Part A：Write an essay on the impact of trade liberalization on productivity. Students are required to write a coherent integrated summary of the allocated Assignment Reading No. 1 (see research article on Moodle) as the foundation work for the essay and then do a critical analysis using relevant other journal studies (at least two). Give reference precisely to the readings you used. (max. words limit 1,200, 40 marks). Part B Due Dates: Wednesday Lecture 10 by 4.20pm. Students are required to read the Assignment Reading 2 (see research article on Moodle) and at least two other relevant recent research articles to get a balanced view of the subject. You must provide your reference list,(word limit does not include the reference list). Required: B1) Discuss available government economic policies in time of crisis (Max words 80, 10 marks) B2) Covid-19 has slowed down the global economy. Many governments have introduced fiscal policies to offset the significant contraction in their economies. Explain those fiscal policies and the theoretical impact on aggregate demand and supply. Incorporate the limitations and the time lags which can hamper the expected outcomes of government policy. Is monetary policy a beneficial tool at this tim? A critical analysis is required (Word limit: max. 1200, 40 marks). Note: Your assignment should be mainly based on the following two research articles labelled as Reading No. 1 and readings No.2, and those articles are available on Moodle. 1) Ahn, J., Dabla-Norris, E., Duval, R., Hu, B. and Njie, L. (2016), ‘Reassessing the Productivity Gains from Trade Liberalization’, IMF, WP/16/77 March., PP. 1-31 Baldwin J., R. and Caves, R., E. (1997), ‘International Competition and Industrial Performance: Allocative Efficiency, Productive Efficiency, and Turbulence ’ , Statistics Canada, Research Paper No. 108. Pp. 1- 27. 2) Spilimbergo, A., Symansky, S., Blanchard, O. and Cottarelli C. (2008), ‘Fiscal Policy for the Crisis’, IMF, December 29, SPN/08/01, pp. 2-32. Crisis Likee No Other, World Economic Outlook Update, IMF, June 2020, PP 1-20 file: Marking Guide for the essays: Introduction (5 Marks) Addressing the core issues(10 marks) Critical analysis (10 marks) Conclusion(2½ Marks) Professionalism (10 Marks) Reference (at least two journal articles in addition to the given Readings 1 and 2, 2 ½ Marks) Due dates will be strictly enforced unless prior arrangements are made. Format/Expectations: Relevant structure will be discussed and clarified in week 1-3. Cover page. Which will be provided, must contain name and student ID of all group members. Students are advised to concentrate on the followings for the Part B of the assignment. • Introduction, • Research objective • Content, • Expression, • Presentation, • Examples and diagrams • Conclusion, and • Reference List Notes: • Your reference list must have academic references and must follow good academic format. Use the link to the TIIS library website for more information. Students are strongly urged to read reviewed academic journal articles. • Assignments must be submitted on or before the due time – late assignments will not be accepted unless alternate arrangements are made. • A high level of scrutiny will be conducted to detect plagiarism, contract cheating, or other academic malfeasance. • See the Subject Learning Guide for the standard of work that is expected in determining the marks relate to grades. Completion and Submission: A hard copy must be submitted directly to the Lecturer on the due time. WP/16/77 Reassessing the Productivity Gains from Trade Liberalization By JaeBin Ahn, Era Dabla-Norris, Romain Duval, Bingjie Hu and Lamin Njie © 2016 International Monetary Fund WP/16/77 IMF Working Paper Fiscal Affairs and Research Departments Reassessing the Productivity Gains from Trade Liberalization Prepared by JaeBin Ahn, Era Dabla-Norris, Romain Duval, Bingjie Hu and Lamin Njie Authorized for distribution by Era Dabla-Norris and Romain Duval March 2016 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. Abstract This paper reassesses the impact of trade liberalization on productivity. We build a new, unique database of effective tariff rates at the country-industry level for a broad range of countries over the past two decades. We then explore both the direct effect of liberalization in the sector considered, as well as its indirect impact in downstream industries via input linkages. Our findings point to a dominant role of the indirect input market channel in fostering productivity gains. A 1 percentage point decline in input tariffs is estimated to increase total factor productivity by about 2 percent in the sector considered. For advanced economies, the implied potential productivity gains from fully eliminating remaining tariffs are estimated at around 1 percent, on average, which do not factor in the presumably larger gains from removing existing non-tariff barriers. Finally, we find strong evidence of complementarities between trade and FDI liberalization in boosting productivity. This calls for a broad liberalization agenda that cuts across different areas. JEL Classification Numbers:F13, F14, F21, F43, O43. Keywords: Trade, Productivity, Tariffs, Inputs, Liberalization, FDI, Reforms, Growth. Author’s E-Mail Address: jahn@imf.org; edablanorris@imf.org; rduval@imf.org; bhu@imf.org; lnjie@imf.org 3 Contents Page I. Introduction ...............................................................................................................................5 II. Data ..........................................................................................................................................7 III. Stylized facts ...........................................................................................................................9 IV. Empirical set-up and econometric results .............................................................................15 A. Empirical set-up .........................................................................................................15 B. Econometric results ....................................................................................................15 C. Robustness Checks .....................................................................................................18 D. Policy Implications of the Results .............................................................................23 V. Concluding remarks ...............................................................................................................25 References ...................................................................................................................................26 Tables 1. Baseline regression: Total Factor Productivity ........................................................................16 2. Baseline regression: Labor Productivity ..................................................................................16 3. Complementarity between tariff and FDI liberalization: Total Factor Productivity ...............18 4. Complementarity between tariff and FDI liberalization: Labor Productivity .........................18 5. Robustness checks for baseline regressions: alternative output and input tariff measures......20 6. Robustness checks for baseline regressions: interpolated tariff data and changes in sample ..20 7. Robustness checks for tariff-FDI complementarity regressions: TFP; alternative output and input tariff measures ....................................................................................................................22 8. Robustness checks for tariff-FDI complementarity regressions: LP; alternative output and input tariff measures ....................................................................................................................22 9. Robustness checks for tariff-FDI complementarity regressions: TFP; interpolated tariff data and changes in sample……………………………………………………………………….....23 10. Robustness checks for tariff-FDI complementarity regressions: LP; interpolated tariff data and changes in sample ..........................................................................................................23 Figures 1. Effective Tariff and Most-Favored-Nation (MFN) Tariff Rates .............................................10 2. Changes in Aggregates Tariff Barriers over 1997-2007 ..........................................................11 3. Output and Input Tariff Rates ..................................................................................................12 4. Heterogeneity in Tariff Rate Changes across Sectors..............................................................13 5a. Total Factor Productivity (TFP) and Output Tariff Rates ......................................................14 5b. Total Factor Productivity (TFP) and Input Tariff Rates ........................................................14 6. Potential Productivity Gains from Eliminating Remaining Tariff Barriers .............................24 4 Annex Tables 1. Sample of Countries .................................................................................................................28 2. Description of Sectors ..............................................................................................................29 3. Baseline Sample Data Countries and Sectors ..........................................................................30 4. Concordance between FDI restrictiveness indicators and the baseline data............................31 5 I. INTRODUCTION Trade liberalization is one of the main potential avenues for countries to boost productivity levels. This issue features high on policymakers’ agendas, as exemplified by the recent TransPacific Partnership (TPP) agreement. Major liberalization has been achieved in the past, but efforts have stalled more recently and there remains some scope for further progress even in advanced economies, particularly as regards to non-tariff barriers to trade and foreign direct investment (FDI). Over and above the classical gains arising from the reallocation of resources across sectors, the literature identifies several channels through which trade liberalization can boost productivity and, hence output. First, lower trade and FDI barriers can strengthen competition in the liberalized sector(s), putting pressure on domestic producers to lower price margins, exploit economies of scale (Helpman and Krugman, 1985), improve efficiency, absorb foreign technology, or innovate (Aghion and others, 2005). Second, productivity gains from liberalization may accrue disproportionately to larger and more productive firms, enabling them to gain market share and amplifying aggregate gains within the liberalized sector (Melitz, 2003; Pavcnik, 2002). Third, trade liberalization can boost productivity by increasing the quality and variety of intermediate inputs available to domestic producers (Ethier, 1982; Grossman and Helpman, 1991; Markusen, 1989). Recent firm-level evidence for a number of countries confirms the quantitative importance of this input channel (Fernandes, 2007; Kasahara and Rodrigue, 2008; Topalova and Khandelwal, 2011; Amiti and Konings, 2013; Halpern et al., 2015). Another important result from recent theoretical and empirical evidence is that the impact of the input channel—and that of trade liberalization more broadly—appears to vary widely across firms depending on their individual characteristics, such as ownership status (foreignowned vs. domestic, see Halpern et al., 2015), the extent to which they use imported inputs (Amiti and Konings, 2007) or the degree of competition in their industry (Topalova and Khandelwal, 2011). This hints at possible interactions between trade liberalization and other policies, such as product market regulation or barriers to FDI that could affect these firm characteristics. This paper reassesses the productivity gains arising from tariff cuts on final goods and intermediate inputs and their complementarities with reductions in barriers to FDI. We use a new, unique database of effective tariffs in 18 sectors across 18 advanced countries spanning over two decades. The productivity effects of both “output tariffs”, which capture competitive pressures from liberalization in the sector considered, and “input tariffs”, which capture the input channel, are assessed empirically. For each country and year observation, the effective output tariff in each sector j is computed as a weighted average of most-favored-nation (MFN), preferential tariff and non-MFN rates, where weights reflect the relative importance of the 6 individual products and trading partners to which each type of rate applies. For each country and year, the effective input tariff in each sector j is then computed as a weighted average of output tariff rates in all sectors, with weights calculated using Input-Output (IO) matrices for each individual country, taking into account all input linkages. That is, we factor in the fact that tariff changes affect not only the imported inputs but also the domestic ones insofar as the latter are produced using imported inputs from other sectors. Our three-dimensional panel econometric analysis finds a significant and robust impact of input tariff liberalization on sector-level total factor productivity (TFP), which is much stronger than the effect of output tariff liberalization. In other words, the input variety and/or quality channels that underpin the input tariff effect appear to matter more for boosting productivity than the pro-competition impact of lower output tariffs. Quantitatively, the estimates imply that a one percentage reduction in input tariffs raises TFP levels by about two percent. In addition, the effect of both output and input tariff liberalization are greater when barriers to FDI are lower, highlighting the importance of complementarities between trade and FDI liberalization. Our results are robust across different specifications. Using alternative lags of the output and input tariff variables, different measures of productivity and time periods, as well as alternative clustering strategies—at country-sector or country-year level—for standard errors only has a limited quantitative impact on the results. We also try to capture competitive pressures in an alternative way, by considering the effective rate of protection a la Corden (1966)—which takes into account potential anti-competitive forces from both high output tariffs and low input tariffs—instead of the output tariff rate; again, results are virtually identical. While tariff barriers in advanced countries have been reduced substantially over the last decades, our analysis suggests that there remains some scope for further reductions, and therefore for additional productivity gains. A back-of-the-envelope calculation of the potential productivity gains from full elimination of remaining tariffs suggests that aggregate productivity could rise, on average, by around 1 percent across advanced economies, varying from about 0.2 percent in Japan to 7.7 percent in Ireland, depending on both remaining sectorlevel tariff rates and each sector’s importance in the country considered. For instance, potential productivity gains for Korea and Ireland are estimated to be larger than those for other advanced economies mainly because of comparatively high remaining tariffs in Korea and the importance of specific sectors for Ireland—the chemical and pharmaceutical industries, which dominate potential productivity gains. Given their comparatively higher tariff barriers to trade, emerging and low-income economies could benefit from tariff liberalization even more than advanced economies, on average. Our paper makes several contributions to the existing literature. We build the first comprehensive dataset of effective import tariffs across countries, sectors and time, starting, and aggregating up from bilateral imports from each partner country at the individual product 7 level. Previous studies employing tariff measures (e.g, Amiti and Konings, 2007; Amiti and Khandelwal, 2013; Fernandes, 2007; Topalova and Khandelwal, 2011) typically only consider MFN rates, which have become increasingly misleading as preferential bilateral or regional agreements have gained prominence around the world. Second, by accounting fully for the gains from resource reallocation across firms, it adds to the recent firm-level literature on trade liberalization that has emphasized the impact of input tariffs. The main advantage of using sector-level data on both tariffs and productivity is that we are able to capture the aggregate impact of liberalization on both within-firm productivity and sector-level productivity via reallocation of resources across firms, including entry and exit. While recent empirical literature essentially focuses on firm-level outcomes to examine the importance of the input channel, “new trade” theory highlights the importance of this resource reallocation across firms for overall sector-level productivity gains (Melitz, 2003; Melitz and Ottaviano, 2008). As regards interactions between trade and FDI liberalization, our results generalize most recent firm-level evidence. Using firm-level data for Hungary, Halpern et al. (2015) find that foreign firms use imported inputs more effectively and pay a lower fixed cost for importing, suggesting that by increasing foreign firm presence, lower FDI barriers could magnify the productivity impact of tariff liberalization. Our paper also contributes to the empirical literature on the impact of market deregulation. Using sector-level EU KLEMS data and a comparable approach, Bourles et al. (2013) identify an input channel of product market liberalization, i.e. reductions in barriers to entry in upstream industries benefit most those downstream industries that use their products as inputs. Aghion et al. (2008) use state-level data for India and find that the 1991 economy-wide removal of entry barriers—the abolition of the so-called License Raj in 1991—benefited most those states that had easier labor market regulations. Our paper is the first to assess the impact of output and input tariff liberalization at the sector level across countries. The remainder of this paper is structured as follows. Section II discusses the data, while further details on the dataset are provided in an accompanying Annex. Section III features stylized facts on effective output and input tariffs rates and their relationship with TFP. Section IV presents our empirical set-up and econometric results. Section V provides concluding remarks. II. DATA We construct a unique database of effective tariffs for 18 manufacturing and nonmanufacturing sectors across 18 advanced countries (see Annex 1 for list of countries) spanning over two decades. For each country-year observation, the effective output tariff at the product level is computed as a weighted average of most-favored-nation (MFN), preferential tariff and non-MFN rates, where weights reflect the relative importance of the individual products and trading partners to which each type of rate applies. This significantly improves on existing studies that typically consider MFN rates only. 8 Specifically, we calculate the effective tariff rate for country i and product p in year t as: where denotes the MFN rate applied to WTO member countries, is the (typically higher) rate applied to countries that are not part of the WTO, and is the jtrading-partner-specific preferential rate under a (regional or bilateral) preferential trade agreement or under a unilateral preferential treatment such as the Generalized System of Preferences (GSP) toward developing countries.1 To calculate the country-product-level weight, , we take the share of imports from country j in country i’s total imports of each product p, which is treated as a constant based on the initial year’s value in order to minimize endogeneity issues.2 Product-level effective tariff rates, , are then aggregated up to the 2 digit sector level using the concordance table between HS6 and ISIC.rev.3 classifications: , ∈ where the weights, imports in sector s. , is derived from the product p’s import share in country i’s total For each country and year, the effective input tariff in each sector s is then computed as a weighted average of output tariff rates in all sectors, with weights reflecting the share of imported inputs from each of these sectors used in the production of sector s’s output. Considering further that the domestic portion of intermediate inputs used in sector s can also be produced using imported inputs (i.e. taking into the full input-output linkages), the effective “input tariff” for sector s can be expressed as: ∑ 1 2 ∑ A complete list of beneficiary countries for each preferential tariff regime is provided by the TRAINS database. Although raw tariff rates are available at HS 8 level from the TRAINS database, since trade data are available only at HS6 level from the UNComtrade database, we first take a simple average of each tariff rate across HS8 level within HS6 level, and then calculate the HS6-level effective tariff rates. 9 ∑ ∑ ∑ ∑ ⋯ (1) So that the 1vector of input tariffs can be written as , where is a 1 matrix whose th element is and is a matrix whose , th element is , where denotes the share of imported inputs from sector k in total inputs used in denotes the share of domestic inputs from sector k in total inputs used in sector s, while sector s, both available from the national Input-Output (IO) tables compiled by the OECD.3 We then match the resulting input and output tariff rates data with corresponding (countrysector-year-level) TFP data at the ISIC rev4 level, which are taken from the EU KLEMS and World KLEMS databases.4 These databases provide annual information on sectoral input, output, prices, and TFP over the period 1991–2012. The resulting, matched industry-level dataset of TFP and tariff rates consists largely of 13 manufacturing sectors, but a number of services sectors as well as agricultural and mining sectors are also included (see Annex 2 for description of sectors, and Annex 3 for data coverage). Actual data coverage is largely determined by the availability of the tariff data, which are missing for a few country-year observations. Finally, in the empirical analysis we also explore interactions between tariffs and the stringency of barriers to FDI. We measure the latter by using the OECD’s FDI Regulatory restrictiveness Index, which measures statutory restrictions on FDI in all of our sample countries for 22 sectors and 8 years (1997, 2003, 2006-2014).5 We map the sectoral FDI restrictiveness indicators to our TFP and tariffs data using the correspondence table shown in Annex 4. In the absence of a comprehensive annual time series for the FDI restrictiveness indicators, we compute and use their average value over the sample period when testing for their interactions with tariffs in the empirical analysis. III. STYLIZED FACTS Figure 1 illustrates the systematic disparity between the simple average of MFN rates and effective tariff rates. When aggregated up to country-year level for illustrative purposes, most 3 To avoid potential endogeneity and measurement issues, we pick one vintage of the input-output table and keep them constant throughout the sample period. 4 The EU KLEMS database includes annual measures of output and input growth, and derived variables such as total factor productivity at the industry level. Two vintages of the database under the ISIC rev.4 and ISIC rev3 classifications were fully harmonized to be consistent at the ISIC rev4-level. See Dabla-Norris et al. (2015). 5 For details, see http://www.oecd.org/investment/fdiindex.htm . 10 of the observations lie below the 45 degree line, indicating that effective tariff rates tend to be lower than simple average MFN rates. This is not entirely straightforward a priori, since effective tariff rates incorporate both preferential rates and non-MFN rates, which are higher and lower than MFN rates, respectively. In practice, however, preferential trade agreements tend to take place between larger trading partners, while non-MFN rates tend to be applied to only a few trading partners with smaller weights. As such, deviations from the 45 degree line depend largely on the coverage and depth of regional and bilateral preferential trade agreements in each country. Figure 1. Effective Tariff and Most-Favored-Nation (MFN)Tariff Rates (In percent; country-year level aggregates) 15 10 10 5 5 0 0 MFN tariff rates 15 0 5 10 15 Effective tariff rates Alternatively, and more relevant of our empirical analysis, the disparity between simple average MFN rates and effective tariff rates can be illustrated in terms of changes over time. Figure 2 displays changes in tariffs—both simple average MFN and effective rates—over 10 years between 1997 and 2007 across countries.6 Two things stand out. First, except for the United States, they show different patterns. Second, these patterns are not uniform across countries. Some countries experienced a larger decline in effective rates, likely reflecting multiple preferential trade agreements that came in effect recently (e.g., Australia and Korea). At the same time, there are countries that experienced a larger decline in MFN rates, notably advanced EU member countries, where major preferential trade agreements outside the EU had 6 It is over 8 years between 1997 and 2007 for Slovenia due to data availability. 11 not taken place during the period considered. In such cases, headline measures of tariff reduction in terms of MFN rates may overstate the degree of actual reduction in tariff barriers. Figure 2. Changes in Aggregate Tariff Barriers over 1997-2007 (In percentage point) 0 -5 -10 USA SWE SVN NLD KOR JPN ITA IRL HUN MFN tariff rates GBR FRA FIN ESP DEU CAN AUT AUS CZE Effective tariff rates -15 Turning to core variables in our country-sector-year-level empirical set-up, Figure 3 plots output tariff (X-axis) and input tariff (Y-axis) rates as deviations from country-sector averages. One of the major concerns of any approach that attempts to separately identify the output and input channels through which trade liberalization boosts productivity stems from potential collinearity between input and output tariff rates. Considering that input tariffs are constructed from output tariffs, this is not entirely implausible because input-output coefficients tend to be concentrated on diagonals—i.e., the biggest contributor to each sector’s inputs tends to be its own output. Indeed, Figure 3 reveals a positive correlation between them—with a correlation coefficient of 0.49. Importantly, this correlation is not strong enough to raise serious concerns of collinearity, as highlighted by the variation around the fitted line. 12 Figure 3. Output and Input Tariff Rates (In deviation from country-sector averages) Input tariff rates (in percent) 5 0 -5 -5 0 5 Output tariff rates (in percent) Another potential concern arises from the limited variation in tariff rates across countries. This is partly suggested by the similarity of aggregate tariff rate changes among EU member countries in Figure 2. This issue would be particularly problematic had we employed countrylevel aggregate data, and, in fact, alleviating it is one of the main advantages of the countrysector-level approach employed in this paper. Although even effective tariff rates tend to be fairly similar across countries in the common customs area, there is substantial variation in tariff rates across sectors, allowing for empirical identification of their productivity effects. This is illustrated in Figure 4 that shows changes over time in median sector-level input tariff rates among advanced EU countries. 13 Figure 4. Heterogeneity in Tariff Rate Changes across Sectors (Sector-level median of input tariff rates in advanced EU countries) 0 0.5 1 1.5 2 2.5 3 Food products, beverages and tobacco Textiles, apparel, leather etc. Wood and of wood and cork Paper, printing and publishing Coke and refined petroleum products Chemicals and chemical products Rubber and plastic products etc. Other non-metallic mineral Basic metals and fabricated metals Electrical and optical equipment Machinery and equipment Transport equipment Other manufacturing Agriculture, forestry, and fishing Mining and quarrying Eletricity, gas, and water supply Professional, scientific, technical etc. Arts, entertainment, recreation etc. 1996-2000 average 2008-13 average Figure 5 describes the relationship between TFP and tariff rates, the main variable of interest of our study. The above panel chart plots log TFP and output tariff rates, while the below panel chart plots log TFP and input tariff rates, all expressed in terms of deviation from countrysector averages so as to control for the role of country-sector-level fixed factors. As can be seen in the figure, compared to output tariff rates, input tariff rates appear to have a slightly stronger negative correlation with TFP, suggesting a possibly dominant productivity effect from the input channel. Subsequent sections examine this relationship using formal econometric analysis. Figure 5 also points to a substantial number of outliers clustered around zero, i.e. clustered around values of input tariffs equal to their country-sector averages. These outliers happen to belong to either “Coke and chemical products” or “Electrical and optical equipment,” and might reflect the volatility of output and prices in these industries. These observations are expected to drive the estimated impact of tariffs on TFP toward zero. Nevertheless, we systematically keep all observations in the empirical analysis that follows—removing them was not found to affect the results. 14 Figure 5A. Total Factor Productivity (TFP) and Output Tariff Rates (In deviation from country-sector averages) log TFP 3 0 -3 -5 0 5 Output tariff rates (in percent) Figure 5B. Total Factor Productivity (TFP) and Input Tariff Rates (In deviation from country-sector means) log TFP 3 0 -3 -5 0 Input tariff rates (in percent) 5 15 IV. EMPIRICAL SET-UP AND ECONOMETRIC RESULTS A. Empirical set-up In order to quantify the respective effects of output and input tariffs on productivity at the country-sector level, the following empirical specification is estimated: ln , , , (2) where subscripts i, s, t denote country, sector, and year, respectively. The dependent variable ln denotes log total factor productivity (TFP) in country i and sector s in year t, and and , are the corresponding country-sector-level output and input tariff rates , lagged l years. Given our interest in the long-run productivity impact of tariffs, we estimate the equation in levels, and use lagged tariffs to mitigate endogeneity issues. Different lag structures (l = 1 to 4) are tested for. The specification also includes country-sector ( ) and country-year ( ) fixed effects. The country-year fixed effects control for any variation that is common to all sectors of a country’s economy, including for instance aggregate output growth or reforms in other areas. The country-industry fixed effects allows us to control for industry-specific factors, including, for instance, cross-country differences in the growth of certain sectors that could arise for instance from differences in comparative advantage. This specification with fixed effects is tantamount to asking how changes in tariff rates in a given sector and country are associated with changes in productivity levels in that country-sector. This specification is extended to test for complementarities between tariffs and barriers to FDI as follows: ln , , , ∗ , (3) where (FDI Barriers)is is the average value of the OECD indicator of FDI restrictiveness in country i and sector s over the sample period, and , is the output or the input tariff rate depending on the specifications—note that the direct effect of FDI barriers on productivity is . absorbed by the country-sector fixed effect We estimate these equations using ordinary least squares in an unbalanced panel for the period 1991-2012. Standard errors are clustered at the country-year level. Robustness to alternative tariff measures, samples and clustering method is then performed. B. Econometric results Table 1 presents the baseline regression results showing the impact of output and input tariffs on TFP. We first regress (the logarithm of) TFP only on final goods tariffs. While the point estimate is negative as expected, the estimated coefficient is statistically insignificant, 16 regardless of the lags considered. By contrast, input tariffs always have a strong and statistically significant impact on productivity growth when incorporated in the estimated regression. Depending on the number of lags considered, a one percentage point decline in input tariffs increases the level of TFP by 1.5 to 2.2 percent, with an average semi-elasticity of close to 2. These results clearly show that the productivity gains from reducing input tariffs dominate those from reducing output tariffs. Table 1. Baseline regression: Total Factor Productivity Dependent variable: ln (TFP)ist l=1 l=2 l=3 l=4 (2) (3) (4) (5) (6) (7) (8) -0.002 -0.003 -0.002 -0.002 -0.001 -0.001 -0.001 (Output tariff)ist-l (0.002) (0.002) (0.002) (0.002) (0.001) (0.002) (0.002) -0.022 ** -0.020 *** -0.018 *** -0.015 *** (Input tariff)ist-l (0.009) (0.006) (0.006) (0.006) Country-Sector FE Yes Yes Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs 3,714 3,714 3,432 3,432 3,167 3,167 2,885 2,885 Adj R squared 0.642 0.644 0.688 0.689 0.718 0.719 0.745 0.746 Note: The dependent variable is log total factor productivity (TFP) in country i and sector s in year t. Independent variables are corresponding output and input tariff rates lagged l years. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. (1) -0.003 (0.003) There may be a concern that the TFP estimates could be biased as TFP is measured as a residual, and any measurement errors in the labor and capital series might be captured in the estimates. To address this, we replace the TFP measure with sector-level labor productivity (LP). In Table 2 we regress log value added per hours worked in each sector on final goods and input tariffs over different time horizons. The findings presented in the table confirm our previous results. In particular, the effect from output tariffs is insignificant once we control for input tariffs, whereas the magnitude of the effect of input tariffs is very close to that estimated in the TFP regressions of Table 1. Table 2. Baseline regression: Labor Productivity Dependent variable: ln (LP)ist l=1 l=2 l=3 l=4 (2) (3) (4) (5) (6) (7) (8) (Output tariff)ist-l -0.001 -0.003 -0.001 -0.002 -0.001 -0.002 -0.001 (0.001) (0.002) (0.001) (0.002) (0.001) (0.002) (0.002) (Input tariff) ist-l -0.024 ** -0.022 *** -0.020 *** -0.018 *** (0.011) (0.008) (0.008) (0.007) Country-Sector FE Yes Yes Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs 3,784 3,784 3,502 3,502 3,237 3,237 2,955 2,955 Adj R squared 0.965 0.966 0.968 0.968 0.970 0.970 0.972 0.972 Note: The dependent variable is log labor productivity (LP) in country i and sector s in year t. Independent variables are corresponding output and input tariff rates lagged l years. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. (1) -0.003 (0.003) 17 We then extend our baseline regressions along the lines of equation (3) above in order to test for interactions between tariffs and FDI restrictiveness.7 We find strong evidence of complementarities between reductions in tariffs and barriers to FDI (Table 3). Input tariff reductions are estimated to have a larger impact on TFP when barriers to FDI are low. This is consistent with the evidence in Halpern et al. (2015) that foreign firms use imported inputs more effectively and pay a lower fixed cost for importing, so that their presence—which is helped by lower barriers to FDI—magnifies the productivity impact of tariff liberalization through the input channel. Interestingly, once they are interacted with FDI restrictiveness, output tariffs show a significant, direct negative effect on TFP that was absent in Tables 1 and 2—for countrysectors with the sample mean level of FDI restrictiveness, output tariff effect can be as strong as input tariff effects. Moreover, the estimated TFP gain from output tariff reduction is greater when barriers to FDI are lower, consistent with the notion that by increasing competitive pressure, the presence of foreign firms amplifies the productivity gain from trade liberalization through this channel. All these results are robust to considering labor productivity rather than TFP as the dependent variable (Table 4), as well as to including interactions between FDI restrictiveness and both input tariffs and output tariffs in the same specification—while also controlling for the interaction between output and input tariffs and the triple interaction between FDI restrictiveness (estimates not reported, but available upon request). These results are also economically significant. For instance, when FDI restrictiveness is at the 75th percentile of its cross-country and cross-sector distribution, the impact of a one percentage point fall in input tariffs on TFP ranges from 0 to 1 percent depending on the number of lags considered for the explanatory variables, while it ranges from 3 to 4 percent when FDI restrictiveness is at the 25th percentile of its distribution. 7 For the sake of easier interpretation, all the independent variables are expressed as deviation from their respective sample averages. 18 Table 3. Complementarity between tariff and FDI liberalization: Total Factor Productivity Dependent variable: ln (TFP)ist l=1 l =2 l=3 l =4 (1) (2) (3) (4) (5) (6) (7) (8) -0.010 ** -0.005 ** -0.011 ** -0.005 ** -0.010 * -0.004 * -0.021 *** -0.008 ** (Output tariff) ist-l (0.004) (0.002) (0.004) (0.002) (0.006) (0.002) (0.006) (0.003) -0.017 * -0.012 -0.014 ** -0.008 * -0.012 ** -0.007 -0.006 * 0.001 (Input tariff) ist-l (0.009) (0.008) (0.006) (0.004) (0.006) (0.005) (0.004) (0.004) 0.0003 *** 0.0003 *** 0.0003 ** 0.0007 *** (Output tariff) ist-l × (FDI) is (0.0001) (0.0001) (0.0002) (0.0002) 0.004 ** 0.005 *** 0.005 ** 0.008 *** (Input tariff) ist-l × (FDI) is (0.002) (0.002) (0.002) (0.003) Country-Sector FE Yes Yes Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs 3,052 3,052 2,818 2,818 2,599 2,599 2,365 2,365 Adj R squared 0.648 0.648 0.694 0.694 0.723 0.723 0.750 0.750 Note: The dependent variable is log total factor productivity (TFP) in country i and sector s in year t. Independent variables are corresponding output and input tariff rates lagged l years as well as their interaction with country-sector level FDI restrictiveness indicators, all of which are expressed as deviation from their respective sample averages for the sake of easier interpretation of interaction terms. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. Table 4. Complementarity between tariff and FDI liberalization: Labor Productivity Dependent variable: ln (LP) ist (Output tariff) ist-l (Input tariff) ist-l (Output tariff) ist-l × (FDI) is l=1 (1) -0.008 ** (0.004) -0.019 * (0.011) 0.0003 ** (0.0001) l=2 (2) -0.005 *** (0.002) -0.011 (0.009) (3) -0.009 * (0.005) -0.017 ** (0.008) 0.0003 ** (0.0001) (4) -0.006 *** (0.002) -0.007 (0.005) l=3 (5) -0.010 (0.007) -0.014 ** (0.007) 0.0003 * (0.0002) (6) -0.005 ** (0.002) -0.006 (0.006) l=4 (7) -0.022 *** (0.007) -0.009 ** (0.004) 0.0007 *** (0.0002) (Input tariff) ist-l × (FDI) is (8) -0.010 *** (0.003) 0.001 (0.003) 0.005 *** 0.006 *** 0.006 ** 0.010 *** (0.002) (0.002) (0.002) (0.003) Country-Sector FE Yes Yes Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs 3,112 3,112 2,878 2,878 2,659 2,659 2,425 2,425 Adj R squared 0.985 0.985 0.986 0.986 0.986 0.986 0.987 0.987 Note: The dependent variable is log labor productivity (LP) in country i and sector s in year t. Independent variables are corresponding output and input tariff rates lagged l years as well as their interaction with country-sector level FDI restrictiveness indicators, all of which are expressed as deviation from their respective sample averages for the sake of easier interpretation of interaction terms. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. C. Robustness Checks In this section we report evidence from a battery of robustness tests to show that the set of regressions presented in Tables 1-4 offers solid evidence of a statistically significant impact of input tariff liberalization on sector-level productivity, which is much stronger and more robust than the effect of output tariff liberalization. Moreover, the estimated coefficients on the input tariff variable are very stable across our robustness checks, and close to our baseline results. 19 Alternative measures of output and input tariffs We first check the robustness of our results to alternative measures of output and input tariffs in Table 5. For the output tariff measure, we use the effective rate of protection, which measures the net protective effect on producers of any product accounting for the structure of protection on both its inputs and outputs. Specifically, the effective rate of protection is computed as: 1 , ∑ where is the share of intermediate inputs k in total output s. Unlike our previous measure, this captures the adverse effect of lower tariffs on intermediate inputs that is likely to weaken the disciplining effect of lower output tariffs for producers. As an alternative measure of input tariffs, we calculate the indirect tariff from immediate linkages only. Specifically, we disregard indirect linkages through domestic inputs (i.e., =0 in equation (1) above), which reduces to: , corresponding to input tariffs employed in previous studies (e.g., Amiti and Konings, 2007; Topalova and Khandelwal, 2011). Table 5 shows that our previous results—focusing here on explanatory variables lagged three periods—are robust across different tariff measures.8 Using the effective rate of protection instead of the output tariff rate still yields statistically insignificant results (columns 1 and 2), while the significance and magnitude of the effect of input tariffs is very close to the baseline results, regardless of whether we control for the effective rate of protection (columns 3-6). 8 Robusteness checks with different lags throughout this section yield virtually identical results. 20 Table 5. Robustness checks for baseline regressions: alternative output and input tariff measures Alt. output tariff Alt. input tariff Alt. output and input tariff ln (TFP) ist ln (LP) ist ln (TFP) ist ln (LP) ist ln (TFP) ist ln (LP) ist (1) (2) (3) (4) (5) (6) (Output tariff) ist-3 -0.001 -0.001 0.000 0.000 0.000 -0.001 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (Input tariff) ist-3 -0.020 ** -0.022 ** -0.027 ** -0.028 * -0.028 ** -0.030 * (0.008) (0.009) (0.013) (0.014) (0.014) (0.016) Country-Sector FE Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Obs 3,167 3,237 3,167 3,237 3,167 3,237 Adj R squared 0.719 0.970 0.719 0.970 0.719 0.970 Note: The dependent variable is log total factor productivity (TFP) in columns 1, 3, 5 and log labor productivity (LP) in columns 2, 4, 6, both in country i and sector s in year t. Independent variables are corresponding 3-years lagged output and input tariff rates. Specifically, the output tariff rate variable in columns 1-2 and 5-6 is the effective rate of protection as defined in the text, and the input tariff rate variable in columns 3-6 is a simpler version of the baseline measure, which considers only immediate linkages in the IO matrix. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. Dependent variable: Interpolating data for missing tariff observations As discussed earlier, the baseline sample is discontinuous for some countries during the sample period due to missing tariff data. It is conceptually possible, albeit unlikely, that tariff data are missing in such a systematic way that biases estimation results—for instance, tariff data might be missing when they are not much changed from the previous year. As reported in columns 1 and 2 of Table 6, accounting for missing years in the tariffs data by interpolating in between available years does not alter our main results. Table 6. Robustness checks for baseline regressions: interpolated tariff data and changes in sample interpolated tariff data excluding service sectors sample period up to 2007 excluding new EU members ln (TFP) ist ln (LP)ist ln (TFP) ist ln (LP) ist ln (TFP) ist ln (LP) ist ln (TFP) ist ln (LP) ist (1) (2) (3) (4) (5) (6) (7) (8) 0.000 0.000 0.000 0.000 -0.001 -0.001 -0.001 -0.001 (Output tariff) ist-3 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) (0.002) -0.017 *** -0.018 *** -0.017 *** -0.019 *** -0.016 ** -0.018 ** -0.015 ** -0.016 ** (Input tariff) ist-3 (0.004) (0.005) (0.006) (0.007) (0.006) (0.008) (0.006) (0.007) Country-Sector FE Yes Yes Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs 3,467 3,537 2,555 2,610 2,675 2,675 2,861 2,931 Adj R squared 0.720 0.970 0.722 0.967 0.751 0.977 0.640 0.988 Note: The dependent variable is log total factor productivity (TFP) in columns 1, 3, 5, 7 and log labor productivity (LP) in columns 2, 4, 6, 8, both in country i and sector s in year t. Independent variables are corresponding 3-years lagged output and input tariff rates. Columns 1-2 employ extended sample by interpolating tariff rates data. Columns 3-4 exclude service sectors, columns 5-6 excludes sample periods after 2007, and columns 7-8 excludes new EU member countries. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. Dependent variable: Effects for different sectors The link between productivity gains and input tariff reductions may differ across sectors. In particular, the input channel might be expected to be stronger for manufacturing industries. In order to explore this possibility, we rerun the baseline regressions by dropping all services- 21 related sectors from the sample.9 The results reported in columns 3 and 4 of Table 6 do not corroborate a stronger effect in manufacturing industries—the significance and magnitude of the estimated impact of input tariffs remains very close to the baseline results. Alternative time periods and country samples Productivity measures tend to behave pro cyclically. Indeed, available data suggest that TFP declined in most countries in the wake of the global financial crisis. While the regression specifications address this by controlling for country-year fixed effects, as a robustness test we restrict the sample to the pre-crisis period. The empirical results obtained on a 1991-2007 sample are broadly consistent with the baseline results (columns 5 and 6 in Table 6), i.e. we continue to find a negative and statistically significant relationship between input tariffs and productivity. Our main results also remain stable and significant when sub-groups of countries are omitted in a systematic way. In particular, our results hold if we exclude Czech Republic, Hungary, and Slovenia, countries which experienced the most significant tariff cuts as they joined the European Union around the middle of the sample period (columns 7 and 8 in Table 6). We also consider alternative clustering approaches, including clustering standard errors at the country-sector level. The findings, not reported here, but available upon request, indicate that the thrust of our results remains essentially unchanged. Robustness of complementarities between tariffs and barriers to FDI Lastly, we confirm the robustness of the results regarding complementarities between tariffs and barriers to FDI along the exactly same dimensions as above—alternative measures of input and output tariffs, interpolating missing tariff data, and excluding service sectors/post-2007 period/new EU member countries, all for both TFP and LP—in Tables 7-10. 9 Specifically, we dropped water supply; sewerage, waste management and remediation activities; professional, scientific and technical activities; administrative and support service activities; arts, entertainment and recreation; and other service activities. 22 Table 7. Robustness checks for tariff-FDI complementarity regressions: TFP; alternative output and input tariff measures Alt. output tariff Alt. input tariff Alt. output and input tariff Dependent variable: ln (TFP) ist (1) -0.002 (0.002) -0.024 *** (0.008) 0.0001 * (0.0001) (Output tariff) ist-3 (Input tariff) ist-3 (Output tariff) ist-3 × (FDI) is (2) -0.002 (0.001) -0.015 *** (0.005) (3) -0.010 * (0.006) -0.019 * (0.011) 0.0003 * (0.0002) (4) -0.008 ** (0.003) -0.011 (0.010) (5) -0.002 (0.001) -0.031 *** (0.012) 0.0001 * (0.0000) (6) -0.002 * (0.001) -0.025 *** (0.009) (Input tariff) ist-3× (FDI) is 0.004 ** 0.009 *** 0.006 *** (0.002) ` (0.003) (0.002) Country-Sector FE Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Obs 2,599 2,599 2,599 2,599 2,599 2,599 Adj R squared 0.723 0.723 0.723 0.724 0.723 0.723 Note: The dependent variable is log total factor productivity (TFP) in country i and sector s in year t. Independent variables are corresponding 3-years lagged output and input tariff rates as well as their interaction with country-sector level FDI restrictiveness indicators, all of which are expressed as deviation from their respective sample averages for the sake of easier interpretation of interaction terms. Specifically, the output tariff rate variable in columns 1-2 and 5-6 is the effective rate of protection as defined in the text, and the input tariff rate variable in columns 3-6 is a simpler version of the baseline measure, which considers only immediate linkages in the IO matrix. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. Table 8. Robustness checks for tariff-FDI complementarity regressions: LP; alternative output and input tariff measures Alt. output tariff Alt. input tariff Alt. output and input tariff Dependent variable: ln (LP) ist (Output tariff) ist-3 (Input tariff)ist-3 (Output tariff) ist-3× (FDI)is (Input tariff)ist-3× (FDI) is (1) -0.010 (0.007) -0.014 ** (0.007) 0.0003 * (0.0002) (2) -0.005 ** (0.002) -0.006 (0.006) (3) -0.010 (0.007) -0.020 (0.012) 0.0003 * (0.0002) (4) -0.009 ** (0.004) -0.010 (0.011) (5) -0.002 (0.002) -0.033 ** (0.013) 0.0001 * (0.0001) (6) -0.003 * (0.001) -0.026 *** (0.009) 0.006 ** 0.011 *** 0.007 *** (0.002) (0.004) (0.002) Country-Sector FE Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Obs 2,659 2,659 2,659 2,659 2,659 2,659 Adj R squared 0.986 0.986 0.969 0.969 0.969 0.969 Note: The dependent variable is log labor productivity (LP) in country i and sector s in year t. Independent variables are corresponding 3-years lagged output and input tariff rates as well as their interaction with country-sector level FDI restrictiveness indicators, all of which are expressed as deviation from their respective sample averages for the sake of easier interpretation of interaction terms. Specifically, the output tariff rate variable in columns 1-2 and 5-6 is the effective rate of protection as defined in the text, and the input tariff rate variable in columns 3-6 is a simpler version of the baseline measure, which considers only immediate linkages in the IO matrix. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. 23 Table 9. Robustness checks for tariff-FDI complementarity regressions: TFP; interpolated tariff data and changes in sample interpolated tariff data excluding service sectors sample period up to 2007 excluding new EU members Dependent variable: ln (TFP)ist (Output tariff)ist-3 (Input tariff)ist-3 (Output tariff)ist-3× (Direct FDI)is (1) -0.011 ** (0.006) -0.011 ** (0.004) 0.0004 ** (0.0002) (2) -0.004 * (0.002) -0.007 (0.005) (3) -0.022 *** (0.008) -0.007 ** (0.003) 0.0007 *** (0.0002) (4) -0.014 *** (0.004) -0.004 (0.004) (5) -0.009 (0.005) -0.011 * (0.006) 0.0003 * (0.0002) (6) -0.006 *** (0.002) 0.000 (0.003) (7) -0.025 *** (0.008) -0.007 ** (0.003) 0.0008 *** (0.0002) (8) -0.007 * (0.004) -0.004 (0.008) (Input tariff)ist-3× (Direct FDI) is 0.004 ** 0.014 *** 0.006 *** 0.007 * (0.002) (0.004) (0.002) (0.004) Country-Sector FE Yes Yes Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs 2,865 2,865 1,981 1,981 2,197 2,197 2,327 2,327 Adj R squared 0.725 0.724 0.645 0.647 0.755 0.756 0.643 0.642 Note: The dependent variable is log total factor productivity (TFP) in country i and sector s in year t. Independent variables are corresponding 3-years lagged output and input tariff rates as well as their interaction with country-sector level FDI restrictiveness indicators, all of which are expressed as deviation from their respective sample averages for the sake of easier interpretation of interaction terms. Columns 1-2 employ extended sample by interpolating tariff rates data. Columns 3-4 exclude service sectors, columns 5-6 excludes sample periods after 2007, and columns 7-8 excludes new EU member countries. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. Table 10. Robustness checks for tariff-FDI complementarity regressions: LP; interpolated tariff data and changes in sample interpolated tariff data excluding service sectors sample period up to 2007 excluding new EU members Dependent variable: ln (LP)ist (Output tariff) ist-3 (Input tariff) ist-3 (Output tariff) ist-3× (Direct FDI) is (1) -0.010 * (0.006) -0.013 *** (0.005) 0.0003 * (0.0002) (2) -0.005 ** (0.002) -0.006 (0.006) (3) -0.023 ** (0.009) -0.008 ** (0.003) 0.0007 *** (0.0003) (4) -0.016 *** (0.005) 0.009 * (0.005) (5) -0.008 (0.006) -0.013 * (0.007) 0.0003 (0.0002) (6) -0.007 *** (0.003) 0.001 (0.003) (7) -0.026 *** (0.009) -0.007 * (0.004) 0.0008 *** (0.0003) (Input tariff) ist-3× (Direct FDI) is (8) -0.009 * (0.005) -0.002 (0.009) 0.006 *** 0.016 *** 0.008 *** 0.009 ** (0.002) (0.005) (0.002) (0.004) Country-Sector FE Yes Yes Yes Yes Yes Yes Yes Yes Country-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs 2,925 2,925 2,031 2,031 2,197 2,197 2,387 2,387 Adj R squared 0.969 0.969 0.984 0.984 0.990 0.990 0.987 0.987 Note: The dependent variable is log labor productivity (LP) in country i and sector s in year t. Independent variables are corresponding 3-years lagged output and input tariff rates as well as their interaction with country-sector level FDI restrictiveness indicators, all of which are expressed as deviation from their respective sample averages for the sake of easier interpretation of interaction terms. Columns 1-2 employ extended sample by interpolating tariff rates data. Columns 3-4 exclude service sectors, columns 5-6 excludes sample periods after 2007, and columns 7-8 excludes new EU member countries. Country-sector as well as country-year fixed effects are included in all columns. Standard errors in parentheses are clustered at the country-year level. Significance: * 10 percent; ** 5 percent; *** 1 percent. D. Policy Implications of the Results Our results have three main policy implications. First, tariff reductions have been important drivers of productivity growth in the past. For the countries in our sample, input tariffs fell on average by 0.5 percentage points over the decade 1997-2007. Using a baseline semi-elasticity of 2, this translates into an average productivity gain of about 1 percent. Second, while tariff barriers in advanced countries have been reduced substantially over the last decades, there is still scope for further reductions, and therefore for further productivity gains, in some sectors in some countries. A back-of-the-envelope calculation of the potential productivity gains from full elimination of remaining tariffs suggests that aggregate productivity could rise by around 1 percent on average across advanced economies. These 24 gains vary from a 0.2 percent gain in Japan to a 7.7 percent gain in Ireland, depending on current sector-level tariff rates as well as each sector’s importance in individual country (Figure 6).10 For instance, potential gains for Ireland and Korea are estimated to be larger than those for other advanced economies. Korea, for instance, has higher remaining tariffs on average than other advanced countries in the sample—partly reflecting that its trade partners differ from those of EU countries that dominate the sample. For Ireland, a strong reliance on imported inputs especially in specific sectors—the chemical and pharmaceutical industries—is estimated to dominate the potential gains. Given their comparatively higher tariff barriers to trade, emerging and low-income economies could benefit from tariff liberalization even more than advanced economies, on average.11 Figure 6. Potential Productivity Gains from Eliminating Remaining Tariff Barriers (In percent; red bars on right axis) 9 1.2 8 1 7 6 0.8 5 0.6 4 0.4 3 2 0.2 1 0 IRL KOR USA SWE SVN NLD JPN ITA HUN GBR FRA FIN ESP DEU CZE CAN AUT AUS 0 10 This is based on tariff data in the latest available years—Japan and Korea are based on tariff data in 2012 and 2010, respectively, while all other countries are based on tariff data in 2013. 11 Applying the same level of semi-elasticity of 2 to the latest sector-level effective input tariff rates, India, for instance, could boost TFP level by around 18 percent on average across sectors, reflecting substantially higher level of remaining tariffs than those in advanced countries. 25 Third, the impact of further tariff reductions on productivity would be amplified if barriers to FDI were also reduced in parallel. This highlights the need for a broad liberalization agenda cutting across different areas. V. CONCLUDING REMARKS This paper empirically reassesses the productivity gains from trade liberalization in a crosscountry cross-industry time-series framework that captures productivity effects arising within each firm as well as from reallocation of resources across firms. Our main result is that trade liberalization in upstream industries matters more for sector-level productivity than liberalization in the sector considered itself. This is consistent with, but generalizes, the findings of recent papers at the firm level. Our findings provide a clear case for further liberalization efforts to raise productivity and output in advanced economies— all the more so as the estimates vastly under-state the potential gains as they ignore the (presumably much larger) benefits to be reaped from easing non-tariff trade barriers. Indeed, recent trade liberalization efforts have increasingly centered on reducing non-tariff barriers, particularly in services sectors, from expediting customs procedures to intellectual property provisions. Ongoing efforts to enhance data availability on non-tariff barrier measures will help complement existing studies of the impact of tariff liberalization (e.g., Bachetta and Beverelli, 2012; Staiger, 2015). Given their comparatively higher barriers to trade, productivity gains for emerging and low-income countries could conceivably be even higher. The results also highlight the existence of complementarities between reductions in barriers to trade and reforms in other areas. While our focus has been on complementarities between reductions in trade and FDI barriers, the productivity effects of trade liberalization could also vary depending on other existing policies and institutions, such as in the areas of labor or product markets. For instance, the effect of tariff liberalization could be greater when domestic product market (“behind-the-border barriers”) and labor market regulations are less stringent. Recent theoretical work by Helpman and Itskhoki (2014) shows that in the wake of trade liberalization, labor market frictions can persistently depress productivity during the transition to the new steady state as they result in misallocation of labor—consistent with the empirical results of Aghion et al. (2008) using state-level data for India. Future empirical research could investigate the existence of complementarities between trade liberalization and other types of structural reforms. 26 REFERENCES Aghion, Philippe, Nick Bloom, Richard Blundell, Rachel Griffith, and Peter Howitt. 2005. “Competition and Innovation: an Inverted-U Relationship.” Quarterly Journal of Economics 120 (2): 701-28. Aghion, Philippe, Robin Burgess, Stephen J. 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Sample of Countries 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Australia Austria Canada Czech Republic Germany Spain Finland France United Kingdom Hungary Ireland Italy Japan Korea Netherlands Slovenia Sweden United States 29 Annex 2. Description of Sectors Description Agriculture, forestry, and fishing Mining and quarrying Food products, beverages and tobacco Textiles, wearing apparel, leather and related prodcuts Wood products Paper products; printing and reproduction of recorded media Coke and refined petroleum products Chemicals and chemical products Rubber and plastics products Other non-metallic mineral products Basic metals and fabricated metal products, except machinery and equipment Electrical and optical equipment Machinery and equipment n.e.c. Transport equipment Other manufacturing; repair and installation of machinery and equipment Electricity, gas, and water supply Professional, scientific, technical, administrative and support service activities Arts, entertainment, recreation and other service activities ISIC Rev 4. Code A B 10-12 13-15 16-18a 16-18b 19 20-21 22-23a 22-23b 24-25 26-27 28 29-30 31-33 D-E M-N R-S Annex 3. Baseline Sample Data Countries and Sectors ISIC Rev. 4 Sector Code Country Australia A B 10-12 13-15 16-18 19 20-21 22-23 24-25 26-27 28 29-30 31-33 D-E M-N R-S 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' 91', 93''-07' Austria 95'-09' 95'-09' 95'-09' 95'-09' - 95'-09' 95'-09' - 95'-09' 95'-09' 95'-09' 95'-09' 95'-09' 95'-09' 95'-09' 95'-09' Canada 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' 93', 95'-10' Czech Republic 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' Germany 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' Finland 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' 95'-12' France 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' United Kingdom 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' Hungary 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' - 94'-09' Spain - 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' 96'-07' Ireland 94'-07' 94'-07' 94'-07' 94'-07' 94'-07' - 94'-07' 94'-07' 94'-07' 94'-07' 94'-07' 94'-07' 94'-07' 94'-07' 94'-07' 94'-07' Italy 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 94'-09' 95'-09' 95'-09' 95'-09' 95'-09' 95'-09' - 94'-09' 95'-09' - 94'-09' Japan 95'-09' 95'-09' 95'-09' 95'-09' 95'-09' 95'-09' 95'-09' 95'-09' - 96', 99',02', 04', 06'-10' Korea 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 96', 99',02', 04', 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 06'-10' 94'-12' 94'-12' 94'-12' - 94'-12' 94'-12' - 94'-12' 94'-12' 94'-12' 94'-12' 94'-12' 94'-12' 94'-12' 94'-12' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' 99', 01'-06' Sweden 95'-11' 95'-11' 95'-11' 95'-11' - 95'-11' 95'-11' - 95'-11' 95'-11' 95'-11' 95'-11' 95'-11' 95'-11' 95'-11' 95'-11' United States 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 91'-09' 30 94'-12' Slovenia Netherlands 31 Annex 4. Concordance between FDI restrictiveness indicators and the baseline data FDI Regulatory Restrictiveness Index Agriculture, Fisheries, and Forestry Mining & Quarrying (incl. Oil extr.) Food and other Oil ref. & Chemicals Metals, machinery and other minerals Electric, electronics, and other Transport equipment Electricity Construction Business services ISIC Rev 4. Code A B 10-12; 13-15; 16-18 19; 20-21; 22-23a 22-23b; 24-25; 28 26-27 29-30 D-E F M-N June 2020 WORLD ECONOMIC OUTLOOK UPDATE A Crisis Like No Other, An Uncertain Recovery Global growth is projected at –4.9 percent in 2020, 1.9 percentage points below the April 2020 World Economic Outlook (WEO) forecast. The COVID-19 pandemic has had a more negative impact on activity in the first half of 2020 than anticipated, and the recovery is projected to be more gradual than previously forecast. In 2021 global growth is projected at 5.4 percent. Overall, this would leave 2021 GDP some 6½ percentage points lower than in the pre-COVID-19 projections of January 2020. The adverse impact on low-income households is particularly acute, imperiling the significant progress made in reducing extreme poverty in the world since the 1990s. As with the April 2020 WEO projections, there is a higher-than-usual degree of uncertainty around this forecast. The baseline projection rests on key assumptions about the fallout from the pandemic. In economies with declining infection rates, the slower recovery path in the updated forecast reflects persistent social distancing into the second half of 2020; greater scarring (damage to supply potential) from the larger-than-anticipated hit to activity during the lockdown in the first and second quarters of 2020; and a hit to productivity as surviving businesses ramp up necessary workplace safety and hygiene practices. For economies struggling to control infection rates, a lengthier lockdown will inflict an additional toll on activity. Moreover, the forecast assumes that financial conditions—which have eased following the release of the April 2020 WEO—will remain broadly at current levels. Alternative outcomes to those in the baseline are clearly possible, and not just because of how the pandemic is evolving. The extent of the recent rebound in financial market sentiment appears disconnected from shifts in underlying economic prospects—as the June 2020 Global Financial Stability Report (GFSR) Update discusses—raising the possibility that financial conditions may tighten more than assumed in the baseline. All countries—including those that have seemingly passed peaks in infections—should ensure that their health care systems are adequately resourced. The international community must vastly step up its support of national initiatives, including through financial assistance to countries with limited health care capacity and channeling of funding for vaccine production as trials advance, so that adequate, affordable doses are quickly available to all countries. Where lockdowns are required, economic policy should continue to cushion household income losses with sizable, well-targeted measures as well as provide support to firms suffering the consequences of mandated restrictions on activity. Where economies are reopening, targeted support should be gradually unwound as the recovery gets underway, and policies should provide stimulus to lift demand and ease and incentivize the reallocation of resources away from sectors likely to emerge persistently smaller after the pandemic. Strong multilateral cooperation remains essential on multiple fronts. Liquidity assistance is urgently needed for countries confronting health crises and external funding shortfalls, including through debt relief and financing through the global financial safety net. Beyond the pandemic, policymakers must cooperate to resolve trade and technology tensions that endanger an eventual recovery from the COVID-19 crisis. Furthermore, building on the record drop in greenhouse gas emissions during the pandemic, policymakers should both implement their climate change mitigation commitments and work together to scale up equitably designed carbon taxation or equivalent schemes. The global community must act now to avoid a repeat of this catastrophe by building global stockpiles of essential supplies and protective equipment, funding research and supporting public health systems, and putting in place effective modalities for delivering relief to the neediest. International Monetary Fund | June 2020 1 COVID-19 Crisis: More Severe Economic Fallout than Anticipated Economic data available at the time of the April 2020 WEO forecast indicated an unprecedented decline in global activity due to the COVID-19 pandemic. Data releases since then suggest even deeper downturns than previously projected for several economies. The pandemic has worsened in many countries, leveled off in others. Following the release of the April 2020 WEO, the pandemic rapidly intensified in a number of emerging market and developing economies, necessitating stringent lockdowns and resulting in even larger disruptions to activity than forecast. In others, recorded infections and mortality have instead been more modest on a per capita basis, although limited testing implies considerable uncertainty about the path of the pandemic. In many advanced economies, the pace of new infections and hospital intensive care occupancy rates have declined thanks to weeks of lockdowns and voluntary distancing. Synchronized, deep downturn. First-quarter GDP was generally worse than expected (the few exceptions include, for example, Chile, China, India, Malaysia, and Thailand, among emerging markets, and Australia, Germany, and Japan, among advanced economies). High-frequency indicators point to a more severe contraction in the second quarter, except in China, where most of the country had reopened by early April. Consumption and services output have dropped markedly. In most recessions, consumers dig into their savings or rely on social safety nets and family support to smooth spending, and consumption is affected relatively less than investment. But this time, consumption and services output have also dropped markedly. The pattern reflects a unique combination of factors: voluntary social distancing, lockdowns needed to slow transmission and allow health care systems to handle rapidly rising caseloads, steep income losses, and weaker consumer confidence. Firms have also cut back on investment when faced with precipitous demand declines, supply interruptions, and uncertain future earnings prospects. Thus, there is a broadbased aggregate demand shock, compounding near-term supply disruptions due to lockdowns. Mobility remains depressed. Globally, lockdowns were at their most intense and widespread from about mid-March through mid-May. As economies have gradually reopened, mobility has picked up in some areas but generally remains low compared to pre-virus levels, suggesting people are voluntarily reducing exposure to one another. Mobility data from cellphone tracking, for example, indicate that activity in retail, recreation, transit stations, and workplaces remains depressed in most countries, although it appears to be returning to baseline in certain areas. Severe hit to the labor market. The steep decline in activity comes with a catastrophic hit to the global labor market. Some countries (notably in Europe) have contained the fallout with effective short-term work schemes. Nonetheless, according to the International Labour Organization, the global decline in work hours in 2020:Q1 compared to 2019:Q4 was equivalent to the loss of 130 million full-time jobs. The decline in 2020:Q2 is likely to be equivalent to more than 300 million full-time jobs. Where economies have been reopening, activity may have troughed in April—as suggested, for example, by the May employment report for the United States, where furloughed workers are returning to work in some of the sectors most affected by the lockdown. 2 International Monetary Fund | June 2020 The hit to the labor market has been particularly acute for low-skilled workers who do not have the option of working from home. Income losses also appear to have been uneven across genders, with women among lower-income groups bearing a larger brunt of the impact in some countries. Of the approximately 2 billion informally employed workers worldwide, the International Labour Organization estimates close to 80 percent have been significantly affected. Contraction in global trade. The synchronized nature of the downturn has amplified domestic disruptions around the globe. Trade contracted by close to –3.5 percent (year over year) in the first quarter, reflecting weak demand, the collapse in cross-border tourism, and supply dislocations related to shutdowns (exacerbated in some cases by trade restrictions). Weaker inflation. Average inflation in advanced economies had dropped about 1.3 percentage points since the end of 2019, to 0.4 percent (year over year) as of April 2020, while in emerging market economies it had fallen 1.2 percentage points, to 4.2 percent. Downward price pressure from the decline in aggregate demand, together with the effects of lower fuel prices, seems to have more than offset any upward cost-push pressure from supply interruptions so far. Policy Countermeasures Have Limited Economic Damage and Lifted Financial Sentiment Some bright spots mitigate the gloom. Following the sharp tightening during January–March, financial conditions have eased for advanced economies and, to a lesser extent, for emerging market economies, also reflecting the policy actions discussed below. Sizable fiscal and financial sector countermeasures deployed in several countries since the start of the crisis have forestalled worse near-term losses. Reduced-work-hour programs and assistance to workers on temporary furlough have kept many from outright unemployment, while financial support to firms and regulatory actions to ensure continued credit provision have prevented more widespread bankruptcies (see Annex 1 and the June 2020 Fiscal Monitor Database of Country Fiscal Measures, which discuss fiscal measures amounting to about $11 trillion that have been announced worldwide, as well as the April 2020 WEO and the IMF Policy Tracker on Responses to COVID-19, which provide a broader list of country-specific measures). Swift and, in some cases, novel actions by major central banks (such as a few emerging market central banks launching quantitative easing for the first time and some advanced economy central banks significantly increasing the scale of asset purchases) have enhanced liquidity provision and limited the rise in borrowing costs (see the June 2020 GFSR Update). Moreover, swap lines for several emerging market central banks have helped ease dollar liquidity shortages. Portfolio flows into emerging markets have recovered after the record outflows in February-March and hard currency bond issuance has strengthened for those with stronger credit ratings. Meanwhile, financial regulators’ actions—including modification of bank loan repayment terms and release of capital and liquidity buffers—have supported the supply of credit. International Monetary Fund | June 2020 3 Stability in the oil market has also helped lift sentiment. West Texas Intermediate oil futures, which in April had sunk deep into negative territory for contracts expiring in the early summer, have risen in recent weeks to trade in a stable range close to the current spot price. Exchange rate changes since early April have reflected these developments. As of mid-June, the US dollar had depreciated by close to 4 percent in real effective terms (after strengthening by over 8 percent between January and early April). Currencies that had weakened substantially in previous months have appreciated since April—including the Australian dollar and the Norwegian krone, among advanced economy currencies, and the Indonesian rupiah, Mexican peso, Russian ruble, and South African rand, among emerging market currencies. Considerations for the Forecast The developments discussed in the previous section help shape the key assumptions for the global growth forecast, in particular with regard to activity disruptions due to the pandemic, commodity prices, financial conditions, and policy support. Disruptions to activity in the forecast baseline. Based on downside surprises in the first quarter and the weakness of high-frequency indicators in the second quarter, this updated forecast factors in a larger hit to activity in the first half of 2020 and a slower recovery path in the second half than envisaged in the April 2020 WEO. For economies where infections are declining, the slower recovery path in the updated forecast reflects three key assumptions: persistent social distancing into the second half of 2020, greater scarring from the larger-than-anticipated hit to activity during the lockdown in the first and second quarters, and a negative impact on productivity as surviving businesses enhance workplace safety and hygiene standards. For economies still struggling to control infection rates, the need to continue lockdowns and social distancing will take an additional toll on activity. An important assumption is that countries where infections have declined will not reinstate stringent lockdowns of the kind seen in the first half of the year, instead relying on alternative methods if needed to contain transmission (for instance, rampedup testing, contact tracing, and isolation). The risk section below considers alternative scenarios, including one featuring a repeat outbreak in 2021. Policy support and financial conditions. The projection factors in the impact of the sizable fiscal countermeasures implemented so far and anticipated for the rest of the year. With automatic stabilizers also allowed to operate and provide further buffers, overall fiscal deficits are expected to widen significantly and debt ratios to rise over 2020–21. Major central banks are assumed to maintain their current settings throughout the forecast horizon to the end of 2021. More generally, financial conditions are expected to remain approximately at current levels for both advanced and emerging market economies. Commodity prices. The assumptions on fuel prices are broadly unchanged from the April 2020 WEO. Average petroleum spot prices per barrel are estimated at $36.20 in 2020 and $37.50 in 2021. Oil futures curves indicate that prices are expected to increase thereafter toward $46, still about 25 percent below the 2019 average. Nonfuel commodity prices are expected to rise marginally faster than assumed in the April 2020 WEO. 4 International Monetary Fund | June 2020 Deep Downturn in 2020, Sluggish Turnaround in 2021 Global growth is projected at –4.9 percent in Figure 1. Quarterly World GDP (2019:Q1 = 100) 2020, 1.9 percentage points below the April 2020 WEO forecast (Table 1). Consumption World Advanced economies growth, in particular, has been downgraded Emerging market and developing economies excluding China for most economies, reflecting the larger-thanChina anticipated disruption to domestic activity. 115 The projections of weaker private consumption reflect a combination of a large 110 adverse aggregate demand shock from social distancing and lockdowns, as well as a rise in 105 precautionary savings. Moreover, investment is expected to be subdued as firms defer 100 capital expenditures amid high uncertainty. Policy support partially offsets the 95 deterioration in private domestic demand. In the baseline, global activity is expected 90 to trough in the second quarter of 2020, recovering thereafter (Figure 1). In 2021 85 growth is projected to strengthen to 5.4 2019: 19: 19: 19: 20: 20: 20: 20: 21: 21: 21: Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 percent, 0.4 percentage point lower than the April forecast. Consumption is projected to Source: IMF staff estimates. strengthen gradually next year, and investment is also expected to firm up, but to remain subdued. Global GDP for the year 2021 as a whole is forecast to just exceed its 2019 level. Uncertainty. Similarly to the April 2020 WEO projections, there is pervasive uncertainty around this forecast. The forecast depends on the depth of the contraction in the second quarter of 2020 (for which complete data are not yet available) as well as the magnitude and persistence of the adverse shock. These elements, in turn, depend on several uncertain factors, including • The length of the pandemic and required lockdowns • Voluntary social distancing, which will affect spending • Displaced workers’ ability to secure employment, possibly in different sectors • Scarring from firm closures and unemployed workers exiting the workforce, which may make it more difficult for activity to bounce back once the pandemic fades • The impact of changes to strengthen workplace safety—such as staggered work shifts, enhanced hygiene and cleaning between shifts, new workplace practices relating to proximity of personnel on production lines—which incur business costs • Global supply chain reconfigurations that affect productivity as companies try to enhance their resilience to supply disruptions International Monetary Fund | June 2020 5 21: Q4 • The extent of cross-border spillovers from weaker external demand as well as funding shortfalls • Eventual resolution of the current disconnect between asset valuations and prospects for economic activity (as highlighted in the June 2020 GFSR Update) Growth in the advanced economy group is projected at –8.0 percent in 2020, 1.9 percentage points lower than in the April 2020 WEO. There appears to have been a deeper hit to activity in the first half of the year than anticipated, with signs of voluntary distancing even before lockdowns were imposed. This also suggests a more gradual recovery in the second half as fear of contagion is likely to continue. Synchronized deep downturns are foreseen in the United States (–8.0 percent); Japan (–5.8 percent); the United Kingdom (–10.2 percent); Germany (–7.8 percent); France (–12.5 percent); Italy and Spain (–12.8 percent). In 2021 the advanced economy growth rate is projected to strengthen to 4.8 percent, leaving 2021 GDP for the group about 4 percent below its 2019 level. Among emerging market and developing economies, the hit to activity from domestic disruptions is projected closer to the downside scenario envisaged in April, more than offsetting the improvement in financial market sentiment. The downgrade also reflects larger spillovers from weaker external demand. The downward revision to growth prospects for emerging market and developing economies over 2020–21 (2.8 percentage points) exceeds the revision for advanced economies (1.8 percentage points). Excluding China, the downward revision for emerging market and developing economies over 2020–21 is 3.6 percentage points. Overall, growth in the group of emerging market and developing economies is forecast at –3.0 percent in 2020, 2 percentage points below the April 2020 WEO forecast. Growth among low-income developing countries is projected at –1.0 percent in 2020, some 1.4 percentage points below the April 2020 WEO forecast, although with differences across individual countries. Excluding a few large frontier economies, the remaining group of low-income developing countries is projected to contract by –2.2 percent in 2020. For the first time, all regions are projected to experience negative growth in 2020. There are, however, substantial differences across individual economies, reflecting the evolution of the pandemic and the effectiveness of containment strategies; variation in economic structure (for example, dependence on severely affected sectors, such as tourism and oil); reliance on external financial flows, including remittances; and precrisis growth trends. In China, where the recovery from the sharp contraction in the first quarter is underway, growth is projected at 1.0 percent in 2020, supported in part by policy stimulus. India’s economy is projected to contract by 4.5 percent following a longer period of lockdown and slower recovery than anticipated in April. In Latin America, where most countries are still struggling to contain infections, the two largest economies, Brazil and Mexico, are projected to contract by 9.1 and 10.5 percent, respectively, in 2020. The disruptions due to the pandemic, as well as significantly lower disposable income for oil exporters after the dramatic fuel price decline, imply sharp recessions in Russia (–6.6 percent), Saudi Arabia (–6.8 percent), and Nigeria (–5.4 percent), while South Africa’s performance (–8.0 percent) will be severely affected by the health crisis. 6 International Monetary Fund | June 2020 Table 1. Overview of the World Economic Outlook Projections (Percent change, unless noted otherwise) Year over Year Projections 2020 2021 Difference from April 2020 WEO Projections 1/ 2020 2021 Q4 over Q4 2/ Projections 2019 2020 2021 2018 2019 3.6 2.9 –4.9 5.4 –1.9 –0.4 2.8 –3.5 4.6 Advanced Economies United States Euro Area Germany France Italy Spain Japan United Kingdom Canada Other Advanced Economies 3/ 2.2 2.9 1.9 1.5 1.8 0.8 2.4 0.3 1.3 2.0 2.7 1.7 2.3 1.3 0.6 1.5 0.3 2.0 0.7 1.4 1.7 1.7 –8.0 –8.0 –10.2 –7.8 –12.5 –12.8 –12.8 –5.8 –10.2 –8.4 –4.8 4.8 4.5 6.0 5.4 7.3 6.3 6.3 2.4 6.3 4.9 4.2 –1.9 –2.1 –2.7 –0.8 –5.3 –3.7 –4.8 –0.6 –3.7 –2.2 –0.2 0.3 –0.2 1.3 0.2 2.8 1.5 2.0 –0.6 2.3 0.7 –0.3 1.5 2.3 1.0 0.4 0.9 0.1 1.8 –0.7 1.1 1.5 1.9 –7.2 –8.2 –8.6 –6.7 –8.9 –10.9 –11.4 –1.8 –9.0 –7.5 –5.1 5.1 5.4 5.8 5.5 4.2 5.5 6.3 0.0 6.9 4.6 5.5 Emerging Market and Developing Economies Emerging and Developing Asia China India 4/ ASEAN-5 5/ Emerging and Developing Europe Russia Latin America and the Caribbean Brazil Mexico Middle East and Central Asia Saudi Arabia Sub-Saharan Africa Nigeria South Africa 4.5 6.3 6.7 6.1 5.3 3.2 2.5 1.1 1.3 2.2 1.8 2.4 3.2 1.9 0.8 3.7 5.5 6.1 4.2 4.9 2.1 1.3 0.1 1.1 –0.3 1.0 0.3 3.1 2.2 0.2 –3.0 –0.8 1.0 –4.5 –2.0 –5.8 –6.6 –9.4 –9.1 –10.5 –4.7 –6.8 –3.2 –5.4 –8.0 5.9 7.4 8.2 6.0 6.2 4.3 4.1 3.7 3.6 3.3 3.3 3.1 3.4 2.6 3.5 –2.0 –1.8 –0.2 –6.4 –1.4 –0.6 –1.1 –4.2 –3.8 –3.9 –1.9 –4.5 –1.6 –2.0 –2.2 –0.7 –1.1 –1.0 –1.4 –1.6 0.1 0.6 0.3 0.7 0.3 –0.7 0.2 –0.7 0.2 –0.5 3.9 5.0 6.0 3.1 4.6 3.4 2.2 –0.2 1.6 –0.8 ... –0.3 ... ... –0.6 –0.5 2.4 4.4 0.2 –1.4 –7.0 –7.5 –9.0 –9.3 –10.1 ... –4.4 ... ... –2.1 4.2 3.9 4.3 1.2 6.1 6.6 5.6 4.1 4.5 4.8 ... 4.1 ... ... –2.8 Memorandum Low-Income Developing Countries World Growth Based on Market Exchange Rates 5.1 3.1 5.2 2.4 –1.0 –6.1 5.2 5.3 –1.4 –1.9 –0.4 –0.1 ... 2.3 ... –4.9 ... 4.8 3.8 3.4 4.5 0.9 1.5 0.1 –11.9 –13.4 –9.4 8.0 7.2 9.4 –0.9 –1.3 –0.5 –0.4 –0.2 –0.7 ... ... ... ... ... ... ... ... ... 29.4 1.3 –10.2 0.8 –41.1 0.2 3.8 0.8 0.9 1.3 –2.5 1.4 –6.1 4.9 –42.6 –0.8 12.2 1.3 2.0 4.8 1.4 5.1 0.3 4.4 1.1 4.5 –0.2 –0.2 –0.4 0.0 1.4 5.0 –0.1 3.1 1.5 4.0 2.5 –0.3 0.0 2.3 –0.4 0.0 0.9 –0.4 0.0 0.6 –0.4 –0.1 0.2 0.0 0.1 0.0 0.0 0.0 ... ... ... ... ... ... ... ... ... World Output World Trade Volume (goods and services) 6/ Advanced Economies Emerging Market and Developing Economies Commodity Prices (U.S. dollars) Oil 7/ Nonfuel (average based on world commodity import weights) Consumer Prices Advanced Economies 8/ Emerging Market and Developing Economies 9/ London Interbank Offered Rate (percent) On U.S. Dollar Deposits (six month) On Euro Deposits (three month) On Japanese Yen Deposits (six month) Note: Real effective exchange rates are assumed to remain constant at the levels prevailing during April 21--May 19, 2020. Economies are listed on the basis of economic size. The aggregated quarterly data are seasonally adjusted. WEO = World Economic Outlook . 1/ Difference based on rounded figures for the current and April 2020 WEO forecasts. Countries whose forecasts have been updated relative to April 2020 WEO forecasts account for 90 percent of world GDP measured at purchasing-power-parity weights. 2/ For World Output, the quarterly estimates and projections account for approximately 90 percent of annual world output at purchasing-power-parity weights. For Emerging Market and Developing Economies, the quarterly estimates and projections account for approximately 80 percent of annual emerging market and developing economies' output at purchasing-powerparity weights. 3/ Excludes the Group of Seven (Canada, France, Germany, Italy, Japan, United Kingdom, United States) and euro area countries. 4/ For India, data and forecasts are presented on a fiscal year basis and GDP from 2011 onward is based on GDP at market prices with fiscal year 2011/12 as a base year. 5/ Indonesia, Malaysia, Philippines, Thailand, Vietnam. 6/ Simple average of growth rates for export and import volumes (goods and services). 7/ Simple average of prices of UK Brent, Dubai Fateh, and West Texas Intermediate crude oil. The average price of oil in US dollars a barrel was $61.39 in 2019; the assumed price, based on futures markets (as of May 19, 2020), is $36.18 in 2020 and $37.54 in 2021. 8/ The inflation rate for the euro area is 0.2% in 2020 and 0.9% in 2021, for Japan is -0.1% in 2020 and 0.3% in 2021, and for the United States is 0.5% in 2020 and 1.5% in 2021. 9/ Excludes Venezuela. International Monetary Fund | June 2020 7 In 2021 the growth rate for emerging market and developing economies is projected to strengthen to 5.9 percent, largely reflecting the rebound forecast for China (8.2 percent). The growth rate for the group, excluding China, is expected to be –5.0 percent in 2020 and 4.7 percent in 2021, leaving 2021 GDP for this subset of emerging market and developing economies slightly below its 2019 level. Global trade will suffer a deep contraction this year of –11.9 percent, reflecting considerably weaker demand for goods and services, including tourism. Consistent with the gradual pickup in domestic demand next year, trade growth is expected to increase to 8 percent. Inflation outlook. Inflation projections have generally been revised downward, with larger cuts typically in 2020 and for advanced economies. This generally reflects a combination of weaker activity and lower commodity prices, although in some cases partially offset by the effect of exchange rate depreciation on import prices. Inflation is expected to rise gradually in 2021, consistent with the projected pickup in activity. Nonetheless, the inflation outlook remains muted, reflecting expectations of persistently weak aggregate demand. Likely Reversal of Progress on Poverty Reduction These projections imply a particularly acute negative impact of the pandemic on low-income households worldwide that could significantly raise inequality. The fraction of the world’s population living in extreme poverty—that is, on less than $1.90 a day—had fallen below 10 percent in recent years (from more than 35 percent in 1990). This progress is imperiled by the COVID-19 crisis, with more than 90 percent of emerging market and developing economies projected to register negative per capita income growth in 2020. In countries with high shares of informal employment, lockdowns have led to joblessness and abrupt income losses for many of those workers (often where migrants work far from home, separated from support networks). Moreover, with widespread school closures in about 150 countries as of the end of May, the United Nations Educational, Scientific and Cultural Organization estimates that close to 1.2 billion schoolchildren (about 70 percent of the global total) have been affected worldwide. This will result in significant loss of learning, with disproportionately negative effects on earnings prospects for children in low-income countries. Risks to the Outlook Fundamental uncertainty around the evolution of the pandemic is a key factor shaping the economic outlook and hinders a characterization of the balance of risks. The downturn could be less severe than forecast if economic normalization proceeds faster than currently expected in areas that have reopened—for example in China, where the recovery in investment and services through May was stronger than anticipated. Medical breakthroughs with therapeutics and changes in social distancing behavior might allow health care systems to cope better without requiring extended, stringent lockdowns. Vaccine trials are also proceeding at a rapid pace. Development of a safe, effective vaccine would lift sentiment and could improve growth outcomes in 2021, even if vaccine production is not scaled up fast enough to deliver herd immunity by the end of 2021. More generally, changes in production, distribution, and payment systems during the pandemic could actually spur productivity gains—ranging from new techniques in medicine to, more broadly, accelerated digitalization or the switch from fossil fuels to renewables. 8 International Monetary Fund | June 2020 Downside risks, however, remain significant. Outbreaks could recur in places that appear to have gone past peak infection, requiring the reimposition of at least some containment measures. A more prolonged decline in activity could lead to further scarring, including from wider firm closures, as sur...
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