A Comprehensive Evaluation Framework on

the Economic Performance of

State-owned Enterprises

Feb 8-9, 2018Mongolia

Farhad Taghizadeh-Hesary Assist. Professor of Economics,

Waseda University,

Tokyo, Japan

Reforming State-Owned Enterprises in Central Asia: Challenges and Solutions26–27 September 2019

Bishkek, Kyrgyz Republic

Naoyuki YoshinoDean and CEO, ADBI

Professor Emeritus

Keio University

Chul Ju KimDeputy Dean,

ADBI

The views expressed in this presentation are the view s of the authors and do not necessarily reflect the views or policies of the Asian Development Bank

Institute (ADBI), the Asian Development Bank (ADB), its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy

of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent

with ADB official terms.

Outline

1- Introduction and Background

2- Variables and Data

3- Statistical Analysis Technique

4- Empirical Results

5- Conclusion and Policy implications

2

3

I. Introduction and Background

Importance of SOEs in global economy

4

12% 13%12% 12% 12% 12% 11%

13% 13%

8%8%

9%9% 9% 10%

8%

7%9%

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

2009 2010 2011 2012 2013 2014 2015 2016 2017

0%

5%

10%

15%

20%

25%

Bill

ion

s o

f U

S$

PRC Rest of the World Total SOE's operating revenue

Share of SOEs in top 200 companies (based on Orbis/BV)

5

• In several Asian economies, SOEs have significant

share in the economy.

• Studies show that, In some countries SOEs have lower

productivity comparing to the private enterprises.

• Lower productive SOEs specially in those economics

that SOEs are dominating the economy, negatively affect

the economic output of the whole economy.

• It is important to evaluate the performance of SOEs

using measureable and defendable tools.

Low productive SOEs negatively affect the

GDP growth rate

6

• SOEs usually do not have difficulty for accessing to finance

• In several central Asian countries, majority of the credit is

allocating to the public sector including SOEs.

• Private sectors have several difficulties for accessing to

finance in the region (high collateral, high interest rate…)

• Low productive SOEs needs more capital therefore more

finance for each unit of their production, hence this makes

the business environment and access to finance severe for

private enterprises.

Low productive SOEs makes the business

environment severe for the private sector

Credit to the private sector in Central Asia remains comparatively modest

7

Figure 1. Domestic credit to private sector in Central Asia

Source: (World Bank, 2017), (EBRD, 2017), (RAEX, 2017), (OECD, 2018)

34%

21%

58%

19% 15%

44%

148%

0

20

40

60

80

100

120

140

160

Kazakhstan Kyrgyzstan Mongolia Tajikistan Uzbekistan Lower middleincome

OECDmembers

Domestic credit to private sector (% of GDP)

Non-performing loans remain high in the region

8

20.4%

8.5%

6.7%

0.4%

9.4%

4.2%

2.7%

0.6%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

Tajikistan* Kyrgyzstan Kazakhstan Uzbekistan RussianFederation

Lower middleincome

OECD members Canada

Bank nonperforming loans to total gross loans (%)

Note: * Data for Tajikistan is from 2014

Sources: (World Bank, 2017; Bank of Mongolia, 2016; OECD, 2018)

Figure 2. Bank nonperforming loans to total gross loans (%)

Credit conditions are tight with high interest rates in the region

9

Interest rates in Central Asia, 2016

22%

20%

26%

16% 16%

3%4%

5%

-5%

0%

5%

10%

15%

20%

25%

30%

Kyrgyzstan Mongolia Tajikistan* Kazakhstan* Uzbekistan Canada Czech Republic Poland

Lending interest rate (%) Inflation Rate (CPI)

Note: *lending interest rates for Kazakhstan and Tajikistan (2015)

Source: (World Bank, 2017; CIA, 2018; State Committee of Uzbekistan on Statistics, 2018; Ministry of National Economy of

Kazakhstan, 2017; OECD, 2018)

Figure 3. Lending interest rate and inflation rate

High and systematic collateral requirements limit access to finance for SMEs

10

140%

150%

160%

170%

180%

190%

200%

210%

220%

230%

60% 65% 70% 75% 80% 85% 90% 95% 100% 105%

Val

ue

of

colla

tera

l ne

ed

ed

for

a lo

an

(% o

f t

he

loan

am

ou

nt)

Proportion of loans requiring collateral (%)

Mongolia

Lower middle income

Kyrgyzstan

Kazakhstan

Tajikistan

Uzbekistan

OECD average

High and systematic collateral requirements

Average but systematic collateral requirements

Source: (EBRD, 2017; World Bank, 2017, OECD, 2018)

Figure 4. Collateral requirements in Central Asia

11

• Many SOEs established in order to provide public services

and their objective is to increase the social welfare and not

profit making.

• However, without relying on a concrete and comprehensive

criteria, it is not possible for the central government to

evaluate the SOEs as it is not easy to calculate the social

welfare measured by the SOEs.

• Its important to have a many-sided evaluation of SOEs’

performance, in order to improve the productivity of the public

budget.

A comprehensive evaluation of SOEs is needed in

order to improve the productivity of the public

capital

II. Variables and Data

12

Variables

13

Notation Definition Group

Var 1 ROE using P/L before tax % Profitability

Var 2 ROA using P/L before tax % Profitability

Var 3 Profit margin % Profitability

Var 4 Cash flow / Operating revenue % Profitability

Var 5 Credit due dates Operational

Var 6 Export revenue / Operating revenue % Operational

Var 7 Liquidity ratio Structure

Var 8 Solvency ratio (Asset based) % Structure

Var 9 Solvency ratio (Liability based) % Structure

Var 10 Profit per employee in USD Per Employee

Var 11 Operating revenue per employee in USD Per Employee

Var 12 Costs of employees / Operating revenue % Per Employee

Var 13 Average cost of employee in USD Per Employee

Var 14 Working capital per employee in USD Per Employee

Var 15 Total assets per employee in USD Per Employee

Data: 1148 SOEs

14

Source of Data: BvD, Orbis

Breakdown of data by Industry

15

Total: 1148 SOEs

Breakdown by operating revenue

16

Total: 1148 SOEs

III. Statistical Analysis

17

Principle Component Analysis (PCA)

• PCA is a standard data reduction technique which extractsdata, removes redundant information, highlights hiddenfeatures, and visualizes the main relationships that existbetween observations.

• PCA is a technique for simplifying a data set, by reducingmulti-dimensional data sets to lower dimensions for analysis.

• PCA does not have a fixed set of basis vectors; Its basisvectors depend on the data set, Unlike other linear transformmethods, .

• PCA also has the advantage of indicating the similarities anddifferences of the various models created (Bruce-Ho, Dash-Wu, 2009).

Through this method, we reduce the 15 variables to determinethe minimum number of components (As in Yoshino andTaghizadeh-Hesary 2014; 2015)

18

Correlation among variables is the main reason behind using PCA

19

Correlation MatrixVar 1 Var 2 Var 3 Var 4 Var 5 Var 6 Var 7 Var 8 Var 9 Var 10 Var 11 Var 12 Var 13 Var 14 Var 15

Corre

latio

n

Var 1 1.000 .421 .321 .129 -.039 .013 .007 .001 .024 .110 .020 -.051 .069 .004 .043

Var 2 .421 1.000 .570 .337 -.097 .017 .062 .371 .123 .114 .002 -.118 -.007 -.007 .001

Var 3 .321 .570 1.000 .645 -.150 -.009 .139 .334 .080 .216 -.009 -.160 -.051 -.022 .128

Var 4 .129 .337 .645 1.000 -.166 -.038 .124 .316 .154 .160 -.032 -.151 -.109 -.031 .214

Var 5 -.039 -.097 -.150 -.166 1.000 -.057 -.089 -.191 -.079 -.026 -.038 -.077 -.022 -.022 -.024

Var 6 .013 .017 -.009 -.038 -.057 1.000 .055 -.038 -.095 .141 .201 -.127 .153 .022 .194

Var 7 .007 .062 .139 .124 -.089 .055 1.000 .264 -.076 .071 .138 -.084 -.001 -.022 -.017

Var 8 .001 .371 .334 .316 -.191 -.038 .264 1.000 .117 .074 -.018 -.030 -.073 -.093 -.033

Var 9 .024 .123 .080 .154 -.079 -.095 -.076 .117 1.000 -.046 -.062 .075 -.008 -.058 -.043

Var 10 .110 .114 .216 .160 -.026 .141 .071 .074 -.046 1.000 .237 -.129 .094 .175 .580

Var 11 .020 .002 -.009 -.032 -.038 .201 .138 -.018 -.062 .237 1.000 -.168 .097 .192 .482

Var 12 -.051 -.118 -.160 -.151 -.077 -.127 -.084 -.030 .075 -.129 -.168 1.000 .186 -.163 -.195

Var 13 .069 -.007 -.051 -.109 -.022 .153 -.001 -.073 -.008 .094 .097 .186 1.000 .242 .073

Var 14 .004 -.007 -.022 -.031 -.022 .022 -.022 -.093 -.058 .175 .192 -.163 .242 1.000 .265

Var 15 .043 .001 .128 .214 -.024 .194 -.017 -.033 -.043 .580 .482 -.195 .073 .265 1.000

5 Significant components achieved

20

Total Variance Explained

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

Z1 2.782 18.549 18.549 2.782 18.549 18.549

Z2 2.164 14.430 32.979 2.164 14.430 32.979

Z3 1.284 8.563 41.542 1.284 8.563 41.542

Z4 1.227 8.178 49.720 1.227 8.178 49.720

Z5 1.114 7.428 57.147 1.114 7.428 57.147

Z6 .964 6.425 63.572

Z7 .902 6.015 69.587

Z8 .865 5.767 75.355

Z9 .821 5.475 80.829

Z10 .696 4.641 85.470

Z11 .653 4.351 89.821

Z12 .524 3.496 93.317

Z13 .433 2.888 96.205

Z14 .314 2.093 98.298

Z15 .255 1.702 100.000

Extraction Method: Principal Component Analysis.

Component Matrix

21

Component Matrixa

Component

Z1 Z2 Z3 Z4 Z5

Var 1 ROE using P/L before tax % Profitability 0.597 -0.096 0.424 -0.313 0.393

Var 2 ROA using P/L before tax % Profitability 0.680 -0.304 0.254 -0.134 0.233

Var 3 Profit margin % Profitability 0.805 -0.263 0.060 -0.120 0.027

Var 4 Cash flow / Operating revenue % Profitability 0.707 -0.233 -0.115 -0.051 -0.259

Var 5 Credit due dates Operational -0.262 0.107 -0.080 -0.559 0.165

Var 6 Export revenue / Operating revenue % Operational 0.122 0.399 -0.006 0.213 0.372

Var 7 Liquidity ratio Structure 0.271 -0.019 -0.400 0.487 0.377

Var 8 Solvency ratio (Asset based) % Structure 0.502 -0.348 -0.218 0.391 -0.017

Var 9 Solvency ratio (Liability based) % Structure 0.129 -0.280 0.231 0.072 -0.576

Var 10 Profit per employee in USD Per Employee 0.473 0.520 0.023 -0.035 -0.198

Var 11 Operating revenue per employee in USD Per Employee 0.237 0.631 -0.128 0.151 0.014

Var 12 Costs of employees / Operating revenue % Per Employee -0.318 -0.244 0.449 0.415 -0.169

Var 13 Average cost of employee in USD Per Employee -0.024 0.307 0.649 0.372 0.156

Var 14 Working capital per employee in USD Per Employee 0.108 0.501 0.242 -0.021 -0.090

Var 15 Total assets per employee in USD Per Employee 0.423 0.704 -0.054 -0.074 -0.312

Extraction Method: Principal Component Analysis.

a. 5 components extracted.

IV. Empirical results

22

Regression result: Dependent variable Z4 (credit due days or default variable)

23

Variables Coefficient t-statistic Std. Error Probability

C

Constant

19.19 9.59 2.00 0.00

Z1

Profitability

-0.14 -10.34 0.01 0.00

Z2

Per Capital

Productivity

-0.22 -48.40 0.004 0.00

Z3

Per Capital

costs

0.26 31.49 0.008 0.00

Z5

Solvency

-0.60 -71.41 0.008 0.00

Note: Dependent variable is Z4,

Observations=1137;

R-squared=0.994;

Adjusted R-squared=0.994;

Durbin-Watson statistics=1.98

Source: Authors’ compilation

24

Negative movements of solvency ratio (Z5)

with credit due (Default variable Z4)

-30000

-25000

-20000

-15000

-10000

-5000

0

5000

10000

15000

13

16

19

11

21

15

11

81

21

12

41

27

13

01

33

13

61

39

14

21

45

14

81

51

15

41

57

16

01

63

16

61

69

17

21

75

17

81

81

18

41

87

19

01

93

19

61

99

11

02

11

05

11

08

11

11

1

Z4 Z5Source: Authors’ compilation

25

Positive movement of Per employee costs

(Z3) with Credit due days (default Z4)

-10000

-5000

0

5000

10000

15000

200001

36

71

10

6

14

11

76

21

12

46

28

13

16

35

13

86

42

14

56

49

1

52

65

61

59

66

31

66

6

70

17

36

77

18

06

84

18

76

91

19

46

98

11

01

61

05

11

08

61

12

1

Z3 Z4Source: Authors’ compilation

26

Negative movements of Per Employee Productivity (Z2)

with Credit due days (default variable Z4)

-120000

-100000

-80000

-60000

-40000

-20000

0

200001

37

73

10

91

45

18

12

17

25

32

89

32

5

36

13

97

43

34

69

50

55

41

57

76

13

64

9

68

57

21

75

7

79

38

29

86

59

01

93

79

73

10

09

10

45

10

81

11

17

Z4 Z2Source: Authors’ compilation

27

Negative Movements of Profitability (Z1) with Credit

Due Days (Default variable Z4)

-70000

-60000

-50000

-40000

-30000

-20000

-10000

0

10000

20000

1

22

43

64

85

10

6

12

7

14

8

16

9

19

0

21

1

23

2

25

3

27

4

29

5

31

6

33

7

35

8

37

9

40

0

42

1

44

2

46

3

48

4

50

5

52

6

54

7

56

8

58

9

61

0

63

1

65

2

67

3

69

4

71

5

73

6

75

7

77

8

79

9

82

0

84

1

86

2

88

3

90

4

92

5

94

6

96

7

98

8

10

09

10

30

10

51

10

72

10

93

11

14

11

35

Z1 Z4Source: Authors’ compilation

IV. Conclusion and Policy Implications

28

29

1. Low productive SOEs, will slow the economic growth in many

economies that SOEs have significant share in the whole

economy.

2. Not only slowing the economic growth but also low productivity

of SOEs will make the business environment more severe for

the private sector.

3. It is important for the central governments to implement

comprehensive evaluation methods for evaluating the

performance of SOEs.

4. Profit making of SOEs is important, however just focusing on

one criteria, will mislead the policy makers, in addition nature

of many SOEs is for generating social welfare and not profit.

5. Empirical part of this research shows that solvency ratios and

per employee variables (cost and revenue) have more

deterministic power on success or failure of SOEs comparingto profitability.

Thank you for your attention!

farhad@aoni.waseda.jp

www.linkedin.com/in/farhadth

30