Measurement of Environmentally Adjusted Agricultural Total Factor Productivity Using the Malmquist-Luenberger Productivity Index in Korea

Transcription

1 OECD Expert Workshop on Measuring EATFP and Its Determinants (14-15 December 2105) Measurement of Environmentally Adjusted Agricultural Total Factor Productivity Using the Malmquist-Luenberger Productivity Index in Korea Improve Agricultural Productivity Kim, Chang-Gil Korea Rural Economic Institute Taeyoung Kim Dept. Food & Resource Economics Gyeongsang National Univ. Reduce GHG Emission

2 Outline I. Introduction II. III. IV. Historical trends of agricultural production and GHG emissions in Korea Malmquist productivity index (MPI) Malmquist-Luenberger (ML) productivity index V. Data VI. Results of ML index in Korean agriculture VII. Concluding remarks 2

3 I. Introduction An environmentally sustainable economic growth in agriculture can be conceptualized as trying to achieve improvements in agricultural productivity and reduction of greenhouse gas (GHG) emissions through the adoption of green technologies. Traditional (conventional) methods to evaluate the productivity performance often neglect the influence of byproducts of the undesirable outputs, such as GHG emissions. 3

4 I. Introduction cont. Traditional productivity measures can lead to a bias in evaluating the real productivity performances when taking environmental impacts into account. It is necessary to employ an environmentally adjusted total factor productivity (EATFP) indicator as an alternative measure of agricultural productivity growth. OECD suggested the EATFP as one of the key indicators for monitoring progress of green growth in agriculture. Green Growth is an approach to pursuing both economic growth and ecological sustainability and moving toward a low carbon-green growth economy. 4

5 I. Introduction cont. Objective To apply a Malmquist-Luenberger (ML) productivity index as a measure of environmentally adjusted productivity index in agricultural sector by taking into account both desirable (agricultural productivity) and undesirable outputs (agricultural GHG emissions) together as opposed to conventional measures considered only desirable output. 5

6 Total farm production (Billion Won) II. Historical trends Trends of Total Production in Korean Agriculture: Sources: KOSIS.kr Annual Growth rate: -0.98% 6

7 GHG Emission (Gg, CO2 eq.) II. Historical trends cont. Trends of GHG emission in Korean Agriculture: Total Crop Livestock Sources: Own calculation based on Tier I. Annual Growth rate: Crop -1.7%, Livestock 0.95%, Total -0.71% 7

8 Emission (metric ton, CO2 eq.) II. Historical trends cont. Trends of GHG emission per farm household: Sources: Own calculation based on Tier I. Annual Growth rate: 1.15% 8

9 =100 II. Historical trends cont. Trends in Total GHG Emissions & Total production: (1993=100) 120 Total production GHG emission Sources: KOSIS.kr 2015, and emission estimation in this report. Annual Growth rate of TP and total GHG emission TP: -0.98%, GHG emission: -0.71% 9

10 =100 II. Historical trends cont. Carbon Productivity (McKinsey Global Institute, 2008): Total agricultural production Carbon Productivity = Level of emissions (CO 2 eq.)

11 =100 II. Historical trends cont. Trends in GHG Emissions & Total production per farm household: (1993=100) Total production GHG emission Sources: KOSIS.kr 2015, and emission estimation in this report. Annual Growth rate of TP and total GHG emission per farm household TP: 0.71%, GHG emission: 1.53%

12 III. Malmquist Productivity Index (MPI) Malmquist Productivity index makes use of distance functions to measure productivity change (Caves, Christensen and Diewert, 1982). <Output-orientated Malmquist productivity Index (MPI)> 12 Based on average change in production for two periods given technology frontier

13 III. MPI cont. MPI is defined as the geometric mean of the two: Efficiency change implies change in relative efficiency (i.e., the change in how far observed production is from maximum potential production) between years t and t + 1 Technical change implies shift in technology between the two periods

14 IV. Malmquist-Luenberger(ML) Productivity Index Malmquist-Luenberger productivity index introduced by Chung, Färe, and Grosskopf (1997), Färe, Grosskopf, and Pasurka (2001) - Nonparametric approach based on directional distance function where g=(g y, -g b ) is a direction vector, β is the maximum feasible expansion of goods and reduction of bads at the same proportions for a given level of inputs, and the goods and bads are asymmetric - Credits a producer for simultaneously reducing the production of the undesirable output and increasing the production of the desirable output 14

15 IV. ML Productivity Index cont. ML Productivity Index the geometric mean of the two Technology of period 1 Technology of period 2 15 Efficiency change The ratio of how each observation is close to its respective frontier in terms of proportional increase in goods and decrease in bads. Technical change The geometric mean of the change in the production possibilities frontier.

16 IV. ML Productivity Index cont. Efficiency change(ec): EC = 1 indicates a producer is at the same distance from the frontier between the period t and t+1 (i.e. no change in output efficiency between the two periods) EC > 1 indicates a producer becomes closer to the frontier in period t+1 than period t (i.e. an improvement in output efficiency over time). EC < 1 indicates a producer becomes further from the frontier in period t+1 than period t (i.e. a decrease in output efficiency over time). Technology change(tc): TC = 1 indicates there is no shift in production possibilities frontier TC > 1 indicates the production possibility frontier shifts into more goods and fewer bads. TC < 1 indicates the production possibilities frontier shifts into the direction of fewer goods and more bads. 16

17 V. Data , province level Desirable output (goods): Total production obtained from KOSIS.kr Undesirable output (bads): Agricultural GHG emission from rice, soil, crop residue, livestock and manure management calculated based on Tier I. Inputs: Material costs (such as seed, feed, fertilizer, pesticide, etc), Labor cost, and other costs (such as depreciation, fuels and Electricity costs, etc.), obtained from KOSIS.kr 17

18 VI. Results of ML index in Korean agriculture ML EC TC ML productivity had been decreased by 0.2% per year. Average ML productivity = < 1, Average EC = 1, Average TC = < 1 The fluctuation of cumulative ML productivity follows similar patterns of technical change(tc) during the periods. This implies that TC makes ML productivity fluctuate. 18

19 VI. Results of ML index in Korean agriculture cont. MPI ML Average ML productivity = < 1, vs. Average MPI = > 1 Ignoring the undesirable output can lead to overestimation of productivity Period 93-98: ML(0.988) < MPI(1.040), where TP, Emission Period 98-03: ML(1.005) < MPI(1.018), where TP fluctuate, Emission (TP in 2003) Period 03-08: ML(1.015) < MPI(0.990), where TP, Emission Period 08-11: ML(1.015) < MPI(0.990), where TP, Emission 19

20 VI. Results of ML index in Korean agriculture cont. Relationship of the relative growth rates of the traditional output (MPI) and adjusted output productivity measures(ml index): Färe, Grosskopf, and Pasurka (2001) mentioned that the relative growth rates of the traditional output and adjusted output productivity measures depend on the relative growth rates of the desirable and undesirable outputs. For a given input vector, if the percentage increase in desirable output exceeds (is less than) the absolute value of the percentage decrease in the undesirable output, then the growth rate of traditional productivity exceeds (is less than) the growth rate of the adjusted productivity.

21 VI. Results of ML index in Korean agriculture cont. Chungnam Jeonbuk Jeonnam ML Index Gyeonggi Gangwon Chungbuk Gyeongbuk Gyeongnam Chungnam MPI Gyeonggi Jeonbuk Jeonnam Gangwon Chungbuk Gyeongbuk Gyeongnam Average MPI>1 for all provinces with no consideration of bads 4 provinces (Gangwon, Gyeongbuk, Gyeongnam, Jeju) have average ML productivities with greater than 1 Jeju Jeju 1.025

22 VI. Results of ML index in Korean agriculture cont. Difference between ML and MPI Chungnam Gyeonggi Jeonbuk Jeonnam Gangwon Chungbuk Gyeongbuk Gyeongnam Difference = MPI-ML Only Gyeongnam province has been increased when an GHG emission takes into account Gyeonggi is the most affected province after taking GHG emission into account See next slide to check why 22 Jeju

23 = =100 VI. Results of ML index in Korean agriculture cont. Why? Difference between Gyeonggi and Gyeongnam TP GHG TP, Emission TP GHG TP, Emission <Gyeonggi> Gyeongnam

24 VII. Concluding Remarks Advantages of M-L index It can simultaneously consider undesirable by-products as well as desirable outputs, so that it allows us to target both increase of good output and reduction of bad outputs, while traditional productivity measures used to ignore bad outputs. This approach measures the productivity given all inputs being controlled. We can also take advantage of using non-parametric frontier technologies which does not require a priori functional form on technology, nor any restrictive assumptions regarding input, as opposed to parametric approaches 24

25 VII. Concluding Remarks cont. Implications of Measuring EATFP M-L productivity index as an alternative measure of green growth Technical change was the source of most of the M-L productivity increase. Ignoring undesirable bad outputs overestimates annual productivity growth Average annual M-L productivity varies considerably across provinces 25

26 VII. Concluding remarks cont. Limitations and Further Research - Lack of data on environmental inputs or outputs - Only considering bad output (GHGs) and not considering sink services (soil sequestration) - Data availability regarding the use of the environmental inputs in production and the associated costs - Need for further research for interpreting changes in the carbon productivity index and M-L productivity index 26

27 Improve Agricultural Productivity Reduce GHG Emission 27