Interaction of FSFDI with the Countries’ Absorptive Capabilities Borensztein et al. (1998) detected a positive and significant interaction between the

stock of human capital and FDI. They interpret this finding with the observation that

“the flow of advanced technology brought along by FDI can increase the growth rate of the host economy only by interacting with that country’s absorptive capabil-ity”. Evans, Green and Murinde (2002) find human capital and bank-sector size to be complements and suggest that the presence of a developed banking system is necessary to exploit the productivity-enhancing potential of human capital.

Following this line of research, we implement as a further improvement interac-tion terms between the stock of FSFDI and the stocks of human and physical capital. We enclose the products of FSFDI and human and physical capital si-multaneously in five different regressions. While the interaction of the FSFDI stock with the index of employees’ education has a positive impact on economic growth, the interaction of the FSFDI stock with the stock of physical capital is associated negatively to growth. Both effects together could explain the in-significant impact of contemporaneous FSFDI values in other equations. The specifications in columns (2) and (3) in Table 5 (financial M&A per employee or to GDP, respectively) replace the financial M&A variable by the mentioned interaction terms and yield coefficients that are highly statistically significant.

The high significance of the interaction terms may be the effect of the omission of other relevant factors, in particular, the FSFDI variable by itself. Therefore we include FSFDI, human capital, and physical capital individually alongside their product in the respective regressions of Table 3 (columns 3-4: change of inward FSFDI stock per employee) and Table 4 (column 4: level of inward FSFDI stock per GDP). In this way, we can test jointly whether these variables affect growth by themselves or through the interaction term (see Borensztein et al. 1998). In Table 3 the two interaction terms do not change their sign and are still significant, albeit only at the 10% level. FSFDI by itself enters the equation positively but is still statistically significant only with a lag of two years.

Let us try to interpret these findings more accurately. Firstly – considering the positive human capital-related interaction term – we can detect complementary effects between FSFDI and human capital on economic growth. FSFDI seems to spur economic growth depending on a higher human capital stock, which is

in line with the finding of Borensztein et al. (1998) that the contribution of FDI to growth holds only when the host country has a minimum stock of human capital.

Knowledge-spillovers to domestic banks associated with the inflow of FSFDI (e.g. in the form of new technology introduced by foreign banks as argued by Zajc 2002, 200) can be an explanation for this phenomenon. These spillovers can take place if domestic banks are able to cope with the increasing competi-tion induced by foreign owners. Furthermore, foreign banks seeking to mitigate their own risk might act as a catalyst for regulatory changes and implementa-tion of internaimplementa-tional corporate standards. Consequently, improvements in ac-counting standards and auditing practices have to follow (BIS 2004, 13). Such implemented higher standards create the need for adoptions and further human capital formation of employees in all industries and companies, not only those seeking for external bank finance. In this context, the particular role of the finan-cial industry within an economy needs to be considered.

Secondly – considering the negative physical capital-related interaction term – substitutive effects between FSFDI and domestic physical capital on economic growth are indicated. De Mello (1999) similarly finds that FDI among OECD economies is growth enhancing only for countries in which domestic and foreign capital are complements. On the one hand, FSFDI may have a weaker impact on economic growth in the case of a higher physical capital stock. On the other hand, the physical capital stock may have a weaker impact on economic growth in the case of a higher FSFDI stock. The latter effect can be interpreted by the crowding-out of local physical capital caused by the entry of a foreign bank.

Schumpeterian effects of creative destruction seem to be at work. The first ef-fect, however, which is probably the stronger one, cannot be interpreted in a straightforward manner. Analogously to Carkovic & Levine (2002) and Campos

& Kinoshita (2002), we could argue that FSFDI is only growth-enhancing in countries with low physical capital stocks. In any case, respective analysis de-serves more attention. In general, market microstructure features like the mode of entry (greenfield vs. M&A), the main line of business of foreign owners and their target groups (retail vs. wholesale/corporate) have to be considered with regard to the impact on local physical capital formation.

Conclusions

Reviewing and combining the finance-growth and the FDI-growth literature, we identified special characteristics of foreign owned banks, such as better risk-management techniques, and discuss research results on how foreign owned banks have an impact on financial sector development. We contribute to the literature by identifying the following possible transmission channels between FSFDI, financial sector development, and economic growth: intermediation/effi-ciency, intermediation/credit volume, corporate governance and institution build-ing, as well as signal effects for total FDI and portfolio investments.

We provided novel data on FSFDI for the CEE region and the foreign ownership structure of banks in the SEE region. We saw that FSFDI accounts for about 18% of total FDI and that foreign penetration in the financial sector of these countries has steadily increased, whereby in recent years---because of a slow-down of bank privatization in CEE---a shift of primary FSFDI to the SEE region and further East (e.g. to Ukraine and Russia) can be observed. In both the CEE and SEE region the importance of foreign involvement is strongly underlined by the fact that 70% of total bank assets are controlled by foreign owners.

Our empirical results indicate that there can be a positive relationship between FSFDI and growth in CEE-10 for the period 1996-2003. It depends on a careful examination of lagged, hump-shaped or interacted effects of FSFDI. While this extension of Eller et al. (2006) to South-Eastern Europe concentrated on one aspect of the FSFDI-growth-relationship – the efficiency channel, further channels need to be investigated in the future. Does FSFDI trigger growth in private do-mestic credit (whereas credit volume frequently is mentioned as a likely cause for GDP growth in the literature)? Does FSFDI trigger shrinking interest rate margins (whereas credit price again should have an impact on investment and growth ac-cording to common assumptions)? Does FSFDI also attract FDI in the real sector or portfolio investment into the host country stock exchange (i.e. are there spillover effects)? The above research clearly calls also for a regional broadening. While we concentrated on “emerging Europe” here, the issue equally applies to Latin America and South-East Asia which show many similarities in transition efforts.

Dependent Variable: �ln(RGDPLit) REGRESSIONS EXPLANATORY VARIABLES

Table 3: Temporary EffectsTable 4: Permanent Effects (1) FSFDIit = �ln(FSFDIEMPit) (2) FSFDIi,t-m = �ln(FSFDIEMPi,t-2) (3) FSFDIit = �ln(FSFDIEMPit) (4) FSFDIi,t-m = �ln(FSFDIEMPi,t-2) (5) FSFDIit = �ln(FSFDIGDPit) (6) FSFDIi,t-m = �(FSFDI HUMPi,t-2) (1) FSFDIit = ln(FSFDIEMPit) (2) FSFDIi,t-m = ln(FSFDIEMPi,t-3) (3) FSFDIit = ln(FSFDIGDPit) (4) FSFDIi,t-m = ln(FSFDIGDPi,t-3)

(5) FSFDIi,t-m = ln(FSFDIHUMPi,t-2) Constant0.036*** (9.320)0.020** (2.363)0.068 (1.536)0.065 (1.026)0.034*** (12.193)0.030*** (4.063)0.027 (0.713)0.101** (2.226)0.035 (1.424)-0.065* (-1.998)0.030*** (3.434) FSFDIit, resp. FSFDIi,t-m0.0004 (0.060)0.019*** (2.750)0.002 (0.241)0.020*** (3.103)-0.0012 (-0.131)0.002 (1.216)0.001 (0.216)-0.013* (-1.792)0.0005 (0.064)-0.022*** (-2.782)0.007 (1.329) �ln(kit)0.087*** (3.077)0.090*** (3.227)0.102*** (5.176)0.113*** (3.692)0.103*** (4.675)0.114*** (3.448)0.087*** (3.102)0.087*** (3.249)0.103*** (4.722)0.100*** (3.936)0.079*** (2.718) �ln(hit)0.037 (0.139)0.179 (0.695)0.078 (0.272)0.251 (0.865)-0.008 (-0.007)0.043 (0.192)0.031 (0.119)-0.074 (-0.323)0.005 (0.019)0.064 (0.252)0.272 (1.043) �ln(GCit)-0.110* (-1.658)-0.016 (-0.180)-0.113* (-1.733)-0.119* (-1.765)-0.112* (-1.717)-0.102 -1.225-0.123* (-1.849)-0.097 (-1.213)-0.028 (-0.406) πit-0.009 (-1.185)0.221 (1.307)0.211 (1.188)0.170 (1.026)-0.009 (-1.108)0.135 (0.694)0.023 (0.116)-0.072 (-0.953) ln(FSFDIit)� ln(hit)0.005* (1.802)0.006* (1.675)0.008*** (2.147) ln(FSFDIit)� ln(kit)-0.003* (-1.859)-0.003* (-1.825)-0.003*** (-2.369) No. of Total Observations6350635063456450645054 Estimation MethodLSDVLSDVLSDVLSDVLSDVLSDVLSDVLSDVLSDVLSDVLSDV DWH: K*; t- or F-aux1; 0.7961; 0.7323; 2.88*3; 1.481; 0.8231; -0.9311; 0.9651; -0.4591; 0.7923; 2.301; 1.050 Adj. R²0.4040.2720.4180.2790.4090.3440.3950.2450.4000.2780.323 F-Value (sig. of R²)3.218***1.960**3.220***1.953**3.391***2.442***3.063***1.837*3.215***1.902*2.406*** Source: Eller et al. (2006). Notes: Static variable-intercept panel data model with country-fixedandtime-fixedeffects(whosecoefficientsarenotreportedhere,butavail-ableuponrequest).t-statistics are in parentheses, basing on heteroskedasticity-robust standard errors (White diagonal s.e. & covariance; no df correction). Asterisks indicate significanceatthe10%(*),5%(**),and1%(***)level, respectively. DWH (Durbin-Wu-Hausman test): K* represents the number of regressors with endogeneity suspi-cion, the t- (for K*=1) or F-statistic (for K*>1) represent the significanceofthefirststage residuals in the auxiliary regression. Romania is not included into this setting be-cause of limited FSFDI data. For the variable definitionsandsourcesseethedataappendix.

Table 3 and Table 4: Fixed effects panel data results - impact of inward FSFDI on economic growth in CEE-10, (cross-country growth accounting, annual data 1996-2003)

Table 5: Fixed effects panel data results - impact of financial M&A on economic growth in CEE-10 (cross-country growth accounting, annual data 1996-2002)

Source: Eller et al. (2006). Notes: Static variable-intercept panel data model with country-fixed and time-fixed ef-fects (whose coefficients are not reported here, but available upon request). t-statistics are in parentheses, basing on heteroskedasticity-robust standard errors (White diagonal s.e. & covariance; no df correction). Asterisks indicate significance at the 10%(*), 5%(**), and 1% (***) level, respectively. DWH (Durbin-Wu-Hausman test): K* represents the number of regressors with endogeneity suspicion, the t- (for K*=1) or F-statistic (for K*>1) represent the signifi-cance of the first stage residuals in the auxiliary regression. Croatia is not included into this setting because of limited financial M&A data. For the variable definitions and sources see the data appendix.

Dependent Variable: �ln(RGDPL_it)

REGRESSIONS

EXPLANATORY

VARIABLES

Permanent Effects FINMA(1) _it

= ln(FIN-MAEMP_it)

FINMA(2) _it

= ln(FIN-MAEMP_it)

FINMA(3) _it

= ln(FIN-MAGDP_it)

FINMA(4) _it

= ln(FIN-MAGDP_it)

FINMA(5) _i,t-m

= ln(FIN-HUMPMA _i,t-2)

FINMA(6) _i,t-m

= ln(FIN-HUMPMA _i,t-3)

Constant 0.036***

(5.008) 0.024***

(3.304) 0.026**

(2.183) 0.048***

(3.684) 0.024***

(5.321) 0.025***

(4.994) FINMA_it , resp.

FINMA_i,t-m -0.0009

(-0.489) -0.001

(-0.577) 0.015***

(2.752) 0.011**

(2.251)

�ln(k_it) 0.111***

(3.717) 0.113***

(4.899) 0.112***

(3.718) 0.077**

(2.607) 0.100***

(4.654) 0.101***

(4.256)

�ln(h_it) 0.958

(1.537) 0.292

(0.459) 0.965

(1.550) 0.405

(0.659) -0.458

(-1.159) 0.177 (0.589)

�ln(GC_it) -0.199***

(-3.512) -0.170***

(-3.076) -0.198***

(-3.481) -0.160***

(-3.077) -0.201***

(-3.471) -0.175***

(-3.089)

π_it -0.008

(-0.968) -0.008

(-0.943) -0.016**

(-2.292) -0.005

(-0.673) -0.009 (-0.932) ln(FINMA_it)� ln(h_it) 0.007***

(3.443) 0.007***

(3.433) ln(FINMA_it)� ln(k_it) -0.003***

(-3.597) -0.002***

(-3.560) No. of Total

Observations 45 45 45 45 70 60

Estimation Method LSDV LSDV LSDV LSDV LSDV LSDV

DWH: K*;

t- or F-aux 1; -0.644 2; 2.27 1; -0.812 2; 1.45 1; 0.118 1; 1.264

Adj. R² 0.416 0.418 0.417 0.491 0.483 0.454

F-Value (sig. of R²) 2.655** 3.352*** 2.660** 3.128*** 4.235*** 3.873***

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Data Appendix

Real GDP per worker (RGDPL): real GDP at 1995 domestic market prices di-vided by the number of employed persons of the total economy. The values for Croatia between 1994 and 1999 are interpolated with the trend growth rate between 2000 and 2005. Source: AMECO (annual macro-economic) database of the European Commission’s Directorate General for Economic and Financial Affairs (DG ECFIN), April 2005. Trend growth rates have been calculated analo-gously to Temple (1999, 119).

Employment (L): number of employed persons of the total economy. Source:

AMECO database (national accounts).

Inward FSFDI stock (FSFDI): inward FDI stocks that are devoted to financial intermediation according to NACE code J, including equity capital, reinvested earnings and loans, mn EUR, for CEE-10, from 1996 to 2004. Source: Hunya &

Stankovsky (2006). Primary source: National Banks according to international investment position (IIP).

Hump-shaped index for FSFDI: is constructed analogously to Eller (2004): in a country ranking the lowest and highest values of FSFDI per GDP get a value of one. The next lowest and highest values get higher values and this procedure is continued up to the medium range values of FSFDI per GDP, which get the high-est values. This procedure is repeated for each year within the high-estimation period.

FDI: total inward FDI stock (mn EUR) including equity capital, reinvested earn-ings and loans from 1996 to 2004. Source: Hunya & Stankovsky (2006). Primary source: National Banks according to international investment position (IIP).

Financial cross-border M&A (FINMA): flow data relating to completed cross-bor-der M&A (mn EUR) in the financial sector for all eleven CEECs (except Croatia) from 1994 to 2002. The data exclude corporate transactions involving less than 5% of ownership of banks and nonblank financial institutions or less than 3% if the transaction value is greater than 1 million US-$. Although, in practice, all transactions referred to are acquisitions, the acronym M&A is used. Source:

European Central Bank (Baudino et al., 2004).

Physical capital stock per worker (k): real physical capital stock per em-ployee at 1995 domestic market prices. Time series on the physical capital stock (K) were calculated by using perpetual inventory methods. The initial capital stock values (K₀) were calculated following Easterly & Levine (2001)

by , where represents annual

aver-age investment rates (gross fixed capital formation (GFCF) of the business sec-tor) over a ten year period, denotes output growth averaged over a ten year period, and is a constant rate of depreciation assumed to be 0.07. Assuming that the growth rate of the capital stock can be approximated by the growth rate of GFCF, further values of the capital stock are calculated by taking the initial value, using annual real changes of GFCF and dividing the values by the number of em-ployed persons of the total economy. Source: WIIW Research Reports 314, March 2005; International Financial Statistics (IFS) of the IMF; AMECO database.

Human capital stock (h): constructed index using reported education levels of employees 1996–2003 (low educated: ISCED-classification 0-2, weight 1; me-dium educated: ISCED 3-4, weight 1.4; high educated: ISCED 5-6, weight 2).

Source: EUROSTAT, labor force surveys, primarily 2nd quarter 1998–2003 (no data for Croatia). Data for 1996–1997 are interpolated using the trend growth rate between 1998 and 2003. Data for Lithuania are adjusted because of a structural break 2000–2001 which has given rise to overestimated high educated and un-derestimated low educated employees. The EUROSTAT data have been favored respecting educational attainment rates of the Barro and Lee (2001) database, since the latter one does not provide sufficient data for the Baltic countries.

Government consumption (GC): Real final consumption expenditure of the gen-eral government to real GDP at 1995 domestic market prices, representing the size of the public sector. Source: AMECO database.

Inflation (π): Growth rate of the GDP price deflator at market prices (national currency). Source: AMECO database.

Interest spreads: ratio of each CEE country’s interest spread to the aggregate Eurozone interest spread (lending rate minus deposit rate, in percent per annum, end-year). Once this spread ratio is bigger than 1, there will be an incentive for a Eurozone bank to invest in the specific CEE country. We used this variable as an instrument for the DWH first stage.Source: IMF, IFS statistics.

In document OF FOREIGN DIRECT INVESTMENTSON BANKING SECTORSIN SOUTH EAST EUROPEAN COUNTRIES IMPACTS (Pldal 50-64)