The effect of anti-corruption policies on the profitability and growth of the firms listed in the stock market-application on Singapore (panel data analysis)

| January 21, 2015

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Developing the idea:
1-History of anti-corruption policies in Singapore
2-The regulatory environment of the stock exchange in Singapore (focus on disclosure and ethical practices)
3-Literature on the effect of anti-corruption policies at micro level
a.Studies show positive effect (lubricating theory)
b.Studies show negative effect (bad environment theory)
4-Objective of the study: using panel data analysis using (30) nonfinancial listed firms in Singapore exchange over the period (1995 – 2013) to investigate the two theories
5-Value of the study: if the lubricating theory is correct, how much corruption should be tolerated? (I.e. what is the optimal tradeoff between the macro and micro objectives) (theoretical model can be developed here)
The model:
Two models will be constructed as follows:
1-Model 1 – Effect on profitability:
Hypothesis: H0: corruption has no relation to the profitability (β CORR = 0)
HA: (β CORR ≠0)
Variables used:
Dependent variable – Profitability: measured by ROE (describe)
Independent variables:
1-Corruption index (explain how calculated)
2-Control variables:
a.Macro variables:
i.Interest rates (expected sign: negative)
ii.Inflation (expected sign: negative)
iii.Crisis factor (dummy variable ) (expected sign: negative)
iv.GDP growth (expected sign: positive)
b.Micro variables
i. Firm size(expected sign: positive)
ii. Firm age (expected sign: positive)
iii. Sector (manufacturing or service – dummy variable) (expected result service sectors are more profitable)
2 – Model 2 – Effect on growth
This model will be similar to the previous model in all variables except the dependent variable which will be the firm’s growth of assets.
The data:
The data used for this study is the values of the dependent, independent, and micro control variables variable for all non-financial firms listed in the (–index) (cross section dimension) over the period between 1995 to 2013 for the dependent, independent, macro and micro control variables (time series dimension).
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Category: Economics

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