Chapter

4 The Growth-Financial Development Nexus

Author(s):
Wanda Tseng, and Markus Rodlauer
Published Date:
February 2003
Share
  • ShareShare
Show Summary Details
Author(s)
Christoph Duenwald and Jahangir Aziz 

In recent years the empirical growth literature has refocused on the linkages between a country’s economic growth and the level of development of its financial system. The observation that such a link exists dates back to Schumpeter (1911), who argued that the services provided by financial intermediaries—mobilizing savings, evaluating projects, managing risk, monitoring managers, and facilitating transactions—are essential for technological innovation and economic development. Although this notion was examined empirically to a limited extent in the 1970s, more sophisticated analysis of the link did not take place until the 1990s. These studies, which use cross-sectional data for large sets of countries, have tended to find a positive association between faster growth and more developed financial systems. This chapter examines this link more closely for China, through an analysis of data from China’s 28 provinces.1

Although China’s growth performance since the onset of economic reform in 1978 has been remarkable, this performance masks substantial differences in the growth rate and level of incomes per capita across different provinces. Indeed, empirical work on income convergence among China’s provinces suggests that convergence weakened in the 1990s, with the coastal provinces tending to grow much faster than the interior provinces and making a commensurately greater contribution to China’s overall growth (see Chapter 3). At the same time, the coastal provinces have the highest relative degree of nonstate sector involvement in the economy. However, almost two-thirds of domestic bank credit continues to go to the state sector, raising the question of how the nonstate sector is financing its rapid growth. More generally, after adjusting for other factors that contribute to variation in interprovincial growth, do differing degrees of financial development help explain differences in growth across provinces?

Data limitations prevent a rigorous examination of the growth-financial intermediation nexus for China as a whole; however, it is possible to analyze the issue by looking at data on China’s provinces, autonomous regions, and municipalities. Accordingly, this chapter seeks to answer the following questions:

  • What are the main characteristics of China’s system of financial intermediation at the national level, and how do they compare with those of similar countries?

  • Do differing degrees of financial development across China’s provinces help explain differences in growth performance? In this context, how has China’s growth been financed?

  • What policy implications flow from the empirical results, especially as they pertain to China’s financial sector reform program?

The main findings of this chapter are as follows:

  • Although the level of financial intermediation in China is relatively high, the financial system is generally viewed as inefficient at converting financial resources into productive investment. China’s large pool of savings—currently equivalent to 38 percent of GDP—has been almost wholly intermediated through the domestic banking system and, in large part, has been allocated to the state enterprise sector. Indeed, the nonstate sector appears to have financed itself mainly out of retained earnings or savings by the principal owner of the enterprise, or by foreign direct investment (FDI), rather than from bank credit or the capital markets.

  • Financial system development, as measured by bank loan-to-GDP ratios, has been much lower in the faster-growing provinces than in provinces that have grown less quickly than the average. Not surprisingly, provinces with higher concentrations of state-owned enterprises (SOEs) had higher loan-to-GDP ratios.

  • Although total bank credit is not significant in explaining interprovincial growth differences, nonstate sector credit is. Panel regressions suggest that, after conditioning on a number of variables including initial GDP per capita, population growth, investment, FDI, concentration of SOEs, and the ratio of fiscal revenue to expenditure, the level of financial development is not a statistically significant explanatory variable for observed interprovincial differences in growth rates of GDP per capita. However, once bank lending is adjusted for lending to SOEs so as to construct a variable that measures nonstate credit (no direct measure is available in the Chinese statistics), variations in this variable were found to affect growth in income per capita to a statistically significant (but small) degree.

Theory and Earlier Findings

Financial intermediation can affect growth through any of three channels. It can increase the marginal productivity of capital by collecting information from which to evaluate alternative investment projects, and by sharing of risk. It can raise the proportion of savings channeled to investment through financial development, by reducing the resources absorbed by financial intermediaries (in the form of interest rate spreads, commissions, and the like), thus increasing the efficiency of financial intermediation. And it can raise the private saving rate.2

The positive correlation between growth and indicators of financial development was first documented by Goldsmith (1969), McKinnon (1973), and Shaw (1973). Since then, a flourishing body of empirical work has emerged.3 These studies, which are typically based on regression analyses of large cross sections of countries (both advanced and developing), generally find that cross-country differences in financial development explain a significant portion of differences in average growth rates.

In testing for the link between growth and financial development, studies generally regress countries’ growth rates on an indicator of financial development and a set of control variables, typically including initial income per capita, educational attainment, political stability, and population growth. Following King and Levine (1993) and Levine (1997), the following indicators of financial development have typically been chosen: the ratio of liquid liabilities of the financial system to GDP (a variable that measures the combined size of the country’s financial intermediaries), the ratio of bank credit to the sum of bank credit and central bank domestic assets, the ratio of private credit to total domestic credit, and the ratio of private credit to GDP.

The main outstanding issues in the literature are threefold. What is the appropriate measurement of financial development, given that the empirical results are sensitive to the measure of financial depth used? What is the true direction of causality between financial development and growth; that is, do financial systems develop in tandem with or ahead of growth?4 And is a bank-based or a securities market-based financial system superior in terms of maximizing finance’s contribution to growth? This chapter will not explore the third issue further, but Levine (1999) and others have argued that the debate is probably misplaced: both sources of finance are important to growth, and financial development is best fostered through the establishment of a strong legal and regulatory system.

China’s System of Financial Intermediation

Despite a large deposit base, China’s system of financial intermediation is generally judged to be relatively underdeveloped and inefficient at converting financial resources into productive investment.5 This view is based on a number of the system’s characteristics:

  • Financial intermediation in China is largely bank based and dominated by four state commercial banks (SCBs), with securities (bond and equity) markets still relatively small (Table 4.1).6 The SCBs together account for two-thirds of financial system assets. Although a financial system dominated by state-owned financial institutions is not necessarily less efficient than one dominated by private firms, the state has been involved in China’s financial system on a very large scale, with government budgetary grants having been replaced since the late 1970s by state bank credit as the main source of funds for SOEs.7 Indeed, a large proportion of savings continues to be channeled to SOEs: state bank claims on SOEs still amounted to two-thirds of GDP at the end of 2000 (Table 4.2).

  • The resources intermediated through bank lending may have been misallocated. Evidence of this is the excess capacity built up in the 1990s in the real estate and manufacturing sectors, which have contributed to over two years of deflation, and the weak performance of the banks themselves. Burdened by portfolios dominated by directed credit, the state banks have been weakly profitable. Substantial nonperforming loans remain in the banking system,8 and capital adequacy needs considerable strengthening to meet international standards.

  • Despite its lack of access to domestic bank credit, China’s nonstate sector has been the most dynamic part of the economy. Thus the International Finance Corporation (IFC; Gregory, Tenev, and Wagle, 2000) estimates that, between 1990 and 1997, new jobs created in the private sector accounted for 56 percent of new formal employment in urban areas. This rapid growth has occurred despite relatively few resources coming from the financial sector: in 1991–97 the share of private investment in national investment was in the range of 15–27 percent, because private investors had little recourse to formal bank loans (less than 1 percent of working capital loans went to the private sector, according to the IFC report). In addition, private firms’ access to equity markets has been limited. The same IFC volume reports that, of the 976 companies listed on the Shanghai and Shenzhen stock exchanges, only 11 are nonstate firms, and that in 1998 and 1999 only four initial public offerings of shares in nonstate firms took place. Based on evidence from a sample survey, the IFC finds that private firms in China tend to rely primarily on internal sources of financing—including retained earnings and principal-owner financing—for both startup capital and subsequent investments.

Table 4.1.Indicators of Financial Development in Selected Countries and Country Groups, 1993–2000

(In percent)1

Country Income Group
RatioChinaIndiaRep. of KoreaJapanUnited StatesHigh incomeMiddle incomeLow income
Private sector credit to GDP40.024.1112.565.176.7121.841.159.6
Domestic assets of deposit money banks to total domestic assets80.077.393.587.990.2
M2 to GDP121.747.5113.348.059.139.373.5
Stock market capitalization to GDP14.132.432.767.8112.681.435.420.5
Memoranda:
Annual growth in real GDP per capita8.94.83.50.82.51.71.05.2
Ratio of FDI to GDP4.80.50.60.11.41.11.83.1
Sources: World Economic Outlook and International Financial Statistics databases; World Bank, World Development Indicators, various years.

Period averages.

Sources: World Economic Outlook and International Financial Statistics databases; World Bank, World Development Indicators, various years.

Period averages.

Table 4.2.Allocation of State Bank Credit(In percent of GDP)
State bank claims19931994199519961997199819992000
On nongovernment sector97.589.588.394.0102.9112.2121.8124.6
of which:
On non-SOE sector35.732.631.835.437.441.248.857.0
On SOE sector61.856.956.558.665.571.073.067.6
Sources: International Financial Statistics, various issues; People’s Bank of China data; and IMF staff estimates.
Sources: International Financial Statistics, various issues; People’s Bank of China data; and IMF staff estimates.

Evidence from Provincial Data

In contrast to most other empirical analyses of this genre, which have been carried out in a cross-country setting, this chapter uses data from China’s provinces, for several reasons:

  • Differences in the level of economic and financial development among the provinces contain information that can be exploited.

  • China’s large population and limited labor and capital mobility between provinces make the study similar to a cross-country study of medium-size countries.

  • Compared with cross-country studies, use of provincial data increases the likelihood of homogeneous data compilation methodologies.

  • The use of provincial-level data expands the sample size considerably.

Although provincial-level data on “national” accounts are available for a relatively long period, information on financial variables in the provinces is more limited. As a result, much of the analysis is conducted for 1988–97, a period for which consistent data covering 27 provinces (listed in Table 4.3) are available.9 Apart from the short time span, other shortcomings in the available information exist and are discussed below.

Table 4.3.Summary Financial and SOE Data by Province, 1988–97

(In percent)1

ProvinceGrowth

in GDP

Per Capita
Ratio of

Bank

Loans to

GDP
Ratio of

Banking

Sector to

GDP
SOE

Share of

Industrial

Output
Ratio of

FDI to GDP
Ratio of SOE

Operating

Surplus to

GDP
All China8.270.56.043.43.322.6
Fujian14.053.55.832.09.521.4
Guangdong13.472.17.329.610.620.0
Zhejiang12.645.34.421.21.827.8
Jiangsu12.350.74.926.74.227.0
Shandong11.751.05.834.22.625.4
Hebei10.961.96.340.11.127.4
Shanghai10.299.89.550.36.934.4
Guangxi10.160.74.655.42.517.0
Jiangxi10.077.26.655.11.220.1
Anhui9.759.24.242.81.017.7
Henan9.461.74.843.90.815.2
Hubei9.277.84.250.91.322.4
Beijing9.0108.210.356.05.230.6
Tianjin8.8113.08.440.87.133.2
Yunnan8.561.85.570.40.419.2
Sichuan8.458.35.351.41.719.5
Jilin8.2111.16.265.81.417.9
Hunan8.257.54.450.61.215.7
Inner Mongolia7.883.95.566.60.421.8
Gansu7.789.35.471.70.521.2
Xinjiang7.786.85.477.90.323.0
Shanxi7.683.97.851.00.522.8
Shaanxi7.591.27.663.01.716.0
Liaoning7.582.06.450.42.930.1
Heilongjiang7.184.15.672.61.122.8
Ningxia6.9107.68.973.00.322.3
Guizhou6.272.64.571.00.513.8
Qinghai5.2115.97.082.10.117.1
Sources: National Bureau of Statistics (1996); China Statistical Yearbook, various issues.

Value added less depreciation, wages, and taxes.

Sources: National Bureau of Statistics (1996); China Statistical Yearbook, various issues.

Value added less depreciation, wages, and taxes.

Overall, the results suggest that the level of financial development has not played a key role in contributing to growth within China. Rather, bank loans appear to have been channeled to provinces with heavy concentrations of SOEs, which are also the provinces that have tended to grow relatively slowly, suggesting that the productivity of lending was relatively low. This picture appears to corroborate an often-told story about China’s transition from plan to market: although things are changing with ongoing SOE and banking reforms, the banking system has been used to keep inefficient SOEs afloat so as not to produce excessive layoffs and raise the cost of the transition to levels that might threaten social stability.

Preliminary Analysis

To get a sense of the role played by financial development in China’s growth, provinces were grouped according to certain economic characteristics, and dummy variables were constructed for each group. Three groupings were made, as follows: provinces with above-average and those with below-average growth, those with above-and below-average levels of financial intermediation (as measured by the bank loans-to GDP ratio), and those with above-average and below-average levels of SOE concentration. For the first grouping, the level of financial intermediation in the high-growth group was compared with that in the low-growth group, and similar exercises were conducted for the other groupings. This exercise produced the results presented in Figures 4.1 to 4.4 and summarized below:

  • Those provinces with above-average GDP growth had bank loans-to-GDP ratios that were significantly lower—by up to 36 percent of GDP—than provinces with below-average growth (Figure 4.1).10

  • Correspondingly, provinces with above-average levels of financial intermediation experienced lower annual growth rates (by up to 4¼ percentage points) than provinces with below-average levels of intermediation (Figure 4.2).

  • Provinces with above-average concentrations of SOEs had higher loan-to-GDP ratios than those with below-average concentrations (Figure 4.3).

  • Provinces with above-average growth rates had relatively larger profit-to-GDP ratios, suggesting that firms in these provinces financed themselves out of retained earnings rather than with bank loans (assuming profits are a good proxy for corporate saving; Figure 4.4). Moreover, as discussed in the next section, firms in fast-growing provinces were able to avail themselves of foreign savings through FDI.

Figure 4.1.Financial Intermediation and Growth

(In percent)1

Sources: National Bureau of Statistics; Lardy (1998); Almanac of China’s Finance and Banking, various issues; and IMF staff estimates.

1Financial intermediation is measured on the vertical axis as the ratio of bank loans to provincial GDP.

Figure 4.2.Growth and Financial Intermediation

(In percent)1

Sources: National Bureau of Statistics; Lardy (1998); Almanac of China’s Finance and Banking, various issues; and IMF staff estimates.

1Growth is measured on the vertical axis as the growth rate of provincial GDP per capita.

Figure 4.3.Financial Intermediation and SOE Concentration

(In percent)1

Sources: National Bureau of Statistics; Lardy (1998); Almanac of China’s Finance and Banking, various issues; and IMF staff estimates.

1Financial intermediation is measured on the vertical axis as the ratio of bank loans to provincial GDP.

Figure 4.4.Profitability and Growth

(In percent)1

Sources: National Bureau of Statistics; and IMF staff estimates.

1Profitability is measured on the vertical axis as the ratio of profits to provincial GDP.

Regression Analysis

The methodology adopted in this section closely follows that established in previous studies. A series of fixed-effects panel regressions are estimated, with various combinations of control variables, to draw inferences about the role played by bank intermediation in growth, resource mobilization, and productivity. The general form of the panel regression equations is the following:

where y is the dependent variable (growth, investment, or productivity), X is the standard set of neoclassical growth factors (lagged real GDP per capita, population growth, and investment), F is the financial intermediation variable of interest (total bank loans, bank loans to SOEs, or bank loans to the nonstate sector), and K is a set of other control variables (such as the fiscal surplus, the share of SOEs in industrial output, a dummy variable for coastal provinces, and FDI). Subscript i indicates a province and subscript t the time period.

This equation follows the practice elsewhere in the literature of supplementing a standard growth regression with variables measuring the level of financial development. The expectation is that the coefficient on the financial development variable is positive and statistically significant.

The following results pertaining to growth emerge from this exercise:

  • The expansion of bank credit during 1988–97 did not exert a statistically significant influence on growth. This can be seen from regression equation (5) in Table 4.4, where, although the ratio of total bank loans to GDP has a marginally positive sign, it is not significant. This result formally confirms the preliminary analysis (Figure 4.1), which indicated that bank credit in the faster-growing provinces was lower than that in the slower-growing provinces. In that discussion it was also pointed out that the likely reason behind this phenomenon was the large proportion of bank credit provided to the SOE sector, which was relatively less productive than the nonstate sector. This hypothesis is also confirmed.

  • Bank credit to the nonstate sector, however, exerts a positive and statistically significant influence on growth. This result is borne out by equation (6), where the coefficient on bank loans to the nonstate sector—although small, possibly because it is a constructed variable—is significant at the 1 percent level. The small coefficient, however, could also reflect the limited importance of this source of financing for China’s fast-growing nonstate sector enterprises.11

  • Apart from the financial sector results, the exercise also shows that FDI has played a significant role in China’s growth process.12 Equations (4) through (6) display a remarkable consistency in the growth elasticity of FDI: regardless of the control variables used, a 1-percentagepoint increase in the FDI-to-GDP ratio raises the per capita growth rate by about ½ percentage point. This again corroborates the widely held view that FDI has been a critical source of financing for China’s growth.13

Table 4.4.Results of Regressions of Output Growth on Financial Intermediation Measures
Variable1(1)(2)(3)(4)(5)(6)
Lagged real GDP per capita1.291.57–3.73***–5.27***–5.48***–5.31***
Population growth (percent)–1.46**–1.45**–1.44***–1.42***–1.41***–1.42***
Ratio of total investment to GDP0.28**0.25**0.25***
Ratio of domestic investment to GDP0.21***0.21***0.21***
Ratio of FDI to GDP0.56***0.57***0.56***
Ratio of government revenue to expenditure0.02***0.03**0.02**0.03**
Share of SOEs in industrial output–0.19***–0.18***–0.2***
Coastal dummy variable–1.56
Ratio of bank loans to GDP0.03
Ratio of bank loans to nonstate sector to GDP0.002***
Summary statistics
Adjusted R20.430.410.490.500.500.51
P value of Hausman test20.000.000.000.000.050.01
No. of observations280277277277277277
Source: IMF staff estimates based on data from China Statistical Yearbook, various issues.

The dependent variable is the growth rate of real GDP per capita; regressions are fixed-effects panel regressions on data from 1988 to 1997.

Null hypothesis: random versus fixed effects.

Indicates statistical significance at the 1 percent level.

Indicates statistical significance at the 5 percent level.

Source: IMF staff estimates based on data from China Statistical Yearbook, various issues.

The dependent variable is the growth rate of real GDP per capita; regressions are fixed-effects panel regressions on data from 1988 to 1997.

Null hypothesis: random versus fixed effects.

Indicates statistical significance at the 1 percent level.

Indicates statistical significance at the 5 percent level.

Several previous studies have also pointed out that financial intermediation helps growth in two ways: by facilitating resource mobilization, and by helping to improve resource allocation, thereby enhancing total factor productivity (mostly of capital). To test this hypothesis, these studies measure the impact of financial intermediation separately on growth, investment, and productivity. Measures of productivity by province are not readily available for China, however. Following the literature, total factor productivity growth is therefore proxied as actual growth less the fraction α of capital growth (net investment), under the assumption that about one-third of national income accrues to capital.

The level of financial development did not have a statistically significant impact on domestic investment (Table 4.5). In many developing countries the deepening of financial development generally raises the rate of investment by lowering the cost of matching the savings of households with the investment needs of the corporate sector. This does not appear to have occurred in China. Neither total bank credit nor nonstate bank credit exerted a significant influence on the rate of domestic investment.

Table 4.5.Results of Regressions of Investment on Financial Intermediation Measures
Equation
Variable1(1)(2)(3)(4)(5)
Lagged growth of real GDP per capita0.26***0.27**0.318**0.24***0.25***
Ratio of bank loans to nonstate sector to GDP0.0010.0010.0010.0010.02
Ratio of government revenue to expenditure–0.01–0.02–0.01–0.01
Ratio of FDI to GDP–0.398**–0.3***–0.27*
Ratio of lagged domestic investment to GDP0.35***0.34***
Share of SOEs in industrial output1.95
Summary statistics
Adjusted R20.790.790.820.830.83
P value of Hausman test20.050.230.270.000.00
Number of observations252249249249249
Source: IMF staff estimates based on data from China Statistical Yearbook, various issues.

The dependent variable is the ratio of domestic investment to GDP; regressions are fixed-effects panel regressions on data from 1988 to 1997.

Null hypothesis: random versus fixed effects.

Indicates statistical significance at the 1 percent level.

Indicates statistical significance at the 5 percent level.

Source: IMF staff estimates based on data from China Statistical Yearbook, various issues.

The dependent variable is the ratio of domestic investment to GDP; regressions are fixed-effects panel regressions on data from 1988 to 1997.

Null hypothesis: random versus fixed effects.

Indicates statistical significance at the 1 percent level.

Indicates statistical significance at the 5 percent level.

On the other hand, nonstate bank credit appears to have exerted a positive and significant influence on productivity (Table 4.6). Thus the deepening of financial intermediation in China seems to have aided growth by allowing savings to be allocated more efficiently rather than simply making more savings available for investment purposes.

Table 4.6.Results of Regressions of Productivity on Financial Intermediation Measures
Equation
Variable1(1)(2)(3)(4)(5)
Lagged real GDP per capita1.71–3.73***–5.28***–5.49***–5.32***
Population growth (percent)–1.45***–1.43***–1.42***–1.42***–1.42***
Ratio of total investment to GDP–0.05–0.05
Ratio of domestic investment to GDP–0.09**–0.09**–0.09**
Ratio of FDI to GDP0.26***0.27***0.26***
Ratio of government revenue to expenditure0.028*0.03**0.02**0.03**
Share of SOEs in industrial output–0.19***–0.18***–0.2***
Ratio of total bank loans to GDP0.03
Ratio of bank loans to nonstate sector to GDP0.002***
Summary statistics
Adjusted R20.500.550.560.560.55
P value of Hausman test20.000.000.010.010.01
Number of observations280277277277277
Source: IMF staff estimates based on data from China Statistical Yearbook, various issues.

The dependent variable is total factor productivity; regressions are fixed-effects panel regressions on data from 1988 to 1997.

Null hypothesis: random versus fixed effects.

Indicates statistical significance at the 1 percent level.

Indicates statistical significance at the 5 percent level.

Source: IMF staff estimates based on data from China Statistical Yearbook, various issues.

The dependent variable is total factor productivity; regressions are fixed-effects panel regressions on data from 1988 to 1997.

Null hypothesis: random versus fixed effects.

Indicates statistical significance at the 1 percent level.

Indicates statistical significance at the 5 percent level.

Data Issues

As noted above, regression analysis of the growth-financial development link is complicated by the paucity of data on financial variables. In particular, data on private credit, an indicator of financial development used extensively in the literature, are not available. At the provincial level, only data on aggregate bank lending are available on a consistent basis. However, a significant portion of bank lending in China has gone to SOEs, so that using total bank lending as a proxy for nonstate sector credit is not appropriate. To circumvent this difficulty, a proxy for non-state sector credit was used in the empirical analyses in this chapter.

The proxy is constructed using the share of credit to SOEs predicted by a province’s share of SOE value added in GDP. Formally, a fixed-effects panel regression of the total bank lending-to-GDP ratio on the share of SOEs in industrial output is estimated, with the error term following a first-order autoregressive process to correct for serial correlation. Loans to the nonstate sector are then proxied by discounting from total bank lending the proportion explained by the share of SOEs in industrial output in the estimated equation.

Conclusions

Contrary to the findings of most previous studies for large cross sections of countries, the results of this chapter suggest that financial development, as proxied by total bank lending, has not significantly boosted growth among China’s provinces. This finding probably reflects the large proportion of lending channeled to the SOE sector. Nonstate credit, on the other hand, has had a statistically significant, although small, effect on growth.

This chapter also finds that nonbank sources of finance have played a significant role in financing China’s growth. In particular, FDI was shown to have a large impact on provinces’ income per capita: the coefficient on the FDI variable displayed remarkable robustness in the face of changing model specifications.

Anecdotal and survey evidence suggests that the internal savings of enterprises have played an important role in financing growth in China. However, this conclusion could not be corroborated empirically, reflecting the lack of consistent data on corporate savings in China’s provinces.

The main implication for China’s financial sector reform agenda is the importance of channeling a higher proportion of savings to the non-state sector. This will allow China’s financial sector to act as an efficient intermediary between savers and borrowers, and thus strengthen the positive link between financial development and growth. Some related conclusions can also be drawn:

  • It will be crucial to raise the efficiency of bank lending through the adoption of market-based lending principles. The commercialization of banking, in turn, will require the establishment of an appropriate legal framework to protect creditor rights, as well as the gradual liberalization of interest rates.

  • The nonstate sector’s access to equity and debt financing should be enhanced. Again, strengthening the legal framework (bankruptcy and company laws, protection of shareholder rights) as well as the application of internationally accepted accounting rules and governance codes will be important in giving firms the incentives to finance themselves through the securities markets, and investors the incentives to participate in this process. Such enhancements to the investment environment would also promote a continued strong inflow of FDI.

References

    CullRobert and L.C.Xu2000“Bureaucrats, State Banks, and the Efficiency of Credit Allocation: The Experience of Chinese State-Owned Enterprises,”Journal of Comparative EconomicsVol. 28No. 1 pp. 131.

    Dayal-GulatiAnuradha and Aasim M.Husain2000“Centripetal Forces in China’s Economic Take-off,”IMF Working Paper 00/86 (Washington: International Monetary Fund).

    GoldsmithRaymond W.1969Financial Structure and Development (New Haven, Connecticut: Yale University Press).

    GregoryNeilStoyanTenev and DileepWagle2000China’s Emerging Private Enterprises—Prospects for the New Century (Washington: International Finance Corporation).

    KhanMohsin S. and Abdelhak S.Senhadji2000“Financial Development and Economic Growth: An Overview,”IMF Working Paper 00/209 (Washington: International Monetary Fund).

    KingRobert G. and RossLevine1993“Finance and Growth: Schumpeter Might Be Right,”Quarterly Journal of EconomicsVol. 108No. 3 (August) pp. 71737.

    LardyNicholas R.1998China’s Unfinished Economic Revolution (Washington: Brookings Institution).

    LardyNicholas R.2000“When Will China’s Financial System Meet China’s Needs?”paper presented at the Conference on Policy Reform in China, Center for Research on Economic Development and Policy Reform, Stanford UniversityNovember 18–20, 1999.

    LevineRoss1997“Financial Development and Economic Growth: Views and Agenda,”Journal of Economic LiteratureVol. 35No. 2 pp. 688726.

    LevineRoss1999“Financial Development and Growth: Where Do We Stand,”Estudios de EconomiaVol. 26No. 2 pp. 11336.

    McKinnonRonald I.1973Money and Capital in Economic Development (Washington: Brookings Institution).

    National Bureau of Statistics1996The Gross Domestic Product of China 1992–95 (Beijing: China Statistical Publishing House).

    PaganoMarco1993“Financial Markets and Growth—An Overview,”European Economic ReviewVol. 37No. 2-3 pp. 61322.

    SchumpeterJoseph A.1911The Theory of Economic Development: An Inquiry into Profits Capital Credit Interest and the Business Cycle tr. by Redvers Opie 1934 (Cambridge, Massachusetts: Harvard University Press).

    ShawEdward S.1973Financial Deepening in Economic Development (New York: Oxford University Press).

    ZhaoRui1998“Capital Mobility and Regional Integration: China 1978–95” (unpublished; Washington: International Monetary Fund).

As discussed further below, data limitations have largely prevented China from inclusion in cross-country studies.

However, the impact of financial development on private saving is ambiguous theoretically. Efficient risk sharing could lower the saving rate, reducing growth. For example, see the discussion in Pagano (1993).

What follows is a partial review of the macroeconomic literature. There have also been numerous studies investigating the growth-financial development link at the industry and at the firm level. Levine (1997) and Khan and Senhadji (2000) provide more comprehensive surveys.

Econometrically, this is a problem of simultaneity bias and has been tackled using instrumental variables or related econometric techniques.

For example, see Lardy (2000).

Average stock market capitalization for China as shown in the table masks the fact that it has risen sharply in recent years—largely reflecting higher prices—to reach 55 percent of GDP in mid-2001. However, two-thirds of market capitalization is nontradable, and equity issuance is dominated by SOEs.

Cull and Xu (2000) find that the shift of SOE financing from government transfers to bank credit increased the SOEs’ productivity (at least in the 1980s).

Despite the transfer of a substantial portion (Y 1.4 trillion) of nonperforming loans to the asset management companies, the average ratio of nonperforming loans to total lending is officially estimated at 25 percent. Market estimates are substantially higher.

The provincial data set used in this chapter was first compiled by Zhao (1998) and later updated by Dayal-Gulati and Husain (2000) and Chapter 3, this volume.

Whether the large drop in the difference in 1997 is an anomaly is difficult to ascertain without an extension of the data base beyond 1997.

Since the nonstate credit variable was constructed by regressing total bank loans on the share of SOEs in industrial output, the latter variable is dropped from this regression to avoid multicollinearity.

This finding is consistent with the results in Chapter 6, which provides a detailed analysis of the impact of FDI on GDP and total factor productivity growth.

The results from the exercise also confirm the findings in Chapter 3 that incomes per capita are converging across China’s provinces not in the absolute sense but only in the conditional sense. For details, see Dayal-Gulati and Husain (2000), who studied convergence using average cross-provincial regressions, and Chapter 3, this volume, which used nonparametric estimators.

    Other Resources Citing This Publication