Temas varios de importancia específica

As discussed in section 3.4.2, this study applies logit regression to examine the probabilities describing the possible relationship between the dependent variable of political connection and the series of independent variables of accounting performance. The dependent variable takes the value of one when a firm has political connections and zero otherwise. On the right side of the regression, this study includes the following variables of operating a business; cash and cash equivalents, net other receivables, total current assets, net fixed assets, other variables of net investment, net construction in progress, net intangible assets, net goodwill, operating liabilities, short-term debts, long- term debts, payables for special projects, other liabilities, operating revenues, net profit, IPM(interest protection multiple), market to book, share capital, capital reserves, surplus reserves, retained earnings, and minority interest. Before running the regressions, all of the independent variables are winsorised at the 1stand 99thpercentiles.

Table 3:4: Political connections and accounting based numbers

This table presents the logistic regression results of the relationship between political connections and corporate governance. The dependent variable in this table is political connection, and equals 1 if the firm is politically connected (zero otherwise). Robust t-statistics are in parentheses. Significance levels 0.01, 0.05 and 0.1 are noted by ***, **, * respectively (The accounting ratios are based on total assets).

1 2 3 4 5 6

Intercept 0.557*** 0.457** 0.279 0.491** 0.614*** -0.300 (3.38) (2.47) (0.49) (2.43) (3.67) (-0.49)

Cash and cash equivalents -0.592*** -0.669*** -0.244 -0.595*** -0.596*** -0.126 (-3.77) (-3.72) (-0.88) (-3.10) (-3.46) (-0.41)

Net other receivables -0.961*** -0.907*** -0.544 -0.698** -0.890*** -0.424 (-4.02) (-3.32) (-1.06) (-2.29) (-3.62) (-0.81)

Total current assets 1.526*** 1.458*** 1.661** 1.418*** 1.315*** 1.062 (3.43) (2.90) (2.04) (2.76) (2.80) (1.18)

Net investment -1.459*** -1.522*** -1.330*** -1.472*** -1.527*** -1.281*** (-7.75) (-7.11) (-3.64) (-6.41) (-7.82) (-3.41)

Net fixed assets -0.429*** -0.464*** -0.056 -0.473*** -0.465*** -0.058 (-3.64) (-3.46) (-0.25) (-3.34) (-3.82) (-0.25)

Net construction in Progress -0.287 -0.594** -0.025 -0.558* -0.251 -0.289 (-1.21) (-2.04) (-0.05) (-1.74) (-1.00) (-0.55)

Net intangible assets -0.294 -0.132 -0.789 -0.060 -0.259 -0.656 (-0.95) (-0.38) (-1.46) (-0.16) (-0.82) (-1.15) Net goodwill 0.894 2.220 4.485 3.886* 0.809 4.050 (0.58) (1.18) (1.47) (1.85) (0.51) (1.28) Operating liabilities -0.055 -0.032 -0.047 -0.122 -0.154 -0.150 (-0.29) (-0.16) (-0.12) (-0.57) (-0.80) (-0.36) Short-term debts -0.696*** -0.696*** -1.063*** -0.737*** -0.766*** -1.028** (-3.51) (-3.27) (-2.54) (-3.28) (-3.79) (-2.37) Long-term debts -0.250 -0.357 -0.659 -0.425 -0.362 -0.841 (-0.96) (-1.25) (-1.24) (-1.42) (-1.37) (-1.55)

Payable for special projects 0.824 0.659 -0.193 0.633 0.659 0.182 (1.13) (0.86) (-0.13) (0.79) (0.90) (0.12) Other liabilities 0.451 1.332 -0.751 2.308** 0.450 -1.221 (0.62) (1.53) (-0.52) (2.45) (0.60) (-0.81) Operating revenues -0.004 -0.003 -0.159** -0.002 0.013 -0.164** (-0.10) (-0.07) (-2.13) (-0.05) (0.30) (-2.10) Net profit 1.107*** 0.846** 0.581 0.976*** 1.052*** 0.559 (3.54) (2.42) (0.93) (2.66) (3.29) (0.86) IPM 0.002** 0.002* 0.003* 0.002** 0.002** 0.002 (2.48) (1.72) (1.88) (2.33) (1.98) (1.45) Share capital -1.076*** -0.976*** -1.354*** -0.938*** -1.169*** -1.457*** (-4.97) (-4.10) (-3.03) (-3.73) (-5.18) (-3.06) Capital reserve -0.224 -0.174 -0.818** -0.138 -0.203 -0.940** (-1.24) (-0.89) (-2.15) (-0.67) (-1.10) (-2.34) Surplus reserves 1.196** 1.169** -1.093 0.584 1.175** -0.948 (2.52) (2.13) (-1.18) (1.01) (2.38) (-0.96) Retained earnings -0.014 0.094 -0.238 0.118 -0.061 -0.390 (-0.05) (0.34) (-0.46) (0.41) (-0.23) (-0.72) Minority interest 1.001** 1.632*** 2.631*** 1.491*** 1.084** 2.790*** (2.32) (3.34) (3.20) (2.93) (2.46) (3.26) Market to Book 0.040*** 0.036*** 0.073*** 0.035** 0.043*** 0.082*** (3.34) (2.76) (3.48) (2.43) (3.18) (3.35) Largest 0.002* 0.009***

(1.68) (4.18) state 0.086 -0.050 (1.05) (-0.33) dully -0.079 -0.069 (-1.15) (-0.68) board 0.004 0.000 (0.28) (0.01) independent -1.082** -0.745 (-2.21) (-1.44) female -0.418 -0.054 (-1.56) (-0.19) age 0.013* 0.008 (1.79) (1.05) edu 0.105** 0.090 (2.03) (1.63) background1 -0.175 -0.101 (-1.63) (-0.85) background2 0.223** 0.300*** (2.15) (2.69) BMEET -0.012 -0.009 (-1.51) (-1.01) Sbsise 0.023 0.073*** (1.56) (2.85) SBMEET -0.005 0.010 (-0.44) (0.55) AR1 0.017 -0.023 (0.65) (-0.57) Politically connected 57.7% 60% 62.29% 59.59% 60.35% 62.82% Politically non-connected 55.27% 55.27% 55.51% 55.84% 55.98% 60.20%

Table 3.4 presents the results of the logistic regressions of political connection on accounting performance and corporate governance for the total samples. This study reports the results of the regressions on accounting ratios based on the total assets in Model 1 and adds corporate governance and other control variables in Models 2-6. For the first group of variables of asset accounts, it can be observed that connected firms have significantly lower cash and cash equivalents, net other receivables, net investments and net fixed assets. However, total current assets are positively related to political connections. The coefficients of these variables are all negative and most are highly statistically significant at the 1% level.

On the liability side, it can be seen that all of the operating liabilities are negative, but none are statistically significant. It can also be seen that connected firms have both less long- term and less short-term debt, but this is only significant for the short-term debts (mostly at the 1% level). As for business revenue and incomes, it can be seen that the operating revenues are mostly negative (2 out of 6 are significant at the 5% level). Again there are significantly (4 out of 6 regressions) positive net profits and significantly positive interest protection multiplies (IPM).

These findings are generally consistent with the results in the preliminary test presented in Table 3.2. Although there are some differences, such as the operating revenues and operating liabilities being negative, stronger positive coefficients in net profits and total current assets consistently indicate that the PCFs have better operating business performance. They also display better production efficiency with less fixed assets. The connected firms are, on average, generating more revenue (Table 3.2) and higher net profits (Table 3.2 and Table 3.4). The negative operating revenue shown in Table 3.4 could possibly be due to a multicollinearity problem; it is highly correlated with many operating business variables (such as Total current assets and Net profits), and some other non-operating variables.

To check the accuracy of the logistic regression model, we check the model accuracy prediction, as reported in Table 3.4. It shows that regardless of which rate version is used, our model has more than 50% ability in predictions for both PCFs and politically unconnected firms.

In Table 3.4 the significantly negative coefficients of net other receivables confirm that PCFs are less likely to conduct cash tunnelling. It is interesting to note that the variable of net other receivables is highly correlated with retained earnings, with a coefficient of -0.39 (shown in Table 3.3). However, the coefficients of net other receivables in the regressions are strongly significant in most of the regressions shown in Table 3.4.

Another important difference between Table 3.2 and Table 3.4 is the variable of retained earnings. On average, the connected firms maintain higher ratios (1% significance in Table 3.2), but after controlling for other accounting ratios, the coefficients turn to having no significance (at any conventional level). The authors think this is caused by a multicollinearity problem. It can be seen that the variable of retained earnings is highly correlated with a few of the other ratios (net other receivables at -0.39, short-term debts at -0.41, net profits at 0.63, and share capital at -0.41). Retained earnings are actually the accumulation of many operating and non-operating results. Therefore, it seems true that the relation between retained earnings and the dependent variable is dominated by the relatedness of through multicollinearity to many other variables included in the regressions.

The last result is in regards to the share capital, with systematic lower ratios (at the 1% significance level) for the connected firms, indicating less original investment from stockholders. The result is consistent with the hypothesis of higher (historical) operating profits; for a similar size of total assets, less share capital is required because these firms have more internal funding.

In addition, all the coefficients of market-to-book ratio in Table 3.4 are shown to be significantly positively correlated to political connection. Although the market-to-book ratio has a significant correlation with share capital (at 0.45 in Table 3.3), the coefficients of share capital are negatively significant at the 1% level, indicating there is no serious identification problem between them.

As for the control variables related to ownership structure, the results in Table 3.4 show that the coefficients of ownership concentration measured by “largest” (in Models 3 and 6) are significantly positively related (10% and 1%) to political connections. This is consistent with Fan et al. (2007). Furthermore, for the control variables of board

characteristics, Table 3.4 shows that the connected board directors are older (Age) and have more academic education background (Background2). All of these are consistent with the findings shown in the univariate test reported in Table 3.2. The remaining variables are not particularly significant in explaining political connections.

To check the power of the logit regression model, instead of examining some indicative econometric statistics, this study uses prediction accuracy. There are two measures; one is the ratio of correct prediction among politically connected firms, and the other is the ratio of correct prediction among politically non-connected firms. The result is reported in the last two rows of Table 3.4. It can be seen from the table that all of the right prediction ratios are above 55%, which is well above the no explanation ratio of 50%.

The logit regression results are generally supportive of the hypothesis that PCFs have a systematically different pattern of accounting ratios (Hypothesis 1). In particular, these firms have managed operating businesses with higher business turnover and lower fixed assets investment. As a result, they show higher net profits (Hypothesis 1a). Additionally, these firms suffer less cash tunnelling (Hypothesis 1b) and so they have more net profits and thereby have more internal funds available for growth (Hypothesis 1c). In terms of the “Golden hens” vs. “Chicken” theory these results show strong evidence that connected firms are “Golden hens”.