Experiencias teatrales en la Casa de la Cultura de Suba

Due to data availability and in order to capture the beginning of the finan- cial globalization process I choose for the regression analysis a 21-year period from 1984 to 2004. The panel of countries is unbalanced. The basic sample includes 122 countries, which is a fairly good size compared with previous empirical studies.54 In order to reduce the influence of extreme outliers I winsorize the upper 95th percentile and the lower 5th percentile of the dis- tribution of the dependent variables. Beside the lagged dependent variable the main regressors are private credit as a ratio to GDP, the logarithm of GDP per capita as well as trade and capital account openness. Tables 2.8 and 2.9 in Appendix A provide summary statistics of the variables as well as pairwise correlations of the regressors. All the explanatory variables are positively correlated with the highest correlation coefficients between private credit, institutions and GDP per capita, which range from 0.69 to 0.84.

Tables 2.2 and 2.3 present the baseline estimation results.55 Standard errors are robust to an arbitrary correlation within countries as well as to heteroskedasticity across countries. Throughout all the specifications in both the tables the Arellano-Bond test suggests the existence of negative first-order serial correlation in the first-differenced residuals at the 1% significance level,

53_{The number of overidentifying restrictions may be higher when I instrument other}

explanatory variables.

54_{Table 2.7 in Appendix A provides a list of all the countries. From the original 145}

countries from Lane and Milesi-Ferretti (2007) 23 countries have been excluded due to missing data on the explanatory variables or because they have only a small number of yearly observations.

55_{I use the xtabond2 routine in Stata provided by Roodman (2009a) to obtain my}

Table 2.2: FDI outflows and financial development: basic sample

Dependent Variable:

(1) (2) (3) (4) (5)

L1.(Net FDI outflows to GDP) 0.081* 0.085** 0.084** 0.082** 0.090** (0.041) (0.041) (0.042) (0.041) (0.042) Private credit to GDP 0.035** 0.034** 0.034** 0.034** 0.033**

(0.014) (0.014) (0.014) (0.014) (0.014) Log of real GDP per capita in PPP -0.012 -0.013

(0.013) (0.013)

Trade openness -0.021** -0.021**

(0.0083) (0.0082)

Capital account openness -0.0076 -0.0076

(0.0049) (0.0047)

Year dummies yes yes yes yes yes

Observations 2064 2064 2064 2064 2064

Number of country clusters 122 122 122 122 122

Number of instruments 25 26 26 28 30

F statistic 3.83 4.10 5.01 4.83 6.42

F-Test (p-value) 0.00 0.00 0.00 0.00 0.00

AR(1) Test -6.82 -6.80 -6.91 -6.90 -6.99

AR(1) Test (p-value) 0.00 0.00 0.00 0.00 0.00

AR(2) Test -0.45 -0.41 -0.35 -0.45 -0.32

AR(2) Test (p-value) 0.66 0.68 0.73 0.65 0.75 Hansen-J statistic 2.00 2.14 2.03 2.86 2.88

Hansen-J (degrees of freedom) 2 2 2 4 4

Hansen-J (p-value) 0.37 0.34 0.36 0.58 0.58 Diff-in-Hansen statistic for private credit 2.00 2.14 0.16 0.08 0.01 Diff-in-Hansen (p-value) 0.16 0.14 0.69 0.78 0.92

Net FDI outflows to GDP

Notes: Robust standard errors adjusted for clustering on country-level in parentheses. Estimates are one-step Difference-GMM. In all columns L2-L4.(Net FDI outflows to GDP) are used as instruments for the (differenced) lagged dependent variable. In columns (4) and (5) the (differenced) capital account openness is instrumented with L1-L3 of it's level. The table shows the Arellano-Bond-Test for first and second order autocorrelation of the first-differenced residuals. The null hypothesis is no autocorrelation. Heteroskedasticity robust test of overidentifying restrictions (Hansen-J-Test) is performed. The null hypothesis is that the instrument set as a group is exogenous. Difference-in-Hansen Test for exogeneity of instrument subset (here of private credit) is performed. Under the null the instrument excluded is exogenous. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Notes: Robust standard errors adjusted for clustering on the country level in parentheses. Estimates are one-step difference GMM. In all the columns L2-L4.(Net FDI outflows to GDP) are used as instruments for the (differenced) lagged dependent variable. In columns (4) and (5) the (differenced) capital account openness is instrumented with L1-L3 of its level. The table shows the Arellano-Bond test for first- and second-order autocorrelation of the first- differenced residuals. The null hypothesis is no autocorrelation. A heteroskedasticity-robust test of overidentifying restrictions (Hansen-J test) is performed. The null hypothesis is that the instrument set as a group is exogenous. A Difference-in-Hansen test for exogeneity of the instrument subset (here of private credit) is performed. Under the null the instrument excluded is exogenous. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

Table 2.3: Debt outflows and financial development: basic sample

Dependent Variable:

(1) (2) (3) (4) (5)

L1.(Net debt outflows to GDP) 0.096*** 0.090** 0.096*** 0.096*** 0.091** (0.035) (0.035) (0.036) (0.035) (0.036) Private credit to GDP -0.051** -0.051** -0.051** -0.050** -0.050**

(0.022) (0.021) (0.022) (0.022) (0.022) Log of real GDP per capita in PPP -0.072 -0.070

(0.044) (0.044)

Trade openness -0.0076 -0.0076

(0.030) (0.031)

Capital account openness 0.015 0.014

(0.014) (0.014)

Year dummies yes yes yes yes yes

Observations 2063 2063 2063 2063 2063

Number of country clusters 122 122 122 122 122

Number of instruments 25 26 26 28 30

F statistic 11.2 11.1 10.8 11.2 10.6

F-Test (p-value) 0.00 0.00 0.00 0.00 0.00

AR(1) Test -7.56 -7.68 -7.55 -7.52 -7.64

AR(1) Test (p-value) 0.00 0.00 0.00 0.00 0.00

AR(2) Test -0.16 -0.19 -0.15 -0.18 -0.20

AR(2) Test (p-value) 0.87 0.85 0.88 0.85 0.84 Hansen-J statistic 0.35 0.26 0.37 1.87 1.66

Hansen-J (degrees of freedom) 2 2 2 4 4

Hansen-J (p-value) 0.84 0.88 0.83 0.76 0.80 Diff-in-Hansen statistic for private credit 0.27 0.21 0.31 0.08 0.03 Diff-in-Hansen (p-value) 0.61 0.65 0.58 0.78 0.86

Net debt outflows to GDP

Notes: Robust standard errors adjusted for clustering on country-level in parentheses. Estimates are one-step Difference-GMM. In all columns L2-L4.(Net debt outflows to GDP) are used as instruments for the (differenced) lagged dependent variable. In columns (4) and (5) the (differenced) capital account openness is instrumented with L1-L3 of it's level. The table shows the Arellano-Bond-Test for first and second order autocorrelation of the first-differenced residuals. The null hypothesis is no autocorrelation. Heteroskedasticity robust test of overidentifying restrictions (Hansen-J-Test) is performed. The null hypothesis is that the instrument set as a group is exogenous. Difference-in-Hansen Test for exogeneity of instrument subset (here of private credit) is performed. Under the null the instrument excluded is exogenous. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Notes: Robust standard errors adjusted for clustering on the country level in parentheses. Estimates are one-step difference GMM. In all the columns L2-L4.(Net debt outflows to GDP) are used as instruments for the (differenced) lagged dependent variable. In columns (4) and (5) the (differenced) capital account openness is instrumented with L1-L3 of its level. The table shows the Arellano-Bond test for first- and second-order autocorrelation of the first- differenced residuals. The null hypothesis is no autocorrelation. A heteroskedasticity-robust test of overidentifying restrictions (Hansen-J test) is performed. The null hypothesis is that the instrument set as a group is exogenous. A Difference-in-Hansen test for exogeneity of the instrument subset (here of private credit) is performed. Under the null the instrument excluded is exogenous. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

which is expected by construction. The test cannot, however, reject the null hypothesis of the absences of AR(2) in the first-differenced residuals. It indicates that my lagged levels of the dependent variable are valid instru- ments. Further, the p-value of the Hansen-J test ranges between 0.34 and 0.88. Therefore, the null hypothesis of exogeneity of the instrument set as a whole cannot be rejected. This indicates that endogeneity is not driving my results. I performed further a Difference-in-Hansen test for the exogeneity of private credit. The test statistic is χ2(1) distributed. The p-value ranges from 0.14 to 0.92 and indicates that the null hypothesis of exogeneity of my financial development measure cannot be rejected.

Looking at column (1) of Table 2.2, private credit has a positive and at the 5% level statistically significant impact on FDI outflows as a share of GDP. This suggests that in line with the theory countries with a better developed financial system export FDI on net. Throughout all 5 specifications the coef- ficient almost does not change quantitatively and remains significant. In the next three columns (2) to (4) I include the other explanatory variables one by one. Column (5) uses all the regressors together.56 Since the estimated coefficients remain unchanged I interpret only column (5). The estimated coefficient on GDP per capita is negative and shows that poor countries observe more FDI outflows (or fewer FDI inflows). However, it is not sta- tistically significant. Further, trade openness has a negative impact on FDI outflows, which is significant at the 5% level. This result is either consistent with the vertical integration motive for FDIs where multinational firms look for cheap production locations or it may be explained by the “export plat- forms” suggestion by Hanson et al. (2001) where multinationals establish large distribution networks in order to serve foreign markets better. Finally, as expected, capital account restrictions impede net FDI inflows. The coef-

56_{In columns (4) and (5) the capital account openness index is instrumented with ap-}

propriate lags because the Difference-in-Hansen test for this variable indicated endogeneity problems. As a consequence there are four instead of only two overidentifying restrictions.

ficient is negative though only marginally significant at the 11% level. A natural question to ask is what is the economic significance of the im- pact of financial development? To quantify the effect I compare a country at the 75th percentile of the distribution of private credit, like Australia, with a country at the 25th percentile, like Turkey.57 The latter has an approxi- mately 50 percentage points lower level of private credit as a percentage of GDP. The coefficient of 0.033 suggests that Australia will have 1.65 percent- age points higher net FDI outflows as a percentage of GDP. However, since I have a dynamic model, the coefficient represents only the contemporaneous effect. The long-run impact is given by β/(1−α). Given the estimate for α

of 0.09, the long-run effect amounts to about 1.65/(1−0.09) = 1.8 percent- age points. Moreover, let’s compare this value with the observed difference between the 75th and the 25th percentiles of the distribution of net FDI out- flows to GDP, which is around 2.7 percentage points.58 Therefore, the effect of financial development would “explain” about two-thirds of this difference. In contrast, the same exercise for the other statistically significant variable, trade openness, leads to a lower long-run effect of 1.1 percentage points, which makes up around 41% of the interquartile range of the distribution of net FDI outflows to GDP.59 _{In summary, financial system development has} not only a statistically but also an economically significant impact on net FDI flows.

Table 2.3 presents the results for net debt outflows. Since the estimated coefficients differ only slightly across specifications I interpret the estimates in column (5) only. Private credit has a negative impact on net debt out- flows as a share of GDP. Further, the estimated coefficient is significant at

57_{In 1994 private credit in Turkey was around 16% of GDP whereas it was around 66%}

for Australia.

58_{This represents the interquartile range of the distribution.}

59_{The difference between a country at the 75th percentile of trade openness distribution}

and a country at the 25th percentile is 0.48. The long-run impact is therefore 0.48∗

the 5% level. Therefore, as postulated by the theory, financially less devel- oped countries export portfolio debt capital on net. The economic effect of financial development is sizable for net debt flows as well. Improving the availability of private credit in Turkey to the level in Australia leads to a 0.5∗0.05/(1−0.091) = 2.75 percentage points increase in net debt inflows as a percentage of GDP over the long run. Furthermore, the interquartile range of net debt outflows amounts to around 6.5% of GDP. As a result the effect of financial system development makes up around 42% of this range. Therefore, the quality of the financial system has an economically significant effect on net portfolio debt flows. Further, the coefficient on the variable GDP per capita is negative; however, it is only marginally significant at the 11.4% level. This is against the prediction of purely neoclassical mod- els because it indicates that high-income countries import capital on net.60 However, this estimate is consistent with the “paradox” of capital flows from poor to rich countries, because empirically in the period 1984-2004 portfolio debt investments were directed on net to rich countries. Finally, the last two explanatory variables, trade and capital account openness, do not have a significant impact on net debt flows. Nevertheless, the positive coefficient on financial openness is consistent with the fact that the patterns of debt flowing from developing to more advanced countries started in the mid 1980s after the beginning of the global capital account liberalization process.