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The impact of equitization on financial and operating performance of state-owned enterprises (SOEs) in Vietnam: An approach using propensity score matching






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Abstract

This paper examines the impact of equitization on financial and operating performance of state-owned enterprises (SOEs) in Vietnam. Previous related privatization theories have not explained whether there is an improvement in financial and operating performance of equitized SOEs compared to non-equitized SOEs or not. This study proposes to use with-without comparison method through the average treatment effect measuring the impact of equitization on financial and operating performance of SOEs. By using data of 114 SOEs equitized in the period from 2012 to 2014, the author finds that equitized SOEs can not improve profitability, operating efficiency, and output when considering non-equitized SOEs. There is also no evidence for a reduction in the number of employees of equitized SOEs after equitization. These findings are in contrast to previous studies in Vietnam, but there are similarities with the results of studies in China. This is because equitized SOEs in the early post-equitization period in Vietnam are still monitored by the Vietnamese government, as well as the equitized enterprises in the period 2012-2014 are mainly large-scale ones with slow change of operating objectives, monitoring mechanism and weak competitiveness after equitization. However, equitization can help equitized SOEs operate more efficiently than non–equitized SOEs when considering non-listing status or industry group. This research provides implications for the Vietnamese government to encourage non-equitized enterprises to participate in the equitization program actively. The research results also help investors to have appropriate long-term investment strategies in equitized SOEs. This paper also has some limitations for further research.

OVERVIEW

Megginson et al. explain that privatization is known as selling public assets to the private sector 1 . Privatization reallocates resources of SOEs through private sector participation. In Vietnam, the state often uses the term ‘equitization’ instead of ‘privatization’ because equitization is the process of transferring assets of SOEs to the private sector, but the state still controls equitized SOEss after equitization in some SOEs.

From 2016 up to present, the number of equitized enterprises was limited. There were only 55 equitized enterprises in 2016, while the equitization plan for the period of 2016 to 2020 would reach 240 enterprises. In this stage, the equitization progress has been slow due to several main reasons as follows: First, there are many ideas that state-owned enterprises should play the leading role, so reducing the number of state-owned enterprises will reduce this role. Second, after more than 15 years of equitization, the remaining SOEs in the equitization list are medium and large scale ones. The equitization of large scale ones is increasingly complex, especially in the valuation of state-owned assets. Third, some leaders or agents of state-owned enterprises fear that they will lose or reduce their control over SOEs when transforming SOEs from state ownership to private ownership, so they have actively slowed equitization progress and interfered equitization process.The equitization process in Vietnam in recent years has shown slow progress due to various reasons. According to Odle, the third privatization stage marks the completion of the privatization program, but there are large-scale SOEs in this stage, and participation of these SOEs has a significant impact on the success of the privatization program 2 .

However, empirical studies have inconsistent results of the equitization impact in Vietnam. Pham also suggests that equitization may not have a positive impact on equitization, especially when compared with non-equitized SOEs 3 . These results are similar to empirical studies in China, where equitization is less likely to improve financial and operating performance of equitized SOEs 4 . However, other studies in the developed and developing countries by Megginson et al., Pohl et al., Frydman et al., Claessens and Djankov confirm that privatization helps equitized SOEs improve their financial and operating performance 1 , 5 , 6 , 7 .

Studies in developed and developing countries mostly use the pre-post comparison method and do not use with-without comparison method. Studies in China and Vietnam, in particular studies by Nhan and Son, Hung et al., Loc and Tran also use with-without comparison method, but these studies use inappropriate characteristics to define the similarity between equitized and non-equitized SOEs 8 , 9 , 10 . These studies do not use industry characteristics to compare these two groups. According to Porter, each industry has a different operating and regulatory environment, so we can only compare firm performance within one specific industry 11 .

However, previous studies have certain limitations: (1) When comparing with the non-equitized SOEs, these studies only used establishment year and firm size to determine propensity score, so the comparison is inaccurate since we can not compare between two firms in different industries. (2) Previous studies in Vietnam focused on SOEs equitized in the first and the second stage, so these studies have not considered large-sized SOEs; (3) Previous studies have not performed robustness testing in propensity score matching technique, and they only used one radius matching to set up common support area. Common support area contains propensity scores where equitized SOEs (treatment group) and non-equitized SOEs (control group) have similarities in some characteristics.

This study solves the above problems when using PSM technique with four control variables, including establishment year, equitization year, firm size and industry to identify common support areas between equitized SOEs (treatment group) and non-equitized SOEs (control group). Research data include large-scale SOEs in the third equitization stage, especially from 2012 to 2014. Furthermore, the authors also perform robustness testing of the PSM technique to evaluate the equitization impact for more accurate results. This study is organized into six parts: (1) introduction, (2) review of prior studies, (3) research methodology and data, (4) empirical results, (5) conclusions and discussion, and (6) summary and implications.

Review of prior studies

Related theories

The public choice theory was first proposed by Tullock and Buchanan to identify the impact of privatization on firm performance. This theoretical focus emphasized on financial and operating performance of SOEs when it explained that SOEs are less efficient because politicians only aim to orientate state-owned enterprises to increase their power without considering financial and operating performance of SOEs 12 . Therefore, privatizing these enterprises is necessary in order to set up the business objectives of the enterprises through transferring ownership rights to private entities. The theory also assumes that state-owned enterprises aim to maximize budgets, disperse risks, maximize labor and investment rather than maximize profits. William L. Megginson et al. argue that if state-owned enterprises were privatized, there would be an improvement in firm performance 1 . Property rights theory is built on the fundamental advantage of ownership. Private-sector firms are more experienced than state-owned enterprises in decision-making and operate more effectively than SOEs, although they operate in the same industry environment. For state-owned enterprises, the ownership of corporate stakeholders is simply state ownership, so it is difficult for them to operate effectively. State-owned enterprise managers generally do not benefit from SOEs' operating profits, so they have no motivation to manage them well. According to this theory, public agents of SOEs do not work hard in management and do not need many innovations in managing SOEs.

The theory of competitive advantage is actually derived from explaining competitive advantages at the industry level and then developing into competitive advantages at the national level. Porter presents this theory and refers to the issue of competition at an industry level or national level 11 . According to Porter, the competitive nature and resources of competitive advantage vary widely among industries or even in small segments within the same industry 11 . A study by Megginson and Netter also suggests that real sales of SOEs in different industries could be improved differently after privatization 13 . Therefore, the industry characteristics and competitiveness of each industry will determine financial and operating performance of enterprises after privatization.

Empirical studies

Primarily, related privatization theories only explain that private ownership has more advantages than state ownership, and these theories approve that privatization will help state-owned enterprises improve their financial and operating performance after privatization.

An important study by Cuervo and Villalonga, demonstrating that privatization and ownership are not the main determinants of firm performance af ter privatization 14 . These authors develop one model to explain the variability in financial and operating performance of enterprises after privatization. Empirical results show that privatization and contextual factors (privatization methods, prior-restructuring, deregulation) help to change in governance, ownership structure. After that, the post-privatized enterprises will change their operating goals, incentives, and control. Next, enterprises will change their operational strategies, organizational structure, and organizational culture. As a result, the variations have to be explained through a process like this. Studies in China also show that some measures of financial and operating performance of privatized enterprises after privatization declined or did not significantly change, such as profitability. This finding is inconsistent with research results by some authors in other developed countries, such as Megginson et al., Boubakri and Cosset, Megginson and Netter, Pohl et al., Claessens and Djankov 1 , 15 , 13 , 5 , 7 . Privatization in China also has several cases where the state still holds many shares in enterprises after privatization in some industries and critical corporations. This is a similar characteristic in the privatization process between China and Vietnam. Jiang et al., Wei et al. also prove that the profitability of privatized enterprises declined after privatization, and this finding is in contrast to research work by Megginson et al. 4 , 16 , 1 .

In order to compare the performance of enterprises after privatization and non-privatized ones, previous studies have used a with-without comparison method. Frydman et al., Claessens and Djankov, and Pohl et al. are propositional authors who use with-without comparison method to assess the impact of privatization in European countries 6 , 7 , 5 . In particular, Claessens and Djankov argue that privatized firms are more efficient than non-equitized firms 7 . Nhan and Son, Hung et al., Loc and Tran also use with-without comparison method between two groups of equitized and non-equitized SOEs for considering differences in their financial and operating performance 8 , 9 , 10 . In general, international studies and empirical studies in Vietnam have demonstrated that privatized SOEs have better financial and operating performance than non-privatized SOEs, so the author proposes the new hypothesis as follows:

Hypothesis 1: Equitized SOEs will have better financial and operating performance after equitization than non-equitized SOEs (considering only post-equitization period)

According to Loc and Tran, the equitization process helps SOEs increase their profitability, reduce leverage, total assets turnover, and employment 10 . However, These studies have shown that there is no evidence of increased labor productivity after equitization (if considered with non-equitized firms). This research has some differences compared to the study conducted by Loc and Tran when profit after tax is applied instead of profit before tax 10 . Besides, this study uses the net income efficiency ratio. Thus, the next research hypothesis can be stated as follows:

H2: Equitized SOEs will have better financial and operating performance after equitization than non-equitized SOEs (considering the difference in measures between pre-post equitization windows).

Research methodology and data

Research methodology

Previous studies used pre-post comparison, with-without comparison, and regression methods. This study mainly uses with-without comparison method. According to Khandker et al., a with-without comparison method is another option when evaluating the effectiveness of a program. This method is used through a technique known as propensity score matching and was first proposed by Rosenbaum and Rubin 17 , 18 . The advantage of this method is that it eliminates the possibility of selection bias because the selection of two participants in the program has some similarities in characteristics. Claessens and Djankov and Pohl et al. suggest using this method to assess the effects of privatization in European countries 7 , 5 . Claessens and Djankov argue that privatized SOEs are also more efficient than non-privatized SOEs 7 . Loc and Tran, Nhan and Son continue to use this method to measure how equitization impacts on firm performance 10 , 8 . Hung et al. used this method but compared between equitized SOEs and private firms 9 .

This study uses the with-without comparison method but chooses four variables of establishment year, firm size, industry, and equitization year to determine the propensity score in order to identify similarities between the treatment and control group. Besides, this study also uses a robustness test for consistent result testing 17 . This study adopts direct nearest-neighbor matching (nnmatch) and five nearest-neighbor matching (psmatch) to test the robustness of the average treatment effect. The studies by Loc and Tran, Nhan and Son, Hung et al. only apply radius matching (0.001), and this is also one limitation of these studies 10 , 8 , 9 .

Data

The initial data includes information about firm performance from the Vietnamese General Statistics Office. The initial data includes 114 equitized SOEs in the period of 2012-2014 and 312 non-equitized SOEs. This paper uses firm performance data from 2010 to 2016. Firm performance measures are calculated in average values for two years before and after equitization. Previous studies use from 2 to 10 years to be privatization windows. This study uses two-year equitization windows because of data characteristics in Vietnam and data of two-year equitization windows was also applied by most of the empirical studies in Vietnam, such as studies by Nhan and Son (2017), Loc and Tran (2016), Hung et al. (2017) 8 , 10 , 9 .

Variables and testable predictions

William L. Megginson et al. develop seven measures based on empirical findings, including (1) profitability; (2) operational efficiency; (3) capital investment; (4) output; (5) employment; (6) financial leverage and (7) payment 1 . Boubakri and Cosset , D'Souza and Megginso also apply the above measures of privatized SOEs after privatization in developing countries 15 , 19 . Nhan and Son apply five measures proposed by William L. Megginson et al. , including profitability, operating efficiency, output, employment and leverage 8 , 1 . Loc and Tran use the following measures, such as profitability, total asset turnover, labor productivity, debt ratio and total employment 10 . The authors argue that post-equitization enterprises will receive tax incentives in the first year after equitization, so using ROA, ROE and ROS will not accurately reflect financial and operating performance after equitization. However, this research will use after-tax earning for calculating ROA, ROE and ROS because many international empirical studies have used earning after tax instead of earning before tax. This research uses the same measure with most of the previous studies to compare findings better. It is also challenging to measure tax incentives of post-equitization enterprises because Vietnamese government have different support for them.

Based on the above empirical results and hypothesis development, the author proposes some variable measurement and testable prediction presented in Table 1 .

Table 1 Testable predictions 1
Variables Proxies Predicted relationship
Profitability ROS = Net Income/ Sales ROSA > ROSB
ROA = Net Income / Total Assets ROAA > ROAB
ROE = Net Income/ Equity ROEA > ROEB
Operating efficiency SALEF =Sales/ Number of employees SALEFFA> SALEFFB
NIEFF = Net Income/ Number of employees NIEFFA > NIEFFB
TAS = total sales/ total assets TASA > TASB
Output SAL = Nominal Sales/ Consumer Price Index SALA > SALB
Employment EMPL = Total Number of employees EMPLA < EMPLB
Leverage LV = Total Debt/ Total Assets LEVA < LEVB

Loc and Tran, Nhan and Son also applied a combination of PSM and DID for measuring the equitization impact (these authors used earning before tax instead of earnings after tax to calculate profitability) 10 , 8 .

Table 2 DID analysis 10
Two-year pre-equitization average of the measure Two-year post-equitization average of the measure Pre- and post-equitization difference Pre- and post-equitization difference between treatment group and control group
Treatment group (equitized SOEs) MA(0) MA(1) dA = MA(1) –MA(0) DID = dA - dB
Control group (non-equitized SOEs) MB(0) MB(1) dB = MB(1) – MB(0)
Table 3 Testable HYPOTHESES 10
Variables Proxies Predicted relationship
Profitability ROS = Net Income/ Sales ROSdA > ROSdB
ROA = Net Income / Total Assets ROAdA > ROAdB
ROE = Net Income/ Equity ROEdA > ROEdB
Operating efficiency SALEF =Sales/ Number of employees SALEFFdA> SALEFFdB
NIEFF = Net Income/ Number of employees NIEFFdA > NIEFFdB
TAS = total sales/ total assets TASdA > TASdB
Output SAL = Nominal Sales/ Consumer Price Index SALdA > SALdB
Employment EMPL = Total Number of employees EMPLdA < EMPLdB
Leverage LV = Total Debt/ Total Assets LEVdA < LEVdB

The authors calculate the two-year average values of measures in pre-post equitization windows presented in Table 2 . Most empirical studies in Vietnam used two-year equitization windows to increase sample size and two-year average values can also be used for different statistical tests.

The symbols A and B ( Table 3 ) denote for measures of equitized SOEs after equitization and non-equitized SOEs in the same period, respectively.

The research work also adopts with-without comparison method through the combination of PSM and DID techniques. There are three steps for this method, and the first step is to define the common support area. The second step is to calculate pre- and post-equitization differences between the equitized group and non-equitized group (DID technique), and the third step is used to assess the average treatment effects of equalization on the performance of the two groups.

Empirical results

Descriptive statistics

The initial data includes 114 equitized SOEs in the period of 2012-2014 and 312 non-equitized SOEs. According to Khandker et al., the number of non-participants in the control group should be larger than the number of participants in the treatment group and this will help to identify common support areas easily 17 .

Table 4 Number of non-equitized SOEs and equitized SOEs
No. of enterprises Frequency Percentage (%) Cumulative percentage (%)
Before applying PSM
Non-equitized SOEs 312 73.24 73.24
Equitized SOEs 114 26.76 100.00
Total 426 100.00
After applying PSM
Non-equitized SOEs 296 72.20 72.20
Equitized SOEs 114 27.80 100.00
Total 410 100

Using criteria of firm size, establishment year, equitization year and industry to identify common support area, the authors eliminated 16 observations (16 non-participating enterprises) to satisfy balancing conditions. The number of non-equitized SOEs i20s 296 (72.20%), and the number of equitized SOEs is 114 (27.8%) ( Table 4 ).

Table 5 Equitization year
Equitization year No. of enterprises Percentage (%) Cumulative percentage (%)
2012 41 10.00 10.00
2013 193 47.07 57.07
2014 176 42.93 100.00
Total 410 100.00

According to the number of SOEs by year presented in Table 5 , Most SOEs are chosen in 2013 (47.07%), followed by the number of SOEs in 2014 with 176 enterprises (accounting for 42.93%). This result comes from the fact that the number of equitized SOEs in 2012 is only 9, so the number of non-equitized enterprises selected in this period is less than in other periods.

Table 6 Descriptive statistics
Variables Observations Mean Std Min Max
ROS 410 0.046 0.202 -0.898 0.730
ROE 410 0.063 0.210 -1.582 1.235
ROA 410 0.023 0.099 -0.992 0.704
SALEF 410 1,602.705 5,263.764 7.316 76,937.54
NIEFF 410 135.240 1,697.372 -3,296.3 33,170.57
TAS 410 1.219 1.841 0.002 22.041
SAL 410 287,937.9 1,335,102 41.260 1.72x107
EMPL 410 634.788 1,635.713 5 22,991
LV 410 0.534 0.464 0.005 3.331

Table 6 shows that SOEs generally have a significant difference in performance, sale efficiency, and employment have the highest standard deviation. This shows that SOEs have a different firm size in terms of employment and real sales. SOEs have high average real sales of nearly 288 billion VND and an average number of employees of 635, indicating that SOEs in this sample are large-scale ones. This is also the practical contribution of this study because previous studies in Vietnam mainly focus on small and medium-sized SOEs (SOEs equitized in the first and second stages). Besides, the statistical results show that the financial performance of SOEs is not high. This can be explained through the net profit of SOEs with negative values in some cases, leading to negative ROS, ROE, and ROA. SOEs also have a difference in financial and operating performance through high standard deviation and the maximum value of these measures.

The equitization impact (considering post-equitization period only)

In this case, the authors use the PSM method for identifying the equitization impact (considering post-equitization period only) by different classification criteria, including general assessment, establishment year, non-listing status, industry group and equitization year. This is also the contribution of this study compared with previous studies in Vietnam, such as the studies by Loc and Tran, Nhan and Son 10 , 8 . Furthermore, not many international empirical studies used comparative with-without comparison method with the PSM technique, such as studies by Megginson et al., Boubakri and Cosset, D'Souza and Megginson, Harper 1 , 15 , 19 , 20 .

Table 7 General estimated results with PSM
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.553 -0.34(0.731) -1.396 -0.55(0.582)
ROE 0.008 0.40(0.686) -0.002 -0.11(0.909)
ROA 0.014 1.46(0.144) 0.008 1.04(0.297)
SALEF -343.0075 -0.79(0.427) -456.519 -1.12(0.263)
NIEFF -41.000 -0.45(0.651) -85.932 -0.62(0.536)
TAS -0.361 -2.01**(0.044) -0.351 -2.05**(0.040)
SAL -282505 -3.16***(0.002) -272104.5 -3.18***(0.001)
EMPL -178.724 -1.98**(0.047) -243.945 -2.76***(0.006)
LV -0.075 -1.63(0.103) -0.0680 -1.68*(0.093)
Sample size 410 (296 non-equitized SOEs and 114 equitized SOEs)

The authors also assess the equitization impact by firm size. To identify firm size, this paper based on the current decree no. 56/2009/ND-CP issued on June 30 rst , 2006 in Vietnam.

Table 8 Estimated results with PSM based on firm size
Small and medium-sized SOEs Large SOEs
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -3.703 -0.95(0.344) -3.755 -0.97(0.331) 0.035 0.62(0.536) 0.046 0.56(0.573)
ROE 0.022 0.63(0.526) 0.004 0.15(0.880) -0.009 -0.43(0.666) -0.011 -0.42(0.678)
ROA 0.005 0.37(0.712) 0.000 0.02(0.984) 0.003 0.23(0.815) 0.001 0.08 (0.933)
SALEF -185.880 -1.83*(0.067) -267.718 -2.47**(0.013) -345.009 -0.43(0.668) -377.259 -0.61(0.542)
NIEFF -34.919 -1.18(0.239) -43.791 -1.33(0.185) -128.932 -0.41(0.681) -200.511 -0.92(0.357)
TAS -0.316 -1.50(0.133) -0.462 -2.05**(0.040) -0.391 -1.67*(0.095) -0.0754 -0.16(0.875)
SAL -1844.598 -1.17(0.241) -2396.664 -1.48(0.140) -242037.5 -1.82*(0.069) -214816.6 -2.02**(0.044)
EMPL -2.243 -0.21(0.832) -2.149 -0.29(0.772) -86.975 -1.13(0.259) -39.969 -0.49(0.623)
LV -0.005 -0.07(0.945) 0.056 0.75(0.451) -0.072 -1.60(0.109) -0.0565 -1.24(0.215)
Sample size 142 (105 non-equitized SOEs and 37 equitized SOEs) 238 (162 non-equitized SOEs and 76 equitized SOEs)

Table 8 shows that the number of valid small and medium-sized SOEs is 142 (37 belongs to the treatment group and 105 belongs to the control group). Also, there are 238 valid large enterprises (76 equitized SOEs and 162 non-equitized SOEs).

Table 9 Estimated results with PSM based on non-listing
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.621 -0.35(0.730) -1.269 -0.47(0.638)
ROE -0.008 -0.41(0.680) -0.007 -0.32(0.752)
ROA 0.009 0.94(0.347) 0.011 1.24(0.215)
SALEF -654.774 -1.93*(0.053) -764.495 -2.48(0.013
NIEFF -108.931 -1.18(0.239) -111.434 -1.19(0.234)
TAS -0.359 -2.62(0.009) -0.377 -3.05(0.002)
SAL -223698.8 -3.24(0.001) -213881.3 -3.11(0.002)
EMPL -141.621 -1.63(0.103) -173.844 -1.78* (0.075)
LV -0.076 -1.41(0.158) -0.066 -1.40(0.162)
Sample size 368 (294 non-equitized SOEs and 74 equitized SOEs)

For unlisted enterprises within one year of equitization, their financial and operating performance is much lower than non-equitized firms in the same period ( Table 9 ).

Table 10 Estimated results with PSM based on industry group
Industry group 1 Industry group 2 Industry group 3
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 0.016 0.83(0.404) 0.017 0.82(0.410) -0.011 -0.16 (0.869) 0.025 0.35(0.725) -2.456 -0.35(0.729) -1.978 -0.26(0.793)
ROE -0.005 -0.18(0.860) -0.009 -0.34(0.731) 0.0126 0.44(0.660) 0.0212 0.80(0.421) 0.009 0.25(0.805) -0.006 -0.21(0.837)
ROA 0.007 0.52(0.601) 0.007 0.55(0.585) 0.003 0.20(0.843) 0.011 0.81 (0.421) 0.033 1.35(0.177) 0.024 1.02(0.308)
SALEF -635.139 -0.71(0.478) -264.556 -0.41(0.685) 324.116 0.47(0.641) -439.321 -0.82(0.415) -1208.097 -1.89*(0.059) -1372.487 -2.01**(0.044)
NIEFF 59.667 0.67(0.504) 77.522 0.82(0.411) -2.386 -0.05(0.959) 3.003 0.10(0.916) -378.331 -1.04(0.297) -490.411 -1.05(0.295)
TAS -0.389 -1.86*(0.063) -0.243 -1.61(0.107) 0.167 0.38(0.702) -0.052 -0.21(0.831) -0.712 -1.94*(0.053) -0.877 -2.60***(0.009)
SAL -348608.2 -1.69*(0.091) -276304 -1.79*(0.073) -38211.33 -0.86(0.389) -58907.24 -1.89*(0.059) -110953.7 -1.92*(0.055) -153028.5 -2.26**(0.024)
EMPL -130.139 -1.33(0.184) -143.041 -2.50**(0.012) -84.37308 -1.14(0.254) -55.908 -0.49(0.624) -2.554 -0.02(0.982) -144.654 -1.86*(0.063)
LV -0.067 -1.19(0.235) -0.115 -1.73*(0.084) -0.131 -1.82*(0.069) -0.196 -2.26**(0.024) -0.051 -0.53(0.596) -0.076 -1.24(0.216)
Sample size 176 (127 non-equitized SOEs and 49 equitized SOEs) 130 (81 non-equitized SOEs and 49 equitized SOEs) 93 (77 non-equitized SOEs and 16 equitized SOEs)

The authors classify SOEs into three industry groups. The first group includes SOEs from the first industry to the third industry (agriculture, mining, and manufacturing industries), the second group includes SOEs from the fourth industry to the sixth industry (power, water supply, and construction industries), and the third group includes SOEs from the seventh industry to the twelfth industry (transportation, retailing, hospitality, telecommunication, banking, insurance, and real estate industries).

Table 11 Estimated results with PSM based on equitization years
2012 2013 2014
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 6.149 0.89(0.375) 6.088 1.14 (0.256) 0.035 1.22 (0.221) 0.010 0.42(0.672) -3.103 -0.99(0.322) -6.271 -0.99(0.320)
ROE .070 2.15**(0.031) 0.056 1.61 (0.107) 0.019 0.58 (0.565) 0.017 0.60(0.547) -0.039 -2.21**(0.027) -0.0367 -2.14**(0.032)
ROA .052 3.18*** (0.001) 0.053 2.86*** (0.004) 0.018 1.28 (0.202) 0.005 0.44(0.659) -0.007 -0.52(0.601) -0.003 -0.25(0.803)
SALEF 537.339 0.64 (0.519) 62.776 0.09 (0.928) -659.694 -0.89 (0.372) -276.766 -0.47(0.639) -137.828 -0.21(0.830) -511.612 -0.98(0.32)
NIEFF 205.815 1.87* (0.062) 201.516 1.58 (0.114) 6.402 0.08 (0.937) 5.507 0.08(0.934) -191.607 -1.01(0.314) -246.265 -1.10(0.272
TAS .242 0.59 (0.553) 0.017 0.06 (0.956) -0.549 -1.64 (0.102) -0.124 -0.20(0.845) -0.273 -1.56(0.118) -0.172 -1.03(0.304)
SAL 11456.83 0.34 (0.737) -15666.87 -0.54 (0.592) -301 -1.94* (0.052) -233814.9 -2.04**(0.041) -345912 -2.84***(0.005) -292231.2 -2.45**(0.014)
EMPL -133.039 -1.55 (0.120) -221.582 -1.98** (0.047) -189.246 -1.25 (0.211) -301.331 -1.93*(0.054) -204.568 -1.81*(0.071) -183.024 -1.63(0.102)
LV -0.165 -1.50 (0.135) -0.244 -2.18**(0.029) -0.078 -1.14(0.253) 0.026 0.47(0.637) -0.051 -0.71(0.478) -0.104 -1.29(0.197)
Sample size 51 (42 non-equitized SOEs and 9 equitized SOEs) 193 (146 non-equitized SOEs and 47 equitized SOEs) 177 (119 non-equitized SOEs and 58 equitized SOEs)

In the case of applying the PSM technique for equitization years, the authors use only three characteristics to determine propensity score, including establishment year, firm size, and industry (as each year is studied separately). The impact of equitization on the post-equitization financial and operating performance of equitized SOEs are as follows:

The equitization impact (considering post-equitization period only)

In this case, the authors use the PSM method for identifying the equitization impact (considering post-equitization period only) by different classification criteria, including general assessment, establishment year, non-listing status, industry group and equitization year. This is also the contribution of this study compared with previous studies in Vietnam, such as the studies by Loc and Tran, Nhan and Son 10 , 8 . Furthermore, not many international empirical studies used comparative with-without comparison method with the PSM technique, such as studies by Megginson et al., Boubakri and Cosset, D'Souza and Megginson, Harper 1 , 15 , 19 , 20 .

Table 7 General estimated results with PSM
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.553 -0.34(0.731) -1.396 -0.55(0.582)
ROE 0.008 0.40(0.686) -0.002 -0.11(0.909)
ROA 0.014 1.46(0.144) 0.008 1.04(0.297)
SALEF -343.0075 -0.79(0.427) -456.519 -1.12(0.263)
NIEFF -41.000 -0.45(0.651) -85.932 -0.62(0.536)
TAS -0.361 -2.01**(0.044) -0.351 -2.05**(0.040)
SAL -282505 -3.16***(0.002) -272104.5 -3.18***(0.001)
EMPL -178.724 -1.98**(0.047) -243.945 -2.76***(0.006)
LV -0.075 -1.63(0.103) -0.0680 -1.68*(0.093)
Sample size 410 (296 non-equitized SOEs and 114 equitized SOEs)

The authors also assess the equitization impact by firm size. To identify firm size, this paper based on the current decree no. 56/2009/ND-CP issued on June 30 rst , 2006 in Vietnam.

Table 8 Estimated results with PSM based on firm size
Small and medium-sized SOEs Large SOEs
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -3.703 -0.95(0.344) -3.755 -0.97(0.331) 0.035 0.62(0.536) 0.046 0.56(0.573)
ROE 0.022 0.63(0.526) 0.004 0.15(0.880) -0.009 -0.43(0.666) -0.011 -0.42(0.678)
ROA 0.005 0.37(0.712) 0.000 0.02(0.984) 0.003 0.23(0.815) 0.001 0.08 (0.933)
SALEF -185.880 -1.83*(0.067) -267.718 -2.47**(0.013) -345.009 -0.43(0.668) -377.259 -0.61(0.542)
NIEFF -34.919 -1.18(0.239) -43.791 -1.33(0.185) -128.932 -0.41(0.681) -200.511 -0.92(0.357)
TAS -0.316 -1.50(0.133) -0.462 -2.05**(0.040) -0.391 -1.67*(0.095) -0.0754 -0.16(0.875)
SAL -1844.598 -1.17(0.241) -2396.664 -1.48(0.140) -242037.5 -1.82*(0.069) -214816.6 -2.02**(0.044)
EMPL -2.243 -0.21(0.832) -2.149 -0.29(0.772) -86.975 -1.13(0.259) -39.969 -0.49(0.623)
LV -0.005 -0.07(0.945) 0.056 0.75(0.451) -0.072 -1.60(0.109) -0.0565 -1.24(0.215)
Sample size 142 (105 non-equitized SOEs and 37 equitized SOEs) 238 (162 non-equitized SOEs and 76 equitized SOEs)

Table 8 shows that the number of valid small and medium-sized SOEs is 142 (37 belongs to the treatment group and 105 belongs to the control group). Also, there are 238 valid large enterprises (76 equitized SOEs and 162 non-equitized SOEs).

Table 9 Estimated results with PSM based on non-listing
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.621 -0.35(0.730) -1.269 -0.47(0.638)
ROE -0.008 -0.41(0.680) -0.007 -0.32(0.752)
ROA 0.009 0.94(0.347) 0.011 1.24(0.215)
SALEF -654.774 -1.93*(0.053) -764.495 -2.48(0.013
NIEFF -108.931 -1.18(0.239) -111.434 -1.19(0.234)
TAS -0.359 -2.62(0.009) -0.377 -3.05(0.002)
SAL -223698.8 -3.24(0.001) -213881.3 -3.11(0.002)
EMPL -141.621 -1.63(0.103) -173.844 -1.78* (0.075)
LV -0.076 -1.41(0.158) -0.066 -1.40(0.162)
Sample size 368 (294 non-equitized SOEs and 74 equitized SOEs)

For unlisted enterprises within one year of equitization, their financial and operating performance is much lower than non-equitized firms in the same period ( Table 9 ).

Table 10 Estimated results with PSM based on industry group
Industry group 1 Industry group 2 Industry group 3
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 0.016 0.83(0.404) 0.017 0.82(0.410) -0.011 -0.16 (0.869) 0.025 0.35(0.725) -2.456 -0.35(0.729) -1.978 -0.26(0.793)
ROE -0.005 -0.18(0.860) -0.009 -0.34(0.731) 0.0126 0.44(0.660) 0.0212 0.80(0.421) 0.009 0.25(0.805) -0.006 -0.21(0.837)
ROA 0.007 0.52(0.601) 0.007 0.55(0.585) 0.003 0.20(0.843) 0.011 0.81 (0.421) 0.033 1.35(0.177) 0.024 1.02(0.308)
SALEF -635.139 -0.71(0.478) -264.556 -0.41(0.685) 324.116 0.47(0.641) -439.321 -0.82(0.415) -1208.097 -1.89*(0.059) -1372.487 -2.01**(0.044)
NIEFF 59.667 0.67(0.504) 77.522 0.82(0.411) -2.386 -0.05(0.959) 3.003 0.10(0.916) -378.331 -1.04(0.297) -490.411 -1.05(0.295)
TAS -0.389 -1.86*(0.063) -0.243 -1.61(0.107) 0.167 0.38(0.702) -0.052 -0.21(0.831) -0.712 -1.94*(0.053) -0.877 -2.60***(0.009)
SAL -348608.2 -1.69*(0.091) -276304 -1.79*(0.073) -38211.33 -0.86(0.389) -58907.24 -1.89*(0.059) -110953.7 -1.92*(0.055) -153028.5 -2.26**(0.024)
EMPL -130.139 -1.33(0.184) -143.041 -2.50**(0.012) -84.37308 -1.14(0.254) -55.908 -0.49(0.624) -2.554 -0.02(0.982) -144.654 -1.86*(0.063)
LV -0.067 -1.19(0.235) -0.115 -1.73*(0.084) -0.131 -1.82*(0.069) -0.196 -2.26**(0.024) -0.051 -0.53(0.596) -0.076 -1.24(0.216)
Sample size 176 (127 non-equitized SOEs and 49 equitized SOEs) 130 (81 non-equitized SOEs and 49 equitized SOEs) 93 (77 non-equitized SOEs and 16 equitized SOEs)

The authors classify SOEs into three industry groups. The first group includes SOEs from the first industry to the third industry (agriculture, mining, and manufacturing industries), the second group includes SOEs from the fourth industry to the sixth industry (power, water supply, and construction industries), and the third group includes SOEs from the seventh industry to the twelfth industry (transportation, retailing, hospitality, telecommunication, banking, insurance, and real estate industries).

Table 11 Estimated results with PSM based on equitization years
2012 2013 2014
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 6.149 0.89(0.375) 6.088 1.14 (0.256) 0.035 1.22 (0.221) 0.010 0.42(0.672) -3.103 -0.99(0.322) -6.271 -0.99(0.320)
ROE .070 2.15**(0.031) 0.056 1.61 (0.107) 0.019 0.58 (0.565) 0.017 0.60(0.547) -0.039 -2.21**(0.027) -0.0367 -2.14**(0.032)
ROA .052 3.18*** (0.001) 0.053 2.86*** (0.004) 0.018 1.28 (0.202) 0.005 0.44(0.659) -0.007 -0.52(0.601) -0.003 -0.25(0.803)
SALEF 537.339 0.64 (0.519) 62.776 0.09 (0.928) -659.694 -0.89 (0.372) -276.766 -0.47(0.639) -137.828 -0.21(0.830) -511.612 -0.98(0.32)
NIEFF 205.815 1.87* (0.062) 201.516 1.58 (0.114) 6.402 0.08 (0.937) 5.507 0.08(0.934) -191.607 -1.01(0.314) -246.265 -1.10(0.272
TAS .242 0.59 (0.553) 0.017 0.06 (0.956) -0.549 -1.64 (0.102) -0.124 -0.20(0.845) -0.273 -1.56(0.118) -0.172 -1.03(0.304)
SAL 11456.83 0.34 (0.737) -15666.87 -0.54 (0.592) -301 -1.94* (0.052) -233814.9 -2.04**(0.041) -345912 -2.84***(0.005) -292231.2 -2.45**(0.014)
EMPL -133.039 -1.55 (0.120) -221.582 -1.98** (0.047) -189.246 -1.25 (0.211) -301.331 -1.93*(0.054) -204.568 -1.81*(0.071) -183.024 -1.63(0.102)
LV -0.165 -1.50 (0.135) -0.244 -2.18**(0.029) -0.078 -1.14(0.253) 0.026 0.47(0.637) -0.051 -0.71(0.478) -0.104 -1.29(0.197)
Sample size 51 (42 non-equitized SOEs and 9 equitized SOEs) 193 (146 non-equitized SOEs and 47 equitized SOEs) 177 (119 non-equitized SOEs and 58 equitized SOEs)

In the case of applying the PSM technique for equitization years, the authors use only three characteristics to determine propensity score, including establishment year, firm size, and industry (as each year is studied separately). The impact of equitization on the post-equitization financial and operating performance of equitized SOEs are as follows:

The equitization impact (considering post-equitization period only)

In this case, the authors use the PSM method for identifying the equitization impact (considering post-equitization period only) by different classification criteria, including general assessment, establishment year, non-listing status, industry group and equitization year. This is also the contribution of this study compared with previous studies in Vietnam, such as the studies by Loc and Tran, Nhan and Son 10 , 8 . Furthermore, not many international empirical studies used comparative with-without comparison method with the PSM technique, such as studies by Megginson et al., Boubakri and Cosset, D'Souza and Megginson, Harper 1 , 15 , 19 , 20 .

Table 7 General estimated results with PSM
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.553 -0.34(0.731) -1.396 -0.55(0.582)
ROE 0.008 0.40(0.686) -0.002 -0.11(0.909)
ROA 0.014 1.46(0.144) 0.008 1.04(0.297)
SALEF -343.0075 -0.79(0.427) -456.519 -1.12(0.263)
NIEFF -41.000 -0.45(0.651) -85.932 -0.62(0.536)
TAS -0.361 -2.01**(0.044) -0.351 -2.05**(0.040)
SAL -282505 -3.16***(0.002) -272104.5 -3.18***(0.001)
EMPL -178.724 -1.98**(0.047) -243.945 -2.76***(0.006)
LV -0.075 -1.63(0.103) -0.0680 -1.68*(0.093)
Sample size 410 (296 non-equitized SOEs and 114 equitized SOEs)

The authors also assess the equitization impact by firm size. To identify firm size, this paper based on the current decree no. 56/2009/ND-CP issued on June 30 rst , 2006 in Vietnam.

Table 8 Estimated results with PSM based on firm size
Small and medium-sized SOEs Large SOEs
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -3.703 -0.95(0.344) -3.755 -0.97(0.331) 0.035 0.62(0.536) 0.046 0.56(0.573)
ROE 0.022 0.63(0.526) 0.004 0.15(0.880) -0.009 -0.43(0.666) -0.011 -0.42(0.678)
ROA 0.005 0.37(0.712) 0.000 0.02(0.984) 0.003 0.23(0.815) 0.001 0.08 (0.933)
SALEF -185.880 -1.83*(0.067) -267.718 -2.47**(0.013) -345.009 -0.43(0.668) -377.259 -0.61(0.542)
NIEFF -34.919 -1.18(0.239) -43.791 -1.33(0.185) -128.932 -0.41(0.681) -200.511 -0.92(0.357)
TAS -0.316 -1.50(0.133) -0.462 -2.05**(0.040) -0.391 -1.67*(0.095) -0.0754 -0.16(0.875)
SAL -1844.598 -1.17(0.241) -2396.664 -1.48(0.140) -242037.5 -1.82*(0.069) -214816.6 -2.02**(0.044)
EMPL -2.243 -0.21(0.832) -2.149 -0.29(0.772) -86.975 -1.13(0.259) -39.969 -0.49(0.623)
LV -0.005 -0.07(0.945) 0.056 0.75(0.451) -0.072 -1.60(0.109) -0.0565 -1.24(0.215)
Sample size 142 (105 non-equitized SOEs and 37 equitized SOEs) 238 (162 non-equitized SOEs and 76 equitized SOEs)

Table 8 shows that the number of valid small and medium-sized SOEs is 142 (37 belongs to the treatment group and 105 belongs to the control group). Also, there are 238 valid large enterprises (76 equitized SOEs and 162 non-equitized SOEs).

Table 9 Estimated results with PSM based on non-listing
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.621 -0.35(0.730) -1.269 -0.47(0.638)
ROE -0.008 -0.41(0.680) -0.007 -0.32(0.752)
ROA 0.009 0.94(0.347) 0.011 1.24(0.215)
SALEF -654.774 -1.93*(0.053) -764.495 -2.48(0.013
NIEFF -108.931 -1.18(0.239) -111.434 -1.19(0.234)
TAS -0.359 -2.62(0.009) -0.377 -3.05(0.002)
SAL -223698.8 -3.24(0.001) -213881.3 -3.11(0.002)
EMPL -141.621 -1.63(0.103) -173.844 -1.78* (0.075)
LV -0.076 -1.41(0.158) -0.066 -1.40(0.162)
Sample size 368 (294 non-equitized SOEs and 74 equitized SOEs)

For unlisted enterprises within one year of equitization, their financial and operating performance is much lower than non-equitized firms in the same period ( Table 9 ).

Table 10 Estimated results with PSM based on industry group
Industry group 1 Industry group 2 Industry group 3
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 0.016 0.83(0.404) 0.017 0.82(0.410) -0.011 -0.16 (0.869) 0.025 0.35(0.725) -2.456 -0.35(0.729) -1.978 -0.26(0.793)
ROE -0.005 -0.18(0.860) -0.009 -0.34(0.731) 0.0126 0.44(0.660) 0.0212 0.80(0.421) 0.009 0.25(0.805) -0.006 -0.21(0.837)
ROA 0.007 0.52(0.601) 0.007 0.55(0.585) 0.003 0.20(0.843) 0.011 0.81 (0.421) 0.033 1.35(0.177) 0.024 1.02(0.308)
SALEF -635.139 -0.71(0.478) -264.556 -0.41(0.685) 324.116 0.47(0.641) -439.321 -0.82(0.415) -1208.097 -1.89*(0.059) -1372.487 -2.01**(0.044)
NIEFF 59.667 0.67(0.504) 77.522 0.82(0.411) -2.386 -0.05(0.959) 3.003 0.10(0.916) -378.331 -1.04(0.297) -490.411 -1.05(0.295)
TAS -0.389 -1.86*(0.063) -0.243 -1.61(0.107) 0.167 0.38(0.702) -0.052 -0.21(0.831) -0.712 -1.94*(0.053) -0.877 -2.60***(0.009)
SAL -348608.2 -1.69*(0.091) -276304 -1.79*(0.073) -38211.33 -0.86(0.389) -58907.24 -1.89*(0.059) -110953.7 -1.92*(0.055) -153028.5 -2.26**(0.024)
EMPL -130.139 -1.33(0.184) -143.041 -2.50**(0.012) -84.37308 -1.14(0.254) -55.908 -0.49(0.624) -2.554 -0.02(0.982) -144.654 -1.86*(0.063)
LV -0.067 -1.19(0.235) -0.115 -1.73*(0.084) -0.131 -1.82*(0.069) -0.196 -2.26**(0.024) -0.051 -0.53(0.596) -0.076 -1.24(0.216)
Sample size 176 (127 non-equitized SOEs and 49 equitized SOEs) 130 (81 non-equitized SOEs and 49 equitized SOEs) 93 (77 non-equitized SOEs and 16 equitized SOEs)

The authors classify SOEs into three industry groups. The first group includes SOEs from the first industry to the third industry (agriculture, mining, and manufacturing industries), the second group includes SOEs from the fourth industry to the sixth industry (power, water supply, and construction industries), and the third group includes SOEs from the seventh industry to the twelfth industry (transportation, retailing, hospitality, telecommunication, banking, insurance, and real estate industries).

Table 11 Estimated results with PSM based on equitization years
2012 2013 2014
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 6.149 0.89(0.375) 6.088 1.14 (0.256) 0.035 1.22 (0.221) 0.010 0.42(0.672) -3.103 -0.99(0.322) -6.271 -0.99(0.320)
ROE .070 2.15**(0.031) 0.056 1.61 (0.107) 0.019 0.58 (0.565) 0.017 0.60(0.547) -0.039 -2.21**(0.027) -0.0367 -2.14**(0.032)
ROA .052 3.18*** (0.001) 0.053 2.86*** (0.004) 0.018 1.28 (0.202) 0.005 0.44(0.659) -0.007 -0.52(0.601) -0.003 -0.25(0.803)
SALEF 537.339 0.64 (0.519) 62.776 0.09 (0.928) -659.694 -0.89 (0.372) -276.766 -0.47(0.639) -137.828 -0.21(0.830) -511.612 -0.98(0.32)
NIEFF 205.815 1.87* (0.062) 201.516 1.58 (0.114) 6.402 0.08 (0.937) 5.507 0.08(0.934) -191.607 -1.01(0.314) -246.265 -1.10(0.272
TAS .242 0.59 (0.553) 0.017 0.06 (0.956) -0.549 -1.64 (0.102) -0.124 -0.20(0.845) -0.273 -1.56(0.118) -0.172 -1.03(0.304)
SAL 11456.83 0.34 (0.737) -15666.87 -0.54 (0.592) -301 -1.94* (0.052) -233814.9 -2.04**(0.041) -345912 -2.84***(0.005) -292231.2 -2.45**(0.014)
EMPL -133.039 -1.55 (0.120) -221.582 -1.98** (0.047) -189.246 -1.25 (0.211) -301.331 -1.93*(0.054) -204.568 -1.81*(0.071) -183.024 -1.63(0.102)
LV -0.165 -1.50 (0.135) -0.244 -2.18**(0.029) -0.078 -1.14(0.253) 0.026 0.47(0.637) -0.051 -0.71(0.478) -0.104 -1.29(0.197)
Sample size 51 (42 non-equitized SOEs and 9 equitized SOEs) 193 (146 non-equitized SOEs and 47 equitized SOEs) 177 (119 non-equitized SOEs and 58 equitized SOEs)

In the case of applying the PSM technique for equitization years, the authors use only three characteristics to determine propensity score, including establishment year, firm size, and industry (as each year is studied separately). The impact of equitization on the post-equitization financial and operating performance of equitized SOEs are as follows:

The equitization impact (considering post-equitization period only)

In this case, the authors use the PSM method for identifying the equitization impact (considering post-equitization period only) by different classification criteria, including general assessment, establishment year, non-listing status, industry group and equitization year. This is also the contribution of this study compared with previous studies in Vietnam, such as the studies by Loc and Tran, Nhan and Son 10 , 8 . Furthermore, not many international empirical studies used comparative with-without comparison method with the PSM technique, such as studies by Megginson et al., Boubakri and Cosset, D'Souza and Megginson, Harper 1 , 15 , 19 , 20 .

Table 7 General estimated results with PSM
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.553 -0.34(0.731) -1.396 -0.55(0.582)
ROE 0.008 0.40(0.686) -0.002 -0.11(0.909)
ROA 0.014 1.46(0.144) 0.008 1.04(0.297)
SALEF -343.0075 -0.79(0.427) -456.519 -1.12(0.263)
NIEFF -41.000 -0.45(0.651) -85.932 -0.62(0.536)
TAS -0.361 -2.01**(0.044) -0.351 -2.05**(0.040)
SAL -282505 -3.16***(0.002) -272104.5 -3.18***(0.001)
EMPL -178.724 -1.98**(0.047) -243.945 -2.76***(0.006)
LV -0.075 -1.63(0.103) -0.0680 -1.68*(0.093)
Sample size 410 (296 non-equitized SOEs and 114 equitized SOEs)

The authors also assess the equitization impact by firm size. To identify firm size, this paper based on the current decree no. 56/2009/ND-CP issued on June 30 rst , 2006 in Vietnam.

Table 8 Estimated results with PSM based on firm size
Small and medium-sized SOEs Large SOEs
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -3.703 -0.95(0.344) -3.755 -0.97(0.331) 0.035 0.62(0.536) 0.046 0.56(0.573)
ROE 0.022 0.63(0.526) 0.004 0.15(0.880) -0.009 -0.43(0.666) -0.011 -0.42(0.678)
ROA 0.005 0.37(0.712) 0.000 0.02(0.984) 0.003 0.23(0.815) 0.001 0.08 (0.933)
SALEF -185.880 -1.83*(0.067) -267.718 -2.47**(0.013) -345.009 -0.43(0.668) -377.259 -0.61(0.542)
NIEFF -34.919 -1.18(0.239) -43.791 -1.33(0.185) -128.932 -0.41(0.681) -200.511 -0.92(0.357)
TAS -0.316 -1.50(0.133) -0.462 -2.05**(0.040) -0.391 -1.67*(0.095) -0.0754 -0.16(0.875)
SAL -1844.598 -1.17(0.241) -2396.664 -1.48(0.140) -242037.5 -1.82*(0.069) -214816.6 -2.02**(0.044)
EMPL -2.243 -0.21(0.832) -2.149 -0.29(0.772) -86.975 -1.13(0.259) -39.969 -0.49(0.623)
LV -0.005 -0.07(0.945) 0.056 0.75(0.451) -0.072 -1.60(0.109) -0.0565 -1.24(0.215)
Sample size 142 (105 non-equitized SOEs and 37 equitized SOEs) 238 (162 non-equitized SOEs and 76 equitized SOEs)

Table 8 shows that the number of valid small and medium-sized SOEs is 142 (37 belongs to the treatment group and 105 belongs to the control group). Also, there are 238 valid large enterprises (76 equitized SOEs and 162 non-equitized SOEs).

Table 9 Estimated results with PSM based on non-listing
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.621 -0.35(0.730) -1.269 -0.47(0.638)
ROE -0.008 -0.41(0.680) -0.007 -0.32(0.752)
ROA 0.009 0.94(0.347) 0.011 1.24(0.215)
SALEF -654.774 -1.93*(0.053) -764.495 -2.48(0.013
NIEFF -108.931 -1.18(0.239) -111.434 -1.19(0.234)
TAS -0.359 -2.62(0.009) -0.377 -3.05(0.002)
SAL -223698.8 -3.24(0.001) -213881.3 -3.11(0.002)
EMPL -141.621 -1.63(0.103) -173.844 -1.78* (0.075)
LV -0.076 -1.41(0.158) -0.066 -1.40(0.162)
Sample size 368 (294 non-equitized SOEs and 74 equitized SOEs)

For unlisted enterprises within one year of equitization, their financial and operating performance is much lower than non-equitized firms in the same period ( Table 9 ).

Table 10 Estimated results with PSM based on industry group
Industry group 1 Industry group 2 Industry group 3
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 0.016 0.83(0.404) 0.017 0.82(0.410) -0.011 -0.16 (0.869) 0.025 0.35(0.725) -2.456 -0.35(0.729) -1.978 -0.26(0.793)
ROE -0.005 -0.18(0.860) -0.009 -0.34(0.731) 0.0126 0.44(0.660) 0.0212 0.80(0.421) 0.009 0.25(0.805) -0.006 -0.21(0.837)
ROA 0.007 0.52(0.601) 0.007 0.55(0.585) 0.003 0.20(0.843) 0.011 0.81 (0.421) 0.033 1.35(0.177) 0.024 1.02(0.308)
SALEF -635.139 -0.71(0.478) -264.556 -0.41(0.685) 324.116 0.47(0.641) -439.321 -0.82(0.415) -1208.097 -1.89*(0.059) -1372.487 -2.01**(0.044)
NIEFF 59.667 0.67(0.504) 77.522 0.82(0.411) -2.386 -0.05(0.959) 3.003 0.10(0.916) -378.331 -1.04(0.297) -490.411 -1.05(0.295)
TAS -0.389 -1.86*(0.063) -0.243 -1.61(0.107) 0.167 0.38(0.702) -0.052 -0.21(0.831) -0.712 -1.94*(0.053) -0.877 -2.60***(0.009)
SAL -348608.2 -1.69*(0.091) -276304 -1.79*(0.073) -38211.33 -0.86(0.389) -58907.24 -1.89*(0.059) -110953.7 -1.92*(0.055) -153028.5 -2.26**(0.024)
EMPL -130.139 -1.33(0.184) -143.041 -2.50**(0.012) -84.37308 -1.14(0.254) -55.908 -0.49(0.624) -2.554 -0.02(0.982) -144.654 -1.86*(0.063)
LV -0.067 -1.19(0.235) -0.115 -1.73*(0.084) -0.131 -1.82*(0.069) -0.196 -2.26**(0.024) -0.051 -0.53(0.596) -0.076 -1.24(0.216)
Sample size 176 (127 non-equitized SOEs and 49 equitized SOEs) 130 (81 non-equitized SOEs and 49 equitized SOEs) 93 (77 non-equitized SOEs and 16 equitized SOEs)

The authors classify SOEs into three industry groups. The first group includes SOEs from the first industry to the third industry (agriculture, mining, and manufacturing industries), the second group includes SOEs from the fourth industry to the sixth industry (power, water supply, and construction industries), and the third group includes SOEs from the seventh industry to the twelfth industry (transportation, retailing, hospitality, telecommunication, banking, insurance, and real estate industries).

Table 11 Estimated results with PSM based on equitization years
2012 2013 2014
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 6.149 0.89(0.375) 6.088 1.14 (0.256) 0.035 1.22 (0.221) 0.010 0.42(0.672) -3.103 -0.99(0.322) -6.271 -0.99(0.320)
ROE .070 2.15**(0.031) 0.056 1.61 (0.107) 0.019 0.58 (0.565) 0.017 0.60(0.547) -0.039 -2.21**(0.027) -0.0367 -2.14**(0.032)
ROA .052 3.18*** (0.001) 0.053 2.86*** (0.004) 0.018 1.28 (0.202) 0.005 0.44(0.659) -0.007 -0.52(0.601) -0.003 -0.25(0.803)
SALEF 537.339 0.64 (0.519) 62.776 0.09 (0.928) -659.694 -0.89 (0.372) -276.766 -0.47(0.639) -137.828 -0.21(0.830) -511.612 -0.98(0.32)
NIEFF 205.815 1.87* (0.062) 201.516 1.58 (0.114) 6.402 0.08 (0.937) 5.507 0.08(0.934) -191.607 -1.01(0.314) -246.265 -1.10(0.272
TAS .242 0.59 (0.553) 0.017 0.06 (0.956) -0.549 -1.64 (0.102) -0.124 -0.20(0.845) -0.273 -1.56(0.118) -0.172 -1.03(0.304)
SAL 11456.83 0.34 (0.737) -15666.87 -0.54 (0.592) -301 -1.94* (0.052) -233814.9 -2.04**(0.041) -345912 -2.84***(0.005) -292231.2 -2.45**(0.014)
EMPL -133.039 -1.55 (0.120) -221.582 -1.98** (0.047) -189.246 -1.25 (0.211) -301.331 -1.93*(0.054) -204.568 -1.81*(0.071) -183.024 -1.63(0.102)
LV -0.165 -1.50 (0.135) -0.244 -2.18**(0.029) -0.078 -1.14(0.253) 0.026 0.47(0.637) -0.051 -0.71(0.478) -0.104 -1.29(0.197)
Sample size 51 (42 non-equitized SOEs and 9 equitized SOEs) 193 (146 non-equitized SOEs and 47 equitized SOEs) 177 (119 non-equitized SOEs and 58 equitized SOEs)

In the case of applying the PSM technique for equitization years, the authors use only three characteristics to determine propensity score, including establishment year, firm size, and industry (as each year is studied separately). The impact of equitization on the post-equitization financial and operating performance of equitized SOEs are as follows:

The equitization impact (considering post-equitization period only)

In this case, the authors use the PSM method for identifying the equitization impact (considering post-equitization period only) by different classification criteria, including general assessment, establishment year, non-listing status, industry group and equitization year. This is also the contribution of this study compared with previous studies in Vietnam, such as the studies by Loc and Tran, Nhan and Son 10 , 8 . Furthermore, not many international empirical studies used comparative with-without comparison method with the PSM technique, such as studies by Megginson et al., Boubakri and Cosset, D'Souza and Megginson, Harper 1 , 15 , 19 , 20 .

Table 7 General estimated results with PSM
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.553 -0.34(0.731) -1.396 -0.55(0.582)
ROE 0.008 0.40(0.686) -0.002 -0.11(0.909)
ROA 0.014 1.46(0.144) 0.008 1.04(0.297)
SALEF -343.0075 -0.79(0.427) -456.519 -1.12(0.263)
NIEFF -41.000 -0.45(0.651) -85.932 -0.62(0.536)
TAS -0.361 -2.01**(0.044) -0.351 -2.05**(0.040)
SAL -282505 -3.16***(0.002) -272104.5 -3.18***(0.001)
EMPL -178.724 -1.98**(0.047) -243.945 -2.76***(0.006)
LV -0.075 -1.63(0.103) -0.0680 -1.68*(0.093)
Sample size 410 (296 non-equitized SOEs and 114 equitized SOEs)

The authors also assess the equitization impact by firm size. To identify firm size, this paper based on the current decree no. 56/2009/ND-CP issued on June 30 rst , 2006 in Vietnam.

Table 8 Estimated results with PSM based on firm size
Small and medium-sized SOEs Large SOEs
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -3.703 -0.95(0.344) -3.755 -0.97(0.331) 0.035 0.62(0.536) 0.046 0.56(0.573)
ROE 0.022 0.63(0.526) 0.004 0.15(0.880) -0.009 -0.43(0.666) -0.011 -0.42(0.678)
ROA 0.005 0.37(0.712) 0.000 0.02(0.984) 0.003 0.23(0.815) 0.001 0.08 (0.933)
SALEF -185.880 -1.83*(0.067) -267.718 -2.47**(0.013) -345.009 -0.43(0.668) -377.259 -0.61(0.542)
NIEFF -34.919 -1.18(0.239) -43.791 -1.33(0.185) -128.932 -0.41(0.681) -200.511 -0.92(0.357)
TAS -0.316 -1.50(0.133) -0.462 -2.05**(0.040) -0.391 -1.67*(0.095) -0.0754 -0.16(0.875)
SAL -1844.598 -1.17(0.241) -2396.664 -1.48(0.140) -242037.5 -1.82*(0.069) -214816.6 -2.02**(0.044)
EMPL -2.243 -0.21(0.832) -2.149 -0.29(0.772) -86.975 -1.13(0.259) -39.969 -0.49(0.623)
LV -0.005 -0.07(0.945) 0.056 0.75(0.451) -0.072 -1.60(0.109) -0.0565 -1.24(0.215)
Sample size 142 (105 non-equitized SOEs and 37 equitized SOEs) 238 (162 non-equitized SOEs and 76 equitized SOEs)

Table 8 shows that the number of valid small and medium-sized SOEs is 142 (37 belongs to the treatment group and 105 belongs to the control group). Also, there are 238 valid large enterprises (76 equitized SOEs and 162 non-equitized SOEs).

Table 9 Estimated results with PSM based on non-listing
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.621 -0.35(0.730) -1.269 -0.47(0.638)
ROE -0.008 -0.41(0.680) -0.007 -0.32(0.752)
ROA 0.009 0.94(0.347) 0.011 1.24(0.215)
SALEF -654.774 -1.93*(0.053) -764.495 -2.48(0.013
NIEFF -108.931 -1.18(0.239) -111.434 -1.19(0.234)
TAS -0.359 -2.62(0.009) -0.377 -3.05(0.002)
SAL -223698.8 -3.24(0.001) -213881.3 -3.11(0.002)
EMPL -141.621 -1.63(0.103) -173.844 -1.78* (0.075)
LV -0.076 -1.41(0.158) -0.066 -1.40(0.162)
Sample size 368 (294 non-equitized SOEs and 74 equitized SOEs)

For unlisted enterprises within one year of equitization, their financial and operating performance is much lower than non-equitized firms in the same period ( Table 9 ).

Table 10 Estimated results with PSM based on industry group
Industry group 1 Industry group 2 Industry group 3
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 0.016 0.83(0.404) 0.017 0.82(0.410) -0.011 -0.16 (0.869) 0.025 0.35(0.725) -2.456 -0.35(0.729) -1.978 -0.26(0.793)
ROE -0.005 -0.18(0.860) -0.009 -0.34(0.731) 0.0126 0.44(0.660) 0.0212 0.80(0.421) 0.009 0.25(0.805) -0.006 -0.21(0.837)
ROA 0.007 0.52(0.601) 0.007 0.55(0.585) 0.003 0.20(0.843) 0.011 0.81 (0.421) 0.033 1.35(0.177) 0.024 1.02(0.308)
SALEF -635.139 -0.71(0.478) -264.556 -0.41(0.685) 324.116 0.47(0.641) -439.321 -0.82(0.415) -1208.097 -1.89*(0.059) -1372.487 -2.01**(0.044)
NIEFF 59.667 0.67(0.504) 77.522 0.82(0.411) -2.386 -0.05(0.959) 3.003 0.10(0.916) -378.331 -1.04(0.297) -490.411 -1.05(0.295)
TAS -0.389 -1.86*(0.063) -0.243 -1.61(0.107) 0.167 0.38(0.702) -0.052 -0.21(0.831) -0.712 -1.94*(0.053) -0.877 -2.60***(0.009)
SAL -348608.2 -1.69*(0.091) -276304 -1.79*(0.073) -38211.33 -0.86(0.389) -58907.24 -1.89*(0.059) -110953.7 -1.92*(0.055) -153028.5 -2.26**(0.024)
EMPL -130.139 -1.33(0.184) -143.041 -2.50**(0.012) -84.37308 -1.14(0.254) -55.908 -0.49(0.624) -2.554 -0.02(0.982) -144.654 -1.86*(0.063)
LV -0.067 -1.19(0.235) -0.115 -1.73*(0.084) -0.131 -1.82*(0.069) -0.196 -2.26**(0.024) -0.051 -0.53(0.596) -0.076 -1.24(0.216)
Sample size 176 (127 non-equitized SOEs and 49 equitized SOEs) 130 (81 non-equitized SOEs and 49 equitized SOEs) 93 (77 non-equitized SOEs and 16 equitized SOEs)

The authors classify SOEs into three industry groups. The first group includes SOEs from the first industry to the third industry (agriculture, mining, and manufacturing industries), the second group includes SOEs from the fourth industry to the sixth industry (power, water supply, and construction industries), and the third group includes SOEs from the seventh industry to the twelfth industry (transportation, retailing, hospitality, telecommunication, banking, insurance, and real estate industries).

Table 11 Estimated results with PSM based on equitization years
2012 2013 2014
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 6.149 0.89(0.375) 6.088 1.14 (0.256) 0.035 1.22 (0.221) 0.010 0.42(0.672) -3.103 -0.99(0.322) -6.271 -0.99(0.320)
ROE .070 2.15**(0.031) 0.056 1.61 (0.107) 0.019 0.58 (0.565) 0.017 0.60(0.547) -0.039 -2.21**(0.027) -0.0367 -2.14**(0.032)
ROA .052 3.18*** (0.001) 0.053 2.86*** (0.004) 0.018 1.28 (0.202) 0.005 0.44(0.659) -0.007 -0.52(0.601) -0.003 -0.25(0.803)
SALEF 537.339 0.64 (0.519) 62.776 0.09 (0.928) -659.694 -0.89 (0.372) -276.766 -0.47(0.639) -137.828 -0.21(0.830) -511.612 -0.98(0.32)
NIEFF 205.815 1.87* (0.062) 201.516 1.58 (0.114) 6.402 0.08 (0.937) 5.507 0.08(0.934) -191.607 -1.01(0.314) -246.265 -1.10(0.272
TAS .242 0.59 (0.553) 0.017 0.06 (0.956) -0.549 -1.64 (0.102) -0.124 -0.20(0.845) -0.273 -1.56(0.118) -0.172 -1.03(0.304)
SAL 11456.83 0.34 (0.737) -15666.87 -0.54 (0.592) -301 -1.94* (0.052) -233814.9 -2.04**(0.041) -345912 -2.84***(0.005) -292231.2 -2.45**(0.014)
EMPL -133.039 -1.55 (0.120) -221.582 -1.98** (0.047) -189.246 -1.25 (0.211) -301.331 -1.93*(0.054) -204.568 -1.81*(0.071) -183.024 -1.63(0.102)
LV -0.165 -1.50 (0.135) -0.244 -2.18**(0.029) -0.078 -1.14(0.253) 0.026 0.47(0.637) -0.051 -0.71(0.478) -0.104 -1.29(0.197)
Sample size 51 (42 non-equitized SOEs and 9 equitized SOEs) 193 (146 non-equitized SOEs and 47 equitized SOEs) 177 (119 non-equitized SOEs and 58 equitized SOEs)

In the case of applying the PSM technique for equitization years, the authors use only three characteristics to determine propensity score, including establishment year, firm size, and industry (as each year is studied separately). The impact of equitization on the post-equitization financial and operating performance of equitized SOEs are as follows:

The equitization impact (considering post-equitization period only)

In this case, the authors use the PSM method for identifying the equitization impact (considering post-equitization period only) by different classification criteria, including general assessment, establishment year, non-listing status, industry group and equitization year. This is also the contribution of this study compared with previous studies in Vietnam, such as the studies by Loc and Tran, Nhan and Son 10 , 8 . Furthermore, not many international empirical studies used comparative with-without comparison method with the PSM technique, such as studies by Megginson et al., Boubakri and Cosset, D'Souza and Megginson, Harper 1 , 15 , 19 , 20 .

Table 7 General estimated results with PSM
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.553 -0.34(0.731) -1.396 -0.55(0.582)
ROE 0.008 0.40(0.686) -0.002 -0.11(0.909)
ROA 0.014 1.46(0.144) 0.008 1.04(0.297)
SALEF -343.0075 -0.79(0.427) -456.519 -1.12(0.263)
NIEFF -41.000 -0.45(0.651) -85.932 -0.62(0.536)
TAS -0.361 -2.01**(0.044) -0.351 -2.05**(0.040)
SAL -282505 -3.16***(0.002) -272104.5 -3.18***(0.001)
EMPL -178.724 -1.98**(0.047) -243.945 -2.76***(0.006)
LV -0.075 -1.63(0.103) -0.0680 -1.68*(0.093)
Sample size 410 (296 non-equitized SOEs and 114 equitized SOEs)

The authors also assess the equitization impact by firm size. To identify firm size, this paper based on the current decree no. 56/2009/ND-CP issued on June 30 rst , 2006 in Vietnam.

Table 8 Estimated results with PSM based on firm size
Small and medium-sized SOEs Large SOEs
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -3.703 -0.95(0.344) -3.755 -0.97(0.331) 0.035 0.62(0.536) 0.046 0.56(0.573)
ROE 0.022 0.63(0.526) 0.004 0.15(0.880) -0.009 -0.43(0.666) -0.011 -0.42(0.678)
ROA 0.005 0.37(0.712) 0.000 0.02(0.984) 0.003 0.23(0.815) 0.001 0.08 (0.933)
SALEF -185.880 -1.83*(0.067) -267.718 -2.47**(0.013) -345.009 -0.43(0.668) -377.259 -0.61(0.542)
NIEFF -34.919 -1.18(0.239) -43.791 -1.33(0.185) -128.932 -0.41(0.681) -200.511 -0.92(0.357)
TAS -0.316 -1.50(0.133) -0.462 -2.05**(0.040) -0.391 -1.67*(0.095) -0.0754 -0.16(0.875)
SAL -1844.598 -1.17(0.241) -2396.664 -1.48(0.140) -242037.5 -1.82*(0.069) -214816.6 -2.02**(0.044)
EMPL -2.243 -0.21(0.832) -2.149 -0.29(0.772) -86.975 -1.13(0.259) -39.969 -0.49(0.623)
LV -0.005 -0.07(0.945) 0.056 0.75(0.451) -0.072 -1.60(0.109) -0.0565 -1.24(0.215)
Sample size 142 (105 non-equitized SOEs and 37 equitized SOEs) 238 (162 non-equitized SOEs and 76 equitized SOEs)

Table 8 shows that the number of valid small and medium-sized SOEs is 142 (37 belongs to the treatment group and 105 belongs to the control group). Also, there are 238 valid large enterprises (76 equitized SOEs and 162 non-equitized SOEs).

Table 9 Estimated results with PSM based on non-listing
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS -0.621 -0.35(0.730) -1.269 -0.47(0.638)
ROE -0.008 -0.41(0.680) -0.007 -0.32(0.752)
ROA 0.009 0.94(0.347) 0.011 1.24(0.215)
SALEF -654.774 -1.93*(0.053) -764.495 -2.48(0.013
NIEFF -108.931 -1.18(0.239) -111.434 -1.19(0.234)
TAS -0.359 -2.62(0.009) -0.377 -3.05(0.002)
SAL -223698.8 -3.24(0.001) -213881.3 -3.11(0.002)
EMPL -141.621 -1.63(0.103) -173.844 -1.78* (0.075)
LV -0.076 -1.41(0.158) -0.066 -1.40(0.162)
Sample size 368 (294 non-equitized SOEs and 74 equitized SOEs)

For unlisted enterprises within one year of equitization, their financial and operating performance is much lower than non-equitized firms in the same period ( Table 9 ).

Table 10 Estimated results with PSM based on industry group
Industry group 1 Industry group 2 Industry group 3
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS 0.016 0.83(0.404) 0.017 0.82(0.410) -0.011 -0.16 (0.869) 0.025 0.35(0.725) -2.456 -0.35(0.729) -1.978 -0.26(0.793)
ROE -0.005 -0.18(0.860) -0.009 -0.34(0.731) 0.0126 0.44(0.660) 0.0212 0.80(0.421) 0.009 0.25(0.805) -0.006 -0.21(0.837)
ROA 0.007 0.52(0.601) 0.007 0.55(0.585) 0.003 0.20(0.843) 0.011 0.81 (0.421) 0.033 1.35(0.177) 0.024 1.02(0.308)
SALEF -635.139 -0.71(0.478) -264.556 -0.41(0.685) 324.116 0.47(0.641) -439.321 -0.82(0.415) -1208.097 -1.89*(0.059) -1372.487 -2.01**(0.044)
NIEFF 59.667 0.67(0.504) 77.522 0.82(0.411) -2.386 -0.05(0.959) 3.003 0.10(0.916) -378.331 -1.04(0.297) -490.411 -1.05(0.295)
TAS -0.389 -1.86*(0.063) -0.243 -1.61(0.107) 0.167 0.38(0.702) -0.052 -0.21(0.831) -0.712 -1.94*(0.053) -0.877 -2.60***(0.009)
SAL -348608.2 -1.69*(0.091) -276304 -1.79*(0.073) -38211.33 -0.86(0.389) -58907.24 -1.89*(0.059) -110953.7 -1.92*(0.055) -153028.5 -2.26**(0.024)
EMPL -130.139 -1.33(0.184) -143.041 -2.50**(0.012) -84.37308 -1.14(0.254) -55.908 -0.49(0.624) -2.554 -0.02(0.982) -144.654 -1.86*(0.063)
LV -0.067 -1.19(0.235) -0.115 -1.73*(0.084) -0.131 -1.82*(0.069) -0.196 -2.26**(0.024) -0.051 -0.53(0.596) -0.076 -1.24(0.216)
Sample size 176 (127 non-equitized SOEs and 49 equitized SOEs) 130 (81 non-equitized SOEs and 49 equitized SOEs) 93 (77 non-equitized SOEs and 16 equitized SOEs)

The authors classify SOEs into three industry groups. The first group includes SOEs from the first industry to the third industry (agriculture, mining, and manufacturing industries), the second group includes SOEs from the fourth industry to the sixth industry (power, water supply, and construction industries), and the third group includes SOEs from the seventh industry to the twelfth industry (transportation, retailing, hospitality, telecommunication, banking, insurance, and real estate industries).

Table 11 Estimated results with PSM based on equitization years
2012 2013 2014
Variable ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch) ATE (nnmatch) z-statistic for ATE (nnmatch) ATE (psmatch) z-statistic for ATE (psmatch)
ROS