Informe de resultados fiscales de la vigencia

The methodology in this paper distinguishes from prior studies on political connection in many ways. First, this study calculates three forms of accounting ratios (rate, deviation rate and absolute deviation) to estimate accounting performances among PCFs and their non- connected peers. Each ratio version refers to different meanings. The rate is measured as the accounting value divided by the book value of total assets at the year end (the result is robust if the beginning value of the total assets is used instead). To control for differences in each accounting measure across industries, this study calculates the deviation by subtracting the corresponding industry mean ratios from each firm’s accounting rate. This study also calculates the absolute deviation of this deviation to examine the hypothesised relation to the volatility of these ratios.

Second, this study uses T-tests and Wilcoxon tests to compare the mean (median) values of the explanatory variables for both PCFs and their non-connected counterparts. Third, this study conducts a multivariate framework by involving two steps. The first step is to apply VIF (variance inflation factors) regression methodology to detect multicollinearity in the model. If the VIF for one or more of the independent variables is greater than 10, then the variable which has the highest value of VIF will be further treated; for example, the variable will be replaced by the residual from a regression of that variable on an intercept and the other independent variables (fortunately no variable included in the study shows greater-than-10 VIF values, so in the report there is no further treatment on the multicollinearity problem). Moreover, this study uses logit regression to compare the relation between political connection and a set of independent variables for connected firms and non-connected firms. The binary logistic model is used to estimate the probability of a binary response for firm i (equal to one if the firm is politically connected and zero otherwise) based on independent variables from financial statements accounts (ȭY_௜,௝,௧) and some controlling variables of corporate governance (ȭcontrol_௜,௞,௧).

Logistic model:

۾(܇=

૚ | ܆) =

૚ାࢋࢄࢼ

Where XȾ=ߚ଴+σ௝ୀଵ௡ ߚ௝ȱ௜,௝,௧+σ௞௞ୀଵߣ௞ܿ݋݊ݐݎ݋݈௜,௞,௧+ߝ௠,௧

To test hypothesis 1, this study uses

Model 1:XȾ= ߚ_଴+ȭߚ_௝ܺ_௜,௝,௧+σ ߣ_௞ܿ݋݊ݐݎ݋݈_௜,௞,௧+ߝ_௠,௧

Where Xࢼis a dummy variable taking the value of 1 if the firm is politically connected, and otherwise 0. ȈXi,j,t denotes the accounting variable ratios from the balance sheet and

income statement. Each ratio is the account balance ratio based on total assets. Beside this simple ratio, this study has also tested the industry-mean-adjusted relative deviation ratio and absolute deviation for each account, although the reported results rely on the simple rate version.

Moreover, this study includes a set of control variables related to corporate governance.

σ ܿ݋݊ݐݎ݋݈_௜,௞௧ refers to these control variables. Largest is the percentage of shares owned

by the top shareholder, which is used to measure ownership concentration. State is the percentage of shares owned by the state, and dually-listed (Dually) is a dummy variable that takes the value of 1 if a firm is listed on at least two stock markets, and 0 otherwise. Board is the number of directors on the board. Independent is the percentage of independent directors. Female is the percentage of female directors. Age is the average age of directors. Edu34 is the average educational level of directors. Background1 is the number of directors who have an accounting, law or finance background. Background2 is the number of directors who have an academic background. BMEET is the frequency of board meetings per year. Sbsize is the number of supervisory directors. SBMEET is the frequency of supervisory board meetings. Additionally, this study also measures the market performance of PCFs by using abnormal stock returns (Ar1). Ar1= Rs-Rm. Rsis the

raw return of sample firms; Rmis the return of an equally-weighted market index.

In addition to logit regression analysis, this study runs the prediction analysis for model accuracy. This study categorises samples into four classifications:

r1: correct connection prediction (Predicted connected/connected);

r0: correct non-connected prediction (Predicted connected/non- connected); e1: incorrect connection prediction (Predicted connected/connected);

e0: incorrect non-connection prediction (Predicted connected/non-connected); The table below shows these 4 classifications:

Connected Non-connected Predicted Connected r1/(r1+e1) e0/(r0+e0) Predicted non-connected e1/(r1+e1) r0/(r0+e0)

The percentage of model accuracy is defined as the number of correct predictions relative to the total number of predictions. In the above table, the ratio of [r1/(r1+e1)] is the accuracy for the Connected sample and the ratio of [r0/(r0+e0)] is the accuracy for the Non-connected sample.

34_{Education level is the average score of the education level of the directors on the board. The value of the}

score is between 0 and 3: 0 means educational level is below junior degree; 1 is junior degree; 2 equals to bachelor degree; 3 refers to master degree; 4 represents PhD.

A ratio result of 50% implies a naïve prediction, suggesting no predictive power for the model. Only when these ratios are over 50% can this study conclude that the resulting model of independent variables from financial statements is helpful in predicting political connections.