Discussion Paper No. 821

THE DETERMINANTS AND
LONG-TERM PROJECTIONSOF SAVING RATESIN DEVELOPING ASIA
Charles Yuji Horioka
Akiko Terada-Hagiwara

October 2011

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

The Determinants and Long-term Projections of Saving Rates in Developing Asia
Charles Yuji Horioka*
Institute of Social and Economic Research, Osaka University, andNational Bureau of Economic Research, Inc., andAkiko Terada-Hagiwara
Asian Development BankMay 25, 2010; revised October 27, 2011, November 18, 2011
Abstract
In this paper, we present data on trends over time in domestic saving rates in twelve economies indeveloping Asia during the 1966-2007 period and analyze the determinants of these trends. We
find that domestic saving rates in developing Asia have, in general, been high and rising but that
there have been substantial differences from economy to economy and that the main determinantsof these trends appear to have been the age structure of the population (especially the aged
dependency ratio), income levels, and the level of financial sector development. We then project
future trends in domestic saving rates in developing Asia for the 2011-2030 period based on ourestimation results and find that the domestic saving rate in developing Asia as a whole will remainroughly constant during the next two decades despite rapid population aging in some economies indeveloping Asia because population aging will occur much later in other economies and because thenegative impact of population aging on the domestic saving rate will be largely offset by the
positive impact of higher income levels.
Key words: Domestic saving rates; saving rates; life cycle model; life cycle hypothesis; age structureof the population; demographics; aging; population aging; financial sector development; developing
Asia; emerging Asia; China

Journal of Economic Literature classification numbers: D91, E21, G10, J11, O53*Address for correspondence:
Institute of Social and Economic Research, Osaka University, 6-1, Mihogaoka, Ibaraki, Osaka 567-0047,
JAPAN.

Telephone:

81-(0)6-6879-8586/8574.

Facsimile:

81-(0)6-6879-8575.

Email:

horioka@iser.osaka-u.ac.jp
We thank Jong-Wha Lee, Kwanho Shin, Dennis Tao Yang, and the other participants of workshops inSeoul and Hong Kong, David Cook, Desiree Desierto, Julen Esteban-Pretel, Alessandra Guariglia,
Minchung Hsu, Alistair Munro, Wade Pfau, Pengfei Wang, and other seminar participants at the AsianDevelopment Bank, Chinese University of Hong Kong, Durham University, Hong Kong University of
Science and Technology, and the National Graduate Institute for Policy Studies, and two anonymousreferees for their helpful comments, and Aleli Rosario and Shiela Camingue for their superb assistance.
The views expressed in this paper are those of the authors and do not necessarily reflect the views orpolicies of the Asian Development Bank or its Board of Governors or the governments they represent.

1. Introduction

Developing Asia has been characterized by high domestic and national saving rates almost acrossthe board in recent years, and these high saving rates have made possible high levels of domestic
investment but have also led to large capital outflows (current account surpluses) (see, for example,
the data presented in Park and Shin (2009)).

To put it another way, the developing economies ofAsia have oversaved and underinvested, leading to large current account imbalances (surpluses),as asserted by Bernanke (2005) and others.

The purpose of this paper is to present data on trends over time in domestic saving rates in twelve
economies in developing Asia during the 1966-2007 period, to analyze the determinants of thosetrends, and to project trends in domestic saving rates in these same economies during the next
twenty years (2011-2030 period) based on the estimation results.

The twelve economies included

in our analysis are the People’s Republic of China (PRC); Hong Kong, China; India; Indonesia;
Republic of Korea (hereafter Korea); Malaysia; Pakistan; Philippines; Singapore; Taipei,China;
Thailand, and Viet Nam, which comprise 95 percent of the GDP of developing Asia.

This paper

contributes to the literature in several respects. First, it presents and compares data on bothnominal and real domestic saving rates, even though few past studies have looked at real rates
despite their importance.

Second, it examines the nonlinear impact of financial sector

development on the saving rate, which is particularly relevant for our sample of countries.

Finally,

it generates long-term projections based on the estimation results.

This paper is organized as follows:

In section 2, we present our estimation model of thedeterminants of domestic saving rates; in section 3, we discuss past trends in domestic saving rates
and in the determinants thereof in developing Asia; in section 4, we present our empirical results
1concerning the determinants of domestic saving rates in developing Asia; in section 5, we discuss
our future projections of domestic saving rates in developing Asia; and section 6 concludes.

To summarize the main findings of this paper, we find that domestic saving rates in developing
Asia have, in general, been high and rising but that there have been substantial differences from
economy to economy, that the main determinants of the domestic saving rate in developing Asia
during the 1960-2007 period appear to have been the age structure of the population (especially theaged dependency ratio), income levels, and the level of financial sector development, and moreover,that the direction of impact of each factor has been more or less as expected.

We also find that thedomestic saving rate in developing Asia as a whole will remain roughly constant during the next
two decades because the negative impact of population aging thereon will be roughly offset by the
positive impact of higher income levels thereon but that there will be substantial variation from
economy to economy, with the rapidly aging economies showing a sharp downturn in their domestic
saving rates by 2030 because the negative impact of population aging thereon will dominate thepositive impact of higher income levels thereon and the less rapidly aging economies showing risingdomestic saving rates, at least until 2020, because the positive impact of higher income levels
thereon will dominate the negative impact of population aging thereon.

2. Estimation Model of the Determinants of Domestic Saving Rates

In this section, we discuss the estimation model we will use in our econometric analysis of thedeterminants of domestic saving rates in developing Asia.

There have been many previous

empirical analyses of the determinants of saving rates using cross-section or panel cross-country
data or time series data for individual countries, among them Modigliani (1970), Feldstein (1977,1980), Modigliani and Sterling (1983), Horioka (1989), Edwards (1996), Dayal-Ghulati and2Thimann (1997), Bailliu and Reisen (1998), Higgins (1998), Loayza, et al. (2000), Chinn and Prasad(2003), Luhrman (2003), International Monetary Fund (2005), Bosworth and Chodorow-Reich(2007), Kim and Lee (2008), Park and Shin (2009), Horioka and Yin (2010), and Hung and Qian
(2010), and a smaller number of studies that conduct similar analyses of the determinants ofcurrent account balances, among them Chinn and Ito (2007, 2008) and Ito and Chinn (2009). The
present study is based most closely on Higgins (1998), Bosworth and Chodorow-Reich (2007), andPark and Shin (2009).

These studies suggest an important role for demographic variables based on the life cycle model.
Looking first at the impact of the age structure of the population, since the aged typically financetheir living expenses by drawing down their previously accumulated savings, the aged dependency
ratio (the ratio of the aged population to the working-age population) should have a negative
impact on the saving rate, and similarly, since children typically consume without earning income,the youth dependency ratio (the ratio of children to the working-age population) should also have anegative impact on the saving rate.

Moreover, a higher youth dependency ratio means morechildren to provide care and financial assistance during old age and less need to save on one’s own
for old age, and hence the youth dependency ratio could have a negative impact on the saving rate
for this reason as well.

Park and Shin (2009) and most other studies find that the aged

dependency ratio and the youth dependency ratio both decrease the saving rate, as expected.

A high growth rate of real GDP is another important factor, creating a virtuous cycle in which rapid
income growth makes it easy to save, and high saving feeds back through capital accumulation topromote further growth.

Bosworth and Chodorow-Reich (2007) as well as Park and Shin (2009)find that both contemporaneous and lagged real per capita GDP growth rates increase the savingrate. Moreover, Park and Shin (2009) also find that the level of per capita income has a significant
3nonlinear or more precisely convex relationship with the saving rate in Asia, but Bosworth andChodorow-Reich (2007) do not find a significant effect.

Aside from demographic and GDP-related variables, financial sector development is also
considered to be a crucial determinant of saving rates, but the direction of its impact is ambiguous
theoretically as well as empirically.

Wang, Xu, and Xu’s (2011) theoretical analysis of the impactof financial sector development on the saving rate finds that, if both households and firms are
subject to financial friction and if the financial sector development occurs first in the corporate
sector and then spreads to the household sector (a likely scenario), then financial sector
development will have a hump-shaped impact on the saving rate, initially increasing the savingrate by increasing firms’ ability to borrow and invest but then reducing the saving rate by
weakening the precautionary saving incentives of households.

Turning to empirical studies,

Loayza, et al. (2000) as well as Horioka and Yin (2010) find that financial sector development has anegative impact on the saving rate, Park and Shin (2009) find that its impact is insignificant, andWang, Xu, and Xu (2011) find a hump-shaped relationship.

By contrast, anecdotal evidence

suggests that relaxing financial constraints by increasing the availability of saving instruments
and accessibility to banks may promote higher saving, as argued by Chinn and Prasad (2003) andJha, et al. (2009).

Thus, both the theoretical and empirical literature suggests the possibility of anonlinear relationship between financial sector development and the saving rate, and this paper
investigates this possibility.

Finally, the real interest rate should, in theory, have an impact on the saving rate although thedirection of its impact is theoretically ambiguous.

Thus, the reduced form estimating equation we will estimate is as follows:4SRi ,t = β 0,i + β1 * AGEi ,t + β 2 * DEPi ,t + β 3 * LNGDPi ,t + β 4 * LNGDPSQi ,t + β 5 * CREDITi ,t +β 6 * CREDITSQi ,t + β 7 * X i ,t + u i ,twhere i = 1, … 12 (1 = PRC, 2 = Hong Kong, China, 3 = Indonesia, 4 = India, 5 = Korea, 6 =Malaysia, 7 = Pakistan, 8 = Philippines, 9 = Singapore, 10 = Thailand, 11 = Taipei,China, and 12 =Viet Nam); and t = 1, … 8 (1 = 1966-70, 2 = 1971-75, 3 = 1976-80, 4 = 1981-85, 5 = 1986-1990, 6 =1991-1995, 7 = 1996-2000, and 8 = 2001-2007).

SRi ,t represents the real domestic saving rate in country i at time t; AGE i ,t is the aged
dependency ratio (the ratio of the population aged 65 or older to the population aged 15-64); DEPi ,tis a youth dependency ratio (the ratio of the population aged 14 or younger to the population aged
15-64); LNGDPi ,t is the log of per capita real GDP; LNGDPSQ i ,t is the square of LNGDPi ,t ,CREDITi ,t is the ratio of private credit from deposit money banks and other financial institutions
to GDP; CREDITSQ i ,t is the square of CREDITi ,t , and X i ,t is a vector of the other explanatory
variables included in the estimation model.
fixed effects model is estimated.

β 0,i is a constant plus country fixed effects when a

In addition, in some variants, we include CHGDPi ,t , the growthrate of real per capita GDP and/or RINTi ,t , the real interest rate.

In addition, we also try using one-period lags of variables relating to LNGDPi ,t , CREDITi ,t , and

CHGDPi ,t in lieu of contemporaneous values because these variables are endogenous and thus
using lagged values thereof would alleviate simultaneity bias.

5

3. Past Trends in Domestic Saving Rates in Developing Asia

In this section, we discuss past trends in the domestic saving rate and in the determinants thereofin the twelve developing Asian economies in our sample.

First, in Table 1 and Figure 1, we

present data on past trends in both the nominal as well as real domestic saving rates for each of thetwelve developing Asian economies in our sample as well as for these twelve countries as a whole(weighted by real GDP) for each five-year subperiod during the 1966-2001 and for the most recent
period, which includes the years from 2001 to 2007.

Nominal domestic saving rates are computed as the ratio of gross domestic saving in current local
currency units to gross domestic product in current local currency units and are taken from theWorld Development Indicators of the World Bank, while real domestic saving rates are computedby subtracting the consumption and government shares of real GDP from 1 and are taken from thePenn World Table, version 6.3 (Heston, et al. (2009)).

The nominal and real measures can show

divergent trends if trends in GDP and capital goods deflators differ significantly.

As can be seen from this table and figure, there is enormous variation among the twelve economies
in the sample in their domestic saving rates, with the nominal domestic saving rate ranging from39.8 percent in Singapore (and also above 30 percent in the PRC, Malaysia, and Hong Kong, China)
to 11.2 percent in Pakistan (and also below 20 percent in Viet Nam and the Philippines) during the
1966-2007 period as a whole.

In fact, Viet Nam showed negative real domestic saving rates until

she transitioned to a market economy in the 1990s.

Korea, Taipei,China, Thailand, Indonesia,

and India showed intermediate nominal domestic saving rates during this period.

Nonetheless,domestic saving rates were relatively high in developing Asia as a whole relative to the rest of theworld throughout the 1966-2007 period.
6

Table 1: Trends over Time in Gross Domestic Saving Rates in Developing Asia
Nominal measure
1966-70 1971-75 1976-80 1981-85 1986-90 1991-95 1996-2000 2001-07 Average
PRC
28.9
29.1
33.0
34.8
37.0
41.9
40.7
46.2
37.8
Hong Kong, China
28.2
29.4
33.5
31.6
36.0
32.6
30.4
31.5
31.6
Indonesia
14.3
23.9
30.8
30.1
31.9
32.4
28.1
29.9
27.8
India
15.5
16.7
18.7
19.4
21.9
23.0
22.4
29.0
22.2
Korea, Rep. of
15.2
19.0
27.0
27.8
36.5
36.4
35.6
31.8
28.8
Malaysia
24.3
24.2
31.1
28.1
33.3
37.8
45.8
42.6
33.4
Pakistan
8.9
8.0
7.9
7.2
10.3
16.4
14.9
16.1
11.2
Philippines
21.9
23.6
26.2
21.0
19.0
16.3
17.1
13.8
19.1
Singapore
18.4
26.3
34.9
43.8
41.0
47.3
49.9
46.3
39.8
Thailand
21.2
22.8
22.1
24.3
30.8
35.8
34.3
32.2
27.9
Taipei,China
25.3
30.4
33.0
31.6
33.4
27.2
26.0
26.0
28.7
Viet Nam
3.9
14.6
22.2
29.0
17.4
Average 1/
19.8
22.0
25.7
26.6
30.0
33.2
32.9
37.5
Note: Computed as Gross domestic savings (current LCU) / GDP (current LCU)
1/ weighted by average real GDP of each period
Data source: World Bank, World Development Indicators
Real measure
1966-70 1971-75 1976-80 1981-85 1986-90 1991-95 1996-2000 2001-07 Average
PRC
27.3
28.3
30.1
28.1
27.0
27.7
29.4
35.6
29.2
Hong Kong, China
35.0
34.6
35.7
33.0
33.1
28.2
27.9
33.7
32.6
Indonesia
35.7
39.3
35.2
20.7
26.0
25.8
21.0
19.4
27.9
India
12.2
13.1
14.5
12.0
12.1
12.4
10.9
16.4
13.0
Korea, Rep. of
18.8
21.9
31.8
33.4
39.3
42.2
42.4
43.7
34.2
Malaysia
28.5
31.0
31.5
29.7
37.5
39.0
46.0
42.5
35.7
Pakistan
3.8
3.4
-3.8
-0.8
4.8
5.9
5.2
7.7
3.3
Philippines
10.2
13.2
18.1
18.9
14.2
10.6
9.7
10.8
13.2
Singapore
35.8
40.4
45.4
53.7
51.3
56.2
60.2
56.4
49.9
Thailand
17.9
21.0
21.7
24.8
30.4
35.2
32.8
31.7
26.9
Taipei,China
13.4
18.4
23.1
25.1
25.5
21.7
22.6
26.4
22.0
Viet Nam
-2.4
-2.4
-2.4
-2.4
-1.7
6.9
12.6
16.8
3.1
Average 1/
18.8
21.0
23.0
21.2
22.8
24.3
24.8
29.4
Note: Computed as 100 - kc - kg, where "kc" is the consumption share of real GDP per capita and "kg" is the government
share of real GDP per capita.
1/ weighted by average real GDP of each period
Data source: Penn World Tables (PWT), version 6.3

7

Figure 1: Trends over Time in Gross Domestic Saving Rates in Developing Asia

India

Indonesia

Korea, Rep. of

70 75 80 85 90 95 00 07

70 75 80 85 90 95 00 07

70 75 80 85 90 95 00 07

70 75 80 85 90 95 00 07

Malaysia

China, People's Rep. ofPakistan

Philippines

70 75 80 85 90 95 00 07

70 75 80 85 90 95 00 07

70 75 80 85 90 95 00 07

70 75 80 85 90 95 00 07

Singapore

Taipei,China

Thailand

Viet Nam

70 75 80 85 90 95 00 07

70 75 80 85 90 95 00 07

70 75 80 85 90 95 00 07

70758085 90 95 00 07

20
0
60
40
20
0
60
40
0

20

Average Domestic Saving Rate

40

60

Hong Kong, China

Nominal measure

Real measure

Graphs by ctry

Data sources:

See Table 1.

The real domestic saving rate ranged from 49.9 percent in Singapore (and also above 30 percent in
Malaysia, Korea, and Hong Kong, China) to 3.1 percent in Viet Nam (and also below 20 percent inPakistan, India, and the Philippines) during the 1966-2007 period as a whole.

The PRC,

Indonesia, Thailand, and Taipei,China showed intermediate real domestic saving rates during thisperiod.

Thus, the ranking ordering of the twelve countries is roughly the same regardless ofwhether we look at nominal or real domestic saving rates, with the biggest difference being that
PRC falls from second to fifth when real domestic saving rates are used.

Turning to trends over time, both the nominal and real domestic saving rates showed upward
trends throughout the 1966-2007 period for the twelve developing Asian economies as a whole,8

increasing from 19.8 (18.8) percent in 1966-70 to 37.5 (29.4) percent in 2001-07 in the case of thenominal (real) domestic saving rate.

Overall, the real saving rate tends to be higher than thenominal measure in higher per capita income economies such as Singapore and Korea, and the
opposite holds in lower per capita income economies such as Pakistan, Viet Nam, India, and PRC.
This is consistent with the notion that capital goods prices will tend to fall or grow more slowly
than the GDP deflator as technological progress occurs and the economy grows.

Moreover, the nominal and real domestic saving rates of most individual economies also showed
upward trends during most or all of the 1966-2007 period, with the primary exceptions being thatthe nominal and real domestic saving rates of the Philippines, the nominal domestic saving rates ofHong Kong, China; Indonesia; the Republic of Korea; and Taipei,China, and the real domestic
saving rate of Thailand have started declining in recent years, that the real domestic saving rate ofIndonesia has been declining throughout the 1966-2007 period, and that the real domestic saving
rates of Hong Kong, China, and Pakistan declined in the early years of the 1966-2007 period before
turning upward.

Given that the rank ordering of countries and trends over time are broadly similar regardless ofwhether we use nominal or real domestic saving rates, given that using a real measure ispreferable from a theoretical point of view, and given that a number of authors (such as Aghion,
Comin, Howitt, and Tecu (2009) and Shioji and Vu (2011)) have used a real measure of saving, we
decided to use the real domestic saving rate as our dependent variable throughout our regression
analysis.

Note, however, that we also tried using the nominal domestic saving rate and that theresults were qualitatively very similar (with the main difference being that the coefficient of theAGE variable (to be defined later) is higher in absolute magnitude when the nominal domestic
saving rate is used than when the real domestic saving rate is used.

9Various factors affected the trends in domestic saving rates described above. First of all, many ofthe economies in our sample experienced rapid demographic transition.

Life expectancy rose

sharply from an average of about 53 in the early 1960s to 73 in the late 2000s in the sample as awhole.

Consequently, the aged dependency rate also increased (from 6.5 to 10.2 percent on

average) during the same period.

Population aging has been particularly significant in Hong

Kong, China; Korea; Singapore; and Taipei,China.

Meanwhile, the aged dependency rate has been

declining somewhat in Pakistan and Viet Nam.

The youth dependency rate shows a uniform

picture, declining in all of the economies in our sample, though to a lesser extent in Pakistan.

Financial sector development played an especially significant role in developing Asia.

James, et al.

(1989) discuss the role played by financial incentives such as raising interest rates on time andsaving deposits in increasing the domestic saving rate when the financial system was still shallow
in the 1970s (in Korea and Singapore, for example).

Financial deepening accelerated in many

Asian economies after the mid-1980s, driven by financial liberalization, which relaxed financial
constraints and eased access to credit.

However, these earlier forms of financial deepening tend to

be more relevant for the corporate sector, as often discussed in the context of the financial
development and growth nexus (see King and Levine, 1993, for example), and thus they
presumably had a positive impact on the saving rate as firms’ investments become more profitable
and the increasing demand for credit pushed up interest rates.

By contrast, improved access tocredit by households and the resultant decline in precautionary saving tend to occur at a later stageof development, and thus the negative impact of financial sector development on saving rates will
presumably tend to come later.

The developing Asian economies in our sample recorded

deepening of their credit markets to more than 100% of GDP by the 2000s except in India,
Indonesia, Pakistan, the Philippines, and Viet Nam, and thus further financial deepening can be10expected to contribute toward reducing their saving rates, unlike in the case of earlier financial
incentives and/or the relaxation of financial constraints.

Moreover, these demographic and financial developments were accompanied by the continuing but
uneven increase in per capita GDP and its growth rate.

4.

Estimation Results concerning the Determinants of Domestic Saving Rates in Developing Asia

In this section, we present our estimation results concerning the determinants of domestic saving
rates in the twelve developing Asian economies in our sample during the 1966-2007 period.

We estimated both a country fixed effects model and a random effects model with robust standard
errors, and we use non-overlapping five-year averages except for the most recent period which
includes the years from 2001 to 2007 in order to eliminate the impact of cyclical fluctuations and to
focus on long-term rather than short-term variations.

Thus, we have maximum of 8 observations

per country and a maximum of 78 total observations.

Details concerning the variables used in our analysis (including data sources) can be found in
Appendix Table 1, and descriptive statistics can be found in Appendix Table 2.

Our estimation results are shown in Table 2.

Models 1-3 are country fixed effects models with nolags, models 4-6 are random effects models with no lags, models 7-9 are country fixed effects models
with lags for LNGDPi ,t , LNGDPSQ i ,t , CREDITi ,t , CREDITSQ i ,t , and CHGDPi ,t , and models
10-12 are random effects models with lags for these same variables.

11While the results of standard specification tests such as the Hausman test and the Breusch andPagan Lagrangian multiplier test suggest the use of random effects models, we show the results forboth fixed effects and random effects models.

This is because a test of the joint significance of thecountry fixed effects rejected the null hypothesis that the coefficients of all of the country fixed
effects are zero, because omitting country fixed effects seems to increase the residuals for some
economies such as the PRC, as also argued by others including Hung and Qian (2010), and because
we are interested in knowing whether there are significant country fixed effects when explaining
domestic saving rates.

As the results in Table 2 show, our results are intuitive and broadly consistent with those ofprevious studies.

Looking first at the results for the models without lags (models 1-6), thecoefficient of AGE (the aged dependency ratio) is negative and significant, as expected, in every
case, ranging from -0.83 to -0.98 in the country fixed effects model and from -1.58 to -1.69 in the
random effects model. Thus, the absolute magnitude of the coefficient of AGE is much larger inthe random effects model than it is in the country fixed effects model and is, in fact, much larger inabsolute magnitude than theoretical expectation (the life cycle model predicts that it should equal
the negative of the ratio of working years to lifespan and thus should be well below one in absolutevalue).

This is presumably due to omitted variable bias arising from the omission of country fixed

effects and other relevant variables.

The coefficient of DEP (the youth dependency ratio) is

negative, as expected, in almost every case but is totally insignificant in all cases.

Turning to the GDP-related variables, the coefficient of LNGDP (the log of real per capita GDP) is
negative and significant, as expected, in all cases, with its square term being positive andsignificant in all cases, suggesting a nonlinear (convex) relationship with the domestic saving rate,as was also found by Park and Shin (2009).

In other words, income levels initially have a negative12

Model
1

2

3

4

5

6

AGE

DEP

-0.95 **
0.35
-2.71
-0.98 **
0.38
-2.58
-0.83 **
0.36
-2.28

-0.03
0.13
-0.23
-0.03
0.13
-0.22
0.05
0.18
0.27

-1.58 ***
0.52
-3.03
-1.63 ***
0.54
-3.02
-1.69 **
0.66
-2.55

-0.08
0.10
-0.72
-0.07
0.10
-0.69
-0.62
0.15
-0.41

Table 2: The Determinants of the Real Domestic Saving Rate in Developing Asia
LNGDP
LNGDPSQCREDIT
CREDITSQCHGDPCountry fixed effects model with no lags-43.13 ***2.92 ***
14.48 **
-6.46 ***
4.94
0.32
4.92
1.62
-8.73
9.24
2.94
-3.99
-44.21 ***2.99 ***
14.58 **
-6.41 ***
0.08
6.19
0.39
5.01
1.70
0.14
-7.15
7.75
2.91
-3.78
0.59
-35.63 **2.53 ***
14.88 ***
-6.25 ***
0.16
13.38
0.74
4.80
1.56
0.14
-2.66
3.40
3.10
-4.00
1.15
Random effects model with no lags
-46.79 ***3.15 ***
15.35 ***
-6.71 ***
8.16
0.50
5.87
1.82
-5.73
6.29
2.61
-3.69
-49.45 ***3.31 ***
15.08 **
-6.39 ***
0.24 *
8.97
0.55
5.92
1.83
0.13
-5.51
5.99
2.55
-3.49
1.75
-40.65 **2.85 ***
14.63 **
-6.15 ***
0.31 **
16.77
1.01
6.23
1.84
0.15
-2.42
2.81
2.35
-3.35
2.09
(continued)

13

RINT

R-squared# obs.
78

0.03
0.16
0.19

0.76
0.61
0.70
0.76
0.62
0.71
0.69
0.65
0.72

78

-0.04
0.19
-0.21

0.75
0.68
0.74
0.75
0.70
0.75
0.66
0.75
0.78

78

70

78

70

Table 2 (continued)
Model AGE

DEP
LNGDP
LNGDPSQCREDIT
CREDITSQ
CHGDP
RINT
R-squared # obs.
Country fixed effects model with lags of LNGDP, LNGDPSQ, CREDIT, CREDITSQ, and CHGDP
7-1.34 ***
-0.02
-24.63 **1.96 ***
6.70
-3.51
0.60
66
0.40
0.20
8.40
0.47
6.22
2.13
0.59
-3.33
-0.09
-2.93
4.17
1.08
-1.65
0.66
8
-1.41 ***
-0.02
-25.64 **2.03 ***
6.75
-3.32
0.19
0.60
66
0.46
0.20
8.86
0.52
6.45
2.24
0.15
0.62
-3.10
-0.09
-2.89
3.90
1.05
-1.48
1.28
0.68
9
-1.42 **
0.09
-14.12
1.43
7.12
-2.98
0.26 *
-0.07
0.55
62
0.51
0.23
16.90
0.86
5.70
1.95
0.14
0.26
0.57
-2.77
0.37
-0.84
1.66
1.25
-1.52
1.84
-0.27
0.64
Random effects model with lags of LNGDP, LNGDPSQ, CREDIT, CREDITSQ, and CHGDP
10-1.91 ***
-0.14
-32.31 ***2.39 ***
7.61
-4.09
0.58
66
0.61
0.13
10.43
0.73
7.40
2.52
0.70
-3.12
-1.08
-3.10
3.29
1.03
-1.62
0.73
11
-2.02 ***
-0.14
-34.12 ***2.51 ***
7.18-3.51
0.40 **
0.58
66
0.68
0.14
12.04
0.83
7.63
2.56
0.18
0.74
-2.99
-0.98
-2.83
3.03
0.94
-1.37
2.17
0.76
12
-1.93 ***
-0.10
-23.92
1.97 *
5.02-2.70
0.37 **
-0.17
0.52
62
0.72
0.16
18.59
1.16
7.37
2.40
0.17
0.24
0.73
-2.68
-0.61
-1.29
1.69
0.68
-1.13
2.22
-0.71
0.76
Notes: The figures are the estimated coefficients (first row), robust standard errors (second row), and t-values (third row). The first R-squared is within, thesecond is between, and the third is overall. The constant terms and country fixed effects are not shown to save space.
*Significant at the 10% level. **Significant at the 5% level. ***Significant at the 1% level.

14

impact on domestic saving rates but their impact becomes more and more positive asincome levels rise.

Our results imply that, by 1976-80, income levels had become highenough for income levels to have a positive impact on domestic saving rates in nine out of
the twelve economies in our sample, with income levels reaching this threshold by 1981-85
in India, by 1986-90 in China, and by 1991-95 in Viet Nam.

Thus, by 1991-95, income

levels had become high enough for income levels to have a positive impact on domestic
saving rates in all twelve economies in our sample, as also found by Park and Shin (2009).

Turning to the financial variables, the availability of private credit exhibits a nonlinear(concave) relationship with the domestic saving rate, with the coefficient of CREDIT (theratio of private credit to GDP) being positive and significant and the coefficient of its
squared term being negative and significant in all cases.

This nonlinear relationshipindicates that financial sector development leads to a higher domestic saving rate up to apoint, after which it works to lower the domestic saving rate, which is consistent with thetheoretical model of Wang, Xu, and Xu (2011) and with the discussions in Chinn andPrasad (2003) and Jha, et al. (2009).

Our results imply that financial sector development

has progressed enough in six of the twelve economies in our sample (PRC; Hong Kong,
China; Korea; Malaysia; Singapore; and Taipei,China) for the availability of private credit
to have a negative impact on the domestic saving rate in these economies but that
financial sector development has not progressed enough in six of the twelve economies inour sample (India, Indonesia, Pakistan, Philippines, Thailand, and Viet Nam) for theavailability of private credit to have a negative impact on the domestic saving rate.

Turning to the impact of CHGDP (the growth rate of real per capita GDP) and RINT (the15real interest rate), the coefficient of CHGDP is positive, as expected, in all cases andsignificant in two out of four cases, and the coefficient of RINT is not stable in sign and is
totally insignificant.

This might be due to the often discussed fact that interest rates

start to matter for saving behavior only after economic development has progressedbeyond a certain threshold (see Ogaki, Ostry, and Reinhart, 1996).

Turning to the results for the models with lags (models 7-12), the results are broadly
consistent with the results for the models without lags:

the coefficient of AGE is negative,

as expected, and significant in all cases, the coefficient of DEP is negative, as expected, but
totally insignificant in all cases, the coefficients of LNGDP and LNGDPSQ are negativeand positive, respectively, and are significant in most (but not all cases), the coefficient of
CHGDP is positive, as expected, and significant in three out of four cases, and thecoefficient of RINT is negative but totally insignificant.

The biggest difference between the results for the models without lags and those for themodels with lags is in the impact of financial sector development.

The significant impactof financial sector development found in models 1-6 without lags disappears in models
7-12 with lags, which suggests the existence of simultaneity bias even though the signs of
the relevant variables still indicate a concave impact of financial sector development on
the domestic saving rate.

Another difference is that the absolute magnitudes of thecoefficients of AGE and CHGDP are larger in the case of the models with lags whereas theabsolute magnitudes of the coefficients of LNGDP, LNGDPSQ, CREDIT, and CREDITSQ
are larger in the case of the models without lags, which suggests that the models withoutlags exaggerate the impact of LNGDP, LNGDPSQ, CREDIT, and CREDITSQ due to16

simultaneity bias.

Finally, the results of the fixed effects models show that the country fixed effects are
significant for most economies (except for Korea, Malaysia, and Singapore) with asignificant negative sign when the PRC is taken as the reference country, indicating amuch higher domestic saving rate in the PRC than predicted by the other explanatory
variables.

As a robustness check, we also tried including time dummies for 7 out of the 8 time periods.
When we did so, we found that the coefficients of 3 out of the 7 time dummies weresignificant at the 10 percent level (with the coefficients indicating a downward trend overtime).

However, a test for the joint significance of the time dummies failed to reject thenull hypothesis that the coefficients of all of the time dummies are zero at the 5 percent
significance level in the case of the benchmark model, and the results for the otherexplanatory variables were roughly comparable to those for the model without time
dummies (except that the absolute magnitude of the coefficient of AGE becomes larger andthe absolute magnitude of the coefficients of CREDIT and its squared term become
smaller).

Thus, we have not shown the results for the model that includes time dummies.

In sum, the main determinants of the domestic saving rate in developing Asia during the
1966-2007 period appear to be the age structure of the population (especially the aged
dependency ratio), income levels, and (to a lesser extent) the level of financial sector
development, except as noted above, and moreover, the direction of impact of each factor ismore or less as expected.

17

5.

Projections of Domestic Saving Rates in Developing Asia for 2011-2030

In this section, we discuss our projections of domestic saving rates in the twelve
developing Asian economies in our sample for 2011-2030.

We should note at the start,

however, that future projections over such a long time horizon are fraught with dangers
and that we implicitly assume that there will be no changes in any factor (e.g., economic
policies, institutions, etc.) other than those explicitly included in our analysis, which is not
likely to be the case.

Thus, our results should be taken with a grain of salt.

We show only the projections based on the fixed effects model because, while the results ofstandard specification tests such as the Hausman test and the Breusch and Pagan
Lagrangian multiplier test suggest the use of random effects models, a test of the joint
significance of the country fixed effects rejected the null hypothesis that the coefficients ofall of the country fixed effects are zero, because the absolute magnitude of the coefficient of
the aged dependency ratio seems unreasonably large in the estimation results based on
the random effects model, as noted earlier, and because comparing out-of-sample
projections based on the fixed effects and random effects models suggests that the random
effects model does not perform as well as the fixed effects model in fitting the domestic
saving rate for a number of economies such as the PRC, Korea, Singapore, Pakistan, and
the Philippines.

The projections from the random effects models underestimate thesaving rates of the former three economies while overestimating those of the latter two
economies.

This is consistently true for all six random effects models.

For the PRC,

omitting the country fixed effect would yield a saving rate of about 24% of GDP for the18

2001-2007 period—10 percentage points lower than the actual rate.

Our finding isremarkably consistent with Hung and Qian’s (2010) finding that China’s national savingrate is about 10 to 12 percentage points higher than that predicted by their regression
analysis.

A possible explanation for the case of the PRC is omitted factors such as theincrease in the corporate saving rate during this period (IMF, 2009) and/or the distorted
sex ratio of those of marrying age (Wei and Zhang, 2009).

Another example of an obvious

deviation of the fitted saving rate from the actual rate is the Philippines, where the fitted
saving rate based on the random effects model does not show the decline observed in theactual rate.

The rapidly increasing coverage of the social security system has beensuggested as one of the explanations for why this might be (Terada-Hagiwara, 2009).

Our projections for the next two decades, 2011-2020 and 2021-2030, rely on the United
Nations’ (U.N.) projections of the age structure of the population (the aged and youth
dependency ratios, median variant) and the GDP projections of Lee and Hong (2010).

Since projections of financial sector development are not available, we assumed that
financial deepening progresses according to the level of per capita income.

We projected

in which World Bank income group each country would belong in 2011-2020 and
2021-2030 using Lee and Hong's (2010) GDP projections and assigned to each country the2008 value of the financial sector development variable (private credit to GDP ratio) for
the income group to which it is projected to belong in each time period except that, foreconomies in which the financial sector has already deepened beyond the average value forthe income group to which they are projected to belong (Hong Kong, China; the PRC,
Taipei,China, and Viet Nam in 2011-2020 and these same countries minus the PRC in

19

2021-2030), the financial sector development variable was assumed to remain at the samevalue as in the recent past (the average value for 2000-2007).

The average 2008 valuesof the financial sector development variable for each income group were 130% for the highincome group, 105% for the upper middle income group, and 46% for the lower middle
income group, as shown in Beck et al. (2008).

Hong Kong, China; Korea; Malaysia;

Singapore; the PRC; and Taipei,China are projected to belong to the high income group,
Thailand to the upper middle income group, and Indonesia, India, Pakistan, thePhilippines, and Viet Nam to the lower middle income group by 2021-2030.

Saving rate projections are generated for the 2011-2020 and 2021-2030 periods using thecoefficients in model 7 of Table 2, the baseline country fixed effects model with lags,because simultaneity bias will presumably be less severe in the model with lags.

Table 3shows future projections of domestic saving rates for the twelve economies in our sample,
and as can be seen from this table, domestic saving rates in Hong Kong, China; Indonesia;
Korea; Malaysia; Philippines; Singapore; Thailand; and Taipei,China are expected todecrease (except for a slight upturn in Korea in 2021-30); that of Viet Nam is expected toincrease in 2011-20, then decrease in 2021-30; that of the PRC is expected to remainroughly flat for the next two decades; and that of India and Pakistan are expected toincrease (comparisons are being made to fitted values for the 2001-07 period).

While all of the developing Asian economies in our sample are projected to post steady
growth, which will lead to higher income levels, which in turn will create upward pressure
on their domestic saving rates, the dramatic differences among these economies inprojected future trends in their domestic saving rates are not surprising because there is a2040- to 50-year gap in the timing of population aging, as can be seen from Table 4.

As aresult of these dramatic differences in the timing of the demographic transition in the
coming decades, the decline in domestic saving rates will not occur simultaneously in theeconomies of developing Asia but will rather be spread out over close to a half-century,with the decline in domestic saving rates in some economies being offset by the increase indomestic saving rates in other economies until at least 2030.

In particular, economies in which population aging is expected to occur the soonest (suchas Hong Kong, China; Korea; Singapore; Thailand; and Taipei,China) are projected toshow the earliest declines in domestic saving rates, economies in which population aging

Table 3: Future Trends in Real Domestic Saving Rates in Developing Asia
2001-072011-202021-30
FittedProjected
Projected
Economy
PRC
31.82
30.30
31.88
Hong Kong, China
29.75
24.33
20.02
Indonesia
24.08
21.59
20.80
India
14.54
14.92
15.91
Korea, Rep. of
42.02
35.53
37.36
Malaysia
44.65
43.74
41.97
Pakistan
6.66
7.01
10.05
Philippines
14.90
12.91
11.81
Singapore
58.74
47.02
40.43
Thailand
31.31
28.59
23.53
Taipei,China
25.10
20.68
15.65
Viet Nam
16.76
19.19
15.44
Developing Asia
27.38
26.33
27.21
Notes: Authors' calculation. Refer to the main text for explanation.

21

Table 4: Population Aging in Developing Asia
The Period during which the
Population Aged 65 andOlder Reaches 14 Percent
Economythe Total Population
PRC
2020-25
Hong Kong, China
2010-15
Indonesia
2040-45
India
2050-55
Korea, Rep. of
2015-20
Malaysia
2040-45
Pakistan
After 2055
Philippines
2050-55
Singapore
2015-20
Thailand2020-25
Taipei,China
2015-20
Viet Nam
2030-35
Japan
1990-95
Data source: The United Nations’ (U.N.) projections available
at http://esa.un.org/unpp, and the Statistical Yearbook for
Taipei, China, available at
http://www.cepd.gov.tw/encontent/m1.aspx?sNo=0000063.

is expected to occur neither early nor late are projected to either show declines in domestic
saving rates right from the start (as in the case of Indonesia, Malaysia, and thePhilippines) or to show an increase in domestic saving rates early on, followed by a decline(as in the case of Viet Nam); and economies in which population aging is expected to occur
the latest (such as India and Pakistan) are projected to show an increase in domestic
saving rates at least until 2030.

The primary exception is the PRC, where the domestic

saving rate is projected to remain roughly constant for the next two decades (for thereason we will discuss shortly).

22

The projected decline in domestic saving rates from the 2000s until the 2020s in the
rapidly aging economies ranges from 5.4 percentage points (Korea) to 12.5 percentage
points (Singapore), which is about the same or larger than what other already aging
economies such as Japan have experienced over the last 20 years.

In Japan, the domestic

saving rate declined from its peak of 39% in the late 1980s to 33% in the early 2000s,
during which time the aged dependency ratio rose from 16% to 29%.

The morepronounced decline in developing Asia’s domestic saving rate might be due to the fact thataging is expected to progress more rapidly.

Finally, we calculate the historical and projected domestic saving rates of developing Asia
as a whole by weighting the domestic saving rates for each economy by its real GDP (seeTable 3).

According to the fixed effects model, the domestic saving rate in developing Asia

as a whole is projected to remain roughly constant for the next two decades—averaging
26.3% during the 2011-2020 period and 27.2% during the 2021-2030 period, compared toan actual value of 29.4% and a fitted value of 27.4% during the 2001-2007 period.

The population of emerging Asia is projected to age rapidly over the next 20 years, withthe aged dependency ratio expected to climb from 11 percent in 2001-2007 to 19 percent in
2021-2030.

However, the domestic saving rate of developing Asia as a whole is expected

to remain high despite the rapid aging of the population because of differences among
economies in the timing of population aging and because the negative impact of population
aging on the domestic saving rate will be largely offset by the positive impact of higher
income (per capita GDP) levels on the domestic saving rate.1

1

Projections based on the random effects model indicate that the domestic saving rate in23

To corroborate this contention, we analyze the determinants of future trends in domestic
saving rates with the help of Table 5, which shows the contribution of each factor to thechange in the domestic saving rate of each country from the 2001-2007 period to the
2011-2010 period and from the 2011-2010 period to the 2021-2030 period.

Thesecontributions were calculated by multiplying the estimated coefficients of each factor fromthe country fixed effects model with lags (model 7 in Table 2) by the projected change inthe factor in question during the period in question.

Note, however, that in the case of(per capita) GDP and CREDIT, the contribution shown represents the sum of thecontribution of the variable itself and that of the square of the variable.

As can be seen from this table, the change (increase) in AGE will lower the domestic
saving rate in every country in both time periods, the change (decrease) in DEP will raisethe domestic saving rate in 21 out of 24 cases, the change (increase) in GDP will increase
the domestic saving rate in every country in both time periods, and the change (increase)in CREDIT will increase the domestic saving rate in 15 out of 24 cases.

Note, however,that the contributions of DEP and CREDIT are not precisely estimated because they arecalculated based on statistically insignificant coefficients and that their contributions arevery small (in absolute magnitude) in any case.

developing Asia as a whole will decline from 29.2% in 2001-07 to 26.6% in 2011-2020 andfurther to 23.5% in 2021-2030, contrary to the projections based on the country fixed effects
model. However, as discussed earlier, the absolute magnitude of the coefficient of the aged
dependency ratio seems unreasonably large in the estimation results based on the random
effects model, and this is what is causing the domestic saving rate to show such a sharp decline.
Thus, the projections based on the fixed effects model appear to be more credible than the
results based on the random effects model.

24

Table 5: The Contributio n of Each Facto r to Future Trends in Real Dome stic Saving
Rate s in De velo ping Asia
Country
Time Period
AGE
DEP
GDP
CREDIT
Total
PRC
2011-2020
-5.83
0.11
4.31
-0.12
-1.52
PRC
2021-2030
-8.80
0.02
10.36
0.00
1.58
Hong Kong, China
2011-2020
-8.63
0.09
2.53
0.58
-5.43
Hong Kong, China
2021-2030
-9.61
-0.05
5.36
0.00
-4.30
Indonesia
2011-2020
-2.69
0.17
0.91
-0.89
-2.49
Indonesia
2021-2030
-5.26
0.10
3.26
1.11
-0.79
India
2011-2020
-2.02
0.23
1.66
0.51
0.38
India
2021-2030
-3.59
0.13
3.99
0.46
0.98
Korea
2011-2020
-9.76
0.15
2.94
0.18
-6.49
Korea
2021-2030
-4.40
-0.01
6.38
-0.14
1.84
Malaysia
2011-2020
-3.96
0.20
2.32
0.53
-0.91
Malaysia
2021-2030
-6.08
0.11
4.49
-0.30
-1.77
Pakistan
2011-2020
-0.92
0.23
0.97
0.06
0.35
Pakistan
2021-2030
-1.63
0.16
3.58
0.92
3.04
Philippines
2011-2020
-2.51
0.22
0.88
-0.58
-2.00
Philippines
2021-2030
-3.58
0.12
1.86
0.51
-1.09
Singapore
2011-2020-14.06
0.20
2.16
-0.02
-11.72Singapore2021-2030-10.70
-0.06
4.33
-0.16
-6.59
Thailand2011-2020
-5.23
0.08
1.54
0.89
-2.72
Thailand2021-2030
-9.59
0.02
4.53
-0.02
-5.06
Taipei,China
2011-2020
-6.44
0.16
2.73
-0.86
-4.42
Taipei,China
2021-2030
-9.16
0.01
4.13
0.00
-5.02
Viet Nam2011-2020
-0.99
0.28
1.93
1.21
2.43
Viet Nam
2021-2030
-8.04
0.04
4.25
0.00
-3.74
Note: Refer to the main text for variable definitions and a description of the calculation method.

The variables making by far the largest contributions (in absolute magnitude) to future
trends in the domestic saving rate are AGE and GDP, with AGE making the largest
contribution (in absolute magnitude) in 18 out of 24 cases and GDP making the largest
contribution (in absolute magnitude) in 6 out of 24 cases.

This corroborates my earlier

contention that the downward pressure on the domestic saving rate caused by the increase
in AGE will be largely offset by the upward pressure on the domestic saving rate caused by
the increase in GDP.

The impact of AGE will dominate in 10 economies in 2011-2020 andin 8 economies in 2021-2030, causing their domestic saving rates to decrease, on balance,
whereas the impact of GDP will dominate in 2 economies in 2011-2020, in 4 economies in25

2021-2030, and in developing Asia as a whole, causing their domestic saving rates toincrease, on balance.

Our results shed light on why the domestic saving rate of the PRC is projected to remain
more or less unchanged during the next two decades.

The reason is that the substantial

downward pressure thereon caused by the rapid aging of her population will be roughly
offset by the substantial upward pressure thereon caused by higher income (per capitaGDP) levels.

The trajectory of the domestic saving rate in developing Asia as a whole appears to beheavily influenced by trends in the PRC, which will account for more than 50% of regional
GDP in the next two decades.

Thus, any developments in the PRC that affect its

domestic saving rate (such as the worsening of its fiscal balance, increases in expenditures
on social services and pensions, and/or changes in the speed of population aging) haveimportant ramifications for future trends in saving rates in developing Asia as a whole.

6. Summary and Conclusions

In this paper, we presented data on trends over time in domestic saving rates in twelve
economies in developing Asia during the 1966-2007 period, conducted an econometricanalysis of the determinants of those trends, and projected trends in domestic saving rates
in these same economies during the next twenty years (2011-2030 period) based on ourestimation results.

We found that domestic saving rates in developing Asia have, in

general, been high and rising but that there have been substantial differences from26

economy to economy, that the main determinants of the domestic saving rate in developing
Asia during the 1960-2007 period appear to have been the age structure of the population(especially the aged dependency ratio), income levels, and the level of financial sector
development, and moreover, that the direction of impact of each factor has been more orless as expected.

We also found that the domestic saving rate in developing Asia as awhole will remain roughly constant during the next two decades because the negative
impact of population aging thereon will be roughly offset by the positive impact of higher
income levels thereon but that there will be substantial variation from economy toeconomy, with the rapidly aging economies showing a sharp downturn in their domestic
saving rates by 2030, because

the negative impact of population aging thereon will

dominate the positive impact of higher income levels thereon, and the less rapidly aging
economies showing rising domestic saving rates, at least until 2020, because the positive
impact of higher income levels thereon will dominate the negative impact of population
aging thereon.

27

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Appendix Table 1: Variable Definitions and Data Sources
Variable
Real domestic

Var. nameSRsaving rate

Data source

Notes

Computed as 100 - kg - kc.

kg is the government share of

Heston, et al. (2009), Penn

real per capita GDP, and kc

World Table (PWT), version 6.3

is the consumption share of1/

real per capita GDP.

Bothfrom PWT.
Aged dependency

AGE

ratio

“SP.POP.DPND.OL” from

Ratio of the population aged

World Development Indicators

65 or older to the population(WDI) of the World Bank 2/ andaged 15-64

the Statistical Yearbook for
Taipei,China 3/Youth dependency

DEP

ratio

Real per capita

LNGDP

GDP
Real per capita

CHGDP

GDP growth rate
Private credit fromCREDIT“SP.POP.DPND.YG” from WDI

Ratio of the population aged

and the Statistical Yearbook for

0-14 to the population aged

Taipei,China

15-64“rgdpch” from Penn World

Real per capita GDP (2005

Table, version 6.3

constant prices: Laspeyres)“grgdpch” from Penn World

Growth rate of real per capita

Table, version 6.3

GDP (chain series) (rgdpch)“pcrdbofgdp” from Beck anddeposit money

Demirguc-Kunt (2009) and line

banks and other

32D from International

financial

Financial Statistics (IFS) of theinstitutions (% ofInternational Monetary FundGDP)

for the PRC

Nominal interest

INT

rate

IFS, and www.cbc.gov.tw

Used data on the deposit rate

(Taipei,China's central bank’s(line 60L of IFS) except forwebsite) for Taipei,China. 8/

India, Pakistan, and theRepublic of Korea, for which
we used the discount rate(line 60 of IFS)

Inflation rate

Real interest rate

INFL

RINT

“NY.GDP.DEFL.KD.ZG” from

Used the rate of change of the

WDI

GDP deflator

IFS, WDI, and www.cbc.gov.tw

Computed as ln ((1+INT/100)/(1+INFL/100))33

Appendix Table 1 (continued)
Notes:
1/ Available at http://pwt.econ.upenn.edu/php_site/pwt_index.php
2/ Available at http://devdata.worldbank.org/dataonline/
3/ Available at http://www.cepd.gov.tw/encontent/m1.aspx?sNo=0000063
4/ Available at http://www.dbm.gov.ph/index.php?id=32&pid=95/ Available at http://www.adb.org/Statistics/ki.asp
6/ Available at http://www.bot.or.th
7/ Available at http://www.bnm.gov.my
8/ Available at http://www.cbc.gov.tw/ct.asp?xItem=30010&CtNode=517&mp=2

34

Appe ndix Table 2: Descriptive Statistics
Variable
No. of Obs.
Mean
Std. Dev.
Minimum
Maximum
SR
78
25.2
14
-3.8
60.2
AGE
78
7.8
2.2
5.7
16.3
DEP
78
56.8
18.6
20.3
87.6
LNGDP
78
8.5
0.9
6.7
10.5
CREDIT
78
0.7
0.5
0.1
2.2
CHGDP
78
4.6
2.8
-2.7
12.3
RINT
70
1.1
3
-7.5
8.7
Note: Refer to Appendix Table 1 for variable definitions and data sources.

35