Research & Policy Briefs From the World Bank Malaysia Hub No. 64 July XX, 2024 Implications of Heightened Global Uncertainty for the East Asia and Pacific Region Jongrim Ha, Ergys Islamaj, and Aaditya Mattoo1 Macroeconomic, financial, and policy-related uncertainty have increased since the COVID-19 pandemic globally and in individual developing economies in the East Asia and Pacific region. Uncertainty shocks can transmit across borders and their economic consequence is quite sizeable, affecting both the financial sector and the real economy in the EAP region. Introduction Box 1 presents more details about the measurement of different types of uncertainty. Extensive literature documents heightened economic uncertainty since the COVID-19 pandemic (Cascaldi-Garcia et Macroeconomic uncertainty is elevated in the United al. 2023). All types of uncertainty have increased: States, reflecting uncertain future output growth and inflation macroeconomic, financial, economic policy, geopolitical, and developments (figure 1, panel a). After peaking in April 2020 at trade uncertainty. However, less is known about the the onset of the COVID-19 pandemic, it spiked again in 2022, implications of global uncertainty for emerging markets and mainly due to a surge in inflation. Despite the decline since developing economies (EMDEs) in the East Asia and Pacific mid-2022, inflation uncertainty is still high in the United States, (EAP) region. This Research and Policy Brief examines the reflecting disagreements on the speed and degree of the future effects of global uncertainty on EAP by addressing the following (dis)inflation process. Global economic policy uncertainty, questions. First, what are the basic sources of economic which aggregates future monetary, fiscal, and financial policies, uncertainty? Second, how has uncertainty evolved globally and is twice as high as the pre-pandemic levels (figure 1, panel b). In in individual EAP EMDEs in recent decades? Third, how has the United States, debates continue about the path of future global uncertainty affected macroeconomic and financial monetary policy decisions by the Federal Reserve; the conditions in EAP? monetary policy uncertainty index has stayed persistently above the long-term averages since 2020. Similarly, financial The empirical results suggest that the effects of uncertainty (VIX) surged in 2020 and 2022 and, despite uncertainty—mainly related to the volatility (second moment) declines, remains above pre-pandemic levels (figure 1, panel c). of the variables—are distinctive from the effects of the level (first moment) fluctuations in underlying business, financial, Uncertainty regarding future output, inflation, financial and policy variables. The economic consequences of the markets, and economic policies is higher than before the uncertainty shocks are quite sizeable, almost comparable to pandemic in many EAP countries. Macro uncertainty, which the effects of the “level” shocks. In addition, the results suggest suffered from record-high levels in 2020–21 (output uncertainty) that different types of uncertainty shocks affect EAP countries or 2022 (inflation uncertainty), has generally declined since differently, individually and as a group cause somewhat 2023 (figure 2). That said, uncertainty measures are at levels heterogeneous effects on EAP countries. In terms of cumulative above pre-pandemic ones, and in some countries (Thailand), effect, macro uncertainty, followed by financial and economic output and inflation uncertainty are still increasing. News-based policy uncertainty, contributes the most to the variations in the economic policy uncertainty remains elevated—particularly in key domestic variables in EAP. In particular, macro uncertainty China. Survey-based uncertainty indicators on short- and can explain up to one-quarter of variations in macro variables in long-term interest rates, which are typically a critical EAP. determinant of investment, have increased across all EAP economies (World Bank 2023). Evolution of global and EAP economic uncertainty Economic effects of heightened uncertainty Global economic uncertainty rose sharply to a record-high level after the outbreak of the COVID-19 pandemic and stayed To assess the effects of global uncertainty on EAP, a series of elevated through 2023 and early 2024.This Brief discusses structural vector autoregressive (SVAR) models are estimated the effect of global uncertainty on macroeconomic and over 2000–19 for five EAP countries: China, Indonesia, financial conditions in the EAP. It highlights three types of Malaysia, the Philippines, and Thailand. The models consist of a uncertainty: macroeconomic uncertainty–forecast-based measures; set of macro and financial variables in the United States (or policy uncertainty–news-based measures; and financial global measures), China, and individual EAP countries. China, uncertainty–stock market volatility-based measures. Where a the largest EMDE, is taken separately in the model to reflect its global index is unavailable, US uncertainty indexes are used as role as both a source and a recipient of global uncertainty proxies for global uncertainty, as is common in the literature. shocks. Three sources of uncertainty are distinguished: 1 Affiliations: Aaditya Mattoo is the Chief Economist of East Asia Pacific, World Bank. Jongrim Ha is with the Prospects Group (DECPG), World Bank. Ergys Islamaj is with the East Asia and Pacific Chief Economist Office, World Bank. Acknowledgements: Nancy Morrison provided editorial assistance. Objective and disclaimer: Research & Policy Briefs synthetize existing research and data to shed light on a useful and interesting question for policy debate. Research & Policy Briefs carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors, or the governments they represent. Implications of Heightened Global Uncertainty for the East Asia and Pacific Region Box 1. Empirical methodology and data Model. Both country-specific and panel SVAR models are run. Country models provide more detailed county-specific results, This Research and Policy Brief takes two sequential empirical while the panel model is more efficient and clearer to present but steps: (1) measuring uncertainty; and (2) estimating SVAR models loses country-specific results. This Brief reports the results based to quantify the economic effects of global uncertainty on EAP. on panel SVARs. Country-specific results are featured as robustness or sensitivity checks. In identifying the uncertainty Measures of uncertainty shocks, the Brief employs recursive restriction. Following the The Brief employs various measures of uncertainty based on the previous literature—such as Baker, Bloom, and Davis (2016); Leduc extensive literature and authors’ calculations. and Lui (2016)—and to overcome the endogeneity problem, the uncertainty measures are ordered last (along with various control • Forecast-based measures. To gauge macroeconomic uncertainty, variables to reduce the endogeneity). this set of measures relies on economic forecasting models for a variable of interest. For instance, a vector autoregressive Data set. The analyses are based on monthly macroeconomic and (VAR) model is recursively estimated, and the standard financial data for the global analysis, US analysis, and analysis of deviation of the forecast error for the variable of interest is EAP countries in a panel setup. Where a global index is assumed to reflect the uncertainty in the variable. unavailable, US uncertainty indexes are used as proxies for global • Survey-based measures. This set of measures uses economic uncertainty, in keeping with the literature. The sample period is surveys from professional forecasters on a variable of interest: 2000–19. Main endogenous variables (in this order) are: US output, inflation, short- and long-term interest rates, and (global) output, inflation, uncertainty measure, domestic output, current account. The uncertainty is measured as the survey inflation, exchange rates, equity prices, and domestic uncertainty responses' standard deviation or cross-sectional differences, measure. Control variables are: volatility (VIX) index, US monetary such as inter-quartile ranges. policy instrument, time trends, and commodity prices. EAP • News-based measures. To gauge policy uncertainty, this set of countries in the sample include China, Indonesia, Malaysia, the measures uses a keyword-searching algorithm. An example is Philippines, and Thailand. These countries were selected after the algorithm used by Baker et al. (2016) and Husted et al. considering the economic scale and the availability of uncertainty (2020), which puts together economic policy uncertainty in the data. China, which is the largest emerging market and developing economy (EMDE), is taken separately in a model that includes both United States and other economies by searching the keywords global (US) and Chinese variables. Three types of uncertainty for momentary, fiscal, and other policies in the local newspapers. measures are tested: macroeconomic, financial, and policy. For China’s uncertainty measures, the dataset in Ahir et al. (2022) is Structural vector autoregressive (SVAR) models also employed. An open-economy model characterizes the data-generating process as follows: Robustness checks. The main empirical results are tested using alternative modeling [factor-augmented vector autoregressive p q AYt=∑i= 1 Bi Yt-i +∑j=0 Cj Xt-j +εt . (FAVAR) framework], alternative measures of uncertainty, different ordering of the variables, the inclusion of other control Yt is an n×1 vector of endogenous macroeconomic and financial variables—in particular, the different types of uncertainty measures variables. Xt is exogenous regressors. A, Bi ( i ≥ 1), and Cj ( j ≥ 0) A A and the inclusion of post-pandemic data, among others. The are nonsingular coefficient matrices. εt is an n×1 structural results confirm that the key empirical results are robust to these disturbances vector and serially uncorrelated. alternative models. Figure 1. Evolution of uncertainty in the global economy and the United States a. US macroeconomic uncertainty b. Global economic policy uncertainty c. US financial uncertainty (VIX) and US monetary policy uncertainty 1.3 Macro uncertainly 450 Global EPU US MPU (RHS) 350 GDP uncertainty (RHS) 400 300 1.2 Inflation uncertainty (RHS) 350 250 300 1.1 250 200 200 150 1.0 150 100 0.9 100 50 50 0.8 0 0 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 Source: World Bank estimates; Haver Analytics; Baker, Bloom, and Davis 2016; Jurado, Ludvigson, and Ng 2015. Note: In panel a, macroeconomic uncertainty is based on forecast-based measures by Jurado, Ludvigson, and Ng (2015). The long-term average is 0.9. GDP and inflation uncertainty are based on a standard deviation of Consensus Forecasts for US GDP and Consumer Price Index (CPI) inflation. In panel b, the Global Economic Policy Uncertainty (EPU) and US Monetary Policy Uncertainty (MPU) index are based on news-based measures by Baker, Bloom, and Davis (2016). In panels b and c, the horizontal lines indicate the long-term average (set to 100). RHS = right-hand side. 2 Research & Policy Brief No.64 Figure 2. Evolution of economic uncertainty in the East Asia and Pacific region a. Macroeconomic uncertainty b. Financial and policy uncertainty 2.5 China policy uncertainty EAP long-term rate uncertainty (RHS) GDP uncertainty 700 EAP short-term rate uncertainty (RHS) 1.0 2.0 Inflation uncertainly 600 0.8 1.5 500 400 0.6 1.0 300 0.4 200 0.5 0.2 100 0.0 0 0.0 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 Source: World Bank estimates; Consensus Economics; Baker, Bloom, and Davis 2016. Note: In panel a, EAP macroeconomic uncertainty is based on the average cross-sectional standard deviation of Consensus Forecast surveys for four emerging markets and developing economies (EMDEs) in EAP (China, Indonesia, Malaysia, Thailand). In panel b, China policy uncertainty is based on news-based measures by Baker et al. (2016). EAP financial uncertainty is based on the average standard deviation of Consensus Forecasts for 3-month Treasury bill yields and 10-year bond yields in the four EAP countries. EAP = East Asia and Pacific; RHS = right-hand side. macroeconomic, financial, and policy (for estimation details, impacts on key domestic financial variables in EAP, although see box 1). their impacts are overall more immediate but short-lived compared with macro uncertainty (figure 3, panel c). Following The findings suggest that global uncertainty shocks have a one- standard-deviation VIX shock, average stock prices in negatively affected macro and financial conditions in EAP EAP declined by 1.2 percent, which is comparable to the effects countries. This result is statistically and economically significant of a negative shock on US equity prices (-1.6 percent). The based on average results (that is, based on the panel effects of policy uncertainty shocks appear to add another layer model), as well as in country-specific perspectives—the of negative effects on EAP countries. While US monetary results hold for all EAP countries in the sample. Following a tightening led to a decline in EAP stock prices by up to 1.4 one-standard-deviation increase in US macro uncertainty, percent, heightened US monetary policy uncertainty resulted growth of output declined by 0.5 percent and stock prices in another 0.7 percent decline in stock prices. declined by 3.0 percent in EAP within one year after the shock (figure 3, panels a and b). The effects are stronger than the The sizeable uncertainty effects reflect various channels of effects from a one-standard-deviation decline in US output (by international transmission of uncertainty shocks. First of all, the 0.3 percent and 1.0 percent, respectively). Global financial dynamic responses of the macro variables are consistent with market and economic policy uncertainty also had significant the theories on the economic effects of uncertainty—typically Figure 3. Impact of US uncertainty on the East Asia and Pacific region a. EAP output responses to negative US b. EAP stock price responses to c. EAP stock price responses to US output and macro uncertainty shocks negative US stock price and monetary policy tightening and financial uncertainty shocks monetary policy uncertainty shocks Percent Percent Percent 0.6 1.0 0.0 0.4 0.5 0.2 0.0 -0.5 0.0 -0.5 -0.2 -1.0 -1.0 -0.4 -0.6 -1.5 -1.5 -0.8 -2.0 -1.0 -2.5 -2.0 t=0 6 12 24 t=0 6 12 24 t=0 6 12 24 t=0 6 12 24 t=0 6 12 24 t=0 6 12 24 Negative US Marco uncertainly Negative US US financial US monetary policy US monetary policy output shock shock stock prices uncertainty (VIX) shock uncertainty shock Source: World Bank estimates; Jarocińksy and Karadi 2020. Note: The results are based on panel vector autoregressions estimated with a sample for January 2000 through December 2019 for the United States and four emerging markets and developing economies (EMDEs) in EAP (Indonesia, Malaysia, Philippines, Thailand). The model includes, in this order, the following measures: the US production, US Consumer Price Index (CPI), US uncertainty; and domestic (EAP) industrial production, prices, stock prices, exchange rates, and uncertainty. Panel a shows dynamic responses of EAP industrial production to a one-standard-deviation increase in macroeconomic uncertainty and decline in output. Bars indicate the median responses, and vertical lines indicate 16 percent–84 percent confidence intervals. Panel b shows dynamic responses of EAP equity prices to a one-standard-deviation increase in financial uncertainty (VIX) and decline in US equity prices. Panel c shows dynamic responses of EAP stock prices to US monetary policy tightening shock and US monetary policy uncertainty shock. EAP = East Asia and Pacific. 3 Implications of Heightened Global Uncertainty for the East Asia and Pacific Region Figure 4. Impact of United States and China uncertainty on the East Asia and Pacific region a. China output and stock price responses to b. EAP responses to heightened uncertainty heightened macro uncertainty in the in China Percent United States Percent 0.2 0.3 0.2 0.0 0.1 0.0 -0.2 -0.1 -0.2 -0.4 -0.3 -0.4 -0.6 -0.5 -0.6 -0.8 -0.7 -1.0 t=0 6 12 24 t=0 6 12 24 t=0 6 12 24 t=0 6 12 24 EAP output to EAP prices to China macro China macro Industrial production CPI inflation uncertainly uncertainly Source: World Bank estimates. Note: The results are based on a panel Vector autoregression estimated for January 2000 through December 2019 for four emerging markets and developing economies (EMDEs) in EAP (Indonesia, Malaysia, Philippines, Thailand). The model includes, in this order, the US production, US Consumer Price Index (CPI), US uncertainty measure; China production, China CPI, China uncertainty measure; and domestic (EAP) industrial production, prices, stock prices, exchange rates, and uncertainty measures. In panel a, bars show dynamic responses of China industrial production and CPI to a one-standard-deviation increase in macroeconomic uncertainty in the United States. In panel b, bars show dynamic responses of EAP production to a one-standard-deviation increase in macroeconomic uncertainty in the United States and China. EAP = East Asia and Pacific. on the “wait-and-see” channel that strains private investment recipient of uncertainty shocks in the global markets, and consumption (Binder 2017; Bloom 2009). In particular, high particularly in connection with EAP countries. levels of macro uncertainty have a negative impact on investment growth, as investors become hesitant to invest in Conclusion new projects or expand their existing businesses (Leduc and Liu 2016). Second, the reactions of financial asset prices and The findings lead to at least two types of novel implications. increases in credit costs also contribute to the contractionary First, the effects of uncertainty—which is mainly related to the effects of economic uncertainty. Third, following the volatility (second moment) of the variables—are distinctive heightened global and US uncertainty, the uncertainty from the effects of the level (first moment) fluctuations in indicators in individual EAP countries also responded underlying business, financial, and policy variables. The significantly, which supports the narratives of internationally economic consequence of the uncertainty shocks is quite correlated risk and uncertainty (Londono et al. 2021). sizeable, almost comparable to the effects of the “level” The heightened uncertainty in the domestic economy shocks. These findings are quite robust across different types of is estimated to have played a negative role in economic uncertainty measures—specifically, across macro, financial, confidence/sentiments in EAP countries (Ha and So 2023). and policy uncertainties. Second, the results suggest that different types of uncertainty shocks affect EAP countries Estimation results also suggest that while China was differently, individually and as a group. In terms of cumulative negatively affected by the global and US uncertainty shocks, its effect, macro uncertainty, followed by financial and economic own uncertainty shocks had an impact on the macroeconomic policy uncertainty, contributes the most to the variations in the and financial conditions in EAP (figure 4). Following a key domestic variables in EAP. In particular, macro uncertainty, one-standard-deviation increase in macro uncertainty in China, which explains up to 40 percent of total variations in US output industrial production in EAP countries declined by around 0.3 and prices, can explain up to one-quarter of variations in macro percent—about half of the responses to the same type of US variables in EAP. This result is in line with the findings of Stock uncertainty. Again, China's heightened policy uncertainty was and Watson (2012), who argue that the global uncertainty followed by heightened uncertainty in EAP countries. These shock has been a key driver of global recessions and other results collectively suggest that China is both a source and severe fluctuations. References Ahir, H., N. Bloom, and D. Furceri. 2022. “The World Uncertainty Index.” NBER Working Paper 29763, Jarociński, M., and P. 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