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STRESSED ASSET SCENARIO OF INDIAN CO-OPERATIVES

A RESEARCH REVIEW

S. Senthil, M. Com., M. Phil., NET., Ph. D., Research Scholar,

Arignar Anna Government Arts College, Vadachennimalai, Attur, Salem,

Tamil Nadu. Pin. 636 121, E. Mail: [email protected]

Dr. D. Thiruniraiselvi, M. Com., M. Phil., Ph.D. M.Ed.,

Head of the Department & Research Supervisor,

P.G. & Research Department of Commerce,

Arignar Anna Government Arts College, Vadachennimalai, Attur, Salem – 636 121

Parishodh Journal

Volume IX, Issue III, March/2020

ISSN NO:2347-6648

Page No:2530

STRESSED ASSET SCENARIO OF INDIAN CO-OPERATIVES

ABSTRACT

The research was conducted to learn and understand the stressed asset position of Indian Co-operatives with

the help of secondary data related to loans in demand and loans overdue of all the Primary Agriculture Co-

operative Societies (PACS) functioning in Indian during the period 2007-08 to 2017-18. These co-operatives

provide more volume of loans to priority sectors like agriculture, micro, small, medium scale industries, housing,

education, etc. that provide more employment but less and highly probable returns hence the loans provided to

priority sector have more chance of turning into stressed assets to lending organisations. This study analyses the

correlation co-efficient between loans in demand and loans over-due of the loans provided by Primary agriculture

cooperative societies to Priority sector1 taken as Agriculture, Priority sector 2 taken as Non-agriculture and Non-

priority sector taken as Business activities. The result after analysis has revealed that the correlation coefficient of

stressed asset of loans to priority sector 1 is lesser than the same happened with the loans to priority sector 2 and

non-priority sectors. The reasons behind such scenario are found and revealed.

Key Words: Co-operative, Correlation,, G21, Loans in demand, Loans over-due, , Priority Sector, Stressed assets.

INTRODUCTION

Stressed Assets - As a revamping measure of the existing Credit Monitoring System, The Central Bank of

India, through its “framework for revitalizing distressed assets in the economy”, dated 30th

January 2014, has made

it mandatory to all the lending institutions under its supervision to adhere and to implement its guidelines from the

date April 1, 2014, to recognize the early symptoms of distress in their loan assets and to take immediate action to

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mitigate losses due to accumulation of Non-performing Assets (NPAs) (RBI, 2014, p.1). The three sub-categories

of NPAs viz. Substandard Assets, Doubtful assets and Loss assets had been introduced by the RBI to be followed

by banking organizations functioning in India from the date 31st March 2005 (RBI, 2009). An updated version of

sorting distressed asset was introduced by the Reserve Bank of India in the year 2014 with the name Stressed Asset

which is an accumulation of Non-performing asset, Restructured loans and Written-off loans. Thus stressed asset

has a comprehensive meaning than non-performing asset. The conventional pattern of non-performing asset consist

of loan accounts of which no service (payment of a part of principal with interest or the part of principal only or the

interest only) is found for a period of 90 days from the due date but the Stressed asset pattern insist that a loan

account should cross over three stages well before it should be categorized under Non-performing asset. The said

stages are contained in the Prudential Framework of RBI for the Resolution of Stressed Assets dated June 7, 2019

has mentioned the categories of Special Mention Account as SMA 1, SMA 2, and SMA 3 of which SMA 1 consist

of loan accounts of which no service was found for 30 days from the due date, SMA 2 consist of loan accounts of

which no service was found between 31 to 60 days and SMA 3 consist of loan account of which no service was

found between 61 to 90 days (RBI, 2019).

The necessity for Stressed Asset pattern

To the lender – by incorporating the stressed asset pattern lending institutions can fairly identify the early

symptoms of performing loans turning into non-performing loans in the near future and could take precautionary

measures to avoid such a dilemma.

To the Apex – it helps to know the real worth of loan assets of the financial institutions performing under its

leadership and to identify the poorly performing lenders at various levels and provide them adequate supports so as

to amend their approaches with lending practice.

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To the Society – it supports the growth of national economy by way of early identification of detriment to the

health of financial system and to take necessary measures to curb major destructions and to save the faith of public

with the banking system.

STRESSED ASSET PATTERN OF CO-OPERATIVES

Though Co-operatives are working alike other banking entities, in terms of receiving deposits and lending

loans it differ from the later in several aspects like/by providing more importance to the development of rural

economy through lending substantially to priority sectors especially to agricultural operation, micro and small scale

industries to which the contribution of co-operative is considerably higher than the contribution of counterparts in

other sectors. More volume of fund is provided as loan to agriculture activities which are dominated by nature than

that of human efforts hence the expected rate of return from this occupation is highly speculative and subject to

probabilities. Thus loan provided by co-operatives to agricultural operation need certain consideration while

categorizing it under the sub-heads of stressed assets. Some rational differences in the treatment of loans provided

for agriculture under the head non-performing asset and under stressed assets are provided in the forthcoming

subhead.

Components of Stressed assets of Co-operatives

The Master circular of Reserve Bank of India dated 1st July 2015, on prudential norms on Income Recognition,

Asset Classification and Provisioning pertaining to Advances has provided explanation for the following as

(a). Non-Performing Asset

(i). when loan is provided for short-term cropping it should be treated as non-performing asset when there

is no service to the loan account for two short-term crop seasons that follows the due date of loan. (In general a

short-term crop season ranges between 3 to 6 months but it varies according to the climatic condition and other

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environmental factors hence it should vary from place to place and thus decided by the State level Banker

Committee individually)

(ii). When loan is provided for long-term cropping it should be treated as non-performing asset when there

is no service to the loan account for a long-term crop season that follows the due date of loan. (In general a long-

term cropping season ranges upto 12 months but in rare cases it can extend one or two months depending upon the

supports of environment hence the same method should be followed as said earlier to decide the time period of

long term crop season).

(iii). When loan is provided for non-agriculture purpose then it should be treated as non-performing asset

only when there is no service to that loan account for a period of 90 days that follows the due date of loan (Reserve

Bank of India, 2015).

(iv). Sub-standard asset: Further classification has to be made with the name sub-standard asset as

mandated by RBI from the date March 31, 2005. The loan assets of which no credit was made for a period not

exceeding 12 months and identified obvious symptoms of leaning the worth of loan should be placed under this

category.

(v). Doubtful asset consist of three sub-classes viz.

Sub-class: D1 of Doubtful asset: it consists of un-served loan accounts for a period of 24 months.

Sub-class: D2 of Doubtful asset: it consists of un-served loan accounts for a period of 36 months

Sub-class: D3 of Doubtful asset: it consists of un-served loan accounts for a period over 36 months.

(b). Loss assets indicates loan accounts that remained in the D3 category of doubtful asset of which necessary

actions were taken by the lending organization to realize it but the stagnated loan could not be collected in full or

a part and hence the efforts failed and found no use of keeping the same under the head loan assets thereafter and

hence they are categorized under the head loss asset after being verified and authenticated by the bank itself or the

internal auditor or the external auditor or by the officer of the RBI (Reserve Bank of India, 2015).

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(c). Restructured assets in the sense of co-operative banking it connotes a loan placed under any of the NPA

categories stated above and when considered for re-construction as a new loan either by adding the unpaid interest

with the principal amount or by relaxing a part of unpaid interest or even a part of principal amount is forgone in

order to rechristen it as a restructured loan. Such loan has to be maintained under sub-standard asset or doubtful

asset categories until it shows satisfactory performance for a period of one year from the date it get restructured.

Thus the head stressed asset comprises restructured loans also (Reserve Bank of India, 2001).

REVIEW OF LITERATURE

(Nivedita, 2018) – the research has been conducted by using secondary data relevant to non-performing assets and

loans outstanding of the State Co-operative banks functioning in four regions of India. Analysis of the said data for

three years viz. 2011, 2015 and 2016 has revealed the existence of variation in the percentage of NPA to

outstanding loans further it found that the percentage of non-performing assets of North-eastern region of the

country having 12 State Co-operative banks is the highest (13.1%) when compared with State Co-operatives of

other regions. The overall result of percentage of NPAs of Indian State Co-operatives during the study years has

decreased from 8.6% in the year 2011 to 4.5% in the year 2016. Furthermore the researcher has stated that NPA

norms of co-operatives are not par with the norms of commercial banks but it is liberal to certain extent and as

suggestion she has stated that appraisal of borrower and post-sanction follow-up are needed measures so as to

minimize the NPAs.

(Raj, Jain, Bansal, Verma. 2018), the researchers had conducted a comparative analysis of the Non-performing

assets of The State Bank of India, a public sector undertaking with the private sector banks The Industrial

Investment Corporation of India with the result of four financial years from 2014 to 2017. They have conducted

their analysis with correlation coefficient between Net NPA and Net Profits of the said banks and they found that

major reason behind the growth of NPAs of both banks is due to relaxed lending norms and poor consideration to

the results of credit rating analysis. In case of SBI the reason behind the hike of NPA is majorly contributed by

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loans provided for agricultural activity but in case of ICICI the reason niche has not discussed. Also they had found

that the Gross NPA and Net NPA of SBI are lesser than the results of ICICI and the negative correlation between

Net NPA and Net Profit of SBI is higher than that of ICICI. Thus they conclude that performance of SBI is

comparatively better than ICICI.

(Islam, 2017), had performed a study by comparing the NPA results of 10 leading banks of India with another set

of 10 leading banks of Bangladesh. The aim of the study is to measure the trends of NPAs of those banks for seven

financial years and to analyze the impacts caused by it on the returns of the banks. By using regression analysis

they had analyzed secondary data and found that in both the countries state owned public sector banks have more

volume of non-performing assets than their counterparts in private sector. The analysis has further revealed that the

NPAs of the ten Indian banks have an inverse relationship between NPAs and Return of Equity and the same result

was found with the banks of both sectors in Bangladesh. Also their findings show that the management of private

sector banks is healthier than that of the management of public sector banks. They had suggested that the

management system of public sector banks of both the countries should focus on better preventive measures and

recovery strategies.

(Singh, 2016), has pursued his study with aim to express the NPA status of Scheduled commercial banks in India,

impact cause by NPAs and strategies of recovery of bad loans applied by them. Data for fourteen years from 2000

to 2014 were collected regarding Net NPAs and Net Loans from the existing sources and simple percentage was

applied as tool for analysis and the results arrived had revealed that the increase of gross NPA during the period of

study is 273.16% and that of net NPA is 177.75%. A sum of Rs.2535 crores through Lok Adalat, Rs.27231 crores

through DRTs and Rs.77241 crores through SARFAESI were recovered during the period 2008 to 2014. Toward

the impact, Return on Investment of all the SCBs were severely affected due to rise in NPAs. In his

recommendation he had suggested for revision of credit appraisal system; strengthening the loan recovery methods;

post sanction follow-up measure; Publishing the defaulters through various media, etc.

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(Poornima, 2013), in her research she intend to find reasons of assets turning into NPA, impacts caused by it and

measures undertaken by the Karnataka State Co-operative Apex Limited to reduce NPAs. She had used graphical

representation and ratio analysis in order to analyze periodic data related to the year 2007-08 to 2011-12 collected

from various existing sources. Her analysis has found that sub-standard asset to gross NPA has grown up 39.83%

during the study period but the percentage of doubtful asset to gross NPA has decreased by 33.59%. Toward Loss

asset to Gross NPA an increase of 31.03% was found, contribution of various sectors to the gross NPA shows that

Sugar Industry has maximum score then come the other sectors and the least contributor is Agriculture in which

the recovery rate is 100% during the last 3 years of the study period. Her findings toward management show that

there is no credit monitoring and controlling devices that could support mitigation of NPAs.

SCOPE AND IMPORTANCE OF THE STUDY

This study covers the stressed asset status of all the Primary Agriculture Co-operative Societies functioning

in India for a period of eleven years from 2007-08 to 2017-18. The reason behind choosing this period for study is

to reveal whether there is any impact caused on the financial health of the base level co-operatives functioning in

India due to the Global Financial Crisis (GFC), happened in the year 2008 that shook the developed countries at

large and it takes several years for the suffered nations to retrieve their economy. The study concentrates on

depicting correlation between the Total Demand of Loans and the Total Loan Over-due (considered as stressed

assets for this study) related to loans provided to agriculture activity, taken as priority sector 1, non-agriculture

activities taken as priority sector 2 and to other activities taken as non-priority sector. This study is vital from the

broader view that the number of PACS functioning in India is much higher in number than any other banking

organization that could provide support for the rural development through loans and advances abundantly.

OBJECTIVES

(a). To know the volume of credit delivered by PACS to Agriculture activities (PS-1: Priority Sector-1),

Non-agriculture activities (PS- 2: Priority Sector-2) and other activities (NPS: Non-Priority Sector) during the

period 2007-08 to 2017-18.

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(b). To ascertain correlation between Loans in demand and Loans over-due (stressed assets) related to loans

provided to PS-1, PS- 2 and NPS.

METHODOLOGY

Descriptive method is adopted to depict the financial results of all the PACS functioning in India.

Secondary data from the published records of RBI and NABARD are used to fulfill data requirement of the study.

Karl Pearson’s Correlation coefficient was applied as core analytical tool to find the relationship between

independent variable (loans in demand) and dependent variable (loan over-due) taken as X and Y respectively.

HYPOTHESIS

H01: Any change in the loans in demand will have no relative change in the loan over-due toward loan provided for

agriculture activities (PS -1)


non-agriculture activities (PS -2)


non-priority sector (NPS)

PRIMARY AGRICULTURE CO-OPERATIVE SOCIETY

Primary Agriculture Co-operative Society (PACS) is the base level co-operative organization having direct

contact with the people being its members of rural residence. It is the third tire of the short term co-operative credit

structure having apex at three levels viz. DCCBs – District Central Co-operative Banks at district level, SCCB –

State Central Co-operative Bank at state level and NABARD – National Bank for Agriculture and Rural

Development at National level. The contribution of PACS for the rural development of India is enormous because

of its count and its presence in rural parts of India. As on 31st March 2018 the total number of PACS functioning

in India is 95,238 having membership of 13.05crores with a paid-up capital of Rs.14,142 crores, while the total

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count of borrower is 5.07crores to whom total loans issued is Rs.2,07,322 crores, total loans in demand is

Rs.1,96,750 crores, total loan outstanding is Rs.169630 crores, total collection made is Rs. 1,48,834 crores and the

total over-due is Rs.47,915 crores. To know the financial health of PACS the volume of loans over-due (stressed

assets) it posses should be compared with loans in demand. Hence loans over-due was taken as dependent variable

and loans in demand is taken as independent variable and correlation between these two could helps in

understanding the financial health of the PACS.

DATA ANALYSIS AND INTERPRETATION

Table- 1 Particulars of Loans to Agriculture activity (Priority Sector – 1) provided by PACS

1 2 3 4 5 6 7

Financi

al Year

Loans issued

Rs.

(in Lakhs)

Loans in

Demand

Rs.

(in Lakhs)

Loans

collected

Rs.

(in Lakhs)

Loans

Outstanding

Rs.

(in Lakhs)

Loans

Over-due

Rs.

(in Lakhs)

Percentage of

Over-due to

Demand

[6/3X100]

2007-08 3247754.72 4697466.56 2971225.24 3751011.29 1726221.32 36.75

2008-09 3091532.75 6553903.58 3187172.03 3795140.02 3366731.55 51.37

2009-10 3762479.82 7011605.29 3086363.29 4112301.54 3925242.00 55.98

2010-11 4646981.35 6358214.55 3946997.19 4463900.07 2411217.36 37.92

2011-12 4912721.26 5896978.67 4211676.95 4723819.59 1685301.72 28.58

2012-13 6449919.71 7543491.00 5580202.85 6656683.56 1963288.15 26.03

2013-14 7204334.96 7932270.65 6053889.31 6137610.03 1878381.34 23.68

2014-15 7628224.79 8702771.43 6466313.29 7386044.91 2236458.14 25.70

2015-16 6645703.32 7895538.82 5667064.07 7521108.09 2228474.75 28.22

2016-17 8061919.03 8947856.98 6946584.63 8607150.62 2001272.35 22.37

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2017-18 9867068.23 8820053.55 6990018.13 8383761.38 1830035.42 20.75

Source: National Federation of State Cooperative Banks Ltd. (2007-08 to 2017-18).Performance of PACS. p. 21-23

Analysis was conducted to check the correlation between the Loans in demand (X) to Loans over-due (Y) and the

result is shown below.

X Values Y Values X and Y Combined values

∑ = 80360151.08 ∑ = 25252624.1 N = 11

Mean = 7305468.28 Mean = 2295693.1 ∑ (X – Mx) (Y – My)

∑ (X - Mₓ)₂ = SSₓ = 18075595080616 ∑ (Y – My)

₂ = SSy = 5108964474368.1 = -700292561817.159

R Value: R = ∑ ((X – My)(Y- Mx)) / √ ((SSx)(SSy))

r = -700292561817.159/ √ ((18075595080616)(5108964474368.1)) = - 0.0729

Results: r = – .07 and the value of p at 5% significant level is .83

The result of p is not significant at p < .05. This shows that positive correlation exist between variable x – loans in

demand and variable y – loans over-due.

Interpretation: The value of “r” arrived through Pearson correlation is -.07 and that of “p” is .83 proving that the

correlation between loans in demand and loans over-due is not strong at 5% significance level. Hence null

hypothesis is accepted and we can conclude that a change in loans in demand would not have a relative change in

the stressed assets of the loans provided to priority sector 1.

Table- 2 Particulars of Loan to Non-Agriculture activity (Priority sector – 2) provided by PACS

1 2 3 4 5 6 7

Financial

Year

Loans

issued

Rs.

(in Lakhs)

Loans in

Demand

Rs.

(in Lakhs)

Loans

collected

Rs.

(in Lakhs)

Loans

Outstanding

Rs.

(in Lakhs)

Loans

Over-due

Rs.

(in Lakhs)

Percentage

of Over-due

to Demand

(6/3x100)

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2007-08 1480574.14 989766.86 783977.28 943954.74 205789.58 20.79

2008-09 1781583.85 1281092.21 1042651.76 1313111.45 238440.45 18.61

2009-10 2547771.08 1733330.66 1255109.77 1064230.44 478220.88 27.59

2010-11 2961131.27 1911251.37 1596908.39 2608136.32 314342.98 16.45

2011-12 5010790.49 2306425.28 1781428.90 3486209.43 524996.38 22.76

2012-13 5745120.09 4150819.79 3787794.85 4408705.67 363025.14 8.75

2013-14 5970171.21 4884599.09 4209821.06 4967918.68 674778.03 13.81

2014-15 6205649.64 4915989.08 4400996.51 5347147.43 514992.57 10.48

2015-16 6300688.03 5054572.00 4868930.40 5078746.79 185641.60 3.67

2016-17 6223483.36 6686306.97 5415256.25 6743960.60 1271050.72 19.01

2017-18 6346620.60 6660478.21 5303507.58 6877328.14 1356970.63 20.37


Analysis was conducted to check the correlation between the total loans in demand (x) and total over-due (y) and

the result is as below.

X Values Y Values X and Y Combined

∑ = 40574631.52 ∑ = 6128248.96 N = 11

Mean = 3688602.865 Mean = 557113.542 ∑ (X – Mx) (Y – My)

∑ (X – Mx)₂ = SSx = 44806978966591.7 ∑ (Y – My)

₂ = SSy = 1631932650683.53 = 6354656894408.26

R Calculation

R = ∑ ((X – My)(Y- Mx)) / √((SSx)(SSy))

r = 6354656894408.26 / √((44806978966591.7)(1631932650683.53)) = 0.7431

Results: r = .74 and p = .00878;

The result of p found to be significant at p < .05. This shows that negative correlation exist between variable x -

loans in demand and variable y- loans over-due.

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Interpretation: The value of “r”, arrived through Pearson correlation is .74 and that of “p” is .00 proving that the

correlation between loans in demand and loans over-due is negative at 5% significance level. Hence rejection of

null hypothesis is favored and hence we can conclude that any change in the loans in demand have no similar effect

on loans over-due.

Table 3 Particulars of Loan to other activities (Non-Priority sector) provided by PACS

1 2 3 4 5 6 7

Financial

Year

Loans

issued

Rs.

(in Lakhs)

Loans in

Demand

Rs.

(in Lakhs)

Loans

collected

Rs.

(in Lakhs)

Loans

Outstanding

Rs.

(in Lakhs)

Loans

Over-due

Rs.

(in Lakhs)

Percentage

of Over-due

to Demand

[6/3X100]

2007-08 1035922.36 1042081.66 573543.44 1974672.35 470088.98 46.16

2008-09 1005557.57 628402.74 439939.88 1296172.77 191165.30 30.42

2009-10 1183503.07 804723.74 1255786.55 2471450.60 176442.40 21.93

2010-11 1522269.64 754616.27 1210417.03 1704757.87 171537.52 22.73

2011-12 8065111.45 871398.29 651333.29 914292.05 220065.00 25.25

2012-13 3995875.45 3844276.82 2341037.51 2875761.83 1504245.31 39.13

2013-14 3967581.20 2768499.75 2358472.31 1901136.13 412208.79 14.89

2014-15 2071152.31 2343849.59 1516240.74 1990644.50 895726.77 38.22

2015-16 5135957.49 4028224.73 3453429.39 8250150.67 1038403.84 25.78

2016-17 5782435.07 4412240.21 2355266.10 1696094.44 2579559.40 58.46

2017-18 4518487.30 4194483.11 2589946.16 1703158.82 2197801.83 52.40


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The result of the analysis conducted to reveal the correlation co-efficient between loans in demand (x) and loans

over-due (y) related with the loan provided by PACS to non-priority sector is provided below.

X Values Y Values X and Y Combined

∑ = 25692796.91 ∑ = 9857245.14 N = 11

Mean = 2335708.81 Mean = 896113.195 ∑ (X – Mx) (Y – My)

∑ (X – Mx) = SSx = 24671125827285.5 ∑ (Y – My)₂ = SSy = 7331044637440.33 = 11856128766676.8

R Calculation

R = ∑ ((X – My)(Y- Mx)) / √((SSx)(SSy))

r = 11856128766676.8 / √((24671125827285.5)(7331044637440.33)) = 0.8816

Results: r = .88 and that of p at 5% significant level = .00034

The result of p found to be significant at p < .05. This shows that negative correlation exist between variable x -

loans in demand and variable y- loans over-due.

Interpretation: The value of “r”, arrived through Pearson correlation is .88 and that of “p” is .00 proving that the

correlation between loans in demand and loans over-due is negative at 5% significance level and thus rejection of

null hypothesis is favored and hence we can conclude that any change caused in the loans in demand have no

similar effect on the loans over-due.

FINDINGS

1. Table 1 show that PACS in India have provided considerable volume of loans to agricultural activities during the

study period. The extent of growth in the volume of loans found to be 3 times higher at the end of 2017-18 when

compared with the volume of loans of the year 2007-08. During the first three years (2007-08 to 2009-10) there

seem an increase in the stressed asset (loan over-due) absolutely correlated with the increase of loans in demand

but after that from the year 2010-11 to 2017-18 increase in the volume of stressed asset has been moderately

correlated with the increase of loans in demand. This shows that the PACS have comparatively low volume of

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stressed asset on the loans provided for agricultural activities from the year 2010-11 onwards and it stood at 20.75

percent at the end of the study period (2017-18).

2. Tale 2 express the volume of loans provided by PACS to Non-agricultural activities (Priority sector 2). When

compared with loans provided to priority sector 1, it is equal to one-third at the opening year of study (2007-08)

but it gradually increases in the later years and reaches two-third of the volume at the end of 2017-18. In the year

2011-12 the issue of loan to non-agriculture activities is higher by 69.22 percent when compared with the earlier

year (2010-11) then thereafter the increase seem to be marginal till the end of 2017-18. Toward the loan over-due

to the loans in demand the correlation coefficient is moderately negative in most of the years and seems to be

volatile during the study years. The minimum and maximum percent of over-due to loans in demand stood at 3.67

and 27.59 respectively.

3. Table 3 depicts loans provided by PACS to other activities categorized under non-priority sector is

comparatively lower than the loan volume provided to Priority sector 1 and Priority sector 2. An increase in the

issue of loan was observed to the extent of 429.81% during the financial year 2011-12 when compared with the

earlier year 2010-11. Thereafter it gets moderated and remains the same till the end of the study period 2017-18.

Toward the loans over-due as a percentage to loans in demand it was 46.16 percent in the year 2007-08 then it get

reduced to 14.89 percent in the year 2013-14 but at the end of 2016-17 it sores up to 58.46 percent and remains at

52.40 percent at the end of 2017-18. also the correlation coefficient between the two variables is negative.

4. Thus it is obvious that throughout the study period the percentage of stressed asset found to be high with the

non-priority sector and it was medium with priority sector 1 and highly volatile with the priority sector 2. The

possible reason for such scenario could be that agriculture and small scale industries were less affected by the

global economic slow-down but not the business activities.

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CONCLUSION

In India the growth of agriculture, micro, small and medium scale industries found to be prosperous after

the arrival of base level co-operative societies like PACS that have been providing enormous support through

money supply for the production activity and marketing supports for the end products. The study was attempted to

know which among the three sectors have contributed more to the volume of stressed assets of Primary Agriculture

Co-operative Societies and the result shows that loans provided to non-priority sector involving other activities

have more stressed assets than other two sectors and being burden to the financial potency of PACS in India. The

result also shows that the volumes of stressed assets are not strongly correlated with the growth of its business

volume. The reasons behind this scenario are due to strategic measures taken by the management of PACS to avoid

the growing trend of stressed assets in the earlier years (2007-08 to 2010-11) when many countries in the world has

suffered due to depression in their economy caused by global financial crisis. Thus the results of the study shows

that stressed asset could not be avoidable in the operation of PACS but can be controlled with the support of its

member, Apex organizations and the Government.

REFERENCE

Manisha Raj, Aashita Jain, Shruyti Bansal, Tanya Verma. (2018). Non-performing Assets: A Comparative study of

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http://www.iosrjournals.org/iosr-jbm/papers/Vol20-issue9/Version-7/K2009077884.pdf

Nivedita. (2018). A Study on NPA of State Cooperative Banks in India. International Journal of Management

Information. 1-7 Retrieved from 1.ijmite - a study on npa of state cooperative banks in india 1 / 8

Poornima. S (2013). Non-Performing Assets and its Impact on Performance of Karnataka State Co-operative Apex

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Sudin Bag, Sajijul Islam. (2017). Non-Performing Assets a Biggest Challenge in Banking Sector – A Comparative

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National Federation of State Cooperative Banks Ltd.(2008). Performance of Primary Agricultural credit societies

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