a look at business environment and non-performing...

International Review of Research in Emerging Markets and the Global Economy (IRREM) An Online International Research Journal (ISSN: 2311-3200)

2015 Vol: 1 Issue 1

234

www.globalbizresearch.org

A look at Business Environment and Non-Performing Loans Factors in

BRICS Economies

Deva Dutta Dubey,

KJ Somaiya Institute of Management Studies and Research,

Mumbai, India.

Email: [email protected]

A. S. Binilkumar,

NITIE, Mumbai, India.

Email: [email protected]

________________________________________________________________________

Abstract

BRICS nations (Brazil, Russia, India, China and South Africa) have been in the news as a bloc

since the publication of the Goldman Sachs report of 2003 and it has been suggested that by 2050

they would contribute to a substantial part of global trade and economy. Whereas individual

countries may be taking steps to grapple with local and global issues in the wake of the emerging

challenges in global finance, this paper attempts to take a look at some economic indicators of

these nations and attempts to determine relationships if any amongst some of these, as also

whether they demonstrate any similarities or otherwise. This paper performs descriptive and

statistical analysis on Panel Data of these five economies for 10 year period from 2003. The data

clearly indicates the impact of the global financial crisis of 2008 and how these economies

managed its effect.

________________________________________________________________________

Key words: BRICS, Panel Data Analysis, causality, NPL Estimation, Economic Indicators

JEL Classification: C 19, G13, G 14


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1. Introduction

Non-Performing Assets (NPA) also known as Problem Loans, Defaulting Loans, Bad Loans

and Non-Performing Loans (NPL), used interchangeably in this document, have been studied in

the financial sector across the world for a long time as they impact on bank profitability and

ultimately on bank survival / existence. However, a structured approach to the study of NPAs

appears to be underway to a large extent only during the last 25 years or so.

Bank of International Settlement (BIS) has taken steps in trying to establish norms for bank

regulators for facilitating smooth international transactions to support cross border and other cash

flows through banks in their jurisdictions. BIS, took a gentle approach of getting most national

regulators to see the problem and take steps to enhance financial stability by improving

supervisory knowhow and the quality of banking supervision.

BIS has evolved prudential norms for adequate protection of banks from unexpected crises.

These are in the nature of prescription of minimum supervisory standards. BIS facilitates

encouraging convergence towards common standards without attempting detailed harmonisation

of member countries’ supervisory approaches. Prescriptions known as Basel I, Basel II and Basel

III have been issued – BIS (2013). However, there exist differences between different national

economic and legal environments. Participating members agreed to adopt the new rules on

varying timescales.

NPA have strong impact on bank profitability and ultimately on bank survival / existence.

NPA originate from the pool of assets / lending done by the bank.

Tirole (2006) presented some stylised facts of lending and outlined that lenders perform a

credit analysis along several directions. They look at the borrower’s financial information,

estimates of market and liquidation values of assets, capability and character of the borrower or

top management. Thus the process of credit sanction is quite challenging. On sanction of financial

assistance, a typical debt liability specifies various terms of the credit. These are contained in a

legally enforceable contractual document. Lending and its management is thus a complex

operation and requires monitoring critical information over the life of the loan. It becomes a

problem for the lender in case the borrower is unable to meet his repayment obligations as per the

amortisation schedule, leading to a situation of default. Depending on the time for which the

default persists, banks are expected to take suitable action in order to protect their capital and

assets position. Ranjan and Dhal (2003) – A credit transaction involves a contract between two

parties: the borrower and the creditor (banks) subject to a mutual agreement on the terms of

credit. The terms of credit are defined over five critical financial parameters: amount of credit,


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interest rate, maturity of loans, frequency of loan servicing and collateral. The mutual agreement

may not necessarily imply be optimal for both.

Higher NPA ratio shakes the confidence of investors, depositors, lenders etc. It also causes

poor recycling of funds, which in turn will have deleterious effect on the deployment of credit.

The non-recovery of loans also affects financial soundness of organisation, Kumar and Singh

(2012)

A World Bank Paper presented results of a survey of the systems prevailing in 23 jurisdictions

as at end of 2001. It was found that in some instances, poor classification and provisioning

practices have led to painting a rosier picture in the 1990s. The evidence this survey provides is

intended to improve regulatory practices – Laurin and Majnoni (2003).

Thus, it can be seen that NPLs affect almost all banks in the world. There could be many

reasons for banks to have NPLs however, what is important is the steps taken by regulators to

contain the harmful effects of NPLs. Meanwhile, it is important to understand what drives NPLs

and this paper is a step in that direction as it makes an attempt to understand a number of

macroeconomic and bank factors which could have an impact on NPLs in countries included in

the BRICS economies.

2. Literature Review

BRICS nations (Brazil, Russia, India, China and South Africa) have been in the news as a bloc

since the publication of the Goldman Sachs Report of 2003 and it has been suggested that by

2050 they would contribute to a substantial part of global trade and economy. The ease of doing

business in these economies is reported as 130, 112, 132 and 2 on a scale of 185, using

euromonitor data, as reported by Finweek. However, as reported in Time articles, it is taking a

beating lately due to their structural and socio-political attributes, as other groups of nations are

also performing just about same or better. They do face a bit of a challenge on account of the

ground realities. However, there is hope and scope for this bloc. Stuenkel, 2013 argues that

despite disappearance of conditions which led to failure of the international financial order, which

led to the emergence of the BRICS economies, giving them a good bargaining power at the IMF

and establishment of the said platform, the bloc survives. And it is also likely to suggest a step in

the direction of intra-BRICS cooperation. This is amply demonstrated by period intra-BRICS

interaction facilitating greater cooperation among the members in areas of international finance,

banking, security, cybersecurity, taxation issues, competition issues, academics, health, trade et

al. As per an article in Markets / Finance, 2013, post global financial crisis, bad bank loans are

increasing across economies, including in BRICS economies. Federal corrective action in these


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countries range from capital infusion in banks including public banks, and enhanced recovery and

writeoff of bad loans, so as to prepare for more. As argued by Monica Herman Caggiano, 2013,

The BRICS as an informal bloc is most appropriate for intra bloc interaction, which have the

potential to deliver good results for the participants, while maintaining their respective

socioeconomic and political structures and maintaining seamlessness at the margins for smoother

interaction and better agreements which may recognise genuine concerns of the members on

merit. As per Harvard International Review, the world is undergoing unprecedented change and

shift in economic power, leading to greater globalisation. Greater intra BRICS cooperation is the

order of the day and is likely to lead to a coordinated growth of the member countries and not

creating competing institutions and rivals. Peter G Hall (2013) has presented that despite a slow-

down and hiccups, BRICS are here to stay. Member countries are leveraging the ‘policy

potential’ and taking steps to bring their economies to shape through policy reforms and actions.

The immediate outlook too looks promising and this will in turn stimulate their local economies.

This is likely to open up further opportunities in the future.

Messai and Jouini (2013) studied effect of GDP growth rate, unemployment rate, real interest

rate, LLR/TL and loan growth on NPL, for 85 banks in Italy, Greece and Spain for 2004-08.

Authors found negative variation of NPL with growth rate of GDP, profitability of banks’ assets

and positive variation with unemployment rate, LLR/TL and real interest rate.

Fofack (2005) investigated countries in Sub-Saharan Africa in 1990s for relationship between

NPA and Real GDP growth rate, GDP Per capita, M2, change in REER, real interest rate,

interbank loans, equity to total assets, equity to liquid assets, RoA, NIM showed a dramatic

increase in NPL and extremely high credit risk. The results also show strong relationship between

NPL and economic growth, real exchange rate appreciation, the real interest rate, NIM and

interbank loans.

Boudriga et al (2009) looked at 59 countries over 2002-06 making a comprehensive model to

explain differences between NPL using level of country financial development, GDP growth rate,

Foreign ownership of banks, bank market concentration, percentage of state owned banks,

regulatory capital to RWA, CAR, loans loss provisions, RoA. Empirical findings indicated that

higher CAR and prudent provisioning policy seems to reduce the level of NPL.

Espinoza and Prasad (2010) studied 80 GCC banks in a dynamic panel estimated over 1995–

2008 for NPA as a function of non-oil real GDP growth, stock market returns, interest rates,

world trade growth, the VIX index. They found that NPL ratio worsens as economic growth

becomes lower and IR and risk aversion increases. Firm-specific factors related to risk-taking and


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efficiency are also related to NPL. Strong evidence of a significant inverse relationship between

real (non-oil) GDP and NPL was found. The study also showed that global financial market

conditions have an effect on NPL of banks.

Beck et al (2013) studied 75 countries during the last decade to ascertain factors, other than

the economic cycle, using variables such as : RGDP, NEER, Lending IR, Share Prices, ICL. They

reported that real GDP growth, share prices, exchange rate and lending interest rate significantly

affect NPL ratios.

3. Methodology

3.1 Research Questions

This paper looks at some key indicators of the economies representing environmental

attributes (exogenous variables) and direct action attributes (endogenous variables). These are

captured through the following variables – Depth of Information Index, Legal Rights Index,

Insolvency Resolution, Annual GDP growth, Capital Formation to GDP, Industry Value Added to

GDP, Bank Capital to Assets, Lending Interest Rates, and Gross NPA to Gross Advances.

Information relating to Depth of Information Index, Legal Rights Index and Insolvency

Resolution are available for shorter durations and have therefore been omitted from the Panel

Data analysis. It was observed in respect of these three variables that these series appear

somewhat static for a few countries and hence may not be reflecting the ground reality in terms of

institutional mechanisms or their localised failure, or there may be some measurement issues or

the effect of certain institutions to address these issues may be yet to be realised, hence there may

be a need for more detailed analysis of these series. Statistically, these also tend to be considered

as representing collinearity.

Accordingly, the following objectives have been outlined in this paper.

Study of trends in each of these variables in the BRICS economies over the study

period.

Study of impact of all or some of these variables in the NPA formation in these

countries represented by Gross NPA to gross advances.

3.2 Modeling

In this paper, we use Panel Data Regression Analysis technique which builds up on the

descriptive analysis and presentation of certain environment specific variables and data.

3.3 Data

The study is based on secondary data. Data for the study has been obtained from time series

data available at IMF webpage. The period of study has been confined to the period 2003-2012 in


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order to obtain maximum information on the variables for all the five countries leading to a

strongly balanced panel. The methodology for this study is descriptive study of the variables

leading to a panel data regression analysis.

4. Results and Discussion

Description / definitions of the variables and variable-wise descriptive statistics is as under:

4.1 Credit depth of information index (0=low to 6=high)

Credit depth of information index measures rules affecting the scope, accessibility, and

quality of credit information available through public or private credit registries. The index ranges

from 0 to 6, with higher values indicating the availability of more credit information, from either

a public registry or a private bureau, to facilitate lending decisions. The plot of this variable for

all the five countries is shown in Figure 1. Brazil has had a very static attribute which remained at

5 during the entire period, indicating quite high index. India grew from 0 (low) to 5 (quite high)

over the period and shows the growth of institutions and mechanisms put in place, possibly to

minimise information asymmetry. China moved from 2 (somewhat low) to 4 (somewhat high),

indicating possible establishment of institutional mechanisms for enhancing depth of information

index. South Africa moved from 5 (quite high) to 6 (high) indicating further improvements in the

index. Russia, quite like India moved from 0 (low) to 5 (quite high) during the said period. Table

1 shows summary statistical information about the variable.

Figure 1

Table 1

Country No of Obs Mean Std Dev Min Max

Brazil 9 5 0 5 5

India 9 4 1.802776 0 5

China 9 3.555556 .8819171 2 4

South Africa 9 5.666667 0.5 5 6

Russia 9 3.111111 2.368778 0 5

0

1

2

3

4

5

6

7

2004 2005 2006 2007 2008 2009 2010 2011 2012

Credit depth of information index (0=low to 6=high)

BRAZIL INDIA CHINA SOUTH AFRICA RUSSIA


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4.2 Strength of legal rights index (0=weak to 10=strong)

Strength of legal rights index measures the degree to which collateral and bankruptcy laws

protect the rights of borrowers and lenders and thus facilitate lending. The index ranges from 0 to

10, with higher scores indicating that these laws are better designed to expand access to credit.

The plot of this variable for all the five countries is in Figure 2. Brazil reported a very static

attribute which remained at 3 (somewhat weak) during the entire period, indicating a somewhat

weak index. India grew from 6 (somewhat strong) to 8 (quite strong) over the period. China

moved from 3 (somewhat weak) to 5 (somewhat strong), indicating possible establishment of

procedures to enhance this index. South Africa remained static at 7 (quite strong) indicating its

position. Russia, quite like Brazil, remained at 3 (somewhat weak). Table 2 shows summary

statistical information about the variable.

Figure 2

Table 2


Brazil 9 3 0 3 3

India 9 7.444444 .8819171 6 8

China 9 4.222222 .9718253 3 5

South Africa 9 7 0 7 7

Russia 9 3 0 3 3

4.3 Time to resolve insolvency (years) Time to resolve insolvency is the number of years

from the filing for insolvency in court until the resolution of distressed assets

There is no range for this, and represents actual years reported by individual countries. The

plot of this variable for all the five countries is at Figure 3. Brazil reported a reduction of the

0

1

2

3

4

5

6

7

8

9

2004 2005 2006 2007 2008 2009 2010 2011 2012

Strength of legal rights index (0=weak to 10=strong)



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period from 10 to 4 years, indicating better opportunities available to parties to resolve distressed

assets. Incidentally, it was also the longest period reported by any of the 5 BRICS countries. India

managed to retain this duration at 4.3 years over the period. China reduced the period from 2.4

years to 1.7 years, indicating steps taken for improving resolution of distressed assets. South

Africa and Russia have reported duration of 2 years which demonstrates a strong and committed

response for resolution of distressed assets. Table 3 shows summary statistical information about

the variable.

Figure 3

Table 3


Brazil 10 5.8 2.898275 4 10

India 10 4.3 0 4.3

China 10 1.98 .3614785 1.7 2.4

South Africa 10 2 0 2 2

Russia 10 2 0 2 2

4.4 GDP growth (annual %)

Annual percentage growth rate of GDP at market prices based on constant local currency.

Aggregates are based on constant 2005 U.S. dollars. GDP is the sum of gross value added by all

resident producers in the economy plus any product taxes and minus any subsidies not included in

the value of the products. It is calculated without making deductions for depreciation of

fabricated assets or for depletion and degradation of natural resources. The plot of this variable

for all the five countries is at Figure 4. Brazil reported a minimum of -0.33 annual growth in 2009

and a maximum of 7.53 during the period of review and a mean growth rate of 3.41%. India

0

2

4

6

8

10

12

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Time to resolve insolvency (years)



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reported minimum growth of 3.24% and maximum growth of 10.55% and a mean growth of

6.85% during the period. China reported minimum growth of 7.8% and maximum growth of

14.2% with a mean of 10.03% during the period. South Africa registered minimum growth of -

1.53% and maximum growth of 5.6% with a mean of 3.51%. Russia reported minimum of -7.82%

and maximum growth of 10% with a mean value of 5.16%. Barring South Africa, all other

countries showed a reversal of trends during 2009. Table 4 shows summary statistical information

about the variable.

Figure 4

Table 4


Brazil 10 3.60 2.551582 -0.33 7.53

India 10 7.66 2.459616 3.24 10.55

China 10 10.46 1.850045 7.80 14.20

South Africa 10 3.51 2.096251 -1.53 5.60

Russia 10 4.72 4.727804 -7.82 8.54

4.5 Gross capital formation (% of GDP)

Gross capital formation (formerly gross domestic investment) consists of outlays on additions to

the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include

land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment

purchases; and the construction of roads, railways, and the like, including schools, offices,

hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are

stocks of goods held by firms to meet temporary or unexpected fluctuations in production or

sales, and "work in progress." According to the 1993 SNA, net acquisitions of valuables are also

considered capital formation. Figure 5 shows the plot of the variables for the 5 countries. Brazil

-10.00

-5.00

0.00

5.00

10.00

15.00

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

GDP Growth (annual %)



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reported minimum of 15.77% and maximum of 20.69% with a mean of 17.91%. India, by far the

leader in the BRICS on this dimension, demonstrated a minimum of 35.11% and maximum of

48.82% with a mean of 42.93%. China reported a minimum of 24.11% and a maximum of

38.03% with a mean of 32.41%. South Africa reported a minimum of 13.15% and maximum of

22.69% with a mean of 16.86% while Russia reported a minimum of 15.29% and maximum of

22.71% with a mean of 18.31%. It may be seen that despite the varying economic situation

prevailing in the world, the BRICS nations demonstrated varying gross capital formation to GDP.

Table 5 shows summary statistical information about the variable.

Figure 5

Table 5


Brazil 10 18.03 1.700444 15.77 20.69

India 10 45.89 3.118161 42.20 49.82

China 10 36.66 3.346857 28.14 40.03

South Africa 10 37.66 3.346857 29.14 41.03

Russia 10 23.05 2.028541 19.87 26.71

4.6 Industry, value added (% of GDP)

Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-

37). It comprises value added in mining, manufacturing (also reported as a separate subgroup),

construction, electricity, water, and gas. Value added is the net output of a sector after adding up

all outputs and subtracting intermediate inputs. It is calculated without making deductions for

depreciation of fabricated assets or depletion and degradation of natural resources. The origin of

0.00

10.00

20.00

30.00

40.00

50.00

60.00

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Gross capital formation (% of GDP)



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value added is determined by the International Standard Industrial Classification (ISIC), revision

3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Figure 6

shows the plot of the variable for the 5 countries for the period. Brazil reported minimum of

26.29% and maximum of 30.11% with a mean of 27.85%. India demonstrated a minimum of

25.08% and 29.03% with a mean of 27.17%. China, by far the leader in the BRICS countries

reported a minimum of 44.79% and a maximum of 47.95% with a mean of 46.39%. South Africa

reported a minimum of 28.41% and maximum of 32.61% with a mean of 31.07% while Russia

reported a minimum of 32.57% and maximum of 38.08% with a mean of 35.71%. It may be seen

that despite the varying economic situation prevailing in the world, the BRICS nations were more

or less able to maintain the industry value added to GDP. Table 6 shows summary statistical

information about the variable.

Figure 6

Table 6


Brazil 10 3.60 2.551582 -0.33 7.53

India 10 7.66 2.459616 3.24 10.55

China 10 10.46 1.850045 7.80 14.20

South Africa 10 3.51 2.096251 -1.53 5.60

Russia 10 4.72 4.727804 -7.82 8.54

4.7 Bank capital to assets ratio (%)

Bank capital to assets is the ratio of bank capital and reserves to total assets. Capital and

reserves include funds contributed by owners, retained earnings, general and special reserves,

20.00

25.00

30.00

35.00

40.00

45.00

50.00

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Industry Value Added (% to GDP)



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provisions, and valuation adjustments. Capital includes tier 1 capital (paid-up shares and common

stock), which is a common feature in all countries' banking systems, and total regulatory capital,

which includes several specified types of subordinated debt instruments that need not be repaid if

the funds are required to maintain minimum capital levels (these comprise tier 2 and tier 3

capital). Total assets include all nonfinancial and financial assets. Figure 7 shows the plot of the

variable for all the 5 countries. Brazil reported a minimum bank capital of 8.9%, maximum of

12.1% and a mean of 10.44%, indicating near compliance with BASEL norms of 8% / 9% capital

to assets. India reported minimum of 5.3%, maximum of 7.3% and a mean of 6.37%, indicating

shortfall in the capital at the aggregate level, thereby, indicating need for capital infusion. China

reported minimum of 3.8%, maximum of 6.4% and a mean of 5.22% indicating need for capital

infusion in Chinese banking entities. South Africa reported minimum of 5.6%, maximum of 9.3%

and average of 7.68% reflecting need for capital infusion in banks. Russia reported minimum of

10.8%, maximum of 14.6% and mean of 12.92%, by and large the largest capital ratio in the

BRICS countries. It reflects capital adequacy of a high standard during the period under review.

Table 7 shows summary statistical information about the variable.

Figure 7

Table 7


Brazil 10 10.55 0.587367 9.60 11.30

India 10 6.64 0.537897 5.70 7.30

China 10 5.34 0.964019 3.80 6.40

South Africa 10 7.40 0.795822 5.60 8.20

Russia 10 12.71 1.02247 10.80 14.60

2

4

6

8

10

12

14

16

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Bank Capital to Assets Ratio (%)



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4.8 Lending interest rate (%)

Lending rate is the bank rate that usually meets the short- and medium-term financing needs

of the private sector. This rate is normally differentiated according to creditworthiness of

borrowers and objectives of financing. The terms and conditions attached to these rates differ by

country, however, limiting their comparability. Figure 8 shows plot of the variable for the 5

countries. Brazil reported minimum of 9.12%, maximum of 67.08% and mean of 50.89%,

indicating very high rates which may be impeding growth. Even at the last levels reported, it may

be considered high. India reported minimum of 8.33%, maximum of 13.31% and mean of 11.4%.

This may also be considered high and may be leading to keeping good credit out of the market.

China reported modest minimum of 5.31%, maximum of 7.47% and mean of 5.85%, reflecting a

reasonable interest rates for financing. South Africa reported minimum of 8.75%, maximum of

15.75% and mean of 12.28% reflecting not too high interest rates. Russia reported minimum of

8.46%, maximum of 24.43% and mean of 13.04% reflecting, once again, not too high rates.

Overall, Brazil had the highest interest rates. Table 8 shows summary statistical information

about the variable.

Figure 8

Table 8


Brazil 10 48.43 8.900648 36.64 67.08

India 10 11.19 1.443106 8.33 13.31

China 10 5.91 0.684418 5.31 7.47

South Africa 10 11.56 2.245817 8.75 15.13

Russia 10 11.15 1.983599 8.46 15.31

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Lending Interest Rate (%)



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4.9 Bank nonperforming loans to total gross loans (%)

Bank nonperforming loans to total gross loans are the value of nonperforming loans divided

by the total value of the loan portfolio (including nonperforming loans before the deduction of

specific loan-loss provisions). The loan amount recorded as nonperforming should be the gross

value of the loan as recorded on the balance sheet, not just the amount that is overdue. Figure 9

shows the plot of the variable for the 5 countries. Brazil reported minimum of 2.9%, maximum of

8.3% and a mean of 4.06%, India reported minimum of 2.3%, maximum of 12.8% and a mean of

5.72%. China reported minimum of 0.9%, maximum of 29.8% and a mean of 10.82%. South

Africa reported minimum of 1.1%, maximum of 5.9% with a mean of 3.25%. Russia reported

minimum of 2.4%, maximum of 9.5% and a mean of 5.43% during the period under review.

Overall, it can be seen that China reported highest mean values, by far in this respect. Table 9

shows summary statistical information about the variable.

Figure 9

Table 9


Brazil 10 3.45 0.442844 2.9 4.2

India 10 3.98 2.313151 2.3 8.8

China 10 6.25 6.445713 0.9 20.4

South Africa 10 3.31 1.8788 1.1 5.9

Russia 10 5.11 2.526944 2.4 9.5

0

5

10

15

20

25

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Bank NPL to Total Gross Loans (%)



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Panel Data Analysis

Panel data was analysed using statistical software package facilitating panel data regression

analysis, with options of fe and re being used.

Equation for Fixed Effects Model

{dv} Gross NPA to Gross Advancesit = bi({iv1}Annual GDP growth, {iv2}Capital Formation

to GDP, {iv3}Industry Value Added to GDP, {iv4}Bank Capital to Assets, {iv5}Lending Interest

Rates) + αi+ uit

Where, the variables have been defined, and the coefficients are as under :

bi is a coefficient for a particular ivi,

αi is the unknown intercept for each entity (5 countries in the group)

uit is the error term.

Equation for the Random Effects Model

{dv} Gross NPA to Gross Advancesit = Bi({iv1}Annual GDP growth, {iv2}Capital

Formation to GDP, {iv3}Industry Value Added to GDP, {iv4}Bank Capital to Assets,

{iv5}Lending Interest Rates) + αi + uit + εit

Where, the variables have been defined, and the coefficients are as under :

Bi is a coefficient for a particular ivi,

αi is the unknown intercept for each entity (5 countries in the group)

uit is the between entity error term

εit is the within entity error term

The two models have been estimated and compared in the Table 10. It can be seen from the

Random Effects model coefficients generated from the estimation that annual GDP growth and

bank capital to assets are inversely related to gross NPA to gross advances. This suggests that as

GDP grows and as bank capital to assets increases, gross NPAs decrease, possibly suggesting that

as GDP grows, there is a positive feeling in the players and tightening of lending norms as capital

increases hence decrease in NPAs. While gross capital formation to GDP, industry value added to

GDP and lending interest rates are directly related to gross NPA to gross advances possibly

indicating an element of recklessness and buyers misreading the economic opportunities and

ending up as NPAs. However, none of the coefficients is significant at the 5% level.

On the other hand, the Fixed Effects model coefficients generated from the estimation

indicate that all variables have inverse relation to gross NPA to gross advances. This suggests that

as GDP, gross capital formation to GDP and industry value added to GDP grow, gross NPAs

decrease, possibly reflecting the focus and positive feeling of borrowers. While relationship


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between bank capital to assets and lending interest rates to gross NPA to gross advances

suggesting that as these increase, gross NPAs to gross advances decrease, possibly suggesting

that as bank capital and lending rates increase, there may be a sense of recklessness amongst

lenders to over lend, and misreading the opportunities amongst borrowers thereby contracting

borrowing at higher rates of interest. All coefficients except those of annual GDP growth and

lending interest rates are significant at the 5% level.

Table 10: Comparison of Models Generated

RE, GLS FE

Descriptor Dependent Variable > Gross NPA to Gross Advances

dv Intercept

3.782

{0.76}

(0.448)

81.358

{6.12}

(0.0)

Independent Variables

iv1 Annual GDP Growth

-0.183

{-1.07}

(0.284)

-0.097

{-0.68}

(0.499)

iv2 Gross Capital Formation to

GDP

0.152

{-0.24}

(0.807)

-0.399

{-3.11}

(0.003)

iv3 Industry Value Added to

GDP

0.137

{1.51}

(0.130)

-1.208

{-3.47}

(0.001)

iv4 Bank Capital to Assets

-0.272

{-1.19}

(0.235)

-2.733

{-6.06}

(0.0)

iv5 Lending Interest Rates

0.009

{-0.25}

(0.802)

-0.086

{-0.97}

(0.338)

GFI R2 Within 0.1427 0.5884

R2 Between 0.4187 0.7905

R2 Overall 0.1206 0.0058

Wald Chi2

6.03

F(5,40)

11.44

Prob > Chi2

0.3028

Prob > F

0.00

Interpretation

As the value of

Prob>Chi2 is > 0.05, the

model is not acceptable

As the value of Prob>F

is < 0.05, the model is

acceptable

Figures in curly brackets { } indicate relevant t values, and figures in brackets ( )

indicate p values of the relevant t values

Post Estimation

The Hausman Test between Fixed and Random Effects Models (all equations) was conducted and

the findings are presented in Table 11.


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Table 11: Findings of the Hausman Test between FE and RE Models

Coefficients b, Fixed B, Random (b-B) sqrt(diag(V_b-

V_B))

Annual GDP Growth -.0970859 -0.1834988 0.0864129 .

Gross Capital Formation

to GDP

-

0.3992764

-0.015176 -0.3841004 0.1125019

Industry Value Added to

GDP

-1.208189 .1370193 -1.345208 .3365773

Bank Capital to Assets -2.732905 -0.2716885 -2.461217 .3882531

Lending Interest Rates -.0869334

-0.0094308 -0.0775026 0.0813247

b = consistent under Ho and Ha; obtained from xtreg

B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Test: Ho: difference in coefficients not systematic

chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 71.20

Prob>chi2 = 0.0000

(V_b-V_B is not positive definite)

Since Prob>chi2 equals 0.0000 (i.e. less than 0,05 – hence it is appropriate to use the Fixed

Effects Model)

5. Conclusions and Recommendations

Within this, for the given period of the study, the coefficients are all negative, indicating that

when annual Growth of GDP rises, OR when Gross Capital Formation increases, OR when

Industry value added to GDP rises, Or when Bank Capital to Assets rises, OR when lending

interest rates rise, NPL falls. These are not unexpected, however, the lending interest rate

relationship with NPL is indeed interesting. The coefficients are large for Bank Capital to Assets,

Industry Value Added to GDP and Annual GDP Growth and Gross Capital Formation, but they

are not significant for Annual GDP Growth and for Lending Interest rates.

The modeling exercise / findings suggest that as indicated by the Hausman Test, Fixed Effects

model is a better estimate of the data underlying the statistical exercise. This finding appears

interesting considering the diversity amongst the economies, however, possibly it suggests the

similarity of the intrinsic role of banks and financial intermediaries in the overall economic

system of each of these countries, irrespective of the apparent differences on the surface and its

manifestations on the ground. This is also due to the fact that the universe of objects in the

statistical exercise is determined.


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This may be a case for further research in future. Thus, it may be possible to forecast the level

of gross NPA to gross advances, given the measurement of the underlying independent variables

in the exercise. The study is presented subject to the following limitations of the study and the

data.

1. The study is based on secondary data and has characteristic problems associated with

secondary data analysis.

2. Some data fields have not been captured and they may lead to some discrepancies and

hence have not been included in the analysis, notwithstanding their otherwise explanatory

power in the phenomena under study.

3. There are various factors which could impact gross NPA to gross advances, including

societal and temporal factors, which would have an impact on the dependent variable,

and these have not been captured in this exercise.

4. The analysis and findings may be studied in detail by including more countries in the

sample, thereby trying to minimize the small sample bias, if any.

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