Estimation and Decomposition of Agricultural Productivity Growth in Asia

Supawat Rungsuriyawiboon Faculty of Economics Thammasat University

Introduction

“

When all people at all times have both physical and economic access to sufficient food to meet their dietary needs for a productive and healthy life

” (USAID) 

F

ood crisis and food security

are back on policy agendas

Food Price Energy Price

Introduction

 Some food price examples from the FAO

Type

White Thailand rice (second grade)

2003

$/ton 198

2007

$/ton 323 Yellow corn 105 160 Wheat 144 207 Powdered milk Soy oil 1,835 521 3,288 714

2008

$/ton 854 (+77%), (+62%) 250 (+58%), (+36%) 401 (+64%), (+48%) 4,750 (+61%), (+30%) 1,400 (+63%), (+49%)

Introduction

 Food commodity price indices have increased across the board

52% Oil&Fat 48% Cereals 32% Dairy

Introduction

 Numerous factors are influencing this price rise 

Supply side:

difficult seasonal conditions in the major production regions and increased input costs. 

Demand side:

increasing food demand, rising demand for grain for biofuels  Given the current world food situation, it is clear from the global perspectives that each world region must have a sufficient supply in agricultural products to meet the growing food demand

Asia has the potential to supply a substantial share of the expected growth in food demand Many countries undergone from CPE to a free market economy

Introduction



Asia

has experienced impressive growth in rice and wheat production

Production of Wheat, Corn and Rice



The Green Revolution

was achieved through the application of the high-yielding varieties of major cereals and irrigation system  Increased input use cannot guarantee a long-run sustainable growth rate of yields and output 

Given the potential sources of factor inputs are being exhausted

, future growth in agriculture will not only rely on mobilizing inputs but will also require rising productivity  Understanding the state of productivity improvements in Asia is important

Literature Review

 A number of studies examine intercountry differences in productivity growth: - The availability of new panel data sets - The development of frontier analysis 

Two types of frontier analysis:

Stochastic Frontier Analysis (SFA): A parametric approach - Data Envelopment Analysis (DEA): A nonparametric approach  This frontier analysis allows to not only calculate productivity, but also decompose productivity growth  Both SFA and DEA models conducted in many studies

investigate intercountry productivity growth in Asia differences in agricultural

using the panel data from the FAO

to

Literature Review



A nonparametric DEA model:

- Bureau, Färe, and Grosskopf (1995) - Fulginiti and Perrin (1997) - Arnade (1998) - Suhariyanto and Thirtle (2001) - Trueblood and Coggins (2003) - Coelli and Rao (2005) 

A parametric SFA model:

- Fulginiti and Perrin (1993) - Craig, Pardey and Roseboom (1997) - Wiebe et al (2000) - Liu and Wang (2005)  Because of data problems of transition countries in Central Asia,

previous studies just ignored these countries

Objectives



First, this study formulates a general model using a parametric technique to measure productivity growth

This approach allows to uncover what sources attributing to productivity growth.



Second, this study measures productivity growth in Asian countries

This study includes 27 countries for 25 years. The size of this sample allows us to examine productivity for almost all major nations in Asia over time.

Theoretical Framework

 Performance of a firm A study about an ability of a firm to convert inputs into outputs given a technology in the production process  Performance measurement is a relative concept  A simple measure of performance is a

productivity ratio



Productivity

is defined as the ratio of outputs to inputs

Productivity

=

outputs inputs

 The greater value implies the better performance

Productivity Measurement

 If a production technology consists of

multiple inputs and outputs

, a measure of productivity becomes more complex  Productivity measured from the multi-input and multi-output production technology is called

total factor productivity (TFP)

 TFP can be measured using a concept of

index number

TFP index

=

output index input index

Other Method to Measure Firm’s Performance

 Another method to measure the performance of a firm is to use a concept of firm’s

efficiency

 In practice, the terms,

productivity and efficiency have been used interchangeably

.  However, they are not precisely the same things.



Efficiency of a firm

is measured using

a production frontier.

A Measure of Technical Efficiency

   Consider a simple production process in which a single input (x) is used to produce a single output (y) Line OF’ represents the maximum output attainable from each input level.

The line OF’ is called a production frontier

Consider three firms, that is A, B and C, are operating as follows • Firm A is operating beneath the frontier OF’ whereas firm B and C are operating on the frontier OF’ • Firm B and C are

efficient technically

• Firm A is

technically inefficient

•

Technical efficiency (TE)

measured by the distance. TE is equal to

0A/0B

or

0C/0A

can be

Distinction between Technical Efficiency and Productivity

 From the figure, firm A is

technically inefficient

whereas firm B and C are

technically efficient



Productivity of these firms are measured by the slope of the rays from origin

 Firm C has higher productivity than firm A and B.

Firm C has the highest productivity



Point C is the point technically optimal scale.

Operation at any other point on the production frontier results in lower productivity. Point C indicates an operation at

of scale economies

Distance function

 Consider a production technology when used to produce

multiple outputs multiple inputs

are   

Production frontier

technology can not use to describe this production Shephard (1953, 1970) proposes

a distance function

describe the structure of production technology with inputs and outputs to multiple

Two types of distance function

1. Input distance function, D I 2. Output distance function, D o

  

Output Distance Function (D

o

)

The minimum amount by which an output vector can be deflated and still remain producible with a given input vector.

Output distance function D o (x,y)

D o    min   : is defined as y   P   

where P(x) = {y: (y,x) Є T}

Consider M = 2

This figure shows that the output vector y is producible with input x, but so is the radially expanded output vector (y/μ*)

B So, D 0 (x,y) = μ * = OA/OB ≤ 1 A D 0 (x,y) = TE 0

Properties of Output Distance Function

 (i) D o (x, 0) = 0 and D o (0, y) = ∞     (ii) D o (x, λy) = λD o (x, y) for λ > 0

(HOD+1 in y)

(iii) D o (λx, y) ≤ D o (x, y) for λ ≥ 1

(non-increasing in x)

(iv) D o (x, λy) ≤ D o (x, y) for 0 ≤ λ ≤ 1

(non-decreasing in y)

(v) D o (x, y) is convex function in y

Methodology



Total Factor Productivity (TFP) growth:

not explained by growth in input uses Residual growth in outputs 

Färe et al. (1989)

proposed a

Malmquist TFP index

productivity growth using the

output distance function

to measure  The

output distance function

D o t



X t

,

Y t

  min at period t   : 

X t

,

Y t

  

T t

 represents the minimum amount by which y t remain producible with x t can be deflated and still

Methodology

 The

Malmquist TFP index

M o t ( x t  1 , in period

t

y t  1 , x t , y t )  D o t ( x t  1 , y t  1 ) D o t ( x t , y t )  The

Malmquist TFP growth index

between

t

and

t

+ 1 M o ( x t  1 , y t  1 , x t , y t )     D o t D t ( o x t  1 ( x t , , y t  1 ) y t )  t  1 D ( o D o t  1 x t ( x  1 t , , y t  1 ) y t )    1 / 2

Period t Period t+1 Malmquist TFP growth (MTC) Technical Efficiency Change (TEC) Technical Change (TC) Scale Efficiency Change (SEC)

TFP growth decomposition

Methodology

 Orea (2002) employs a parametric technique to derive a generalized MPC decomposition.

 The

output distance function

taking the Translog functional form ln

D o it

(  )   0 

m M

  1 

y m

ln

Y mit

 1 2

m M M

   1 1

y m y m

ln

Y mit

 ln

Y mit



k K

  1 

x k

ln

X kit

 1 2

k K K

  1 1 

x k x l

ln

X kit

 ln

X lit



k K M

   1 1

x k y m

ln

X kit

 ln

Y mit

 

t t

 1 2 

tt t

2 

k K

  1 

x k t

ln

X kit



t



m M

  1 

y m t

ln

Y mit



t

 Young’s theorem requires

linear homogeneity in outputs

M m   1  y m  1 , n M   1  y m y n  0 , M m   1  x k y m  0 , M m   1  y m t  0  ln

Y Mit

   0 

M



m

 1   1

y m k K M

   1  1

m

 1 

x k y m

ln

Y

*

mit

ln  1 2

M m

 1

M

   1

X kit

 ln

Y

*

mit n

 1   1  

z t



y m y m

ln

Y

*

mit

1 2 

tt t

2   ln

Y

*

mit k K

  1 

x k t

ln 

k K

  1 

x k X kit



t

 ln

X kit M



m

 1  1 

y m t

 1 2

k K K

    ln

Y

*

mit



t



x k

 ln

x l

ln

D o it X kit

 ln

X lit

Methodology

 The decomposition of MTC can be calculated as

Technical Efficiency Change (TEC) Technical Change (TC) Scale Efficiency Change (SEC)

    ln ln

D t o

 1

D t o

    1 2      ln

D t o

 1 

t

(  )   ln

D t o



t

(  )     1 2    

k K

 1    

K k

 1

e kt

 1  1   

s kt

 1      

k K

  1

e kt

 1   

s kt

       ln ln

X kt

 1

X kt

  

Data

 The empirical analysis in this study focuses on agricultural production of

27 Asian countries

over the period from

1980-2004

 The primary source of data is obtained from the website of the

and Agricultural Organization (FAO)

AGROSTAT system

Food

acquired from the  Production technology consists of

input variables two output variables

and

five

Data

Output Variables:

 The output series are derived by aggregating detailed output quantity data on

115 cropping commodities and 12 livestock commodities

expressed in terms of the international average prices (in US dollars)

Input Variables:

 Land: Arable land in hectare includes both land under permanent crops as well as the area under permanent pasture     Tractor: the total number of wheeled- and crawler tractors used in agriculture Labor: the number of economically active people in agriculture Fertilizer: the commercial use of nitrogen, potassium and phosphate fertilizers in nutrient-equivalent terms expressed in thousands of metric tons Livestock: the sheep-equivalent of the six categories of animals (buffaloes, cattle, pigs, sheep, goats and poultry)

Country Profile

Region Central Asia (CA) East Asia (EA) West Asia (WA) Southeast Asia (SEA) South Asia (SA) Country Kazakhstan (KAZ) Kyrgyzstan (KGZ) Tajikistan (TKM) Turkmenistan (TJK) Uzbekistan (UZB) China (CHN) Japan (JPN) Republic of Korea (PRK) Mongolia (MNG) Iraq (IRQ) Israel (ISR) Saudi Arabia (SAU) Syrian Arab Republi (SYR) Cambodia (KHM) Indonesia (IDN) Lao PDR (LAO) Malaysia (MYS) Myanmar (MMR) Philippines (PHL) Thailand (THA) Vietnam (VNM) Bangladesh (BGD) India (IND) Islamic Rep of Iran (IRN) Nepal (NPL) Pakistan (PAK) Sri Lanka (LKA)

Estimated Parameters of the Output Distance Model Parameter a

β

0

β

y1 (crop)

β

x1 (land)

β

x2 (tractor)

β

x3

β

x4 (labor) (fertilizer)

β

x5 (livestock)

β y

1

y

1

β x

1

x

1

β x

2

x

2

β x

3

x

3

β x

4

x

4

β x

5

x

5

β x

1

x

2

β x

1

x

3

β x

1

x

4

β x

1

x

5

β x

2

x

3

β x

2

x

4

β x

2

x

5

β x

3

x

4

β x

3

x

5

β x

4

x

5

β x

1

y

1

β x

4

y

1

β x

5

y

1

β t β tt β x

1

t β x

5

t β y

1

t

Estimates

0.277

0.490

-0.099

-0.184

-0.192

-0.224

-0.334

0.331

-0.101

0.033

0.151

-0.022

-0.228

0.043

-0.103

0.048

0.035

0.195

-0.060

-0.128

-0.214

-0.008

0.296

-0.051

0.189

0.114

-0.008

-0.001

-0.008

-0.006

-0.001

t-Statistic

8.781**

20.114

**

-7.126

**

-15.228

**

-8.222

**

-16.310

**

-11.067

** 5.253** -7.517** 3.321* 2.455* -3.161** -2.034

5.147** -4.426

5.470** 1.179

8.454** -7.818** -4.866** -10.331** -0.103

12.564** -2.115* 10.061** 2.067* -6.887** -2.590* -6.996** -2.564* -0.410

MTC and Decomposition for All Asian Countries Region Asia Period

1980-1985 1985-1990 1990-1995 1995-2000 2000-2004

1980-2004 TEC

-0.598

0.371

-0.218

-0.885

0.835

-0.138

TC

1.422

1.897

2.376

2.847

3.245

2.321

SEC

-0.481

-0.494

-0.300

0.061

-0.165

-0.280

MTC

0.343

1.775

1.857

2.023

3.916

1.902

 TFP growth across all of Asia was positive and nearly 2%  The high TFP growth has relied on TC.

 The high TFP growth for Asia is largely driven by rises in TFP during the past 5 years.

 TFP growth has been pulled down due to declining TEC and SEC.  This decline may be due to the continued rise in off-farm employment.  Asian TFP growth was relatively robust and rising. This is good news for those concerned about keeping balance in Asia and world food markets.

MTC and Decomposition for Each Region (in %) Region A) SA B) SEA C) WA D) EA E) CA Period 1980-2004 1980-2004 1980-2004 1980-2004 1992-2004 TEC -0.176

0.292

-0.402

-0.218

-0.087

TC 2.456

0.825

0.081

2.739

1.940

SEC -0.064

-0.050

-0.056

-0.495

-0.509

MTC 2.216

1.066

-0.376

2.026

1.344

 SA and EA exhibited high TFP growth. TC was a major factor driving TFP growth. TFP growth would have been higher had efficiency levels not fallen  TFP growth rate in SEA was only 1.1%. Both TEC and TC contributed to TFP growth in SEA.  WA was the only region exhibiting TFP regress. However, average TFP growth is small. Both TEC and SEC dragged down TFP growth. 

Without including transition countries in CA,

TFP growth rate in CA reached 1.4%. Asian TFP growth would have been lower.

MTC and Decomposition by Transition Countries (in %) Transition Country A) China B) Mongolia C) Vietnam D) Laos E) Myanmar F) Kazakhstan G) Kyrgyzstan H) Tajikistan I) Turkmenistan J) Uzbekistan Periods 1980-2004 1991-2004 1986-2004 1986-2004 1989-2004 1992-2004 1992-2004 1992-2004 1992-2004 1992-2004 TEC -0.250

0.078

-0.062

-1.320

0.008

0.225

-0.219

0.517

0.069

-0.950

TC 3.209

3.983

0.052

0.542

1.704

3.412

0.587

0.232

1.529

1.215

SEC -0.358

-0.347

-0.734

0.544

0.545

-1.689

-1.020

0.268

0.687

0.122

MTC 2.600

3.714

-0.744

-0.234

2.256

1.948

-0.653

1.018

2.285

0.387

Conclusion

 With nearly half of the potential agricultural resources, Asia has the potential to supply an increase in world food demand  By including more member countries into the analysis especially the transition economies,

Asian countries exhibited a healthy TFP growth with a growth rate of 1.9 per annum.

 Investments in R&D was a major contribution to TFP growth in Asian agriculture 

The healthy TFP growth in Asian agriculture

enhanced by countries in EA and SA.

is greatly 

Focusing on transition countries,

terms of the magnitude and direction of agricultural TFP growth during the past two decades. large differences exist in  Some transition countries such as

Turkmenistan China, Mongolia and

exhibited above average growth. Others, such as,

Kyrgyzstan, Uzbekistan, Laos, and Vietnam

did not do so well