Evaluation of banking sector liberalization in India and China

Part of the Contributions to Economics book series (CE)


In this chapter, the framework and the identified indicators developed in the previous chapter are used to evaluate the liberalization of the banking sectors in India and China. The assessment begins with a qualitative evaluation according to the propositions for liberalizing a banking sector. This is followed by a quantitative evaluation of the process and the results at the sector level. Finally, the overall macroeconomic effects are tested (Table 6). The combined results provide a basis for the discussion of further reform steps in the following chapter.


Financial Development Banking Sector Foreign Bank Financial Liberalization Private Sector Bank 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 2.
    See Chow (2004), p. 128f.Google Scholar
  2. 3.
    See International Monetary Fund (2006b); Kusic, Zhang and Cvijanovic (2003), p. 1.Google Scholar
  3. 4.
    In China the overall institutional framework was also far less developed than in India especially due to the deficiencies of the legal system. The two primary reasons are that the traditional Chinese legal system is based on informal social relations (guanxi) to ensure fulfillment of contracts so that by Western standards it may be considered a “semi-legal system”, and that the Communist Party is defacto above the law since it claims absolute power to rule China. See Chow (1997), p. 322 and Chow (2004), p. 145.Google Scholar
  4. 5.
    See Reserve Bank of India (2005c), p. 16.Google Scholar
  5. 6.
    See Kusic, Zhang and Cvijanovic (2003), p. 9.Google Scholar
  6. 7.
    See Allen, Qian and Qian (2005a), p. 16; Bai et al. (1999), p. 51; Lardy (2000), p. 35.Google Scholar
  7. 8.
    See Lardy (2000), p. 40.Google Scholar
  8. 10.
    The Working Group on Restructuring Weak Public Sector Banks pointed out that “although such failures to achieve agreed targets or to fulfill commitments were frequent, there never were any penalties for such failures. Banks reporting operating losses were, no doubt, barred from opening new branches, recruitment of staff and fresh capital expenditure without RBI approval, but these restrictions did not serve as a disciplining measure as, in any case, they were already overstaffed and were in no position to undertake branch expansion or to incur any major capital expenditure.” Reserve Bank of India (1999), section 5.4.Google Scholar
  9. 11.
    See Shirai (2002c), p. 28.Google Scholar
  10. 12.
    See Deutsche Bank Research (2004), p. 8.Google Scholar
  11. 13.
    This is also triggered by the current political environment with weak coalition governments that increasingly have to rely on single-state parties and are heavily influenced by vested interest groups. These weak governments are also particularly vulnerable to a loss of voter support. See Echeverri-Gent (2001), pp. 1–5; Jalan (2005), pp. 88–90.Google Scholar
  12. 14.
    India has annual budget deficits of around 10% of GDP and government debt of around 65% of GDP (see Figure 8). China has relatively moderate official deficit and debt levels of 3–4% of GDP and about 40% of GDP respectively. However, China has relatively high contingent liabilities from the bad loans that are accumulated in the banking sector, which are estimated to stand at 30% of GDP. See Mukherji (2005), p. 64f.; The Economist (2004), p. 18f.Google Scholar
  13. 15.
    See Demetriades and Luintel (1997), p. 320.Google Scholar
  14. 16.
    The net interest margins in Europe and the United States have been between 140 and 400 basis points over the last years. See International Monetary Fund (2006a), pp. 160–162.Google Scholar
  15. 17.
    Author’s calculation based on International Monetary Fund (2006b).Google Scholar
  16. 18.
    Author’s calculation based on International Monetary Fund (2006b).Google Scholar
  17. 20.
    See Reserve Bank of India (2005c), p. 73. An inverse relationship between interest rates and bond prices exists: lower interest rates result in higher bond prices.Google Scholar
  18. 21.
    Another factor that may have affected the limited expansion of credit is that credit officers of PSBs may be charged with corruption if a borrower defaults. This leads to strong incentives not to extend credit even to profitable enterprises. See Banerjee and Duflo (2004), p. 6.Google Scholar
  19. 22.
    See Garcia-Herrero and Santabarbara (2004), p. 43; Garcia-Herrero, Gavila and Santabarbara (2005), p. 40f.; Reserve Bank of India (2005c), p. 305.Google Scholar
  20. 24.
    See Wolken (1990), p. 55.Google Scholar
  21. 25.
    See BankScope (2006); Garcia-Herrero and Santabarbara (2004), p. 42; Lardy (1998), p. 100; Reserve Bank of India (2005a).Google Scholar
  22. 27.
    Furthermore, it is important to keep in mind that the calculation of the ROA is based on accounting data. Differing accounting standards across countries may consequently limit the comparability to some extent. See Ferris et al. (2000), p. 8.Google Scholar
  23. 29.
    The phased implementation of the Basel II standards in India is not expected to start before April 2007. See Gopinath (2006), p. 1f.; Mohan (2006a), p. 3.Google Scholar
  24. 30.
    See BankScope Database (2006); Garcia-Herrero and Santabarbara (2004), p.42; Reserve Bank of India (2004b), p. 85; Reserve Bank of India (2005c), p. 95.Google Scholar
  25. 31.
    At least until 2004, the aggregate CRAR of the Big Four has been well below the mandated 8%. See for example Fitch Ratings (2005), p. 10; Morgan Stanley (2004), p. 33; Pei and Shirai (2004), p. 9.Google Scholar
  26. 32.
    See Barton, Newell and Wilson (2003), pp. 53–56.Google Scholar
  27. 34.
    Author’s calculation based on Garcia-Herrero, Gavila and Santabarbara (2005), p. 40 and Reserve Bank of India (2005a).Google Scholar
  28. 36.
    Holz (2000) argues that the higher level of financial depth in China is the result of expansionary government policies. The NPLs and the implicit government guarantee of the banking system mean that a large share of the funds in the banking system is de-facto government debt. This means that financial depth may not lead to the same positive effects like in other countries because of the distorted underlying mechanisms so that the comparatively high level is somewhat misleading. See Holz (2000), p. 91.Google Scholar
  29. 37.
    Author’s calculation based on International Monetary Fund (2006b).Google Scholar
  30. 38.
    See Hope and Hu (2006), p. 45; International Monetary Fund (2005), p. 196; Lardy (1998), p. 119 and 122; Muniappan (2002), p. 2f.; Pei and Shirai (2004), p. 7.Google Scholar
  31. 39.
    The exception is the takeover of Indian private sector banks that the RBI declares as in need of restructuring. See Reserve Bank of India (2005b), p. 2.Google Scholar
  32. 42.
    See Garcia-Herrero, Gavila and Santabarbara (2005), p. 40; Reserve Bank of India (2005a). Note: assets in cooperative banks excluded from calculations.Google Scholar
  33. 44.
    The distribution of bank loans in China shows that this pattern continues: private enterprises received only 27% of bank loans in 2003, but accounted for 52% of GDP. See McKinsey Global Institute (2006b), p. 11.Google Scholar
  34. 45.
    See Llewellyn (2002), pp. 168–171; Mehrez and Kaufmann (2000), p. 2; Williamson and Mahar (1998), pp. 52–54.Google Scholar
  35. 46.
    Another factor is that foreign banks are according to China’s WTO commitments barred from offering services to Chinese individuals until the end of 2006. See Deutsche Bank Research (2004), p. 2.Google Scholar
  36. 47.
    As reported by Saez and Yang (2001), this not only appears to be the case in the banking sector but also in the electricity and telecommunications sector, where India has made more progress than China. See Saez and Yang (2001), p. 90. Other political-economy factors were discussed in the previous section. For a summary of statistical studies on the relationship between different political regimes and economic growth, see for example Przeworski and Limongi (1993).Google Scholar
  37. 48.
    See Arestis and Demetriades (1997), p. 784; Kennedy (2003), p. 301.Google Scholar
  38. 49.
    See for example the studies by Galindo, Micco and Ordonez (2002), King and Levine (1993) and Roubini and Sala-i-Martin (1992).Google Scholar
  39. 50.
    See Arestis and Demetriades (1997), p. 784; Demetriades and Hussein (1996), p. 390f.; Hu (2002), p. 3; Quah (1993), p. 1.Google Scholar
  40. 51.
    See Kennedy (2003), p. 325f. The number of differencing operations necessary to make the time series stationary is the order of integration. For example, a variable that requires one differencing operation to achieve stationarity is referred to as integrated of order one, which is expressed as “I(1)”. See Kennedy (2003), p. 326.Google Scholar
  41. 53.
    See Backhaus et al. (2003), p. 87f.Google Scholar
  42. 56.
    See Backhaus et al. (2003), p. 91; Demetriades and Luintel (1996b), p. 366; Mankiw (1995), p. 304f.Google Scholar
  43. 57.
    See Backhaus et al. (2003), pp. 79–82.Google Scholar
  44. 58.
    See Backhaus et al. (2003), pp. 84–87; Platek (2002), p. 187f.Google Scholar
  45. 59.
    See Mankiw (1995), p. 303f.; Temple (1999), p. 128f. In the former example of two-way causation the independent variable would be endogenous.Google Scholar
  46. 60.
    See Temple (1999), p. 130.Google Scholar
  47. 61.
    See National Bureau of Statistics of China (2006). For a discussion of the limitations of Chinese economic indicators and the problems of China’s statistical system, see Wu (2003).Google Scholar
  48. 62.
    See Levine and Renelt (1992), p. 944.Google Scholar
  49. 66.
    See Asian Development Bank (2006); Government of India (2006); International Monetary Fund (2006b).Google Scholar
  50. 67.
    Author’s calculation based on International Monetary Fund (2006b).Google Scholar
  51. 68.
    Author’s calculation based on International Monetary Fund (2006b).Google Scholar
  52. 69.
    See King and Levine (1993), p. 718; Wachtel (2001), p. 342.Google Scholar
  53. 70.
    See Quantitative Micro Software (2004), p. 226f.Google Scholar
  54. 71.
    See Government of India (2006), table 15; International Monetary Fund (2006b).Google Scholar
  55. 72.
    See International Monetary Fund (2006b).Google Scholar
  56. 74.
    Author’s calculation based on International Monetary Fund (2006b).Google Scholar
  57. 75.
    Author’s calculation based on International Monetary Fund (2006b).Google Scholar
  58. 76.
    See Reserve Bank of India (2004a), p. 1.Google Scholar
  59. 77.
    Lal (2006) points out that the faster-growing and more efficient private sector in China is crowded out from credit by inefficient SOEs, which lowers the ICOR and the growth rate. See Lal (2006), p. 280.Google Scholar
  60. 78.
    See Makin (2006), p. 313. This leads to a dilemma for Chinese policy makers: opening the capital account could enhance the efficiency of domestic investments and offer Chinese savers higher returns by investing abroad. However, given the fragile state of the Chinese banking system, a large capital outflow would significantly increase the risk of a banking crisis. See Lardy (2005), p. 46.Google Scholar
  61. 79.
    See Morgan Stanley (2004), p. 10.Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2008

Personalised recommendations