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Measuring Interactions among Agricultural Productivity, Trade Openness, Agricultural
GDP, and Income in Korea (1972- 2007)
Seong-Hoon Cho1, Tun-Hsiang “Edward” Yu1, and Yong-Taek Kim2
Poster prepared for presentation at the Agricultural & Applied Economics Association 2010
AAEA, CAES, & WAEA Joint Annual Meeting, Denver, Colorado, July 25-27, 2010
Copyright 2010 by Cho, Yu, and Kim. All rights reserved. Readers may make verbatim copies of
this document for non-commercial purposes by any means, provided that this copyright notice
appears on all such copies.
1
Assistant Professor, Department of Agricultural Economics, University of Tennessee,
Knoxville, TN.
2
Senior Director, Korea Rural Economic Institute, Seoul, Korea.
Measuring Interactions among Agricultural Productivity
Productivity, Trade Openness,
Openness
Agricultural GDP, and Income in Korea (1972- 2007)
Seong
eong--Hoon Cho, Tun
Tun--Hsiang “Edward” Yu, and YongYong-Taek Kim
Agriculture in South Korea g
has developed in line with the progress of the national p
p g
economy. While agriculture used to be the backbone of the country’s economy, it has become a smaller component of national income. Even with the decline, the agricultural sector is still crucial for the country’s rural economy as 7 percent of the economically active population lives in rural areas where agriculture provides most
economically active population lives in rural areas, where agriculture provides most employment opportunity (Statistics Korea 2010). Given the importance of the agricultural sector, the South Korean government has expanded the public funds allocated to the agricultural sector to sustain agricultural growth. The support for the agricultural sector has increased further since the country opened its agricultural
agricultural sector has increased further since the country opened its agricultural market through the Uruguay Round of General Agreement on Tariffs and Trade of multilateral negotiations (the “Uruguay Round”) that was completed in 1997. The change of South Korean agricultural structure seems inevitable as liberalizing agricultural trade continues and its corresponding agricultural policies have been restructured. This paper applies the directed graph and times series model to measure interactions among agricultural productivity, trade openness, agricultural 2007.
GDP, and income in South Korea during the period of 1972–
g
p
20,000
0.800
15,000
0.600
10,000
0.400
5,000
0.200
0.000
1972
1974
1976
1978
1980
 s [ln z
j1
j
j
( t )  ln z j ( t  1)]
where is total input of j
h
t f jth category (j=1,..4)** during time t and is t
(j 1 4)** d i ti
t d sj i
z j(t) i t t l i
th
weight of j category between t and t‐1
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
0
2006
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Openness
Real Ag GDP
0.80
25,000
0.60
20,000
0.40
15,000
10,000
0.20
5 000
5,000
0
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
Openness
1994
1996
1998
2000
2002
2004
0.00
2006
1972
1974
1976
1978
1980
1982
1984
I
Income
Forecast Error Variance Decomposition (FEVD)
F
E
V i
D
i i (FEVD)
Openness
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
The directed graph is generated based on the correlation matrix of the residuals from the vector autoregression (VAR) equations of those four variables The graph suggests
those four variables. The graph suggests that, in the contemporaneous period, agricultural productivity and trade openness are exogenous. Shocks in agricultural d ti it
dt d
ff t
productivity and trade openness affect agricultural GDP and the impacts pass through to the national income per capita.
TFP
Ag GDP
Y ,where Y is total product and Z is total input
Z
dT FP
(2)
[ln Y ( t )  ln
l Y ( t  1)]  [ln
[l Z ( t )  ln
l Z ( t  1)]
 [l
9
dt
where ln Y ( t )  ln Y ( t  1) 
  i [ln y i ( t )  ln y i ( t  1)]
4
1982
30,000
(1) T FP 
i
where ln Z ( t )  ln Z ( t  1) 
25,000
1.000
TFP (Total‐factor productivity): A variable which accounts for effects in total output not caused by inputs
i  1th
p
g y
g
(
)
g
where is total product of i
category of agriculture (i=1,..9)* during yi (t)
time t and is weight of ith category of agriculture between t and t‐1
Real Income Per Capita
TFP
1.200
Openness Ag GDP
Income
Ag GDP
80
Income
TFP
TFP
100
100
80
80
60
60
40
40
20
20
100
60
80
60
40
40
Real Ag GDP ($1,000,000): Real Ag GDP adjusted by exchange rate g
g
j
y
g
Real Income Per Capita ($1,000,000): Real income divided by population
Openness: (Ag including livestock exports + Ag including livestock imports) / g
Nominal Ag GDP
* The 9 agricultural categories consist of rice, barley, grains, legumes, potatoes, fruits, vegetables, livestock, and others including specialty crops such as cottons and sesame, tobaccos and ginsengs, and silkworms. ** Th 4 i
** The 4 inputs consist of labor, capital, fertilizer, and land.
t
i t fl b
it l f tili
d l d
20
20
0
0
2
3
4
5
6
7
8
9
0
0
1
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
10
The variance in agricultural GDP is explained by the shocks in itself and agricultural TFP initially, however, in the long run, the impact of productivity significantly i
increases while national income also contributes to the agricultural GDP. For hil
i
li
l
ib
h
i l
l GDP F
national income, although the forecast error variance is primarily explained by itself and agricultural GDP in the early stage, the impact of agricultural GDP quickly p
p
g
declines in the outer period. The variances in trade openness and agricultural productivity are primarily affected by national income in the long run.