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analysis on rbi growth

  1. 1. ANALYSIS ON RBI STUNNING GROWTH AFTER 1991 1.Post-Reform Period: A State Level AnalysisBiswa Swarup MisraThis paper examines whether allocative efficiency of the Indian Banking systemhas improved after the introduction of financial sector reforms in the early 1990s.Allocative efficiency has been studied for twenty three States of India. To get acomparative perspective, allocative efficiency has been estimated for two periods1981-1992 and 1993- 2001; broadly corresponding to the pre financial sectorreforms and the post reforms periods, respectively. The analysis carried underpanel cointegration framework reveals that overall allocative efficiency of thebanking system has almost doubled in the post reform period. This goes to suggestthe success of reforms in improving allocative efficiency of the banking system inIndia. Allocative efficiency at the State and sectoral level hasalso been estimated to get a deeper insight. While allocative efficiency of Banksfunds deployed in the services sector has improved that in the agriculture andindustry has deteriorated in the post reform period for the majority of the States.The study finds improvement in the overall allocative efficiency in the post reformperiod for the majority of the States. Further, the improved allocative efficiency ismore marked for the services sector than for industry across the States. 1.1 IntroductionTY.B.F.M Page 1
  2. 2. ANALYSIS ON RBI STUNNING GROWTH AFTER 1991 Enduring growth, in the context of a developing economy like India invariablyrequires that the economy be put to a trajectory of higher savings and ensuring,further, that the realised savings are chanelised into productive investment. In thisscheme of growth, the banking system has a dual role to play. The banking systemacts both as a mobiliser of savings as well as an allocator of credit for productionand investment. Effectiveness of the banking sector ’s contribution to the economicgrowth and development is broadly determined by its efficiency in the allocation ofthe mobilised savings amongst competing projects. Financial sector reforms wereinitiated in India in 1992-93 to promote a diversified, efficient and competitivefinancial system with the prime objective of improving the allocative efficiency ofavailable resources. Banking sector being the dominant segment in Indiasfinancial system, a number of measures specific to the banking system wereinitiated to improve its allocative efficiency. Freedom to price their products alongcommercial considerations, relaxation in various balance sheet restrictions in theform of statutory pre-emptions, exposing the banking sector to an increasedcompetition by allowing entry of new private sector banks and the introduction ofprudential norms relating to income recognition, asset classification and capitaladequacy were some of the ingredients of the banking sector reforms. Improvedallocative efficiency was sought to be achieved through operational flexibility,improved financial viability and institutional strengthening. The early initiatives inthe banking reforms were geared towards removing the functional and operationalconstraints impinging upon bank operations, and subsequently, providing themwith greater operational autonomy to take decision based on commercialconsiderations. With gradual relaxation of administered controls, banks andfinancial institutions were expected to evolve as truly commercial entities. Moreimportantly, the operation of banks under free interplay of market forces in aderegulated atmosphere was expected to lead to increased allocative efficiency ofscarce resources among competing sources of demand. Banking sector reformshave been in vogue for more than a decade in India. In this context, it would beappropriate to study whether the various reform measures have helped inimproving the allocative efficiency of the banking system.This study seeks toTY.B.F.M Page 2
  3. 3. ANALYSIS ON RBI STUNNING GROWTH AFTER 1991enquire whether the financial sector reforms in general, and banking sectorreforms in particular had any beneficial impact on the allocative efficiency of thebanking system. To get a comparative perspective, the allocative efficiency of thebanking system in the post banking sector reforms period has been compared andcontrasted with that of the pre-reform period. Allocative efficiency is measured forthe twenty-three States of India, individually and as well for all the States takentogether. In addition to the scenario at the aggregate level, the allocative efficiencyin the sectoral context has also been studied to get a deeper insight. Therest of thestudy is schematised as follows. Section I discusses the manner in which allocativeefficiency has been construed in this study. Section II reviews the literature onallocative efficiency. Some of the stylized facts regarding the credit deploymentpattern are discussed in Section III. The data and the empirical framework havebeen discussed in Section IV. The econometric findings are discussed in Section V.Finally, Section VI presents some concluding observations.Section IInterpreting Allocative Efficiency Efficiency of a financial system is generally described through four broadnomenclatures i.e., information arbitrage efficiency, fundamental valuationefficiency, full insurance efficiency and functional efficiency. The ensuingdiscussion in this paper would centre around the concepts of functional orallocative efficiency. Allocative efficiency can be judged either directly bymonitoring some proxy of allocative efficiency or indirectly by estimating thecontribution of a financial variable to economic growth. As far as direct measuresare concerned, the interest rate structure, cost of intermediation and net interestmargin (RBI, 2002a) as measures of bank efficiency are the oftenly-used criterionsto evaluate the allocative efficiency of the banking system. Allocative efficiency,however, can also be inferred indirectly by studying whether a banks resources areallocated to most productive uses or not. Most productive use, in turn, can bedefined in terms of the economic rate of return (ERR) of a project financed by thebanking system. Allocative efficiency would mean that projects with very high ERRare being financed by the banks. It would imply that the funds of the bankingsystem are so deployed as to maximise the rate of return (ERR) of the projectsfinanced by them. The ERR o f individual bank financed projects, however, isdifficult to quantify in practice. Akin to the interpretation of allocative efficiency ofa banks resources in terms of the ERR for individual projects, one canTY.B.F.M Page 3
  4. 4. ANALYSIS ON RBI STUNNING GROWTH AFTER 1991conceptualise the allocative efficiency of the entire banking system. In anaggregated sense, allocative efficiency would imply that maximum output isobtained from the deployment of banking systems resources. The concept ofmaximum output, however, is rather vague. As such, studying changes inallocative efficiency reflected in changes in output from a given pool of financialresources under two different time periods or circumstances is morecomprehendible than the concept of allocative efficiency per se. Allocative efficiencyof an individual bank involves some sort of constrained optimisation. When studiedin the cross section dimension, efficiency measurement generally involves use ofnonparametric frontier methodology (English, Grosskopfet al., 1993). In the panelcontext, however, the frontier approach does not capture the panel nature of thedata and treats each observation as a separate unit. So it is like a pooledregression, unlike random/ fixed effects models. There are recent developments toovercomethis problem, but it is still in a nascent stage. Consequently in a panelcontext, following RBI (2002a) allocative efficiency has been approximated by theelasticity of output with respect to credit in this studySection IIReview of Literature There has been a revival of finance and economic development linkage by theendogenous growth theory over the past decade. In the endogenous growth theoryframework, bank finance has a scope to influence economic growth by eitherincreasing the productivity of capital, lowering the intermediation cost, oraugmenting the savings rate. The role of financial institutions is to collect andanalyse information so as to channel investible funds into investment activitiesthat yield the highest returns [Greenwood and Jovanovic (1990)]. Though in a pureneo-classical framework, the financial system is irrelevant to economic growth, inpractice, an efficient financial system can simultaneously lower the cost of externalborrowing, raise the return to savers, and ensure that savings are allocated inpriority to projects that promise the highest returns ; all of which have thepotential for improving growth rates (RBI, 2001a). Commercial banks are the mainconduit for resource allocation in a bank dominated financial system like India.Commercial banks generally provide the working capital needs of business. Thereis no strict boundary of division, however, in the us age of the funds;oncedisbursed by financial institutions. Once allocated, a part ofthe bank funds mayvery well be put towards building up fixedcapital. This is because, a businessenterprise would be encouraged to undertake fixed capital formation, once it isassured of working capital needs. Though in India there have been institutionscreated specifically to meet the long term investment needs of business enterprise,the pervasive character of the scheduled commercialbanks had a greater role toplay in reaching to a wider mass of people through its vast branch-bankingnetwork. Pattrick (1966) provides a reference framework to study financialTY.B.F.M Page 4
  5. 5. ANALYSIS ON RBI STUNNING GROWTH AFTER 1991development by enunciating the demand-following approach and the supply-leading approach to financial development. Demand following is defined as asituation where financial development is an offshoot of the developments in thereal sector. In the case of supply leading, financial development precedes andstimulates the process of economic growth; the supply of financial services andinstruments create the demand for them. Patrick suggested that in the early stagesof economic development, a supply-leading relation is more likely since a directstimulus is needed to mobilise savings to finance investment for growth. At a laterstage, when the financial sector is more developed, the demand-following relationwill be more prevalent. Empirical studies such as Gupta (1984), Jung (1986) andSt. Hill (1992) are broadly suggestive of the pattern of financial developmentenvisaged by Patrick (1966). However, such a theoretical dichotomy betweendemand following and supply leading is difficult to defend in the context ofcontinuous interaction between the real and the financial sectors in practice.Regarding the impact of bank finance on growth, a number of empirical studiesdrive home the positive impact of bank credit on output. Employing GMM panelestimators on a panel data set of 74 countries and a cross sectional instrumentalvariable estimator for 71 countries, Levine et al(2000) find that the exogenouscomponent of financial intermediary development is positively associated witheconomic growth. Further, empirical studies by King and Levine (1993), Gregorioand Guidotti (1995) strongly borne out the positive effect of financial developmenton the long run growth of real per capita GDP. In the tradition of disentangling theimpact of bank credit on growth, Reserve Bank of India (2002a) explored therelative impact of finance in inducing output growth using panel regressiontechniques. Estimates of elasticity of output with respect to credit improved from0.30 during the period 1981-1991 to 0.35 during 1992- 2001 indicating asimprovement in the allocative efficiency of the banking system at the all India level(RBI 2002a). Sector-wise credit elasticities of output also indicate as improvementin the allocative efficiency for most of the sectors in the post reform periodcompared to the 1980s. However, no attempt has been made to study allocativeefficiency at the State level and across the sectors. The present study seeks to fillthis gap.Section IIICredit and Output in the Spatial Dimension: Some Stylised FactsThe relative growth rates in credit and output in the pre and post- reforms periodscan act as pointers to allocative efficiency. Aggregate credit has grown at a similarpace both in the pre reform and the postTable 1: Growth of Output and Credit(Per cent)TY.B.F.M Page 5
  6. 6. ANALYSIS ON RBI STUNNING GROWTH AFTER 1991 1981-1992 1993-2001 1981-2001VARIABLE Output Credit Output Credit Output CreditNSDP* 2.7 12.9 4.1 12.9 3.1 13.2Agriculture 1.6 11.1 0.7 9.6 1.5 9.1Industries 3.6 15.1 5.6 11.5 4.2 14.2Services 4.0 11.2 6.0 15.3 4.6 13.3* Net State Domestic ProductSource : Central Statistical Organisation and Reserve Bank of Indiareform period, aggregate output, however, grew at a distinctly higher rate in thepost reform phase. This indicates that at the aggregate level, there could be someimprovement in the allocative efficiency. However, one finds a mixed picture at thesectoral level. While both output and credit growth has decelerated for theagricultural sector, that for services sector has accelerated in the post reformphase as compared to the pre reform phase. For industry, however, highergrowth in output is witnessed in spite of deceleration in credit growth in the reformperiod. Focusing only on growth rates of output and credit to comment on theallocative efficiency may be quite misleading, if the share of different sectors inaggregate credit and output has not remained the same. In fact, the share in creditand output has increased for both industry and services sector and has declinedfor the agriculture sector in the post reform period (Table 2). Thus, a much deeperTable 2: Share in Output and Credit(Per cent) Average Share in the pre- Average Share in the postSector banking sector reform banking sector reform period period Output Credit Output CreditAgriculture 37 15.7 29 10Industry 23 43.5 25.5 48Services 40 40.8 45.5 42Source : Central Statistical Organisation and Reserve Bank of India.analysis is required to comment on the allocative efficiency in different sectors inthe post reform phase. At the State level, all the States under study can be broadlyclassified into four categories based on their shares in aggregatecredit and output.TY.B.F.M Page 6
  7. 7. ANALYSIS ON RBI STUNNING GROWTH AFTER 1991States with increased share in output and credit in the post reform phase ascompared to the pre reform period are the Group A States. States with increasedshare in output but reduced share in credit are the Group B States. States ithincreased share in credit and reduced share in output are Group C status, andStates with decline in their share in output and credit belong to the Group Dcategory. As can be seen from Table 3, the majority of the States (Thirteen) belongto Group D, which have suffered a decline in their share in aggregate output andcredit. In total, share of credit in the aggregate credit has gone down for 16 Statesand has improved for 7 States in the post reform phase. Considerable inequality isthus , seen among the States in terms of their share in overall credit. In such ascenario, it becomes interesting to enquire, whether, States receiving anincreasing share of the credit resource have been able to make the most of it. Inother words, whether, rising credit shares are also accompanied with improvedallocative efficiency. Further, if allocative efficiency of credit has improved even Table 3 : Changing Share of Different States in Output and Credit: A Comparison of Pre-Reform and Post-Reform Period States with States with States with States with decline increased share in increased share in increased share in in their share in output and credit output but reduced credit and reduced output and credit share in credit share in output (Group A) (Group B) (Group C) (Group D) Andhra Pradesh, Arunachal Pradesh, Kerala Assam, Bihar, Delhi, Tamil Nadu, Rajasthan and Himachal Pradesh, Maharastra, West Bengal Jammu & Kashmir, Karnataka Pondicherry, and Gujarat Manipur, MadhyaPradesh, Punjab, Orissa, Uttar Pradesh, Tripura, Meghalaya and Haryana Source : Central Statistical Organisation and Reserve Bank of India.TY.B.F.M Page 7
  8. 8. for States that have undergone a decline in their share of credit, it would have wellserved the purpose of reforms in the banking sector. Hence, it would be useful todecipher,if any pattern is emerging at the State level, when allocative efficiency ofthe banking system is seen in conjunction with their credit shares. Apart fromdifferences in their shares in output and credit, States have also exhibited a variedpattern in their growth of output and credit in the post reform period. Based ontheir growth in aggregate credit and output, there can be four categories of States.States with increased share in output and credit in the post reform phase ascompared to the pre reform period are the Group E States. States with highergrowth in output but lower growth in credit belong to Group F. Group G Statesare those with higher growth in credit and lower growth in output and States withreduced growth both in output and credit belong to the Group H category. Thedifferential growth pattern in credit and output can act as a guide to comment on allocative efficiencyacross States. Group F States that have shown an increased growth in output along with low creditgrowth in the post reform period are likely to exhibit higher allocative efficiency. On the other hand,Group G States with lower output and higher credit growth are clear candidates where allocativeefficiency would be deteriorating. However, it is tricky to judge about the allocative efficiency forStates belonging to the Group E and group H, that have experienced either increased orTable 4: Growth in Output and Credit of Different States:A Comparison of Pre – Reform and Post - Reform Period States with higher States with States with higher States with growth in output higher growth growth in credit and lower growth in and credit output but lower lower growth in output and credit growth in credit output (Group E) (Group F) (Group G) (Group H) Delhi, Karnataka, Andhra Pradesh, Punjab and Haryana Arunachal Pradesh, Kerala Maharastra, Gujarat, Assam, Bihar, Orissa and Rajasthan Himachal Pradesh, and Uttar Pradesh Jammu & Kashmir, MadhyaPradesh, Manipur,Meghalaya Pondicherry, Tamil Nadu, Tripura and West Bengal Source : Central Statistical Organisation and Reserve Bank of India.reduced growth both in credit and output. For Group E States, that have witnessed
  9. 9. higher growth both in credit and output, allocative efficiency would be guided bythe relative growth of output vis-a-visthat of credit. Similarly, for Group H Statesthat have experienced a lower growth of both credit and output in the post reformphase, allocative efficiency would depend on the relative decline in onevis-a-vis theother. The indications for allocative efficiency obtained from the above informalanalysis, however, need to be corroborated with more rigorous analysis to arrive atrobust inferences. The empirical framework to estimate the allocative efficiency isdiscussed in the next section.Section IVData and Empirical MethodologyThe study examines the allocative efficiency of the banking system for 23 States ofIndia. Allocative efficiency has been estimated separately for the two periods1981-1992 (first period) and 1993-2001(second period). The periods have been sochosen as torepresent the pre banking sector reforms and the post banking sectorreforms scenario s, respectively. The credit output dynamics has been studied forthree broad sectors of each State viz, agriculture, industry and services. Whilemeasuring output; the following classification has been used. Agriculture includesagriculture, forestry and fishing and logging. Industry includes mining, quarryingand manufacturing (registered and non-registered) and services include electricity,gas and water supply, transport, storage and communication, trade, hotels andrestaurants, banking and insurance, real estate, ownership of dwellings andbusiness services, public administration and other services. Income originatingfrom the States rather than income accruing to State concept has been used tomeasure output. The data on output has been taken from the information suppliedby the various States to the Central Statistical Organisation. SDP data at the1993-94 base has been used in the study. The data on credit refers to theoutstanding credit to different sectors from all scheduled commercial banks in aregion. The data for credit has been taken from the Basic Statistical Returnspublished by the Reserve Bank of India. The output variable is represented by logof per capita net State Domestic Product (LPNSDP) and the credit variable by thelog of per capita credit for the State (LPTCAS). Though certain new regions havebeen carved out from the existing ones in the year 2000, for analytical purposes,necessary adjustments have been made to make the output and credit figures forthe year 2001 comparable to that for the previous years. The choice of the regionsand the time period have been completely motivated by the availability andconsistency of the data. However, with inclusion of regions having share of lessthan one percent and as well having more than ten percent in the combined NSDPfor all the 25 regions, heterogeneity that prevails across the regions in India hasbeen captured considerably.
  10. 10. Empirical MethodologyTo estimate the credit elasticities of output, we have twelve data points for the prereform and nine data points in the post reform period. Use of time seriesestimation techniques, however, isprecluded given the small number ofobservations for estimation.However, taking advantage of the panel nature of thedata, one canuse panel data techniques. With panel data techniques, informationfrom the time-series dimension is combined with that obtained from the cross-sectional dimension, in the hope that inference about the existence of unit rootsand cointegration can be made more straightforward and precise. To ascertain theappropriate estimation technique , the variables have been first examined forstationarity in a panel context. If the variables are found to contain a unit root, thevariables are then examined for possible cointegration. In the event cointegrationbetween the variables, Fully Modified OLS (FMOLS) estimation technique is usedto obtain coefficient estimates. Specifically, the panel unit root tests developed byLevin, Lin and Chu and Im, Pesaran and Shin have been employed. Pedronismethod is used to test for panel cointegration. Fully modified OLS estimationtechnique given by Pedroni is used to derive the elasticities. The details of theempirical methodology are given in the Annex 6.Section VEmpirical ResultsThe results of the panel unit root tests for each of our variables are shown inAnnex 3. In no case, can we reject the null hypothesis that every country has aunit root for the series in log levels. Once ascertained that both the variables are I(1), we turn to the question of possible cointegration between log of per capita SDPand log of per capita credit. In the absence of cointegration, we can firstDifferentiate the data and then work with these transformed variables.However, inthe presence of cointegration, the first differences do not capture the long runrelationships in the data and the cointegration relationship must be taken intoaccount. Annex 4 depicts the evidence on the cointegration property between per-capita SDP
  11. 11. and per-capita credit for the Indian States. The panel cointegration tests suggestedby Pedroni (1999) have been applied. In general, the Pedroni (1999) tests turn outto be in favour of a cointegrating relation between the variables that are nonstationary. The agriculture sector has not been studied for cointegration as theoutput variable for agriculture is stationary and the credit variable is nonstationary. 2 Efficient FMOLS estimation technique is used to obtain the estimateof elasticity of output with respect to credit for each sub-period. The results aregiven in Annex 5. The changing allocative efficiency over time and across Statescan be seen from Chart 1. The results broadly indicate an improvement in theallocative efficiency for the majority of the States.3 For instance, for fifteen States,there was an improvement in allocative efficiency with respect to the StateDomestic Product. It may be noted that eight out of these fifteen States hadundergone a decline in their share in aggregate credit in the post reform period. Asindicated by the analysis of growth in terms of credit and output, the allocativeefficiency of banks funds has improved for all States that had higher output andlower credit growth in the post reform phase.For all States taken together,allocative efficiency has improved from 0.18 to 0.34 as indicated by the pooledestimates. An overview of the results in terms of States and sectors that havewitnessed an improvement in allocative efficiency of bank funds is given in Table 5.At the sectoral level, an improvement in allocative efficiency of bank funds in theservices sector is witnessed for 18 States and in the industrial sector for 12States (Table 5).
  12. 12. Table 5: Allocative Efficiency Across Sectors and StatesRESERVE BANK OF INDIA in the Post reform periodOCCASIONAL PAPERS Sectors State Industry Services Overall5 ANDHRAPRADESH Ö Ö Ö ARUNACHAL PRADESH Ö ASSAM Ö Ö BIHAR Ö Ö DELHI GUJARAT Ö Ö HARYANA Ö HIMACHAL PRADESH Ö Ö Ö JAMMU & KASHMIR Ö Ö KARNATAKA Ö Ö Ö KERALA Ö Ö Ö MADHYAPRADESH Ö Ö MAHARASHTRA Ö Ö Ö MANIPUR MEGHALAYA Ö Ö ORISSA Ö PONDICHERRY Ö Ö Ö PUNJAB Ö Ö RAJASTHAN Ö TAMIL NADU Ö Ö Ö TRIPURA Ö Ö UTTARPRADESH Ö WEST BENGAL Ö Ö Ö Note :Ö indicates improvement in allocative efficiency in the post reform phase as compared to the pre reform period. Blank cells indicate deterioration in allocative efficiency in the post reform period.
  13. 13. Section VIConclusionOne of the main aims of financial sector reforms in the post 1990s was to improvethe allocative efficiency of the financial system. The efficiency improvement of thebanking system has a bearing on the overall efficiency of the Indian financialsystem as the banking sector has a dominant role to play in the entire financialedifice. This study attempted to enquire into the allocative efficiency of the Indianbanking system on a wider canvass encompassing twenty three States and acrossthe agriculture, industry and services sectors. Th e finding of the study broadlycorroborates that there hasbeen an improvement in allocative efficiency for allStates taketogether as far as elasticity of total output to total credit is concerned.At the sectoral level, however, the picture is mixed. For the services sector therehas been a distinct improvement in allocative efficiency of credit in the post reformperiod. The agriculture and industry sector, however, have witnessed a decline inthe allocative efficiency of credit in the same period. At theState level, majority ofthe States witnessed an improvement in the overall allocative efficiency in the postreform period. The improved allocative efficiency is more marked for the servicessector than for industry across the States. Notes 1 Given that credit – output relations involve relatively short time series dimen- sions, and the well known low power of conventional unit root tests when applied to a single time series, there may be considerable potential for tests that can be employed in an environment where the time series may be of limited length, but very similar data may be available across a cross–section of countries, regions, firms, or industries. 2 Both fixed and random effects estimation of elasticity of output with respect to credit shows deterioration in allocative efficiency in the post reform period for the agriculture sector. 3 Allocative efficiency as defined by elasticity of SDP with respect to total credit. The individual and pooled FMOLS estimates are given in Annex-5. 4 Manipur is an exception
  14. 14. 5 Overall refers to the State Domestic Product
  15. 15. State Agriculture Industry Services NSDP 1981 1993 1981 1981 1993 1981 1981 1993 1981 1981 1993 1981 -1992 -2001 -2001 -1992 -2001 -2001 -1992 -2001 -2001 -1992 -2001 -2001ANDHRA 0.1 1.5 0.7 6.1 6.2 6.3 6.0 5.8 5.4 3.6 4.5 3.8PRADESHARUNACHAL 5.1 -3.5 2.4 5.1 0.9 5.3 6.0 6.8 6.6 5.4 1.0 4.4PRADESHASSAM 0.1 -0.3 -0.1 1.4 2.0 0.5 2.4 1.4 2.3 1.2 0.8 1.0BIHAR 0.2 -0.4 -1.3 4.3 3.8 2.1 3.2 3.6 2.7 2.2 2.1 0.9DELHI -0.3 -10.8 -6.8 4.1 -0.3 2.7 3.4 5.9 4.5 3.5 4.1 3.8GUJARAT -2.8 -3.1 -0.2 4.8 4.3 5.9 5.0 6.8 5.5 2.4 3.7 4.0HARYANA 2.1 -0.3 1.3 6.4 4.1 4.3 5.4 7.2 5.1 4.0 3.5 3.3HIMACHAL 0.3 -1.8 -0.2 5.4 7.2 6.5 5.0 5.1 4.1 3.0 3.6 3.1PRADESHJAMMU & -2.6 1.2 -0.8 2.4 -2.9 0.2 1.1 3.7 2.2 -0.3 1.8 0.7KASHMIRKARNATAKA 0.7 3.0 1.9 4.9 5.8 4.8 5.5 9.0 6.4 3.4 6.1 4.3KERALA 1.2 0.4 1.8 1.9 4.1 4.3 2.8 6.8 4.8 2.0 4.3 3.7MADHYA -0.4 -1.8 0.3 2.7 7.4 6.8 4.1 4.0 3.5 1.6 2.1 2.1PRADESHMAHARA- 0.7 -0.9 1.7 3.9 4.4 4.3 5.0 5.9 6.2 3.6 4.2 4.6SHTRAMANIPUR -0.4 1.9 0.2 4.0 8.1 3.0 4.1 5.3 4.2 2.2 4.9 2.7MEGHALAYA -1.6 2.7 -1.1 2.6 6.7 4.0 4.9 2.8 3.6 2.3 3.4 2.2ORISSA -0.8 -0.9 -1.4 5.1 -1.9 4.1 4.3 5.9 4.4 2.0 1.6 1.4PONDI- -1.8 -2.7 -2.6 1.0 21.6 3.2 2.2 10.0 5.2 0.9 12.3 2.8CHERRYPUNJAB 3.1 0.2 1.9 5.1 4.9 5.0 2.5 4.9 2.8 3.3 2.8 2.9RAJASTHAN 1.9 0.0 1.7 4.3 7.0 5.6 6.2 5.8 5.4 3.7 4.1 3.8TAMILNADU 2.6 0.8 2.7 3.2 4.4 4.1 5.1 8.2 6.2 3.9 5.3 4.7TRIPURA -0.1 0.4 -0.6 -1.2 12.3 4.2 6.2 5.0 5.9 2.6 4.4 3.1UTTAR 0.5 0.0 0.3 5.2 2.5 3.3 3.9 2.9 3.0 2.5 1.7 1.9PRADESHWEST 3.2 2.1 2.9 1.3 4.4 2.6 2.7 8.3 4.6 2.4 5.5 3.5BENGAL1 Compound annual growth rates.
  16. 16. Annex 2: Growth of Sector-wise Credit2 (Per cent) State Agriculture Industry Services TotalCredit 1981 1993 1981 1981 1993 1981 1981 1993 1981 1981 1993 1981 -1992 -2001 -2001 -1992 -2001 -2001 -1992 -2001 -2001 -1992 -2001 -2001 ANDHRA 14.0 11.1 11.0 17.1 12.3 14.9 19.7 17.2 17.4 17.0 14.1 14.8 PRADESH ARUNACHAL 37.3 7.7 19.6 36.4 -7.2 11.1 23.8 20.3 18.5 32.3 5.7 15.2 PRADESH ASSAM 15.3 -1.9 7.2 19.4 1.7 8.9 17.8 13.7 13.2 18.0 6.8 10.6 BIHAR 14.8 0.3 10.0 11.0 1.6 8.7 20.2 8.4 14.8 15.1 4.9 11.5 DELHI -5.9 19.4 9.1 14.1 10.3 16.2 4.3 15.1 11.0 7.9 12.3 13.2 GUJARAT 14.3 6.7 11.1 15.1 15.4 14.0 15.3 16.0 15.6 15.0 14.5 14.0 HARYANA 11.4 8.5 7.6 12.8 15.8 12.4 13.2 13.3 12.1 12.4 13.5 11.0 HIMACHAL 13.4 7.1 7.6 18.0 12.2 12.4 16.8 12.2 13.3 16.5 11.6 12.1 PRADESH JAMMU & KASHMIR 13.0 8.6 7.3 16.6 4.8 8.9 16.1 17.9 14.8 15.9 14.2 12.6 KARNATAKA 16.1 12.2 12.1 14.8 15.1 14.0 17.2 19.5 16.0 15.9 16.3 14.3 KERALA 13.6 12.3 11.1 11.8 11.1 11.0 14.9 17.6 15.3 13.5 14.9 13.2 MADHYA 17.1 10.2 12.1 18.7 14.6 14.6 19.2 10.7 15.0 18.5 12.1 14.1 PRADESH MAHARA 12.0 12.8 10.6 14.1 16.6 15.5 13.1 17.6 15.4 13.4 16.9 15.1 -SHTRA MANIPUR 23.3 7.9 13.0 38.8 1.3 19.9 21.2 12.8 14.1 25.3 8.6 15.3 MEGHALAYA 27.2 -3.7 10.1 36.0 5.7 16.0 17.1 9.5 14.3 23.3 6.3 13.7 ORISSA 14.0 8.1 9.2 19.8 7.9 12.2 20.1 14.1 14.9 18.5 11.0 12.7 PONDI 7.8 7.5 6.9 15.4 7.1 12.2 16.2 15.1 15.8 14.0 10.6 12.5 -CHERRY PUNJAB 7.9 11.0 7.0 15.9 14.2 13.4 10.1 14.7 12.7 11.3 13.8 11.3 RAJASTHAN 14.2 12.3 11.1 12.9 12.7 13.0 14.6 16.1 14.1 13.8 13.9 12.9 TAMILNADU 16.1 8.4 12.2 16.0 16.1 15.5 17.9 17.6 17.8 16.6 15.8 15.9 TRIPURA 20.4 1.7 10.1 26.9 -2.3 10.9 21.8 4.6 12.5 22.5 2.8 11.6 UTTAR 13.6 9.0 10.8 13.8 8.5 11.3 16.7 11.3 13.2 14.8 9.8 11.9 PRADESH WEST 14.4 3.9 8.1 11.8 8.7 10.9 16.7 13.1 14.4 13.4 10.0 11.8 BENGAL2 Compound annual growth rates.
  17. 17. Annex 3 : Panel Unit Root Tests 1981-1992 1993-2001Variable Levin- Levin- Levin- IPS Levin- Levin- Levin- IPS Lin rho Lin t-rho Lin ADF Lin rho Lin t-rho Lin ADF -stat -stat ADF-stat -stat -stat -stat ADF-stat -statLPAGRI -7.80 -4.52 -2.58 -6.13 -6.67 -4.56 -3.73 -6.31LPINDS 1.15 2.27 2.37 2.45 0.47 0.73 0.73 -0.42LPSERV 2.45 3.36 3.53 4.54 2.49 3.46 3.25 2.85LPNSDP 1.75 2.91 3.58 3.99 1.58 2.18 2.51 2.29LPACS 0.82 0.68 1.33 1.46 1.67 2.82 2.63 2.36LPICS 2.09 2.40 1.98 0.74 1.49 2.57 1.87 0.17LPSCS 1.08 1.20 2.81 5.31 2.36 3.49 3.22 3.88LPTCAS 1.64 1.73 2.58 2.20 2.47 3.53 3.33 2.54Notes : a. The critical values are from Levin and Lin (1992). b. IPS indicates the Im et al. (1997) test. The critical values are taken from Table 4. c. Unit root tests include a constant and heterogeneous time trend in the data. Annex 4 : Panel Cointegration Tests 1981-1992 1993-2001Statistics LPINDS LPSERV LPNSDP LPINDS LPSERV LPNSDP and and and and and and LPICS LPSCS LPTCAS LPICS LPSCS LPTCASPanel v-statistics 4.52 2.49 2.97 1.02 2.80 1.79Panel rho-statistics -1.96 -1.71 -1.51 -0.39 -0.84 -0.80Panel pp-statistics -3.57 -2.96 -2.96 -3.83 -2.89 -3.65Panel adf-statistics -4.45 -3.47 -1.99 -2.03 -3.32 -2.48Group rho-statistics -0.34 0.21 0.0006 1.01 1.35 0.47Group pp-statistics -4.31 -3.02 -3.20 -6.66 -3.56 -6.44Group adf-statistics -5.75 -5.09 -3.75 -23.83 -15.36 -22.65Notes : The critical values for the panel cointegration tests are base on Pedroni (2001a). LPAGRI = Log of per capita agricultural output LPINDS = Log of per capita industrial output LPSERV = Log of per capita services sector output LPNSDP = Log of per capita net State domestic product
  18. 18. LPACS = Log of per capita agricultural creditLPICS = Log of per capita industrial creditLPSCS = Log of per capita services sector creditLPTCAS = Log of per capita total credit outstanding for all sectors of the State
  19. 19. Annex 5 : Individual and Pooled FMOLS ResultsStates 1981-1992 1993-2001 1981-1992 1993-2001 1981-1992 1993-2001 LPNSDP LPNSDP LPINDS LPINDS LPSERV LPSERVANDHRAPRADESH 0.22 0.31 0.41 0.44 0.32 0.35 (-12.95) (-33.96) (-10.60) (-27.86) (-13.61) (-45.14)ARUNACHAL PRADESH 0.17 0.06 0.15 0.1 0.34 0.38 (-42.90) (-26.11) (-31.56) (-6.07) (-19.96) (-8.08)ASSAM 0.05 0.11 -0.03 0.25 0.14 0.09 (-78.06) (-48.25) (-86.56) (-11.31) (-37.71) (-52.65)BIHAR 0.14 0.19 0.34 0.05 0.17 0.37 (-26.38) (-8.86) (-12.21) (-6.08) (-153.24) (-8.82)DELHI 0.42 0.33 0.32 -0.09 0.55 0.36 (-10.74) (-11.09) (-32.89) (-16.46) (-2.82) (-9.69)GUJARAT 0.15 0.21 0.28 0.27 0.34 0.47 (-29.75) (-13.17) (-15.23) (-24.29) (-27.64) (-14.50)HARYANA 0.37 0.26 0.52 0.25 0.43 0.52 (-11.96) (-85.73) (-9.53) (-235.33) (-8.25) (-31.67)HIMACHAL PRADESH 0.22 0.29 0.03 0.47 0.34 0.46 (-12.84) (-41.42) (-14.24) (-7.34) (-11.42) (-18.74)JAMMU & KASHMIR -0.02 0.1 -0.19 -0.24 0.08 0.2 (-38.75) (-61.07) (-13.13) (-13.86) (-67.00) (-51.85)KARNATAKA 0.21 0.39 0.02 0.4 0.34 0.47 (-25.53) (-13.58) (-43.88) (-12.76) (-24.92) (-15.15)KERALA 0.15 0.28 0.09 0.3 0.2 0.4 (-15.67) (-49.23) (-13.33) (-36.07) (-31.35) (-25.86)MAHARASHTRA 0.08 0.15 -0.05 0.29 0.23 0.38 (-33.23) (-36.83) (-47.65) (-27.18) (-47.62) (-18.57)MANIPUR 0.31 0.24 0.03 0.25 0.4 0.35 (-14.19) (-74.81) (-9.61) (-55.47) (-5.83) (-24.06)MEGHALAYA 0.09 0.48 -0.01 0.02 0.2 0.44 (-97.31) (-2.92) (-129.84) (-1.38) (-47.02) (-7.77)MADHYAPRADESH 0.08 0.2 -0.06 0.14 0.29 0.24 (-22.14) (-6.10) (-75.61) (-5.11) (-10.05) (-9.58)ORISSA 0.14 0.11 0 -0.59 0.25 0.43 (-55.82) (-58.34) (-16.08) (-9.70) (-76.56) (-60.82)PONDICHERRY 0.06 1.09 -0.12 2.19 0.14 0.66 (-57.65) -0.48 (-13.49) -1.18 (-133.73) (-8.45)PUNJAB 0.29 0.22 0.16 0.34 0.27 0.37 (-11.00) (-86.15) (-7.50) (-17.70) (-18.51) (-16.08)RAJASTHAN 0.32 0.27 0.14 0.53 0.46 0.37 (-12.24) (-11.18) (-6.93) (-13.45) (-8.75) (-16.27)TAMILNADU 0.25 0.33 0.16 0.24 0.32 0.5 (52.30) (-63.08) (-23.21) (-28.70) (-65.09) (-15.10)TRIPURA 0.11 1.46 0 -2.31 0.3 0.97
  20. 20. (-22.23) -1.91 (-39.83) (-3.05) (-19.08) (-0.64) UTTARPRADESH 0.19 0.17 0.05 0.29 0.27 0.28 (-63.23) (-38.75) (-51.47) (-11.36) (-30.85) (-64.79) WESTBENGAL 0.21 0.5 0.21 0.49 0.17 0.63 (-30.22) (-29.80) (-16.57) (-29.83) (-70.59) (-9.82) POOLED 0.18 0.34 0.03 0.18 0.28 0.42 (-162.03) (-166.41) (-156.24) (-124.94) (-194.26) (-111.37) Note : Figures are estimated elasticities of output with respect to credit of the respective sectors. Figuresinparenthesisindicatet-value Annex 6Panel Unit Root, Panel Cointegration and Fully Modified OLS EstimationPanel unit root Tests There are several techniques, which can be used to test for a unit root inpanel data. Specifically, we are interested to test for non- stationarity against thealternative that the variable is trend stationary. Levin, Lin and Chu (LLC) TestOne of the first unit root tests to be developed for panel data is that of Levin andLin, as originally circulated in working paper form in 1992 and 1993. Their workwas finally published, with Chu as a coauthor, in 2002. Their test is based onanalysis of the equation:∆yy na l y t na tt na l yi tiii i t,, 1i , 1,2,.. ,N t 1,2,... .1 , 2 , ,This model allows for two-way fixed effects (a and q) and unit-specific time trends. The unit-specific fixed effects are an important source ofheterogeneity, since the coefficient of the lagged dependent variable is restricted tobe homogeneous across all units of the panel. The test involves the null hypothesisH0: ri= 0 for all I against the alternativeHA: ri =r< 0 for all I with auxiliary assumptions under the null also being requiredabout the coefficients relating to the deterministic components. Like most of theunit root tests in the literature, LLC assume that the individual processes arecross- sectionally independent. Given this assumption, they derive conditions andcorrection factors under which the pooled OLS estimate will have a standardnormal distribution under the null hypothesis. Their work focuses on theasymptotic distributions of this pooled panel estimate of r under differentassumptions on the existence of fixed effects and homogeneous time trends. TheLLC test may be viewed as a pooled Dickey-Fuller (or ADF) test, potentially withdiffering lag lengths across the units of the panel.The Im-Pesaran-Shin TestThe Im-Pesaran-Shin (IPS, 1997) test extends the LLC framework to allow forheterogeneity in the value of riunder the alternative hypothesis. Given the sameequation:∆q ua t i o n: yi ti t iitt tt yi i 1,2,.. ,N t 1,2,... .1 , 2 , ,The null andalternative hypotheses are defined as:H0: : t e ∀i0 I and H AA:ii N0,i , 1,2,...,1;; ii , 0,i , N11 1, N11 , 2,...N Thus under the null hypothesis, all series
  21. 21. in the panel are nonstationary processes; under the alternative, a fraction of theseries in the panel are assumed to be stationary. This is in contrast to the LLCtest, which presumes that all series are stationary under the alternativehypothesis. The errors are assumed to be serially autocorrelated, with differentserial correlation properties and differing variances across units. IPS propose theuse of a group- mean Lagrange multiplier statistic to test the null hypothesis. TheADF regressions are computed for each unit, and a standardized statisticcomputed as the average of the LM tests for each equation. Adjustment factors(available in their paper) are used to derive a test statistic that is distributedstandard Normal under the null hypothesis. IPS also propose the use of a group-mean t-bar statistic, where the t statistics from each ADF test are averaged acrossthe panel; again, adjustment factors are needed to translate the distribution of t-bar into a standard Normal variate under the null hypothesis. IPS demonstratesthat their test has better finite sample performance than that of LLC. The test isbased on the average of the augmented Dickey-Fuller (ADF) test statisticscalculated independently for each member of the panel, with appropriate lags toadjust for auto- correlation. The adjusted test statistics, [adjusted using the tablesin Im, Pesaran, and Shin (1995)] are distributed as N(0,1) under the null of a unitroot and large negative values lead to the rejection of a unit root in favor ofstationarity.Panel Cointegration Tests and Efficient Estimation Cointegration analysis is carried out using a panel econometric approach.Since the time series dimension is enhanced by the cross section, the analysisrelies on a broader information set. Hence, panel tests have greater power thanindividual tests, and more reliable findings can be obtained. We use Pedronis(1995, 1997) panel cointegration technique, which allows for heterogeneouscointegrating vectors. The panel cointegration tests suggested by Pedroni (1999)extend the residual based Engle and Granger (1987) cointegration strategy. First,the cointegration equation is estimated separately for each panel member. Second,the residuals are examined with respect to the unit root feature. If the null of no-cointegration is rejected, the long run equilibrium exists, but the cointegrationvector may be different for each cross section. Also, deterministic components areallowed to be individual specific. To test for cointegration, the residuals are pooledeither along the within or the between dimension of the panel, giving rise to thepanel and group mean statistics (Pedroni, 1999). In the former, the statistics areconstructed by summing both numerator and denominator terms over theindividuals separately; while in the latter, the numerator is divided by thedenominator prior to the summation. Consequently, in the case of the panelstatistics the autoregressive parameter is restricted to be the same for all crosssections. If the null is rejected, the variables in question are cointegrated for all
  22. 22. panel members. In the group statistics, the autoregressive parameter is allowed tovary over the cross section,as the statistics amounts to the average of individualstatistics. If the null is rejected, cointegration holds at least for one individual.Therefore, group tests offer an additional source of heterogeneity among the panelmembers. Both panel and group statistics are based on augmented Dickey Fuller(ADF) and Phillips- Perron (PP) method. Pedroni (1999) suggests 4 panel and 3group s tatistics. Under appropriate standardization, each statistic is distributedas standard normal, when both the cross section and the time series dimensionbecome large. The asymptotic distributions can be stated in the form Z Z*−e c N(1)v where Z* is the panel or group statistic, respectively, N the crosssection dimension m and n and arise from of the moments of the underlyingBrownian motion functionals. They depend on the number of regressors andwhether or not constants or trends are included in the co-integration regressions.Estimates for m and n are based on stochastic simulations and are reported inPedroni (1999). Thus, to test the null of no co-integration, one simply computes thevalue of the statistic so that it is in the form of (1) above and compares these to theappropriate tails of the normal distribution. Under the alternative hypothesis, thepanel variance statistic diverges to positive infinity, and consequently the right tailof the normal distribution is used to reject the null hypothesis. Consequently, forthe panel variance statistic, large positive values imply that the null of no co-integration is rejected. For each of the other six test statistics, these diverge tonegative infinity under the alternative hypothesis, and consequently the left tail ofthe normal distribution is used to reject the null hypothesis. Thus, for any of theselatter tests, large negative values imply that the null of no co- integration isrejected. The intuition behind the test is that using the average of the overall teststatistic allows more ease in interpretation: rejection of the null hypothesis meansthat enough of the individual cross sections have statistics far away from themeans predicted by theory were they to be generated under the null.Panel FMOLSIn the event the variables are co-integrated, to get appropriate estimates of the co-integration relationship, efficient estimation techniques are employed. Theappropriate estimation method is so designed that the problems arising from theendogeneity of the regressors and serial correlation in the error term are avoided.Due to the corrections, the estimators are asymptotically unbiased. Especially,fully modified OLS (FMOLS) is applied. In the modelyitxy i t x y iiitxx i uiti t xx ,, t (uu )(2) (2)itit −1ititit,itthe asymptotic distribution of theOLS estimator depends on the long run covariance matrix of the residual processw. This matrix is given by Ω. lim1l i T∑E ϖ T∑i mϖϖi m1 hi s ′mϖϖ,ϖu,, i (3)(3)iT→∞) TtT 1itti 1itiiϖi u iϖu u ,ifor the i-th panel member,where1TT he r e 1 22 h∑r e ∑ϖϖϖϖ′∑r u iilimTEititT ,, E2,2 T →∞→t1u iu ,ii (4)1 T−1T∑4 ∑1 t ,u iu i iiT→∞TkT k t1E wwitit k′t k ui,, i ,i(4)denote the matrices of
  23. 23. contemporaneous correlation coefficients and theauto-covariance, respectively,where the latter are weighted according to the Newey and West (1994) proposal.For convenience, the matrix F o r c o ,, ui,, i r ∑o nv e n ii∞∑r E wwE(5)(5)ii ,, ( ,ij 0ii 0 is defined. The endogeneity correction is achieved by the transformation** −0 ϖˆuiˆyityit,ϖ, , −,1i∆xit(6) and the fully modified estimator is ˆ( 6 * 6 ) −1*ˆ1 *iiiX yii−i TT u)((7) (7)ˆ( where,*wuu hˆ ˆ u − e r ϖϖϖˆ ˆˆ −i1ˆ1 ,providesthe autocorelation correction, The estimates needed for thetransformations are based on OLS residuals obtained in a preliminary step. Thepanel FMOLS estimator is just the average of the individuals parameters.Narasimham Committee Report - Some Further Ramifications andSuggestionsJayanth R. Varma, V. Raghunathan, A.Korwar and M.C. BhattWorking Paper No. 1009February 1992Indian Institute of Management, Ahmedabad 2. Narasimham Committee ReportSome Further Ramifications and SuggestionsAbstractThis paper while agreeing with the general thrust of the Narasimham CommitteeReport, calls attention to some logical corollaries of the Report and analyses somepossible fallout from implementing the Report. We agree with the view that controlof banking system should be under an autonomous body supervised by the RBI.
  24. 24. However at the level of individual banks, closer scrutiny of lending proceduresmay be called for than is envisaged in the Report. In a freely functioning capitalmarket the potential of government bonds is enormous, but this necessitatesrestructuring of the government bond market. The government bonds may thenalso be used as suitable hedging mechanisms by introducing options and futurestrading. We recommend freeing up the operation of pension and provident fund toenable at least partial investment of such funds in risky securities. In thecorporate sector, we believe that the current 2:1 debt equity norm is too high andnot sustainable in the long term. We envisage that high debt levels and higherinterest rates, combined with higher business risk may result in greater incidenceof corporate sickness. This may call for various schemes for retrenched workersand amendment to land laws for easy exit of companies. On account ofinterdependencies across different policies, any sequencing of their implementationmay be highly problematic. We therefore suggest a near simultaneity in theimplementation of various reforms in order to build up a momentum which wouldbe irreversible if people are to have confidence that the reforms will endure, and ifwe are to retain our credibility with international financial institutions.Narasimham Committee ReportSome Further Ramifications and SuggestionsThe Narasimham Committee Report is without doubt a major path- breaking pieceof work and deserves the support of all who yearn for a more rational and effectivebanking system in this country. We strongly agree with the general thrust of thereport and enthusiastically endorse its major recommendations. In particular, wewelcome its proposals to delink the entire issue of concessional credit from theissue of banking operations, to reduce the SLR limits, to strengthen the capitalbase of banks, and to bring about a general freeing of interest rates. We alsostrongly endorse the call for greater transparency in banking reports as well as theproposal to strengthen the regulatory role of SEBI while abolishing the office of theCCI. The concept of ARF for bad debts and the idea of having special tribunals toexpedite recovery of dues are also very practical and eminently implementable. Theintent of this note is not to comment paragraph by paragraph on the CommitteeReport or to attempt to pick holes in what is a welcome as well as a comprehensiveset of recommendations to reform the banking system. Instead, what we shallattempt to do here is to call attention to some natural corollaries of the Report, andto speculate about some possible fall-out from implementing the Report which theGovernment and the financial system in general may want to look out for. The noteis structured in five parts: in the first, we shall examine the implications of theReport for the government bond markets. This will be followed by a look at theimplications for the corporate sector. After this section, a brief look at theimplications for the rural sector will be followed by some speculations regardingthe financial auditing and consulting sector. Finally, a look at the interlinkagesbetween the financial sector and the real economy, and we conclude with a word
  25. 25. about the pace of reform.I. Restructuring the Government Bond MarketToday, the government bond market is exclusively the province of banks andbanking institutions. From the point of view of the banks, the chief function ofgovernment bonds is to satisfy the SLR requirements. One likely consequence ofthe proposed reduction in SLR limits from 38.5% to 25% is that government bondswill increasingly be subject to some of the market pressures other bondsexperience in financial markets. The government bond market is likely to beincreasingly integrated into the mainstream capital market with investorscomparing the yields on government bonds with yields available on comparablefinancial instruments elsewhere. A considerable widening and deepening of thegovernment bond market will be necessary to handle these changes. Currently,while government bonds are listed on the stock exchanges, they are not activelytraded. Trading is essentially restricted to the interbank market. The potential roleof government bonds in a freely functioning capital market is enormous - one hasonly to observe that the U.S. treasury bill and bond market is the largest in theworld, to recognize this fact. Because of the virtual absence of default risk ongovernment debts, government bonds have the potential to offer investors ariskless investment with which to manage overall portfolio risk. Private corporatefunds, both large and small, would be attracted to such an investment as a placeto park cash without undue risk. Mutual funds could use the government bondmarkets to manage the risk of their overall portfolios on a day to day basis -switching in and out of government bonds depending on their perception of thelikely course of the stock markets. Government bonds are also an excellent vehicleto manage inflation risk - in a freely functioning bond market, yields ongovernment bonds would have high correlations with expected inflation rates.Forecasting of inflation rates would also become possible as the government bondmarket develops and matures. Various organizations including corporations, tradeassociations and trade unions could use such forecasts in pricing and bargaining.Individuals would be able to use government bonds as part of their investmentstrategy, especially for trusts and legacies for their children. To cater to suchdemands, a number of bond trading firms would probably arise, specializing indealing in government bonds. Operating on thin, almost invisible margins, suchfirms would help keep the government bond markets efficient in the informationalefficiency sense, rather like Salomon Brothers, for instance, in the U.S. Publicsector enterprises and government agencies may well find that an active, efficientbond market which attracts private capital could be a major source of much-needed funds.SLRIt is clear that the SLR limits are intended mainly to ensure that banks maintainadequate liquidity to discharge their obligations. It is difficult to see how long-termbonds - government or otherwise - could qualify as liquid assets. At the same time,there are a number of other financial assets which could qualify - short-termcorporate debt instruments like commercial paper of the highest quality, for
  26. 26. instance. There is a need to rethink the meaning of liquidity, keeping foremost thebasic intent of the SLR. This would be in line with the spirit of the NarasimhamCommittee Report - to return to sound banking practices. It would, in any case, benecessitated by the expected integration of the government bond market with therest of the financial markets.Trust SecuritiesBringing government bonds into the mainstream of financial markets would alsomean that they should compete openly with other high-grade securities forinclusion in the portfolios of provident funds and pension funds. These, andsimilar bodies, are currently required to invest only in approved Trust securitieswhich are essentially government bonds. We believe that non-governmentsecurities of comparable risk should be permitted as investment vehicles. In afurther move to free up the operation of pension and provident funds, employees -the ultimate investors - should be permitted the option of choosing to have theirfunds deployed at least partly in equity securities. We believe such liberalisation ofthe investment activities of pension and provident funds will fuel an unprecedentedboom in such funds. Strong funds of this kind can help mobilize savings just asmutual funds have in the past few years. Strong pension funds can serve twopurposes - they can act as major sources of funding, both loans and equity, forcompanies in both the private and public sector. This would help alleviate some ofthe financing crunch so many companies are facing today. Secondly, well-managedpension funds can provide the banking system some healthy competition, whichwould force them to strive for greater efficiency and productivity.Interest Rate HedgingWith interest rates deregulated, there will be a need to develop suitable hedgingmechanisms in the form of futures and options. In the long run, these mechanismsmay well be needed for all securities. However, since government bonds would beinfluenced by a relatively small number of factors such as inflation and the termstructure of interest rates, they would provide an ideal vehicle to experiment andlearn how to operate options and futures markets in the Indian context. We believegovernment bonds should be the first choice of securities exchange boardscontemplating introducing options and futures trading.II. The Corporate SectorIf we compare corporate debt levels in India with those elsewhere, we would findthat Indian companies operate with an astoundingly high degree of borrowing.Debt levels of 2:1 and 3:1 are commonplace in India - whereas they would beunthinkable in most other financial markets of the world. There are many aspectsto this issue - a high debt level permits control of the company with a very smallequity investment. The results of such control without commitment are not alwayshealthy for the company, to say the least. When major shareholders strip acompany of its productive earning power and leave a shell behind, at least part ofthe blame must be ascribed to a system which allows such extraordinary levels of
  27. 27. debt financing. In economic downturns and recessions - inevitable in any economy- high levels of debt will often cause a company to fall when it should only stumble.Why have such high debt levels been permitted? There are probably mean reasons,rooted in the history of the growth pains of a developing economy. One such reasonwould be that government controlled financial institutions have often seen it astheir duty to provide funds to an approved company - namely, any companywhich has been able to secure a license. Even companies implementing the riskiestof projects have been able to find debt financing, often at concessional rates, oncethey have been able to get a license for the project. With the reform of the financialsystem proposed by the Narasimham Committee, financial institutions will begin tomove away from such concerns with developmental or societal objectives. Oneresult will be that corporations will be forced to reduce their reliance on debtfinancing. There are at least three other reasons why the historical high debt levelsof corporations cannot be sustained in the future. One is that, as the interest ratesare deregulated, they are likely to rise, at least in the short term. This is especiallythe case because so much of corporate debt has been obtained in the past atconcessional rates from financial institutions. The increase in interest rates willincrease the debt service burden sharply at current levels of borrowings. As theequity markets grow, equity financing will appear more and more attractive incomparison. Further, with the greater reliance upon borrowing from the capitalmarkets rather than from Development Finance Institutions, there will be lessflexibility in terms of rescheduling of payments, since it is hardly practicable toconvene a meeting ofdebenture-holders at every turn. Finally, since high debt levels increase the overallrisk of the corporation, companies will have to seek ways to control their financialrisk as they struggle to cope with the increased business risks they will face inopenly competitive product markets. With the risk of mistakes and stumblesgreatly increased, companies will find their equity values depressed if they burdenthemselves with debt and thereby invite financial disasters. This is one of the likelybut thus far unheralded consequences of the liberalization of industrial policy bythe present government, which has left few protected markets for companies tokeep harvesting as they have in the past.Corporate SicknessUntil such time as the corporate debt levels are brought down to more manageablelevels, the corporate sector will probably see a greater incidence of sickness onaccount of its inability to absorb the higher debt service charges. This is especiallytrue of the older, more established companies which will, at the same time, findtheir hitherto profitableand protected markets invaded by new and more aggressive competitors. Theerosion of profitability and the increase in debt service burden will be a vise manysuch companies will find themselves inexorably squeezed in. Needless to say, thisbrings up issues such as exit policy, which we address in the section onInterlinkages. At this stage, however, we suggest that the debt equity norm shouldbe reduced in a time-bound manner, say over a period of two years, from 2:1 to
  28. 28. 1:1, in order to give the corporate sector some time to adjust their long-termfinancing mix. Eventually, of course, the debt equity norm will have to bedetermined purely on business considerations, and will vary in a complex mannerfrom industry to industry if not from company to company. However, a phasedmove in this direction must be implemented as soon as the NarasimhamCommittee report itself is implemented in its final form.III. Rural Sector BanksWith the implementation of the Narasimham Committee Report, commercial bankswill no longer be cross-subsidizing loans to the rural sector with earnings from theurban sector. While this will certainly put an end to the strategic schizophreniabanks have been afflicted with in the past, it does mean that commercial banks,including their rural subsidiaries, will find it increasingly difficult to compete withspecialized rural banks. We anticipate that the need and the demand for credit inthe rural sector will only grow as the economy grows. To meet this demand, anumber of such specialized banks are likely to arise, probably floated byentrepreneurs with strong rural roots. Because such entrepreneurs are likely toperform much better than the rural subsidiaries of the existing commercial banksat the critical tasks of credit appraisal and understanding the real needs of ruralpeople, we expect these new financial institutions to serve rural markets better.However, they will always suffer from two major problems: they will always belocalized and therefore not adequately diversified, which will make them prone tofailure with every local disaster; secondly, they will be short of capital in the shortrun. We expect that government will have to find ways to provide capital to suchnew banks, preferably in the form of venture capital in the form of equity. It is hardto see what can be done to solve the problem of inadequate geographicaldiversification without jeopardizing the strong local expertise which will be themain competitive advantage for these new banks.IV. Financial Auditing and ConsultingWe believe that the scheme proposed by the Committee for supervision of bankswill be found to be inadequate, in as much as it relies strongly on self-regulationby banks with a small supervisory board. The main aim of bank supervisionshould be to protect the interests of depositors and to prevent any run on thebanking system which may be follow any significant bank failures. We propose thatthe best way to ensure this would be a strong system of bank examiners, coupledwith a system of insurance of bank deposits. Bank examiners would be chargedwith the task of auditing the portfolios of individual banks, at a detailed level, andto assess the overall portfolio of the individual bank. Examiners should be able toprovide an early warning system to the bank itself as well as to the RBI if the bankhas excessive exposure to particular risks, for instance. Such examiners wouldneed to be independent of the both the bank and the RBI. Ideally, they would beprofessionals, trained in financial and investment management. We suggest thatsuch the RBI hire such professional services on a contract basis. A number of
  29. 29. other financial services would need to be developed. For instance, we haveproposed in the section on government bonds that pension and provident funds beallowed to invest in high grade debt securities other than government bonds.Naturally, then, there will need to be a number of independent agenciesspecializing in the appraisal of debt securities.V. Interlinkages with the Real EconomyStrong interrelationships obviously exist between the banking system and the restof the economy.Exit PolicyOpening up the entries but keeping the exit clogged is clearly not a viableprocedure. The need for a workable exit policy to go along with the liberal entrypolicies introduced by the current government, is a rather obvious one. The pointto be made here is that this need for a workable exit policy will be greatly increasedby some of the fallouts from the proposed reform of the banking sector. Quite apartfrom the fact that some banks themselves will become unviable and will have tostart downsizing or adopting a more regional focus, we expect that the incidence ofcorporate failures will also increase as the debt burden increases. We have dealtwith this issue at length in a previous section.Labour LawsThe retrenchment of workers arising from the sickness of firms could be taken careof by the following options:a) Rather than force sick units to continue retaining the labour force, which is notfeasible in the long run in any case and results in a downward spiraling of moraleand productivity in the short run, employers could be forced to find alternativeemployment for workers elsewhere. In practice, an employer who wishes to lay offworkers may have to pay a new employer to take them on. Some form of insurancecould be obtained by the old employer to help defray such costs in the event ofsickness. We expect an active market in this area if this option is resorted to.b) An employment retrenchment insurance scheme wherein the employer pays aninsurance premium to an insurance company to cover retrenchment payments toemployees (not covering retrenchment on disciplinary grounds etc.) The insurancecompany could pay the retrenched worker directly to provide him or her somecushion or to pay finance any retraining which would be needed for him or her tofind a new job. Various combinations of the above schemes could also be workedout. In any case, as sickness and layoffs become more common, workers also needto have a variety of insurance and pension schemes which would not be dependenton any one employer. We anticipate a growing demand for independent insuranceand pension fund companies as the proposed reforms are implemented.Land LawsCertain restrictions on the sale of certain kinds of land properties have acted asmajor impediments in the way of sick companies which could otherwise have soldthe land to raise funds to finance rehabilitation efforts. With the increased
  30. 30. incidence of corporate sickness we predict as a consequence of both the liberalizedindustrial policy and the reforms proposed in the Narasimhan Committee report,some major amendments to land laws appear to be urgently called for.VI. Pace of ReformMajor economic reforms are being contemplated today. One issue which naturallyarises is that of sequencing these reforms. At first blush, it may appear that itwould be logical to implement reforms in some logical order of priority, basedperhaps on some sense of relative urgency. However, a closer examination revealsthat there is some sort of circular sequencing requirement here, where each reformappears to be a precondition for another. For example, it would make little sense toreform the banking system first, since the real urgency driving this set of reformscomes from the need to rationalize the entire economic system. On the other hand,how feasible would it be to implement the reform of the industrial system first, ifthere is not a strong banking system to finance the new entrants into newlyderegulated industries? Again, how feasible would it be to implement an easy entrypolicy without an easy exit policy and how would an exit policy work without asystem of insurance for retrenched workers, which would require a reformedfinancial system as a precondition? Indeed, reforms in industrial policy are hardlylikely to win the enthusiastic support of industry if industry leaders did not havereason to believe that reforms in the financial system are imminent if notconcurrent. We believe the simplest way out of such a dilemma is to aim for a nearsimultaneity in these reforms. This will necessarily mean a rapid pace of reform inwhich time is measured in days, not years. Days as units connote a sense ofurgency not communicated by months and years. At the same time, there is a needto build up a momentum which would be irreversible if the people are to haveconfidence that the reforms will endure. A slow pace of reform will breed a waitand see attitude, which would neither bring the benefits of reform nor permitcontinued economic growth under the old rules of the game. The greatest danger isuncertainty - he who hesitates is indeed lost. As we look around us, we see evenmore momentous reforms being introduced in the world today, especially inEurope and the erstwhile Soviet Union. India cannot afford to be slower than thesecountries, especially if we are to retain our credibility with international financialinstitutions.Capital Adequacy RatioINTRODUCTION The instructions regarding the components of capital and capital charge requiredto be provided for by the banks for credit and market risks. It deals with providingexplicit capital charge for credit and market risk and addresses the issues involvedin computing capital charges for interest rate related instruments in the tradingbook, equities in the trading book and foreign exchange risk (including gold and
  31. 31. other precious metals) in both trading and banking books. Trading book for thepurpose of these guidelines includes securities included under the Held forTrading category, securities included under the Available For Sale category, opengold position limits, open foreign exchange position limits, trading positions inderivatives, and derivatives entered into for hedging trading book exposures.Measurement of capital charge for foreign exchange and gold open positionsForeign exchange open positions and gold open positions are at present riskweighted at 100%. Thus, capital charge for foreign exchange and gold openposition is 9% at present. These open positions, limits or actual whichever ishigher, would continue to attract capital charge at 9%. This is in line with theBasel Committee requirement.Capital Adequacy for Subsidiaries1.The Basel Committee on Banking Supervision has proposed that the New CapitalAdequacy Framework should be extended to include, on a consolidated basis,holding companies that are parents of banking groups. On rudentialconsiderations, it is necessary to adopt best practices in line with internationalstandards, while duly reflecting local conditions.2.Accordingly, banks may voluntarily build-in the risk weighted components oftheir subsidiaries into their own balance sheet on notional basis, at par with therisk weights applicable to the banks own assets. Banks should earmark additionalcapital in their books over a period of time so as to obviate the possibility ofimpairment to their net worth when switchover to unified balance sheet for thegroup as a whole is adopted after sometime. Thus banks were asked to provideadditional capital in their books in phases, beginning from the year ended March2001.3.A consolidated bank defined as a group of entities which include a licensed bankshould maintain a minimum Capital to Risk-weighted Assets Ratio (CRAR)asapplicable to the parent bank on an ongoing basis. While computing capital funds,parent bank may consider the following points :i.Banks are required to maintaina inimum capital to risk weighted assets ratio of 9%. Non-bank subsidiaries arerequired to maintain the capital adequacy ratio prescribed by their respectiveregulators. In case of any shortfall in the capital adequacy ratio of any of thesubsidiaries, the parent should maintain capital in addition to its ownregulatory requirements to cover the shortfall. ii.Risks inherent in deconsolidatedentities (i.e., entities which are not consolidated in the Consolidated Prudential
  32. 32. Reports) in the group need to be assessed and any shortfall in the regulatorycapital in the econsolidated entities should be deducted (in equal proportion fromTier I and Tier II capital) from the consolidated banks capital in the proportionof its equity stake in the entity.Procedure for computation of CRAR1. While calculating the aggregate of funded and non-funded exposure of aborrower for the purpose of assignment of risk weight, banks may ‘net-off’ againstthe total outstanding exposure of the borrower -(a) advances collateralised by cashmargins or deposits,(b) credit balances in current or other accounts which are notearmarked for specific purposes and free from any lien,(c) in respect of any assetswhere provisions for depreciation or for bad debts have been made (d) claimsreceived from DICGC/ ECGC and kept in a separate account pending adjustment,and (e) subsidies received against dvances in respect of Government sponsoredschemes and kept in a separate account.2.After applying the conversion factor as indicated in Annex 10, the adjusted offBalance Sheet value shall again be multiplied by the risk weight attributable to therelevant counter-party as specified.3. Computation of CRAR for Foreign Exchange Contracts and Gold: Foreignexchange contracts include- Cross currency interest rate swaps, Forward foreignexchange contracts, Currency futures, Currency options purchased, and othercontracts of a similar nature Foreign exchange contracts with an original maturityof 14 calendar days or less, irrespective of the counterparty, may be assigned "zero"risk weight as perinternational practice. As in the case of other off-Balance Sheetitems, a two stage calculation prescribed below shall be applied: (a) Step 1- The notional principal amount of each instrument is multiplied by the conversion factor given below: Residual Maturity Conversion Factor One year or less 2% Over one year to five years 10% Over five years 15%(b) Step 2 - The adjusted value thus obtained shall be multiplied by the risk weightage allotted to the relevant counter-party as given in Step 2 in section D of Annex10.4. Computation of CRAR for Interest Rate related Contracts::
  33. 33. Interest rate contracts include the Single currency interest rate swaps, Basisswaps, Forward rate agreements, Interest rate futures, Interest rate optionspurchased and other contracts of a similar nature. As in the case of other off-Balance Sheet items, a two stage calculation prescribed below shall be applied:(a)Step 1 - The notional principal amount of each instrument is multiplied by thepercentages given below :Residual Maturity Conversion FactorOne year or less 0.5% 1.0%Over one year to five yearsOver five years 3.0%(b) Step 2 -The adjusted value thus obtained shall be multiplied by the riskweightage allotted to the relevant counter-party as given in Step 2 in Section I.D.of AnnexThe Committee on Banking Regulations and Supervisory Practices (BaselCommittee) had released the guidelines on capital measures and capitalstandards in July 1988 which were been accepted by Central Banks in variouscountries including RBI. In India it has been implemented by RBI w.e.f. 1.4.92Objectives of CAR : The fundamental objective behind the norms is tostrengthen the soundness and stability of the banking system.Capital Adequacy Ratio or CAR or CRAR : It is ratio of capital fund to riskweighted assets expressed in percentage terms i.e.Minimum requirements of capital fund in India:* Existing Banks 09 %* New Private Sector Banks 10 %* Banks undertaking Insurance business 10 %* Local Area Banks 15%Tier I Capital should at no point of time be less than 50% of the total capital.This implies that Tier II cannot be more than 50% of the total capital.Capital fundCapital Fund has two tiers - Tier I capital include*paid-up capital*statutory reserves*other disclosed free reserves
  34. 34. *capital reserves representing surplus arising out of sale proceeds of assets.Minus*equity investments in subsidiaries,*intangible assets, and*losses in the current period and those brought forward from previous periodsto work out the Tier I capital.Tier II capital consists of:*Un-disclosed reserves and cumulative perpetual preference shares:*Revaluation Reserves (at a discount of 55 percent while determining their valuefor inclusion in Tier II capital)*General Provisions and Loss Reserves upto a maximum of 1.25% of weightedrisk assets:*Investment fluctuation reserve not subject to 1.25% restriction*Hybrid debt capital Instruments (say bonds):*Subordinated debt (long term unsecured loans:Risk weighted assets - Fund Based : Risk weighted assets mean fund basedassets such as cash, loans, investments and other assets. Degrees of credit riskexpressed as percentage weights have been assigned by RBI to each such assets.Non-funded (Off-Balance sheet) Items : The credit risk exposure attached tooff-balance sheet items has to be first calculated by multiplying the face amountof each of the off-balance sheet items by the credit conversion factor. This willthen have to be again multiplied by the relevant weightage.Reporting requirements :Banks are also required to disclose in their balance sheet the quantum of Tier Iand Tier II capital fund, under disclosure norms.An annual return has to be submitted by each bank indicating capital funds,conversion of off-balance sheet/non-funded exposures, calculation of risk-weighted assets, and calculations of capital to risk assets ratio,Asset - Liability Management System in banks - GuidelinesOver the last few years the Indian financial markets have witnessed wide rangingchanges at fast pace. Intense competition for business involving both the assetsand liabilities, together with increasing volatility in the domestic interest rates aswell as foreign exchange rates, has brought pressure on the management of banksto maintain a good balance among spreads, profitability and long-term viability.These pressures call for structured and comprehensive measures and not just adhoc action. The Management of banks has to base their business decisions on adynamic andintegrated risk management system and process, driven by corporate strategy.
  35. 35. Banks are exposed to several major risks in the course of their business - creditrisk, interest rate risk, foreign exchange risk, equity / commodity price risk,liquidity risk and operational risks.2. This note lays down broad guidelines in respect of interest rate and liquidityrisks management systems in banks which form part of the Asset-LiabilityManagement (ALM) function. The initial focus of the ALM function would be toenforce the risk management discipline viz. managing business after assessing therisks involved. The objective of good risk management programmes should be thatthese programmes will evolve into a strategic tool forbank management.3. The ALM process rests on three pillars: ALM information systems=> Management Information System=> Information availability, accuracy, adequacy and expediency= ALM organisation=> Structure and responsibilities=> Level of top management involvement= ALM process=> Risk parameters=> Risk identification=> Risk measurement=> Risk management=> Risk policies and tolerance levels.TRENDS IN DOMESTIC RATES AND YILED CURVEThe major focus of prudential regulation in developing countries has traditionallybeen on credit risk. While banks and their supervisors have grappled with non-performing loans for several decades, interest rate risk is a relatively new problem.Administrative restrictions on interest rates in India have been steadily eased since1993. This has led to increased interest rate volatility. Table I shows the trends indomestic interest rates in India during the study period. It is clear that the ratesare increasing.
  36. 36. Table I - Trends in Domestic Interest Rates in India (in %)Effective since reverse repo rate repo rate CRRMar 31, 2004 4.50 6.00 4.50Sep 18, 2004 4.50 6.00 4.75Oct 2, 2004 4.50 6.00 5.00Oct 27, 2004 4.75 6.00 5.00Apr 29, 2005 5.00 6.00 5.00Oct 26, 2005 5.25 6.00 5.00Jan 24, 2006 5.50 6.25 5.00Jun 9, 2006 5.75 6.50 5.00Jul 25, 2006 6.00 6.75 5.00Oct 31, 2006 6.00 7.00 5.00Dec 23, 2006 6.00 7.25 5.25Jan 6, 2007 6.00 7.25 5.50 Source: RBI Bulletin The yield curve has shifted upward since March ‘04, with the 10-year yields moving from 5% to 7% (Fig.I). However,the longer end of the curve has flattened. The significant drop in turnover in 2004-05 and 2005-06 could be due to a ‘buy and hold’ tendency of the participants other than commercialbanks (like insurance companies) and also due to the asymmetric response ofinvestors to the interest rate cycle. Inthe absence of a facility of short selling ingovernment securities, participants generally refrained from taking positions whichresulted in volumes drying up in a falling market. The Reserve Banks efforts toelongate the maturity profile resulted in a smooth and reliable yield curve to act asa benchmark for the other markets for pricing and valuationpurposes. The weighted average maturity of securitiesincreased from 5.5 years in1995-96 to 14.6 years during2006-07. The weighted average yield of securitiesalsodeclined to 5.7 per cent in 2003-04 and since then, it has increased to 7.3 percent in 2005-06 and further to 7.9 percent in 2006-07.The Indian yield curve todaycompares with not only emerging market economies but also the developed world.
  37. 37. RBI repo rate - Indian central bank’s interest rateCharts - historic RBI interest ratesGraph Indian interest rate RBI - interest Graph Indian interest rate RBI - long-rates last year term graphThe current Indian interest rate RBI (base rate) is 8.500 %RBI - Reserve Bank of IndiaThe Reserve Bank of India (RBI) is the Indian central bank. The RBI’s mostimportant goal is to maintain monetary stability - moderate and stable inflation - inIndia.. The RBI uses monetary policy to maintain price stability and an adequateflow of credit. Rates which the Indian central bank uses for this are the bank rate,repo rate, reverse repo rate and the cash reserve ratio. Reducing inflation has beenone of the most important goals for some time.
  38. 38. Other important tasks of the Reserve Bank of India are: • to maintain the population’s confidence in the system, to safeguard the interests of those who have entrusted their money and to supply cost-effective banking systems to the population; • to manage foreign currency controls: facilitating exports, imports and international payment traffic and developing and maintaining the trade in foreign currencies in India; • issuing money (the rupee) and adequately ensuring a high quality money supply; • providing loans to commercial banks in order to maintain or grow the Gross National Product (GNP); • acting as the government’s banker; • acting as the banks’ banker.RBI Repo rate or key short term lending rateWhen reference is made to the Indian interest rate this often refers to the repo rate,also called the key short term lending rate. If banks are short of funds they canborrow rupees from the Reserve Bank of India (RBI) at the repo rate, the interestrate with a 1 day maturity. If the central bank of India wants to put more moneyinto circulation, then the RBI will lower the repo rate. The reverse repo rate is theinterest rate that banks receive if they deposit money with the central bank. Thisreverse repo rate is always lower than the repo rate. Increases or decreases in therepo and reverse repo rate have an effect on the interest rate on banking productssuch as loans, mortgages and savings.This page shows the current and historic values of Indian central banks Repo rateBase Ratei.The Base Rate system will replace the BPLR system with effect from July 1, 2010.Base Rate shall include all those elements of the lending rates that arecommonacross all categories of borrowers. Banks may choose any benchmark to arrive atthe Base Rate for a specific tenor that may be disclosed transparently. An ilustration for computing the ase Rate is set out in theAnnex. Banks are free to useany other methodology, as considered appropriate, provided it is consistent and ismade available for supervisory review/scrutiny, as and when required.ii. Banks may determine their actual lending rates on loans and advances withreference to the Base Rate and by including such other customer specific chargesas considered appropriate.iii.In order to give banks some time to stabilize the system of Base Rate calculation,