___ PS - 2313
POLICY RESEARCH WORKING PAPER 2313
How Interest Rates As financial liberalization
progressed, the general level
Changed under of real interest rates increased
Financial LiberalizaTin *more in developing countries
than it did in industrial
countries. Volatility in
A Cross-Country Review wholesale interest rates also
jumped, often markedly, In
most liberalizing countries.
Patrick Honohan Treasury bill rates and bank
spreads showed the greatest
increase in developing
countnes, shifting substantiai
rents from the public sector
and from favored borrowers.
The World Bank
Development Research Group
Finance U
April 2000
I POLICY RESEARCH WORKING PAPER 2313
Summary findings
Financial liberalization was expected to make interest the greatest increase as liberalization progressed -
rates and asset prices more volatile, with distributional shifting substantial rents from the public sector and from
consequences such as reduced or relocated rents and favored borrowers.
increased competition in financial services. Honohan Whereas quoted bank spreads in industrial countries
examines available data on money market and bank contracted somewhat in the late 1990s, spreads in
interest rates for evidence of whether these things developing countries remained much higher, presumably
happened. reflecting both market power and the higher risks of
He shows that as more and more countries liberalized, lending in the developing world.
the level and dynamic behavior of developing-country There was no clear-cut change in mean rates of
interest rates converged to industrial-country norms. In inflation, monetary depth, or GDP growth. If anything,
the short term, volatility increased in both real and there was a small average improvement in inflation, but a
nominal money market interest rates. Treasury bill rates decline in monetary depth and economic growth, relative
and bank spreads, evidently the most repressed, showed to trends in industrial countries.
This paper - a product of Finance, Development Research Group - is part of a larger effort in the group to explore optimal
policy under financial liberalization. Copies of the paper are available free from the World Bank, 1818 H Street NW,
Washington, DC 20433. Please contact Agnes Yaptenco, room MC3-446, telephone 202-473-1823, fax 202-522-1155,
email address ayaptenco@worldbank.org. Policy Research Working Papers are also posted on the Web atwww.worldbank.org/
research/workingpapers. The author may be contacted at phonohan@worldbank.org. April 2000. (48 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center
How Interest Rates Changed under Financial Liberalization
A Cross-Country Review
Patrick Honohan1,
Development Research Group
'Thanks to Ash Demirguc,-Kunt, Aart Kraay, and Philip Lane for helpful comments and suggestions, not all of
which have yet been acted upon., and to Anqing Shi for painstaking research assistance.
I
Introduction
The process of financial liberalization was expected to increase the volatility of interest
rates and asset prices, to have distributional consequences in the form of reduced or relocated
rents, and to have increased competition in the financial services industry. In this paper we
examine the available data on money market and bank interest rates for evidence on these
propositions.
WVe show that, as more and more countries liberalized, the level and dynamic behavior of
developing country interest rates converged to industrial country norms. Liberalization did mean
an increased short-term volatility in both real and nominal money market interest rates. Treasury
bill rates and bank spreads were evidently the most repressed, and they showed the greatest
increase as liberalization progressed: this shifted substantial rents from the public sector and
from favored borrowers. Whereas quoted bank spreads in industrial countries contracted again
somewhat during the late 1990s, spreads in developing countries remained much higher,
presumably reflecting both market power and the higher risks of lending in the developing world.
Sections 1 and 2 review the global pattern of long-term and short-term dynamics in interest
rate levels and spreads. Section 3 proposes an approach to judging when the de facto
liberalizaLtion of wholesale rates occurred, and Section 4 measures the speed of adjustment of
developing country interest rates to external interest rate shocks before and after these dates.
Section 5 examines the way in vwhich changes in wholesale rates passes through to bank lending
and deposit rates. Using the date of de facto wholesale interest rate liberalization, Section 6
compares overall economic performance before and after. Section 7 contains concluding
remarks.
1. Global trends in interest rate levels anfd spreads (annual data)
1.1 Global trends since 1960
Broad trends in global interest rates since 1960 are summarized by the world medians
shown in Table 1 .2 There appears to have been a general upward trend in the level of world
median real interest rates, but the most striking feature is a pronounced secular swing in real rates
over the past forty years, with a sharp dip into negative rates in the 1970s followed by a recovery
to higher than previous levels in the 1980s and 1990s, and the beginnings of a reduction again
more recently. The swing is evident in both money market and deposit rates.
From a theoretical point of view, variations over time in the general level of unregulated
wholesale ex post real interest rates can be explained by deviations of actual from expected
inflation, and because of cyclical or trend changes in the productivity of capital and the
propensity to save and perceptions of risk. Changes in the degree to which these interest rates
are administratively controlled will also be a factor.
The causes of the secular swing in world interest rates since the 1960s, a we][l known
feature of industrial country data, have been debated in the literature at length.3 Was there a
downturn in the marginal efficiency of capital (possibly associated with the surge in petroleum
and other primary product prices); or was there a transitory an increase in the propensity to save?
These are probably the leading explanations. In a fully integrated world capital market, these
real factors would be fully transmitted across all markets, and would not retain any national
features. Nominal, currency-specific factors such as shifts in the relation between actual and
expected inflation are of greater interest in the present context, where we are looking at
differences in the behavior of interest rates from country to country. Thus, a fairly plausible and
2 In this section, unless otherwise stated, "real" rate dala shown are computed as ex post real interest rates simply
adjusted for consumer price inflation, see the data appendix. In Table 1, for each year the median is formed from all
of the countries for which IFS data exists for that year.
3 An important early analysis of the episode is Blanchard and Summers (1984). Bank of England (1996) presents a
useful overview of empirical work explaining long-trends in real interest rates in the industrial countries.
2
parsimonious (albeit somewhat underrated) interpretation attributes part of the U-shaped
evolution to a long lag in the formation of inflation expectations. In this account the relatively
high inflation of the 1970s in most industrial countries was unexpected and its persistence
continued to be underestimated for most of that decade.4 Furthermore, even where the market
did revise its inflation expectations upwards, interest rate controls inhibited the response of some
markets to the expected inflation. In contrast, although inflation began to come under control in
most industrial countries by the mid-1980s, by that time inflation expectations were high and
remained stubbornly so, placing upward pressure on nominal market interest rates. By that stage,
many interest rate controls had been dismantled, so that actual rates more closely reflected
market forces. A subsequent decline in real rates by the mid-1990s is explained in this account
by the gradual decline in inflation expectations in recent years. In addition to these effects of the
"great inflation" of the 1970s, the stance of countercyclical monetary policy has also been a
factor.
The degree to which developing country rates have tracked the long swing is an
indication of the degree to which elements of global financial integration were already in effect
by 1960.
Table 1: Median World Real Interest Rates, 1960-95
Money market Deposit rate Lending
less deposit
1960 1.3 2.4 3.6
1965 -0.5 0.5 3.5
1970 -0.7 0.8 2.9
1975 -5.8 -3.5 3.5
1980 -6.1 0.0 3.7
1985 2.0 5.0 4.2
1990 0.4 5.1 5.5
1995 2.2 3.4 5.9
Note: In this table deflation is by curTent inflation, i.e. these are ex ante real rates with stationary expectations.
' That is not to say that inflation was underestimated in each quarter, but investing in inflation hedges was inhibited
by set-up costs, as well as liquidity and other risks, the assumption of which could only be justified by expectation
of sustained inflation.
3
The median quoted internediation spread between deposit and lending rates remained
broadly constant during 1960-1980 but has risen rather sharply since then. A numlber of
interpretations are possible. For one thing there could have been an increase in the market power
of banks, possibly associated with the relaxation of interest rate controls. Another factor could
be the deterioration in loan-loss experience in the latter part of the sample: equilibriurm spreads
should have widened to take account of the credit risk. Finally, the degree to which the quoted
rates are representative will have varied over time, with large depositors and first-rate borrowers
beginning to have new non-bank opportunities.
1.2 The developing countries catch up: annual data from 1975
Data for the early years in Table 1 are sparse: the early years included vety few
observations. Only from about 1980 on is there data for at least several dozen countries in each
case. Table 2 and Figures 1 to 4 provide more detail for the period since 1975, distinguiishing
between industrial and developing countries. These show mean and percentile figures in addition
to the median on an annual basis. Along with rnoney market and treasury bill rates, we show
bank deposit rates and the quoted intermediation spread, i.e. the difference between quoted
deposit rates and quoted lending rates for as many countries as have sufficient annual
observations included in International Financial Statistics.5 The general trend is summarized by
three-yearly averages of the medians shovn in Table 2.
Table 2: Median Ex-post Real World Interest Rates: 1970s to 1990s
Money Market Treasury Bill Deposit Lending less deposit
% Industrial Developing Industrial Developing Industrial Developing Industrial Developing
1975-77 -1.0 -0.4 -0.9 -3.1 -2.8 -4.2 2.8 4.1
1980-82 3.3 1.6 2.4 -1.9 0.1 -2.3 4.1 4.6
1985-87 5.8 3.7 5.3 0.9 3.2 1.5 4.4 5.0
1990-92 6.7 6.8 6.2 2.5 3.3 2.5 5.1 6.7
1995-96 3.1 4.4 3.5 5.0 1.8 3.5 4.0 6.6
Note: The mean of the median across countries is shown. For the spread mean shown is for 1995-97.
S These figures and data are based on countries for which annual data is available for at least 12 years within 1980-
93. It is not a balanced pool: the number of countries varies somewhat from year to year, but more according to the
series, from a mean of 35 for money market and 36 for treasury bill rates to 59 for intermediation spreads and 62
for deposit rates. This sample selection strategy represents a compromise between the desirability of including as
many countries as possible with the risks of too unbalanced a pool. One potentially important but hidden source of
sample-selection bias could arise to the extent that reporting of data to IFS is correlated with a liberalized interest
rate regime.
4
Figure 1 :Median Ex-post Real World Interest Rates: 19 70s to 1990s
Industrial countries Developing countries
Median real interest rates Nldian real interest rates
% per annum % per annum
-6-4-202468 -6-4-20246 8
75-77 75-77
80-82 _*Money Mkt 80-82 *-ADney Mkt
*TB *~~~~~~~~~~ITB
X 85-87 _TB 5 85-87 _ fca
>! =_COfficial N fficial
90-92 r-IDeposit 90-92 _tDeposit
95-96 95-96
Quoted hntermediation Spreads
ledian for Industrial and Developing Countries
% per annum
0 2 4 6 8
75-77
80-82
X 85-87 *Industrial
C) *Developing
90-92
95-97 -
This annual data reveals some similarities and some contrasts between developing and
industrial country interest rates,
Developing country real interest rates on an upward trend
The real interest rates shown begin at predominantly negative levels, with even the third
quartile generally negative or close to zero in the late 1970s. Developing country rates were, on
average, even lower than those in industrial countries up to the mid-1980s; but thereafter
developing country rates increased and passed out the industrial countries to end the period
5
higher. The reduction in industrial country rates from the mid-1990s was not systematically
followed in the developing world
Market re-ranks different interest rates
Market forces can be expected to push deposit rates below, and lending rates above,
wholesale money market rates, reflecting costs and risks. Assuming that quoted interbank
money market rates .elate to lending that is highly liquid and virtually free of credit risk,
Treasury bill rates at the same maturity should be very close to money market rates. In the data,
median6 deposit rates were generally lower than money market rates, but not always lower than
Treasury bill rates. Until the 1990s, Treasury bill rates fell below money market rates in
developing countries, probably reflecting controls, taxes or other administrative requirements
(including compulsory take-up rules) more than a market assessment of differential risk. T he fact
that official (discount) rates switch from being lower than money market rates to being higher
may reflect changing mechanisms of central bankc liquidity support to the market as more central
banks shifted away from a subsidized and rationed facility to a penalty rate facility as their main
off-market method of intervening.
International dispersion of real interest rates does notfall
Evidence on trends in the international dispersion of real interest rates is ambiguous. All
standard measures of dispersion increase from the 1960s to the 1970s, though the small number
of countries included in the early years may affect this. Subsequently the interquartile range and
the gap between top and bottom decile show no clear trend in any of the series, 7 but the standard
deviation and range increase, reflecting more extreme outliers.8 This finding is, perhaps, slightly
surprising: had the data been drawn from countries with and without interest controls we might
have expected an increase in dispersion in the 1980s when real interest rates increased in the
6Note that in general a different country will be the median for each rate.
7 For example, the estimated LS time trend for the interquartile range of money market rates is -6.5 basis points per
annum - small relative to a mean range of 462 basis points - with a standard error of 3.3, not quite significant at
the 5 per cent level. The steepest shrinkage of interquartile is for the deposit rate, with an estimated annual trends of
-12.6 basis points, highly significant with a standard error of 4.3. (Note in contrast that the interquartile range for
the intermediation spread does show a statistically significant widening over time.)
Especially negative outliers - in several years the distribution across countries has a highly negative skew.
6
uncontrolled countries, followed by a narrowing as more and more countries decontrolled. This
alerts us to the possibility that iinterest rate controls may not have been fully effective in the early
years, at least for these countries. Bear in mind, however, that the sample of countries may suffer
from selection bias to the extent that reporting of statistics to IFS may be correlated with degree
of regime liberalization.
Bank spreads increase
Bank quoted gross intermediation spreads (as measured by subtracting quoted deposit
from quoted lending rates) increase sharply with the general increase in rates during the 1980s. In
industrial countries the increase is from 2.8 per cent in the mid-1970s to 5.2 per cent in the early
1990s; in the developing countries the spreads are wider: increasing from 4.1 per cent to 6.7 per
cent. During the 1 990s, these spreads remain high in developing countries, whereas they decline
in the industrial countries.9 Except for one year, the median quoted rates for the developing
countries are higher than for the industrial countries. The gap becomes quite wide by the late
1990s. Once again, this result cannot be extrapolated to an increase in bank profitability for a
variety of reasons. For one thing, the single rates used do not purport to be average rates, but
quoted rates for instruments of standard quality. Furthermore, the risk profile of borrowers and
the extent to which quoted rates bundle the cost of other banking services to customers may have
changed systematically over time.
2. Short-run dynamics of wholesale rates - overview
We have nearly complete monthly data since 1980 on wholesalel° interest rates and
inflation for some 28 significant" developing countries. In a later section we will have
something to say about nominal rates, but here the focus is on rates adjusted for exchange rate
9 To what extent this recent reversal in industrial countries reflects disintermediation from banking is not clear: it is
widely believed that industrial country banks have lost some market power in the past decade, which would have
narrowecl spreads. But although it is their most creditworthy customers that they have lost to securities markets, the
shifting composition of their loan portfolio towards lower quality is unlikely to influence the quoted spreads, which
are usually for standard borrower categories.
0 We use the term to imply either money market or treasury bill rates.
1 The number would be 43 before excluding microstates and multiple members of currency unions (see Data
Annex)
7
change and expected inflation. We find that both forms of adjusted exchange rates have
displayed extremes of high and low - both spikes and on a sustained basis.
2.1 Expected or ex ante real interest rates in developing countries
Our approximation for expected, or ex ante real exchange rates, is to subtract a smoothed
rate of inflation (Hodrick-Prescott filter - see D)ata Annex) from actual nominal rates. This
simple procedure has the advantage of eliminating the volatile month-to-month noise in the
inflation data.12 Because of the high smoothing parameter used, short-run changes in the real
interest series thus derived are largely attributable to interest rate changes rather than expected
inflation (and this is true of all spikes).
The resulting data are plotted in Figure 5. The plots are characterized by gentle
fluctuations with a period of a few years punctuated by intervals of sometimes violent
fluctuations on a month-to-month basis.'3 So, in contrast to what is predicted by simple models
of market efficiency, rational expectations and static preferences, real interest rates in developing
countries have had a considerable degree of persistence, as well being subject to short term
reversible shocks.
Developing country interest rates have been high Some of these countries have
experienced extended periods of very high real interest rates. Of the 17 treasury bill countries in
our data set, eight have had mean real interest rates in double digits continuously for at least three
years. Guyana had the highest three-year mean real interest rate at over 26 per cent. Much
higher real interest rates have been sustained for periods as long as one year: five of the
countries had one-year means of over 20 per cent: one (Sierra Leone) with 45 per cent and
another (Mexico) with 33 per cent.
12 We applied a single filter to each country's entire inflation series, rather than using a Kalman filter. A one-sided
backward-looking univariate filter on this data would use too little information to provide a credible expectations
proxy, especially (but not only) for early periods. This outweighs the obvious drawback of the procedure we have
adopted, namely that the expected inflation for time t is computed using data that was not available at time t. While
that would make this approximation questionable for exarnining issues of informational efficiency, those issues are
not a central focus of this paper (cf. Baxter, 1996; Edison and Pauls, 1993).
c,
... And volatile But there have also been very low real interest rate observations in
developing countries, and, despite a low (negative) mean value, the mean of 25 developing
country monthly standard deviations (excluding Argentina and Brazil) in our sample is 877 basis
points, compared with just 18 7 basis points for the eight control industrial countries. And this is
not just due to some outliers: the smallest of developing country standard deviations is 221 basis
point fDr Singapore.
2.2 Negatively skewed distribution of $-adjusted rates
Volatility and extreme values are also evident in Table 3, which compares the 1980-97
average statistics of monthly wholesale returns for developing and industrial countries adjusted
for actual change in exchange rates against the US dollar. Although the mean of developing
country $-adjusted money market rates was on average much lower than for industrial countries,
this mainly reflected the wider variation over time of exchange-rate adjusted interest rates for
developing countries, and in particular the negative skewness (influence of extreme negative
observations). In other words, occasional sharp devaluations were not fully compensated-for by
a sufficient excess return or peso premia in developing country interest rates in normal times.
Table 3: Ex post $-adjusted Money Market Rates: Industrial and Developing Countries,
Monthly Data: 1980-97
% per annum 26 Developing Countries 12 Industrial Countries
mean over countries of:
mean (standard error) 0.49 (9.0) 7.34 (1.1)
median 3.04 7.56
maximum 107.3 86.2
minimum -490 -94.9
standard deviation 53.3 28.9
skewness -3.95 -0.1
median over countries of:
median 6.95 6.11
standard deviation 35.2 32.5
Note: 26 large developing countries not including Argentina or Brazil
13 Unit root tests can reject non-stationarity of at least half of the country series at the 5 per cent level, suggesting
that these apparent slow oscillations are not just an optical illusion.
9
3. The timing of liberalization: wholesale rates
For the purpose of describing liberalization, we need at least to distinguish between three
main types of control: first, external capital (exchange) controls which drive a wedge between
domestic and foreign wholesale rates but need not involve any administrative control of domestic
rates. Second, administrative control of domestic wholesale rates (this is unlikely to be very
effective in the absence of exchange controls). Third, control of retail bank deposit and lending
rates. Note that even in a liberalized environment, the authorities can also influence 'wholesale
interest rates by use of monetary policy instruments, but such action is to be distinguished from
"control".
Because relaxation of these three controls is rarely simultaneous, it is not normally
possible to define a single date on which liberalization occurred. Worse, multiplicity of different
interest rates in any country and the varied array of administrative controls"4 that have been
employed make it impossible in most cases to define a single liberalization date even for one of
the three types of control. Besides, not infrequently, there have been partial reversals of prior
liberalizations."5 Finally. all observers concur that the timing in practice of particular relaxations
often does not coincide with the fornal relaxation: sometimes the control has become a dead
letter long before formally removed; in other instances formal control has been replaced by
informal administrative suasion, or de facto control exercised by the government through its
ownership of dominant banks.
By the mid-1 990s the process of liberalization has proceeded in many countries to full or
almost full abolition of all three types of control; but the process has been a protracted one with
many stages varying in importance. This clearly points to the need for detailed country-by-
" For example, foreign exchange controls may be relaxed for certain classes of investors; or ceilings on capital
exports may be increased. Controls on treasury bill rates may be retained, along with compulsory investment
requirements from banks and other institutions, while other wholesale rates are freed. Controls on lending rates
may be relaxed for certain sectors of borrowers, or for certain categories of bank or near-bank. Controls on bank
deposit rates may be relaxed for certain size categories, or maturities, or for accounts attracting a particular class of
income tax treatment.
1 0
country analysis."6 But it is also an obstacle to econometric estimation of the impact of
liberalization on a cross-country basis, as knowledge of the timing is all but indispensable.
An alternative is to try to infer the timing of key aspects of liberalization from the
statistical properties of the interest rate data themselves."7 Two classic approaches to measuring
the degree of external capital account liberalization by the looking at interest rate and other
macroeconomic time series have been proposed by Edwards (1985) and Edwards and Khan
(1985), where interest rate data is available, and by Haque and Montiel (1991) where interest rate
data is not available. Each of these approaches assumes that the effective interest rate is a
weighted average of that which would prevail in fully controlled and uncontrolled regimes
respectively; the estimated weiglht then becomes an indicator variable representing the degree to
which the domestic money market is open and uncontrolled. They could be adapted to allow
some tiime-variation in the indicator, and hence in principle to identify a liberalization date.
While both approaches thus offer elegant solutions to the problem at hand, they have the
important shortcoming that each assumes that uncovered interest parity UIP prevails in an
uncontrolled market. The well-known fact, that UIP is empirically questionable even for
countries without any form of foreign exchange control, mars the use of this as an identifying
assumption. There are also difficulties with the specification of equilibrium in the controlled
market. Further commentary on these approaches, together with some discussion of possible
alternatives, is included as Appendix 1.
A simpler approach that does not rely on UIP depends instead on the assumption that the
short-run dynamic behavior of interest rates changes with liberalization.
'5 Thus it is not surprising to find, for example, that a recent study's table presenting just 27 liberalization dates had
to be accompanied by two-and-a-half pages of qualifying notes (Galbis, 1993).
16 As illustrated by several contributions to the present research (Cho, 1999; Montes-Negret and Landa, 1999;
Wyplosz, 1999).
17 If an unregulated curb market exists alongside the formal market, and if interest rate data for the curb market
exist, then differences between the rates can be used as a measure of the degree to which the controls bite. This
approach has been used for domestic curb markets by Reisen and Yeches (1993), and extensively for off-shore
"euro"-markets, cf. Wyplosz (1999).
11
A For administratively controlled interest rates this assumption seems readily acceptable. If
rates that were held absolutely constant for extended periods are suddenly found to change from
month to month, there has to be a presumption i;hat controls have been relaxed. We find below
that simple filters designed to detect shifts of this type from administratively fixed rates to
variable identify plausible regime shift dates for many developing country bank nominal
wholesale rates.
B Where rates are market-determined bul: behind effective exchange controls, they are
exposed to fluctuations in money supply, but may be partly insulated from the pressures of
speculation related to changing exchange rate expectations. (A simple model is discussed in
Appendix 1). If the second form of disturbance is likely to be higher than the first, liberalization
of capital controls will be marked by an increase in short-run interest rate volatility. This
assumption is necessarily controversial. However, a sharp increase in short-run wholesale
interest rate volatility during the period known to be one of liberalization may then indicate a
critical effective date of liberalization. We will see below that a filter of this type based on
recursive residuals of a simple dynamic model of wholesale interest rate unambiguously
identifies regime shift dates for many developing countries, plausibly marking significant shifts
towards elimination of capital controls.
Regime shift of type A: Relaxation of nominal rate controls
Figure 6 shows changes in nominal money market rates. Several countries begin the
period with a pattem of occasional non-zero changes only, and then make a transition into
frequent changes. To identify key dates for relaxation (or reimposition) of fixed rate
administrative control we applied a filter whic]h triggers "control off' whenever the number of
changes in the following seven months is four or more, and subsequently triggers "control on" if
there is a period of more than 12 months without any change. "Control off' periods are
identified as such in Table 4.
12
Regime shift of type B: Marked increases in real interest volatility
In order to identify shifts of this type, we estimated an econometric model of each
country's ex ante real interest rate and looked for large forecast errors. Specifically, we fitted a
simple error-correction model for each country's real interest rate, assuming that it could be
modeled as a function of changes in the world interest rate,'8 of the gap between the world and
the local interest rate, and by some autoregressive dynamics.'9 The estimates were by recursive
least squares, and we tested the fitted equations for break-points indicated by systematic failure
of one-month ahead recursive forecasts.20 Examples of the procedure are shown in Figures 7 (a)
and (b) for India and Kenya. The recursive residuals from the dynamic interest rate model are
shown, bracketed by 5 per cent confidence intervals. In the lower panel is plotted the probability
level at which the hypothesis of no structural change can be rejected in each period. For the
Indian data, March 1990 is identified as the break point, and for Kenya, March 1993. Plots for
all the countries, together with details of the estimated regressions on the full period, and on the
before- and after-liberalization sub-periods are provided in Appendix 2. Note that this method
cannot detect a gradual increase in volatility.
As the method identifies regime change events with short-term increases in volatility, the
subsequent finding that volatility remained high after the change is not an inevitable and
tautological consequence of the identification method, but represents an independent observation.
(Indeed, to verify this, we also computed post-event volatility removing a six-month window
after the event.)
1 We used the first principal component of the 8 large industrial country real TB rates as a proxy for the world rate.
'9 The model employed is equivalent to that of equation (1') in the next section below, with k1.
20 The criterion for a break was three forecast errors in four consecutive months each statistically significant at least
the 1 per cent level. (A single data outlier in the level of interest rates could have triggered a break if the criterion
had required only two significant forecast errors).
13
Figure 7: Recursive residuals from dyinamic model of real interest change
(a) Kenya; (b) India
Kenya India
10 010
.0~~~~~~~~~~~~~~~~
------------- -AA ~~~~~~~~~~~~~~~~---1
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 10 0.0000 0 0-2
0.05 0 80 0.05 . ,
0.10 0 0 0.10
0.15 0
82 8 4 8 6 8 8 9 0 9 2 9 4 9 6 ~~ ~~~0.15 _ _ _ _ _ _ _ _ _ _ _ _
82 84 86 88 90 92 94 96
Repeating the exercise for all of the coumtries we found a plausible pattern of breaks
(Table 4). For 15 of the 17 developing country TB rates, there was a single break during the
sample period21. Following the break, the residual standard error was much higher - the imedian
ratio of the before and after residual standard errors was 4.3.22 Thus we find not only an episode
of increased volatility as estimated by the recursive prediction failure, but also that subsequent
volatility is higher on a sustained basis.
The filter flags sudden increases in volatLility, but, based as it is on recursive (backward-
looking) regressions, it does not imply a sustained increase in volatility after the liberalization
date. Our finding that volatility did stay high after the liberalization date does therefore represent
an independent finding.
21 Two breaks for Trinidad & Tobago.
22 The calculation was also made after deleting six obsenrations at the break in order to verify that the increase in
variance was peristent and not solely driven by a few months around the date of the break.
14
The coefficients oif the error-correction process are not all well-determined, but
sometimes there is also an indication of a stronger impact of world interest rates after the break.
All iin all, it the empirical patterns detected seem to confirm the a priori belief that this method
would capture a significant date in the liberalization of wholesale interest rates.23
The same approach was extended to 8 of the 10 money market rates (Argentina and
Brazil excluded because of the difficulty of defining a satisfactory smoothed inflation series).
Here a further three break points were detected as shown in Table 4, again with high volatility
ratios. The indications were that most of the remaining countries may have crossed that
threshold before 1980.24
Also included in Table 4 are the liberalization dates provided for these countries by other
recent studies.25 The differences between the dates reflect differences in the concept of
liberalization date being used. They should thus be considered as complementary to the dates
obtained bv approaches (a) and (b) here. Galbis (1993) study uses dates at which preferential
lendiing rates, or controls on key bank deposit or lending rates were removed. Demirguic-Kunt
and Detragiache (1998) use deregulation of bank interest rates as the observable policy change
to date liberalization. Williamson and Mahar (1998) are looking at a wider concept of
financial liberalization and provide two dates: 'start of liberalization' and 'largely liberalized'.
23 We also carried out the same exercise for the thirteen industrial countries. Break points within the sample period
were also found for four of these. (An unusual situation arose for one country, Ireland, where a break existed, but
the post-break residual variance was lower than the pre-break. In fact the identified break was in this instance
related to the EMS crisis of 1992-93 rather than to liberalization.)
24 Liberalization is not irreversible; the data from Malaysia (late 1981), and to a lessor extent India (early 1984) and
Pakistan (late 1985) provides some indications of a reversal to a narrower range of fluctuation.
25 Appendix Table A gives the dates from these studies for additional developing countries.
15
Table 4: Date of de facto liberalization of wholesale interest rates
Approach: "Control off" Marked volatility Expert datings
increase 1980-97
Volatility Galbis D&D W&M
Date A Date B ratio Start Largely
60C Treasury Bill
Fiji 82:08 85:06 4.7
Ghana 84:08-84:11; 91:10 2.1
87:10
Guyana 87:03-88:07; 89:10 5.5 91
89:10
Jamaica 80:08 91:03 4.3 85:10 91
Kenya pre-80 93:03 4.2 91
Sri Lanka 85:09 88:03 1L.6 80 78 -
Mexico pre-80 83:11 2.6 85:03 89 74; 89 92
Malawi 92:06 92:06 5.3
Nepal 89:08 89:10 32.5 86:05 89 -
Philippines pre-80 84:06 3.8 82:12 81 81 94
Papua New Guinea 80:07 86:01 1.1 pre-80
Swaziland 82:12 None
Trinidad and Tobago 80:02 84:07; 6.7
94:11
Sierra Leone 91:12 87:08 4.9
Uganda 92:03 81:10 135.0 88:07
South Africa pre-80 None 80 84
Zimbabwe pre-80 92:04 2.4
60B Money market
India pre-80 90:03 4.6 91 92 -
Korea pre-80 None Not lib 84-88; 91 83 --
Malaysia pre-80 None 78:10 pre-80 78 92
Pakistan pre-80-86:12; 92:03 3.7
89:12
Singapore pre-80 None 78 73
Thailand pre-80 90:03 2.1 90:03 89 mid-80s 92
Cote d'Ivoire 82:07-90:05 None 89:10
Mauritius 85:02 None 81:11
Industrial countries
Australia pre-80 82:02 2.3 81
New Zealand 85:02 83:08 7.9 pre-80; 84
Spain 84:01 83:01 21.3 87:03
Portugal 83:01 82:06 90.5 94
Notes: Industrial countries included are those for which regime changes (b) were identified post 1980.
Volatility ratio is the ratio of the standard error of estimate of dynamic regression model in the post-
liberalization period to that in the pre-liberalization period.
Dates from other studies: Galbis (1993); D&D=Demirguc-Kunt and Detragiache (1998); W&M:
Williamson and Mahar (1998) - showing start of liberalization and 'largely liberalized' dates.
16
4 Convergence of inlterest rates
What does liberalization mean for global integration of world financial markets? Some
indication can be found by modelling the dynamic behavior of real interest rates. We arrive at
two main conclusions.
First, real ex ante wholesale interest rates in developing countries are quite strongly
influenced by world real interest rate movements. Furthermore, if we distinguish between
before- and after- the liberalization events (type B) reported in Table 4,26 we find that the impact
of world interest rates and the speed of convergence both increase following liberalization.
Second, nominal wholesale interest rates help predict subsequent exchange rate
movements to a larger extent than is the case in the industrial countries. Following liberalization,
their predictive power is no better than before.
The textbook model of an efficient and frictionless expectations-driven financial market
without risk aversion (ancl with sufficient goods-market integration to ensure purchasing-power
parity) implies that real interest rates will be equalized across countries and that nominal interest
rates differentials will represent unbiased predictions of inflation and exchange rate change.
Imperfectly integrated and partially efficient financial markets will still tend to be influenced by
world interest rates and by expectations, though perhaps partially and with a lag. This section
provides a quantification cif the imperfection, and how it evolves with liberalization.
4.1 Dynamic error-correction model
For our real ex ante wholesale interest rates, we estimated a dynamic error-correction model
in which the change in the interest rate is influenced by current world interest rates changes, and
by the lagged gap between domestic and world (real) interest rates, together, perhaps with the
lagged dependent variable.
26 In respect of the countries for which no liberalization events of type B are detected, we treat the wholesale rates as
liberalized throughout the sample
17
Thus, if the real world interest rate at time t is denoted rtw and the real interest rate for
country i is denoted rti then the convergence model can be written:
Ar,'i = a, + bA,v + c, (r,w -- r,'. I) + dAr,' l + u,.
(1)
or,
Ar,i ai + biAr,' + ci ('rrw - r,'. I) + u,.
(1')
with (in 1') u, = ,=, p,t-i + Et
Here the coefficient ai indicates an average deviation between country i's real interest rate
and that of the "world", bi measures the impact effect of a change in world interest rates on those
in i and the "catch-up effect" ci indicates the speed with which deviations from the mean
relationship with the world interest rate are closed.. Provided d<1 (or that the autoregressive
dynamics of the residual are stable), a positive value of ci implies that the impact of any
transitory shock Ar,' or Et on ri is eventually completely damped.27 would The coefficient * °~in I i
a~~~~~ in ID . i N0/'a . N a
XD~~~~~~ XObXz SooInM>
Data Appendix
The interest rates data used is from International Financial Statistics. Five interest rate
categories are used: Official rates (60) represent rates at which the central banks lend to
financial institutions. Money market rates (6013) - representing interbank lending - and
treasury bill rates (60C) are the two wholesale rates, while bank deposit (60L) and
lending (60P) rates are described as retail rates, though the data collected does typically
refer to rather large transactions. IFS also contains some long-term government bond
interest rates which we have not examined in this paper.
Up to the mid-1970s interest rate data other than official rates was only available for a
handful of countries. Country coverage of the iinterest rate series in IFS improved rapidly
in the late 1970s so that from 1980 on fairly comprehensive coverage exists for the
wholesale interest rates of over 50 countries, and the official and retail rates for over 70
countries.
For the monthly time series analysis of wholesale rates, we confined our analysis to
countries for which complete or nearly34 com-plete data was available over the period
1980-97. Treasury bill rates (60C) are available for some 40 countries, of which 27
developing countries.35 As it happens, 5 of these countries are all tiny members of the
East Caribbean Currency Board (ECCB), with a common interest rate and exchange rate
policy. A further five have populations of under 0.5 million. Therefore most of our
analysis concentrates on the remaining 17 larger developing countries, together with one
representative for the ECCB. In addition we find a further 17 developing countries for
which substantially complete monthly data on imoney market rates (60B) is available, of
which seven share a comnmon rate in the West African Monetary Union. Excluding all
but one of the latter, this gives a total of 28 developing countries for which complete data
on the movements in wholesale rates can be analyzed. These countries are: Argentina,
Brazil, Cote d'Ivoire, Fiji, Ghana, Guyana, India, Jamaica, Kenya, Korea, Malaysia, India,
Sri Lanka, Mexico, Malawi, Mauritius, Nepal, Pakistan, Papua New Guinea, Philippines,
St. Lucia, Sierra Leone, Singapore, Swaziland, Thailand, Trinidad and Tobago, Uganda,
South Africa, Zimbabwe. Although a microstate, St. Lucia is included as a
representative of the ECCB. C6te d'Ivoire represents the UMOA. The following 12
industrial countries, for which monthly data on 60C exist, were included as controls:
Australia, Belgium, Canada, Germany, Ireland, Italy, New Zealand, Portugal, Spairn,
Sweden, Switzerland, United Kingdom, United States.
For the expected or ex ante real interest rates, a Hodrick-Prescott filter with parameter
16500 was applied to the log-change in each coulmtry's CPI, and the result subtracted frorn
the nominal exchange rate (cf. Edison, 199[6]).
3" In a few cases short stretches of missing data were filled by interpolating available quarterly figures, or
by using regression relationships with available data.
35 i.e. excluding those who were members of the OECD throughout. In this definition Korea and Mexico
are included with the developing countries, as they were not members of the OECD for most of the sample.
40
For the econometric analysis using quarterly data on deposit and lending rates, similar
sample selection criteria were applied (substantially complete availability of the relevant
data over 1980-97; no mnicrostates, only one country per currency union). This left 37
developing countries for which more or less complete quarterly data on the movements in
bank rates can be analyzed. These countries are: Argentina, Botswana, Brazil, Cameroon,
Costa Rica, Cote d'Ivoire, Cyprus, Fiji, the Gambia, Ghana, Guatemala, Guyana,
Honduras, Indonesia, Jamaica, Korea, Malawi, Malta, Mauritius, Mexico, Morocco,
Nigeria, Papua New Guinea, Philippines, Rwanda, St. Lucia, Sierra Leone, Singapore,
South Africa, Sri Lanka, Swaziland, Trinidad and Tobago, Turkey, Uganda, Uruguay,
Zambia, Zimbabwe. For Ghana, Mexico and Turkey, deposit rate only was used.
Argentina and Brazil were excluded from the econometrics because of their outliers. For
analysis requiring both wholesale and bank rates (Table 5) the following countries also
had to be excluded for want of data: Botswana, Cameroon, Costa Rica, Cyprus, the
Gambia, Guatemala, Honduras, Malta, Nigeria, Rwanda, Uruguay. That left 21 countries
in the standard sample used. Data for 19 industrial countries were used as controls:
Australia, Belgium, Canada, Denmark, Finland, Germany, Iceland, Ireland, Italy, Japan,
Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United
Kingdom, United States.
The shortcomings of the data must be acknowledged. Long series like the ones we are
using necessarily involve changing definitions of the underlying assets, as institutions and
data-collection methods evolve. Furthermore, there is typically a very wide range of
interest rates prevalent in any financial market, depending on size, creditworthiness,
maturity and other asselt characteristics. The limited number of series available here will
capture this diversity very imperfectly.
41
Appendix 1: Using economic theory to assess wholesale interest rate liberalization
Interest rates that are administratively controlled may differ both in their average level,
their volatility and in their correlation with mLovements in market-determined interest
rates. Analysis of these characteristics of interest rates can help characterize the extent
and nature of the interest rate controls. It can also help distinguish between countries that
have biting controls, and those that do not.
Economic theory gives some pointers as to the likely influences on, and correlates of,
uncontrolled interest rates. For example, if there is a stable demand for money function,
then interest rates will be correlated with the money stock and the other determinants of
money demand. For instance, if the demand for money function is,
d
ml, -p, :a. +a,i, +a2y, + a3(m,l -p,)+ u,t,
then the money market equilibrium condition can be solved for the interest rate i to
obtain:
it =i° --[m - a3m,.l - (1 - a3)p, - a. - a2y, - u,. 2
a,
A stable demand for money should mean that the parameters a, are constant over a
relevant period and a moderate variance of the disturbance u. More complicated money
demand specifications may be required (including different dynamic specifications), but
the general idea of a stable relationship between the interest rate, money and the
determinants of its demand remain. Note that these relationships should hold if domestic
money markets are uncontrolled; they do not require liberalization of the external capital
account.
Another theory-derived relationship for market-determined interest rates is uncovered
interest parity (UIP). This relates domestic to foreign interest rates by the expected rate
of exchange rate change plus a possible risk premium:
it = i -= i,1 + E, s,+ - s, +, 3
Evidently (3) can only be expected to prevail if there are no effective exchange controls.
The two equations (2) and (3) thus define two shadow interest rates: i* being the interest
rate that would prevail under UIP, and iO being the rate consistent with internal money
equilibrium.
The volatility of the two shadow rates depends on the volatility of the determinants and
their mutual correlation. If short-run output changes and stochastic shocks to the money
demand equation are small, or are negatively correlated with change in the real money
stock (accommodating monetary policy), then the variance of the change in i° will be
relatively low. If expected exchange rate change and the risk premium demanded b,y
speculators are volatile and mutually uncorrelated, then i* will be relatively volatile. This
plausible ranking is used in approach (b) of the text to dating liberalization of wholesale
rates.
42
Of course, the two shadow rate equations are not incompatible. Some or all of the other
variables shown may be endogenous. If both UIP and domestic monetary equilibrium
(with the money demand function adopted) prevail, then the two shadow interest rates
will be equal. Thus, for example, in a fixed exchange rate regime, the money stock may
adjust to ensure that the money market equilibrium condition (2) prevails, with i,° = i,+.
Previous literature: majcor contributions to measuring liberalization
Two main approaches in the literature draw on this equilibrium framework to assess the
degree of capital account openness. The first, due to Edwards (1985) and Edwards and
Khan (1985), defines an open capital accounts as one which satistfies UIP. This approach
nests the UIP in a broader maintained hypothesis encompassing both the domestic
monetary equilibrium and UIP conditions as special cases. For example, we can
estimate36 the parameter (p in a regression defined by:
it = (p i, + Q - )i,°+ , (4 )
The value (o = 1 is interpreted as complete capital mobility, q = 0 as a closed financial
system.
The Edwards-Khan approach requires the use of data on interest rates. However, such
data may not be available, at least for some of the relevant domestic financial markets.
For example, published official interest rates may be applicable only to a fraction of
borrowings and allocate(d by administrative directive. If there is a parallel market, then
the unobserved interest rate on that market may give a better indication of the degree to
which the financial system is integrated in the World economy. In order to deal with the
unobserved interest rate issue, Haque and Montiel (1991) proposed a way of solving for
the unobserved shadow interest rate that would prevail in the absence of capital mobility.
In contrast to Edwards and Khan, who use the shadow rate i° defined as the rate that
clears the money market for the observed money stock, Haque and Mcntiel use the
concept of the hypothetical money stock m" that would prevail if there were no capital
flows. They then define a new shadow rate i' as the rate that would clear the money
market for the hypothetical money stock. Specifically, let mt in equation (2) be replaced
with m' defined by:
m,' = ml + kP + v, m,+v, (5)
where kP is the net private capital flow, and vt is a measurement error. The new shadow
interest rate is then defined by:
i, ~- a[m' - a3m, - (-a3)p, - ao - a2y, - u] (2')
a,
The true (but unobserved) interest rate it is now hypothesized to be a weighted average of
the UIP and the hypothetical closed-market shadow interest rate i' giving:
3 Provided we have some empirical substitute for the unobserved exchange rate expectation.
43
i, = Vi,++ (4')
The money market equilibrium condition, evaluated at this (unobserved) interest rate tt,
then provides the regression equation to be estimated:
mt -Ap =70 +f1i, +7r2yt +,T3(m,, - pA)+ 4 (m' -p,)+ W (5)
where the parameters 7ri satisfy 7ri = aijp for i=1,2,3; 7r4=1- (, and the disturbance wt is:
W, =Ut-U, -± 4V,w + ,* (6)
The estimate of 7r4 is the key parameter here, as a value insignificantly different from zero
corresponds to an estimate of , insignificantly different from unity, and thus to non-
rejection of the hypothesis of perfect capital mobility.
WhWile both EK and HM models estimate a constant parameter ,p, it would easily be
possible to adapt their approach to allow some time variation in p , and hence in principle
to estimate the timing of liberalization.
Difficulties
(i) Recasting the Edwards-Khan approach to recognize that UIP and monetary
equilibrium are not incompatible:
These two approaches present some issues of irnterpretation. To begin with the Edwards
and Khan approach, there is the problem of interpreting values of v that are
insignificantly different from both zero and unity. A point estimate of ? lying between
zero and one at first sight appears to imply a regime partially reflecting convergence to
domestic money-market equilibrium,37 and partly to UIP. But, as already noted, there is
x10 logical incompatibility between these two hypotheses. Under the joint hypothesis h'1
of perfect capital mobility and money market equilibrium, if both i; and i° are knowrn
they will be equal and hence perfectly correlated. Even recognizing that neither is known
exactly, multicollinearity can be expected in the estimating equation if UIP truly prevails,
preventing significant coefficients. Besides, under HI either of the proxies could be as
good as the other for either of it and i,0. In short, to find p significantly different fromn
zero is not consistent with H,, and it is not enitirely clear how such a finding is to be
interpreted. We return to some ways out of this difficulty in (v) below.
(ii) Does the Haque-Montiel approach simply add measurement error to an identity?
The Haque and Montiel approach adds some additional difficulties of its own. First is the
definition of m' . Simply subtracting actual private capital flows from the observed
money stock certainly does not provide a reliable estimate of what the money stock
would be if capital were totally restricted. Public capital flows and the current account
are also likely to be endogenous. The measurement error v is likely to be significant,
biasing the estimate of p towards unity. More generally, one may be allowed to be
skeptical of a hypothesis test that depends on the value of the coefficient of a variable that
has been constructed by adding a novel variable to the dependent variable.
3 Recall that the 'equilibrium' of equation (1) has a lagged adjustment built-in.
44
Indeed, inspection of Haque and Montiel's 15 country regressions reveal that only three of
the countries have estirmated money demand functions with a significant interest rate
variable. The estimated equations are consistent with what one would expect if kP, i* and
y were just uncorrelated noise, and the postulated monetary equilibrium and UIP were not
true. In effect, what is being estimated is a noisy identity. In particular note that for
India, where kP has low variance, the estimated coefficient 7r4 is insignificantly different
from unity, and all the other variables insignificantly different from zero.
(iii) What does the variable m' measure?
One may also question the theoretical basis for constructing the weighted average interest
rate using (2'), based oIn money market equilibrium computed at hypothetical money
stlock m' instead of (2), based on the actual money stock mt. For suppose that 0 = 0 is
true. Then the HM model asserts that (2') prevails. But the actual money stock is m, not
n,'. So the postulated relationship (2') actually implies monetary disequilibrium (since in
general m," mi,). As such, the KM model is not consistent with domestic money-
market equilibrium.
Nlote that, if we try to modify HM's approach by simply substituting (2) for (2') we cannot
proceed to recover the parameter q , which is no longer identified in the new estimating
e,quation (5'):
mt - p, =ao0 + aii +a2y, +±a3(m,l - pI)+w, (5')
The only leverage one has here is that (5') is derived under the condition ( # 0. A finding
that the coefficient of i* in (5') was non-zero would thus be a test against complete
closure of the capital market.
(iv) UIP doesn't fit well for most countries
Finally there is a broacder problem with both of these approaches, and that is that,
although well-founded theoretically, UIP and a stable money demand function are both
conditions for which there is comparatively little empirical support even for industrial
countries with open cariital accounts. Indeed, UIP is generally rejected in empirical
studies.38 Demand for money functions often shift, and need to be quite complex to have
a chance of remaining stable over lengthy periods. Therefore, however solid the
theoretical basis of these tests for capital integration, they rest on rather flimsy empirical
underpinnings.
There are possible explanations which allow us to believe in UIP even though it fails
most stringent tests. McCallum (1994) proposes that if UIP is augmented with a
disturbance termn, and if that error should be correlated with the interest differential (as it
would if policymakers responded to the disturbance by liquidity policy) then one would
observe failure of the usual UIP tests. Kaminsky (1993) suggests that UIP fails in small
" Though it may pass weak tests, such as stationarity and non-zero mean of deviations from UIP (Tanner,
1998).
45
samples because of a peso problem: agents are not sure which regime they are in and
hedge their behavior in a way that becomes a systematic forecasting error over intervals
of several quarters or more. Mishkin (1992) observes that the Fisher equation (a
domestic economy equivalent of interest parity) is more closely satisfied in periods of
rapid inflation. This might also be relevant for UIP.
(v) Decomposition of UIP deviations
The underlying idea behind the Edwards-Khan approach is to try to detect the extent to
which domestic factors cause a deviation from UIP. The issue we have identified in (ii)
above is that EK's regressions imply that any correlation between interest rate and
domestic variables is seen as a deviation from l:JIP even though some of such correlations
are consistent with UIP.
A. -theoretically more robust way of drawing inferences from correlations between
domestic variables and interest rates (within a UIP context) is to recognize that such
correlations can exist, but must enter through the interest parity condition. Specifically, if
we assume that a linear combination of domestic variables Xt observed at time t, helps
forecast future exchange rates, and that rational expectations prevails, then we can write:
s,+, - s, = Xi ,B + u, (7)
wahere u is uncorrelated with Xt. Now if UIF' prevails, we can take expectations in (7)
and substitute into (3) to obtain:
i, =i, if + -t,, + (3)
Now (3') and (7) can be jointly estimated and the restriction of equality of the parameteirs
,B in each can be tested. That should provide a rnore robust test of the open capital market
hypothesis. However, for this we do (of course) require the maintained hypothesis that
the risk premium 4t is also uncorrelated with Xi,.
(We did implement the above approach for both industrial and developing countries.
Writing the forecast of exchange rate change (r±t+I - st) from (7) as zt we estimated the
equation:
i, = a(if + z,) + X,,8'+,
and found that ,' is always significantly different from zero. Interestingly, a , which
could be thought of as an index of the degree to which UIP prevails, is insignificant for
pre-liberalization periods in the developing countries, but estimated at 0.37 (with a t-
statistic of 10.5) for post-liberalization periodLs. Note, however, that a is estimated as
insignificant for industrial countries, recalling the empirical weakness of UIP for those
countries.)
Several recent papers employ an equation similar to (7) to probe the correlates of
deviations from UIP. One useful and popular approach involves decomposition
deviations from UIP (forward bias) into changes in real exchange rates and real interest
differentials. Levine (1991) provides evidence to show that anticipated real exchange rate
46
changes pass through tc UIP deviations one-for-one and that these (rather than deviations
in expected real interest rates) are the primary component of the forward bias (for five
industrial countries). Ihis conclusion meshes well with the well-known fact (for these
countries) that short-run nominal exchange rate changes are much more volatile than
nominal interest or inflation rates: thus short-run fluctuations in real exchange rates are
dominated by nominal exchange rate changes.
Tanner (1998) applies a similar decomposition to a larger sample, including developing
countries, but assesses the relative importance of the different components mainly by
comparing the size their variances. His finding that real interest rate variations are
relatively important in cleveloping countries is thus chiefly a reflection of that importance
of inflation fluctuations in many of the developing countries that are in his sample.
The findings of Levine and Tanner are not incompatible. With his regression approach,
Levine is essentially stating that it is only to the extent that real exchange rate changes
can be forecast that UII' deviations can be forecast: in explaining UIP deviations it does
not help to add variables additional to the forecast real exchange rate change. Tanner's
result is not concerned with forecastibility: he merely ranks the contribution of variances
and covariances. His point that real interest rates are highly variable in developing
countries does not imply that their changes are forecastible. (Though they are, as we
show in the text).
In fact, applying Levine's general approach to our developing country data, we find that
the best forecast of real exchange rate change (linear projection on available macro data)
does help forecast UIF' deviations, but that it can be improved upon by the separate
inclusion of some of the other explanatory macro variables.
(vi) Alternatives to IJIP
Other equilibrium relationships could conceivably be used in lieu of UIP as a benchmark
for testing for liberalization of wholesale rates. Among possible candidates might be the
theory of correlation between deviations from purchasing-power parity and real interest
differentials, at least at business cycle frequencies (Baxter, 1994, Levine, 1992; but see
Edison and Pauls, 1993).39 However, it is probably fair to say that these are not much
more empirically robust than UIP.
More generally one can model the response of interest rates to inflation (Fisher effect),
exchange rate change and foreign real rates. Relevant here is the hypothesis of constant
expected real interest rate differentials (cf. Cumby and Obstfeld, 1984, Levine, 1991).
The speed of convergence of real interest rates towards a world norm can be measured
(Faruqee, 1991, Cavaglia, 1992, cf. O'Connell, 1998). A version of this approach is
presented in the paper.
(vii) Statistical multi-factor models
3 Could also compare other models of interest rate determination (e.g. Orr, Edey and Kennedy, 1995).
47
Since there are so many unobserved variables and the theoretical determination of interest
rates is so complex, it may be useful to approach the question of modeling the entire set
of world interest rates as being determined by a small number of unobserved common
factors plus idiosyncratic local factors. This approach has been employed, for example,
by Koedijk and Kool (1994), in order to analyze European interest rates, and the method
is potentially even more effective when data. on a larger number of countries is
available.40 It can allow both identification of a core world interest rate for comparative
purposes (measure mean deviation in real rates), and also allow the experience of
different developing countries to be grouped. The interesting issues include: (a) How
many significant common factors are there in real interest rates across the world? (b)
What are the characteristics of countries have low correlations with the main common
factors? In particular, what is the role of financial repression; and of national inflation
vol]atility in determining low correlations.
In practice, we find that on the larger samnple of 24 developing country wholesale rates4'
the picture that emerges is of a diverse and volatile interest rate experience, with
conisiderable persistence in real interest rate rnovements. The degree to which the
movements are common across countries was assessed by computing the eigenvalues and
principal components of the real interest rate data. With the first principal component
accounting for only some 28 per cent of the total variance it is clear that the degree of
commnonality is rather modest. Indeed, a three-factor model explains only 61 per cent of
the total variance, and a six factor model is required to reach 80 per cent). With such
modest explanatory power, it is not surprising that no evident economic interpretation
could be placed on these principal components, nor are they highly correlated with
inclustrial country interest rates.
A itechnical difficulty that emerges in using factor models is that high volatility countries
tend to dominate the overall variability to be exp:lained, and a natural normalization of the
dala that would reduce this dependency on outliers is not obviously available.
40 The model of unobserved common factors has also been applied to the term structure of interest rates in
one country; see for example, Jegadeesh and Pennacchi (1996).
41 This is the monthly sample (excluding Argentina and BTazil) discussed in the text.
48
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Strategies 'or Coping vhith Poverty Kathleen Nlullan Harris 33902
Barry Popkin
WvPS231 identifying Welifare Effects from Martin Ravallion March 2000 P. Sader
Subiective Questions Michael Lokshin 33902
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2302 VVhy Liberalization Alone Has Not Klaus Deininger March 2000 M. Fernandez
Improved Agricultural Productivity Pedro Olinto 33766
irn Zambia: The Role of Asset
C)wnership and Working Capital
Constraints
WPS2303 Mlalaria and Growth F. Desmond McC(arthy March 2000 H. Sladovich
Holger Wolf 37698
Yi Wu
WPS2304 [)isinflation and the Supply Side Pierre-Richard Agenor March 2000 T. Loftus
Lodovico Pizzati 36317
WPS2'305 1The Impact of Banking Crises on Maria Soledad Mvlartinez March 2000 A. Yaptemco
Motney Demand and Price Stability Peria 31823
WPS2306 International Contagion: Implications Roberto Chang March 2000 E. Mekhova
for Policy Giovanni Majnoni 85984
\VPS2:307 Surveying Surveys and Questioning Francesca Racanatini March 2000 P. Sintim-Aboagye
Questions: Learning from World Bank Scott J. Wallsteri 37644
Experience Lixin Coiin Xu
VVPS2:308 How Small Should an Economy's Paul Beckermar March 2000 H. Vargas
Fiscal Deficit Be? A Monetary 38546
F'rogramming Approach
\NPS2:309 What Drives Private Saving around Norman Loayza March 2000 E. Khine
the World? Klaus Schmidt-Hebbel 37471
Luis Serven
WPS2310 How Politics and Institutions Affect Mitchell A. Orenstein March 2000 M. Leenaerts
FPension Reform in Three 84264
F'ostcommunist Countries
WPS2311 1The Buenos Aires Water Concession Lorena Alcazar April 2000 P. Sintim-Aboagye
Manuel A. Abdala 38526
Mary M. Shirley
WPS2.312 Measuring Governance, Corruption, Joel S. Hellman April 2000 D. Bouvet
ancl State Capture: How Firms and Geraint Jones 35818
Bureaucrats Shape the Business Daniel Kaufmanri
Environment in Transition Economies Mark Schankern-ian
,.
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