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Finance and Growth
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INTRODUCTION
What are the effect of the quality of the financial system oneconomic growth?
Theory (Levine, Finance and Growth, Handbook of EconomicGrowth, 2005)
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Empirical CROSS-COUNTRY studies on finance and growth
How is “Finance” measured?
-- SIZE MEASURES
(1) BANK CREDITGDP
CORPORATE BONDSGDP
(2)
STOCK MARKET CAPITALIZATIONGDP
(3)
And, most importantly, (1)+(2)+(3)
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-- STRUCTURE MEASURES
(1) Measures of bank competition
(2) Measures of efficiency of contract enforcement
(3) Measures of protection of minority shareholders
(4) Measures of “informativeness” of stock market
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Empirical CROSS-COUNTRY studies on finance and growth (1)
Cross-country work linking size measures of financial markets to (subsequent) growth (many studies, see Levine’s review)
,1996 1960
1 ,1960
2
3 ,1960 1960 4 1960
log log*100
36
log log
c c
c
c
c c c
y y
FD
X
FD y y
β
β
β β
−
= +
+
+ +
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Empirical CROSS-COUNTRY studies on finance and growth (2)
Cross-country before-after studies linking financial liberalization to growth
( ), , 1
1 , 2 , 4 , 1
log log
log
c t c t
c t c t c t
y y
FD X yβ β β
−
−
−
= + +
e.g. Bekaert, Harvey, and Lundblad, “Does Financial Liberalization Spur Growth,” Journal of Financial
Economics, forthcoming.
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Empirical CROSS-COUNTRY studies on finance and growth (3)
Cross-country empirical work on how FINANCE improvesthe country-level response to “growth opportunities”
e.g. Bekaert, Harvey, and Lundblad, “Global growth opportunities and market integration,” Journal of Finance,
forthcoming.
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Hypothesis:Financial system mobilized funds when they can be productively employed (when there is a growth opportunity)
“Growth opportunity for the country”?
1 21 1
...
ct
ct ct Ictt t It
GrowthOpportunity
P P PE E E
ω ω ω⎛ ⎞ ⎛ ⎞ ⎛ ⎞= + + +⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠
Global sectoralprice-earningsratios
Country-sector-specific weights
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PE ratio: Measure the value of a stock relative to currentearnings. High values are seen to proxy for high futurerelative to current earnings.
Value Production(1)Total Production
ictict
ct
ω =
Stock Market Cap(2)Total Stock Market Cap
ictict
ct
ω =
Weights:
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( ), , 1 1 , 1 2 ,log logc t c t c t c ty y GrOpp Xβ β− −− = +
( ), , 1
1 , , 1 2 , 4 , 1
log log
log
c t c t
c t c t c t c t
y y
FD GrOpp X yβ β β
−
− −
−
= + +
EMPIRICAL SPECIFICATIONS:
(1)
(2)
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Do Growth Opportunities Affect Economic Growth?
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“OPENNESS MEASURES”
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“STRUCTURAL MEASURES”
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CHANNEL ANALYSIS USING
DISAGGREGATE,
INDUSTRY ANALYSIS
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More disaggregate studies of “channels” through which finance potentially impacts growth
ONE AGGREGATE CHANNEL:Lowers cost of capital. More capital productivity.
DISAGGREGATE, INDUSTRY STUDY:
Lower cost of capital should be more important for
industries relying more heavily on external capital
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Analyzing channels using industry data
Rajan and Zingales, AER, 1998
Does greater financial development have dis-proportionate growth effecton industries that are finance dependent?
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(COMPUSTAT) Finance dependence industry i?
, ,,
,
i U S i U Si i U S
i U S
I C A S H F L O WE X T F I N E X T F I N
I−
= =
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Dis-proportionate growth effect?
, , , ,ln i c i c i US c i c i cY EXTFIN FD SHARE uη τ α γ⎡ ⎤∆ = + + + +⎣ ⎦
, , , ,ln i c i c iUS c i c i cY EXTFIN FD SHARE uη τ α γ⎡ ⎤∆ = + + + +⎣ ⎦
PERSPECTIVE 1
PERSPECTIVE 2
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Questions
RZ-1-- Rybczynski effect?
FD up Cost of (external) capital down Shift towards external capital intensive industries?
THEN should it not be?: ,c i USFD EXTFIN∆ ×
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QuestionsRZ-2
-- Heckscher-OhlinGreater FD Lower cost of (external) capital Greater production in (external) capital intensive industries?
& -- transition dynamics after shock that took economy out of steady state?
THEN Why should production structure return to steady state? E.g. shock to cost of capital.
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Questions
RZ-3
-- Cost of external capital would seem to affect industries through entire debt, not just externally funded investment
-- Interest rate affects more capital intensive industries more; must be controlled for
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Questions
RZ-4
--
-- I could be proxy for growth opportunities
, ,,
,
i U S i U Si i U S
i U S
I C A S H F L O WE X T F I N E X T F I N
I−
= =
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“Channels” through which finance potentially impacts growth
Reallocates capital more quickly to where it is most productive
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Analyzing channels using industry data
Fisman & Love, JF, 2004; NBER, 2004Does greater financial development speed up resource reallocation to industries that have good growth opportunities?
(COMPUSTAT) Growth opportunity industry i
Speed up resource reallocation?
, ,ln ...i c i c i US cY GROWTHOPP FDη τ β∆ = + + +
, ,i i US i USGROWTHOPP GROWTHOPP SALESGROWTH= =
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, ,i US i i USEXTFIN EXTFIN ε= +
Questions
RZ-FL
--
-- , ,i US i i USGROWTHOPP GROWTHOPP ν= +
Measurement error bias really necessarilydownwards?
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FURTHER WORK ON FINANCE INTERSECTORAL REALLOCATION in response to “Growth Opportunities”
-- Theoretical framework to study link between finance and speed of capital reallocation-- Analysis of the consequences of measurement error due to “country idiosyncratics”-- Deal with bias due to country idiosyncratics
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Overview
1. Theoretical framework 2. Model estimation issues
1. Bias when employing US (or any other country) data to construct global industry characteristics
2. Dealing with measurement error bias 3. Data4. Results
1. Using US-based proxy only2. Accounting for measurement error
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Theoretical Framework: Perfect Financial Markets
Shifts (country; industry; country-industry)in productivity and preferences
Industry-country optimal capital if financial markets were perfect (target)
Industry value added growth
Theoretical Framework – Estimation – Data - Results
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Theoretical Framework: Financial Development as an ADJUSTMENT MECHANISM
Shifts (country; industry; country-industry)in productivity and preferences
Industry-country optimal capital if financial markets were perfect (target)
Industry value added growth
FinancialDevelopment
Actual capital growth
Theoretical Framework – Estimation – Data - Results
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Model Set-Up
, ,
, , , ,
1
1, ,
0 0
(1 )11
, , (1 ), , , , , ,
, , , 0
, , , , , ,
Preferences
ln 0 1
Demand
Supply
i c t
i c t i c t
I C
t i c t
Ci c tt
i c t i c t i c ti t i c t
i c t i c t i c t
U x B dc
pMx B P p B dcP P
Z A K
ρρ ρ
ρρ ρρ
ρ
ρ−
− −− − −−
⎛ ⎞= < ≤⎜ ⎟
⎝ ⎠
⎛ ⎞ ⎛ ⎞= =⎜ ⎟ ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠
=
∫ ∫
∫
Theoretical Framework – Estimation – Data - Results
Industries Countries
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Long-Run Equilibrium (Perfect Financial Markets)
, , , ,
* * *, ,
, ,
*, , , , , , , , , ,
1, ,*
, ,, , ,
or
ln ln ln Country-effect ln
i c t i c t
ti c t t
i c t
i c i c T i c T i c S i c S i c
i c t ti c t t
t i c t i t
rA p r p MCA
Y p Z p Z K
B rK Mr A P
ρρ− −
= = =
∆ = − = + ∆
⎛ ⎞= ⎜ ⎟⎜ ⎟
⎝ ⎠
Theoretical Framework – Estimation – Data - Results
Target capital increases reflect anticipated future industry growth opportunities (due to technical change, demand shifts, and
changing prices of international competitors)
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Financial Development and Target Capital Adjustment
cφ
*,ln i cK∆
( ) *, ,ln 1 ln 0 1i c c i c cK Kλ λφ φ∆ = − + ∆ ≤ ≤⎡ ⎤⎣ ⎦
Theoretical Framework – Estimation – Data - Results
,ln i cK∆
1
λ
0
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Financial Development and Target Capital Adjustment
Target capital increases translate into faster value added growth in countries with high levels of financial development
( )
( )
* *, , , ,
*, ,
Price adjustment:
ln (1 ) ln ln ln
Value added and target capital growth:
ln 1 ln ln
i c i c i c i c
i c c i c t
p K K MC
Y K r
ρ
θ θφ θ λρ
∆ = − ∆ −∆ + ∆
∆ = − + ∆ + ∆ =⎡ ⎤⎣ ⎦
Theoretical Framework – Estimation – Data - Results
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Technology & preference shifts, and target capital
, , , ,
* * *, ,
, , ,
ln ; ln
ln ln ln
/(1 )
i c c i i c i c c i i c
i c c i i c
i c i c i c
A a a a B b b b
K K K
b a
ε
ε ρ ρ
∆ = + + ∆ = + +
∆ = ∆ + ∆ +
= + −
Theoretical Framework – Estimation – Data - Results
Global industry investment
opportunity
“Country Indiosyncratics”
Country effect
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Value added growth andglobal industry investment opportunities
*, ,ln Country-effects [(1 ) ] ln [(1 ) ]i c c i c i cY Kθ φ θ θ φ θ ε∆ = + − + ∆ + − +
Theoretical Framework – Estimation – Data - Results
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Effect of financial development on value added growth
cφ
Theoretical Framework – Estimation – Data - Results
0
1
1
(1 ) cθ φ θ− +
θ
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Model Estimating Equation:
Theoretical Framework – Estimation – Data - Results
*, ,
* *, ,
ln Country-effects [(1 ) ] ln [(1 ) ]
Target capital growth=Actual capital growth in US
ln ln ln
i c c i c i c
i US US i i US
Y K
K K K
θ φ θ θ φ θ ε
ε
∆ = + − + ∆ + − +
∆ = ∆ + ∆ +
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Classical measurement error:
Theoretical Framework – Estimation – Data - Results
*, ,
* *, ,
, ,
(1) ln Country-effects [(1 ) ] ln [(1 ) ]
(2) ln ln ln
and are independent
i c c i c i c
i US US i i US
i c i US
Y K
K K K
θ φ θ θ φ θ ε
ε
ε ε
∆ = + − + ∆ + − +
∆ = ∆ + ∆ +
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Downward bias due to classical measurement error:
[ ]*
,
( )(1 )( )
ic
i US
Var KVar K
θ φ θ⎛ ⎞
− + ⎜ ⎟⎜ ⎟⎝ ⎠
cφ
Theoretical Framework – Estimation – Data - Results
0
1
1
(1 ) cθ φ θ− +
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Upward bias due to measurement error:
Theoretical Framework – Estimation – Data - Results
( ), , , ,
, ,
(2) in (1) yields US-proxy ESTIMATING EQUATION
ln Country-effects [(1 ) ] ln [(1 ) ]
TWO GROUPS OF COUNTRIES
Group 1--HIGH Financial Development:
Group 1--LOW Finan
i c c i US c i c i US
i c i US
Y Kθ φθ θ φθ ε ε
ε ε
∆ = + − + ∆ + − + −
=
, ,cial Development: and are independenti c i USε ε
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Upward bias due to measurement error:
[(1 ) ]HIGHθ φ θ− +
LOWφ HIGHφ
Theoretical Framework – Estimation – Data - Results
[(1 ) ]LOWθ φ θ− +
True values
Least squaresestimates
0
1
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Accounting for measurement error:
*, ,
,
* *,
*,
(1) ln Country-effects [(1 ) ] ln [(1 ) ]
Ignore for a moment
ln Country-effects (1 ) ln ln
ln Country-effects Industry-effects ln
i c c i c i c
i c
i c i c i
i c c i
Y K
Y K K
Y K
θ φ θ θ φ θ ε
ε
θ φ θ
φ θ
∆ = + − + ∆ + − +
∆ = + − ∆ + ∆
∆ = + + ∆
Theoretical Framework – Estimation – Data - Results
,
,
ln Country-effect
ln
i c i c i
PREDICTED EST ESTi US i US i
Y
Y
φ
φ
∆ = +Θ + Λ
∆ = Θ + Λ
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Accounting for measurement error:
( )
( )
, , , ,
, , , ,
(2) in (1) yields US-proxy EQUATION
ln Country-effects [(1 ) ] ln [(1 ) ]
ln Country-effects Industry-effects ln [(1 ) ]
i c c i US c i c i US
i c c i US c i c i US
Y K
Y K
θ φ θ θ φ θ ε ε
θφ θ φ θ ε ε
∆ = + − + ∆ + − + −
∆ = + + ∆ + − + −
Theoretical Framework – Estimation – Data - Results
,
,
Instrument for ln :
ln
i US
PREDICTED EST ESTi US i US i
K
Y φ
∆
∆ = Θ + Λ
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Data1. Country-Industry level (from UNIDO)
• Value added growth in the eighties• 66 countries; 28 manufacturing industries
2. Country-level• FD: main measure is private credit to GDP (PRIVC)• Other control variables: GDP, institutions, human capital
3. Industry-levelExploit the NBER-Manufacturing database. In contrast to COMPUSTAT more
representative (key for international comparisons)• Capital growth• Other proxies of sector growth opportunities, employed in the literature
Theoretical Framework – Estimation – Data - Results
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Least squares approach
Theoretical Framework – Estimation – Data - Results
, , ,
LEAST-SQUARES ESTIMATION OF
ln Country-effects Industry-effects lni c c i US i cY FD K uα∆ = + + ∆ +
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Least squares approach: Opportunities and RZ financedependence
(1) (2) (3) (4) (5) (6)
SHARE80i,c -0.1900 -0.1981 -0.1955 -0.0724 -0.0809 -0.0803(3.75) (3.83) (3.79) (3.12) (3.49) (3.47)
Finance X Investment Opportunities 0.3485 0.2905 0.3433 0.2261[PRIVCR X CAPGR ] (3.35) (2.34) (5.22) (3.22)
Finance X External Finance Dependence 0.0179 0.0109 0.0228 0.0146[PRIVCR X EXTFIN ] (2.25) (1.28) (5.17) (3.09)
Adjusted R2 0.2986 0.2962 0.2991 0.4489 0.4565 0.4540Countries 66 66 66 66 66 66Observations 1589 1589 1589 1589 1589 1589Industry Fixed-Effects Yes Yes Yes Yes Yes YesCountry Fixed-Effects Yes Yes Yes Yes Yes Yes
OLS Robust Estimation
Theoretical Framework – Estimation – Data - Results
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Least squares approach: Alternative proxies of opportunities
Theoretical Framework – Estimation – Data - Results
,( )i i USGROWTHOPP Fisman Love SALESGROWTH− =
= Anticipated Growth Opportunity
+
Effect of Unanticipated Demand Shifts on Sales
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Least squares approach: Alternative proxies of opportunities
(1) (2) (3) (4)
SHARE80i,c -0.1875 -0.1922 -0.1838 -0.1971(3.68) (3.81) (3.61) (3.83)
Finance X Investment Opportunities 0.5551 0.5349[PRIVCR X CAPGR ] (3.05) (3.26)
Finance X Sales Growth 0.1724 -0.2309[PRIVCR X SALESGR ] (1.77) (1.38)
Finance X Value Added Growth 0.1391 -0.1860[PRIVCR X VAGR ] (1.58) (1.37)
adj. R-squared 0.2946 0.2992 0.2944 0.2991Countries 66 66 66 66Observations 1589 1589 1589 1589Industry Fixed-Effects Yes Yes Yes YesCountry Fixed-Effects Yes Yes Yes Yes
OLS
Theoretical Framework – Estimation – Data - Results
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Least squares approach: Alternative adjustment channels
(1) (2) (3) (4) (9) (10) (11) (12)
Finance X Invest. Opport. 0.3520 0.2986 0.3664 0.3026[PRIVCR X CAPGR ] (2.93) (4.11) (3.54) (4.26)
Law X Invest. Opport. -0.2141 -0.0604 -0.1977 -0.0782[LAW X CAPGR ] (2.71) (0.68) (3.26) (1.15)
Schooling X Invest. Opport. 0.0567 0.0238 0.0537 0.0327[SCH X CAPGR ] (2.36) (1.03) (3.15) (1.80)
adjusted R-squred 0.3159 0.3207 0.4758 0.4800 0.3037 0.3086 0.4559 0.4581Countries 57 57 57 57 1524 1524 1524 1524Observations 1435 1435 1435 1435 62 62 62 62Industry Fixed-Effects Yes Yes Yes Yes Yes Yes Yes YesCountry Fixed-Effects Yes Yes Yes Yes Yes Yes Yes Yes
Legal System Quality (LAW ) Schooling (SCH )OLS Robust OLS Robust
Theoretical Framework – Estimation – Data - Results
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Least squares approach: Controlling for income
OLS Robust IV(1) IV(2) OLS Robust IV(1) IV(2)
(1) (2) (3) (4) (5) (6) (7) (8)
SHARE80i,c -0.1889 -0.0683 -0.1936 -0.1906 -0.1755 -0.0704 -0.1786 -0.1773 (3.70) (2.93) (3.70) (3.71) (3.04) (2.86) (3.05) (3.05)
Finance X Invest. Opport. 0.2162 0.1942 1.0587 0.5104 0.3528 0.3400 1.0448 0.7632[PRIVCR X CAPGR ] (1.41) (1.90) (2.78) (1.98) (3.45) (4.66) (4.61) (4.58)
Income X Invest. Opport. 0.0939 0.0936 0.2278 0.1506[Y X CAPGR ] (1.33) (1.52) (1.39) (1.16)
R-squaredCountries 0.2988 0.4516 0.3069 0.3366 0.3344 0.4889 0.3561 0.3686Observations 5.76 14.68 12.19 13.05 6.10 16.54 13.39 14.18Industry Fixed-Effects 65 65 65 65 53 53 53 53Country Fixed-Effects 1562 1562 1562 1562 1317 1317 1317 1317
All countries Dropping Low Income Countries
Theoretical Framework – Estimation – Data - Results
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New (IV) Approach to deal with country-idiosyncratics
, , ,
INSTRUMENTAL-VARIABLES ESTIMATION OF
ln Country-effects Industry-effects lni c c i US i cY FD K uα∆ = + + ∆ +
Theoretical Framework – Estimation – Data - Results
,
,
Instrument for ln :
ln
i US
PREDICTED EST ESTi US i US i
K
Y FD
∆
∆ = Θ + Λ
Estimated across countrieswithout US
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Predicted and Actual US Industry Growth
311313
314
321
322
323324 331
332 341
342
351
352
353
354
355
356
361
362
369
371
372
381382
383
384
385
390
-.02
0.0
2.0
4.0
6C
apita
l Gro
wth
in th
e U
nite
d St
ates
(KH
AT)
0 .02 .04 .06 .08 .1Predicted Capital-Value Added Growth in the United States (est. with OLS)
Theoretical Framework – Estimation – Data - Results
slope=0.48
t-stat=3.39
1/(reverse regressionslope)=1.62
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Predicted and Actual US Industry Growth
311313
314
321
322
323324 331
332 341
342
351
352
353
354
355
356
361
362
369
371
372
381382
383
384
385
390
-.04
-.02
0.0
2.0
4.0
6C
apita
l Gro
wth
in th
e U
nite
d St
ates
(KH
AT)
0 .02 .04 .06 .08 .1Predicted Capital-Value Added Growth in the United States (est. with Huber)
Theoretical Framework – Estimation – Data - Results
slope=0.66
t-stat=4.69
1/(reverse regressionslope)=1.65
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New Approach: Instrumental Variable Estimates
IV(1) IV(2)
(1) (2)
SHARE80i,c -0.1931 -0.1913(3.74) (3.75)
Finance X Investment Opportunities 1.1683 0.6872[PRIVCR X CAPGR ] (4.01) (3.72)
centered R-squared 0.3108 0.3351F-score 12.15 13.51Countries 66 66Observations 1589 1589Industry Fixed-Effects Yes YesCountry Fixed-Effects Yes Yes
Theoretical Framework – Estimation – Data - Results
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New Approach: Other opportunities measures
Theoretical Framework – Estimation – Data - Results
,( )i i USGROWTHOPP Fisman Love SALESGROWTH− =
= Anticipated Growth Opportunity
+
Effect of Unanticipated Demand Shifts on US Sales
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New Approach: Other opportunities measures
(1) (2) (3) (4)
SHARE80i,c -0.1812 -0.1541 -0.1851 -0.1709(3.41) (3.00) (3.53) (3.36)
Finance X Sales Growth 1.1794 0.5541[PRIVCR X SALESGR ] (2.78) (2.80)
Finance X Value Added Growth 1.0054 0.5156[PRIVCR X VAGR ] (2.75) (2.77)
centered R-squared 0.282 0.2823 0.3286 0.326Countries 66 66 66 66Observations 1589 1589 1589 1589Industry Fixed-Effects Yes Yes Yes YesCountry Fixed-Effects Yes Yes Yes Yes
IV(1) IV(2)
Theoretical Framework – Estimation – Data - Results
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New approach: Controlling for income
OLS Robust IV(1) IV(2) OLS Robust IV(1) IV(2)
(1) (2) (3) (4) (5) (6) (7) (8)
SHARE80i,c -0.1889 -0.0683 -0.1936 -0.1906 -0.1755 -0.0704 -0.1786 -0.1773 (3.70) (2.93) (3.70) (3.71) (3.04) (2.86) (3.05) (3.05)
Finance X Invest. Opport. 0.2162 0.1942 1.0587 0.5104 0.3528 0.3400 1.0448 0.7632[PRIVCR X CAPGR ] (1.41) (1.90) (2.78) (1.98) (3.45) (4.66) (4.61) (4.58)
Income X Invest. Opport. 0.0939 0.0936 0.2278 0.1506[Y X CAPGR ] (1.33) (1.52) (1.39) (1.16)
R-squaredCountries 0.2988 0.4516 0.3069 0.3366 0.3344 0.4889 0.3561 0.3686Observations 5.76 14.68 12.19 13.05 6.10 16.54 13.39 14.18Industry Fixed-Effects 65 65 65 65 53 53 53 53Country Fixed-Effects 1562 1562 1562 1562 1317 1317 1317 1317
All countries Dropping Low Income Countries
Theoretical Framework – Estimation – Data - Results
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Endogeneity of Financial Development and Measurement Error in US-based proxy of opportunities
Instrumental Variable Technique IV FULL IV(1) FULL IV(2)(1) (2) (3)
SHARE80i,c -0.1906 -0.1918 -0.1914(3.75) (3.76) (3.76)
Finance X Investment Opportunities 0.5194 0.8166 0.7263[PRIVCR X CAPGR ] (3.53) (2.74) (2.98)
centered R-squared 0.3388 0.3305 0.3339F-score 12.44 12.25 12.67Countries 66 66 66Observations 1589 1589 1589Industry Fixed-Effects Yes Yes YesCountry Fixed-Effects Yes Yes Yes
Theoretical Framework – Estimation – Data - Results
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Conclusion
-- Greater financial development translates into faster growth of industries with good investment opportunities
-- This effect strengthens when mis-measurement of global investment opportunities is taken into account