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Philippines: Selected Issues

Author(s):
International Monetary Fund. Asia and Pacific Dept
Published Date:
November 2017
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Spillover Effects from United States Policy Shifts and Lower Growth in China1

The Philippines trade and financial exposures to the United States and China are more moderate than the more open ASEAN neighbors, albeit with the role of China rising recently. Thus, the potential spillovers from policy shifts in the United States and lower growth in China is expected to be more modest in the Philippines, although historically U.S. financial spillovers had a large impact, perhaps reflecting the shallower financial depth. From a more forward looking perspective, we assess potential spillover effects to the Philippines under the following three illustrative scenarios: (a) monetary policy normalization in the United States, (b) a lower growth path in China owing to the materialization of downside risks, including tighter domestic funding conditions (IMF, 2017b), and (c) a deficit-financed fiscal expansion in the United States through reduced labor and corporate income taxes and increased infrastructure spending (IMF, 2017a).

A. Introduction

1. We consider two main channels through which U.S. policy shifts and a slowdown in China can affect the Philippines: trade and financial markets.2 Trade is likely the most important channel as the United States and China are key trading partners not only in goods, but also in services such as business process outsourcing (BPOs), remittances and tourism (Figure 1). The destinations of merchandise exports have changed during the past decade as the weight of the United States has fallen while those of Asian countries, especially China, Hong Kong SAR and Japan have risen. However, the share of exports to the United States has remained significant, mainly related to electronic exports. BPO exports have shown rapid growth, with the Philippines becoming the call center (voice) capital of world with export receipts approaching $20 billion or nearly as large as remittances from overseas Filipino workers (OFWs). More important, BPOs have fueled service export growth and domestic value added employing about 1 million workers and creating demand for office space and nearby condominiums. Remittances remains relatively stable and the largest source of external finance, albeit moderating in importance. The share of remittances from the United States remains large, notwithstanding most of the OFWs being employed in Saudi Arabia and UAE. The financial channel is also important, especially through spikes in global financial volatility but the external liabilities of the Philippines are relatively low. Direct financial spillovers from the United States are significant, mainly through FDI and cross border bank lending, while direct financial links with China are currently limited (Dizioli and others, 2016).

Figure 1.Trade and Financial Exposures

Trade in Goods and Services

2. The Philippines has significant trade exposures to the United States and China. Given Asia’s supply chains, trade openness as a share of nominal GDP may be misleading due to large re-exports and processing trade. Thus, value-added trade provides a complementary perspective. Although data on valued-added merchandise exports are not available for the Philippines, the exposures to the United States and China appear significant, with a share of exports less than the more open economies of Korea, Malaysia, Thailand and Vietnam but larger than the more closed Asian economies of India and Indonesia (Figure 1). Value added trade data on service exports is not available for the Philippines, but the share of modern service exports in GDP and growth captures the booming IT-BPO sector, which is an important source of productivity growth and output. Anecdotal evidence suggests that about 80 percent of BPO revenues are destined to the United States, highlighting vulnerability to potential changes in U.S. policies, particularly to the outsourcing sector.

Exports of Modern Services 1/2/

Citation: 2017, 335; 10.5089/9781484326862.002.A001

Sources: IMF. Balance of Payments statistics; CEIC Data Company Ltd.; Bloomberg LP.;and IMF staff estimates.

1/ Modern services include telecommunications. computer, information, business, intellectual property, and financial services.

2/ Note: BPM6-based series start at 2002 for SGP and KOR; 2005 for MVS, PHL, and THA; 2010 for IDN and IND; and 2012 for CHN. Older data are based on the the BPMS framework.

3/ 2002–2006 average growth rate and percent of GDP figures cover 2005–2006 and 2004–2006. respectively.

Exports and Value Added to United States

(In percent of National GDP, 2014)

Sources: IMF, Direction of Trade Statistics; and IMF staff estimates.

Exports and Value Added to China

(In percent of National GDP, 2014)

Sources: IMF, Direction of Trade Statistics: and IMF staff estimates.

Financial Linkages and Spillovers

3. Portfolio flows to the Philippines is closely related to the VIX and, more recently to U.S. dollar strength, as in the rest of the ASEAN-5. Portfolio outflows in response to the taper tantrum in 2013, China equity market sell-off in 2015, and the U.S. Presidential election has been sizeable with asset prices reacting in tandem (Figure 2). In general, equity prices and the exchange rate in the Philippines has been more sensitive than regional peers, while sovereign and corporate debt spreads have been more resilient. This could be related to the relatively high foreign exposure in the equity market (about 50 percent of daily volumes) and low foreign participation in the local currency bond market (about 7 percent of stock). The exchange rate has been acting as a shock absorber with market implied sovereign risks remaining low, perhaps due to the Philippines’ low vulnerabilities.

Figure 2.Financial Spillovers and Regional Comparisons

BSP-Registered Foreign Portfolio Investment Transactions and External Factors

(In million U.S. dollars)

Sources: Bloomberg, LP.; and Bangko Sentral ng Pilipinas.

Philippines: Selected Vulnerability Indicators 1/

Citation: 2017, 335; 10.5089/9781484326862.002.A001

Sources; IMF, Vulnerability Exercise database; and IMF staff estimates.

1/ The diagram is designed to show dec rea sing vulnerability from the center to the periphery. The indicator values are based on staff estimates of 2015 forthe nonfinancial corporate interest coverage and 2016 forai the other indicators. The indicators are defined as follows; Foreign exchange reserve coverage Is the official foreign exchange reserves in percent of the IMF Assessing Reserve Adequacy metric; the external financing requirement is the short-term debt plus the long-term amortization paid plus the current account balance in percent of GDP; Foreign exchange share of nonfinancial corporater’public debt is the share of foreign exchange denominated debt in total nonfinancial corporations /general government debt; the bank capital adequacy ratio isthe banking system capital in percent of total risk-weighted assets; and nonfinancial corporate interest coverage is the ratio of total nonfinancial corporation earnings before interest and taxes (EBIT) to interest payments due. The minimum and the maximum axis values for each indicator are 0 and the cross-country distribution average plus one standard deviation in 2016 (2015 for nonfinancial corporate interest coverage), respectively.

2/ Inverted axis with the maximum axis value at the center and the minimum at the periphery.

B. Asset Price Spillovers

Equity Prices

4. This section uses a spillover index developed by Diebold and Yilmaz (2014) to analyze the interdependence of asset returns and volatilities in the ASEAN-5, China and the United States The index quantifies the contribution of shocks from one country’s asset returns and volatilities to another’s at different points in time. The time-varying spillover index is obtained as the generalized impulse responses, which are derived using two lags in the vector auto regression estimation and a 150-day rolling window. Because the generalized impulse response functions and variance decompositions are invariant to the ordering of the variables, four key indicators are derived from the approach: (1) gross shocks transmitted by one country to all other countries (outward spillovers); (2) gross shocks a country receives from all others (inward spillovers); (3) the net contribution of the country to the gross shocks (net spillovers) and (4) the evolution of the shocks overtime (dynamic total connectedness).

5. The results show sizeable spillovers among the ASEAN-5, China and the United States (Yilmaz, 2010; Guimaraes-Filho and Hong, 2016). Specifically, we find that:

  • Own country’s contribution price dynamics is much higher than other countries’ contribution to the equity market (Table 1).

  • The United States and Singapore equity market have been the major spillover contributors having a net contribution of 33.9 percent and 10.5 percent, respectively (Table 1). Whereas, the Philippines’ market has the largest spillover vulnerability among the ASEAN-5 countries with a −19.2 percent net contribution.

  • Equity return and volatility spillovers have increased substantially since the global financial crisis (GFC), with a mild decrease in recent years. During the 2007–2008 GFC, the interconnectedness index spiked, showing strong interlinkages across the countries. The taper tantrum in 2013 and China equity market sell-off in 2015 were also associated with elevated spillovers.

  • Finally, while interconnectedness and spillovers from China has risen as reported in IMF 2016a and IMF 2016b, the U.S. and ASEAN-5 markets remain the main source of spillover to the Philippines, perhaps due to greater regional financial integration. Singapore has been the more resilient market in the region.

Table 1.Variance Decomposition Matrix—Equity Market
PhilippinesIndonesiaMalaysiaThailandChinaUSASingaporeFROM
Philippines40.2913.119.169.722.8812.4112.4359.71
Indonesia9.5539.3810.1911.72.8412.3613.9960.62
Malaysia7.0712.2142.028.723.4111.5415.0357.98
Thailand8.1611.837.7645.252.’5’512.0912.2554.75
China3.985.625.735.0867.44.447.7532.6
USA4.436.045.957.232.7462.311.337.7
Singapore7.2911.2810.979.963.9818.7637.7762.23
TO40.4960.149.7652.4118.571.672.7552.23
NET−19.22−0.53−8.22−2.35−14.0933.910.51
Sources: Bloomberg L.P.; and IMF staff estimates.
Sources: Bloomberg L.P.; and IMF staff estimates.

Stock Market Index—Interconnectedness and Market Performance

Sources: Bloomberg L.P.; and IMF tuff estimate.

Global and Regional Spillovers—Net Indicator by Region

Sources: Bloomberg L.P.; and IMF staff estimates.

6. We examine the spillovers on ASEAN-5 domestic interest rates. While the role of global risk aversion on emerging markets’ equity prices has been well studied (IMF, 2014a; Yilmaz, 2010; and IMF, 2015), spillovers on ASEAN-5’s domestic interest rates are important given their direct implications on the monetary policy framework. How the “center economy” monetary policies are transmitted to domestic long-term sovereign bond yields is of particular interest as they act as a benchmark for pricing corporate bonds and household mortgages. The influence of global financial factors and risk aversion on domestic retail bank rates, directly or indirectly, through the monetary transmission mechanism is also important given the dominance of banks in the Philippines

  • Domestic long-term market interest rates. The methodology followed Peiris (2013) and IMF (2016), estimating an EGARCH (1,1) model of sovereign bond yields in the ASEAN-5 economies during 2000–2015 using a comprehensive set of macrofinancial variables including global factors. The results show that a decline in the shadow federal funds rate3 reduces long-term government bond yields in all ASEAN-5 economies. An increase in U.S. term premium, such as during the “taper tantrum,” also results in higher long-term bond yields in all ASEAN-5 economies. The results indicate a greater impact in the Philippine domestic rates, owing to a rise in the shadow federal funds rate and U.S. term premium (Table 2). Greater global risk aversion proxied by the VIX has a mixed effect on long rates, with a rise in the VIX increasing yields in Indonesia and the Philippines while lowering yields in Thailand, likely reflecting the greater home bias of Thai financial institutions. Strong fundamentals such as stronger external balances and lower public debt tend to keep bond yields down. Expectations of currency depreciation can also drive bond yields higher. Interestingly, better growth expectations often result in lower bond yields than vice versa, suggesting that investors may see better growth prospects as a sign of improved credit worthiness rather than just a cyclical consideration. Overall, the susceptibility of long-term bond yields to global factors is consistent with the high degree of foreign participation in the ASEAN-5 economies, with foreign portfolio capital flows being a key channel of spillovers, albeit with expectations and domestic residents continuing to play a significant role.4

  • Retail bank rates. Spillovers of global factors to retail bank rates in the ASEAN-5 countries were investigated following the approach of Ricci and Shi (2016) and IMF (2016) by estimating the domestic and global determinants of both deposit and loan rates (Table 3 and 4).5 In addition, the specification allows for liquidity effects and rigidities in interest rate transmission. The results indicate that global financial factors significantly affect bank behavior in the Philippines and other ASEAN-5 economies except possibly in the case of Thailand.6 Lending rates are also affected by lagged equity prices, which are a proxy for net worth of corporates and reflect balance sheet or financial accelerator effects affecting the cost of bank credit. However, the domestic policy rates and liquidity conditions (measured by the deviation of reserve money from a Hodrick-Prescott trend) also post a significant effect in the Philippine deposit and lending activities. Subsequently, affirming the important role of domestic monetary policy and liquidity management operations in influencing the credit cycles.

Table 2.Determinants of Sovereign Bond Yields 1/2/3/(10-year government bond)
Domestic FactorsExternal Factors
Debt to GDP ratioExpected GDP (real % change, 1-yr forecast)InflationCurrent account balance in percent of GDP (-1)Expected exchange rates (1-year forecast)Share of foreign holdings in total local currency government bondsVIXEffective Federal funds rateU.S. term premium
Indonesia0.062333 *−2.08002 *0.22776 *0.110600.04632 **0.37055 **0.80325 *
−0.046404 **−0.5195220.274776 *0.000656 *−0.174364 *0.033914 **0.1102440.63379 *
Malaysia0.018206−0.194963 ***0.081960 **0.013592−0.0056500.0954690.142382
−0.0045240.1123540.048385 **0.455059 *−0.0135910.000604−0.0340090.174713 *
Philippines0.033204 *−0.899722 *0.024455−0.178439 *0.015214 *0.413160 *0.527717 *
0.118536 *−0.642977 **0.208446 *0.187917 *−0.0036980.107680.605144 *
Singapore−0.008626 *−0.148974 *−0.085395 *−0.0192630.0030950.181435 *0.309268 *
−0.007277 **−0.029602−0.041678 ***1.686303 *−0.0045030.0519120.218736 *
Thailand−0.033360 **0.1409610.046901−0.045019 *−0.008170.269024 *0.411737 *
−0.1073660.1634510.10453 *0.066449 *0.05807 **0.0018420.288077 *0.48909 *

*significant at p<0.01 level; **significant at p<0.05 level; **significant at p<0.10 level.

The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage rise in the explanatory variables.

Results of alternative specification considering changes in economy-specific terms of trade remain robust.

*significant at p<0.01 level; **significant at p<0.05 level; **significant at p<0.10 level.

The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage rise in the explanatory variables.

Results of alternative specification considering changes in economy-specific terms of trade remain robust.

Table 3.Determinants of Deposit Rates 1/2/3/
Domestic FactorsExternal Factors
Policy rateReserve money gapDeposit interest rate(-l)VIXFederal Funds rateU.S. term premium
Indonesia0.027175 **−0.00000050.933521 *−0.0009490,0089740.027535
0.148977 *−0.000002−0.0090330,395125 *0.607063 *
Malaysia0.043452 *−0.000001 **0.941046 *−0.001112 *0,0020530.013723 *
0.0513230.000013 *−0.0035900,094911 *0.085377 *
Philippines0.064238 ***0.0000000.888499 *0.001056−0,0042740.022956
0.693344 *−0.000003 *−0.003013−0,0501550.241218 *
Singapore−0.0005920.0000010.025152 *0.001321 *0,017002 *−0.002479
−0.0011910.0000000.001087 *0,029868 *0.015474 *
Thailand0.051272 **0.0001120.876080 *−0.0024160,0008880.009568
0.309664 *−0.000103−0.008819 **0,075830 *0.022943

For Singapore, NEER month-on-month growth was used forthe variable “policy rate.”

* significant at p<0.01 level; “significant at p<0.05 level; ““significant at p<0.10 level.

The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

For Singapore, NEER month-on-month growth was used forthe variable “policy rate.”

* significant at p<0.01 level; “significant at p<0.05 level; ““significant at p<0.10 level.

The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

Table 4.Determinants of Lending Rates 1/2/3/
Domestic FactorsExternal Factors
Policy rateReserve money gapLending interest rate (-1)Equity pricesVIXFederal Funds rateU.S. term premium
Indonesia0.062514 *−0.00000050.955839 *0.002044−0.014678−0.011645
0.072285−0.00000660.014710 ***0.674774 *0.829765 *
−0.011667−0.000002−0.000707 *0.0055350.277657 *0.16202 **
Malaysia0.0256670.00000120.912929 *−0.001410 ***0.032964 *0.027321 **
0.0298590.000013 *0.009545 *0.383984 *0.243941 *
0.540194 *0.000004−0.001056 *0.0005060.206565 *0.189615 *
Philippines0.198824 **−0.00000020.736750 *0.0081070.0591090.152256 **
0.832216 *−0.000002 *0.027624 *0.189371 **0.627367 *
0.337952 *−0.000001−0.000486 *0.014131 **0.161869 **0.05785
Singapore0.000107−0.0000008 *0.982296 *0.0000650.0005250.001203
0.004795−0.00000060.001331 *−0.008960 *0.000812
0.003364−0.00000050.000030 *0.001496 *−0.006920 *0.006025 *
Thailand0.051432 *0.0002460.976985 *−0.000205−0.031450 *−0.008097
0.167468 *−0.000223−0.010757 ***−0.49984 *−0.636922 *
0.0169850.0002840.002209 *0.019712 *−0.130755 *−0.173794 *

For Singapore, NEER month-on-month growth was used forthe variable “policy rate,”

* significant at p<0.01 level; “significant at p<0.05 level; ‘“significant at p<0.10 level.

The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

For Singapore, NEER month-on-month growth was used forthe variable “policy rate,”

* significant at p<0.01 level; “significant at p<0.05 level; ‘“significant at p<0.10 level.

The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

C. Role of External Factors in Driving Business Cycle Fluctuations

7. We also examine the quantitative impacts of external shocks on the Philippines’ business cycle. The role of external factors in driving emerging market economic growth is well established.7 We follow the approach of IMF (2014b) to analyze the relationship between emerging market business cycles and external conditions by assuming that global economic conditions are exogenous to small open emerging market economies, at least on impact.8 The section uses Bayesian structural vector auto regression (BVAR) model to quantify the growth effects of external shocks. The external variables (the “external block”) include U.S. real GDP growth, the U.S. Term Premium, the VIX index, China GDP growth and economy-specific terms of trade growth.9 In alternative specifications (Kim and Peiris, forthcoming), the external block will be modified by additional proxies for global financing conditions, such as net capital flows, and FX currency sovereign (EMBIG) spreads.

8. The impact of external shocks on economic activity could be transmitted through different channels and amplified by structural features and policies. Therefore, we consider a few alternative specifications based on the literature. The baseline specification for domestic variables (the “internal block”) include real GDP growth, domestic credit growth to the private sector, interest rates, the rate of appreciation of the economy’s real exchange rate against the U.S. dollar, the budget balance, and remittances. The external block is assumed to be contemporaneously exogenous to the internal block—that is, external variables are not affected by internal variables within a quarter. This specification captures the traditional transmission channels of external demand and financing conditions through the trade channels and domestic monetary policy response including credit and exchange rate channels. An alternative model specification evaluates global and domestic policy transmission through local currency long term bond yields and EMBIG spreads given the large capital inflows and pick up in FX borrowing since the GFC.

9. The model is estimated for the Philippines using quarterly data from the first quarter of 2000 through the latest available quarter in 2017. The impulse response functions (IRFs) show that domestic economic activity (real GDP) is significantly affected by U.S. GDP growth, U.S. long-term bond yields and the VIX. As a consequence, external factors explain most of the variation in real GDP growth excluding own shocks with remittances the only other significant domestic driver of business cycles. In terms of transmission and amplification of shocks including capital flows, domestic bank credit and local currency bond yields play an important role, which are affected by both domestic policy and external factors.

IRF of Philippines’ Domestic Activity to an External Shock—Baseline Model

IRF of Philippines’ Domestic Activity to an External Shack—Baseline Model

Variance Decomposition of Domestic Activity— Baseline Model

(Share of own shock excluded)

IRF of Philippines’ Domestic Activity to an External Shock—Alternative Model

IRF of Philippines’ Domestic Activity to an External Shock—Alternative Model

Variance Decomposition of Domestic Activity— Alternative Model

(Share of own shock excluded)

D. Illustrative Policy Scenarios

10. Global policy uncertainties are at an elevated level and some types of external shocks can have large spillovers on the ASEAN-5 and emerging markets, based on historical experience. Despite a decline in election and new administrations related risks, policy uncertainty could well rise further, reflecting—for example—difficult-to-predict U.S. fiscal policies (Obstfeld, 2017). In China, failure to address financial stability risks and curb excessive credit growth could result in an unwanted, abrupt growth slowdown, with adverse spillovers to other countries through trade, commodity price, and confidence channels. A faster-than-expected monetary policy normalization in the United States could tighten global financial conditions and trigger reversals in capital flows to emerging economies, along with U.S. dollar appreciation (Obstfeld, 2017). Recent experience with the taper tantrum in 2013, China equity market sell-off in 2015, and initial reaction to the 2016 U.S. election, suggests potentially significant spillovers to the ASEAN-5 should any of the three key risks identified above materializes (Figure 2). The spillovers of United States and China shocks to the ASEAN-5 estimated and traced in the previous sections also indicate likely channels of impact and magnitudes, that can be used gauge the spillovers of the hypothetical risks above.

11. We use a four-region version of the IMF’s Global Integrated Monetary and Fiscal Model—consisting of the United States, China, the Philippines, and the rest of the world—to quantify potential spillover effects to the Philippines. The model also features a financial accelerator effect, with financing costs of firms varying in response to changes in their debt-equity ratios. We examine the following illustrative scenarios.

Monetary Policy Normalization in United States

12. Assumptions. The monetary policy normalization in the United States, including through a gradual reduction of the Federal Reserve’s securities holdings, causes a greater-than-expected tightening of global financial conditions. As discussed in IMF (2014a), this unexpected tightening could be triggered by market misperception over the speed of future monetary policy normalization in the United States. The U.S. term premium rises by 20 basis points in 2018 and 2019, respectively, and 15 basis points each in the subsequent two years.10 These in turn raise the term premia in other countries, consistent with the historical correlation for this type of shock (IMF, 2014a). Furthermore, emerging market sovereign risk premia increase temporarily by 50-70 basis points in 2018,11 as investors become more reluctant to hold bonds issued by these economies.

13. Results(Figure 3). As financial conditions unexpectedly tighten, U.S. real GDP falls by 0.5 percent in 2018 and 0.7 percent in 2019. The Federal Reserve responds quickly to market fears by easing its monetary stance relative to the baseline, which helps contain the rise in U.S. short-term interest rates.

Figure 3.Monetary Policy Normalization in United States

(Percent deviation from case with no shocks)

14. The adverse spillovers to the Philippines could be significant, with the real GDP falling by close to one percent in 2018 and 2019. The increase in the sovereign risk premium and the term premium raise the real interest rate and the external financing premium of leveraged firms, leading to weaker investment. The increase in the user cost of capital also reduces firm profitability and dividend payments to households, and lowers production and labor demand, leading to weaker consumption. In response to the weaker domestic private demand and the resulting moderate decline in inflation, the authorities lower the nominal policy interest rates and increase government spending. Improvement in the trade balance, which mainly reflects lower imports and weaker currency, provides some partial offset to the output loss.

Lower Growth Path in China

15. Assumptions. China follows a lower growth path over the medium term owing to a temporary but persistent funding shock. The shock could be triggered by a system-wide turbulence in the Chinese wholesale funding market or a run on short-term asset management products issued by nonbank financial institutions, as described in IMF (2017b). Under this scenario, real GDP growth falls by about 2.5 percentage point below the baseline in 2018 and 2019, and remain below the baseline over the medium term. Furthermore, sovereign risk premia rise in 2018, by 100 basis points in China and by 25 basis points in other economies excluding the United States.

16. Results(Figure 4). Notwithstanding the significant output decline in China, the estimated spillovers to the Philippines are relatively moderate. Real GDP declines by about 0.6 percent in 2018 and 2019. The external financing premium for Filipino firms rise about 15 basis points in 2018. The currency remains broadly stable in real effective terms, but depreciates by almost one percent against the U.S. dollar in real terms.

Figure 4.Low Growth in China

(Percent deviation from case with no shocks)

Unproductive U.S. Fiscal Expansion12

17. Assumptions. The United States embarks on a four-year debt-financed fiscal expansion (2018–21) through a combination of reduced labor and corporate income taxes and increased infrastructure spending (IMF, 2017a). After four years, the U.S. government adjusts its policy to stabilize the long-run government debt-to-GDP ratio. During the first two years, households and firms take the fiscal stimulus as temporary in nature and behave accordingly. While U.S. monetary policy responds endogenously to the change in demand, the rest of the world—except China and the Philippines—keeps their policy rates at the effective lower bounds. The infrastructure spending is assumed to be unproductive, leading to higher U.S. inflation rates and a faster normalization of the U.S. term premium (25 basis points in 2018 and an additional 25 basis points in 2019) than with productive infrastructure spending. Labor tax cuts go mostly to wealthy households.

18. Results(Figure 5). During the fiscal expansion period, U.S. real GDP rises by about 0.5 percent, and U.S. monetary policy tightens in response to higher domestic demand and inflation pressures. Real U.S. interest rates also rise, and the U.S. dollar appreciates in real effective terms.

Figure 5.Fiscal Expansion with Unproductive Infrastructure Investment

(Percent deviation from case with no shocks)

19. The U.S. fiscal expansion affects the Philippines economy through the interest rate and the trade channel. The net spillover impact on the Philippines’ GDP is negative (about 0.2 percent) in the short term as global financial conditions tighten more than to offset the expected positive gains in trade. Compared to the productive case where the net output impact is positive, the U.S. nominal policy rate rises by less but the faster normalization of the U.S. term premium leads to higher real interest rates. On the other hand, the gain from trade is smaller owing to the weaker domestic demand expansion in the United States.

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Prepared by Shanaka Jayanath Peiris and Minsuk Kim (both APD). The authors would like to thank Mia Agcaoili and Khristine Racoma (IMF Resident Representative’s Office) for their excellent research support and Dirk Muir (APD) for his valuable inputs and guidance on model simulation.

Spillovers from global commodity prices are expected to be positive for net commodity importers like the Philippines but relatively small given the low energy intensity of the Philippine economy. The full pass-through of petroleum prices given the liberalized fuel pricing market is inflationary but energy is only 6 percent of the CPI basket. Thus, this paper controls for the impact of global commodity prices rather than assessing it as a separate channel. See Dizioli and others (2016) for the limited role of global commodity prices in the Philippines in conjunction with shocks to China.

The Federal funds rate provides the conventional measure of U.S. monetary policy stance but at a near-zero rate since the end of 2008 cannot capture the role of unconventional monetary policy. This prompts the consideration of other measures including a shadow short rate (Krippner, 2014). The shadow short rate is computed using estimates from a two-state variable shadow yield curve and has historically tracked the actual federal funds rate very closely, prior to reaching the zero lower bound.

The degree of foreign participation has a direct impact on sovereign bond yields in the ASEAN-5 as in other EMs (Peiris, 2013) while the role of global financial factors also remains significant. The impact of Quantitative Easing in the Euro Area and Japan was not distinguishable with U.S. financial variables which are the dominant global factor for the ASEAN-5. The increasing spillovers from China to EME financial markets reported in IMF (2016b) were also not discernible in the quarterly data from 2000–15 given the frequency of the sample.

The empirical methodology followed Ricci and Shi (2016) in assessing the robustness of the findings to alternative specifications and sub-sample estimations, but the results were largely unchanged from the Ordinary Least Squares estimates below for the full sample period, allaying concerns of omitted variable bias and/or structural breaks. The robustness of the results to alternative publicly available retail bank rate data were also tested, although supervisory data on banks deposit and loan rates were unavailable and may provide a more accurate measure of financing costs.

The increase of provisioning rates by the Bank of Thailand and tightening of banks’ lending standards, likely related to rising household leverage (see next section), may explain the different results for Thailand.

Studies analyzing the role of external conditions in emerging markets’ growth include Österholm and Zettelmeyer (2007) for Latin America; Utlaut and van Roye (2010) for Asia; and Adler and Tovar (2012) for a more diverse group of emerging markets.

On the other hand, IMF (2017a) for the impact of external factors on trend or medium term growth in emerging markets.

With the federal funds rate constant at near zero since 2008 and the Federal Reserve’s focus on lowering U.S. interest rates at the long end, the 10-year Treasury bond rate is likely a better proxy for U.S. monetary policy for the analysis. That said, none of the main results of the analysis would be affected if the federal funds rate were added or used instead.

See Bonis and others (2017) for the effect of the U.S. Federal Reserve’s balance sheet adjustment on the term premium.

This is about half of the size of the shock observed in 2008, as measured by the average annual increase in the J.P. Morgan Global Emerging Market Bond Index for emerging Asian countries.

This scenario is based on the “unproductive” infrastructure spending scenario in Scenario Box 1 of IMF (2017a). The latter, however, used the IMF’s G-20 model for simulation. The simulation results here and in the WEO are qualitatively similar, although the magnitude of impacts is generally smaller in this simulation.

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