Estimation and Decomposition of Agricultural Productivity Growth in Asia

Supawat Rungsuriyawiboon

Faculty of Economics Thammasat University

Introduction

Food crisis and food security are back on policy agendas

“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)

Food Price

Introduction

Some food price examples from the FAO

Type 2003 2007 2008$/ton $/ton $/ton

White Thailand rice(second grade)

198 323 854(+77%), (+62%)

Yellow corn 105 160 250(+58%), (+36%)

Wheat 144 207 401(+64%), (+48%)

Powdered milk 1,835 3,288 4,750(+61%), (+30%)

Soy oil 521 714 1,400(+63%), (+49%)

Introduction

Food commodity price indices have increased across the board

Cereals

48%

Oil&Fat

52%

Dairy

32%

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

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

Production of Wheat, Corn and Rice

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 to investigate intercountry differences in agricultural productivity growth in Asia using the panel data from the FAO

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

P roductivity is defined as the ratio of outputs to inputs

P roductivity = outputs inputs

The greater value implies the better performance

P roductivity 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 to tal factor produc

tivity (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 technically efficient

• Firm A is technically inefficient

• Technical efficiency (TE) can be measured by the

distance. TE is equal to 0A/0B or 0 C/0A

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 of technically optimal scale. Operation at any other point

on the production frontier results in lower productivity.

Point C indicates an operation at scale economies

Distance function Consider a production technology when multiple inputs

are used to produce multiple outputs

Production frontier can not use to describe this production technology

Shephard (1953, 1970) proposes a distance function to describe the structure of production technology with

multiple inputs and outputs

Two types of distance function 1. Input distance function, DI

2. Output distance function, Do

Output Distance Function (Do) The minimum amount by which an output vector can

be deflated and still remain producible with a given input vector.

Output distance function Do(x,y) is defined as

where P(x) = {y: (y,x) Є T}

xPy:miny,xDo

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*)

So, D0(x,y) = μ * = OA/OB ≤ 1

D0(x,y) = TE0

B

A

Properties of Output Distance Function (i) Do(x, 0) = 0 and Do (0, y) = ∞

(ii) Do (x, λy) = λDo (x, y) for λ > 0 (HOD+1 in y)

(iii) Do (λx, y) ≤ Do (x, y) for λ ≥ 1 (non-increasing in x)

(iv) Do (x, λy) ≤ Do (x, y) for 0 ≤ λ ≤ 1 (non-decreasing in y)

(v) Do (x, y) is convex function in y

Methodology

Total Factor Productivity (TFP) growth: Residual growth in outputs not explained by growth in input uses

Färe et al. (1989) proposed a Malmquist TFP index to measure productivity growth using the output distance

function

The output distance function at period t

represents the minimum amount by which yt can be deflated and still remain producible with xt

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Methodology

The Malmquist TFP index in period t

The Malmquist TFP growth index between t and t + 1

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growth

(MTC)Scale Efficiency Change (SEC)

Technical Efficiency Change (TEC)

Technical Change (TC)

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

Young’s theorem requires linear homogeneity in outputs

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Scale Efficiency Change(SEC)

Technical Efficiency Change (TEC)

Technical Change (TC)

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 Food and Agricultural Organization (FAO) acquired from the AGROSTAT system

Production technology consists of two output variables and five input variables

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 Country Central Asia (CA) Kazakhstan (KAZ)

Kyrgyzstan (KGZ) Tajikistan (TKM)

Turkmenistan (TJK) Uzbekistan (UZB)

East Asia (EA) China (CHN) Japan (JPN)

Republic of Korea (PRK) Mongolia (MNG)

West Asia (WA) Iraq (IRQ)Israel (ISR)

Saudi Arabia (SAU) Syrian Arab Republi (SYR)

Southeast Asia (SEA) Cambodia (KHM) Indonesia (IDN)

Lao PDR (LAO)Malaysia (MYS)

Myanmar (MMR) Philippines (PHL)

Thailand (THA) Vietnam (VNM)

South Asia (SA) Bangladesh (BGD) India (IND)

Islamic Rep of Iran (IRN) Nepal (NPL) Pakistan (PAK)

Sri Lanka (LKA)

Estimated Parameters of the Output Distance Model

Parametera Estimates t-Statistic

β0βy1 (crop)

βx1 (land)

βx2 (tractor)

βx3 (labor)

βx4 (fertilizer)

βx5 (livestock)

βy1y1

βx1x1

βx2x2

βx3x3

βx4x4

βx5x5

βx1x2

βx1x3

βx1x4

βx1x5

βx2x3

βx2x4

βx2x5

βx3x4

βx3x5

βx4x5

βx1y1

βx4y1

βx5y1

βt

βtt

βx1t

βx5t

βy1t

0.2770.490-0.099-0.184-0.192-0.224-0.3340.331-0.1010.0330.151-0.022-0.2280.043-0.1030.0480.0350.195-0.060-0.128-0.214-0.0080.296-0.0510.1890.114-0.008-0.001-0.008-0.006-0.001

8.781**20.114**-7.126**

-15.228**-8.222**

-16.310**-11.067**5.253**-7.517**3.321*2.455*-3.161**-2.0345.147**-4.4265.470**1.1798.454**-7.818**-4.866**-10.331**-0.10312.564**-2.115*10.061**2.067*-6.887**-2.590*-6.996**-2.564*-0.410

MTC and Decomposition for All Asian Countries

Region Period TEC TC SEC MTC

Asia 1980-1985 -0.598 1.422 -0.481 0.3431985-1990 0.371 1.897 -0.494 1.7751990-1995 -0.218 2.376 -0.300 1.857

1995-2000 -0.885 2.847 0.061 2.023

2000-2004 0.835 3.245 -0.165 3.916

1980-2004 -0.138 2.321 -0.280 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 past5 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 Period TEC TC SEC MTC

A) SA 1980-2004 -0.176 2.456 -0.064 2.216

B) SEA 1980-2004 0.292 0.825 -0.050 1.066

C) WA 1980-2004 -0.402 0.081 -0.056 -0.376

D) EA 1980-2004 -0.218 2.739 -0.495 2.026

E) CA 1992-2004 -0.087 1.940 -0.509 1.344

SA and EA exhibited high TFP growth. TC was a major factor driving TFP growth. T FP 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, Asian TFP growth would have b een lower. TFP growth rate in CA reached 1.4% .

MTC and Decomposition by Transition Countries (in %)

Transition Country

Periods TEC TC SEC MTC

A) China 1980-2004 -0.250 3.209 -0.358 2.600

B) Mongolia 1991-2004 0.078 3.983 -0.347 3.714

C) Vietnam 1986-2004 -0.062 0.052 -0.734 -0.744

D) Laos 1986-2004 -1.320 0.542 0.544 -0.234

E) Myanmar 1989-2004 0.008 1.704 0.545 2.256

F) Kazakhstan 1992-2004 0.225 3.412 -1.689 1.948

G) Kyrgyzstan 1992-2004 -0.219 0.587 -1.020 -0.653

H) Tajikistan 1992-2004 0.517 0.232 0.268 1.018

I) Turkmenistan 1992-2004 0.069 1.529 0.687 2.285

J) Uzbekistan 1992-2004 -0.950 1.215 0.122 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.

I nvestments in R&D was a major contribution to TFP growth in Asian agriculture

The healthy TFP growth in Asian agriculture is greatly enhanced by countries in EA and SA.

Focusing on transition countries, large differences exist in t erms of the magnitude and direction of agricultural TFP growth

during the past two decades.

Some transition countries such as China, Mongolia and Turkmenistan exhibited above average growth. Others, such as, Kyrgyzstan, Uzbekistan, Laos, and Vietnam did not do

so well