## Has the Phillips Curve Become Steeper? — Excerpt (Introduction & Data Sources) ## Source details **Canonical URL:** [Has the Phillips Curve Become Steeper? — Excerpt (Introduction & Data Sources)](https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023100-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2023/english/wpiea2023100-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2023/english/wpiea2023100-print-pdf.pdf.json) --- ### Background and puzzle - The Phillips curve is defined as the empirical relationship between unemployment and inflation and captures the trade-off between price stability and full capacity utilization. - Recent empirical work reports a flattening of the Phillips curve in advanced economies in recent decades (examples cited: Blanchard, 2016; Del Negro and others, 2020; Coibion and Gorodnichenko, 2015; Heise and others, 2022). - Proposed explanations for the flattening include: - Implementation of inflation targeting increasing monetary policy capacity to neutralize demand shocks (Broadbent, 2020; Bergholt, and others, 2023). - Firmer anchoring of inflation expectations reducing second-round effects (Borio and others, 2021). - Globalization reducing responsiveness of inflation to domestic slack and weakening labor bargaining power (Heise and others, 2022; Lombardi and others, 2020). - Increased market power allowing firms to absorb cost-push shocks in markups (Baqaee, Farhi and Sangani, 2021). ### Recent developments that may reverse trends - The recent rise in inflation to historically high levels in advanced economies could weaken the anchoring of inflation expectations built up over the past two decades. - A de-globalization scenario could make the domestic output gap more relevant and potentially restore some labor bargaining power (Goodhart and Pradhan, 2020). - Digitalization, accelerated by the COVID-19 pandemic, has raised the share of online retail where prices are typically more flexible (Gorodnichenko and Talavera, 2017; Cavallo, 2018), which could reduce nominal rigidities and steepen the Phillips curve. - Figure 1 (10 year rolling correlation of the unemployment rate and CPI inflation) shows that the empirical correlation which had gone from negative to broadly zero in the US and the euro area (and positive in the UK) has turned negative again since the pandemic. ### Research objective and empirical strategy - Analyze the impact of recent structural changes in trade intensity and digitalization on the slope of the Phillips curve. - Primary empirical approach: - Estimate a sectoral Phillips curve using quarterly data from 17 sectors in 24 advanced economies in Europe over 2012Q1–2019Q4. - Use sectoral variation to increase identification power, relying on the assumption that production factors are not perfectly mobile across sectors (allowing sectoral output gaps to differ). - Complementary analysis: - Use price quote data from the UK (individual prices for goods in specific establishments) with monthly data from January 2008 to June 2021 to test whether sectors with higher e-commerce intensity have higher frequencies of individual price changes. - Robustness checks include defining price flexibility with either positive or negative price changes only and including sector and year fixed effects. ### Key empirical findings (summary) - Both de-globalization (trade intensity) and digitalization have a statistically significant impact in steepening the Phillips curve in the sectoral panel estimates. - UK price quote analysis: greater e-commerce intensity at the sectoral level is associated with a significantly higher frequency of individual price changes (consistent with Gorodnichenko and Talavera, 2017; Cavallo, 2018). - Application to post-pandemic inference: - The sectoral Phillips curve results are applied to infer changes in the post-pandemic Phillips curve for France, Germany, Italy, Spain, the UK, and a euro area aggregate. - The Phillips curve has steepened in the UK, Spain, Italy, and the euro area, but the extent of steepening is limited and post-pandemic Phillips curves remain largely flat. - This conclusion holds even under a hypothetical de-globalization scenario where trade intensity falls substantially. - The limited steepening implies an important role for outwards shifts in the Phillips curve (possible drivers: higher inflation expectations, supply shocks, or other structural changes) in explaining the surge in inflation in 2021–22. ### Robustness and related concerns addressed - Estimation includes controls for non-linearities and endogenous monetary policy responses to shocks; results are robust to these considerations. - Robustness checks: - Clustering standard errors at the sector-country combination level. - Estimating using only countries in the euro area or with an exchange rate peg to the euro to address endogenous monetary policy responses (time fixed effects capture common policy). - Considering non-linear Phillips curve specifications with quadratic terms for the output gap and inflation expectations. - For the UK price flexibility analysis, re-defining the flexibility measure to exclude all positive or all negative price changes to test for “deflationary bias.” ### Coverage and sample (data construction) - Panel dataset for the Phillips curve estimates covers the period 2014Q4–2019Q4 at quarterly frequency. - Tables and regressions elsewhere in the chapter pertain to sectoral Phillips curve regressions covering 17 sectors at NACE Rev. 2 two-digit classifications in 24 advanced economies in Europe between 2012Q1–2019Q4 at quarterly frequency. - In 2019, the included sectors accounted on average for 42 percent of total gross value added for the countries in the dataset. - Among the countries, Czechia, Norway, Sweden and the UK have independent monetary policies, leading to their exclusion from robustness checks for endogenous monetary policy reactions. The remaining countries are euro area members, except for Denmark which has an exchange rate peg with the euro. ### Countries and sectors included - Countries: Austria, Belgium, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Portugal, Slovakia, Slovenia, Spain, Sweden and the United Kingdom. - Sectors (NACE Rev. 2 two-digit groupings listed exactly as in source): - C10-C12 Manufacture of food products; beverages and tobacco products - C13-C15 Manufacture of textiles, wearing apparel, leather and related products - C16-C18 Manufacture of wood, paper, printing and reproduction - C19 Manufacture of coke and refined petroleum products - C20 Manufacture of chemicals and chemical products - C21 Manufacture of basic pharmaceutical products and pharmaceutical preparations - C22-C23 Manufacture of rubber and plastic products and other non-metallic mineral products - C24-C25 Manufacture of basic metals and fabricated metal products, except machinery and equipment - C26 Manufacture of computer, electronic and optical products - C27-C28 Manufacture of electrical equipment and machinery and equipment n.e.c. - C29-C30 Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment - C31-C33 Manufacture of furniture; jewelry, musical instruments, toys; repair and installation of machinery and equipment - H49-H53 Land transport and transport via pipelines, water and air transport, warehousing and support activities for transportation, postal and courier activities - I Accommodation and food service activities - J58-J60 Publishing activities, motion picture, video and television programme production, sound recording and music publishing activities, programming and broadcasting activities - J61 Telecommunications - J62-J63 Computer programming, consultancy, and information service activities ### Key variables and construction - Digitalization proxy: percentage share of enterprises with e-commerce sales amounting to at least 1 percent of total turnover (Eurostat) at the sectoral breakdown shown above. - Sectoral inflation: measured using implied GDP deflators (implicit deflator). - Sectoral output gap: constructed by applying a Hodrick-Prescott filter to real gross value added (GVA) and taking the residual in percentage terms; output gap is the residual of an HP-filter of sectoral GVA and is in terms of percent of potential sectoral GVA, with positive values indicating output above potential. - Sectoral trade intensity (proxy for (de-)globalization): ratio of sectoral imports and exports to sectoral GVA. Sectoral imports and exports are available from 2012Q1 onwards. - Inflation expectations (country level): proxied by one-year-ahead CPI inflation forecasts from Consensus Economics. - Real effective exchange rates (country level): (CPI-based) REER data obtained from the IMF’s World Economic Outlook database. - All variables are in annualized terms when used in regressions. ### Price-quote data and price flexibility measure (UK ONS) - Data source: UK Office for National Statistics (ONS) consumer price quote dataset, monthly frequency, used to construct CPI. - Data processing rules: - Exclude price quotes not validated by the ONS and quotes where a different comparable product was used (retain price changes due to sales). - For duplicate item-shop-region-month observations, select the observation with the lowest price (selection tends to diminish the rate of price changes). - Calculate the rate of price changes for each item-shop pairing. - Aggregate price flexibility by COICOP sectors and manually match COICOP sectors to NACE 2-digit sectoral categories for which the digitalization proxy is available. The manual matching is available upon request. - Caveat: ONS data cover physical shops only; this likely under-estimates price flexibility in sectors with a higher share of online retail. The authors note Gorodnichenko and Talavera (2017)’s finding of higher price flexibility in online markets and therefore interpret ONS-based estimates as speaking to whether physical shops in sectors with higher penetration of e-commerce responded with more frequent price adjustments, rather than capturing price flexibility for the sector inclusive of e-commerce. ### Notes on timeframes and projection inputs for post-pandemic analysis - Post-pandemic Phillips curve slopes use the coefficients from Table 2, Column (vi) and: - Latest available digitalization data from 2022 (with the exception of the UK, latest available data for which are from 2020). - IMF WEO projections for trade shares in 2023–24, except for the Euro area composite where Eurostat data on extra-Euro area trade is extended using IMF WEO projections. - In the de-globalization scenario, trade intensity is assumed to decline to the midpoint of 2023-24 projections and the 1980-90 average. For the Euro area, trade intensity is assumed to decline by the same amount as the average decline in DE, ES, FR and IT. *Source: wpiea2023100-print-pdf — IMF Working Paper, 1. INTRODUCTION & 3.3 Data Sources and Construction (excerpts).* ### 1. INTRODUCTION ........................................................................................................ ### 1. INTRODUCTION ### Background and puzzle - The Phillips curve is defined as the empirical relationship between unemployment and inflation and captures the trade-off between price stability and full capacity utilization. - Recent empirical work reports a flattening of the Phillips curve in advanced economies in recent decades (examples cited: Blanchard, 2016; Del Negro and others, 2020; Coibion and Gorodnichenko, 2015; Heise and others, 2022). - Proposed explanations for the flattening include: - Implementation of inflation targeting increasing monetary policy capacity to neutralize demand shocks (Broadbent, 2020; Bergholt, and others, 2023). - Firmer anchoring of inflation expectations reducing second-round effects (Borio and others, 2021). - Globalization reducing responsiveness of inflation to domestic slack and weakening labor bargaining power (Heise and others, 2022; Lombardi and others, 2020). - Increased market power allowing firms to absorb cost-push shocks in markups (Baqaee, Farhi and Sangani, 2021). ### Recent developments that may reverse trends - The recent rise in inflation to historically high levels in advanced economies could weaken the anchoring of inflation expectations built up over the past two decades. - A de-globalization scenario could make the domestic output gap more relevant and potentially restore some labor bargaining power (Goodhart and Pradhan, 2020). - Digitalization, accelerated by the COVID-19 pandemic, has raised the share of online retail where prices are typically more flexible (Gorodnichenko and Talavera, 2017; Cavallo, 2018), which could reduce nominal rigidities and steepen the Phillips curve. - Figure 1 (10 year rolling correlation of the unemployment rate and CPI inflation) shows that the empirical correlation which had gone from negative to broadly zero in the US and the euro area (and positive in the UK) has turned negative again since the pandemic. ### Research objective and empirical strategy - The paper analyzes the impact of recent structural changes in trade intensity and digitalization on the slope of the Phillips curve. - Primary empirical approach: - Estimate a sectoral Phillips curve using quarterly data from 17 sectors in 24 advanced economies in Europe over 2012Q1–2019Q4. - Use sectoral variation to increase identification power, relying on the assumption that production factors are not perfectly mobile across sectors (allowing sectoral output gaps to differ). - Complementary analysis: - Use price quote data from the UK (individual prices for goods in specific establishments) with monthly data from January 2008 to June 2021 to test whether sectors with higher e-commerce intensity have higher frequencies of individual price changes. - Robustness checks include defining price flexibility with either positive or negative price changes only and including sector and year fixed effects. ### Key empirical findings (summary) - Both de-globalization (trade intensity) and digitalization have a statistically significant impact in steepening the Phillips curve in the sectoral panel estimates. - UK price quote analysis: greater e-commerce intensity at the sectoral level is associated with a significantly higher frequency of individual price changes (consistent with Gorodnichenko and Talavera, 2017; Cavallo, 2018). - Application to post-pandemic inference: - The sectoral Phillips curve results are applied to infer changes in the post-pandemic Phillips curve for France, Germany, Italy, Spain, the UK, and a euro area aggregate. - The Phillips curve has steepened in the UK, Spain, Italy, and the euro area, but the extent of steepening is limited and post-pandemic Phillips curves remain largely flat. - This conclusion holds even under a hypothetical de-globalization scenario where trade intensity falls substantially. - The limited steepening implies an important role for outwards shifts in the Phillips curve (possible drivers: higher inflation expectations, supply shocks, or other structural changes) in explaining the surge in inflation in 2021–22. ### Robustness and related concerns addressed - Estimation includes controls for non-linearities and endogenous monetary policy responses to shocks; results are robust to these considerations. - Robustness checks: - Clustering standard errors at the sector-country combination level. - Estimating using only countries in the euro area or with an exchange rate peg to the euro to address endogenous monetary policy responses (time fixed effects capture common policy). - Considering non-linear Phillips curve specifications with quadratic terms for the output gap and inflation expectations. - For the UK price flexibility analysis, re-defining the flexibility measure to exclude all positive or all negative price changes to test for “deflationary bias.” ### Organization of the paper - Section 2: Related literature. - Section 3: Empirical strategy and data. - Section 4: Results and robustness checks. - Section 5: Estimates of Phillips curve slopes in the aftermath of the pandemic and in a de-globalization scenario. - Section 6: Conclusion. *IMF Working Paper — 1. INTRODUCTION (excerpt).* ### 3.3 Data Sources and Construction ### 3.3 Data Sources and Construction ### Coverage and sample - Panel dataset for the Phillips curve estimates covers the period 2014Q4–2019Q4 at quarterly frequency. - Tables and regressions elsewhere in the chapter pertain to sectoral Phillips curve regressions covering 17 sectors at NACE Rev. 2 two-digit classifications in 24 advanced economies in Europe between 2012Q1–2019Q4 at quarterly frequency. - In 2019, the included sectors accounted on average for 42 percent of total gross value added for the countries in the dataset. - Among the countries, Czechia, Norway, Sweden and the UK have independent monetary policies, leading to their exclusion from robustness checks for endogenous monetary policy reactions. The remaining countries are euro area members, except for Denmark which has an exchange rate peg with the euro. ### Countries and sectors included - Countries: Austria, Belgium, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Portugal, Slovakia, Slovenia, Spain, Sweden and the United Kingdom. - Sectors (NACE Rev. 2 two-digit groupings listed exactly as in source): - C10-C12 Manufacture of food products; beverages and tobacco products - C13-C15 Manufacture of textiles, wearing apparel, leather and related products - C16-C18 Manufacture of wood, paper, printing and reproduction - C19 Manufacture of coke and refined petroleum products - C20 Manufacture of chemicals and chemical products - C21 Manufacture of basic pharmaceutical products and pharmaceutical preparations - C22-C23 Manufacture of rubber and plastic products and other non-metallic mineral products - C24-C25 Manufacture of basic metals and fabricated metal products, except machinery and equipment - C26 Manufacture of computer, electronic and optical products - C27-C28 Manufacture of electrical equipment and machinery and equipment n.e.c. - C29-C30 Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment - C31-C33 Manufacture of furniture; jewelry, musical instruments, toys; repair and installation of machinery and equipment - H49-H53 Land transport and transport via pipelines, water and air transport, warehousing and support activities for transportation, postal and courier activities - I Accommodation and food service activities - J58-J60 Publishing activities, motion picture, video and television programme production, sound recording and music publishing activities, programming and broadcasting activities - J61 Telecommunications - J62-J63 Computer programming, consultancy, and information service activities ### Key variables and construction - Digitalization proxy: percentage share of enterprises with e-commerce sales amounting to at least 1 percent of total turnover (Eurostat) at the sectoral breakdown shown above. - Sectoral inflation: measured using implied GDP deflators (implicit deflator). - Sectoral output gap: constructed by applying a Hodrick-Prescott filter to real gross value added (GVA) and taking the residual in percentage terms; output gap is the residual of an HP-filter of sectoral GVA and is in terms of percent of potential sectoral GVA, with positive values indicating output above potential. - Sectoral trade intensity (proxy for (de-)globalization): ratio of sectoral imports and exports to sectoral GVA. Sectoral imports and exports are available from 2012Q1 onwards. - Inflation expectations (country level): proxied by one-year-ahead CPI inflation forecasts from Consensus Economics. - Real effective exchange rates (country level): (CPI-based) REER data obtained from the IMF’s World Economic Outlook database. - All variables are in annualized terms when used in regressions. ### Price-quote data and price flexibility measure (UK ONS) - Data source: UK Office for National Statistics (ONS) consumer price quote dataset, monthly frequency, used to construct CPI. - Data processing rules: - Exclude price quotes not validated by the ONS and quotes where a different comparable product was used (retain price changes due to sales). - For duplicate item-shop-region-month observations, select the observation with the lowest price (selection tends to diminish the rate of price changes). - Calculate the rate of price changes for each item-shop pairing. - Aggregate price flexibility by COICOP sectors and manually match COICOP sectors to NACE 2-digit sectoral categories for which the digitalization proxy is available. The manual matching is available upon request. - Caveat: ONS data cover physical shops only; this likely under-estimates price flexibility in sectors with a higher share of online retail. The authors note Gorodnichenko and Talavera (2017)’s finding of higher price flexibility in online markets and therefore interpret ONS-based estimates as speaking to whether physical shops in sectors with higher penetration of e-commerce responded with more frequent price adjustments, rather than capturing price flexibility for the sector inclusive of e-commerce. ### Notes on timeframes and projection inputs for post-pandemic analysis - Post-pandemic Phillips curve slopes use the coefficients from Table 2, Column (vi) and: - Latest available digitalization data from 2022 (with the exception of the UK, latest available data for which are from 2020). - IMF WEO projections for trade shares in 2023–24, except for the Euro area composite where Eurostat data on extra-Euro area trade is extended using IMF WEO projections. - In the de-globalization scenario, trade intensity is assumed to decline to the midpoint of 2023-24 projections and the 1980-90 average. 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Forthcoming in Econometrica. - Speigner, B. (2014). Long-Term Unemployment and Convexity in the Phillips Curve. Bank of England Working Paper No. 519. - Tuckett, A. (2018). What can regional data tell us about the UK Phillips Curve? Bank of England. Available online at: https://bankunderground.co.uk/2018/04/13/what-can-regional-data-tell-us-about-the-uk-phillips-curve/. - Witt, M.A. (2019). De-globalization: Theories, predictions, and opportunities for international business research. Journal of International Business Studies 50, 1053–1077. *Has the Phillips Curve Become Steeper? Working Paper No. WP/2023/100* --- _Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023100-print-pdf.pdf_