FINANCE AND GROWTH: DOES ONE SIZE FIT ALL? EVIDENCES … · 2016. 3. 26. · Sudip Ranjan Basu, Economic Affairs Office, ESCAP For more information, contact: Macroeconomic Policy

PDD WORKING PAPERS Discussion Paper First High-Level Follow-up Dialogue on Financing for Development in Asia and the Pacific Incheon, Republic of Korea

30-31 March 2016

DP/16

March 2016

Monica Das and Sudip Ranjan Basu

FINANCE AND GROWTH: DOES ONE SIZE FIT ALL? EVIDENCES FROM A

NONPARAMETRIC ESTIMATION

For participant only

FINANCE AND GROWTH: DOES ONE SIZE FIT ALL? EVIDENCES FROM ANONPARAMETRIC

ESTIMATION


Monica Das, Department of Economics, Skidmore

College, 815 N Broadway, Saratoga Springs, NY

12866; [email protected]; 518-580-5096 and

Sudip Ranjan Basu, Economic Affairs Office,

ESCAP

For more information, contact:

Macroeconomic Policy and Financing for Development Division (MPFD) Economic and Social Commission for Asia and the Pacific United Nations Building, Rajadamnern Nok Avenue, Bangkok 10200, Thailand Email: [email protected]

Dr. Aynul Hasan Director Macroeconomic Policy and Financing for Development Division

mailto:[email protected]


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Contents

I. Introduction ........................................................................................................................... 2 II. Literature ................................................................................................................................ 4 A. Impact of financial sector development on growth ........................................................ 4 B. Impact of financial structure on growth .......................................................................... 6 III. Empirical Methodology ......................................................................................................... 7 IV. Data and Empirical Model ..................................................................................................... 9 A. Data .................................................................................................................................... 9 B. Dependent and independent variables ............................................................................ 9 C. The Empirical model ........................................................................................................ 10 V. Results ............................................................................................................................... 10 A. Core model results with regional classification ............................................................. 11 B. Core model results with UN regional groupings ............................................................ 12 C. Core model results with size of financial sector ............................................................ 12 D. Core model results with institutional quality................................................................. 13 E. Comparing estimates across bank-based versus market-based system ...................... 13 VI. Conclusions .......................................................................................................................... 14 References ............................................................................................................................... 15 Annex ............................................................................................................................... 17

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Discussion Paper

Macroeconomic Policy and Financing for Development Division

Finance and Growth: Does One Size Fit All? Evidences from a Nonparametric Estimation

by


March 2016

Abstract

The objective of the paper is to use nonparametric methodology to examine the

relationship between indicators of financial development and economic growth. The

paper uses the Li-Racine (2004) generalized kernel estimation methodology to

examine the role of financial development in understanding economic performance

across countries and country-groups. We also control for other factors that influence

economic performance such as, trade openness, government policies and institutional

quality. The paper supports the view that countries with larger and well-diversified

financial markets are in a better position to reap benefits from financial sector

deepening and forward-looking public policies. This holds true irrespective of the

initial size of the financial sector in the country as well as the strength of institutional

quality.

Keywords: Bank, Capital market, nonparametric estimation, growth.

Authors’ e-mail addresses: [email protected] and [email protected].

The views expressed in this discussion paper are those of the author(s) and should not necessarily be considered as reflecting the views or carrying the endorsement of the United Nations. Discussion papers describe research in progress by the author(s) and are published to elicit comments and to further debate. This publication has been issued without formal editing.


Financial development and growth: Does one size fit all? Evidences from a nonparametric

2

I. Introduction

The financial sector activity is significantly linked to economic growth. From Armenia

to America, and from Bangladesh to Botswana, the countries have not only seen the

progression in the deepening of the financial sector, but it has also raised challenges in

policymaking that require balancing between the size and depth, and its impact on

economic growth. Increasingly, over the past decades, the volatility and stress in the

financial sectors have created concerns both for the prospects of sustainable growth as

well as in ensuring jobs creation. The transmission channel from finance and economic

growth is not uni-directional, rather there are multiple ways in which these two

macroeconomic factors are related.

This paper uses nonparametric regression methodology to estimate the impact of the

financial system on growth at the cross-country level during the past two decades.

Therefore, the key issue here is to understand the following: Is financial sector

deepening positively linked to economic growth at all levels of development? or does it

depend on the size of the economy and the nature of the financial system? The paper

provides empirical evidence on the issue that “a one-size-fits-all” policies is not

applicable across countries, and during the period.

In the traditional macroeconomic theory, the financial sector development is intertwined

to capital accumulation, one of the input factors in the production function. In particular,

an improvement in the financial market can produce positive outcomes through:

reducing the loss of resources required to allocate capital; increasing the capital saving

ratio; and raising capital productivity. A development in financial sector therefore

contributes to capital accumulation and effectively that subsequently ensure robust

economic growth, and also helps to increase the productivity by selecting the most

profitable investment projects. This linkage between financial sector development and

productivity growth is particularly critical in periods of rapid technological progress and

innovation, which allow countries to benefit and enjoy higher rates of economic growth.

Another discussion that has taken centre stage of the current policy regimes is that of the

expansion of integrated capital market, the evolution of financial structure in different

economies on growth. The countries financial structure is mostly defined either in the

form of a bank-based or market-based financial system. In the bank-dominated financial

system, banks take the dominant role in mobilizing resource, identifying bankable

projects, and managing risk, and incentivizing technological progress and innovation. On

the other hand, with the market-dominated financial system, market operations provides

the necessary lead in identifying investment portfolios, devising risk management

activities, and supporting for high-risk high-return projects, thus reducing the inherent

inefficiencies associated with banks-based financing system.

With the growth of financial services around the world, it is argued that financial

structure does not have added valued to economic growth. Importantly, for finance and

growth to work towards increasing the overall economic prosperity, there is a role of the

property rights and institutional quality to improve the effectiveness of the financial

system in facilitating the economic growth process.

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The contribution of the paper is in the application of the Li and Racine (2007)

nonparametric methodology to estimate the relationship between the size of an

economy and the size of its financial sector in a panel with both time and country

effects. In the estimation of any parametric model, misspecification bias and

endogeneity/omitted variable bias could be present.

The nonparametric estimates in the paper effectively deal with misspecification bias.

Another advantage of the nonparametric methodology is that the paper obtains

estimates of slope coefficients (ΔY/ΔX) for every data point. In other words, paper

estimates the impact of every finance variable on economic growth for every country

in every time period. This allows to provide several group specific aggregates of the

slope coefficients. The standard errors of the group aggregates are obtained via

bootstrapping.

Apart from key role of the size and composition of financial institutions to boost

economic performance, there are growing number of research papers in literature to

document the critical role of efficient domestic institutional conditions as well as

human capital accumulation and geography (Acemoglu et al. 2001; Sachs, 2003;

Easterly and Levine, 2003; Rodrik et al. 2004; and Basu, 2008). Among other group

aggregates, paper provides the nonparametric estimates the economy-finance

relationship for country groups based on property rights (pr) and strength of

institutional quality (ief). These results add to the discussion on the role of

institutional quality in shaping the linkages between financial sector development and

economic growth performance.

The nonparametric estimates find strong support for positive significant impact of

higher level of financial sector development on GDP per capita, by using several

control variables. For majority of the countries examined, the impact of higher level

of financial sector development on the GDP per capita are quite favorable, while the

role of trade policies and institutional quality remain key complementary policy

initiatives. Since the Li-Racine methodology provides weighted estimates (weights

determined by all observations) of the regression function and its slope at every data

point, the paper also examines the nonparametric estimates for various sub-groups by

continents and country characteristics.

The impact of financial sector development, both bank-based and market-based on

GDP per capita is far from uniform across countries or time periods. However, the

favorable relationship between these two or minimal support for a negative relation

between the two variables, is robust to most sub groups and country characteristics.

The paper employs nonparametric methodology that conducts the analysis without

pre-specifying any functional form. Also, it obtains the estimates of the slope

coefficients for every data point. The slope estimates can be aggregated for every sub

groups based on several criteria, such as quality of institutions or size of the financial

sector.

We now sketch a course for the rest of the paper. Section 2 presents a brief survey of the

most relevant papers in the literature. Section 3 provides the Li-Racine estimation

technique for mixed data, utilized in the paper to the estimation of economy-finance

relationship. Section 4 discusses the data set and the empirical model. Main results of the

paper are presented in section 5 and section 6 concludes the paper.


4

II. Literature

A large body of literature dating as far back as Schumpeter (1911) argues for the positive

relationship between development of a country's financial sector and the rate of growth

of its per capita income (see Levine 1997). The paper by Rajan and Zingales (1998)

argues a reason why financial development leads to economic growth could be that it

reduces the costs of external finance. Their paper demonstrates that industrial sectors that

are relatively more in need of external finance develop disproportionately faster in

countries with more-developed financial markets. Most empirical works seem to agree

with the Schumpeterian arguments.

Similarly, King and Levine (1993) present cross country evidence of a positive

relationship between financial development and economic growth via the mechanism of

physical capital accumulation and growth of economic efficiencies, using data from 80

countries from 1960 to 1989 after controlling for numerous country and policy

characteristics. Furthermore, Levine and Zervos (1998) shows that stock market

liquidity and banking development both positively predict growth, capital accumulation,

and productivity improvements when entered together in regressions, even after

controlling for economic and political factors. Levine et al (2000) estimate a GMM

dynamic panel model with data from 74 countries averaged over 5-year intervals during

1960 – 1995. They find that legal and accounting reforms can boost financial

development and accelerate economic growth.

Some of the papers in the empirical literature question the previous findings. Easterly

and Stiglitz (2000) take a different view that financial development is desirable up to a

point. They find that financial systems that feature debt more prominently than equity

are more vulnerable to growth collapses. Cecchetti and Kharraoubi (2012) conclude as is

the case with many things in economic literature, with financial development countries

can have too much of a good thing. The relationship between financial development and

economic growth is a non-linear inverted-U relationship.

The discussion on this issue can further be grouped into two key areas as follows:

A. Impact of financial sector development on growth

A large number of studies have identified empirically the positive role of financial

activity in economic growth and development. Finance Watch (2014) is a survey study

on relevant literature for Europe, which collectively points to the inverted U-shaped

curve that illustrates the non-linear relation between private sector credit and economic

growth. Higher private credit to GDP ratio is associated with more growth until private

credit reaches 80-100% of GDP, where further financial deepening undermines growth,

as suggested by Arcand et al (2012) and Manganelli (2013). The same pattern emerges

when financial sector’s share of employment surpasses a certain threshold. Whilst

emerging economies benefit from a growing financial sector, it is correlated with weaker

economic growth in advanced economies (Cecchetti and Kharroubi, 2012; Assa, 2012).

Disparate effects on growth are also found for different types of banking activities. Beck

et el (2012) and Bezemer et al (2014) reveal that financial intermediation, especially

lending to non-financial sectors, lead to positive growth yet large European Banks invest

mostly in credit flows to the asset markets (incl. real estate), which brings insignificant

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or negative growth. Explanations for negative growth suggest that the growing capital

markets incur high costs and compete with other sectors in the economies for scarce

resources. The non-linear effect of finance on growth thus implies that current financial

system is too large relative to the real economy, but the trend is hard to revert because as

Benoît Cœuré (2014) suggests, the financial system can extract excessively high

informational rents and pay excessively high wages.

Cecchetti and Kharroubi (2012) is one of the first studies to rigorously measure the

impact of financial market on growth using a regression model: five-year average private

credit to GDP ratio and its quadratic form as the main regressors and a five-year average

GDP per worker growth as the dependent variable. Using the estimated coefficients, the

authors approximate the peak of the inverted U-curve at 100% of credit to GDP ratio,

suggesting than a banking system larger than the real economy will have detrimental

effects on growth. Replacing the independent variable with the five-year average

financial intermediation share in total employment, they arrive at the turning-point

employment share at roughly 3.85%.

Gambocorta, Yang and Tssatsaronis (2014) provides additional evidence for the non-

linear linkage between bank credit and GDP growth, and takes one step further to

compare the differential effect on growth from banks and financial market development.

Based on Beck and Levine (2004), their benchmark model uses the turnover ratio (the

ratio of the value of total shares traded to average market capitalization) to measure

financial market development (this ratio is used because it is not affected by asset price

evaluations). The non-linear specification suggests that both bank and market activity

positively affect growth only up to a certain point, setting the peak credit over GDP ratio

at 40% and the critical turnover ratio around 95%. Advanced economies are in the

declining part of both the bank and market expansion curves, yet EMEs can still benefit

from equity market deepening. The authors then use a cross section of downturns and

recoveries from a sample of 24 developed countries over the 1960-2013 period (database

from Bech et al, 2012) and relate financial structure to economic volatility. Findings are

that in the absence of financial crisis, bank-based systems appear more resilient in an

economic downturn, possibly because stronger banks help absorb economic shocks.

However when recession is accompanied by financial crises, countries with bank-based

systems experience three times more severe losses than those with market-oriented

financial structure. Notwithstanding the intuitively non-linear effect of finance on

growth, the implication varies for each country differs because of their stage of

development. For example, Estrada, Noland, Park and Ramayandi (2015) ascertains that

although it is possible that the relationship turns insignificant or even negative beyond

some threshold, developing Asia is well short of that possible turning point.

Beck (2013) summarizes the above arguments from existing literature and provides

guidance for future research. The paper emphasizes the role of theory in linking data and

specific identification strategies in empirical research. It also acknowledges the large

variety of methodologies and data sources applied in the finance for development

literature, ranging from RCTs and micro-experiments to historical macro analyses. Beck

also highlights the importance of close interactions between practitioners and

policymakers, as well as the relevance of developed country experience in the

developing country settings.

https://en.wikipedia.org/wiki/Beno%C3%AEt_C%C5%93ur%C3%A9


6

B. Impact of financial structure on growth

The financial structure of a country is based on bank-dominated systems or market

dominated system. In the bank-based systems are characterized by a few lenders in the

economy, whereas market-based systems are characterized by a large number of lenders.

Given the cost of information acquisition and the problem of free-riding, banks are more

efficient in the provision of information. However, markets have the capacity to

aggregate views and opinions on the profitability of an investment from different parties,

therefore more efficient in dealing with uncertain investment environment. Markets are

also more conducive for young innovative companies that do not have the collateral

requirements demanded by most banks.

In particular, these two systems also differ in the provision of corporate control. The

plausible long-term relationship with a bank enhances the credibility of the bank’s

commitment to provide staged funding and therefore reduce monitoring costs and

uncertainty in funding sources. On the other hand, the established relationship may

encourage collusion and over-investment in inefficient projects. A decentralized

financial system and stock markets will nevertheless stimulate greater control, facilitate

take-overs and promote operative efficiency.

There is no consensus about the structure of financial system and its correlation with

output growth. Carlin and Mayer (2003) examines the relation between the institutional

structures of 14 advanced OECD countries and their comparative growth and investment

of 27 industries from 1970 to 1995. The analysis includes three proxies for country

structure of financial systems – accounting standards (information disclosure), bank

concentration, and ownership concentration; and three industry characteristics – equity

dependence, bank dependence, and skill dependence. The authors estimate the relation

between the interaction terms and industry growth and investment. Statistical results

support the hypotheses that information disclosure, fragmentation of banking systems,

and concentration of ownership have a strong relation with growth and R&D investment

in equity financed and skill intensive industries, but there is no significant effect on fixed

capital investment. The growth of industries relying on R&D is strongly affected by

financial variables. Thus finance stimulates economic growth by affecting investment in

R&D. In order to study the features of a growth supportive financial structure, Thiel

(2001) researches on the pros and cons of a bank based or market based financial

structure, motivated by the different systems witnessed in the developed world (e.g.

Germany and Japan are bank-based, USA, UK are market-based). The results highlight

the trend of growing reliance on markets and disintermediation through banks,

manifested in the increasing concentration of banks, increasing spread of strategic

alliances and mergers among equity market organizations, and in the growth of new

forms of financial intermediaries such as pension funds, venture capitalists and risk

capital markets.

Another stream of literature, however, plays down the role of financial structure in

predicting economic growth and emphasizes the role of law and finance. Beck and

Levine (2000a), for instance, use a country-industry panel to show that financial

dependent industries do not grow faster in bank- or market-based financial systems.

Levine (2000b) finds strong support that the efficiency of the legal system is positively

related to growth and innovation. Demirgüç-Kunt and Levine (2000) evaluates the

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impact of financial structure on economic growth using the large international dataset

constructed by Beck, Demirgüç-Kunt and Levine (2000) that includes country level data

of 48 countries. Using three indicators for legal system – creditor, anti-director and rule

of law – as instruments for financial development, they find evidence supporting the

financial services and the law and finance views. Financial development and the

component defined by the legal protection of outsider investors explain long-term cross

country growth rates, whilst financial structure does not offer any additional information.

Noticeably, this paper provides a system of indicators of country-level financial

structure, financial development, and legal environment for an array of developed and

developing states, thus providing useful framework for future research on this topic.

III. Empirical Methodology

The basic principle behind the nonparametric estimation technique is to fit a window h

(also known as smoothing parameter) around every observation of the data set and

estimate the relationship of interest between variables in each window. A kernel density

function K(.) is used to give high weights to data points close to the window and low

weights to data points far from the window. Thus the regression relationship is

estimated, piece by piece or window by window as shown in figure (1). One of the

advantages of nonparametric estimation is that it estimates the regression function m(.)

as well as the slope coefficients β(.) at every data point.

Figure 1

If yi is the target variable (GDP per capita) and xi the policy variable (a set of financial

sector variables, policy variables or institutional quality variables), (E(yi|xi) < ) the

relation among them may be expressed in terms of the conditional moment E(yi|xi)

=m(xi). When the actual functional form is unknown, parametric specifications including

complex ones like the translog functions are deemed inadequate. Compared with the

parametric procedures, the nonparametric methodology is more proficient in capturing

non linearities in the underlying system thus dealing with the problem of model

misspecification.


8

The paper uses the Li-Racine Generalized Kernel Estimation Methodology (by Li and

Racine (2004) and Racine and Li (2004)) to examine the relationship between

financial sector development structue and GDP per capita. Equation (1) represents the

basic regression model.

iii xmy (1)

In equation (1), yi represents the ith

observation on the dependent variable (GDP per

capita) and i indexes country-time observations of N countries and T time intervals.

Also, m(.) is an unknown smooth regression function with argument xi=[ u

i

c

i xx , ],

where c

ix is a NTk vector of continuous variables (a set of financial sector variables,

policy variables or institutional quality variables), u

ix is a NT1 vector of unordered

discrete variables (country effects) and i is a NT1 vector of errors. Following the Li-

Racine methodology, we take a first order Taylor expansion of (1) around xj to obtain

equation (2).

ij

c

j

c

iji xxxxmy (2)

Here, (xj) is the partial derivative of m(xj) with respect to xc. The estimate of (xj)

[m(xj) (xj)]’ is represented by equation (3).

j

j

j x

xmx

ˆ

ˆˆ

i

ic

j

c

ih

ic

j

c

i

c

j

c

i

c

j

c

i

c

j

c

i

hy

xxK

xxxxxx

xxK

1

'

1ˆ

1

ˆ (3)

In equation (3),

r

s

u

s

u

sj

u

si

uq

s s

c

sj

c

si

shxxl

h

xxwhK

11

1ˆ

ˆ,,ˆ

ˆ is the generalized kernel

function. The commonly used product kernel Kh is from Pagan and Ullah (1999),

where w is the standard normal product kernel function with window width hs =

hs(NT) associated with the sth

component of xc. The kernel function l

u is a variation of

Aitchison and Aitken (1976) kernel function which equals one if u

sj

u

si xx and u

s

otherwise.1

It is well known in the nonparametric literature that estimation of the bandwidths (h,

u) is crucial. N implements a number of ‘data-driven’ numerical algorithms to

determine the appropriate bandwidth or smoothing parameters for a given sample.

The paper uses the Least squares cross validation method as discussed in Racine and

Li (2004). Least squares cross validation selects h1, h2, … hq, u

1 , u

2 , … u

r to

minimize the following cross validation function:

n

i

iiii xMxmyCV1

2ˆ (4)

1 See for details of this estimation methodology in Li and Racine (2004) and Racine and Li (2004).

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Here, ii xmˆ = ./. KKy n

ill

n

il is the leave-one-out kernel estimate of m(xi)

and 0M(.)1 is a weight function. The purpose of M(.) is to avoid difficulties caused

by dividing by zero or by the slow convergence rate induced by boundary effects.

IV. Data and Empirical Model

A. Data

Utilizing the Li-Racine nonparametric estimation technique for mixed data, developed

by Li and Racine (2004) and Racine and Li (2004), our paper explores the

relationship between GDP per capita (GDPPC) and various indicators of financial

sector development and structure. The finance indicators are represented by several

macro variables such as, the share of GDP in private credit (pcby), share of GDP in

domestic credit (dcpy), market capitalization (smk), turnover ratio of stocks (str) and

total value of stocks traded (sva). The nonparametric technique of choice allows us to

examine the finance-economy relationship in a data driven specification free manner.

Our paper is based on 156 countries, of which 111 developing countries for the whole

sample. The discussion of results based on smk, str and sva consist of 110 countries

due to lack of data on three market-based variables across the years and countries.

The developing country lists also include 36 Least Developed Countries (LDCs) as

defined by United Nations.2 We obtained data from the UN sources and several

international and research institutions3.

B. Dependent and independent variables

Our main dependent variable is rpcGDP or real GDP per capita (international $, 2005

Constant Prices, Chain series) to identify level of economic performance at the cross-

country level. We use five variables to measure the size and nature of the financial

sector: pcby measures private credit held by banks, dcpy measures domestic credit

held by the private sector, smk is market capitalization of listed companies, str is the

turnover ratio of stocks traded and sva is the value of total stocks. All variables are

measured as a percent of GDP.

To control for non-financial variables, we use two more exogenous variables,

government’s expenditure (GC) and value of merchandise trade (TY), both also

measured as a % of GDP. We also use variables pr and ief to measure the strength of

institutional quality.

The Figure A.2 provides graphs of the nonparametric density estimates of various

financial quality indicators (dcpy, dcpy, smk, str and sva) and real per capita GDP. We

look at how the density functions shift between 1993 and 2011. These graphs plot the

values of the financial indicators along the x-axis and the frequency of countries

(proportion of countries in that range) along the y-axis. The results are mixed while

2 See Annex Table A1 for a complete list of countries.

3 See Annex Table A2 for data sources of the variables used in the paper.


10

comparing pdfs in 1993 and 2011; for some indicators (dcpy, dcpy, smk) we see the

density function shifts to the right, thus suggesting a greater fraction of countries in

our dataset have better quality of financial institutions. However, for others, (str and

sva) the density function does not move much or even shifts upwards. The density

function shifts to the right for the variable measuring real per capita GDP. Fewer

fraction of countries in our data are in the low income range between 1993 and 2011.

The descriptive statistics of the variables are reported in Table A.3.

C. The Empirical model

The objective is to examine the impact of the size and structure of financial sector

(measured by pcby, dcpy, smk, str and sva) on GDP per capita (rpcGDP). The main

model is a semi-log function converted to a nonparametric model represented by

equation (5). Here, m(.) is an unknown smooth function of the covariates, i are

unobserved country characteristics that are constant over time. This flexible

estimation strategy helps to avoid any functional form misspecification bias and

enables to explore the shape of the underlying relationship without superimposing any

a priori functional form restriction.

ln(rpcGDPit) = m(i, Fit, GCit, TYit) (5)

Fit = pcbyit,, dcpyit,, smkit, strit or svait

V. Results

This section discusses results for the core empirical model as represented by equation

(5), which has three main independent variables for a sample of 156 countries over

the period of 1993-2011. The three independent variables are the financial variable

(pcby/ dcpy/ smk/ str/ sva), government expenditures (GC) and value of trade (TY).

The results reported by dividing the data into various sub-groups of countries.

The dependent variable (rpcGDP) is measured in logs. All independent variables are

measures as percentage of GDP. All estimated coefficients are interpreted as a model

like in a semi log model. If the value of any x-variable changes by 1%, the real per

capita GDP (rpcGDP) changes by the coefficient multiplied by 100%.

As noted earlier, the nonparametric estimation technique gives an estimate of the

value of the regression function (the conditional moment) and its slope at every

country-time period combination. To help with the analysis and interpretation of

results, paper provides the slope estimates at the 25th

, 50th

and 75th

percentiles (labeled

quartiles 1, 2 and 3 or Q1, Q2 and Q3) and their standard errors obtained via

bootstrapping. The table also indicates which estimates are significant at the 90%,

95% or 99% confidence level.

In section A, the results reported for three group of countries, namely: regional

groupings as East Asia and Pacific, Europe and Central Asia, Latin America & the

Caribbean, Middle East and North Africa, South Asia, and Sub-Saharan Africa, as

well as for two income groups such as high income OECD countries and high income

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non OECD countries. The results are also shown for regional groupings based on the

United Nations Regional Commissions country classifications, namely: ESCAP,

ECA, ECE, ECLAC and ESCWA. We also discuss robustness checks of the results

for the model which then include two additional types of classification of results

based on the size of the financial sector and institutional quality.

In table 1, the subgroups are: (i) East Asia and Pacific, (ii) Europe and Central Asia,

(iii) Latin America & the Caribbean, (iv) Middle East and North Africa, (v) South

Asia, (vi) Sub-Saharan Africa, (vii) high income OECD countries and (viii) high

income non OECD countries. Classification is table 2 is based on United Nations

regional classifications (unrgcr).

In table 3, we divide the countries by size of the financial sector. Tables 4 and 5

divide the available countries on the basis of property rights (pr) and strength of

institutions (ief).

A. Core model results with regional classification

Table 1 displays the nonparametric estimates of the responsiveness of real GDP per

capita (rpcGDP) to changes in the financial variables (pcby/ dcpy/ smk/ str/ sva) for

various country groups. For countries in the East Asia and Pacific region, in 75% of

the observations, we find a positive relationship between economic growth

performance and the financial sector variables.

First, the results are shown for the East Asia and Pacific region, in columns 2 – 4, which

is based on the estimates in row titled pcby. In this region, as indicated by the 3rd

Quartile being equal to .017, in 75% of cases considered (in this case 34 countries across

7 time periods), during certain time periods, countries with similar sized governments

(GC) and degree of openness (TY) show a positive relationship between pcby and

economic growth. Specifically, in these cases, if share of GDP in private credit held by

banks or pcby increases by 1%, the per capital GDP increases by 1.7%. This relationship

is also statistically significant. In other words, the ceteris paribus relationship between

pcby and economic growth is positive significant for 75% of cases considered. Similarly,

from row titled GC, we can infer that the ceteris paribus relationship between GC and

economic growth is positive significant for 75% of cases, however the relationship is

significant negative for 50% of cases considered. And also from the row titled TY, the

ceteris paribus relationship between TY and economic growth is positive significant for

75% of cases. Unlike parametric results which provide a single global estimate for the

entire dataset, nonparametric methodology provides more heterogeneous results,

highlighting the differences across countries and country groups.

Second, the results for dcpy are similar as seen from columns 5 - 7. The ceterus paribus

relationship is significant positive for 75% of cases considered for the relationships

between economic growth and dcpy, economic growth and GC as well as economic

growth and TY.

Our general observation from the set of two parts under table 1 is a positive

relationship between the finance variables, especially the bank-based system and

growth. Almost all 3rd

quartile estimates are positive and significant. This observation


12

holds true for market-based system as show in the second panel of table 1 with

financial variables such as smk/ str/ sva. However, across regions, we find that the

size and significance of the results vary as well in descending order: Sub-Saharan

Africa, Middle—East and North Africa, East Asia and the Pacific, South Asia, East

Asia and the Pacific and Latin America and Caribbean in the case of pcby variable,

while the order for dcpy is the following: Sub-Saharan Africa, Europe and Central

Asia, South Asia, Middle—East and North Africa, East Asia and the Pacific and Latin

America and Caribbean.

For example, consider Table 1 (g) presenting the 3rd

Quartile estimates for high

income OECD countries. The table at the end of this set of tables presents a

comparison of the nonparametric estimates among various financial variables. We

find the magnitude of the estimates is the highest for pcby. A 1% increase in pcby will

increase real per capita GDP by 3.7%. At the same time, the magnitude is the lowest

for str; a 1% increase in str will increase per capita GDP by only 0.2%. All the

financial variables have a positive impact on economic growth. However, a better

banking system may have a stronger impact on growth compared with a larger stock

market.

B. Core model results with UN regional groupings

Table 2 presents medians, 1st quartiles and 3

rd quartiles of nonparametric estimates

categorized by the United Nations regional commissions classifications (unrgcr). The

theme continues of a ceteris paribus positive significant relationship between the

finance variables (pcby/ dcpy/ smk/ str/ sva) and economic growth for 75% of the

sample for all sub groups considered. Irrespective of the nature of classification, in

75% of the sample, the size of the financial sector has a positive impact on growth, for

countries with similar sized governments and degree of openness.

C. Core model results with size of financial sector

With the set of results under table 3, we try to evaluate the claims of Easterly and Stiglitz

(2000) and Cecchetti and Kharraoubi (2012) who find the positive relationship between

growth of the financial sector and GDP only under certain conditions. We particularly

want to see if a bloated financial system slows down the economy as claimed by

Cecchetti and Kharraoubi (2012). To do so, we divided the data into three subgroups

based on size of the financial sector.

For every financial variable (pcby/ dcpy/ smk/ str/ sva), we roughly sort the data in

increasing order of size of the variable and then divide it by three quartiles. The purpose

is to see if the positive finance-economy relationship is different for countries with the

small sized, medium sized and large sized financial sectors, or if some kind of U-shaped

relationship exits between size of the financial sector and economic growth. We find the

3rd

quartile is significant positive for all financial variables for all sub-groups in this

category. No matter what the size of the financial sector, we find the positive and

significant relationship does exist between the finance variables and growth

DP/16

13

D. Core model results with institutional quality

As mentioned earlier, the nonparametric methodology gives us a slope estimate for every

country in every year. Table 4 presents medians, 1st quartiles and 3

rd quartiles of

nonparametric estimates categorized by property rights or pr (weak to ideal), and table 5

provides the same based on institutional quality or ief (weak to ideal), which is a much

broader concept than the property rights. Again, the 3rd

quartile is positive significant for

all finance variables for all sub groups. The positive significant finance-economy

relationship holds irrespective of the strength or property rights or institutional quality.

In other words, even with different levels of institutions or property rights, a bigger

financial sector may have a transitory higher positive impact on growth.

E. Comparing estimates across bank-based versus market-based system

To compare the 3rd

quartile estimates of the relationship between the various financial

variables (pcby/ dcpy/ smk/ str/ sva) and growth, we add a comparison table 6 for each

of the groups presenting the 1st, 2

nd and 3

rd quartile estimates obtained from the

nonparametric methodology. In general we find the magnitude of the estimates for

pcby and dcpy are higher than the magnitude of the estimates for smk or str.

Here, we examine the impact of a growing financial system on growth or evaluate the

views of Easterly and Stiglitz (2000) as well as Cecchetti and Kharraoubi (2012)

about an inverted U relationship between financial development and growth. Table 7

presents a comparison table of the 3rd

quartile results across all financial variables and

the size of the financial sector. First we look at countries with small sized financial

sectors, i.e. pcby <15, dcpy < 19, smk < 30, str < 5 and sva < 1. The data are sorted

into three quartiles by magnitude of the various financial variables to determine the

cut off points. In this category of results, the banking sector has a significant positive

impact on growth. Increasing pcby or dcpy by 1% will increase per capita GDP by

3.7% or 3.8%. At the same time increasing smk or str by 1% will increase per capita

GDP by 1.4% or 1.3%. Total value of stocks traded also has a significant impact on

growth. Increasing sva by 1% will increase per capita GDP by 7.3%. The impact of

these financial variables on growth deteriorates as the size of the financial sector

expands. To understand the declining impact of a bloated financial sector in growth,

we look at countries with large sized financial sectors, i.e., pcby <15, dcpy < 19, smk

< 30, str < 5 and sva < 1. Here, all financial variables have a weaker impact on

growth. Increasing pcby or dcpy or smk or str or sva by 1% will increase per capita

GDP by only 0.8% or 0.9% or 0.3% or 0.2% or 0.45%, a much smaller magnitude

compared with the results for the sub group with smaller financial sector. A very large

financial sector may be too much of a good thing.

Now we examine the impact of property rights and institutions on the finance-growth

relationship. Table 8 compares the nonparametric 3rd

quartile estimates of the finance-

growth relationship for various sub groups of countries based on an index of property

rights (pr). For countries with low pr (0 < pr < 39), the impact of increasing pcby or

dcpy by 1% on real per capita GDP is 3.3% or 3.1%. The impact is much lower in

magnitude for stock market variables such as smk or str. However, if sva increases by

1%, real per capita GDP will increase by 5.1%. The magnitude of the impact is much

lower for the countries grouped together on the basis of high values of pr (70 < pr <

100). For this group, if pcby or dcpy increases by 1% then real per capita GDP rises


14

by 1.1% or 0.9%. The magnitudes of the impact on economic growth is much smaller

for str or sva. However, smk does have a strong impact of growth in this subgroup. If

smk increases by 1%, per capita GDP increases by 6%.

Table 9 conducts a similar comparison across financial variables by the value of the

index of institutional quality (ief). For the groups of countries with low values of ief

(i.e., 0 < ief < 39), pcby, dcpy and sva have a large impact on economic growth. In

this subgroup if pcby, dcpy or sva increase by 1%, per capita GDP will increase by

4.7%, 3.1% or 5.3%. However, the magnitude of this impact is much lower for the

subgroup of countries with high values of ief ((i.e., 70 < ief < 100). If pcby, dcpy or

sva increase by 1%, per capita GDP will increase by 0.7%, 0.8% or 1%.

Given the general relationship between well-defined property rights, good

institutional quality and income, one could claim, countries with high values of pr and

ief would on average have high incomes. It appears, financial variables have a low

impact on growth in these countries. Although the estimates may be statistically

significant, their economic significance is considerably low.

VI. Conclusions

The impact of finance variables on economic performance has enormous policy

implications for international institutions such as the United Nations to achieve the

newly adopted Sustainable Development Goals (SDGs). In this paper, we reassess the

relationship between two types of finance variables, bank-based (pcby/ dcpy) and

market-based (smk/ str/ sva) and GDP per capita by utilizing the Li-Racine

methodology.

We examine here a dataset of 156 countries over 1993-2011 time periods. There is

strong evidence of a statistically significant, positive impact of financial sectors on

GDP per capita. It’s worth noting that this positive significant trend is fairly uniform

for all subgroups by country characteristics, size of the financial sector, strength of

property rights and institutional quality. Flow of credit as well as functioning financial

markets are essential to support higher level of economic performance across

countries.

Our paper supports the view that countries with larger and better financial markets are

in a better position to reap benefits from trade integration and public policies. This

holds true irrespective of the initial size of the financial sector in the country as well

as the strength of property rights or institutional quality. The financial sector

deepening and its diversification are catalyst of economic growth by moving capital to

the highest value user without substantial risk of loss through moral hazard, adverse

selection, or transactions costs. Although some of the empirical literature supports this

argument, these studies establish correlation rather than causation.

In the long run, it is important that the financial structure is complete, stable,

transparent, diversified and able to offer financing through both banks and capital

markets, while being sufficiently adaptive to new developments. Policies aimed at

financial structure reform also depend on legal issues in creditor protection,

accounting standards in the particular country, and the stage of a country’s

development, as previously discussed.

DP/16

15

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DP/16

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ANNEX TABLES

Table A.1. List of countries

Country

UN

Classification

UN Regional

Commissions Regional Classification Income Classification

Albania Transition ECE Europe and Central Asia Lower-middle-income economies

Algeria Developing ECA Middle East and North Africa Upper-middle-income economies

Antigua and

Barbuda SIDS ECLAC Latin America & the Caribbean Upper-middle-income economies

Argentina Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Armenia Transition ECE Europe and Central Asia Lower-middle-income economies

Australia Developed ESCAP High-income OECD members High-income OECD members

Austria Developed ECE High-income OECD members High-income OECD members

Azerbaijan Transition ECE Europe and Central Asia Upper-middle-income economies

Bahamas, The SIDS ECLAC High-income nonOECD members High-income nonOECD members

Bahrain SIDS ESCWA High-income nonOECD members High-income nonOECD members

Bangladesh LDCs ESCAP South Asia Low-income economies

Barbados SIDS ECLAC High-income nonOECD members High-income nonOECD members

Belarus Transition ECE Europe and Central Asia Upper-middle-income economies

Belgium Developed ECE High-income OECD members High-income OECD members

Belize SIDS ECLAC Latin America & the Caribbean Lower-middle-income economies

Benin LDCs ECA Sub-Saharan Africa Low-income economies

Bhutan LDCs ESCAP South Asia Lower-middle-income economies

Bolivia Developing ECLAC Latin America & the Caribbean Lower-middle-income economies

Botswana Developing ECA Sub-Saharan Africa Upper-middle-income economies

Brazil Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Brunei Darussalam Developing ESCAP High-income nonOECD members High-income nonOECD members

Bulgaria Developed ECE Europe and Central Asia Upper-middle-income economies

Burkina Faso LDCs ECA Sub-Saharan Africa Low-income economies

Burundi LDCs ECA Sub-Saharan Africa Low-income economies

Cambodia LDCs ESCAP East Asia and Pacific Low-income economies

Cameroon Developing ECA Sub-Saharan Africa Lower-middle-income economies

Canada Developed ECLAC High-income OECD members High-income OECD members

Central African

Republic LDCs ECA Sub-Saharan Africa Low-income economies

Chad LDCs ECA Sub-Saharan Africa Low-income economies

Chile Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

China Developing ESCAP East Asia and Pacific Upper-middle-income economies

Colombia Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Comoros LDCs/SIDs ECA Sub-Saharan Africa Low-income economies

Congo, Dem. Rep. LDCs ECA Sub-Saharan Africa Low-income economies

Congo, Rep. Developing ECA Sub-Saharan Africa Lower-middle-income economies

Costa Rica Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Cote d'Ivoire Developing ECA Sub-Saharan Africa Lower-middle-income economies

Croatia Developed ECE High-income nonOECD members High-income nonOECD members

Cyprus Developed ECE High-income nonOECD members High-income nonOECD members

Czech Republic Developed ECE High-income OECD members High-income OECD members


18

Country

UN

Classification

UN Regional


Denmark Developed ECE High-income OECD members High-income OECD members

Djibouti LDCs ECA Middle East and North Africa Lower-middle-income economies

Dominica SIDS ECLAC Latin America & the Caribbean Upper-middle-income economies

Dominican Republic SIDS ECLAC Latin America & the Caribbean Upper-middle-income economies

Ecuador Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Egypt, Arab Rep. Developing ECA Middle East and North Africa Lower-middle-income economies

El Salvador Developing ECLAC Latin America & the Caribbean Lower-middle-income economies

Equatorial Guinea LDCs ECA High-income nonOECD members High-income nonOECD members

Eritrea LDCs ECA Sub-Saharan Africa Low-income economies

Estonia Developed ECE High-income OECD members High-income OECD members

Ethiopia LDCs ECA Sub-Saharan Africa Low-income economies

Fiji SIDS ESCAP East Asia and Pacific Lower-middle-income economies

Finland Developed ECE High-income OECD members High-income OECD members

France Developed ECE High-income OECD members High-income OECD members

Gabon Developing ECA Sub-Saharan Africa Upper-middle-income economies

Gambia, The LDCs ECA Sub-Saharan Africa Low-income economies

Georgia Transition ECE Europe and Central Asia Lower-middle-income economies

Germany Developed ECE High-income OECD members High-income OECD members

Ghana Developing ECA Sub-Saharan Africa Lower-middle-income economies

Greece Developed ECE High-income OECD members High-income OECD members

Grenada SIDS ECLAC Latin America & the Caribbean Upper-middle-income economies

Guatemala Developing ECLAC Latin America & the Caribbean Lower-middle-income economies

Guinea LDCs ECA Sub-Saharan Africa Low-income economies

Guyana Developing ECLAC Latin America & the Caribbean Lower-middle-income economies

Honduras Developing ECLAC Latin America & the Caribbean Lower-middle-income economies

Hong Kong SAR,

China Developing ESCAP High-income nonOECD members High-income nonOECD members

Hungary Developed ECE High-income OECD members High-income OECD members

Iceland Developed ECE High-income OECD members High-income OECD members

India Developing ESCAP South Asia Lower-middle-income economies

Indonesia Developing ESCAP East Asia and Pacific Lower-middle-income economies

Iran, Islamic Rep. Developing ESCAP Middle East and North Africa Upper-middle-income economies

Israel Developing ECE High-income OECD members High-income OECD members

Italy Developed ECE High-income OECD members High-income OECD members

Japan Developed ESCAP High-income OECD members High-income OECD members

Jordan Developing ESCWA Middle East and North Africa Upper-middle-income economies

Kazakhstan Transition ECE Europe and Central Asia Upper-middle-income economies

Kenya Developing ECA Sub-Saharan Africa Low-income economies

Korea, Rep. Developing ESCAP High-income OECD members High-income OECD members

Kuwait Developing ESCWA High-income nonOECD members High-income nonOECD members

Kyrgyz Republic Transition ECE Europe and Central Asia Low-income economies

Lao PDR LDCs ESCAP East Asia and Pacific Lower-middle-income economies

Latvia Developed ECE Europe and Central Asia Upper-middle-income economies

Lebanon Developing ESCWA Middle East and North Africa Upper-middle-income economies

Lesotho LDCs ECA Sub-Saharan Africa Lower-middle-income economies

Libya Developing ECA Middle East and North Africa Upper-middle-income economies

DP/16

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Country

UN

Classification

UN Regional


Lithuania Developed ECE Europe and Central Asia Upper-middle-income economies

Macao SAR, China Developing ESCAP High-income nonOECD members High-income nonOECD members

Macedonia, FYR Transition ECE Europe and Central Asia Upper-middle-income economies

Madagascar LDCs ECA Sub-Saharan Africa Low-income economies

Malawi LDCs ECA Sub-Saharan Africa Low-income economies

Malaysia Developing ESCAP East Asia and Pacific Upper-middle-income economies

Mali LDCs ECA Sub-Saharan Africa Low-income economies

Malta Developed ECE High-income nonOECD members High-income nonOECD members

Mauritius SIDS ECA Sub-Saharan Africa Upper-middle-income economies

Mexico Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Moldova Transition ECE Europe and Central Asia Lower-middle-income economies

Mongolia Developing ESCAP East Asia and Pacific Lower-middle-income economies

Morocco Developing ECA Middle East and North Africa Lower-middle-income economies

Mozambique LDCs ECA Sub-Saharan Africa Low-income economies

Namibia Developing ECA Sub-Saharan Africa Upper-middle-income economies

Nepal LDCs ESCAP South Asia Low-income economies

Netherlands Developed ECE High-income OECD members High-income OECD members

New Zealand Developed ESCAP High-income OECD members High-income OECD members

Nicaragua Developing ECLAC Latin America & the Caribbean Lower-middle-income economies

Niger LDCs ECA Sub-Saharan Africa Low-income economies

Nigeria Developing ECA Sub-Saharan Africa Lower-middle-income economies

Norway Developed ECE High-income OECD members High-income OECD members

Oman Developing ESCWA High-income nonOECD members High-income nonOECD members

Pakistan Developing ESCAP South Asia Lower-middle-income economies

Panama Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Papua New Guinea SIDS ESCAP East Asia and Pacific Lower-middle-income economies

Paraguay Developing ECLAC Latin America & the Caribbean Lower-middle-income economies

Peru Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Philippines Developing ESCAP East Asia and Pacific Lower-middle-income economies

Poland Developed ECE High-income OECD members High-income OECD members

Portugal Developed ECE High-income OECD members High-income OECD members

Russian Federation Transition ESCAP Europe and Central Asia Upper-middle-income economies

Rwanda LDCs ECA Sub-Saharan Africa Low-income economies

Saudi Arabia Developing ESCWA High-income nonOECD members High-income nonOECD members

Senegal LDCs ECA Sub-Saharan Africa Lower-middle-income economies

Sierra Leone LDCs ECA Sub-Saharan Africa Low-income economies

Singapore Developing ESCAP High-income nonOECD members High-income nonOECD members

Slovak Republic Developed ECE High-income OECD members High-income OECD members

Slovenia Developed ECE High-income OECD members High-income OECD members

Solomon Islands LDCs/SIDs ESCAP East Asia and Pacific Lower-middle-income economies

South Africa Developing ECA Sub-Saharan Africa Upper-middle-income economies

Spain Developed ECE High-income OECD members High-income OECD members

Sri Lanka Developing ESCAP South Asia Lower-middle-income economies

St. Kitts and Nevis SIDS ECLAC High-income OECD members High-income OECD members

St. Lucia SIDS ECLAC Latin America & the Caribbean Upper-middle-income economies


20

Country

UN

Classification

UN Regional


St. Vincent and the

Grenadines SIDS ECLAC Latin America & the Caribbean Upper-middle-income economies

Sudan LDCs ECA Sub-Saharan Africa Lower-middle-income economies

Swaziland Developing ECA Sub-Saharan Africa Lower-middle-income economies

Sweden Developed ECE High-income OECD members High-income OECD members

Switzerland Developed ECE High-income OECD members High-income OECD members

Syrian Arab

Republic Developing ESCWA Middle East and North Africa Lower-middle-income economies

Tajikistan Transition ECE Europe and Central Asia Low-income economies

Tanzania LDCs ECA Sub-Saharan Africa Low-income economies

Thailand Developing ESCAP East Asia and Pacific Upper-middle-income economies

Togo LDCs ECA Sub-Saharan Africa Low-income economies

Tonga SIDS ESCAP East Asia and Pacific Lower-middle-income economies

Trinidad and Tobago SIDS ECLAC High-income nonOECD members High-income nonOECD members

Tunisia Developing ECA Middle East and North Africa Upper-middle-income economies

Turkey Developing ECE Europe and Central Asia Upper-middle-income economies

Uganda LDCs ECA Sub-Saharan Africa Low-income economies

Ukraine Transition ECE Europe and Central Asia Lower-middle-income economies

United Arab

Emirates Developing ESCWA High-income nonOECD members High-income nonOECD members

United Kingdom Developed ECE High-income OECD members High-income OECD members

United States Developed ECLAC High-income OECD members High-income OECD members

Uruguay Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Vanuatu LDCs/SIDs ESCAP East Asia and Pacific Lower-middle-income economies

Venezuela, RB Developing ECLAC Latin America & the Caribbean Upper-middle-income economies

Vietnam Developing ESCAP East Asia and Pacific Lower-middle-income economies

Yemen, Rep. LDCs ESCWA Middle East and North Africa Lower-middle-income economies

Zambia LDCs ECA Sub-Saharan Africa Lower-middle-income economies

Zimbabwe Developing ECA Sub-Saharan Africa Low-income economies

Source: United Nations, and classification is based on the World Bank

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Table A.2. Description and sources of variables

Variable/code Description Source

rpcGDP GDP per capita (constant 2005 US$) UN

pcby Private credit by deposit money banks to GDP

(%) IMF/World Bank

dcpy Domestic credit to private sector (% of GDP) IMF/World Bank

smk Market capitalization of listed companies (% of

GDP)

IMF/World Bank

str Turnover ratio of stocks traded (% of GDP) IMF/World Bank

sva Total stock value traded IMF/World Bank

GC General government final consumption

expenditure (% of GDP) World Bank

TY Merchandise trade (% of GDP) UNCTAD

pr Property rights Heritage Foundation

ief Institutions classification Heritage Foundation

Note: All variables are converted in logs, denoted by ‘ln’ in the text, tables and figures.

Table A.3. Descriptive statistics

ln(rpcGDP) pcby dcpy smk str sva GC TY

Mean 8.02 42.32 46.72 46.15 47.06 26.13 15.81 65.88

Std Dev 1.59 40.65 44.48 58.92 72.15 53.48 6.01 41.26

Min 4.79 0.12 0.83 0.03 0.02 .002 2.92 8.62

Max 11.11 272.7 282.7 539.33 605.02 673 55.87 358.4

N 1001 1001 1092 677 700 700 1001 1001


22

Figure A.2. List of Graphs

0

.005

.01

.015

.02

0 100 200 300x

kdensity pcby_1993 kdensity pcby_2011

(1993 vs 2011)

pcby density estimates

0

.005

.01

.015

.02

0 100 200 300x

kdensity dcpy_1993 kdensity dcpy_2011

(1993 vs 2011)

dcpy density estimates0

.005

.01

.015

.02

.025

0 100 200 300 400x

kdensity smk1993 kdensity smk2011

(1993 vs 2011)

smk density estimates

0

.005

.01

.015

0 200 400 600x

kdensity str_1993 kdensity str_2011

(1993 vs 2011)

str density estimates

0

.02

.04

.06

.08

0 200 400 600 800x

kdensity sva_1993 kdensity sva_2011

(1993 vs 2011)

sva density estimates

0

.00002

.00004

.00006

0 20000 40000 60000x

kdensity rpcGDP_1993 kdensity rpcGDP_2011

(1993 vs 2011)

rpcGDP density estimates

DP/16

23

Table 1. Nonparametric slope coefficients by region

(a) Estimates for East Asia and Pacific

Dependent

variable

ln(rpcGDP)

1st Quartile Median 3

rd Quartile 1

st Quartile Median 3

rd Quartile

pcby -0.0007

(0.0006)

0.005

(0.003)

.017***

(0.002)

dcpy

.002***

(.0004)

.007**

(.002)

.014***

(.001)

GC -0.042**

(0.019)

-.013*

(.007)

0.029**

(0.014)

-.01

(.009)

-.0005

(.003)

.08***

(.02)

TY 0.0006

(0.0008)

0.003***

(0.0006)

0.006***

(0.001)

.001***

(.0005)

.003***

(.0002)

.008***

(.001)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk .000

(.000)

.002***

(.000)

.009***

(.002)

str

-.003**

(.001)

.001

(.001)

.004***

(.001)

sva

.000

(.001)

.005**

(.002)

.027*

(.016)

GC -.048***

(.016)

-.001

(.009)

.033

(.024)

-.059***

(.009)

-.014

(.012)

.057***

(.016)

-.035**

(.017)

.008

(.010)

.063***

(.011)

TY -.005***

(.001)

.002

(.001)

.007***

(.001)

-.004***

(.001)

.003***

(.000)

.007***

(.001)

-.002*

(.001)

.003***

(.001)

.008***

(.001)

(b) Estimates for Europe and Central Asia

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby 0.004***

(0.001)

0.012***

(0.002)

0.021***

(0.003)

dcpy

.006***

(.001)

.015***

(.001)

.024***

(.001)

GC -0.080***

(0.014)

-0.019**

(0.008)

0.020**

(0.009)

-.059***

(.005)

-.024***

(.002)

.001

(.003)

TY -0.004

(0.003)

0.006***

(0.0009)

0.012***

(0.001)

-.008***

(.002)

.005***

(.0007)

.008***

(.0009)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk .006***

(.001)

.012***

(.002)

.032***

(.008)

str

-.007

(.004)

-.000*

(.000)

.005**

(.002)

sva

.014***

(.003)

.042***

(.004)

.063***

(.004)

GC .080***

(.025)

.007

(.015)

.041***

(.015)

-.11***

(.02)

-.024***

(.009)

.016

(.011)

-.077***

(.021)

-.022**

(.010)

.023**

(.010)

TY -.004

(.006)

.004***

(.001)

.011***

(.001)

-.002

(.006)

.009***

(.001)

.018***

(.005)

-.003

(.002)

.005***

(.001)

.014***

(.001)


24

(c) Estimates for Latin America & the Caribbean

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby -0.001

(.001)

0.004***

(.0008)

0.01***

(.001)

dcpy

.005***

(.0006)

.0088***

(.0003)

.013***

(.0005)

GC -0.025***

(.003)

0.001

(.002)

0.018***

(.005)

-.025***

(.004)

.006

(.003)

.025***

(.005)

TY -0.002***

(.0006)

0.001**

(.0009)

0.007***

(.0009)

-.002***

(.0007)

.003***

(.0004)

.007***

(.0008)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.002**

(.001)

.002***

(.000)

.005***

(.001)

str

.000

(.000)

.003***

(.000)

.008***

(.002)

sva

.000

(.003)

.007***

(.001)

.025***

(.004)

GC -.006

(.007)

.021***

(.005)

.075***

(.009)

-.003

(.004)

.045***

(.009)

.102***

(.016)

-.000

(.004)

.030***

(.007)

.075***

(.011)

TY .001**

(.000)

.004***

(.000)

.009***

(.001)

-.000

(.001)

.007***

(.001)

.016***

(.001)

.003*

(.001)

.007***

(.000)

.013***

(.001)

(d) Estimates for Middle East and North Africa

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .006***

(.001)

.011***

(.002)

.027***

(.006)

dcpy

.005***

(.001)

.007***

(.001)

.017***

(.002)

GC -.035***

(.006)

-.013*

(.008)

.009

(.020)

-.027***

(.008)

-.011*

(.006)

.014**

(.006)

TY -.002**

(.001)

.002

(.002)

.013***

(.0009)

.0004

(.001)

.004*

(.002)

.012***

(.001)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.003*

(.001)

.001*

(.000)

.005***

(.001)

str

-.016***

(.004)

-.001

(.002)

.006**

(.002)

sva

.000

(.003)

.012***

(.004)

.031***

(.004)

GC -.087

(.058)

-.020*

(.012)

.074*

(.041)

-.062***

(.017)

-.012

(.023)

.14**

(.05)

-.1***

(.029)

-.009

(.029)

.12***

(.036)

TY -.003

(.003)

-.000

(.001)

.008**

(.003)

-.004***

(.001)

.000

(.002)

.016**

(.007)

-.01***

(.004)

-.001

(.001)

.003

(.003)

DP/16

25

(e) Estimates for South Asia

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .011***

(.003)

.016***

(.001)

.019***

(.0009)

dcpy

.006**

(.002)

.016***

(.001)

.018***

(.0009)

GC -.017

(.012)

.024**

(.01)

.063***

(.008)

.048**

(.019)

.062***

(.012)

.11***

(.009)

TY .001

(.002)

.006***

(.001)

.017***

(.005)

.002

(.002)

.003***

(.0004)

.015***

(.002)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.008**

(.003)

-.001

(.002)

.01**

(.005)

str

.000

(.000)

.001**

(.000)

.003

(.003)

sva

-.001

(.0010

.005

(.005)

.038***

(.013)

GC .000

(.033)

.059***

(.007)

0.11***

(.031)

-.039**

(.017)

.039

(.034)

0.15***

(.03)

.040

(.037)

.084***

(.011)

.127***

(.020)

TY .002

(.004)

.017***

(.004)

0.03***

(.008)

-.000

(.003)

.013***

(.004)

.023***

(.003)

.009***

(.002)

.023***

(.008)

.041***

(.002)

(f) Estimates for Sub-Saharan Africa

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .008***

(.001)

.022***

(.002)

.038***

(.001)

dcpy

.016***

(.001)

.026***

(.001)

.039***

(.001)

GC -.03***

(.003)

-.007**

(.003)

.014***

(.002)

-.017***

(.002)

-.003

(.001)

.019***

(.002)

TY -.001*

(.0008)

.003***

(.0004)

.007***

(.0007)

-.0007

(.001)

.003***

(.0003)

.009***

(.0006)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk .000

(.000)

.002*

(.001)

.013***

(.003)

str

-.000

(.001)

.008***

(.001)

.015***

(.001)

sva

.037***

(.004)

.057***

(.005)

.083***

(.003)

GC -.029***

(.006)

-.007

(.005)

.014*

(.008)

-.023***

(.007)

-.009*

(.005)

.019***

(.003)

-.035***

(.007)

.001

(.005)

.022***

(.003)

TY -.003***

(.001)

.001

(.001)

.008***

(.001)

-.005***

(.001)

.003**

(.001)

.009***

(.001)

-.008***

(.002)

.000

(.001)

.008***

(.002)


26

(g) Estimates for high income OECD countries

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby -.037***

(.01)

-.004

(.005)

.037***

(.011)

dcpy

.001***

(.000)

.005***

(.000)

.008***

(.000)

GC -.004**

(.001)

.001*

(.001)

.008***

(.002)

-.025***

(.003)

.022***

(.008)

.053***

(.004)

TY -.002***

(.000)

.000**

(.000)

.003**

(.001)

-.006***

(.001)

.002***

(.000)

.006***

(.000)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.000

(.000)

.002***

(.000)

.005***

(.000)

str

-.000*

(.000)

.000***

(.000)

.002***

(.000)

sva

.000*

(.000)

.003***

(.000)

.007***

(.000)

GC -.017***

(.005)

.015***

(.004)

.059***

(.007)

-.041***

(.008)

.005

(.003)

.038***

(.007)

-.017**

(.008)

.020***

(.005)

.085***

(.010)

TY -.004***

(.001)

.004***

(.000)

.008***

(.000)

-.005***

(.001)

.003***

(.000)

.007***

(.000)

-.014***

(.001)

-.001

(.001)

.006***

(.001)

(h) Estimates for high income non OECD countries

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby -.000

(.001)

.003***

(.000)

.009***

(.002)

dcpy

.001

(.001)

.005***

(.000)

.010***

(.001)

GC -.051***

(.007)

-.011**

(.004)

-.000

(.002)

-.040***

(.004)

-.013

(.008)

.012**

(.005)

TY -.003**

(.001)

.000

(.000)

.004***

(.001)

-.001

(.001)

.003***

(.000)

.006***

(.001)

Dependent variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk .000

(.000)

.002***

(.000)

.003***

(.000)

str

-.002***

(.000)

.000**

(.000)

.003**

(.001)

sva

.000

(.000)

.006***

(.001)

.020***

(.002)

GC -.029***

(.008)

-.008

(.006)

.017

(.014)

-.037***

(.009)

-.004

(.004)

.037***

(.011)

-.032**

(.014)

.003

(.008)

.048***

(.013)

TY -.002***

(.000)

.000

(.001)

.006***

(.002)

-.004**

(.002)

.001*

(.001)

.008***

(.002)

-.007***

(.001)

.000

(.001)

.005***

(.001)

All standard error are in parentheses and are obtained via bootstrapping

* indicates significance at 10% level

** indicates significance at 5% level

*** indicates significance at 1% level

DP/16

27

Table 2. Nonparametric slope coefficients by UN regional commissions

(a) Estimates for unrgcr = ECA

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .006***

(.0009)

.018***

(.002)

.037***

(.002)

dcpy

.01***

(.002)

.02***

(.001)

.037***

(.001)

GC -.031***

(.003)

-.006**

(.003)

.014***

(.002)

-.016***

(.001)

-.002*

(.001)

.02***

(.001)

TY -.0008

(.0008)

.004***

(.0004)

.010***

(.001)

-.0001

(.0008)

.003***

(.0003)

.01***

(.0006)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk .000

(.000)

.002*

(.001)

.011***

(.002)

str

-.002

(.002)

.006***

(.001)

.014***

(.001)

sva

.027***

(.004)

.051***

(.004)

.080***

(.003)

GC -.029***

(.005)

.001

(.006)

.034**

(.015)

-.023***

(.006)

.001

(.006)

.033***

(.008)

-.032***

(.008)

.006

(.006)

.037***

(.010)

TY -.003*

(.001)

.001

(.001)

.009***

(.001)

-.005***

(.001)

.003**

(.001)

.010***

(.001)

-.006***

(.002)

.000

(.000)

.009***

(.001)

(b) Estimates for unrgcr = ECE

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .002***

(.0002)

.005***

(.0004)

.01***

(.001)

dcpy

.002***

(.0002)

.006***

(.0003)

.013***

(.0009)

GC -.058***

(.003)

-.021***

(.006)

.02***

(.003)

-.027***

(.002)

-.002

(.004)

.045***

(.003)

TY -.001*

(.0007)

.003***

(.0004)

.009***

(.001)

-.004***

(.001)

.004***

(.0005)

.007***

(.0003)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk .000

(.000)

.003***

(.000)

.010***

(.000)

str

-.001***

(.000)

.000

(.000)

.002***

(.000)

sva

.000

(.000)

.005***

(.001)

.032***

(.005)

GC -.018***

(.006)

.017***

(.005)

.062***

(.006)

-.047***

(.007)

.001

(.004)

.037***

(.004)

-.036***

(.005)

.013**

(.005)

.077***

(.012)

TY -.001

(.001)

.004***

(.000)

.008***

(.000)

-.002*

(.001)

.005***

(.000)

.010***

(.000)

-.010***

(.001)

.001**

(.001)

.008***

(.000)


28

(c) Estimates for unrgcr = ECLAC

Dependent

variable

lp(rpcGDP)


rd Quartile 1


rd Quartile

pcby .002***

(.0002)

.005***

(.0004)

.01***

(.001)

dcpy

.004***

(.0003)

.008***

(.0005)

.013***

(.0007)

GC -.058***

(.003)

-.021***

(.006)

.02***

(.003)

-.031***

(.002)

-.002

(.003)

.023***

(.001)

TY -.001*

(.0007)

.003***

(.0004)

.009***

(.001)

-.002***

(.0008)

.002***

(.0004)

.007***

(.0009)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.001**

(.000)

.002***

(.000)

.005***

(.000)

str

.000

(.000)

.003***

(.000)

.007***

(.001)

sva

.000

(.001)

.007***

(.001)

.020***

(.003)

GC -.023**

(.012)

.017***

(.003)

.071***

(.010)

-.009

(.007)

.028**

(.012)

.073***

(.012)

-.004

(.004)

.026***

(.005)

.067***

(.007)

TY .000

(.001)

.004***

(.000)

.009***

(.000)

-.003**

(.001)

.005***

(.001)

.014***

(.0010

-.000

(.001)

.006***

(.000)

.013***

(.001)

(d) Estimates for unrgcr = ESCAP

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .0004

(.0009)

.012***

(.001)

.019***

(.0009)

dcpy

.002***

(.0004)

.007***

(.001)

.017***

(.0009)

GC -.056***

(.009)

-.014**

(.006)

.019***

(.007)

-.035***

(.002)

.003

(.002)

.053***

(.011)

TY -.004**

(.001)

.002***

(.0004)

.006***

(.0007)

-.001

(.002)

.003***

(.0002)

.007***

(.001)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.000

(.001)

.001***

(.000)

.006***

(.001)

str

-.000

(.000)

.001***

(.000)

.004***

(.001)

sva

.000

(.000)

.006***

(.001)

.020***

(.003)

GC -.044***

(.009)

.010

(.008)

.066***

(.012)

-.060***

(.007)

-.003

(.010)

.062***

(.009)

-.025**

(.011)

.029***

(.007)

.084***

(.010)

TY -.004***

(.001)

.002***

(.001)

.009***

(.001)

-.005***

(.000)

.001**

(.000)

.007***

(.002)

-.003***

(.001)

.002**

(.000)

.010***

(.002)

DP/16

29

(e) Estimates for unrgcr = ESCWA

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .001

(.002)

.012***

(.002)

.02***

(.004)

dcpy

.004**

(.002)

.01***

(.001)

.017***

(.001)

GC -.03***

(.011)

-.009*

(.005)

.003

(.003)

-.03*

(.016)

-.011

(.009)

.02***

(.007)

TY -.002***

(.0006)

.0006

(.0006)

.001

(.001)

.0008

(.0007)

.004***

(.0003)

.006

(.006)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.000

(.000)

.001***

(.000)

.002***

(.000)

str

-.002**

(.001)

-.000

(.000)

.001***

(.000)

sva

-.000

(.001)

.004***

(.001)

.010**

(.004)

GC -.045***

(.018)

-.015***

(.005)

.003

(.004)

-.040***

(.007)

-.017***

(.004)

.002

(.004)

-.089***

(.018)

-.015

(.010)

.005

(.012)

TY -.003

(.002)

-.000

(.001)

.007**

(.003)

-.001

(.001)

.000

(.001)

.010***

(.003)

-.012***

(.003)

-.003*

(.001)

.002

(.002)






30

Table 3. Nonparametric slope coefficients by size of the financial sector

(roughly arranging by quartiles)

(a) Estimates for Countries with a Small Sized Financial Sector

pcby <15 dcpy<19

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .006***

(.001)

.02***

(.002)

.037***

(.001)

dcpy

.014***

(.001)

.024***

(.0008)

.038***

(.001)

GC -.034***

(.005)

-.003

(.002)

.018***

(.002)

-.024***

(.002)

-.005***

(.001)

.018***

(.002)

TY -.002***

(.0008)

.003***

(.0005)

.01***

(.001)

-.002***

(.0009)

.003***

(.0005)

.008***

(.0006)

smk <30 str < 5 sva < 1

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.000

(.000)

.004***

(.000)

.014***

(.001)

str

-.003*

(.002)

.004***

(.001)

.013***

(.001)

sva

.012***

(.003)

.042***

(.004)

.073***

(.004)

GC -.02***

(.006)

.007***

(.002)

.055***

(.007)

-.023***

(.006)

.003

(.003)

.037***

(.010)

-.034***

(.007)

.005

(.003)

.041***

(.007)

TY -.000

(.000)

.004***

(.000)

.008***

(.000)

-.004***

(.001)

.002***

(.000)

.009***

(.001)

-.004***

(.001)

.003***

(.001)

.010***

(.001)

(b) Estimates for Countries with a Medium Sized Financial Sector

15 < pcby <50 19<dcpy<50

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .002***

(.0007)

.009***

(.0008)

.017***

(.0009)

dcpy

.003***

(.0004)

.009***

(.0007)

.015***

(.0006)

GC -.04***

(.003)

-.008***

(.003)

.016***

(.002)

-.026***

(.003)

-.001

(.002)

.03***

(.003)

TY -.0009**

(.0004)

.003***

(.0005)

.009***

(.0009)

.0002

(.0007)

.004***

(.0005)

.01***

(.0006)

DP/16

31

30<smk <60 5 < str < 20 1 < sva < 4.5

Dependent

variable

ln(rpcGDP)


rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.000

(.000)

.002***

(.000)

.004***

(.000)

str

-.003*

(.001)

.001**

(.000)

.009***

(.001)

sva

.005***

(.002)

.026***

(.002)

.045***

(.002)

GC -.044***

(.008)

.006

(.006)

.045***

(.01)

-.049***

(.006)

-.007

(.0050

.049***

(.012)

-.045***

(.006)

.003

(.006)

.085***

(.010)

TY -.005***

(.001)

.002**

(.001)

.011***

(.001)

-.005***

(.001)

.003***

(.001)

.012***

(.002)

-.004***

(.001)

.004***

(.001)

.011***

(.000)

(c) Estimates for Countries with a Large Sized Financial Sector

pcby > 50

dcpy>50

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .001***

(.0003)

.004***

(.0005)

.008***

(.0005)

dcpy

.002***

(.0002)

.005***

(.0003)

.009***

(.0004)

GC -.05***

(.003)

-.019***

(.004)

.012***

(.003)

-.03***

(.001)

.006*

(.003)

.048***

(.003)

TY -.003***

(.0006)

.002***

(.0002)

.006***

(.0003)

-.003**

(.001)

.002***

(.0002)

.005***

(.0005)

smk > 60 str > 20 sva > 4.5

Dependent

variable

ln(rpcGDP)


rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.000

(.000)

.001***

(.000)

.003***

(.000)

str

-.000***

(.000)

.000***

(.000)

.002***

(.000)

sva

.005**

(.002)

.026***

(.002)

.045***

(.002)

GC -.026***

(.008)

.009***

(.003)

.059***

(.008)

-.045***

(.006)

.004

(.003)

.050***

(.007)

-.045***

(.006)

.003

(.007)

.085***

(.011)

TY -.004***

(.001)

.002***

(.000)

.009***

(.001)

-.003***

(.000)

.004***

(.000)

.010***

(.000)

-.004***

(.001)

.004***

(.001)

.011***

(.000)






32

Table 4. Non parametric slope estimates by property rights (weak to ideal)

(a) Estimates for countries with low pr (0 – 39)

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .005***

(.000)

.016***

(.001)

.033***

(.002)

dcpy

.007***

(.001)

.018***

(.001)

.031***

(.001)

GC -.050***

(.006)

-.014***

(.006)

.012***

(.004)

-.033***

(.002)

-.011***

(.001)

.013***

(.004)

TY -.001*

(.000)

.004***

(.000)

.009***

(.001)

-.001*

(.001)

.003***

(.000)

.009***

(.000)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.000

(.000)

.005***

(.001)

.016***

(.002)

str

-.001

(.001)

.002***

(.000)

.010***

(.001)

sva

.004*

(.002)

.022***

(.003)

.051***

(.003)

GC -.045***

(.008)

.001

(.007)

.074***

(.008)

-.063***

(.008)

-.010*

(.005)

.090***

(.028)

-.057***

(.008)

-.007

(.007)

.063***

(.012)

TY -.001

(.001)

.004***

(.000)

.010***

(.000)

-.004***

(.001)

.004***

(.000)

.011***

(.001)

-.002*

(.001)

.006***

(.000)

.013***

(.001)

(b) Estimates for countries with medium pr (40 – 69)

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .001*

(.000)

.008***

(.000)

.019***

(.001)

dcpy

.003***

(.000)

.009***

(.000)

.018***

(.000)

GC -.032***

(.004)

.001

(.001)

.021***

(.004)

-.014***

(.003)

.009***

(.002)

.030***

(.002)

TY -.001**

(.000)

.004***

(.000)

.010***

(.001)

-.000

(.001)

.004***

(.000)

.009***

(.000)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.031***

(.005)

.000

(.004)

.044***

(.006)

str

-.002***

(.000)

.001**

(.000)

.007***

(.001)

sva

.003***

(.001)

.019***

(.003)

.048***

(.003)

GC -.002***

(.000)

.002***

(.000)

.007***

(.000)

-.031***

(.004)

.002

(.004)

.051***

(.008)

-.023***

(.006)

.009***

(.003)

.055**

(.005)

TY -.000

(.000)

.002***

(.000)

.007***

(.001)

-.001*

(.001)

.004***

(.000)

.014***

(.001)

-.002***

(.000)

.004***

(.001)

.011***

(.001)

DP/16

33

(c) Estimates for countries with high pr (70 – 100)

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .002***

(.000)

.005***

(.000)

.011***

(.000)

dcpy

.001***

(.000)

.005***

(.000)

.009***

(.001)

GC -.048***

(.005)

-.017***

(.005)

.014***

(.003)

-.026***

(.004)

.018***

(.004)

.048***

(.003)

TY -.002***

(.000)

.002***

(.000)

.006***

(.000)

-.001

(.000)

.003***

(.000)

.006***

(.000)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.017***

(.006)

.014***

(.002)

.060***

(.007)

str

-.000

(.000)

.001***

(.000)

.003***

(.000)

sva

.000**

(.000)

.004***

(.000)

.010***

(.001)

GC -.002**

(.001)

.004***

(.000)

.009***

(.000)

-.040***

(.006)

.006**

(.003)

.044***

(.006)

-.005

(.007)

.025***

(.003)

.085***

(.008)

TY .000

(.000)

.001***

(.000)

.004***

(.000)

-.004***

(.000)

.003***

(.000)

.008***

(.000)

-.014***

(.001)

-.000

(.001)

.006***

(.001)


34

Table 5. Non parametric slope estimates by overall institutions classification

(weak to ideal)

(a) Estimates for countries with low ief (0 – 39)

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .017***

(.005)

.034***

(.004)

.047***

(.007)

dcpy

.002

(.003)

.019***

(.004)

.031***

(.010)

GC -.023***

(.004)

-.006

(.007)

.033

(.035)

-.023***

(.007)

-.012***

(.004)

.016

(.013)

TY -.006**

(.002)

.000

(.001)

.003**

(.001)

-.007**

(.003)

.000

(.001)

.005**

(.002)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk .000

(.000)

.001

(.001)

.004

(.003)

str

.006*

(.004)

.012***

(.003)

.016***

(.001)

sva

.001

(.013)

.027**

(.015)

.053***

(.015)

GC -.042*

(.023)

-.014

(.010)

-.000

(.006)

-.074***

(.0110

-.048***

(.018)

-.022***

(.008)

-.056***

(.022)

-.034

(.033)

-.001

(.17)

TY -.013*

(.005)

-.000

(.006)

.003

(.003)

-.014***

(.003)

-.003

(.005)

.000

(.004)

-.009

(.006)

-.001

(.003)

.003

(.003)

(b) Estimates for countries with medium ief (40 – 69)

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .002***

(.000)

.009***

(.000)

.020***

(.000)

dcpy

.004***

(.000)

.012***

(.000)

.022***

(.000)

GC -.044***

(.003)

-.007***

(.001)

.018***

(.001)

-.024***

(.002)

.000

(.002)

.031***

(.001)

TY -.000**

(.000)

.003***

(.000)

.009***

(.000)

-.000

(.000)

.004***

(.000)

.009***

(.000)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.000

(.000)

.002***

(.000)

.009***

(.000)

str

-.001***

(.000)

.001***

(.000)

.006***

(.000)

sva

.002***

(.000)

.012***

(.001)

.045***

(.002)

GC -.032***

(.005)

.009***

(.003)

.060***

(.005)

-.040***

(.004)

.002

(.003)

.054***

(.006)

-.032***

(.004)

.014***

(.003)

.066***

(.005)

TY -.002***

(.000)

.003***

(.000)

.009***

(.000)

-.002***

(.000)

.004***

(.000)

.010***

(.000)

-.005***

(.000)

.003***

(.000)

.010***

(.000)

DP/16

35

(c) Estimates for countries with high ief (70 – 100)

Dependent

variable

ln(rpcGDP)


rd Quartile 1


rd Quartile

pcby .001***

(.000)

.004***

(.000)

.007***

(.001)

dcpy

.001***

(.000)

.005***

(.000)

.008***

(.000)

GC -.064***

(.006)

-.020***

(.007)

.010**

(.005)

-.031***

(.004)

.000

(.005)

.035***

(.007)

TY -.006**

(.002)

.002***

(.000)

.007***

(.001)

-.001

(.001)

.003***

(.000)

.005***

(.000)

Dependent

variable

ln(rpcGDP)

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

1st

Quartile

Median 3rd

Quartile

smk -.000

(.000)

.001**

(.000)

.003***

(.000)

str

-.000

(.000)

.001***

(.000)

.004***

(.000)

sva

.000

(.000)

.002***

(.000)

.010***

(.002)

GC -.017**

(.007)

.011***

(.004)

.045***

(.010)

-.046***

(.011)

.005

(.004)

.043***

(.006)

-.018***

(.006)

.022***

(.005)

.072***

(.016)

TY -.001

(.000)

.003***

(.000)

.008***

(.001)

-.006***

(.001)

.002***

(.001)

.007***

(.001)

-.012***

(.003)

.000

(.001)

.007***

(.001)


36

Table 6. Nonparametric slope coefficients by financial sector structure

(a) Estimates by region

Dependent variable

ln(rpcGDP)

pcby dcpy smk str sva

3rd

Quartile

East Asia and Pacific .017***

(0.002)

.014***

(.001)

.009***

(.002)

004***

(.001)

.027*

(.016)

Europe and Central Asia 0.021***

(0.003)

.024***

(.001)

.032***

(.008)

.005**

(.002)

.063***

(.004)

Latin America & the

Caribbean

0.01***

(.001)

.013***

(.0005)

.005***

(.001)

.008***

(.002)

.025***

(.004)

Middle East and North

Africa

.027***

(.006)

.017***

(.002)

.005***

(.001)

.006**

(.002)

.031***

(.004)

South Asia .019***

(.0009)

.018***

(.0009)

.01**

(.005)

.003

(.003)

.038***

(.013)

Sub-Saharan Africa .038***

(.001)

.039***

(.001)

.013***

(.003)

.015***

(.001)

.083***

(.003)

(b) Estimates by regions income level

Dependent variable

ln(rpcGDP)


3rd

Quartile

high income OECD

countries

.037***

(.011)

.008***

(.000)

.005***

(.000)

.002***

(.000)

.007***

(.000)

high income non OECD

countries

.009***

(.002)

.010***

(.001)

.003***

(.000)

.003**

(.001)

.020***

(.002)

(c) Estimates by UN regional commissions

Dependent variable

ln(rpcGDP)


3rd

Quartile

ECA .037***

(.002)

.037***

(.001)

.011***

(.002)

.014***

(.001)

.080***

(.003)

ECE .01***

(.001)

.013***

(.0009)

.010***

(.000)

.002***

(.000)

.032***

(.005)

ECLAC .01***

(.001)

.013***

(.0007)

.005***

(.000)

.007***

(.001)

.020***

(.003)

ESCAP .019***

(.0009)

.017***

(.0009)

.006***

(.001)

.004***

(.001)

.020***

(.003)

ESCWA .02***

(.004)

.017***

(.001)

.002***

(.000)

.001***

(.000)

.010**

(.004)

DP/16

37

Table 7. Nonparametric slope coefficients by size of the financial sector

(roughly arranging by quartiles)

(d) Comparing Estimates for Countries by size of Financial Sector

Dependent variable

ln(rpcGDP) pcby <15 dcpy<19

smk <30 str < 5 sva < 1

3rd

Quartile .037***

(.001)

.038***

(.001)

.014***

(.001)

.013***

(.001)

.073***

(.004)

Dependent variable

ln(rpcGDP) 15 < pcby

<50

19<dcpy<50 30<smk <60 5 < str < 20 1 < sva < 4.5

3rd

Quartile .017***

(.0009)

.015***

(.0006)

.004***

(.000)

.009***

(.001)

.045***

(.002)

Dependent variable

ln(rpcGDP) pcby > 50

dcpy>50 smk > 60 str > 20 sva > 4.5

3rd

Quartile .008***

(.0005)

.009***

(.0004)

.003***

(.000)

.002***

(.000)

.045***

(.002)

Table 8. Non parametric slope estimates by property rights (weak to ideal)

(d) Comparing 3rd

Quartile Estimates for countries by pr

Dependent variable

ln(rpcGDP)

low pr (0 – 39) medium pr (40 – 69) high pr (70 – 100)

pcby .033***

(.002)

.019***

(.001)

.011***

(.000)

dcpy .031***

(.001)

.018***

(.000)

.009***

(.001)

smk .016***

(.002)

.044***

(.006)

.060***

(.007)

str .010***

(.001)

.007***

(.001)

.003***

(.000)

sva

.051***

(.003)

.048***

(.003)

.010***

(.001)

Table 9. Non parametric slope estimates by overall institutions classification

(weak to ideal)

(d) Comparing 3rd

Quartile Estimates for countries by ief

Dependent variable

ln(rpcGDP)

low ief (0 – 39) medium ief (40 – 69) high ief (70 – 100)

pcby .047***

(.007)

.020***

(.000)

.007***

(.001)

dcpy .031***

(.010)

.022***

(.000)

.008***

(.000)

smk .004

(.003)

.009***

(.000)

.003***

(.000)

str .016***

(.001)

.006***

(.000)

.004***

(.000)

sva

.053***

(.015)

.045***

(.002)

.010***

(.002)

All standard errors in parentheses are obtained via bootstrapping

* indicates significance at 10% level.

** indicates significance at 5% level.

*** indicates significance at 1% level.

MPDD Working Papers WP/16/..

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