Abstract
Selected Issues
The Evolution of Peru’s Multi-Instrument Policy Framework1
As in many EMEs, Peru’s inflation targeting framework is characterized by a strong focus on exchange rate and financial stability, and the use of several instruments to attain those additional objectives. This framework has significantly evolved since the adoption of inflation targeting in 2002, but its complexity presents a challenge when describing its evolution because of the multiplicity of instruments that have been used at different times in response to shocks of different nature. This paper attempts to do so by summarizing the evolution of the framework with a policy triangle, which shows how tensions and trade-offs between different objectives have developed and been resolved. Our preliminary analysis suggests that Peru’s multi-instrument policy framework has worked well since the adoption of inflation targeting although the absense of a counterfactual prevents us from drawing definite conclusions. Over this period, Peru’s macroeconomic performance in terms of inflation and output compares well with that of other EMEs, particularly on inflation. The success of this framework in achieving macroeconomic stability and lowering dollarization has likely strengthened the monetary policy transmission mechanism, allowing a gradual reduction of exchange rate intervention. Going forward, it will be important to assess how instruments interact with each other and how their combination can be improved.
A. Introduction
1. Peru’s monetary framework strengthened significantly after a long period of macroeconomic instability. Two decades of severe macroeconomic imbalances and hyperinflation until the early 1990s entrenched low confidence in the national currency, leaving widespread dollarization (Rossini et al., 2011). Monetary regulation was enhanced with new instruments, including certificates of deposit (1994) and repos (1997), while reserve requirements (RRs) on domestic deposits were reduced substantially to foster an effective interbank market (1991–93). The BCRP (Banco Central de Reserva del Perú), mandated by the Constitution to maintain monetary stability, adopted an inflation targeting framework in 2002, becoming the first monetary authority to implement it under a dual monetary system.2 The reference interest rate has been the main monetary policy instrument. Owing to the possible effects of exchange rate movements on dollar liabilities, foreign-exchange intervention—sterilized to preserve the inflation objective—remained a key policy tool.
2. The crisis at the end of the 1990s paved the way for financial system reform and new macroprudential tools. Following the Asian and Russian crisis, Peru experienced a series of bank failures and a collapse in credit. The SBS (Superintendencia de Banca, Seguros y AFP) took steps to strengthen banking supervision and the regulatory framework and improve its enforcement capacity.3 New tools were introduced to enhance resilience, dampen the credit cycle, prevent liquidity shortages, and mitigate risks stemming from dollarization.
3. This paper is organized as follows. In section B, we outline the concept of policy triangle that we use to characterize the history of Peru’s multi-instrument framework since the adoption of inflation targeting in 2002. In section C, we describe the events in the period leading to the Global Financial Crisis (GFC), (2002–07). In section D, we look at the GFC and its aftermath (2008–11). In section E, we focus on the episodes of global risk aversion that have characterized the period after the GFC (2012–19). In section F, we examine Peru’s macroeconomic performance over the whole period 2002–19. Section G concludes. Performance of key macroeconomic variables during the periods is presented in Figures 1–5 in the Annex. Annex I further details policy trade-offs with the role of financial stability.
B. A Stylized Representation of The Multi-Instrument Policy Framework
4. The evolution of Peru’s multi-instrument policy framework can be described using a policy triangle (Figure 1). The sides represent different combinations of three policy objectives of price, financial, and exchange rate stability—the latter being instrumental to attaining the first two fundamental goals; and the vertices indicate the three main policy instruments used to attain these goals, i.e., the policy interest rate (MP), exchange rate intervention, (FXI), and macro-prudential policies (MPPs).4 Movements inside the triangle toward one of the vertices show that the use of a particular instrument has increased over time. Movements toward the sides indicates how trade-offs between objectives have evolved in response to different shocks. Obviously, this does not mean to say that objectives changed over time, only that trade-offs between objectives changed depending on the shocks hitting the economy. In this respect, the movements inside the triangle represent movements that can be interpreted as “revealed preferences” of policy makers in response to a changing monetary and financial environment.
Peru’s Multi-Instrument Monetary Policy Framework: Policy Triangle
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Peru’s Multi-Instrument Monetary Policy Framework: Policy Triangle
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Peru’s Multi-Instrument Monetary Policy Framework: Policy Triangle
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
5. Declining dollarization, new macroprudential tools, and the changing nature of shocks are pushing Peru’s framework towards a more intense use of the MP and away from FXI. In 2002–03, when the economy was experiencing large capital inflows and favorable terms of trade shocks, FXI was used intensely to accumulate reserves and reduce exchange rate pressures. The movement to the north east continued until the GFC and initially involved little use of MPPs (points 1 and 2 in Figure 1). During the GFC and immediately after, in 2008–11, when terms trade worsened and capital inflows stalled, the framework moved further to the north east (point 3), as many new MPPs and RRs were used together with foreign currency sales. In 2012–16, with declining terms of trade and volatile capital flows, MPPs continued to be aggressively deployed and more flexibility was allowed in the exchange rate (from 3 to 4 and 5). In the last few years, as terms of trade have continued to decline and capital flows have remained volatile, the policy rate has been used more intensely and FXI less so, continuing to rely on a large number of MPP tools (point 6). The next three sections provide a more detailed descriptions of these three periods.
C. The Commodity Price Boom (2002–07)
6. In response to favorable terms of trade and large capital inflows, the BCRP began accumulating foreign-exchange reserves. External conditions were favorable, with large metal price increases creating appreciation pressures, which the BCRP used to strengthen its reserve buffers and limit the impact of capital inflows on the exchange rate.5 Between January 2002 and December 2007, foreign exchange (FX) reserves increased by more than US$18 billion (12 percent of GDP), nearly all of which was sterilized.6
Peru: Selected Macroeconomic Variables, 2002–07
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Sources: Central Bank of Peru; Alam and others 2018; Haver Analytics; IMF staff calculations.Peru: Selected Macroeconomic Variables, 2002–07
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Sources: Central Bank of Peru; Alam and others 2018; Haver Analytics; IMF staff calculations.Peru: Selected Macroeconomic Variables, 2002–07
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Sources: Central Bank of Peru; Alam and others 2018; Haver Analytics; IMF staff calculations.7. After the adoption of inflation targeting, monetary policy was tightened to lower inflation and new MPPs were taken to contain credit growth. After interest rates fell to record lows in early 2002, the BCRP raised its reference rate by 150 basis points from September in response to heightened regional uncertainties, pressures in the FX market, and rising inflationary pressures. Inflation was reduced to about 2 percent on average while the output gap was negative at 0.5 percent. Owing to dollarization, banks were required to assess exchange-rate-related credit risks, and mortgage lending in foreign currency to unhedged borrowers was put under scrutiny. Reserve requirements, both in domestic and foreign currency, were increased. Loan loss provision requirements were augmented to include dynamic provisioning and sectoral provisions (on housing loans, in particular).7
Selected Calculations 1/
Average for the period. Volatilites are measured as 8 quarters rolling standard deviation.
T stands for tightening measures, L for loosening measures.
Measured by the change in the policy rate for the entire period by end points. T stands for tightening, L for loosening, and N for neutral stance. Within period changes are discussed in the text.
Selected Calculations 1/
| 2002q1–2019q1 | 2002q1–2007q4 | 2008q1–2011q4 | 2012q1–2019q1 | |
|---|---|---|---|---|
| Inflation | 2.71 | 1.92 | 3.41 | 2.97 |
| Output gap | -0.08 | -0.46 | 0.05 | 0.17 |
| Inflation volatility | 1.08 | 1.14 | 1.79 | 0.65 |
| Output volatility | 1.90 | 1.67 | 3.41 | 1.25 |
| Real credit growth, y-y, average for the period | 9.10 | 5.03 | 16.53 | 8.36 |
| REER y-y change, average for the period | 0.07 | -1.67 | 1.66 | 0.64 |
| REER volatility | 3.00 | 2.50 | 2.69 | 3.59 |
| FXIs, net million USD | 43375.30 | 20306.10 | 15461.90 | 7607.30 |
| FXI, in percent of GDP | 30.29 | 27.36 | 10.99 | 3.73 |
| MPP measures 2/ | T | T | T | T |
| MP stance 3/ | L | T | L | L |
| MPP measures (share of total) | 12 | 29 | 59 | |
| NIR, average for the period, million USD | 41926.56 | 15247.33 | 39313.00 | 63242.75 |
| NIR, in percent of GDP | 29.28 | 20.55 | 27.94 | 31.03 |
Average for the period. Volatilites are measured as 8 quarters rolling standard deviation.
T stands for tightening measures, L for loosening measures.
Measured by the change in the policy rate for the entire period by end points. T stands for tightening, L for loosening, and N for neutral stance. Within period changes are discussed in the text.
Selected Calculations 1/
| 2002q1–2019q1 | 2002q1–2007q4 | 2008q1–2011q4 | 2012q1–2019q1 | |
|---|---|---|---|---|
| Inflation | 2.71 | 1.92 | 3.41 | 2.97 |
| Output gap | -0.08 | -0.46 | 0.05 | 0.17 |
| Inflation volatility | 1.08 | 1.14 | 1.79 | 0.65 |
| Output volatility | 1.90 | 1.67 | 3.41 | 1.25 |
| Real credit growth, y-y, average for the period | 9.10 | 5.03 | 16.53 | 8.36 |
| REER y-y change, average for the period | 0.07 | -1.67 | 1.66 | 0.64 |
| REER volatility | 3.00 | 2.50 | 2.69 | 3.59 |
| FXIs, net million USD | 43375.30 | 20306.10 | 15461.90 | 7607.30 |
| FXI, in percent of GDP | 30.29 | 27.36 | 10.99 | 3.73 |
| MPP measures 2/ | T | T | T | T |
| MP stance 3/ | L | T | L | L |
| MPP measures (share of total) | 12 | 29 | 59 | |
| NIR, average for the period, million USD | 41926.56 | 15247.33 | 39313.00 | 63242.75 |
| NIR, in percent of GDP | 29.28 | 20.55 | 27.94 | 31.03 |
Average for the period. Volatilites are measured as 8 quarters rolling standard deviation.
T stands for tightening measures, L for loosening measures.
Measured by the change in the policy rate for the entire period by end points. T stands for tightening, L for loosening, and N for neutral stance. Within period changes are discussed in the text.
8. These policies were successful in lowering inflation, stabilizing inflation expectations, and containing the impact of capital inflows. They can be represented as a move from point 1 to point 2 in the policy triangle (Figure 1). Nonetheless, despite the favorable terms of trade shock, the output gap was negative on average and output volatility was high, probably reflecting the disinflation effort (Figure 5).
D. The Global Financial Crisis (2008–11)
9. As global external conditions worsened, monetary policy became accommodative in 2008. After the long boom experienced in the first part of the decade, commodity prices started declining and capital flows became very volatile. The BCRP responded by using a broad range of unconventional instruments: currency swaps, repo operations, as well as the repurchase of central bank certificates of deposit, and sold nearly US$7 billion (20 percent of international reserves). Only later, starting in February 2009, the BCRP began a series of aggressive rate cuts (with the reference rate reduced overall by 525 bps to 1.25 percent), partially replacing unconventional measures. These cuts were reversed rapidly as commodity prices and demand recovered in mid-2010 when the policy rate was increased by a total of 300 bps to 4.25 percent by May 2011.
Peru: Selected Macroeconomic Variables, 2008–11
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others 2018; Haver Analytics; Bloomberg; IMF staff calculations.Peru: Selected Macroeconomic Variables, 2008–11
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others 2018; Haver Analytics; Bloomberg; IMF staff calculations.Peru: Selected Macroeconomic Variables, 2008–11
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others 2018; Haver Analytics; Bloomberg; IMF staff calculations.10. Many MPPs, changes in RRs, and CFMs were used to stabilize credit conditions and further enhance the management of capital flows. RRs were also tightened considerably to further curb credit growth which has stayed at about 20 percent in 2010–11, with growth in foreign currency credit surpassing that in domestic currency. The marginal requirement for domestic currency deposits increased from 15 to 25 percent in the same period and the required ratio for non-residents increased from 15 to 120 percent by October 2008. RRs for foreign currency deposits and short-term debt increased from 30 percent to 49 percent by September 2008, respectively. RRs on long-term external liabilities were reintroduced at 9 percent in July 2008. As conditions stabilized, the BCRP began to reverse some of these measures. At times, the opposite movements of RRs, MPPs and policy rate changes could have weakened the impact of the monetary policy. The first phase of a comprehensive reform of capital requirements was announced by SBS in July 2008 and in 2010, when new asset-based instruments were created. The first CFM was introduced in February 2010 as a RR on foreign credit lines and other external obligations.
Peru: Reserve Requirements
(Percent, 2008–2011)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others (2018).Peru: Reserve Requirements
(Percent, 2008–2011)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others (2018).Peru: Reserve Requirements
(Percent, 2008–2011)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others (2018).
Peru: Changes in the Policy Rate and MPPs without RRs (2008–2011)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.Peru: Changes in the Policy Rate and MPPs without RRs (2008–2011)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.Peru: Changes in the Policy Rate and MPPs without RRs (2008–2011)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.11. Large use of FXI and macroprudential policies may have helped preserve financial stability during the GFC. These policies can be represented as a movement from point 2 to point 3 (Figure 1). Nonetheless, global turbulence led to a temporary increase in inflation expectations and the inflation target was missed twice. Peru still performed better than many of its peers on inflation, and growth picked up quite quickly (Figure 5).
E. Global Risk Aversion Events (2012–19)
12. Following the GFC, commodity prices continued declining and capital flows remained very volatile. Episodes of global risk aversion and volatile capital flows to EMEs were triggered by China’s slowdown in 2012, lingering U.S. fiscal policy uncertainty and the taper tantrum in 2013, further Renminbi depreciation in 2016, and trade and geopolitical tensions in 2017–19.
13. In 2012, monetary policy remained on hold, while FXI and MPPs were used to limit the impact of large capital inflows. In the first three quarters of 2012, net capital inflows were over $16 billion (about 8 percent of GDP), of which only about half was in the form of foreign direct investment. Nonresident holdings of domestically-issued debt also continued to increase, reaching over half of the total (about 3.5 percent of GDP) in 2012. The BCRP kept the policy rate unchanged at 4.25 percent until the third quarter of 2013 but placed large amounts of central bank securities to sterilize FX purchases.
Peru: Selected Macroeconomic Variables, 2012–19
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others 2018; Haver Analytics; Bloomberg; IMF staff calculations.Peru: Selected Macroeconomic Variables, 2012–19
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others 2018; Haver Analytics; Bloomberg; IMF staff calculations.Peru: Selected Macroeconomic Variables, 2012–19
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others 2018; Haver Analytics; Bloomberg; IMF staff calculations.14. RRs were further tightened, and the second phase of capital reforms started. Through November 2013, the BCRP tightened RRs further and allowed more investment abroad of the private pension funds, while the Ministry of Finance announced additional efforts to reduce appreciation pressures, including prepayment of external debt, larger accumulation of FX to repay debt, and a significant increase in the Fiscal Stabilization Fund. Gross international reserves reached US$64 billion (32 percent of GDP). Despite the large intervention, the sol appreciated by about 4 percent (yoy) against the US dollar and by almost 9 percent (yoy) in real effective terms during January-November 2012. A countercyclical capital buffer as part of the second phase of raising capital requirements was introduced in 2012 through October 2014. The newly introduced noncyclical capital buffer consisted of various concentration risk buffers, a risk-propensity buffer and, for the four largest banks, a very small systemic risk buffer.8
Peru: Reserve Requirements (RRs) (Percent, 2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others (2018).Peru: Reserve Requirements (RRs) (Percent, 2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others (2018).Peru: Reserve Requirements (RRs) (Percent, 2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; Alam and others (2018).15. External conditions started to deteriorate rapidly in 2013, prompting a preemptive easing of monetary policy, FX sales, and new MPPs aimed at supporting monetary policy. Concerns about the unwinding of unconventional monetary policy in the U.S. and weaker metal prices contributed to a deterioration of domestic confidence and increased uncertainty. The BCRP cut the policy rate by 0.25 bps to 4 percent in November 2013, citing slower domestic and global growth, and declining inflationary expectations. The reference rate was further reduced in steps to 3.25 percent through January 2015. RRs on domestic currency deposits were also cut by a cumulative 23 bps to 7 percent between August 2013 and May 2015 and the legal minimum requirement rate was further reduced from 9 percent to 6.5 percent by year-end.
Peru: Changes in the Policy Rate and RRs (2012–2019Q4)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.Peru: Changes in the Policy Rate and RRs (2012–2019Q4)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.Peru: Changes in the Policy Rate and RRs (2012–2019Q4)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.
Peru: Policy Rates and RRs (Percent, 2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: IMF World Economic Outlook; Central Bank of Peru, IMF staff calculations.Peru: Policy Rates and RRs (Percent, 2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: IMF World Economic Outlook; Central Bank of Peru, IMF staff calculations.Peru: Policy Rates and RRs (Percent, 2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: IMF World Economic Outlook; Central Bank of Peru, IMF staff calculations.16. RRs and MPPs were also used to reduce dollarization and enhance the resilience of banks. The BCRP raised marginal RRs on FX deposits from 60 to 70 percent and introduced additional RRs on short positions in FX derivatives and others conditional on FX loan balances which could at times weakened the impact of the monetary policy during this period too.9 Other MPPs were mostly related to increasing capital buffers on FX loans to reduce FX-related credit risk, additional capital buffers on consumption loans, and higher risk weights on mortgages with high loan-to-value (LTV) ratios. New liquidity coverage ratios (LCRs) were introduced in 2014. In January 2015, LCRs in both local and foreign currency were increased from 90 to 100 percent. Two additional RRs for financial institutions considered as CFMs were introduced in February and March 2015.
Peru: Changes in the Policy Rate and MPPs without RRs (2012–2019Q4)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.Peru: Changes in the Policy Rate and MPPs without RRs (2012–2019Q4)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.Peru: Changes in the Policy Rate and MPPs without RRs (2012–2019Q4)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.17. In 2016, following the large Renminbi depreciation and lower metal prices, monetary policy was tightened, FXI was used less, and new MPPs were introduced. The BCRP raised the policy rate from September 2015 by a full percentage point to 4.25 percent aiming at re-anchoring inflation expectations. The sol depreciated by 14 percent with respect to the U.S. dollar by the end of 2015 over a year, the largest yearly exchange rate movement since the adoption of inflation targeting, but the volatility of the exchange rate was otherwise contained. In 2016, as part of the capital reforms, the SBS continued to tighten the countercyclical capital buffers on past lending.
Peru: One Year Ahead Expected Inflation (Percent, 2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.Peru: One Year Ahead Expected Inflation (Percent, 2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.Peru: One Year Ahead Expected Inflation (Percent, 2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru.18. In 2017, global trade tensions and adverse domestic factors prompted monetary easing and relaxation of some MPPs, with very little use of FXI. The BCRP gradually reduced the policy rate in May 2017 from 4.25 percent to 2.75 in April 2018 in response to increased uncertainty in the global outlook, weak commodity prices, domestic political uncertainty, and an adverse weather event. As economic activity continued to weaken, the BCRP started easing again with two 25 bps rate cuts in August and November 2019 to 2.25 percent. The BCRP also reduced reserve requirements to 5 percent for domestic currency deposits and 35 percent in mid-2017 and mid-2018, respectively for FX deposits while introducing new limits on banks’ FX transactions. While quantitative limits on FX credit growth remained in place, further decline in RRs for FX deposits was made possible by the progress attained in reducing dollarization.10 In March 2018, the BCRP introduced new measures, allowing foreign currency loans to increase if local currency credit to enterprises rises more than proportionally, subject to certain thresholds. The operational limit on private pension funds’ investment in foreign securities increased from 46 to 50 percent of their portfolio by September 2018. In March 2019, the BCRP relaxed limits on FX operations with forwards and swaps. During this period the use of FXIs were limited in size amounting to less than 3 percent of GDP in 2018–2019 with balances on swap agreements reduced to zero and they were broadly symmetric.
19. As dollarization declined, the use of FXI was reduced in favor of a more active use of the policy rate, while the MPP toolkit was further expanded. After the 2012 events, when FXI was still substantial (point 4, Figure 1), the response to the later risk aversion episodes focused on new MPP measures, including to reduce dollarization (point 5, Figure 1). The success achieved in reducing dollarization supported less FXI, some relaxation of MPPs, and a more intense use of the policy rate (point 6, Figure 1). Inflation performance was good relative to other EMEs although target misses were more frequent than in Chile and Brazil.
F. Overall Performance (2002–19)
20. Although the use of instruments has been largely determined by the nature of the shocks, Peru’s policy framework has moved gradually away from its reliance on FXI toward an increased use of traditional tools—the policy rate—and MPPs (Figure 1). This change can be explained by the success achieved in stabilizing inflation and reducing dollarization, which has improved the effectiveness of the interest transmission channel and mitigated the financial stability risks associated with exchange rate movements. The latter have also become less correlated with inflation dynamics (Annex I).
21. With increasing success in preserving financial stability, the overall macroeconomic performance was also good. With a strong and resilient financial system, Peru was one of the best EME performers in terms of output growth, inflation, and inflation volatility. Output volatility, however, was higher than in most EMEs (Figure 5).11
Output Growth and Inflation: Selected Countries, 2002–19
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; World Economic Outlook; Haver Analytics; IMF staff calculations.Output Growth and Inflation: Selected Countries, 2002–19
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; World Economic Outlook; Haver Analytics; IMF staff calculations.Output Growth and Inflation: Selected Countries, 2002–19
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; World Economic Outlook; Haver Analytics; IMF staff calculations.
Output and Inflation Volatilities: S elected co u n tr ies, 2002–19 1/
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
1/ Growth and output volatilities are calculated based on 8 quarters standard deviations.Source: Central Bank of Peru; World Economic Outlook; Haver Analytics; IMF staff calculations.Output and Inflation Volatilities: S elected co u n tr ies, 2002–19 1/
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
1/ Growth and output volatilities are calculated based on 8 quarters standard deviations.Source: Central Bank of Peru; World Economic Outlook; Haver Analytics; IMF staff calculations.Output and Inflation Volatilities: S elected co u n tr ies, 2002–19 1/
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
1/ Growth and output volatilities are calculated based on 8 quarters standard deviations.Source: Central Bank of Peru; World Economic Outlook; Haver Analytics; IMF staff calculations.22. FXI may have helped stabilize output fluctuations, but the benefits of FXI would have dissipated as dollarization declined. Model simulations show that FXI can mitigate output responses to both real and financial shocks when balance sheet effects resulting from dollarization are associated with exchange rate movements.12 Using the FPAS model, we simulated two shocks to: (i) the foreign interest rate and (ii) terms of trade.13 When the foreign interest rate increases (declines) with a capital outflow (inflow) or terms of trade deteriorate (improve), the FXI can reduce the impact of shocks on the output gap and inflation based on the assumption that the balance sheet effects of the exchange rate fluctuations are large (see also Ostry, (2012)). However, with dollarization declining, the positive impact of FXI would correspondingly diminish.
Peru: Impact of Foreign Exchange Intervention
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Peru: Impact of Foreign Exchange Intervention
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Peru: Impact of Foreign Exchange Intervention
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
23. FXI and MPPs may have contributed to attaining low and stable inflation.14 Augmented Taylor rules—including terms for the REER, real credit, and the US policy rate—show that the policy rate in Peru was more expansionary and less volatile than predicted by the rules.15 Significant coefficients for FXI and a variable for financial stability proxied by the domestic lending-deposit interest rate spread suggest that, without these policies, the reference rate would have had to be higher on average and more volatile in order to attain the inflation target16
Peru: Taylor Rules versus Actual, Policy Rates (Percent)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; IMF staff calculations.TR = rbar(0) + policy rate(-1) +0.5*(inflation gap) +0.5*(output gap)TRA = rbar(0)+ policy rate(-1) +0.5*(inflation gap) +0.5*(output gap) + 0.1*usa_fedfunds+0.5*(reer gap) + 0.5*(credit gap)Peru: Taylor Rules versus Actual, Policy Rates (Percent)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; IMF staff calculations.TR = rbar(0) + policy rate(-1) +0.5*(inflation gap) +0.5*(output gap)TRA = rbar(0)+ policy rate(-1) +0.5*(inflation gap) +0.5*(output gap) + 0.1*usa_fedfunds+0.5*(reer gap) + 0.5*(credit gap)Peru: Taylor Rules versus Actual, Policy Rates (Percent)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; IMF staff calculations.TR = rbar(0) + policy rate(-1) +0.5*(inflation gap) +0.5*(output gap)TRA = rbar(0)+ policy rate(-1) +0.5*(inflation gap) +0.5*(output gap) + 0.1*usa_fedfunds+0.5*(reer gap) + 0.5*(credit gap)Taylor Rules in Peru: OLS Regressions
*, ** and *** denote statistical significance at the 10, 5 and 1 percent level, respectively. Sample: 2003q4–2019q1.
Taylor Rules in Peru: OLS Regressions
| Dependent Variable: Policy rate – Inflation Target 1/ | ||||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Lagged (policy rate – inflation target) | 0.9339*** | 0.7444*** | 0.7428*** | |
| Expected inflation – inflation target | 1.3356*** | 0.2817* | 0.102 | 0.2856* |
| REER deviation from trend | -4.2255 | -4.4529* | ||
| Lagged output gap | 0.2551*** | 0.2471*** | ||
| Domestic interest rate spread (lending minus deposit rate) | 0.0528*** | |||
| Dummy for global financial crisis | 0.7313 | -1.2338*** | -0.8604*** | -1.0524*** |
| Constant | 0.8661 | 0.0266 | 0.4201 | -0.5718 |
| Observations | 63 | 62 | 62 | 62 |
| Adjusted R-squared | 0.3622 | 0.8274 | 0.8712 | 0.8776 |
*, ** and *** denote statistical significance at the 10, 5 and 1 percent level, respectively. Sample: 2003q4–2019q1.
Taylor Rules in Peru: OLS Regressions
| Dependent Variable: Policy rate – Inflation Target 1/ | ||||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Lagged (policy rate – inflation target) | 0.9339*** | 0.7444*** | 0.7428*** | |
| Expected inflation – inflation target | 1.3356*** | 0.2817* | 0.102 | 0.2856* |
| REER deviation from trend | -4.2255 | -4.4529* | ||
| Lagged output gap | 0.2551*** | 0.2471*** | ||
| Domestic interest rate spread (lending minus deposit rate) | 0.0528*** | |||
| Dummy for global financial crisis | 0.7313 | -1.2338*** | -0.8604*** | -1.0524*** |
| Constant | 0.8661 | 0.0266 | 0.4201 | -0.5718 |
| Observations | 63 | 62 | 62 | 62 |
| Adjusted R-squared | 0.3622 | 0.8274 | 0.8712 | 0.8776 |
*, ** and *** denote statistical significance at the 10, 5 and 1 percent level, respectively. Sample: 2003q4–2019q1.
24. Despite episodic reductions in RRs, Peru’s MPPs have been mostly tightening. Alam and others (2018) classified Peru’s MPPs as tightening and a large part of them as capital-related measures followed by provisioning and dollarization measures. They have also been designed for resilience building rather than cyclical reasons. Most of the MPPs adopted have never been removed, which makes it difficult to evaluate their effectiveness.
Peru: Macroprodential Measures, Prevalence of Use by Instrument
(Percent of total, 1996-present)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Alam and others (2018).Peru: Macroprodential Measures, Prevalence of Use by Instrument
(Percent of total, 1996-present)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Alam and others (2018).Peru: Macroprodential Measures, Prevalence of Use by Instrument
(Percent of total, 1996-present)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Alam and others (2018).
Peru: Type of Macroprudential Measures 1/
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Alam and others (2018); IMF staff calculations.1/ In percent. All loosening measures except the one in 2019:Q1 related to FX position are changes in the RRs.Peru: Type of Macroprudential Measures 1/
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Alam and others (2018); IMF staff calculations.1/ In percent. All loosening measures except the one in 2019:Q1 related to FX position are changes in the RRs.Peru: Type of Macroprudential Measures 1/
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Alam and others (2018); IMF staff calculations.1/ In percent. All loosening measures except the one in 2019:Q1 related to FX position are changes in the RRs.25. MPPs seem to have been successful at dampening the credit cycle, especially in 2012–19, but the evidence on their impact is inconclusive. Fang et al., (2018) find no effects of higher capital requirements on credit in Peru. Following Boar et al. (2017), we estimated a regression equation of MPPs on output and output volatility that shows no impact of MPPs on real GDP growth in the short run.17 MPPs increase output volatility (at the 10-percent significance level only) which can be reversed with deeper financial development and more capital account openness. In the longer run (using 2- and 5-year averages), the impact of MPPs on growth and its volatility dissipates.
Do Macroprudential Policies Reduce Output Volatility and Enhance Output Growth?1/
*, ** and *** denote statistical sjgnificance at the 10, 5 and 1 percent level, respectively. Sample: 2003q4–2019ql.
Do Macroprudential Policies Reduce Output Volatility and Enhance Output Growth?1/
| Dependent Variables | ||||
|---|---|---|---|---|
| Real GDP Growth | Output Volatility | |||
| Coeff. | P-value | Coeff. | P-value | |
| MPPs | 0.64 | 0.59 | 1.37 | 0.06* |
| FD | -1.78 | -0.31 | -0.43 | 0.9 |
| OPEN | 3.95 | 0.25 | -0.73 | 0.7 |
| FD x Map x OPEN | -0.86 | 0.59 | -1.78 | 0.07* |
| Otlier controls (induding GFC) | yes | yes | yes | yes |
| Observations | 62 | 62 | 62 | 62 |
| R-squared | 0.75 | 0.75 | 0.75 | 0.75 |
*, ** and *** denote statistical sjgnificance at the 10, 5 and 1 percent level, respectively. Sample: 2003q4–2019ql.
Do Macroprudential Policies Reduce Output Volatility and Enhance Output Growth?1/
| Dependent Variables | ||||
|---|---|---|---|---|
| Real GDP Growth | Output Volatility | |||
| Coeff. | P-value | Coeff. | P-value | |
| MPPs | 0.64 | 0.59 | 1.37 | 0.06* |
| FD | -1.78 | -0.31 | -0.43 | 0.9 |
| OPEN | 3.95 | 0.25 | -0.73 | 0.7 |
| FD x Map x OPEN | -0.86 | 0.59 | -1.78 | 0.07* |
| Otlier controls (induding GFC) | yes | yes | yes | yes |
| Observations | 62 | 62 | 62 | 62 |
| R-squared | 0.75 | 0.75 | 0.75 | 0.75 |
*, ** and *** denote statistical sjgnificance at the 10, 5 and 1 percent level, respectively. Sample: 2003q4–2019ql.
G. Conclusions
26. Since the adoption of inflation targeting in 2002, Peru’s multi-instrument policy framework has gradually reduced its reliance on FXI and increased its use of the policy rate and a rapidly expanding macroprudential toolkit. The evolution of the framework is described in this paper by movements within a triangular space, where the vertices represent the instruments and the sides the trade-offs between policy objectives. The main reason for these changes is the success attained in gaining credibility, stabilizing inflation, limiting the impact of large capital flows, and reducing dollarization, thereby enhancing the effectiveness of the traditional monetary policy transmission mechanism.
27. The evolution of this framework is marked by several external shocks. The first period (2002–2007) was characterized by a commodity price boom and large capital inflows. The policy reaction focused on large FX purchases while monetary policy was tightened to cement low inflation, and a more systematic MPP framework started to emerge. In the second period (2008–11), the reaction to the GFC included monetary policy easing, large FX sales, and increased use of MPPs. In recent years (2012–19), Peru experienced unfavorable terms of trade and financial market volatility connected with risk-off episodes. The use of MPPs increased further, including to reduce dollarization. With the success of these measures, the use of FXI declined gradually, while the policy rate was used more frequently to attain the inflation target.
28. While further developments in this direction are expected as dollarization declines, some general lessons may be derived from this experience. Our analysis shows that Peru’s macroeconomic performance compares well with that of other EMEs, particularly on inflation. It also indicates that FXI may have contributed to stabilizing output fluctuations (although the benefits of FXI would have declined over time with de-dollarization); FXI and MPPs may have helped monetary policy achieve its objectives; and MPPs seem to have had some role in dampening credit cycles but perhaps also contributing to output fluctuations. Nonetheless, the evidence presented here is merely suggestive and the absence of a clear counterfactual makes it difficult to draw definite conclusions. Thus, further analysis is necessary to fully understand the interaction of different instruments, which could open new possibilities for a broader definition of the policy stance and improved combinations of instruments and policies.
References
Agénor P., R., and Silva, L., “Integrated inflation targeting: another perspective from the developing world”, Bank for International Settlements, 2019.
Alam, Z., et al, “Digging Deeper--Evidence on the Effects of Macroprudential Policies from a New Database”, WP/19/66, 2018, IMF Working paper.
Armas, A and F Grippa: “Targeting inflation in a dollarized economy: the Peruvian experience”, Financial dollarization: the policy agenda, IMF, 2006.
Armas, A., Castillo. P., Vega, M.: “Inflation targeting and Quantitative Tightening: Effects of Reserve Requirements in Peru”, BCRP Working Paper series, 2014.
Berg et al., “A Practical Model-Based Approach to Monetary Policy Analysis – Overview”, IMF Working Paper, , WP/06/80, 2006.
Boar, C., et al., “What are the effects of macroprudential policies on macroeconomic performance”, BIS Quarterly Review, September 2017.
Cabello. M., Lupu. J., Minaya, E.: “Macroprudential Policies in Peru: The effects of Dynamics Provisioning and Conditional Reserve Requirements”, February 2017, Bank of International Settlements, 2017.
Castillo, P., Vega, H., Serrano, E., Burga, C.: “De-dollarization of credit in Peru: the role of unconventional monetary policy tools”, BCRP, Working Paper series, 2016.
Choi, G., and Cook, D.: “Policy Conflicts and Inflation Targeting: The Role of Credit Markets”, IMF Working Paper, WP/18/72, 2018.
Claessens, S, Gosh and R Mihet: “Macro-prudential policies to mitigate financial system vulnerabilities”, Journal of International Money and Finance, vol 39, pp 153–85, 2013.
De Paoli, B., and M. Paustian.,“Coordinating Monetary and Macroprudential Policies”, Federal Reserve Bank of New York Staff Reports, no. 653, November 2013.
Fang, X. et. al, “The Effects of Higher Bank Capital Requirements on Credit in Peru”, IMF Working Paper, WP/18/222, 2018.
Ghazanchyan M. et al., “The Evolution of Peru’s Multi-Instrument Policy Framework”, IMF Working paper, forthcoming.
Lahura, E., “Monetary aggregates and monetary policy in Peru”, BCRP, working paper series, 2017.
Malovana, S., and Frait, J., “Monetary policy and macroprudential policy: Rivals or teammates”? Journal of Financial stability 32 (2017) 1–16.
Mano, R. and Sgherri, S. “One shock, many policy responses”, IMF Working paper, 2019, forthcoming.
Ostry, J., et al (2012), “Targets, Two Instruments: Monetary and Exchange Rate Policies in Emerging Market Economies”, IMF Staff discussion Note, SDN/12/01.
Rossini, R. and Z. Quispe: “Monetary policy during the global financial crisis of 2007–2009: the case of Peru”, 2010, BIS Papers, 54.
Rossini, R. and Z. Quispe “Implementation of Macroprudential Policy in Peru”, BIS Papers No 94 257, 2014.
Rossini, R., Vega, M., Quispe, Z., and Rodriguez, D., “Capital flows, monetary policy and forex intervention in Peru”, 2011, BCRP, working paper series.
Rossini, R., Vega, M., Quispe, Z., and Perez, F., “Inflation expectations and dollarization in Peru”, BIS89.
Velarde, J.: “The future of monetary policy”, in A Werner and A Santos (eds), Peru: Staying the Course of Economic Success, IMF Publications, 2015.
Annex I. Policy Goals and Trade-Offs
1. Stabilizing financial and foreign exchange market conditions may have conflicted with the goals of price and output stability in Peru. Broader financial stability concerns at times can outweigh price stability concerns where monetary policy actions may not be enough to have an impact (Agénor and Silva, 2019; Malovaná and Frait, 2017). Following Choi and Cook (2018), we document frequencies in Peru during the period after the adoption of inflation targeting, 2012–19 at which stabilizing volatility in capital flows, real credit and exchange rate were not correlated with inflationary and output gap dynamics echoing policy challenges.
2. Our methodological strategy focuses on frequencies and the significance of co-movements in macroeconomic variables.1 For business cycle considerations, the expectation is that output gap moves in the same direction as inflation except at times of supply shocks. For the exchange rate, depreciation periods will be associated with higher inflation and appreciation periods with lower inflation. For financial stability, credit expansions precede high inflation calling for policy rate hikes and, conversely, when inflation is considerably below the target range, credit conditions are more likely to be contractionary (possibly owing to reduced demand for credit). For capital flows, inflows will be associated with higher inflation and outflows with lower inflation, other things being equal.
3. While real credit and capital flows were generally correlated with inflation developments in Peru, exchange rate movements were not correlated with inflation in most periods owing to interventions.2 While the real credit was off sync in the aftermath of the adoption of the IT framework it was broadly in line with inflation in all other sub-periods. Except the brief period of 2008–2011, capital flows were largely in line with the movements of inflation though not with the exchange rate. In addition, where large negative output gaps were present (the pre-crisis period) and associated with higher inflation perhaps due to supply shocks, the BCRP responded with policy hikes, which otherwise could have stayed neutral. The closer link of capital flows with inflation in Peru suggests that inflation was more correlated with financial rather than real shocks. These observations also bond well with our findings on Peru’s relatively better inflation performance compared to output with respect to other emerging market countries facing similar shocks.
Peru: Correlations, Selected Macroeconomic Variables 1/2/
The table shows coefficient estimates from a simple regression of the inflation gap (inflation minus its target value) separately from also the status index of inflation relative to target.
***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively.
The inflation status is calculated as follows: It equals -2 when headline inflation is below the target lower bound, -1 when headline inflation is below target but within the target range, 1 when headline inflation is above target but within the range, and 2 when inflation is above the target upper bound.
Peru: Correlations, Selected Macroeconomic Variables 1/2/
| Dependent variable | Period: 2002q1–2019q1 Independent variables | Period: 2002q1–2007q4 Independent variables | Period: 2008q1–2011q4 Independent variables | Period: 2012q1–2019q1 Independent variables | ||||
|---|---|---|---|---|---|---|---|---|
| Inflation status 3/ | Inflation gap | Inflation status | Inflation gap | Inflation status | Inflation gap | Inflation status | Inflation gap | |
| Output gap | 0.41*** | 0.53*** | 0.19 | 0.31* | 1.04*** | 0.94*** | 0.08 | 0.05 |
| Change in ER | 0.9* | 0.55 | 0.08 | 0.06 | -0.19 | -0.46 | 2.62*** | 3.69*** |
| Change in Real credit | 2.35*** | 2.99*** | 0.75 | 1.41 | 1.74 | 2.18** | 2.56*** | 2.66*** |
| Share of net capital flows in GDP | 1.29*** | 1.24*** | 1.33** | 1.40** | 0.31 | 0.32 | 2.09** | 2.69** |
The table shows coefficient estimates from a simple regression of the inflation gap (inflation minus its target value) separately from also the status index of inflation relative to target.
***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively.
The inflation status is calculated as follows: It equals -2 when headline inflation is below the target lower bound, -1 when headline inflation is below target but within the target range, 1 when headline inflation is above target but within the range, and 2 when inflation is above the target upper bound.
Peru: Correlations, Selected Macroeconomic Variables 1/2/
| Dependent variable | Period: 2002q1–2019q1 Independent variables | Period: 2002q1–2007q4 Independent variables | Period: 2008q1–2011q4 Independent variables | Period: 2012q1–2019q1 Independent variables | ||||
|---|---|---|---|---|---|---|---|---|
| Inflation status 3/ | Inflation gap | Inflation status | Inflation gap | Inflation status | Inflation gap | Inflation status | Inflation gap | |
| Output gap | 0.41*** | 0.53*** | 0.19 | 0.31* | 1.04*** | 0.94*** | 0.08 | 0.05 |
| Change in ER | 0.9* | 0.55 | 0.08 | 0.06 | -0.19 | -0.46 | 2.62*** | 3.69*** |
| Change in Real credit | 2.35*** | 2.99*** | 0.75 | 1.41 | 1.74 | 2.18** | 2.56*** | 2.66*** |
| Share of net capital flows in GDP | 1.29*** | 1.24*** | 1.33** | 1.40** | 0.31 | 0.32 | 2.09** | 2.69** |
The table shows coefficient estimates from a simple regression of the inflation gap (inflation minus its target value) separately from also the status index of inflation relative to target.
***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively.
The inflation status is calculated as follows: It equals -2 when headline inflation is below the target lower bound, -1 when headline inflation is below target but within the target range, 1 when headline inflation is above target but within the range, and 2 when inflation is above the target upper bound.
Peru: Correlations with Selected Macroeconomic Variables 2012–19
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: World Economic Outlook; Haver Analytics; IMF staff calculations.Peru: Correlations with Selected Macroeconomic Variables 2012–19
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: World Economic Outlook; Haver Analytics; IMF staff calculations.Peru: Correlations with Selected Macroeconomic Variables 2012–19
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: World Economic Outlook; Haver Analytics; IMF staff calculations.
Peru: Selected Macroeconomic Variables (1990–2002)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.Peru: Selected Macroeconomic Variables (1990–2002)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.Peru: Selected Macroeconomic Variables (1990–2002)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.
Peru: Selected Macroeconomic Variables (2002–2007)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.Peru: Selected Macroeconomic Variables (2002–2007)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.Peru: Selected Macroeconomic Variables (2002–2007)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.
Peru: Selected Macroeconomic Variables (2008–2011)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.Peru: Selected Macroeconomic Variables (2008–2011)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.Peru: Selected Macroeconomic Variables (2008–2011)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.
Peru: Selected Macroeconomic Variables (2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.Peru: Selected Macroeconomic Variables (2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.Peru: Selected Macroeconomic Variables (2012–2019)
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Haver Analytics, BCRP.
Peru and other EMEs: Selected Macroeconomic Variables, 2002–19 1/
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; World Economic Outlook; Haver Analytics; IMF staff calculations.1/ Growth and output volatilities are calculated based on 8 quarters standard deviations.Peru and other EMEs: Selected Macroeconomic Variables, 2002–19 1/
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; World Economic Outlook; Haver Analytics; IMF staff calculations.1/ Growth and output volatilities are calculated based on 8 quarters standard deviations.Peru and other EMEs: Selected Macroeconomic Variables, 2002–19 1/
Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A002
Source: Central Bank of Peru; World Economic Outlook; Haver Analytics; IMF staff calculations.1/ Growth and output volatilities are calculated based on 8 quarters standard deviations.Prepared by Manuk Ghazanchyan, Joannes Mongardini, and Alexei Goumilevski. Special thanks to Bas Bakker and Leo Bonato for invaluable comments and suggestions. We also thank Renzo Rossini, Adrian Adams, and Paul Castillo and all seminar participants at the Central Reserve Bank of Peru for their very helpful comments.
The target range for inflation was initially set at 1 -3 percent. In 2002, the target was set to 2.5 per cent with a tolerance margin of +/- 1 per cent. From 2007 onwards, the target was lowered to 2 percent with the same tolerance margin.
Both the BCRP and the SBS operate macroprudential policy tools. The BCPR’s instruments target mainly liquidity risk, both in domestic and foreign currency, and excessive credit growth, whereas the SBS’s instruments focus primarily on strengthening the financial system’s loss absorption capacity (Rossini and Quispe, 2014).
Capital flow measures (CFMs) and reserve requirements (RRs) have a somewhat special status as they do no fit perfectly in this classification. Between 2010–15, Peru introduced five measures that were classified as CFMs at the time of their adoption according to the IMF taxonomy. Currently, only one of them—a tax on non-resident income gains from short-term financial derivatives—remains classified as CFM, and the other four measures are assessed as CFM/MPMs (for additional details on CFMs for Peru, see https://www.imf.org/external/np/g20/pdf/2018/IMF2018CFMTaxonomy.xlsx). RRs are also a key component of the policy framework and there is no clear-cut answer as to whether they belong to MP or MPPs. In our discussion, we follow Alam et al. (2018) by classifying RRs under MPPs, but we also show the behavior of RRs alone.
Dollarization was still high, with 55 percent of broad money and 70 percent of credit to the private sector denominated in foreign currency at end-2005.
During this period, Peru had two stand-by arrangements with the IMF.
For a detailed account of MPPs in Peru see Alam et al. (2018), Cabello et al. (2017), and Rossini (2014).
For more details on Peruvian capital reforms, see Fang et al., (2018).
Peru’s public debt management strategy has been focused on developing a yield curve of government bonds in soles while reducing FX-denominated public debt. In line with this objective, in 2013 the Ministry of Economy and Finance launched a program to develop a market for fixed-rate sovereign instruments in domestic currency. As a result, government bonds in soles have gained liquidity and the yield curve has been extended considerably.
Latest data shows that credit and deposit dollarization in Peru have declined to about 27 and 35 percent from more than 65 and 55 percent, respectively in the early 00s.
Please see Ghazanchyan et al., (2019), forthcoming, for more details on cross country evidence on the effectiveness of the combination of instruments and macroeconomic performance.
Simulations using the Forecasting and Policy Analysis System (FPAS) model (see Berg et al., 2016). For the detailed discussion and results for Peru, see Ghazanchyan et al. (2019, forthcoming).
Shocks are 1 -percent deviations from baseline values.
Following Ostry (2012), we have also estimated the following Taylor rule it – πt+4 = β0 + β(it-1 – π*t-1) + β2(πet+4 – π *t+4) – β3(log(REERt)) – log(REERHPt)) + β4YGAPt-1 – βD08:4-09:2 + εt, where the dependent variable is the “target real interest rate (the policy interest rate, it minus the four-quarter ahead inflation target) and where REER is the log of the real effective exchange rate (an increase is an appreciation of the domestic currency), REER is the level implied by a rolling HP filter, YGAP is the output gap, D08:4–09:2 is a dummy variable for the GFC. We extended this equation to include the credit gap (the results for credit gap are not significant) and lending market interest rate spread. For more general discussion see Ostry (2012).
Similar results were obtained by using the specification by Velarde (2015) with a neutral rate of 3.2 percent.
We used both credit gap and lending-deposit interest rate spread as proxies for financial stability with the former being insignificant in the regressions. The lending-deposit interest rate spread is another measure of credit condition in the financial system.
The variable of interest in the specification is the macroprudential measure MPP and its impact on real GDP growth and its volatility. FD indicates the degree of financial development (FD, from Sahay et al. (2015)) with higher values indicating higher degree of financial development, and openness, OPEN, measures capital account openness (from Chinn and Ito (2006)) with higher values indicating more openness. The regressions also include interactions of these three variables. Xt includes a set of control variables such as the average number of years of schooling, real government consumption, the inflation rate, the real interest rate, the effective exchange rate, and a global financial crisis dummy.
To capture the frequencies and comovements for each macroeconomic variable of interest, we constructed the standard deviation of it over the period from the inception of the IT regime (2002) to the second quarter of 2008 (the set of the Lehman Brothers crisis). If, for example, the output gap in a given quarter is above (below the negative of) the pre-crisis standard deviation, we characterized that quarter as a boom (recession). To compare inflation movements with other variables, we constructed a discrete variable for headline inflation status which equals -2 when headline inflation is below the target lower bound, -1 when headline inflation is below target but within the target range, 1 when headline inflation is above target but within the range, and 2 when inflation is above the target upper bound. Similarly, we measured depreciation (appreciation) quarters when the year-on-year growth of the exchange rate against the U.S. dollar was higher (lower) than the rate measured as one-standard deviation above (below) the average for the period between the inception of an IT regime and the second quarter of 2008. The same technique was used for real credit booms and busts. Our capital flow measure is the change in capital flows in GDP when inflow means capital flows are more (less) than pre-crisis mean plus (minus) one pre-crisis standard deviation. With dummy variables representing the status changes for all the variables, we also run statistical tests to check for the significance of correlations of these variables with inflation and inflation status.
Independent shocks could shift the direction of macroeconomic variables and not necessarily the combination of policies. We only propose that the policy makers when facing the trade-offs between alternative goals, were not fully able to achieve them with the currrent set of instruments.
