LIQ
0.003 (0.004) | ||||||||
GOV- BOND | -0.006 (0.005) | |||||||
CORP- BOND | -0.014* (0.006) | |||||||
SIZE | 0.003*** (0.001) | 0.003*** (0.001) | 0.003*** (0.001) | 0.003*** (0.001) | 0.003*** (0.001) | 0.002*** (0.001) | 0.003*** (0.001) | 0.003*** (0.001) |
FANG | 0.017 (0.021) | 0.019 (0.021) | 0.017 (0.021) | 0.017 (0.021) | -0.003 (0.018) | -0.001 (0.018) | -0.002 (0.018) | -0.003 (0.018) |
PTB | 0.002 (0.002) | 0.002 (0.002) | 0.002 (0.002) | 0.002 (0.002) | -0.001 (0.002) | -0.001 (0.002) | -0.001 (0.002) | -0.001 (0.002) |
ROA | -0.160* (0.062) | -0.162** (0.062) | -0.155* (0.062) | -0.164** (0.063) | -0.129* (0.058) | -0.128* (0.059) | -0.130* (0.059) | -0.133* (0.059) |
GDPGR | 0.098 (0.082) | 0.079 (0.075) | 0.058 (0.077) | 0.141 (0.084) | -0.000 (0.024) | 0.006 (0.027) | -0.003 (0.025) | -0.003 (0.024) |
INF | 0.160*** (0.039) | 0.158*** (0.039) | 0.120** (0.037) | 0.157*** (0.035) | 0.088* (0.035) | 0.128*** (0.035) | 0.105** (0.035) | 0.097** (0.035) |
IND1 | 0.013* (0.006) | 0.013* (0.006) | 0.013* (0.006) | 0.013* (0.006) | 0.013* (0.005) | 0.012* (0.005) | 0.012* (0.005) | 0.013* (0.005) |
IND2 | 0.008 (0.005) | 0.008 (0.005) | 0.008 (0.005) | 0.008 (0.005) | 0.009* (0.004) | 0.008 (0.004) | 0.008* (0.004) | 0.009* (0.004) |
IND3 | 0.009* (0.004) | 0.008 (0.004) | 0.009* (0.004) | 0.010* (0.004) | 0.012** (0.004) | 0.011** (0.004) | 0.012** (0.004) | 0.012** (0.004) |
IND4 | 0.004 (0.004) | 0.003 (0.004) | 0.003 (0.004) | 0.004 (0.004) | 0.005 (0.004) | 0.006 (0.004) | 0.005 (0.004) | 0.005 (0.004) |
IND5 | 0.007 (0.004) | 0.007 (0.004) | 0.007 (0.004) | 0.007 (0.004) | 0.010* (0.004) | 0.009* (0.004) | 0.010* (0.004) | 0.010* (0.004) |
IND6 | 0.011* (0.005) | 0.011* (0.005) | 0.011* (0.005) | 0.012* (0.005) | 0.009 (0.005) | 0.008 (0.005) | 0.009 (0.005) | 0.009* (0.005) |
IND7 | 0.007 (0.006) | 0.006 (0.006) | 0.007 (0.006) | 0.008 (0.006) | 0.004 (0.005) | 0.003 (0.005) | 0.004 (0.005) | 0.004 (0.005) |
_cons | -0.074** (0.024) | -0.081** (0.027) | -0.063** (0.022) | -0.081*** (0.022) | -0.031 (0.018) | -0.035* (0.017) | -0.032 (0.018) | -0.033 (0.018) |
Number of officers close | 14,085 | 14,085 | 14,085 | 14,085 | 16,017 | 16,017 | 16,017 | 16,017 |
Number of research groups | 1,898 | 1,898 | 1,898 | 1,898 | 1,951 | 1,951 | 1,951 | 1,951 |
Number of instrumental variables | 63 | 63 | 63 | 63 | 86 | 86 | 86 | 86 |
AR(1) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
AR(2) | 0.129 | 0.126 | 0.133 | 0.129 | 0.057 | 0.056 | 0.056 | 0.055 |
Hansen | 0.221 | 0.230 | 0.213 | 0.222 | 0.128 | 0.119 | 0.120 | 0.114 |
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Note: *,**,*** correspond to significance levels of 10%, 5%, 1%
Source: Results from Stata
The results of the Hansen test show that there is no endogeneity occurring in the model, because the p-values of the Hansen test are all greater than or equal to α (5%), indicating the hypothesis H 0 that the variables are exogenous. Accepted at 5% significance level. Result
Arellano-Bond AR(2) autocorrelation test also shows that no autocorrelation occurs at all levels of the model with a significance level of 5%. At the same time, according to Arellano et al. (1991) and Hansen et al. (1996), the AR(2) and Hansen test results have the p-value as large as possible, so all models have two values. This is greater than 10%; Particularly for models 21 to 24, the p-value of AR(2) is only over 5%. In addition, in order for the estimate not to be weak, the number of instrumental variables must be less than or equal to the groups. The estimation results presented in Appendix 6 all have a number of instrumental variables smaller than the number of groups, ensuring robustness. of the model. The specific regression model results are as follows:
The results in tables 4.4 to 4.6 show that the impact of financial market development on corporate capital structure in the period 2010 - 2020 will depend on the aspect as well as the debt term being considered. Specifically, when using indicators representing the development of financial markets compiled by the IMF, the development of financial markets in general (FMI), market access (FMA) and market depth (FMD) will have a negative impact. ; while market efficiency (FME) has a positive impact on total debt. As for long-term debt, FMI and FMD still have a negative impact, but FMA and FME do not reach statistical significance; and only the FME variable has a positive impact on short-term debt (reaching 0.014 at the 5% significance level), the remaining three variables do not reach statistical significance. In terms of the magnitude of the impact, FMA has the largest impact (reaching -0.035 at the 1% significance level) and FME has the smallest impact (only reaching 0.009 at the 5% significance level); FMD and FMI are in second and third place, respectively, and the impact of these two variables on total debt is relatively stronger than long-term debt, specifically: for total debt, the two variables are - respectively - 0.027 and -0.019 at the 1% significance level, as for long-term debt, these two values are quite similar, reaching -0.011 and -0.015 respectively at the 10% significance level. In other words, when the financial market develops overall or improves financial depth, it will help businesses reduce the use of debt (especially long-term debt); This result is similar to Demirguc-Kunt et al (1996), Le and Ooi (2012), hypotheses H4 and H2. When financial markets are easily accessible, businesses will be encouraged to reduce their use of debt, similar to hypothesis H1; While improved market efficiency encourages businesses to increase the use of debt, this result is similar to Doku et al. (2011), but contrary to hypothesis H3 and the study of Deesomsak et al.
(2004). Besides, when the development of the financial market is divided into the development of the stock market and the bond market, the interesting thing is that the development of both markets encourages businesses to reduce the use of debt, similar with hypothesis H5 but contrary to hypothesis H6. Specifically, when the ratio of market capitalization to GDP increases, it will cause businesses to reduce the use of debt (coefficient reaches -0.016), this is true in both the short term (-0.008) and long term (-0.010) with a significant level. mean 5% or more; This result is similar to Agarwal and Mohtadi (2004), Yartey (2009), Deesomsak et al. (2009), Lemma and Negash (2012), Masoud (2013), Yadav et al. (2019). An improvement in the ratio of stock transaction value to GDP will help businesses reduce their long-term debt ratio, with the coefficient reaching -0.007 at the 10% significance level; This result is similar to Bokpin (2010), Doku et al (2011), Masoud (2013), Le (2017). As for the bond market, when the government bond or corporate bond market develops, it will help businesses reduce debt ratios, especially long-term debt ratios; Particularly, the development of the corporate bond market also helps reduce the ratio of short-term debt. The impact seems to be strongest on total debt, then long-term debt and short-term debt; Typically for the development of the government bond market, the rates reach -0.017 and -0.015 at the 5% significance level (short-term debt does not reach statistical significance); while the development of the corporate bond market reached -0.033, -0.020 (both at the 1% significance level) and -0.014 (at the 10% significance level).
For the group of business characteristics variables: Lags of the dependent variables (including total debt, long-term debt and short-term debt) all have a positive impact on the dependent variable and reach a significance level of 1%, this shows that the structure Enterprise capital structure is relatively stable over the years. Business size (SIZE) has a positive impact on capital structure (both total debt, long-term debt and short-term debt), the impact on total debt (reaching 0.010 - 0.011) and long-term debt (0.009) is different. are not significantly different, while short-term debt is quite small (only 0.003) and all coefficients reach the 1% statistical significance level. Under the condition that other factors remain unchanged, when SIZE increases (decreases) by 1 unit, the corporate debt ratio will increase (decrease) in the range of 0.003 - 0.011%; The results support the trade-off theory and are similar to the studies of Deesomsak et al (2004, 2009), Sbeiti (2010), Lucey and Zhang (2011), Le and Ooi (2012), Dang Thi Quynh Anh and Quach Thi Hai Yen (2014),
Vo Thi Thuy Anh and colleagues (2014), and Le (2017). Similar to SIZE, the nature of corporate assets (TANG) also affects capital structure in the same direction, but only reaches statistical significance for the total debt variable in models measuring the development of financial market according to the IMF, but the The impact is quite large, reaching 0.030 to 0.033 at the 5% and 10% significance levels; This result supports the trade-off theory and is similar to the results of studies like Deesomsak et al (2004, 2009), Yartey (2009), Le and Ooi (2012), Dang Thi Quynh Anh and Quach Thi Hai Yen (2014). ). Business growth opportunities (PTB) do not reach statistical significance in all models. Profitability (ROA) has a negative impact on total debt and short-term debt of the enterprise; In which, the impact of ROA on total debt is larger (from -0.138 to -0.190) and reaches 1% significance, while short-term debt reaches from -0.128 to -0.164 at the 10% significance level. In other words, under the condition that other factors remain unchanged, if a business's profitability increases (decreases) by 1%, corporate debt will decrease (increase) by 0.128 - 0.190% (this is a large coefficient). best of all variables representing business characteristics, except for the dependent variable lagged); This result supports the pecking order theory and is similar to the research results of Bokpin (2009), Yartey (2009), Zafar et al. (2019), Le Dat Chi (2013), Dang Thi Quynh Anh and Quach Thi Hai Yen (2014). At the same time, the research results also show that the industry in which the business operates will impact the corporate capital structure, similar to the research of Kayo and Kimura (2011), Lemma and Negash (2012), Le Dat Chi (2012), and Le Dat Chi (2011). 2013), Palacin-Sanchez and Pietro (2015).
For the group of macroeconomic variables: Inflation rate (INF) has a positive impact on total debt and short-term debt (model 5 alone does not reach statistical significance for total debt) with all three levels of significance 1 %, 5% and 10%; in which INF impact on short-term debt (0.088
– 0.160) is stronger than total debt (from 0.066 to 0.122); The impact trend is similar to the research of Bokpin (2009), Lucey and Zhang (2011), Lemma and Negash (2012), Zafar et al. (2019). This implies that when inflation increases, businesses tend to increase debt ratios (especially short-term debt) to take advantage of currency depreciation to reduce interest costs. Meanwhile, GDP per capita growth rate (GDPGR) has a positive impact on total debt (but only reaches statistical significance at the 5% and 1% levels in models 2 and 4) with values of 0.141 and 0.190, respectively. ; this
shows that the impact of GDPGR on capital structure is not clear, but there is also evidence that when the economy grows, businesses will tend to increase debt to expand production and business activities. .
4.4.2. Impact of financial market development on corporate capital structure by national
The quality of national institutions varies widely between countries; In which Vietnam is the country with the lowest value with an average value of -0.969, followed by Indonesia reaching -0.136, the Philippines -0.064, Thailand -0.055 and the highest is Malaysia reaching 1.342 (details in Appendix 10). . Meanwhile, the quality of national institutions can have a huge impact on the development of financial markets as well as corporate capital structure (analyzed in section 2.2.2). However, including the variable of national institutional quality in the general model with the variable representing the development of financial markets (in direct or interactive form) causes the model to encounter multicollinearity and will produce inconsistent estimation results. Exactly. Therefore, the thesis will divide the research sample into two based on the median of the GOV variable into: a group with low national institutional quality (GOV_LOW) and a group with high national institutional quality (GOV_HIGH) to consider. In more detail the impact of financial market development on corporate capital structure.
Table 4.7: Impact of financial market development on total debt in the group with low national institutional quality (GOV_LOW)
MH 25 | MH 26 | MH 27 | MH 28 | MH 29 | MH 30 | MH 31 | MH 32 | |
L.LEV | 0.847*** (0.077) | 0.869*** (0.073) | 0.806*** (0.084) | 0.848*** (0.073) | 0.681*** (0.071) | 0.812*** (0.065) | 0.774*** (0.071) | 0.709*** (0.079) |
FMI | 0.005 (0.013) | |||||||
FMA | 0.078 (0.057) | |||||||
FMD | -0.013 (0.016) | |||||||
FME | 0.004 (0.008) | |||||||
MACAP | -0.032** (0.010) | |||||||
LIQ | 0.012 (0.006) | |||||||
GOV- BOND | -0.001 (0.013) |
CORP- BOND
-0.065 (0.037) | ||||||||
SIZE | 0.008** (0.003) | 0.008** (0.003) | 0.010** (0.003) | 0.008** (0.003) | 0.017*** (0.003) | 0.011*** (0.002) | 0.013*** (0.002) | 0.015*** (0.003) |
FANG | 0.012 (0.041) | 0.013 (0.040) | 0.012 (0.042) | 0.012 (0.041) | -0.060 (0.041) | -0.063 (0.037) | -0.063 (0.039) | -0.048 (0.042) |
PTB | 0.003 (0.003) | 0.003 (0.003) | 0.005 (0.003) | 0.004 (0.003) | 0.010** (0.003) | 0.007* (0.003) | 0.007* (0.003) | 0.008** (0.003) |
ROA | 0.092 (0.132) | 0.115 (0.132) | 0.057 (0.132) | 0.088 (0.128) | -0.315** (0.113) | -0.156 (0.106) | -0.187 (0.108) | -0.270* (0.113) |
GDPGR | 0.339 (0.248) | 0.275 (0.213) | 0.324 (0.206) | 0.357 (0.259) | 0.014 (0.038) | 0.030 (0.044) | 0.013 (0.043) | 0.010 (0.039) |
INF | 0.157** (0.057) | 0.162* (0.063) | 0.146*** (0.044) | 0.157** (0.057) | 0.122** (0.039) | 0.187*** (0.046) | 0.165*** (0.045) | 0.152*** (0.039) |
IND1 | 0.011 (0.011) | 0.013 (0.010) | 0.008 (0.012) | 0.011 (0.011) | -0.020 (0.011) | -0.008 (0.010) | -0.012 (0.010) | -0.016 (0.011) |
IND2 | 0.006 (0.013) | 0.009 (0.012) | 0.001 (0.014) | 0.006 (0.013) | -0.030* (0.012) | -0.014 (0.011) | -0.018 (0.011) | -0.023 (0.012) |
IND3 | -0.003 (0.013) | 0.001 (0.012) | -0.008 (0.014) | -0.003 (0.012) | -0.039** (0.012) | -0.022* (0.011) | -0.027* (0.011) | -0.032** (0.012) |
IND4 | 0.006 (0.013) | 0.010 (0.013) | 0.001 (0.015) | 0.006 (0.013) | -0.031* (0.014) | -0.013 (0.012) | -0.018 (0.013) | -0.025 (0.013) |
IND5 | -0.020 (0.016) | -0.017 (0.016) | -0.027 (0.018) | -0.020 (0.016) | -0.056*** (0.016) | -0.034* (0.014) | -0.040** (0.015) | -0.047** (0.016) |
IND6 | 0.009 (0.013) | 0.011 (0.013) | 0.005 (0.014) | 0.009 (0.013) | -0.028* (0.013) | -0.014 (0.011) | -0.018 (0.012) | -0.022 (0.012) |
IND7 | 0.003 (0.018) | 0.007 (0.018) | -0.004 (0.019) | 0.002 (0.018) | -0.055** (0.017) | -0.034* (0.015) | -0.039* (0.016) | -0.046** (0.016) |
_cons | -0.156*** (0.043) | -0.184** (0.063) | -0.164*** (0.037) | -0.158*** (0.045) | -0.172*** (0.030) | -0.142*** (0.028) | -0.147*** (0.028) | -0.170*** (0.034) |
Number of officers close | 7,019 | 7,019 | 7,019 | 7,019 | 7,998 | 7,998 | 7,998 | 7,998 |
Number of research groups | 1,374 | 1,374 | 1,374 | 1,374 | 1,398 | 1,398 | 1,398 | 1,398 |
Number of instrumental variables | 44 | 44 | 44 | 44 | 63 | 63 | 63 | 63 |
Similar level 1 official – AR(1) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Second-order autoregression - AR(2) | 0.810 | 0.837 | 0.778 | 0.804 | 0.551 | 0.625 | 0.600 | 0.553 |
Hansen test | 0.869 | 0.863 | 0.860 | 0.876 | 0.421 | 0.249 | 0.249 | 0.278 |
Note: *,**,*** correspond to significance levels of 10%, 5%, 1%
Source: Results from Stata
Table 4.8: Impact of financial market development on long-term debt in groups with low national institutional quality (GOV_LOW)
MH 33 | MH 34 | MH 35 | MH 36 | MH 37 | MH 38 | MH 39 | MH 40 | |
L.LLEV | 0.478*** (0.115) | 0.554*** (0.106) | 0.402*** (0.122) | 0.512*** (0.111) | 0.346*** (0.098) | 0.490*** (0.096) | 0.429*** (0.096) | 0.366*** (0.100) |
FMI | -0.021 (0.012) | |||||||
FMA | -0.013 (0.040) | |||||||
FMD | -0.030* (0.012) | |||||||
FME | -0.008 (0.006) | |||||||
MACAP | -0.023** (0.008) | |||||||
LIQ | -0.001 (0.006) | |||||||
GOV- BOND | -0.023 (0.013) | |||||||
CORP- BOND | -0.061* (0.030) | |||||||
SIZE | 0.017*** (0.004) | 0.014*** (0.003) | 0.020*** (0.004) | 0.015*** (0.004) | 0.021*** (0.003) | 0.016*** (0.003) | 0.018*** (0.003) | 0.020*** (0.003) |
FANG | 0.017 (0.039) | 0.021 (0.036) | 0.017 (0.042) | 0.019 (0.038) | 0.018 (0.036) | 0.018 (0.033) | 0.021 (0.035) | 0.033 (0.037) |
PTB | 0.004 (0.003) | 0.002 (0.003) | 0.006 (0.003) | 0.004 (0.003) | 0.005* (0.003) | 0.002 (0.002) | 0.003 (0.002) | 0.004 (0.003) |
ROA | 0.067 (0.092) | 0.062 (0.088) | 0.037 (0.093) | 0.060 (0.093) | 0.003 (0.076) | 0.075 (0.071) | 0.055 (0.071) | 0.027 (0.074) |
GDPGR | -0.068 (0.202) | -0.023 (0.167) | -0.068 (0.190) | -0.020 (0.204) | -0.072** (0.028) | -0.065* (0.030) | -0.090** (0.030) | -0.074** (0.027) |
INF | 0.105* (0.045) | 0.097* (0.049) | 0.104** (0.037) | 0.113* (0.046) | 0.077* (0.031) | 0.088* (0.036) | 0.067 (0.035) | 0.094** (0.033) |
IND1 | -0.073*** (0.019) | -0.061*** (0.017) | -0.084*** (0.021) | -0.067*** (0.018) | -0.091*** (0.017) | -0.069*** (0.016) | -0.079*** (0.017) | -0.087*** (0.017) |
IND2 | -0.068*** (0.019) | -0.055** (0.017) | -0.079*** (0.021) | -0.062*** (0.018) | -0.082*** (0.017) | -0.059*** (0.016) | -0.068*** (0.016) | -0.075*** (0.017) |
IND3 | -0.083*** (0.021) | -0.068*** (0.019) | -0.097*** (0.023) | -0.076*** (0.020) | -0.104*** (0.018) | -0.078*** (0.017) | -0.089*** (0.018) | -0.097*** (0.018) |
IND4 | -0.053** (0.018) | -0.043** (0.016) | -0.063** (0.020) | -0.048** (0.017) | -0.067*** (0.017) | -0.047** (0.016) | -0.056*** (0.016) | -0.062*** (0.017) |
IND5 | -0.094*** (0.023) | -0.078*** (0.021) | -0.109*** (0.025) | -0.087*** (0.022) | -0.113*** (0.021) | -0.087*** (0.019) | -0.097*** (0.020) | -0.106*** (0.020) |
IND6 | -0.061*** (0.018) | -0.050** (0.016) | -0.072*** (0.020) | -0.056** (0.017) | -0.077*** (0.016) | -0.056*** (0.015) | -0.064*** (0.015) | -0.070*** (0.016) |
IND7 | -0.062** (0.021) | -0.050** (0.019) | -0.074** (0.023) | -0.057** (0.020) | -0.082*** (0.019) | -0.060*** (0.017) | -0.068*** (0.018) | -0.074*** (0.019) |
_cons | -0.195*** (0.053) | -0.169** (0.061) | -0.230*** (0.055) | -0.190*** (0.054) | -0.249*** (0.045) | -0.198*** (0.043) | -0.213*** (0.043) | -0.250*** (0.047) |
Number of observations
7,019 | 7,019 | 7,019 | 7,019 | 7,998 | 7,998 | 7,998 | 7,998 | |
Number of research groups rescue | 1,374 | 1,374 | 1,374 | 1,374 | 1,398 | 1,398 | 1,398 | 1,398 |
Number of instrumental variables | 44 | 44 | 44 | 44 | 63 | 63 | 63 | 63 |
AR(1) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
AR(2) | 0.691 | 0.737 | 0.610 | 0.716 | 0.698 | 0.985 | 0.888 | 0.773 |
Hansen | 0.424 | 0.339 | 0.524 | 0.383 | 0.358 | 0.146 | 0.202 | 0.231 |
Note: *,**,*** correspond to significance levels of 10%, 5%, 1%
Source: Results from Stata
Table 4.9: Impact of financial market development on short-term debt in the group with low national institutional quality (GOV_LOW)
MH 41 | MH 42 | MH 43 | MH 44 | MH 45 | MH 46 | MH 47 | MH 48 | |
L.SLEV | 0.673*** (0.090) | 0.646*** (0.089) | 0.657*** (0.093) | 0.664*** (0.089) | 0.620*** (0.061) | 0.637*** (0.060) | 0.634*** (0.061) | 0.623*** (0.062) |
FMI | 0.017 (0.012) | |||||||
FMA | 0.141** (0.045) | |||||||
FMD | -0.019 (0.011) | |||||||
FME | 0.018* (0.007) | |||||||
MACAP | -0.021** (0.006) | |||||||
LIQ | 0.002 (0.005) | |||||||
GOV- BOND | -0.006 (0.009) | |||||||
CORP- BOND | -0.049* (0.023) | |||||||
SIZE | 0.003* (0.001) | 0.003** (0.001) | 0.003* (0.001) | 0.003** (0.001) | 0.003** (0.001) | 0.003** (0.001) | 0.003** (0.001) | 0.003** (0.001) |
FANG | 0.008 (0.041) | 0.003 (0.041) | 0.007 (0.040) | 0.009 (0.041) | -0.008 (0.029) | -0.017 (0.028) | -0.014 (0.028) | -0.015 (0.029) |
PTB | 0.002 (0.003) | 0.003 (0.003) | 0.002 (0.003) | 0.003 (0.003) | 0.002 (0.002) | 0.002 (0.002) | 0.002 (0.002) | 0.001 (0.002) |
ROA | 0.040 (0.093) | 0.040 (0.092) | 0.049 (0.093) | 0.029 (0.091) | -0.080 (0.076) | -0.081 (0.075) | -0.083 (0.075) | -0.098 (0.077) |
GDPGR | 0.550* (0.238) | 0.438* (0.196) | 0.361 (0.220) | 0.635** (0.240) | 0.022 (0.028) | 0.055 (0.032) | 0.043 (0.032) | 0.024 (0.029) |
INF | 0.136** (0.045) | 0.158*** (0.045) | 0.073 (0.041) | 0.144*** (0.043) | 0.063 (0.035) | 0.125*** (0.037) | 0.109** (0.037) | 0.084* (0.033) |
IND1 | 0.047*** (0.010) | 0.051*** (0.010) | 0.046*** (0.010) | 0.049*** (0.010) | 0.039*** (0.008) | 0.038*** (0.008) | 0.038*** (0.008) | 0.038*** (0.009) |