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Estimates of Labor and Total Fac tor Productivity by 72 industries in Korea (1970-2003)

OECD Bern Workshop , October 16-18, 2006. Estimates of Labor and Total Fac tor Productivity by 72 industries in Korea (1970-2003). Oct 17. 2006 Hak K. Pyo, Keun Hee Rhe e and Bongchan Ha Institute of Economic Research Seoul National University. Contents. 1. Introduction

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Estimates of Labor and Total Fac tor Productivity by 72 industries in Korea (1970-2003)

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  1. OECD Bern Workshop , October 16-18, 2006 Estimates of Labor andTotal Factor Productivity by 72 industries in Korea (1970-2003) Oct 17. 2006 Hak K. Pyo, Keun Hee Rhee and Bongchan Ha Institute of Economic Research Seoul National University

  2. Contents 1. Introduction 2. Data Structure 2.1 Gross Output Data 2.2 Measurement of Capital Input 2.3 Measurement of Labor Input 2.4 Energy, Material, and Service and Input Shares 3. Estimates of Labor Productivity and TFP by 72-industry 3.1 Trend of Labor Productivity Level and Growth Rates by Sector 3.2 Gross Output Growth Accounting and TFP Growth 3.3 Cumulative Contribution of Sectors to TFP growth 3.4 Relations between Labor Productivity and TFP growth 4. Conclusion

  3. 1. Introduction • The purpose - To explain the data structure of Korea for the estimation of productivities by industry in KLEMS model - To present preliminary estimates of labor productivity and total factor productivity (TFP) at reasonably detailed industry level. • We have used 72-sector industrial classificationfollowing the guidelines of EU KLEMS project for the future comparability with EU member countries, the United States, and Japan. • An analysis based on detailed industrial classification gives us better views on productivity and growth, which is difficult to grasp in broader industrial classifications.

  4. Economy growth and investment growth

  5. Average economy growth and average investment growth

  6. 2. Data Structure • Gross Output Data (1) Estimation of Use Tables - In order to reconcile the National Accounts data to our industrial classification, we have used other data sources, such as Mining & Manufacturing Census and Surveys, Wholesale and Retail Surveys, and so on. - Since we do not have detailed information on intermediate input structures, we have assumed the same intermediate input structures for the industries belonging to the same category of National Accounts classification. - As for Input-Output Tables, they have detailed commodity classifications enough to match the 21-commodity classification in National Accounts. However, since they are not annually published, we have used interpolation method for the missing years.

  7. 2. Data Structure • Gross Output Data (1) Estimation of Use Tables - In order to reconcile the National Accounts data to our industrial classification, we have used other data sources, such as Mining & Manufacturing Census and Surveys, Wholesale and Retail Surveys, and so on. - Since we do not have detailed information on intermediate input structures, we have assumed the same intermediate input structures for the industries belonging to the same category of National Accounts classification. - As for Input-Output Tables, they have detailed commodity classifications enough to match the 21-commodity classification in National Accounts. However, since they are not annually published, we have used interpolation method for the missing years.

  8. 2. Data Structure • Gross Output Data (2) Estimation of Make and Use Tables for the Missing Years - We have estimated the Make and Use Tables for the missing years, 1970-1984 and 2003-2004 through a biproportional adjustment methodology, RAS. - For the years 1970-1984 we have used the 1985 tables as benchmark tables, and for the years 2003-2004 we have used the 2002 tables. - We have annual series of each industry's gross output, value-added, intermediate input, and so on. However, because we do not have annual series of each commodity's data in Input-Output Tables, we have applied the interpolation method between existing tables and normalized them to the National Accounts data

  9. 2. Data Structure • Gross Output Data (3) Aggregation Issues - We have applied a simple summation for the Make Table aggregation over commodities under the assumption of the same deflator over all commodities produced in the same industry following Timmer (2005). - With regard to the aggregation in Use Tables, we have not applied any aggregation technique considering each commodity as different inputs.

  10. 2. Data Structure • Gross Output Data (3) Make Tables at Purchase Prices and Use Tables at Basic Prices

  11. 2. Data Structure • Gross Output Data

  12. 2. Data Structure • Measurement of Capital Input (1) Estimation of Capital Stock - Estimating Method for 1970-1997: we have applied the polynomial benchmark year estimation method to estimating depreciation by types of assets only. Thus we have generated net stocks by types of assets first for the period of 1968-97 and then, distributed them over different sectors of industries by using interpolated industrial weights between the respective benchmark years. - Estimating Method after 1997 we have to estimate capital stocks by a modified perpetual inventory method using 1997 NWS as benchmark estimates .

  13. 2. Data Structure • Measurement of Capital Input (1) Estimation of Capital Stock - Reconciliation with Database of Pyo (2003) Since the database of Pyo (2003) covers 10 broad categories of industrial sector together with 28 sub-sectors of Manufacturing, it has been reclassified and reconciled with 72 industry classification using other sources .

  14. 2. Data Structure • Measurement of Labor Input (1) data - For the present study, we have obtained the raw data file of Survey Report on Wage Structure from the Ministry of Labor and Economically Active Population Survey from National Statistical Office for the period of 1980-2003. - The data are classified by two types of gender (Male and Female), three types of age (below 30, 30-49, and 50 above), and four types of education (middle school and under, high school, college, and university above).

  15. 2. Data Structure • Measurement of Labor Input (2) Estimating Labor Quantity and Quality Inputs

  16. 2. Data Structure • Energy, Material, and Service and Input Shares - In order to decompose intermediate inputs into energy (E), material (M), and service (S) inputs, we have identified coal and lignite, crude petroleum and natural gas, uranium and thorium ores, metal ores, coke, refined petroleum products and nuclear fuel, gas, water, and electricity commodities as energy inputs, both primary commodities and remaining manufacturing commodities as material inputs, and remaining service inputs as service inputs. - Regarding shares of inputs, we have used compensation of employees as shares of labor inputs and remaining value-added as shares of capital inputs. This method may underestimate the shares of labor input by allocating the compensation of self-employed to the shares of capital input, and this gap would be especially large in primary industry.

  17. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (1) The Level of Labor Productivity and its Trend - The general trend of labor productivity reveals a rising trend but with a remarkable difference between Manufacturing and Service. the catch-up process of Korea has been well-documented by Timmer (1999) and Pyo (2001). - As observed in Pyo and Ha (2005), the labor productivity level was not reduced during the years (1997-1998) of the Asian Financial Crisis because of IMF-mandated industrial restructuring: the reduced output was matched by reduced employment leaving labor productivity level unaffected.

  18. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (1) The Level of Labor Productivity and its Trend Figure 4 Trend of labor productivity level <unit: gross output per hour(won)>

  19. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (1) The Level of Labor Productivity and its Trend - The relatively sluggish productivity gain in Service sector has been pointed out by IMF in their recent consultation with the Korean authorities as a bottleneck of sustainable growth for Korea. Inklaar, Timmer and van Ark (2006) also pointed out the slower productivity gain of service industries in Europe relative to those in the United States.

  20. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (2) The growth rates of labor productivity by Sector - The growth rates of labor productivity as summarized in Table 4 and shown in Figure 5 confirm the remarkable difference between Manufacturing and Service sector. Throughout the entire period of 1971-2003, the economy-wide labor productivity has grown at the average rate of 5.59 percent but with the sectoral difference between Manufacturing (6.99 %) and Service (2.91 %). The difference did not shrink but rather has expanded as the process of industrialization continued. For example, the difference in the 1990’s (9.55 % vs. 2.64 %) has been more than doubled since 1970’s (4.01 % vs. 2.15 %).

  21. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (2) The growth rates of labor productivity by Sector - The observed difference in both levels and growth rates of labor productivity between Manufacturing and Service can signal the difference in the degree of foreign competition, the proportion of tradable and non-tradable and the degree of domestic competition due to historically different regulatory environments. - For example, the proportion of public enterprises and their subsidiaries in total output of many service industries such as utilities (electricity, water and gas), transportation and communication is a lot greater than their proportion in Manufacturing so that their productivity improvement could have been sluggish over time. In addition, many non-tradable sectors of service industries such as retail trade, real estate and financial services, hotels and restaurants etc. have been subject to all kinds of regulations such as zoning, sanitary standards and segregated financial market services etc

  22. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (2) The growth rates of labor productivity by Sector Table 6 Growth Rates of Labor Productivity by Sector (%)

  23. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (2) The growth rates of labor productivity by Sector Figure 5 The growth rates of labor productivity <unit: log growth rates(%)>

  24. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (2) The growth rates of labor productivity by Sector Figure 5 The growth rates of labor productivity <unit: log growth rates(%)>

  25. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (2) The growth rates of labor productivity by Sector Figure 6 Growth Rates of Labor Productivity in Manufacturing (1972-03/ %)

  26. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (2) The growth rates of labor productivity by Sector Figure 7 Growth Rates of Labor Productivity in Service (1972-03/ %)

  27. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (3) Trend of TFP Growth by Sector - The growth rates of TFP by sector are shown in Figure 8. Throughout the entire period 1972-2003, Korean economy experienced about 2 break-points: mid-1970s which was the first oil shock and in 1997 which was the financial crisis. - The difference between two break points can be summarized as follows. During the second half of 1970’s, the growth rate of gross output was not low, but the growth rates of inputs such as capital(4.56%), labor(1.79%), energy(0.69%), intermediate goods(3.34%) especially, were relatively higher. - Therefore, the growth rates of TFP have been estimated as negative. In case of late 1990’s the negative growth of TFP has been resulted from the shrink of gross output rooted from economic crisis.

  28. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (3) Trend of TFP Growth by Sector - In addition we observe that the estimated TFP growth rates in Manufacturing are in general greater than in Service. It maybe due to the fact that an innovation process such as product innovation or process innovation is more sensitive and stronger in manufacturing than in service. - Also the R&D investment for innovation is in general more intensive in manufacturing than in service. So the growth rates of TFP in Manufacturing seem to be greater than in Service.

  29. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (3) Trend of TFP Growth by Sector Figure 8 The growth rates of TFP (%)

  30. 3. Estimates of Labor Productivity and TFP by 72-industry • Trend of Labor Productivity Level and Growth Rates by Sector (3) Trend of TFP Growth by Sector Figure 8 The growth rates of TFP (%)

  31. 3. Estimates of Labor Productivity and TFP by 72-industry • Gross Output Growth Accounting and TFP Growth Table 9 Gross Output Growth Accounting and TFP growth in economy-wide <unit: log growth rates(%)>

  32. 3. Estimates of Labor Productivity and TFP by 72-industry • Gross Output Growth Accounting and TFP Growth Table 10 Gross Output Growth Accounting and TFP growth in manufacturing <unit: log growth rates(%)>

  33. 3. Estimates of Labor Productivity and TFP by 72-industry • Gross Output Growth Accounting and TFP Growth Table 11 Gross Output Growth Accounting and TFP growth in service <unit: log growth rates(%)>

  34. 3. Estimates of Labor Productivity and TFP by 72-industry • Gross Output Growth Accounting and TFP Growth Table 12 The investment in IT sector

  35. 3. Estimates of Labor Productivity and TFP by 72-industry • Cumulative Contribution of Sectors to TFP growth - Following Fukao et. al, (2006) we can examine the sectoral contribution of TFP growth in Manufacturing and we can identify what are core sectors for enhancing productivity. As shown in Figure 9, the weight of gross output of the sectors with positive TFP growth is 72.4% while the weight with negative TFP growth is 27.6% during 1972-2003. - The former are basic metals, chemicals, machinery, textiles, rubber and plastic, fabricated metal, wood, other non metallic mineral, motor vehicles and trailers as non IT sectors, and electronic valves and tubes, office, accounting and computing machinery, telecommunications, radio and TV receivers as IT sectors. The latter are leather and footwear, wearing and apparel, coke and refined petroleum etc.

  36. 3. Estimates of Labor Productivity and TFP by 72-industry • Cumulative Contribution of Sectors to TFP growth Figure 9 Cumulative Contribution of sectors to TFP Growth in Economy-wide (1972-2003)

  37. 3. Estimates of Labor Productivity and TFP by 72-industry • Cumulative Contribution of Sectors to TFP growth Figure 10 Cumulative Contribution of sectors to TFP Growth in Manufacturing (1972-2003)

  38. 3. Estimates of Labor Productivity and TFP by 72-industry • Cumulative Contribution of Sectors to TFP growth Figure 11 Cumulative Contribution of sectors to TFP Growth in Service (1972-2003)

  39. 3. Estimates of Labor Productivity and TFP by 72-industry • Relations between Labor Productivity and TFP growth Figure 12 Plotting between Sectoral Labor Productivity  Growth and TFP Growth (1972-2003, %) * In case of EU-KLEMS code, #5,#6,#33,#39,#66#,#72 are excluded because of data insufficiency

  40. 3. Estimates of Labor Productivity and TFP by 72-industry • Relations between Gross Output Growth and TFP growth Figure13 Plotting between Sectoral Gross output Growth and TFP Growth (1972-2003, %) * In case of EU-KLEMS code, #5,#6,#33,#39,#66#,#72 are excluded because of data insufficiency

  41. 3. Estimates of Labor Productivity and TFP by 72-industry • Relations between Labor Productivity and TFP growth

  42. 3. Estimates of Labor Productivity and TFP by 72-industry • Relations between Gross Output Growth and TFP growth

  43. Wilcoxon Rank-Sum(Mann-Whitney) Test This test is used in place of a two sample t test when the populations being compared are not normal.Null hypothesis: two distributions are the same.

  44. (1)TFP-LP Rank Test *These values are approximate The two samples are not significantly different(P>=0.05, two-tailed test)

  45. (2)TFP-Gross output Rank Test *These values are approximate The two samples are not significantly different(P>=0.05, two-tailed test)

  46. 4. Conclusion • Throughout the entire period of 1971-2003, the economy-wide labor productivity has grown at the average rate of 5.59 percent but with the sectoral difference between Manufacturing (6.99 %) and Service (2.91 %). • The difference did not shrink but rather has expanded as the process of industrialization continued.

  47. 4. Conclusion • The growth rate of economy-wide TFP has been estimated as -0.59 percent. The growth rates of TFP in Manufacturing and Service are estimated as 0.48 percent and -0.92 percent respectively throughout the entire period of 1972-2003.

  48. 4. Conclusion • We can identify sectors that have contributed to the growth of economy-wide TFP positively by decomposing relative contribution of each sector to total TFP growth (Y-axis) with each sector’s relative weight of output (X-axis). • Leading sectors in this group include Financial Intermediation and Post and Telecommunications in Service and Basic Metals and Electronic Valves and Tubes in Manufacturing among others. • We also identify sectors with negative contribution to Economy-wide TFP growth such as Agriculture, Hotels and Restaurants, Imputation of owner-occupied housing and Media activities etc.

  49. 4. Conclusion • The relations of TFP with labor productivity and output growth can be examined by looking at the scatter diagrams and a regression analysis. • A visual inspection tells us that TFP growth is positively correlated with both labor productivity growth and output growth and TFP-LP relation is stronger than TFP –Output relation. We have adopted an implicit hypotheses that higher LP and output growth induces TFP growth through enhanced human capital and economies of scale. • In both regressions, the coefficients of LP growth and Output Growth are significant. The TFP-LP regression seems more significant than TFP-Output regression.

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