A. Introduction

1. A large number of countries rely on the policy rate as their main monetary policy instrument. This is the case in countries that have adopted an inflation targeting (IT) regime. Under this regime, the monetary authority sets a reference interest rate—i.e., the policy rate—to achieve a pre-announced inflation target. Although the ultimate goal is generally stated in terms of inflation, monetary policy decisions are transmitted first through various interrelated channels and variables, and only with a delay to inflation.

2. The paper studies the transmission of monetary policy in Uruguay after the adoption of the policy rate as its main monetary policy instrument in late 2007. That is when Uruguay began transitioning towards an IT regime. The paper uses a vector autoregressive (VAR) model to estimate the strength of the transmission channels of monetary policy, and evaluates the extent to which policy rate changes affect inflation and economic activity.² To put Uruguay in perspective, the paper also undertakes a cross-country comparison considering the cases of Chile, Peru, and New Zealand, which are three small open economies with well-established IT regimes. Peru is of particular relevance for Uruguay, since like Uruguay it has a high degree of banking dollarization.

3. The paper suggests that in Uruguay the policy rate has a more significant effect on inflationary pressures than in output. A similar result is obtained in the case of Peru. Due to the relatively large exchange rate pass through of these two countries, the exchange rate channel plays an important role in the transmission of monetary policy. Results also suggest that the credibility of the inflation target in Uruguay has increased recently, though it is still below the level of the other three countries. In addition, the implicit monetary policy reaction function of Uruguay appears to be only mildly responsive to changes in inflation and the stance of economic activity, a finding that mirrors the yet limited number of policy rate modifications that have taken place in the country. Evidence also indicates that a higher degree of local-currency lending to the private sector may reinforce the effectiveness of policy rate changes in controlling inflation and aggregate demand.

4. The paper is organized as follows. Sections B and C discuss the recent economic developments and the credibility of the inflation target in the four economies under study. Section D discusses the strength of the policy rate pass through in these countries. Section E presents the empirical analysis of the transmission of monetary policy decisions using a VAR model, while Section F deals with the role of financial-system variables in the monetary policy transmission. Finally, Section G suggests a number of policy recommendations to strengthen the transmission of monetary policy in Uruguay.

B. Recent Macroeconomic Developments

5. Chile, New Zealand, Peru and Uruguay are all small open economies that currently operate IT regimes. The four countries have relatively similar degrees of trade openness and all of them are important commodity exporters. One key difference among them is the degree of banking-sector dollarization. Whereas in Chile and New Zealand almost all credit and deposits are denominated in local currency, in Peru and Uruguay the share of foreign-currency denominated credits and deposits is large, though on a downward trend.

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Selected Macroeconomic Indicators

Citation: IMF Staff Country Reports 2011, 063; 10.5089/9781455219025.002.A002

Sources: countries’ authorities and IMF staff calculations.

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6. There are important differences across these four countries in the performance of key fundamentals. In Uruguay and Peru, the variability of the nominal exchange rate against the U.S. dollar (NER) and the real effective exchange rate (REER) has been relatively low. This might reflect a ‘fear of floating’ behavior, owing to their significant bank dollarization. Also, Uruguay has experienced a considerably higher volatility in industrial production (IP) relative to all other countries.³ In contrast, the volatility of CPI inflation in Uruguay is low and has recently decreased, contrary to the other economies in which it has shown an upward trend. Expected inflation has followed a similar pattern: in all countries but Uruguay, its volatility has increased. Finally, the volatility of international reserves has moved upward in all countries but New Zealand, suggesting more active foreign exchange interventions in the three Latin-American countries in recent years.

Table 1.

Volatility of selected variables ^1/

Source: countries’ authorities and IMF staff calculations.

^1/Standard deviations of y/y changes. For inflation-related variables, statistics are computed considering actual values of the variables.

(1): January 2005 to December 2007.

(2): January 2008 to June 2010.

Table 1.

Volatility of selected variables ^1/

									Expected inflation		Foreign Reserves
	REER		NER		IP		CPI inflation		(12-months ahead)		to GDP
	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)
Chile	6.4	7.8	5.0	18.3	1.0	3.6	1.4	4.4	0.3	1.1	7.1	34.6
New Zealand	8.0	13.0	10.1	24.4	1.1	2.0	0.6	1.0	0.2	0.6	20.2	12.9
Peru	2.3	3.1	3.4	7.9	1.8	5.0	1.6	3.7	0.3	0.6	15.5	34.0
Uruguay	5.9	7.6	6.8	15.5	5.3	8.7	1.5	0.8	0.5	0.5	15.8	23.5

Source: countries’ authorities and IMF staff calculations.

^1/Standard deviations of y/y changes. For inflation-related variables, statistics are computed considering actual values of the variables.

(1): January 2005 to December 2007.

(2): January 2008 to June 2010.

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Table 1.

Volatility of selected variables ^1/

									Expected inflation		Foreign Reserves
	REER		NER		IP		CPI inflation		(12-months ahead)		to GDP
	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)
Chile	6.4	7.8	5.0	18.3	1.0	3.6	1.4	4.4	0.3	1.1	7.1	34.6
New Zealand	8.0	13.0	10.1	24.4	1.1	2.0	0.6	1.0	0.2	0.6	20.2	12.9
Peru	2.3	3.1	3.4	7.9	1.8	5.0	1.6	3.7	0.3	0.6	15.5	34.0
Uruguay	5.9	7.6	6.8	15.5	5.3	8.7	1.5	0.8	0.5	0.5	15.8	23.5

Source: countries’ authorities and IMF staff calculations.

^1/Standard deviations of y/y changes. For inflation-related variables, statistics are computed considering actual values of the variables.

(1): January 2005 to December 2007.

(2): January 2008 to June 2010.

7. Although Uruguay’s CPI inflation has been relatively more stable, it has also been higher than in the other three countries, particularly in 2010. A similar pattern arises when considering core inflation (not shown). Furthermore, actual and expected inflation have remained systematically in the upper band (or above) the target range in Uruguay, whereas in all the other cases both variables have hovered around their respective mid-point target ranges. Figure 2 shows that the policy rate has closely tracked inflation in all four cases, pointing to an active role of monetary policy in curbing inflationary pressures. As expected, inflation expectations have moved in tandem with actual inflation, but showing a lower degree of volatility.

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Actual Inflation, Expected Inflation, Target Bands and Policy Rates

Citation: IMF Staff Country Reports 2011, 063; 10.5089/9781455219025.002.A002

Sources: Contries’ authorities and IMF staff calculations.

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8. There are significant differences in terms of the evolution of the policy rate in Uruguay relative to the other countries (Table 2).⁴ The sample is divided in two periods: the first period starts with the introduction of the policy rate as the main monetary policy instrument for Peru in September 2003, going through August 2007; the second period starts with its introduction in Uruguay, in September 2007, going through the latest available observation (August 2010). Since Uruguay has had the highest average inflation level, the policy rate has accordingly taken the highest value after its implementation. Uruguay also had the fewest number of policy rate modifications, though the median change of the policy rate was the largest. These results differ substantially from those of Peru, where the IT regime is also somewhat more recent. Overall, evidence suggests a less frequent use of the policy rate in Uruguay yet with relatively larger changes whenever it was modified.

Table 2.

Inflation and Policy Rate

(In percent, unless otherwise specified)

Source: countries’ authorities and IMF staff calculations.

^1/Minimum, maximum, and median are based on the absolute value of the policy changes, in basis points.

Table 2.

Inflation and Policy Rate

(In percent, unless otherwise specified)

	CPI inflation	Policy rate		Policy rate changes 1/
	Avg	Avg level	Std deviation	No of times	Minimum	Maximum	Median
2003M9-2007M8
Chile	2.5	3.7	1.4	29	6	58	15
New Zealand	2.7	6.7	0.9	13	25	25	25
Peru	2.1	3.5	0.8	10	25	25	25
Uruguay	7.3	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
2007M9-2010M8
Chile	4.4	3.8	3.1	21	8	261	33
New Zealand	2.8	4.9	2.6	9	25	150	50
Peru	3.6	4.0	2.1	18	25	100	25
Uruguay	7.4	7.5	1.1	8	25	225	100

Source: countries’ authorities and IMF staff calculations.

^1/Minimum, maximum, and median are based on the absolute value of the policy changes, in basis points.

View Table

Table 2.

Inflation and Policy Rate

(In percent, unless otherwise specified)

	CPI inflation	Policy rate		Policy rate changes 1/
	Avg	Avg level	Std deviation	No of times	Minimum	Maximum	Median
2003M9-2007M8
Chile	2.5	3.7	1.4	29	6	58	15
New Zealand	2.7	6.7	0.9	13	25	25	25
Peru	2.1	3.5	0.8	10	25	25	25
Uruguay	7.3	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
2007M9-2010M8
Chile	4.4	3.8	3.1	21	8	261	33
New Zealand	2.8	4.9	2.6	9	25	150	50
Peru	3.6	4.0	2.1	18	25	100	25
Uruguay	7.4	7.5	1.1	8	25	225	100

Source: countries’ authorities and IMF staff calculations.

^1/Minimum, maximum, and median are based on the absolute value of the policy changes, in basis points.

C. Credibility of the Inflation Target

9. The strength of the transmission of monetary policy decisions depends to a large extent on agents’ inflation expectations. When inflation expectations are well-anchored in an IT regime, expected inflation is largely determined by the pre-announced inflation target of the central bank.

10. To evaluate how well anchored inflation expectations are in these countries, the following oLs recursive regressions are estimated:

View Expanded

where for any country i, ${{\pi }^e}_{i,t}$ is the 12-month ahead expected inflation at period t, ${{\pi }^T}_{i,t}$ is the 12-month ahead inflation target set at period t, π_{i t}-₁ is the lagged y-o-y realized inflation and e_it is a white noise component. The coefficient α_i is the weight agents attach to the inflation target: a high value for a_i means that inflation expectations are well anchored, and that the IT regime is credible.

11. Recursive estimations show that the degree of credibility in the inflation target significantly varies across countries and time. The evidence also suggests that after the introduction of the policy rate as the main monetary policy instrument in Uruguay, in September 2007, the credibility of the inflation target increased significantly (Figure 3). The credibility level still remains well below that of other countries, however, thus suggesting that agents in Uruguay still form expectations largely based on past inflation.

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Credibility Coefficients

(OLS recursive regressions)

Citation: IMF Staff Country Reports 2011, 063; 10.5089/9781455219025.002.A002

Source: IMF staff estimations.

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D. The Policy Rate Pass Through

12. Monetary policy decisions in IT regimes are initially transmitted to the rest of the economy through the effect of the policy rate on the money market rate. Changes in the latter are, in turn, transmitted to deposit and lending rates, thus affecting the consumption and saving decisions of individuals and firms, and hence aggregate demand and inflation. Simultaneously, monetary policy decisions may affect the overall availability of credit as well as different asset prices that react to short-run interest rates, thus enhancing the initial transmission through the so-called credit and asset price channels. Moreover, as domestic and foreign interest rates differ for comparable assets, arbitrage between them gives rise to nominal exchange rate fluctuations, which in turn affect inflation and economic activity through the so-called exchange rate channel.

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Local-Currency Deposit and Lending Rates, and Policy Rate

Citation: IMF Staff Country Reports 2011, 063; 10.5089/9781455219025.002.A002

Source: Contries’ authorities.

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13. As expected, in all four countries there is a positive co-movement between the policy rate and both the lending and deposit rates. However, in Peru and Uruguay the co-movement is significantly weaker than in Chile and New Zealand. This weaker relationship likely reflects differences in the structure, depth, and the degree of dollarization of their financial systems.

14. To assess the relation between these interest rates and the strength of the policy rate pass through, the following OLS regression is estimated:$\begin{array}{cccccc}{y}_{\mathit{it}}=& c_i+& {\alpha }_i{y}_{\mathit{it}-1}+& {\beta }_i{x}_{\mathit{it}}+& {\gamma }_i{x}_{\mathit{it}-1}+& {ϵ}_{\mathit{it}}\end{array}$

where for any country i and period t, y^it is either the deposit or the lending rate, cⁱ is a constant, x_it is the policy rate and ε_it is a white noise component. The short-run effect of the policy rate is

thus given by βⁱ whereas the long-run effect is given by the coefficient$\frac{{\beta }_i+{\gamma }_i}{1-{\alpha }_i}$. Tables 3 and 4 show the results.

Table 3.

Pass-Through from the Policy to the Deposit Rate

Source: IMF staff estimations.

Table 3.

Pass-Through from the Policy to the Deposit Rate

	c	α	β	γ	Short-run Effect	Long-run Effect	R-squared	Sample
Chile	0.10	0.53	0.98	-0.50	0.98	1.00	0.92	2001M1-2010M9
t-Statistic	0.77	6.89	7.94	-3.61
New Zealand	0.18	0.95	0.56	-0.53	0.56	0.47	0.98	1999M4-2010M9
t-Statistic	2.25	28.39	9.96	-9.40
Peru	0.04	0.89	0.29	-0.22	0.29	0.62	0.99	2003M9-2010M8
t-Statistic	0.77	22.68	8.11	-4.80
Uruguay	0.97	0.52	0.26	-0.07	0.26	0.40	0.44	2007M9-2010M9
t-Statistic	1.12	3.50	1.28	-0.35

Source: IMF staff estimations.

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Table 3.

Pass-Through from the Policy to the Deposit Rate

	c	α	β	γ	Short-run Effect	Long-run Effect	R-squared	Sample
Chile	0.10	0.53	0.98	-0.50	0.98	1.00	0.92	2001M1-2010M9
t-Statistic	0.77	6.89	7.94	-3.61
New Zealand	0.18	0.95	0.56	-0.53	0.56	0.47	0.98	1999M4-2010M9
t-Statistic	2.25	28.39	9.96	-9.40
Peru	0.04	0.89	0.29	-0.22	0.29	0.62	0.99	2003M9-2010M8
t-Statistic	0.77	22.68	8.11	-4.80
Uruguay	0.97	0.52	0.26	-0.07	0.26	0.40	0.44	2007M9-2010M9
t-Statistic	1.12	3.50	1.28	-0.35

Source: IMF staff estimations.

Table 4.

Pass-Through from the Policy to the Lending Rate

Source: IMF staff estimations

Table 4.

Pass-Through from the Policy to the Lending Rate

	c	α	β	γ	Short-run Effect	Long-run Effect	R-squared	Sample
Chile	0.36	0.80	0.77	-0.47	0.77	1.54	0.94	2001M1-2010M9
t-Statistic	1.77	19.00	4.87	-2.60
New Zealand	0.23	0.96	0.22	-0.18	0.22	0.79	0.97	1999M4-2010M9
t-Statistic	1.44	51.96	2.67	-2.22
Peru	-0.20	1.01	0.35	-0.37	0.35	2.00	0.96	2003M9-2010M8
t-Statistic	-0.35	38.45	2.03	-2.17
Uruguay	2.41	0.65	0.36	0.32	0.36	1.95	0.67	2007 M9-2010M9
t-Statistic	0.93	5.47	0.62	0.56

Source: IMF staff estimations

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Table 4.

Pass-Through from the Policy to the Lending Rate

	c	α	β	γ	Short-run Effect	Long-run Effect	R-squared	Sample
Chile	0.36	0.80	0.77	-0.47	0.77	1.54	0.94	2001M1-2010M9
t-Statistic	1.77	19.00	4.87	-2.60
New Zealand	0.23	0.96	0.22	-0.18	0.22	0.79	0.97	1999M4-2010M9
t-Statistic	1.44	51.96	2.67	-2.22
Peru	-0.20	1.01	0.35	-0.37	0.35	2.00	0.96	2003M9-2010M8
t-Statistic	-0.35	38.45	2.03	-2.17
Uruguay	2.41	0.65	0.36	0.32	0.36	1.95	0.67	2007 M9-2010M9
t-Statistic	0.93	5.47	0.62	0.56

Source: IMF staff estimations

15. The estimations suggest a significant pass through from the policy rate to both lending and deposit rates. For Chile and New Zealand, the short-run effect of the policy rate is larger for the deposit rate. In contrast, for Peru and Uruguay, the short-run effect is strongest on the lending rate, though the significance of the estimated coefficients tends to be weak.⁵

E. Empirical Analysis of the Transmission of Monetary Policy Decisions

16. To analyze empirically the impact of changes in the policy rate on inflation and economic activity, the following vector autoregressive (VAR) model is estimated:

Y_t = A(L)Y_t-1 + B(L)X_t +U_t

where A(L) and B(L) are a n x n and a n x k polynomial matrices in the lag operator L, respectively, Y_t is a n x 1 vector of endogenous variables, X_t is a k x 1 vector of exogenous variables, and U_t is a n x 1 vector of estimated residuals. X_t is included to control for exogenous disturbances that may affect the dynamics of the model that could lead to counter–intuitive results.⁶ The benchmark model takes the following form:

Y_t=[R_t IP_t π_t reer_t]

where R_t is the policy rate, IP_t is the y-o-y change of an index of economic activity; n_t is a measure of annual core inflation; and reer_t is the y-o-y change in the Real Effective Exchange Rate.⁷

17. The vector of exogenous variables is in turn given by

View Expanded

where FF_t is the U.S. Federal Funds rate; WCPI_t is the y-o-y change of the world commodity price index, and $I{P}_t^{us}$ is the U.S. industrial production index gap in logs.⁸

18. The model uses a standard identification framework. Structural shocks are identified using a Cholesky decomposition, with the variables ordered as in vector Yt.⁹ This implies, for instance, that R_t is contemporaneously affected only by its own shock, whereas reert (or neert , when later specified), being the most endogenous variable of the model, is affected on impact by all structural innovations.

19. The estimations are based on monthly data, with the sample period tailored to each country. In the case of Chile, the sample starts with the introduction of the inflation targeting regime in September 1999, going through July 2010. For New Zealand, it goes from April 1999, when the so-called cash rate was set as the main policy instrument, until June 2010.¹⁰ For Peru, the sample starts in September 2003, the month in which the full-fledged IT regime was implemented, going through July 2010. Finally, for Uruguay, the sample starts in September 2007, with the introduction of the policy rate, and it goes until September 2010. The model is estimated with two lags, except for Uruguay, where only one lag was considered due to the short sample period.

20. The strength of the transmission of the policy rate change varies greatly across countries. The impulse-response (IR) functions to a 100 basis point increase in the policy rate—a contractionary monetary policy shock—are presented below. In New Zealand, there is a significant and persistent contraction in the growth rate of economic activity and inflation. In Chile, the negative impact on economic activity takes about four months to materialize, yet it is persistent. The effect on inflation in Chile is more immediate but it is also less persistent.

21. The two dollarized economies appear to have distinct results. The estimations for Peru and Uruguay indicate that the policy rate hike leads to a short-run reduction in inflation, and an increase in economic activity (Peru) or an insignificant effect on it (Uruguay). The transmission from monetary policy decisions to inflation does not seem to operate through credit and aggregate demand in these countries, but rather through the exchange rate channel, (as discussed below). In addition, the absence of a contraction in economic activity after the rise in the policy rate may be related to the existence of balance-sheet effects. That is, the associated exchange rate appreciation that follows the interest rate increase may lead to an improvement in the balance-sheets of those agents indebted in the foreign currency, generating an indirect positive effect on aggregate demand that may outweight the contractionary effect initially given by the traditional interest rate channel. ¹¹

22. The estimation also shows differences in how the policy rate reacts endogenously to shocks. The reaction of the policy rate in Uruguay is associated with a lower degree of persistence relative to the other three countries. This result supports the conclusion that monetary policy has reacted relatively less frequently to changes in economic activity and inflation in Uruguay, implying that the policy rate still provides a rather weak signal of the monetary policy stance in the country.¹²

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Impulse Response Functions to a 100 Basis Points Increase in the Policy Rate

(In Percent)

Citation: IMF Staff Country Reports 2011, 063; 10.5089/9781455219025.002.A002

Source: IMF staff estimations.

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23. To explore the relevance of the exchange rate channel, a slightly revised version of the VAR model is estimated. Specifically, the vector of endogenous variables now becomes

where π_t represents the annual CPI inflation rate whereas neer_t indicates the y-o-y change in the NEER. The rest of the model remains as in the baseline specification.

24. To help understand the role of the exchange rate transmission channel, the IR functions below consider how a NEER depreciation affects CPI inflation. Peru and Uruguay have the largest exchange rate pass through, likely reflecting the high dollarization and the relevance of the exchange rate channel in these countries. For Peru, a 10 percent depreciation of the NEER raises CPI inflation up to 1.5 percent five months after the shock. In Uruguay, CPI inflation increases 1.7 percent after three months.

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Impulse Response Functions to a 10 Percent NEER Depreciation

(In percent)

Citation: IMF Staff Country Reports 2011, 063; 10.5089/9781455219025.002.A002

Source: IMF staff estimations.

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F. The Role of Financial Factors in the Transmission of Monetary Policy

25. Policy rate changes are transmitted through different channels, whose strength depend on various factors, notably those related to the financial system. In a dollarized economy, the traditional interest rate channel tends to be weaker, as the monetary authority has a lower influence on the relevant interest rates that affect consumption and investment decisions. With lower financial depth, the credit channel may also be less pronounced, as a low ratio of local-currency credit to output may reduce the overall impact of monetary policy on the flow of credit and thus on aggregate demand.¹³ Similarly, the low relevance of domestic capital markets may impinge on the tranmission from asset price changes to aggregate demand, also limitting the effects of policy rate changes.

26. Although financial factors may affect the strenght of the transmission, other critical factors are at play. These include the overall impact of monetary policy of course also depends on the credibility of the monetary regime, the extent of the exchange rate pass through, and the frequency and size of changes in the policy rate. Thus, a low level of financial depth does not mean that monetary policy cannot affect inflation. It means that the other transmission channels will be more important, and it reinforces the need for an active and credible monetary policy.

27. Table 5 shows some key financial system parameters in the four countries. Uruguay ranks low in terms of credit to the private sector over GDP, closely followed by Peru—again, the two countries where the transmission of monetary policy to aggregate demand is comparatively weaker. Particularly striking is the very small size of the stock market capitalization in Uruguay. Not surprisingly, Uruguay also has the largest share of international debt issues over GDP—followed by Peru—reflecting the yet limited scope for funding in local markets. This trend has recently started to revert, in line with the significant de-dollarization process experienced by these two economies.

Table 5.

Financial system indicators

Source: countries’ authorities, Federacion Iberoamericana de Bolsas, New Zealand Exchange, BIS and IMF staff calculations.

^1/As of June 2010.

^2/As of July 2010

^3/International debt securities of all issuers (amortizations outstanding) from BIS Securities Statistics (Table 12A) as of June 2010.

^4/2010 GDP taken from WEO forecasts.

Table 5.

Financial system indicators

	Total	Credit to	Stock Market	International
	Deposits 1/	Private Sector 1/	Capitalization 2/	Debt Issues 3/
In percent of GDP 4/
Chile	49.4	71.1	123.9	6.1
New Zealand	85.3	141.7	26.5	7.4
Peru	25.8	24.6	47.5	9.8
Uruguay	43.1	18.3	0.3	22.2

Source: countries’ authorities, Federacion Iberoamericana de Bolsas, New Zealand Exchange, BIS and IMF staff calculations.

^1/As of June 2010.

^2/As of July 2010

^3/International debt securities of all issuers (amortizations outstanding) from BIS Securities Statistics (Table 12A) as of June 2010.

^4/2010 GDP taken from WEO forecasts.

View Table

Table 5.

Financial system indicators

	Total	Credit to	Stock Market	International
	Deposits 1/	Private Sector 1/	Capitalization 2/	Debt Issues 3/
In percent of GDP 4/
Chile	49.4	71.1	123.9	6.1
New Zealand	85.3	141.7	26.5	7.4
Peru	25.8	24.6	47.5	9.8
Uruguay	43.1	18.3	0.3	22.2

Source: countries’ authorities, Federacion Iberoamericana de Bolsas, New Zealand Exchange, BIS and IMF staff calculations.

^1/As of June 2010.

^2/As of July 2010

^3/International debt securities of all issuers (amortizations outstanding) from BIS Securities Statistics (Table 12A) as of June 2010.

^4/2010 GDP taken from WEO forecasts.

28. A set of VARs for each country is run to understand how the degree of local-currency credit may affect the transmission of changes in the policy rate. ¹⁴ The paper then explores whether the three-month response of inflation to a 100 basis points increase in the policy rate has changed with the degree of financial deepening.¹⁵

29. The evidence suggests that a higher level of local-currency credit to the private sector may help strengthen the effectiveness of monetary policy in controlling inflation.¹⁶ As shown in Figure 7, a greater ratio of private sector credit (in local currency) to GDP, gives rise to a more important (negative) response of the inflation rate after the increase in the policy rate. This trend is also present in the cases Peru and Uruguay, suggesting that the strenght of the transmission of monetary policy has increased somewhat with the ongoing de-dollarization process.

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Response of inflation after 3 months to a 100 basis points increase in the policy rate

(In percent)

Citation: IMF Staff Country Reports 2011, 063; 10.5089/9781455219025.002.A002

Source: IMF staff estimations.

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G. Policy Recommendations

30. Policy rate changes seem to have a significant transmission to interest rates and inflation in Uruguay since late 2007 in the expected direction. The effect on economic activity is, however, less significant, suggesting that the exchange rate is still a relevant channel to control inflation in Uruguay. Other transmission channels that tend to operate mostly through either aggregate demand or expectations may not be that operative in the country yet. However, the ongoing de-dollarization process of Uruguay is likely to reduce the relevance of the exchange rate channel over time. This gives rise to the need for a further strengthening of the other channels of transmission to effectively control inflation over the medium term.

31. A number of policy recommendations to strengthen the transmission of monetary policy in Uruguay follow from the paper. It is important to further enhance the credibility of the inflation target in the country to foster the effectiveness of monetary policy decisions. To do this, authorities should aim for and deliver an inflation rate within the pre-announced target range, close to its mid-point level. Further strengthening the operational autonomy of the central bank will help fulfill this objective. A better communication strategy with the public and a more frequent and persistent use of the policy rate will also help in this endeavor. In addition, continuing with the ongoing de-dollarization process while fostering financial deepening is commendable, as evidence suggests that a larger fraction of local-currency credit and a deeper financial system may also help strengthen the transmission of monetary policy decisions.