Empirical Models and Potential Specification Errors

To test the hypotheses and to address those potential sample data issues mentioned in the previous sections, the following regression models are developed to gather evidence to support the following research questions.

86 6.4.1 Regression Models and Hypotheses 1st Stage:

As discussed, before testing for the first stage of research hypothesis, it is necessary to control for the potential endogeneity issue by performing the 2-SLS test. Accordingly, the following Tables 6.1 and 6.2 are constructed.

Table 6.1

First stage regression model to develop the Possible Mandatory Adopters (PMAdopters) This model is developed to control for the possible endogeneity issue in estimating the effect of mandatory IFRS adoption

PMAdopters = β0 + β1Size +β2(Log of BM) + β3(ROE) + country dummy variables + industry dummy variables + year dummy variables + error terms

Equation 6.2

Equation 6.2 is composed of the following variables:

1. PMAdopters: a dummy variable is used to predict the probability of being a mandatory adopter (i.e. 1 being mandatory adopter, otherwise 0);

2. Size = Firms’ size, calculated by the natural logarithm of total assets as of year- end;

3. Log of BM = Log of Book-to-Market ratios for each firm-year observation as of year-end;

4. ROE = Return on Equity calculated as the ratio of Earnings Before Interest Tax (EBIT) to Common Equities as of year-end for each firm year observation; 5. Country dummy variables = All18 EU countries dummy (=1 if yes, 0

otherwise);

6. Industry dummy variables = Industry dummy specified by sectors determined by Standard & Poor (If yes =1, 0 otherwise);

7. Year dummy variables = Year dummies from 2000 to 2009 (=1 if yes, 0 otherwise).

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As illustrated in Equation 6.2, in the first stage, instrumental variables for size, ROE and book-to-market ratios are included that are seen to be most likely to influence firms’ decisions to become mandatory adopters (e.g. Barth et al. 2005), As a result, the instrumental variable predicted mandatory IFRS adopter called Possible Mandatory IFRS Adopters (PMAdopters) is estimated in order to capture the likelihood of becoming a mandatory IFRS adopter for each firm-year observation. Similar to prior studies, country dummies are included to control for differences of cross-country factors that may be important to mandatory IFRS adoption, and with year dummies and

industry dummies to control for the year and industry fixed effects.

In the second stage, the PMAdopters (derived from the 1st stage) are included as an additional explanatory variable in the research model to control for potential endogeneity problems associated with the feedback response between mandatory adoption and the COE. Therefore, Equation 6.3 becomes the main regression model to perform all operating hypotheses stipulated in Table 4.1.

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Table 6.2

Second stage regression model to include the Possible Mandatory Adopters (PMAdopters)

This model is derived from Table 6.1 in estimating the cost of equity effect from mandatory IFRS adoption

2nd Stage:

COE = β0 + β1PMAdopters + Control variables + Firm-specific CBF variables + Firm-specific ICG variables + Country-specific IEF variables + Real GDP Growth + Stock Market Capitalization + Industry Dummies + Year Dummies + Country Dummies + error terms

Equation 6.3 Where:

1. COE PEG = ex-ante cost of equity, where PEG refers to price-earnings growth model;

2. Control variables include:

Size = Firms’ size, calculated by the natural logarithm of total assets as of year-end;

Log of BM = Log of Book-to-Market ratios for each firm-year observation as of year-end;

ROE = Return on Equity calculated as the ratio of Earnings Before Interest Tax (EBIT) to Common Equities as of year-end for each firm year

observation;

3. Firm-specific CBF = Sum of seven key dummy variables (i.e. max. = 7) to proxy for each firm-year observation’s particular characteristics as Core Business Factor to reporting incentives. Thus, each variable of dummies equals one if any firm-year observation’s CBF is above average for it is in larger size, more equity-based capital structure, more profitable, higher TobinQ, larger market value of equity, more capital-intensive assets and more analysts followed.

4. Firm-specific ICG variables = Deminor Corporate Governance score to proxy for firms’ internal corporate governance (ICG) quantity and quality rating for firms of each country. Deminor CG Score based on FTSE Eurotop 300 companies with a grid consisting of over 300 corporate governance criteria. The maximum score is 40.

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5. Country-specific IEF variables have two different proxies: 8.A and 8.B 5.A1 Legal Origin - GO = GO means German-origin countries (La Porta et al. 2002)

5.A2 Legal Origin - FO = FO means French-origin countries (La Porta et al. 2002).

5.A3 Legal Origin - SO means Scandinavian-origin countries (La Porta et al. 2002)

5.A4 Legal Origin - BO means British- origin countries (La Porta et al. 2002)

5.B1 Sector A: Public Institutions & Civil Society= Composite scores measured areas of political institutions; security, law & order; and functioning of public administration.

5.B2 Sector B: high business activities: Composite scores captured areas of functioning and regulations of goods/services market; security of transactions & contracts; and openness to outside world.

5.B3: Sector C: high capital activities which is the composite indices measured areas of capital market functioning;

5.B4: Sector D: high labor/social activities which are the composite scores measured the level of labor market and labor relations; social cohesion and social mobility.

Note of 5.B1 to 5.B4: All these composite scores are extracted from Profiles of Institutional Characteristics of 85 Developed & Developing Countries in 2010

(http://www.cepii.fr)

6. Country dummy variables = All 18 EU countries dummy (=1 if yes, 0 otherwise);

7. Industry dummy variables = Industry dummy specified by sectors determined by Standard & Poor (If yes =1, 0 otherwise); and

8. Year dummy variables = Year dummies from 2000 to 2009 (=1 if yes, 0 otherwise).

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6.3.2 Specification Error Test (RESET) for 2 SLS Omitted Variables Problems When various functional variables are specified in the regression model, prior studies highlight that another possible issue for running 2 SLS is the potential specification errors when an independent variable in the regression model is correlated with the error term. The two possible causes for specification errors are incorrect functional form and a variable omitted from the model which may have a relationship with both the

dependent variable and one or more independent variables. Pesaran and Taylor (1999) propose to use the RESET general specification error test for appropriate functional form and/or omitted variables in the 2 SLS regression model. If the 2 SLS model functions well, the predicted variable of mandatory adopters derived in the first stage from the model should not have any explanatory power in the original OLS model. In order to perform the RESET for the 2SLS, basically, it is necessary to create another variable which is computed from squaring the forecasted variable in the first stage OLS regression and test if it has any significance.

6.3.3 Control for potential multicollinearity issues in independent variables In multivariate regression models, multicollinearity is a condition that exists when two and more than two predictor (independent) variables correlate very strongly. Predictors with multicollinearity would distort the interpretation of multiple regression results because the variables are largely confounded with one another. That is, they are essentially measuring the same characteristics. As such, it would be difficult to know which of the two is the more relevant. To control for this problem, all independent variables in the regression model (Table 6.2) will be tested with (i) tolerance parameter and (ii) variance inflation factor (VIF). A tolerance parameter for each independent variable of 0.1 or below and a VIF for each independent variable of more than 10 will be considered to be problematic.

Specifically, a 2SLS model is used to control for potential endogeneity issue. To improve the reliability of the model due to problems from omitted variables, a RESET test is conducted. In addition, tolerance parameter and VIF tests are used to remedy the potential multicolinearity issues for the predictor variables. The next few chapters present the empirical results for all the hypotheses discussed earlier, together with the various descriptive, univariate and multivariate tests.

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