A dynamic theory of the Feldstein-Horioka puzzle andfinancial frictions: Re-estimation of the saving
retention coefficient
Harutaka Takahashi
June, 2020
Discussion Paper No.2007

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANA dynamic theory of the Feldstein–Horioka puzzle
and financial frictions:Re-estimation of the saving retention coefficientbyHarutaka Takahashi 1
Research Fellow, Graduate School of Economics, Kobe University
Professor Emeritus, Meiji Gakuin University(December, 2019)
Abstract
Many empirical studies of the Feldstein-Horioka Puzzle (FHP) conducted so far havecommon drawbacks; among others, those estimates are free from economic models, andtheir specification errors are not avoidable. In other words, statistically significant
estimates of the saving retention coefficient are not known yet. Taking a complete
different approach from earlier studies to elucidate FHP, I re-estimate the savings
retention coefficient (called “beta”) indirectly based on the perfect-foresight saddle-path
dynamics of investment under convex adjustment cost. I assume that the error term of
the regression model will represent various shocks and effect of financial frictions. Our
main empirical results are; (1) Replication of the FHP in the 16 OECD countries during
1964–1974 yields 0.52 as the estimate of the beta, which is much smaller than theestimate of Feldstein and Horioka (1980). (2) Estimates using samples for every 10
years during 1960–1999 exhibit that the beta decreases gradually. (3) During 2000–2008,
the FHP temporarily disappeared and then the higher beta returned after 2008.
(167 words).
JEL: C23, F21, F32
Keyword: savings retention coefficient; financial friction; capital mobility

1

The earlier version of the paper was presented at the economic department seminars: Kobe University, Sichuan

University and Marseille University. I thank for useful comments from Yan Bai at the University of Rochester and theparticipants of the seminars. Corresponding Email: Harutaka Takahashi, haru@eco.meijigakuin.ac.jp

11. Introduction
In their controversial paper, Feldstein and Horioka (1980; hereinafter, FH) studied therelation between domestic investment and domestic saving shares of GDP among 16
OECD countries. Their empirical studies revealed that the estimated correlation, 0.89,
was very high across countries. They interpreted their findings as an indication of a highdegree of financial friction. They concluded that they were inconsistent with the perfectcapital mobility assumption commonly made for international finance studies. This
problem has often been designated as the “Feldstein–Horioka puzzle” (hereinafter, FHP).
I will leave earlier research of the FHP for the detailed survey by Coakley et al. (1998)
and, Apergis and Tsoumas (2009), which will mainly focus on the most recent research
results.
Recently Bai and Zhang (2010) specifically examined financial friction to solve thepuzzle theoretically. They considered financial friction of two kinds: “limited
enforcement” and “limited spanning” and found that the calibrated model with frictionof both types produced a saving–investment correlation close to value found from thedata: 0.52. Recent empirical studies examining FHP also mainly emphasize estimationof the savings retention coefficient as the indices of globalization and capital mobility.
Younas and Nandwa (2010) introduced not only the financial openness index for theOECD countries but also the foreign aid index for developing countries as omitted
variables. Applying the dynamic panel estimation, they obtained much lower estimation
values of savings retention coefficient (“beta”) than that of FH. Furthermore, Younas
and Chakraborty (2011) used the globalization index instead of the financial openness
index to re-estimate the beta and yielded the lower estimation values too. Both papers,
however, share a common defect. The estimators of the savings retention coefficientdepend on the values of indices. Therefore, some ambiguity persists. Georgopoulos andHejazi (2005) introduced a time trend interaction term in the panel data model andreported that the value of savings retention coefficient fell over time by demonstratingthat the estimated coefficient of the time trend interaction term was negative. That result
implies that the economy will become more open and financially integrated over time.
Because the trend interaction term is assumed as linear, financial integration will
become perfect over time: beta will approach and eventually reach zero. But and Morley(2017) examined the time-varying beta across the OECD using recursive estimations
and found that the estimated beta for OECD countries had a negative trend. Furthermore,they found that the FHP disappeared briefly prior to the 2008 crisis.
It is important to note that the majority of preceding empirical researches share the2common drawbacks; among others, they were not based on economic models. In otherwords, specification errors of their estimates will not be avoidable. Taking a completely
different approach from earlier empirical studies to elucidate FHP, I estimate the savings
retention coefficient indirectly. To do so, I start from setting up the representative firm’s
investment behavior model. According to the System of National Accounts (SNA),
gross domestic investment (GDI) comprises housing investment, gross private
investment, and public investment. Among them, gross private investment predominatesand serves a crucial role in the behavior of GDI. Therefore, I shall start fromparticularly addressing a representative firm’s investment behavior and then based on
the perfect-foresight saddle-path dynamics of investment under convex adjustment costs.
Because the optimal investment behavior includes lagged investment variables, adding
domestic saving variables as independent variables to estimate the beta, the dynamic
panel regression method will be employed to avoid a biased estimate problem.
My main empirical findings are the following:
1. Replication of the FHP in the 16 OECD countries during 1964–1974 yields 0.52 as
the estimate of the beta, which is much smaller than the estimate of 0.89 by FH(1980).
2. Estimates using samples for every 10 years during 1960–1999 exhibit that the beta
decreases gradually from 0.57 to 0.32.
3. During 2000–2014, the estimate of beta is not statistically significant from 2000
through 2008. It becomes statistically significant again after 2008, where theestimated beta is 0.51, which is similar to the result for 1960s and 1970s. In otherwords, the FHP temporarily disappeared, then the higher beta returned after 2008.
The results described above clearly support the theoretical solution of the FHP
presented by Bai and Zhang (2010). Additionally, it is noteworthy that our estimatedbeta value closely approximates that obtained from their calibration: 0.52. Globalizationand financial integration facilitated the cross-border movements of portfolio and direct
investment in 1980s and 1990s. Therefore, I am able to observe that thesaving–investment correlation estimated by the beta was gradually declining until 1999
because some degree of financial friction (e.g. home country bias) persists and theestimate of the beta remained significant. One might say that the FHP was robust during
the observation period of 1960–1999 if one considers these estimated beta values as
high. Then, during 2000–2008, I observed that beta turned out to be statistically notsignificant and that the FHP had temporarily disappeared. Worldwide trends of financial
deregulation in the late 1990s caused these results. Indeed, after the 2008 financial crisis,3

tougher financial regulations were implemented again 2 and higher beta of 0.51 cameback. These results firmly support Bai and Zhang’s financial friction solution for the
FHP.
Each section is organized as follows: Section 2 is assigned for presenting a
representative firm’s investment behavior model. I derive the estimation equation bysolving the firm’s optimal problem. All the mathematical derivations are demonstrated
in Appendix. In Section 3, setting up the regression model based on the firm’s
investment function derived in Section 2, I employ the dynamic panel method toestimate the saving retention rate. I will report and discuss the results. In Section 4, I
will briefly summarize and conclude my work.

2. The Model and Assumptions
Let us consider the following representative firm investment behavior model:

1( R(kt ) − It − C ( It ) ){It } t =0 (1 + r )ts.t. kt = I t + (1 − δ )kt −1 and k0 .
where
: discount rate (0 < r < 1),r
R(kt ) : revenue function ,: gross investment,It
C (kt ) : adjustment cost function .
∞

max ∑

The model implies that the representative firm will choose its investment level tomaximize the total discounted pure revenue flow where investment will increase capitalstock and raise the revenue, but it will pay costs of investment adjustment as well as
investment itself. Showing the saddle-path stability of the model, I derive the following
investment share equation as demonstrated in Mathematical Appendix.
=
I it β 0 I it −1 + β1 I it − 2(1)(1 + λ1 )where I it is the national investment share in the period t, β 0 =and(1 + g )2

For example, the Dodd-Frank Wall Street Reform and Consumer Protection Act enacted on July 21, 2010.

4β1 = −

λ1

(1 + g )2.

Note that due to the derivation of Eq. (1) demonstrated in Appendix, β 0 and β1 should
satisfy the following sign conditions: β 0 > 0 and -1<β1 < 0.

3. Estimation and Results
Based on the optimal investment behavior presented as Eq. (1) and adding variables of
the national saving share, the following regression model can be finally set up:(*)=
I it β 0 I it −1 + β1 I it − 2 + β 2 Sit + β3 Sit −1 + γ i + uit
where i country
index, t time index,==
I it : national investment share of GDP,
Sit : national saving share of GDP,uit : error term (shocks and effect of frictions),

γ i : fixed effect of the country i.
Since the deterministic part of the above model derived based on the perfect-foresight
firm’s model, the error term exhibits various fundamental shocks 3 and effect offrictions explained in Section 1. The fixed effect of the country will stand for the homecountry bias, as discussed by Kraay et al. (2000) and Kraay and Ventura (2000).
It is also noteworthy that signs of coefficients of (*) must satisfy the following
restrictions because of the theoretical model discussed in Appendix:
Condition I:
β 0 and β1 are statistically significant, and they satisfy (i ) 0 < β 0 , (ii ) -1<β1 < 0.
Because I assume that the investment share and the saving share should satisfy the()

equation; I it= α + β S it , it follows that I it − I it=
β S it − S it −=β S it + ( − β ) S it −1 .
−11Thus the following additional sign condition should be also satisfied for the coefficientsof the saving shares:
3

They indicate temporary changes in the terms of trade, transfers from abroad, and fluctuations in production.

5

Condition II:

β 2 and β3 are statistically significant, and (iii ) β 2 = − β3 .
If condition (iii) would not hold, then the estimate of β 2 should not be regarded as thesavings retention coefficient.
The regression model (*) includes the lagged dependent variables as independent
variables as well as the fixed effect. Therefore, the standard ordinary least squares(OLS) estimates will exhibit strong bias. To avoid that difficulty I apply dynamic panel
estimation (DPE) developed by Arellano and Bond (1998) and Arellano and Bover(1996). Although these estimation methods are powerful, one must be careful about
weak instrument and over-identification problems. Actually, DPE is well-known togenerate a huge number of instruments easily. To examine these problems, twoimportant statistics will be reported in our tables: for the weak-instrument problem,
Arellano–Bond AR(1) (A-B AR(1) test) and Arellano–Bond AR(2) tests (A-B AR(2)test) for first and second serial correlations, and for the over-identification problem, theHansen tests for over-identification of the instruments. To avoid the weak instrument
problem, the null hypothesis of “no serial correlation” must be rejected in the A-B
AR(1) test. Conversely, in the A-B AR(2) test the null should be accepted. For theover-identification problem, the null of the Hansen J-test “not over-identified” should be
accepted. For system GMM, result of the Difference-in-Hansen test for the validity of
the additional moment restriction will be reported.
I examine the following three cases:Case 1: Replication studies of the Feldstein–Horioka Puzzle
Data:
Gross national investment GDP share and gross saving GDP share
Period:1960–1974
Countries: 21 OECD countries



Case 2: Ten-year sub-samples
Data:
Same as Case 1
Period:1960–1969, 1970–1979, 1980–1989, 1990–1999
Countries: 28 OECD countries



Case 3: Breaks in 2000s
Data:
Same as Case 1
Period:2000–2008, 2009–2014, 2000–20146Countries:

28 OECD countries

All the data come from the National Account Data included in the Penn World Table
Ver.9 4.
The estimation results of Case 1 are reported in Table 1. I apply one-step andtwo-step difference GMM with collapsed instruments for this case with and without aconstant term. The results are reported as Columns (1) and (2), and one-step andtwo-step system GMM are reported as Columns (3) and (4), respectively in Table 1. The
Arellano–Bond AR(1) and AR(2) tests exhibit a weak-instrument problem for Columns(1) and (2). All the over-identification test results are satisfactory. I will choose Column(3) of Table 1 as our final result because all sign conditions i) through iii) derived fromthe theoretical model are satisfied. Consequently, the replicated estimated savings
retention coefficient is 0.52, which is much smaller than the value estimated by
Feldstein and Horioka (1980): 0.89. Our estimated value coincides with the value
calibrated by Bai and Zhang (2010). I can regard 0.52 as a benchmark value of thesavings retention coefficient.
The estimation results of Case 2 are presented in Table 2 below as Columns (1)through (3), where one-step difference or one-step system GMM is employed. For theobservation period of the 1990s, the one-step system GMM is applied and reported as
Column (4) in Table 2. Furthermore, the two-step system GMM is applied to Case 2. Its
results are reported in Table 3. For results of Columns (2) and (3) in Table 2, the sign ofS it −1 is not statistically significant. Consequently, the estimates in Columns (2) and (3)of the beta cannot be regarded as savings retention coefficients. However, allcoefficient-sign conditions of i) through ii) are satisfied in Table 3. Condition iii) also
holds except the estimates in Column (3). Thus, overall required sign conditions are
satisfied and robust. Furthermore the weak-instrument tests and over-identification tests
present favorable results. To sum up, our test statistics hit at a proper specification.
Table 4 reported the estimation results from 2000 to 2014, and 2008 is the break year.
All the estimates are also statistically significant. It is important to emphasize that
before the break year the estimates of β 2 and β3 had turned to be insignificant andthen returned to be significant after the break year.
Finally, I might infer that the estimation values of the beta can be summarized asshown as Table 5 below.

4 http://www.ggdc.ne/pwt

7

Table 5: Summaryβ2

1960–19691970–1979

1980–1989

1990–1999

0.50

0.54

0.42

0.32–0.33

It is clear that the savings retention coefficient beta declines over time. This result is
consistent with results reported by Georgopoulos and Hejazi (2005), and by But andMorley (2017). It follows from the fact that the recent economic openness and financial
market integration increase capital mobility and reduce financial friction.
In Case 3, I will not only estimate the beta for the overall observation period: 2000–
2014, but also estimate it for two sub-periods: 2000–2008 and 2009–2014. The main
reason I use 2008 as the break point is that it marks the 2008 financial crisis.
-Figure 1 According to Figure 1, where the diagram of total foreign claims 5 of BIS reportingbanks (32 countries, 246 banks) is depicted, the total foreign claims surged suddenly to
34 M USD in the early 2000s. Subsequently, it plummeted to the 26 M USD level after2008.
- Figure 2Total world direct investment shown in Figure 2 also exhibits a similar process to that offoreign claims in Figure 1. Our results from the 2000s are reported in Table 4.
Estimation results of Columns (1) and (2) in Table 4 indicate that the estimates of beta
become not significant during 2000–2008. In other words, the saving–investment
relation temporarily disappeared during this period. It also implies that the financial
friction disappeared too. I will identify that the period when the saving–investment
relation disappeared as coinciding with a worldwide financial bubble period:
2000–2008. Consequently, Column (3) in Table 4 exhibits that after 2008, thesaving–investment relation returned: the estimated beta was 0.51, which is a similarvalue to that of the 1960s and 1970s. After the crisis, financial frictions also returned, asreported by But and Morley (2017).

4. Conclusion
I set up a representative firm’s investment model to avoid specification errors. Based on
the perfect-foresight saddle-path dynamics of investment under convex adjustment cost,
5 Note that Foreign claims = Cross-border claims + Local claims of foreign affiliates in foreign currency +Local claims of foreign affiliates in local currency.

8I have derived the dynamic regression model. It is worth to note that the error term will
include various shocks and effect of financial frictions. Then, using the dynamic panel
method, I estimated the savings retention coefficient during 1960–2014. Replication of
the FH estimation yields 0.52, which is consistent with the calibration result of 0.52
reported by Bai and Zhang (2010) from their specific examination of the financial
friction for solving FHP. Furthermore, I demonstrate that beta fell from 0.54 to 0.33 in
the 1980s and 1990s and the saving–investment relation temporarily disappeared from2000 through 2008. Then the higher beta value of 0.51 returns after 2008. Theseempirical results support the theoretical solution of the FHP presented by Bai and Zhang(2010).
Acknowledgements Taisei Ogawa at the Graduate School of Economics, Meiji Gakuin

University helped me for this research. I am grateful for valuable comments from Yan Bai at the
University of Rochester and the participants of the seminars at GREQAM, Dalhousie University
and Kobe University, especially Alain Venditti, Kuwan Xu, Norov Tumennasan, Talan Iscan,
Mevlude Akbulut-Yuksel and Swapan Dasgupta. This work was partially founded by ResearchInstitute of Business and Economics at Meiji Gakuin University.

References
Apergis, N. and C. Tsoumas (2009), “A survey of the Feldstain-Horioka puzzle: What
has been done and where we stand,” Research in Economics 63, 64-76.
Bai, Y., and J. Zhang (2010), “Solving the Feldstein–Horioka puzzle with financial
frictions,” Econometrica 78 (2), 603 - 632.
But, B. and B. Morley (2017), “The Feldstein–Horioka puzzle and capital mobility: The
role of the recent financial crisis,” Economic Systems 41, 139-150.
Coakley, J., F. Kulasi, and R. Smith (1998), “The Feldstein–Horioka puzzle and capital
mobility: A review,” International Journal of Finance and Economics 3 (2), 169-188.
Feldstein, M. and Y. Horioka (1980), “Domestic saving and international capital flow,”
Economic Journal 90, 314-329.

9

Georgopoulos, G. and W. Hejai (2005), “Feldstein–Horioka meets a time trend,”
Economics Letters 86, 353-357.
Kraay, A., N. Loayza, L. Serven and J. Ventura (2000), “Country Portfolio,”
Department of Economics Working Paper 00-16 (MIT).
Kraay, A. and J. Ventura (2000), “Current accounts in debtor and creditor countries,”
Quarterly Journal of Economics Vol.115, No. 4, 1137-1166.
Younas, J. and D. Chakraborty (2011), “Globalization and the Feldstein–Horioka
puzzle,” Applied Economics 43, 2089-2096.
Younas, J. and B. Nandwa (2010), “Financial openness and capital mobility: a dynamic
panel analysis,” International Review of Applied Economics 24 (2), 239-246.

Mathematical Appendix
In order to derive the estimation equation, I start making the following assumptions.
Assumption A1.

( )C ( I ) is C 2 on the interval 0, I and strictly convex, where I is the upper bound of investment.
Moreover, lim C ( I ) ==lim C ' ( I ) ∞ and C ' (0) =
0.
I →I

I →I

Assumption A2.
 I
R(k ) is C 2 defined on ℑ ≡  0,  = 0, k , and strictly concave with respect to k . 6
 δ

( )

To solve the problem, I rewrite the problem by using the following objective function
as defined below.
Definition A1.
V (kt −1 , kt ) ≡ R (kt ) − I t − C ( I=
R (kt ) − (kt − (1 − δ )kt −1 ) − C ( kt − (1 − δ )kt −1 ) (t ≥ 1)t)
Then using the newly defined objective function, the original problem is rewritten as
6

k is the maximum reproducible capital when I is given.
10

follows.

1
V ( kt , kt +1 ){kt } t =0 (1 + r )ts.t. ( kt , kt +1 ) ∈ ℑ× ℑ and k0 .
∞

max ∑

Concerning with the rewritten optimal problem, the following Euler equations can bederived quickly.
∂V (kt −1 , kt )  1  ∂V (kt , kt +1 )
(A1)0 (t ≥ 1)
+
=

∂kt
∂kt
 1+ r 
From the Euler equation (A1), the optimal steady state is defined as presented below.
Definition A2. The optimal steady state k * is a solution of the following equation.
∂V (k * , k * )  1  ∂V (k * , k * )+=0 (t ≥ 1)∂kt∂kt 1+ r 
Because of Assumption 1, there exists an upper-bound k =I> kt > 0 (t ≥ 0) . It follows
δthat lim(1 + r ) − t kt < lim(1 + r ) − t k =0 . Therefore, the transversality condition should hold.
t →∞

t →∞

Consequently, any optimal path is expected to satisfy this condition.
Next, to study the local stability around the steady state k * , let us linearize theEuler equations around k * . Then after some calculations, I obtain2
 1  ∂V 2 (k , k )∂V 2 (kt −1 , kt )
 1   ∂V (kt , kt +1 ) 
t
t +1
Z
+
+

 Zt


t +1




∂kt2
∂kt2
 1 + r   ∂kt ∂kt +1  ( k * ,k * ) 1 + r ( k* ,k* )( k* ,k* )  ∂V 2 (kt −1 , kt ) 0,+ Z t −1 = ∂kt ∂kt −1 

(A2)where Z=kt − k * ,tUsing Definition A1 and calculating each second order partial derivative evaluated at
k * , Eq. (A2) can be rewritten as follows.

{}(1 − δ )C "* Z t +1 + (1 + r )  R"* − C "*  − C "* (1 − δ ) 2 Z t + (1 + r )(1 − δ )C "* Z t −1 =0,where the symbol * denotes that the functions are evaluated in the optimal steadystate k * . Then further simplification will yield the following second-order characteristic11equation f (λ ) = 0 related to the Euler equation.
 1 + r   C "* − R"* − (1 − δ )  λ + (1 + r ) =f (λ ) ≡ λ 2 + 
0.


"*

 1 − δ   C


(A3)

Then I can prove the following important proposition. I will derive the estimation
equation based on it. I leave all proof in Appendix.
Proposition. If f(1) < 0 and f(0) > 0 hold, then the steady state k * is saddle-point stable.
Proof. Note that f (0) = (1 + r ) > 0 already holds. Therefore, I need show that f (1) < 0
will also hold. Eq. (A3) quickly provides the following relation:

 1 + r   R"* f (1) =1 + 
−
−
−
1
(1
)
δ
 + (1 + r )


 "*

 1 − δ   C

"*
"*
 
 1+ r  R
 1 + r   1 + r   R = (1 + r + δ ) +   "* − 1 = (1 + r + δ ) −  + 
  "*  1− δ  C
 1 − δ   1 − δ   C  −δ (r + δ )  1 + r   R"* =+  "*  < 01− δ
 1− δ  C (from the fact that R"* < 0 and C "* > 0).

Therefore, the needed property is established.■
The proposition implies that there are two positive roots and one of two roots has its
absolute value less than one.
As claimed before, the transversality conditions are held. Therefor saddle-point
stability implies that the optimal path should be on stable manifolds. For our estimationof the savings retention coefficient, I use the saddle-point stability property of the
optimal accumulation path, instead of estimating the firm’s investment function directly.
Let λ1 be the root satisfying 0 < λ1 < 1 . Then the optimal path is expected to satisfy
the following difference equations:(kt − k *=) λ1 (kt −1 − k * ) for all t ≥ 1 .

(A4)

From Eq. (A4), subtraction to eliminate k * gives
(kt − kt −1 )= λ1 (kt −1 − kt − 2 )(k − k )= λ (k − k ).
t −2
t −3
1
 t −1 t − 2

12From the capital accumulation equations, it follows that
 I t − I t −1 = (kt − kt −1 ) − (1 − δ )(kt −1 − kt − 2 ),
 I − I = (k − k ) − (1 − δ )(k − k ).
t −1
t −2
t −2
t −3
 t −1 t − 2
Combining both relations will readily give I t − I t −=λ1 ( I t −1 − I t − 2 ) . Finally, I obtain the1
following relation along the saddle-point path as the optimal investment behavior:
It =(1 + λ1 ) I t −1 − λ1 I t − 2 .

(A5)Next, I must rewrite the equation (A5) obtained above into the GDP share terms
originally used by Feldstein and Horioka (1980). To do so, I make the following
assumption.
Assumption A3. The rate of average GDP growth g is constant and 0 < g < 1 .
Dividing both sides by Yt = GDPt gives Y  I 
 Y  I It(1 + λ1 )  t −1   t −1  − λ1  t − 2   t − 2  .
=Yt
 Yt   Yt −1  Yt   Yt − 2 
Due to Assumption A3, the following theoretical model will be finally obtained:

I t (1 + λ1 )  I t −1 
λ1  I t − 2  (1 + λ1 ) 
λ1 =
I t −1 −
I

−
=
2 
2 t −2
Yt (1 + g )  Yt −1  (1 + g )  Yt  (1 + g ),(1 + g )and finallyI it β 0 I it −1 + β1 I it − 2 ,=where β 0 =(1 + λ1 )λ1and β1 = −.
2(1 + g )(1 + g )Since 0 < λ1 < 1 holds, it follows that β 0 > 0 and −1 < β1 < 0 should hold.

13

Table 1: Replication of the FHP
Dependent variable: nominal national investment share of GDP ( I it )Sample period: 1960–1974(1)
Specification
One-step difference GMM(collapsed instrument)
Regressor(2)
Two-step difference GMM(collapsed instrument)(3)
One-step system GMM(collapsed instrument)(4)
Two-step system GMM(collapsed instrument)I it −1

0.34360 (0.2203)

0.15888 (0.2730)

0.94495(0.1304)***0.93204(0.1215)***I it − 2

- 0.18563 (-0.1856) ***- 0.12046 (0.6025)*- 0.17236 (0.0826)**- 0.14632 (0.1075)

S it

0.66894 (0.1058)***0.76820 (0.1316)***0.51702 (0.1076)***

0.63176 (0.1553)***S it −1

-0.01540 (-0.0154)- 0.0050 (0.1439)- 0.42195 (0.1418)***- 0.52326 (0.1767)***0.03875 (0.0250)

0.03088 (0.0243)

28
18
0.001
0.171
0.147
0.479
0.4407

28
18
0.007
0.161
0.147
0.479
0.3996

Const.
# of groups (countries)# of instruments
A-B test for AR(1)A-B test for AR(2)Hansen J-test
Diff-in-Hansen test
Wald test: β 2 = − β3

28
16
0.174
0.102
0.215

28
16
0.625
0.039
0.215

0.000

0.000

Notes

Significant at * 10%, ** 5%, *** 1%. ( ) are standard errors. P-values are reported for Arellano-Bond tests, Hansen J-test, Difference-in –Hansen
test and Wald test.

14

Table 2: Sample by 10 years
Dependent variable: nominal national investment share of GDP ( I it )Sample period:
Specification
Regressors(1) 1960–1969
One-step system GMM( maximum lag = 2 )(2) 1970–1979
One-step difference GMM( collapsed instruments)(3) 1980–1989
One-step difference GMM(maximum lag = 2)(4) 1990–1999
One-step system GMM( maximum lag = 2)I it −1

0.97956 (0.1200)***0.50447 (0.0946)***

0.97424 (0.1447)***I it − 2

- 0.14896 (0.0735)**- 0.13925 (0.0429)***- 0.11557 (0.0828)- 0.14686 (0.6800)**S it

0.49905 (0.1610)***0.56865 (0.1479)**

0.55954 (0.1355)***0.31699 (0.1215)**

S it −1

- 0.43101(0.1895)***- 0.02761 (0.1111)- 0.24231 (0.1451)- 0.28881 (0.1318)**

Const.

0.02755 (0.0187)# of groups (countries)# of instruments
A-B test for AR(1)A-B test for AR(2)Hansen J-test
Diff-in-Hansen test
Wald test: β 2 = − β 3

28
26
0.002
0.365
0.312
0.421
0.45211.0311 (0.0666)***0.01926 (0.0082)
28
21
0.000
0.306
0.211

28
23
0.002
0.504
0.190

0.00000.0113

28
34
0.001
0.721
0.559
0.992
0.2703

Note

Significant at * 10%, ** 5%, *** 1%. ( ) are standard errors. P-values are reported for Arellano-Bond tests, Hansen J-test, Difference-in –Hansen test
and Wald test.

15

Table 3: Sample by 10 years with Two-step System GMM
Dependent variable: nominal national investment share of GDP ( I it )Sample period:
Specification
Regressors

I it −1I it −2

(1) 1960–1969
Two-step system GMM( maximum lag = 2 )(2) 1970–1979(3) 1980–1989
Two-step system GMM Two-step system GMM( collapsed instruments) (maximum lag = 1)

(4) 1990–1999
Two-step system GMM( maximum lag = 1)

0.98967 (0.0979)***0.68673 (0.1424)***

0.97518 (0.0604)***1.04328 (0.0695)***- 0.14559 (0.0802)*- 0.15147 (0.0887)*- 0.02993 (0.0819)- 0.13789 (0.0732)*

S it
S it −1

0.50419 (0.1686)***

0.53558 (0.1583)***0.42619 (0.1216)***0.33307 (0.1143)***- 0.44019 (0.1899)***- 0.34202 (0.1310) **- 0.34943 (0.1207)***- 0.30925 (0.1217)**

Const.

0.02441 (0.0175)

0.07520 (0.0270)***- 0.00569 (0.0145)

0.01546 (0.0100)

28
23
0.001
0.316
0.233
0.203
0.2368

28
24
0.006
0.375
0.148
0.507
0.0206# of groups (countries)# of instruments
A-B test for AR(1)A-B test for AR(2)Hansen J-test
Diff-in-Hansen test
Wald test: β 2 = − β 3

28
26
0.008
0.425
0.312
0.421
0.0903

28
24
0.013
0.772
0.118
0.266
0.2995

Note

Significant at * 10%, ** 5%, *** 1%. ( ) are standard errors. P-values are reported for Arellano-Bond tests, Hansen J-test, Difference-in –Hansen
test and Wald test.
16

Table 4: Break sample in 2000s
Dependent variable: nominal national investment share of GDP ( I it )Sample period:
Specification
Regressors(1) 2000–2008
One-step difference GMM( maximum lag = 1 )(2) 2000–2008
Two-step difference GMM( maximum lag = 1)

(3) 2009–2014
One-step system GMM(maximum lag = 2)(4) 2000–2014
Two-step difference GMM( maximum lag = 1)I it −1

1.05583 (0.1164)***

1.16306 (0.1413)***0.96651 (0.0838)***0.87132 (0.1506)***I it −2

- 0.55433 (0.1719)***- 0.69848 (0.2223)***- 0.04373 (0.1001)- 0.34514 (0.1265)***S it

0.20021(0.1652)

0.26918 (0.1538)*

0.51255 (0.1306)***0.51774 (0.1617)***

S it −1

-0.14750 (0.1317)- 0.16673 (0.1696)- 0.48142 (0.1374)***- 0.28662 (0.1781)

Const.
# of groups (countries)# of instruments
A-B test for AR(1)A-B test for AR(2)Hansen J- test
Diff-in-Hansen test
Wald test: β 2 = − β 3Note0.00592 (0.0135)
28
12
0.006
0.311
0.103

28
12
0.011
0.320
0.103

0.7141

0.5116

28
22
0.000
0.099
0.170
0.821
0.1872

28
18
0.014
0.189
0.102
0.1426Significant at * 10%, ** 5%, *** 1%. ( ) are standard errors. P-values are reported for Arellano-Bond tests, Hansen J-test, Difference-in –Hansen
and Wald test.

test

17

Foreign claims of BIS reporting banks in USD mn
35,000,000
30,000,000
25,000,000
20,000,000
15,000,000
10,000,000
5,000,000
0
1985

1990

1995

2000

2005

2010

2015

Figure 1

World Direct Investment in USD mn
19500
17500
15500
13500
11500
9500
7500
5500
3500
1500
-500

Total

Figure 218