Ref. code: 25595822040100EWT

AN ASSOCIATION BETWEEN CASSAVA PLEDGING SCHEME AND THE

FINANCIAL PERFORMANCE OF CASSAVA PRODUCT MANUFACTURER

BY

TRINUJ VONGSOMTAKUL

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF MASTER OF

ENGINEERING (LOGISTICS AND SUPPLY CHAIN SYSTEMS

ENGINEERING)

SIRINDHORN INTERNATIONAL INSTITUTE OF TECHNOLOGY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2016

Ref. code: 25595822040100EWT

AN ASSOCIATION BETWEEN CASSAVA PLEDGING SCHEME AND THE

FINANCIAL PERFORMANCE OF CASSAVA PRODUCT MANUFACTURER

BY

TRINUJ VONGSOMTAKUL

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF MASTER OF

ENGINEERING (LOGISTICS AND SUPPLY CHIAN SYSTEMS

ENGINEERING)

SIRINDHORN INTERNATIONAL INSTITUTE OF TECHNOLOGY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2016

Ref. code: 25595822040100EWT

ii

Acknowledgements

First, I would like express my sincere gratitude to my advisor, Assoc. Prof. Dr.

Jirachai Buddhakulsomsiri for his encouragement, sacrifice, patience and dedication in

guiding, teaching, and giving me advice throughout my master degree at SIIT.

Besides my advisor, I would like to thank Assoc. Prof. Dr.Parthana Parthanadee

who suggested me the thesis topic, provided me an access to essential source of data as

well as supporting me through my entire research thesis.

Furthermore, I would like to thank my thesis committees, Assist. Prof. Dr.

Morrakot Raweewan and Assoc. Prof. Dr. Tanachote Boonvorachote who constantly

guided me and gave me suggestions to improve my thesis results.

Last but not least, I would like to thank my friends at SIIT who endlessly gave

me encouragement and practical support throughout the study at SIIT. Most

importantly, I would like to thank my family who is a big support behind my education

success.

Ref. code: 25595822040100EWT

iii

Abstract

[AN ASSOCIATION BETWEEN CASSAVA PLEDGING SCHEME AND THE

FINANCIAL PERFORMANCE OF CASSAVA PRODUCT MANUFACTURER]

by

TRINUJ VONGSOMTAKUL

Bachelor of Engineering. (Industrial Engineering), Sirindhorn International Institute

of Technology, Thammasat University, 2015.

Master of Engineering (Logistics and Supply Chain Systems), Sirindhorn

International Institute of Technology, Thammasat University, 2017.

Abstract

This paper involves a study that investigates the association between government

pledging program and the financial performance of cassava product manufacturers in

Thailand using multiple linear regression modelling. Financial performance in terms of

return on equity was the response variable obtained from financial statement of the year

after the pledging program. The list of manufacturers who joined the pledging program

is the key research variable. Internal factors and financial statement of 24 samples of

starch manufacturers and 12 chip manufacturers were collected and treated as control

variables. The results indicate that there is a positive association between

manufacturers’ financial performance and participating in the pledging program. The

study also investigated characteristics of companies that joined the pledging program

using binary logistics regression.

Keywords: Association, pledging scheme, financial performance, cassava product

manufacturers

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iv

Table of Contents

Chapter Title Page

Signature Page i

Acknowledgements ii

Abstract iii

Table of Contents iv

List of Figures vi

List of Tables vii

1 Introduction 1

1.1 Research Overview 1

1.2 Problem Statements 3

1.3 Objectives 3

2 Literature Review 4

2.1 Background of the Study 4

2.2 Financial Performance Indicator 5

2.3 Company’s Success Factors 5

3 Methodology 12

3.1 Sample and Data Collection 12

3.2 Research Questions 12

3.3 Regression Modelling 13

4 Results and Discussions 17

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4.1 Sample Demographic 17

4.2 Multiple Regression to Evaluate an Association between 18

Participating in the Pledging Scheme and Financial Performance

4.3 Binary Logistics Regression to Determine the Characteristics of 21

the Manufacturers who joined the Pledging Scheme.

5 Conclusions and Recommendations 24

References 25

Appendices 28

Appendix A 29

Appendix B 32

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List of Figures

Figures Page

4.1 Residual plot of return on equity 21

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List of Tables

Tables Page

2.1. Literature review of banking industry with the response variables and 7

independent variables

2.2. Literature review of manufacturing industries with the response variables 9

and the independent variables.

2.3. Literature review of service industries with the response variables and the 10

independent variables.

2.4. Study using control variables 11

3.1. List of response variable, research variable and control variable 14

4.1 Regression model results 17

4.2 Regression model results 18

4.3 Odds ratios for continuous predictors 18

4.4 Binary logistic regression: deviance table 21

4.5 Odds ratios for continuous predictors 22

4.6 Goodness of fit tests 22

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Chapter 1

Introduction

1.1 Research Overview

Cassava is the third most important economic crops of Thailand after rice and

para rubber. In 2015, Thailand is the world’s largest exporter of cassava (2,771 USD),

followed by Vietnam (1,139 million USD), Costa Rica (72 million USD), Peru (29

million USD) and China (25 million USD). The world’s largest importer of cassava is

China (3,246 million USD), Japan (418 million USD), US (363 million USD), and

Germany (326 million USD). Cassava can be converted into several forms including

cassava chips, tapioca starch, sago, and pallets. In 2015, Thailand export values of these

products were 51,868.82 million thb for cassava chips, 41,166.70 million thb for

tapioca, 771.04 million thb for sago, and 293.6 million thb for pellets (NSTDA, 2017).

Due to the Thai cassava farmer’s poverty, the Thai government had launched

the price intervention policies in some harvest years to secure farmer’s income. The

Thai government started launching the price intervention policies during 1999/2000 to

2008/2009 cassava seasons (Parthanadee et al., 2016). From 2011-2017, Thailand faced

an economic loss of approximately 5.8 billion thb as the government set price 50%

higher than the market price. Thai cassava farmers gained little benefits from the

pledging program, while most benefit went to cassava exporters, manufacturers, and

cassava yards (TDRI, 2011). The government pledging program was set again in

2011/2012 and 2012/2013 seasons (Public Warehouse Organization, 2013; North

Eastern Tapioca Trade Association, 2013). Some researcher suggested that the Thai

government should promote the market oriented policy instead of launching the

pledging program (Laiprakobsup, 2014). Interestingly, another researcher found out

that the Thai cassava farmers preferred the price guarantee policy over the pledging

program (Poramacom et al., 2013).

Net income of cassava manufacturers may depend on both internal and external

factors. Internal factors may include how they manage their asset, liability, and owner

equity. It may also include how they manage their production processes, human

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2

resource, and supplier and customer relationships. External factors may include the

government intervention policies, cassava production from farmers (supply), domestic

and oversea demand, cassava root and cassava product prices, alternative crop price,

and so on.

This paper focuses on investigating whether the government cassava pledging

program has an association with the cassava product manufacturers’ financial

performance. The financial performance data of starch and chip manufacturers were

collected during the year 2013 when pledging programs were in effect. The list of

cassava product manufacturers who participated in the pledging program in the

2012/2013 season were collected and used as the key research variable. Also, the

research would like to identify the internal factors that influence the financial

performance of cassava product manufacturers. In addition, the characteristics of those

manufacturers who participated in the program were identified using binary logistics

regression.

Past research that studied the benefits received by the farmers from government

pledging policies involves the rice pledging program. The rice pledging program, which

was implemented during 2011-2014, had severely damaged the Thai rice milling

business in the long term. However, the benefits from the pledging program that

actually went to the farmers might be much less than hoped for. The rice might have

exposed to corruptions at every stage, ranging from farmers, collectors, surveyors,

yards, manufacturers, to government officials, as evidence from many stakeholders are

under investigation by the current government. (TDRI, 2014). Moreover, the rice

pledging program was conducted without the conditions on the rice quality. Many

researchers (Laiprakobsup, 2014; Permani and Vanzetti, 2015; Attavanich, 2016;

Laiprakobsup, 2014; and Attavanich, 2016) concluded that the Thai rice pledging

program was set up merely for the government to win the election. They also suggested

that the Thai government should instead focus on the long term benefits such as

developing the Thai rice quality, warehousing, fertilizers, harvesting tools, providing

low interest loan for farmers and even educating the farmers to produce higher rice

standard.

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3

1.2 Problem Statement

Net income of cassava manufacturers may depend on both internal and external

factors. Internal factors may include how they manage their asset, liability, and owner

equity. It may also include how they manage their production process, human capital

and their downstream industries. The external factors may include the government

intervention policies, cassava yield from farmers (supply), demand from China,

alternative crop price, and so on. In this study, the researcher would like to determine

if the financial performance of the cassava product manufacturers is influenced

specifically by the Thai government cassava pledging policy. Also, the research would

like to determine what internal factors that influence the financial performance of

cassava manufacturers.

1.3 Objectives

To determine an association between the cassava pledging policy and the

financial performance of cassava manufacturers.

To determine factors that affect the financial performance of cassava

manufacturers.

To determine the characteristics for the manufacturers who participated in

the pledging policy.

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Chapter 2

Literature Review

2.1 Background of the Study

Several research studies focused on the impact of the agricultural policy on the

benefits of the farmers and on economic impact of the nation as a whole in developing

countries. Due to the farmer poverty, government had to intervene to assist farmers in

increasing crop price. The papers reviewed will be based on the government

intervention policy on rice and palm oil and the benefits received by the farmers and

other related consequences of such policy.

Government intervention policy is intensive in the developing countries. During

2011-2014, Thai government was heavily criticized for its massive public spending of

$3.78 billion for the rice pledging policy which incurred a loss of $16 billion. Despite

large spending, the benefit gained was less than expected as the program aimed to

improve the net income of the farmers. Price guarantee is the more popular among the

short term subsidy program. Not only that the pledging scheme did not improve the

economic viability in rice farming, it also has a negative effect on the national politics

and economy in the long term.

Rice pledging scheme had damaged the Thai rice milling business, particularly the

rice millers that did not participate in the program, as the government sets the price

much higher than the market price. The program was also exposed to corruption at

every stage. The government had to keep large stock of rice and sold them at a loss.

Permani and Vanzetti (2016) pointed out that out of 18 million ton of rice accumulated

from the pledging scheme, only 10% were on standard quality thus government had to

stock rice for more than five years considering the rice spoilage.

In addition, it encouraged the farmers to sell their rice without assessing the rice

quality, thus lowering Thai rice standard. The study concluded that the pledging scheme

did not improve the yield of farmers as it did not give any incentive to improve and

increase rice productivity. The program was set up merely for government to win the

election. Laiprakobsup (2014), Permani and Vanzetti (2015), Attavanich (2016),

Laiprakobsup (2014) and Attavanich (2016) suggested that government should instead

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5

assist farmers in the long run such as providing supports in terms of fertilizers,

harvesting tools, rice variety, warehouse and even providing low interest loan for

farmers.

Castiblanco, et al. (2015) investigated the impact of subsidy policy and blending

mandates for the palm oil producers and biodiesel producers in Columbia. Similar to

the Thai rice pledging scheme, the program benefits the crude palm oil producers in a

short term only. However, in a long term, biodiesel producers gained the most benefits

from the subsidy program. Moreover, the efficiency loss and deadweight loss are

insignificant in the short term but become important in the long term. The study

suggested that the subsidy policy alone was not effective and thus should be combined

with the blending mandates. Integrating the subsidy policy and the blending mandates

led to positive effect on the biodiesel producers as they increased productivity.

2.2 Financial Performance Indicator

From the literature review, no research has studied the relationship between the

government intervention policy and financial performance of cassava product

manufacturers. This study aims to fill this research gap. Regarding financial

performance, one of the most widely used measures of business performance is return

on equity (ROE). ROE measures how much return is received from an investment.

Research studies that focused on ROE are Sufian and Habibullah, 2010 in Banking

industry; Dietrich and Wanzenried, 2011; Gul et al., 2011; and Shubita and Alsawalhah,

2012.

2.3 Company’s Success Factors

Much research studied the factors that contribute to company’s success. The

company’s success may include return on asset (ROA), return on equity (ROE), return

on investment (ROI), export performance, turnover, productivity, and profitability.

Research that studied the factors to company’s success were mostly found in the

banking industries, followed by manufacturing and service industries. Table 2.1 shows

that most researchers prefer using ROA and ROE as profitability indicator in banking

industry. Most banking industries were found using capital adequacy, size in terms of

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6

asset, GDP and inflation as the independent variables in explaining the profitability.

Some used loan to customer (measure of liquidity), credit risk (loan loss provisions/total

loans), and a few used cost to income ratio, age of the bank, market concentration,

market capitalization, and ownership (public bank if public sector runs more than 50%

of market share). From table 2.2, most researchers used ROA and ROE as profitability

indicator and used as size (in terms of asset) and debt ratio as the common independent

variable in the manufacturing companies. Agiomirgianakis et al. (2006) used size, age,

management efficiency, and debt leverage to predict profitability. Kim and Hemmert,

(2016) used export, managerial skills, human resource, technological resources,

marketing resources, strength of customer relationship and number of network ties as

the Table 2.3 shows that the common variables used in the service industry are size,

age and location of the business. Gursoy and Swanger (2007) used human resource,

technological resource, marketing resource, and strength of customer relationship in

testing the profitability and ROI. Aissa and Goaied (2016) used the size, age, debt,

affiliation, location, management efficiency and managerial skills to explain the return

on asset (ROA).

Company’s characteristics, management personal characteristics, strategic internal

factors (human resource), and financial conditions will be used for setting up the

hypothesis in this study. Many studies use control variables to help explain the

variability in the response variable. Table 2.4 shows that firm size, inflation, and sales

growth were widely used as control variables in banking industry (Athanasoglou et al.,

2008; Shubita and Alsawalhah, 2012; Kanasa et al., 2012; Djalilov and Piesse, 2016

and Hamid et al., 2015). In addition, Antoniou et al. (2008) used equity premium, term-

structure of interest rate, laws and regulations, ownership concentration, creditor rights

and anti-director rights as control variables.

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7

Tab

le 2.1

Literatu

re review

of b

ankin

g in

du

stry with

the resp

onse v

ariables an

d in

dep

enden

t variab

les

Stu

dy

Auth

ors, y

ear

Resp

onse

variable

Cap

ital

adeq

uecy

Size

(Aset)

Loan

Cost:Inco

me

Ratio

Age

GD

PInflatio

nC

oncentratio

n

Cred

it

risk

Mark

et

capitalizatio

nO

wnership

Ban

k-sp

ecific

,

industry

-specific

and m

acro

eco

no

mic

dete

rmin

ants o

f

ban

k p

rofitab

ility

(Sufian

and

Hab

ibullah

, 20

10

)R

OE

or R

OA

xx

xx

xx

Fac

tors in

fluencin

g

the p

rofitab

ility o

f

do

mestic

and

fore

ign c

om

merc

ial

ban

ks in

the

Euro

pean

Unio

n

(Pasio

uras an

d

Ko

smid

ou, 2

00

7)

RO

Ax

xx

xx

xx

Dete

rmin

ants o

f

ban

k p

rofitab

ility

befo

re an

d d

urin

g

the

crisis: E

vidence

from

Sw

itzerlan

d

(Die

trich an

d

Wan

zenrie

d, 2

01

1)

RO

Ex

xx

xx

x

Fac

tors A

ffectin

g

Ban

k

Pro

fitability

in

Pak

istan

(Gul e

t al., 20

11

)R

OE

, RO

Ax

xx

xx

x

Ref. code: 25595822040100EWT

8

Tab

le 2.1

(contin

ued

)D

ete

rmin

ants o

f

ban

k p

rofitab

ility in

Pak

istan: In

tern

al

facto

r analy

sis

(Javaid e

t al., 20

11

)R

OA

xx

x

The R

elatio

nsh

ip

betw

een C

apital

Stru

ctu

re an

d

Pro

fitability

(Shubita an

d

Alsaw

alhah

, 20

12

)R

OE

, RO

Ax

Dete

rmin

ants o

f

ban

ks’ p

rofitab

ility:

evid

ence fro

m E

U

27

ban

kin

g sy

stem

s

(Petria e

t al., 20

15

)R

OE

, RO

Ax

xx

xx

Revisitin

g b

ank

pro

fitability

:

A se

mi-p

arametric

appro

ach

(Kan

asa et al.,

20

12

)R

OA

, RO

Ex

xx

Dete

rmin

ants o

f

ban

k p

rofitab

ility in

transitio

n c

ountrie

s:

What m

atters m

ost?

(Djalilo

v and

Pie

sse, 2

01

6)

RO

Ax

xx

xx

Ref. code: 25595822040100EWT

9

Tab

le 2.2

Literatu

re review o

f man

ufactu

ring in

du

stries with

the resp

on

se variables an

d th

e ind

epen

den

t variables.

Stu

dy

Auth

ors, y

earR

espo

nse

Size

Age

Man

agem

ent

efficien

cy

Exp

ort

Man

agerial

skills

Hum

an

resourc

e

Tec

hno

logic

al

resourc

es

Mark

eting

resourc

es

Stren

gth

of

Custo

mer

relatiosn

hip

Num

ber

of

Netw

ork

ties

Deb

t

ratio

Fin

ancial fac

tors affec

ting

pro

fitability

and

emp

loym

ent g

row

th: th

e

case o

f Greek

man

ufac

turin

g

(Agio

mirg

ianak

is

et al., 2006)

Pro

fitability

xx

xx

x

What d

rives th

e exp

ort

perfo

rman

ce o

f small an

d

med

ium

-sized

sub

co

ntrac

ting firm

s? A

stud

y o

f Ko

rean

man

ufac

turers

(Kim

and

Hem

mert, 2

016)

Exp

ort

perfo

rman

c

e

xx

xx

xx

x

A p

anel d

ata analy

sis of

pro

fitability

determ

inan

ts

(Prath

eepan

,

2014)

RO

Ax

x

Fac

tors d

etermin

ing

Pro

fitability

:

A S

tud

y o

f Selec

ted

Man

ufac

turin

g C

om

pan

ies

listed o

n C

olo

mb

o S

tock

Exchan

ge in

Sri L

anka

(Siv

athaasan

et al., 2013)

RO

E,R

OA

xx

Ref. code: 25595822040100EWT

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Tab

le 2.3

Literatu

re review o

f service ind

ustries w

ith th

e respo

nse variab

les and

the in

dep

end

ent variab

les.

Stu

dy

Auth

ors, y

ear

Industry

Resp

onse

variable

Size

Age

Inflation

Deb

tA

ffiliation

locatio

nM

anagement

efficiency

Managerial

skills

Hum

an

resource

Techno

logical

resources

Mark

eting

resources

Strength o

f

Custo

mer

relationship

Mark

et

segment

Pro

duct

develo

pm

ent

Service

Perfo

rman

ce-

enhan

cin

g in

tern

al

strategic

facto

rs and

co

mpete

ncie

s:

Impac

ts on fin

ancial

success

(Gurso

y an

d

Sw

anger, 2

00

7)

Servic

e

Pro

fitability

,

RO

I

xx

xx

xx

Strate

gic

Ho

tel

Deve

lopm

ent an

d

Po

sitionin

g:

The E

ffects o

f

Reve

nue D

rivers o

n

Pro

fitability

(O’n

eill

and M

attila,

20

06

)

Ho

tel

Net

operatin

g

inco

me

perc

entag

e

xx

xx

Dete

rmin

ants o

f

Tunisian

ho

tel

pro

fitability

:

The ro

le o

f

man

agerial

effic

iency

(Aissa an

d

Go

aied, 2

01

6)

Ho

tel

RO

Ax

xx

xx

xx

x

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Tab

le 2.4

Stu

dies u

sing co

ntro

l variab

les

Stud

yA

uth

ors, ye

arIn

du

stryFirm

Age

SizeG

DP

Inflatio

nSale

s

grow

th

Inte

rest

rateC

apital

Finan

cial

structu

reC

ost

Equ

ity

pre

miu

mO

wn

ersh

ipC

on

cen

tration

cred

it rights

&an

ti

cred

it rights

Laws an

d

regu

lation

s

Ind

ustry

type

Cyclical

ou

tpu

t

Ban

k-spe

cific, ind

ustry-sp

ecific

and

macro

eco

no

mic

de

term

inan

ts

of b

ank p

rofitab

ility.

(Ath

anaso

glou

et al., 2008)

Ban

kx

xx

xx

The

Re

lation

ship

be

twe

en

Cap

ital

Structu

re an

d P

rofitab

ility.

(Shu

bita an

d

Alsaw

alhah

,

2012)

Ban

kx

x

Wh

at drive

s the

exp

ort

pe

rform

ance

of sm

all and

me

diu

m-size

d su

bco

ntractin

g

firms? A

stud

y of K

ore

an

man

ufactu

rers.

(Kim

and

He

mm

ert,

2016)

Man

u

facturin

gx

x

Re

visiting b

ank p

rofitab

ility:

A se

mi-p

arame

tric app

roach

.

(Kan

asa et al.,

2012)B

ank

xx

x

De

term

inan

ts of b

ank

pro

fitability in

transitio

n co

un

tries: W

hat

matte

rs mo

st?

(Djalilo

v and

Pie

sse, 2016)

Ban

kx

xx

The

De

term

inan

ts of C

apital

Structu

re: C

apital M

arket-

Orie

nte

d ve

rsus B

ank-O

rien

ted

Institu

tion

s.

An

ton

iou

et al. (2008)

Ban

k

x

xx

xx

x

Cap

ital Structu

re an

d

Pro

fitability in

Family an

d N

on

-

Family Firm

s: Malaysian

evid

en

ce.

Ham

id

et al. (2015)

Ban

kx

xx

Ref. code: 25595822040100EWT

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CHAPTER 3

METHODOLOGY

3.1 Sample and Data Collection

The research used panel data from financial statements of starch manufacturers and

cassava chip manufacturers in the year 2013 that were submitted to the Department of

Business Development, Ministry of Commerce, Thailand. Additional data about the

companies’ internal factors were collected through in-depth interviews using

questionnaires by phone, e-mails and by post. The input data contain a total of 24 starch

manufacturers and 12 chips manufacturers that had complete information of both

financial statement and internal factors. Moreover, an important piece of information is

the list of cassava product manufacturers who participated in the pledging scheme in

2013, which was obtained from the Public Warehouse Organization, Ministry of

Commerce, Thailand.

3.2 Research Questions

There are two research questions. The key research question is whether or not there

is a relationship between government intervention policy, specifically, the pledging

scheme, and financial performance of cassava product manufacturers. The second

hypothesis is to identify the characteristics of those manufacturers who joined the

pledging policies. The hypothesis can be stated as follows.

Hypothesis 1: There is a significant association between cassava product

manufacturers’ financial performance in terms of ROE and pledging scheme

participation.

Hypothesis 2: What are the characteristics of those manufacturers who joined the

pledging policies?

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13

In addition, the third research questions are to identify the companies’ internal factors

that are highly associated with their financial performance, and thus, can help describe

the variability in the financial performance, while evaluating the primary research

question. The following hypotheses can be stated.

Hypothesis 3: There are associations between companies’ internal factors and ROE of

cassava product manufacturers, where internal factors include:

(1) managerial capability in terms of work experience (years), level of education, and

gender of manager;

(2) company characteristics in terms of company age, size, percentage of company’s

sales in domestic market, level of workforce, amount and topic of training, type of

customers’ industry, standards and certifications that company has, production

capacity, sources of cassava, yield of cassava (ton/ha), minimum, average and

maximum inventory days of finished goods, and number of months that production

process is in operation;

(3) supplier relationship in terms of number of farmers that are regular suppliers, having

knowledge sharing among farmers, and having regular meetings with farmers;

(4) customer relationship in terms of having purchase contracts, having knowledge

sharing with customers and percentage of return customers.

3.3 Regression Modelling.

This study used the return on equity (ROE) in 2013 as the response variable.

The key research variable is a variable that indicates whether or not a manufacturer

joined the pledging scheme in 2013. The control variables are from the manufacturers’

financial performance data, including cash ratio, inventory turnover, fixed assets

turnover, total assets turnover, debt ratio, account payable turnover, payable deferral

period, and debt to equity ratio in 2013. Another set of control variable is the firm’s

internal factors including managerial capability, company characteristics, supplier

relationship, and customer relationship as listed previously.

General form of the regression model that relates the response and the research variables

is as shown below.

ROE = β0 + β1PS + β2CR + … +

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where the variables are listed in Table 3.1 and is the error term.

Table 3.1 List of response variable, research variable and control variable

Variables Definition and formula

Response variable

ROE Return on equity

Research variable

PS Indicator variable, which takes on the value of 1 if a

manufacturer joined the pledging program; or 0

otherwise.

Control variable

CR Cash ratio = cash / current liability

INV Inventory turnover = Cost of goods sold / average

inventory

Fixed Fixed assets turnover = Net sales / fixed asset

TAT Total assets turnover = Sales / total assets

DR Debt ratio = Total debts / total assets

APT Account payables turnover = Cost of goods sold /

average payable

PDP Payable deferral period = Payables / (cost of goods

sold/365)

DER Debt to equity ratio = Total debts / total assets

Company characteristics

Age The age at which the company registered with the

Department of Business Development

RegCap Company’s registered capital

PT Product type starch or pallet

DS Percentage of company’s sales in domestic market

Labor No. of labor in production process

TR No. days of training/year/person

Topic Topic of training: safety, production, managerial

skill, and software skill

AvgC, MaxC Average and maximum capacity in ton per day

Cert Standard certification: GMP, HACCP, Halal,

Kosher, ISO 9000, ISO 9001, etc.

Cus_d Type of customers’ in domestic industry: native

starch, modified starch, sweetener, glue, paper

Cus_e Type of customers’ in export industry: Modified

starch, food, glue, paper

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Source

Sources of cassava: buying in front of factory,

middle merchant, or factory farm

Area Planting area (hectare) of a company

Yield Yield of cassava (ton per hectare)

Lead Manufacturing lead time (min)

Min_invt, Avg_invt,

Max_invt

Minimum, average and maximum inventory days of

finished goods

Month_manu Number of months that production process is in

operation

Relationship to farmers

(supplier)

Mem Supplier relationship in terms of number of farmers

that are regular suppliers

TF* Sharing agricultural knowledge to farmers

Manufacturer meeting

with farmers (MF)*

Strengthening relationship with farmers

Buying contract (BuyC)* Having buying contract?

Sharing manufacturing

knowledge (ShareC)*

Is there an exchange of information, giving

specification

% Old customer (Old.c) Percentage of old customer

Managerial capability

Gender* Male/Female

Edu* Primary, secondary, bachelor, or master degree

Exp Number of years of experience of manager

*Categorical variable

The final model is fitted such that it contains only significant variables with p-value

being less than 0.05. However, other variables having p-value more than 0.05 were kept

in order to help explain the variability in the model.

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3.4 Binary Logistics Regression

Logistics regression is commonly used for predicting the probability of occurrence of

two or more binary dependent variable(s) using one or more quantitative or categorical

variables.

𝑝(𝑥)

1 − 𝑝(𝑥)= 𝑒𝛽0+𝛽1𝑋

P(Y) = exp(Y')/(1 + exp(Y'))

𝑦 = {1, 𝛽0 + 𝛽1𝑋 + > 0

0, 𝑒𝑙𝑠𝑒

The binary logistic regression can be explained using the odd ratio. The nominator

represents the probability of success and the denominator represents the probability of

failure. The expression on the left is the “odd”. Similar to the simple linear regression,

increasing one unit of x will change the slope of β accordingly.

For this study, the researcher would like to determine the characteristics of the

manufactures who joined the pledging policies. Thus, the dependent variables are the

list of manufacturers who participated in the pledging policy. The independent variables

are a mixture of categorical and quantitative variable based on the questionnaire.

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Chapter 4

Results and Discussions

4.1 Sample Demographic

The sample of processing factories where internal factor data were collected

were obtained from personal interview, phone interview, e-mail, and mail using

questionnaire form (see Appendices A and B). A total of 36 responses were obtained,

24 of which are starch manufacturers and 12 are chips manufacturers. Note that there

are a total of 87 starch manufacturers and 335 chip manufacturers in Thailand (NSTDA,

2017). Tables 3.2-3.3 provides geographic data of the response by region and by

province, respectively. According to the Revenue Department, the small, medium, and

large companies were distinguished by size of fixed asset. The small enterprise would

possess the fixed asset being less than 50 million baht, the medium enterprise would

possess the fixed asset between 51-200 million baht and the large enterprise would have

the fixed asset more than 200 million baht (Department of Revenue, 2017).

Table 4.1 Number of samples collected from each region in Thailand

Region Large Medium Small

NE 11 2 2

C 4 2 1

W 2 0 0

E 3 2 2

Total 20 6 5

There are more number of starch and cassava chips factories in the northeast of

Thailand. Therefore, most samples were collected from Nakhon Ratchasima in the

northeast region. The second most sample collected is from the central region. Even

though only 36 samples were collected, many samples were from the largest chain of

manufacturers, Eiam group. Eiam group consists of Eiamheng and Eiamburapa group.

Eiamheng group includes Eiamheng Topica Starch Industry Co., Ltd.; Eiam E-San

Topica Starch Co., Ltd.; Eiamheng Modified Starch Co., Ltd.; and Eiamrungruang

Industry Co., Ltd. Eiamburapa Group includes Eiamsiri Starch Co., Ltd.; Eiamburapa

Starch Co., Ltd.; and Eiam Ubon Co., Ltd. Samples collected covered all companies in

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the chain except Eiam Ubon Co., Ltd. as the company is in ethanol industry, which is

out of the scope of this study.

4.2 Multiple Regression to Evaluate an Association between Participating in the

Pledging Scheme and Financial Performance

The regression model results are shown in Table 4.1, followed by the regression

equations.

Table 4.2 Regression model results.

Source DF Adj Dev Adj Mean Chi-Square P-value

Regression 7 18.0139 2.5734 20.51 0.000

Min_invt 1 0.4314 0.4314 3.44 0.074

Fixed 1 1.0052 1.0052 8.01 0.008

DER 1 12.1741 12.1741 97.05 0.000

TP 1 1.9539 1.9539 15.58 0.000

HACCP 1 0.4765 0.4765 3.8 0.061

Exporter2 1 0.7216 0.7216 5.75 0.023

PS 1 1.0264 1.0264 8.18 0.008

Error 28 3.5124 0.1254

Total 33 21.5263

Table 4.3 Regression equation.

HACCP Exporter2 PS TP

0 0 0 1

ROE

= -0.612 + 0.000985 Min_invt

+ 0.0462 Fixed - 0.06249 DER

0 0 0 2

ROE

= -0.034

+ 0.000985 Min_invt

+ 0.0462 Fixed - 0.06249 DER

0 0 1 1

ROE

= -0.221 + 0.000985 Min_invt

+ 0.0462 Fixed - 0.06249 DER

0 0 1 2

ROE

= 0.357 + 0.000985 Min_invt

+ 0.0462 Fixed - 0.06249 DER

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0 1 0 1

ROE

= -0.252 + 0.000985 Min_invt

+ 0.0462 Fixed - 0.06249 DER

0 1 0 2

ROE

= 0.326 + 0.000985 Min_invt

+ 0.0462 Fixed - 0.06249 DER

0 1 1 1

ROE

= 0.139

+ 0.000985 Min_invt

+ 0.0462 Fixed - 0.06249 DER

0 1 1 2

ROE

= 0.717 + 0.000985 Min_invt

+ 0.0462 Fixed - 0.06249 DER

1 0 0 1

ROE

= -0.332 + 0.000985 Min_invt

+ 0.0462 Fixed + 0.000985 Min_invt

From figure 1 below, the model assumption is conformed. The residuals are

normally distributed and independent of one another. The research variable of interest,

PS, is significant with p-value of 0.000. Its positive coefficient of 0.0463 indicates that

the government pledging scheme is positively associated with ROE for both tapioca and

ethanol manufacturers that participated in the program, even though the average

pledging price in 2013 is slightly lower than 2012. Thus, a null hypothesis can be

rejected. The significant factors that affect ROE can be explained below.

1. Minimum inventory days (Min_Inv)

This is the amount of inventory that the company keeps before releasing to the market.

From Table 4.2, p-value is significant being 0.018 and the coefficient is positively

correlated to the ROE. For those who keep their stock more than 1 day, ROE is 25%

higher than those who release stock immediately after production since manufacturers

would wait for price to rise before selling.

2. Fixed asset turnover (Fixed)

Fixed asset turnover is significant with p-value being 0.001. Fixed asset turnover is

sales over total asset. Thus, the higher the sales, the more profitability in terms of ROE.

3. Type of product (TP_1)

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Type of product 1 represents starch industry. It is positively correlated to ROE which

means that starch industry could gain more profitability in terms of ROE than the chip-

pallet industry.

4. HACCP

Manufacturers who have HACCP have more creditability in terms of food safety and

hazard control. The manufacturers having HACCP could gain more profitability than

others by 40% than those who do not have HACCP certificate.

5. Exporter2

Exporter2 is the outbound industry in which the manufacturer export their product to

the exporter and the exporter sell the product to another exporter. The manufacturers

who export their product to the exporter has significantly higher ROE than those who

do not by 97% on average.

6. DER

Debt to equity ratio is negatively correlated to return to equity ratio. Several studies

found the negative correlation between leverage and profitability. Shubita and

Maroofalsawalhah (2012); Pedro Proença, Raul M. S. Proença, Laureano, and

Laureano (2014); Hamid, Abdullah and Kamaruzzaman (2015). However, some

researchers found a positive correlation between leverage and profitability (MacKay

and Phillips, 2001 and Gaud et. all, 2007).

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Figure 4.1 Residual plots of return on equity

4.3 Binary Logistics Regression to Determine the Characteristics of the

Manufacturers who joined the Pledging Scheme.

Since, PS is significant, a researcher would like to determine the characteristics of the

manufacturers who participated in the pledging scheme using logistics regression.

Table 4.4 shows the binary logistic regression result. Table 4.3 presents the odd ratios.

Table 4.4 Binary logistic regression: deviance table

Source DF Adj Dev Adj Mean Chi-Square P-value

Regression 10 21.9477 2.19477 21.95 0.015

Age 1 8.0164 8.01636 8.02 0.005

Member 1 7.1453 7.14532 7.15 0.008

Labor 1 2.6637 2.66374 2.66 0.103

RC 1 6.0594 6.05944 6.06 0.014

Avg_cap 1 0.9444 0.94439 0.94 0.331

Yield 1 6.2199 6.21985 6.22 0.013

Lead 1 5.4491 5.44911 5.45 0.020

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Month_manu 1 7.9799 7.97994 7.98 0.005

Min_invt 1 3.0296 3.02960 3.03 0.082

TR 1 0.0404 0.04039 0.04 0.841

Error 23 25.1863 1.09506

Total 33 47.1340

Table 4.5 Odds ratios for continuous predictors

Variable Odd Ratio 95% CI

Age 1.1760 (1.0151, 1.3624)

Member 1.0050 (0.9998, 1.0103)

Labor 1.0086 (0.9938, 1.0237)

RC 1.000 (1.0000, 1.0000)

Avg_cap 0.9969 (0.9905, 1.0033)

Yield 0.4164 (0.1394, 1.2435)

Lead 0.9638 (0.9269, 1.0021)

Month_manu 0.3741 (0.1612, 0.8682)

Min_Invt 1.0105 (0.9834, 1.0383)

TR 0.9287 (0.4384, 1.9674)

Regression Equation

P(yes) = exp(Y')/(1 + exp(Y'))

Y' = 4.39 + 0.1621 Age + 0.00503 Member + 0.00859 Labor + 0.000000 RC

- 0.00311 Avg_cap - 0.876 Yield - 0.0369 Lead - 0.983 Month_manu

+ 0.0104 Min_invt - 0.074 TR

Table 4.6 Goodness-of-Fit tests

Test DF Chi-square P-value

Deviance 23 25.19 0.341

Pearson 23 32.13 0.098

Hosmer-Lemeshow 8 5.93 0.655

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According to Table 4.5, the significant variables are not be included in the 95%

confidence interval. Hosmer-Lemeshow shows a p-value of 0.655 which indicates that

the model is not lack of fit. From the binary logistic regression result in Table 4.4, the

company that participated in the pledging policy tends to be the company that has been

established for a longer period of time than others, p-value of age is 0.005. These

companies tend to have a large registered capital and large number of labors. Moreover,

these companies have shorter manufacturing lead time and operate for a shorter month

period in a year which show that they can operate at a higher efficiency than others.

They also keep their inventory for longer time and release them to the market when

price increases. This behavior can be done for those who have considerably large

amount of stock in which some part is sold directly into the market and some is kept

for price to increase. Yield is significant with p-value of 0.013 and the coefficient is

negative. Since, only 9 companies out of 36 companies grow their own cassava farm,

thus it cannot be concluded that manufacturers who tend to join the pledging scheme

will have low yield of their cassava product. To conclude, these characteristics show

that the companies that tend to join the cassava pledging policy are those who are big

companies. This research result may indicate that the SME may not benefit from the

cassava pledging policy since the pledging policy did not motivate the SME to

participate.

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Chapter 5

Conclusion and Recommendations

The study investigates the relationship between the government market intervention

policy and the financial performance of the cassava product manufacturers measured in

terms of return on equity. The model results suggested that ROE was positively

associated with the manufacturers participating in the program. This suggested that the

pledging program, which aimed at elevating the farmers’ financial situation, also had a

positive impact on the cassava chip and starch manufacturers’ financial performance.

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Appendices

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Appendix A

Questionnaire form for cassava starch manufacturers

แบบสอบถามส าหรบผประกอบการอตสาหกรรมแปงมนส าปะหลงดบ / แปงดดแปร

วนทใหขอมล

สวนท 1 ขอมลทวไปเกยวกบสถานประกอบการ

1) ชอผใหขอมล

ชอสถานประกอบการ

เลขท หมท ซอย ถนน

ต าบล/แขวง อ าเภอ/เขต จงหวด

รหสไปรษณย โทรศพท โทรศพทมอถอ

โทรสาร เวบไซต

เรมตนกจการเมอ หรอระบอายของกจการ ป

ปจจบนทานด ารงต าแหนง □ เจาของกจการ □ ผบรหาร □ หนสวนกจการ □ พนกงาน □ อนๆ โปรดระบ

เพศ □ชาย □ หญง ประสบการณในต าแหนงปจจบน ป วฒการศกษา

2) ประเภทอตสาหกรรมและก าลงการผลต (ตอบไดมากกวา 1 ขอ) อตสาหกรรม ก าลงการผลตรวมสงสด ก าลงการผลตทใชโดยเฉลย

□ แปงดบ (ตนตอวน) (ตนตอวน)

□ แปงดดแปร (ตนตอวน) (ตนตอวน)

3) ตลาดและลกคาหลกของกจการ □ ในประเทศ % โปรดระบประเภทอตสาหกรรมของลกคา

□ แปงมนดบ □ แปงมนดดแปร □ เอทานอล □ ผงชรส □ อาหารสตว □ แปงเปยก □ กระดาษ

□ ยา □ บะหมกงส าเรจรป □ สาค □ กาว □ กรดมะนาว □ ซอสปรงรส □ เครองดม

□ ไมอด □ สารความหวาน □ สงทอ □ ซกรด □ ผสงออก □ อนๆ โปรดระบ

□ ตางประเทศ % โปรดระบประเภทอตสาหกรรมของลกคา □ แปงมนดดแปร □ เอทานอล □ ผงชรส □ อาหารสตว □ แปงเปยก □ กระดาษ □ เครองดม

□ ยา □ บะหมกงส าเรจรป □ สาค □ กาว □ กรดมะนาว □ ซอสปรงรส □ ไมอด

□ สงทอ □ ซกรด □ สารความหวาน □ อนๆ โปรดระบ

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ระบปลายทางในประเทศ ทาเรอ □ กรงเทพ □ แหลมฉบง □ อยธยา □ อนๆ โปรดระบ

4) บรษทไดรบ ใบรบรองมาตรฐานอะไรบาง □ ISO

□GMP □ HACCP □ TISI □ HALAL □ อนๆ โปรดระบ

5) ความสมพนธกบเกษตรกร □ มการจดอบรม/ใหความรกบเกษตรกรดานการเพาะปลก □ มเกษตรกรเปนสมาชก ราย □ อนๆ โปรดระบ

6) ความสมพนธกบลกคา ลกคาใหม (นอยกวา 1 ป) % ลกคาเกา %

□ มการตกลงซอขายลวงหนา (ทไมใชการซอขายตามความตองการของลกคาหรอ Spot market) %

□ มการใหความรดานการผลต ปจจยการผลต และเทคโนโลยจากลกคา

สวนท 2 การส ารวจและวเคราะหสภาพขอเทจจรง (As-Is analysis)

การจดหาวตถดบ

7) รปแบบการจดหาหวมนส าปะหลง □ รบซอหนาโรงงาน □ ซอทไรของเกษตรกร □ ซอจากสหกรณการเกษตร

□ รบซอผานพอคาคนกลาง □ น าเขาจากประเทศ

□ เพาะปลกในไรของบรษท พนทเพาะปลก ไร ผลผลตตอไร ตน/ไร

การผลต

8) จ านวนแรงงานทใชในสายการผลต คน อตราคาจางแรงงาน บาท ตอ

9) เวลาทท าการผลต ชวโมง/วน ท าการผลตรวมทงสน วนตอเดอน เดอนตอป

10) ระยะเวลาทในการแปรรปหวมนสดจนเปนแปง ชม.

11) กระบวนการผลตเปนแบบ □ Semi-automation □ Automation

การฝกอบรม

12) จ านวนพนกงานทไดรบการฝกอบรม คนตอป จากทงหมด คน

13) ระยะเวลาการฝกอบรม วนตอคนตอป

14) หวขอการฝกอบรม

15) □ ความปลอดภย □ กระบวนการผลต □ การเพมทกษะการบรหารการจดการ

16) □ การใชเครองจกร □ อนๆ

การจดเกบสนคา การขนสง และการสงออก

17) เวลาทใชในการจดเกบสนคา โดยเฉลย วน นอยทสด วน มากทสด วน

18) รปแบบการขนสงผลตภณฑมนส าปะหลงทใช (ตอบไดมากกวา 1 ขอ)

รปแบบการขนสง ปรมาณ ระยะเวลาเดนทางไปกลบ (ช.ม.)

คาใชจาย

(บาท/เทยว)

ของบรษท ผานผใหบรการขนสง

Ref. code: 25595822040100EWT

31

(ตน/

เทยว)

ในประเทศ

รถบรรทก 6 ลอ □ □

รถบรรทก 10 ลอ □ □

รถพวง □ □

รถหวลาก Trailer □ □

อนๆ □ □

สงออก

รถบรรทก 6 ลอ □ □

รถบรรทก 10 ลอ □ □

รถพวง □ □

รถหวลาก □ □

สวนท 3 ความคดเหนและขอเสนอแนะ

19) โครงการ การใหความชวยเหลอผประกอบการ ป 2557/58 ป 2558/59

□ การเพมสภาพคลองทางการคาผประกอบการ □ การยกระดบมาตรฐานการแปรรปมนส าปะหลง

□ โครงการสนบสนนสนเชอเพอรวบรวมและสรางมลคาเพมมนส าปะหลงแกสหกรณภาคการเกษตร กลมเกษตรกร และกลมวสาหกจชมชนทเกยวของ เพอเปนเงนทนหมนเวยนในการรบซอผลผลตมนส าปะหลงจากเกษตรกร

โครงการอนๆทเขารวม โปรดระบปทเขารวม

20) ขอเสนอแนะตอทางภาครฐ

ผวจยขอขอบคณทานเปนอยางยงทใหความอนเคราะหในการใหขอมล

Ref. code: 25595822040100EWT

32

Appendix B

Questionnaire form for chip-pellet industry

แบบสอบถามส าหรบผประกอบการอตสาหกรรมมนเสน

สวนท 1 ขอมลทวไปเกยวกบสถานประกอบการ

1) ชอผใหขอมล

ชอสถานประกอบการ

เลขท หมท ซอย ถนน

ต าบล/แขวง อ าเภอ/เขต จงหวด

รหสไปรษณย โทรศพท โทรศพทมอถอ

โทรสาร เวบไซต

เรมตนกจการเมอ ทนจดทะเบยน บาท

ปจจบนทานด ารงต าแหนง □ เจาของกจการ □ ผบรหาร □ หนสวนกจการ □ พนกงาน □ อนๆ โปรดระบ

เพศ □ชาย □ หญง ประสบการณในต าแหนงปจจบน ป วฒการศกษา

2) ประเภทอตสาหกรรม (ตอบไดมากกวา 1 ขอ) อตสาหกรรม ก าลงการผลตสงสด ก าลงการผลตทใชโดยเฉลย

□ มนเสน (ตนตอวน) (ตนตอวน)

3) ตลาดและลกคาหลกของกจการ (ตอบไดมากกวา 1 ขอ) □ ในประเทศ % ระบประเภทอตสาหกรรม

□ เอทานอล □ อาหารสตว □ แอลกอฮอล □ ผรวบรวมเพอสงออก

□ อนๆ โปรดระบ

□ ตางประเทศ %

ระบปลายทางในประเทศ ทาเรอ □ กรงเทพ □ แหลมฉบง □ อยธยา □ อนๆ โปรดระบ

4) ความสมพนธกบลกคา ลกคาใหม (นอยกวา 1 ป) % ลกคาเกา %

สวนท 2 การส ารวจและวเคราะหสภาพขอเทจจรง (As-Is analysis)

การจดหาวตถดบ

5) รปแบบการจดหาหวมนส าปะหลง □ รบซอหนาโรงงาน % □ ซอทไรของเกษตรกร %

Ref. code: 25595822040100EWT

33

□ ซอจากสหกรณการเกษตร %

□ รบซอผานพอคาคนกลาง % □ น าเขาจากประเทศ %

□ เพาะปลกในไรของบรษท % พนทเพาะปลก ไร ผลผลตตอไร ตน/ไร

การผลต

6) จ านวนแรงงานทใชในสายการผลต คน อตราคาจางแรงงาน บาท ตอ

7) เวลาทท าการผลต ชวโมง/วน ท าการผลตรวมทงสน วนตอเดอน เดอนตอป

8) เวลาน าในการผลต วน

การฝกอบรม

9) จ านวนพนกงานทไดรบการฝกอบรม คนตอป จากทงหมด คน

10) ระยะเวลาการฝกอบรม วนตอคนตอป

11) หวขอการฝกอบรม

□ กฎระเบยบขอบงคบ ขอกฎหมายเกยวกบการน าเขาสงออก หรอภาษการคา □ การเพมทกษะการบรหารการจดการ □ การใช Software

การจดเกบสนคา การขนสง และการสงออก เวลาทใชในการจดเกบสนคา โดยเฉลย เดอน นอยทสด เดอน มากทสด วน

12) รปแบบการขนสงผลตภณฑมนส าปะหลงทใช (ตอบไดมากกวา 1 ขอ)

รปแบบการขนสง ปรมาณ

(ตน/เทยว)

ระยะเวลาเดนทางไปกลบ

(ช.ม.)

คาใชจาย

(บาท/เทยว)

ของบรษท ผานผใหบรการขนสง

ในประเทศ

รถบรรทก 6 ลอ □ □

รถบรรทก 10 ลอ □ □

รถพวง □ □

รถหวลาก Trailer □ □

สงออก

รถบรรทก 10 ลอ □ □

Ref. code: 25595822040100EWT

34

รถพวง □ □

รถหวลาก □ □

สวนท 3 ความคดเหนและขอเสนอแนะ 13) โครงการ การใหความชวยเหลอผประกอบการ

ป 2557/58 ป 2558/59

□การเพมสภาพคลองทางการคาผประกอบการ □การยกระดบมาตรฐานการผลตและแปรรปมนส าปะหลง

□สงเสรมโครงการสนบสนนสนเชอเพอรวบรวมและสรางมลคาเพมมนส าปะหลง

โครงการอนๆทเขารวม โปรดระบปทเขารวม

คณะวจยขอขอบคณทานเปนอยางยงทใหความอนเคราะหในการใหขอมล