The Case Of India ECS 4030 Foreign Banks entering developing countries: The case of India Shweta Chaudhary* Msc Banking and Finance Middlese – University Business Sc…

The Case Of India Economics Essay ECS 4030 Foreign Banks entering developing countries: The case of India Shweta Chaudhary* Msc Banking and Finance Middlese – University Business School May 2013 mdx logo.jpg *Views of the author are that of his own and does not reflect or influence the views of an individual Acknowledgment Abstract Indian banking sector is a combination of different structure banks working in the same environment. Public sector units, private banks, foreign banks, scheduled and non-scheduled banks and even non-banking finance corporations are the key players of this economy. Here, in this paper we will be analysis the data and the hypothesis that did the entry of foreign banks as a part of globalisation policy accepted by India in the 1990s beneficial to the economy or it was a not needed step. And we could have done without the liberalisation and globalisation policies. We will judge the branch locations in different states and districts over a time period covering post and pre-privatisation period. Did these firms make access to finance easier for the client? Or it only made the rich richer. This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order Table of contents: Introduction A foreign bank is a bank that has been setup in a different country, which is its home country and is also serving customers of another country, the foreign countries. Foreign banks are active in India, just the way the local banks are. They need to adhere to all the rules and regulations that are applicable to banks based out of India and they are governed by the Reserve Bank of India that controls/governs all banks operating in India. In the recent years there had been a lot of transition of banks from Developed countries like US and Japan to Developing countries i.e. emerging economies of the world. In this paper we will study the establishment of foreign banks from various countries making a mark in the Indian banking sector. As stated above these banks work under the rules and regulations of the Apex bank of India which is Reserve Bank of India. It was a conception that foreign banks benefit the healthy competition, giving a boost to accessibility to financial instruments and services, improves financial and economic efficiency of their clients, and introduce a huge financial stability (Clarke, cull, Martinez Pera and Sanchez, 2003, Claessens 2006. Chopra 2007 and cull and Martinez peria, 2011). Normally, lesser costs of financial intermediation and less profitability are noticed with huge foreign bank occurrence (claessens, demirguc-kunt, and Huizinga, 2001, mian 2003, berger, Clarke, cull, klapper and udell, 2005). We would also like to add that foreign banks probably forced govts. To improvise regulation and management, imparts transparency, and catalyze national reforms (Levine 1996, Dobson, 2005, and mishkin, 2006). The foreign banks working in India are as follows: Foreign banks branches in India as on March 31, 2013 Sl.No Name of bank Country of Incorporation No of Branches in India 1 AB Bank Ltd. Bangladesh 1 2 The Royal Bank of Scotland N.V. Netherlands 31 3 Abu Dhabi Commercial Bank Ltd. UAE 2 4 American E -press Banking Corporation USA 1 5 Antwerp Diamond Bank N.V. Belgium 1 6 Bank Internasional Indonesia Indonesia 1 7 Bank of America USA 5 8 Bank of Bahrain & Kuwait BSC Bahrain 2 9 Bank of Ceylon Sri Lanka 1 10 Bank of Nova Scotia Canada 5 11 Barclays Bank Plc. United Kingdom 7 12 BNP Paribas France 8 13 Credit Agricole Corporate & Investment Bank France 5 14 Chinatrust Commercial Bank Taiwan 2 15 Citibank N.A. USA 42 16 DBS Bank Ltd. Singapore 12 17 Deutsche Bank Germany 18 18 HSBC Ltd Hong Kong 50 19 J.P. Morgan Chase Bank N.A. USA 1 20 JSC VTB Bank Russia 1 21 Krung Thai Bank Public Co. Ltd. Thailand 1 22 Mashreq Bank PSC UAE 1 23 Mizuho Corporate Bank Ltd. Japan 2 24 Oman International Bank SAOG Sultanate of Oman 2 25 Shinhan Bank South Korea 3 26 Societe Generale France 3 27 Sonali Bank Ltd. Bangladesh 2 28 Standard Chartered Bank United Kingdom 101 29 State Bank of Mauritius Mauritius 3 30 The Bank of Tokyo- Mitsubishi UFJ Ltd. Japan 4 31 UBS AG Switzerland 1 32 FirstRand Bank Ltd South Africa 1 33 United Overseas Bank Ltd Singapore 1 34 Commonwealth Bank of Australia Australia 1 35 Sberbank Russia 1 36 Credit Suisse A.G Switzerland 1 37 Australia and New Zealand Banking Group Ltd. Australia 1 38 Rabobank International Netherlands 1 39 Industrial & Commercial Bank of China Ltd. China 1 40 Woori Bank South Korea 1 41 National Australia Bank Australia 1 42 Westpac Banking Corporation Australia 1 43 Sumitomo Mitsui Banking Corporation Japan 1       331 List of Foreign banks having Representative Offices in India as on March 2013. Sr. No. Name of the representative office Country of incorporation Centre Date of opening 1 Raiffeisen Zentral Bank Osterreich AG Austria Mumbai 1.11.1992 2 Fortis Bank Belgium Mumbai 6.10.1987 3 K.B.C. Bank N.V. Belgium Mumbai 1.02.2003 4 Royal Bank of Canada Canada Mumbai 1.2.2008 5 Toronto Dominion Bank Canada Mumbai 16.11.2009 6 Credit Industriel et Commercial France New Delhi 1.04.1997 7 Natixis France Mumbai 4.01.1999 8 Bayerische Hypo – und Vereinsbank Germany Mumbai 12.07.1995 9 DZ Bank AG Deutsche Zentral – Genossenschafts Bank Germany Mumbai 22.02.1996 10 Landesbank Baden – Wurttemberg Germany Mumbai 1.11.1999 11 Commerzbank Germany Mumbai 23.12.2002 12 BayernLB Germany Mumbai 15.4.2008 13 Norddeutsche Landesbank Girozentrale (NORD LB) Germany Mumbai 1.9.2008 14 KfW IPEX Bank GmbH Germany Mumbai 1.4.2009 15 DEPFA Bank Ireland Mumbai 9.3.2007 16 Intesa Sanpaolo S.p.A Italy Mumbai 1.11.1988 17 Uni Credito Italiano Italy Mumbai 1.08.1998 18 This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order Banca Populare Di Verona E Novara Italy Mumbai 18.06.2001 19 BPU Banca –Banche Popolari Unite S.c.r.l Italy Mumbai 16.01.2006 20 Monte Dei Paschi Di Sienna Italy Mumbai 07.04.2006 21 Banca Popolare di Vicenza Italy Mumbai 29.04.2006 22 CIMB Bank Berhad Malaysia Mumbai 23.11.2010 23 Everest Bank Ltd. Nepal New Delhi 24.03.2004 24 DNB Bank ASA Norway Mumbai 27.8.2008 25 Caixa Geral de Depositos Portugal Mumbai Goa (EC) 8.11.1999 26 Vnesheconombank (Bank for Foreign Economic Affairs) Russia New Delhi 1.3.1983 27 Promsvyazbank Russia New Delhi 25.04.2006 28 Gazprombank Russia New Delhi 12.7.2010 29 Hana Bank South Korea New Delhi   30 Korea Exchange Bank South Korea New Delhi 27.8.2008 31 Kookmin Bank South Korea Mumbai 1.06.2012 32 Industrial Bank of Korea South Korea New Delhi 22.11.2012 33 Banco de Sabadell SA Spain New Delhi 2.08.2004 34 Banco Bilbao Vizcaya Argentaria Spain Mumbai 2.4.2007 35 CaixaBank S.A. Spain New Delhi 1.2.2011 36 Hatton National Bank Sri Lanka Chennai 1.01.1999 37 Svenska Handlesbanken Sweden Mumbai 1.08.2006 38 Skandinaviska Enskilda Banken AB Sweden New Delhi 1.02.2008 39 Zurcher Kantonalbank Switzerland Mumbai 27.06.2006 40 Mega International commercial Bank Taiwan Mumbai 2.12.2008 41 Asya Katilim Bankasi AS Turkey Mumbai 1.9.2012 42 Emirates Bank International UAE Mumbai 16.06.2000 43 First Gulf Bank UAE Mumbai 26.10.2009 44 Duncan Lawrie Ltd United Kingdom Kolkata 30.10.2009 45 The Bank of New York Mellon USA Mumbai 27.10.1983 46 Wells Fargo Bank N.A. USA Mumbai (Sub-office at Chennai & New Delhi) 1.11.1996 Indian Banking history: When in 1947, India got independence from East India company rule, at that time India had main five banks namely: Central Bank of India, Punjab National Bank, United Commercial Bank, Bank of Baroda, and Bank of India. This year and the preceding year were vicious for Indian Economy as the following year in 1948, India and Pakistan got separated into two different countries and it lead to division of the bigger banks. Liberalisation was introduced in Indian economy 18 years ago. Indian banking system is an animated cluster of competence enhanced Public Sector units and progress thirsty private sector banks. The services, money instruments, efficiency, IT facilities and management would have been a far off our vision 10 years ago. The amount of conveniences banks are providing to their corporate clients and retail customers has been improving and it was something no one ever imagined or even in their thoughts. Indian banking industry has witnessed exponential escalation the CNB Bank Index has shown a growth of 1100% in absolute terms, a compounded rate annual growth rate of 25% in the time period of 2000-2010. And if we look at the sensex, it grew at an compounded annual growth rate of 14%. The year 2010 was a good year for the Indian banking sector as it contributed to the GDP by 16.35%. Data: The data used in this paper has been extracted from the databases of Reserve Bank of India. The data regarding the geographical locations of the new banks and the existing Indian banks is taken from the publications of the RBI issued by the Director General. The time frame of the data is 1988-2004. We have taken this time frame because it covers the pre-privatisation period and the post privatisation period also. This was the time when there have been extreme changes in the Indian Banking system and the economy as a whole. By using this data, we will now map out the locations of the foreign bank openings in different regions of India. This is presented in the form of a table in table1. Strategy for Empirical study and their hypothesis: The OLS arrangement used here is: yi ,d ,t = β0 +β1Foreign Bankd ,t +αi +δ t +ε i ,d ,t … (1) Here, we have taken firm level outcome y of firm i as the dependent variable, in year t based in district d. The value y will involve measures of firms, unsettled stock of loans from Banks and FIs. Other various variables will narrated below. In district d of year t, the indicator of existence of foreign bank is Foreign Bank. that is twisted on for all the firms in the district if an outsider bank was already present in that year. When district-level allocation of foreign banks is put to use instead of an indicator, every following were seen to be similar. Though, there have been a theory which says the sheer entry of foreign lenders and investors is enough to persuade a segmentation of the market and intensify information asymmetries1 . A full set of firm dummies, αi , absorb any fixed differences in firms’ use of loans in a way that the coefficient of interest, 1β , is approximately only using within firm changes. This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order By this we are sure of one thing that the change in the firm specific and location specific changes of the bank branch won’t influence the empirical analysis. Running of country-level pattern in lending trend that might be caused by other alterations in government policies or regulations will be time dummy variable δt. Now, the last thing being the standard errors, as we know the disparity of foreign entry appears at this district level, the standard errors are concentrated here. Here, the consequence of foreign bank entry is captivated by the coefficient β1 . It is to be estimated using the changes in the patterns of borrowing of the firms located in the eight districts who got their foreign bank from 1991-2002 comparative to firms in districts who never got a foreign bank. There are certain benefits of this within-the boundary analysis as compared to the more criterion approach of making use of cross-sectional, country-level analysis. Here firstly the specified made usage of the spread out timing of entrance by foreign banks in the time frame of 1994-2001. The coefficient β1 . Is found out by mere usage of variations in borrowing trends at the same time as the time of foreign bank entrance in each and every district. Secondly, we also saw that in house variation ensured that 1 (Dell’Arricia and Marquez, 2004; Sengupta, 2006; Gormley, 2006) the results for Beta are traceable in the entry of the FB instead of other country-level economic reforms introduced during the early 90s. What happens after such country-level changes is that the consequences are absorbed in by the y(year) dummies, and the results of foreign bank entrance will be genuinely recognised under the assumption that the pattern in use and loan firm size in those eight districts should have been similar like those in control group in the absenteeism of the foreign bank’s entrance. The initial organizing group uses all firms found in the areas who soen’t have a foreign bank in the time gap of 1991-2002. Head offices in these 18 places who already had a foreign bank saw a decline in the regression. Table no.2. Keeping in mind the robustness test, regressions seem to be also running with a tiny control group comprising of firms seen in the nine places who got their initial foreign bank in the time frame of ’03-’04. The nine districts who showed a healthy control and were seen as a potential map points for foreign bank entrance were Fardabad, Nagpur, Surat, Aurangabad, Lucknow, Bhopal,Thane, Patna and Rajkot. As this wasn’t different than the earlier eight districts who had an early foreign bank. But the problem being that in equation (1) there is a limitation, it doesn’t let us to check whether the entrance of foreign bank has an differentially influence on the firms. So, we come up with another equation to overcome the limitation as it takes into consideration the interaction , Foreign Bank X ROA, here ROA is said to be the demean percent average return of the total assets of t firm in the time gap of ‘91-‘93. The profits are calculated by putting use of profit minus taxes and the net of non-recurring operation. The whole set of year and profit interactions, δ ×ROA, will also be considered to let the firms cross the country to variants in trend as a property of their earlier profitability. yi ,d ,t = β0 + β1Foreign Bankd ,t +β2 Foreign Bankd ,t ×ROAi +αi +δt + δ t ×ROAi +ε … (2) Now we move on to the next specification, β1 constantly seems to be defining the major effects of foreign bank entrance since ROA is demean. Simultaneously , βhere describes marginal effect of keeping elevated ROA in advance. Considering the interaction of Foreign Bank x ROA checks whether the firm’s profit issues matter a lot for credit accessibility after foreign bank entrance the places, under the supposition a firm’s previous ROA is optimistic forecaster of future possibilities. Hence, this permits us to analyse whether foreign bank entrance is engaged with a shit of credit from comparetivly less profitable organisations to more profitable organisations. Because it is indicated by 2 β > 0. Furthermore, we see a drop in credit accelerated predominately by lesser loans advanced to politically engaged, unproductive firms, it should also be shown by 2 β > 0. This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order Lastly, we see that it is absolutely supposed that the impact of foreign bank entrance is restricted and Realised predicatively dominated by the organisations in the districts with a foreign bank. Both these Arrangements suppose that organisations in India scrounge to banks located closer to their registered addresses and along with it they are put up in the same district. In a broad spectrum it is to be expected that this shows as a empirical work concerning lending association in various countries has shown up the mean distance between their office and their bank is normally pretty short. Yet, even when this supposition does not hold true completely. It could be only a bias approach towards the results alongside figuring out the impact of foreign bank entrance. Even if the organisation asks for advances from a different district then organisations out up outside those eight interacted districts might be actually “interacted” bring about the estimates to know the real effects. Even further, the capacity to borrow from banks from other districts may only lessen the neighbouring impact of foreign bank entrance hence making it tricky to discriminate an effect of foreign bank entrance at the local plane. In order to calculate organisations accessibility to advances, a few number of dependant variables, y, shall be put to use. Primarily, to check the impact on the value of advances of the organisations reports, we will need to use three various variables namely: the stash of advances from the local development banks, the stash of long-term commercial bank advances and the stash of advances from both commercial and development banks which would mean total of long-term advances. These above mentioned ways are standardised by organisations total assets as they were in the earlier stages of the samples in ’91. Next, a collection of indicators equalizing one of organisations with advances from the given resources are considered to check the variations in the probability of a organisation having a loan. Meanwhile the two sets of fiscal measures are alike in character, their discrimination is important. The primary set will detain whether organisations in districts gaining a foreign bank know-how a comparative variation in amount of financing they gain, while the next set of predictors will check whether an organisations chances of having a loan is impacted by entrance of an foreign bank. ESTIMATES OF OLS: OLS estimate of the correspondence among long-term advances and the incidence of a foreign bank are stated in Table 3. Columns1-4 state the coefficients by means of and predictor for having a long-term advance as dependant variable, and columns5-6 state the coefficients whilst dependant variable in the stash of aggregated long-term advance standardised by assets. Instead of being a help to the local organisations, foreign bank entrance is closely linked with a decrease in local firms possibilities of borrowing long-term advances that is discrete to organisations previous profits. In the regular regression in all organisations and no add on control or interactions – column 1. It is been seen that the foreign banks entrance coincides to a 7.5% percentile drop in organisations possibility of borrowing a long-term advance comparative to organisations put up in districts not having a foreign bank. Insertion of ROA intersects in column-2 reveals that the fall is on a bigger scale not related to organisations ROA, and even if there is anything which exists here is a fragile proof a organisations ROA is relatively of less importance pursuing foreign bank entrance. The decrease in long-term loans is vigorous to the addition of industry-year contact, column-3 and prohibiting the ultimately “interact” control group. There are facts, moreover that foreign bank entrance is closely linked with an elevation in the comparative importance of organisations ROA in the value of long-term financing delegated. In the regressions where in usage of stash of long-term advances standardises by assets as the dependant variable, a privileged ROA matches to an elevation in the advance to asset ratio for organisations in the local districts with a foreign bank comparative to an organisation sans a foreign bank. The scale of the coefficients in column-6 entail that a one standard deviation elevates in organisations ROA is linked with an increase in their advances to asset ratio which on an average 1/10th standard deviations bigger when a foreign bank is existing in their district. Moreover, there is no noteworthy proof of an average decline in loan sizes. The advance-asset ratio being a louder way of credit relative to the predictors might explain so as to why we found a decline in average number of advances but not average size of advances. This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order In general, the findings and assumptions of Table 3 are constant with the theoretical structure of Gormley(2006) where in foreign entrance is linked with a restructuring of credit that doesn’t inevitably be beneficial to all borrowers. We also saw that more of profitable organisations saw a rise in their comparative value of loans, while other organisations saw a decline in their possibility of borrowing a long-term loan of any kind. In spite of all the findings the question remains as it is. Moreover, as to whether this restructuring is efficient. In this prospect, the decline in advances doesn’t come into view to be determined by a turn down in advances owed to only the most unfeasible, politically connected organisations. This should give away a optimistic coefficient for the marginal effect of a organisations ROA, not a pessimistic coefficient as found in columns1-4 of Table 3. As an alternative, the decline in credit seems to be non comparative to organisations potential and even if has something, the relation is contradictory to what one may expect to be. The decline in the number of organisations holding a long-term loan is comparatively bigger. As seen in table 2, Almost 88% of all the organisations had a long-term advance in ’93. By year ’02, though this declined to around 78% as there was a normal decrease in the long-term advances given by India’s development Banks. The decline however, was majorly in districts with newer foreign banks as shown in Fig. 2 and the instance of time coincides with the normal expansion of foreign banks from ’94-’02. Extra robustness confirms on the instance of time of this decline within every district provided in section 5. If few organistions get improved financial services after entrance, a part of the decrease in advances may also be demand driven if locally domestic organisations in the same industries counter larger competition in their yield market. Known that these are comparatively larger firms, yet such local variations are not likely to make an impact on the total demand of their yield, and a robustness test in section 5.3 also says that the decrease in advances is not related to demand. In order to understand where the variations in advance allotment are from, the regressions are now Carried out separately for advances from banks and financial institutes. Yet again, the regressions pertaining to bank advances will substitute for the “direct” impact of new foreign bank advances, while the financial institutes advance regressions will entail the “indirect” impact of foreign bank entrance on the local advances from India’s Development Bank. The regressions for banks and financial institutes are stated in tables 4-5. In table 4, we can see that the decline in possibilities of having a long-term advance is determined entirely by a down turn in financial institutes advances column5-8 than advances from commercial banks columns. This entails that competition from foreign banks indirectly impacts the allotment of credit by India’s local domestic banks. Yet, an organisations ROA doesn’t seem to show any sort of effect on whether it is not as much of likely to gain an financial institute loan. Hence, there doesn’t seem to be any proof to carry the hypothesis that local lenders react optimistically by accepting new broadcasted technologies and improvising their credit allotment. Rather, national development banks react to the clash from foreign banks by methodically deducting the number of local organisations they expand long-term advances to, in spite of their potential, and this decline in advances from the national banks is not equalizing by an elevation in advances from foreign banks. In table 5 we can see that comparative rise in importance of organisations ROA for the value of loan is principally by an rise in slope of bank advances considerably than financial institute advances. This proposes a rise in advances to more profitable organisations is driven by newer advances from the foreign banks than national banks. For the reason that commercial bank advances are also added in the measure of “bank” advances, although, the rise in comparative important of ROA might also be forced by new national commercial bank advances. This may happen that national commercial bank’s enhanced efficiency following foreign bank entrance. Though if this holds true, we shall also expect to explore an alike enhancement in efficiency for national development banks, however as seen in table 4. There isn’t a proof. Furthermore, the restructure of bank advances seems to be driven by lending trends constant with the “cream skimming” nature and aiming of lesser informational-dense organisations that is normally related with foreign banks more so over than national banks. This fact is stated in section 4.2 and 6.1 This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order Opposing to a normal reallocation of credit from fewer to more profitable organisations, the optimistic interactions in table 5 seems to be an impact of an elevation in bank advance to only top most percentile of organisations. But in regards to ROA. It is rather easier to see this in figure3, which splits down the drift in bank advances or assets of organisations within the time frame of ’91-’02 build on their ROA from ’91-’93. As we can see in figure 3, the drift in advances to the more profitable 10% of organisations was comparatively plane from ’91-’95. Commencing in ’96, nevertheless there is a huge expansion in advances for the top 10% in districts with a foreign bank, and the top 10% in other districts did not illustrate and rise in advances. The rise is advances to profitable organisations is prohibited to the top 10%, even so, there is no proof that foreign bank entrance is linked with an rise in advances amounts for organisations with an ROA more than the median but not also in the top 10% of organisation with an ROA lesser than the median. The calculations in table 6 ensure that the optimistic interface on advance sizes is due to predominately because of the top 10% of organisations. In coluns1-3, the top 10% of organisations in regards of ROA have fallen from the regressions. The optimistic impact on the size of advances to more profitable organisations observed in table 5 is now entirely gone, following the judgement that the previous results were firstly driven by a rise in bank advances to very profit making organisations. The rise in advances also was found to be primarily due to only the biggest organisations. Dipping organisations with assets in ’91 greater than the median, as shown in columns 4-6 also abolishes rise in advances. These observations hold the theory that information asymmetries are normally tricky for foreign banks to beat leading them to only provide finance to the most profit making and biggest organisations in the economy. Interpretation of the OLS estimate: In general, the OLS estimates are confirming of models incorporating asymmetric information. The rise in advances to the most profit making 10% of organisations is indicative that these organisations were under-financed in the closed economy, and foreign bank entrance improvised the allotment of credit by aiming more advances to these organisations. Yet, the rise is advances seems to be restricted to small division of profit making organisations, which is constant with theories that asymmetric information persuades “cream skimming” nature by foreign banks and a section credit market (Dell’Ariccia and Marquez , 2004; Sengupta, 2006; Gormley, 2006). Besides, competition from foreign banks also looks to guide to a symmetric deduction in long-term by the national development banks is not counteract by a equivalent rise in advances from foreign banks which is steady with models that indicate the sectioned market may unfavourably affect some organisations (Gormley, 2006). While a portion of this decline in credit might be the result of an efficient deduction in advances to very not profit making or politically-connected organisations, the reach of the drop and its no relation to a organisations previous profits is indicative that few viable national organisations were also unlikely to gain a loan after entrance. It is tricky to discern precisely where the national capital has gone. One probable illustration is that financial institutes rose less capital on outside markets via the commercial papers, issuance of bonds, etc. Even though the data is only accessible earlier in ’96, the actual value of outside capital raised by development banks was steady from ’96-’98 and down turn thereafter. Another possible illustration is that bank capital was forwarded somewhere else. In ’92-‘93, twenty four percent of bank deposits were hypothecated as govt. Securities, but in ’94-’98 it rose to twenty nine percent, beyond the statutory requisite of twenty five percent. Data restrictions, nonetheless, did not permit to check whether either of these variations were pumped by development banks put up in districts with newer foreign banks. These results have suggested for financial policy in lower development countries, which in current years has rousingly Drifted towards the allotment of bigger foreign bank entrances. While the possivle gains of foreign bank entrance have inference of foreign bank entrance are many, the proof implicates that information asymmetries may curb a lot of firms in these economies from getting to know these benefits. This result equates an active literature that tests the relative disadvantage of big banks in the production and usage of soft information(Berger, Miller, Petersen, Rajan, and Stein, 2005), and the unexpected results that bigger competition might have on the lending relationships that small and medium sized entrepreneurs hold on (boot and thakor 2000; Peterson and rajan 1995). On a whole this proof indicates that it might be necessary to accept extra policies exceeding permitting foreign banks entrance- to improve efficiency and to improvise credit accessibility in developing countries. Exceeding the existence of asymmetric information, it is likely that the local institutes and disappearance of banking competition in India preceding the foreign entry is partly accountable for the seen restructures of credit, preceding 10 ’91, India’s national development banks repeatedly engaged in govt. Directed lending schemes and were protected from competing via regulatory prohibitions to entrance. Meanwhile the financial reforms in 1991 on a great extent liberalised the national banking sector years beforehand the real entry of foreign banks into different Indian districts, it is likely the development banks are differently influenced by foreign bank entry owing to their history and immaturity at successfully viewing possible clients. Though, it is worth taking a note that alike directed lending schemes and regulatory prohibitions are very common in developing countries that permit larger foreign bank entrance as a means of rising banking competition. Hence, the understanding of India’s development banks might not be all that exceptional. This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order Verifying the Robustness and four estimates Although the previous regressions are indicative of predictions that extra competition from foreign banks will boost a restructure of credit when information asymmetries are great, one may be thoughtful about a possible choice bias in the ols estimates. As foreign banks endogenously selected where to establish new branches in India, it is likely the foreign banks chose into districts that were either pre-trending uniquely in bank of financial institutes advances or were leading to trend uniquely in the future motivation other than the entry of the foreign banks. Checking the pre-trends To check for a prevailing trend in bank and financial institute loans, two variables are summed up to the key regressions of tables3-4. The primary variable is an predictor, fake, which turns optimistic in the 3 years preceding the entry of a foreign bank. Illustration, in Ludhiana where the primary foreign bank reached in ’01, this variable equalises to 1 in years ’98-’00 and 0 all other years. Secondly variable newly added is the interface fake x ROA. Findings of this illustration can be found in table 7. But if the most profit making organisations have previously seen a rise in their bank advances in three years preceding to foreign bank’s onset, then we can figure out a optimistic coefficient for the interface term, fake X ROA. Further, if national organisations in the foreign bank districts have previously showing a decline in their accessibility to development bank advances prior the foreign bank prior the foreign bank entrance must find a depressing coefficient for fake in the regressions making use of an predictor for financial institutes as a dependant variable. Although, in table 7, the rise in bank advances values to most profit making organisations and the decline in financial institute advances to most profit making in the three years prior foreign bank entrance. In none of the case we can ignore the null hypothesis that the direct estimate for fake x ROA is 0. There is no such proof that foreign banks chosen into districts with pre-established differential patterns in bank of financial institute loans. Henceforth, the rise in bank advances to most profit making organisations and the decline in financial institutes advances to appear 1 or 2 years preceding foreign bank entrance within every district. Fig 4 board A plots the direct estimates from OLS regression of bank advances onto predictors for years To be relevant to foreign bank entrance for organisations with a ROA in the top 10%. As seen in fig 4, there is no such proof of a rise in bank advances in the years prior foreign bank entrance or in the year itself of the entrance. Nonetheless, 1 year preceding entrance, bank advances rose, and the rise became and stayed important at 5% level initiating 2 years after foreign bank entrance. Fig.4 board B plots the direct estimates from a alike regression making use of all organisations and an predictor for financial institutes loans as a dependant variable. Yet again, the direct estimates show the down turn in financial advances began exactly 1 or 2 years after primary entrance. The four estimates Although, we still concern that ofreign banks may have chosen districts that were Leaving to trend unlikely in the future for basis unrelated to the exact entrance. A re-evaluation of press releases of the foreign banks put up newer branches in India during late ‘90s shows newer branch points in India were selected to decline the remoteness to established borrowers and to put up a presence in high-growth cities. Installation of newer branch in Surat in ’04 Citigroup country office for India said : we’re very happy to shift nearer to the customers. As the map points selection showed basis on prior clients it is indifferently to pose an recognition problem, the assortment into high-growth districts may also cause a pessimistic bias if the constant rise of new industries in those districts intersects with slow growth rate of organisations in old and more firm industries. To consider this possible recognition problem, the pre-’94 occurrence of foreign organisations is to be used as an measure for the location of newer foreign banks. I presume that foreign banks are further probable to enter districts with organisations form their home land to hold pre-established relations or to take benefit of their competitive benefits in acquiring information regarding the organisations in their home land. This behaviour of foreign banks to guide their clients outside the country is been noticed in a various number of countries and seems to happen in India too. In the sample data, there have been 52 foreign-owned organisations scattered across twenty six of the one sixty two districts. 5 of 8 districts gaining their 1st foreign bank in ‘90s had a foreign-owned organisation already existing in ’93. This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order In order to check the relation between the map points of foreign bank and foreign-owned organisation, the below mentioned 1st stage regression is put to use: Foreign Bank = const + Foreign organisationd11993 × Post-1993 +α +δ +εi,d, t, …. (3) The measure for foreign bank is the intersection in a district panel predictor variable for having a foreign-owned organisation established in ’93, foreign organisation, and a post ’93 year predictor. The firm-level regression in organisation and time dummies, and SE are concentrated at the district panel. The consequences of the 1st stage are stated in table 8. And as it can be shown the occurrence of a foreign owned organisation in ’93 is a optimistic and important indicator of a foreign bank being established in the years ’94-’00. The findings show an occurrence of foreign-owned organisation in ’93 rose a districts possibility of gaining a foreign bank after ’94 to say about thirty four percent points comparative to districts who doesn’t have had a foreign-owned organisation. So as to see if the measurement is applicable, nonetheless, the location of foreign organisation, itself could be uncorrelated with the borrowing pattern of national firms. Whilst the unique location selection of foreign organisations might also be a strategy. This suppositions show reasonable that the median year of establishment of foreign firms used in the data is ’74. Almost twenty years preceding to liberalization in mid ‘90s. Henceforth, the location of foreign-owned organisations is unlikely to be straightly correlated to national lending trend in mid and late ‘90s. And the location selection is prohibited to foreign organisations setup 10 years before India’s liberalization in ’94. By using only those elder foreign organisations might in addition restrict endogeneity concerns in relation to the location selection of current setup foreign organisations. Henceforth, we don’t have any proof that national firms in districts with a foreign organisation are trending unlikely to their usage of long-term advances in 5 years before signing of GATS in ’94. This may happen due to the occurrence of the foreign organisations innovating spill over impacts who indirectly affected national borrowing trend and their growth. Still, there is no such proof so as to say that the districts with foreign organisations were unlikely different with regards to national loan advancing trend before the entrance of foreign banks. It is observed in fig5, which boards the % of organisations with an financial institute advance and the mean bank advance to asset ratio of organisations between ’89-’02. Bank advances seem to be similarly down trending in 5 years before the foreign bank entrance in districts with and sand a foreign firm in ’93 and no. Of organisations with financial institute loans were upward-trend in both districts from ’89-93. So, to make the measurement to infringe this prohibiting restriction, one should imagine story of a foreign organisations occurrence in India may just happen to pump a direct variation in national bank advances at the time of foreign bank entrance in ever district, yet not prior it. Stated here doesn’t seems to be any other variations in govt. Policies that both rose the significance of foreign organisations and intersects with the timing of foreign bank entrance in each special district, the measure seems to be valid here. Now as the measure seems to be satisfying the recognition suppositions we would now like to move further to the four estimates of eq (1). The intersection Foreign organisation x post-93 was used as a measure for the location of foreign banks, Foreign bank, and the interaction Foreign organisation x post-93 X ROA was used to measure Foreign bank X ROA. I now proceed to the IV estimates of equation (2). The interaction Foreign Firm × Post -1993 is used to instrument for the location of foreign banks, Foreign Bank , and the interaction Foreign Firm × Post -1993×ROA is used to instrument for Foreign Bank ×ROA . The IV estimates are reported in Table 9. The IV estimates confirm the OLS estimates. The arrival of a foreign bank is still associated with a drop in the average firm’s likelihood of receiving a long-term loan [Table 9, column (3)]. The IV estimates suggest foreign bank entry is associated with a 12.4 percentage point reduction in firms’ This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order likelihood of having either a bank or FI loan, which is larger than OLS estimate of 7.6 percentage points. Moreover, foreign bank entry is still associated with a positive and significant increase in the marginal importance of ROA for bank loan sizes [Table 9, column (5)], and the magnitude of the effect is similar to the OLS estimate. While not shown, the IV estimates are also robust to including industry-year interactions as done in some of the OLS specifications. 5.3 Additional robustness checks Overall, both the drop in firms’ likelihood of having a long-term loan and the increase in the Table 1 District and year wise no. Of foreign banks in India. Number of foreign bank branches calculated using the Directory of Bank Offices . Bank numbers represent total branches as of March 31 for each year. District Name State Name 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Districts with Pre-Existing Foreign Bank Branches Amritsar Punjab 3 3 3 3 3 3 3 3 3 2 1 1 1 Bangalore Urban Kanrataka 2 2 2 3 3 5 6 7 7 10 11 11 12 Coimbatore Tamil Nadu 1 1 1 1 1 1 1 1 2 2 2 3 4 Darjiling West Bengal 1 1 1 1 1 1 1 1 1 1 1 1 1 Delhi Delhi 22 23 24 24 26 28 28 31 35 36 37 38 37 Ernakulam Kerala 3 3 3 3 4 4 4 4 4 3 3 3 4 Greater Mumbai Maharashtra 51 52 52 51 51 55 58 63 65 63 64 64 63 Haora West Bengal 2 2 2 2 2 2 2 2 2 2 2 2 2 Hyderabad Andhra Pradesh 1 1 1 1 1 2 2 2 2 4 6 8 8 Kamrup Assam 1 1 1 1 1 1 1 1 1 1 1 1 1 Kanpur City Uttar Pradesh 3 3 3 3 3 3 3 3 3 3 3 3 3 Kolkata West Bengal 43 43 42 42 42 42 42 43 43 34 34 34 34 Kozhikode Kerala 1 1 1 1 1 1 1 1 1 1 Chennai Tamil Nadu 11 11 11 12 12 12 14 15 16 16 16 16 16 Simla Himachal Pradesh 1 1 1 1 1 1 1 1 1 1 1 1 1 South Goa Goa 1 1 1 1 1 1 1 1 1 1 Srinagar Jammu & Kashmir 1 1 1 1 1 1 1 1 1 1 1 1 1 Vishakhapatnam Andhra Pradesh 1 1 1 1 1 1 1 1 1 1 1 1 1 Thiruvananthapuram Kerala 1 1 1 1 1 1 1 1 1 Ahmedabad Gujarat 2 2 3 3 5 5 8 8 Pune Maharashtra 1 1 4 5 5 5 6 Chandigarh Chandigarh 1 1 1 1 2 2 Gurgaon Haryana 1 1 1 2 Vadodara Gujarat 1 1 2 2 Jaipur Rajasthan 1 1 Ludhiana Punjab 1 1 Total Foreign Bank B ranches 149 151 151 152 156 167 174 187 198 196 198 209 212 Districts Receiving First Foreign Bank Table 2 District wise summary statistics, Using 1993 Data . Districts with Districts with No Foreign Bank in 1991 Pre-Existing Foreign Banks Foreign Bank by 2002 No Foreign Bank by 2002 (1) (2) (3) Firm Characteristics Total Assets (10 mn. Rp.) 511.78 229.21 259.12 1991-1993 Average ROA (%) 2.48 3.75 2.07 Short-Term Bank Credit / Total Borrowings 0.380 0.344 0.350 Long-Term Bank & FI Loans / Total Borrowings 0.298 0.337 0.373 Short-Term Bank Credit / Assets 0.148 0.123 0.148 Long-Term Bank Loans / Assets 0.041 0.023 0.034 FI Loans / Assets 0.106 0.118 0.168 % of Firms with Long-Term Loan 80.2 87.5 88.1 % Firms with Bank Loan 42.2 43.1 44.1 % Firms with FI Loan 69.3 80.6 81.4 District Banking & Population Characteristics Population / Km2 6591 1228 476 Total Banks / Million People 135 118 72 % Share of Private Banks 11.32 6.13 6.13 Number of Districts 14 8 154 Number of Firms 1047 156 500 Table 3 Impact of foreing bank entry on entrance on aggregate long-term advances. . Standard errors, clustered at the district-level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = Indicator for Long-Term Loan Long-Term Loans / 1991 Assets (1) (2) (3) (4) (5) (6) (7) (8) Foreign Bank -0.076*** -0.075*** -0.080*** -0.077** -0.049 -0.078 -0.181 -0.046 (0.028) (0.028) (0.028) (0.033) (0.150) (0.141) (0.249) (0.106) Foreign Bank * ROA -0.003* -0.003 -0.002 0.011* 0.011 0.019*** (0.002) (0.002) (0.002) (0.006) (0.006) (0.005) Observations 7088 7088 7088 2617 7088 7088 7088 2617 R-squared 0.55 0.56 0.67 0.56 0.50 0.51 0.61 0.56 Number of Districts 162 162 162 17 162 162 162 17 ROA-Year Interactions 4-Digit Industry-Year Interactions – — – – – – – “Treated” Control Group Used This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order – – Table 4 Impact of foreign Bank entrance on accessibility to banks and financial institutes advances. Standard errors, clustered at the district-level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = Indicator for Bank advances Indicator for Finc. Inst. advances (1) (2) (3) (4) (5) (6) (7) (8) Foreign Bank 0.008 0.009 -0.014 0.038 -0.084** -0.087** -0.065* -0.073** (0.038) (0.039) (0.035) (0.038) (0.040) (0.039) (0.037) (0.035) Foreign Bank * ROA -0.003* -0.002 -0.003 -0.001 0.000 -0.001 (0.002) (0.002) (0.002) (0.001) (0.002) (0.001) Observations 7088 7088 7088 2617 7088 7088 7088 2617 R-squared 0.46 0.47 0.56 0.45 0.64 0.65 0.72 0.65 Number of Districts 162 162 162 17 162 162 162 17 ROA-Year Interactions 4-Digit Industry-Year Interactions – – – – – – – – “Treated” Control Group Used – – Table 5 Impact of foreign bank entrance on size of bank and financial institute advances. Standard errors, clustered at the district-level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = Bank advances / 1991 Assets FI advances / 1991 Assets (1) (2) (3) (4) (5) (6) (7) (8) Foreign Bank 0.041 0.029 0.030 0.035 -0.089 -0.108 -0.211 -0.081 (0.069) (0.063) (0.070) (0.046) (0.093) (0.090) (0.202) (0.081) Foreign Bank * ROA 0.006** 0.007*** 0.007*** 0.005 0.004 0.012*** (0.002) (0.003) (0.002) (0.006) (0.007) (0.004) Observations 7088 7088 7088 2617 7088 7088 7088 2617 R-squared 0.42 0.43 0.51 0.53 0.50 0.50 0.61 0.54 Number of Districts 162 162 162 17 162 162 162 17 ROA-Year Interactions 4-Digit Industry-Year Interactions – – – – – – – – “” Control Group Used – – Table 6 Scope of Foreign Bank Entry Effect on Size of Bank Loans Standard errors, clustered at the district-level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Firms Dropped = Dependent Variable = Bank Loans / 1991 Assets ROA > 90th Percentile 1991 Assets > 50th Percentile (1) (2) (3) (4) (5) (6) Foreign Bank -0.012 0.008 0.005 -0.026 -0.081 -0.010 (0.029) (0.024) (0.020) (0.061) (0.074) (0.049) Foreign Bank * ROA -0.001 -0.001 0.000 -0.002 -0.006 -0.002 (0.001) (0.001) (0.001) (0.002) (0.004) (0.004) Observations 6387 6387 2332 3412 3412 1233 R-squared 0.39 0.46 0.41 0.35 0.52 0.48 Number of Districts 162 162 17 162 162 17 4-Digit Industry-Year Interactions “Treated” Control Group Used – – – – Table 7 Pre-Trend Falsification Test Standard errors, clustered at the district-level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = Bank advances / 1991 Assets Indicator for Finc inst. advances (5) (6) (7) (8) (1) (2) (3) (4) Fake -0.028 -0.026 -0.050 -0.034 -0.006 -0.004 -0.018 0.004 (0.021) (0.019) (0.032) (0.038) (0.026) (0.025) (0.030) (0.035) Foreign Bank 0.023 0.012 -0.002 0.008 -0.088* -0.090* -0.077 -0.069 (0.060) (0.054) (0.057) (0.033) (0.046) (0.046) (0.052) (0.051) Fake * ROA -0.001 0.001 -0.000 -0.001 -0.001 -0.001 (0.001) (0.001) (0.002) (0.003) (0.003) (0.003) Foreign Bank * ROA 0.005** 0.008** 0.006*** -0.001 -0.000 -0.002 (0.003) (0.003) (0.002) (0.003) (0.003) (0.003) Observations 7088 7088 7088 2617 7088 7088 7088 2617 R-squared 0.42 0.43 0.50 0.53 0.64 0.65 0.72 0.65 Number of Districts 162 162 162 17 162 162 162 17 ROA-Year Interactions 4-Digit Industry-Year Interactions – – – – – — – “Treated” Control Group Used – – Table 8 First Stage Regression Yearly observations from ‘91 to ‘02 are included for firms with positive sales and assets in ‘91 but not situated in a district with a foreign bank by 1991. Standard errors, cconcentrated at the district-level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = ‘Foreign Bank’ Foreign-Owned Firms in 1993 * Post-1993 0.341*** (0.131) Observations 7088 R-squared 0.65 Table 9 Instrumental Variable Estimates of Foreign Bank Entry Standard errors, concentrated at the district- level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = FI advance Bank advance Either advance FI advance Bank advance Both advancee (1) (2) (3) (4) (5) (6) Foreign Bank -0.131 -0.124 -0.124* -0.346 0.011 -0.336 (0.089) (0.137) (0.068) (0.279) (0.113) (0.360) Foreign Bank * ROA 0.000 -0.001 -0.002 0.019 0.010*** 0.030 (0.004) (0.005) (0.003) (0.017) (0.004) (0.019) Observations 7088 7088 7088 7088 7088 7088 R-squared 0.65 0.46 0.56 0.50 0.43 0.50 Number of Districts 162 162 162 162 162 162 Indicator for value / 1991 Assets for Table 10 Access to FI Loans for Group versus Non-Group Firms Measurement used in the first stage are ‘Foreign-Owned Firm in 1993’ * post-1993 year dummy and ‘Foreign-Owned Firm in 1993’ * ‘ROA’ * post-1993 year dummy. Standard errors, concentrated at the district-level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = Indicator for FI advance Group organisation Non-Group organisation (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Foreign Bank -0.105*** -0.107*** -0.089** -0.080*** -0.233*** -0.034 -0.021 0.038 -0.026 0.131 (0.038) (0.037) (0.043) (0.027) (0.084) (0.060) (0.060) (0.061) (0.063) (0.181) Foreign Bank * ROA 0.003 0.004 0.003 0.007 -0.006*** -0.006*** -0.007*** -0.005 (0.004) (0.004) (0.004) (0.006) (0.001) (0.002) (0.001) (0.006) Observations 4140 4140 4140 1673 4140 2948 2948 2948 944 2948 R-squared 0.58 0.59 0.68 0.58 0.58 0.69 0.69 0.81 0.72 0.69 Number of Districts 121 121 121 17 This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order 121 115 115 115 16 115 ROA-Year Interactions 4-Digit Industry-Year Interactions – – – – – – – – – – “Treated” Control Group Used – – Specification OLS OLS OLS OLS IV OLS OLS OLS OLS IV Table 11 Access to Bank Loans for Group versus Non-Group Firms Standard errors, concentrated at the district-level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = Indicator for Bank advances Group organisations Non-Group organisations (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Foreign Bank 0.015 0.015 -0.026 0.058 -0.075 -0.008 -0.026 0.077 -0.019 -0.279 (0.051) (0.056) (0.060) (0.060) (0.154) (0.054) (0.049) (0.110) (0.053) (0.268) Foreign Bank * ROA -0.011*** -0.009** -0.010** -0.012* 0.005** 0.010** 0.004* 0.008 (0.004) (0.004) (0.004) (0.006) (0.002) (0.004) (0.002) (0.007) Observations 4140 4140 4140 1673 4140 2948 2948 2948 944 2948 R-squared 0.45 0.45 0.58 0.43 0.45 0.46 0.47 0.64 0.49 0.46 Number of Districts 121 121 121 17 121 115 115 115 16 115 ROA-Year Interactions 4-Digit Industry-Year Interactions – – – – – – — – – “Treated” Control Group Used – – Specification OLS OLS OLS OLS IV OLS OLS OLS OLS IV Table 12 Effect of Foreign Bank Entry on Bankruptcy Rates and Sales This table reports coefficients from regressions using OLS and IV with firm and year fixed effects. The dependent variable is an indicator equal to 1 if the firm has declared bankruptcy with the Board for Industrial and Financial Reconstruction in columns (1)-(5) and the log of total sales in columns (6)-(10). Yearly observations from 1991 to 2002 are included for domestic, non-financial firms with positive sales and assets in 1991 but not located in a district with a foreign bank by 1991. ‘Foreign Bank’ is equal to one for firms located in a district with a foreign bank in the given year, and zero otherwise. ‘ROA’ is a firm’s 1991- 1993 average percent return on assets, demeaned. Columns (3) & (8) include 4-digit industry-year interactions. Columns (4) & (9) restrict the sample to ‘treated’ firms located in districts with a foreign bank by 2004. Columns (5) & (10) report the IV estimates. Instruments used in the first stage are ‘Foreign- Owned Firm in 1993’ * post-1993 year dummy and ‘Foreign-Owned Firm in 1993’ * ‘ROA’ * post-1993 year dummy. Standard errors, clustered at the district- level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = Bankruptcy Log(Sales) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Foreign Bank -0.0264 -0.0244 -0.0097 -0.0001 -0.0763 0.046 0.002 -0.114 -0.128 -0.444 (0.0243) (0.0252) (0.0201) (0.0285) (0.0504) (0.101) (0.110) (0.117) (0.118) (0.329) Foreign Bank * ROA -0.0007 0.0000 -0.0011 0.0016 0.025** 0.030** 0.038*** 0.061** (0.0016) (0.0011) (0.0015) (0.0017) (0.012) (0.012) (0.012) (0.023) Observations 7872 7872 7872 2880 7872 7032 7032 7032 2593 7032 R-squared 0.53 0.54 0.62 0.59 0.53 0.81 0.82 0.85 0.85 0.81 Number of Districts 162 162 162 17 162 162 162 162 17 162 ROA-Year Interactions – – – – – – – – 4-Digit Industry-Year Interactions – “Treated” Control Group Used – Specification OLS OLS OLS OLS IV OLS OLS OLS OLS IV Table 13 Effect of Foreign Bank Entry on Loans from Other Firms This table reports coefficients from regressions using OLS and IV with firm and year fi -ed effects. The dependent variable is stock of loans from all firms normalized by 1991 assets in columns (1)-(5) and loans from group firms only in columns (6)-(10). Yearly observations from 1991 to 2002 are included for domestic, non- financial firms with positive sales and assets in 1991 but not located in a district with a foreign bank by 1991. ‘Foreign Bank’ is equal to one for firms located in a district with a foreign bank in the given year, and zero otherwise. ‘ROA’ is a firm’s 1991-1993 average percent return on assets, demeaned. Columns (3) & (8) include 4-digit industry-year interactions. Columns (4) & (9) restrict the sample to ‘treated’ firms located in districts with a foreign bank by 2004. Columns (5) & (10) report the IV estimates. Instruments used in the first stage are ‘Foreign-Owned Firm in 1993’ * post-1993 year dummy and ‘Foreign-Owned Firm in 1993’ * ‘ROA’ * post- 1993 year dummy, where ‘Foreign-Owned Firms in 1993’ is an indicator equal to 1 for firms located in districts with at least one foreign-owned firm in 1993. Standard errors, clustered at the district-level, are reported in parentheses. * = 10% level, ** = 5% level, *** = 1% level. Dependent Variable = Loans from All Firms / 1991 Assets Loans from Group Firms / 1991 Assets (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Foreign Bank 0.013 0.020 0.010 0.018 -0.019 0.015* 0.023 0.022 0.020 0.037 (0.021) (0.026) (0.038) (0.023) (0.059) (0.009) (0.016) (0.019) (0.013) (0.031) Foreign Bank * ROA -0.008** -0.009** -0.005* -0.016*** -0.007** -0.008** -0.006** -0.014*** (0.003) (0.004) (0.003) (0.005) (0.003) (0.004) (0.003) (0.004) Observations 7088 7088 7088 2617 7088 7088 7088 7088 2617 7088 R-squared 0.47 0.48 0.52 0.65 0.48 0.69 0.71 0.73 0.75 0.70 Number of Districts 162 162 162 17 162 162 162 162 17 162 ROA-Year Interactions 4-Digit Industry-Year Interactions – – – – – – – – – – “Treated” Control Group Used – – Specification OLS OLS OLS OLS IV OLS OLS OLS OLS IV Appendix Table 1 Organisation Observations Dropped by Year 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Total Number of organistions in Prowess Data 2068 2415 3013 4004 5144 5607 5720 5658 5984 6385 5559 3571 Observations Dropped Missing District Location 13 21 42 71 106 127 131 126 146 163 119 50 No Sales or Assets in 1991 61 458 1085 2021 3135 3589 3723 3697 4006 4369 3748 2249 Foreign-Owned Firms 202 198 199 194 192 189 192 198 199 200 188 118 Financial or Banking organisation 89 86 82 86 83 85 85 84 81 81 72 58 In District with Foreign Bank in 1991 1047 1015 992 1000 994 984 970 959 969 993 922 698 Number of organisations in Regressions 656 637 613 632 634 633 619 594 583 579 510 398