1

What Types of Financial Reforms are Important for the Innovation activity?

Spyridon Boikos* Ioannis Bournakis† Dimitris Christopoulos‡

Abstract

Although the positive effect of financial development on growth is vast, there is only a recent study of Ang (2011) that investigates the relationship between financial reforms and innovation. Our paper takes the existing scarce literature a step ahead by investigating whether policies of financial liberalisation cause different effects on R&D investment. We develop a horizontal R&D growth model with human capital accumulation, riskiness into the production of new ideas and a banking sector that performs under specific status of financial reforms. We distinguish between micro and macro type of financial reforms specifying the following hypotheses: a micro type reform increases competition in the banking sector thus lowering the cost of borrowing, while a macro type reform increases the cost of deposit insurance followed from lowering reserve requirements, which impacts negatively on the cost of borrowing. Our empirics fit very well the theoretical propositions of the model.

JEL Classification numbers: G2,C23, E44, O43.

Key Words: Financial Reforms, R&D, Economic growth.

* Department of Economics, University of Crete, 74100 Rethymno, Crete, Greece. † Department of Economics and International Development, Middlesex University, London, NW4 4BT, UK. ‡ Corresponding Author: Department of Economic and Regional Development, Panteion University, Syngrou Ave. 136, 176 71 Athens, Greece. Email:DK.Christopoulos@gmail.com

2

1. Introduction

The role of finance in long-run output is widely regarded in recent growth literature (Tobin

and Brainard, 1963; King and Levine, 1993; Rajan and Zingales, 1998; Beck et al.2000).1

The main argument in this literature is that an economy with a well-developed financial

system contributes to a better allocation of resources as it provides more effective financial

intermediaries that monitor investment risks channelling funds towards projects that stimulate

growth. Financial intermediaries can more easily coordinate transactions lowering the cost of

information and monitoring, which essentially reduces the cost of external finance for

investors. Therefore, a frictionless financial market is a condition for existing firms to grow

and new dynamic firms to enter, which promotes aggregate industry productivity. In a similar

line of argument, innovation-the most crucial growth determinant-can more effectively be

promoted with a well-functioned financial system as financial intermediation can easily

evaluate risky investment projects so as making loans available at competitive interest rates

and appropriate borrowing conditions (Bekaert et al. 2005; Hsu et al. 2014).

Although the above literature provides clear evidence for the positive role of financial

development on growth2, the mechanisms of this nexus remains an issue of both theoretical

scrutiny and empirical investigation. More crucially, little is known yet about the response of

innovative intensive sectors of the economy to policy changes in the financial markets. The

main objective of the present paper is to address this question examining how different

aspects of financial liberalisation policies affect the finance of innovation. A highly repressed

financial environment is expected to weaken the role of financial intermediation, which

increases transaction costs thus worsening the allocation of financial resources across

alternative uses (Blackburn and Hung, 1998). On the other hand, a more liberalised financial

environment with fewer entry restrictions fosters competition among financial intermediaries,

which is reflected in a lower cost of lending. This scenario of “financial liberalisation” is

perfectly compatible to the needs of an innovation-oriented economy that undertakes R&D

projects for producing and patenting new ideas. The empirical pattern revealed in Ang (2011)

shows a negative relationship between indices of financial liberalisation and innovation

1 See Ang (2008) for a survey on Finance and Growth and Goodhart (2016) for an overview of the channels between financial development and economic growth. 2 Cross-country evidence for the positive role of financial development on economic growth can be found in (King and Levine, 1993; Atje and Jovanovic, 1993; Levine and Zervos, 1998; McCaig and Stengos, 2005) while firm level evidence that exerts the positive role of financial development on the growth of firms can be seen in (Levine, 2002; Demirguc-Kunt and Maksimovic,2002).

3

output, which are mainly attributed to reform induced volatility, which does not favour the

finance of innovative activity. Bandiera et al. (2000) find a similar negative effect of financial

liberalisation on savings. In fact, there are dimensions of financial liberalisation that might

cause adverse effects in the financing of risky investment projects, such as the removal of

credit controls. The latter is an aspect of financial reform that reduces the reserve

requirements of banks and then leads to an increase in the insurance cost of the deposits in an

event of default, which will result in a higher cost of lending (Calomiris et al., 2015). The

inconclusive pattern of empirical results so far point out that financial reforms embody

theoretical settings that might not always be favourable to financing innovation and

knowledge creation activities. In this scenario, a more liberalised financial environment

redistributes resources towards sectors that promise high short-term returns leaving the

technological sector of the economy underdeveloped.

To this end, we develop a theoretical model to capture the differentiating effects of

financial reforms in innovation. Specifically, we develop an endogenous growth model that

incorporates different type of financial reforms and examine through what channels these

policy changes affect R&D investment, one of the main factors driving the creation of new

knowledge. In this framework, we consider two types of policy reforms: A micro policy

reform and a macro policy one. The micro policy reform includes the removal of entry barrier

restrictions in the banking sector. Next, we assume that micro policy reforms increase

competition in the banking sector, leading thus to lower cost of lending which in turn

promote investment in R&D. Regarding the macro policy reforms they refer to a decrease in

the reserve requirements, which essentially increases deposits’ insurance premium thus

increasing the cost of lending for R&D projects. The empirical validity of our model’s

theoretical predictions is tested using the recently compiled data set of Abiad et al. (2010).

The paper is organised as follows: section 2 describes the assumptions of the theoretical

model and its main propositions; section 3 contains the empirical part with the description of

the data, estimation methodology and results. The final section 4 concludes the paper.

2. Theoretical Model

2.1 Model Set up

We develop an endogenous growth model a la Romer (1990) allowing for a banking sector

which regulates the mark-up between deposit and lending interest rates in a manner similar to

4

Berthelemy and Varoudakis (1996).3 Our model assumes that human capital is endogenously

accumulated in the education sector while R&D expenditure is funded with loans (Romero-

Avila et al, 2008). The economy consists of four production sectors, final output, intermediate

sector, R&D sector and financial sector. There are two types of households of mass one. The

first type of household is the owners of the intermediate firms and the second one the owners

of the banks. The final output sector produces a consumption good with the use of

intermediate inputs and human capital. The intermediate input is assumed to be produced

with human capital (Grossman and Helpman, 1991) while R&D sector produces new ideas

with human capital and financial resources obtained from the financial sector. 4 The

innovation process is uncertain, implying that a fraction of R&D projects will not lead to new

ideas, thus loans used to finance these R&D activities will not be repaid. We introduce

deposit insurance in the financial sector to allow for the possibility that some loans will be

not be repaid (Boyd and De Nicolo, 2005). The cost of deposit insurance is assumed to be a

decreasing function of banks’ reserve requirements (Calomiris et al., 2015). Both final good

and R&D sectors are competitive with zero long-run profits while firms in the intermediate

sector receive profits from monopolistic rents. The first type of households owns the

intermediate firms and accumulates human capital. Human capital is assumed to be

distributed among four sectors, final output, intermediate output, R&D sector and education

sector. The second type of households is the owners of the banks who keep their asset

holdings as deposits into the banks. In equilibrium, the rate of return from equity holdings in

intermediate good firms equals the deposit interest rate offered by banks.5

Finally, it is assumed that R&D expenditure and the number of ideas are endogenously

determined while there are two financial reforms (both exogenously determined) that drive

outcomes in our model. First, the removal of entry requirements in the financial market that

increase competition, the micro policy reform; and second the relaxation of capital control

restrictions that lead to lower reserve requirements, the macro policy reform. For simplicity,

we follow the original assumption of Romer (1990) that the economy is closed with zero

3 In our model, contrary to Berthelemy and Varoudakis (1996), banks compete a la Cournot for providing loans and not for receiving deposits. The reason is that the deposit interest rate is determined and it is equal to the rate of return of the intermediate firms. 4 Financial resources are necessary for R&D expenditure to pay for facilities, equipment, machinery and access to JSTOR (Sánchez Losada, 2014). 5 This is a non-arbitrage condition implying that bankers save their wealth only in bank deposit forms while the firm owners save their wealth only in equity forms in the intermediate firms.

5

population growth, which make the aggregate and per capital variables to coincide in the

steady state.

2.2 Production

Final goods are produced with human capital ( YH ) and specialized inputs ( ix ). The price of

final goods is normalized to one. We follow a standard aggregate production technology

(Spence, 1976; Dixit and Stiglitz, 1977; Ethier 1982; Gancia and Zilibotti, 2005):

1

0

, 0,1t

t Yt itY H x di

(1)

where tY denotes aggregate output (GDP) at time t, t is the number of intermediate-input

varieties discovered until time t, while and 1 represent the elasticity of final good with

respect to intermediate input and human capital, respectively. From the first order condition

of profit maximization, the inverse demand function for the i-th intermediate input is derived

as:

1 1, 0 , [0, )it Yt it t tp H x i ,Ω Ω (2)

From (2) it follows that the price elasticity of the i-th intermediate is equal to 1/ 1 and

coincides with the elasticity of substitution between two generic varieties of intermediate

goods used in the production of final goods.

Firms in the intermediate sector are monopolistically competitive. Each firm produces

only one horizontally differentiated intermediate good with the use of a patent purchased

from the R&D sector. We consider that the production function of the i-th intermediate good

is one-to-one technology in human capital (Grossman and Helpman, 1991):

, 0, , [0, )it it t tx h i (3)

Therefore, the marginal cost of producing a new intermediate output equals the wage w.

Assuming symmetry in production and demand of intermediate goods, we focus

on: tit xx , 0 ti ,Ω . The assumption of symmetric equilibrium implies that the total

amount of human capital used in the intermediate sector at time t ItH is:

0 0

= t t

it it Itx di h di H

(4)

6

Assuming there is no strategic interaction across firms, profit maximization in firm i leads to

the following mark-up rule:

, 0, It tit t t

w wp p i

(5)

Equation (5) shows that the price for all intermediate goods is the same and equal to a

constant mark-up 1/ on marginal cost ( tw ). Human capital is used to produce

consumption goods YH , durables IH and ideas H . Symmetry implies:

, 0, Itit t t

t

Hx x i Ω

(6)

Given tx , the instantaneous profits of intermediate firm i is written as:

11 , 0, it Yt It t t tH H i Ω (7)

Under symmetry, (1) can be transformed into:

1 1t Yt It tY H H (8)

2.3 R&D Sector

There is a large number of R&D competitive firms that produce new ideas with Ω to

indicate total stock of knowledge in the economy. The representative R&D firm employs a

research technology similar to Jones (1995a) and Arnold (1998) augmented in our model

with an additional input of financial resources F which is necessary to facilitate the work of

researchers. R&D firms produce ideas without knowing in advance the economic success of

these new ideas.6 The probability of gaining a patent 0 1, is constant over time. The

accumulation of stock of ideas in the economy evolves as follows:

t H F

(9)

with 1 and ℝ. The assumption of 1 satisfies the empirical regularity that the

production of new ideas falls as the number of researchers increases (Kortum, 1993). H is

the amount of human capital (researchers) working in the R&D sector. Parameter captures 6 Some R&D firms either they will not manage to produce a patent or they will not manage to get an economic rent out of it.

7

the existence of spillover effects in the production of new ideas (Patel and Soete,1988; Nadiri,

1991; Wolff, 1997; Guellec and Van Pottelsberghe, 2004).7

The assumption of perfect competition in the R&D sector implies that the market value of

the i-th patent needed for the production of the intermediate input is equal to the flow of

instantaneous profits of the intermediate sector:

> t x

t

r s d s

t I

t

V e d ,

(10)

tV is the price of the i-th patent at time t, It is the instantaneous profit of the i-th

intermediate patent and xr is the instantaneous interest rate on first type of households’ asset

holdings. Therefore, the profit maximization of a representative R&D firm is given by:

LV w H r F

(11)

Differentiating (11) with respect to ΩH and ΩF , we get:

1w H F V (12)

1Lr H F V (13)

Due to the assumption of constant returns to scale in (9) profits are zero per unit of time. By

taking logs and differentiating (9) with respect to t and taking logs we obtain the following

condition, which guarantees a constant growth rate of ideas in the Balanced Growth Path

(BGP) equilibrium:

1 1F Hg g g

(14)

The above condition requires 1 in order for both Fg and Hg to have a positive effect

on the growth rate of patentsΩ .8

7If 0 , the rate of innovation declines when new ideas are discovered (fishing-out effect); if 0 , old discoveries improve the production of new ideas (standing-on-shoulders effect); and if 0 , the rate of current innovation is independent from the stock of knowledge. 8 The term jg represents the growth rate of variable j.

8

2.4 Households

Both types of households gain utility from the consumption of final good output as follows:

1 1

1 1jt

jt

Cu C , j f ,B

(15)

where is the inverse of the intertemporal elasticity of substitution of consumption.9 The

first type of households is the owners of the intermediate firms (f) and their asset holdings

equal the aggregate value of intermediate firms:

ft t tA V (16)

We also assume that the first type of households accumulates human capital (Lucas, 1988):

1 , >0t t tH u H

(17)

Parameter represents the efficiency of the education sector, 1 0 1tu , is the share of

human capital employed in the education sector for further accumulation of human capital

while tu is endogenously allocated among the other three production sectors (i.e. Y IH ,H

and H ).10 Under homogeneity and perfect human capital mobility, wages are equalized

across the three sectors. The probability of an unsuccessful R&D activity (no production of a

new patent) implies that a firm will not be able to pay wages to researchers.11 Therefore, the

fraction of human capital employed in final output and intermediate sectors receive income:

tw u s H while the fraction of human capital employed in the R&D sector receives

income: tw s H . Since there is a possibility of wages not being paid in the R&D sector; we

introduce a wage premium for human capital employed in the R&D sector.12 The saving of

assets is defined as households’ income not used for consumption. Thus, the equation of asset

accumulation for the first type of households is written as:

xft t ft t t ftA r A w u s H w s H C

(18)

9 Blundell et al. (1994) and Attanasio and Browning (1995) find θ to be close to 1 at country level while Evans (2004) and Percoco (2008) with market demand preferences show that θ is closer to 1.5. 10 The share of the human capital working in the three production sectors is Y Is s s u while 1 u enters the education sector. 11 The probability of wages to be paid in the R&D sector is . 12 As we show in Appendix A, human capital is allocated across all the production sectors when the wage in the R&D sector is higher relative to the wage in the other sectors: w w / . If R&D firms experience zero risk 1 then wages are equal across sectors Y Iw w w w , otherwise Y Iw w w w .

9

Finally, the representative agent of the first type of households solves the following

maximization problem:

0

1

0

1Max , 0 1

1ft t t ft t

ft t

C ,u ,H ,A

CU e dt ,

(19)

s.t.: xft t ft t t ftA r A w u s H w s H C

(20)

1t t tH u H

(21)

Bankers are the second type of households which-for simplicity-is assumed to have the same

utility function with firm owners without accumulating human capital. Their asset holdings

equal the aggregate value of the deposits and the redistributed banking profits:

Bt t BtA D (22)

The second type of households accumulates assets as follows:

dBt Bt t Bt BtA S r A C

(23)

Finally, the representative agent of second type of households is solving the following

problem:

0

1

0

1Max , 0 11Bt Bt t

tBt

C ,A

CU e dt ,

(24)

s.t.: dBt Bt t Bt BtA S r A C

(25)

2.5 Financial Sector

We model the financial sector upon the set-up of Berthelemy and Varoudakis (1996). Banks

use deposit insurance to protect the savings of depositors. We assume that the two regulatory

policy instruments of the financial sector are exogenously determined. The first policy

instrument is a micro financial reform that determines the level of competition in the financial

sector.13 The second policy instrument is a macro financial reform that affects capital controls.

Accordingly, each bank can only lend a fraction 0,1 of deposits received while the

13 Before implementing the micro financial reform the number of banks is finite. After the implementation of the micro financial reforms, the increased competition between banks leads to zero banking profits. In the absence of monitoring or fixed costs, the number of banks approach infinity.

10

remaining 1 0,1 is used as reserve requirements. From the amount of deposits given for

loans jD , there is another fraction jz D which is used to pay for the deposit insurance

with 0 1z , and 0'z . This set up captures the idea that if reserve requirements

decrease (i.e. increases) after the implementation of a macro type financial reform then

insurance cost of deposits increases.14 Therefore, the net amount of deposits available for

loans is: jz D with z indicating that the amount of loan is positive.

The equilibrium interest rate of deposits is equal to the rate of return of intermediate firms

in order both firm owners and bankers to be indifferent in investing between assets (banking

deposits vs buying equity of the intermediate firms). Contrary to Berthelemy and Varoudakis

(1996), in our model the deposit interest is given so banks compete a la Cournot in providing

loans to R&D firms. Therefore, banks set up a mark-up between the interest rate for loans and

deposits. The higher the mark-up the more expensive is the cost of borrowing for R&D firms

which leads into a reduction in R&D expenditure. The mark-up between interest rate for

loans and deposits is:

1L Dr r (26)

Cross-bank symmetry implies that the loans provided to R&D firms from bank j are

jL F / n .

The second type of households holds savings into banks and under bank-symmetry:

Btj

SDn

(27)

Equilibrium in the loans market is determined as follows:

j jFz D Ln (28)

Another important condition is that the share of deposits paid to the insurance companies is

enough to cover the loss of deposits due to the defaulted loans:

1 1j jFz D Ln (29)

with 1 to be the fraction of unsuccessful R&D projects. The present value of j’s bank

profit is:

14 We consider insurance cost to be exogenous as it is beyond the scope of the paper to model the behaviour of the insurance sector.

11

L D

j jBj D

r L r Dr

(30)

where Ljr L is the revenue from repayments of successful R&D projects. Bank j maximizes

profit given the demand for loans (Eq.13) and the interest rate elasticity of

loans: 1 01

L

L L

dL rdr L

. Substituting (28) and (29) into (30) the discounted profit

function for bank j becomes:

2 L

j jD

r z L LBj zr z

(31)

Based on (31), the equilibrium mark- up between deposits and loans interest rates is:

11 01

2 1

L

D

rr

zn

(32)

It should be noted that the term 1 which captures the unsuccessful R&D projects is absent

from equation (32) due to the existence of the deposits insurance. After a micro reform with

free entry and zero profit, the mark-up is:

12

L

D

rr z

which is smaller in relation to

mark-up (32) before the implementation of the micro financial reform. This is because the

number of banks now tends to infinity n in the absence of any operating costs (i.e.

monitoring cost).

2.6 General Equilibrium and BGP Analysis

In equilibrium the following conditions must hold:

t t t It Ytu H H H H (33)

Yt It tw w w w (34)

xt t t itV r V

(35)

D xt tr r (36)

The no-arbitrage equation suggests that the return on the value of the i-th intermediate firm

xt tr V at equilibrium must equal the sum of the instantaneous monopoly profit accruing to

12

the i-th input producer ( it ) and the capital gains/losses matured on V during the time

interval dt ( tV

).

We can now turn to the formal definition and characterization of the Balanced Growth

Path (BGP) equilibrium.

Definition: Balanced Growth Path (BGP) Equilibrium

In our case a BGP equilibrium is a situation in which: (i) All variables depending on time

grow at constant (possibly positive) exponential rates and (ii) The sectoral shares of human

capital ( j js H / H , j = Y, I, Ω) are constant. Along, the BGP, the fraction of human capital

employed in the different production sectors is constant 0tu u, t .

Below it follows the Proposition 1 which gives the BGP values for the endogenous variables

of the model. The mathematical derivations are in the Appendix A.

Proposition 1: The BGP Equilibrium Conditions

1 1 11 1 1 1Y Cf CB Af AB Fg g g g g g

(37)

1 11 1 1 1Hg

(38)

1 1 1 1

g

(39)

1 1 1 1 1 1

1 1 1 1u

(40)

21 1Ys u s / (41)

2 21Is u s / (42)

2

11 1d

H

g us

r g g g

(43)

1 1 1 1 1

1 1 1 1dr

(44)

1 1 2 1 1L dr / r / z / n (45)

1H F / g / s (46)

Proposition 2 describes the parameter constraints in order for the main endogenous variables of the model to take economically meaningful values in the BGP equilibrium

13

Proposition 2 For 1 2 0, , , z or for 1 , , and 1 1 0 the

following results hold:

>0,Y Cf CB Af AB Fg g g g g g 0Hg , 0g , 0V , 0 1D Lr r 0 1u , ,

0 1 , =js , j Y ,H , and , =ju s j Y ,H , .

Proof: By using the results from Proposition 1 and imposing the following parameter values:

0 6 0 7 0 3 0 03 0 12 0 02 2 0 8 0 05. , . , . , . , . , . , , . , z . , the conditi-

ons of Proposition 2 hold. We assume that 0 8. while the reserve requirement ratio is

1 is 20% (0.2) of the total deposits. Also, we consider that a fraction of 5% of the total

deposits is paid to the deposit insurance companies. Finally, we set a small value for the

parameter , since there is no clear evidence regarding the magnitude and the size of the spill

over effect of previous invented innovations to the current level of the stock of patents. The

rest of the parameter values are according to the literature.

Proposition 3 summarizes the effects the micro and macro financial reforms have on the

equilibrium value of the mark up between the interest rates of loans and deposits.

Proposition 3

Table1: The Effect of the different financial policy parameters in the mark up for interest rates.

n

1L

d

rr

- +/-

Proof: It comes immediately by differentiating Eq. (45) with respect to the parameters n and

.The effect of macro financial reforms on the interest rate of loans might be positive or

negative depending on the sign of the following condition 2 1'z . If the previous

condition is positive the cost of borrowing is increasing and it implies that the insurance

companies increase harshly the insurance cost of deposits in case of a reduction of the reserve

requirements.

14

3. Empirical Model

3.1 Data Description

We test the theoretical proposition 3 developed in the previous section using an unbalanced

panel of 25 OECD countries that mainly cover the period 1977-2005. There are countries

with unavailable patent data for the entire period so enter the sample with a shorter time span

(Appendix B). Financial reforms of entry barriers and credit control are taken from Abiad et

al. (2010)15 . These indices are assigned values from 0 to 3 to represent the degree of

repression (liberalization). Accordingly, 0: fully repressed system; 1: partially repressed

system; 2: partially liberalized system; 3: fully liberalized system. In our analysis, entry

barriers are classified as a micro type financial reform and credit controls as a macro type

one. 16 With reference to entry barriers, a more liberalized financial system withdraws

government restrictions on entry of new competitors (including foreign banks) in the

domestic market while as far as credit controls are concerned removal restrictions indicate

lower reserve requirements, abolishment of government restrictions for financing “priority

sectors” at more beneficial terms and fewer restrictions on credit expansion.17 According to

figure 1, OECD countries implemented financial reforms towards a more liberalized system.

In mid 1970s both micro and macro reform indices indicate a partially repressed environment,

which has been fully liberalized by the first half of 2000s. Figure 1 also demonstrates the lack

of policy reversals, once financial reforms are undertaken then it is very rare to be undone.

Exceptionally, Turkey reversed policy for credit controls in 1990 and 1995 while the

Netherlands did the same in 1976 and 1986. These are only small reversals from a partially

liberalized to a partially repressed credit controls regime.

15The Abiad et al.(2010) data set of financial reforms is available for 1973-2005, which mainly determines the time coverage of our panel. 16Bandiera et al. (2000) and Pina (2012) discuss how financial reforms could be better understood if categorized into micro and macro. 17We refer to Abiad et al. (2010) for a more detailed description of all policy aspects included in these financial reform indices.

15

Figure 1: Entry Barriers and Credit Controls

Notes: Values close to 3 indicate fully liberalized regime in terms of entry of new banks and no credit restrictions for beneficial financial terms in “priority” sectors.

More crucially, figure 2 provides a scatter plot for the link between risk premium on

lending and reserves ratio. This is essentially, the mechanism through which the macro type

financial reform tends to increase the interest rate mark-up. As discussed in proposition 3,

less credit controls lead to lower reserve requirements, which increase the cost of insurance

deposits, thus the cost of lending. Figure 2 shows a robust negative nexus between risk on

lending rate premium and the ratio of reserves to total money (Money plus Quasi Money).

Figure 2: Reserves Ratio and Risk Premium

BEL

BEL

BELBEL

BEL

BEL

BEL

BEL

BEL

BEL

BELBEL

BEL

BEL

BELBEL

BEL

FRA

FRA

FRA

FRA

FRA

FRA

FRA

FRA

FRA

FRA

FRA

FRA

FRA

FRAFRA

FRA

FRA

GER

GERGER

GER

GER

GER GER

GER

GERGER

GER

GER

GER

GER

GER

GER

GERGER

HUNHUN

HUN

HUNHUN

HUN

HUNHUNHUN

HUN

HUN HUN

IRL

IRL

IRLIRL

IRL

IRLIRL

IRL

IRLIRL

IRL

IRL

IRL

IRLIRL IRL

ISR

ISR

ISR

ISRISR

MEX

MEX

MEX

MEXMEX

MEX

MEXMEX

MEX

MEX

MEX

MEX

SWI

SWI

SWI

SWI

SWI

SWI

SWISWI

SWI

SWI

SWI

SWISWI

SWI

SWI

SWI

SWI

SWISWI

SWI

SWI

SWI

SWI

SWI

SWI

-20

24

6

Risk Pr

emium

on Le

nding

5 10 15 20 25 30Reserves Ratio

Data on patent applications are taken from OECD Patent statistics. The raw data of

patents counts follow the International Patent Classification (IPC) and allow identifying the

16

country of residence of each inventor. Aggregate data from World Industrial Property office

(WIPO) provide only the location of company’s headquarters of each patent without

specifying where innovation activity has taken place. For our analysis the country of

residence is crucial information as it shows where the inventor is located and how the

financial system in the country of residence affects the innovation effort.18 We use patents

registered in the USA patents office (USPTO) and reflect that USA is the largest global

market with the highest potential of international technology diffusion. Therefore, these

patents represent the “high quality” innovations originated in each country. 19 The

technological domains covered in the USPTO patents are mainly derived from ICT,

nanotechnology, biotechnology, environment and health. A preliminary investigation for the

relationship between financial reforms and innovation output is shown in Figure 3. This

scatter plots captures all seven aspects of the financial environment of Abiad et al. (2010)20

and the descriptive message provided is clear, the higher is the degree of financial

liberalization the higher is the level of patents per head in the economy. A more rigorous

econometric specification is used in the next section to test about the differentiating effect of

micro and macro type of financial reforms on the stock of patents.

18 See Jaffe and Trajtenberg (2002) for a more detailed discussion 19 Patent is the most common proxy for the production of a new idea (Kortum 1993, Madsen, 2008, Ang, 2011) but there are some inherited drawbacks in this measure of innovation output, first, they do not specify the economic value of the patent and second, the inventor might not use the patent system to protect a new idea preferring others methods such as: secrecy, lead time and franchising. 20 The other five components of the financial reform index in Abiad et al. (2010) are interest rate controls, state ownership in the banking sector, financial account restrictions, prudential regulations and financial security markets.

17

Figure 3: Financial Reforms and Patents per Head, 1976-2005

0.1

.2.3

.4.5

Pate

nts

per H

ead

0 5 10 15 20Financial Reform Index

Notes: The financial reforms index is an aggregate index of the seven reform indices in Abiad et al.

(2010). The maximum value of the aggregate index is 21 signifying a fully liberalized financial environment. Patents per head are the number of patents registered in the USPTO over the population.

R&D is taken from OECD (R&D Statistics) and it is defined as the ratio of total R&D expenditures (business and government) to GDP. R&D and GDP values are expressed in millions of local currency. As shown in Figure 4 the higher is the intensity of innovation input (R&D) the higher is the level of innovation output (patents per head). Appendix C displays in a descending order the average values of patent per head and R&D share for each country in the sample. USA is at the top of the rank with Japan to follow while Turkey and Poland are at the bottom. Finally, human capital is years of tertiary schooling and data are taken from Barro-Lee (2013).

Figure 4: Patents per Head versus R&D Share, 1976-2005

0.1

.2.3

.4.5

Pate

nts pe

r Hea

d

0 1 2 3 4R&D Share (% GDP)

18

3.2 Econometric Specification and Estimation

To empirically assess the relationship between the two different types of financial reforms

and patents we estimate the following specification, which is the empirical counterpart of the

equilibrium conditions (9) and equations (13) and (45) displayed in the previous section:

0ln ln ln ln ln

(ln ) (ln )it 0 P i,0 Risk it RD it H it

micro micro macro macroRD it it RD it it it

P = β + β P + β Risk + β RD + β H

+β RD × F + β RD ×F +e

(47)

where ln itP is the difference in the log value of stock of USPTO patents (P) in country

Ni ,...2,1 at year Tt ,...2,1 . Following Ang (2011) we construct the stock of patents

applying the perpetual inventory method assuming a depreciation rate δ at 10%. To estimate

the initial value of patent stock 0iP we derive the steady state condition of initial knowledge

stock: 0 0 / ( )patt i i iP P g , specified in standard neoclassical growth models, with g to be

the average growth rate of patents in country i over the sample period. itRisk is the risk

associated with R&D investment and measured with an EGARCH(1,1) approach; RD is the

ratio of total R&D expenditure to GDP; H is human capital measured as years of Tertiary

Education; Fmicro is the micro type financial reform of barriers to entry in the financial market

and Fmacro is the macro type financial reform of credit control requirements. The interaction

terms ( ln microit itRD × F ) and ( ln macro

it itRD × F ) capture the effects of R&D expenditure on the

annual growth rate of patents ( ln itP ) subject to different type of financial reforms. The

empirical specification includes a usual statistical noise ite with zero mean and constant

variance.

The estimation of (47) is subject to various endogeneity issues that need further

investigation. To start with, R&D and human capital are highly correlated since countries

with high R&D shares are also well-endowed with human capital. Additionally, there are

feedback effects between innovation input (R&D) and innovation output (stock of patents); a

higher rate of patents production increases profitability of industries’, which in turn makes it

easier the continuous financing of R&D activity (i.e. , ,cov( ) 0i t i te RD ). These effects point

out towards the existence of substantial endogeneity in (47), which indicates the

inappropriateness of OLS. Before estimating (47), we first provide a formal endogeneity test

19

implemented in two stages (Terza et al (2008)). In the first stage, endogenous variables are

regressed on a set of instruments while in the second stage, residuals from stage one are

included as additional regressors in equation (47). The joint significance of the additional

regressors is tested using a standard F-test. The instruments used in the first stage are country

fixed effects, the share of manufacturing to GDP (Mant-1), the share of inward FDI to GDP

(FDIt-1), domestic credit to private sector (FinDt-1), the share of merchandise trade to GDP

(Tradet-1), RDt-1, RDt-2, Ht-1 and Ht-2. The F-test at the bottom of Table 2 signifies the existence

of endogeneity, thus OLS produces spurious results.

An obvious choice for estimating of (47) in the presence of endogeneity is a Two Stage

Least Squares Estimator (2SLS) subject to appropriate instruments (i.e. highly correlated with

the endogenous regressors and uncorrelated with the error term). However, the existence of

strong instruments is always an ambiguous task in country level studies, which easily leads to

finite sample bias of IV estimates quantitatively not different from the OLS bias (Bound et al.

1995). To this end, we employ a copula method, which models the joint distribution of the

endogenous regressors and the error term and this information is used to obtain consistent

estimates independent from instruments (Park and Gupta, 2012). Appendix D provides a

detailed exposition of the copula method. Table 2 shows estimates of (47) based on OLS,

2SLS and Copula OLS approach. Given the value of F-statistic in column (2), OLS estimates

are inconsistent. Turning to 2SLS and Copula estimates the coefficients of primary interest

(ln )microit itRD × F and (ln )micro

it itRD × F are statistically significant and with the sign expected

following the theoretical propositions of our model. This means that R&D expenditure

combined with ta micro type reform exerts a positive influence on the growth rate of patents

while R&D expenditure combined with a macro type reform has an adverse effect on the

growth rate of patents. The first result highlights a competition effect, which leads essentially

to a lower lending interest rate after financial market liberalization. This is beneficial for

innovators that can finance R&D activity of larger scale at a lower cost of borrowing. On the

other hand, the negative sign of the second interaction term identifies the channel in which

lowering restrictions in reserve ratios increase liquidity risks thus a higher loan rate is

requested as collateral insurance. The higher lending interest rate increases the cost of

borrowing and subsequently decreases the innovation output. Both results are fully

compatible to proposition 3 of the patent growth model developed in section 2. Regarding the

coefficients of the remaining variables, R&D has a positive sign which is consistent with the

20

most recent empirical evidence from Bottazzi and Peri (2007), Arora et al.(2008) and Ang

(2011).21 The positive estimate confirms the key proposition of the endogenous growth

theory22 for the role of R&D on accelerating the growth rate of stock of ideas. Human capital

has a positive effect on the growth rate of patents stock. This finding is in line with a long

tradition in the innovation-diffusion literature about the crucial role of human capital in

facilitating the production of new ideas (Eaton and Kortum, 1996). The initial value of patent

stock ( 0tP ) has no effect (coefficient is statistically insignificant) on the subsequent growth

rate of patents in the 2SLS estimation while the coefficient is negative and statistically

significant in the Copula estimation. The negative coefficient of initial patent stock points out

a convergence scenario as countries that initially placed behind in innovation output tend to

accumulate new ideas at a faster rate than countries already well-endowed with stock of ideas.

This process indicates that R&D capital is likely to be subject to diminishing returns. Finally,

human capital ( itH ) exerts the expected positive influence on the growth rate of patents while

itRisk suggests that the higher the risk associated with an R&D project the lower the number

of new patents. On the whole, the empirical findings lend support to the theoretical

predictions that a more liberalized environment with free entry of new financial competitors

impacts positively on the evolution of patents while relaxing credit control restrictions have

deleterious effects on innovation. This finding remains robust regardless whether we estimate

(47) with the copula method or a 2SLS regression.

21 See Denicoló (2007) for a comprehensive review on the theoretical and empirical evidence of the R&D patents nexus. 22 Romer (1990); Aghion and Howitt (1998).

21

Table 2: Growth of Patent Stock, R&D and Financial Reforms, Equation, (47) Variables OLS 2SLS COPULA

itit rCreditContRD -0.016 (0.001)

-0.029 (0.001)

-0.014 (0.002)

itit EntryRD 0.006 (0.152)

0.019 (0.001)

0.007 (0.070)

itRD 0.157 (0.001)

0.123 (0.001)

0.066 (0.049)

itH 0.042 (0.002)

0.077 (0.001)

0.072 (0.001)

0)( iPGR -0.001 (0.277)

0.019 (0.247)

-0.003 (0.038)

itRisk 0.0003 (0.590)

-0.0009 (0.002)

-0.004 (0.483)

Constant -0.112 (0.001)

-0.134 (0.001)

-0.299 (0.001)

*itRD -0.098

(0.001) *itH 0.103

(0.001) Schwarz B.I.C -708.997 -719.931

)469,6(F endogeneity test

105.142

SSR 0.735

Nobs 622 392 622 Notes: Figures in parentheses show p-values associated with the tests. In the case of Copula, these are bootstrapped p-values with 1000 replications. Following approach *

itRD and *itH in the Copula estimation are

the generated variables included in the fitted model to control for endogeneity. Boldfaced values show statistical significance at conventional statistical significance levels (5% and 1%). The instruments used in the 2SLS estimation are: 1tRD , 2tRD , 1tH , 2tH , 1tMan (Manufacturing share to GDP), 1tFinD (Domestic credit to private sector ), 1tTrade (Merchandise trade as a share to GDP), 1tFDI (Inward FDI as a share to GDP) and country fixed effects. The F-statistic tests whether instruments used are uncorrelated with the error term so correctly included in the first stage regression (i.e. the over-identification restriction). The critical value for the F-distribution at 5% statistical level is 2.11.

To obtain a better understanding of the economic value of the coefficients itRD ,

ln microit itRD × F and ln micro

it itRD × F in Table 2, we calculate the total marginal effect of R&D

for each country and we show point estimates in Figure 5. The average marginal effect is

1.11, which indicates a highly responsive relationship between innovation output and R&D

for the sample of OECD countries. The highest R&D elasticity is found in Ireland, Norway

and Finland while the smallest in the USA. These estimates are considerably higher than

22

those documented in the related literature, which are ranked between 0.5 and 0.7.

Nonetheless, our marginal elasticity estimates should be interpreted with caution when

compared to other findings as the econometric specification of the current paper is different

from the standard patent production function formulated, for instance, in Arora et al.(2008)

and Jones and Williams (1998,2000).

Figure 5: Total Marginal Effects of R&D on Growth of Patent Stock

4. Concluding Remarks

This paper seeks to investigate how financial liberalisation affects the efficiency of

innovation output. The main proposition the paper puts forward is how different types of

financial reforms drive investment in the R&D sector, which is one of the most important

factor affecting the production and accumulation of new ideas. The literature on the effects of

financial reforms on innovation activity is limited, with the exception of Ang, 2011 who

although he considers an aggregate measure of financial reforms ignores the fact that policy

changes in the financial environment might cause opposite effects in the way banks provide

loans to the R&D sector. To this end, we develop a theoretical framework that models the

opposing effects induced in the finance of R&D from financial policy reforms. Our model

23

distinguishes two main effects; the first one is that a micro-type reform affects the degree of

competition in the financial market, which impacts on the lending interest rate. Accordingly, a

more liberalised environment brings more rivals in the market and the resulting outcome is a

more competitive interest rate loan. This policy change increases demand for R&D

investment which contributes to a faster accumulation of stock of patents. The second effect

is a macro-type reform, which relaxes restrictions in reserve requirements and expansion of

credit. This policy change increases liquidity in the financial market while, on the other hand,

it increases the risk of default thus the cost of insurance of deposits. The higher insurance

premium raises the mark-up between deposit and loan rate, which entails a higher cost of

borrowing for the R&D sector. Overall, the macro-type reform has an adverse effect in the

innovative activity reducing the production stock of ideas.

We capture these concepts with an endogenous growth model of horizontal differentiation

of patents, human capital accumulation and financial market that competes a la Cournot for

loans in an environment with policy changes that affect differently the behaviour of banks in

financing the needs of the R&D sector. The empirical test of the theoretical propositions of

our model in a sample of 25-OECD countries shows that the data obey the theory very well.

Some clear lessons can be taken from the present analysis. Not all types of financial

reforms bring beneficial results to sectors that are crucial for technological progress.

Providing easy access to credit at a competitive rate are always important for R&D intensive

firms but this can be more easily achieved with fostering competition in the financial sector

rather than deregulating the control of credit. Finally, our results show that policy makers

should take into account the asymmetrical effect of financial reforms might have on R&D

activity although financial sector reforms are suggested by different reasons.

Policy changes in the financial environment must be targeted otherwise can cause

adverse effects in financing activities vital for the long term performance of the economy. To

this end, both theoretical and empirical findings of the present study generate some

scepticism as to whether removal of credit ceilings can work for sectors that undertake

activities with a high degree of inherited risk. The fertility of R&D is never granted; hence

maintaining some favourable credit channels can be proved useful for the high-technological

sectors of the economy.

24

REFERENCES

Abiad, Abdul, Enrica Detragiache, and Thierry Tressel. 2010. A new database of financial reforms. IMF Staff Papers: 281-302.

Aghion, Philippe, and Howitt Peter. 1998. Endogenous growth theory. Cambridge (MA), London.

Ang, James B. 2011. Financial development, liberalization and technological deepening. European Economic Review 55 (5): 688-701.

Arnold, L.G. 1998. Growth, welfare, and trade in an integrated model of human-capital accumulation and research. Journal of Macroeconomics 20 (1): 81-105.

Arora, Ashish, Marco Ceccagnoli, and Wesley M. Cohen. 2008. R&D and the patent premium. International Journal of Industrial Organization 26 (5): 1153-79.

Atje, Raymond, and Boyan Jovanovic. 1993. Stock markets and development. European Economic Review 37 (2): 632-40.

Attanasio O.P. and Browning M. 1995. Consumption over the life cycle and over the business cycle. American Economic Review 85 (5): 1118-37.

Bandiera, Oriana, Gerard Caprio, Patrick Honohan, and Fabio Schiantarelli. 2000. Does financial reform raise or reduce saving? Review of Economics and Statistics 82 (2): 239-63.

Beck, Thorsten, Ross Levine, and Norman Loayza. 2000. Finance and the sources of growth. Journal of Financial Economics 58 (1): 261-300.

Berthelemy, J. C., and Varoudakis, A. 1996. Economic growth, convergence clubs, and the role of financial development. Oxford Economic Papers 48 (2): 300-328.

Blackburn, Keith, and Victor TY Hung. 1998. A theory of growth, financial development and trade. Economica 65 (257): 107-24.

Blundell R., Browning M. and Meghir C. 1994. Consumer demand and the life-cycle allocation of household expenditures. Review of Economic Studies 61(1): 57-80.

Bottazzi, Laura, and Giovanni Peri. 2007. The international dynamics of R&D and innovation in the long run and in the short run. The Economic Journal 117 (518): 486-511.

Bound John, David A. Jaeger, and Regina M. Baker. 1995. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association 90 (430): 443-50.

Boyd, J. H., and De Nicolo, G. 2005. The theory of bank risk taking and competition revisited. The Journal of finance 60 (3): 1329-1343.

25

Calomiris W. Charles, Heider F. and Hoerova M. 2015. A theory of bank liquidity requirements.

Demirgüç-Kunt, Aslı, and Vojislav Maksimovic. 2002. Funding growth in bank-based and market-based financial systems: Evidence from firm-level data. Journal of Financial Economics 65 (3): 337-63.

Dixit, A.K. and J.E. Stiglitz. 1977. Monopolistic Competition and Optimum Product Diversity. American Economic Review 67 (3): 297-308.

Evans D. 2004. The elevated status of the elasticity of marginal utility of consumption. Applied Economics Letters 11(7): 443-47.

Ethier, W.J. 1982. National and International Returns to Scale in the Modern Theory of International Trade. American Economic Review 72 (3): 389-405.

Gancia, G. and F. Zilibotti. 2005. Horizontal Innovation in the Theory of Growth and Development. In: P. Aghion and S.N. Durlauf (Eds.), Handbook of Economic Growth. Amsterdam: Elsevier-North Holland, Chap. 3, 111-70.

Grossman, Gene M. and Elhanan Helpman. 1991. Innovation and growth in the global economy. Cambridge, MA: MIT Press.

Goodhart, Charles. 2016. Financial development and economic growth: Explaining the links Springer.

Guellec, D., and B. Van Pottelsberghe. 2004. From R&D to productivity growth: Do the institutional settings and the source of funds of R&D matter? Oxford Bulletin of Economics and Statistics 66 (3): 353-378.

Jaffe, Adam B., and Manuel Trajtenberg. 2002. Patents, citations, and innovations: A window on the knowledge economy. MIT Press.

Jones, Charles I. 1995a. R&D-based models of economic growth. Journal of Political Economy 103 (4): 759–784.

Jones, Charles I., and John C. Williams. 2000. Too much of a good thing? Τhe economics of investment in R&D. Journal of Economic Growth 5 (1): 65-85.

———. 1998. Measuring the social return to R&D. Quarterly Journal of Economics: 1119-35.

King, Robert G., and Ross Levine. 1993. Finance and growth: Schumpeter might be right. The Quarterly Journal of Economics: 717-37.

Levine, Ross. 2002. Bank-based or market-based financial systems: Which is better? Journal of Financial Intermediation 11 (4): 398-428.

26

Levine, Ross, and Sara Zervos. 1998. Stock markets, banks, and economic growth. American Economic Review: 537-58.

Lucas, Robert. 1988. On the mechanics of economic development. Journal of Monetary Economics 22 (1): 3-42.

McCaig, B. and Stengos, T., 2005. Financial intermediation and growth: Some robustness results. Economics Letters, 88(3), pp.306-312.

Percoco M. 2008. A social discount rate for Italy. Applied Economics Letters 15(1-3): 73-77.

Pina, Gonçalo. 2012. Financial reforms and growth: The role of savings.

Rajan, Raghuram G., and Luigi Zingales. 1998. Financial dependence and growth. American Economic Review: 559-86.

Romer, Paul M. 1990. Endogenous technological change. Journal of Political Economy 98 (5 pt 2).

Romero-Ávila, D., Monnet, C., & Temple, J. (2008). Banking Deregulation, Banking Development and Endogenous Growth.

Sánchez Losada, F. (2014). Endogenous growth with capital in R&D production functions. UB Economics Working Papers 2014/306, Universitat de Barcelona, Facultat d’Economia I Empresa, UB Economics.

Spence, M. 1976. Product Selection, Fixed Costs, and Monopolistic Competition. Review of Economic Studies 43 (2): 217-35.

Terza, Joseph V., Anirban Basu, and Paul J. Rathouz. 2008. Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling. Journal of Health Economics 27 (3): 531-43.

Tobin, James, and William C. Brainard. 1963. Financial intermediaries and the effectiveness of monetary controls. The American Economic Review: 383-400.

Wolff, E. 1997. Spillovers, linkages and technical changes. Economic Systems Research, No. 9.

27

APPENDIX A PROOF OF PROPOSITION 1: EQUATIONS (37)-(46)

TO BE COMPLETED

28

Appendix B: Countries with Shorter Time Span of Patent Data Availability Country Period Available Czech 1990-2005 Hungary 1991-2005 Mexico 1981-2005 Poland 1990-2005 Turkey 1981-2005 Notes: For the remaining 20 countries of the sample the period covered is 1977-2005.

Appendix C: Patents per Head and R&D Expenditure, 1977 -2005 Country Patents per Head R&D

United States 0.307 2.350 Japan 0.242 2.434

Sweden 0.153 2.627 Germany 0.128 2.187 Finland 0.128 1.963 Canada 0.108 1.278

Netherlands 0.088 1.670 Korea 0.085 2.225

Denmark 0.077 1.439 France 0.070 1.889

United Kingdom 0.068 1.624 Austria 0.067 1.320 Belgium 0.059 1.487 Australia 0.053 1.332 Norway 0.052 1.435 Ireland 0.034 0.836

New Zealand 0.033 0.874 Italy 0.028 0.919

Hungary 0.007 0.771 Spain 0.006 0.626

Czech Rep 0.005 1.007 Portugal 0.001 0.422 Mexico 0.001 0.277 Poland 0.001 0.591 Turkey 0.000 0.437 Total 0.072 1.410

Notes: Patents per head is measured as patents applications to USPTO divided by the population and R&D is R&D expenditure to GDP.

29

Appendix D: The Copula Estimation

Consider the following model:

ititit exy ' (D1)

ititit zx ' (D2)

0)|( itit xeE , 0)|( itit zvE , 0),cov( itit ve , itiit v (D3)

where ity is the left hand endogenous variable, itx is a 1k vector of right hand variables

where the first element is 1, is a 1k vector of unknown parameters, itz is 1p vector of

instrumental variables, i are country specific effects to account for cross-country

unobserved heterogeneity and ite , itv are error terms.

Equations (D2) and (D3) postulate that the presence of endogeneity leads to inconsistent

OLS estimates as: 0)|( itit xeE and therefore ')|( ititit xxyE . To tackle this problem we

control for the effect of itv on ite by including itv on equation (D2). Thus, we get:

itititit vxy '' (D4)

where evvv 1 , 'ititit ve .

Estimating (D4) with OLS yields consistent estimates of as 0)|( itit xE and

0),cov( itit v . Thus we can estimate the following model

itititit vxy '' (D5)

where 'ititit zxv .

The null hypothesis that itx is a vector of exogenous variables is tested with a standard F-

statistic, 0:0 H .

30

It is possible to obtain consistent estimates of (D1) even without the auxiliary equation

(D2) if the joint distribution of the endogenous regressor itx and the error term ite is known.

However, this joint distribution is unknown. To overcome this problem, Park and Gupta

(2012) suggest the use of a unique copula model. In particular consider that * 1[ ( )]it x itx F x

and )]([1*iteit eFe variables have continuous marginal distribution functions xF and eF ,

respectively. 1 stands for the standard normal CDF. We assume that '** ][ ititex follows

bivariate standard normal distribution. This can be re written as:

2

1

2*

*

101

it

it

it

it

ex

(D6)

where 1it and 2

it are independent random normal variables and denotes the correlation

coefficient. After some manipulation (D1) can be written as:

22*221* 11 ititititit xe (D7)

Using the assumption that the error term ite is normally distributed we get:

**1*1 )]([)]([ 2 iteititeit eeeFe

e

(D8)

where )]([1*iteit eFe .

By substituting (B3) in (A1) we take:

]1[ 22*'ititeitit xbxy (D9)

Given that 2it is not correlated with any other right hand term in (D9) we can estimate

consistently equation (D9) by OLS.