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Foreign capital and bank lending

Is foreign capital fuel for the FIRE-sector?

October 16, 2013 University of Groningen

Abstract

The research in this paper explains the influence of foreign capital on the composition of credit in several countries. The research distinguishes between Foreign Direct Investment (FDI), Portfolio Equity Investment (PEI) and Debt Flows (DF) as foreign capital flows. The novelty of this research that the composition of bank credit extended is taken into account. This parameter measures the share of bank credit that goes to non-financial businesses with respect to the total bank credit (Credit Real Sector Share (CRSH)). The results show a positive effect of FDI and PEI below certain thresholds of savings and investments. Levels of savings above the identified threshold change the relationship of foreign equity flows and CRSH. The results also show that DF has no effect on CRSH.

Author: Jelmer de Ruiter

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Introduction

An issue that is often overlooked in the financial globalization literature is the influence of foreign capital flows on the composition of the credit extended in countries. Foreign capital can ease the problems of asymmetric information that prevent banks from lending out credit towards the real economy. Foreign capital can increase the profit, cash flow and stock or asset prices. Foreign equity flows are more likely than foreign debt flows to increase the profit and cash flow of a firm, because of the positive attributes associated with foreign equity flows. Foreign capital flows that increase stock and asset prices are associated with a larger share of credit extended towards the financial sector. An increase in asset prices attracts more investors towards the asset markets, and makes current asset owners more creditworthy. The central question of this research is:

• What is the effect of foreign capital flows on the share of credit extended to the real economy with respect to the total credit extended?

In this research the distinction is made between credit extended to the real economy and credit extended towards the financial, insurance and real estate sector (FIRE). This distinction is made because credit to the real sector helps economic growth and credit towards the financial sector hinders or even hurts economic growth. The financial sector can support growth but it can also cause crises. In this paper the research on the effect of the different capital flows on the share of credit to the real sector with respect to total credit (CRSH) is presented.

In his paper Bezemer (2012) also makes the distinction between credit flows that grow the economy of goods and services, the real sector, and credit that causes markets for financial assets and properties to inflate, the FIRE sector. The former helps economic growth in terms of GDP and the latter does not contribute directly towards economic growth. The credit towards the FIRE sector does increase the debt/GDP ratio. A higher debt/GDP ratio means that a larger share of income is used to repay the debt. This means that a smaller share of income is available to buy goods and services which hurts demand for goods and services and this hurts income growth. In this research it is therefore assumed that a smaller CRSH leads to lower economic growth than could have been realized with a larger CRSH.

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Literature review

Foreign capital and economic growth

Kose et al. (2009) provide a unified conceptual framework for organizing the growing literature on the effect of financial globalization on economic growth. Economists in favor of financial openness argue that financial globalization should increase economic growth, reduce macroeconomic volatility, and increase sharing of income risk. In addition to these direct effects, financial flows could increase development of the domestic financial sector and lead to more stable policies. The empirical evidence shows mixed evidence of the direct effect of financial flows. Most evidence is based on cross-country regressions that use an aggregate measure of financial flows, and do not take the different types of capital flows into account. In further research researchers find evidence to support the theory that the absorptive capacity is lower for certain capital flows (Kose et al, 2011). They observe that thresholds are lower for FDI and PEI compared to those for the debt liabilities.

In Kose et al. ( 2009) it is clarified why FDI and PEI are easier to absorb, compared to debt flows, for countries. It is predicted that FDI should yield more benefits than other types of financial flows because it has a positive impact on productivity through transfers of technology and managerial expertise. FDI is less volatile, which probably makes countries less volatile to sudden stops of capital flows. However, the empirical data do not conclusively proof the positive impact of FDI on economic growth.

The research of Carkovic and Levine (2005) provides the field with a comprehensive analysis on the growth effect of FDI. They conclude that FDI has no direct link with economic growth. Their baseline results indicate a positive correlation between economic growth and FDI but, when controlling for trade and domestic financial credit, this correlation disappears.

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5 government or international financial institutions. Foreign debt allows banks to expose their balance sheet to currency risk and by permitting banks to take speculative open positions in foreign exchange. Figure 1 shows the schematic representation of the main threshold conditions why different kinds of foreign capital have different influences. The main threshold conditions are financial market development, institutional quality, governance, macroeconomic policies, and trade integration.

Figure 1. Treshold conditions of foreign capital flows. source: (Kose et al, 2009)

As explained above, financial development affects the correlation between foreign capital flows and economic growth. According to Anwar and Sun (2011) financial development contributes to economic growth in two ways: First of all, financial development increases confidence in the financial system and encourages households to save more, which increases the supply of funds that could be made available to large investors, Secondly, financial development allows a more efficient use of financial capital. Obstfeld (2009) also addresses the issue of financial development, foreign capital flows and economic growth. He argues that financial liberalization improves domestic financial development. Domestic financial development is attractive for several reasons as it:

• promotes growth,

• enhances welfare more generally, • allows easier government borrowing,

• Eases the conduct of a domestically oriented monetary policy.

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6 investment-constrained countries. According to them, there are two main reasons for low investments in a country’s economy: a lack of access to finance and low perceived returns. Prasad et al. (2007) argue that nonindustrial countries that have relied on foreign capital have not grown faster and that there is a growth premium associated for countries not relying on foreign finance. They argue that the reliance of these countries on domestic savings rather than foreign savings comes at a cost. If nonindustrial countries were able to draw in foreign capital, at the same costs as industrial countries, or on the same terms as they can use their own domestic capital, their investments and consumption would be higher. According to Prasad et al. (2007), developing countries have limited absorptive capacity for foreign resources. This limitation is possible because of three reasons: the underdevelopment of their financial markets; their economies being prone to overvaluation caused by capital inflows; and overly rapid consumption.

CRSH and economic growth

In their paper Beck et al. (2009) make the distinction between enterprise credit and household credit. Whereas the theory suggests that household credit has an ambiguous effect on economic growth, enterprise credit is expected to be positively related with economic growth. Beck et al. also make predictions whether enterprise credit or household credit can explain the negative relationship between financial sector development and the changes in income inequality. Their robust results make them more confident that enterprise credit is directly related to GDP per capita growth and indirectly related via income inequality. The enterprise credit has a positive effect on GDP per capita growth and reduces income inequality. Household credit does not have a relation with inequality and GDP per capita growth but is negatively associated with excess consumption sensitivity.

The research of Büyükkarabacak and Krause (2009) distinguishes between household and business credit as well, and studies the implications on the trade balance in Turkey. They find that household credit has a larger negative effect than business credit. In the case of business credit the credit for the extra imports is compensated with more exports or higher valued exports. The household credit increases imports substantially leading to major deterioration of the trade balance. These results indicate that business credit leads to higher value activities or higher economies of scale and therefore a rise in GDP, whereas household credit does not have this benefit.

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7 pressures and larger debt/capital ratio. An increasing demand for household credit will also increase the level of household lending and therefore enhances the levels of financial risk.

In their paper Büyükkarabacak and Valev (2009) also make the distinction between household and enterprise credit. Their results showthat an expansion of household credit is an important indicator of a banking crisis; due to the fact that household credit creates certain vulnerabilities. Enterprise credit can have the same effect, but this effect is moderated by the increase in income associated with the enterprise credit.

The discussion of the current literature until now compassed out of discussion of the effect of CRSH on economic growth and the effect of foreign capital flows on economic growth. A third important relationship is the relationship between foreign capital flows and CRSH. At this point it can be concluded that high levels of credit lead to a lower CRSH and that the real economy can get saturated. Moreover, the evidence on foreign capital flows is ambiguous and depends not only on the type of capital flow but also on the institutional setting in the country. What follows is a discussion of the relationship between CRSH and foreign capital flows.

Foreign capital and CRSH

Investors take two determinants into account when considering an investment: risk and return. If risk is high the returns should also be high and vice versa. Investors choose the projects with lowest perceived risk/return ratio, and investors will use credit to pay for their investments. The literature provides two theories that explain the level of credit in the context of this research: the loan pricing theory and the firm and country characteristics theory.

The loan pricing theory explains that banks cannot always set high interest rates to maximize income. The high interest rates could attract only risky investments and scare the save investments away. Banks consider the problem of adverse selection and moral hazard at the start of the banking relationship. In some cases this leads to interest rates that do not commensurate with the risk of the borrowers. The level of credit determines if the higher risk projects get credit from the bank, whereas the higher interest rates are no longer able to compensate for the risk of these projects (Stiglitz and Weiss, 1981; ECB, 2009).

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8 financial markets also have more information available for banks which reduces the problem of adverse selection as well (Godlewski and Ziane, 2008; ECB, 2009).

The two theories shed light on the channels through which foreign capital could affect the composition of bank lending. The demand channels are the channels that increase demand for credit and the willingness of firms and consumers to increase their debt burden. The supply channels show the mechanisms through which the financial institutions are willing to increase the supply of credit. The demand channels are presented in figure 2 and the supply channels are shown in figure 3. As shown in those figures the relation between foreign capital and stock/asset prices is a recurring one. Large inflows of foreign capital are likely to generate a capital account surplus. The central banks intervene in the foreign exchange market to avoid large appreciations of the nominal exchange rate, by buying the excess supply of foreign currency at the prevailing exchange rate. This would lead to an expansion in the monetary base. The base expansion would lead to growth in broader monetary aggregates, this increases aggregate demand and this will increase domestic price levels. This mechanism would also work in well-functioning financial systems, but a malfunctioning domestic financial system further destabilizes aggregate income. This happens for example by amplifying the effects of the macroeconomic expansion by issuing excessive aggregate credit or by misallocating credit to sectors that may be prone to asset-price bubbles (Montiel, 2013). However, a malfunctioning domestic financial system is not necessarily needed to create asset price bubbles associated with capital inflows. The foreign capital flows increase liquidity of assets which in turn increases their value (Calvo, 1998; Calvo, 2012).

The larger part of the credit channels have a straightforward effect on the composition of credit. The way the channels affect the composition of credit is explained below and presented in figure 4. The firm profit channel assumes that firms will increase profits because foreign investors bring physical capital, modern technology, production techniques, managerial and market knowledge, and higher standards of governance (Kose et al., 2009). These properties will increase the firm value because its income rises. The rise in income will make the managers of the firm more confident that the firm can take on a higher debt. This channel is applicable for FDI and PEI because these capital flows have the above described properties. The firm that increases her income can be expected to do so in the future and can use the credit for further productivity enhancing projects. According to this mechanism FDI and PEI will increase CRSH.

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9 capital flows. If the rise in asset values is perceived as permanent or even if higher asset values are expected than this will influence the decision of managers with respect to debt levels. The increase in wealth of the firm makes the managers feel like that the firm can take on more debt. As FDI and PEI increase asset and stock prices the managers of the firm are more and more seduced to use the new credit to buy new assets. This mechanism indicates a negative effect of FDI and PEI on CRSH. However considering the properties and long term focus of FDI this effect should be weaker for FDI (Harrison and McMillan, 2003; Héricourt and Poncet, 2009)

The wealth effect works almost the same as the firm value channel. The wealth channel however, is from a consumer’s point of view. The increase in asset value will make the consumers feel wealthy and take on more debt. Consumers will use this increase in credit to consume more instead of investing in productivity. This mechanism indicates a negative effect of FDI and PEI on CRSH (Beck et al., 2009; Aoki et al., 2004).

Figure 2: Demand channels of bank lending.

The bank capital channel shows that foreign capital will increase funds available in the domestic financial system. This increase will increase credit extended by banks to more risky projects in line with the loan pricing theory. The DF will increase resources available in the domestic financial system. This channel has therefore no clear cut effect of DF on the credit extended towards the real economy. The DF will increase the effect of the other supply channels.

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10 and moral hazard. If the asset values rise faster than income, then the firms that have more assets will get more credit. This mechanism works in cohesion with the Firm value demand channel that predicts a negative association of FDI and PEI with CRSH.

The bank profit channel works almost the same as the balance sheet channel, only in this case the firm becomes more creditworthy because it becomes more profitable. This factor decreases both the adverse selection and moral hazard problem, and it will increase the willingness of banks to supply credit. This channel works together with the firm profit demand channel and predicts a positive effect of PEI and FDI on CRSH.

Foreign capital increases cash flows of firms of where the capital flows are directed to. This increase in cash flows indicates that the firm is more liquid and therefore the adverse selection and moral hazard problems will decrease. The financial intermediaries are therefore more willing to lend out towards these firms with higher cash flow (Cassano et al., 2013). These firms tend to have higher productivity growth and therefore more credit is invested in productivity enhancing projects (Nickell et al., 1997). Hence, the cash flow channel therefore predicts a positive effect of FDI and PEI on CRSH.

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Figure 3 Supply channels of bank lending.

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12 The distinction between the supply and demand channels for credit is important because, if the supply channels have a stronger effect than the demand channels, credit constraints will be looser and vice versa. Thus banks will take more risk if the supply channels have a stronger effect. At a certain point productive investment in the real economy becomes harder to find due to of diminishing returns. This development causes the bank/firm profit channel to become less influential in affecting lending behavior. The link between the balance sheet and firm value channel with investments in productivity will also diminish. In this situation a higher share of credit will flow towards the non-productive sector. This is the point where a country goes from savings constrained towards investment constrained, as described in (Rodrik and Subramanian, 2009). If credit constraints are loose than even more credit will flow towards the non-productive sector. This causes assets and stock prices to rise further and the financial markets now look very profitable for foreign investors as well. Foreign investors, who first were interested in the productive sector of the economy, will start investing in financial markets. This is where the distinction between FDI, PEI and DF becomes important.

FDI brings physical capital, modern technology and production techniques, and managerial and market knowledge. These properties of FDI spill over to domestic firms which causes domestic firms to upgrade their operational capacity and competitive position. The increase in competitiveness of the firm causes the firm to grow and consequently the firm needs more credit. FDI increases the Firm/Bank profit channel via this mechanism. At high levels of investments FDI does not add to productive investment because most investment opportunities are already filled in. At this point domestic firms can still prefer FDI over domestic credit and FDI will crowd out domestic credit from the productive sector. The crowding out effect will weaken the firm’s profit demand channel. At low levels of domestic savings FDI can work complementary to domestic savings by reducing the problems of low capital available in the domestic financial system, and in the case of high domestic savings FDI increases demand and supply of credit. High savings is also an indication of low investment possibilities in a country and therefore FDI will increase supply more than demand indicating a negative effect of FDI on CRSH. This gives form to four hypotheses:

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13 PEI increases the resources and liquidity available in the financial markets. Large firms are able to access these financial markets for resources whereas consumers are not able to do this. Foreign investors demand higher standards of governance, which could have a positive impact on profitability, efficiency, and other measures of operating performance. The same arguments applicable for FDI are applicable to PEI. However, due to the short term nature of portfolio investors and their influence on financial markets it will not crowd out domestic investment towards the productive sector. The portfolio investors will go where the returns on investment are higher and if the productive sector does not have much productive investment opportunities they will invest in financial markets. This leads to a stronger balance sheet channel, household liquidity effect and wealth effects. Therefore, the positive effect at low levels of savings and investments is the same as FDI, the negative effect a high levels however will be stronger than that of FDI.

HPEI1: At low levels of investments, PEI has a positive effect on CRSH. HPEI2: At high levels of investments, PEI has a negative effect on CRSH. HPEI3: At low levels of savings, PEI has a positive effect on CRSH. HPEI4: At high levels of savings, PEI has a negative effect on CRSH.

HFDIPEI: The negative effect of PEI is stronger than the negative effect of FDI.

DF will increase the resources available in the domestic financial system and as a result increase the effect of the supply channels. The financial intermediaries now have more resources to their disposal to meet demand. The DF does not strengthen the demand channels, as this depends on where demand for credit is highest and this is influenced by country characteristics. At a certain interest rate demand in the productive sector will not increase and investors for productive investments do not even want credit. This increases credit towards the non-productivity investments. At low interest rates savings are low and DF will positively influence CRSH by solving the supply problem that is cause by reserve requirements. The hypothesis for DF therefore is:

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Data, method and model.

Data description

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Table 1 descriptive statistics

Variable Obs Mean Min Max

CRSH 528 47.86% 13.93%(GBR 2008) 93.84%(EGY 1991)

FDI 487 0.95% -50,43%(LUX 2008) 30.59%(BGR 2007)

PEI 433 -1.01% -22.21%(NOR 2008) 18.18%(HKG 1999)

DF 426 1.79% 26,85%(HKG 1999) 58.46%(ISL 2007)

Grossnationalsavings 528 22.50% 8.89%(BGR 2007) 52.68%(SGP 1997)

Investment 528 23.32% 10.78%(ARG 2002) 43.81%(ARM 2008)

government expenditure 516 39.02% 12.08%(SGP 2007) 60.18%(EGY 2001)

inflation 528 3.81% -3.95%(HKG 1999) 28.30%(HUN 1995

GDPpcppp 528 21,255 1,599 (IND 2001) 52,790(NOR 2008)

total bank credit/GDP 526 76.58% 2.56%(EST 1999) 379.89%(MAR 2002) Market capitalization 528 70.03% 0.50%(URY 2008) 606.00%(HKG 2008)

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Trends in the data

Figures 5 through 7 show the trend of the variables CRSH, FDI, PEI, debt flows and total bank credit as a share of GDP for six selected countries. Data of the six countries is available for the entire time period and the average from the dataset from the year 1990 until 2008. Since 1990 there is a decreasing trend in the credit extended towards the non-financial businesses as a share of total credit, which is shown in figure 2. However, total credit extended as a share of GDP increased as can be seen in figure 3.

Figure 5 compiled from (Bezemer, 2013) see appendix A table 6

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17 This means that more money went towards financial businesses, mortgages and households. Banks in countries lend more to these categories than before. This is also pointed out in Beck et al(2009). Over time countries become more dependent on stock markets and these markets can be used as a substitute for credit by firms. Consumers that use household credit and need mortgages do not have this luxury. People in many countries are encouraged to buy a home which resulted into more mortgages. The decreasing regulation in the financial sector also made it easier for consumers to get household credit. Figure 4 shows the trends of foreign capital flows in the dataset.

Figure 7 source: IFS of the IMF. See appendix A, table 12

Relevant methods in the literature

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18 and the results indicate that there is no relationship with economic growth with regard to debt flows and portfolio investment. This suggests that the non-FDI flows have no observable effect on economic growth.

Ding and Jinjarak (2012) have used a cross-country regression and a panel data regression, to investigate the relationship between capital account openness, financial development and economic growth. The novelty in their model is to investigate the turning point on the pattern of capital flows. The turning point is when the relationship changes sign. They do this by implementing the squared term of GDP per capita in their regression. The results indicate that capital flight increases with income and that at a certain point of income capital flight decreases again. This research contributes to (Prasad et al., 2007) because it takes economic development as threshold measure instead of financial development. A possible limitation for the research with this model could be endogeneity. The authors also use an aggregate measure of capital flows and this measure does not allow observing different effects of the different foreign capital flows.

Elboiashi (2011) has investigated the relationship between foreign capital, domestic investments and economic growth. The endogeneity issue also plays a role in his study and he avoids this problem by using a three-stage least square method with instrumental variables. The three-stage least square method is easier to compute and delivers robust results. The author finds evidence for a positive direct correlation between the different kinds of foreign capital flows and economic growth in developing countries. He finds that FDI has the largest positive effect on economic growth, Portfolio investments have the smallest positive effect on economic growth and loans are somewhere in between. This research contributes to (Prasad et al., 2007) because it takes not only FDI into account but also portfolio investments and loans.

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Model

In order to empirically investigate the effect of foreign capital flows on credit to the real sector, this research uses three variables to measure foreign capital flows namely: direct investment, portfolio investment and other investment. In line with Elboiashi (2011) and Ding and Jinjarak (2012) the foreign capital flows are all taken as a share of GDP. The novelty of this research is the dependent variable CRSH. This variable allows for observations of changes in credit to real sector that are caused by foreign capital flows.

In their paper Djankov et al. (2007) investigated private credit determinants in 129 countries and it was established that there are different determinants that influence the private credit ratio. These determinants that influence credit to the private sector are total bank credit to GDP, inflation, legal origin, GDP and GDP per capita growth. These variables are included in the model as control variables. Other control variables in the models are savings, stock market capitalization, investments and a time dummy. The model is presented in equation 1, and X represents are the control variables and T represents the Time dummy. The variables are explained in table 6 of appendix A. The results of the model in equation 1 answer the research question: What is the effect of foreign capital flows on the share of credit extended to the real economy with respect to the total credit extended?

= + + + + + + (1)

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20 As explained in the literature review, β1, β2 and β3 in equation one are expected to have different effects on credit extended to the real sector. If a β has a positive sign it means that the capital flow has increased the share of credit extended to the real sector. The coefficient can also be negative, indicating a less efficient capital allocation by banks because of the foreign capital flow. The expectation that is explained in the literature review is a positive coefficient β1 for FDI, a positive coefficient β2 for PEI and a negative coefficient β3 for DF.

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Results

The coefficients are estimated with a random effects instrumental variable estimator. The description in appendix B explains why this model is appropriate for this estimation. The analysis starts with a set of control variables that has been determined by the literature as relatively robust determinants of private credit. The novelty of this research is the investigation of the effect of foreign capital into the composition of credit. A second contribution is the distinction between the different capital flows. The results of the regression are reported in table 2. The instrument used for PEI is the first difference of PEI, and the instruments used for savings are the first two lagged values. The random effects GLS estimator is the basic specification of the analysis that will be used for further analysis. The coefficients of FDI, PEI Savings, Investment, Inflation, Total bank credit, Market capitalization and GDP per capita have the expected signs. DF and Government expenditure do not have the expected sign. If a country has a German legal origin it has in general a larger CRSH than countries that have other legal origins. The three main coefficients important for the research question are positive but insignificant. The time dummies are included in the model but are not reported.

Table 2 baseline results of random effects GLS regression

The robustness of the results is tested for removal of variables from the equation. The variables savings, investment, and share prices are selected for this check. As explained in Prasad et al. (2007) the relation

Dependent variable is CRSH (1) (2) (3) FDI 0.101 PEI 0.103 DF 0.039 Savings 0.590* 0.515* 0.589* Investment -0.161 -0.144**-0.135 government expenditure -0.078 -0.141**0.023 Inflation 0.223* -0.140**-0.036 LoFrench 0.054 .105* 0.043 LoGer 0.200* 0.207* 0.189* LoScan 0.009 0.025 -0.019 LoSocia 0.100* 0.102* 0.088** lntotalbankcredit -0.036* -0.023* -0.041* lnmarketcapitalization -0.015* -.009** -0.001 lnGDPppppc -0.120* -0.159* -0.153* Observations 396 350 339 R squared 60.69% 64.01% 62.47%

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Table 3 results with savings interaction

Table 4 results with investment interaction

Dependent variable is CRSH (1) (2) FDI 0.355* PEI 1.453* interaction savings -1.071 -4.888* Savings 0.629* 0.226** Investment -0.189* -0.207* government expenditure -0.072 -0.278* Inflation 0.216* -0.075 LoFrench 0.051 0.118* LoGer 0.196* 0.231* LoScan -0.015 0.073 LoSocia 0.096* 0.121* lntotalbankcredit -0.033* -0.033 lnmarketcapitalization -0.017* -0.008 lnGDPppppc -0.123* -0.129* Observations 395 350 R squared 60.50% 64.68%

*,** means significance at 95% and 90% confidence interval

Dependent variable is CRSH (1) FDI PEI 2.127* DF interaction investment -9.035* Savings 0.384* Investment -0.349* government expenditure -0.301* Inflation -0.090 LoFrench 0.120* LoGer 0.225* LoScan 0.020 LoSocia 0.125* lntotalbankcredit -0.022** lnmarketcapitalization -0.010 lnGDPppppc -0.139* Observations 350 R squared 64.08%

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Figure 8 ↑

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Figure 10 ↑

Table 5 slopes and their moderator

FDI/CRSH PEI/CRSH PEI/CRSH savings savings investment

0.00% 0.355* 1.453* 2.127* 5.00% 0.301* 1.208* 1.675* 10.00% 0.248* 0.964* 1.223* 15.00% 0.194* 0.720* 0.771* 20.00% 0.141* 0.475* 0.320* 25.00% 0.087 0.231* -0.132* 30.00% 0.033 -0.014 -0.584* 35.00% -0.021 -0.258* -1.036* 40.00% -0.074 -0.502* -1.487* 45.00% -0.127 -0.746* -1.939* 50.00% -0.181 -0.991* -2.391*

*,** means significance at 95% and 90% confidence interval Savings/Investment

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Discussion of the results

The baseline results in Table 2 show that the relationship between foreign capital flows and CRSH is not as straight forwards as it seems from the literature review. Previous literature also indicated mixed results of the effects of foreign capital on economic growth. In the paper of Kose et al. (2009) a framework is provided to organize the growing literature on the effect of financial globalization on economic growth. The empirical evidence in that paper shows mixed evidence of the nature of the relationship between foreign capital flows and economic growth.

In their research Carkovic and Levine (2005) argue that FDI has no direct link with economic growth. The results in this research indicate a direct link between FDI and CRSH but it is moderated by savings. Kose et al. (2011) observe that there are different thresholds of financial development for the different foreign capital flows. The current results also show different turning points of different capital flows. FDI loses its positive significant effect at 20% savings level and never turned into a significant negative relationship. This is an indication that FDI does increase CRSH at the lower levels of savings. The spillover effect for domestic firms inherent with FDI reduces the problem of asymmetric information. This creates extra incentives for banks to increase their level of lending towards the real sector. After the turning point FDI does not change CRSH anymore. These results indicate no evidence for HFDI1, HFDI2 and HFDI4, however it does find evidence for HFDI3.

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28 turning point is a general calculation and could be different for every country. It would be interesting to see the determinants of the turning point, and potential candidates are: financial development, consumption, overvaluation (Prasad et al., 2007) and there could be many more candidates. The research of Prasad et al., (2007) also concludes that the role of foreign capital in extending the resources may be over stated in previous literature, and these results also support that claim. The results of Prasad also become insignificant when the savings variable is added; this is in conjunction with these results and could be the result of a turning point in their data.

Conclusion

In this paper the relationship between different kinds of foreign capital flows and CRSH has been investigated. The development of an empirical model is presented to investigate this relationship. There are three contributions in which this research adds value to the current literature.

First, it uses a unique variable allowing for a distinction between credit that goes to the real sector and credit that goes to the financial sector. This distinction enables the investigation of the central research question: “What is the effect of foreign capital flows on the share of credit extended to the real economy with respect to the total credit extended?”

The second contribution is the distinction between three different kinds of foreign capital flows. The results indicate a positive effect of FDI and PEI on the dependent variable at a low level of savings. At high levels of savings, FDI shows no significant effect on the composition of credit and PEI shows a strong negative effect. There is no significant effect of debt flows on the credit composition.

A third important contribution is the identification of a turning point in terms of level of savings and investments, where foreign resources do not have a positive effect on CRSH. The estimation indicates that the level of savings lies between 20% and 30% for FDI and around 25% for PEI, and the level of investment where the relation of PEI and CRSH becomes negative is 25%. This threshold has important implications for further research, the threshold shows why there are mixed results in the literature of foreign capital. Datasets with many countries that are below the threshold mainly see encouraging results for foreign equity flows, and datasets with many countries above the threshold show discouraging results for equity flows.

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29 This research also has some limitations and unaddressed issues. The first and most obvious limitation is that of data availability, as the data is from middle and high income countries and runs until 2008. The effects could be completely different in countries with low levels of financial development, and the turning point of savings and investments would lie at a different level as well. In less developed financial markets, high adverse selection and moral hazard make it harder for banks to collect savings and distribute this money flow towards the most productive uses. The results of the 2008 credit crunch are not visible in this dataset it would be interesting to see results of the time period after 2008 in future research. The results after 2008 probably show a large increase in asymmetric information problems for financial intermediaries and therefore a reduction in supply by the financial intermediaries.

Further research should focus on specific properties of savings or investment-constrained countries and distinguish between them if a proper estimation has to be made of foreign capital flows. It would also be helpful to see further determinants of CRSH in future research. This research offers a good step into the direction of creating meaningful understanding of the relation between foreign capital and bank lending, but as is clearly shown there is still a lot left to discover.

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32

Appendix

A: Data, method and model.

Table 6 . Variables in the dataset

Variable name Description source

Legalorigin Legal origin

nonfin_bus 1 Credit towards non-financial businesses Central Bank of reporting country fin_bus 2 Credit towards financial businesses Central Bank of reporting country mortgage 3 Credit towards mortgages Central Bank of reporting country consumer 4 Credit towards consumers Central Bank of reporting country Governmentexp 5 Government expenditure as share of GDP WEO of IMF

savings 6 Gross national savings as share of GDP WEO of IMF investment 7 Total investments as share of GDP WEO of IMF inflation 8 Inflation as consumer prices percentage change WEO of IMF GDPcurrentprices$ 9 GDP current prices in USD WEO of IMF GDPcurrentpricesnational 10 GDP current prices in national currency WEO of IMF GDPPPPC 11 GPD per capita corrected for PPP in USD WEO of IMF

Stock market capitalization 12Market capitalization of listed companies as share

of GDP WDI of World Bank

Directinvestmentabroad 13 Direct investment abroad IFS of IMF Directinvestmentinrepor 14 Direct investment in reporting country IFS of IMF Portfolioinvassets 15 Portfolio investment assets IFS of IMF Portfolioinvassetseq 16 Portfolio investment assets equity IFS of IMF Portfolioinvassetsde 17 Portfolio investment assets debt IFS of IMF Portfolioinvlia 18 Portfolio investment liabilities IFS of IMF Portfolioinvliaeq 19 Portfolio investment liabilities equity IFS of IMF Portfolioinvliade 20 Portfolio investment liabilities debt IFS of IMF Otherinvassets 21 Other investments assets IFS of IMF Otherinvlia 22 Other investments liabilities IFS of IMF

Variable name Calculation

FDI Foreign direct investment (13+14)/9 PEI Portfolio equity investment (16+19)/9

DF Debt flows (17+20+21+22)/9

Totalbankcredit Total bank credit 1+2+3+4

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33

B. Data analysis

The test to identify random effects in the data is the “Breusch and Pagan Lagrangian multiplier test for random effects”. The null hypothesis can be described as: there are no random effects across individuals, and the pooled least squares model is appropriate. The alternative is: there are random effects across individuals, and the pooled least squares model is not appropriate. The conclusion that follows from the test indicates that there are random effects across individuals present so the pooled least squared model is not appropriate. With random effects present two models could be suitable to analyze the data, namely the fixed effects model and the random effects model. The previous literature mostly uses the fixed effect model to estimate the equations. However, if the random effects model is suitable than it is preferred over the fixed effects model because the random effect model:

1. Takes random sampling into account,

2. Permits to estimate the effect of variables that are individually time-invariant, 3. Is a generalized squares estimation procedure.

The Hausman test is appropriate to test for the suitability of the random effects model. The null hypothesis for this test can be described as: there is no correlation between the random effects and the independent variables, and the random effects model is appropriate. The alternative is there is correlation between the random effects and the independent variables, and the random effects model is not appropriate. The results of the test lead to the conclusion that there is no correlation between the random effects and the independent variable. Therefore the random effects model is appropriate and used for this research (Carter Hill et al, 2011). Before the random effects model can be actually applied the assumptions inherent with the model are tested. A violation of the assumption can cause a bias in the estimates that are calculated. The random effects model has two kinds of error terms: namely the random effect error terms and the overall error terms. The error term assumptions are summarized below:

1. The combined error terms have a zero mean and constant variance (homoscedasticity), 2. The combined error terms for different individuals are uncorrelated,

3. The overall error terms are uncorrelated with the independent variables, 4. The random effects error term is uncorrelated with the independent variables.

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35 are removed. The FDI model has 26 influential observations, the PEI model has 56 influential observations and the DF model has 39 influential observations.

Figure 11 Kernel density estimate Table 7 VIF 0 5 1 0 1 5 2 0 D e n s it y -.1 -.05 0 .05 .1 residuals

Kernel density estimate Normal density

kernel = epanechnikov, bandwidth = 0.0062

Kernel density estimate

VIF VIFsingle FDI 3.22 2.12 PEI 2.65 1.45

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