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Theimpactofanti-corruptionpolicieson
theprofitabilityandgrowthofthefirms
listedinthestockmarket-applicationon
Singapore
(Paneldataanalysis)
Ahmed Badawi
Anas Al-Qudah
American University of the Middle East (AUM), Kuwait
Table of
contents
 Introduction
 Previous research
 Methodology and data
 Results and conclusions
 discussions
introduction
 Anti-corruption policies have been instituted over the past few
decades in countries around the globe to limit the economic and
government impacts of corruption, both politically and
economically.
 Since Singapore has become known as one of the least corrupt
countries in the world, it is used in this research to show the
impact of anticorruption policies on the performance of the
companies listed in its stock market
Previous
research
 Some studies found that the ‘the return to financial sector
development at the level of massive corruption, exacerbate
income inequality, offsetting the benefits of financial
development’ (S. Batabyal &A. Chowdhury, 2015)
 Some studies show that corruption can be problematic in that
‘corruption is likely to lower the productivity of capital due to a
variety of channels’ J.G. Lambsdorff, (2003)
 Also (Rose-Ackerman, 2013) found that Corruption makes sales
slow, unpredictable and inefficient
 On the other hand many scholars argue that imposing anti-
corruption policies will not benefit the economy since corruption
may help in capital formation, promoting economic benefits.
(Morris & Blake, 2010).

Research
question
Main research question is:
do anti-corruption policies result in improving firms performance or
do they hinder firms activities and hence negatively affect their
performance?
Methodology
and data
 The study uses panel data regression to examine the effect of
corruption on firm profitability and growth.
 To estimate these models we used the all Singaporean listed
firms. After removing the firms with incomplete data and firms
with extreme values (outliers), we are left with 392 firms covering
the period from 1995 to 2014
 We used 2 models; in the first model we measure the relation
between corruption and profitability measured by net profit
margin. Under this model we run 2 sub models; in the first we
added only micro variables to corruption, in the second we
incorporated macro and micro variables together.
 The second model we measure the relation between corruption
and company growth measured by total asset growth. Under this
model we run 2 sub models; in the first we added only micro
variables to corruption, in the second we incorporated macro and
micro variables together.
Methodology
and data
Model 1
 a – Micro variables only:
 𝑁𝑃𝑀𝑖,𝑡 = 𝛼 + 𝛽1 𝐶𝑂𝑅𝑅 + 𝛽2 𝐷𝑅 + 𝛽3 𝑆𝐼𝑍𝐸 + 𝛽4 𝐴𝐺𝐸 + 𝛽5 𝐴𝐺 + 𝜖𝑖,𝑡
 b – Macro and micro variables:
 𝑁𝑃𝑀𝑖,𝑡 = 𝛼 + 𝛽1 𝐴𝐺 + 𝛽2 𝐶𝑂𝑅𝑅 + 𝛽3 𝐷𝑅 + 𝛽4 𝑆𝐼𝑍𝐸 + 𝛽5 𝐴𝐺𝐸 +
𝛽7 𝐼𝑁𝐹 + 𝛽8 𝐺𝐷𝑃𝐺 + 𝛽8 𝐼 + 𝜖𝑖,𝑡
Model 2
 a – Micro variables only:
 𝐴𝐺𝑖,𝑡 = 𝛼 + 𝛽1 𝑁𝑃𝑀 + 𝛽2 𝐶𝑂𝑅𝑅 + 𝛽3 𝐷𝑅 + 𝛽4 𝑆𝐼𝑍𝐸 + 𝛽5 𝐴𝐺𝐸 + +𝜖𝑖,𝑡
 b – Macro and micro variables:
 𝐴𝐺𝑖,𝑡 = 𝛼 + 𝛽1 𝑁𝑃𝑀 + 𝛽2 𝐶𝑂𝑅𝑅 + 𝛽3 𝐷𝑅 + 𝛽4 𝑆𝐼𝑍𝐸 + 𝛽5 𝐴𝐺𝐸 +
𝛽7 𝐼𝑁𝐹 + 𝛽8 𝐺𝐷𝑃𝐺 + 𝛽8 𝐼 + 𝜖𝑖,𝑡
Results –
Model 1
Variables Model 1 -a Model 1-b
Dependent variable NPM NPM
AG .1790509***
(.0203511)
.1741998 ***
(.02482)
CORR 12.81692***
(3.259897)
10.24906***
(3.495657)
DR -.0429991
(.1034901)
-.0276853
(.1039759)
SIZE 10.58102***
(1.083907)
10.34123*** (1.097665 )
AGE -.1051755
(.1115777)
-.1233492
(.1122431)
INF - 0.6028876
(.5659432)
GDPG - 0.5794218**
(.2728077)
I - .0559096
(.4270291)
Wald test 201.77
(0.0000)
207.68
(0.0000)
Model used Random effect Random effect
Number of observations 5127 5127
Number of firms 392 392
R2 .047 .0481
Results -
Model 2
Variables Model 2 -a Model 2-b
Dependent variable AG AG
NPM .0870135***
(.0177257)
.0829404 ***
(.016968)
CORR 11.77884***
(2.696277)
1.571488
(3.102839)
DR .071343
(.131929)
.0966244
(.1314588)
SIZE 13.59091***
(2.578806)
13.30037***
(2.55851)
AGE -1.625109***
(.3223144)
-2.471063***
(.394006)
INF - 1.339928***
(.4912555)
GDPG - 1.219341***
(.1600843)
I - -1.347597***
(.3304593)
F test 29.29
(0.0000)
27.3
(0.0000)
Model used Fixed effect Fixed effect
Number of observations 5127 5127
Number of firms 392 392
Adjusted R2 0.0887 0.1018
Hausman test 91.15 186.54
Conclusions
 The main conclusion of this study is that anti-corruption policies
that was conducted in Singapore over the period 1995 to 2013
resulted in better firms performance measured by net profit
margin (1 point index improvement increases net profit margin by
about 12.8%) and asset growth (1 point index improvement
increases total asset growth by about 11.7%).
 Further research is suggested to investigate the factors that
differentiate the anti-corruption policy effect among different
countries
Discussion

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THE IMPACT OF ANTI-CORRUPTION POLICIES ON THE PROFITABILITY AND GROWTH IN THE STOCK MARKET – APPLICATION ON SINGAPORE

  • 2. Table of contents  Introduction  Previous research  Methodology and data  Results and conclusions  discussions
  • 3. introduction  Anti-corruption policies have been instituted over the past few decades in countries around the globe to limit the economic and government impacts of corruption, both politically and economically.  Since Singapore has become known as one of the least corrupt countries in the world, it is used in this research to show the impact of anticorruption policies on the performance of the companies listed in its stock market
  • 4. Previous research  Some studies found that the ‘the return to financial sector development at the level of massive corruption, exacerbate income inequality, offsetting the benefits of financial development’ (S. Batabyal &A. Chowdhury, 2015)  Some studies show that corruption can be problematic in that ‘corruption is likely to lower the productivity of capital due to a variety of channels’ J.G. Lambsdorff, (2003)  Also (Rose-Ackerman, 2013) found that Corruption makes sales slow, unpredictable and inefficient  On the other hand many scholars argue that imposing anti- corruption policies will not benefit the economy since corruption may help in capital formation, promoting economic benefits. (Morris & Blake, 2010). 
  • 5. Research question Main research question is: do anti-corruption policies result in improving firms performance or do they hinder firms activities and hence negatively affect their performance?
  • 6. Methodology and data  The study uses panel data regression to examine the effect of corruption on firm profitability and growth.  To estimate these models we used the all Singaporean listed firms. After removing the firms with incomplete data and firms with extreme values (outliers), we are left with 392 firms covering the period from 1995 to 2014  We used 2 models; in the first model we measure the relation between corruption and profitability measured by net profit margin. Under this model we run 2 sub models; in the first we added only micro variables to corruption, in the second we incorporated macro and micro variables together.  The second model we measure the relation between corruption and company growth measured by total asset growth. Under this model we run 2 sub models; in the first we added only micro variables to corruption, in the second we incorporated macro and micro variables together.
  • 7. Methodology and data Model 1  a – Micro variables only:  𝑁𝑃𝑀𝑖,𝑡 = 𝛼 + 𝛽1 𝐶𝑂𝑅𝑅 + 𝛽2 𝐷𝑅 + 𝛽3 𝑆𝐼𝑍𝐸 + 𝛽4 𝐴𝐺𝐸 + 𝛽5 𝐴𝐺 + 𝜖𝑖,𝑡  b – Macro and micro variables:  𝑁𝑃𝑀𝑖,𝑡 = 𝛼 + 𝛽1 𝐴𝐺 + 𝛽2 𝐶𝑂𝑅𝑅 + 𝛽3 𝐷𝑅 + 𝛽4 𝑆𝐼𝑍𝐸 + 𝛽5 𝐴𝐺𝐸 + 𝛽7 𝐼𝑁𝐹 + 𝛽8 𝐺𝐷𝑃𝐺 + 𝛽8 𝐼 + 𝜖𝑖,𝑡 Model 2  a – Micro variables only:  𝐴𝐺𝑖,𝑡 = 𝛼 + 𝛽1 𝑁𝑃𝑀 + 𝛽2 𝐶𝑂𝑅𝑅 + 𝛽3 𝐷𝑅 + 𝛽4 𝑆𝐼𝑍𝐸 + 𝛽5 𝐴𝐺𝐸 + +𝜖𝑖,𝑡  b – Macro and micro variables:  𝐴𝐺𝑖,𝑡 = 𝛼 + 𝛽1 𝑁𝑃𝑀 + 𝛽2 𝐶𝑂𝑅𝑅 + 𝛽3 𝐷𝑅 + 𝛽4 𝑆𝐼𝑍𝐸 + 𝛽5 𝐴𝐺𝐸 + 𝛽7 𝐼𝑁𝐹 + 𝛽8 𝐺𝐷𝑃𝐺 + 𝛽8 𝐼 + 𝜖𝑖,𝑡
  • 8. Results – Model 1 Variables Model 1 -a Model 1-b Dependent variable NPM NPM AG .1790509*** (.0203511) .1741998 *** (.02482) CORR 12.81692*** (3.259897) 10.24906*** (3.495657) DR -.0429991 (.1034901) -.0276853 (.1039759) SIZE 10.58102*** (1.083907) 10.34123*** (1.097665 ) AGE -.1051755 (.1115777) -.1233492 (.1122431) INF - 0.6028876 (.5659432) GDPG - 0.5794218** (.2728077) I - .0559096 (.4270291) Wald test 201.77 (0.0000) 207.68 (0.0000) Model used Random effect Random effect Number of observations 5127 5127 Number of firms 392 392 R2 .047 .0481
  • 9. Results - Model 2 Variables Model 2 -a Model 2-b Dependent variable AG AG NPM .0870135*** (.0177257) .0829404 *** (.016968) CORR 11.77884*** (2.696277) 1.571488 (3.102839) DR .071343 (.131929) .0966244 (.1314588) SIZE 13.59091*** (2.578806) 13.30037*** (2.55851) AGE -1.625109*** (.3223144) -2.471063*** (.394006) INF - 1.339928*** (.4912555) GDPG - 1.219341*** (.1600843) I - -1.347597*** (.3304593) F test 29.29 (0.0000) 27.3 (0.0000) Model used Fixed effect Fixed effect Number of observations 5127 5127 Number of firms 392 392 Adjusted R2 0.0887 0.1018 Hausman test 91.15 186.54
  • 10. Conclusions  The main conclusion of this study is that anti-corruption policies that was conducted in Singapore over the period 1995 to 2013 resulted in better firms performance measured by net profit margin (1 point index improvement increases net profit margin by about 12.8%) and asset growth (1 point index improvement increases total asset growth by about 11.7%).  Further research is suggested to investigate the factors that differentiate the anti-corruption policy effect among different countries