform the data from the district form into the regional form.
Data Reliability
The branch and regional data contain the same biases and errors of the data detailed in Chapter 3 describing the industrial sector since, for the most part, they were derived from the industrial sector data. These biases and errors arise from the a) index number problems (the linkage of indices with different base years and that the indices are only published to the nearest percent) and b) non-scarcity pricing.
The output data of the industrial branches and regions contains an additional error since the percentage share of industrial production is rounded to the nearest tenth of one percent, which introduces the possibility of an error of one-half of a percent. The labor data does not contain any additional errors or bias since it was available in its raw form. The capital data series introduce two additional sources of errors, the first originating from the fact that 111 the share of investment is rounded to the nearest tenth of one percent, which introduces the possibility of an error of one-half of a percent. The second is introduced by the author’s use of an estimation method to calculate the capital stock of the branches and regions for the year of 1955.
Finally, some of the regional data sets contain an additional error because several districts overlapped regional territories and the districts’ contributions to output, labor, and investment of the regions had to be estimated. Therefore, the reliability of the regional data series is less than that of the branch data series, however, none of these biases and errors within the data series undermine the results produced by these data series6 .
Total Factor Inputs
There is only one possible combination of the input and output data series describing the industrial branches over the entire 35 year period, namely the TIEB labor series and RBK capital series.
When the 26 year period of 1960-85 is studied the additional availability of the WSK capital series allows for a second combination of inputs data series, TIEB with WBK to be used. The regional study spans the 26 year period of 1960-85 and therefore has two possible combinations of input data series, namely, TIER
s Ben-Avner and Montias indicate that the data of the agricultural sector, post 1985, has somewhat unreliable and give no indication that the industrial data is any less reliable than what has been presented here. See Chapter 2 for specific details. 112 with RRK and TIER with WRK.
Measures and Results: Industrial Branches
There are 17 industrial branches that comprise the Romanian industrial sector and these are listed in Table 16. In order to simplify the analysis, the industrial sector has been partitioned into two groups. The first group (A) contains those branches of heavy industry, while the second group (B) contains the traditional or fight industrial branches. The analysis proceeds with general conclusions that are characteristic of each group and specific industrial branches are discussed for emphasis.
Direct Effects
First, the growth rates of of production of each group (A and B) and branch is examined in an attempt to discern if the groups and branches exhibit a uniform rate of growth. Secondly, the growth rates of the principal inputs, labor and capital, are compared with the growth rates of the industrial branches and industrial groups
(compilation of branches) to determine the direct effects of the inputs on production. The growth rates of production of the two industrial groups (Group A = Heavy Industry, Group B = Light
Industry) and those of each industrial branch are presented in Table
17. 113 Table 16 The Industrial Branches Comprising the Romanian Industrial Sector
Group A Heavy Industry
1. Electric and Thermal Power {Electric} 2. Fuels (Including Coal, Coke, Petroleum, Gas extraction,Petroleum processing, Methane gas) 3. Ferrous Metallurgy (Including the mining of ferrous ores) {Ferrous} 4. Non-Ferrous Metallurgy (Including the mining of non-ferrous ores) {Non-Ferrous} 5. Machine Building and Metal Working {MBMW} 6. Chemicals 7. Mining of non-metalliferous ores and abrasive substances {Mining} 8. Construction Materials
Group B Light Industry
9. Forestry Operation and Woodworking {Woodworking} 10. Pulp and Paper {Paper} 11. Glass, China, and Faience {Glass} 12. Textiles 13. Ready-Made Clothing {Manufacturing} 14. Leather Goods, Fur, and Footwear {Fur} 15. Food Processing 16. Soap and Cosmetics {Soaps} 17. Poligraphy {Printing}
Notes: The titles given in {} are those that are used in the dissertation. 114 Table 17 Growth Rates of Industrial Production By Industrial Group (A and B) and Branch Romania, Selected Five and Ten Year Midpoint Averages, 1951-1985
Branch 51-60 61-65 66-70 71-75 76-80 81-85
Fuels 10.4 8.0 6.0 6.1 6.9 24.4 Electric 15.5 20.6 16.6 0.8 3.1 20.2 Mining 27.1 10.9 13.2 18.1 11.0 15.1 Ferrous 14.2 11.3 12.3 12.1 9.2 12.6
Chemical 20.7 25.6 21.4 6.2 8.1 8.6 Non-Ferr. 14.5 25.5 12.4 10.1 8.5 7.7
Constr. 15.1 16.0 13.0 11.5 11.9 4.8 Materials MBMW 19.3 17.0 15.8 19.9 11.6 3.4
Group A 15.6 13.9 14.4 15.7 9.9 7.4
Soap 10.8 8.4 9.3 11.9 7.1 11.0 Printing 13.4 14.8 7.1 6.8 5.2 10.2
Fur 9.4 10.3 9.5 11.8 10.3 5.9 Glass 13.3 12.3 12.0 16.8 13.8 5.7 Paper 8.7 19.1 18.6 6.9 8.8 5.5
Food 9.1 8.4 6.5 7.6 6.5 3.8 Processing Wood 10.4 16.6 6.5 7.0 7.0 3.4 Manu. 4.5 7.1 7.5 8.6 2.4 2.0 Textiles 8.9 10.4 11.1 8.1 3.3 1.1
Group B 8.9 15.0 8.3 10.0 7.8 4.9
Industrial 12.3 14.1 11.9 13.6 9.2 6.5 Sector 115 Chapter 3 indicated that the growth rate of production of the
industrial sector followed a cyclical pattern until 1974. It then began its sharp decrease through 1981 and recovered slightly thereafter. The question of whether this drop in the growth rates was experienced uniformly by each of the industrial branches is now
addressed.
Table 17 presents the five year average (midpoint) growth rates of industrial production for each industrial group and branch.
The growth rates of output and inputs are also presented for each branch in Figures 10-26 (which are located in the Graphical
Appendix). The sharp decrease in the industrial sector’s growth rate of production was felt economy-wide. Both industrial groups, A and
B, indicate a similar decline in their growth rates. However, heavy industry felt a sharper decline as its average growth rate fell from
15.7 percent to 9.9 percent in the latter half of the 1970's. Six of the eight heavy industrial branches and seven of the nine light industrial branches have five year average growth rates of production that declined over the 1971-81 period. 116 Since the decline was felt throughout the entire industrial sector it is necessary to investigate the share of production contributed by each branch which are presented in Table 18. The heavy industrial group has steadily increased its share of industrial output since 1951. During the late 1950’s, heavy industry began to contribute a greater share of production than light industry. At the time of the slowdown in industrial production, the contribution to industrial production made by heavy industry was greater than 60 percent and was continuing to increase. Notable branches that saw enormous increases in their contribution to overall industrial output were the Chemical and MBMW branches (Group A). On the other hand, the branches which which lost shares were the Food Processing,
Textiles, Woodworking (Group B), and Fuel (Group A) branches. As heavy industry continually contributed a larger share to industrial production, its influence upon the growth of the industrial sector grew. Thus, any change in the growth rates of the heavy industrial branches would carry greater weight in determining the growth of the industrial sector than changes in the growth rates of light industry. The growth rate of industrial production was then primarily determined by the growth of the heavy industrial branches.
The branches which experienced the greatest increase in the share of production, MBMW and Chemicals, were also the branches which 117 Table 18 Percentage Shares of Industrial Production By Industrial Group (A and B) and Branch Romania, Selected Years, 1951-85
Branch 1951 1961 1971 1981 1 985
MBMW 15.06 25.47 27.25 33.96 31.52 Chemicals 3.38 6.91 10.57 11.98 11.87 Ferrous 5.07 6.49 8.28 8.15 10.23
Fuels 11.37 8.52 5.04 5.86 6.82 E lectrical 1.84 2.46 3.20 2.83 3.53 Construction 2.46 3.13 3.47 3.61 3.25 M aterials Non-Ferrous 1.84 2.08 2.87 3.28 2.83 Mining 0.11 0.30 0.27 0.46 0.53
Group A 41.1 5 55.37 60.95 70.13 70.58
Food Processing 24.28 18.18 17.00 10.20 10.10
Manufacturing 7.10 5.68 4.51 5.21 6.01 T extiles 11.37 8.43 7.35 6.61 5.89
Woodworking 9.84 7.20 6.16 4.23 3.79 Fur 3.75 2.63 2.08 1.98 1.99 Paper 1.17 1.01 1.38 1.07 1.09 P rinting 0.88 1.01 0.28 0.24 0.25 Soap 0.39 0.41 0.24 0.22 0.23 Glass 0.07 0.07 0.05 0.09 0.07
Group B 58.85 44.63 39.05 29.87 29.42 118 experienced a significant decrease in their growth rates of
production. Therefore, these branches “dragged” the growth rate of
the industrial sector downward more so than the decreases in the
growth rates of other branches.
Capital
The capital stock of the industrial sector exhibited increasing
rates of growth until 1972. Since 1972 the growth rates of capital
stock have declined. The five year average (midpoint) growth rates
of the capital stock for industrial groups and branches are presented
in Table 19. Again, both the heavy and light industrial groups exhibit
the similar pattern of capital growth followed by the industrial sector in that the decline in the growth rates begin in the earlier
1970’s. Most branches followed a similar pattern of growth also. The exceptions were the MBMW, Chemical, and Construction material
branches of heavy industry and the Soap branch of light industry. As in the case of industrial production, the decline in the growth rates of capital were, for the most part, felt economy wide. The shares of the industrial capital stock of each industrial group and branch are
presented in Table 20. There has been little variance in the shares of capital stock between industrial groups, as heavy industry continues to contain approximately an 80 percent share. Most industrial Table 19 Growth Rates of Industrial Capital Stock By Industrial Group (A and B) and Branch Romania, Selected Five and Ten Year Midpoint Averages, 1951-1985
Branch 51-60 61-65 66-70 71-75 76-80 81-85
E lectric 0.9 9.9 16.0 13.6 10.2 11.7 MBMW 1.5 13.8 18.7 22.2 15.7 11.4 Mining 1.0 5.6 18.3 14.5 10.9 11.0 Fuels 1.1 7.7 9.1 6.9 8.8 10.3
Chemical 1.2 9.1 13.9 14.5 11.6 8.0 Non-Ferr. 1.0 9.5 9.7 10.0 8.2 7.0 Ferrous 0.8 9.0 13.3 10.8 12.4 6.9 Constr. 1.0 9.8 14.1 16.4 10.4 6.1 Materials
Group A 1.1 8.6 12.3 11.3 10.5 9.2
Food 3.5 9.4 13.3 3.5 10.7 9.5 Processing
T extiles 0.8 11.5 18.0 16.3 10.7 7.0 Manu. 2.5 19.2 22.8 20.0 9.6 6.7 Soap 1.0 4.2 7.2 0.5 15.6 6.2 Paper 0.7 1.3 7.0 7.6 5.7 6.0 Wood 1.0 10.3 10.1 8.6 7.0 5.3
Fur 5.1 14.6 20.1 9.5 11.8 5.3 Glass 1.2 6.4 24.5 11.9 8.6 5.0 Printing 0.1 6.2 10.3 9.5 4.6 4.5
Group B 2.1 10.1 13.3 12.2 9.4 7.4
Industrial 1.3 8.9 12.5 11.5 10.3 8.8 S e cto r 120 Table 20 Percentage Shares of Industrial Capital Stock By Industrial Group (A and B) and Branch Romania, Selected Years 1951-85
Branch 1951 1961 1971 1981 1985
MBMW 7.20 8.07 13.57 25.68 27.17 Fuels 34.31 34.06 26.69 20.77 22.34 Electrical 13.28 13.00 16.38 17.27 19.75 Chemicals 15.81 15.36 16.21 19.91 19.09
Ferrous 12.53 12.01 12.63 13.80 12.35
Construction 4.50 4.30 5.17 6.11 5.36 Materials
Mining 0.58 0.58 0.70 0.70 0.78 Non-Ferrous 0.70 0.67 0.60 0.52 0.48
Group A 81.70 79.99 78.39 79.08 80.15
Food Processing 5.37 7.06 7.32 7.87 8.26
Woodworking 7.79 7.74 7.17 5.31 4.67 Textiles 2.75 2.75 3.89 4.86 4.39
Glass 0.79 0.79 1.27 1.04 0.88 Fur 0.27 0.42 0.67 0.64 0.56 Manufacturing 0.16 0.18 0.45 0.58 0.54 Paper 0.48 0.48 0.38 0.26 0.24 Printing 0.53 0.45 0.36 0.25 0.21 Soap 0.16 0.15 0.09 0.11 0.10
Group B 18.30 20.01 21.61 20.92 19.85 121 branches' share of industrial capital stock have remained
approximately consistent since 1951, however, there are several
notable exceptions. The MBMW branch’s share increased from 7.2
percent to 25.7 percent, while the shares of the Chemical and
Electrical also rose, but not by the same rate as MBMW. The Fuel
branch saw a notable decrease in its share of industrial capital
stock.
Again, as in the case of production, the MBMW and Chemical
branches increased their share of industrial capital stock during the
period in which their growth rates of capital declined significantly.
The weighted average of the heavy industrial branches’ (especially
those of MBMW and Chemicals) “dragged down” the growth of
industrial capital stock.
Labor
The five year average (midpoint) growth rates of labor for both
the heavy and light industrial groups followed a similar cyclical
pattern as is shown in Table 21. The growth rates of each group
decline significantly after 1975. Most individual branches also
follow a cyclical growth pattern, including the MBMW branch. The
Chemical branch’s growth rate of labor began to decline in the 1966-
70 period, earlier than the other branches. These two branches saw
the sharpest decreases in the growth of the labor input during the 122 Table 21 Growth Rates of Industrial Labor By Industrial Group (A and B) and Branch Romania, Selected Five and Ten Year Midpoint Averages, 1951-1985
Branch 51-60 61-65 66-70 71-75 76-80 81-85
Mining 6.1 7.4 4.2 5.0 2.6 10.0 Chemical 9.3 17.9 21.4 16.2 8.0 8.6
E lectrical 4.9 16.9 2.3 1.5 1.2 3.4 Non-Ferrous 7.5 10.0 2.8 3.5 1.4 2.3 Fuels 3.5 2.2 0.0 0.1 4.2 2.2 MBMW 5.0 6.8 6.1 10.8 5.4 1.9
Construction 4.0 4.2 5.1 1.9 1.6 -3.8 Materials Ferrous 5.3 4.1 3.0 3.8 7.8 -7.8
Group A 4.9 6.6 5.0 7.4 4.6 1.8
Manu. 9.9 8.1 12.1 17.7 8.9 10.5
Glass 5.4 9.9 11.7 16.8 13.8 5.7
Textiles 8.1 11.8 12.0 13.1 9.4 2.7 Fur 2.3 2.8 5.5 4.0 3.2 2.2 Paper 5.5 10.9 3.2 4.0 1.3 2.1
Wood 2.8 5.6 1.7 1.4 0.0 -0.2 Food 2.6 5.1 3.1 4.1 2.8 -0.4 Processing Printing 4.9 3.1 -0.3 -1.0 0.1 -0.5 Soap 1.9 4.1 1.9 11.4 4.3 -3.9
Group B 2.8 5.1 3.7 4.9 2.0 0.8
Industrial 1.3 8.9 12.5 11.5 10.3 8.8 Sector 123 period in which industrial production slowed.
Table 22 presents the shares of industrial labor of each of the industrial groups and branches. The heavy industrial group’s share of labor has steadily increased since 1951. In fact, by 1971 the heavy industrial group accounted for more than 50 percent of industrial labor. It is interesting to note that the heavy industrial group, though capital intensive in production, accounts for more than one- half of the industrial work force.
The branch which accounted for the increasing share of the heavy industrial group was MBMW branch, although the Chemical branch’s share also increased. The light industrial group’s share of labor declined over time and this was due primarily to decreases in the shares of labor in the Food Processing and Woodworking branches.
Summary of Direct Effects
The influence of the heavy industrial branches grew steadily from 1951 onward and by the 1970’s they produced the majority of industrial production, employed more than half of all industrial workers and held approximately 80 percent of the industrial capital stock. When these branches experienced a decline in their growth rates of production, especially the MBMW and Chemical branches, the growth rate of the industrial sector was disproportionately 124 Table 22 Percentage Shares of Industrial Labor By Industrial Group (A and B) and Branch Romania, Selected Years, 1951-85
Branch 1951 1961 1971 1981 1985
MBMW 21.40 23.32 27.35 35.96 36.38
Chemicals 2.69 4.53 6.57 6.88 7.22
Ferrous 3.90 4.20 3.65 4.17 3.99 Fuels 7.39 6.54 4.45 3.68 3.85 Construction 5.83 5.57 5.19 3.74 3.25 Materials Non-Ferrous 1.87 2.84 2.92 2.40 2.38 Electrical 1.24 1.34 1.76 1.35 1.42 Mining 0.37 0.44 0.51 0.62 0.55
Group A 44.69 48.77 52.40 58.81 59.06
Textiles 13.00 12.03 11.40 11.93 11.73 Woodworking 18.38 16.04 13.59 9.47 9.41
Food Processing 10.64 9.28 8.53 6.50 6.42 Manufacturing 4.37 4.98 6.02 5.99 6.05
Fur 5.14 4.52 4.17 3.71 3.78 Glass 1.23 1.49 1.46 1.76 1.71 Paper 1.02 1.20 1.35 1.11 1.16 Printing 1.36 1.54 0.97 0.58 0.55 Soap 0.17 0.14 0.12 0.14 0.12
Group B 55.31 51.23 47.60 41.19 40.94 125 affected.
Why have the heavy industrial branches, especially the MBMW and Chemical branches, gained prominence in the Romanian economy?
The primary answer to this is the economic development philosophy of the communists after WWII. The development of heavy industry was a political priority because of the belief that the communist and Soviet command-type economy would produce greater levels of production than a market based economy. In order to produce these levels of production, machinery was needed, therefore the development of the MBMW branch was seen as a priority. This philosophy was also driven by the desire for Romania to become self-sufficient, thus other heavy industrial branches, especially that of Electricity (a massive electrification program began in the
1960’s), were also promote to the forefront of the economy.
A second reason for the development of these and other branches is the resource base of Romania. The Carpathian Mountains provide many natural resources including natural gas, iron ore and various minerals and chemicals. Thus, with numerous resources available, the Chemical, Ferrous, and Mining branches grew rapidly.
Romania also has a large amount of forests and therefore the
Woodworking and Paper branches experienced rapid growth. The Food
Processing branch has not grown as rapidly as it might have since 126 the 1960’s because of the inefficiencies in Romanian agriculture.
Indirect Effects: Industrial Branches
Heavy Industrial Group
The analysis of the indirect effects (the elasticity of substitution, returns to scale, and total factor productivity) of the industrial branches draws upon the production function results of the branches. It describes the general characteristics found in the heavy and light industrial groups.
The industrial branches were examined over the two separate periods of 1951-85 (35 years) and 1960-85 (26 years). Additional input data, that was not available prior to 1960, was included in the examination of the production functions in the latter period. Hence, it was necessary to examine the branches over these two periods in order to include all possible combinations of the inputs and their respective effects on output. Table 23 presents the production function results for the heavy industrial branches over these two periods.
The first characteristic seen in Table 23 is that the majority
(five of eight branches in the 1951-85 period and seven of eight in the 1960-85 period) of the heavy industrial branches are best described by the CD production function. Given the mathematical 127 Table 23 Accepted Production Function Models Heavy Industrial Group Romania, 1951-85 and 1960-85
Chemical
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 8.421 3.195 2.636 TIEB 1.1761 0.4892 2.404 Romstock -0.1761 0.4892 0.360 Constant 0.1008 0.0132 7.636 Variable -0.0015 0.0012 1.250 Restrict 0.0093 0.0036 2.580
DW = 1.440 SSE = 0.0728 R-Sq = 0.9986 Obs. = 35 Lag = 0.411 F-Stat CD v. CES = 2.21 F-StatCDv. TL =1.98
1960-85
Parameter Estimate St. Error T-Statistic
Intercept 6.384 4.4520 1.453 TIEB 0.5853 0.3670 1.595 Romstock 0.4147 0.3670 1.300 Constant 0.0623 0.0093 6.700 Variable -0.0012 0.0006 2.000 Restrict 0.0806 0.0624 1.298
DW = 1.665 SSE = 0.1371 R-Sq = 0.9938 Obs. = 26 Lag = 0.316 F-Stat CD v. CES = 1.49 F-StatCDv. TL =1.31 128 Table 23 (continued)
Construction Material
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 25.5648 8.6490 2.955 TIEB -2.8060 1.6440 1.707 Romstock 3.8060 1.6440 2.135 G 0.1220 0.0780 1.564 Constant 0.1125 0.0123 9.146 Variable -0.00072 0.00072 1.000 R estrict 0.00451 0.00724 0.623
DW = 1.887 SSE = 0.0564 R-Sq = 0.9776 Obs. = 35 F-Stat CD v. CES = 10.53 F-StatCDv. TL =13.56 F-Stat CES v. TL = 1.47
1960-85
Parameter Estimate St. Error T-Statistic
Intercept 7.882 3.882 2.030 TIEB 1.1937 0.2230 5.350 Romstock 0.0230 0.1550 0.015 Constant 0.0680 0.0024 28.330 Variable 0.0005 0.0004 1.250
DW = 1.804 SSE = 0.0299 R-Sq = 0.9853 Obs. = 26 CRS F-Stat = 0.733 F-Stat CD v. CES = 2.98 F-Stat CD v. TL =1.28 129 Table 23 (continued)
Electrical
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 10.241 3.8734 2.644 TIEB 0.5009 0.2012 2.489 Romstock 0.4991 0.2012 2.479 K x K -0.0075 0.0031 2.412 L x L -0.0075 0.0031 2.412 K x L 0.0075 0.0031 2.412 Constant 0.1563 0.0267 5.862 Variable -0.0014 0.0009 1.560 Restrictl 0.00048 0.00046 1.049 R estrict2 0.03215 0.02877 1.118 Restrict3 0.01406 0.02316 0.607
DW = 2.192 SSE = 0.221 R-Sq = 0.9972 Obs. = 35 Elasticity of Substitution 1985 = 0.689 Elasticity of Substitution 1968 = 0.623 Elasticity of Substitution 1951 = 0.597 F-Stat CD v. CES = 5.17 F-Stat CD v. TL = 6.74 F-Stat CES v. TL = 6.60 130 Table 23 (continued)
Electrical
1960-85
Parameter Estimate St. Error -S tatistic
Intercept 15.121 12.664 1.194 TIEB 0.6073 0.2623 2.315 Romstock 0.3927 0.2623 1.497 Kx K -0.0103 0.0128 0.801 L x L -0.0103 0.0128 0.801 Kx L 0.0103 0.0128 0.801 Constant 0.1713 0.1692 1.012 Variable -0.0017 0.0021 0.836 R estrictl 0.00049 0.00028 1.761 Restrict2 0.03985 0.02458 1.622 Restrict3 0.01752 0.01401 1.250
DW = 1.869 SSE - 0.253 R-Sq = 0.9921 = 26 Elasticity of Substitution 1985 = 0.324 Elasticity of Substitution 1972 = 0.289 Elasticity of Substitution 1960 = 0.233 F-Stat CD v. CES = 6.69 F-Stat CD v. TL = 4.80 F-Stat CES v. TL = 5.92 131 Table 23 (continued)
Ferrous
1951-85
Parameter Estimate St. Error T -S tati
Intercept -2.7800 5.7869 0.480 TIEB 0.0803 0.2924 0.276 Romstock 0.9197 0.2924 3.143 K x K 0.0021 0.0072 0.299 L x L 0.0021 0.0072 0.299 K x L -0.0021 0.0072 0.299 Constant 0.1638 0.0280 5.848 Variable -0.0038 0.0011 3.587 Restrict! 0.00077 0.00013 1.113 Restrict2 0.02183 0.02000 1.091 Restrict3 0.02201 0.01317 1.671
DW = 1.652 SSE = 0.009 R-Sq = 0.9975 Obs. = 35 Elasticity of Substitution 1985 = 0.835 Elasticity of Substitution 1968 = 0.841 Elasticity of Substitution 1951 - 0.846 F-Stat CD v. CES = 28.23 F-Stat CD v. TL =31.80 F-Stat CES v. TL = 4.21 132 Table 23 (continued)
Ferrous
1960-85
Parameter Estimate St. Error T-Statistic
Intercept 5.523 4.528 1.220 Sal/Wage 0.4991 0.2285 1.751 Westock 0.5001 0.2285 1.757 Constant 0.2187 0.0024 7.010 Variable -0.0037 0.0005 7.440 Restrict 0.0092 0.0092 1.000
DW = 1.805 SSE = 0.1278 R-Sq = 0.9876 Obs. = 26 Lag = 0.2447 F-Stat CD v. CES = 2.29 F-Stat CD v. TL =1.25 133 Table 23 (continued)
Fuel
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 7.303 4.840 1.508 TIEB 0.7147 0.2840 2.516 Romstock 0.2853 0.2840 1.004 Constant 0.0512 0.0292 1.753 Variable -0.00004 0.0004 0.100 R estrict 0.0204 0.0900 2.222
DW = 2.028 SSE = 0.3346 R-Sq = 0.9846 Obs. = 35 Lag = 0.3401 F-Stat CD v. CES - 1.15 F-Stat CD v. TL =2.11
1960-85
Parameter Estimate St. Error T-Statistic
Intercept -14.740 11.570 1.274 TIEB 1.625 0.7340 2.214 Westock 0.7801 0.6410 1.217 Constant 0.0040 0.0348 1.150
DW = 1.753 SSE = 0.3139 R-Sq = 0.9646 Obs. = 26 CRS F-Stat = 4.362 (reject) Lag = 0.2404 F-Stat CD v. CES = 2.07 F-Stat CD v. TL =1.07 134 Table 23 (continued)
MBMW
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 9.343 2.396 3.899 TIEB 0.9245 0.2386 3.875 Romstock 0.0755 0.2386 0.316 Constant 0.1327 0.0225 5.898 Variable -0.00064 0.00037 1.730 Restrict 0.0072 0.0096 0.750
DW= 1.726 SSE = 0.1195 R-Sq = 0.9954 Obs. = 35 Lag = 0.7293 F-Stat CD v. CES = 1.91 F-Stat CD v. TL = 1.01
1960-85
Parameter Estimate St. Error T-Statistic
Intercept 4.422 2.3967 1.357 TIEB 0.3415 0.3543 0.964 Westock 0.6585 0.3543 1.859 Constant 0.0432 0.0377 1.146 Variable -0.0011 0.0008 1.274 Restrict 0.0138 0.0084 1.643
DW = 1.726 SSE = 0.0859 R-Sq = 0.9922 Obs. = 26 Lag = 0.6455 F-Stat CD v. CES = 1.52 F-Stat CD v. TL =1.69 135 Table 23 (continued)
Mining
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 21.130 4.840 4.510 TIEB 0.5338 0.3840 1.861 Romstock 0.4032 0.3840 0.743 Constant 0.1567 0.0292 5.173 Variable -0.0002 0.0004 0.500 R estrict 0.0202 0.0095 2.092
DW = 1.868 SSE = 0.3455 R-Sq = 0.9753 Obs. = 35 F-Stat CD v. CES = 1.95 F-Stat CD v. TL = 1.78
1960-85
Parameter Estimate St. Error T-Statistic
Intercept 7.169 1.610 4.453 TIEB 0.7336 0.1550 4.732 Romstock 0.2664 0.1550 1.719 Constant 0.0580 0.0120 4.833 Restrict 0.0392 0.0202 1.921
DW = 2.030 SSE = 0.1414 R-Sq = 0.9901 Obs. = 26 Lag = 0.4191 F-Stat CD v. CES = 0.77 F-Stat CD v. TL = 1.29 136 Table 23 (continued)
Non-Ferrous
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 0.8332 4.840 0.172 TIEB 0.2267 0.3840 0.591 Romstock 0.7733 0.3840 2.014 Constant 0.1190 0.0298 4.075 Variable -0.0019 0.0004 4.675 Restrict 0.0117 0.0204 0.537
DW = 1.784 SSE * 0.3782 R-Sq = 0.9649 Obs. = 35 Lag = 0.277 F-Stat CD v. CES = 1.09 F-Stat CD v. TL =1.32
1960-85
Parameter Estimate St. Error T-Statistic
Intercept 1.162 5.298 0.219 TIEB 0.2717 0.4479 0.607 Westock 0.7283 0.4479 1.626 Constant 0.1322 0.0447 2.956 Variable -0.0020 0.0012 1.571 Restrict 0.0107 0.0073 1.499
DW = 1.844 SSE * 0.1897 R-Sq = 0.9822 Obs. = 26 Lag = 0.6207 F-Stat CD v. CES = 1.16 F-Stat CD v. TL = 2.06 137 structure of the CD model, it restricts the elasticity of substitution to be constant and equal to one. Thus, as in the case of the industrial sector, the decline in the growth rate of production in the heavy
industrial branches can not be attributed to a low elasticity of substitution. In fact, only the Electrical branch, as described by the
TL model, exhibits a low elasticity of substitution that is significantly below one. The reason for the low elasticity of substitution is that the Electrical branch is very capital intensive, so it is much more difficult to replace a unit of labor with a unit of capital. However, this only has a very minor effect on the heavy industrial group and the industrial sector since the Electricalbranch has a relatively low share of industrial output.
Secondly, the majority of the heavy industrial branches are characterized by constant returns to scale. Therefore, decreasing
returns to scale are not present and did not cause the slowdown in the growth rate of production within these branches. The acceptance of constant returns to scale reinforces the downward trendsin the growth rates of the principal inputs, which have caused the slowdown in the growth of production since it implies that the equivalent changes in the growth rates of inputs cause an equally 138 proportionate change in the growth rate of output. This in turn implies that output will grow at the rate of the slower growing input, here that is labor.
Finally, the heavy industrial branches, in both periods, exhibit decreasing growth rates of total factor productivity (TFP). This is indicated by the negative coefficient of the variable rate of TFP
(listed as “ Variable” in Table 23). The decline in the growth rates of
TFP can also be seen by comparing the estimates of TFP over the two periods. The estimates of the rates of TFP in the latter period,
1960-85, are much lower, than those of the earlier period 1951-85.
The constant rates of TFP fell sharply between the periods, which indicates that the rate of TFP was lower in 1960 than in 1951, primarily due to the negative variable rates in the 1951-85 period.
However, the more interesting fact here is that the variable rates themselves worsened between the two periods. That is, the variable rate of TFP of these branches became more negative implying that the growth of TFP was declining at a faster rate during the latter period.
The MBMW and Chemical branches experienced the greatest decline in the variable rates of TFP between the two periods. These were the same branches which experienced the greatest declines in the growth of production in the heavy industrial group while 139 maintaining the largest shares of industrial production. Thus, the
MBMW and Chemical branches were the heavy industrial branches which contributed most to the slowdown of production in the Romanian industrial sector.
Why are the rates of TFP of the heavy industrial branches declining? There are several possible explanations, the first being that the rates of TFP were extraordinarily high during the early
formative years (1950’s) of the industrial sector and therefore would naturally fall somewhat. A second possibility is that the capital implemented by Romanian industry did not meet Western standards and is generally considered poor, therefore, technological improvements were not obtained. Capital equipment is used for a much longer time in Romania than most Western nations, so capital improvements or replacements were not common occurrences.
Laborers using the older equipment could not increase their productivity as equipment was outdated and worn. A third explanation arises from the philosophy that “bigger is better.”
Romanian industrial projects of the early 1960’s stressed the size of the industrial complex, borrowed from their teachers, the
Soviets. Too large of an industrial complex leads to inefficiencies in production and management which lower the rate of TFP. The
Romanian institutional framework creates the lack of competitive 140 pressure and improper incentive structures for firms and workers,
which lowers productivity as it restricts self-assertion and
initiative. Finally, the educational level of the work force may also
play a role in the decreasing rate of TFP.
Light Industrial Group
The production function analysis of the light industrial
branches is also conducted over the two periods of 1951-85 and
1960-85. Table 24 contains the production function results of the
light industrial branches. As in the case of the heavy industrial
branches, the majority (five of the nine branches in the 1951-85
period and seven of the nine branches in the 1960-85 period) of the
light industrials branches were characterized by the CD production function. Therefore the elasticity of substitution does not play a
major role in explanation of the declining rate of industrial
production. Only the Food Processing branch, over the 1960-85
period, exhibited an elasticity of substitution significantly below one, since it is a capital intensive branch.
The branches of Woodworking and Textiles have also experienced sharp declines in the growth rates of inputs while their
percentage shares of light industrial production have remained
relatively (for the light industrial group) high, though they have 141 Table 24 Accepted Production Function Models Light Industrial Group Romania, 1951-85 and 1960-85
Food Processing
1951-85
Parameter Estimate St. Error T -S ta tistic
Intercept -30.632 13.329 2.298 TIEB -2.2660 2.3870 0.949 Romstock 4.5880 2.3560 1.947 G -0.1920 0.1030 1.858 Constant 0.0135 0.0323 0.417 Variable -0.0025 0.0023 1.063
DW = 1.501 SSE = 0.1326 R-Sq = 0.9999 Obs. = 35 Lag = 0.300 Elasticity of Substitution = 1.006 F-Stat CD v. CES = 3.64 F-Stat CD v. TL = 3.48 F-Stat CES v. TL = 0.55 1960-85 Parameter Estimate St. Error T -S ta tistic
Intercept 8.483 14.423 0.588 TIEB -0.3860 2.2890 0.167 Romstock 1.3860 2.2890 0.606 G -0.1010 0.0880 1.148 Constant 0.1329 0.0318 4.179 Variable -0.0037 0.0821 0.045
DW = 1.888 SSE = 0.1264 R-Sq = 0.9428 Obs. = 26 Elasticity of Substitution = 1.606 F-Stat CD v. CES = 4.34 F-Stat CD v. TL = 5.78 F-Stat CES v. TL = 2.13 142 Table 24 (continued)
F ur
1 9 5 1 -8 5
Parameter Estimate St. Error T-Statistic
Intercept 6.709 1.590 4.219 TIEB 0.5469 0.1340 4.081 Romstock 0.4531 0.1340 3.381 Constant 0.0319 0.0201 1.587 Restrict 0.0220 0.0131 1.679
DW= 1.438 SSE = 0.1545 R-Sq = 0.9883 Obs. = 35 Lag = 0.7976 F-Stat CD v. CES = 1.04 F-Stat CD v. TL = 2.02 1960-85
Parameter Estimate St. Error T-Statistic
Intercept 8.589 1.429 6.011 TIEB 0.8016 0.1864 4.300 Romstock 0.1984 0.1864 1.064 Constant 0.0413 0.0186 2.216 R estrict 0.0216 0.0042 5.143
DW = 1.423 SSE = 0.0453 R-Sq = 0.9895 Obs. = 26 Lag = 0.7689 F-Stat CD v. CES = 1.75 F-Stat CD v. TL = 1.85 143 Table 24 (continued)
Glass
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 9.257 3.012 3.073 TIEB 0.9205 0.2791 3.298 Romstock 0.0795 0.2791 0.285 Constant 0.0573 0.0171 3.351 Restrict 0.0085 0.0051 1.667
DW = 1.985 SSE = 0.1810 R-Sq = 0.9937 Obs. = 35 Lag = 0.4159 F-Stat CD v. CES = 1.75 F-Stat CD v. TL = 1.20 1960-85
Parameter Estimate St. Error T-Statistic
Intercept 5.134 1.852 2.772 TIEB 1.4739 0.2020 7.296 Romstock 0.0308 0.0310 0.994 Constant 0.0256 0.0128 2.000 Variable 0.0017 0.00002 8.500
DW = 1.973 SSE = 0.0945 R-Sq = 0.9952 Obs. = 26 CRS F-Stat = 5.201 (reject) F-Stat CD v. CES = 0.92 F-Stat CD v. TL = 2.33 144 Table 24 (continued)
Manufacturing
1951-85
Parameter Estimate St. Error T -S ta tis tic
Intercept 11.193 1.0013 11.179 TIEB 1.4414 0.2129 6.789 Romstock -0.4414 0.2129 2.073 K x K 0.0051 0.0025 2.004 L x L 0.0051 0.0025 2.004 K x L -0.0051 0.0025 2.004 Constant -0.0028 0.0175 0.164 Variable 0.0009 0.0004 2.158 Restrict! 0.000293 0.00013 2.163 Restrict2 0.06150 0.02657 2.314 Restrict3 0.03953 0.01747 2.262
DW = 1.832 SSE = 0.107 R-Sq = 0.9975 Obs. = 35 Elasticity of Substitution 1985 = 1.525 Elasticity of Substitution 1968 = 1.394 Elasticity of Substitution 1951 = 1.350 F-Stat CD v. CES = 5.79 F-Stat CD v. TL =11.11 F-Stat CES v. TL = 12.37 145 Table 24 (continued)
Manufacturing
1960-85
Parameter Estimate St. Error T-Statistic
Intercept 10.795 1.6025 6.736 Sal/Wage 0.9697 0.2319 4.182 Romstock 0.0303 0.2319 0.131 Constant 0.0363 0.0278 1.308 R estrict 0.0097 0.0084 1.548
DW= 1.545 SSE = 0.0687 R-Sq = 0.9910 Obs. = 26 F-Stat CD v. CES = 1.66 F-Stat CD v. TL =2.15 146 Table 24 (continued)
Paoer
1951-85
Parameter Estimate St. Error •S tatistic
Intercept -243.10 40.725 5.969 TIEB -17.008 5.6790 2.995 Romstock 30.136 4.4710 6.740 K x K -0.9160 0.2020 4.534 L x L -0.4570 0.3790 I.205 K x L 1.1790 0.5162 2.282 Constant 0.0660 0.0060 II.000
DW = 1.824 SSE = 0.374 R-Sq = 0.9992 = 35 Elasticity of Substitution 1985 = 1.000 Elasticity of Substitution 1 968 = 1.001 Elasticity of Substitution 1 951 = 1.002 F-Stat CD v. CES = 32.79 F-Stat CD v. TL = 26.76 F-Stat CES v. TL = 9.41
1960-85
Parameter Estimate St. Error •S tatistic
Intercept 10.329 3.1050 3.330 Sal/Wage 0.9336 0.2499 3.736 Romstock 0.0664 0.2499 0.267 Constant 0.0373 0.0041 9.098 Restrict 0.0092 0.0093 0.989
DW = 1.654 SSE = 0.0678 R-Sq = 0.9913 Obs. = 26 Lag = 0.4158 F-Stat CD v. CES = 1.36 F-Stat CD v. TL = 2.56 147 Table 24 (continued) Printing
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 126.038 25.838 4.878 TIEB 32.591 6.1480 5.301 Romstock -28.730 5.3100 5.402 G 1.443 0.2740 5.259 Constant -0.0275 0.0725 0.308 Variable -0.0015 0.0011 1.333 R estrict 0.0202 0.0102 1.999
DW = 1.835 SSE = 0.16143 R-Sq = 0.948 Obs. = 35 Elasticity of Substitution = 0.9883 F-Stat CD v. CES = 12.45 F-Stat CD v. TL = 6.97 F-Stat CES v. TL = 1.49 1960-85
Parameter Estimate St. Error T-Statistic
Intercept 109.587 65.349 1.678 TIEB 20.441 12.519 1.642 Romstock -19.441 12.519 1.554 G 0.956 0.0597 1.601 Constant -0.0085 0.0070 1.214 Variable -0.0011 0.0029 0.379 R estrict 0.1189 0.1139 1.044
DW = 1.835 SSE = 0.5583 R-Sq = 0.9397 Obs. = 26 Elasticity of Substitution = 0.9995 F-Stat CD v. CES = 11.23 F-Stat CD v. TL = 4.56 F-Stat CES v. TL = 1.96 148 Table 24 (continued)
Soap
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 4.394 2.582 1.702 TIEB 0.3653 0.228 2.784 Romstock 0.6347 0.228 1.602 Constant 0.0982 0.0148 6.635 Variable -0.0015 0.0004 3.750 Restrict 0.0588 0.0359 1.638
DW = 1.926 SSE = 0.3076 R-Sq = 0.9841 Obs. = 35 Lag = 0.3043 F-Stat CD v. CES = 1.65 F-Stat CD v. TL =1.92 1960-85
Parameter Estimate St. Error T-Statistic
Intercept 8.744 1.260 6.940 Sal/Wage 0.7069 0.1220 7.061 Westock 0.2931 0.1220 2.402 Constant 0.0238 0.0064 3.179 R estrict 0.1104 0.0439 2.515
DW= 1.572 SSE = 0.1782 R-Sq = 0.9821 Obs. = 26 F-Stat CD v. CES = 1.15 F-Stat CD v. TL =1.55 Table 24 (continued)
Textiles
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 7.0069 0.9974 7.025 TIEB 0.6580 0.1042 6.426 Romstock 0.3420 0.1042 3.282 Constant 0.0675 0.0059 11.941 Variable -0.0005 0.00014 5.571 Restrict 0.0492 0.0082 6.000
DW= 1.905 SSE = 0.0463 R-Sq = 0.9978 Obs. = 35 Lag = 0.5206 F-Stat CD v. CES = 2.88 F-Stat CD v. TL =1.56 1960-85
Parameter Estimate St. Error T-Statistic
Intercept 6.742 1.8117 3.722 TIEB 0.6227 0.2115 2.994 Romstock 0.3773 0.2115 1.784 Constant 0.0567 0.2831 2.003 Variable -0.0008 0.00046 1.739 R estrict 0.00495 0.0047 1.053
DW = 1.711 SSE = 0.0294 R-Sq = 0.9954 Obs. = 26 Lag = 0.6352 150 Table 24 (continued)
Woodworking
1951-85
Parameter Estimate St. Error T-Statistic
Intercept 8.258 0.9850 8.382 TIEB 0.8430 0.0995 8.464 Romstock 0.1570 0.0995 1.580 Constant 0.0548 0.0054 10.217 Restrict 0.0211 0.0016 13.188
DW = 2.125 SSE =0.0627 R-Sq = 0.9909 Obs. = 35 Lag = 0.4067 F-Stat CD v. CES = 0.97 F-Stat CD v. TL = 0.95 1960-85
Parameter Estimate St. Error T-Statistic
Intercept 2.638 1.946 1.356 TIEB 0.7267 0.1853 3.988 Westock 0.2733 0.1853 0.938 Constant 0.0306 0.0111 2.758 Variable -0.0006 0.0002 3.000 R estrict 0.0025 0.0024 1.042
DW = 1.845 SSE = 0.0485 R-Sq = 0.9882 Obs. = 26 Lag = 0.5302 F-Stat CD v. CES = 1.77 F-Stat CD v. TL = 2.35 151 declined since 1960. Therefore, the Textile and Woodworking branches have contributed the most (although not by the same amount as the MBMW and Chemical branches), of the light industrial branches, to the decline in the growth rate of industrial production.
These branches’ experiences is mainly due to the philosophy of promoting the heavy industrial branches, as resources were diverted away from the light industrial branches to those of heavy industry.
If a generalization is to be made concerning TFP and the light industrial branches, it must be that the growth rates of TFP in the light industrial branches are lower than those of the heavy industrial branches and that they decline at a slower rate than those of the heavy industrial branches.
The rate of TFP was estimated by two separate methods. First, the regression method estimated the rate of TFP as a function of time, as described in Chapter 2. The second method, referred to as the Solow method, estimated the growth rate of TFP from equation
10 of Chapter 3, which actually derives the yearly growth rate of the residual. Both methods were used to estimate the rate of TFP for each of the industrial branches.
The regression estimates are contained in Tables 23 (heavy industry) and 24 (light industry). The branches which are described only by a constant rate of TFP have their respective Solow method 152 estimates, in five year averages, (corresponding to the five year
plans) and the Solow midpoint averages and regression estimates
listed in Table 25. The regression and Solow midpoint average
estimates are approximately equivalent to one another. It is
interesting to note, however, that even these branches described by
a constant rate of TFP (regression estimates) have Solow estimates
that actually decline overtime. This suggests that these branches
may also be affected, at least slightly, by a declining rate of TFP.
The industrial branches best described by a constant and
variable rate (regression estimate) of TFP have their respective
yearly Solow method estimates of TFP, along with the regression
estimates, depicted in Figures 27-40 (heavy industry) and 41-50
(light industry, both of which are located in the Graphical Appendix).
The comparison between the regression and Solow method estimates
indicate that the Solow method estimates are rather noisy. The
majority of the heavy industrial branches (with both the constant
and variable rates of TFP) exhibit declining growth rates of TFP.
This is indicated by by the regression estimates, the negative
variable rate of TFP (Table 23), and the declining Solow estimates
over time. This downward trend in the Solow estimates continues
through 1985 except for the Fuel, Electrical and Mining branches.
Though these branches see an increase over the 1981-85 period in 153 the rate of TFP their share of industrial production is so small that it has little effect on the downward spiral of the industrial sector.
Those light industrial branches described by both a constant and variable rate of TFP have their regression and Solow method estimates of TFP presented in Figures 41-50 (which are located in the Graphical Appendix). As can be seen from the figures, these branches also exhibit declining growth rates of TFP, evidenced by the negative variable rates of TFP (Table 24), with the lone exceptions being the Manufacturing branch. The Solow method estimates, which are noisier than those of the heavy industrial branches, also indicate that the growth rate of TFP is declining in the light industrial branches. Though the light industrial branches contribute less to industrial output than those of heavy industry, they do contribute to the declining rate of TFP of the industrial sector.
The Manufacturing branch, which has one of the largest shares of light industrial production, has experienced an increase in its rate of TFP between the two periods. Therefore, the growth of TFP in the Manufacturing branch did not contribute to the decline of industrial production at all, since it was able to obtain greater production levels with the same amount of resources. 154 Table 25 Regression and Solow Method Estimates of Total Factor Productivity Of Industrial Branches Characterized By a Constant Rate of Total Factor Productivity (Regression) Five Year Midpoint Averages
Industrial Branch
Year PaDer 1951 Fur 1951 Fur 1960
1951-55 6.23 7.96 1956-60 16.68 8.27 1961-65 27.17 8.14 5.68 1966-70 15.96 3.01 0.39 1971-75 11.25 5.33 6.73 1976-80 8.10 3.25 5.45 1981-85 3.01 -0.45 0.63 Midpoint 12.82 2.44 3.79
Year Pacer 1960 Fuel 1960 SoaD 1960
1961-65 7.75 -1.51 4.91 1966-70 11.84 -1.22 0.83 1971-75 5.56 -0.79 2.44 1976-80 7.20 -6.74 4.60 1981-85 0.64 10.92 4.19 Midpoint 6.54 0.02 3.61
Year Minina 1960 Food 1960
1961-65 7.96 8.03 1966-70 8.27 16.22 1971-75 8.14 7.79 1976-80 3.01 6.97 1981-85 8.68 1.70 Midpoint 6.51 10.54 155
Two general observations can be drawn from these figures. The
first, as shown by the negative variable rates of TFP, is that the growth rates of TFP are declining. Further support of this is shown
by the downward trend of the Solow method estimates of TFP.
Secondly, the majority of the Solow method estimates of TFP fall within the 95 percent confidence intervals of the regression estimates indicating the close approximation of the two estimates of TFP. Finally, the reasons for the declining rate of TFP in the light
industrial branches stems from the priority of the heavy industrial
branches and the stagnant technological and educational development within these branches.
Romanian and Soviet Comparison: Industrial Branches
How do the above results concerning the Romanian industrial branches compare to the industrial experience of the Soviet Union
(the only other country with a command economy whose industrial branches have been studied) Desai (1987) examined the growth patterns of the industrial branches of the Soviet Union to determine the factors causing the slowdown in the growth rate of production in
Soviet industry. Desai’s results are replicated in Table 26. Desai examined the Soviet industrial branches using only the CD and CES
production functions. She found that the CD model best described the Table 26 Accepted Production Function Models Soviet Industrial Branches Estimated By Padma Desai Soviet Union, 1950-79, 1960-79
Electrical 1950-79 Parameter Estimate St. Error T-Statistic
Intercept -0.3408 0.1981 1.720 Labor 0.3849 0.1170 3.289 Capital 0.6151 0.1170 5.255 Constant 0.0283 0.0042 6.690
DW= 1.4498 SSE = R-Sq = 0.9962 Lag = 0.8186
Fuel 1950-79
Parameter Estimate St. Error T-Statistic
Intercept -0.4677 0.2869 1.630 Labor 0.1315 0.1315 1.000 Capital 0.8685 0.1315 6.600 Constant 0.0488 0.0197 2.480 Variable -0.0013 0.0003 3.880
DW = 1.2683 SSE = R-Sq = 0.9702 Lag = 0.8353 Table 26 (continued)
Ferrous 1950-79
Parameter Estimate St. Error T-Statistic
Intercept -0.2582 0.2245 1.150 Labor 0.5192 0.1042 4.983 Capital 0.4808 0.1042 4.610 Constant 0.0203 0.0149 1.360 Variable -0.00034 0.0002 1.640
DW = 1.5528 SSE = R-Sq = 0.9919 Lag = 0.5919
MBMW 1960-79 Parameter Estimate St. Error T-Statistic
Intercept -1.0748 0.2418 4.446 Labor 0.6363 0.1749 3.638 Capital 0.3637 0.1749 2.079 Constant 0.0512 0.0117 4.373
DW = 0.9726 SSE = 0.0046 R-Sq = 0.9986
Construction Material 1960-79
Parameter Estimate St. Error T-Statistic
Intercept -1.0099 0.1297 7.786 Labor 0.1312 0.1286 1.020 Capital 0.8688 0.1286 6.754 Constant 0.0980 0.0106 9.244 Variable -0.0024 0.0003 7.861
DW = 1.9444 SSE = 0.0018 R-Sq = 0.9900 Lag = 0.5958 158
Table 26 (continued)
Woodworking 1960-79
Parameter Estimate St, Error T-Statistic
Intercept -1.8308 0.3115 5.880 Labor 0.3937 0.1184 3.325 Capital 0.6063 0.1184 5.117 Constant 0.1477 0.0264 5.597 Variable -0.0030 0.0006 5.000
DW = 2.136 SSE = 0.0027 R-Sq = 0.9511 Lag = 0.7783
Food Processing 1950-79
Parameter Estimate St. Error T-Statistic
Intercept -0.6148 0.1388 4.429 Labor 0.7282 0.0732 9.948 Capital 0.2718 0.0732 3.713 Constant 0.0383 0.0097 3.959 Variable -0.00043 2.896
DW = 2.207 SSE = 0.0110 R-Sq = 0.9934 Lag = 0.3549
Notes and Sources: Data taken directly from Desai {1987, pp.83-85). Desai accepted the CD production function for all of the branches she examined. The labor and capital variable refer to official Soviet industrial Capital and Labor data and therefore are comparable to the Romanian data employed in this study. The terms Constant and Variable refer to the constant and variable rates of total factor productivity. The DW term represents the Durbin Watson statistic, SSE represents the sum of the squared errors, the R-Sq term is the goodness of fit of the regression, and the lag variable is the correction factor used to adjust for autocorrelation. 159 production processes of the Soviet industrial branches. The lone exception was the Chemical branch for which no specification could be accepted.
The comparison of Romanian and Soviet industrial branches is limited to six heavy industrial branches (all but Mining and Non-
Ferrous) and two light industrial branches (Food Processing and
Woodworking). These were the only Soviet branches for which Desai found data. Desai’s acceptance of the CD production function is similar to that of the Romanian industrial branches. The majority of the Soviet industrial branches, heavy and light, have experienced decreasing growth rates of output. Given that the CD model has been accepted the elasticity of substitution is assumed not to contribute to these declines. Another similarity is that the industrial branch of
Romania and the Soviet Union indicate the presence of constant returns to scale. This finding rules out decreasing returns to scale as a factor in the decline of output growth and it also reinforces the direct effect of input growth on production growth.
The rates of TFP of the heavy industrial branches of Romania and the Soviet Union are also similar in that they are described by negative variable rates of TFP. Thus, the growth rates of TFP of these branches is declining over time and this contributes to the declining growth rate of industrial production in both Romania and 160 the USSR. Also, the Soviet industrial branches which are best described by a constant rate of TFP only are relatively low as are those of the Romanian branches. Generally, the industrial branches of Romania and the Soviet Union are best described by the CD production function, exhibit constant returns to scale and have declining growth rates of TFP. However, the growth rates of TFP are higher than those of Soviet industry, though the Romanian rates decline faster than those of the Soviet branches. The same systemic and institutional philosophies of Romanian and Soviet industry had led to the similarities of their industrial production development.
Regional Analysis
The regional analysis is based on the general characteristics exhibited by groupings of regions, termed here as territories. Table
27 lists the five territories and the regions comprising them. The five territories are the Northwest (3), Northeast (3), Central (3),
Southwest (3) and Southeast (5). (Parentheses indicate the number of regions in each territory.)
Output
The growth rate of industrial production followed a cyclical pattern, as shown in Chapter 3, and four of the five territories also experienced a similar pattern of growth. This can be seen by Table 27 The Industrial Regions Comprising The Romanian Territories
Central
1. Brasov 2. Hunedoara 3. Mures
Northeast
4. Bacau 5. Iasi 6. Suceava
Northwest
7. Cluj 8. Crisana 9. Maramures
Southeast
10. Bucharest (city) 11. Dobrogea 12. Galati 13. Ilfov 14. Ploiesti
Southwest
15. Arges 16. Banat 17. Olt 162 examining Table 28 which presents the five year average (midpoint) growth rates of production for the territories and regions. The year to year growth rates of output, as well as the inputs, are presented in Figures 51-67 (found in the Graphical Appendix), for each region with the territories. Only the Central territory did not follow this pattern as it experienced a steady decline in its growth rate of production.
To assess the effect each territory’s growth rate of production on the growth rate of industrial production, presented in Table 29, are the territorial shares of industrial production must be considered. The Southeast territory comprises approximately 38 percent of overall industrial production. During the 1970’s, the period in which the growth rate of industrial production slowed, the growth rate of production of the Southeast territory fell from 14.4 percent to 8.5 percent (the sharpest decline of all territories). This decrease was due to the extremely sharp decreases in the growth rates of production in Bucharest and Galati, where the largest concentrations of the Fuel and Electrical, Chemical and MBMW branches are located.
The Northeast and Southwest territories saw declines of 5 and
4 percentage points, respectively, in their growth rates of production during the 1970’s. The share of industrial production of 163 Table 28 Growth Rates of Industrial Production By Industrial Territory and Region Romania, Five Year Midpoint Averages, 1 9 60-85
Reoion 1961-65 1 966-70 1 971-75 1 976-80 1981-85
Arges 17.5 19.1 16.6 13.4 6.2 Banat 13.6 8.2 10.9 10.6 -0.2 Olt 16.9 17.6 16.0 7.5 3.5
S outhw est 15.1 13.1 14.0 10.4 3.0
Brasov 10.8 11.8 13.7 10.0 -1.7 Hunedoara 17.5 11.3 5.8 7.5 3.2 Mures 16.4 11.4 12.0 9.7 1.5
C en tral 13.5 11.5 11.1 9.3 0.0
Cluj 13.5 12.0 14.3 10.9 4.0 Crisana 14.1 12.7 13.7 9.6 1.9 Maramures 17.6 8.6 11.3 12.9 2.7
Northwest 14.8 11.1 13.2 11.1 3.1
Bacau 17.0 9.6 12.1 7.4 6.2 Iasi 19.6 16.0 17.1 8.8 2.3 Suceava 20.9 9.5 12.3 10.6 3.0
N ortheast 18.1 11.2 13.7 8.3 4.2
Bucharest 11.5 11.8 13.9 5.4 0.0 Dobrogea 18.5 13.7 14.3 11.4 6.4 Galati 16.1 17.2 17.0 9.9 1.8 Ploiesti 9.4 10.7 14.2 10.2 5.6 llfov 26.1 7.7 13.3 16.7 4.5
Southeast 12.7 12.1 14.4 8.5 3.0 164 Table 29 Percentage Shares of Industrial Production By Industrial Territory and Region Romania, Selected Years, 1960-85
Reaion 1960 1970 1980 1985
Arges 3.11 4.83 6.51 7.76 Banat 9.89 8.18 7.77 6.80 Olt 3.71 5.38 5.56 5.87
S o u th w e st 16.71 18.39 19.83 20.42
Brasov 12.96 11.04 11.59 9.45 Hunedoara 6.732 6.93 4.47 4.60 Mures 3.81 4.09 3.88 3.70
C e n tra l 23.09 22.07 19.95 17.75
Cluj 4.41 4.32 4.82 5.20 Crisana 2.60 2.70 2.78 2.70 Maramures 3.11 3.01 3.15 3.20
N o rth w e s t 10.12 10.02 10.75 11.11
Bacau 5.82 5.94 5.12 6.09 Iasi 2.31 3.46 3.86 3.83 Suceava 2.11 2.42 2.45 2.51
N o rth e a s t 10.24 11.82 11.44 12.44
Bucharest 20.22 17.80 15.15 13.46 Dobrogea 1.98 2.58 2.94 3.53 Galati 4.42 6.03 7.24 6.96 llfo v 1.80 2.39 3.12 3.45 Ploiesti 11.42 8.90 9.57 10.87
S o u th e a s t 39.84 37.70 38.03 38.28 165 the Southwest territory steadily increased over time, thus, as its
share of production increased and its growth rate of production7
decreased. This indicates a significant portion of the slowdown in
industrial production was centered in the Southwest territory. The
Southwest territory contains major industrial complexes of MBMW
steel production (in fact the district of Hunedoara produced 65% of
Romanian steel in the early 1970’s) and mining8.
The Northeast territory, where paper and wood production are
the most important industries (followed by chemical production),
provides a relatively low share of industrial production, and hence
its decreasing growth rate of production made a small contribution to the slowdown in industrial production. Meanwhile, the Central,
though its largest and most important branch is MBMW, and
Northwest since its only resources are non-ferrous materials, territories experienced relatively small declines in their growth rates of industrial production. Thus, these territories only made small contributions to the decline in the industrial growth rate of production.
The analysis suggests that the decline in the industrial growth rate of production was primarily centered in the Southeast and
Southwest territories. This is so because these territories provide
7Turnock, p. 196. 166 the majority of industrial production and experienced the sharpest
decline in the growth rate of production.
Capital
The growth rate of industrial capital stock steadily increased
until 1972 and declined thereafter. Table 30 presents the five year
average (midpoint) growth rates of industrial capital stock of the
territories and regions.
The Northeast and Southwest territories follow the exact
pattern of capital growth of the industrial sector as their five year
average growth rates increase until 1970 and decline afterwards.
The remaining territories’ growth rates of industrial capital stock
their decline in the 1966-70 period.
The Southwest territory experienced the greatest decline in
the growth rate of industrial capital between 1970 and 1980. It also
has the second highest share of industrial capital stock, shown in
Table 31. Therefore, the decline in the industrial capital stock was
centered in the Southwest territory. The cause of the decline in the
growth rate of capital was due to the Romania’s austerity program which eradicated the foreign debt, but led to cutbacks throughout all
areas of the economy. The Southwest experienced sharper declines
as a prejudice of the regime, so the Southeast territory would not suffer as greatly. The remaining territories also experienced a 167 Table 30 Growth Rates of Industrial Capital Stock By Industrial Territory and Region Romania, Five Year Midpoint Averages, 1960-85
Reaion 1961-65 1966-70 1971-75 1976-80 1981-85
Arges 9.8 21.1 19.2 9.1 9.5 Banat 8.2 8.8 11.0 12.5 9.6 Olt 11.7 18.2 17.2 14.2 13.8
Southwest 9.7 16.0 16.3 11.8 11.4
Brasov 8.0 12.1 15.2 11.8 8.3 Hunedoara 7.3 17.2 8.4 9.6 9.5 Mures 11.8 13.0 13.3 8.8 7.2
Central 8.3 14.2 11.9 10.4 8.5
Cluj 11.3 14.5 12.2 13.0 13.5 Crisana 9.3 13.9 13.9 10.8 10.7 Maramures 13.1 10.6 11.0 11.4 7.2
Northwest 11.0 13.3 12.4 11.9 11.2
Bacau 6.5 7.2 11.9 9.3 7.3 Iasi 10.5 12.3 15.4 14.7 9.2 Suceava 11.2 13.5 11.0 13.8 10.6
N orth ea st 7.8 9.1 12.5 11.5 8.5
Bucharest 7.0 10.9 8.1 8.3 6.1 Dobrogea 6.8 10.2 8.9 12.4 12.2 Galati 11.1 16.5 11.8 10.9 6.9 llfov 15.5 14.3 11.7 10.2 12.5 Ploiesti 6.1 11.4 14.9 12.2 7.6
Southeast 8.8 12.5 10.9 10.6 8.3 168 Table 31 Percentage Shares of Industrial Capital Stock By Industrial Territory and Region Romania, Selected Years, 1960-85
Reqion 1960 1970 1980 1985 Arges 4.69 7.63 8.40 8.56 Banat 6.36 5.05 5.03 5.14 Olt 4.79 7.18 9.75 11.48
S o u th w e st 15.84 19.86 23.17 25.1 8
Brasov 6.44 6.11 6.79 6.60 Hunedoara 7.17 7.48 5.95 6.07 Mures 2.76 3.26 2.89 2.67
Central 16.37 16.85 15.63 15.34
Cluj 3.30 3.80 4.98 5.06 Crisana 2.80 3.09 3.14 3.34 Maramures 2.36 2.16 2.00 1.84
Northwest 8.46 9.05 10.12 10.24
Bacau 10.97 7.38 6.36 6.15 Iasi 2.56 2.69 3.57 3.53 Suceava 1.97 2.18 2.37 2.43
N o rth e a s t 15.49 12.26 12.58 12.1 1
Bucharest 16.51 13.28 9.67 8.65 Dobrogea 6.56 5.16 4.82 5.46 Galati 7.14 10.05 9.53 8.96 llfo v 3.62 5.08 4.80 5.35 Ploiesti 10.01 8.41 9.53 8.96
S o u th e a s t 43.84 41.98 38.50 37.13 169 decrease in their five year average growth rates of industrial capital, however the largest decline was only 1.5 percentage points
(Central). Therefore, these territories contributed proportionately
(to their shares of industrial capital stock) to the decline of capital growth.
Labor
Industrial labor growth was characterized by a cyclical growth pattern as described in Chapter 3. Each of the five territories also exhibited a similar pattern of labor growth. Table 32 presents the five year average (midpoint) growth rates of industrial labor of the territories and the regions. Each territory’s growth rate of industrial labor fell during the 1966-70 period, increased in the
1971-75 period and declined thereafter. The Southeast and
Southwest territories experienced the greatest decline, approximately 4 percentage points, in industrial labor growth during the 1970’s. The remaining territories experienced declines of 1.2 to
1.8 percentage points.
Table 33 presents the shares of industrial labor present within each territory and region. The combination of the Southeast and
Southwest territories comprises approximately 55 percent of all industrial labor. These territories experienced the sharpest decline in the growth rate of industrial labor. Thus, the decline in the 170 Table 32 Growth Rates of Industrial Labor By Industrial Territory and Region Romania, Five Year Midpoint Averages, 1960-1985
Reaion 1961-65 1966-70 1971-75 1 9 7 6 -8 0 1 9 8 1 -8 5
Arges 9.4 7.2 10.2 3.8 2.3 Banat 5.0 2.3 5.1 2.5 -0.2 Olt 9.9 7.7 7.9 5.2 3.4
Southwest 6.9 4.7 7.2 3.6 1.6
Brasov 6.9 3.6 5.6 3.0 0.8 Hunedoara 5.5 1.3 2.2 3.2 2.0 Mures 6.7 5.1 6.5 3.2 1.6
C e n tra l 6.4 3.2 4.9 3.1 1.2
Cluj 5.6 4.8 6.3 4.2 2.5 Crisana 6.0 5.8 5.8 2.6 1.4 Maramures 7.2 3.9 5.5 5.0 1.5
Northwest 6.2 4.8 5.9 4.0 2.8
Bacau 7.3 2.9 6.0 4.4 1.9 Iasi 9.4 9.2 8.5 6.8 3.5 Suceava 7.3 5.9 5.7 5.2 3.2
N o rth e a st 7.7 5.2 6.5 5.3 2.8
Bucharest 5.5 3.2 5.0 1.3 -0.3 Dobrogea 8.5 6.3 7.1 4.3 1.3 Galati 7.6 6.9 6.7 4.2 3.0 llfov 11.5 10.7 11.8 1.2 2.5 Ploiesti 4.8 3.3 7.5 4.7 1.3
Southeast 6.0 4.4 6.6 2.8 1.0 171 Table 33 Percentage Shares of Industrial Labor By Industrial Territory and Region Romania, Selected Years, 1960-85
Reaion 1960 1970 1980 1985
Arges 3.53 4.65 5.66 5.90 Banat 10.76 9.10 8.22 7.52 O lt 3.45 4.70 5.51 6.04
S o u th w e s t 17.75 18.45 19.38 19.46
Brasov 11.33 11.18 10.63 10.26 Hunedoara 6.75 5.57 4.51 4.64 Mures 3.92 4.16 4.15 4.08
C e n tra l 21.99 20.91 19.29 18.98
Cluj 5.94 5.81 6.02 6.32 Crisana 3.53 3.73 3.49 3.47 Maramures 3.86 3.92 4.06 4.05
Northwest 13.33 13.46 13.57 13.84
Bacau 5.38 5.18 5.32 5.42 Iasi 2.09 3.00 3.85 4.25 Suceava 2.89 3.20 3.37 3.69
Northeast 10.36 11.38 12.55 13.36
Bucharest 18.64 16.90 14.33 13.09 Dobrogea 2.26 2.68 2.90 2.87 Galati 3.94 4.65 4.91 5.28 llfo v 1.69 2.81 3.22 3.38 P loiesti 10.04 8.76 9.84 9.73
S o u th e a s t 36.57 35.80 35.21 34.36 172 growth of industrial labor during the 1970’s was centered in the
Southeast and Southwest territories.
Summary of Direct Effects
The above analysis of the direct effects of the growth rates of the territories and regions suggests that the slowdown in growth of industrial production was centered in the Southeast and Southwest territories. The regions which have contributed most to this decline in the growth rate of production were Bucharest (SE), Galati (SE), and Olt (SW). These regions experienced sharp decreases in the growth rate of production while comprising 30 percent of overall industrial production.
Indirect Effects: Regions
The analysis of the indirect effects, at the regional level, are assessed with reference to specific region to emphasize the result, otherwise the description is general and remains at the territory level. The regions are examined over the 1960-85 (26 years) period only because of the availability of the data. The production functions which best describe each region individually are listed in Tables 34-
38, by territories. Only one of the five territories, the Northwest territory, has each of its regions described by the same production function, the CES. Each of these regions has an elasticity of substitution that is approximately equal to one. Therefore, a low 173 Table 34 Accepted Production Function Models Southwest Territory Regions of Arges, Banat and Olt Romania 1960-85
Arges
Parameter Estimate St. Error T-Statistic
Intercept 4.833 2.927 1.651 TIER 0.5395 0.3053 2.667 Romstock 0.5066 0.1899 1.767 Constant 0.0380 0.0196 1.941
DW = 1.631 R-Sq = 0.9960 SSE = 0.0902 Obs. = 26 Lag = 0.387 F-Stat CD v. CES = 0.91 F-Stat CD v. TL =1.78 CRS test t-stat = 1.534 174 Table 34 (continued)
Banat
Parameter Estimate St. Error T-Statistic
Intercept -13.003 4.958 2.632 TIER -0.5652 0.268 2.107 Romstock 1.5652 0.268 5.834 KxK 0.0082 0.00332 2.469 LxL 0.0082 0.00332 2.469 KxL 0.0082 0.00332 2.469 Constant -0.00525 0.0251 0.209 Variable -0.00399 0.00063 6.310 Restrictl 0.00238 0.00442 0.539 Restrict2 0.00073 0.00216 0.336 Restrict3 0.00018 0.00007 2.764
DW= 1.553 R-Sq = 0.9996 SSE = 0.0264 Obs. « 26 Lag = 0.581 Elasticity of Substitution 1985 = 1.23 Elasticity of Substitution 1972 = 1.20 Elasticity of Substitution 1985 = 1.18 F-Stat CD v. CES = 7.24 F-Stat CD v. TL == 5.91 F-Stat CES v. TL = 3.42 175 Table 34 (continued)
Olt
Parameter Estimate St. Error T-Statistic
Intercept 5.959 5.959 1.000 TIER 0.2034 0.2669 0.762 Romstock 0.7965 0.2669 2.985 KxK -0.0067 0.00451 1.495 LxL -0.0067 0.00451 1.495 KxL -0.0067 0.00451 1.495 Constant 0.1864 0.0723 2.575 Variable -0.0022 0.00058 3.296 R e s tric tl 0.00029 0.00012 2.351 R estrict2 0.0120 0.00473 2.543 Restrict3 0.00852 0.00333 2.564
DW = 1.798 R-Sq = 0.9991 SSE = 0.0264 Obs. = 26 Elasticity of Substitution 1985 = 0.452 Elasticity of Substitution 1972 = 0.397 Elasticity of Substitution 1985 = 0.327 F-Stat CD v. CES = 55.74 F-Stat CD v. TL =91.00 F-Stat CES v. TL =1 5.79 176 Table 35 Accepted Production Function Models Central Territory Regions of Brasov, Hunedoara and Mures Romania 1960-85
Brasov
Parameter Estimate St. Error T-Statistic
Intercept 0.0929 3.983 0.023 TIER -0.1981 0.2398 0.826 Romstock 1.1981 0.2398 4.996 KxK -0.0023 0.00356 0.646 LxL -0.0023 0.00356 0.646 KxL -0.0023 0.00356 0.646 Constant 0.0885 0.0348 2.546 Variable -0.0022 0.00091 2.423 R estrictl 0.0003 0.00012 2.599 Restrict2 0.0076 0.00415 1.836 Restrict3 0.0043 0.00236 1.856
DW= 1.815 R-Sq = 0.9978 SSE = 0.0272 Obs. *» 26 Lag = 0.625 Elasticity of Substitution 1985 = 0.940 Elasticity of Substitution 1972 = 0.941 Elasticity of Substitution 1985 = 0.941 F-Stat CD v. CES = 4.79 F-Stat CD v. TL == 10.63 F-Stat CES v. TL = 9.48 177 Table 35 (continued)
Hunedoara
Parameter Estimate St. Error T-Statistic
Intercept -63.32 50.55 1.253 TIER -11.670 8.638 1.343 Romstock 12.670 8.638 1.459 G -0.5379 0.372 1.445 Constant 0.0727 0.0252 2.878 Variable 0.0003 0.0016 0.216 R estrict 0.00035 0.0009 0.376
DW = 1.887 R-Sq = 0.9934 SSE = 0.0913 Obs. = 26 Lag = 0.677 Elasticity of Substitution = 1.0073 F-Stat CD v. CES = 29.43 F-Stat CD v. TL = 30.27 F-Stat CES v. TL =2.45 178 Table 35 (continued)
Mures
Parameter Estimate St. Error T-Statistic
Intercept -165.47 126.11 1.312 TIER -28.563 21.97 1.300 Romstock 29.563 21.97 1.345 G -1.231 0.954 1.290 Constant -0.0312 0.0658 0.470 Variable 0.0016 0.00202 0.815 R estrict 0.0066 0.0033 2.000
DW= 1.762 R-Sq = 0.9955 SSE = 0.0694 Obs. = 26 Elasticity of Substitution = 1.00292 F-Stat CD v. CES = 4.03 F-Stat CD v. TL = 8.27 F-Stat CES v. TL = 2.42 179 Table 36 Accepted Production Function Models Northwest Territory Regions of Cluj, Crisana and Maramures Romania 1960-85
Clui
Parameter Estimate St. Error T-Statistic
Intercept 38.846 8.424 4.611 TIER 0.046 0.213 0.217 Romstock 0.831 0.445 2.865 G 0.0028 0.005 0.502 Constant 0.1930 0.0390 4.940
DW = 1.472 R-Sq = 0.9954 SSE = 0.0269 Obs. = 26 Lag = 0.487 Elasticity of Substitution = 1.1474 F-Stat CD v. CES = 6.84 F-Stat CD v. TL == 5.20 F-Stat CES v. TL = 1.97 CRS test t-stat = = 0.327
Crisana
Parameter Estimate St. Error T-Statistic
Intercept -5.432 51.56 0.105 TIER -0.912 9.297 0.098 Romstock 1.912 9.297 0.206 G -0.0386 0.419 0.092 Constant 0.0393 0.0255 1.540 Variable -0.0075 0.0022 1.512 Restrict 0.0002 0.0005 0.400
DW = 1.515 R-Sq = 0.9852 SSE = 0.0554 Obs. - 26 Lag = 0.581 Elasticity of Substitution = 1.0463 F-Stat CD v. CES = 7.91 F-Stat CD v. TL == 10.13 F-Stat CES v. TL = 2.56 180 Table 36 (continued)
Maramures
Parameter Estimate St. Error -S ta tistic
Intercept -19.103 8.031 2.379 TIER 0.4389 0.425 3.533 Romstock 1.6982 0.481 1.033 Constant -0.0896 0.0389 2.302
DW — 1.406 R-Sq = 0.9980 SSE = 0.1689 Obs. = 26 Lag = 0.358 F-Stat CD v. CES = 0.34 F-StatCDv. TL =1.81 CRS test t-stat = 0.213 181 Table 37 Accepted Production Function Models Northeast Territory Regions of Bacau, Iasi and Suceava Romania 1960-85
Bacau
Parameter Estimate St. Error -S ta tistic
Intercept 6.569 5.129 1.281 TIER 0.2603 0.210 1.238 Romstock 0.7397 0.210 3.519 KxK 0.00591 0.0038 1.548 LxL 0.00591 0.0038 1.548 KxL 0.00591 0.0038 1.548 Constant 0.1506 0.0277 5.424 Variable -0.0018 0.0009 2.011 R estrict! 0.00021 0.00010 2.152 Restrict2 0.00221 0.00333 0.674 Restrict3 0.00082 0.00235 0.352
DW = 1.692 R-Sq = 0.9972 SSE = 0.0353 Obs. = 26 Elasticity of Substitution 1985 = 1.11 Elasticity of Substitution 1972 = 1.15 Elasticity of Substitution 1985 = 1.28 F-Stat CD v. CES = 9.98 F-Stat CD v. TL == 14.13 F-Stat CES v. TL = 3.58 182 Table 37 (continued)
Suceava
Parameter Estimate St. Error T-Statistic
Intercept 9.2685 2.451 3.781 TIER 0.8672 0.2164 4.008 Westock 0.1328 0.2164 0.613 Constant 0.1046 0.0168 3.331 Variable -0.0022 0.00065 3.781 R estrict -00443 0.00312 1.498
DW = 1.398 R-Sq = 0.9925 SSE - 0.0731 Obs. = 26 Lag = 0.6055 F-Stat CD v. CES = 0.80 F-Stat CD v. TL := 1.70 183 Table 37 (continued)
Iasi
Parameter Estimate St. Error -S ta tistic
Intercept 3.904 4.382 0.891 TIER 0.4708 0.268 1.505 Romstock 0.5292 0.268 1.691 KxK 0.00172 0.00402 0.430 LxL 0.00172 0.00402 0.430 KxL 0.00172 0.00402 0.430 Constant 0.00825 0.0418 1.975 Variable -0.00308 0.00054 5.632 R estrictl 0.01664 0.00935 1.780 Restrict2 0.01357 0.00727 1.869 Restrict3 0.00057 0.00039 1.503
DW = 1.665 R-Sq = 0.9895 SSE = 0.0764 Obs. = 26 Lag = 0.606 Elasticity of Substitution 1985 = 0.907 Elasticity of Substitution 1972 = 0.911 Elasticity of Substitution 1985 = 0.916 F-Stat CD v. CES = 20.06 F-Stat CD v. TL *= 16.63 F-Stat CES v. TL = 5.57 184 Table 38 Accepted Production Function Models Southeast Territory Regions of Bucharest, Dobrogea, Galati, llfov and Ploiesti Romania 1960-85
Bucharest
Parameter Estimate St. Error T-Statistic
Intercept 12.470 11.072 1.126 TIER 0.5020 0.402 1.247 Romstock 0.4980 0.402 1.237 KxK 0.0081 0.0079 1.021 LxL 0.0081 0.0079 1.021 KxL 0.0081 0.0079 1.021 Constant 0.1441 0.098 1.470 Variable -0.0011 0.0010 1.069 R estrictl 0.00023 0.00009 2.470 Restrict2 0.00345 0.00306 1.129 Restrict3 0.0027 0.00201 1.342
DW = 1.880 R-Sq = 0.9961 SSE * 0.0282 Obs. = 26 Lag = 0.443 Elasticity of Substitution 1985 = 0.525 Elasticity of Substitution 1972 = 0.397 Elasticity of Substitution 1985 = 0.142 F-Stat CD v. CES = 26.57 F-Stat CD v. TL == 19.43 F-Stat CES v. TL = 12.17 185 Table 38 (continued)
G a la ti
Parameter Estimate St. Error T-Statistic
Intercept 2.621 2.261 0.973 TIER 0.2356 0.201 1.167 Westock 0.7644 0.201 3.789 Constant 0.0559 0.0199 2.812 Variable -0.00168 0.00056 1.159 R estrict -0.00855 0.00877 0.975
DW - 1.681 R-Sq = 0.9960 SSE = 0.1326 Obs. = 26 Lag = 0.358 F-Stat CD v. CES = 0.76 F-Stat CD v. TL =2.53
I l f ov
Parameter Estimate St. Error T-Statistic
Intercept 4.6175 1.223 3.743 TIER 0.4751 0.0869 4.806 Westock 0.5249 0.0869 5.462 Constant 8.81 E-11 1.83E-11 4.086 Variable -2.32E-21 1.22E-22 6.046 R estrict 0.00002 0.00002 1.000
DW = 1.627 R-Sq = 0.9889 SSE = 0.0615 Obs. = 26 Lag = 0.382 F-Stat CD v. CES = 1.58 F-Stat CD v. TL =1.74 186 Table 38 (continued)
Dobroaea
Parameter Estimate St. Error T-Statistic
Intercept 5.573 10.161 0.549 TIER 0.3828 0.3098 1.235 Westock 0.6172 0.3098 1.992 KxK -0.00445 0.00816 0.546 LxL -0.00445 0.00816 0.546 KxL -0.00445 0.00816 0.546 Constant 0.1707 0.0494 3.451 Variable -0.00189 0.00069 2.729 R estrict! 0.00029 0.000116 2.583 Restrict2 0.01720 0.00964 1.793 Restrict3 0.01426 0.00804 1.773
DW = 1.873 R-Sq = 0.9980 SSE = 0.0360 Obs. = 26 Lag = 0.363 Elasticity of Substitution 1985 = 0.339 Elasticity of Substitution 1972 = 0.267 Elasticity of Substitution 1985 = 0.225 F-Stat CD v. CES = 5.98 F-Stat CD v. TL ;= 6.51 F-Stat CES v. TL = 3.91 187 Table 38 (continued)
P loiesti
Parameter Estimate St. Error T-Statistic
Intercept 3.752 1.829 2.051 Wage Earners 0.3112 0.166 1.875 Romstock 0.6888 0.166 4.149 Constant 0.1410 0.102 1.374 Restrict 0.0154 0.0128 1.203
DW= 1.504 R-Sq = 0.9938 SSE = 0.0655 Obs. = 26 Lag = 0.255 F-Stat CD v. CES = 1.02 F-Stat CD v. TL = 2.09 1 8 8 elasticity of substitution of the regions cannot explain the slowdown in industrial production.
The remaining territories have regions that are described by a
mixture of the three production functions. Those regions described
by the CD production function do not contribute to the slowdown in
industrial production because of a low elasticity of substitution.
Those remaining regions not described by the CD production function,
generally have elasticities of substitution that are approximately
equal to one so the elasticity of substitution is not a factor in the
slowdown of industrial production. Only the regions of Bucharest and
Dobrogea, since capital intensive industries are located in these
regions, contribute to the slowdown because of their low elasticities of substitution.
The majority of the regional production functions indicate that constant .returns to scale are present. Therefore decreasing returns to scale are not a cause of the declining growth rates of industrial
production. The presence of constant returns to scale, in fact,
reinforces the effect of the growth rates of inputs on the growth
rates of output (direct effect).
Finally, the overwhelming majority of the regions have
declining rates of TFP, evidenced by the negative variable rates of
TFP. Only the regions of Mures and Hunedoara exhibit positive 189 variable rates of TFP, indicating that the growth rate of TFP is increasing. Thus, the declining growth rates of TFP by the majority of the regions have definitely contributed to the declining growth rate of industrial production. Again, the Southeast territory contributes greatly to the decline since it produces the largest share of industrial output of the territories and all its regions have declining growth rates of TFP. Also, the regions of Banat, Crisana and Iasi, even though they contribute smaller shares to industrial output, have growth rates of TFP that are declining at a much faster rate than those regions of the Southeast territory. Thus, these three regions, because of their higher declining rates of TFP also make a healthy contribution to the decline of TFP in the industrial sector as well as the decline in the growth rate of industrial production.
As in the investigation of the industrial branches, two separate measures of TFP were estimated, those being the regression estimate and the Solow method estimate. Table 39 contains the regression and Solow method estimates of TFP for the regions best described by a constant rate of TFP (regression estimate) only. The Solow method estimates are in five year average
(midpoint) rates which correspond to the five year plans. The midpoint average Solow method estimates (over the 1960-85 period) are approximately identical to the constant rates found by the 190 Table 3 9 Regression and Solow Method Estimates of Total Factor Productivity of Industrial Regions Characterized By a Constant Rate of Total Factor Productivity (Regression) Five Year Midpoint Averages
Industrial Region
Year A roes Clui llfov
1961-65 7.50 19.12 9.17 1966-70 4.61 27.75 -7.11 1971-75 1.37 12.07 1.67 1976-80 6.69 14.04 10.84 1981-85 0.14 10.78 -3.17
Midpoint 4.06 16.75 0.02
Year Maramures Ploiesti
1961-65 -7.74 4.47 1966-70 -11.02 1.83 1971-75 -9.77 1.51 1976-80 -8.70 0.33 1981-85 -10.05 -0.09
Midpoint -9.45 1.61 191 regression method.
Figures 68-79 (found in the Graphical Appendix) depict the regression estimates and the Solow method estimates of TFP for regions best described by both the combination of a constant and variable rate of TFP. Again, the Solow method estimates are noisier than the regression estimates, however, the Solow method estimates fall primarily within the 95 percent confidence intervals of the regression estimates. The exceptions here are the regions of
Banat and Brasov. The regression and Solow method estimates of TFP follow the same general trends for most of the regions, however,
Banat and Brasov are again the exceptions. Finally, the regions of
Hunedoara and Mures exhibit increasing rates of TFP whereas the remainder of the regions exhibit a declining rate of TFP. Thus, the
Solow method estimates support the findings and conclusion made from the regression estimates of TFP., mainly that the growth rate of TFP is declining within the majority of the regions..
Romanian and Soviet Comparison: Regions
A comparison of regional production between Romania and the
Soviet Union is difficult to conduct and may not add much, if any, to a greater understanding of regional production in this part of the world for several reasons. First, Romania itself would be an averaged sized region of the Soviet Union (it would be like 192 comparing counties and states) and it may prove more interesting to analyze Romanian industrial production with the industrial production of each Soviet republic. However, that is beyond he scope of this paper. Secondly, industrial production of the Soviet republics was studied by Koropeckyj in 1980. In his study, he assumes a CD production function with constant returns to scale. He does not use regression techniques in his study but uses the shares of income attributed to capital and labor (assuming a CD production function) and utilizes the Solow method to estimate the growth rate of TFP.
His results were not tested for statistical significance, however, assuming that the CD production function results are correct in describing the Soviet republics’ industrial production then
Koropeckyj’s estimated rates of TFP can be compared with the
Romanian rates of TFP for regional industrial production.
Koropeckyj’s estimated rates of TFP for industrial production of the Soviet republics and the authors’ estimated rates of TFP of
Romanian regions are presented in Table 40. Koropeckyj does not partition his estimates into five year averages therefore for reasons of comparison the Romanian estimates were transformed into growth rates of TFP over the 15 year period of 1960-75. The Soviet republics’ rates of TFP range from a low of 1.7 percent in 193 Table 40 Estimated Growth Rates of Total Factor Productivity of Industry Solow Method Estimates Soviet Republics (Koropeckyj) Romanian Regions (Hunt) 1960-85
Soviet ReDublic Rate of TFP Romanian Reaion Rate of TFP
Soviet Union 3.90 Romania 4.97
Armenia 3.00 Arges 3.80 Azerbaidzhan 3.20 Banat 5.74 Georgia 4.60 Olt 11.95
Estonia 4.20 Brasov 6.52 Latvia 5.00 Hunedoara 7.90 Lithuania 3.90 Mures 9.65
Betorussia 4.90 Cluj 16.70 Moldavia 4.40 Crisana 3.93 Russia 4.00 Maramures -9.40 Ukraine 3.60
Kazakistan 2.10 Bacau 10.23 Kirgizistan 1.70 Iasi 6.97 Tadzakistan 6.00 Suceava 4.89 Turkmenistan 2.80 Uzbekistan 2.70
Bucharest 6.44 Dobrogea 12.67 Galati 1.51 llfo v 0.29 Ploiesti 1.41 194 Kirgizistan to a high of 6.0 percent in Tadzikistan over this period.
The Romanian regional rates of TFP, on average, are much higher than those of the Soviet republics’. This was expected since the
Romanian industrial sector exhibits a higher rate of TFP than that of
Soviet industry.
Implications
The previous sections developed measures for describing the direct and indirect effects of the inputs on output, based on the use of the CD, CES and TL production functions. This section describes the implications of these effects, using the above results, on
Romanian industrial branches and regions and their respective patterns of growth.
How did the growth rates of the inputs affect the growth rate of industrial production in Romania? The majority of the growth rates of output of the industrial branches and regions followed a similar growth pattern as that of the industrial sector. The growth rates followed a cyclical pattern through 1972 and then experienced a sharp decline through the 1980’s. The major contributing factor for the latter decline in the growth of industrial production has been due to the declining growth rates of the principal inputs in all branches and regions since the earlier 1970’s. Chapter 3 described the reasons behind the declining growth rates of the inputs. This 195 section is concerned with finding the specific branches and regions which have contributed the most to this decline.
The above analysis indicates that the sharpest decline in input growth rates occurred in the MBMW and Chemical branches. These branches contribute a relatively large portion of total industrial production. Thus, since the leading branches of the industrial sector experienced the greatest decline in the growth rates of inputs, they contributed greatly to the decline in the growth rate of production of the industrial sector. This effect of the growth rates of inputs on output growth is reinforced by the acceptance of constant returns to scale in the industrial branches. Therefore, industrial production proceeded at the rate of the slower growing input and that is labor.
In addition to the declining growth rates of inputs the majority of the industrial branches are experiencing declining growth rates of
TFP. The sharpest declines in TFP growth are also seen in the leading branches of the industrial sector MBMW and Chemical branches. Thus, the MBMW and Chemical branches have lead the industrial sector to slower growth since the early 1970’s.
A similar situation is found in the analysis of the industrial regions of Romania. The Southeast is the dominant territory in the sense that it contributes a relatively large share of industrial production. The Southwest territory has grown so that it contributes 196 the second largest share of the territories to industrial production.
These territories, as well as the remaining three territories, have all experienced declining growth rates of inputs since the early
1970’s. The sharpest decreases in input growth occurred in the
Southeast and Southwest territories which produce over one-half of the industrial production of Romania. The regions of Bucharest,
Galati, Ploiesti and Arges have experienced the sharpest declines in the growth rates of inputs. Though the production function results indicate that there is no single dominant production function in describing regional industrial production, the elasticities of substitution are approximately equal to one and therefore have not aided in the decline of industrial production. The exceptions here are the regions of Bucharest and Dobrogea which have elasticities of substitution well below one, suggesting that it is increasingly difficult to substitute capital in place of labor. As in the case of the industrial branches, the majority of the regions are described by constant returns to scale. Thus the impact of the growth rates of inputs has a direct and important effect on the growth rate of industrial production. Finally, the majority of the regions are exhibit declining growth rates of TFP, especially the regions of the
Southeast territory (because it contributes a large share of industrial output). Other regions, such as, Banat, Crisana and Iasi, 197 exhibit rates of TFP that are declining faster than those in the
Southeast region, also cause a drag on the growth of industrial production.
Why has the Southeast and Southwest territories become the areas of relative concentration of Romanian industry? This question also asks, “Why has Romanian regional industry developed as it has?” Prior to WWII Romanian industry developed near sites where the relevant natural resources were abundantly available. Following
WWII, though still a consideration, industrial development was based on the desire for self-sufficiency and as a political tool of the regime. Ceausescu’s prejudice of the Southeastern part of Romania, including Bucharest, and even more so Tigorveste, led to greater industrial development in these areas. The sheer hatred by
Ceausescu for the Hungarian population in the Central and
Northwestern territories led to a general neglect industrial-wise, as well as in all other aspects of life. Industrial development in other nodal points of the country, such as Iasi, Ordea, and Constanta is due to the available infrastructures and labor force availability9, but also reflects those areas which were recognized by the regime as being politically safe and the industrialization allowed for greater control over these areas. Thus, resource location, prejudice, and political safety played significant roles in the determination of
“Turnock, p. 200. 198 the dispersion of Romanian industry.
Direct and Indirect Data Concerns
A number of the plots of the growth rate of production and Solow method estimates of TFP of the industrial branches and regions indicate abrupt changes in some instances from one year to another.
The drastic changes in the growth rates of production and TFP appear as spikes in the figures. Generally, the spikes in these growth rates are due, at least partially, if not entirely, to the presence of a command type economy. Production within one branch or region may be diverted so that another branch or region’s production can be increased. Also, severe bottlenecks and shortages of resources, which are common in such an economy, act to decrease the growth rates of production and TFP. Thus, political decisions and the inherent bottlenecks (caused by this system) generally explain these severe peaks and troughs.
There are three years, 1965, 1977 and 1981 in which a number of branches and regions exhibit these spiked growth rates of production and TFP. The above political explanation is seen to be a major factor in explaining the 1965 spikes and plays a minor role in those of 1981, however it is necessary to investigate each of these years separately. 199 In 1965 the Chemical, MBMW, Electrical and Construction
Material branches experienced sharp decreases in their respective growth rates of production and TFP while the Ferrous, Non-Ferrous and Food Processing branches saw large increases. The five year plan covering the 1960-65 period called for increases in the development of Romania’s natural resources and urged for greater independence from the USSR. New discoveries of iron ore (280% increase), coal reserves (185% increase) and non-ferrous ores (300% increase) over the 1960-65 period led to the significant increases in the Ferrous and Non-ferrous branches. These increases were directly felt by in the regions of Hunedoara, Mures, and Bacau.
The sharp declines in the growth rates of production and TFP of the regions of Bucharest, Iasi, Brasov, Galati, and Ploiesti reflect the sharp decreases in the MBMW, Chemical, Construction Material and Electrical branches. A possible explanation for the decreases in these branches and regions is that during the 1966-70 period enormous industrial projects came on line. That implies a large capital build-up, as well as labor, that was producing very little if anything prior to the opening of these complexes, and thus lowered the productivity of the resources. This is further evidenced by the large increases in the growth rates of these branches and regions in the 1966-70 period. During this period three large MBMW sites began 2 0 0 production in Bucharest, Galati and Hunedoara that were to account for 70% of MBMW production by 197010. The “ Iron Gates” hydroelectric project was launched in 1966 in cooperation with
Yugoslavia11. Thus, prior to these new facilities coming on-line, capital and labor accumulated at these sites while production was minimal at best.
The increase in the growth rates of production and TFP of the
Food Processing branch may have been due to Ceausescu’s appeasement of the populace, as 1965 marked his first year as the chairman of the Romanian Communist Party, as well as attempting to limit the dependence on the USSR. There was also a significant decline in the growth rates of production and TFP in the Printing branch in 1965. This may have been due to Ceausescu’s attempting to limit political opposition and criticism of his ascension following the death of Gheorghe-Dej.
The year 1977 marked significant decreases in the growth rates of production and TFP in the Electrical and Chemical branches as well as in the regions of Iasi and Bucharest. Extraordinary increases in these growth rates were experienced in the
Construction Material and Woodworking branches, centered mainly in the regions of Arges, Dobrogea, llfov and Mures. An earthquake of 7.2
10 Dobrescu, p 35.
11 Ibid.. d p 36-7. 201 on the Richter scale devastated Bucharest and the surrounding area in March of 197712. Over 200 industrial complexes were shutdown for over a month, 33,000 buildings were destroyed and over 35,000 people were homeless. Thus, the rebuilding that took place following the earthquake significantly increased the growth rates in the
Construction Material and Woodworking branches, while it adversely affected the the Electrical and Chemical branches.
The Fuel branch experienced an extraordinarily large increase in its growth rates of production and TFP in 1981. A portion of this increase was due to falsified coal production figures in 1980 that were discovered by Ceausescu. In order to make up for the shortage of coal enormous amounts of oil were imported in 1981 from Iran'3.
The imports of oil benefited the regions of Dobrogea, Bacau, and
Ploiesti since large oil refineries are located in these regions. Also in 1981, severe flooding in the Danube area destroyed a large portion of the agricultural produce and caused widespread food shortages'4.
The floods also affected the MBMW, Paper, and Fur branches since they are located near the Danube and use its water in the production of their respective products. Finally, cabinet changes in 1981 split the MBMW directorship into two positions, one specifically accountable for machine building and the other concerned with
12 Keesinas Contemporary Archives. Vol23.. p. 28298. 13 M L Vol. 27. p. 30860. 2 0 2 engineering and metal goods production15. This administrative change
may also have caused a decrease in the growth rates, since it is not
easy to change the structure of a command economy.
A final question addressed here is the correlation between the
production function models of the branches and regions. In other words, should a region’s industrial production be comprised
predominantly by one or a few industrial branches, is this region’s production function model the same as that of these industrial branches?
The first observation is that regions which are characterized by an agglomeration of industries, irrespective of the region’s share of total industrial output, are described by the TL production function model. Regions which exhibit this characteristic include
Bucharest (city), Brasov, Banat, Iasi and Dobrogea. This is primarily because each of these regions houses an industrialized city (Brasov,
Timisoara, Iasi, and Constanta respectively).
Secondly, a number of the regions’ production function models reflect the influence of a particular industrial branch. Bacau (TL) reflects the Paper branch, Suceava (CD) the Woodworking branch,
Maramures (CD) the Non-Ferrous Metals branch, llfov (CD) the
Electrical branch and Ploiesti (CD) the Fuel branch.
15 Ibid.. p. 31351. 203 Finally, the remaining regions’ production functions indicate no apparent relationship with any specific industrial branch.
Structural Change: Industrial Branches
The present section addresses the question of whether the industrial branches have experienced a structural change in their production processes over the 1951-85 period. In order to discern if in fact the individual branches have experienced such a change it is necessary to examine their production functions in two distinct time periods. The two periods chosen are the 1951-65 (15 years) and the 1966-85 (20 years) periods, as in Chapter 3 when the question of structural change was addressed at the industrial sector level.
Again, 1965 was chosen as the “ break” year because it signaled a change in political leadership as Ceausescu assumed control of
Romania and its economy. In order to determine whether there has been a structural change in production of an industrial branch it was necessary to hypothesize that, in fact, there has not been a structural change. In other words, the hypothesis that the production functions are equivalent between periods is assumed to be true unless the F-test rejects this hypothesis. 204 Tests of no structural change were conducted for each of the
17 industrial branches and only five branches rejected this
hypothesis. Four of these branches, MBMW, Chemical, Ferrous, and
Non-Ferrous are heavy industrial branches while only one, Glass, is
from the light industrial branches. Thus, these results suggest that
there has been greater change (development) in the heavy industrial
branches since 1951 than in the light industrial branches. The
production functions of each of these branches are presented in
Table 41. The remaining industrial branches did not exhibit any
evidence of structural change and these results are not reported16.
Since these five branches presented evidence of a structural change
in production, it was necessary to investigate where the structural
change had occurred in the production function. That is, did the
change occur in the residual or intercept term17 or did it occur in the
" However, these results can be obtained from the author. "The following model was used in testing for differential intercepts between the two periods: Y = f( l{1951}, 1(1966), K, L, T. T2) where Y is output, K is capital, L is labor, T is the constant rate of TFP, T2 is the variable rate of TFP, 1(1951} is the intercept for the 1951-65 period and 1(66} is the intercept for the 1966-85 period. An F-test was conducted using the sum of squared errors of the “common regression” given by: Y = f(l, K, L. T, T2) and that of the model with the uncommon intercepts (the first model listed above). The test is conducted under the hypothesis that the intercepts are equal between the two periods. The degrees of freedom for the CD production function for the test of differential intercepts is F(1,28) and for the TL production function F(1,25) since there are 35 observations. Tabled F-values for the CD function at the five and one percent significance levels are 7.64 and 4.20 respectively. Tabled F-values for the tests when the TL production function is used at the five and one percent significance levels are 7.77 and 4.24 respectively. (Johnston, p. 218.) 205 Table 41 Production Functions Used in the Tests of Structural Change Industrial Branches Chemical, Ferrous, Glass, MBMW, and Non-Ferrous Romania, 1951-65 and 1966-85
Chemical
1951-65
Parameter Estimate St. Error T-Statistic
Intercept 13.314 6.661 1.999 Wage Earners 1.2077 0.5109 2.364 Romstock -0.2077 0.5109 0.407 Constant 0.0925 0.0583 1.589 Variable -0.0009 0.0021 0.420 Restrict 0.0097 0.0086 1.119
DW = 1.754 R-Sq = 0.9938 SSE = 0.0844 Obs. = 15
1966-85
Parameter Estimate St. Error T-Statistic
Intercept 8.841 4.653 1.900 Wage Earners 0.9618 0.4182 2.299 Romstock 0.0382 0.4182 0.091 Constant 0.2086 0.0311 6.709 Variable -0.0029 0.0003 10.306 Restrict 0.00131 0.0030 0.438
DW = 2.01 R-Sq = 0.9896 SSE = 0.0221 Obs. = 20 Structural Change F-statistic = 4.97 Table 41 (continued)
G lass
1951-65
Parameter Estimate St. Error T-Statistic
Intercept -0.7733 3.435 0.225 Wage Earners 0.2181 0.3160 0.690 Romstock 0.7819 0.3160 2.474 Constant 0.1120 0.0223 5.011 Variable -0.0032 0.0010 3.219 R estrict 0.0083 0.0049 1.700
DW= 1.757 R-Sq = 0.9961 SSE = 0.0445 Obs. = 15
1966-85
Parameter Estimate St. Error T -S ta tis tic
Intercept 9.8202 0.8543 11.494 Wage Earners 1.3424 0.1151 11.662 Romstock -0.3424 0.1151 2.975 Constant 0.1680 0.0411 4.078 Variable -0.0019 0.0007 2.975 R estrict 0.0031 0.0101 0.306
DW = 2.575 R-Sq = 0.9759 SSE - 0.0488 Obs. = 2 Structural Change F-statistic = 8.48 Table 41 (continued)
MBMW
1951-65
Parameter Estimate St. Error T-Statistic
Intercept 9.223 4.969 1.856 Wage Earners 0.4857 0.4881 0.995 Romstock 0.5143 0.4881 1.054 Constant 0.2386 0.0456 5.227 Variable -0.0068 0.0031 2.215 R e strict 0.0040 0.0066 0.605
DW = 1.812 R-Sq = 0.9983 SSE « 0.0478 Obs. = 15
1966-85
Parameter Estimate St. Error T-Statistic
Intercept 17.543 3.3853 5.182 Wage Earners 1.6507 0.4325 3.817 Romstock -0.6507 0.4325 1.505 Constant 0.4355 0.0819 5.295 Variable -0.0057 0.00089 6.435 R e strict 0.0196 0.00897 2.186
DW = 0.682 R-Sq = 0.9759 SSE = 0.0575 Obs. = 20
Structural Change F-statistic = 5.92 Table 41 (continued)
Non-Ferrous
1951-65
Parameter Estimate St. Error T-Statistic
Intercept -11.3717 7.2314 1.573 Wage Earners -0.7373 0.5709 1.290 Romstock 1.7373 0.5709 3.034 Constant 0.2276 0.0627 3.623 Variable -0.0060 0.0025 2.399 Restrict 0.0133 0.0063 2.114
DW= 1.882 R-Sq = 0.9854 SSE = 0.2163 Obs. = 15
1966-85
Parameter Estimate St. Error T-Statistic
Intercept -14.767 6.3883 2.312 Wage Earners -1.3440 0.5889 2.282 Romstock 1.3440 0.5889 3.890 Constant -0.1164 0.0537 2.165 Variable 0.0008 0.0005 1.430 Restrict 0.0032 0.00651 0.429
DW = 1.711 R-Sq = 0.97894 SSE = 0.05709 Obs. = 20 Structural Change F-statistic = 4.98 Table 41 (continued)
F e rro u s
1951-65 Parameter Estimate St. Error T-Statistic
Intercept 62.3499 10.398 5.996 Wage Earners -1.1908 0.7175 1.660 Romstock 2.1908 0.7175 3.053 K x K -0.2003 0.0416 4.806 K x L 0.2003 0.0416 4.806 LXL -0.2003 0.0416 4.806 Constant -0.0490 0.0407 1.203 Variable 0.0251 0.0047 5.361 R e strictl -0.00005 0.00004 1.370 R estrict2 -0.00229 0.00201 1.139 R estrict3 0.00181 0.00115 1.574
DW = 1.826 R-Sq = 0.9902 SSE = 0.2900 Obs. = 15
1966-85
Parameter Estimate St. Error T-Statistic
Intercept -5.2104 6.2177 0.838 Wage Earners -0.0962 0.4863 0.198 Romstock 1.0962 0.4863 2.254 K x K 0.0096 0.0102 0.946 K x L -0.0096 0.0102 0.946 LXL 0.0096 0.0102 0.946 Constant -0.0182 0.0579 0.315 Variable -0.0021 0.0010 1.972 Restrict 0.00024 0.00011 2.177 R estrict2 -0.00860 0.00278 3.093 Restrict3 -0.00860 0.00278 3.093
DW = 1.711 R-Sq = 0.9932 SSE = 0.0239 Obs. = 20 Structural Change F-statistic = 4.61 2 1 0 slope vector18, measuring the output elasticities of the inputs. The
F-tests of the hypothesis that the intercept was unchanged between
the two periods was rejected of the MBMW and Ferrous industrial
branches19. This suggests that the “base level” of production had
shifted in these two periods. Two of the probable causes of this
would be a change in TFP or a change in efficiency. The F-tests of
the hypothesis that the slope vectors of the inputs were unchanged
between the two periods was rejected for the Chemical, Non-
Ferrous, and Glass branches20. This suggests that the combination of the inputs and their effect on output has changed over time. One
possibility to explain this is that the quality of inputs has improved
over time so the input combination has changed.
ia In order to test for differential slope vectors the model Y = f( l{1951>. I{1966>, K, L, T, T2) is used as the unrestricted model. The model consisting of different slope vectors and different intercepts, used in determining whether or no there has been a structural change (see Chapter 3) acts as the restricted model here.An F-test is performed with the degrees of freedom of F(k-1, n-2k). Thus, for the CD production function the degrees of freedom are F(5,23) and for the TL production function the degrees of freedom become F(8,17). The tabled F-values at the five and one percent significance levels for the CD function are 3.94 and 2.64, respectively. The tabled F-values at the five and one percent significance levels for the TL function are 3.79 and 2.55, respectively (Johnston, p. 218.)
'"The calculated F-value for the MBMW branch, under the CD production function, was 36.32 and is well above the tabled F-values at the one and five percent significance levels when tested under the hypothesis of equivalent intercepts. Under the same hypothesis, the calculated F- value for the Ferrous branch was 10.47 and this again was well above the tabled F-values at the one and five percent significance levels. "The calculated F-value for the Chemical branch was 5.665, that of the Non-Ferrous branch was 4.57, and that of the Glass branch was 4.32. Each of these branches was represented by the CD production function and therefore has degrees of freedom of F(5,23). The tabled F-values at the five an d one percent significance levels, with degrees of freedom F(5,23) are 3.94 and 2.64 respectively Therefore, the hypothesis that the slope vectors were equal between the two periods is rejected since the calculated F-value is greater than the tabled F-value. 211 Conclusions
The major conclusions of Chapter 4 are given below in summary form.
1. The branches of the Romanian industrial sector have
followed similar patterns of growth of production, capital and labor
as the total. No single branch escaped the slowdown in industrial
production. In general, the heavy industrial branches experienced the
sharpest declines in their growth rates of production. This was
especially true for the MBMW and Chemical branches. The light
industrial branches also experienced a slowdown in their growth
rates of production, however, the decline was not as sharp as in the
heavy industrial branches.
2. A similar situation is found when the growth rates of labor and capital are examined. That is, generally, the growth rates of
labor and capital fall sharply in the heavy industrial branches, especially those of MBMW and Chemicals. The light industrial branches experience a decline in their growth rates of labor and capital, however, the declines are not as sharp as those in the heavy industrial branches. 2 1 2 3. The branch shares of industrial production indicate that since 1951 the share of industrial production of heavy industrial branches have continued to increase and by the 1960’s surpassed that of the light industrial branches. In fact, as of 1985, the heavy industrial branches account for approximately 70 percent of all industrial production, whereas in 1951 they only accounted for approximately 40 percent. The same phenomenon occurred with respect to the share of industrial labor in the heavy industrial branches. That is, the heavy industrial branches continued to increase it share of industrial labor from 1951 (about 45 percent) to
1985 (nearly 60 percent). The two branches which experienced the largest increases in their shares of industrial production and labor were the MBMW and Chemical branches. The relative share of the industrial capital stock remain fairly constant over time when the heavy (80 percent) and light (20 percent) industrial branches are compared. Again, however, the MBMW, Chemical, Electrical and Food branches’ shares of industrial capital increased. Therefore, the slowdown in the growth rate of production in the industrial sector seems to be centered in the heavy industrial branches, primarily in the MBMW and Chemical branches.
4. The production function analysis of the industrial branches indicates that the majority of the industrial branches are best 213 described by the CD production function. Since the CD model is chosen to describe the production of these branches it is assumed, because of the mathematical restrictions, that the elasticity of substitution is constant and equal to one. Therefore, a low elasticity of substitution, indicating that it is becoming increasingly difficult to substitute capital for labor, is not a primary factor in the explanation of the decline in the growth rate of industrial production.
5. The majority of industrial branches exhibit the presence of constant returns to scale. Thus, decreasing returns to scale cannot be seen as a factor in the slowdown of the growth of industrial production. Constant returns to scale, a proportionate change in the inputs leads to an equivalent proportional change in output, also reinforces the direct effects of the input growth rates on the growth rate of output. It also suggests that output growth will follow more closely the growth rate of the slower growing input, here labor.
6. The majority of the industrial branches also exhibit declining growth rates of total factor productivity (TFP). Therefore, over time the growth of TFP has slowed and in turn has contributed to the slowdown in the growth of industrial production. The branches experiencing the sharpest decline (most negative variable rate of 214 TFP) were the MBMW and Chemical branches, due to the lack of new capital with embodied technological change, lack of improvements in the education of the work force, and poor management techniques.
7. The declining growth rates of the principal inputs and the declining growth rate of TFP of the industrial branches has caused the decline in the growth rate of industrial production. The weighted share of the heavy industrial branches implies that the slowdown has been centered in this group, especially the MBMW and Chemical branches.
8. As is evidenced by the rapid changes in the shares of production and labor of the heavy industrial branches, as well as their relatively high growth rates, rapid change has occurred in these branches. The MBMW, Chemical, Ferrous and Non-Ferrous branches exhibit evidence of a structural change in their production processes over the periods of 1951-65 and 1966-85.
9. The industrial regions of Romanian industry have followed a similar patterns of production, capital and labor growth. No single region escaped the slowdown in industrial production. In general, each of the five territories experienced sharp declines in their growth rates of production. This was especially true for the
Southeast territory in the 1970’s. The remaining territories also experienced a slowdown in their growth rates of production, which, 215 in fact, nearly fell by 10 percentage points from 1971 to 1985.
10. A similar situation is found when the growth rates of labor and capital are examined. The growth rate of capital fell sharply in the Southeast and Southwest territories, while each territory experienced a slight decline in its growth rate of labor, the
Southeast territory again experienced the sharpest decline.
11. The shares of industrial production indicate that since
1960 the share of industrial production of Southeast territory has remained fairly constant at approximately 38 percent, while the
Southwest’s share has increased over time to 20 percent. The same phenomenon occurred with respect to the shares of industrial labor and capital in the Southeast and Southwest territories (however, the
Southeast’s shares have declined over time).
12. The Southeast and Southwest territories produce over 50 percent of Romanian industrial output, due to the regime’s prejudice and need for political control. These territories also experienced the sharpest declines in production, especially the regions of Bucharest,
Galati, and Olt. The analysis of the growth rates and shares of industrial production indicate that the slowdown of industrial production was centered in these regions. This is because the heavy industrial sectors of MBMW, Chemicals and Ferrous Metals are primarily located in these regions. 216 13. No single production function was found to describe the
majority of the regions* production. However, the regions describe
by the CES and TL production functions exhibited elasticities of
substitution that were approximately equal to one. This combined
with the regions described by the CD production function leads to
the conclusion that a low elasticity of substitution is not a major
factor in the explanation of the slowdown in the growth rate of
industrial production. However, the regions of Bucharest and
Dobrogea do exhibit low elasticities of substitution and this has
affected their production growth, as well as that of the industrial sector (since they account for approximately 18 percent of
industrial output).
14. As in the case of the industrial branches, the majority of the regional production function indicate the presence of constant returns to scale. This rules out the possibility of decreasing returns to scale as a factor in causing the slowdown in the growth rate of
industrial production.
15. The majority of the regions also indicate a declining growth rate of total factor productivity (negative variable rates of
TFP). Thus, the combination of declining growth rates of the inputs and TFP have caused the decline in the growth rate of industrial
production. This decline in the growth rate of production appears to 217 be centered in the Southeast (primarily) and Southwest territories of Romania.
16. Regional industrial development has progressed rapidly in
Romania since 1950. The Southwest and Southeast territories have experienced the largest gains from this development. Prior to WWII industrial development was primarily based upon the location of natural resources. Cities also developed a wide of industries. This first occurred in Bucharest, while cities such as Iasi, Brasov,
Constanta, and Timisoara soon followed. These cities were allowed to develop a wide range of industries because they were able to asure a faithful political backing of the Communist Party, in particular the Ceausescu regime. Other cities and regions in the
Northeast and Northwest territories were relatively neglected because of Ceausescu’s personnal prejudice and hatred of the
Hungarian minority living there and because these areas were not as politically safe. CHAPTER V
CONCLUSIONS
The post-war development and growth of Romanian industry has been extraordinary. Although this was also true for many of the
European nations following WWII, what is striking about the
Romanian case is that Romania’s industry grew at a relatively more rapid rate through 1971 than its European (Western and Eastern) counterparts. By the 1960’s all other European nations’ growth rates had slowed, however, Romania’s continued to increase. Romanian industrial growth ranks amongst the highest in all of Europe since the 1950’s.
Why did Romanian industry experience such extraordinary growth? After WWII, the previously existing market economy of
Romania was replaced by a Soviet-styled command economy complete with a centralized planning apparatus which focussed predominantly upon the development of heavy industry. Prior to WWII industry served as a sector that supported the growth and development of agricultural production. Therefore, the Romanian industrial sector was relatively smaller (and surely smaller in
218 219 implementation of the command economy and the emphasis upon heavy industry, which forced resources into the development of these industrial branches, caused industry to grow at extraordinary rates. Though absolute increases in industrial production were meager in the early 1950’s, in comparison to the rest of Europe, they were relatively large when one considers the point at which industrial production began. Thus, one reason for the extraordinary growth of Romanian industrial production has been because it began behind most other European nations' industrial production in 1950 and thus had a greater potential for growth.
The primary factors behind this extraordinary growth of
Romanian industrial production were the growth rates of the principal inputs, labor and capital due to both internal and external causes (as well as their combination). The growth rate of labor follows a cyclical pattern, primarily due to the fluctuations in the birth rate. As Chapter 3 described, the lower birth rates during the
First and Second World Wars and their subsequent echo effects caused the industrial labor growth rate to follow this pattern.
However at the same time external forces, such as the mechanization of agriculture, allowed for the surplus labor in this sector to be transferred to industrial production. But this did not completely offset the declines in the birth rates, especially in the 2 2 0 post 1970 period. By this time the agricultural labor surplus was no longer available since the Agricultural work force was, by 1980, predominantly female1. Thus, the combination of birth rate effects during the WWI and WWII periods, their respective echo effects, and the drying up of the agricultural labor surplus lead to a steady decline in the growth rate of industrial labor since the mid 1970’s.
The growth rate of industrial capital steadily increased from
1951 until the early 1970’s. Agricultural mechanization also grew during the same time period, however, not nearly at the same rate as in industry. There are primarily three factors explaining this occurrence. First, the political priorities were skewed toward the development of heavy industry, especially after 1965, causing the majority of capital investment to be received by the industrial sector, particularly the heavy industrial branches.
Secondly, the rates of mechanization in agricultural slowed from the late 1960’s onward. Although agricultural mechanization increased in Romania during this period, it still lagged well behind other Eastern European countries, so much so that East Germany and
Czechoslovakia had three times the amount of mechanization than
Romania2. There is also the question of the quality and effectiveness of the use of this mechanization in all of the Eastern European
1 Jackson, p. 52. 2 Lazarcik, p. 414. 221 agricultural sectors3.
The third factor pertains to the external terms of trade and how they affected the internal sectors of the Romanian economy.
Although specific terms of trade data (Export prices/import prices) are not readily available for Romania, rough estimates, subject to substantial error do exist. During the 1970's it was estimated that the overall terms of trade declined by over 8% for Romania, including a 4-6% decline in their terms of trade with other CMEA nations, but these are ’hazardous” estimates with a substantial margin of error4. It was also estimated that Romania suffered heavily due to price changes since it primarily trades with underdeveloped countries and with the Middle East for oil5. Thus, during the 1970’s the terms of trade turned against Romania, especially for energy, and Romania accumulated a debt of over $10 billion. The terms of trade for agricultural (internationally) also continued to worsen over the entire period of 1951-85.
Ceausescu followed a policy of retiring the debt by exporting any marketable good that Romania could offer. Thus, exports increased in the latter period at the sacrifice of industrial
(primarily) and agricultural investment. This policy was the major cause of the declining growth rates of industrial capital from the
*Marer, p.50. 8 Ibid.. p. 53. 2 2 2 mid 1 9 7 0 ’s onward.
The growth rate of industrial labor steadily increased from
1951 until the mid 1960’s and then rebounded until the sharp
decrease in the early 1970’s. The growth rate of industrial
production followed approximately an identical growth pattern as that of the labor input, though industry continued to receive large
injections of capital in the earlier decades, suggesting that
Romanian industrial production was more sensitive to changes in labor growth than that of capital.
It was shown, in Chapter 3, that the production of the
Romanian industrial sector is best characterized by the CD production function with constant returns to scale. However, the growth rate of total factor productivity, although positive and relatively high (when compared to other nations), has declined over throughout successive five year periods. Thus, despite the declining rate of TFP, industrial production continued to grow in the earlier period due to the rapid accumulation of capital and in the influx of labor. The same general results are seen when electrical consumption is included in the model.
The estimates of the production function models by industrial branches are generally characterized by the CD model with constant returns to scale and decreasing rates of TFP. Though all industrial 223 branches are not described by the CD model, the majority of the industrial branches exhibit these characteristics. The heavy industrial group has increased its share of industrial production by so much that as of 1985 it produced approximately 70 percent of industrial output. The heavy industrial group also experienced the sharpest declines in the growth rate of production, especially the
MBMW and Chemical branches (which were the two branches with the greatest shares of industrial production). Therefore, since these branches which produce a large percentage of industrial output, they carry a disproportionate amount of the decline in industrial production. The declining growth rates of inputs in these branches coupled with the declining rates of TFP led to the slowdown in the growth of Romanian industrial production.
Although a single production function cannot be used to generalize the industrial production of the individual regions, similar causes of the slowdown in industrial production were exhibited by many regions. But regional production did, for the most part, follow the general growth pattern of the industrial sector.
Most regions experience continuous growth in the capital input from
1960 through the early 1970’s while the labor growth rate increased until the mid 1960’s, fell and by the late 1960’s was on the rise again until 1972. 224 Those regions best described by the CD function exhibit the same general characteristics as the industrial branches, those being constant returns to scale, declining growth rates of inputs and a declining rate of TFP. The majority of the regions best described by the CES and TL production functions also exhibit declining growth rates of inputs and TFP. Approximately half of these regions have elasticities of substitution that are in the vicinity of one, while the remaining regions are less than one, suggesting that diminishing returns also plays a role in the slowdown of industrial production since capital growth had outstripped labor growth by such a wide margin. But once again the majority of the regions have the characteristics of declining growth rates of inputs and TFP therefore these factors are seen to be the primary causes of the industrial slowdown.
The regions most affected by the declining growth rates of inputs and TFP are those regions in the Southeast and Southwest territories. These are precisely the same regions that produce over half of Romania’s industrial production. Therefore, the slowdown in the growth rates of inputs and TFP in these territories adversely affects the growth rate of the aggregate industry in a disproportionate way. Any decrease (or increase, for that manner) in these territories’ production carries a large effect on the aggregate 225 industrial level of production.
The political succession from the Georghe-Dej regime (1951-
65) to that of Ceausescu (1965-88) seems not to have affected the
production process of the industrial sector as a whole. The hypothesis of no structural change in industrial production between these two regimes (1951-65 and 1966-85, respectively) was accepted in this study. However, there were five industrial branches which exhibited evidence of a structural change in production. Four of these were in the heavy industrial group and included the branches of MBMW and Chemical, which had experienced the largest percentage share gains of industrial production since 1951. The hypothesis of structural change could not be addressed in the regional analysis because of the lack of data.
The models presented in this paper are not the only models or specifications available to examine the industrial production of Romania, however, they are among the most frequently used models in the economic literature of this type. The Romanian industrial data is relatively very poor when compared to western data, however, it is comparable to the data of other eastern European nations.
Valuation of output and capital stock are derived by government assigned prices (non-scarcity prices), however, it is possible to use this data to discover the relationship between the inputs and output 226 to produce a production function since prices need not be considered for this physical relationship.
The Ceausescu regime remained in control of Romania until
December of 1988 when a public uprising forced Nicolai, his wife
Elena, family members and other political personnel to flee for their lives. The Ceausescus were later captured, tried and executed by a military court for their crimes against the Romanian people.
However, the Romania people still feel their wrath as their political and economic policies linger on today and must be addressed.
Romania faces many problems. First, new political institutions, as well as players (without the taint of communism or the securitate6 ), are needed to replace the old regime. During the time needed to establish these new institutions, the economy and people have survived through the use of martial law (favored by the communists and frowned upon by the citizens) and by emergency measures. Even following the first “free” elections in Romania this century7 there are countless questions to be answered on political grounds alone concerning the establishment of a new Romania.
Economic policies follow directly from the political ideology and objectives. Therefore, Romania's economy will be somewhat
"unstable” as long as its political system remains awry. Without
a Romanian secret police. 7 Romania was a monarchy prior to WWII. 227 focussing on the different economic policies of the competing political groups, it is perhaps best to contain the industrial
"forecast” at the most general level.
Industrial production continued to decline after the revolution8 according to the official production statistics9. The reasons for the persistent decline in the growth rate of industrial production, as stated by Ben-Ner and Montias (1991) are:
“ 1) the curtailment of the work week; 2) a slackening in the work discipline; 3) the shortage of inputs, due to the elimination of the partial coordination that planning did provide; 4) dislocation due to reorganization of large firms; 5) the difficulties faced by management in coping with the new economic environment; 6) and the relative inexperience of managers after the Revolution.10”
Other problems faced by the Romanian economy are that their markets are not functioning correctly yet and the tradition of using bribes in order to secure goods and services is being reduced with great reluctance. Montias and Ben-Ner argue that the decline in industrial production was unavoidable since..."a growth strategy of the type the Party {Communist Party of Romania} adopted in 1948 can reap initial dividends; only that, when it is carried out to an extreme, it inevitably leads to a slowdown and eventually to a
* Data after 1985 was not used in this study. The official statistics for the year following 1985 were meager at best and therefore an attempt was not made to use this data or find the missing data. * Ben-Ner, Avner and Michael Montias, p. 164 (1991). 10ibid-. PP- 167-8. 228 decline, such has actually occurred in Romania.11”
In addition to these systemic consequences, Ben-Ner and
Montias add the policy of placing the bulk of investment into new industries, at the expense of the established industries, has lead to the continuation of the older industries producing product lines that are obsolete and cannot compete on the world market. The author concurs with Ben-Ner and Montias in that the industrial sector needs to be streamlined and the inefficient firms should be closed.
Romania was known as the “ Breadbasket of the Balkans” prior to
WWII and therefore it may be in Romania’s best interest to place its resources into agricultural production, especially with the food crisis in Eastern Europe, the republics of the USSR, and famine in the third world. Therefore, Romania would have a very marketable product that could compete with foreign produce. At the same time
Romania would earn foreign currency which in turn could be used to modernize industry and promote its agricultural production.
11 Ibid.. p. 169. APPENDIX A
Graphical Appendix
Growth Rates of Output, Labor and Capital: Industrial Branches
Rates of Total Factor Productivity: Industrial Branches
Growth Rates of Output, Labor and Capital: Regions
Rates of Total Factor Productivity: Regions
2 2 9 Growth Rates of Output, Labor and Capital Chemical Branch Romania, 1952-85
35 30 25 lutpi 20 15 of 10
Growth 5 Labor 0 •5 -10 15
Year
Figure 10
Chemical 230 Growth Rates of Output, Labor and Capital Construction Materials Branch Romania, 1952-85
40
Output % Rate of Growth
,-L-abor-
56 6 7 7 8 8 5 0 5 0 5 0 5 Year
Figure 11 Construction Material Growth Rates of Output, Labor and Capital Electrical Branch Romania, 1952-85
40 OutputLabor
30
20 % Rate of 10 Growth 0
10
20
Year
Figure 12 Electrical Growth Rates of Output, Labor and Capita! Ferrous Metals Branch Romania, 1952-85
65 j ■Output 55 -
45
% Rate 35 of iLabor Growth 25 "
1 1 1 1 1 1 1 9 9 9 9 9 9 9 5 6 6 7 7 8 8 5 0 5 0 5 0 5 Year 3 3 2 Figure 13
Ferrous Growth Rates of Output, Labor and Capital Fuel Branch Romania, 1952-85
70 t
% Rate Output of Growth
'tabor.
1 1 1 1 1 1 1 9 9 9 9 9 9 9 5 6 6 7 7 8 8 5 0 5 0 5 0 5 Year
Figure 14 4 3 2 Fuel Growth Rates of Output, Labor and Capital MBMW Branch Romania, 1952-85
30 Capital
25 Output 20
15
% Rate 10 of Labor Growth 5 0
5
10
15
Year
Figure 15 MBMW 235 Growth Rates of Output, Labor and Capital Mining Branch Romania, 1952-85
Output
(Labor % Rate of Growth
1 1 1 1 1 1 1 9 9 9 9 9 9 9 5 6 6 7 7 8 8 5 0 5 0 5 0 5 Year
Figure 16 6 3 2 Mining Growth Rates of Output, Labor and Capital Non-Ferrous Metals Branch Romania, 1952-85
^Output 60 -
% Rate of Growth
Capital
■Labor 1 1 1 1 1 1 1 9 9 9 9 9 9 9 5 6 6 7 7 8 8 5 0 5 0 5 0 5 Year
Figure 17 ro Non-Ferrous oo -vl Growth Rates of Output, Labor and Capital Food Processing Branch Romania, 1952-85
Output
Labor % Rate 30 - * Capital of Growth 20
M \ * 1 1 1 1 1 V 9 9 9 9 9 9 5 6 6 7 7 8 5 0 5 0 5 0 Year
Figure 18 Food Processing 238 Growth Rates of Output, Labor and Capital Fur and Leather Branch Romania, 1952-85
Output
\Capital
of Growth
0 — 0
-10
50 5 0 5 0 5 Year
Figure 19 Fur 239 Growth Rates of Output, Labor and Capital Glass Branch Romania, 1952-85
50 Output
40
30 % Rate of 20 Growth I ' 10
0 ■)Lafooi7
10 5 6 6 7 7 8 8
Year
Figure 20
Glass 240 Growth Rates of Output, Labor and Capital Manufacturing Branch Romania, 1952-85
% Rate of Growth
5 1 1 1 1 1 1 1 9 9 9 9 9 9 5 6 6 7 7 8 5 0 5 0 5 0 Year
Figure 21 Manufacturing Growth Rates of Output, Labor and Capital Paper Branch Romania, 1952-85
30
25 Output 20
15
10 of X 5 Growth Capital Labor 0
-5
10
15
Year
Figure 22 Paper 2 4 2 Growth Rates of Output, Labor and Capital Printing Branch Romania, 1952-85
Output
% Rate of -10 -20
-30
-40
-50
-60
Year
Figure 23 Printing 243 Growth Rates of Output, Labor and Capital Textiles Branch Romania, 1952-85
35 \Capital 30
25 Labor 20
15 of 10 Growth 5 Output 0
-5 10
15
Year
Figure 24 244 Textiles Growth Rates of Output, Labor and Capital Soap and Cosmetics Branch Romania, 1952-85
40 Output / Capital
30
20 Labi
10 of Growth 0
10
20
-30
Year
Figure 25 Soap 5 4 2 Growth Rates of Output, Labor and Capital Woodworking Branch Romania, 1952-85
35 Output 30
25
20
15 of 10
5 0
5
10 5 0 5 0 5 0 5 Year
Figure 26 Woodworking 6 4 2 247
Rates of Total Factor Productivity: Industrial Branches
Legend
Upper _____
Lower ______
Solow ------
TFP ......
Where: Upper and Lower refer to the 95 percent confidence interval for the regression estimate of total factor productivity. Solow refers to the Solow method estimate of total factor productivity. TFP refers to the regression estimate of total factor productivity. Rate of Total Factor Productivity Chemical Branch Romania, 1951-85
100 Upper
% Rate of 1 _V-Kr! \
TFP (1/ ' -20 # Solow 9 9 9 -40
-60
-80 Lower
-100
Year 248 Figure 27
Chemical 1951-85 Rate of Total Factor Productivity Chemical Branch Romania, 1960-85
100
% Rate of TFP -25
-50
-75
-100 Lower
Year 249 Figure 28
Chemical 1960-85 Rate of Total Factor Productivity Construction Material Sector Romania 1951-85
Upper
Solow % Rate of V7 TFP
-40
-60 f Year
Figure 29
Construction Material 1951-85 0 5 2 Rate of Total Factor Productivity Construction Material Sector Romania 1960-85
|jper 40 % Rate
TFP TFP -20 80 -40 Lower -60
Year
Figure 30
Construction Material 1960-85 251 Rate of Total Factor Productivity Electrical Sector Romania 1951-85
50
40 Solow
30 Upper 20 of TFP 10 ____ V T - r t - T W - 0
-10 Lower
-20
Year
Figure 31 Electrical 1951-85 252 Rate of Total Factor Productivity Electrical Sector Romania 1960-85
Upper 40 / v Solow % Rate TFP of TFP
-20 Lower
Year
Figure 32 253 Electrical 1960-85 Rate of Total Factor Productivity Ferrous Metals Branch Romania, 1951-85
60 +
40 |
20 i ...... V... of TFP -v
-20 4-
-40
Lower
Year
Figure 33
Ferrous 1951-85 254 Rate of Total Factor Productivity Ferrous Metals Sector Romania 1960-85
% Rate
TFP "T9 -20
-40 Lower
-60
Year
Figure 34 5 5 2 Ferrous 1960-85 Rate of Total Factor Productivity MBMW Branch Romania, 1951-85
25 t
20 -- Upper 15 -
10 % Rate Lower of TFP \19 \ 19/
-10 -
-15 ■L Year 256
Figure 35 MBMW 1951-85 Rate of Total Factor Productivity MBMW Branch Romania, 1960-85
\Solow
% Rate ttpper
TFP M9 Lower -10 -15
Year
Figure 36
MBMW 1960-85 257 Rate of Total Factor Productivity Non-Ferrous Metals Branch Romania, 1951-85
6olow
40 % Rate of 20 Upper TFP /v / \ -H ...... 1---1--W¥------_ _ \ t 1 ■ \f t- -/■ i -10 -20 -30 Lofter
Year
Figure 37
Non-ferrous 1951-85 258 Rate of Total Factor Productivity Non-Ferrous Metals Branch Romania, 1960-85
% Rate 20 of TFP 10 ^
Year 259 Figure 38 Non-ferrous 1960-85 Rate of Total Factor Productivity Fuel Branch Romania, 1951-85
80 60 i \ Solow / i 40 % Rate of 20 TFP 0
-20 Low2r -40
Year
Figure 39
Fuel 1951-85 260 Rate of Total Factor Productivity Mining Branch Romania, 1951-85
60
50 1 1 Solow 40
% Rate 30 of 20 TFP 10 TFP
0 -VH
-10 Lowed
-20 50 5 0 5 0 5 Year
Figure 40
Mining 1951 ro <71 Rate of Total Factor Productivity Food Processing Branch Romania, 1951-85
+ w % Rate
Upjjer TFP -10 TFP - 8 - -15 Lower -20 -25 -30
Year
Figure 41
Food Processing 1951-85 262 Total Factor Productivity
Romania, 1960-85
%Rate of TFP
65
-10 Year
Figure 42 1960-8S Food Processing Rate of Total Factor Productivity Printing Branch Romania, 1951-85
* I • 30 i ' / \ Solow 9 1 20 » n / \ ■ . »# 1 1 * ' 1 < f « 10 • ‘ ■bPWf •i »i 1 » ' • , ' —1~—i—______*... i— t— ,<■ ■ * 1 — i— i— i— i— i-—t---H----1----1----1 1 + - r * * % Rate 0 J A J - J 1 O
o ...... i 1 \ i : T ...... TFPl 1 n \! Q 9 y 9 9 i3i t ■ TFP -20 7 ' 8 8 5 6 *6* 7 -30 %cf 0 Lo\jer 0 5 5 0 \5:
-50 - -60 - Year
Figure 43 264 Printing 1951-85 Rate of Total Factor Productivity Printing Branch Romania, 1960-85
Upper
of a- 7 — --..V.. -10 \ / TFP
-30
-50 Low< -70
Year
Figure 44 5 6 2 Printing 1960-85 Rate of Total Factor Productivity Textiles Branch Romania, 1951-85
Solow
Upper •V.. % Rate
'er TFP
-10
-15
Year
Figure 45 266 Textiles 1951-85 Rate of Total Factor Productivity Textiles Branch Romania, 1960-85
15
10 Upper . _ — >$olow * A /V % Rate 5 / s'TFP* K - r 5 ^ ' V...... H-----' 1----- H- -t------1- of o ■V/ ------1 ------~V ^ 9 19 19\ 7 19 19 Lower 85 TFP 75 60 \ / -5 f 65 70 -10 -15 Year
Figure 46 267 Textiles 1960-85 Rate of Total Factor Productivity Manufacturing Sector Romania 1951-85
Solow
30
20 Upper of TFP 10 r TFP.
-10 . Lcft/er
Year
Figure 47
Manufacturing 1951-85 268 Rate of Total Factor Productivity Soap and Cosmetics Branch Romania, 1951-85
l Solow
V % Rate ■TFP TFP —/ -10 Lower -20
-30
Year
Figure 48 Soap and Cosmetics 1960-85 ro
folow
% Rate of Ah TFP -10 75 -20
-30 Lower
-40
Year
Figure 49
Glassware 1960-85 270 Rate of Total Factor Productivity Woodworking Branch Romania, 1960-85
iolow Upper
% Rate
TFP TFP
-15 Lower -25
Year
Figure 50
Woodworking 1960-85 r\> -N l Growth Rates of Output, Labor and Capital Regions
Legend
Output
Labor
Capital Growth Rates of Output, Labor and Capital Region of Arges Romania, 1961-85
35
Output 30
25
20 % Rate of 15 Growth ta6ot 10
5
0
5
Year
Figure 51 273 Arges Growth Rates of Output, Labor and Capital Region of Banat Romania, 1961-85
25
20
15 % Rate 10 Growth lutput 5
0
Year
Figure 52 4 7 2
Banat Growth Rates of Output, Labor and Capital Region of Olt Romania, 1961-85
35
30
25
20 % Rate of 15 Growth 10 ■LSb'or 5
0
Year
Figure 53 5 7 2
Olt Growth Rates of Output, Labor and Capital Region of Brasov Romania,'1961-85
25
20
15 % Rate of 10 Growth 5 .Output 0
5
Year
Figure 54 276 Brasov Growth Rates of Output, Labor and Capital Region of Hunedoara Romania, 1961-85
60
50
40 LOutput
% Rate 30 of Growth 20 10
0 Labor
Year
Figure 55 277 Hunedoara Growth Rates of Output, Labor and Capital Region of Mures Romania, 1961-85
25 Output
20
15
% Rate io of •Labor. Growth 5
0
-5
-10 x
Year
Figure 56 278 Mures Growth Rates of Output, Labor and Capital Region of Cluj Romania, 1961-85
% Rate of 10 Growth Labor
Year 279 Figure 57 Cluj Growth Rates of Output, Labor and Capital Region of Crisana Romania, 1961-85
25
20
15 % Rate of 10 CapitaP .Output Growth 5
0
Year
Figure 58 280 Crisana Growth Rates of Output, Labor and Capital Region of Maramures Romania, 1961-85
50 45 Output 40 35 30 % Rate 25 of 20 Growth 15 10 Gapjtal 5 tabor 0 -5 65 75 8070 85 Year
Figure 59
Maramures Growth Rates of Output, Labor and Capital Region of Bacau Romania, 1961-85
30 ; Labor
25
20 Output
15 of 10 •Capital
N. 5
0
5
Year
Figure 60 2 8 2 Bacau Growth Rates of Output, Labor and Capital Region of Iasi Romania, 1961-85
30
25 .Output
20
15
10 of Growth 5 0
-5 Labor 10
15
Year
Figure 61 283 Iasi Growth Rates of Output, Labor and Capital Region of Suceava Romania, 1961-85
60
50 Output
40
% Rate 30
Growth 20
10 tabor 0
70 75
Year
Figure 62 284 Suceava Growth Rates of Output, Labor and Capital City of Bucharest Romania, 1961-85
25
20
15 % Rate of 10 Growth 5 -GapifaT
0
Year
Figure 63 285 Bucharest Growth Rates of Output, Labor and Capital Region of Dobrogea Romania, 1961-85
30
25 Output 20
15 •Capital of 10
5 Labor'
0
5
Year
Figure 64 6 8 2
Dobrogea Growth Rates of Output, Labor and Capital Region of Galati Romania, 1961-85
30
25
20 putput
15 % Rate of 10 Growth Labor 5
0
5 70 10
Year
Figure 65 7 8 2
Galati Growth Rates of Output, Labor and Capital Region of llfov Romania, 1961-85
50 Output 40
30 % Rate of 20 Growth 10 Labor 0
75
Year
Figure 66 288
llfov Growth Rates of Output, Labor and Capital Region of Ploiesti Romania, 1961-85
30 Output 25
20
15 of 10 •Labor 5
0
5
Year
Figure 67 289 Ploiesti 29 0
Rates of Total Factor Productivity: Regions
Legend
Upper ______
Lower _____
Solow ------
TFP ......
Where: Upper and Lower refer to the 95 percent confidence interval for the regression estimate of total factor productivity. Solow refers to the Solow method estimate of total factor productivity. TFP refers to the regression estimate of total factor productivity. Rate of Total Factor Productivity Region of Banat Romania, 1960-85
Upper
Lower
-50 Year
Figure 68
Banat Rate of Total Factor Productivty Region of Olt Romania, 1960-85
■Upper
30 + Solow 20 I TFP %Rate of 10 t TFP
-20 Year
Figure 69 292
Olt Rate of Total Factor Productivity Region of Crisana Romania, 1960-85
Solow -Upper Rate ✓ s. V
■f F TFP TFP
80 Lowi -30
Year
Figure 70
Crisana 293 < £ CO
o-
> b i r> o 5 $ & I s £%S
V i ° £ St & 9 - & S i d > t 3 fl>. o Vr "&
% s Rate of Total Factor Productivity Region of Hunedoara Romania, 1960-85
Upper
Solow % Rate TFP
TFP
65 -10 - -Lowet_ _ .
Year
Figure 72
Hunedoara 295 Rate of Total Factor Productivity Region of Mures Romania, 1960-85
50
40 Upper
30
20 Solow of 10 TFP- TFP 0
10
-20 Lower -30
Year
Figure 73
Mures 6 9 2 Rate o f Total Factor Productivity Region o f Bacau Romania, 1960-85
1° t Rate 5 t of 0 1 TFP -5 AO AS -20
Figure 74 ro v£> • '■ s i
Bacau Rate of Total Factor Productivity Region of Iasi Romania, 1960-85
Upper
% Rate Solow \ TFP TFP -10 -20
-30 Lower -40
Year
Figure 75 8 9 2 Iasi Rate of Total Factor Productivity Region of Suceava Romania, 1960-85
40 t /\ Solow 30 +
20 t Upper % Rate of 10 + — ✓ \ TFP
Lower -20 Year
Figure 76 299 Suceava Rate of Total Factor Productivity Region of Bucharest Romania, 1960-85
% Rate of TFP TFP
TD -10 70 8075 Lower -20
Year
Figure 77
Bucharest 300 Rate of Total Factor Productivity Region of Dobrogea Romania, 1960-85
Upper
TFP %Rate of TFP Lower
-10 Year
Figure 78 W o Dobrogea -* Rate of Total Factor Productivity Region of Galati Romania, 1960-85
h Solow
A % Rate Upper — — V--TEE TFP 19 .OWi
-10
-15
Year
Figure 79
Galati 302 APPENDIX B
DATA TRANSFORMATIONS
Indexing of the Data Output
The official value of industrial production of the Romanian industrial sector is used as the measure of industrial output in this study. It is available in a lei denominated form over the 1975-85 period from the Anaurul Statistice al RPR/RSR (1976-861. The value of industrial production covering the years of 1975-77 was valued in 1963 constant prices while production for the years of 1977 through 1985 was valued in 1977 constant prices. A consistent data series was produced by converting the industrial production valued in 1977 prices into a series valued in 1963 prices. This was accomplished by applying the following relation:
12) {(P77 Q80 ) 1 (p77 $77^ x (p63 Q77 ) = (p63 ^80^ where P represents the price, Q the physical quantities, and the subscripts represent the respective years of the prices and quantities. The first bracketed term, {}, is the growth rate of industrial production, using 1977 prices. The following term is the
303 304 value of 1977 production in 1963 prices and is used to convert the value of production valued in 1977 prices into 1963 prices.
The gross value of production of industrial goods for the remaining years, 1951-1974, is not published directly, but is indirectly attainable from the indices of industrial production. The value of industrial production for each of the remaining years was derived by applying the familiar relation of index numbers:
13) {(P63 Q75 /(P6 3 063)* x 100(base 1963) = index number of 1975(base 1963) where the value of production of 1963 (in 1963 prices) is the unknown. This can be solved for since the base year has an index number equal to 100 and the 1975 production and index number are known. The value of industrial production for the years of 1960-
1974 were easily obtained by using the index numbers and the value of industrial production of 1963. The industrial production of the years prior to 1960 was obtained by applying the same method in converting the figures from 1955 prices into production valued in
1963 prices.
The official value of industrial production attributed to the individual industrial branches and regions was derived directly from the above value of industrial production. The statistical yearbooks publish the percentage shares of production of each branch and 305 region for each year1. Thus, the following expression results in the
industrial output of the respective branch and regions for a specific year:
14) Outputj t = Sector outputt x %sharej t where i is the respective branch or region and t is the year, sector output is the value of industrial production of the industrial sector and output is the value of industrial production of the respective branch and region.
Capital
The statistical yearbooks publish the industrial sector’s capital stock, however, similar to the value of industrial output, the figures are denominated in the prices of different years. Therefore, it was again necessary to transform the data into a consistent form by using a single year’s prices. The prices of the year 1963 were also chosen for this purpose here. The same method used to convert the value of industrial production into a single year’s prices was
used for the calculation of the industrial capital stock values. This was only necessary for the RK (Romanian capital stock) series. The capital stock series obtained from the L. W. Financial International
Research Company, WK (Western estimate), was already valued in
1977 constant prices, therefore no conversion was necessary.
1 This data is available for the entire 1951-85 period for the industrial branches. However, it is only available over the 1960-85 period for the regions. 306 Both the RK and WK data series were used to estimate the capital stock of the industrial branches and regions. The statistical yearbooks do not publish the capital stock of the individual industrial branches nor the individual regions, and therefore it was necessary to estimate these (discussed in the next section).
The capital stock estimates of the branches and regions, which are derived from the RK and WK capital series, are also subject to the linkage errors discussed in Chapter 2. Further information concerning the derivation of the WK capital stock series was not provided to the author from the L. W. Financial International
Research Company.
Labor
The labor data series used in this study are not subject to the indexing error since the the figures are reported in absolute terms.
Capital Stock Estimation
Although the value of the industrial sector’s capital stock is available, the capital stock of the industrial branches and regions is not directly available. Therefore, the capital stock of the branches and regions had to be estimated.
Only the capital stock of aggregate industry is directly available from Romanian statistical sources, as well as from the F.
W. Financial international Research Company. To obtain the 307 estimates of the branches’ and regions’ capital stock it was necessary to begin by finding the change in the capital stock of industry, otherwise known to as net investment. Therefore, given the value of the industrial capital stock (represented by RK and WK respectively), the changes in the capital stock of industry were derived by the following relation:
15) AKt = Kt - Kt-1 where K is the capital stock of industry, AK is the change in the capital stock and t is the year.
The next step is to establish what portion of this net investment of industry was allocated to each of the branches and regions. The statistical yearbooks do provide the share of total industrial investment that was allocated to each branch and region for every year2 thus it is possible to estimate the change in the capital stock of the industrial branches and regions. The following expression determines the flow of capital into the branch or region for each year:
16) Akj t = AKt x % sharet of K where K is defined as above, k the value of capital stock in the respective branch or region, i is the respective branch or region, t the year, and Ak the capital flow to the branch or region. These
2 Again, the shares were available for the branches and regions from 1951 onward, however, labor and production data for the regions is not available prior to 1960. 308 flows were added to the estimates of the branches’ or regions’ capital stock (addressed below) to estimate their respective capital stock series.
The estimates of the branches’ percentage shares of the industrial capital stock for 1955 were estimated by the average of the percentage shares of net investment (discussed above) within each respective branch over the 1951-60 time period. The following expression indicates how these shares were estimated:
I960 17)% share of Kjfi 955 = (2 1=1951 0 /0 share ° f capital flowj t /1 0) where t is the year, i represents the respective branch, K is the capital stock of the industrial sector. By obtaining an estimate of the percentage share of the industrial sector’s capital stock for each branch, an estimate of the capital stock held by each branch in
1955 could be made. This was done by the following relation:
18) ki, 1 955 = % share of Ki, 1955 x K1955 where k represents the capital stock of the respective branch, K the capital stock of the industrial sector, i the respective industrial branch for the year of 1955.
The derivation of the remainder of the branches’ capital stock series entailed multiplying the change in industrial capital stock by the percentage share of investment in of each respective branch. 309 Finally, this figure was added to the estimate of the capital stock.
This procedure is expressed by the following relationship:
19) Akj t = AKt x % share of investment; t where Ak is the change in the branch's capital stock, AK, i, and t are defined as before. Thus, the change in the branch’s capital stock is added to the estimate of the capital stock derived in equation 18.
The capital stock series of the regions was estimated in a similar fashion and employed the same assumptions and techniques as that described above for the estimation of the capital stock series of the branches. However, there are two differences in the regional capital stock estimates as compared to the branch estimates.
First, the average share of investment in each region was taken over the 1960-69 period since the data was not available prior to 1960. Secondly, a portion of the data had to be transformed from district data into regional data. This transformation is discussed in the Geographical Conversion section (Appendix C).
The question that now arises is, “ Is the average of the percentage share of investment (over these ten year periods) a reasonable substitute for the percentage share of capital stock?”
The answer is, yes. Tables 42 and 43 indicate that the average percentage share of investment produces a close approximation of 310 Table 42 Percentage of Industrial Capital Stock and the Average Annual Percentage of the Industrial Gross Investment Within Each Industrial Branch in the Soviet Union 1960-80
Branch % Capital Stock % Ave. Annual Gross Inv. 1965 1969 1960-70 1960-80
Fuel/Electric 30.30 30.20 31.44 30.44 Ferrous 10.30 10.30 10.31 9.02 Chemical 7.62 8.54 8.90 9.22 MBMW 18.81 19.36 19.72 19.64 Construction 5.60 5.54 5.60 5.46 Timber/Paper 5.30 5.30 4.98 4.93
Data was derived from US Government document SOV 82-10093, pp. 3-7. 311 Table 43 Percentage of Industrial Capital Stock and the Average Annual Percentage of Industrial Gross Investment Within Each Industrial Branch for Hungary, Poland, Bulgaria And the Soviet Union, 1960-64
Hunoarv Branch 1960 1964 1961-64
Fuel/Electric 31.6 32.5 33.4 Metallurgy 12.1 11.4 10.5 MBMW 17.9 17.4 14.4 Chemical 8.8 10.6 16.6 Construction 4.9 5.8 7.1 Food 13.0 10.7 9.8
Poland Branch % Caoital Stock % Ave. Annual Total Gross Inv. 1960 1961-64
Fuel/Electric 30.8 34.4 Metallurgy 10.9 11.9 MBMW 15.7 14.9 Chemical 8.8 11.8 Construction 8.4 8.1
B u lga ria Branch % CaDital Stock % Ave. Annual Total Gross Inv. 1960 1964 1961-64
Fuel/Electric 29.4 28.7 33.4 Metallurgy 13.6 18.1 23.1 MBMW 11.4 11.5 10.3 Chemical 6.6 8.7 8.8 Construction 5.2 5.3 5.3 Timber/Paper 5.4 5.1 4.6 Food 6.9 11.9 15.2 312
Table 43 Continued
Soviet Union Branch % Caoital Stock % Ave. Annual Total 1960 1964 1961-64
Fuel/Electric 29.7 28.7 30.4 Metallurgy 10.6 10.7 9.4 MBMW 19.6 19.7 15.7 Chemical 5.7 7.8 9.3 Construction 6.2 6.5 6.2 Timber/Paper 6.1 5.7 5.4 Food 9.6 9.1 7.4
Data taken from Survey of Europe 1965, p. 64. 313 the share of capital stock within industrial branches. Table 42 contains data from Gillula (1983) pertaining to his estimation the percentage share of branch capital stock in the Soviet Union. It compares the average percentage of total gross industrial investment of each branch to the percentage of the total capital stock held within each respective branch in 1965 and 1969. These figures indicate that the average annual percentage of gross industrial investment does produce a close approximation of the share of capital stock within each of these branches. This is true whether one uses the 1965 or 1969 figures of the share of industrial capital stock held within each branch.
The USSR is not the only country for which this relation holds.
Table 43 indicates that the same relationship holds for Poland,
Hungary, Bulgaria and the Soviet Union when a shorter time period is used for the average rate of investment within the respective branches. Thus, it is assumed that the same relationship, that between the average share of investment within the branch and the share of industrial capital stock, holds in the case of Romania during similar time periods. This assumption is also extended to the regions in the case of Romania. The estimates of the average annual shares of total investment for the industrial branches and regions are presented in Table 44. The underlying intuition here is that after 314 Table 44 Average Annual Percentage Share of Industrial Gross Investment In Romanian Industrial Branches and Regions Romania, 1951-1960
Branch % Share Region % Share
Chemical 8.7 Arges 4.9 Construction 4.2 Bacau 10.4 Electricity 11.4 Banat 6.5 Ferrous Metals 8.7 Brasov 6.5 Food Processing 5.6 Bucharest 15.5 Fuel 34.7 Cluj 3.9 Fur and Leather 0.4 Crisana 2.8 Glassware 0.5 Dobrogea 6.3 Machine Building 9.5 Galati 5.1 Manufacturing 0.1 Hunedoara 6.7 Mining 0.6 Iasi 2.8 Non-Ferrous Metals 5.5 llfo v 4.8 Paper 2.4 Maramures 2.4 Printing 1.0 Mures 2.9 Soap 0.1 Oltania 5.4 Textiles 2.1 Ploiesti 9.6 Woodworking 6.2 Suceava 2.3 Other 0.7
Data obtained from official Romanian statistical yearbooks, 1955- 1986 315 WWII these nations needed to rebuild their economies and therefore the average annual rate of investment approximates the share of the branches’ and regions’ capital stocks. Though the capital stock of industry was not zero immediately following the war, it was low enough so that the percentage share of investment is a good approximation for the percentage share of the capital stock held within a branch or region. APPENDIX C
GEOGRAPHICAL CONVERSION
Conversions
The year 1968 marked the first year in which regional data was no longer published, as it was being published in a district form.
The adoption of the new administrative district system in Romania made it necessary to covert the value of industrial production, percentage share of investment and labor figures back into the regional form (used prior to 1968). Under the regional system
Romania was divided into 18 regions, two of which were the cities of Bucharest and Constanta. Data pertaining to Constanta was published only for a short time and thereafter was included in the region of Dobrogea. Hence, there are 17 regions in this study since the city of Constanta was included in the data pertaining to the region of Dobrogea. Only the data pertaining to the city of Bucharest spanned both periods (1960-68 regional, 1969-85 district) so it alone was unaffected by the political transformation. At the present time Romania has 41 districts, including the city of Bucharest. In
316 317 1980, the district of llfov was partitioned into the two separate districts of Calarasi and Giurgiu. The data of these two new districts was combined to arrive at the totals for llfov when transforming the district data into the regional form.
It was not possible to partition the data pertaining to the old
regions in such a way to reflect the the new districting. However, it was possible to convert the district data into the old regional form.
The majority of the new districts lied within the borders of the older regions. Figure 8 presents maps A and B which indicate the old regions and new districts in Romania, respectively. Only several districts overlap two old regions. When this occurred, the data of all the districts within the two regions were aggregated and compared to the regional total. Regional and district data were published for the years of 1965-67, so these comparison could be made. The comparisons of the district totals with the regional totals made it possible to assign the percentage share of output, labor, and capital of a district to a region. These percentages were used to estimate the remaining years’ values of industrial production, labor and capital stock. For example, to reach the value of industrial
production for the region of Arges, it was necessary that 75 percent of the district of Olt’s production be assigned to Arges. The
remaining 25 percent was assigned to the region of Oltania. 318 Table 45 lists the regions and the new districts which comprisethe old regions. Table 46 lists the percentages of the districts’ value of industrial output, labor, and share of investment which were assigned to each of the regions the district overlapped.
Areas
The areas of the regions were compared to the summed areas of the new districts using data taken from the 1962 (regional) and
1985 (district) statistical yearbooks. The sum of the district areas were approximately the same as the areas of the regions which were
“cleanly partitioned” (those regions divided equally into districts).
The regions which were not “ partitioned cleanly” (Alba, Arad, Mures and Olt) also have close approximations in area, when the proper percentages of the areas of the districts contained in the region are applied. These percentages are very close to those used in the above estimation of production, rate of investment and labor. The lone exception was the the district of Alba, of which 18 percent was included in the region of Cluj and 82 percent in Hunedoara. This is quite possible since the district of Alba is relatively highly industrialized. The corresponding areas of the regions and the district which comprise the regions are reproduced in Table 47. 319 Table 45 Listing of the New Districts Which Comprise the Old Regions of Romania
Old Reaions New Districts
Arges Arges, Olt#, Vilcea Bacau Bacau, Neamt Banat Arad#, Caras, Timis Brasov Brasov,Covasna, Mures#, Sibiu llfo v lalomata, llfov, Teleorman Cluj Alba#, Bistrita, Cluj, Salaj Crisana Arad#, Bihor Dobrogea* Constanta, Tulcea Galati Braila, Galati, Vrancea Hunedoara Alba#, Hunedoara Iasi Iasi, Vaslui Maramures Maramures, Satu Mare Mures Harghita, Mures# Oltania Dolj, Gorj, Mehendinta, Olt# Ploiesti Buzau, Dimbovita, Prahova Suceava Botosani, Suceava
*- indicates that separate data of the city of Constanta is included in the district and regional total through 1959. Prior to 1959 Constanta city data was reported a separate region in the statistical yearbook, but it is always included in the regional data in this study.
#- indicates that part of the district lies across the borders of two regions. In order to arrive at a percentage of contribution to output, capital, and labor made by the districts to the region the method described in the Appendix was used. 320 Table 46 The Percentages of Production, Investment, and Labor of The Districts That Lie Between Two Regions
Production D istrict Region Alba 40% Cluj, 60% Hunedoara Arad 15% Banat, 85% Crisana Mures 8% Brasov, 92% Mures Olt 75% Arges, 25% Oltania
Labor D istrict Region Alba 50% Cluj, 50% Hunedoara Arad 11% Banat, 89% Crisana Mures 15% Brasov, 85% Mures Olt 60% Arges, 40% Oltania
C a p ita l D istrict Region Alba 60% Cluj, 40% Hunedoara Arad 15% Banat, 85% Crisana Mures 18% Brasov, 82% Mures Olt 80% Arges, 20% Oltania
The above percentages were derived from the overall production,labor and investment totals. The percentages were applied to the corresponding industrial totals of their respective districts to arrive at the district’s contribution to each region’s total. 321 Table 47 Comparison of the Area of the Old Regions and the Summed Areas of the New Districts Romania, 1962 and 1985, in Square Kilometers
Old Reaions Area New Districts Summed Area
Arges 15,800 Arges, Olt(75%), Vilcea 15,800 Bacau 13,400 Bacau, Neamt 12,496 Banat 21,820 Arad (40%), Caras, Timis 20,614 Brasov 15,090 Brasov, Covasna 1 5,090 Sibiu, Mures(3%) Bucharest 20,480 lolamata,llfov,Teleorman 20,311 Cluj 16,820 Alba (18%), Bistrita 16,280 Cluj, Salaj Crisana 12,240 Arad(60%), Bihor 12,340 Dobrogea 15,460 Constanta, Tulcea 15,485 Galati 12,190 Braila, Galati, Vrancea 14,012 Hunedoara 11,000 Alba(82%), Hunedoara 12,100 Iasi 11,100 Iasi, Vaslui 10,766 Maramures 12,250 Maramures, Satu Mare 12,825 Mures 12,250 Harghita, Mures(97%) 12,514 Oltania 20,130 Dolj.Gorj, Mehendinta 20,152 Olt(25%) Ploiesti 13,100 Buzau, Dimbovita, Prahova 13,504 Suceava 13,750 Botosani, Suceava 13,520 BIBLIOGRAPHY
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